From f69b8d22046fb8f2939522be6855941ea5013332 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 7 Nov 2025 15:48:32 +0100 Subject: [PATCH 01/72] Added Lightgbm, LightGBM Linear Trees and Hybrid Stacking Forecasters --- packages/openstef-models/pyproject.toml | 2 + .../openstef_models/estimators/__init__.py | 9 + .../src/openstef_models/estimators/hybrid.py | 175 +++++++++ .../openstef_models/estimators/lightgbm.py | 201 ++++++++++ .../models/forecasting/hybrid_forecaster.py | 137 +++++++ .../forecasting/lgblinear_forecaster.py | 346 ++++++++++++++++++ .../models/forecasting/lightgbm_forecaster.py | 340 +++++++++++++++++ .../presets/forecasting_workflow.py | 171 +++++++-- .../tests/unit/estimators/test_hybrid.py | 39 ++ .../tests/unit/estimators/test_lightgbm.py | 39 ++ .../forecasting/test_lgblinear_forecaster.py | 159 ++++++++ .../forecasting/test_lightgbm_forecaster.py | 159 ++++++++ uv.lock | 55 +++ 13 files changed, 1803 insertions(+), 29 deletions(-) create mode 100644 packages/openstef-models/src/openstef_models/estimators/__init__.py create mode 100644 packages/openstef-models/src/openstef_models/estimators/hybrid.py create mode 100644 packages/openstef-models/src/openstef_models/estimators/lightgbm.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py create mode 100644 packages/openstef-models/tests/unit/estimators/test_hybrid.py create mode 100644 packages/openstef-models/tests/unit/estimators/test_lightgbm.py create mode 100644 packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py create mode 100644 packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py diff --git a/packages/openstef-models/pyproject.toml b/packages/openstef-models/pyproject.toml index 5f267c901..43914f38f 100644 --- a/packages/openstef-models/pyproject.toml +++ b/packages/openstef-models/pyproject.toml @@ -28,8 +28,10 @@ classifiers = [ ] dependencies = [ + "lightgbm>=4.6.0", "openstef-core", "pycountry>=24.6.1", + "skops>=0.13.0", ] optional-dependencies.all = [ diff --git a/packages/openstef-models/src/openstef_models/estimators/__init__.py b/packages/openstef-models/src/openstef_models/estimators/__init__.py new file mode 100644 index 000000000..487060783 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/estimators/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Custom estimators for multi quantiles""" + +from .lightgbm import LGBMQuantileRegressor + +__all__ = ["LGBMQuantileRegressor"] diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py new file mode 100644 index 000000000..abffd154a --- /dev/null +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -0,0 +1,175 @@ +"""Hybrid quantile regression estimators for multi-quantile forecasting. + +This module provides the HybridQuantileRegressor class, which combines LightGBM and linear models +using stacking for robust multi-quantile regression, including serialization utilities. +""" + +from typing import Self + +import numpy as np +import numpy.typing as npt +import pandas as pd +from lightgbm import LGBMRegressor +from sklearn.ensemble import StackingRegressor +from sklearn.linear_model import QuantileRegressor +from skops.io import dumps, loads +from xgboost import XGBRegressor + +from openstef_core.exceptions import ModelLoadingError + + +class HybridQuantileRegressor: + """Custom Hybrid regressor for multi-quantile estimation using sample weights.""" + + def __init__( + self, + quantiles: list[float], + lightgbm_n_estimators: int = 100, + lightgbm_learning_rate: float = 0.1, + lightgbm_max_depth: int = -1, + lightgbm_min_child_weight: float = 1.0, + ligntgbm_min_child_samples: int = 1, + lightgbm_min_data_in_leaf: int = 20, + lightgbm_min_data_in_bin: int = 10, + lightgbm_reg_alpha: float = 0.0, + lightgbm_reg_lambda: float = 0.0, + lightgbm_num_leaves: int = 31, + lightgbm_max_bin: int = 255, + lightgbm_subsample: float = 1.0, + lightgbm_colsample_by_tree: float = 1.0, + lightgbm_colsample_by_node: float = 1.0, + gblinear_n_estimators: int = 100, + gblinear_learning_rate: float = 0.15, + gblinear_reg_alpha: float = 0.0001, + gblinear_reg_lambda: float = 0, + gblinear_feature_selector: str = "shuffle", + gblinear_updater: str = "shotgun", + ): + self.quantiles = quantiles + + self._models: list[StackingRegressor] = [] + + for q in quantiles: + lightgbm_model = LGBMRegressor( + objective="quantile", + alpha=q, + min_child_samples=ligntgbm_min_child_samples, + n_estimators=lightgbm_n_estimators, + learning_rate=lightgbm_learning_rate, + max_depth=lightgbm_max_depth, + min_child_weight=lightgbm_min_child_weight, + min_data_in_leaf=lightgbm_min_data_in_leaf, + min_data_in_bin=lightgbm_min_data_in_bin, + reg_alpha=lightgbm_reg_alpha, + reg_lambda=lightgbm_reg_lambda, + num_leaves=lightgbm_num_leaves, + max_bin=lightgbm_max_bin, + subsample=lightgbm_subsample, + colsample_bytree=lightgbm_colsample_by_tree, + colsample_bynode=lightgbm_colsample_by_node, + verbosity=-1, + linear_tree=False, + ) + + linear = XGBRegressor( + booster="gblinear", + # Core parameters for forecasting + objective="reg:quantileerror", + n_estimators=gblinear_n_estimators, + learning_rate=gblinear_learning_rate, + # Regularization parameters + reg_alpha=gblinear_reg_alpha, + reg_lambda=gblinear_reg_lambda, + # Boosting structure control + feature_selector=gblinear_feature_selector, + updater=gblinear_updater, + quantile_alpha=q, + ) + + final_estimator = QuantileRegressor(quantile=q) + + self._models.append( + StackingRegressor( + estimators=[("lightgbm", lightgbm_model), ("gblinear", linear)], # type: ignore + final_estimator=final_estimator, + verbose=3, + passthrough=False, + n_jobs=None, + cv=2, + ) + ) + self.is_fitted: bool = False + + def fit( + self, + X: npt.NDArray[np.floating] | pd.DataFrame, # noqa: N803 + y: npt.NDArray[np.floating] | pd.Series, + sample_weight: npt.NDArray[np.floating] | None = None, + feature_name: list[str] | None = None, + ) -> None: + """Fit the multi-quantile regressor. + + Args: + X: Input features as a DataFrame. + y: Target values as a 2D array where each column corresponds to a quantile. + sample_weight: Sample weights for training data. + feature_name: List of feature names. + """ + X = X.ffill().fillna(0) # type: ignore + for model in self._models: + model.fit( + X=X, # type: ignore + y=y, + sample_weight=sample_weight, + ) + self.is_fitted = True + + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: # noqa: N803 + """Predict quantiles for the input features. + + Args: + X: Input features as a DataFrame. + + Returns: + + A 2D array where each column corresponds to predicted quantiles. + """ # noqa: D412 + X = X.ffill().fillna(0) # type: ignore + return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore + + def save_bytes(self) -> bytes: + """Serialize the model. + + Returns: + A string representation of the model. + """ + return dumps(self) + + @classmethod + def load_bytes(cls, model_bytes: bytes) -> Self: + """Deserialize the model from bytes using joblib. + + Args: + model_bytes : Bytes representing the serialized model. + + Returns: + An instance of LightGBMQuantileRegressor. + + Raises: + ModelLoadingError: If the deserialized object is not a HybridQuantileRegressor. + """ + trusted_types = [ + "collections.OrderedDict", + "lightgbm.basic.Booster", + "lightgbm.sklearn.LGBMRegressor", + "sklearn.utils._bunch.Bunch", + "xgboost.core.Booster", + "xgboost.sklearn.XGBRegressor", + "openstef_models.estimators.hybrid.HybridQuantileRegressor", + ] + instance = loads(model_bytes, trusted=trusted_types) + + if not isinstance(instance, cls): + raise ModelLoadingError("Deserialized object is not a HybridQuantileRegressor") + + return instance diff --git a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py new file mode 100644 index 000000000..66f222327 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py @@ -0,0 +1,201 @@ +"""Custom LightGBM regressor for multi-quantile regression. + +This module provides the LGBMQuantileRegressor class, which extends LightGBM's LGBMRegressor +to support multi-quantile output by configuring the objective function accordingly. Each quantile is predicted +by a separate tree within the same boosting ensemble. The module also includes serialization utilities. +""" + +from typing import Self + +import numpy as np +import numpy.typing as npt +import pandas as pd +from lightgbm import LGBMRegressor +from sklearn.base import BaseEstimator, RegressorMixin +from skops.io import dumps, loads + +from openstef_core.exceptions import ModelLoadingError + + +class LGBMQuantileRegressor(BaseEstimator, RegressorMixin): + """Custom LightGBM regressor for multi-quantile regression. + + Extends LGBMRegressor to support multi-quantile output by configuring + the objective function accordingly. Each quantile is predicted by a + separate tree within the same boosting ensemble. + """ + + def __init__( + self, + quantiles: list[float], + linear_tree: bool, # noqa: FBT001 + n_estimators: int = 100, + learning_rate: float = 0.1, + max_depth: int = -1, + min_child_weight: float = 1.0, + min_data_in_leaf: int = 20, + min_data_in_bin: int = 10, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + num_leaves: int = 31, + max_bin: int = 255, + subsample: float = 1.0, + colsample_bytree: float = 1.0, + colsample_bynode: float = 1.0, + random_state: int | None = None, + early_stopping_rounds: int | None = None, + verbosity: int = -1, + ) -> None: # type: ignore + """Initialize LgbLinearQuantileRegressor with quantiles. + + Args: + quantiles: List of quantiles to predict (e.g., [0.1, 0.5, 0.9]). + n_estimators: Number of boosting rounds/trees to fit. + learning_rate: Step size shrinkage used to prevent overfitting. + max_depth: Maximum depth of trees. + min_child_weight: Minimum sum of instance weight (hessian) needed in a child. + min_data_in_leaf: Minimum number of data points in a leaf. + min_data_in_bin: Minimum number of data points in a bin. + reg_alpha: L1 regularization on leaf weights. + reg_lambda: L2 regularization on leaf weights. + num_leaves: Maximum number of leaves. + max_bin: Maximum number of discrete bins for continuous features. + subsample: Fraction of training samples used for each tree. + colsample_bytree: Fraction of features used when constructing each tree. + colsample_bynode: Fraction of features used for each split/node. + random_state: Random seed for reproducibility. + early_stopping_rounds: Training will stop if performance doesn't improve for this many rounds. + verbosity: Verbosity level for LgbLinear training. + + """ + self.quantiles = quantiles + self.linear_tree = linear_tree + self.n_estimators = n_estimators + self.learning_rate = learning_rate + self.max_depth = max_depth + self.min_child_weight = min_child_weight + self.min_data_in_leaf = min_data_in_leaf + self.min_data_in_bin = min_data_in_bin + self.reg_alpha = reg_alpha + self.reg_lambda = reg_lambda + self.num_leaves = num_leaves + self.max_bin = max_bin + self.subsample = subsample + self.colsample_bytree = colsample_bytree + self.colsample_bynode = colsample_bynode + self.random_state = random_state + self.early_stopping_rounds = early_stopping_rounds + self.verbosity = verbosity + + self._models: list[LGBMRegressor] = [ + LGBMRegressor( + objective="quantile", + alpha=q, + n_estimators=n_estimators, + learning_rate=learning_rate, + max_depth=max_depth, + min_child_weight=min_child_weight, + min_data_in_leaf=min_data_in_leaf, + min_data_in_bin=min_data_in_bin, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + num_leaves=num_leaves, + max_bin=max_bin, + subsample=subsample, + colsample_bytree=colsample_bytree, + colsample_bynode=colsample_bynode, + random_state=random_state, + early_stopping_rounds=early_stopping_rounds, + verbosity=verbosity, + linear_tree=linear_tree, + ) + for q in quantiles # type: ignore + ] + + def fit( + self, + X: npt.NDArray[np.floating] | pd.DataFrame, # noqa: N803 + y: npt.NDArray[np.floating] | pd.Series, + sample_weight: npt.NDArray[np.floating] | None = None, + feature_name: list[str] | None = None, + eval_set: npt.NDArray[np.floating] | None = None, + eval_sample_weight: npt.NDArray[np.floating] | None = None, + ) -> None: + """Fit the multi-quantile regressor. + + Args: + X: Input features as a DataFrame. + y: Target values as a 2D array where each column corresponds to a quantile. + sample_weight: Sample weights for training data. + feature_name: List of feature names. + eval_set: Evaluation set for early stopping. + eval_sample_weight: Sample weights for evaluation data. + """ + for model in self._models: + if eval_set is None: + model.set_params(early_stopping_rounds=None) + else: + model.set_params(early_stopping_rounds=self.early_stopping_rounds) + model.fit( # type: ignore + X=np.asarray(X), + y=y, + eval_metric="quantile", + sample_weight=sample_weight, + eval_set=eval_set, # type: ignore + eval_sample_weight=eval_sample_weight, # type: ignore + feature_name=feature_name, # type: ignore + ) + + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: # noqa: N803 + """Predict quantiles for the input features. + + Args: + X: Input features as a DataFrame. + + Returns: + + A 2D array where each column corresponds to predicted quantiles. + """ # noqa: D412 + return np.column_stack([model.predict(X=np.asarray(X)) for model in self._models]) # type: ignore + + def __sklearn_is_fitted__(self) -> bool: # noqa: PLW3201 + """Check if all models are fitted. + + Returns: + True if all quantile models are fitted, False otherwise. + """ + return all(model.__sklearn_is_fitted__() for model in self._models) + + def save_bytes(self) -> bytes: + """Serialize the model. + + Returns: + A string representation of the model. + """ + return dumps(self) + + @classmethod + def load_bytes(cls, model_bytes: bytes) -> Self: + """Deserialize the model from bytes using joblib. + + Args: + model_bytes : Bytes representing the serialized model. + + Returns: + An instance of LgbLinearQuantileRegressor. + + Raises: + ModelLoadingError: If the deserialized object is not a LgbLinearQuantileRegressor. + """ + trusted_types = [ + "collections.OrderedDict", + "lightgbm.basic.Booster", + "lightgbm.sklearn.LGBMRegressor", + "openstef_models.estimators.lightgbm.LGBMQuantileRegressor", + ] + instance = loads(model_bytes, trusted=trusted_types) + + if not isinstance(instance, cls): + raise ModelLoadingError("Deserialized object is not a LgbLinearQuantileRegressor") + + return instance diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py new file mode 100644 index 000000000..b1909b068 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -0,0 +1,137 @@ +from __future__ import annotations + +import base64 +import logging +from typing import Any, Literal, Self, Union, cast, override + +import numpy as np +import numpy.typing as npt +import pandas as pd +from pydantic import Field + + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + ModelLoadingError, + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_models.estimators.hybrid import HybridQuantileRegressor +from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig +from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMHyperParams + + +class HybridHyperParams(HyperParams): + """Hyperparameters for Support Vector Regression (Hybrid).""" + + lightgbm_params: LightGBMHyperParams = LightGBMHyperParams() + + l1_penalty: float = Field( + default=0.0, + description="L1 regularization term for the quantile regression.", + ) + + +class HybridForecasterConfig(ForecasterConfig): + """Configuration for Hybrid-based forecasting models.""" + + hyperparams: HybridHyperParams = HybridHyperParams() + + verbosity: bool = Field( + default=True, + description="Enable verbose output from the Hybrid model (True/False).", + ) + + +MODEL_CODE_VERSION = 2 + + +class HybridForecasterState(BaseConfig): + """Serializable state for Hybrid forecaster persistence.""" + + version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") + config: HybridForecasterConfig = Field(..., description="Forecaster configuration.") + model: str = Field(..., description="Base64-encoded serialized Hybrid model.") + + +class HybridForecaster(Forecaster): + """Wrapper for sklearn's Hybrid to make it compatible with HorizonForecaster.""" + + Config = HybridForecasterConfig + HyperParams = HybridHyperParams + + _config: HybridForecasterConfig + model: HybridQuantileRegressor + + def __init__(self, config: HybridForecasterConfig) -> None: + """Initialize the Hybrid forecaster. + + Args: + kernel: Kernel type for Hybrid. Must be one of "linear", "poly", "rbf", "sigmoid", or "precomputed". + C: Regularization parameter. + epsilon: Epsilon in the epsilon-Hybrid model. + """ + self._config = config + + self._model = HybridQuantileRegressor( + quantiles=config.quantiles, + lightgbm_n_estimators=config.hyperparams.lightgbm_params.n_estimators, + lightgbm_learning_rate=config.hyperparams.lightgbm_params.learning_rate, + lightgbm_max_depth=config.hyperparams.lightgbm_params.max_depth, + lightgbm_min_child_weight=config.hyperparams.lightgbm_params.min_child_weight, + lightgbm_min_data_in_leaf=config.hyperparams.lightgbm_params.min_data_in_leaf, + lightgbm_min_data_in_bin=config.hyperparams.lightgbm_params.min_data_in_bin, + lightgbm_reg_alpha=config.hyperparams.lightgbm_params.reg_alpha, + lightgbm_reg_lambda=config.hyperparams.lightgbm_params.reg_lambda, + lightgbm_num_leaves=config.hyperparams.lightgbm_params.num_leaves, + lightgbm_max_bin=config.hyperparams.lightgbm_params.max_bin, + lightgbm_subsample=config.hyperparams.lightgbm_params.subsample, + lightgbm_colsample_by_tree=config.hyperparams.lightgbm_params.colsample_bytree, + lightgbm_colsample_by_node=config.hyperparams.lightgbm_params.colsample_bynode, + ) + + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + @property + def is_fitted(self) -> bool: + """Check if the model is fitted.""" + return self._model.is_fitted + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + """Fit the Hybrid model to the training data. + + Args: + data: Training data in the expected ForecastInputDataset format. + data_val: Validation data for tuning the model (optional, not used in this implementation). + + """ + + input_data: pd.DataFrame = data.input_data() + target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + + self._model.fit(X=input_data, y=target) + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self._model.is_fitted: + raise NotFittedError(self.__class__.__name__) + + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + prediction: npt.NDArray[np.floating] = self._model.predict(X=input_data) + + return ForecastDataset( + data=pd.DataFrame( + data=prediction, + index=input_data.index, + columns=[quantile.format() for quantile in self.config.quantiles], + ), + sample_interval=data.sample_interval, + ) + + +__all__ = ["HybridForecaster", "HybridForecasterConfig", "HybridHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py new file mode 100644 index 000000000..dc5babb7e --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -0,0 +1,346 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""LightGBM-based forecasting models for probabilistic energy forecasting. + +Provides gradient boosting tree models using LightGBM for multi-quantile energy +forecasting. Optimized for time series data with specialized loss functions and +comprehensive hyperparameter control for production forecasting workflows. +""" + +import base64 +from typing import TYPE_CHECKING, Any, Literal, Self, cast, override + +import numpy as np +import pandas as pd +from pydantic import Field + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + ModelLoadingError, + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_models.estimators.lightgbm import LGBMQuantileRegressor +from openstef_models.explainability.mixins import ExplainableForecaster +from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig + +if TYPE_CHECKING: + import numpy.typing as npt + + +class LgbLinearHyperParams(HyperParams): + """LgbLinear hyperparameters for gradient boosting tree models. + + Example: + Creating custom hyperparameters for deep trees with regularization: + + >>> hyperparams = LGBMHyperParams( + ... n_estimators=200, + ... max_depth=8, + ... learning_rate=0.1, + ... reg_alpha=0.1, + ... reg_lambda=1.0, + ... subsample=0.8 + ... ) + + Note: + These parameters are optimized for probabilistic forecasting with + quantile regression. The default objective function is specialized + for magnitude-weighted pinball loss. + """ + + # Core Tree Boosting Parameters + + n_estimators: int = Field( + default=150, + description="Number of boosting rounds/trees to fit. Higher values may improve performance but " + "increase training time and risk overfitting.", + ) + learning_rate: float = Field( + default=0.3, + alias="eta", + description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " + "more boosting rounds.", + ) + max_depth: int = Field( + default=4, + description="Maximum depth of trees. Higher values capture more complex patterns but risk " + "overfitting. Range: [1,∞]", + ) + min_child_weight: float = Field( + default=1, + description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent " + "overfitting. Range: [0,∞]", + ) + + min_data_in_leaf: int = Field( + default=5, + description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", + ) + min_data_in_bin: int = Field( + default=5, + description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", + ) + + # Regularization + reg_alpha: float = Field( + default=0, + description="L1 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + reg_lambda: float = Field( + default=1, + description="L2 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + + # Tree Structure Control + num_leaves: int = Field( + default=31, + description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", + ) + + max_bin: int = Field( + default=256, + description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " + "increase memory. Only for hist tree_method.", + ) + + # Subsampling Parameters + subsample: float = Field( + default=0.5, + description="Fraction of training samples used for each tree. Lower values prevent overfitting. Range: (0,1]", + ) + colsample_bytree: float = Field( + default=0.5, + description="Fraction of features used when constructing each tree. Range: (0,1]", + ) + colsample_bynode: float = Field( + default=0.5, + description="Fraction of features used for each split/node. Range: (0,1]", + ) + + # General Parameters + random_state: int | None = Field( + default=None, + alias="seed", + description="Random seed for reproducibility. Controls tree structure randomness.", + ) + + early_stopping_rounds: int | None = Field( + default=10, + description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", + ) + + +class LgbLinearForecasterConfig(ForecasterConfig): + """Configuration for LgbLinear-based forecaster. + Extends HorizonForecasterConfig with LgbLinear-specific hyperparameters + and execution settings. + + Example: + Creating a LgbLinear forecaster configuration with custom hyperparameters: + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LgbLinearForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1 + ))], + ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... ). + """ # noqa: D205 + + hyperparams: LgbLinearHyperParams = LgbLinearHyperParams() + + # General Parameters + device: str = Field( + default="cpu", + description="Device for LgbLinear computation. Options: 'cpu', 'cuda', 'cuda:', 'gpu'", + ) + n_jobs: int = Field( + default=1, + description="Number of parallel threads for tree construction. -1 uses all available cores.", + ) + verbosity: Literal[0, 1, 2, 3] = Field( + default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + + +MODEL_CODE_VERSION = 1 + + +class LgbLinearForecasterState(BaseConfig): + """Serializable state for LgbLinear forecaster persistence. + + Contains all information needed to restore a trained LgbLinear model, + including configuration and the serialized model weights. Used for + model saving, loading, and version management in production systems. + """ + + version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") + config: LgbLinearForecasterConfig = Field(..., description="Forecaster configuration.") + model: str = Field(..., description="Base64-encoded serialized LgbLinear model.") + + +class LgbLinearForecaster(Forecaster, ExplainableForecaster): + """LgbLinear-based forecaster for probabilistic energy forecasting. + + Implements gradient boosting trees using LgbLinear for multi-quantile forecasting. + Optimized for time series prediction with specialized loss functions and + comprehensive hyperparameter control suitable for production energy forecasting. + + The forecaster uses a multi-output strategy where each quantile is predicted + by separate trees within the same boosting ensemble. This approach provides + well-calibrated uncertainty estimates while maintaining computational efficiency. + + Invariants: + - fit() must be called before predict() to train the model + - Configuration quantiles determine the number of prediction outputs + - Model state is preserved across predict() calls after fitting + - Input features must match training data structure during prediction + + Example: + Basic forecasting workflow: + + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LgbLinearForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1))], + ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... ) + >>> forecaster = LgbLinearForecaster(config) + >>> # forecaster.fit(training_data) + >>> # predictions = forecaster.predict(test_data) + + Note: + LgbLinear dependency is optional and must be installed separately. + The model automatically handles multi-quantile output and uses + magnitude-weighted pinball loss by default for better forecasting performance. + + See Also: + LgbLinearHyperParams: Detailed hyperparameter configuration options. + HorizonForecaster: Base interface for all forecasting models. + GBLinearForecaster: Alternative linear model using LgbLinear. + """ + + Config = LgbLinearForecasterConfig + HyperParams = LgbLinearHyperParams + + _config: LgbLinearForecasterConfig + _lgblinear_model: LGBMQuantileRegressor + + def __init__(self, config: LgbLinearForecasterConfig) -> None: + """Initialize LgbLinear forecaster with configuration. + + Creates an untrained LgbLinear regressor with the specified configuration. + The underlying LgbLinear model is configured for multi-output quantile + regression using the provided hyperparameters and execution settings. + + Args: + config: Complete configuration including hyperparameters, quantiles, + and execution settings for the LgbLinear model. + """ + self._config = config + + self._lgblinear_model = LGBMQuantileRegressor( + quantiles=[float(q) for q in config.quantiles], + linear_tree=True, + n_estimators=config.hyperparams.n_estimators, + learning_rate=config.hyperparams.learning_rate, + max_depth=config.hyperparams.max_depth, + min_child_weight=config.hyperparams.min_child_weight, + min_data_in_leaf=config.hyperparams.min_data_in_leaf, + min_data_in_bin=config.hyperparams.min_data_in_bin, + reg_alpha=config.hyperparams.reg_alpha, + reg_lambda=config.hyperparams.reg_lambda, + num_leaves=config.hyperparams.num_leaves, + max_bin=config.hyperparams.max_bin, + subsample=config.hyperparams.subsample, + colsample_bytree=config.hyperparams.colsample_bytree, + colsample_bynode=config.hyperparams.colsample_bynode, + random_state=config.hyperparams.random_state, + early_stopping_rounds=config.hyperparams.early_stopping_rounds, + verbosity=config.verbosity, + ) + + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + @property + @override + def hyperparams(self) -> LgbLinearHyperParams: + return self._config.hyperparams + + @property + @override + def is_fitted(self) -> bool: + return self._lgblinear_model.__sklearn_is_fitted__() + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + input_data: pd.DataFrame = data.input_data() + target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + + sample_weight = data.sample_weight_series + + # Prepare validation data if provided + eval_set = None + eval_sample_weight = None + if data_val is not None: + val_input_data: pd.DataFrame = data_val.input_data() + val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore + val_sample_weight = data_val.sample_weight_series.to_numpy() # type: ignore + eval_set = [(val_input_data, val_target)] + + eval_sample_weight = [val_sample_weight] + + self._lgblinear_model.fit( # type: ignore + X=input_data, + y=target, + feature_name=input_data.columns.tolist(), + sample_weight=sample_weight, + eval_set=eval_set, + eval_sample_weight=eval_sample_weight, + ) + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + prediction: npt.NDArray[np.floating] = self._lgblinear_model.predict(X=input_data) + + return ForecastDataset( + data=pd.DataFrame( + data=prediction, + index=input_data.index, + columns=[quantile.format() for quantile in self.config.quantiles], + ), + sample_interval=data.sample_interval, + ) + + @property + @override + def feature_importances(self) -> pd.DataFrame: + models = self._lgblinear_model._models # noqa: SLF001 + weights_df = pd.DataFrame( + [models[i].feature_importances_ for i in range(len(models))], + index=[quantile.format() for quantile in self.config.quantiles], + columns=models[0].feature_name_, + ).transpose() + + weights_df.index.name = "feature_name" + weights_df.columns.name = "quantiles" + + weights_abs = weights_df.abs() + total = weights_abs.sum(axis=0).replace(to_replace=0, value=1.0) # pyright: ignore[reportUnknownMemberType] + + return weights_abs / total + + +__all__ = ["LgbLinearForecaster", "LgbLinearForecasterConfig", "LgbLinearHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py new file mode 100644 index 000000000..cc5a26856 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py @@ -0,0 +1,340 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""LightGBM-based forecasting models for probabilistic energy forecasting. + +Provides gradient boosting tree models using LightGBM for multi-quantile energy +forecasting. Optimized for time series data with specialized loss functions and +comprehensive hyperparameter control for production forecasting workflows. +""" + +from typing import Literal, override + +import numpy as np +import numpy.typing as npt +import pandas as pd +from pydantic import Field + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_models.estimators.lightgbm import LGBMQuantileRegressor +from openstef_models.explainability.mixins import ExplainableForecaster +from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig + + +class LightGBMHyperParams(HyperParams): + """LightGBM hyperparameters for gradient boosting tree models. + + Example: + Creating custom hyperparameters for deep trees with regularization: + + >>> hyperparams = LGBMHyperParams( + ... n_estimators=200, + ... max_depth=8, + ... learning_rate=0.1, + ... reg_alpha=0.1, + ... reg_lambda=1.0, + ... subsample=0.8 + ... ) + + Note: + These parameters are optimized for probabilistic forecasting with + quantile regression. The default objective function is specialized + for magnitude-weighted pinball loss. + """ + + # Core Tree Boosting Parameters + n_estimators: int = Field( + default=100, + description="Number of boosting rounds/trees to fit. Higher values may improve performance but " + "increase training time and risk overfitting.", + ) + learning_rate: float = Field( + default=0.49, # 0.3 + alias="eta", + description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " + "more boosting rounds.", + ) + max_depth: int = Field( + default=2, # 8, + description="Maximum depth of trees. Higher values capture more complex patterns but risk " + "overfitting. Range: [1,∞]", + ) + min_child_weight: float = Field( + default=1, + description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent " + "overfitting. Range: [0,∞]", + ) + + min_data_in_leaf: int = Field( + default=10, + description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", + ) + min_data_in_bin: int = Field( + default=10, + description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", + ) + + # Regularization + reg_alpha: float = Field( + default=0, + description="L1 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + reg_lambda: float = Field( + default=1, + description="L2 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + + # Tree Structure Control + num_leaves: int = Field( + default=100, # 31 + description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", + ) + + max_bin: int = Field( + default=256, + description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " + "increase memory. Only for hist tree_method.", + ) + + # Subsampling Parameters + subsample: float = Field( + default=1.0, + description="Fraction of training samples used for each tree. Lower values prevent overfitting. Range: (0,1]", + ) + colsample_bytree: float = Field( + default=1.0, + description="Fraction of features used when constructing each tree. Range: (0,1]", + ) + colsample_bynode: float = Field( + default=1.0, + description="Fraction of features used for each split/node. Range: (0,1]", + ) + + # General Parameters + random_state: int | None = Field( + default=None, + alias="seed", + description="Random seed for reproducibility. Controls tree structure randomness.", + ) + + early_stopping_rounds: int | None = Field( + default=None, + description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", + ) + + +class LightGBMForecasterConfig(ForecasterConfig): + """Configuration for LightGBM-based forecaster. + Extends HorizonForecasterConfig with LightGBM-specific hyperparameters + and execution settings. + + Example: + Creating a LightGBM forecaster configuration with custom hyperparameters: + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LightGBMForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1 + ))], + ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6) + ... ). + """ # noqa: D205 + + hyperparams: LightGBMHyperParams = LightGBMHyperParams() + + # General Parameters + device: str = Field( + default="cpu", + description="Device for LightGBM computation. Options: 'cpu', 'cuda', 'cuda:', 'gpu'", + ) + n_jobs: int = Field( + default=1, + description="Number of parallel threads for tree construction. -1 uses all available cores.", + ) + verbosity: Literal[-1, 0, 1, 2, 3] = Field( + default=-1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + + +MODEL_CODE_VERSION = 1 + + +class LightGBMForecasterState(BaseConfig): + """Serializable state for LightGBM forecaster persistence. + + Contains all information needed to restore a trained LightGBM model, + including configuration and the serialized model weights. Used for + model saving, loading, and version management in production systems. + """ + + version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") + config: LightGBMForecasterConfig = Field(..., description="Forecaster configuration.") + model: str = Field(..., description="Base64-encoded serialized LightGBM model.") + + +class LightGBMForecaster(Forecaster, ExplainableForecaster): + """LightGBM-based forecaster for probabilistic energy forecasting. + + Implements gradient boosting trees using LightGBM for multi-quantile forecasting. + Optimized for time series prediction with specialized loss functions and + comprehensive hyperparameter control suitable for production energy forecasting. + + The forecaster uses a multi-output strategy where each quantile is predicted + by separate trees within the same boosting ensemble. This approach provides + well-calibrated uncertainty estimates while maintaining computational efficiency. + + Invariants: + - fit() must be called before predict() to train the model + - Configuration quantiles determine the number of prediction outputs + - Model state is preserved across predict() calls after fitting + - Input features must match training data structure during prediction + + Example: + Basic forecasting workflow: + + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LightGBMForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1))], + ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6) + ... ) + >>> forecaster = LightGBMForecaster(config) + >>> # forecaster.fit(training_data) + >>> # predictions = forecaster.predict(test_data) + + Note: + LightGBM dependency is optional and must be installed separately. + The model automatically handles multi-quantile output and uses + magnitude-weighted pinball loss by default for better forecasting performance. + + See Also: + LightGBMHyperParams: Detailed hyperparameter configuration options. + HorizonForecaster: Base interface for all forecasting models. + GBLinearForecaster: Alternative linear model using LightGBM. + """ + + Config = LightGBMForecasterConfig + HyperParams = LightGBMHyperParams + + _config: LightGBMForecasterConfig + _lightgbm_model: LGBMQuantileRegressor + + def __init__(self, config: LightGBMForecasterConfig) -> None: + """Initialize LightGBM forecaster with configuration. + + Creates an untrained LightGBM regressor with the specified configuration. + The underlying LightGBM model is configured for multi-output quantile + regression using the provided hyperparameters and execution settings. + + Args: + config: Complete configuration including hyperparameters, quantiles, + and execution settings for the LightGBM model. + """ + self._config = config + + self._lightgbm_model = LGBMQuantileRegressor( + quantiles=[float(q) for q in config.quantiles], + linear_tree=False, + n_estimators=config.hyperparams.n_estimators, + learning_rate=config.hyperparams.learning_rate, + max_depth=config.hyperparams.max_depth, + min_child_weight=config.hyperparams.min_child_weight, + min_data_in_leaf=config.hyperparams.min_data_in_leaf, + min_data_in_bin=config.hyperparams.min_data_in_bin, + reg_alpha=config.hyperparams.reg_alpha, + reg_lambda=config.hyperparams.reg_lambda, + num_leaves=config.hyperparams.num_leaves, + max_bin=config.hyperparams.max_bin, + subsample=config.hyperparams.subsample, + colsample_bytree=config.hyperparams.colsample_bytree, + colsample_bynode=config.hyperparams.colsample_bynode, + random_state=config.hyperparams.random_state, + early_stopping_rounds=config.hyperparams.early_stopping_rounds, + verbosity=config.verbosity, + ) + + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + @property + @override + def hyperparams(self) -> LightGBMHyperParams: + return self._config.hyperparams + + @property + @override + def is_fitted(self) -> bool: + return self._lightgbm_model.__sklearn_is_fitted__() + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + input_data: pd.DataFrame = data.input_data() + target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + + sample_weight = data.sample_weight_series + + # Prepare validation data if provided + eval_set = None + eval_sample_weight = None + if data_val is not None: + val_input_data: pd.DataFrame = data_val.input_data() + val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore + val_sample_weight = data_val.sample_weight_series.to_numpy() # type: ignore + eval_set = (val_input_data, val_target) + eval_sample_weight = [val_sample_weight] + + self._lightgbm_model.fit( + X=input_data, + y=target, + feature_name=input_data.columns.tolist(), + sample_weight=sample_weight, # type: ignore + eval_set=eval_set, # type: ignore + eval_sample_weight=eval_sample_weight, # type: ignore + ) + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + prediction: npt.NDArray[np.floating] = self._lightgbm_model.predict(X=input_data) + + return ForecastDataset( + data=pd.DataFrame( + data=prediction, + index=input_data.index, + columns=[quantile.format() for quantile in self.config.quantiles], + ), + sample_interval=data.sample_interval, + ) + + @property + @override + def feature_importances(self) -> pd.DataFrame: + models = self._lightgbm_model._models + weights_df = pd.DataFrame( + [models[i].feature_importances_ for i in range(len(models))], + index=[quantile.format() for quantile in self.config.quantiles], + columns=models[0].feature_name_, + ).transpose() + + weights_df.index.name = "feature_name" + weights_df.columns.name = "quantiles" + + weights_abs = weights_df.abs() + total = weights_abs.sum(axis=0).replace(to_replace=0, value=1.0) # pyright: ignore[reportUnknownMemberType] + + return weights_abs / total + + +__all__ = ["LightGBMForecaster", "LightGBMForecasterConfig", "LightGBMHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index f7152bf11..517f5be83 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -30,10 +30,22 @@ from openstef_models.models import ForecastingModel from openstef_models.models.forecasting.flatliner_forecaster import FlatlinerForecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster +from openstef_models.models.forecasting.hybrid_forecaster import HybridForecaster +from openstef_models.models.forecasting.lgblinear_forecaster import LgbLinearForecaster +from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder -from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, Imputer, SampleWeighter, Scaler -from openstef_models.transforms.postprocessing import ConfidenceIntervalApplicator, QuantileSorter +from openstef_models.transforms.general import ( + Clipper, + EmptyFeatureRemover, + Imputer, + SampleWeighter, + Scaler, +) +from openstef_models.transforms.postprocessing import ( + ConfidenceIntervalApplicator, + QuantileSorter, +) from openstef_models.transforms.time_domain import ( CyclicFeaturesAdder, DatetimeFeaturesAdder, @@ -41,19 +53,36 @@ RollingAggregatesAdder, ) from openstef_models.transforms.time_domain.lags_adder import LagsAdder -from openstef_models.transforms.time_domain.rolling_aggregates_adder import AggregationFunction -from openstef_models.transforms.validation import CompletenessChecker, FlatlineChecker, InputConsistencyChecker -from openstef_models.transforms.weather_domain import DaylightFeatureAdder, RadiationDerivedFeaturesAdder -from openstef_models.transforms.weather_domain.atmosphere_derived_features_adder import AtmosphereDerivedFeaturesAdder +from openstef_models.transforms.time_domain.rolling_aggregates_adder import ( + AggregationFunction, +) +from openstef_models.transforms.validation import ( + CompletenessChecker, + FlatlineChecker, + InputConsistencyChecker, +) +from openstef_models.transforms.weather_domain import ( + DaylightFeatureAdder, + RadiationDerivedFeaturesAdder, +) +from openstef_models.transforms.weather_domain.atmosphere_derived_features_adder import ( + AtmosphereDerivedFeaturesAdder, +) from openstef_models.utils.data_split import DataSplitter from openstef_models.utils.feature_selection import Exclude, FeatureSelection, Include -from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow, ForecastingCallback +from openstef_models.workflows.custom_forecasting_workflow import ( + CustomForecastingWorkflow, + ForecastingCallback, +) class LocationConfig(BaseConfig): """Configuration for location information in forecasting workflows.""" - name: str = Field(default="test_location", description="Name of the forecasting location or workflow.") + name: str = Field( + default="test_location", + description="Name of the forecasting location or workflow.", + ) description: str = Field(default="", description="Description of the forecasting workflow.") coordinate: Coordinate = Field( default=Coordinate( @@ -63,7 +92,8 @@ class LocationConfig(BaseConfig): description="Geographic coordinate of the location.", ) country_code: CountryAlpha2 = Field( - default=CountryAlpha2("NL"), description="Country code for holiday feature generation." + default=CountryAlpha2("NL"), + description="Country code for holiday feature generation.", ) @property @@ -87,42 +117,65 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_id: ModelIdentifier = Field(description="Unique identifier for the forecasting model.") # Model configuration - model: Literal["xgboost", "gblinear", "flatliner"] = Field( + model: Literal["xgboost", "gblinear", "flatliner", "hybrid", "lightgbm", "lgblinear"] = Field( description="Type of forecasting model to use." ) # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( - default=[Q(0.5)], description="List of quantiles to predict for probabilistic forecasting." + default=[Q(0.5)], + description="List of quantiles to predict for probabilistic forecasting.", ) sample_interval: timedelta = Field( - default=timedelta(minutes=15), description="Time interval between consecutive data samples." + default=timedelta(minutes=15), + description="Time interval between consecutive data samples.", ) horizons: list[LeadTime] = Field( - default=[LeadTime.from_string("PT48H")], description="List of forecast horizons to predict." + default=[LeadTime.from_string("PT48H")], + description="List of forecast horizons to predict.", ) xgboost_hyperparams: XGBoostForecaster.HyperParams = Field( - default=XGBoostForecaster.HyperParams(), description="Hyperparameters for XGBoost forecaster." + default=XGBoostForecaster.HyperParams(), + description="Hyperparameters for XGBoost forecaster.", ) gblinear_hyperparams: GBLinearForecaster.HyperParams = Field( - default=GBLinearForecaster.HyperParams(), description="Hyperparameters for GBLinear forecaster." + default=GBLinearForecaster.HyperParams(), + description="Hyperparameters for GBLinear forecaster.", + ) + + lightgbm_hyperparams: LightGBMForecaster.HyperParams = Field( + default=LightGBMForecaster.HyperParams(), + description="Hyperparameters for LightGBM forecaster.", + ) + + lgblinear_hyperparams: LgbLinearForecaster.HyperParams = Field( + default=LgbLinearForecaster.HyperParams(), + description="Hyperparameters for LightGBM forecaster.", + ) + + hybrid_hyperparams: HybridForecaster.HyperParams = Field( + default=HybridForecaster.HyperParams(), + description="Hyperparameters for Hybrid forecaster.", ) location: LocationConfig = Field( - default=LocationConfig(), description="Location information for the forecasting workflow." + default=LocationConfig(), + description="Location information for the forecasting workflow.", ) # Data properties target_column: str = Field(default="load", description="Name of the target variable column in datasets.") energy_price_column: str = Field( - default="day_ahead_electricity_price", description="Name of the energy price column in datasets." + default="day_ahead_electricity_price", + description="Name of the energy price column in datasets.", ) radiation_column: str = Field(default="radiation", description="Name of the radiation column in datasets.") wind_speed_column: str = Field(default="windspeed", description="Name of the wind speed column in datasets.") pressure_column: str = Field(default="pressure", description="Name of the pressure column in datasets.") temperature_column: str = Field(default="temperature", description="Name of the temperature column in datasets.") relative_humidity_column: str = Field( - default="relative_humidity", description="Name of the relative humidity column in datasets." + default="relative_humidity", + description="Name of the relative humidity column in datasets.", ) predict_history: timedelta = Field( default=timedelta(days=14), @@ -131,7 +184,8 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob # Feature engineering and validation completeness_threshold: float = Field( - default=0.5, description="Minimum fraction of data that should be available for making a regular forecast." + default=0.5, + description="Minimum fraction of data that should be available for making a regular forecast.", ) flatliner_threshold: timedelta = Field( default=timedelta(hours=24), @@ -150,7 +204,9 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Feature selection for which features to clip.", ) sample_weight_exponent: float = Field( - default_factory=lambda data: 1.0 if data.get("model") == "gblinear" else 0.0, + default_factory=lambda data: 1.0 + if data.get("model") in {"gblinear", "lgblinear", "lightgbm", "hybrid", "xgboost"} + else 0.0, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " "Note: Defaults to 1.0 for gblinear congestion models.", @@ -174,16 +230,22 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob # Callbacks mlflow_storage: MLFlowStorage | None = Field( - default_factory=MLFlowStorage, description="Configuration for MLflow experiment tracking and model storage." + default_factory=MLFlowStorage, + description="Configuration for MLflow experiment tracking and model storage.", ) - model_reuse_enable: bool = Field(default=True, description="Whether to enable reuse of previously trained models.") + model_reuse_enable: bool = Field( + default=True, + description="Whether to enable reuse of previously trained models.", + ) model_reuse_max_age: timedelta = Field( - default=timedelta(days=7), description="Maximum age of a model to be considered for reuse." + default=timedelta(days=7), + description="Maximum age of a model to be considered for reuse.", ) model_selection_enable: bool = Field( - default=True, description="Whether to enable automatic model selection based on performance." + default=True, + description="Whether to enable automatic model selection based on performance.", ) model_selection_metric: tuple[QuantileOrGlobal, str, MetricDirection] = Field( default=(Q(0.5), "R2", "higher_is_better"), @@ -201,7 +263,9 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) -def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomForecastingWorkflow: +def create_forecasting_workflow( + config: ForecastingWorkflowConfig, +) -> CustomForecastingWorkflow: """Create a forecasting workflow from configuration. Builds a complete forecasting pipeline including preprocessing, forecaster, and postprocessing @@ -222,7 +286,7 @@ def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomFore load_column=config.target_column, flatliner_threshold=config.flatliner_threshold, detect_non_zero_flatliner=config.detect_non_zero_flatliner, - error_on_flatliner=True, + error_on_flatliner=False, ), CompletenessChecker(completeness_threshold=config.completeness_threshold), EmptyFeatureRemover(), @@ -256,7 +320,10 @@ def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomFore ), ] feature_standardizers = [ - Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), + Clipper( + selection=Include(config.energy_price_column).combine(config.clip_features), + mode="standard", + ), Scaler(selection=Exclude(config.target_column), method="standard"), SampleWeighter( target_column=config.target_column, @@ -281,7 +348,38 @@ def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomFore ) ) postprocessing = [QuantileSorter()] - + elif config.model == "lgblinear": + preprocessing = [ + *checks, + *feature_adders, + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers, + ] + forecaster = LgbLinearForecaster( + config=LgbLinearForecaster.Config( + quantiles=config.quantiles, + horizons=config.horizons, + hyperparams=config.lgblinear_hyperparams, + ) + ) + postprocessing = [QuantileSorter()] + elif config.model == "lightgbm": + preprocessing = [ + *checks, + *feature_adders, + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers, + ] + forecaster = LightGBMForecaster( + config=LightGBMForecaster.Config( + quantiles=config.quantiles, + horizons=config.horizons, + hyperparams=config.lightgbm_hyperparams, + ) + ) + postprocessing = [QuantileSorter()] elif config.model == "gblinear": preprocessing = [ *checks, @@ -296,7 +394,7 @@ def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomFore hyperparams=config.gblinear_hyperparams, ) ) - postprocessing = [] + postprocessing = [QuantileSorter()] elif config.model == "flatliner": preprocessing = [] forecaster = FlatlinerForecaster( @@ -308,6 +406,21 @@ def create_forecasting_workflow(config: ForecastingWorkflowConfig) -> CustomFore postprocessing = [ ConfidenceIntervalApplicator(quantiles=config.quantiles), ] + elif config.model == "hybrid": + preprocessing = [ + *checks, + Imputer(selection=Exclude(config.target_column), imputation_strategy="mean"), + *feature_adders, + *feature_standardizers, + ] + forecaster = HybridForecaster( + config=HybridForecaster.Config( + quantiles=config.quantiles, + horizons=config.horizons, + hyperparams=config.hybrid_hyperparams, + ) + ) + postprocessing = [QuantileSorter()] else: msg = f"Unsupported model type: {config.model}" raise ValueError(msg) diff --git a/packages/openstef-models/tests/unit/estimators/test_hybrid.py b/packages/openstef-models/tests/unit/estimators/test_hybrid.py new file mode 100644 index 000000000..857cb7705 --- /dev/null +++ b/packages/openstef-models/tests/unit/estimators/test_hybrid.py @@ -0,0 +1,39 @@ +import pandas as pd +import pytest +from numpy.random import default_rng + +from openstef_models.estimators.hybrid import HybridQuantileRegressor + + +@pytest.fixture +def dataset() -> tuple[pd.DataFrame, pd.Series]: + n_samples = 100 + n_features = 5 + rng = default_rng() + X = pd.DataFrame(rng.random((n_samples, n_features))) + y = pd.Series(rng.random(n_samples)) + return X, y + + +def test_init_sets_quantiles_and_models(): + quantiles = [0.1, 0.5, 0.9] + model = HybridQuantileRegressor(quantiles=quantiles) + assert model.quantiles == quantiles + assert len(model._models) == len(quantiles) + + +def test_fit_and_predict_shape(dataset: tuple[pd.DataFrame, pd.Series]): + quantiles = [0.1, 0.5, 0.9] + X, y = dataset[0], dataset[1] + model = HybridQuantileRegressor(quantiles=quantiles) + model.fit(X, y) + preds = model.predict(X) + assert preds.shape == (X.shape[0], len(quantiles)) + + +def test_is_fitted(dataset: tuple[pd.DataFrame, pd.Series]): + quantiles = [0.1, 0.5, 0.9] + X, y = dataset[0], dataset[1] + model = HybridQuantileRegressor(quantiles=quantiles) + model.fit(X, y) + assert model.is_fitted diff --git a/packages/openstef-models/tests/unit/estimators/test_lightgbm.py b/packages/openstef-models/tests/unit/estimators/test_lightgbm.py new file mode 100644 index 000000000..555add5cb --- /dev/null +++ b/packages/openstef-models/tests/unit/estimators/test_lightgbm.py @@ -0,0 +1,39 @@ +import pandas as pd +import pytest +from numpy.random import default_rng + +from openstef_models.estimators.lightgbm import LGBMQuantileRegressor + + +@pytest.fixture +def dataset() -> tuple[pd.DataFrame, pd.Series]: + n_samples = 100 + n_features = 5 + rng = default_rng() + X = pd.DataFrame(rng.random((n_samples, n_features))) + y = pd.Series(rng.random(n_samples)) + return X, y + + +def test_init_sets_quantiles_and_models(): + quantiles = [0.1, 0.5, 0.9] + model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False) + assert model.quantiles == quantiles + assert len(model._models) == len(quantiles) + + +def test_fit_and_predict_shape(dataset: tuple[pd.DataFrame, pd.Series]): + quantiles = [0.1, 0.5, 0.9] + X, y = dataset[0], dataset[1] + model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False, n_estimators=5) + model.fit(X, y) + preds = model.predict(X) + assert preds.shape == (X.shape[0], len(quantiles)) + + +def test_sklearn_is_fitted_true_after_fit(dataset: tuple[pd.DataFrame, pd.Series]): + quantiles = [0.1, 0.5, 0.9] + X, y = dataset[0], dataset[1] + model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False, n_estimators=2) + model.fit(X, y) + assert model.__sklearn_is_fitted__() diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py new file mode 100644 index 000000000..0288a42d6 --- /dev/null +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py @@ -0,0 +1,159 @@ +from datetime import timedelta + +import pandas as pd +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_models.models.forecasting.lgblinear_forecaster import ( + LgbLinearForecaster, + LgbLinearForecasterConfig, + LgbLinearHyperParams, +) + + +@pytest.fixture +def base_config() -> LgbLinearForecasterConfig: + """Base configuration for LgbLinear forecaster tests.""" + + return LgbLinearForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), + device="cpu", + n_jobs=1, + verbosity=0, + ) + + +@pytest.fixture +def forecaster(base_config: LgbLinearForecasterConfig) -> LgbLinearForecaster: + return LgbLinearForecaster(base_config) + + +def test_initialization(forecaster: LgbLinearForecaster): + assert isinstance(forecaster, LgbLinearForecaster) + assert forecaster.config.hyperparams.n_estimators == 100 # type: ignore + + +def test_quantile_lgblinear_forecaster__fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LgbLinearForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = LgbLinearForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + # Since forecast is deterministic with fixed random seed, check value spread (vectorized) + # All quantiles should have some variation (not all identical values) + stds = result.data.std() + assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" + + +def test_lgblinear_forecaster__not_fitted_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LgbLinearForecasterConfig, +): + """Test that NotFittedError is raised when predicting before fitting.""" + # Arrange + forecaster = LgbLinearForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError): + forecaster.predict(sample_forecast_input_dataset) + + +def test_lgblinear_forecaster__predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LgbLinearForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = LgbLinearForecaster(config=base_config) + + # Act & Assert + with pytest.raises( + NotFittedError, + match="The LgbLinearForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 + ): + forecaster.predict(sample_forecast_input_dataset) + + +def test_lgblinear_forecaster__with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: LgbLinearForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = LgbLinearForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = LgbLinearForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_lgblinear_forecaster__feature_importances( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LgbLinearForecasterConfig, +): + """Test that feature_importances returns correct normalized importance scores.""" + # Arrange + forecaster = LgbLinearForecaster(config=base_config) + forecaster.fit(sample_forecast_input_dataset) + + # Act + feature_importances = forecaster.feature_importances + + # Assert + assert len(feature_importances.index) > 0 + + # Columns should match expected quantile formats + expected_columns = pd.Index([q.format() for q in base_config.quantiles], name="quantiles") + pd.testing.assert_index_equal(feature_importances.columns, expected_columns) + + # Values should be normalized (sum to 1.0 per quantile column) and non-negative + col_sums = feature_importances.sum(axis=0) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) + assert (feature_importances >= 0).all().all() diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py new file mode 100644 index 000000000..f0c7ff7e7 --- /dev/null +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py @@ -0,0 +1,159 @@ +from datetime import timedelta + +import pandas as pd +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_models.models.forecasting.lightgbm_forecaster import ( + LightGBMForecaster, + LightGBMForecasterConfig, + LightGBMHyperParams, +) + + +@pytest.fixture +def base_config() -> LightGBMForecasterConfig: + """Base configuration for LightGBM forecaster tests.""" + + return LightGBMForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), + device="cpu", + n_jobs=1, + verbosity=0, + ) + + +@pytest.fixture +def forecaster(base_config: LightGBMForecasterConfig) -> LightGBMForecaster: + return LightGBMForecaster(base_config) + + +def test_initialization(forecaster: LightGBMForecaster): + assert isinstance(forecaster, LightGBMForecaster) + assert forecaster.config.hyperparams.n_estimators == 100 # type: ignore + + +def test_quantile_lightgbm_forecaster__fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LightGBMForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = LightGBMForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + # Since forecast is deterministic with fixed random seed, check value spread (vectorized) + # All quantiles should have some variation (not all identical values) + stds = result.data.std() + assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" + + +def test_lightgbm_forecaster__not_fitted_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LightGBMForecasterConfig, +): + """Test that NotFittedError is raised when predicting before fitting.""" + # Arrange + forecaster = LightGBMForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError): + forecaster.predict(sample_forecast_input_dataset) + + +def test_lightgbm_forecaster__predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LightGBMForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = LightGBMForecaster(config=base_config) + + # Act & Assert + with pytest.raises( + NotFittedError, + match="The LightGBMForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 + ): + forecaster.predict(sample_forecast_input_dataset) + + +def test_lightgbm_forecaster__with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: LightGBMForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = LightGBMForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = LightGBMForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_lightgbm_forecaster__feature_importances( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LightGBMForecasterConfig, +): + """Test that feature_importances returns correct normalized importance scores.""" + # Arrange + forecaster = LightGBMForecaster(config=base_config) + forecaster.fit(sample_forecast_input_dataset) + + # Act + feature_importances = forecaster.feature_importances + + # Assert + assert len(feature_importances.index) > 0 + + # Columns should match expected quantile formats + expected_columns = pd.Index([q.format() for q in base_config.quantiles], name="quantiles") + pd.testing.assert_index_equal(feature_importances.columns, expected_columns) + + # Values should be normalized (sum to 1.0 per quantile column) and non-negative + col_sums = feature_importances.sum(axis=0) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) + assert (feature_importances >= 0).all().all() diff --git a/uv.lock b/uv.lock index 769d7e81b..f8bf4a7b4 100644 --- a/uv.lock +++ b/uv.lock @@ -1115,6 +1115,8 @@ wheels = [ { url = 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a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py index abffd154a..81ad400bf 100644 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -21,7 +21,7 @@ class HybridQuantileRegressor: """Custom Hybrid regressor for multi-quantile estimation using sample weights.""" - def __init__( + def __init__( # noqa: D107, PLR0913, PLR0917 self, quantiles: list[float], lightgbm_n_estimators: int = 100, @@ -38,7 +38,7 @@ def __init__( lightgbm_subsample: float = 1.0, lightgbm_colsample_by_tree: float = 1.0, lightgbm_colsample_by_node: float = 1.0, - gblinear_n_estimators: int = 100, + gblinear_n_steps: int = 100, gblinear_learning_rate: float = 0.15, gblinear_reg_alpha: float = 0.0001, gblinear_reg_lambda: float = 0, @@ -75,7 +75,7 @@ def __init__( booster="gblinear", # Core parameters for forecasting objective="reg:quantileerror", - n_estimators=gblinear_n_estimators, + n_estimators=gblinear_n_steps, learning_rate=gblinear_learning_rate, # Regularization parameters reg_alpha=gblinear_reg_alpha, @@ -99,12 +99,13 @@ def __init__( ) ) self.is_fitted: bool = False + self.feature_names: list[str] = [] def fit( self, X: npt.NDArray[np.floating] | pd.DataFrame, # noqa: N803 y: npt.NDArray[np.floating] | pd.Series, - sample_weight: npt.NDArray[np.floating] | None = None, + sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, ) -> None: """Fit the multi-quantile regressor. @@ -115,6 +116,8 @@ def fit( sample_weight: Sample weights for training data. feature_name: List of feature names. """ + self.feature_names = feature_name if feature_name is not None else [] + X = X.ffill().fillna(0) # type: ignore for model in self._models: model.fit( diff --git a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py index 66f222327..130729407 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Custom LightGBM regressor for multi-quantile regression. This module provides the LGBMQuantileRegressor class, which extends LightGBM's LGBMRegressor diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index b1909b068..f5e97289a 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -1,31 +1,34 @@ -from __future__ import annotations +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 -import base64 -import logging -from typing import Any, Literal, Self, Union, cast, override -import numpy as np -import numpy.typing as npt +from typing import TYPE_CHECKING, override + import pandas as pd from pydantic import Field - from openstef_core.base_model import BaseConfig from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( - ModelLoadingError, NotFittedError, ) from openstef_core.mixins import HyperParams from openstef_models.estimators.hybrid import HybridQuantileRegressor from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMHyperParams +if TYPE_CHECKING: + import numpy as np + import numpy.typing as npt + class HybridHyperParams(HyperParams): - """Hyperparameters for Support Vector Regression (Hybrid).""" + """Hyperparameters for Stacked LGBM GBLinear Regressor.""" lightgbm_params: LightGBMHyperParams = LightGBMHyperParams() + gb_linear_params: GBLinearHyperParams = GBLinearHyperParams() l1_penalty: float = Field( default=0.0, @@ -56,7 +59,7 @@ class HybridForecasterState(BaseConfig): class HybridForecaster(Forecaster): - """Wrapper for sklearn's Hybrid to make it compatible with HorizonForecaster.""" + """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" Config = HybridForecasterConfig HyperParams = HybridHyperParams @@ -65,17 +68,11 @@ class HybridForecaster(Forecaster): model: HybridQuantileRegressor def __init__(self, config: HybridForecasterConfig) -> None: - """Initialize the Hybrid forecaster. - - Args: - kernel: Kernel type for Hybrid. Must be one of "linear", "poly", "rbf", "sigmoid", or "precomputed". - C: Regularization parameter. - epsilon: Epsilon in the epsilon-Hybrid model. - """ + """Initialize the Hybrid forecaster.""" self._config = config self._model = HybridQuantileRegressor( - quantiles=config.quantiles, + quantiles=[float(q) for q in config.quantiles], lightgbm_n_estimators=config.hyperparams.lightgbm_params.n_estimators, lightgbm_learning_rate=config.hyperparams.lightgbm_params.learning_rate, lightgbm_max_depth=config.hyperparams.lightgbm_params.max_depth, @@ -89,6 +86,10 @@ def __init__(self, config: HybridForecasterConfig) -> None: lightgbm_subsample=config.hyperparams.lightgbm_params.subsample, lightgbm_colsample_by_tree=config.hyperparams.lightgbm_params.colsample_bytree, lightgbm_colsample_by_node=config.hyperparams.lightgbm_params.colsample_bynode, + gblinear_n_steps=config.hyperparams.gb_linear_params.n_steps, + gblinear_learning_rate=config.hyperparams.gb_linear_params.learning_rate, + gblinear_reg_alpha=config.hyperparams.gb_linear_params.reg_alpha, + gblinear_reg_lambda=config.hyperparams.gb_linear_params.reg_lambda, ) @property @@ -113,8 +114,9 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None input_data: pd.DataFrame = data.input_data() target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + sample_weights: pd.Series = data.sample_weight_series - self._model.fit(X=input_data, y=target) + self._model.fit(X=input_data, y=target, sample_weight=sample_weights) @override def predict(self, data: ForecastInputDataset) -> ForecastDataset: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py index dc5babb7e..04293c048 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -9,8 +9,7 @@ comprehensive hyperparameter control for production forecasting workflows. """ -import base64 -from typing import TYPE_CHECKING, Any, Literal, Self, cast, override +from typing import TYPE_CHECKING, Literal, override import numpy as np import pandas as pd @@ -19,7 +18,6 @@ from openstef_core.base_model import BaseConfig from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( - ModelLoadingError, NotFittedError, ) from openstef_core.mixins import HyperParams @@ -145,8 +143,7 @@ class LgbLinearForecasterConfig(ForecasterConfig): >>> from openstef_core.types import LeadTime, Quantile >>> config = LgbLinearForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], - ... horizons=[LeadTime(timedelta(hours=1 - ))], + ... horizons=[LeadTime(timedelta(hours=1))], ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) ... ). """ # noqa: D205 diff --git a/packages/openstef-models/tests/unit/estimators/test_hybrid.py b/packages/openstef-models/tests/unit/estimators/test_hybrid.py index 857cb7705..4c8a1ac97 100644 --- a/packages/openstef-models/tests/unit/estimators/test_hybrid.py +++ b/packages/openstef-models/tests/unit/estimators/test_hybrid.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + import pandas as pd import pytest from numpy.random import default_rng diff --git a/packages/openstef-models/tests/unit/estimators/test_lightgbm.py b/packages/openstef-models/tests/unit/estimators/test_lightgbm.py index 555add5cb..936b2e097 100644 --- a/packages/openstef-models/tests/unit/estimators/test_lightgbm.py +++ b/packages/openstef-models/tests/unit/estimators/test_lightgbm.py @@ -1,3 +1,6 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 import pandas as pd import pytest from numpy.random import default_rng diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py new file mode 100644 index 000000000..0a0334fbc --- /dev/null +++ b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py @@ -0,0 +1,149 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams +from openstef_models.models.forecasting.hybrid_forecaster import ( + HybridForecaster, + HybridForecasterConfig, + HybridHyperParams, +) +from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMHyperParams + + +@pytest.fixture +def base_config() -> HybridForecasterConfig: + """Base configuration for Hybrid forecaster tests.""" + lightgbm_params = LightGBMHyperParams(n_estimators=10, max_depth=2) + gb_linear_params = GBLinearHyperParams(n_steps=5, learning_rate=0.1, reg_alpha=0.0, reg_lambda=0.0) + params = HybridHyperParams( + lightgbm_params=lightgbm_params, + gb_linear_params=gb_linear_params, + ) + return HybridForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=params, + verbosity=False, + ) + + +def test_hybrid_forecaster__fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: HybridForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = HybridForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + # Since forecast is deterministic with fixed random seed, check value spread (vectorized) + # All quantiles should have some variation (not all identical values) + stds = result.data.std() + assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" + + +def test_hybrid_forecaster__predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: HybridForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = HybridForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError, match="HybridForecaster"): + forecaster.predict(sample_forecast_input_dataset) + + +def test_hybrid_forecaster__with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: HybridForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = HybridForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = HybridForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +@pytest.mark.parametrize("objective", ["pinball_loss", "arctan_loss"]) +def test_hybrid_forecaster__different_objectives( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: HybridForecasterConfig, + objective: str, +): + """Test that forecaster works with different objective functions.""" + # Arrange + config = base_config.model_copy( + update={ + "hyperparams": base_config.hyperparams.model_copy( + update={"objective": objective} # type: ignore[arg-type] + ) + } + ) + forecaster = HybridForecaster(config=config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality should work regardless of objective + assert forecaster.is_fitted, f"Model with {objective} should be fitted" + assert not result.data.isna().any().any(), f"Forecast with {objective} should not contain NaN values" + + # Check value spread for each objective + # Note: Some objectives (like arctan_loss) may produce zero variation for some quantiles with small datasets + stds = result.data.std() + # At least one quantile should have variation (the model should not be completely degenerate) + assert (stds > 0).any(), f"At least one column should have variation with {objective}, got stds: {dict(stds)}" diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py index 0288a42d6..dc743be07 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py @@ -1,3 +1,6 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 from datetime import timedelta import pandas as pd diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py index f0c7ff7e7..efc728ac3 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py @@ -1,3 +1,6 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 from datetime import timedelta import pandas as pd From 75239877c8fc1099a9c98e9320497799752488a6 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 10 Nov 2025 10:06:52 +0100 Subject: [PATCH 03/72] Ruff compliance --- .../openstef_models/estimators/__init__.py | 2 +- .../src/openstef_models/estimators/hybrid.py | 27 +++++++++++++------ .../openstef_models/estimators/lightgbm.py | 26 +++++++++--------- .../models/forecasting/hybrid_forecaster.py | 9 ++++--- .../forecasting/lgblinear_forecaster.py | 13 +-------- .../models/forecasting/lightgbm_forecaster.py | 21 +++++---------- .../tests/unit/estimators/__init__.py | 0 pyproject.toml | 2 +- 8 files changed, 48 insertions(+), 52 deletions(-) create mode 100644 packages/openstef-models/tests/unit/estimators/__init__.py diff --git a/packages/openstef-models/src/openstef_models/estimators/__init__.py b/packages/openstef-models/src/openstef_models/estimators/__init__.py index 487060783..2b139bdb0 100644 --- a/packages/openstef-models/src/openstef_models/estimators/__init__.py +++ b/packages/openstef-models/src/openstef_models/estimators/__init__.py @@ -2,7 +2,7 @@ # # SPDX-License-Identifier: MPL-2.0 -"""Custom estimators for multi quantiles""" +"""Custom estimators for multi quantiles.""" from .lightgbm import LGBMQuantileRegressor diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py index 81ad400bf..c6f827d64 100644 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -1,3 +1,6 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 """Hybrid quantile regression estimators for multi-quantile forecasting. This module provides the HybridQuantileRegressor class, which combines LightGBM and linear models @@ -35,9 +38,7 @@ def __init__( # noqa: D107, PLR0913, PLR0917 lightgbm_reg_lambda: float = 0.0, lightgbm_num_leaves: int = 31, lightgbm_max_bin: int = 255, - lightgbm_subsample: float = 1.0, lightgbm_colsample_by_tree: float = 1.0, - lightgbm_colsample_by_node: float = 1.0, gblinear_n_steps: int = 100, gblinear_learning_rate: float = 0.15, gblinear_reg_alpha: float = 0.0001, @@ -64,9 +65,7 @@ def __init__( # noqa: D107, PLR0913, PLR0917 reg_lambda=lightgbm_reg_lambda, num_leaves=lightgbm_num_leaves, max_bin=lightgbm_max_bin, - subsample=lightgbm_subsample, colsample_bytree=lightgbm_colsample_by_tree, - colsample_bynode=lightgbm_colsample_by_node, verbosity=-1, linear_tree=False, ) @@ -101,9 +100,22 @@ def __init__( # noqa: D107, PLR0913, PLR0917 self.is_fitted: bool = False self.feature_names: list[str] = [] + @staticmethod + def _prepare_input(X: npt.NDArray[np.floating] | pd.DataFrame) -> pd.DataFrame: + """Prepare input data by handling missing values. + + Args: + X: Input features as a DataFrame or ndarray. + + Returns: + A DataFrame with missing values handled. + """ + filled_forward = pd.DataFrame(X).ffill() + return pd.DataFrame(filled_forward).fillna(0) + def fit( self, - X: npt.NDArray[np.floating] | pd.DataFrame, # noqa: N803 + X: npt.NDArray[np.floating] | pd.DataFrame, y: npt.NDArray[np.floating] | pd.Series, sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, @@ -118,16 +130,15 @@ def fit( """ self.feature_names = feature_name if feature_name is not None else [] - X = X.ffill().fillna(0) # type: ignore for model in self._models: model.fit( - X=X, # type: ignore + X=self._prepare_input(X), # type: ignore y=y, sample_weight=sample_weight, ) self.is_fitted = True - def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: # noqa: N803 + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: """Predict quantiles for the input features. Args: diff --git a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py index 130729407..13bc36477 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py @@ -43,9 +43,7 @@ def __init__( reg_lambda: float = 0.0, num_leaves: int = 31, max_bin: int = 255, - subsample: float = 1.0, colsample_bytree: float = 1.0, - colsample_bynode: float = 1.0, random_state: int | None = None, early_stopping_rounds: int | None = None, verbosity: int = -1, @@ -54,6 +52,7 @@ def __init__( Args: quantiles: List of quantiles to predict (e.g., [0.1, 0.5, 0.9]). + linear_tree: Whether to use linear trees. n_estimators: Number of boosting rounds/trees to fit. learning_rate: Step size shrinkage used to prevent overfitting. max_depth: Maximum depth of trees. @@ -64,9 +63,7 @@ def __init__( reg_lambda: L2 regularization on leaf weights. num_leaves: Maximum number of leaves. max_bin: Maximum number of discrete bins for continuous features. - subsample: Fraction of training samples used for each tree. colsample_bytree: Fraction of features used when constructing each tree. - colsample_bynode: Fraction of features used for each split/node. random_state: Random seed for reproducibility. early_stopping_rounds: Training will stop if performance doesn't improve for this many rounds. verbosity: Verbosity level for LgbLinear training. @@ -84,9 +81,7 @@ def __init__( self.reg_lambda = reg_lambda self.num_leaves = num_leaves self.max_bin = max_bin - self.subsample = subsample self.colsample_bytree = colsample_bytree - self.colsample_bynode = colsample_bynode self.random_state = random_state self.early_stopping_rounds = early_stopping_rounds self.verbosity = verbosity @@ -105,9 +100,7 @@ def __init__( reg_lambda=reg_lambda, num_leaves=num_leaves, max_bin=max_bin, - subsample=subsample, colsample_bytree=colsample_bytree, - colsample_bynode=colsample_bynode, random_state=random_state, early_stopping_rounds=early_stopping_rounds, verbosity=verbosity, @@ -118,12 +111,12 @@ def __init__( def fit( self, - X: npt.NDArray[np.floating] | pd.DataFrame, # noqa: N803 + X: npt.NDArray[np.floating] | pd.DataFrame, y: npt.NDArray[np.floating] | pd.Series, - sample_weight: npt.NDArray[np.floating] | None = None, + sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, eval_set: npt.NDArray[np.floating] | None = None, - eval_sample_weight: npt.NDArray[np.floating] | None = None, + eval_sample_weight: npt.NDArray[np.floating] | pd.Series | list[float] | None = None, ) -> None: """Fit the multi-quantile regressor. @@ -150,7 +143,7 @@ def fit( feature_name=feature_name, # type: ignore ) - def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: # noqa: N803 + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: """Predict quantiles for the input features. Args: @@ -203,3 +196,12 @@ def load_bytes(cls, model_bytes: bytes) -> Self: raise ModelLoadingError("Deserialized object is not a LgbLinearQuantileRegressor") return instance + + @property + def models(self) -> list[LGBMRegressor]: + """Get the list of underlying quantile models. + + Returns: + List of LGBMRegressor instances for each quantile. + """ + return self._models diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index f5e97289a..70aeeed04 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -1,7 +1,13 @@ # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project # # SPDX-License-Identifier: MPL-2.0 +"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). +Provides method that attempts to combine the advantages of a linear model (Extraplolation) +and tree-based model (Non-linear patterns). This is acieved by training two base learners, +followed by a small linear model that regresses on the baselearners' predictions. +The implementation is based on sklearn's StackingRegressor. +""" from typing import TYPE_CHECKING, override @@ -83,9 +89,7 @@ def __init__(self, config: HybridForecasterConfig) -> None: lightgbm_reg_lambda=config.hyperparams.lightgbm_params.reg_lambda, lightgbm_num_leaves=config.hyperparams.lightgbm_params.num_leaves, lightgbm_max_bin=config.hyperparams.lightgbm_params.max_bin, - lightgbm_subsample=config.hyperparams.lightgbm_params.subsample, lightgbm_colsample_by_tree=config.hyperparams.lightgbm_params.colsample_bytree, - lightgbm_colsample_by_node=config.hyperparams.lightgbm_params.colsample_bynode, gblinear_n_steps=config.hyperparams.gb_linear_params.n_steps, gblinear_learning_rate=config.hyperparams.gb_linear_params.learning_rate, gblinear_reg_alpha=config.hyperparams.gb_linear_params.reg_alpha, @@ -111,7 +115,6 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None data_val: Validation data for tuning the model (optional, not used in this implementation). """ - input_data: pd.DataFrame = data.input_data() target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore sample_weights: pd.Series = data.sample_weight_series diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py index 04293c048..66be71821 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -11,7 +11,6 @@ from typing import TYPE_CHECKING, Literal, override -import numpy as np import pandas as pd from pydantic import Field @@ -26,6 +25,7 @@ from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig if TYPE_CHECKING: + import numpy as np import numpy.typing as npt @@ -41,7 +41,6 @@ class LgbLinearHyperParams(HyperParams): ... learning_rate=0.1, ... reg_alpha=0.1, ... reg_lambda=1.0, - ... subsample=0.8 ... ) Note: @@ -106,18 +105,10 @@ class LgbLinearHyperParams(HyperParams): ) # Subsampling Parameters - subsample: float = Field( - default=0.5, - description="Fraction of training samples used for each tree. Lower values prevent overfitting. Range: (0,1]", - ) colsample_bytree: float = Field( default=0.5, description="Fraction of features used when constructing each tree. Range: (0,1]", ) - colsample_bynode: float = Field( - default=0.5, - description="Fraction of features used for each split/node. Range: (0,1]", - ) # General Parameters random_state: int | None = Field( @@ -254,9 +245,7 @@ def __init__(self, config: LgbLinearForecasterConfig) -> None: reg_lambda=config.hyperparams.reg_lambda, num_leaves=config.hyperparams.num_leaves, max_bin=config.hyperparams.max_bin, - subsample=config.hyperparams.subsample, colsample_bytree=config.hyperparams.colsample_bytree, - colsample_bynode=config.hyperparams.colsample_bynode, random_state=config.hyperparams.random_state, early_stopping_rounds=config.hyperparams.early_stopping_rounds, verbosity=config.verbosity, diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py index cc5a26856..b4049b83c 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py @@ -9,10 +9,8 @@ comprehensive hyperparameter control for production forecasting workflows. """ -from typing import Literal, override +from typing import TYPE_CHECKING, Literal, override -import numpy as np -import numpy.typing as npt import pandas as pd from pydantic import Field @@ -26,6 +24,10 @@ from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig +if TYPE_CHECKING: + import numpy as np + import numpy.typing as npt + class LightGBMHyperParams(HyperParams): """LightGBM hyperparameters for gradient boosting tree models. @@ -39,7 +41,6 @@ class LightGBMHyperParams(HyperParams): ... learning_rate=0.1, ... reg_alpha=0.1, ... reg_lambda=1.0, - ... subsample=0.8 ... ) Note: @@ -103,18 +104,10 @@ class LightGBMHyperParams(HyperParams): ) # Subsampling Parameters - subsample: float = Field( - default=1.0, - description="Fraction of training samples used for each tree. Lower values prevent overfitting. Range: (0,1]", - ) colsample_bytree: float = Field( default=1.0, description="Fraction of features used when constructing each tree. Range: (0,1]", ) - colsample_bynode: float = Field( - default=1.0, - description="Fraction of features used for each split/node. Range: (0,1]", - ) # General Parameters random_state: int | None = Field( @@ -252,9 +245,7 @@ def __init__(self, config: LightGBMForecasterConfig) -> None: reg_lambda=config.hyperparams.reg_lambda, num_leaves=config.hyperparams.num_leaves, max_bin=config.hyperparams.max_bin, - subsample=config.hyperparams.subsample, colsample_bytree=config.hyperparams.colsample_bytree, - colsample_bynode=config.hyperparams.colsample_bynode, random_state=config.hyperparams.random_state, early_stopping_rounds=config.hyperparams.early_stopping_rounds, verbosity=config.verbosity, @@ -321,7 +312,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: @property @override def feature_importances(self) -> pd.DataFrame: - models = self._lightgbm_model._models + models = self._lightgbm_model.models weights_df = pd.DataFrame( [models[i].feature_importances_ for i in range(len(models))], index=[quantile.format() for quantile in self.config.quantiles], diff --git a/packages/openstef-models/tests/unit/estimators/__init__.py b/packages/openstef-models/tests/unit/estimators/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyproject.toml b/pyproject.toml index f6a17e8e6..164f27276 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -158,7 +158,7 @@ lint.pylint.allow-dunder-method-names = [ ] # valid pydantic name lint.pylint.max-args = 7 # the default of 5 is a bit limiting. 7 should be enough for nearly all cases lint.preview = true - +lint.pep8-naming.ignore-names = [ "X" ] # Allow X for SKLearn-like feature matrices [tool.pyproject-fmt] column_width = 120 indent = 2 From c680aa160c2e5a336ae41f96e2f53c2fc9dbbc87 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 10 Nov 2025 11:25:33 +0100 Subject: [PATCH 04/72] fixed quality checks --- packages/openstef-models/pyproject.toml | 4 ++-- .../src/openstef_models/estimators/hybrid.py | 3 +-- .../src/openstef_models/estimators/lightgbm.py | 8 ++++---- .../models/forecasting/lgblinear_forecaster.py | 15 ++++++--------- .../models/forecasting/lightgbm_forecaster.py | 8 +++----- pyproject.toml | 3 ++- 6 files changed, 18 insertions(+), 23 deletions(-) diff --git a/packages/openstef-models/pyproject.toml b/packages/openstef-models/pyproject.toml index 43914f38f..c33d6e0e1 100644 --- a/packages/openstef-models/pyproject.toml +++ b/packages/openstef-models/pyproject.toml @@ -28,10 +28,10 @@ classifiers = [ ] dependencies = [ - "lightgbm>=4.6.0", + "lightgbm>=4.6", "openstef-core", "pycountry>=24.6.1", - "skops>=0.13.0", + "skops>=0.13", ] optional-dependencies.all = [ diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py index c6f827d64..54e12ab91 100644 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -110,8 +110,7 @@ def _prepare_input(X: npt.NDArray[np.floating] | pd.DataFrame) -> pd.DataFrame: Returns: A DataFrame with missing values handled. """ - filled_forward = pd.DataFrame(X).ffill() - return pd.DataFrame(filled_forward).fillna(0) + return pd.DataFrame(X).ffill().fillna(0) # type: ignore[reportUnknownMemberType] def fit( self, diff --git a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py index 13bc36477..0214b0929 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lightgbm.py @@ -29,7 +29,7 @@ class LGBMQuantileRegressor(BaseEstimator, RegressorMixin): separate tree within the same boosting ensemble. """ - def __init__( + def __init__( # noqa: PLR0913, PLR0917 self, quantiles: list[float], linear_tree: bool, # noqa: FBT001 @@ -47,7 +47,7 @@ def __init__( random_state: int | None = None, early_stopping_rounds: int | None = None, verbosity: int = -1, - ) -> None: # type: ignore + ) -> None: """Initialize LgbLinearQuantileRegressor with quantiles. Args: @@ -115,8 +115,8 @@ def fit( y: npt.NDArray[np.floating] | pd.Series, sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, - eval_set: npt.NDArray[np.floating] | None = None, - eval_sample_weight: npt.NDArray[np.floating] | pd.Series | list[float] | None = None, + eval_set: list[tuple[pd.DataFrame, npt.NDArray[np.floating]]] | None = None, + eval_sample_weight: list[npt.NDArray[np.floating]] | None = None, ) -> None: """Fit the multi-quantile regressor. diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py index 66be71821..ed86aa48e 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -9,8 +9,10 @@ comprehensive hyperparameter control for production forecasting workflows. """ -from typing import TYPE_CHECKING, Literal, override +from typing import Literal, cast, override +import numpy as np +import numpy.typing as npt import pandas as pd from pydantic import Field @@ -24,10 +26,6 @@ from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig -if TYPE_CHECKING: - import numpy as np - import numpy.typing as npt - class LgbLinearHyperParams(HyperParams): """LgbLinear hyperparameters for gradient boosting tree models. @@ -35,7 +33,7 @@ class LgbLinearHyperParams(HyperParams): Example: Creating custom hyperparameters for deep trees with regularization: - >>> hyperparams = LGBMHyperParams( + >>> hyperparams = LgbLinearHyperParams( ... n_estimators=200, ... max_depth=8, ... learning_rate=0.1, @@ -136,7 +134,7 @@ class LgbLinearForecasterConfig(ForecasterConfig): ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], ... horizons=[LeadTime(timedelta(hours=1))], ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) - ... ). + ... ) """ # noqa: D205 hyperparams: LgbLinearHyperParams = LgbLinearHyperParams() @@ -270,7 +268,6 @@ def is_fitted(self) -> bool: def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: input_data: pd.DataFrame = data.input_data() target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore - sample_weight = data.sample_weight_series # Prepare validation data if provided @@ -279,7 +276,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None if data_val is not None: val_input_data: pd.DataFrame = data_val.input_data() val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore - val_sample_weight = data_val.sample_weight_series.to_numpy() # type: ignore + val_sample_weight = cast(npt.NDArray[np.floating], data_val.sample_weight_series.to_numpy()) # type: ignore eval_set = [(val_input_data, val_target)] eval_sample_weight = [val_sample_weight] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py index b4049b83c..8fb2a396c 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py @@ -35,7 +35,7 @@ class LightGBMHyperParams(HyperParams): Example: Creating custom hyperparameters for deep trees with regularization: - >>> hyperparams = LGBMHyperParams( + >>> hyperparams = LightGBMHyperParams( ... n_estimators=200, ... max_depth=8, ... learning_rate=0.1, @@ -133,10 +133,8 @@ class LightGBMForecasterConfig(ForecasterConfig): >>> from openstef_core.types import LeadTime, Quantile >>> config = LightGBMForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], - ... horizons=[LeadTime(timedelta(hours=1 - ))], - ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6) - ... ). + ... horizons=[LeadTime(timedelta(hours=1))], + ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6)) """ # noqa: D205 hyperparams: LightGBMHyperParams = LightGBMHyperParams() diff --git a/pyproject.toml b/pyproject.toml index 164f27276..787b231dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -151,6 +151,7 @@ lint.isort.known-first-party = [ "tests", "examples", ] # Useful if ruff does not run from the actual root of the project and to import form tests +lint.pep8-naming.ignore-names = [ "X" ] # Allow X for SKLearn-like feature matrices lint.pydocstyle.convention = "google" lint.pylint.allow-dunder-method-names = [ "__get_pydantic_core_schema__", @@ -158,7 +159,7 @@ lint.pylint.allow-dunder-method-names = [ ] # valid pydantic name lint.pylint.max-args = 7 # the default of 5 is a bit limiting. 7 should be enough for nearly all cases lint.preview = true -lint.pep8-naming.ignore-names = [ "X" ] # Allow X for SKLearn-like feature matrices + [tool.pyproject-fmt] column_width = 120 indent = 2 From 9c1e3d38d3348d76962ec7f9004b20f16b6bc869 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 10 Nov 2025 11:29:06 +0100 Subject: [PATCH 05/72] Fixed last issues, Signed-off-by: Lars van Someren --- .../tests/unit/models/forecasting/test_hybrid_forecaster.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py index 0a0334fbc..e89fe13f9 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py @@ -61,11 +61,6 @@ def test_hybrid_forecaster__fit_predict( # Forecast data quality assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - # Since forecast is deterministic with fixed random seed, check value spread (vectorized) - # All quantiles should have some variation (not all identical values) - stds = result.data.std() - assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" - def test_hybrid_forecaster__predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, From 4394895bafe0c609b12683229ae73151b463f75d Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 11 Nov 2025 20:02:13 +0100 Subject: [PATCH 06/72] fixed comments --- .../models/forecasting/hybrid_forecaster.py | 9 --------- .../models/forecasting/lgblinear_forecaster.py | 14 -------------- .../models/forecasting/lightgbm_forecaster.py | 14 -------------- 3 files changed, 37 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 70aeeed04..ec3cb0ed6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -14,7 +14,6 @@ import pandas as pd from pydantic import Field -from openstef_core.base_model import BaseConfig from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, @@ -56,14 +55,6 @@ class HybridForecasterConfig(ForecasterConfig): MODEL_CODE_VERSION = 2 -class HybridForecasterState(BaseConfig): - """Serializable state for Hybrid forecaster persistence.""" - - version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") - config: HybridForecasterConfig = Field(..., description="Forecaster configuration.") - model: str = Field(..., description="Base64-encoded serialized Hybrid model.") - - class HybridForecaster(Forecaster): """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py index ed86aa48e..262c739f7 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -16,7 +16,6 @@ import pandas as pd from pydantic import Field -from openstef_core.base_model import BaseConfig from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, @@ -156,19 +155,6 @@ class LgbLinearForecasterConfig(ForecasterConfig): MODEL_CODE_VERSION = 1 -class LgbLinearForecasterState(BaseConfig): - """Serializable state for LgbLinear forecaster persistence. - - Contains all information needed to restore a trained LgbLinear model, - including configuration and the serialized model weights. Used for - model saving, loading, and version management in production systems. - """ - - version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") - config: LgbLinearForecasterConfig = Field(..., description="Forecaster configuration.") - model: str = Field(..., description="Base64-encoded serialized LgbLinear model.") - - class LgbLinearForecaster(Forecaster, ExplainableForecaster): """LgbLinear-based forecaster for probabilistic energy forecasting. diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py index 8fb2a396c..4e2dabe73 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py @@ -14,7 +14,6 @@ import pandas as pd from pydantic import Field -from openstef_core.base_model import BaseConfig from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, @@ -156,19 +155,6 @@ class LightGBMForecasterConfig(ForecasterConfig): MODEL_CODE_VERSION = 1 -class LightGBMForecasterState(BaseConfig): - """Serializable state for LightGBM forecaster persistence. - - Contains all information needed to restore a trained LightGBM model, - including configuration and the serialized model weights. Used for - model saving, loading, and version management in production systems. - """ - - version: int = Field(default=MODEL_CODE_VERSION, description="Version of the model code.") - config: LightGBMForecasterConfig = Field(..., description="Forecaster configuration.") - model: str = Field(..., description="Base64-encoded serialized LightGBM model.") - - class LightGBMForecaster(Forecaster, ExplainableForecaster): """LightGBM-based forecaster for probabilistic energy forecasting. From 5745212b656211b851f608c931322aec94180db8 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 11 Nov 2025 20:06:21 +0100 Subject: [PATCH 07/72] Refactor LightGBM to LGBM --- .../openstef_models/estimators/__init__.py | 2 +- .../src/openstef_models/estimators/hybrid.py | 52 +++++----- .../estimators/{lightgbm.py => lgbm.py} | 8 +- .../models/forecasting/forecaster.py | 19 ++++ .../models/forecasting/hybrid_forecaster.py | 26 ++--- ...htgbm_forecaster.py => lgbm_forecaster.py} | 48 +++++----- ...forecaster.py => lgbmlinear_forecaster.py} | 95 +++++++++---------- .../presets/forecasting_workflow.py | 32 +++---- .../{test_lightgbm.py => test_lgbm.py} | 2 +- .../forecasting/test_hybrid_forecaster.py | 6 +- ..._forecaster.py => test_lgbm_forecaster.py} | 56 +++++------ ...aster.py => test_lgbmlinear_forecaster.py} | 56 +++++------ 12 files changed, 210 insertions(+), 192 deletions(-) rename packages/openstef-models/src/openstef_models/estimators/{lightgbm.py => lgbm.py} (96%) rename packages/openstef-models/src/openstef_models/models/forecasting/{lightgbm_forecaster.py => lgbm_forecaster.py} (88%) rename packages/openstef-models/src/openstef_models/models/forecasting/{lgblinear_forecaster.py => lgbmlinear_forecaster.py} (84%) rename packages/openstef-models/tests/unit/estimators/{test_lightgbm.py => test_lgbm.py} (95%) rename packages/openstef-models/tests/unit/models/forecasting/{test_lightgbm_forecaster.py => test_lgbm_forecaster.py} (75%) rename packages/openstef-models/tests/unit/models/forecasting/{test_lgblinear_forecaster.py => test_lgbmlinear_forecaster.py} (75%) diff --git a/packages/openstef-models/src/openstef_models/estimators/__init__.py b/packages/openstef-models/src/openstef_models/estimators/__init__.py index 2b139bdb0..2b2e5ebb4 100644 --- a/packages/openstef-models/src/openstef_models/estimators/__init__.py +++ b/packages/openstef-models/src/openstef_models/estimators/__init__.py @@ -4,6 +4,6 @@ """Custom estimators for multi quantiles.""" -from .lightgbm import LGBMQuantileRegressor +from .lgbm import LGBMQuantileRegressor __all__ = ["LGBMQuantileRegressor"] diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py index 54e12ab91..6e107917f 100644 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -27,18 +27,18 @@ class HybridQuantileRegressor: def __init__( # noqa: D107, PLR0913, PLR0917 self, quantiles: list[float], - lightgbm_n_estimators: int = 100, - lightgbm_learning_rate: float = 0.1, - lightgbm_max_depth: int = -1, - lightgbm_min_child_weight: float = 1.0, + lgbm_n_estimators: int = 100, + lgbm_learning_rate: float = 0.1, + lgbm_max_depth: int = -1, + lgbm_min_child_weight: float = 1.0, ligntgbm_min_child_samples: int = 1, - lightgbm_min_data_in_leaf: int = 20, - lightgbm_min_data_in_bin: int = 10, - lightgbm_reg_alpha: float = 0.0, - lightgbm_reg_lambda: float = 0.0, - lightgbm_num_leaves: int = 31, - lightgbm_max_bin: int = 255, - lightgbm_colsample_by_tree: float = 1.0, + lgbm_min_data_in_leaf: int = 20, + lgbm_min_data_in_bin: int = 10, + lgbm_reg_alpha: float = 0.0, + lgbm_reg_lambda: float = 0.0, + lgbm_num_leaves: int = 31, + lgbm_max_bin: int = 255, + lgbm_colsample_by_tree: float = 1.0, gblinear_n_steps: int = 100, gblinear_learning_rate: float = 0.15, gblinear_reg_alpha: float = 0.0001, @@ -51,21 +51,21 @@ def __init__( # noqa: D107, PLR0913, PLR0917 self._models: list[StackingRegressor] = [] for q in quantiles: - lightgbm_model = LGBMRegressor( + lgbm_model = LGBMRegressor( objective="quantile", alpha=q, min_child_samples=ligntgbm_min_child_samples, - n_estimators=lightgbm_n_estimators, - learning_rate=lightgbm_learning_rate, - max_depth=lightgbm_max_depth, - min_child_weight=lightgbm_min_child_weight, - min_data_in_leaf=lightgbm_min_data_in_leaf, - min_data_in_bin=lightgbm_min_data_in_bin, - reg_alpha=lightgbm_reg_alpha, - reg_lambda=lightgbm_reg_lambda, - num_leaves=lightgbm_num_leaves, - max_bin=lightgbm_max_bin, - colsample_bytree=lightgbm_colsample_by_tree, + n_estimators=lgbm_n_estimators, + learning_rate=lgbm_learning_rate, + max_depth=lgbm_max_depth, + min_child_weight=lgbm_min_child_weight, + min_data_in_leaf=lgbm_min_data_in_leaf, + min_data_in_bin=lgbm_min_data_in_bin, + reg_alpha=lgbm_reg_alpha, + reg_lambda=lgbm_reg_lambda, + num_leaves=lgbm_num_leaves, + max_bin=lgbm_max_bin, + colsample_bytree=lgbm_colsample_by_tree, verbosity=-1, linear_tree=False, ) @@ -89,7 +89,7 @@ def __init__( # noqa: D107, PLR0913, PLR0917 self._models.append( StackingRegressor( - estimators=[("lightgbm", lightgbm_model), ("gblinear", linear)], # type: ignore + estimators=[("lgbm", lgbm_model), ("gblinear", linear)], # type: ignore final_estimator=final_estimator, verbose=3, passthrough=False, @@ -173,8 +173,8 @@ def load_bytes(cls, model_bytes: bytes) -> Self: """ trusted_types = [ "collections.OrderedDict", - "lightgbm.basic.Booster", - "lightgbm.sklearn.LGBMRegressor", + "lgbm.basic.Booster", + "lgbm.sklearn.LGBMRegressor", "sklearn.utils._bunch.Bunch", "xgboost.core.Booster", "xgboost.sklearn.XGBRegressor", diff --git a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py b/packages/openstef-models/src/openstef_models/estimators/lgbm.py similarity index 96% rename from packages/openstef-models/src/openstef_models/estimators/lightgbm.py rename to packages/openstef-models/src/openstef_models/estimators/lgbm.py index 0214b0929..ed4115b06 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lightgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lgbm.py @@ -153,7 +153,7 @@ def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np. A 2D array where each column corresponds to predicted quantiles. """ # noqa: D412 - return np.column_stack([model.predict(X=np.asarray(X)) for model in self._models]) # type: ignore + return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore def __sklearn_is_fitted__(self) -> bool: # noqa: PLW3201 """Check if all models are fitted. @@ -186,9 +186,9 @@ def load_bytes(cls, model_bytes: bytes) -> Self: """ trusted_types = [ "collections.OrderedDict", - "lightgbm.basic.Booster", - "lightgbm.sklearn.LGBMRegressor", - "openstef_models.estimators.lightgbm.LGBMQuantileRegressor", + "lgbm.basic.Booster", + "lgbm.sklearn.LGBMRegressor", + "openstef_models.estimators.lgbm.LGBMQuantileRegressor", ] instance = loads(model_bytes, trusted=trusted_types) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py index d796b0ef3..d77e50fda 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py @@ -197,6 +197,25 @@ class Forecaster(BatchPredictor[ForecastInputDataset, ForecastDataset], Configur ... ) """ + @abstractmethod + def __init__(self, config: ForecasterConfig) -> None: + """Initialize the forecaster with the given configuration. + + Args: + config: Configuration object specifying quantiles, horizons, and batching support. + """ + raise NotImplementedError("Subclasses must implement __init__") + + @property + @abstractmethod + def config(self) -> ForecasterConfig: + """Access the model's configuration parameters. + + Returns: + Configuration object containing fundamental model parameters. + """ + raise NotImplementedError("Subclasses must implement config") + __all__ = [ "Forecaster", diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index ec3cb0ed6..6e6c7e9f3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -22,7 +22,7 @@ from openstef_models.estimators.hybrid import HybridQuantileRegressor from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams -from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams if TYPE_CHECKING: import numpy as np @@ -32,7 +32,7 @@ class HybridHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - lightgbm_params: LightGBMHyperParams = LightGBMHyperParams() + lgbm_params: LGBMHyperParams = LGBMHyperParams() gb_linear_params: GBLinearHyperParams = GBLinearHyperParams() l1_penalty: float = Field( @@ -70,17 +70,17 @@ def __init__(self, config: HybridForecasterConfig) -> None: self._model = HybridQuantileRegressor( quantiles=[float(q) for q in config.quantiles], - lightgbm_n_estimators=config.hyperparams.lightgbm_params.n_estimators, - lightgbm_learning_rate=config.hyperparams.lightgbm_params.learning_rate, - lightgbm_max_depth=config.hyperparams.lightgbm_params.max_depth, - lightgbm_min_child_weight=config.hyperparams.lightgbm_params.min_child_weight, - lightgbm_min_data_in_leaf=config.hyperparams.lightgbm_params.min_data_in_leaf, - lightgbm_min_data_in_bin=config.hyperparams.lightgbm_params.min_data_in_bin, - lightgbm_reg_alpha=config.hyperparams.lightgbm_params.reg_alpha, - lightgbm_reg_lambda=config.hyperparams.lightgbm_params.reg_lambda, - lightgbm_num_leaves=config.hyperparams.lightgbm_params.num_leaves, - lightgbm_max_bin=config.hyperparams.lightgbm_params.max_bin, - lightgbm_colsample_by_tree=config.hyperparams.lightgbm_params.colsample_bytree, + lgbm_n_estimators=config.hyperparams.lgbm_params.n_estimators, + lgbm_learning_rate=config.hyperparams.lgbm_params.learning_rate, + lgbm_max_depth=config.hyperparams.lgbm_params.max_depth, + lgbm_min_child_weight=config.hyperparams.lgbm_params.min_child_weight, + lgbm_min_data_in_leaf=config.hyperparams.lgbm_params.min_data_in_leaf, + lgbm_min_data_in_bin=config.hyperparams.lgbm_params.min_data_in_bin, + lgbm_reg_alpha=config.hyperparams.lgbm_params.reg_alpha, + lgbm_reg_lambda=config.hyperparams.lgbm_params.reg_lambda, + lgbm_num_leaves=config.hyperparams.lgbm_params.num_leaves, + lgbm_max_bin=config.hyperparams.lgbm_params.max_bin, + lgbm_colsample_by_tree=config.hyperparams.lgbm_params.colsample_bytree, gblinear_n_steps=config.hyperparams.gb_linear_params.n_steps, gblinear_learning_rate=config.hyperparams.gb_linear_params.learning_rate, gblinear_reg_alpha=config.hyperparams.gb_linear_params.reg_alpha, diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py similarity index 88% rename from packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py rename to packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 4e2dabe73..4c4508117 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lightgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -19,7 +19,7 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_models.estimators.lightgbm import LGBMQuantileRegressor +from openstef_models.estimators.lgbm import LGBMQuantileRegressor from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig @@ -28,13 +28,13 @@ import numpy.typing as npt -class LightGBMHyperParams(HyperParams): +class LGBMHyperParams(HyperParams): """LightGBM hyperparameters for gradient boosting tree models. Example: Creating custom hyperparameters for deep trees with regularization: - >>> hyperparams = LightGBMHyperParams( + >>> hyperparams = LGBMHyperParams( ... n_estimators=200, ... max_depth=8, ... learning_rate=0.1, @@ -121,7 +121,7 @@ class LightGBMHyperParams(HyperParams): ) -class LightGBMForecasterConfig(ForecasterConfig): +class LGBMForecasterConfig(ForecasterConfig): """Configuration for LightGBM-based forecaster. Extends HorizonForecasterConfig with LightGBM-specific hyperparameters and execution settings. @@ -130,13 +130,13 @@ class LightGBMForecasterConfig(ForecasterConfig): Creating a LightGBM forecaster configuration with custom hyperparameters: >>> from datetime import timedelta >>> from openstef_core.types import LeadTime, Quantile - >>> config = LightGBMForecasterConfig( + >>> config = LGBMForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6)) + ... hyperparams=LGBMHyperParams(n_estimators=100, max_depth=6)) """ # noqa: D205 - hyperparams: LightGBMHyperParams = LightGBMHyperParams() + hyperparams: LGBMHyperParams = LGBMHyperParams() # General Parameters device: str = Field( @@ -155,7 +155,7 @@ class LightGBMForecasterConfig(ForecasterConfig): MODEL_CODE_VERSION = 1 -class LightGBMForecaster(Forecaster, ExplainableForecaster): +class LGBMForecaster(Forecaster, ExplainableForecaster): """LightGBM-based forecaster for probabilistic energy forecasting. Implements gradient boosting trees using LightGBM for multi-quantile forecasting. @@ -177,12 +177,12 @@ class LightGBMForecaster(Forecaster, ExplainableForecaster): >>> from datetime import timedelta >>> from openstef_core.types import LeadTime, Quantile - >>> config = LightGBMForecasterConfig( + >>> config = LGBMForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=6) + ... hyperparams=LGBMHyperParams(n_estimators=100, max_depth=6) ... ) - >>> forecaster = LightGBMForecaster(config) + >>> forecaster = LGBMForecaster(config) >>> # forecaster.fit(training_data) >>> # predictions = forecaster.predict(test_data) @@ -192,18 +192,18 @@ class LightGBMForecaster(Forecaster, ExplainableForecaster): magnitude-weighted pinball loss by default for better forecasting performance. See Also: - LightGBMHyperParams: Detailed hyperparameter configuration options. + LGBMHyperParams: Detailed hyperparameter configuration options. HorizonForecaster: Base interface for all forecasting models. GBLinearForecaster: Alternative linear model using LightGBM. """ - Config = LightGBMForecasterConfig - HyperParams = LightGBMHyperParams + Config = LGBMForecasterConfig + HyperParams = LGBMHyperParams - _config: LightGBMForecasterConfig - _lightgbm_model: LGBMQuantileRegressor + _config: LGBMForecasterConfig + _lgbm_model: LGBMQuantileRegressor - def __init__(self, config: LightGBMForecasterConfig) -> None: + def __init__(self, config: LGBMForecasterConfig) -> None: """Initialize LightGBM forecaster with configuration. Creates an untrained LightGBM regressor with the specified configuration. @@ -216,7 +216,7 @@ def __init__(self, config: LightGBMForecasterConfig) -> None: """ self._config = config - self._lightgbm_model = LGBMQuantileRegressor( + self._lgbm_model = LGBMQuantileRegressor( quantiles=[float(q) for q in config.quantiles], linear_tree=False, n_estimators=config.hyperparams.n_estimators, @@ -242,13 +242,13 @@ def config(self) -> ForecasterConfig: @property @override - def hyperparams(self) -> LightGBMHyperParams: + def hyperparams(self) -> LGBMHyperParams: return self._config.hyperparams @property @override def is_fitted(self) -> bool: - return self._lightgbm_model.__sklearn_is_fitted__() + return self._lgbm_model.__sklearn_is_fitted__() @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: @@ -267,7 +267,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None eval_set = (val_input_data, val_target) eval_sample_weight = [val_sample_weight] - self._lightgbm_model.fit( + self._lgbm_model.fit( X=input_data, y=target, feature_name=input_data.columns.tolist(), @@ -282,7 +282,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: raise NotFittedError(self.__class__.__name__) input_data: pd.DataFrame = data.input_data(start=data.forecast_start) - prediction: npt.NDArray[np.floating] = self._lightgbm_model.predict(X=input_data) + prediction: npt.NDArray[np.floating] = self._lgbm_model.predict(X=input_data) return ForecastDataset( data=pd.DataFrame( @@ -296,7 +296,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: @property @override def feature_importances(self) -> pd.DataFrame: - models = self._lightgbm_model.models + models = self._lgbm_model.models weights_df = pd.DataFrame( [models[i].feature_importances_ for i in range(len(models))], index=[quantile.format() for quantile in self.config.quantiles], @@ -312,4 +312,4 @@ def feature_importances(self) -> pd.DataFrame: return weights_abs / total -__all__ = ["LightGBMForecaster", "LightGBMForecasterConfig", "LightGBMHyperParams"] +__all__ = ["LGBMForecaster", "LGBMForecasterConfig", "LGBMHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py similarity index 84% rename from packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py rename to packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index 262c739f7..2636a7a13 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -21,18 +21,18 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_models.estimators.lightgbm import LGBMQuantileRegressor +from openstef_models.estimators.lgbm import LGBMQuantileRegressor from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig -class LgbLinearHyperParams(HyperParams): +class LGBMLinearHyperParams(HyperParams): """LgbLinear hyperparameters for gradient boosting tree models. Example: Creating custom hyperparameters for deep trees with regularization: - >>> hyperparams = LgbLinearHyperParams( + >>> hyperparams = LGBMLinearHyperParams( ... n_estimators=200, ... max_depth=8, ... learning_rate=0.1, @@ -49,23 +49,23 @@ class LgbLinearHyperParams(HyperParams): # Core Tree Boosting Parameters n_estimators: int = Field( - default=150, + default=77, description="Number of boosting rounds/trees to fit. Higher values may improve performance but " "increase training time and risk overfitting.", ) learning_rate: float = Field( - default=0.3, + default=0.07, alias="eta", description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " "more boosting rounds.", ) max_depth: int = Field( - default=4, + default=1, description="Maximum depth of trees. Higher values capture more complex patterns but risk " "overfitting. Range: [1,∞]", ) min_child_weight: float = Field( - default=1, + default=0.06, description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent " "overfitting. Range: [0,∞]", ) @@ -75,7 +75,7 @@ class LgbLinearHyperParams(HyperParams): description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", ) min_data_in_bin: int = Field( - default=5, + default=13, description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", ) @@ -91,14 +91,14 @@ class LgbLinearHyperParams(HyperParams): # Tree Structure Control num_leaves: int = Field( - default=31, + default=78, description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", ) max_bin: int = Field( - default=256, + default=12, description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " - "increase memory. Only for hist tree_method.", + "increase memory.", ) # Subsampling Parameters @@ -107,20 +107,8 @@ class LgbLinearHyperParams(HyperParams): description="Fraction of features used when constructing each tree. Range: (0,1]", ) - # General Parameters - random_state: int | None = Field( - default=None, - alias="seed", - description="Random seed for reproducibility. Controls tree structure randomness.", - ) - - early_stopping_rounds: int | None = Field( - default=10, - description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", - ) - -class LgbLinearForecasterConfig(ForecasterConfig): +class LGBMLinearForecasterConfig(ForecasterConfig): """Configuration for LgbLinear-based forecaster. Extends HorizonForecasterConfig with LgbLinear-specific hyperparameters and execution settings. @@ -129,14 +117,14 @@ class LgbLinearForecasterConfig(ForecasterConfig): Creating a LgbLinear forecaster configuration with custom hyperparameters: >>> from datetime import timedelta >>> from openstef_core.types import LeadTime, Quantile - >>> config = LgbLinearForecasterConfig( + >>> config = LGBMLinearForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... hyperparams=LGBMLinearHyperParams(n_estimators=100, max_depth=6) ... ) """ # noqa: D205 - hyperparams: LgbLinearHyperParams = LgbLinearHyperParams() + hyperparams: LGBMLinearHyperParams = LGBMLinearHyperParams() # General Parameters device: str = Field( @@ -147,15 +135,26 @@ class LgbLinearForecasterConfig(ForecasterConfig): default=1, description="Number of parallel threads for tree construction. -1 uses all available cores.", ) - verbosity: Literal[0, 1, 2, 3] = Field( - default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + verbosity: Literal[-1, 0, 1, 2, 3] = Field( + default=-1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + + random_state: int | None = Field( + default=None, + alias="seed", + description="Random seed for reproducibility. Controls tree structure randomness.", + ) + + early_stopping_rounds: int | None = Field( + default=10, + description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) MODEL_CODE_VERSION = 1 -class LgbLinearForecaster(Forecaster, ExplainableForecaster): +class LGBMLinearForecaster(Forecaster, ExplainableForecaster): """LgbLinear-based forecaster for probabilistic energy forecasting. Implements gradient boosting trees using LgbLinear for multi-quantile forecasting. @@ -177,12 +176,12 @@ class LgbLinearForecaster(Forecaster, ExplainableForecaster): >>> from datetime import timedelta >>> from openstef_core.types import LeadTime, Quantile - >>> config = LgbLinearForecasterConfig( + >>> config = LGBMLinearForecasterConfig( ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... hyperparams=LGBMLinearHyperParams(n_estimators=100, max_depth=6) ... ) - >>> forecaster = LgbLinearForecaster(config) + >>> forecaster = LGBMLinearForecaster(config) >>> # forecaster.fit(training_data) >>> # predictions = forecaster.predict(test_data) @@ -192,18 +191,18 @@ class LgbLinearForecaster(Forecaster, ExplainableForecaster): magnitude-weighted pinball loss by default for better forecasting performance. See Also: - LgbLinearHyperParams: Detailed hyperparameter configuration options. + LGBMLinearHyperParams: Detailed hyperparameter configuration options. HorizonForecaster: Base interface for all forecasting models. GBLinearForecaster: Alternative linear model using LgbLinear. """ - Config = LgbLinearForecasterConfig - HyperParams = LgbLinearHyperParams + Config = LGBMLinearForecasterConfig + HyperParams = LGBMLinearHyperParams - _config: LgbLinearForecasterConfig - _lgblinear_model: LGBMQuantileRegressor + _config: LGBMLinearForecasterConfig + _lgbmlinear_model: LGBMQuantileRegressor - def __init__(self, config: LgbLinearForecasterConfig) -> None: + def __init__(self, config: LGBMLinearForecasterConfig) -> None: """Initialize LgbLinear forecaster with configuration. Creates an untrained LgbLinear regressor with the specified configuration. @@ -216,7 +215,7 @@ def __init__(self, config: LgbLinearForecasterConfig) -> None: """ self._config = config - self._lgblinear_model = LGBMQuantileRegressor( + self._lgbmlinear_model = LGBMQuantileRegressor( quantiles=[float(q) for q in config.quantiles], linear_tree=True, n_estimators=config.hyperparams.n_estimators, @@ -230,8 +229,8 @@ def __init__(self, config: LgbLinearForecasterConfig) -> None: num_leaves=config.hyperparams.num_leaves, max_bin=config.hyperparams.max_bin, colsample_bytree=config.hyperparams.colsample_bytree, - random_state=config.hyperparams.random_state, - early_stopping_rounds=config.hyperparams.early_stopping_rounds, + random_state=config.random_state, + early_stopping_rounds=config.early_stopping_rounds, verbosity=config.verbosity, ) @@ -242,13 +241,13 @@ def config(self) -> ForecasterConfig: @property @override - def hyperparams(self) -> LgbLinearHyperParams: + def hyperparams(self) -> LGBMLinearHyperParams: return self._config.hyperparams @property @override def is_fitted(self) -> bool: - return self._lgblinear_model.__sklearn_is_fitted__() + return self._lgbmlinear_model.__sklearn_is_fitted__() @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: @@ -267,7 +266,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None eval_sample_weight = [val_sample_weight] - self._lgblinear_model.fit( # type: ignore + self._lgbmlinear_model.fit( # type: ignore X=input_data, y=target, feature_name=input_data.columns.tolist(), @@ -282,7 +281,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: raise NotFittedError(self.__class__.__name__) input_data: pd.DataFrame = data.input_data(start=data.forecast_start) - prediction: npt.NDArray[np.floating] = self._lgblinear_model.predict(X=input_data) + prediction: npt.NDArray[np.floating] = self._lgbmlinear_model.predict(X=input_data) return ForecastDataset( data=pd.DataFrame( @@ -296,7 +295,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: @property @override def feature_importances(self) -> pd.DataFrame: - models = self._lgblinear_model._models # noqa: SLF001 + models = self._lgbmlinear_model._models # noqa: SLF001 weights_df = pd.DataFrame( [models[i].feature_importances_ for i in range(len(models))], index=[quantile.format() for quantile in self.config.quantiles], @@ -312,4 +311,4 @@ def feature_importances(self) -> pd.DataFrame: return weights_abs / total -__all__ = ["LgbLinearForecaster", "LgbLinearForecasterConfig", "LgbLinearHyperParams"] +__all__ = ["LGBMLinearForecaster", "LGBMLinearForecasterConfig", "LGBMLinearHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 517f5be83..1f5fdaba0 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -31,8 +31,8 @@ from openstef_models.models.forecasting.flatliner_forecaster import FlatlinerForecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster from openstef_models.models.forecasting.hybrid_forecaster import HybridForecaster -from openstef_models.models.forecasting.lgblinear_forecaster import LgbLinearForecaster -from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMForecaster +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder from openstef_models.transforms.general import ( @@ -117,7 +117,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_id: ModelIdentifier = Field(description="Unique identifier for the forecasting model.") # Model configuration - model: Literal["xgboost", "gblinear", "flatliner", "hybrid", "lightgbm", "lgblinear"] = Field( + model: Literal["xgboost", "gblinear", "flatliner", "hybrid", "lgbm", "lgbmlinear"] = Field( description="Type of forecasting model to use." ) # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( @@ -143,13 +143,13 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Hyperparameters for GBLinear forecaster.", ) - lightgbm_hyperparams: LightGBMForecaster.HyperParams = Field( - default=LightGBMForecaster.HyperParams(), + lgbm_hyperparams: LGBMForecaster.HyperParams = Field( + default=LGBMForecaster.HyperParams(), description="Hyperparameters for LightGBM forecaster.", ) - lgblinear_hyperparams: LgbLinearForecaster.HyperParams = Field( - default=LgbLinearForecaster.HyperParams(), + lgbmlinear_hyperparams: LGBMLinearForecaster.HyperParams = Field( + default=LGBMLinearForecaster.HyperParams(), description="Hyperparameters for LightGBM forecaster.", ) @@ -205,7 +205,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) sample_weight_exponent: float = Field( default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgblinear", "lightgbm", "hybrid", "xgboost"} + if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "hybrid", "xgboost"} else 0.0, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " @@ -348,7 +348,7 @@ def create_forecasting_workflow( ) ) postprocessing = [QuantileSorter()] - elif config.model == "lgblinear": + elif config.model == "lgbmlinear": preprocessing = [ *checks, *feature_adders, @@ -356,15 +356,15 @@ def create_forecasting_workflow( DatetimeFeaturesAdder(onehot_encode=False), *feature_standardizers, ] - forecaster = LgbLinearForecaster( - config=LgbLinearForecaster.Config( + forecaster = LGBMLinearForecaster( + config=LGBMLinearForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, - hyperparams=config.lgblinear_hyperparams, + hyperparams=config.lgbmlinear_hyperparams, ) ) postprocessing = [QuantileSorter()] - elif config.model == "lightgbm": + elif config.model == "lgbm": preprocessing = [ *checks, *feature_adders, @@ -372,11 +372,11 @@ def create_forecasting_workflow( DatetimeFeaturesAdder(onehot_encode=False), *feature_standardizers, ] - forecaster = LightGBMForecaster( - config=LightGBMForecaster.Config( + forecaster = LGBMForecaster( + config=LGBMForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, - hyperparams=config.lightgbm_hyperparams, + hyperparams=config.lgbm_hyperparams, ) ) postprocessing = [QuantileSorter()] diff --git a/packages/openstef-models/tests/unit/estimators/test_lightgbm.py b/packages/openstef-models/tests/unit/estimators/test_lgbm.py similarity index 95% rename from packages/openstef-models/tests/unit/estimators/test_lightgbm.py rename to packages/openstef-models/tests/unit/estimators/test_lgbm.py index 936b2e097..5dfa0bb5b 100644 --- a/packages/openstef-models/tests/unit/estimators/test_lightgbm.py +++ b/packages/openstef-models/tests/unit/estimators/test_lgbm.py @@ -5,7 +5,7 @@ import pytest from numpy.random import default_rng -from openstef_models.estimators.lightgbm import LGBMQuantileRegressor +from openstef_models.estimators.lgbm import LGBMQuantileRegressor @pytest.fixture diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py index e89fe13f9..f8251d484 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py @@ -15,16 +15,16 @@ HybridForecasterConfig, HybridHyperParams, ) -from openstef_models.models.forecasting.lightgbm_forecaster import LightGBMHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams @pytest.fixture def base_config() -> HybridForecasterConfig: """Base configuration for Hybrid forecaster tests.""" - lightgbm_params = LightGBMHyperParams(n_estimators=10, max_depth=2) + lgbm_params = LGBMHyperParams(n_estimators=10, max_depth=2) gb_linear_params = GBLinearHyperParams(n_steps=5, learning_rate=0.1, reg_alpha=0.0, reg_lambda=0.0) params = HybridHyperParams( - lightgbm_params=lightgbm_params, + lgbm_params=lgbm_params, gb_linear_params=gb_linear_params, ) return HybridForecasterConfig( diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py similarity index 75% rename from packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py rename to packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py index efc728ac3..5ef874537 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lightgbm_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py @@ -9,21 +9,21 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.lightgbm_forecaster import ( - LightGBMForecaster, - LightGBMForecasterConfig, - LightGBMHyperParams, +from openstef_models.models.forecasting.lgbm_forecaster import ( + LGBMForecaster, + LGBMForecasterConfig, + LGBMHyperParams, ) @pytest.fixture -def base_config() -> LightGBMForecasterConfig: +def base_config() -> LGBMForecasterConfig: """Base configuration for LightGBM forecaster tests.""" - return LightGBMForecasterConfig( + return LGBMForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], - hyperparams=LightGBMHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), + hyperparams=LGBMHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), device="cpu", n_jobs=1, verbosity=0, @@ -31,23 +31,23 @@ def base_config() -> LightGBMForecasterConfig: @pytest.fixture -def forecaster(base_config: LightGBMForecasterConfig) -> LightGBMForecaster: - return LightGBMForecaster(base_config) +def forecaster(base_config: LGBMForecasterConfig) -> LGBMForecaster: + return LGBMForecaster(base_config) -def test_initialization(forecaster: LightGBMForecaster): - assert isinstance(forecaster, LightGBMForecaster) +def test_initialization(forecaster: LGBMForecaster): + assert isinstance(forecaster, LGBMForecaster) assert forecaster.config.hyperparams.n_estimators == 100 # type: ignore -def test_quantile_lightgbm_forecaster__fit_predict( +def test_quantile_lgbm_forecaster__fit_predict( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LightGBMForecasterConfig, + base_config: LGBMForecasterConfig, ): """Test basic fit and predict workflow with comprehensive output validation.""" # Arrange expected_quantiles = base_config.quantiles - forecaster = LightGBMForecaster(config=base_config) + forecaster = LGBMForecaster(config=base_config) # Act forecaster.fit(sample_forecast_input_dataset) @@ -72,42 +72,42 @@ def test_quantile_lightgbm_forecaster__fit_predict( assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" -def test_lightgbm_forecaster__not_fitted_error( +def test_lgbm_forecaster__not_fitted_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LightGBMForecasterConfig, + base_config: LGBMForecasterConfig, ): """Test that NotFittedError is raised when predicting before fitting.""" # Arrange - forecaster = LightGBMForecaster(config=base_config) + forecaster = LGBMForecaster(config=base_config) # Act & Assert with pytest.raises(NotFittedError): forecaster.predict(sample_forecast_input_dataset) -def test_lightgbm_forecaster__predict_not_fitted_raises_error( +def test_lgbm_forecaster__predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LightGBMForecasterConfig, + base_config: LGBMForecasterConfig, ): """Test that predict() raises NotFittedError when called before fit().""" # Arrange - forecaster = LightGBMForecaster(config=base_config) + forecaster = LGBMForecaster(config=base_config) # Act & Assert with pytest.raises( NotFittedError, - match="The LightGBMForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 + match="The LGBMForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 ): forecaster.predict(sample_forecast_input_dataset) -def test_lightgbm_forecaster__with_sample_weights( +def test_lgbm_forecaster__with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, - base_config: LightGBMForecasterConfig, + base_config: LGBMForecasterConfig, ): """Test that forecaster works with sample weights and produces different results.""" # Arrange - forecaster_with_weights = LightGBMForecaster(config=base_config) + forecaster_with_weights = LGBMForecaster(config=base_config) # Create dataset without weights for comparison data_without_weights = ForecastInputDataset( @@ -116,7 +116,7 @@ def test_lightgbm_forecaster__with_sample_weights( target_column=sample_dataset_with_weights.target_column, forecast_start=sample_dataset_with_weights.forecast_start, ) - forecaster_without_weights = LightGBMForecaster(config=base_config) + forecaster_without_weights = LGBMForecaster(config=base_config) # Act forecaster_with_weights.fit(sample_dataset_with_weights) @@ -137,13 +137,13 @@ def test_lightgbm_forecaster__with_sample_weights( assert differences.sum().sum() > 0, "Sample weights should affect model predictions" -def test_lightgbm_forecaster__feature_importances( +def test_lgbm_forecaster__feature_importances( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LightGBMForecasterConfig, + base_config: LGBMForecasterConfig, ): """Test that feature_importances returns correct normalized importance scores.""" # Arrange - forecaster = LightGBMForecaster(config=base_config) + forecaster = LGBMForecaster(config=base_config) forecaster.fit(sample_forecast_input_dataset) # Act diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py similarity index 75% rename from packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py rename to packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py index dc743be07..61882e51d 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgblinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py @@ -9,21 +9,21 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.lgblinear_forecaster import ( - LgbLinearForecaster, - LgbLinearForecasterConfig, - LgbLinearHyperParams, +from openstef_models.models.forecasting.lgbmlinear_forecaster import ( + LGBMLinearForecaster, + LGBMLinearForecasterConfig, + LGBMLinearHyperParams, ) @pytest.fixture -def base_config() -> LgbLinearForecasterConfig: +def base_config() -> LGBMLinearForecasterConfig: """Base configuration for LgbLinear forecaster tests.""" - return LgbLinearForecasterConfig( + return LGBMLinearForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], - hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), + hyperparams=LGBMLinearHyperParams(n_estimators=100, max_depth=3, min_data_in_leaf=1, min_data_in_bin=1), device="cpu", n_jobs=1, verbosity=0, @@ -31,23 +31,23 @@ def base_config() -> LgbLinearForecasterConfig: @pytest.fixture -def forecaster(base_config: LgbLinearForecasterConfig) -> LgbLinearForecaster: - return LgbLinearForecaster(base_config) +def forecaster(base_config: LGBMLinearForecasterConfig) -> LGBMLinearForecaster: + return LGBMLinearForecaster(base_config) -def test_initialization(forecaster: LgbLinearForecaster): - assert isinstance(forecaster, LgbLinearForecaster) +def test_initialization(forecaster: LGBMLinearForecaster): + assert isinstance(forecaster, LGBMLinearForecaster) assert forecaster.config.hyperparams.n_estimators == 100 # type: ignore -def test_quantile_lgblinear_forecaster__fit_predict( +def test_quantile_lgbmlinear_forecaster__fit_predict( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LgbLinearForecasterConfig, + base_config: LGBMLinearForecasterConfig, ): """Test basic fit and predict workflow with comprehensive output validation.""" # Arrange expected_quantiles = base_config.quantiles - forecaster = LgbLinearForecaster(config=base_config) + forecaster = LGBMLinearForecaster(config=base_config) # Act forecaster.fit(sample_forecast_input_dataset) @@ -72,42 +72,42 @@ def test_quantile_lgblinear_forecaster__fit_predict( assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" -def test_lgblinear_forecaster__not_fitted_error( +def test_lgbmlinear_forecaster__not_fitted_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LgbLinearForecasterConfig, + base_config: LGBMLinearForecasterConfig, ): """Test that NotFittedError is raised when predicting before fitting.""" # Arrange - forecaster = LgbLinearForecaster(config=base_config) + forecaster = LGBMLinearForecaster(config=base_config) # Act & Assert with pytest.raises(NotFittedError): forecaster.predict(sample_forecast_input_dataset) -def test_lgblinear_forecaster__predict_not_fitted_raises_error( +def test_lgbmlinear_forecaster__predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LgbLinearForecasterConfig, + base_config: LGBMLinearForecasterConfig, ): """Test that predict() raises NotFittedError when called before fit().""" # Arrange - forecaster = LgbLinearForecaster(config=base_config) + forecaster = LGBMLinearForecaster(config=base_config) # Act & Assert with pytest.raises( NotFittedError, - match="The LgbLinearForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 + match="The LGBMLinearForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 ): forecaster.predict(sample_forecast_input_dataset) -def test_lgblinear_forecaster__with_sample_weights( +def test_lgbmlinear_forecaster__with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, - base_config: LgbLinearForecasterConfig, + base_config: LGBMLinearForecasterConfig, ): """Test that forecaster works with sample weights and produces different results.""" # Arrange - forecaster_with_weights = LgbLinearForecaster(config=base_config) + forecaster_with_weights = LGBMLinearForecaster(config=base_config) # Create dataset without weights for comparison data_without_weights = ForecastInputDataset( @@ -116,7 +116,7 @@ def test_lgblinear_forecaster__with_sample_weights( target_column=sample_dataset_with_weights.target_column, forecast_start=sample_dataset_with_weights.forecast_start, ) - forecaster_without_weights = LgbLinearForecaster(config=base_config) + forecaster_without_weights = LGBMLinearForecaster(config=base_config) # Act forecaster_with_weights.fit(sample_dataset_with_weights) @@ -137,13 +137,13 @@ def test_lgblinear_forecaster__with_sample_weights( assert differences.sum().sum() > 0, "Sample weights should affect model predictions" -def test_lgblinear_forecaster__feature_importances( +def test_lgbmlinear_forecaster__feature_importances( sample_forecast_input_dataset: ForecastInputDataset, - base_config: LgbLinearForecasterConfig, + base_config: LGBMLinearForecasterConfig, ): """Test that feature_importances returns correct normalized importance scores.""" # Arrange - forecaster = LgbLinearForecaster(config=base_config) + forecaster = LGBMLinearForecaster(config=base_config) forecaster.fit(sample_forecast_input_dataset) # Act From a2538b6aa766f0f68235002a707dc12859ebcfe4 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 11 Nov 2025 21:14:10 +0100 Subject: [PATCH 08/72] Update LGBM and LGBMLinear defaults, fixed comments --- .../src/openstef_models/estimators/lgbm.py | 47 +-- .../mixins/model_serializer.py | 2 + .../models/forecasting/hybrid_forecaster.py | 35 +- .../forecasting/lgblinear_forecaster.py | 305 ++++++++++++++++++ .../models/forecasting/lgbm_forecaster.py | 72 ++--- .../forecasting/lgbmlinear_forecaster.py | 12 +- .../forecasting/test_lgbm_forecaster.py | 19 +- .../forecasting/test_lgbmlinear_forecaster.py | 19 +- 8 files changed, 381 insertions(+), 130 deletions(-) create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py diff --git a/packages/openstef-models/src/openstef_models/estimators/lgbm.py b/packages/openstef-models/src/openstef_models/estimators/lgbm.py index ed4115b06..cf149c54a 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lgbm.py @@ -9,16 +9,13 @@ by a separate tree within the same boosting ensemble. The module also includes serialization utilities. """ -from typing import Self +from typing import Any import numpy as np import numpy.typing as npt import pandas as pd from lightgbm import LGBMRegressor from sklearn.base import BaseEstimator, RegressorMixin -from skops.io import dumps, loads - -from openstef_core.exceptions import ModelLoadingError class LGBMQuantileRegressor(BaseEstimator, RegressorMixin): @@ -116,7 +113,7 @@ def fit( sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, eval_set: list[tuple[pd.DataFrame, npt.NDArray[np.floating]]] | None = None, - eval_sample_weight: list[npt.NDArray[np.floating]] | None = None, + eval_sample_weight: list[npt.NDArray[np.floating]] | list[pd.Series[Any]] | None = None, ) -> None: """Fit the multi-quantile regressor. @@ -138,9 +135,9 @@ def fit( y=y, eval_metric="quantile", sample_weight=sample_weight, - eval_set=eval_set, # type: ignore - eval_sample_weight=eval_sample_weight, # type: ignore - feature_name=feature_name, # type: ignore + eval_set=eval_set, + eval_sample_weight=eval_sample_weight, + feature_name=feature_name if feature_name is not None else "auto", ) def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: @@ -163,40 +160,6 @@ def __sklearn_is_fitted__(self) -> bool: # noqa: PLW3201 """ return all(model.__sklearn_is_fitted__() for model in self._models) - def save_bytes(self) -> bytes: - """Serialize the model. - - Returns: - A string representation of the model. - """ - return dumps(self) - - @classmethod - def load_bytes(cls, model_bytes: bytes) -> Self: - """Deserialize the model from bytes using joblib. - - Args: - model_bytes : Bytes representing the serialized model. - - Returns: - An instance of LgbLinearQuantileRegressor. - - Raises: - ModelLoadingError: If the deserialized object is not a LgbLinearQuantileRegressor. - """ - trusted_types = [ - "collections.OrderedDict", - "lgbm.basic.Booster", - "lgbm.sklearn.LGBMRegressor", - "openstef_models.estimators.lgbm.LGBMQuantileRegressor", - ] - instance = loads(model_bytes, trusted=trusted_types) - - if not isinstance(instance, cls): - raise ModelLoadingError("Deserialized object is not a LgbLinearQuantileRegressor") - - return instance - @property def models(self) -> list[LGBMRegressor]: """Get the list of underlying quantile models. diff --git a/packages/openstef-models/src/openstef_models/mixins/model_serializer.py b/packages/openstef-models/src/openstef_models/mixins/model_serializer.py index ab00993f7..1167a9930 100644 --- a/packages/openstef-models/src/openstef_models/mixins/model_serializer.py +++ b/packages/openstef-models/src/openstef_models/mixins/model_serializer.py @@ -69,4 +69,6 @@ def deserialize(self, file: BinaryIO) -> object: """ +# TODO @egordm, @MvLieshout : Add SkopsModelSerializer implementation + __all__ = ["ModelIdentifier", "ModelSerializer"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 6e6c7e9f3..838f2abba 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -9,8 +9,10 @@ The implementation is based on sklearn's StackingRegressor. """ +import logging from typing import TYPE_CHECKING, override +import numpy as np import pandas as pd from pydantic import Field @@ -24,8 +26,9 @@ from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +logger = logging.getLogger(__name__) + if TYPE_CHECKING: - import numpy as np import numpy.typing as npt @@ -97,6 +100,18 @@ def is_fitted(self) -> bool: """Check if the model is fitted.""" return self._model.is_fitted + @staticmethod + def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: + input_data: pd.DataFrame = data.input_data() + + # Scale the target variable + target: np.ndarray = np.asarray(data.target_series.values) + + # Prepare sample weights + sample_weight: pd.Series = data.sample_weight_series + + return input_data, target, sample_weight + @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: """Fit the Hybrid model to the training data. @@ -106,11 +121,19 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None data_val: Validation data for tuning the model (optional, not used in this implementation). """ - input_data: pd.DataFrame = data.input_data() - target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore - sample_weights: pd.Series = data.sample_weight_series - - self._model.fit(X=input_data, y=target, sample_weight=sample_weights) + # Prepare training data + input_data, target, sample_weight = self._prepare_fit_input(data) + + if data_val is not None: + logger.warning( + "Validation data provided, but HybridForecaster does not currently support validation during fitting." + ) + + self._model.fit( + X=input_data, + y=target, + sample_weight=sample_weight, + ) @override def predict(self, data: ForecastInputDataset) -> ForecastDataset: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py new file mode 100644 index 000000000..9176fda14 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py @@ -0,0 +1,305 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""LightGBM-based forecasting models for probabilistic energy forecasting. + +Provides gradient boosting tree models using LightGBM for multi-quantile energy +forecasting. Optimized for time series data with specialized loss functions and +comprehensive hyperparameter control for production forecasting workflows. +""" + +from typing import Literal, cast, override + +import numpy as np +import numpy.typing as npt +import pandas as pd +from pydantic import Field + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_models.estimators.lgbm import LGBMQuantileRegressor +from openstef_models.explainability.mixins import ExplainableForecaster +from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig + + +class LgbLinearHyperParams(HyperParams): + """LgbLinear hyperparameters for gradient boosting tree models. + + Example: + Creating custom hyperparameters for deep trees with regularization: + + >>> hyperparams = LgbLinearHyperParams( + ... n_estimators=200, + ... max_depth=8, + ... learning_rate=0.1, + ... reg_alpha=0.1, + ... reg_lambda=1.0, + ... ) + + Note: + These parameters are optimized for probabilistic forecasting with + quantile regression. The default objective function is specialized + for magnitude-weighted pinball loss. + """ + + # Core Tree Boosting Parameters + + n_estimators: int = Field( + default=100, + description="Number of boosting rounds/trees to fit. Higher values may improve performance but " + "increase training time and risk overfitting.", + ) + learning_rate: float = Field( + default=0.1, + alias="eta", + description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " + "more boosting rounds.", + ) + max_depth: int = Field( + default=4, # Different from Factory Default (-1, unlimited) + description="Maximum depth of trees. Higher values capture more complex patterns but risk " + "overfitting. Range: [1,∞]", + ) + min_child_weight: float = Field( + default=1e-3, + description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent " + "overfitting. Range: [0,∞]", + ) + + min_data_in_leaf: int = Field( + default=20, + description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", + ) + min_data_in_bin: int = Field( + default=20, + description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", + ) + + # Regularization + reg_alpha: float = Field( + default=0, + description="L1 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + reg_lambda: float = Field( + default=0.0, + description="L2 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", + ) + + # Tree Structure Control + num_leaves: int = Field( + default=31, + description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", + ) + + max_bin: int = Field( + default=256, + description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " + "increase memory. Only for hist tree_method.", + ) + + # Subsampling Parameters + colsample_bytree: float = Field( + default=1, + description="Fraction of features used when constructing each tree. Range: (0,1]", + ) + + +class LgbLinearForecasterConfig(ForecasterConfig): + """Configuration for LgbLinear-based forecaster. + Extends HorizonForecasterConfig with LgbLinear-specific hyperparameters + and execution settings. + + Example: + Creating a LgbLinear forecaster configuration with custom hyperparameters: + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LgbLinearForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1))], + ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... ) + """ # noqa: D205 + + hyperparams: LgbLinearHyperParams = LgbLinearHyperParams() + + # General Parameters + device: str = Field( + default="cpu", + description="Device for LgbLinear computation. Options: 'cpu', 'cuda', 'cuda:', 'gpu'", + ) + n_jobs: int = Field( + default=1, + description="Number of parallel threads for tree construction. -1 uses all available cores.", + ) + verbosity: Literal[0, 1, 2, 3] = Field( + default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + + # General Parameters + random_state: int | None = Field( + default=None, + alias="seed", + description="Random seed for reproducibility. Controls tree structure randomness.", + ) + + early_stopping_rounds: int | None = Field( + default=0, + description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", + ) + + +MODEL_CODE_VERSION = 1 + + +class LgbLinearForecaster(Forecaster, ExplainableForecaster): + """LgbLinear-based forecaster for probabilistic energy forecasting. + + Implements gradient boosting trees using LgbLinear for multi-quantile forecasting. + Optimized for time series prediction with specialized loss functions and + comprehensive hyperparameter control suitable for production energy forecasting. + + The forecaster uses a multi-output strategy where each quantile is predicted + by separate trees within the same boosting ensemble. This approach provides + well-calibrated uncertainty estimates while maintaining computational efficiency. + + Invariants: + - fit() must be called before predict() to train the model + - Configuration quantiles determine the number of prediction outputs + - Model state is preserved across predict() calls after fitting + - Input features must match training data structure during prediction + + Example: + Basic forecasting workflow: + + >>> from datetime import timedelta + >>> from openstef_core.types import LeadTime, Quantile + >>> config = LgbLinearForecasterConfig( + ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], + ... horizons=[LeadTime(timedelta(hours=1))], + ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) + ... ) + >>> forecaster = LgbLinearForecaster(config) + >>> # forecaster.fit(training_data) + >>> # predictions = forecaster.predict(test_data) + + Note: + LgbLinear dependency is optional and must be installed separately. + The model automatically handles multi-quantile output and uses + magnitude-weighted pinball loss by default for better forecasting performance. + + See Also: + LgbLinearHyperParams: Detailed hyperparameter configuration options. + HorizonForecaster: Base interface for all forecasting models. + GBLinearForecaster: Alternative linear model using LgbLinear. + """ + + Config = LgbLinearForecasterConfig + HyperParams = LgbLinearHyperParams + + _config: LgbLinearForecasterConfig + _lgblinear_model: LGBMQuantileRegressor + + def __init__(self, config: LgbLinearForecasterConfig) -> None: + """Initialize LgbLinear forecaster with configuration. + + Creates an untrained LgbLinear regressor with the specified configuration. + The underlying LgbLinear model is configured for multi-output quantile + regression using the provided hyperparameters and execution settings. + + Args: + config: Complete configuration including hyperparameters, quantiles, + and execution settings for the LgbLinear model. + """ + self._config = config + + self._lgblinear_model = LGBMQuantileRegressor( + quantiles=[float(q) for q in config.quantiles], + linear_tree=True, + random_state=config.random_state, + early_stopping_rounds=config.early_stopping_rounds, + verbosity=config.verbosity, + **config.hyperparams.model_dump(), + ) + + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + @property + @override + def hyperparams(self) -> LgbLinearHyperParams: + return self._config.hyperparams + + @property + @override + def is_fitted(self) -> bool: + return self._lgblinear_model.__sklearn_is_fitted__() + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + input_data: pd.DataFrame = data.input_data() + target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + sample_weight = data.sample_weight_series + + # Prepare validation data if provided + eval_set = None + eval_sample_weight = None + if data_val is not None: + val_input_data: pd.DataFrame = data_val.input_data() + val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore + val_sample_weight = cast(npt.NDArray[np.floating], data_val.sample_weight_series.to_numpy()) # type: ignore + eval_set = [(val_input_data, val_target)] + + eval_sample_weight = [val_sample_weight] + + self._lgblinear_model.fit( # type: ignore + X=input_data, + y=target, + feature_name=input_data.columns.tolist(), + sample_weight=sample_weight, + eval_set=eval_set, + eval_sample_weight=eval_sample_weight, + ) + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + prediction: npt.NDArray[np.floating] = self._lgblinear_model.predict(X=input_data) + + return ForecastDataset( + data=pd.DataFrame( + data=prediction, + index=input_data.index, + columns=[quantile.format() for quantile in self.config.quantiles], + ), + sample_interval=data.sample_interval, + ) + + @property + @override + def feature_importances(self) -> pd.DataFrame: + models = self._lgblinear_model._models # noqa: SLF001 + weights_df = pd.DataFrame( + [models[i].feature_importances_ for i in range(len(models))], + index=[quantile.format() for quantile in self.config.quantiles], + columns=models[0].feature_name_, + ).transpose() + + weights_df.index.name = "feature_name" + weights_df.columns.name = "quantiles" + + weights_abs = weights_df.abs() + total = weights_abs.sum(axis=0).replace(to_replace=0, value=1.0) # pyright: ignore[reportUnknownMemberType] + + return weights_abs / total + + +__all__ = ["LgbLinearForecaster", "LgbLinearForecasterConfig", "LgbLinearHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 4c4508117..0c4442328 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -108,18 +108,6 @@ class LGBMHyperParams(HyperParams): description="Fraction of features used when constructing each tree. Range: (0,1]", ) - # General Parameters - random_state: int | None = Field( - default=None, - alias="seed", - description="Random seed for reproducibility. Controls tree structure randomness.", - ) - - early_stopping_rounds: int | None = Field( - default=None, - description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", - ) - class LGBMForecasterConfig(ForecasterConfig): """Configuration for LightGBM-based forecaster. @@ -151,6 +139,17 @@ class LGBMForecasterConfig(ForecasterConfig): default=-1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) + random_state: int | None = Field( + default=None, + alias="seed", + description="Random seed for reproducibility. Controls tree structure randomness.", + ) + + early_stopping_rounds: int | None = Field( + default=None, + description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", + ) + MODEL_CODE_VERSION = 1 @@ -219,20 +218,10 @@ def __init__(self, config: LGBMForecasterConfig) -> None: self._lgbm_model = LGBMQuantileRegressor( quantiles=[float(q) for q in config.quantiles], linear_tree=False, - n_estimators=config.hyperparams.n_estimators, - learning_rate=config.hyperparams.learning_rate, - max_depth=config.hyperparams.max_depth, - min_child_weight=config.hyperparams.min_child_weight, - min_data_in_leaf=config.hyperparams.min_data_in_leaf, - min_data_in_bin=config.hyperparams.min_data_in_bin, - reg_alpha=config.hyperparams.reg_alpha, - reg_lambda=config.hyperparams.reg_lambda, - num_leaves=config.hyperparams.num_leaves, - max_bin=config.hyperparams.max_bin, - colsample_bytree=config.hyperparams.colsample_bytree, - random_state=config.hyperparams.random_state, - early_stopping_rounds=config.hyperparams.early_stopping_rounds, + random_state=config.random_state, + early_stopping_rounds=config.early_stopping_rounds, verbosity=config.verbosity, + **config.hyperparams.model_dump(), ) @property @@ -250,30 +239,35 @@ def hyperparams(self) -> LGBMHyperParams: def is_fitted(self) -> bool: return self._lgbm_model.__sklearn_is_fitted__() + @staticmethod + def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: + input_data: pd.DataFrame = data.input_data() + target: np.ndarray = np.asarray(data.target_series.values) + sample_weight: pd.Series = data.sample_weight_series + + return input_data, target, sample_weight + @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - input_data: pd.DataFrame = data.input_data() - target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore + # Prepare training data + input_data, target, sample_weight = self._prepare_fit_input(data) - sample_weight = data.sample_weight_series + # Evaluation sets + eval_set = [(input_data, target)] + sample_weight_eval_set = [sample_weight] - # Prepare validation data if provided - eval_set = None - eval_sample_weight = None if data_val is not None: - val_input_data: pd.DataFrame = data_val.input_data() - val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore - val_sample_weight = data_val.sample_weight_series.to_numpy() # type: ignore - eval_set = (val_input_data, val_target) - eval_sample_weight = [val_sample_weight] + input_data_val, target_val, sample_weight_val = self._prepare_fit_input(data_val) + eval_set.append((input_data_val, target_val)) + sample_weight_eval_set.append(sample_weight_val) self._lgbm_model.fit( X=input_data, y=target, feature_name=input_data.columns.tolist(), - sample_weight=sample_weight, # type: ignore - eval_set=eval_set, # type: ignore - eval_sample_weight=eval_sample_weight, # type: ignore + sample_weight=sample_weight, + eval_set=eval_set, + eval_sample_weight=sample_weight_eval_set, ) @override diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index 2636a7a13..202dd1e4e 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -218,20 +218,10 @@ def __init__(self, config: LGBMLinearForecasterConfig) -> None: self._lgbmlinear_model = LGBMQuantileRegressor( quantiles=[float(q) for q in config.quantiles], linear_tree=True, - n_estimators=config.hyperparams.n_estimators, - learning_rate=config.hyperparams.learning_rate, - max_depth=config.hyperparams.max_depth, - min_child_weight=config.hyperparams.min_child_weight, - min_data_in_leaf=config.hyperparams.min_data_in_leaf, - min_data_in_bin=config.hyperparams.min_data_in_bin, - reg_alpha=config.hyperparams.reg_alpha, - reg_lambda=config.hyperparams.reg_lambda, - num_leaves=config.hyperparams.num_leaves, - max_bin=config.hyperparams.max_bin, - colsample_bytree=config.hyperparams.colsample_bytree, random_state=config.random_state, early_stopping_rounds=config.early_stopping_rounds, verbosity=config.verbosity, + **config.hyperparams.model_dump(), ) @property diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py index 5ef874537..47bed1774 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py @@ -85,22 +85,6 @@ def test_lgbm_forecaster__not_fitted_error( forecaster.predict(sample_forecast_input_dataset) -def test_lgbm_forecaster__predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: LGBMForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = LGBMForecaster(config=base_config) - - # Act & Assert - with pytest.raises( - NotFittedError, - match="The LGBMForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 - ): - forecaster.predict(sample_forecast_input_dataset) - - def test_lgbm_forecaster__with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, base_config: LGBMForecasterConfig, @@ -160,3 +144,6 @@ def test_lgbm_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +# TODO : Add tests on different loss functions @MvLieshout diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py index 61882e51d..1a2ce31cf 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py @@ -85,22 +85,6 @@ def test_lgbmlinear_forecaster__not_fitted_error( forecaster.predict(sample_forecast_input_dataset) -def test_lgbmlinear_forecaster__predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: LGBMLinearForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = LGBMLinearForecaster(config=base_config) - - # Act & Assert - with pytest.raises( - NotFittedError, - match="The LGBMLinearForecaster has not been fitted yet. Please call 'fit' before using it.", # noqa: RUF043 - ): - forecaster.predict(sample_forecast_input_dataset) - - def test_lgbmlinear_forecaster__with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, base_config: LGBMLinearForecasterConfig, @@ -160,3 +144,6 @@ def test_lgbmlinear_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +# TODO : Add tests on different loss functions @MvLieshout From 3d5460413a54a43acf1c8d393a2a434c899dfca5 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 12 Nov 2025 09:49:26 +0100 Subject: [PATCH 09/72] Fixed comments --- .../src/openstef_models/estimators/hybrid.py | 44 +-- .../src/openstef_models/estimators/lgbm.py | 4 +- .../models/forecasting/hybrid_forecaster.py | 2 + .../forecasting/lgblinear_forecaster.py | 305 ------------------ .../models/forecasting/lgbm_forecaster.py | 2 +- 5 files changed, 5 insertions(+), 352 deletions(-) delete mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py index 6e107917f..1660d8707 100644 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ b/packages/openstef-models/src/openstef_models/estimators/hybrid.py @@ -7,19 +7,14 @@ using stacking for robust multi-quantile regression, including serialization utilities. """ -from typing import Self - import numpy as np import numpy.typing as npt import pandas as pd from lightgbm import LGBMRegressor from sklearn.ensemble import StackingRegressor from sklearn.linear_model import QuantileRegressor -from skops.io import dumps, loads from xgboost import XGBRegressor -from openstef_core.exceptions import ModelLoadingError - class HybridQuantileRegressor: """Custom Hybrid regressor for multi-quantile estimation using sample weights.""" @@ -94,7 +89,7 @@ def __init__( # noqa: D107, PLR0913, PLR0917 verbose=3, passthrough=False, n_jobs=None, - cv=2, + cv=1, ) ) self.is_fitted: bool = False @@ -149,40 +144,3 @@ def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np. """ # noqa: D412 X = X.ffill().fillna(0) # type: ignore return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore - - def save_bytes(self) -> bytes: - """Serialize the model. - - Returns: - A string representation of the model. - """ - return dumps(self) - - @classmethod - def load_bytes(cls, model_bytes: bytes) -> Self: - """Deserialize the model from bytes using joblib. - - Args: - model_bytes : Bytes representing the serialized model. - - Returns: - An instance of LightGBMQuantileRegressor. - - Raises: - ModelLoadingError: If the deserialized object is not a HybridQuantileRegressor. - """ - trusted_types = [ - "collections.OrderedDict", - "lgbm.basic.Booster", - "lgbm.sklearn.LGBMRegressor", - "sklearn.utils._bunch.Bunch", - "xgboost.core.Booster", - "xgboost.sklearn.XGBRegressor", - "openstef_models.estimators.hybrid.HybridQuantileRegressor", - ] - instance = loads(model_bytes, trusted=trusted_types) - - if not isinstance(instance, cls): - raise ModelLoadingError("Deserialized object is not a HybridQuantileRegressor") - - return instance diff --git a/packages/openstef-models/src/openstef_models/estimators/lgbm.py b/packages/openstef-models/src/openstef_models/estimators/lgbm.py index cf149c54a..666f5d8cf 100644 --- a/packages/openstef-models/src/openstef_models/estimators/lgbm.py +++ b/packages/openstef-models/src/openstef_models/estimators/lgbm.py @@ -9,8 +9,6 @@ by a separate tree within the same boosting ensemble. The module also includes serialization utilities. """ -from typing import Any - import numpy as np import numpy.typing as npt import pandas as pd @@ -113,7 +111,7 @@ def fit( sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, feature_name: list[str] | None = None, eval_set: list[tuple[pd.DataFrame, npt.NDArray[np.floating]]] | None = None, - eval_sample_weight: list[npt.NDArray[np.floating]] | list[pd.Series[Any]] | None = None, + eval_sample_weight: list[npt.NDArray[np.floating]] | list[pd.Series] | None = None, ) -> None: """Fit the multi-quantile regressor. diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 838f2abba..2e9ff448b 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -152,5 +152,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + # TODO(@MvLieshout, @Lars800): Make forecaster Explainable + __all__ = ["HybridForecaster", "HybridForecasterConfig", "HybridHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py deleted file mode 100644 index 9176fda14..000000000 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgblinear_forecaster.py +++ /dev/null @@ -1,305 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""LightGBM-based forecasting models for probabilistic energy forecasting. - -Provides gradient boosting tree models using LightGBM for multi-quantile energy -forecasting. Optimized for time series data with specialized loss functions and -comprehensive hyperparameter control for production forecasting workflows. -""" - -from typing import Literal, cast, override - -import numpy as np -import numpy.typing as npt -import pandas as pd -from pydantic import Field - -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.exceptions import ( - NotFittedError, -) -from openstef_core.mixins import HyperParams -from openstef_models.estimators.lgbm import LGBMQuantileRegressor -from openstef_models.explainability.mixins import ExplainableForecaster -from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig - - -class LgbLinearHyperParams(HyperParams): - """LgbLinear hyperparameters for gradient boosting tree models. - - Example: - Creating custom hyperparameters for deep trees with regularization: - - >>> hyperparams = LgbLinearHyperParams( - ... n_estimators=200, - ... max_depth=8, - ... learning_rate=0.1, - ... reg_alpha=0.1, - ... reg_lambda=1.0, - ... ) - - Note: - These parameters are optimized for probabilistic forecasting with - quantile regression. The default objective function is specialized - for magnitude-weighted pinball loss. - """ - - # Core Tree Boosting Parameters - - n_estimators: int = Field( - default=100, - description="Number of boosting rounds/trees to fit. Higher values may improve performance but " - "increase training time and risk overfitting.", - ) - learning_rate: float = Field( - default=0.1, - alias="eta", - description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require " - "more boosting rounds.", - ) - max_depth: int = Field( - default=4, # Different from Factory Default (-1, unlimited) - description="Maximum depth of trees. Higher values capture more complex patterns but risk " - "overfitting. Range: [1,∞]", - ) - min_child_weight: float = Field( - default=1e-3, - description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent " - "overfitting. Range: [0,∞]", - ) - - min_data_in_leaf: int = Field( - default=20, - description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", - ) - min_data_in_bin: int = Field( - default=20, - description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", - ) - - # Regularization - reg_alpha: float = Field( - default=0, - description="L1 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", - ) - reg_lambda: float = Field( - default=0.0, - description="L2 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]", - ) - - # Tree Structure Control - num_leaves: int = Field( - default=31, - description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", - ) - - max_bin: int = Field( - default=256, - description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " - "increase memory. Only for hist tree_method.", - ) - - # Subsampling Parameters - colsample_bytree: float = Field( - default=1, - description="Fraction of features used when constructing each tree. Range: (0,1]", - ) - - -class LgbLinearForecasterConfig(ForecasterConfig): - """Configuration for LgbLinear-based forecaster. - Extends HorizonForecasterConfig with LgbLinear-specific hyperparameters - and execution settings. - - Example: - Creating a LgbLinear forecaster configuration with custom hyperparameters: - >>> from datetime import timedelta - >>> from openstef_core.types import LeadTime, Quantile - >>> config = LgbLinearForecasterConfig( - ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], - ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) - ... ) - """ # noqa: D205 - - hyperparams: LgbLinearHyperParams = LgbLinearHyperParams() - - # General Parameters - device: str = Field( - default="cpu", - description="Device for LgbLinear computation. Options: 'cpu', 'cuda', 'cuda:', 'gpu'", - ) - n_jobs: int = Field( - default=1, - description="Number of parallel threads for tree construction. -1 uses all available cores.", - ) - verbosity: Literal[0, 1, 2, 3] = Field( - default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" - ) - - # General Parameters - random_state: int | None = Field( - default=None, - alias="seed", - description="Random seed for reproducibility. Controls tree structure randomness.", - ) - - early_stopping_rounds: int | None = Field( - default=0, - description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", - ) - - -MODEL_CODE_VERSION = 1 - - -class LgbLinearForecaster(Forecaster, ExplainableForecaster): - """LgbLinear-based forecaster for probabilistic energy forecasting. - - Implements gradient boosting trees using LgbLinear for multi-quantile forecasting. - Optimized for time series prediction with specialized loss functions and - comprehensive hyperparameter control suitable for production energy forecasting. - - The forecaster uses a multi-output strategy where each quantile is predicted - by separate trees within the same boosting ensemble. This approach provides - well-calibrated uncertainty estimates while maintaining computational efficiency. - - Invariants: - - fit() must be called before predict() to train the model - - Configuration quantiles determine the number of prediction outputs - - Model state is preserved across predict() calls after fitting - - Input features must match training data structure during prediction - - Example: - Basic forecasting workflow: - - >>> from datetime import timedelta - >>> from openstef_core.types import LeadTime, Quantile - >>> config = LgbLinearForecasterConfig( - ... quantiles=[Quantile(0.1), Quantile(0.5), Quantile(0.9)], - ... horizons=[LeadTime(timedelta(hours=1))], - ... hyperparams=LgbLinearHyperParams(n_estimators=100, max_depth=6) - ... ) - >>> forecaster = LgbLinearForecaster(config) - >>> # forecaster.fit(training_data) - >>> # predictions = forecaster.predict(test_data) - - Note: - LgbLinear dependency is optional and must be installed separately. - The model automatically handles multi-quantile output and uses - magnitude-weighted pinball loss by default for better forecasting performance. - - See Also: - LgbLinearHyperParams: Detailed hyperparameter configuration options. - HorizonForecaster: Base interface for all forecasting models. - GBLinearForecaster: Alternative linear model using LgbLinear. - """ - - Config = LgbLinearForecasterConfig - HyperParams = LgbLinearHyperParams - - _config: LgbLinearForecasterConfig - _lgblinear_model: LGBMQuantileRegressor - - def __init__(self, config: LgbLinearForecasterConfig) -> None: - """Initialize LgbLinear forecaster with configuration. - - Creates an untrained LgbLinear regressor with the specified configuration. - The underlying LgbLinear model is configured for multi-output quantile - regression using the provided hyperparameters and execution settings. - - Args: - config: Complete configuration including hyperparameters, quantiles, - and execution settings for the LgbLinear model. - """ - self._config = config - - self._lgblinear_model = LGBMQuantileRegressor( - quantiles=[float(q) for q in config.quantiles], - linear_tree=True, - random_state=config.random_state, - early_stopping_rounds=config.early_stopping_rounds, - verbosity=config.verbosity, - **config.hyperparams.model_dump(), - ) - - @property - @override - def config(self) -> ForecasterConfig: - return self._config - - @property - @override - def hyperparams(self) -> LgbLinearHyperParams: - return self._config.hyperparams - - @property - @override - def is_fitted(self) -> bool: - return self._lgblinear_model.__sklearn_is_fitted__() - - @override - def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - input_data: pd.DataFrame = data.input_data() - target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore - sample_weight = data.sample_weight_series - - # Prepare validation data if provided - eval_set = None - eval_sample_weight = None - if data_val is not None: - val_input_data: pd.DataFrame = data_val.input_data() - val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore - val_sample_weight = cast(npt.NDArray[np.floating], data_val.sample_weight_series.to_numpy()) # type: ignore - eval_set = [(val_input_data, val_target)] - - eval_sample_weight = [val_sample_weight] - - self._lgblinear_model.fit( # type: ignore - X=input_data, - y=target, - feature_name=input_data.columns.tolist(), - sample_weight=sample_weight, - eval_set=eval_set, - eval_sample_weight=eval_sample_weight, - ) - - @override - def predict(self, data: ForecastInputDataset) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - input_data: pd.DataFrame = data.input_data(start=data.forecast_start) - prediction: npt.NDArray[np.floating] = self._lgblinear_model.predict(X=input_data) - - return ForecastDataset( - data=pd.DataFrame( - data=prediction, - index=input_data.index, - columns=[quantile.format() for quantile in self.config.quantiles], - ), - sample_interval=data.sample_interval, - ) - - @property - @override - def feature_importances(self) -> pd.DataFrame: - models = self._lgblinear_model._models # noqa: SLF001 - weights_df = pd.DataFrame( - [models[i].feature_importances_ for i in range(len(models))], - index=[quantile.format() for quantile in self.config.quantiles], - columns=models[0].feature_name_, - ).transpose() - - weights_df.index.name = "feature_name" - weights_df.columns.name = "quantiles" - - weights_abs = weights_df.abs() - total = weights_abs.sum(axis=0).replace(to_replace=0, value=1.0) # pyright: ignore[reportUnknownMemberType] - - return weights_abs / total - - -__all__ = ["LgbLinearForecaster", "LgbLinearForecasterConfig", "LgbLinearHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 0c4442328..50dd9f50b 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Literal, override +import numpy as np import pandas as pd from pydantic import Field @@ -24,7 +25,6 @@ from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig if TYPE_CHECKING: - import numpy as np import numpy.typing as npt From 34fc3e5af68b89eec8ec39c168bbbd773c6e3cfd Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 12 Nov 2025 11:41:01 +0100 Subject: [PATCH 10/72] Added SkopsModelSerializer --- .../integrations/skops/__init__.py | 15 +++ .../skops/skops_model_serializer.py | 105 ++++++++++++++++++ .../mixins/model_serializer.py | 3 +- .../presets/forecasting_workflow.py | 2 +- .../tests/unit/integrations/skops/__init__.py | 5 + .../skops/test_skops_model_serializer.py | 72 ++++++++++++ 6 files changed, 199 insertions(+), 3 deletions(-) create mode 100644 packages/openstef-models/src/openstef_models/integrations/skops/__init__.py create mode 100644 packages/openstef-models/src/openstef_models/integrations/skops/skops_model_serializer.py create mode 100644 packages/openstef-models/tests/unit/integrations/skops/__init__.py create mode 100644 packages/openstef-models/tests/unit/integrations/skops/test_skops_model_serializer.py diff --git a/packages/openstef-models/src/openstef_models/integrations/skops/__init__.py b/packages/openstef-models/src/openstef_models/integrations/skops/__init__.py new file mode 100644 index 000000000..16fcbd789 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/integrations/skops/__init__.py @@ -0,0 +1,15 @@ +"""Joblib-based model storage integration. + +Provides local file-based model persistence using Skops for serialization. +This integration provides a safe way for storing and loading ForecastingModel instances on +the local filesystem, making it suitable for development, testing, and +single-machine deployments. +""" + +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from .skops_model_serializer import SkopsModelSerializer + +__all__ = ["SkopsModelSerializer"] diff --git a/packages/openstef-models/src/openstef_models/integrations/skops/skops_model_serializer.py b/packages/openstef-models/src/openstef_models/integrations/skops/skops_model_serializer.py new file mode 100644 index 000000000..6296d3abb --- /dev/null +++ b/packages/openstef-models/src/openstef_models/integrations/skops/skops_model_serializer.py @@ -0,0 +1,105 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Local model storage implementation using joblib serialization. + +Provides file-based persistence for ForecastingModel instances using joblib's +pickle-based serialization. This storage backend is suitable for development, +testing, and single-machine deployments where models need to be persisted +to the local filesystem. +""" + +from typing import BinaryIO, ClassVar, override + +from openstef_core.exceptions import MissingExtraError +from openstef_models.mixins.model_serializer import ModelSerializer + +try: + from skops.io import dump, get_untrusted_types, load +except ImportError as e: + raise MissingExtraError("joblib", package="openstef-models") from e + + +class SkopsModelSerializer(ModelSerializer): + """File-based model storage using joblib serialization. + + Provides persistent storage for ForecastingModel instances on the local + filesystem. Models are serialized using joblib and stored as pickle files + in the specified directory. + + This storage implementation is suitable for development, testing, and + single-machine deployments where simple file-based persistence is sufficient. + + Note: + joblib.dump() and joblib.load() are based on the Python pickle serialization model, + which means that arbitrary Python code can be executed when loading a serialized object + with joblib.load(). + + joblib.load() should therefore never be used to load objects from an untrusted source + or otherwise you will introduce a security vulnerability in your program. + + Invariants: + - Models are stored as .pkl files in the configured storage directory + - Model files use the pattern: {model_id}.pkl + - Storage directory is created automatically if it doesn't exist + - Load operations fail with ModelNotFoundError if model file doesn't exist + + Example: + Basic usage with model persistence: + + >>> from pathlib import Path + >>> from openstef_models.models.forecasting_model import ForecastingModel + >>> storage = LocalModelStorage(storage_dir=Path("./models")) # doctest: +SKIP + >>> storage.save_model("my_model", my_forecasting_model) # doctest: +SKIP + >>> loaded_model = storage.load_model("my_model") # doctest: +SKIP + """ + + extension: ClassVar[str] = ".skops" + + @override + def serialize(self, model: object, file: BinaryIO) -> None: + dump(model, file) # type: ignore[reportUnknownMemberType] + + @staticmethod + def _get_stateful_types() -> set[str]: + return { + "tests.unit.integrations.skops.test_skops_model_serializer.SimpleSerializableModel", + "openstef_core.mixins.predictor.BatchPredictor", + "openstef_models.models.forecasting.forecaster.Forecaster", + "openstef_models.models.forecasting.xgboost_forecaster.XGBoostForecaster", + "openstef_models.models.component_splitting_model.ComponentSplittingModel", + "openstef_core.mixins.transform.TransformPipeline", + "openstef_core.mixins.transform.TransformPipeline[EnergyComponentDataset]", + "openstef_core.mixins.transform.TransformPipeline[TimeSeriesDataset]", + "openstef_models.models.forecasting.lgbm_forecaster.LGBMForecaster", + "openstef_models.models.component_splitting.component_splitter.ComponentSplitter", + "openstef_models.models.forecasting_model.ForecastingModel", + "openstef_core.mixins.transform.Transform", + "openstef_core.mixins.transform.TransformPipeline[ForecastDataset]", + "openstef_core.mixins.predictor.Predictor", + "openstef_models.models.forecasting.lgbmlinear_forecaster.LGBMLinearForecaster", + } + + @override + def deserialize(self, file: BinaryIO) -> object: + """Load a model's state from a binary file and restore it. + + Returns: + The restored model instance. + + Raises: + ValueError: If no safe types are found in the serialized model. + """ + safe_types = self._get_stateful_types() + + # Weak security measure that checks a safe class is present. + # Can be improved to ensure no unsafe classes are present. + model_types: set[str] = set(get_untrusted_types(file=file)) # type: ignore + + if len(safe_types.intersection(model_types)) == 0: + raise ValueError("Deserialization aborted: No safe types found in the serialized model.") + + return load(file, trusted=list(model_types)) # type: ignore[reportUnknownMemberType] + + +__all__ = ["SkopsModelSerializer"] diff --git a/packages/openstef-models/src/openstef_models/mixins/model_serializer.py b/packages/openstef-models/src/openstef_models/mixins/model_serializer.py index 1167a9930..40e74a52a 100644 --- a/packages/openstef-models/src/openstef_models/mixins/model_serializer.py +++ b/packages/openstef-models/src/openstef_models/mixins/model_serializer.py @@ -34,6 +34,7 @@ class ModelSerializer(BaseConfig, ABC): See Also: JoblibModelSerializer: Concrete implementation using joblib. + SkopsModelSerializer: Concrete implementation using skops. """ extension: ClassVar[str] @@ -69,6 +70,4 @@ def deserialize(self, file: BinaryIO) -> object: """ -# TODO @egordm, @MvLieshout : Add SkopsModelSerializer implementation - __all__ = ["ModelIdentifier", "ModelSerializer"] diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 1f5fdaba0..999ed701f 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -31,8 +31,8 @@ from openstef_models.models.forecasting.flatliner_forecaster import FlatlinerForecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster from openstef_models.models.forecasting.hybrid_forecaster import HybridForecaster -from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder from openstef_models.transforms.general import ( diff --git a/packages/openstef-models/tests/unit/integrations/skops/__init__.py b/packages/openstef-models/tests/unit/integrations/skops/__init__.py new file mode 100644 index 000000000..63d543f53 --- /dev/null +++ b/packages/openstef-models/tests/unit/integrations/skops/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +__all__ = [] diff --git a/packages/openstef-models/tests/unit/integrations/skops/test_skops_model_serializer.py b/packages/openstef-models/tests/unit/integrations/skops/test_skops_model_serializer.py new file mode 100644 index 000000000..8d4bb9eb7 --- /dev/null +++ b/packages/openstef-models/tests/unit/integrations/skops/test_skops_model_serializer.py @@ -0,0 +1,72 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from __future__ import annotations + +from io import BytesIO +from typing import TYPE_CHECKING + +import pytest + +from openstef_core.mixins import Stateful +from openstef_core.types import LeadTime, Q +from openstef_models.integrations.skops.skops_model_serializer import SkopsModelSerializer +from openstef_models.models.forecasting.forecaster import ForecasterConfig +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster + +if TYPE_CHECKING: + from openstef_models.models.forecasting.forecaster import Forecaster + + +class SimpleSerializableModel(Stateful): + """A simple model class that can be pickled for testing.""" + + def __init__(self) -> None: + self.target_column = "load" + self.is_fitted = True + + +def test_skops_model_serializer__roundtrip__preserves_model_integrity(): + """Test complete serialize/deserialize roundtrip preserves model state.""" + # Arrange + buffer = BytesIO() + serializer = SkopsModelSerializer() + model = SimpleSerializableModel() + + # Act - Serialize then deserialize + serializer.serialize(model, buffer) + buffer.seek(0) + restored_model = serializer.deserialize(buffer) + + # Assert - Model state should be identical + assert isinstance(restored_model, SimpleSerializableModel) + assert restored_model.target_column == model.target_column + assert restored_model.is_fitted == model.is_fitted + + +@pytest.mark.parametrize( + "forecaster_class", + [ + XGBoostForecaster, + LGBMForecaster, + LGBMLinearForecaster, + ], +) +def test_skops_works_with_different_forecasters(forecaster_class: type[Forecaster]): + buffer = BytesIO() + serializer = SkopsModelSerializer() + + config: ForecasterConfig = forecaster_class.Config(horizons=[LeadTime.from_string("PT12H")], quantiles=[Q(0.5)]) # type: ignore + assert isinstance(config, ForecasterConfig) + forecaster = forecaster_class(config=config) + + # Act - Serialize then deserialize + serializer.serialize(forecaster, buffer) + buffer.seek(0) + restored_model = serializer.deserialize(buffer) + + # Assert - Model state should be identical + assert isinstance(restored_model, forecaster.__class__) From bad4c449bc0a9180d2c6fca1d9e30bb9753f534f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 12 Nov 2025 12:32:01 +0100 Subject: [PATCH 11/72] Fixed issues --- .../src/openstef_models/models/forecasting/hybrid_forecaster.py | 2 +- .../tests/unit/models/forecasting/test_lgbm_forecaster.py | 2 +- .../tests/unit/models/forecasting/test_lgbmlinear_forecaster.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 2e9ff448b..a3f2849a3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -152,7 +152,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) - # TODO(@MvLieshout, @Lars800): Make forecaster Explainable + # TODO(@Lars800): #745: Make forecaster Explainable __all__ = ["HybridForecaster", "HybridForecasterConfig", "HybridHyperParams"] diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py index 47bed1774..b4fe1c989 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py @@ -146,4 +146,4 @@ def test_lgbm_forecaster__feature_importances( assert (feature_importances >= 0).all().all() -# TODO : Add tests on different loss functions @MvLieshout +# TODO(@MvLieshout): Add tests on different loss functions # noqa: TD003 diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py index 1a2ce31cf..cc4b4701e 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbmlinear_forecaster.py @@ -146,4 +146,4 @@ def test_lgbmlinear_forecaster__feature_importances( assert (feature_importances >= 0).all().all() -# TODO : Add tests on different loss functions @MvLieshout +# TODO(@MvLieshout): Add tests on different loss functions # noqa: TD003 From 99c9bc5e8e3dbed522ae7874c1bc78d14ea1c202 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 13 Nov 2025 12:00:44 +0100 Subject: [PATCH 12/72] Gitignore optimization and dev sandbox --- .gitignore | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index b181a07c5..81c981c66 100644 --- a/.gitignore +++ b/.gitignore @@ -123,5 +123,7 @@ certificates/ *.html *.pkl -# Benchmark outputs -benchmark_results/ \ No newline at end of file +# Experiment outputs +benchmark_results/ +optimization_results/ +dev_sandbox/ \ No newline at end of file From 4027de7fd0d0834da2b636ea1866cdaec3995857 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 13 Nov 2025 14:48:53 +0100 Subject: [PATCH 13/72] Added MultiQuantileAdapter Class --- .../utils/multi_quantile_regressor.py | 119 ++++++++++++++++++ .../tests/unit/estimators/test_lgbm.py | 42 ------- .../utils/test_multi_quantile_regressor.py | 107 ++++++++++++++++ 3 files changed, 226 insertions(+), 42 deletions(-) create mode 100644 packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py delete mode 100644 packages/openstef-models/tests/unit/estimators/test_lgbm.py create mode 100644 packages/openstef-models/tests/unit/utils/test_multi_quantile_regressor.py diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py new file mode 100644 index 000000000..d48e01b1e --- /dev/null +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -0,0 +1,119 @@ +import logging + +import numpy as np +import numpy.typing as npt +import pandas as pd +from sklearn.base import BaseEstimator, RegressorMixin + +logger = logging.getLogger(__name__) + +ParamType = float | int | str | bool | None + + +class MultiQuantileRegressor(BaseEstimator, RegressorMixin): + """Adaptor for multi-quantile regression using a base quantile regressor. + + This class creates separate instances of a given quantile regressor for each quantile + and manages their training and prediction. + """ + + def __init__( + self, + base_learner: type[BaseEstimator], + quantile_param: str, + quantiles: list[float], + hyperparams: dict[str, ParamType], + ): + """Initialize MultiQuantileRegressor. + + This is an adaptor that allows any quantile-capable regressor to predict multiple quantiles + by instantiating separate models for each quantile. + + Args: + base_learner: A scikit-learn compatible regressor class that supports quantile regression. + quantile_param: The name of the parameter in base_learner that sets the quantile level. + quantiles: List of quantiles to predict (e.g., [0.1, 0.5, 0.9]). + hyperparams: Dictionary of hyperparameters to pass to each base learner instance. + """ + self.quantiles = quantiles + self.hyperparams = hyperparams + self.quantile_param = quantile_param + self.base_learner = base_learner + self.is_fitted = False + self._models = [self._init_model(q) for q in quantiles] + + def _init_model(self, q: float) -> BaseEstimator: + params = self.hyperparams.copy() + params[self.quantile_param] = q + base_learner = self.base_learner(**params) + + try: + q == base_learner.get_params()[self.quantile_param] # type: ignore + except AttributeError as e: + msg = f"The base learner does not support the quantile parameter '{self.quantile_param}'." + raise ValueError(msg) from e + + return base_learner + + def fit( + self, + X: npt.NDArray[np.floating] | pd.DataFrame, + y: npt.NDArray[np.floating] | pd.Series, + sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, + feature_name: list[str] | None = None, + eval_set: list[tuple[pd.DataFrame, npt.NDArray[np.floating]]] | None = None, + eval_sample_weight: list[npt.NDArray[np.floating]] | list[pd.Series] | None = None, + ) -> None: + """Fit the multi-quantile regressor. + + Args: + X: Input features as a DataFrame. + y: Target values as a 2D array where each column corresponds to a quantile. + sample_weight: Sample weights for training data. + feature_name: List of feature names. + eval_set: Evaluation set for early stopping. + eval_sample_weight: Sample weights for evaluation data. + """ + for model in self._models: + if eval_set is None and "early_stopping_rounds" in self.hyperparams: + model.set_params(early_stopping_rounds=None) # type: ignore + elif "early_stopping_rounds" in self.hyperparams: + model.set_params(early_stopping_rounds=self.hyperparams.early_stopping_rounds) # type: ignore + + if eval_set or eval_sample_weight: + logger.warning( + "Evaluation sets or sample weights provided, but MultiQuantileRegressor does not currently support " + "these during fitting." + ) + + if feature_name: + logger.warning( + "Feature names provided, but MultiQuantileRegressor does not currently support feature names during fitting." + ) + model.fit( # type: ignore + X=np.asarray(X), + y=y, + sample_weight=sample_weight, + ) + self.is_fitted = True + + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: + """Predict quantiles for the input features. + + Args: + X: Input features as a DataFrame. + + Returns: + + A 2D array where each column corresponds to predicted quantiles. + """ # noqa: D412 + return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore + + @property + def models(self) -> list[BaseEstimator]: + """Get the list of underlying quantile models. + + Returns: + List of BaseEstimator instances for each quantile. + """ + return self._models diff --git a/packages/openstef-models/tests/unit/estimators/test_lgbm.py b/packages/openstef-models/tests/unit/estimators/test_lgbm.py deleted file mode 100644 index 5dfa0bb5b..000000000 --- a/packages/openstef-models/tests/unit/estimators/test_lgbm.py +++ /dev/null @@ -1,42 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -import pandas as pd -import pytest -from numpy.random import default_rng - -from openstef_models.estimators.lgbm import LGBMQuantileRegressor - - -@pytest.fixture -def dataset() -> tuple[pd.DataFrame, pd.Series]: - n_samples = 100 - n_features = 5 - rng = default_rng() - X = pd.DataFrame(rng.random((n_samples, n_features))) - y = pd.Series(rng.random(n_samples)) - return X, y - - -def test_init_sets_quantiles_and_models(): - quantiles = [0.1, 0.5, 0.9] - model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False) - assert model.quantiles == quantiles - assert len(model._models) == len(quantiles) - - -def test_fit_and_predict_shape(dataset: tuple[pd.DataFrame, pd.Series]): - quantiles = [0.1, 0.5, 0.9] - X, y = dataset[0], dataset[1] - model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False, n_estimators=5) - model.fit(X, y) - preds = model.predict(X) - assert preds.shape == (X.shape[0], len(quantiles)) - - -def test_sklearn_is_fitted_true_after_fit(dataset: tuple[pd.DataFrame, pd.Series]): - quantiles = [0.1, 0.5, 0.9] - X, y = dataset[0], dataset[1] - model = LGBMQuantileRegressor(quantiles=quantiles, linear_tree=False, n_estimators=2) - model.fit(X, y) - assert model.__sklearn_is_fitted__() diff --git a/packages/openstef-models/tests/unit/utils/test_multi_quantile_regressor.py b/packages/openstef-models/tests/unit/utils/test_multi_quantile_regressor.py new file mode 100644 index 000000000..d2e8ad7be --- /dev/null +++ b/packages/openstef-models/tests/unit/utils/test_multi_quantile_regressor.py @@ -0,0 +1,107 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +import pandas as pd +import pytest +from lightgbm import LGBMRegressor +from numpy.random import default_rng +from pydantic import BaseModel +from sklearn.base import BaseEstimator +from sklearn.linear_model import QuantileRegressor +from xgboost import XGBRegressor + +from openstef_models.utils.multi_quantile_regressor import MultiQuantileRegressor, ParamType + +ParamDict = dict[str, ParamType] +BaseLearner = BaseEstimator + + +class BaseLearnerConfig(BaseModel): + base_learner: type[BaseLearner] + quantile_param: str + hyperparams: ParamDict + + +@pytest.fixture +def dataset() -> tuple[pd.DataFrame, pd.Series]: + n_samples = 100 + n_features = 5 + rng = default_rng() + X = pd.DataFrame(rng.random((n_samples, n_features))) + y = pd.Series(rng.random(n_samples)) + return X, y + + +@pytest.fixture(params=["sklearn_quantile", "lgbm", "xgboost"]) +def baselearner_config(request: pytest.FixtureRequest) -> BaseLearnerConfig: # type : ignore + model: str = request.param + if model == "sklearn_quantile": + return BaseLearnerConfig( + base_learner=QuantileRegressor, + quantile_param="quantile", + hyperparams={"alpha": 0.1, "solver": "highs", "fit_intercept": True}, + ) + if model == "lgbm": + return BaseLearnerConfig( + base_learner=LGBMRegressor, # type: ignore + quantile_param="alpha", + hyperparams={ + "objective": "quantile", + "n_estimators": 10, + "learning_rate": 0.1, + "max_depth": -1, + }, + ) + return BaseLearnerConfig( + base_learner=XGBRegressor, + quantile_param="quantile_alpha", + hyperparams={ + "objective": "reg:quantileerror", + "n_estimators": 10, + "learning_rate": 0.1, + "max_depth": 3, + }, + ) + + +def test_init_sets_quantiles_and_models(baselearner_config: BaseLearnerConfig): + quantiles = [0.1, 0.5, 0.9] + + model = MultiQuantileRegressor( + base_learner=baselearner_config.base_learner, + quantile_param=baselearner_config.quantile_param, + quantiles=quantiles, + hyperparams=baselearner_config.hyperparams, + ) + + assert model.quantiles == quantiles + assert len(model._models) == len(quantiles) + + +def test_fit_and_predict_shape(dataset: tuple[pd.DataFrame, pd.Series], baselearner_config: BaseLearnerConfig): + quantiles = [0.1, 0.5, 0.9] + + X, y = dataset[0], dataset[1] + model = MultiQuantileRegressor( + base_learner=baselearner_config.base_learner, + quantile_param=baselearner_config.quantile_param, + quantiles=quantiles, + hyperparams=baselearner_config.hyperparams, + ) + + model.fit(X, y) + preds = model.predict(X) + assert preds.shape == (X.shape[0], len(quantiles)) + + +def test_is_fitted_true_after_fit(dataset: tuple[pd.DataFrame, pd.Series], baselearner_config: BaseLearnerConfig): + quantiles = [0.1, 0.5, 0.9] + X, y = dataset[0], dataset[1] + model = MultiQuantileRegressor( + base_learner=baselearner_config.base_learner, + quantile_param=baselearner_config.quantile_param, + quantiles=quantiles, + hyperparams=baselearner_config.hyperparams, + ) + model.fit(X, y) + assert model.is_fitted From 064a92db8dd5910615149b5e2e750cf09c1104ad Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 13 Nov 2025 14:50:49 +0100 Subject: [PATCH 14/72] small fix --- .../src/openstef_models/utils/multi_quantile_regressor.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py index d48e01b1e..e96a92993 100644 --- a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -47,11 +47,9 @@ def _init_model(self, q: float) -> BaseEstimator: params[self.quantile_param] = q base_learner = self.base_learner(**params) - try: - q == base_learner.get_params()[self.quantile_param] # type: ignore - except AttributeError as e: + if self.quantile_param not in base_learner.get_params(): # type: ignore msg = f"The base learner does not support the quantile parameter '{self.quantile_param}'." - raise ValueError(msg) from e + raise ValueError(msg) return base_learner From ed83b3a9d5bce96b8e04d6c00d5c910af5b9bef7 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 17 Nov 2025 15:43:36 +0100 Subject: [PATCH 15/72] Hybrid V2 --- .../openstef_models/estimators/__init__.py | 4 +- .../src/openstef_models/estimators/lgbm.py | 168 ----------- .../models/forecasting/forecaster.py | 9 + .../models/forecasting/gblinear_forecaster.py | 17 ++ .../models/forecasting/hybrid_forecaster.py | 273 +++++++++++++----- .../models/forecasting/lgbm_forecaster.py | 48 ++- .../forecasting/lgbmlinear_forecaster.py | 85 ++++-- .../models/forecasting/xgboost_forecaster.py | 17 ++ .../utils/multi_quantile_regressor.py | 52 +++- .../forecasting/test_hybrid_forecaster.py | 48 +-- 10 files changed, 393 insertions(+), 328 deletions(-) delete mode 100644 packages/openstef-models/src/openstef_models/estimators/lgbm.py diff --git a/packages/openstef-models/src/openstef_models/estimators/__init__.py b/packages/openstef-models/src/openstef_models/estimators/__init__.py index 2b2e5ebb4..07a4cbc99 100644 --- a/packages/openstef-models/src/openstef_models/estimators/__init__.py +++ b/packages/openstef-models/src/openstef_models/estimators/__init__.py @@ -4,6 +4,4 @@ """Custom estimators for multi quantiles.""" -from .lgbm import LGBMQuantileRegressor - -__all__ = ["LGBMQuantileRegressor"] +__all__ = [] diff --git a/packages/openstef-models/src/openstef_models/estimators/lgbm.py b/packages/openstef-models/src/openstef_models/estimators/lgbm.py deleted file mode 100644 index 666f5d8cf..000000000 --- a/packages/openstef-models/src/openstef_models/estimators/lgbm.py +++ /dev/null @@ -1,168 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""Custom LightGBM regressor for multi-quantile regression. - -This module provides the LGBMQuantileRegressor class, which extends LightGBM's LGBMRegressor -to support multi-quantile output by configuring the objective function accordingly. Each quantile is predicted -by a separate tree within the same boosting ensemble. The module also includes serialization utilities. -""" - -import numpy as np -import numpy.typing as npt -import pandas as pd -from lightgbm import LGBMRegressor -from sklearn.base import BaseEstimator, RegressorMixin - - -class LGBMQuantileRegressor(BaseEstimator, RegressorMixin): - """Custom LightGBM regressor for multi-quantile regression. - - Extends LGBMRegressor to support multi-quantile output by configuring - the objective function accordingly. Each quantile is predicted by a - separate tree within the same boosting ensemble. - """ - - def __init__( # noqa: PLR0913, PLR0917 - self, - quantiles: list[float], - linear_tree: bool, # noqa: FBT001 - n_estimators: int = 100, - learning_rate: float = 0.1, - max_depth: int = -1, - min_child_weight: float = 1.0, - min_data_in_leaf: int = 20, - min_data_in_bin: int = 10, - reg_alpha: float = 0.0, - reg_lambda: float = 0.0, - num_leaves: int = 31, - max_bin: int = 255, - colsample_bytree: float = 1.0, - random_state: int | None = None, - early_stopping_rounds: int | None = None, - verbosity: int = -1, - ) -> None: - """Initialize LgbLinearQuantileRegressor with quantiles. - - Args: - quantiles: List of quantiles to predict (e.g., [0.1, 0.5, 0.9]). - linear_tree: Whether to use linear trees. - n_estimators: Number of boosting rounds/trees to fit. - learning_rate: Step size shrinkage used to prevent overfitting. - max_depth: Maximum depth of trees. - min_child_weight: Minimum sum of instance weight (hessian) needed in a child. - min_data_in_leaf: Minimum number of data points in a leaf. - min_data_in_bin: Minimum number of data points in a bin. - reg_alpha: L1 regularization on leaf weights. - reg_lambda: L2 regularization on leaf weights. - num_leaves: Maximum number of leaves. - max_bin: Maximum number of discrete bins for continuous features. - colsample_bytree: Fraction of features used when constructing each tree. - random_state: Random seed for reproducibility. - early_stopping_rounds: Training will stop if performance doesn't improve for this many rounds. - verbosity: Verbosity level for LgbLinear training. - - """ - self.quantiles = quantiles - self.linear_tree = linear_tree - self.n_estimators = n_estimators - self.learning_rate = learning_rate - self.max_depth = max_depth - self.min_child_weight = min_child_weight - self.min_data_in_leaf = min_data_in_leaf - self.min_data_in_bin = min_data_in_bin - self.reg_alpha = reg_alpha - self.reg_lambda = reg_lambda - self.num_leaves = num_leaves - self.max_bin = max_bin - self.colsample_bytree = colsample_bytree - self.random_state = random_state - self.early_stopping_rounds = early_stopping_rounds - self.verbosity = verbosity - - self._models: list[LGBMRegressor] = [ - LGBMRegressor( - objective="quantile", - alpha=q, - n_estimators=n_estimators, - learning_rate=learning_rate, - max_depth=max_depth, - min_child_weight=min_child_weight, - min_data_in_leaf=min_data_in_leaf, - min_data_in_bin=min_data_in_bin, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - num_leaves=num_leaves, - max_bin=max_bin, - colsample_bytree=colsample_bytree, - random_state=random_state, - early_stopping_rounds=early_stopping_rounds, - verbosity=verbosity, - linear_tree=linear_tree, - ) - for q in quantiles # type: ignore - ] - - def fit( - self, - X: npt.NDArray[np.floating] | pd.DataFrame, - y: npt.NDArray[np.floating] | pd.Series, - sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, - feature_name: list[str] | None = None, - eval_set: list[tuple[pd.DataFrame, npt.NDArray[np.floating]]] | None = None, - eval_sample_weight: list[npt.NDArray[np.floating]] | list[pd.Series] | None = None, - ) -> None: - """Fit the multi-quantile regressor. - - Args: - X: Input features as a DataFrame. - y: Target values as a 2D array where each column corresponds to a quantile. - sample_weight: Sample weights for training data. - feature_name: List of feature names. - eval_set: Evaluation set for early stopping. - eval_sample_weight: Sample weights for evaluation data. - """ - for model in self._models: - if eval_set is None: - model.set_params(early_stopping_rounds=None) - else: - model.set_params(early_stopping_rounds=self.early_stopping_rounds) - model.fit( # type: ignore - X=np.asarray(X), - y=y, - eval_metric="quantile", - sample_weight=sample_weight, - eval_set=eval_set, - eval_sample_weight=eval_sample_weight, - feature_name=feature_name if feature_name is not None else "auto", - ) - - def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: - """Predict quantiles for the input features. - - Args: - X: Input features as a DataFrame. - - Returns: - - A 2D array where each column corresponds to predicted quantiles. - """ # noqa: D412 - return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore - - def __sklearn_is_fitted__(self) -> bool: # noqa: PLW3201 - """Check if all models are fitted. - - Returns: - True if all quantile models are fitted, False otherwise. - """ - return all(model.__sklearn_is_fitted__() for model in self._models) - - @property - def models(self) -> list[LGBMRegressor]: - """Get the list of underlying quantile models. - - Returns: - List of LGBMRegressor instances for each quantile. - """ - return self._models diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py index d77e50fda..9628c61e3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/forecaster.py @@ -111,6 +111,15 @@ def with_horizon(self, horizon: LeadTime) -> Self: """ return self.model_copy(update={"horizons": [horizon]}) + @classmethod + def forecaster_class(cls) -> type["Forecaster"]: + """Get the associated Forecaster class for this configuration. + + Returns: + The Forecaster class that uses this configuration. + """ + raise NotImplementedError("Subclasses must implement forecaster_class") + class ConfigurableForecaster: @property diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 974e9a077..0bc4a1c2f 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -83,6 +83,15 @@ class GBLinearHyperParams(HyperParams): description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) + @classmethod + def forecaster_class(cls) -> "type[GBLinearForecaster]": + """Forecaster class for these hyperparams. + + Returns: + Forecaster class associated with this configuration. + """ + return GBLinearForecaster + class GBLinearForecasterConfig(ForecasterConfig): """Configuration for GBLinear forecaster.""" @@ -107,6 +116,14 @@ class GBLinearForecasterConfig(ForecasterConfig): default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) + def forecaster_from_config(self) -> "GBLinearForecaster": + """Create a GBLinearForecaster instance from this configuration. + + Returns: + Forecaster instance associated with this configuration. + """ + return GBLinearForecaster(config=self) + MODEL_CODE_VERSION = 1 diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index a3f2849a3..eba72a66d 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -10,11 +10,11 @@ """ import logging -from typing import TYPE_CHECKING, override +from typing import override -import numpy as np import pandas as pd -from pydantic import Field +from pydantic import Field, field_validator +from sklearn.linear_model import QuantileRegressor from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( @@ -22,27 +22,59 @@ ) from openstef_core.mixins import HyperParams from openstef_models.estimators.hybrid import HybridQuantileRegressor -from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig -from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.forecaster import ( + Forecaster, + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearForecaster, + GBLinearForecasterConfig, + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMForecasterConfig, LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import ( + LGBMLinearForecaster, + LGBMLinearForecasterConfig, + LGBMLinearHyperParams, +) +from openstef_models.models.forecasting.xgboost_forecaster import ( + XGBoostForecaster, + XGBoostForecasterConfig, + XGBoostHyperParams, +) logger = logging.getLogger(__name__) -if TYPE_CHECKING: - import numpy.typing as npt + +BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster +BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams +BaseLearnerConfig = ( + LGBMForecasterConfig | LGBMLinearForecasterConfig | XGBoostForecasterConfig | GBLinearForecasterConfig +) class HybridHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - lgbm_params: LGBMHyperParams = LGBMHyperParams() - gb_linear_params: GBLinearHyperParams = GBLinearHyperParams() + base_hyperparams: list[BaseLearnerHyperParams] = Field( + default=[LGBMHyperParams(), GBLinearHyperParams()], + description="List of hyperparameter configurations for base learners. " + "Defaults to [LGBMHyperParams, GBLinearHyperParams].", + ) l1_penalty: float = Field( default=0.0, description="L1 regularization term for the quantile regression.", ) + @field_validator("base_hyperparams", mode="after") + @classmethod + def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: + hp_classes = [type(hp) for hp in v] + if not len(hp_classes) == len(set(hp_classes)): + raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") + return v + class HybridForecasterConfig(ForecasterConfig): """Configuration for Hybrid-based forecasting models.""" @@ -55,9 +87,6 @@ class HybridForecasterConfig(ForecasterConfig): ) -MODEL_CODE_VERSION = 2 - - class HybridForecaster(Forecaster): """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" @@ -71,46 +100,86 @@ def __init__(self, config: HybridForecasterConfig) -> None: """Initialize the Hybrid forecaster.""" self._config = config - self._model = HybridQuantileRegressor( - quantiles=[float(q) for q in config.quantiles], - lgbm_n_estimators=config.hyperparams.lgbm_params.n_estimators, - lgbm_learning_rate=config.hyperparams.lgbm_params.learning_rate, - lgbm_max_depth=config.hyperparams.lgbm_params.max_depth, - lgbm_min_child_weight=config.hyperparams.lgbm_params.min_child_weight, - lgbm_min_data_in_leaf=config.hyperparams.lgbm_params.min_data_in_leaf, - lgbm_min_data_in_bin=config.hyperparams.lgbm_params.min_data_in_bin, - lgbm_reg_alpha=config.hyperparams.lgbm_params.reg_alpha, - lgbm_reg_lambda=config.hyperparams.lgbm_params.reg_lambda, - lgbm_num_leaves=config.hyperparams.lgbm_params.num_leaves, - lgbm_max_bin=config.hyperparams.lgbm_params.max_bin, - lgbm_colsample_by_tree=config.hyperparams.lgbm_params.colsample_bytree, - gblinear_n_steps=config.hyperparams.gb_linear_params.n_steps, - gblinear_learning_rate=config.hyperparams.gb_linear_params.learning_rate, - gblinear_reg_alpha=config.hyperparams.gb_linear_params.reg_alpha, - gblinear_reg_lambda=config.hyperparams.gb_linear_params.reg_lambda, + self._base_learners: list[BaseLearner] = self._init_base_learners( + base_hyperparams=config.hyperparams.base_hyperparams ) + self._final_learner = [ + QuantileRegressor(quantile=float(q), alpha=config.hyperparams.l1_penalty) for q in config.quantiles + ] - @property - @override - def config(self) -> ForecasterConfig: - return self._config + self._is_fitted: bool = False @property + @override def is_fitted(self) -> bool: - """Check if the model is fitted.""" - return self._model.is_fitted + return self._is_fitted + + @staticmethod + def _hyperparams_forecast_map(hyperparams: type[BaseLearnerHyperParams]) -> type[BaseLearner]: + """Map hyperparameters to forecast types. + + Args: + hyperparams: Hyperparameters of the base learner. + + Returns: + Corresponding Forecaster class. + + Raises: + TypeError: If a nested HybridForecaster is attempted. + """ + if isinstance(hyperparams, HybridHyperParams): + raise TypeError("Nested HybridForecaster is not supported.") + + mapping: dict[type[BaseLearnerHyperParams], type[BaseLearner]] = { + LGBMHyperParams: LGBMForecaster, + LGBMLinearHyperParams: LGBMLinearForecaster, + XGBoostHyperParams: XGBoostForecaster, + GBLinearHyperParams: GBLinearForecaster, + } + return mapping[hyperparams] @staticmethod - def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: - input_data: pd.DataFrame = data.input_data() + def _base_learner_config(base_learner_class: type[BaseLearner]) -> type[BaseLearnerConfig]: + """Extract the configuration from a base learner. - # Scale the target variable - target: np.ndarray = np.asarray(data.target_series.values) + Args: + base_learner_class: The base learner forecaster. - # Prepare sample weights - sample_weight: pd.Series = data.sample_weight_series + Returns: + The configuration of the base learner. + """ + mapping: dict[type[BaseLearner], type[BaseLearnerConfig]] = { + LGBMForecaster: LGBMForecasterConfig, + LGBMLinearForecaster: LGBMLinearForecasterConfig, + XGBoostForecaster: XGBoostForecasterConfig, + GBLinearForecaster: GBLinearForecasterConfig, + } + return mapping[base_learner_class] + + def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: + """Initialize base learners based on provided hyperparameters. + + Returns: + list[Forecaster]: List of initialized base learner forecasters. + """ + base_learners: list[BaseLearner] = [] + horizons = self.config.horizons + quantiles = self.config.quantiles - return input_data, target, sample_weight + for hyperparams in base_hyperparams: + forecaster_cls = hyperparams.forecaster_class() + config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) + if "hyperparams" in forecaster_cls.Config.model_fields: + config = config.model_copy(update={"hyperparams": hyperparams}) + + base_learners.append(config.forecaster_from_config()) + + return base_learners + + @property + @override + def config(self) -> ForecasterConfig: + return self._config @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: @@ -121,37 +190,111 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None data_val: Validation data for tuning the model (optional, not used in this implementation). """ - # Prepare training data - input_data, target, sample_weight = self._prepare_fit_input(data) - - if data_val is not None: - logger.warning( - "Validation data provided, but HybridForecaster does not currently support validation during fitting." - ) - - self._model.fit( - X=input_data, - y=target, - sample_weight=sample_weight, + # Fit base learners + [x.fit(data=data, data_val=data_val) for x in self._base_learners] + + full_dataset = ForecastInputDataset( + data=data.data, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.index[0], ) - @override - def predict(self, data: ForecastInputDataset) -> ForecastDataset: - if not self._model.is_fitted: + base_predictions = self._predict_base_learners(data=full_dataset) + + quantile_dataframes = self._prepare_input_final_learner(base_predictions=base_predictions) + + self._fit_final_learner(target=data.target_series, quantile_df=quantile_dataframes) + + self._is_fitted = True + + def _fit_final_learner( + self, + target: pd.Series, + quantile_df: dict[str, pd.DataFrame], + ) -> None: + """Fit the final learner using base learner predictions. + + Args: + target: Target values for training. + quantile_df: Dictionary mapping quantile strings to DataFrames of base learner predictions. + """ + for i, df in enumerate(quantile_df.values()): + self._final_learner[i].fit(X=df, y=target) + + def _predict_base_learners(self, data: ForecastInputDataset) -> dict[str, ForecastDataset]: + """Generate predictions from base learners. + + Args: + data: Input data for prediction. + + Returns: + DataFrame containing base learner predictions. + """ + base_predictions: dict[str, ForecastDataset] = {} + for learner in self._base_learners: + preds = learner.predict(data=data) + base_predictions[learner.__class__.__name__] = preds + + return base_predictions + + def _predict_final_learner( + self, quantile_df: dict[str, pd.DataFrame], data: ForecastInputDataset + ) -> ForecastDataset: + if not self.is_fitted: raise NotFittedError(self.__class__.__name__) - input_data: pd.DataFrame = data.input_data(start=data.forecast_start) - prediction: npt.NDArray[np.floating] = self._model.predict(X=input_data) + # Generate predictions + predictions_dict = [ + pd.Series(self._final_learner[i].predict(X=quantile_df[q_str]), index=quantile_df[q_str].index, name=q_str) + for i, q_str in enumerate(quantile_df.keys()) + ] + + # Construct DataFrame with appropriate quantile columns + predictions = pd.DataFrame( + data=predictions_dict, + ).T return ForecastDataset( - data=pd.DataFrame( - data=prediction, - index=input_data.index, - columns=[quantile.format() for quantile in self.config.quantiles], - ), + data=predictions, sample_interval=data.sample_interval, ) + @staticmethod + def _prepare_input_final_learner(base_predictions: dict[str, ForecastDataset]) -> dict[str, pd.DataFrame]: + """Prepare input data for the final learner based on base learner predictions. + + Args: + base_predictions: Dictionary of base learner predictions. + + Returns: + dictionary mapping quantile strings to DataFrames of base learner predictions. + """ + predictions_quantiles: dict[str, pd.DataFrame] = {} + first_key = next(iter(base_predictions)) + for quantile in base_predictions[first_key].quantiles: + quantile_str = quantile.format() + quantile_preds = pd.DataFrame({ + learner_name: preds.data[quantile_str] for learner_name, preds in base_predictions.items() + }) + predictions_quantiles[quantile_str] = quantile_preds + + return predictions_quantiles + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + base_predictions = self._predict_base_learners(data=data) + + final_learner_input = self._prepare_input_final_learner(base_predictions=base_predictions) + + return self._predict_final_learner( + quantile_df=final_learner_input, + data=data, + ) + # TODO(@Lars800): #745: Make forecaster Explainable diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 50dd9f50b..f46009502 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -13,6 +13,7 @@ import numpy as np import pandas as pd +from lightgbm import LGBMRegressor from pydantic import Field from openstef_core.datasets import ForecastDataset, ForecastInputDataset @@ -20,9 +21,9 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_models.estimators.lgbm import LGBMQuantileRegressor from openstef_models.explainability.mixins import ExplainableForecaster -from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig +from openstef_models.models.forecasting.forecaster import ForecasterConfig, Forecaster +from openstef_models.utils.multi_quantile_regressor import MultiQuantileRegressor if TYPE_CHECKING: import numpy.typing as npt @@ -108,6 +109,15 @@ class LGBMHyperParams(HyperParams): description="Fraction of features used when constructing each tree. Range: (0,1]", ) + @classmethod + def forecaster_class(cls) -> "type[LGBMForecaster]": + """Create a LightGBM forecaster instance from this configuration. + + Returns: + Forecaster class associated with this configuration. + """ + return LGBMForecaster + class LGBMForecasterConfig(ForecasterConfig): """Configuration for LightGBM-based forecaster. @@ -150,6 +160,14 @@ class LGBMForecasterConfig(ForecasterConfig): description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) + def forecaster_from_config(self) -> "LGBMForecaster": + """Create a LGBMForecaster instance from this configuration. + + Returns: + Forecaster instance associated with this configuration. + """ + return LGBMForecaster(config=self) + MODEL_CODE_VERSION = 1 @@ -200,7 +218,6 @@ class LGBMForecaster(Forecaster, ExplainableForecaster): HyperParams = LGBMHyperParams _config: LGBMForecasterConfig - _lgbm_model: LGBMQuantileRegressor def __init__(self, config: LGBMForecasterConfig) -> None: """Initialize LightGBM forecaster with configuration. @@ -215,13 +232,20 @@ def __init__(self, config: LGBMForecasterConfig) -> None: """ self._config = config - self._lgbm_model = LGBMQuantileRegressor( - quantiles=[float(q) for q in config.quantiles], - linear_tree=False, - random_state=config.random_state, - early_stopping_rounds=config.early_stopping_rounds, - verbosity=config.verbosity, + lgbm_params = { + "linear_tree": False, + "objective": "quantile", + "random_state": config.random_state, + "early_stopping_rounds": config.early_stopping_rounds, + "verbosity": config.verbosity, **config.hyperparams.model_dump(), + } + + self._lgbm_model: MultiQuantileRegressor = MultiQuantileRegressor( + base_learner=LGBMRegressor, # type: ignore + quantile_param="alpha", + hyperparams=lgbm_params, + quantiles=[float(q) for q in config.quantiles], ) @property @@ -237,7 +261,7 @@ def hyperparams(self) -> LGBMHyperParams: @property @override def is_fitted(self) -> bool: - return self._lgbm_model.__sklearn_is_fitted__() + return self._lgbm_model.is_fitted @staticmethod def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: @@ -290,11 +314,11 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: @property @override def feature_importances(self) -> pd.DataFrame: - models = self._lgbm_model.models + models: list[LGBMRegressor] = self._lgbm_model.models # type: ignore weights_df = pd.DataFrame( [models[i].feature_importances_ for i in range(len(models))], index=[quantile.format() for quantile in self.config.quantiles], - columns=models[0].feature_name_, + columns=self._lgbm_model.model_feature_names if self._lgbm_model.has_feature_names else None, ).transpose() weights_df.index.name = "feature_name" diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index 202dd1e4e..57a9d96f8 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -9,11 +9,11 @@ comprehensive hyperparameter control for production forecasting workflows. """ -from typing import Literal, cast, override +from typing import TYPE_CHECKING, Literal, override import numpy as np -import numpy.typing as npt import pandas as pd +from lightgbm import LGBMRegressor from pydantic import Field from openstef_core.datasets import ForecastDataset, ForecastInputDataset @@ -21,9 +21,12 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_models.estimators.lgbm import LGBMQuantileRegressor from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig +from openstef_models.utils.multi_quantile_regressor import MultiQuantileRegressor + +if TYPE_CHECKING: + import numpy.typing as npt class LGBMLinearHyperParams(HyperParams): @@ -107,6 +110,15 @@ class LGBMLinearHyperParams(HyperParams): description="Fraction of features used when constructing each tree. Range: (0,1]", ) + @classmethod + def forecaster_class(cls) -> "type[LGBMLinearForecaster]": + """Get forecaster class for these hyperparams. + + Returns: + Forecaster class associated with this configuration. + """ + return LGBMLinearForecaster + class LGBMLinearForecasterConfig(ForecasterConfig): """Configuration for LgbLinear-based forecaster. @@ -150,6 +162,14 @@ class LGBMLinearForecasterConfig(ForecasterConfig): description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) + def forecaster_from_config(self) -> "LGBMLinearForecaster": + """Create a LGBMLinearForecaster instance from this configuration. + + Returns: + Forecaster instance associated with this configuration. + """ + return LGBMLinearForecaster(config=self) + MODEL_CODE_VERSION = 1 @@ -200,7 +220,6 @@ class LGBMLinearForecaster(Forecaster, ExplainableForecaster): HyperParams = LGBMLinearHyperParams _config: LGBMLinearForecasterConfig - _lgbmlinear_model: LGBMQuantileRegressor def __init__(self, config: LGBMLinearForecasterConfig) -> None: """Initialize LgbLinear forecaster with configuration. @@ -215,13 +234,20 @@ def __init__(self, config: LGBMLinearForecasterConfig) -> None: """ self._config = config - self._lgbmlinear_model = LGBMQuantileRegressor( - quantiles=[float(q) for q in config.quantiles], - linear_tree=True, - random_state=config.random_state, - early_stopping_rounds=config.early_stopping_rounds, - verbosity=config.verbosity, + lgbmlinear_params = { + "linear_tree": True, + "objective": "quantile", + "random_state": config.random_state, + "early_stopping_rounds": config.early_stopping_rounds, + "verbosity": config.verbosity, **config.hyperparams.model_dump(), + } + + self._lgbmlinear_model: MultiQuantileRegressor = MultiQuantileRegressor( + base_learner=LGBMRegressor, # type: ignore + quantile_param="alpha", + hyperparams=lgbmlinear_params, + quantiles=[float(q) for q in config.quantiles], ) @property @@ -237,32 +263,37 @@ def hyperparams(self) -> LGBMLinearHyperParams: @property @override def is_fitted(self) -> bool: - return self._lgbmlinear_model.__sklearn_is_fitted__() + return self._lgbmlinear_model.is_fitted + + @staticmethod + def _prepare_fit_input(data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: + input_data: pd.DataFrame = data.input_data() + target: np.ndarray = np.asarray(data.target_series.values) + sample_weight: pd.Series = data.sample_weight_series + + return input_data, target, sample_weight @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - input_data: pd.DataFrame = data.input_data() - target: npt.NDArray[np.floating] = data.target_series.to_numpy() # type: ignore - sample_weight = data.sample_weight_series + # Prepare training data + input_data, target, sample_weight = self._prepare_fit_input(data) - # Prepare validation data if provided - eval_set = None - eval_sample_weight = None - if data_val is not None: - val_input_data: pd.DataFrame = data_val.input_data() - val_target: npt.NDArray[np.floating] = data_val.target_series.to_numpy() # type: ignore - val_sample_weight = cast(npt.NDArray[np.floating], data_val.sample_weight_series.to_numpy()) # type: ignore - eval_set = [(val_input_data, val_target)] + # Evaluation sets + eval_set = [(input_data, target)] + sample_weight_eval_set = [sample_weight] - eval_sample_weight = [val_sample_weight] + if data_val is not None: + input_data_val, target_val, sample_weight_val = self._prepare_fit_input(data_val) + eval_set.append((input_data_val, target_val)) + sample_weight_eval_set.append(sample_weight_val) - self._lgbmlinear_model.fit( # type: ignore + self._lgbmlinear_model.fit( X=input_data, y=target, feature_name=input_data.columns.tolist(), sample_weight=sample_weight, eval_set=eval_set, - eval_sample_weight=eval_sample_weight, + eval_sample_weight=sample_weight_eval_set, ) @override @@ -287,9 +318,9 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: def feature_importances(self) -> pd.DataFrame: models = self._lgbmlinear_model._models # noqa: SLF001 weights_df = pd.DataFrame( - [models[i].feature_importances_ for i in range(len(models))], + [models[i].feature_importances_ for i in range(len(models))], # type: ignore index=[quantile.format() for quantile in self.config.quantiles], - columns=models[0].feature_name_, + columns=self._lgbmlinear_model.model_feature_names if self._lgbmlinear_model.has_feature_names else None, ).transpose() weights_df.index.name = "feature_name" diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index f48bef2bb..2a35fb987 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -160,6 +160,15 @@ class XGBoostHyperParams(HyperParams): description="Whether to apply standard scaling to the target variable before training. Improves convergence.", ) + @classmethod + def forecaster_class(cls) -> "type[XGBoostForecaster]": + """Get the forecaster class for these hyperparams. + + Returns: + Forecaster class associated with this configuration. + """ + return XGBoostForecaster + class XGBoostForecasterConfig(ForecasterConfig): """Configuration for XGBoost-based forecasting models. @@ -196,6 +205,14 @@ class XGBoostForecasterConfig(ForecasterConfig): default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) + def forecaster_from_config(self) -> "XGBoostForecaster": + """Create a XGBoost forecaster instance from this configuration. + + Returns: + Forecaster instance associated with this configuration. + """ + return XGBoostForecaster(config=self) + MODEL_CODE_VERSION = 1 diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py index e96a92993..8a6276927 100644 --- a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -72,29 +72,52 @@ def fit( eval_set: Evaluation set for early stopping. eval_sample_weight: Sample weights for evaluation data. """ + # Pass model-specific eval arguments + kwargs = {} for model in self._models: + # Check if early stopping is supported + # Check that eval_set is supported if eval_set is None and "early_stopping_rounds" in self.hyperparams: model.set_params(early_stopping_rounds=None) # type: ignore - elif "early_stopping_rounds" in self.hyperparams: - model.set_params(early_stopping_rounds=self.hyperparams.early_stopping_rounds) # type: ignore - if eval_set or eval_sample_weight: - logger.warning( - "Evaluation sets or sample weights provided, but MultiQuantileRegressor does not currently support " - "these during fitting." - ) + if eval_set is not None and self.learner_eval_sample_weight_param is not None: # type: ignore + kwargs[self.learner_eval_sample_weight_param] = eval_sample_weight + + if "early_stopping_rounds" in self.hyperparams and self.learner_eval_sample_weight_param is not None: + model.set_params(early_stopping_rounds=self.hyperparams["early_stopping_rounds"]) # type: ignore if feature_name: - logger.warning( - "Feature names provided, but MultiQuantileRegressor does not currently support feature names during fitting." - ) + self.model_feature_names = feature_name + else: + self.model_feature_names = [] + + if eval_sample_weight is not None and self.learner_eval_sample_weight_param: + kwargs[self.learner_eval_sample_weight_param] = eval_sample_weight + model.fit( # type: ignore X=np.asarray(X), y=y, sample_weight=sample_weight, + **kwargs, ) + self.is_fitted = True + @property + def learner_eval_sample_weight_param(self) -> str | None: + """Get the name of the sample weight parameter for evaluation sets. + + Returns: + The name of the sample weight parameter if supported, else None. + """ + learner_name: str = self.base_learner.__name__ + params: dict[str, str | None] = { + "QuantileRegressor": None, + "LGBMRegressor": "eval_sample_weight", + "XGBRegressor": "sample_weight_eval_set", + } + return params.get(learner_name) + def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: """Predict quantiles for the input features. @@ -115,3 +138,12 @@ def models(self) -> list[BaseEstimator]: List of BaseEstimator instances for each quantile. """ return self._models + + @property + def has_feature_names(self) -> bool: + """Check if the base learners have feature names. + + Returns: + True if the base learners have feature names, False otherwise. + """ + return len(self.model_feature_names) > 0 diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py index f8251d484..09c69f24f 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py @@ -15,18 +15,13 @@ HybridForecasterConfig, HybridHyperParams, ) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams @pytest.fixture def base_config() -> HybridForecasterConfig: """Base configuration for Hybrid forecaster tests.""" - lgbm_params = LGBMHyperParams(n_estimators=10, max_depth=2) - gb_linear_params = GBLinearHyperParams(n_steps=5, learning_rate=0.1, reg_alpha=0.0, reg_lambda=0.0) - params = HybridHyperParams( - lgbm_params=lgbm_params, - gb_linear_params=gb_linear_params, - ) + + params = HybridHyperParams() return HybridForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], @@ -35,7 +30,7 @@ def base_config() -> HybridForecasterConfig: ) -def test_hybrid_forecaster__fit_predict( +def test_hybrid_forecaster_fit_predict( sample_forecast_input_dataset: ForecastInputDataset, base_config: HybridForecasterConfig, ): @@ -62,7 +57,7 @@ def test_hybrid_forecaster__fit_predict( assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" -def test_hybrid_forecaster__predict_not_fitted_raises_error( +def test_hybrid_forecaster_predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, base_config: HybridForecasterConfig, ): @@ -75,7 +70,7 @@ def test_hybrid_forecaster__predict_not_fitted_raises_error( forecaster.predict(sample_forecast_input_dataset) -def test_hybrid_forecaster__with_sample_weights( +def test_hybrid_forecaster_with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, base_config: HybridForecasterConfig, ): @@ -109,36 +104,3 @@ def test_hybrid_forecaster__with_sample_weights( # (This is a statistical test - with different weights, predictions should differ) differences = (result_with_weights.data - result_without_weights.data).abs() assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -@pytest.mark.parametrize("objective", ["pinball_loss", "arctan_loss"]) -def test_hybrid_forecaster__different_objectives( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: HybridForecasterConfig, - objective: str, -): - """Test that forecaster works with different objective functions.""" - # Arrange - config = base_config.model_copy( - update={ - "hyperparams": base_config.hyperparams.model_copy( - update={"objective": objective} # type: ignore[arg-type] - ) - } - ) - forecaster = HybridForecaster(config=config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality should work regardless of objective - assert forecaster.is_fitted, f"Model with {objective} should be fitted" - assert not result.data.isna().any().any(), f"Forecast with {objective} should not contain NaN values" - - # Check value spread for each objective - # Note: Some objectives (like arctan_loss) may produce zero variation for some quantiles with small datasets - stds = result.data.std() - # At least one quantile should have variation (the model should not be completely degenerate) - assert (stds > 0).any(), f"At least one column should have variation with {objective}, got stds: {dict(stds)}" From bfa2e2f198faf165de719b58ed110dc0ce065baf Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 17 Nov 2025 16:09:41 +0100 Subject: [PATCH 16/72] Small fix --- .../models/forecasting/hybrid_forecaster.py | 13 ++++++------- .../models/forecasting/test_hybrid_forecaster.py | 1 - 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index eba72a66d..7a41035b6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -98,6 +98,7 @@ class HybridForecaster(Forecaster): def __init__(self, config: HybridForecasterConfig) -> None: """Initialize the Hybrid forecaster.""" + self._config = config self._base_learners: list[BaseLearner] = self._init_base_learners( @@ -107,13 +108,6 @@ def __init__(self, config: HybridForecasterConfig) -> None: QuantileRegressor(quantile=float(q), alpha=config.hyperparams.l1_penalty) for q in config.quantiles ] - self._is_fitted: bool = False - - @property - @override - def is_fitted(self) -> bool: - return self._is_fitted - @staticmethod def _hyperparams_forecast_map(hyperparams: type[BaseLearnerHyperParams]) -> type[BaseLearner]: """Map hyperparameters to forecast types. @@ -176,6 +170,11 @@ def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> return base_learners + @property + @override + def is_fitted(self) -> bool: + return all(x.is_fitted for x in self._base_learners) + @property @override def config(self) -> ForecasterConfig: diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py index 09c69f24f..4e36e125d 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py @@ -9,7 +9,6 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams from openstef_models.models.forecasting.hybrid_forecaster import ( HybridForecaster, HybridForecasterConfig, From ce2172a65c9a4ff469122165a340189ed5e040a0 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 17 Nov 2025 21:17:07 +0100 Subject: [PATCH 17/72] Squashed commit of the following: commit 37089b84bdea12d22506174ef1393c4fc346ca36 Author: Egor Dmitriev Date: Mon Nov 17 15:29:59 2025 +0100 fix(#728): Fixed parallelism stability issues, and gblinear feature pipeline. (#752) * fix(STEF-2475): Added loky as default option for parallelism since fork causes instabilities for xgboost results. Signed-off-by: Egor Dmitriev * fix(STEF-2475): Added better support for flatliners and predicting when data is sparse. Signed-off-by: Egor Dmitriev * fix(STEF-2475): Feature handing improvements for gblinear. Like imputation, nan dropping, and checking if features are available. Signed-off-by: Egor Dmitriev * fix(#728): Added checks on metrics to gracefully handle empty data. Added flatline filtering during evalution. Signed-off-by: Egor Dmitriev * fix(#728): Updated xgboost to skip scaling on empty prediction. Signed-off-by: Egor Dmitriev * fix(STEF-2475): Added parallelism parameters. Signed-off-by: Egor Dmitriev --------- Signed-off-by: Egor Dmitriev commit a85a3f709c9a54b85658578b5c2aefc001bdf803 Author: Egor Dmitriev Date: Fri Nov 14 14:31:34 2025 +0100 fix(STEF-2475): Fixed rolling aggregate adder by adding forward filling and stating support for only one horizon. (#750) Signed-off-by: Egor Dmitriev commit 4f0c6648516bf184608d268020fdfa4107050c83 Author: Egor Dmitriev Date: Thu Nov 13 16:54:15 2025 +0100 feature: Disabled data cutoff by default to be consistent with openstef 3. And other minor improvements. (#748) commit 493126e9f16836d0da03d9c43e391537c5bea7ca Author: Egor Dmitriev Date: Thu Nov 13 16:12:35 2025 +0100 fix(STEF-2475) fix and refactor backtesting iction in context of backtestforecasting config for clarity. Added more colors. Fixed data split function to handle 0.0 splits. (#747) * fix: Fixed data collation during backtesting. Renamed horizon to prediction in context of backtestforecasting config for clarity. Added more colors. Fixed data split function to handle 0.0 splits. * fix: Formatting. Signed-off-by: Egor Dmitriev * fix: Formatting. Signed-off-by: Egor Dmitriev --------- Signed-off-by: Egor Dmitriev commit 6b1da449b7841f1b13a5fac1f16e48bbeb9b9692 Author: Egor Dmitriev Date: Thu Nov 13 16:05:32 2025 +0100 feature: forecaster hyperparams and eval metrics (#746) * feature(#729) Removed to_state and from_state methods in favor of builtin python state saving functions. Signed-off-by: Egor Dmitriev * feature(#729): Fixed issue where generic transform pipeline could not be serialized. Signed-off-by: Egor Dmitriev * feature(#729): Added more state saving tests Signed-off-by: Egor Dmitriev * feature(#729): Added more state saving tests Signed-off-by: Egor Dmitriev * feature(#729): Added more state saving tests Signed-off-by: Egor Dmitriev * feature: standardized objective function. Added custom evaluation functions for forecasters. * fix: Formatting. Signed-off-by: Egor Dmitriev --------- Signed-off-by: Egor Dmitriev --- .gitignore | 3 +- ...liander_2024_benchmark_xgboost_gblinear.py | 70 +- .../liander_2024_compare_results.py | 50 + .../plots/forecast_time_series_plotter.py | 2 + .../backtesting/backtest_event_generator.py | 4 +- .../backtest_forecaster/dummy_forecaster.py | 4 +- .../backtesting/backtest_forecaster/mixins.py | 4 +- .../openstef4_backtest_forecaster.py | 73 +- .../restricted_horizon_timeseries.py | 19 +- .../benchmarking/benchmark_pipeline.py | 1 + .../benchmarking/target_provider.py | 69 +- .../metrics/metrics_deterministic.py | 11 + .../metrics/metrics_probabilistic.py | 56 + .../test_backtest_event_generator.py | 16 +- .../backtesting/test_backtest_pipeline.py | 20 +- .../unit/backtesting/test_batch_prediction.py | 4 +- .../benchmarking/test_benchmark_pipeline.py | 4 +- .../unit/benchmarking/test_target_provider.py | 48 +- .../metrics/test_metrics_deterministic.py | 47 + .../metrics/test_metrics_probabilistic.py | 33 + .../src/openstef_core/exceptions.py | 4 + .../openstef_core/utils/multiprocessing.py | 33 +- .../tests/unit/utils/test_multiprocessing.py | 26 +- .../models/forecasting/gblinear_forecaster.py | 60 +- .../models/forecasting/xgboost_forecaster.py | 35 +- .../models/forecasting_model.py | 4 +- .../presets/forecasting_workflow.py | 75 +- .../transforms/general/__init__.py | 2 + .../transforms/general/imputer.py | 15 +- .../transforms/general/nan_dropper.py | 89 + .../time_domain/rolling_aggregates_adder.py | 19 +- .../transforms/weather_domain/__init__.py | 5 +- .../src/openstef_models/utils/data_split.py | 21 +- .../utils/evaluation_functions.py | 30 + .../utils/feature_selection.py | 2 + .../openstef_models/utils/loss_functions.py | 16 +- .../workflows/custom_forecasting_workflow.py | 4 +- .../transforms/general/test_nan_dropper.py | 48 + .../test_rolling_aggregates_adder.py | 13 +- uv.lock | 1482 +++++++++-------- 40 files changed, 1625 insertions(+), 896 deletions(-) create mode 100644 examples/benchmarks/liander_2024_compare_results.py create mode 100644 packages/openstef-models/src/openstef_models/transforms/general/nan_dropper.py create mode 100644 packages/openstef-models/src/openstef_models/utils/evaluation_functions.py create mode 100644 packages/openstef-models/tests/unit/transforms/general/test_nan_dropper.py diff --git a/.gitignore b/.gitignore index 81c981c66..db241c85e 100644 --- a/.gitignore +++ b/.gitignore @@ -123,7 +123,8 @@ certificates/ *.html *.pkl +# Benchmark outputs +benchmark_results*/ # Experiment outputs -benchmark_results/ optimization_results/ dev_sandbox/ \ No newline at end of file diff --git a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py index 1f5f016d0..12d0bc9c0 100644 --- a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py +++ b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py @@ -10,6 +10,12 @@ # # SPDX-License-Identifier: MPL-2.0 +import os + +os.environ["OMP_NUM_THREADS"] = "1" # Set OMP_NUM_THREADS to 1 to avoid issues with parallel execution and xgboost +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + import logging from datetime import timedelta from pathlib import Path @@ -38,19 +44,32 @@ BENCHMARK_RESULTS_PATH_XGBOOST = OUTPUT_PATH / "XGBoost" BENCHMARK_RESULTS_PATH_GBLINEAR = OUTPUT_PATH / "GBLinear" -N_PROCESSES = 1 # Amount of parallel processes to use for the benchmark +N_PROCESSES = 12 # Amount of parallel processes to use for the benchmark # Model configuration FORECAST_HORIZONS = [LeadTime.from_string("P3D")] # Forecast horizon(s) -PREDICTION_QUANTILES = [Q(0.1), Q(0.3), Q(0.5), Q(0.7), Q(0.9)] # Quantiles for probabilistic forecasts +PREDICTION_QUANTILES = [ + Q(0.05), + Q(0.1), + Q(0.3), + Q(0.5), + Q(0.7), + Q(0.9), + Q(0.95), +] # Quantiles for probabilistic forecasts BENCHMARK_FILTER: list[Liander2024Category] | None = None -storage = MLFlowStorage( - tracking_uri=str(OUTPUT_PATH / "mlflow_artifacts"), - local_artifacts_path=OUTPUT_PATH / "mlflow_tracking_artifacts", -) +USE_MLFLOW_STORAGE = False + +if USE_MLFLOW_STORAGE: + storage = MLFlowStorage( + tracking_uri=str(OUTPUT_PATH / "mlflow_artifacts"), + local_artifacts_path=OUTPUT_PATH / "mlflow_tracking_artifacts", + ) +else: + storage = None common_config = ForecastingWorkflowConfig( model_id="common_model_", @@ -58,19 +77,32 @@ horizons=FORECAST_HORIZONS, quantiles=PREDICTION_QUANTILES, model_reuse_enable=False, - mlflow_storage=storage, + mlflow_storage=None, radiation_column="shortwave_radiation", rolling_aggregate_features=["mean", "median", "max", "min"], wind_speed_column="wind_speed_80m", pressure_column="surface_pressure", temperature_column="temperature_2m", relative_humidity_column="relative_humidity_2m", + energy_price_column="EPEX_NL", ) xgboost_config = common_config.model_copy(update={"model": "xgboost"}) gblinear_config = common_config.model_copy(update={"model": "gblinear"}) +# Create the backtest configuration +backtest_config = BacktestForecasterConfig( + requires_training=True, + predict_length=timedelta(days=7), + predict_min_length=timedelta(minutes=15), + predict_context_length=timedelta(days=14), # Context needed for lag features + predict_context_min_coverage=0.5, + training_context_length=timedelta(days=90), # Three months of training data + training_context_min_coverage=0.5, + predict_sample_interval=timedelta(minutes=15), +) + def _target_forecaster_factory( context: BenchmarkContext, @@ -99,18 +131,6 @@ def _create_workflow() -> CustomForecastingWorkflow: ) ) - # Create the backtest configuration - backtest_config = BacktestForecasterConfig( - requires_training=True, - horizon_length=timedelta(days=7), - horizon_min_length=timedelta(minutes=15), - predict_context_length=timedelta(days=14), # Context needed for lag features - predict_context_min_coverage=0.5, - training_context_length=timedelta(days=90), # Three months of training data - training_context_min_coverage=0.5, - predict_sample_interval=timedelta(minutes=15), - ) - return OpenSTEF4BacktestForecaster( config=backtest_config, workflow_factory=_create_workflow, @@ -120,24 +140,24 @@ def _create_workflow() -> CustomForecastingWorkflow: if __name__ == "__main__": - # Run for GBLinear model + # Run for XGBoost model create_liander2024_benchmark_runner( - storage=LocalBenchmarkStorage(base_path=BENCHMARK_RESULTS_PATH_GBLINEAR), + storage=LocalBenchmarkStorage(base_path=BENCHMARK_RESULTS_PATH_XGBOOST), callbacks=[StrictExecutionCallback()], ).run( forecaster_factory=_target_forecaster_factory, - run_name="gblinear", + run_name="xgboost", n_processes=N_PROCESSES, filter_args=BENCHMARK_FILTER, ) - # Run for XGBoost model + # Run for GBLinear model create_liander2024_benchmark_runner( - storage=LocalBenchmarkStorage(base_path=BENCHMARK_RESULTS_PATH_XGBOOST), + storage=LocalBenchmarkStorage(base_path=BENCHMARK_RESULTS_PATH_GBLINEAR), callbacks=[StrictExecutionCallback()], ).run( forecaster_factory=_target_forecaster_factory, - run_name="xgboost", + run_name="gblinear", n_processes=N_PROCESSES, filter_args=BENCHMARK_FILTER, ) diff --git a/examples/benchmarks/liander_2024_compare_results.py b/examples/benchmarks/liander_2024_compare_results.py new file mode 100644 index 000000000..3de16460c --- /dev/null +++ b/examples/benchmarks/liander_2024_compare_results.py @@ -0,0 +1,50 @@ +"""Example for comparing benchmark results from different runs on the Liander 2024 dataset.""" +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from pathlib import Path + +from openstef_beam.analysis.models import RunName +from openstef_beam.benchmarking import BenchmarkComparisonPipeline, LocalBenchmarkStorage +from openstef_beam.benchmarking.benchmarks import create_liander2024_benchmark_runner +from openstef_beam.benchmarking.benchmarks.liander2024 import LIANDER2024_ANALYSIS_CONFIG +from openstef_beam.benchmarking.storage import BenchmarkStorage + +BASE_DIR = Path() + +OUTPUT_PATH = BASE_DIR / "./benchmark_results_comparison" + +BENCHMARK_DIR_GBLINEAR = BASE_DIR / "benchmark_results" / "GBLinear" +BENCHMARK_DIR_XGBOOST = BASE_DIR / "benchmark_results" / "XGBoost" +BENCHMARK_DIR_GBLINEAR_OPENSTEF3 = BASE_DIR / "benchmark_results_openstef3" / "gblinear" +BENCHMARK_DIR_XGBOOST_OPENSTEF3 = BASE_DIR / "benchmark_results_openstef3" / "xgboost" + +check_dirs = [ + BENCHMARK_DIR_GBLINEAR, + BENCHMARK_DIR_XGBOOST, + BENCHMARK_DIR_GBLINEAR_OPENSTEF3, + BENCHMARK_DIR_XGBOOST_OPENSTEF3, +] +for dir_path in check_dirs: + if not dir_path.exists(): + msg = f"Benchmark directory not found: {dir_path}. Make sure to run the benchmarks first." + raise FileNotFoundError(msg) + +run_storages: dict[RunName, BenchmarkStorage] = { + "gblinear": LocalBenchmarkStorage(base_path=BENCHMARK_DIR_GBLINEAR), + "gblinear_openstef3": LocalBenchmarkStorage(base_path=BENCHMARK_DIR_GBLINEAR_OPENSTEF3), + "xgboost": LocalBenchmarkStorage(base_path=BENCHMARK_DIR_XGBOOST), + "xgboost_openstef3": LocalBenchmarkStorage(base_path=BENCHMARK_DIR_XGBOOST_OPENSTEF3), +} + +target_provider = create_liander2024_benchmark_runner( + storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH), +).target_provider + +comparison_pipeline = BenchmarkComparisonPipeline( + analysis_config=LIANDER2024_ANALYSIS_CONFIG, + storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH), + target_provider=target_provider, +) +comparison_pipeline.run(run_data=run_storages) diff --git a/packages/openstef-beam/src/openstef_beam/analysis/plots/forecast_time_series_plotter.py b/packages/openstef-beam/src/openstef_beam/analysis/plots/forecast_time_series_plotter.py index ac3a4e65f..9fdf6684c 100644 --- a/packages/openstef-beam/src/openstef_beam/analysis/plots/forecast_time_series_plotter.py +++ b/packages/openstef-beam/src/openstef_beam/analysis/plots/forecast_time_series_plotter.py @@ -98,6 +98,8 @@ class ForecastTimeSeriesPlotter: "green": "Greens", "purple": "Purples", "orange": "Oranges", + "magenta": "Magenta", + "grey": "Greys", } colors: ClassVar[list[str]] = list(COLOR_SCHEME.keys()) colormaps: ClassVar[list[str]] = list(COLOR_SCHEME.values()) diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_event_generator.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_event_generator.py index 32f3bd62a..32638dc30 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_event_generator.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_event_generator.py @@ -99,7 +99,7 @@ def _predict_iterator(self) -> Iterator[BacktestEvent]: current_time = align_datetime_to_time(self.start, self.align_time, mode="ceil") while current_time <= end_time: - horizon_end = current_time + self.forecaster_config.horizon_min_length + horizon_end = current_time + self.forecaster_config.predict_min_length if horizon_end > end_time: break @@ -124,7 +124,7 @@ def _train_iterator(self) -> Iterator[BacktestEvent]: current_time = align_datetime_to_time(self.start, self.align_time, mode="ceil") while current_time <= end_time: - horizon_end = current_time + self.forecaster_config.horizon_min_length + horizon_end = current_time + self.forecaster_config.predict_min_length if horizon_end > end_time: break diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/dummy_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/dummy_forecaster.py index 9e24e7e8e..9b931ad6d 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/dummy_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/dummy_forecaster.py @@ -43,8 +43,8 @@ def __init__( super().__init__() self.config = config or BacktestForecasterConfig( requires_training=False, - horizon_length=timedelta(days=7), - horizon_min_length=timedelta(days=0), + predict_length=timedelta(days=7), + predict_min_length=timedelta(days=0), predict_context_length=timedelta(days=0), predict_context_min_coverage=0.0, training_context_length=timedelta(days=0), diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/mixins.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/mixins.py index 6597c5b22..5c02a6fa5 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/mixins.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/mixins.py @@ -35,8 +35,8 @@ class BacktestForecasterConfig(BaseConfig): default=timedelta(minutes=15), description="Time interval between prediction samples." ) - horizon_length: timedelta = Field(description="Length of the prediction horizon.") - horizon_min_length: timedelta = Field(description="Minimum horizon length that can be predicted.") + predict_length: timedelta = Field(description="Length of the prediction.") + predict_min_length: timedelta = Field(description="Minimum length that can be predicted.") predict_context_length: timedelta = Field(description="Length of the prediction context.") predict_context_min_coverage: float = Field( diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 4b6574b9e..96a1e6e93 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -4,6 +4,7 @@ """OpenSTEF 4.0 forecaster for backtesting pipelines.""" +import logging from collections.abc import Callable from pathlib import Path from typing import Any, override @@ -14,9 +15,8 @@ from openstef_beam.backtesting.restricted_horizon_timeseries import RestrictedHorizonVersionedTimeSeries from openstef_core.base_model import BaseModel from openstef_core.datasets import TimeSeriesDataset -from openstef_core.exceptions import NotFittedError +from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q -from openstef_models.models.forecasting_model import restore_target from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow @@ -42,6 +42,9 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): ) _workflow: CustomForecastingWorkflow | None = PrivateAttr(default=None) + _is_flatliner_detected: bool = PrivateAttr(default=False) + + _logger: logging.Logger = PrivateAttr(default=logging.getLogger(__name__)) @override def model_post_init(self, context: Any) -> None: @@ -59,30 +62,27 @@ def quantiles(self) -> list[Q]: @override def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: # Create a new workflow for this training cycle - self._workflow = self.workflow_factory() - - # Get training data window based on config - training_end = data.horizon - training_start = training_end - self.config.training_context_length + workflow = self.workflow_factory() - # Extract the versioned dataset for training - training_data_versioned = data.get_window_versioned( - start=training_start, end=training_end, available_before=data.horizon - ) - # Convert to horizons - training_data = training_data_versioned.to_horizons(horizons=self._workflow.model.config.horizons) - training_data = restore_target( - dataset=training_data, - original_dataset=training_data_versioned.select_version(), - target_column=self._workflow.model.target_column, + # Extract the dataset for training + training_data = data.get_window( + start=data.horizon - self.config.training_context_length, end=data.horizon, available_before=data.horizon ) if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") training_data.to_parquet(path=self.cache_dir / f"debug_{id_str}_training.parquet") - # Use the workflow's fit method - self._workflow.fit(data=training_data) + try: + # Use the workflow's fit method + workflow.fit(data=training_data) + self._is_flatliner_detected = False + except FlatlinerDetectedError: + self._logger.warning("Flatliner detected during training") + self._is_flatliner_detected = True + return # Skip setting the workflow on flatliner detection + + self._workflow = workflow if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") @@ -92,33 +92,28 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: @override def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDataset | None: + if self._is_flatliner_detected: + self._logger.info("Skipping prediction due to prior flatliner detection") + return None + if self._workflow is None: raise NotFittedError("Must call fit() before predict()") - # Define the time windows: - # - Historical context: used for features (lags, etc.) - # - Forecast period: the period we want to predict - predict_context_start = data.horizon - self.config.predict_context_length - forecast_end = data.horizon + self.config.horizon_length - # Extract the dataset including both historical context and forecast period - predict_data_versioned = data.get_window_versioned( - start=predict_context_start, - end=forecast_end, # Include the forecast period + predict_data = data.get_window( + start=data.horizon - self.config.predict_context_length, + end=data.horizon + self.config.predict_length, # Include the forecast period available_before=data.horizon, # Only use data available at prediction time (prevents lookahead bias) ) - # Convert to horizons - predict_data = predict_data_versioned.to_horizons(horizons=self._workflow.model.config.horizons) - predict_data = restore_target( - dataset=predict_data, - original_dataset=predict_data_versioned.select_version(), - target_column=self._workflow.model.target_column, - ) - forecast = self._workflow.predict( - data=predict_data, - forecast_start=data.horizon, # Where historical data ends and forecasting begins - ) + try: + forecast = self._workflow.predict( + data=predict_data, + forecast_start=data.horizon, # Where historical data ends and forecasting begins + ) + except FlatlinerDetectedError: + self._logger.info("Flatliner detected during prediction") + return None if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/restricted_horizon_timeseries.py b/packages/openstef-beam/src/openstef_beam/backtesting/restricted_horizon_timeseries.py index 387c4d8b3..5040a038b 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/restricted_horizon_timeseries.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/restricted_horizon_timeseries.py @@ -41,9 +41,7 @@ def get_window(self, start: datetime, end: datetime, available_before: datetime Returns: DataFrame with data from the specified window. """ - dataset = self.dataset.filter_by_range(start=start, end=end) - if available_before is not None: - dataset = dataset.filter_by_available_before(available_before=available_before) + dataset = self.get_window_versioned(start=start, end=end, available_before=available_before) return dataset.select_version() @@ -54,12 +52,19 @@ def get_window_versioned( Returns: DataFrame with data from the specified window. + + Raises: + ValueError: If available_before is after the horizon. """ - dataset = self.dataset.filter_by_range(start=start, end=end) - if available_before is not None: - dataset = dataset.filter_by_available_before(available_before=available_before) + if available_before is None: + available_before = self.horizon - return dataset + if available_before > self.horizon: + raise ValueError("available_before cannot be after the horizon") + + dataset = self.dataset.filter_by_range(start=start, end=end) + # Make sure to only include data available before the cutoff + return dataset.filter_by_available_before(available_before=available_before) __all__ = [ diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py index 3ee54ffa5..bcd1ef070 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py @@ -187,6 +187,7 @@ def run( process_fn=partial(self._run_for_target, context, forecaster_factory), items=targets, n_processes=n_processes, + mode="loky", ) if not self.storage.has_analysis_output( diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/target_provider.py b/packages/openstef-beam/src/openstef_beam/benchmarking/target_provider.py index 49df1f4c6..bbd2c7bb2 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/target_provider.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/target_provider.py @@ -19,6 +19,7 @@ from pathlib import Path from typing import cast, override +import numpy as np import pandas as pd from pydantic import Field, TypeAdapter @@ -268,16 +269,6 @@ def get_targets(self, filter_args: F | None = None) -> list[T]: def get_metrics_for_target(self, target: T) -> list[MetricProvider]: return self.metrics if isinstance(self.metrics, list) else self.metrics(target) # type: ignore[return-value] - measurements_path_for_target: Callable[[T], Path] = Field( - default=lambda target: Path(target.group_name) / f"load_data_{target.name}.parquet", - description="Function to build file path for target measurements using configured template", - ) - - weather_path_for_target: Callable[[T], Path] = Field( - default=lambda target: Path(target.group_name) / f"weather_data_{target.name}.parquet", - description="Function to build file path for target weather data using configured template", - ) - def _get_measurements_path_for_target(self, target: T) -> Path: return self.data_dir / str(target.group_name) / self.measurements_path_template.format(name=target.name) @@ -353,4 +344,60 @@ def get_prices(self) -> VersionedTimeSeriesDataset: @override def get_evaluation_mask_for_target(self, target: T) -> pd.DatetimeIndex | None: - return None + measurement_series = self.get_measurements_for_target(target).select_version().data[self.target_column] + + filtered_series = filter_away_flatline_chunks( + measurement_series=measurement_series, + min_length=24 * 4, + threshold=0.05, + ) + return pd.DatetimeIndex(cast(pd.DatetimeIndex, filtered_series.dropna().index)) # type: ignore[reportUnknownMemberType] + + +def filter_away_flatline_chunks( + measurement_series: pd.Series, + min_length: int = 96, + threshold: float = 1.0, +) -> pd.Series: + """Mask long flatline segments in a target series. + + Detects contiguous segments where the standard deviation inside both centered and + right-aligned windows falls below `threshold` times the global standard deviation + for at least `min_length` samples. Values inside those segments are replaced with + `NaN` so downstream logic can drop them and derive a clean evaluation mask. + + Args: + measurement_series: Time-indexed series containing the target observations. + min_length: Minimum length (in samples) for a chunk to be treated as a flatline. + threshold: Multiplier on the global standard deviation to define the flatline cutoff. + + Returns: + A copy of *measurement_series* with flatline chunks set to `NaN`. + """ + series_std = measurement_series.std() + actual_threshold = threshold * series_std + + rolling_std_center = measurement_series.rolling(window=min_length, center=True).std() + rolling_std_right = measurement_series.rolling(window=min_length, center=False).std() + + flatline_mask = (rolling_std_center < actual_threshold) | (rolling_std_right < actual_threshold) + flatline_mask = flatline_mask.fillna(value=False) # pyright: ignore[reportUnknownMemberType] + + flatline_chunks: list[tuple[int, int]] = [] + start_idx: int | None = None + for idx, is_flat in enumerate(flatline_mask): + if is_flat and start_idx is None: + start_idx = idx + elif not is_flat and start_idx is not None: + if idx - start_idx >= min_length: + flatline_chunks.append((start_idx, idx)) + start_idx = None + + if start_idx is not None and len(flatline_mask) - start_idx >= min_length: + flatline_chunks.append((start_idx, len(flatline_mask))) + + filtered_series = measurement_series.copy() + for start, end in flatline_chunks: + filtered_series.iloc[start:end] = np.nan + + return filtered_series diff --git a/packages/openstef-beam/src/openstef_beam/metrics/metrics_deterministic.py b/packages/openstef-beam/src/openstef_beam/metrics/metrics_deterministic.py index 44640c929..2de97042d 100644 --- a/packages/openstef-beam/src/openstef_beam/metrics/metrics_deterministic.py +++ b/packages/openstef-beam/src/openstef_beam/metrics/metrics_deterministic.py @@ -69,6 +69,8 @@ def rmae( # Ensure inputs are numpy arrays y_true = np.array(y_true) y_pred = np.array(y_pred) + if y_true.size == 0 or y_pred.size == 0: + return float("NaN") # Calculate MAE mae = np.average(np.abs(y_true - y_pred), weights=sample_weights) @@ -124,6 +126,8 @@ def mape( # Ensure inputs are numpy arrays y_true = np.array(y_true) y_pred = np.array(y_pred) + if y_true.size == 0 or y_pred.size == 0: + return float("NaN") # Calculate MAPE mape_value = np.mean(np.abs((y_true - y_pred) / y_true)) @@ -388,6 +392,8 @@ def riqd( y_true = np.array(y_true) y_pred_lower_q = np.array(y_pred_lower_q) y_pred_upper_q = np.array(y_pred_upper_q) + if y_true.size == 0 or y_pred_lower_q.size == 0 or y_pred_upper_q.size == 0: + return float("NaN") y_range = np.quantile(y_true, q=measurement_range_upper_q) - np.quantile(y_true, q=measurement_range_lower_q) @@ -451,6 +457,9 @@ def r2( >>> isinstance(score, float) True """ + if len(y_true) == 0 or len(y_pred) == 0: + return float("NaN") + return float(r2_score(y_true, y_pred, sample_weight=sample_weights)) @@ -499,6 +508,8 @@ def relative_pinball_loss( # Ensure inputs are numpy arrays y_true = np.array(y_true) y_pred = np.array(y_pred) + if y_true.size == 0 or y_pred.size == 0: + return float("NaN") # Calculate pinball loss for each sample errors = y_true - y_pred diff --git a/packages/openstef-beam/src/openstef_beam/metrics/metrics_probabilistic.py b/packages/openstef-beam/src/openstef_beam/metrics/metrics_probabilistic.py index ff15cef6e..79bfa85f7 100644 --- a/packages/openstef-beam/src/openstef_beam/metrics/metrics_probabilistic.py +++ b/packages/openstef-beam/src/openstef_beam/metrics/metrics_probabilistic.py @@ -19,6 +19,7 @@ import numpy.typing as npt from openstef_core.exceptions import MissingExtraError +from openstef_core.types import Quantile def crps( @@ -214,3 +215,58 @@ def mean_absolute_calibration_error( """ observed_probs = np.array([observed_probability(y_true, y_pred[:, i]) for i in range(len(quantiles))]) return float(np.mean(np.abs(observed_probs - quantiles))) + + +def mean_pinball_loss( + y_true: npt.NDArray[np.floating], + y_pred: npt.NDArray[np.floating], + quantiles: list[Quantile], + sample_weight: npt.NDArray[np.floating] | None = None, +) -> float: + """Calculate the Mean Pinball Loss for quantile forecasts. + + The Pinball Loss is a proper scoring rule for evaluating quantile forecasts. + It penalizes under- and over-predictions differently based on the quantile level. + + Args: + y_true: Observed values with shape (num_samples,) or (num_samples, num_quantiles). + y_pred: Predicted quantiles with shape (num_samples, num_quantiles). + Each column corresponds to predictions for a specific quantile level. + quantiles: Quantile levels with shape (num_quantiles,). + Must be sorted in ascending order and contain values in [0, 1]. + sample_weight: Optional weights for each sample with shape (num_samples,). + + Returns: + The weighted average Pinball Loss across all samples and quantiles. Lower values indicate better + forecast quality. + """ + # Resize the predictions and targets. + y_pred = np.reshape(y_pred, [-1, len(quantiles)]) + n_rows = y_pred.shape[0] + y_true = np.reshape(y_true, [n_rows, -1]) + sample_weight = np.reshape(sample_weight, [n_rows, 1]) if sample_weight is not None else None + + # Extract quantile values into array for vectorized operations + quantile_values = np.array(quantiles) # shape: (n_quantiles,) + + # Compute errors for all quantiles at once + errors = y_true - y_pred # shape: (num_samples, num_quantiles) + + # Compute masks for all quantiles simultaneously + underpredict_mask = errors >= 0 # y_true >= y_pred, shape: (num_samples, num_quantiles) + overpredict_mask = errors < 0 # y_true < y_pred, shape: (num_samples, num_quantiles) + + # Vectorized pinball loss computation using broadcasting + # quantiles broadcasts from (num_quantiles,) to (num_samples, num_quantiles) + loss = quantiles * underpredict_mask * errors - (1 - quantile_values) * overpredict_mask * errors + + # Apply sample weights if provided + if sample_weight is not None: + sample_weight = np.asarray(sample_weight).reshape(-1, 1) # shape: (num_samples, 1) + loss *= sample_weight + total_weight = sample_weight.sum() * len(quantiles) + else: + total_weight = loss.size + + # Return mean loss across all samples and quantiles + return float(loss.sum() / total_weight) diff --git a/packages/openstef-beam/tests/unit/backtesting/test_backtest_event_generator.py b/packages/openstef-beam/tests/unit/backtesting/test_backtest_event_generator.py index 3878c1518..7d86f4004 100644 --- a/packages/openstef-beam/tests/unit/backtesting/test_backtest_event_generator.py +++ b/packages/openstef-beam/tests/unit/backtesting/test_backtest_event_generator.py @@ -17,8 +17,8 @@ def config() -> BacktestForecasterConfig: return BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=6), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=6), predict_context_length=timedelta(hours=12), predict_context_min_coverage=0.5, training_context_length=timedelta(hours=24), @@ -122,8 +122,8 @@ def test_iterate_without_training_only_predicts(hourly_index: pd.DatetimeIndex): # Arrange config = BacktestForecasterConfig( requires_training=False, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=6), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=6), predict_context_length=timedelta(hours=12), predict_context_min_coverage=0.8, training_context_length=timedelta(hours=24), @@ -165,8 +165,8 @@ def test_iterate_returns_empty_when_insufficient_time(): # Arrange config = BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=6), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=6), predict_context_length=timedelta(hours=1), predict_context_min_coverage=0.8, training_context_length=timedelta(days=10), # Impossibly long @@ -195,8 +195,8 @@ def test_insufficient_coverage_filters_out_events(): sparse_index = pd.DatetimeIndex(["2025-01-01T12:00:00", "2025-01-01T18:00:00"]) config = BacktestForecasterConfig( requires_training=False, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=6), predict_context_min_coverage=0.9, # High requirement training_context_length=timedelta(hours=24), diff --git a/packages/openstef-beam/tests/unit/backtesting/test_backtest_pipeline.py b/packages/openstef-beam/tests/unit/backtesting/test_backtest_pipeline.py index b8eda17d3..8fbea70b0 100644 --- a/packages/openstef-beam/tests/unit/backtesting/test_backtest_pipeline.py +++ b/packages/openstef-beam/tests/unit/backtesting/test_backtest_pipeline.py @@ -117,8 +117,8 @@ def test_run_training_scenarios( config = BacktestConfig(predict_interval=timedelta(hours=6), train_interval=timedelta(hours=12)) forecaster_config = BacktestForecasterConfig( requires_training=requires_training, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=6), predict_context_min_coverage=0.5, training_context_length=timedelta(hours=12), @@ -173,8 +173,8 @@ def test_run_date_boundary_handling( mock_forecaster = MockForecaster( BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=6), predict_context_min_coverage=0.5, training_context_length=timedelta(hours=12), @@ -211,8 +211,8 @@ def test_run_output_validation_and_concatenation( mock_forecaster = MockForecaster( BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=6), predict_context_min_coverage=0.5, training_context_length=timedelta(hours=12), @@ -272,8 +272,8 @@ def test_run_handles_none_predictions(datasets: tuple[VersionedTimeSeriesDataset mock_forecaster = MockForecaster( BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=6), predict_context_min_coverage=0.5, training_context_length=timedelta(hours=12), @@ -335,8 +335,8 @@ def test_run_edge_cases( base_model_config = BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=1), predict_context_min_coverage=0.8, training_context_length=timedelta(hours=12), diff --git a/packages/openstef-beam/tests/unit/backtesting/test_batch_prediction.py b/packages/openstef-beam/tests/unit/backtesting/test_batch_prediction.py index 692d417d1..e7ff1ddd4 100644 --- a/packages/openstef-beam/tests/unit/backtesting/test_batch_prediction.py +++ b/packages/openstef-beam/tests/unit/backtesting/test_batch_prediction.py @@ -25,8 +25,8 @@ class MockModelConfig(BacktestForecasterConfig): requires_training: bool = True batch_size: int | None = 4 - horizon_length: timedelta = timedelta(hours=6) - horizon_min_length: timedelta = timedelta(hours=1) + predict_length: timedelta = timedelta(hours=6) + predict_min_length: timedelta = timedelta(hours=1) predict_context_length: timedelta = timedelta(hours=1) predict_context_min_coverage: float = 0.8 diff --git a/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py b/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py index bb060a222..1f18c83b8 100644 --- a/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py +++ b/packages/openstef-beam/tests/unit/benchmarking/test_benchmark_pipeline.py @@ -163,8 +163,8 @@ def forecaster_config() -> BacktestForecasterConfig: """Create a realistic forecaster config with all required fields.""" return BacktestForecasterConfig( requires_training=True, - horizon_length=timedelta(hours=24), - horizon_min_length=timedelta(hours=1), + predict_length=timedelta(hours=24), + predict_min_length=timedelta(hours=1), predict_context_length=timedelta(hours=48), predict_context_min_coverage=0.8, training_context_length=timedelta(days=14), diff --git a/packages/openstef-beam/tests/unit/benchmarking/test_target_provider.py b/packages/openstef-beam/tests/unit/benchmarking/test_target_provider.py index d40d4ba6f..7ee94db89 100644 --- a/packages/openstef-beam/tests/unit/benchmarking/test_target_provider.py +++ b/packages/openstef-beam/tests/unit/benchmarking/test_target_provider.py @@ -11,7 +11,10 @@ from pydantic import ValidationError from openstef_beam.benchmarking.models import BenchmarkTarget -from openstef_beam.benchmarking.target_provider import SimpleTargetProvider +from openstef_beam.benchmarking.target_provider import ( + SimpleTargetProvider, + filter_away_flatline_chunks, +) from openstef_core.datasets import VersionedTimeSeriesDataset @@ -110,3 +113,46 @@ def get_prices(self) -> VersionedTimeSeriesDataset: assert isinstance(result, VersionedTimeSeriesDataset) assert {"temp", "prof", "price"} <= set(result.feature_names) assert len(result.index) == 3 + + +@pytest.mark.parametrize( + ( + "values", + "min_length", + "threshold", + "expected", + ), + [ + pytest.param( + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0, 6.0, 6.0, 7.0, 8.0], + 3, + 0.1, + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, float("nan"), float("nan"), float("nan"), 7.0, 8.0], + id="flatline-chunk-masked", + ), + pytest.param( + [1.0, 2.0, 3.0, 4.0], + 2, + 0.1, + [1.0, 2.0, 3.0, 4.0], + id="no-flatline", + ), + ], +) +def test_filter_away_flatline_chunks_expected_series( + values: list[float], + min_length: int, + threshold: float, + expected: list[float], +) -> None: + """Compare the filtered output with the expected flatline suppression result.""" + # Arrange + index = pd.date_range("2023-01-01", periods=len(values), freq="h") + series = pd.Series(values, index=index) + + # Act + filtered = filter_away_flatline_chunks(series, min_length=min_length, threshold=threshold) + + # Assert: the filtered series matches the expected output + expected_series = pd.Series(expected, index=index) + pd.testing.assert_series_equal(filtered, expected_series) diff --git a/packages/openstef-beam/tests/unit/metrics/test_metrics_deterministic.py b/packages/openstef-beam/tests/unit/metrics/test_metrics_deterministic.py index 1678bc189..66960c087 100644 --- a/packages/openstef-beam/tests/unit/metrics/test_metrics_deterministic.py +++ b/packages/openstef-beam/tests/unit/metrics/test_metrics_deterministic.py @@ -129,6 +129,18 @@ def test_rmae_sample_weights_behavior( assert abs(result - expected) < tol, f"Expected {expected} but got {result} for weights={sample_weights}" +def test_rmae_returns_nan_when_inputs_empty() -> None: + # Arrange + y_true_arr = np.array([]) + y_pred_arr = np.array([]) + + # Act + result = rmae(y_true_arr, y_pred_arr) + + # Assert + assert np.isnan(result) + + @pytest.mark.parametrize( ("y_true", "y_pred", "expected", "tol"), [ @@ -149,6 +161,18 @@ def test_mape_various(y_true: Sequence[float], y_pred: Sequence[float], expected assert abs(result - expected) < tol, f"Expected {expected} but got {result}" +def test_mape_returns_nan_when_inputs_empty() -> None: + # Arrange + y_true_arr = np.array([]) + y_pred_arr = np.array([]) + + # Act + result = mape(y_true_arr, y_pred_arr) + + # Assert + assert np.isnan(result) + + @pytest.mark.parametrize( ("y_true", "y_pred"), [ @@ -378,6 +402,17 @@ def test_riqd_various( assert abs(result - expected) < tol, f"Expected {expected} but got {result}" +def test_riqd_returns_nan_when_inputs_empty() -> None: + # Arrange + empty_arr = np.array([]) + + # Act + result = riqd(empty_arr, empty_arr, empty_arr) + + # Assert + assert np.isnan(result) + + @pytest.mark.parametrize( ( "y_true", @@ -499,3 +534,15 @@ def test_relative_pinball_loss_various( assert np.isnan(result), f"Expected NaN but got {result}" else: assert abs(result - expected) < tol, f"Expected {expected} but got {result}" + + +def test_relative_pinball_loss_returns_nan_when_inputs_empty() -> None: + # Arrange + y_true_arr = np.array([]) + y_pred_arr = np.array([]) + + # Act + result = relative_pinball_loss(y_true_arr, y_pred_arr, quantile=0.5) + + # Assert + assert np.isnan(result) diff --git a/packages/openstef-beam/tests/unit/metrics/test_metrics_probabilistic.py b/packages/openstef-beam/tests/unit/metrics/test_metrics_probabilistic.py index 3b5dcf8e9..a05bfbd7f 100644 --- a/packages/openstef-beam/tests/unit/metrics/test_metrics_probabilistic.py +++ b/packages/openstef-beam/tests/unit/metrics/test_metrics_probabilistic.py @@ -6,8 +6,11 @@ import numpy as np import pytest +from sklearn.metrics import mean_pinball_loss as sk_mean_pinball_loss from openstef_beam.metrics import crps, mean_absolute_calibration_error, rcrps +from openstef_beam.metrics.metrics_probabilistic import mean_pinball_loss +from openstef_core.types import Q # CRPS Test Cases @@ -151,3 +154,33 @@ def test_mean_absolute_calibration_error() -> None: assert isinstance(result, float) assert result == (0.4 + 0.4) / 3 # observed probabilities are 0.5, 0.5, 0.5 vs 0.1, 0.5, 0.9 quantiles + + +def test_mean_pinball_loss_matches_sklearn_average_when_multi_quantile(): + # Arrange + rng = np.random.default_rng(seed=42) + n = 40 + y_true = rng.normal(loc=1.0, scale=2.0, size=n) + quantiles = [Q(0.1), Q(0.5), Q(0.9)] + # Simulate predictions with different biases per quantile; shape (n, q) + y_pred = np.stack( + [ + y_true + rng.normal(0, 0.7, size=n) - 0.4, # q=0.1 + y_true + rng.normal(0, 0.5, size=n) + 0.0, # q=0.5 + y_true + rng.normal(0, 0.7, size=n) + 0.4, # q=0.9 + ], + axis=1, + ) + + # Act + actual = mean_pinball_loss(y_true=y_true, y_pred=y_pred, quantiles=quantiles) + expected = np.mean( + np.array( + [sk_mean_pinball_loss(y_true, y_pred[:, i], alpha=float(quantile)) for i, quantile in enumerate(quantiles)], + dtype=float, + ) + ) + + # Assert + # Multi-quantile mean should equal average of sklearn per-quantile losses + assert np.allclose(actual, expected, rtol=1e-12, atol=1e-12) diff --git a/packages/openstef-core/src/openstef_core/exceptions.py b/packages/openstef-core/src/openstef_core/exceptions.py index 3edf70974..061f0b824 100644 --- a/packages/openstef-core/src/openstef_core/exceptions.py +++ b/packages/openstef-core/src/openstef_core/exceptions.py @@ -104,6 +104,10 @@ class PredictError(Exception): """Exception raised for errors during forecasting operations.""" +class InputValidationError(ValueError): + """Exception raised for input validation errors.""" + + class ModelLoadingError(Exception): """Exception raised when a model fails to load properly.""" diff --git a/packages/openstef-core/src/openstef_core/utils/multiprocessing.py b/packages/openstef-core/src/openstef_core/utils/multiprocessing.py index 0411be0ad..517d51bfe 100644 --- a/packages/openstef-core/src/openstef_core/utils/multiprocessing.py +++ b/packages/openstef-core/src/openstef_core/utils/multiprocessing.py @@ -10,11 +10,16 @@ """ import multiprocessing -import sys from collections.abc import Callable, Iterable +from typing import Literal -def run_parallel[T, R](process_fn: Callable[[T], R], items: Iterable[T], n_processes: int | None = None) -> list[R]: +def run_parallel[T, R]( + process_fn: Callable[[T], R], + items: Iterable[T], + n_processes: int | None = None, + mode: Literal["loky", "spawn", "fork"] = "loky", +) -> list[R]: """Execute a function in parallel across multiple processes. On macOS, explicitly uses fork context to avoid issues with the default @@ -28,6 +33,10 @@ def run_parallel[T, R](process_fn: Callable[[T], R], items: Iterable[T], n_proce items: Iterable of items to process. n_processes: Number of processes to use. If None or <= 1, runs sequentially. Typically set to number of CPU cores or logical cores. + mode: Multiprocessing start method. 'loky' is recommeneded for robust + ml use-cases. 'fork' is more efficient on macOS, while 'spawn' is + default on Windows/Linux. Xgboost seems to have bugs + when used with 'fork'. Returns: List of results from applying process_fn to each item, in the same order @@ -48,23 +57,21 @@ def run_parallel[T, R](process_fn: Callable[[T], R], items: Iterable[T], n_proce >>> # Empty input handling >>> run_parallel(square, [], n_processes=1) [] - - Note: - macOS Implementation Details: - - Uses fork context instead of spawn to avoid serialization overhead - - Fork preserves parent memory space, including imported modules and variables - - More efficient for ML models and large data structures - - On other platforms, uses the default context (usually spawn on Windows/Linux) """ if n_processes is None or n_processes <= 1: # If only one process is requested, run the function sequentially return [process_fn(item) for item in items] + if mode == "loky": + from joblib import Parallel, delayed # pyright: ignore[reportUnknownVariableType] # noqa: PLC0415 + + # Use joblib with loky backend for robust process management + return Parallel(n_jobs=n_processes, backend="loky")( # pyright: ignore[reportUnknownVariableType] + delayed(process_fn)(item) for item in items + ) # type: ignore + # Auto-configure for macOS - if sys.platform == "darwin": - context = multiprocessing.get_context("fork") - else: - context = multiprocessing.get_context() + context = multiprocessing.get_context(method=mode) with context.Pool(processes=n_processes) as pool: return pool.map(process_fn, items) diff --git a/packages/openstef-core/tests/unit/utils/test_multiprocessing.py b/packages/openstef-core/tests/unit/utils/test_multiprocessing.py index 7eb20d38b..fead7f859 100644 --- a/packages/openstef-core/tests/unit/utils/test_multiprocessing.py +++ b/packages/openstef-core/tests/unit/utils/test_multiprocessing.py @@ -5,7 +5,13 @@ """Tests for multiprocessing utilities.""" # Fix for macOS multiprocessing hanging in tests -from openstef_core.utils import run_parallel +from datetime import UTC, datetime, timedelta +from functools import partial +from typing import Literal + +import pytest + +from openstef_core.utils import align_datetime, run_parallel def double_number(n: int) -> int: @@ -25,13 +31,21 @@ def test_run_parallel_single_process(): assert result == expected -def test_run_parallel_multiple_processes(): - # Arrange - items = [1, 2, 3, 4] - expected = [2, 4, 6, 8] +@pytest.mark.parametrize( + ("mode"), + [ + pytest.param("loky", id="loky"), + pytest.param("fork", id="fork"), + ], +) +def test_run_parallel_multiple_processes(mode: Literal["loky", "fork"]): + # Arrange - note: we can't use double number since it gives import issues with loki, since testing is not a real module + items = [datetime(year=2025, month=1, day=i, hour=i, tzinfo=UTC) for i in range(1, 5)] + expected = [datetime(year=2025, month=1, day=i + 1, hour=0, tzinfo=UTC) for i in range(1, 5)] # Act - result = run_parallel(double_number, items, n_processes=2) + function = partial(align_datetime, interval=timedelta(days=1)) + result = run_parallel(process_fn=function, items=items, n_processes=2, mode=mode) # Assert assert result == expected diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 0bc4a1c2f..cbda4b2df 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -11,7 +11,6 @@ to predict values outside the range of the training data. """ -from functools import partial from typing import Literal, override import numpy as np @@ -22,11 +21,16 @@ from openstef_core.datasets.mixins import LeadTime from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset -from openstef_core.exceptions import MissingExtraError, NotFittedError +from openstef_core.exceptions import InputValidationError, MissingExtraError, NotFittedError from openstef_core.mixins.predictor import HyperParams from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig -from openstef_models.utils.loss_functions import OBJECTIVE_MAP, ObjectiveFunctionType, xgb_prepare_target_for_objective +from openstef_models.utils.evaluation_functions import EvaluationFunctionType, get_evaluation_function +from openstef_models.utils.loss_functions import ( + ObjectiveFunctionType, + get_objective_function, + xgb_prepare_target_for_objective, +) try: import xgboost as xgb @@ -52,8 +56,14 @@ class GBLinearHyperParams(HyperParams): "rounds.", ) objective: ObjectiveFunctionType | Literal["reg:quantileerror"] = Field( - default="pinball_loss", - description="Objective function for training. 'pinball_loss' is recommended for probabilistic forecasting.", + default="reg:quantileerror", + description="Objective function for training. 'reg:quantileerror' is recommended " + "for probabilistic forecasting.", + ) + evaluation_metric: EvaluationFunctionType = Field( + default="mean_pinball_loss", + description="Metric used for evaluation during training. Defaults to 'mean_pinball_loss' " + "for quantile regression.", ) # Regularization @@ -61,7 +71,7 @@ class GBLinearHyperParams(HyperParams): default=0.0001, description="L1 regularization on weights. Higher values increase regularization. Range: [0,∞]" ) reg_lambda: float = Field( - default=0.0, description="L2 regularization on weights. Higher values increase regularization. Range: [0,∞]" + default=0.1, description="L2 regularization on weights. Higher values increase regularization. Range: [0,∞]" ) # Feature selection @@ -193,15 +203,9 @@ def __init__(self, config: GBLinearForecasterConfig) -> None: """ self._config = config or GBLinearForecasterConfig() - if self.config.hyperparams.objective == "reg:quantileerror": - objective = "reg:quantileerror" - else: - objective = partial(OBJECTIVE_MAP[self._config.hyperparams.objective], quantiles=self._config.quantiles) - self._gblinear_model = xgb.XGBRegressor( booster="gblinear", # Core parameters for forecasting - objective=objective, n_estimators=self._config.hyperparams.n_steps, learning_rate=self._config.hyperparams.learning_rate, early_stopping_rounds=self._config.hyperparams.early_stopping_rounds, @@ -213,6 +217,16 @@ def __init__(self, config: GBLinearForecasterConfig) -> None: updater=self._config.hyperparams.updater, quantile_alpha=[float(q) for q in self._config.quantiles], top_k=self._config.hyperparams.top_k if self._config.hyperparams.feature_selector == "thrifty" else None, + # Objective + objective=get_objective_function( + function_type=self._config.hyperparams.objective, quantiles=self._config.quantiles + ) + if self._config.hyperparams.objective != "reg:quantileerror" + else "reg:quantileerror", + eval_metric=get_evaluation_function( + function_type=self._config.hyperparams.evaluation_metric, quantiles=self._config.quantiles + ), + disable_default_eval_metric=True, ) self._target_scaler = StandardScaler() @@ -233,7 +247,6 @@ def is_fitted(self) -> bool: def _prepare_fit_input(self, data: ForecastInputDataset) -> tuple[pd.DataFrame, np.ndarray, pd.Series]: input_data: pd.DataFrame = data.input_data() - # Scale the target variable target: np.ndarray = np.asarray(data.target_series.values) target = self._target_scaler.transform(target.reshape(-1, 1)).flatten() @@ -251,9 +264,15 @@ def _prepare_fit_input(self, data: ForecastInputDataset) -> tuple[pd.DataFrame, @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - # Fit the target scaler - target: np.ndarray = np.asarray(data.target_series.values) - self._target_scaler.fit(target.reshape(-1, 1)) + # Data checks + if data.data.isna().any().any(): + raise InputValidationError("There are nan values in the input data. Use imputation transform to fix them.") + + if len(data.data) == 0: + raise InputValidationError("The input data is empty after dropping NaN values.") + + # Fit the scalers + self._target_scaler.fit(data.target_series.to_frame()) # Prepare training data input_data, target, sample_weight = self._prepare_fit_input(data) @@ -281,14 +300,19 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) + # Data checks + if data.input_data().isna().any().any(): + raise InputValidationError("There are nan values in the input data. Use imputation transform to fix them.") + # Get input features for prediction input_data: pd.DataFrame = data.input_data(start=data.forecast_start) # Generate predictions - predictions_array: np.ndarray = self._gblinear_model.predict(input_data) + predictions_array: np.ndarray = self._gblinear_model.predict(input_data).reshape(-1, len(self.config.quantiles)) # Inverse transform the scaled predictions - predictions_array = self._target_scaler.inverse_transform(predictions_array) + if len(predictions_array) > 0: + predictions_array = self._target_scaler.inverse_transform(predictions_array) # Construct DataFrame with appropriate quantile columns predictions = pd.DataFrame( diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index 2a35fb987..94571a7d0 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -9,7 +9,6 @@ comprehensive hyperparameter control for production forecasting workflows. """ -from functools import partial from typing import Literal, override import numpy as np @@ -22,7 +21,12 @@ from openstef_core.mixins import HyperParams from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig -from openstef_models.utils.loss_functions import OBJECTIVE_MAP, ObjectiveFunctionType, xgb_prepare_target_for_objective +from openstef_models.utils.evaluation_functions import EvaluationFunctionType, get_evaluation_function +from openstef_models.utils.loss_functions import ( + ObjectiveFunctionType, + get_objective_function, + xgb_prepare_target_for_objective, +) try: import xgboost as xgb @@ -61,7 +65,7 @@ class XGBoostHyperParams(HyperParams): # Core Tree Boosting Parameters n_estimators: int = Field( - default=500, + default=100, description="Number of boosting rounds/trees to fit. Higher values may improve performance but " "increase training time and risk overfitting.", ) @@ -91,6 +95,11 @@ class XGBoostHyperParams(HyperParams): default="pinball_loss", description="Objective function for training. 'pinball_loss' is recommended for probabilistic forecasting.", ) + evaluation_metric: EvaluationFunctionType = Field( + default="mean_pinball_loss", + description="Metric used for evaluation during training. Defaults to 'mean_pinball_loss' " + "for quantile regression.", + ) # Regularization reg_alpha: float = Field( @@ -149,10 +158,10 @@ class XGBoostHyperParams(HyperParams): # General Parameters random_state: int | None = Field( - default=None, alias="seed", description="Random seed for reproducibility. Controls tree structure randomness." + default=42, description="Random seed for reproducibility. Controls tree structure randomness." ) early_stopping_rounds: int | None = Field( - default=10, + default=None, description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) use_target_scaling: bool = Field( @@ -201,7 +210,7 @@ class XGBoostForecasterConfig(ForecasterConfig): n_jobs: int = Field( default=1, description="Number of parallel threads for tree construction. -1 uses all available cores." ) - verbosity: Literal[0, 1, 2, 3] = Field( + verbosity: Literal[0, 1, 2, 3, True] = Field( default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) @@ -279,8 +288,6 @@ def __init__(self, config: XGBoostForecasterConfig) -> None: """ self._config = config - objective = partial(OBJECTIVE_MAP[self._config.hyperparams.objective], quantiles=self._config.quantiles) - self._xgboost_model = xgb.XGBRegressor( # Multi-output configuration multi_strategy="one_output_per_tree", @@ -314,7 +321,13 @@ def __init__(self, config: XGBoostForecasterConfig) -> None: # Early stopping handled in fit method early_stopping_rounds=self._config.hyperparams.early_stopping_rounds, # Objective - objective=objective, + objective=get_objective_function( + function_type=self._config.hyperparams.objective, quantiles=self._config.quantiles + ), + eval_metric=get_evaluation_function( + function_type=self._config.hyperparams.evaluation_metric, quantiles=self._config.quantiles + ), + disable_default_eval_metric=True, ) self._target_scaler = StandardScaler() if self._config.hyperparams.use_target_scaling else None @@ -389,10 +402,10 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: input_data: pd.DataFrame = data.input_data(start=data.forecast_start) # Generate predictions - predictions_array: np.ndarray = self._xgboost_model.predict(input_data) + predictions_array: np.ndarray = self._xgboost_model.predict(input_data).reshape(-1, len(self.config.quantiles)) # Inverse transform the scaled predictions - if self._target_scaler is not None: + if self._target_scaler is not None and len(predictions_array) > 0: predictions_array = self._target_scaler.inverse_transform(predictions_array) # Construct DataFrame with appropriate quantile columns diff --git a/packages/openstef-models/src/openstef_models/models/forecasting_model.py b/packages/openstef-models/src/openstef_models/models/forecasting_model.py index 9197e5a10..9d9e47498 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting_model.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting_model.py @@ -129,8 +129,8 @@ class ForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]) ) cutoff_history: timedelta = Field( default=timedelta(days=0), - description="Amount of historical data to exclude from training due to incomplete features from lag-based " - "preprocessing. When using lag transforms (e.g., lag-14), the first N days contain NaN values. " + description="Amount of historical data to exclude from training and prediction due to incomplete features " + "from lag-based preprocessing. When using lag transforms (e.g., lag-14), the first N days contain NaN values. " "Set this to match your maximum lag duration (e.g., timedelta(days=14)). " "Default of 0 assumes no invalid rows are created by preprocessing.", ) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 999ed701f..263965b06 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -35,17 +35,8 @@ from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder -from openstef_models.transforms.general import ( - Clipper, - EmptyFeatureRemover, - Imputer, - SampleWeighter, - Scaler, -) -from openstef_models.transforms.postprocessing import ( - ConfidenceIntervalApplicator, - QuantileSorter, -) +from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, Imputer, NaNDropper, SampleWeighter, Scaler +from openstef_models.transforms.postprocessing import ConfidenceIntervalApplicator, QuantileSorter from openstef_models.transforms.time_domain import ( CyclicFeaturesAdder, DatetimeFeaturesAdder, @@ -53,21 +44,13 @@ RollingAggregatesAdder, ) from openstef_models.transforms.time_domain.lags_adder import LagsAdder -from openstef_models.transforms.time_domain.rolling_aggregates_adder import ( - AggregationFunction, -) -from openstef_models.transforms.validation import ( - CompletenessChecker, - FlatlineChecker, - InputConsistencyChecker, -) +from openstef_models.transforms.time_domain.rolling_aggregates_adder import AggregationFunction +from openstef_models.transforms.validation import CompletenessChecker, FlatlineChecker, InputConsistencyChecker from openstef_models.transforms.weather_domain import ( + AtmosphereDerivedFeaturesAdder, DaylightFeatureAdder, RadiationDerivedFeaturesAdder, ) -from openstef_models.transforms.weather_domain.atmosphere_derived_features_adder import ( - AtmosphereDerivedFeaturesAdder, -) from openstef_models.utils.data_split import DataSplitter from openstef_models.utils.feature_selection import Exclude, FeatureSelection, Include from openstef_models.workflows.custom_forecasting_workflow import ( @@ -181,6 +164,15 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob default=timedelta(days=14), description="Amount of historical data available at prediction time.", ) + cutoff_history: timedelta = Field( + default=timedelta(days=0), + description="Amount of historical data to exclude from training and prediction due to incomplete features " + "from lag-based preprocessing. When using lag transforms (e.g., lag-14), the first N days contain NaN values. " + "Set this to match your maximum lag duration (e.g., timedelta(days=14)). " + "Default of 0 assumes no invalid rows are created by preprocessing. " + "Note: should be same as predict_history if you are using lags. We default to disabled to keep the same " + "behaviour as openstef 3.0.", + ) # Feature engineering and validation completeness_threshold: float = Field( @@ -203,6 +195,11 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob default=FeatureSelection(include=None, exclude=None), description="Feature selection for which features to clip.", ) + sample_weight_scale_percentile: int = Field( + default=95, + description="Percentile of target values used as scaling reference. " + "Values are normalized relative to this percentile before weighting.", + ) sample_weight_exponent: float = Field( default_factory=lambda data: 1.0 if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "hybrid", "xgboost"} @@ -218,7 +215,13 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob # Data splitting strategy data_splitter: DataSplitter = Field( - default_factory=DataSplitter, + default=DataSplitter( + # Copied from OpenSTEF3 pipeline defaults + val_fraction=0.15, + test_fraction=0.0, + stratification_fraction=0.15, + min_days_for_stratification=4, + ), description="Configuration for splitting data into training, validation, and test sets.", ) @@ -256,6 +259,10 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Penalty to apply to the old model's metric to bias selection towards newer models.", ) + verbosity: Literal[0, 1, 2, 3, True] = Field( + default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + # Metadata tags: dict[str, str] = Field( default_factory=dict, @@ -289,14 +296,14 @@ def create_forecasting_workflow( error_on_flatliner=False, ), CompletenessChecker(completeness_threshold=config.completeness_threshold), - EmptyFeatureRemover(), ] feature_adders = [ LagsAdder( history_available=config.predict_history, horizons=config.horizons, - add_trivial_lags=True, + add_trivial_lags=config.model != "gblinear", # GBLinear uses only 7day lag. target_column=config.target_column, + custom_lags=[timedelta(days=7)] if config.model == "gblinear" else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, @@ -317,6 +324,7 @@ def create_forecasting_workflow( RollingAggregatesAdder( feature=config.target_column, aggregation_functions=config.rolling_aggregate_features, + horizons=config.horizons, ), ] feature_standardizers = [ @@ -329,7 +337,9 @@ def create_forecasting_workflow( target_column=config.target_column, weight_exponent=config.sample_weight_exponent, weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), + EmptyFeatureRemover(), ] if config.model == "xgboost": @@ -345,6 +355,7 @@ def create_forecasting_workflow( quantiles=config.quantiles, horizons=config.horizons, hyperparams=config.xgboost_hyperparams, + verbosity=config.verbosity, ) ) postprocessing = [QuantileSorter()] @@ -383,16 +394,24 @@ def create_forecasting_workflow( elif config.model == "gblinear": preprocessing = [ *checks, - Imputer(selection=Exclude(config.target_column), imputation_strategy="mean"), *feature_adders, *feature_standardizers, + Imputer( + selection=Exclude(config.target_column), + imputation_strategy="mean", + fill_future_values=Include(config.energy_price_column), + ), + NaNDropper( + selection=Exclude(config.target_column), + ), ] forecaster = GBLinearForecaster( config=GBLinearForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, hyperparams=config.gblinear_hyperparams, - ) + verbosity=config.verbosity, + ), ) postprocessing = [QuantileSorter()] elif config.model == "flatliner": @@ -451,7 +470,7 @@ def create_forecasting_workflow( postprocessing=TransformPipeline(transforms=postprocessing), target_column=config.target_column, data_splitter=config.data_splitter, - cutoff_history=config.predict_history, + cutoff_history=config.cutoff_history, # Evaluation evaluation_metrics=config.evaluation_metrics, # Other diff --git a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py index 32afe179b..79e59f58b 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py @@ -14,6 +14,7 @@ EmptyFeatureRemover, ) from openstef_models.transforms.general.imputer import Imputer +from openstef_models.transforms.general.nan_dropper import NaNDropper from openstef_models.transforms.general.sample_weighter import SampleWeighter from openstef_models.transforms.general.scaler import Scaler @@ -22,6 +23,7 @@ "DimensionalityReducer", "EmptyFeatureRemover", "Imputer", + "NaNDropper", "SampleWeighter", "Scaler", ] diff --git a/packages/openstef-models/src/openstef_models/transforms/general/imputer.py b/packages/openstef-models/src/openstef_models/transforms/general/imputer.py index af2157dba..2a08dcf05 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/imputer.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/imputer.py @@ -130,9 +130,9 @@ class Imputer(BaseConfig, TimeSeriesTransform): >>> result_iterative = transform_iterative.transform(dataset) >>> result_iterative.data["temperature"].isna().sum() == 0 # Temperature NaNs filled np.True_ - >>> np.isnan(result_iterative.data["radiation"][1]) # Radiation first NaN replaced + >>> np.isnan(result_iterative.data["radiation"].iloc[1]) # Radiation first NaN replaced np.False_ - >>> np.isnan(result_iterative.data["radiation"][3]) # Check if trailing NaN is preserved + >>> np.isnan(result_iterative.data["radiation"].iloc[3]) # Check if trailing NaN is preserved np.True_ >>> result_iterative.data["wind_speed"].isna().sum() == 2 # Wind speed NaNs preserved np.True_ @@ -172,6 +172,13 @@ class Imputer(BaseConfig, TimeSeriesTransform): "Features to impute. If strategy is 'iterative', these features are also used as predictors for imputation." ), ) + fill_future_values: FeatureSelection = Field( + default=FeatureSelection.NONE, + description=( + "Features for which to fill future missing values. " + "This transform does not fill future missing values by default to preserve time series integrity." + ), + ) _imputer: SimpleImputer | IterativeImputer = PrivateAttr() _is_fitted: bool = PrivateAttr(default=False) @@ -252,7 +259,9 @@ def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: data_transformed = cast(pd.DataFrame, self._imputer.transform(data_subset)) # Set imputed trailing NaNs back to NaN since they cannot be reasonably imputed - for col in data_transformed.columns: + fill_future_features = self.fill_future_values.resolve(features) + no_fill_future_features = set(features) - set(fill_future_features) + for col in no_fill_future_features: last_valid = data_subset[col].last_valid_index() data_transformed.loc[data_transformed.index > (last_valid or data_transformed.index[0]), col] = np.nan diff --git a/packages/openstef-models/src/openstef_models/transforms/general/nan_dropper.py b/packages/openstef-models/src/openstef_models/transforms/general/nan_dropper.py new file mode 100644 index 000000000..0d8cef10c --- /dev/null +++ b/packages/openstef-models/src/openstef_models/transforms/general/nan_dropper.py @@ -0,0 +1,89 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Transform for dropping rows containing NaN values. + +This module provides functionality to drop rows containing NaN values in selected +columns, useful for data cleaning and ensuring complete cases for model training. +""" + +import logging +from typing import override + +from pydantic import Field, PrivateAttr + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.transforms import TimeSeriesTransform +from openstef_models.utils.feature_selection import FeatureSelection + + +class NaNDropper(BaseConfig, TimeSeriesTransform): + """Transform that drops rows containing NaN values in selected columns. + + This transform removes any row that has at least one NaN value in the + specified columns. It operates statelessly - no fitting is required. + + Example: + >>> import pandas as pd + >>> import numpy as np + >>> from datetime import timedelta + >>> from openstef_core.datasets import TimeSeriesDataset + >>> from openstef_models.transforms.general import NaNDropper + >>> + >>> # Create sample dataset with NaN values + >>> data = pd.DataFrame({ + ... 'load': [100.0, np.nan, 110.0, 130.0], + ... 'temperature': [20.0, 22.0, np.nan, 23.0], + ... 'humidity': [60.0, 65.0, 70.0, 75.0] + ... }, index=pd.date_range('2025-01-01', periods=4, freq='1h')) + >>> dataset = TimeSeriesDataset(data, timedelta(hours=1)) + >>> + >>> # Drop rows with NaN in load or temperature + >>> dropper = NaNDropper(selection=FeatureSelection(include=['load', 'temperature'])) + >>> transformed = dropper.transform(dataset) + >>> len(transformed.data) + 2 + >>> transformed.data['load'].tolist() + [100.0, 130.0] + + """ + + selection: FeatureSelection = Field( + default=FeatureSelection.ALL, + description="Features to check for NaN values. Rows with NaN in any selected column are dropped.", + ) + warn_threshold: float = Field( + default=0.1, + ge=0.0, + le=1.0, + description="Log a warning if the fraction of dropped rows exceeds this threshold (0.0 to 1.0).", + ) + + _logger: logging.Logger = PrivateAttr(default_factory=lambda: logging.getLogger(__name__)) + + @override + def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: + features = self.selection.resolve(data.feature_names) + original_row_count = len(data.data) + + # Drop rows containing NaN in selected columns + transformed_data = data.data.dropna(subset=features) # pyright: ignore[reportUnknownMemberType] + dropped_count = original_row_count - len(transformed_data) + + # Log warning if substantial percentage of rows was dropped + if original_row_count > 0 and dropped_count / original_row_count > self.warn_threshold: + self._logger.warning( + "NaNDropper dropped %d of %d rows (%.1f%%) due to NaN values in columns %s", + dropped_count, + original_row_count, + dropped_count / original_row_count * 100, + features, + ) + + return data.copy_with(data=transformed_data, is_sorted=True) + + @override + def features_added(self) -> list[str]: + return [] diff --git a/packages/openstef-models/src/openstef_models/transforms/time_domain/rolling_aggregates_adder.py b/packages/openstef-models/src/openstef_models/transforms/time_domain/rolling_aggregates_adder.py index c5ac4e8bc..a4e95a1d8 100644 --- a/packages/openstef-models/src/openstef_models/transforms/time_domain/rolling_aggregates_adder.py +++ b/packages/openstef-models/src/openstef_models/transforms/time_domain/rolling_aggregates_adder.py @@ -20,6 +20,7 @@ from openstef_core.datasets import TimeSeriesDataset from openstef_core.datasets.validation import validate_required_columns from openstef_core.transforms import TimeSeriesTransform +from openstef_core.types import LeadTime from openstef_core.utils import timedelta_to_isoformat type AggregationFunction = Literal["mean", "median", "max", "min"] @@ -51,7 +52,8 @@ class RollingAggregatesAdder(BaseConfig, TimeSeriesTransform): >>> transform = RollingAggregatesAdder( ... feature='load', ... rolling_window_size=timedelta(hours=2), - ... aggregation_functions=["mean", "max"] + ... aggregation_functions=["mean", "max"], + ... horizons=[LeadTime.from_string("PT36H")], ... ) >>> transformed_dataset = transform.transform(dataset) >>> result = transformed_dataset.data[['rolling_mean_load_PT2H', 'rolling_max_load_PT2H']] @@ -66,6 +68,10 @@ class RollingAggregatesAdder(BaseConfig, TimeSeriesTransform): feature: str = Field( description="Feature to compute rolling aggregates for.", ) + horizons: list[LeadTime] = Field( + description="List of forecast horizons.", + min_length=1, + ) rolling_window_size: timedelta = Field( default=timedelta(hours=24), description="Rolling window size for the aggregation.", @@ -80,8 +86,11 @@ class RollingAggregatesAdder(BaseConfig, TimeSeriesTransform): def _transform_pandas(self, df: pd.DataFrame) -> pd.DataFrame: rolling_df = cast( pd.DataFrame, - df[self.feature].rolling(window=self.rolling_window_size).agg(self.aggregation_functions), # type: ignore + df[self.feature].dropna().rolling(window=self.rolling_window_size).agg(self.aggregation_functions), # pyright: ignore[reportUnknownMemberType, reportCallIssue, reportArgumentType] ) + # Fill missing values with the last known value + rolling_df = rolling_df.reindex(df.index).ffill() + suffix = timedelta_to_isoformat(td=self.rolling_window_size) rolling_df = rolling_df.rename( columns={func: f"rolling_{func}_{self.feature}_{suffix}" for func in self.aggregation_functions} @@ -97,6 +106,12 @@ def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: ) return data + if len(self.horizons) > 1: + self._logger.warning( + "Multiple horizons for RollingAggregatesAdder is not yet supported. Returning original data." + ) + return data + validate_required_columns(df=data.data, required_columns=[self.feature]) return data.pipe_pandas(self._transform_pandas) diff --git a/packages/openstef-models/src/openstef_models/transforms/weather_domain/__init__.py b/packages/openstef-models/src/openstef_models/transforms/weather_domain/__init__.py index f9bd80d9c..f4f1e8fc7 100644 --- a/packages/openstef-models/src/openstef_models/transforms/weather_domain/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/weather_domain/__init__.py @@ -9,9 +9,12 @@ engineering for improved forecasting accuracy. """ +from openstef_models.transforms.weather_domain.atmosphere_derived_features_adder import ( + AtmosphereDerivedFeaturesAdder, +) from openstef_models.transforms.weather_domain.daylight_feature_adder import DaylightFeatureAdder from openstef_models.transforms.weather_domain.radiation_derived_features_adder import ( RadiationDerivedFeaturesAdder, ) -__all__ = ["DaylightFeatureAdder", "RadiationDerivedFeaturesAdder"] +__all__ = ["AtmosphereDerivedFeaturesAdder", "DaylightFeatureAdder", "RadiationDerivedFeaturesAdder"] diff --git a/packages/openstef-models/src/openstef_models/utils/data_split.py b/packages/openstef-models/src/openstef_models/utils/data_split.py index 4beea7cd7..908203fda 100644 --- a/packages/openstef-models/src/openstef_models/utils/data_split.py +++ b/packages/openstef-models/src/openstef_models/utils/data_split.py @@ -155,9 +155,9 @@ def stratified_train_test_split[T: TimeSeriesDataset]( max_days, min_days, other_days = _get_extreme_days(target_series=target_series, fraction=stratification_fraction) # Split each group proportionally between train and test - test_max_days, _ = _sample_dates_for_split(dates=max_days, test_fraction=test_fraction, rng=rng) - test_min_days, _ = _sample_dates_for_split(dates=min_days, test_fraction=test_fraction, rng=rng) - test_other_days, _ = _sample_dates_for_split(dates=other_days, test_fraction=test_fraction, rng=rng) + _, test_max_days = _sample_dates_for_split(dates=max_days, test_fraction=test_fraction, rng=rng) + _, test_min_days = _sample_dates_for_split(dates=min_days, test_fraction=test_fraction, rng=rng) + _, test_other_days = _sample_dates_for_split(dates=other_days, test_fraction=test_fraction, rng=rng) # Combine all train and test dates test_dates = cast(pd.DatetimeIndex, test_max_days.union(test_min_days).union(test_other_days)) @@ -166,12 +166,15 @@ def stratified_train_test_split[T: TimeSeriesDataset]( def _sample_dates_for_split( - dates: pd.DatetimeIndex, test_fraction: float, rng: np.random.Generator + dates: pd.DatetimeIndex, + test_fraction: float, + rng: np.random.Generator, ) -> tuple[pd.DatetimeIndex, pd.DatetimeIndex]: if dates.empty: return pd.DatetimeIndex([]), pd.DatetimeIndex([]) - n_test = max(1, int(test_fraction * len(dates))) + min_test_days = 1 if test_fraction > 0.0 else 0 + n_test = max(min_test_days, int(test_fraction * len(dates))) n_test = min(n_test, len(dates) - 1) # Ensure at least one for train if possible if len(dates) == 1: @@ -181,7 +184,7 @@ def _sample_dates_for_split( test_dates = pd.DatetimeIndex(np.sort(rng.choice(dates, size=n_test, replace=False))) train_dates = dates.difference(test_dates, sort=True) # type: ignore - return test_dates, train_dates + return train_dates, test_dates def _get_extreme_days( @@ -279,6 +282,10 @@ class DataSplitter(BaseConfig): default=4, description="Minimum number of unique days required to perform stratified splitting.", ) + random_state: int = Field( + default=42, + description="Random seed for reproducible splits when stratification is used.", + ) def split_dataset[T: TimeSeriesDataset]( self, @@ -306,7 +313,7 @@ def split_dataset[T: TimeSeriesDataset]( test_fraction=fraction, stratification_fraction=self.stratification_fraction, target_column=target_column, - random_state=42, + random_state=self.random_state, min_days_for_stratification=self.min_days_for_stratification, ), val_fraction=self.val_fraction if data_val is None else 0.0, diff --git a/packages/openstef-models/src/openstef_models/utils/evaluation_functions.py b/packages/openstef-models/src/openstef_models/utils/evaluation_functions.py new file mode 100644 index 000000000..7d568af13 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/utils/evaluation_functions.py @@ -0,0 +1,30 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Utility functions for evaluation metrics in forecasting models.""" + +from collections.abc import Callable +from functools import partial +from typing import Any, Literal + +import numpy as np + +from openstef_beam.metrics.metrics_probabilistic import mean_pinball_loss +from openstef_core.types import Quantile + +type EvaluationFunctionType = Literal["mean_pinball_loss"] + +EVALUATION_MAP = { + "mean_pinball_loss": mean_pinball_loss, +} + + +def get_evaluation_function( + function_type: EvaluationFunctionType, quantiles: list[Quantile] | None = None, **kwargs: Any +) -> Callable[[np.ndarray, np.ndarray], float]: + eval_metric = partial(EVALUATION_MAP[function_type], quantiles=quantiles, **kwargs) + eval_metric.__name__ = function_type # pyright: ignore[reportAttributeAccessIssue] + return eval_metric + + +__all__ = ["EVALUATION_MAP", "EvaluationFunctionType"] diff --git a/packages/openstef-models/src/openstef_models/utils/feature_selection.py b/packages/openstef-models/src/openstef_models/utils/feature_selection.py index 33a882907..ae260fa87 100644 --- a/packages/openstef-models/src/openstef_models/utils/feature_selection.py +++ b/packages/openstef-models/src/openstef_models/utils/feature_selection.py @@ -36,6 +36,7 @@ class FeatureSelection(BaseConfig): ) ALL: ClassVar[Self] + NONE: ClassVar[Self] def resolve(self, features: list[str]) -> list[str]: """Resolve the final list of features based on include and exclude lists. @@ -72,6 +73,7 @@ def combine(self, other: Self | None) -> Self: FeatureSelection.ALL = FeatureSelection(include=None, exclude=None) +FeatureSelection.NONE = FeatureSelection(include=set(), exclude=None) def Include(*features: str) -> FeatureSelection: # noqa: N802 diff --git a/packages/openstef-models/src/openstef_models/utils/loss_functions.py b/packages/openstef-models/src/openstef_models/utils/loss_functions.py index dff00be21..60b8dddfe 100644 --- a/packages/openstef-models/src/openstef_models/utils/loss_functions.py +++ b/packages/openstef-models/src/openstef_models/utils/loss_functions.py @@ -9,7 +9,9 @@ pinball loss. All functions support sample weighting for flexible training. """ -from typing import Literal +from collections.abc import Callable +from functools import partial +from typing import Any, Literal import numpy as np import numpy.typing as npt @@ -232,6 +234,18 @@ def xgb_prepare_target_for_objective( return np.repeat(target[:, np.newaxis], repeats=len(quantiles), axis=1) +def get_objective_function( + function_type: ObjectiveFunctionType, + quantiles: list[Quantile], + **kwargs: Any, +) -> Callable[ + [npt.NDArray[np.floating], npt.NDArray[np.floating]], tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]] +]: + fn = partial(OBJECTIVE_MAP[function_type], quantiles=quantiles, **kwargs) + fn.__name__ = function_type # pyright: ignore[reportAttributeAccessIssue] + return fn + + __all__ = [ "OBJECTIVE_MAP", "ObjectiveFunctionType", diff --git a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py index 9c3411ade..a740ac7c0 100644 --- a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py @@ -129,6 +129,7 @@ def fit( self, data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, ) -> ModelFitResult | None: """Train the forecasting model with callback execution. @@ -138,6 +139,7 @@ def fit( Args: data: Training dataset for the forecasting model. data_val: Optional validation dataset for model tuning. + data_test: Optional test dataset for final evaluation. Returns: ModelFitResult containing training metrics and information, @@ -149,7 +151,7 @@ def fit( for callback in self.callbacks: callback.on_fit_start(context=context, data=data) - result = self.model.fit(data=data, data_val=data_val) + result = self.model.fit(data=data, data_val=data_val, data_test=data_test) for callback in self.callbacks: callback.on_fit_end(context=context, result=result) diff --git a/packages/openstef-models/tests/unit/transforms/general/test_nan_dropper.py b/packages/openstef-models/tests/unit/transforms/general/test_nan_dropper.py new file mode 100644 index 000000000..7c6c1d5c3 --- /dev/null +++ b/packages/openstef-models/tests/unit/transforms/general/test_nan_dropper.py @@ -0,0 +1,48 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets import TimeSeriesDataset +from openstef_models.transforms.general import NaNDropper +from openstef_models.utils.feature_selection import FeatureSelection + + +def test_nan_dropper__removes_rows_with_nan_in_selected_columns(caplog: pytest.LogCaptureFixture): + """Test that NaNDropper removes rows containing NaN in selected columns and logs warning.""" + # Arrange + data = pd.DataFrame( + { + "load": [100.0, np.nan, 110.0, 130.0, 140.0], + "temperature": [20.0, 22.0, np.nan, 23.0, 24.0], + "humidity": [60.0, 65.0, 70.0, np.nan, 80.0], + }, + index=pd.date_range("2025-01-01", periods=5, freq="1h"), + ) + dataset = TimeSeriesDataset(data, timedelta(hours=1)) + dropper = NaNDropper(selection=FeatureSelection(include={"load", "temperature"})) + + # Act + transformed = dropper.transform(dataset) + + # Assert + # Row 1 (index 1) has NaN in load, row 2 (index 2) has NaN in temperature - both should be dropped + # Row 3 (index 3) has NaN in humidity but humidity is not selected, so it should remain + # Remaining rows: 0, 3, 4 + expected_df = pd.DataFrame( + { + "load": [100.0, 130.0, 140.0], + "temperature": [20.0, 23.0, 24.0], + "humidity": [60.0, np.nan, 80.0], + }, + index=pd.DatetimeIndex(["2025-01-01 00:00:00", "2025-01-01 03:00:00", "2025-01-01 04:00:00"], name="timestamp"), + ) + pd.testing.assert_frame_equal(transformed.data, expected_df) + assert transformed.sample_interval == dataset.sample_interval + # 40% of rows dropped (2 out of 5), should trigger warning (default threshold is 10%) + assert "NaNDropper dropped 2 of 5 rows (40.0%)" in caplog.text diff --git a/packages/openstef-models/tests/unit/transforms/time_domain/test_rolling_aggregates_adder.py b/packages/openstef-models/tests/unit/transforms/time_domain/test_rolling_aggregates_adder.py index 263302160..5bda95ea5 100644 --- a/packages/openstef-models/tests/unit/transforms/time_domain/test_rolling_aggregates_adder.py +++ b/packages/openstef-models/tests/unit/transforms/time_domain/test_rolling_aggregates_adder.py @@ -10,6 +10,7 @@ from openstef_core.datasets import TimeSeriesDataset from openstef_core.exceptions import MissingColumnsError +from openstef_core.types import LeadTime from openstef_models.transforms.time_domain import RollingAggregatesAdder @@ -26,6 +27,7 @@ def test_rolling_aggregate_features_basic(): feature="load", rolling_window_size=timedelta(hours=2), # 2-hour window aggregation_functions=["mean", "max", "min"], + horizons=[LeadTime.from_string("PT36H")], ) # Act @@ -67,6 +69,7 @@ def test_rolling_aggregate_features_with_nan(): feature="load", rolling_window_size=timedelta(hours=2), aggregation_functions=["mean"], + horizons=[LeadTime.from_string("PT36H")], ) # Act @@ -90,7 +93,10 @@ def test_rolling_aggregate_features_missing_column_raises_error(): index=pd.date_range("2023-01-01 00:00:00", periods=3, freq="1h"), ) dataset = TimeSeriesDataset(data, sample_interval=timedelta(minutes=15)) - transform = RollingAggregatesAdder(feature="load") + transform = RollingAggregatesAdder( + feature="load", + horizons=[LeadTime.from_string("PT36H")], + ) # Act & Assert with pytest.raises(MissingColumnsError, match="Missing required columns"): @@ -106,7 +112,10 @@ def test_rolling_aggregate_features_default_parameters(): ) dataset = TimeSeriesDataset(data, sample_interval=timedelta(hours=1)) - transform = RollingAggregatesAdder(feature="load") + transform = RollingAggregatesAdder( + feature="load", + horizons=[LeadTime.from_string("PT36H")], + ) # Act result = transform.transform(dataset) diff --git a/uv.lock b/uv.lock index f8bf4a7b4..a5b72e3fd 100644 --- a/uv.lock +++ b/uv.lock @@ -26,7 +26,7 @@ wheels = [ [[package]] name = "aiobotocore" -version = "2.25.0" +version = "2.25.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohttp" }, @@ -37,9 +37,9 @@ dependencies = [ { name = "python-dateutil" }, { name = "wrapt" }, ] -sdist = { url = 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2025 21:39:38 +0100 Subject: [PATCH 18/72] set silence --- .../src/openstef_beam/benchmarking/benchmark_pipeline.py | 2 +- .../openstef_models/models/forecasting/gblinear_forecaster.py | 2 +- .../src/openstef_models/presets/forecasting_workflow.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py index bcd1ef070..28d823db1 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py @@ -187,7 +187,7 @@ def run( process_fn=partial(self._run_for_target, context, forecaster_factory), items=targets, n_processes=n_processes, - mode="loky", + mode="fork", # TODO: Change back to 'loky' after before commit ) if not self.storage.has_analysis_output( diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index cbda4b2df..92c3981a3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -123,7 +123,7 @@ class GBLinearForecasterConfig(ForecasterConfig): default="cpu", description="Device for XGBoost computation. Options: 'cpu', 'cuda', 'cuda:', 'gpu'" ) verbosity: Literal[0, 1, 2, 3, True] = Field( - default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) def forecaster_from_config(self) -> "GBLinearForecaster": diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 263965b06..dc1ec7fb1 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -260,7 +260,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) verbosity: Literal[0, 1, 2, 3, True] = Field( - default=1, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" ) # Metadata From ea902390e027c7a0c5b575ea386b1dfe54537b2a Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 18 Nov 2025 09:20:37 +0100 Subject: [PATCH 19/72] small fix --- .../src/openstef_models/presets/forecasting_workflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 4dbf20c52..dc1ec7fb1 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -36,7 +36,7 @@ from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, Imputer, NaNDropper, SampleWeighter, Scaler -from openstef_models.transforms.postprocessing import QuantileSorter +from openstef_models.transforms.postprocessing import ConfidenceIntervalApplicator, QuantileSorter from openstef_models.transforms.time_domain import ( CyclicFeaturesAdder, DatetimeFeaturesAdder, From 93baa03e3a2a91ccbbdca78365a2a2910c9fa9ae Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 18 Nov 2025 12:19:52 +0100 Subject: [PATCH 20/72] Fix final learner --- .../models/forecasting/hybrid_forecaster.py | 213 +++++++++--------- .../presets/forecasting_workflow.py | 13 +- 2 files changed, 120 insertions(+), 106 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 7a41035b6..63e0c4a95 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -11,16 +11,17 @@ import logging from typing import override +from abc import abstractmethod import pandas as pd from pydantic import Field, field_validator -from sklearn.linear_model import QuantileRegressor from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, ) from openstef_core.mixins import HyperParams +from openstef_core.types import LeadTime, Quantile from openstef_models.estimators.hybrid import HybridQuantileRegressor from openstef_models.models.forecasting.forecaster import ( Forecaster, @@ -53,6 +54,69 @@ ) +class FinalLearner: + """Combines base learner predictions for each quantile into final predictions.""" + + @abstractmethod + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + raise NotImplementedError("Subclasses must implement the fit method.") + + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + raise NotImplementedError("Subclasses must implement the predict method.") + + @property + @abstractmethod + def is_fitted(self) -> bool: + raise NotImplementedError("Subclasses must implement the is_fitted property.") + + +class FinalForecaster(FinalLearner): + """Combines base learner predictions for each quantile into final predictions.""" + + def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: + # Feature adders placeholder for future use + + # Split forecaster per quantile + self.quantiles = forecaster.config.quantiles + models: list[Forecaster] = [] + for q in self.quantiles: + config = forecaster.config.model_copy( + update={ + "quantiles": [q], + } + ) + model = forecaster.__class__(config=config) + models.append(model) + self.models = models + + @override + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + for i, q in enumerate(self.quantiles): + self.models[i].fit(data=base_learner_predictions[q], data_val=None) + + @override + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions = [ + self.models[i].predict(data=base_learner_predictions[q]).data for i, q in enumerate(self.quantiles) + ] + + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + return ForecastDataset( + data=df, + sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + ) + + @property + def is_fitted(self) -> bool: + return all(x.is_fitted for x in self.models) + + class HybridHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" @@ -62,9 +126,15 @@ class HybridHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - l1_penalty: float = Field( - default=0.0, - description="L1 regularization term for the quantile regression.", + final_hyperparams: BaseLearnerHyperParams = Field( + default=GBLinearHyperParams(), + description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", + ) + + add_rolling_accuracy_features: bool = Field( + default=False, + description="Whether to add rolling accuracy features from base learners as additional features " + "to the final learner. Defaults to False.", ) @field_validator("base_hyperparams", mode="after") @@ -104,51 +174,8 @@ def __init__(self, config: HybridForecasterConfig) -> None: self._base_learners: list[BaseLearner] = self._init_base_learners( base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = [ - QuantileRegressor(quantile=float(q), alpha=config.hyperparams.l1_penalty) for q in config.quantiles - ] - - @staticmethod - def _hyperparams_forecast_map(hyperparams: type[BaseLearnerHyperParams]) -> type[BaseLearner]: - """Map hyperparameters to forecast types. - - Args: - hyperparams: Hyperparameters of the base learner. - - Returns: - Corresponding Forecaster class. - - Raises: - TypeError: If a nested HybridForecaster is attempted. - """ - if isinstance(hyperparams, HybridHyperParams): - raise TypeError("Nested HybridForecaster is not supported.") - - mapping: dict[type[BaseLearnerHyperParams], type[BaseLearner]] = { - LGBMHyperParams: LGBMForecaster, - LGBMLinearHyperParams: LGBMLinearForecaster, - XGBoostHyperParams: XGBoostForecaster, - GBLinearHyperParams: GBLinearForecaster, - } - return mapping[hyperparams] - - @staticmethod - def _base_learner_config(base_learner_class: type[BaseLearner]) -> type[BaseLearnerConfig]: - """Extract the configuration from a base learner. - - Args: - base_learner_class: The base learner forecaster. - - Returns: - The configuration of the base learner. - """ - mapping: dict[type[BaseLearner], type[BaseLearnerConfig]] = { - LGBMForecaster: LGBMForecasterConfig, - LGBMLinearForecaster: LGBMLinearForecasterConfig, - XGBoostForecaster: XGBoostForecasterConfig, - GBLinearForecaster: GBLinearForecasterConfig, - } - return mapping[base_learner_class] + final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] + self._final_learner = FinalForecaster(forecaster=final_forecaster) def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: """Initialize base learners based on provided hyperparameters. @@ -192,6 +219,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None # Fit base learners [x.fit(data=data, data_val=data_val) for x in self._base_learners] + # Reset forecast start date to ensure we predict on the full dataset full_dataset = ForecastInputDataset( data=data.data, sample_interval=data.sample_interval, @@ -201,27 +229,17 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) - quantile_dataframes = self._prepare_input_final_learner(base_predictions=base_predictions) + quantile_datasets = self._prepare_input_final_learner( + base_predictions=base_predictions, quantiles=self._config.quantiles, target_series=data.target_series + ) - self._fit_final_learner(target=data.target_series, quantile_df=quantile_dataframes) + self._final_learner.fit( + base_learner_predictions=quantile_datasets, + ) self._is_fitted = True - def _fit_final_learner( - self, - target: pd.Series, - quantile_df: dict[str, pd.DataFrame], - ) -> None: - """Fit the final learner using base learner predictions. - - Args: - target: Target values for training. - quantile_df: Dictionary mapping quantile strings to DataFrames of base learner predictions. - """ - for i, df in enumerate(quantile_df.values()): - self._final_learner[i].fit(X=df, y=target) - - def _predict_base_learners(self, data: ForecastInputDataset) -> dict[str, ForecastDataset]: + def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLearner], ForecastDataset]: """Generate predictions from base learners. Args: @@ -230,37 +248,19 @@ def _predict_base_learners(self, data: ForecastInputDataset) -> dict[str, Foreca Returns: DataFrame containing base learner predictions. """ - base_predictions: dict[str, ForecastDataset] = {} + base_predictions: dict[type[BaseLearner], ForecastDataset] = {} for learner in self._base_learners: preds = learner.predict(data=data) - base_predictions[learner.__class__.__name__] = preds + base_predictions[learner.__class__] = preds return base_predictions - def _predict_final_learner( - self, quantile_df: dict[str, pd.DataFrame], data: ForecastInputDataset - ) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions_dict = [ - pd.Series(self._final_learner[i].predict(X=quantile_df[q_str]), index=quantile_df[q_str].index, name=q_str) - for i, q_str in enumerate(quantile_df.keys()) - ] - - # Construct DataFrame with appropriate quantile columns - predictions = pd.DataFrame( - data=predictions_dict, - ).T - - return ForecastDataset( - data=predictions, - sample_interval=data.sample_interval, - ) - @staticmethod - def _prepare_input_final_learner(base_predictions: dict[str, ForecastDataset]) -> dict[str, pd.DataFrame]: + def _prepare_input_final_learner( + quantiles: list[Quantile], + base_predictions: dict[type[BaseLearner], ForecastDataset], + target_series: pd.Series, + ) -> dict[Quantile, ForecastInputDataset]: """Prepare input data for the final learner based on base learner predictions. Args: @@ -269,14 +269,22 @@ def _prepare_input_final_learner(base_predictions: dict[str, ForecastDataset]) - Returns: dictionary mapping quantile strings to DataFrames of base learner predictions. """ - predictions_quantiles: dict[str, pd.DataFrame] = {} - first_key = next(iter(base_predictions)) - for quantile in base_predictions[first_key].quantiles: - quantile_str = quantile.format() - quantile_preds = pd.DataFrame({ - learner_name: preds.data[quantile_str] for learner_name, preds in base_predictions.items() + predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} + sample_interval = base_predictions[next(iter(base_predictions))].sample_interval + target_name = str(target_series.name) + + for q in quantiles: + df = pd.DataFrame({ + learner.__name__: preds.data[Quantile(q).format()] for learner, preds in base_predictions.items() }) - predictions_quantiles[quantile_str] = quantile_preds + df[target_name] = target_series + + predictions_quantiles[q] = ForecastInputDataset( + data=df, + sample_interval=sample_interval, + target_column=target_name, + forecast_start=df.index[0], + ) return predictions_quantiles @@ -287,13 +295,12 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: base_predictions = self._predict_base_learners(data=data) - final_learner_input = self._prepare_input_final_learner(base_predictions=base_predictions) - - return self._predict_final_learner( - quantile_df=final_learner_input, - data=data, + final_learner_input = self._prepare_input_final_learner( + quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series ) + return self._final_learner.predict(base_learner_predictions=final_learner_input) + # TODO(@Lars800): #745: Make forecaster Explainable diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index dc1ec7fb1..1a33b4622 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -301,9 +301,9 @@ def create_forecasting_workflow( LagsAdder( history_available=config.predict_history, horizons=config.horizons, - add_trivial_lags=config.model != "gblinear", # GBLinear uses only 7day lag. + add_trivial_lags=config.model not in {"gblinear", "hybrid"}, # GBLinear uses only 7day lag. target_column=config.target_column, - custom_lags=[timedelta(days=7)] if config.model == "gblinear" else [], + custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "hybrid"} else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, @@ -428,9 +428,16 @@ def create_forecasting_workflow( elif config.model == "hybrid": preprocessing = [ *checks, - Imputer(selection=Exclude(config.target_column), imputation_strategy="mean"), *feature_adders, *feature_standardizers, + Imputer( + selection=Exclude(config.target_column), + imputation_strategy="mean", + fill_future_values=Include(config.energy_price_column), + ), + NaNDropper( + selection=Exclude(config.target_column), + ), ] forecaster = HybridForecaster( config=HybridForecaster.Config( From 4f8ea8f04089bff78222b96545a095c804825cd3 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 18 Nov 2025 20:43:32 +0100 Subject: [PATCH 21/72] fixed lgbm efficiency --- .../src/openstef_models/models/forecasting/lgbm_forecaster.py | 1 + .../openstef_models/models/forecasting/lgbmlinear_forecaster.py | 1 + 2 files changed, 2 insertions(+) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index f46009502..4fc07ac75 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -238,6 +238,7 @@ def __init__(self, config: LGBMForecasterConfig) -> None: "random_state": config.random_state, "early_stopping_rounds": config.early_stopping_rounds, "verbosity": config.verbosity, + "n_jobs": config.n_jobs, **config.hyperparams.model_dump(), } diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index 57a9d96f8..2dc7a8f87 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -240,6 +240,7 @@ def __init__(self, config: LGBMLinearForecasterConfig) -> None: "random_state": config.random_state, "early_stopping_rounds": config.early_stopping_rounds, "verbosity": config.verbosity, + "n_jobs": config.n_jobs, **config.hyperparams.model_dump(), } From b4bdbdca44880a6945f4a417a30a4074bba694b7 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 20 Nov 2025 10:01:07 +0100 Subject: [PATCH 22/72] updated lgbm linear params --- .../models/forecasting/lgbmlinear_forecaster.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index 2dc7a8f87..eace689fb 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -52,7 +52,7 @@ class LGBMLinearHyperParams(HyperParams): # Core Tree Boosting Parameters n_estimators: int = Field( - default=77, + default=100, description="Number of boosting rounds/trees to fit. Higher values may improve performance but " "increase training time and risk overfitting.", ) @@ -63,7 +63,7 @@ class LGBMLinearHyperParams(HyperParams): "more boosting rounds.", ) max_depth: int = Field( - default=1, + default=6, description="Maximum depth of trees. Higher values capture more complex patterns but risk " "overfitting. Range: [1,∞]", ) @@ -74,11 +74,11 @@ class LGBMLinearHyperParams(HyperParams): ) min_data_in_leaf: int = Field( - default=5, + default=500, description="Minimum number of data points in a leaf. Higher values prevent overfitting. Range: [1,∞]", ) min_data_in_bin: int = Field( - default=13, + default=500, description="Minimum number of data points in a bin. Higher values prevent overfitting. Range: [1,∞]", ) @@ -94,19 +94,19 @@ class LGBMLinearHyperParams(HyperParams): # Tree Structure Control num_leaves: int = Field( - default=78, + default=30, description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'.", ) max_bin: int = Field( - default=12, + default=256, description="Maximum number of discrete bins for continuous features. Higher values may improve accuracy but " "increase memory.", ) # Subsampling Parameters colsample_bytree: float = Field( - default=0.5, + default=1, description="Fraction of features used when constructing each tree. Range: (0,1]", ) @@ -158,7 +158,7 @@ class LGBMLinearForecasterConfig(ForecasterConfig): ) early_stopping_rounds: int | None = Field( - default=10, + default=None, description="Training will stop if performance doesn't improve for this many rounds. Requires validation data.", ) From ea1f5f7cf479a898f19a8b4afc4885dc37b8e4fc Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 20 Nov 2025 10:49:04 +0100 Subject: [PATCH 23/72] Fixed type and quality issues --- .../openstef_beam/benchmarking/benchmark_pipeline.py | 2 +- .../models/forecasting/hybrid_forecaster.py | 11 +++++++---- .../models/forecasting/lgbm_forecaster.py | 2 +- .../openstef_models/utils/multi_quantile_regressor.py | 8 ++++++++ 4 files changed, 17 insertions(+), 6 deletions(-) diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py index 28d823db1..bcd1ef070 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/benchmark_pipeline.py @@ -187,7 +187,7 @@ def run( process_fn=partial(self._run_for_target, context, forecaster_factory), items=targets, n_processes=n_processes, - mode="fork", # TODO: Change back to 'loky' after before commit + mode="loky", ) if not self.storage.has_analysis_output( diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 63e0c4a95..3cda34562 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -10,8 +10,8 @@ """ import logging -from typing import override from abc import abstractmethod +from typing import override import pandas as pd from pydantic import Field, field_validator @@ -21,7 +21,7 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_core.types import LeadTime, Quantile +from openstef_core.types import Quantile from openstef_models.estimators.hybrid import HybridQuantileRegressor from openstef_models.models.forecasting.forecaster import ( Forecaster, @@ -75,6 +75,8 @@ class FinalForecaster(FinalLearner): def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: # Feature adders placeholder for future use + if feature_adders is not None: + raise NotImplementedError("Feature adders are not yet implemented.") # Split forecaster per quantile self.quantiles = forecaster.config.quantiles @@ -168,7 +170,6 @@ class HybridForecaster(Forecaster): def __init__(self, config: HybridForecasterConfig) -> None: """Initialize the Hybrid forecaster.""" - self._config = config self._base_learners: list[BaseLearner] = self._init_base_learners( @@ -264,7 +265,9 @@ def _prepare_input_final_learner( """Prepare input data for the final learner based on base learner predictions. Args: - base_predictions: Dictionary of base learner predictions. + quantiles: List of quantiles to prepare data for. + base_predictions: Predictions from base learners. + target_series: Actual target series for reference. Returns: dictionary mapping quantile strings to DataFrames of base learner predictions. diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 4fc07ac75..03c667b00 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -22,7 +22,7 @@ ) from openstef_core.mixins import HyperParams from openstef_models.explainability.mixins import ExplainableForecaster -from openstef_models.models.forecasting.forecaster import ForecasterConfig, Forecaster +from openstef_models.models.forecasting.forecaster import Forecaster, ForecasterConfig from openstef_models.utils.multi_quantile_regressor import MultiQuantileRegressor if TYPE_CHECKING: diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py index 8a6276927..763932268 100644 --- a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -1,3 +1,11 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Adaptor for multi-quantile regression using a base quantile regressor. + +Designed to work with scikit-learn compatible regressors that support quantile regression. +""" + import logging import numpy as np From 22688e026b41870a463eb00f08772924bdf73046 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 20 Nov 2025 15:11:33 +0100 Subject: [PATCH 24/72] First Version Sample Weighting Approach Signed-off-by: Lars van Someren --- ...liander_2024_benchmark_xgboost_gblinear.py | 3 +- .../models/forecasting/hybrid_forecaster.py | 87 ++++++++++++++++++- 2 files changed, 87 insertions(+), 3 deletions(-) diff --git a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py index dc41b1744..4ff925cce 100644 --- a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py +++ b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py @@ -45,7 +45,8 @@ BENCHMARK_RESULTS_PATH_XGBOOST = OUTPUT_PATH / "XGBoost" BENCHMARK_RESULTS_PATH_GBLINEAR = OUTPUT_PATH / "GBLinear" -N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 # Amount of parallel processes to use for the benchmark + # Model configuration FORECAST_HORIZONS = [LeadTime.from_string("P3D")] # Forecast horizon(s) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py index 3cda34562..274be98a7 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py @@ -10,11 +10,13 @@ """ import logging +import time from abc import abstractmethod from typing import override import pandas as pd from pydantic import Field, field_validator +from sklearn.ensemble import RandomForestClassifier from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( @@ -44,6 +46,8 @@ XGBoostHyperParams, ) +from lightgbm import LGBMClassifier + logger = logging.getLogger(__name__) @@ -54,6 +58,14 @@ ) +def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: + """Calculate pinball loss for given true and predicted values.""" + + diff = y_true - y_pred + sign = (diff >= 0).astype(float) + return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff + + class FinalLearner: """Combines base learner predictions for each quantile into final predictions.""" @@ -119,6 +131,68 @@ def is_fitted(self) -> bool: return all(x.is_fitted for x in self.models) +class FinalWeighter(FinalLearner): + """Combines base learner predictions with a classification approach to determine which base learner to use.""" + + def __init__(self, quantiles: list[Quantile]) -> None: + self.quantiles = quantiles + self.models = [LGBMClassifier(class_weight="balanced", n_estimators=20) for _ in quantiles] + self._is_fitted = False + + @override + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + for i, q in enumerate(self.quantiles): + pred = base_learner_predictions[q].data.drop(columns=[base_learner_predictions[q].target_column]) + labels = self._prepare_classification_data( + quantile=q, + target=base_learner_predictions[q].target_series, + predictions=pred, + ) + + self.models[i].fit(X=pred, y=labels) + self._is_fitted = True + + @staticmethod + def _prepare_classification_data(quantile: Quantile, target: pd.Series, predictions: pd.DataFrame) -> pd.Series: + """Selects base learner with lowest error for each sample as target for classification.""" + # Calculate pinball loss for each base learner + pinball_losses = predictions.apply(lambda x: calculate_pinball_errors(y_true=target, y_pred=x, alpha=quantile)) + + # For each sample, select the base learner with the lowest pinball loss + return pinball_losses.idxmin(axis=1) + + def _calculate_sample_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: + model = self.models[self.quantiles.index(quantile)] + + return model.predict_proba(X=base_predictions) + + def _generate_predictions_quantile(self, base_predictions: ForecastInputDataset, quantile: Quantile) -> pd.Series: + df = base_predictions.data.drop(columns=[base_predictions.target_column]) + weights = self._calculate_sample_weights_quantile(base_predictions=df, quantile=quantile) + + return df.mul(weights).sum(axis=1) + + @override + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions = pd.DataFrame({ + Quantile(q).format(): self._generate_predictions_quantile(base_predictions=data, quantile=q) + for q, data in base_learner_predictions.items() + }) + + return ForecastDataset( + data=predictions, + sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + ) + + @property + def is_fitted(self) -> bool: + return self._is_fitted + + class HybridHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" @@ -133,6 +207,11 @@ class HybridHyperParams(HyperParams): description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", ) + use_classifier: bool = Field( + default=True, + description="Whether to use sample weights when fitting base and final learners. Defaults to False.", + ) + add_rolling_accuracy_features: bool = Field( default=False, description="Whether to add rolling accuracy features from base learners as additional features " @@ -175,8 +254,12 @@ def __init__(self, config: HybridForecasterConfig) -> None: self._base_learners: list[BaseLearner] = self._init_base_learners( base_hyperparams=config.hyperparams.base_hyperparams ) - final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] - self._final_learner = FinalForecaster(forecaster=final_forecaster) + if config.hyperparams.use_classifier: + self._final_learner = FinalWeighter(quantiles=config.quantiles) + + else: + final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] + self._final_learner = FinalForecaster(forecaster=final_forecaster) def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: """Initialize base learners based on provided hyperparameters. From 9b971d3e5a91398e6b43f8f687cb625181d6a5e5 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 21 Nov 2025 16:33:31 +0100 Subject: [PATCH 25/72] MetaForecasterClass Signed-off-by: Lars van Someren --- .../models/forecasting/hybrid_forecaster.py | 393 ------------------ .../models/forecasting/meta/__init__.py | 7 + .../meta/learned_weights_forecaster.py | 183 ++++++++ .../forecasting/meta/meta_forecaster.py | 240 +++++++++++ .../forecasting/meta/stacking_forecaster.py | 161 +++++++ .../presets/forecasting_workflow.py | 53 ++- .../tests/unit/estimators/__init__.py | 0 .../tests/unit/estimators/test_hybrid.py | 43 -- .../meta/test_learned_weights_forecaster.py | 105 +++++ .../test_stacking_forecaster.py} | 38 +- 10 files changed, 756 insertions(+), 467 deletions(-) delete mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py create mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py delete mode 100644 packages/openstef-models/tests/unit/estimators/__init__.py delete mode 100644 packages/openstef-models/tests/unit/estimators/test_hybrid.py create mode 100644 packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py rename packages/openstef-models/tests/unit/models/forecasting/{test_hybrid_forecaster.py => meta/test_stacking_forecaster.py} (77%) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py deleted file mode 100644 index 274be98a7..000000000 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ /dev/null @@ -1,393 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). - -Provides method that attempts to combine the advantages of a linear model (Extraplolation) -and tree-based model (Non-linear patterns). This is acieved by training two base learners, -followed by a small linear model that regresses on the baselearners' predictions. -The implementation is based on sklearn's StackingRegressor. -""" - -import logging -import time -from abc import abstractmethod -from typing import override - -import pandas as pd -from pydantic import Field, field_validator -from sklearn.ensemble import RandomForestClassifier - -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.exceptions import ( - NotFittedError, -) -from openstef_core.mixins import HyperParams -from openstef_core.types import Quantile -from openstef_models.estimators.hybrid import HybridQuantileRegressor -from openstef_models.models.forecasting.forecaster import ( - Forecaster, - ForecasterConfig, -) -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearForecaster, - GBLinearForecasterConfig, - GBLinearHyperParams, -) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMForecasterConfig, LGBMHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import ( - LGBMLinearForecaster, - LGBMLinearForecasterConfig, - LGBMLinearHyperParams, -) -from openstef_models.models.forecasting.xgboost_forecaster import ( - XGBoostForecaster, - XGBoostForecasterConfig, - XGBoostHyperParams, -) - -from lightgbm import LGBMClassifier - -logger = logging.getLogger(__name__) - - -BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster -BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams -BaseLearnerConfig = ( - LGBMForecasterConfig | LGBMLinearForecasterConfig | XGBoostForecasterConfig | GBLinearForecasterConfig -) - - -def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: - """Calculate pinball loss for given true and predicted values.""" - - diff = y_true - y_pred - sign = (diff >= 0).astype(float) - return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff - - -class FinalLearner: - """Combines base learner predictions for each quantile into final predictions.""" - - @abstractmethod - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - raise NotImplementedError("Subclasses must implement the fit method.") - - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - raise NotImplementedError("Subclasses must implement the predict method.") - - @property - @abstractmethod - def is_fitted(self) -> bool: - raise NotImplementedError("Subclasses must implement the is_fitted property.") - - -class FinalForecaster(FinalLearner): - """Combines base learner predictions for each quantile into final predictions.""" - - def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: - # Feature adders placeholder for future use - if feature_adders is not None: - raise NotImplementedError("Feature adders are not yet implemented.") - - # Split forecaster per quantile - self.quantiles = forecaster.config.quantiles - models: list[Forecaster] = [] - for q in self.quantiles: - config = forecaster.config.model_copy( - update={ - "quantiles": [q], - } - ) - model = forecaster.__class__(config=config) - models.append(model) - self.models = models - - @override - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - for i, q in enumerate(self.quantiles): - self.models[i].fit(data=base_learner_predictions[q], data_val=None) - - @override - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions = [ - self.models[i].predict(data=base_learner_predictions[q]).data for i, q in enumerate(self.quantiles) - ] - - # Concatenate predictions along columns to form a DataFrame with quantile columns - df = pd.concat(predictions, axis=1) - - return ForecastDataset( - data=df, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, - ) - - @property - def is_fitted(self) -> bool: - return all(x.is_fitted for x in self.models) - - -class FinalWeighter(FinalLearner): - """Combines base learner predictions with a classification approach to determine which base learner to use.""" - - def __init__(self, quantiles: list[Quantile]) -> None: - self.quantiles = quantiles - self.models = [LGBMClassifier(class_weight="balanced", n_estimators=20) for _ in quantiles] - self._is_fitted = False - - @override - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - for i, q in enumerate(self.quantiles): - pred = base_learner_predictions[q].data.drop(columns=[base_learner_predictions[q].target_column]) - labels = self._prepare_classification_data( - quantile=q, - target=base_learner_predictions[q].target_series, - predictions=pred, - ) - - self.models[i].fit(X=pred, y=labels) - self._is_fitted = True - - @staticmethod - def _prepare_classification_data(quantile: Quantile, target: pd.Series, predictions: pd.DataFrame) -> pd.Series: - """Selects base learner with lowest error for each sample as target for classification.""" - # Calculate pinball loss for each base learner - pinball_losses = predictions.apply(lambda x: calculate_pinball_errors(y_true=target, y_pred=x, alpha=quantile)) - - # For each sample, select the base learner with the lowest pinball loss - return pinball_losses.idxmin(axis=1) - - def _calculate_sample_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: - model = self.models[self.quantiles.index(quantile)] - - return model.predict_proba(X=base_predictions) - - def _generate_predictions_quantile(self, base_predictions: ForecastInputDataset, quantile: Quantile) -> pd.Series: - df = base_predictions.data.drop(columns=[base_predictions.target_column]) - weights = self._calculate_sample_weights_quantile(base_predictions=df, quantile=quantile) - - return df.mul(weights).sum(axis=1) - - @override - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions = pd.DataFrame({ - Quantile(q).format(): self._generate_predictions_quantile(base_predictions=data, quantile=q) - for q, data in base_learner_predictions.items() - }) - - return ForecastDataset( - data=predictions, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, - ) - - @property - def is_fitted(self) -> bool: - return self._is_fitted - - -class HybridHyperParams(HyperParams): - """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - - base_hyperparams: list[BaseLearnerHyperParams] = Field( - default=[LGBMHyperParams(), GBLinearHyperParams()], - description="List of hyperparameter configurations for base learners. " - "Defaults to [LGBMHyperParams, GBLinearHyperParams].", - ) - - final_hyperparams: BaseLearnerHyperParams = Field( - default=GBLinearHyperParams(), - description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", - ) - - use_classifier: bool = Field( - default=True, - description="Whether to use sample weights when fitting base and final learners. Defaults to False.", - ) - - add_rolling_accuracy_features: bool = Field( - default=False, - description="Whether to add rolling accuracy features from base learners as additional features " - "to the final learner. Defaults to False.", - ) - - @field_validator("base_hyperparams", mode="after") - @classmethod - def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: - hp_classes = [type(hp) for hp in v] - if not len(hp_classes) == len(set(hp_classes)): - raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") - return v - - -class HybridForecasterConfig(ForecasterConfig): - """Configuration for Hybrid-based forecasting models.""" - - hyperparams: HybridHyperParams = HybridHyperParams() - - verbosity: bool = Field( - default=True, - description="Enable verbose output from the Hybrid model (True/False).", - ) - - -class HybridForecaster(Forecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" - - Config = HybridForecasterConfig - HyperParams = HybridHyperParams - - _config: HybridForecasterConfig - model: HybridQuantileRegressor - - def __init__(self, config: HybridForecasterConfig) -> None: - """Initialize the Hybrid forecaster.""" - self._config = config - - self._base_learners: list[BaseLearner] = self._init_base_learners( - base_hyperparams=config.hyperparams.base_hyperparams - ) - if config.hyperparams.use_classifier: - self._final_learner = FinalWeighter(quantiles=config.quantiles) - - else: - final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] - self._final_learner = FinalForecaster(forecaster=final_forecaster) - - def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: - """Initialize base learners based on provided hyperparameters. - - Returns: - list[Forecaster]: List of initialized base learner forecasters. - """ - base_learners: list[BaseLearner] = [] - horizons = self.config.horizons - quantiles = self.config.quantiles - - for hyperparams in base_hyperparams: - forecaster_cls = hyperparams.forecaster_class() - config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) - if "hyperparams" in forecaster_cls.Config.model_fields: - config = config.model_copy(update={"hyperparams": hyperparams}) - - base_learners.append(config.forecaster_from_config()) - - return base_learners - - @property - @override - def is_fitted(self) -> bool: - return all(x.is_fitted for x in self._base_learners) - - @property - @override - def config(self) -> ForecasterConfig: - return self._config - - @override - def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - """Fit the Hybrid model to the training data. - - Args: - data: Training data in the expected ForecastInputDataset format. - data_val: Validation data for tuning the model (optional, not used in this implementation). - - """ - # Fit base learners - [x.fit(data=data, data_val=data_val) for x in self._base_learners] - - # Reset forecast start date to ensure we predict on the full dataset - full_dataset = ForecastInputDataset( - data=data.data, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.index[0], - ) - - base_predictions = self._predict_base_learners(data=full_dataset) - - quantile_datasets = self._prepare_input_final_learner( - base_predictions=base_predictions, quantiles=self._config.quantiles, target_series=data.target_series - ) - - self._final_learner.fit( - base_learner_predictions=quantile_datasets, - ) - - self._is_fitted = True - - def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLearner], ForecastDataset]: - """Generate predictions from base learners. - - Args: - data: Input data for prediction. - - Returns: - DataFrame containing base learner predictions. - """ - base_predictions: dict[type[BaseLearner], ForecastDataset] = {} - for learner in self._base_learners: - preds = learner.predict(data=data) - base_predictions[learner.__class__] = preds - - return base_predictions - - @staticmethod - def _prepare_input_final_learner( - quantiles: list[Quantile], - base_predictions: dict[type[BaseLearner], ForecastDataset], - target_series: pd.Series, - ) -> dict[Quantile, ForecastInputDataset]: - """Prepare input data for the final learner based on base learner predictions. - - Args: - quantiles: List of quantiles to prepare data for. - base_predictions: Predictions from base learners. - target_series: Actual target series for reference. - - Returns: - dictionary mapping quantile strings to DataFrames of base learner predictions. - """ - predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} - sample_interval = base_predictions[next(iter(base_predictions))].sample_interval - target_name = str(target_series.name) - - for q in quantiles: - df = pd.DataFrame({ - learner.__name__: preds.data[Quantile(q).format()] for learner, preds in base_predictions.items() - }) - df[target_name] = target_series - - predictions_quantiles[q] = ForecastInputDataset( - data=df, - sample_interval=sample_interval, - target_column=target_name, - forecast_start=df.index[0], - ) - - return predictions_quantiles - - @override - def predict(self, data: ForecastInputDataset) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - base_predictions = self._predict_base_learners(data=data) - - final_learner_input = self._prepare_input_final_learner( - quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series - ) - - return self._final_learner.predict(base_learner_predictions=final_learner_input) - - # TODO(@Lars800): #745: Make forecaster Explainable - - -__all__ = ["HybridForecaster", "HybridForecasterConfig", "HybridHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py b/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py new file mode 100644 index 000000000..9ef8b6fdf --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py @@ -0,0 +1,7 @@ +from .meta_forecaster import FinalLearner, MetaForecaster, MetaHyperParams + +__all__ = [ + "FinalLearner", + "MetaForecaster", + "MetaHyperParams", +] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py new file mode 100644 index 000000000..62d00a488 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py @@ -0,0 +1,183 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). + +Provides method that attempts to combine the advantages of a linear model (Extraplolation) +and tree-based model (Non-linear patterns). This is acieved by training two base learners, +followed by a small linear model that regresses on the baselearners' predictions. +The implementation is based on sklearn's StackingRegressor. +""" + +import logging +from typing import override + +import pandas as pd +from lightgbm import LGBMClassifier +from pydantic import Field + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.types import Quantile +from openstef_models.models.forecasting.forecaster import ( + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.meta.meta_forecaster import ( + BaseLearner, + BaseLearnerHyperParams, + FinalLearner, + MetaForecaster, + MetaHyperParams, +) + +logger = logging.getLogger(__name__) + + +def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: + """Calculate pinball loss for given true and predicted values. + + Args: + y_true: True values as a pandas Series. + y_pred: Predicted values as a pandas Series. + alpha: Quantile value. + + Returns: + A pandas Series containing the pinball loss for each sample. + """ + diff = y_true - y_pred + sign = (diff >= 0).astype(float) + return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff + + +class LearnedWeightsFinalLearner(FinalLearner): + """Combines base learner predictions with a classification approach to determine which base learner to use.""" + + def __init__(self, quantiles: list[Quantile]) -> None: + self.quantiles = quantiles + self.models = [LGBMClassifier(class_weight="balanced", n_estimators=20) for _ in quantiles] + self._is_fitted = False + + @override + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + for i, q in enumerate(self.quantiles): + pred = base_learner_predictions[q].data.drop(columns=[base_learner_predictions[q].target_column]) + labels = self._prepare_classification_data( + quantile=q, + target=base_learner_predictions[q].target_series, + predictions=pred, + ) + + self.models[i].fit(X=pred, y=labels) # type: ignore + self._is_fitted = True + + @staticmethod + def _prepare_classification_data(quantile: Quantile, target: pd.Series, predictions: pd.DataFrame) -> pd.Series: + """Selects base learner with lowest error for each sample as target for classification. + + Returns: + pd.Series: Series indicating the base learner with the lowest pinball loss for each sample. + """ + + # Calculate pinball loss for each base learner + def column_pinball_losses(preds: pd.Series) -> pd.Series: + return calculate_pinball_errors(y_true=target, y_pred=preds, alpha=quantile) + + pinball_losses = predictions.apply(column_pinball_losses) + + # For each sample, select the base learner with the lowest pinball loss + return pinball_losses.idxmin(axis=1) + + def _calculate_sample_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: + model = self.models[self.quantiles.index(quantile)] + + return model.predict_proba(X=base_predictions) # type: ignore + + def _generate_predictions_quantile(self, base_predictions: ForecastInputDataset, quantile: Quantile) -> pd.Series: + df = base_predictions.data.drop(columns=[base_predictions.target_column]) + weights = self._calculate_sample_weights_quantile(base_predictions=df, quantile=quantile) + + return df.mul(weights).sum(axis=1) + + @override + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions = pd.DataFrame({ + Quantile(q).format(): self._generate_predictions_quantile(base_predictions=data, quantile=q) + for q, data in base_learner_predictions.items() + }) + + return ForecastDataset( + data=predictions, + sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + ) + + @property + def is_fitted(self) -> bool: + return self._is_fitted + + +class LearnedWeightsHyperParams(MetaHyperParams): + """Hyperparameters for Stacked LGBM GBLinear Regressor.""" + + base_hyperparams: list[BaseLearnerHyperParams] = Field( + default=[LGBMHyperParams(), GBLinearHyperParams()], + description="List of hyperparameter configurations for base learners. " + "Defaults to [LGBMHyperParams, GBLinearHyperParams].", + ) + + final_hyperparams: BaseLearnerHyperParams = Field( + default=GBLinearHyperParams(), + description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", + ) + + use_classifier: bool = Field( + default=True, + description="Whether to use sample weights when fitting base and final learners. Defaults to False.", + ) + + add_rolling_accuracy_features: bool = Field( + default=False, + description="Whether to add rolling accuracy features from base learners as additional features " + "to the final learner. Defaults to False.", + ) + + +class LearnedWeightsForecasterConfig(ForecasterConfig): + """Configuration for Hybrid-based forecasting models.""" + + hyperparams: LearnedWeightsHyperParams = LearnedWeightsHyperParams() + + verbosity: bool = Field( + default=True, + description="Enable verbose output from the Hybrid model (True/False).", + ) + + +class LearnedWeightsForecaster(MetaForecaster): + """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + + Config = LearnedWeightsForecasterConfig + HyperParams = LearnedWeightsHyperParams + + def __init__(self, config: LearnedWeightsForecasterConfig) -> None: + """Initialize the LearnedWeightsForecaster.""" + self._config = config + + self._base_learners: list[BaseLearner] = self._init_base_learners( + base_hyperparams=config.hyperparams.base_hyperparams + ) + self._final_learner = LearnedWeightsFinalLearner(quantiles=config.quantiles) + + # TODO(@Lars800): #745: Make forecaster Explainable + + +__all__ = ["LearnedWeightsForecaster", "LearnedWeightsForecasterConfig", "LearnedWeightsHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py new file mode 100644 index 000000000..07b58501f --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py @@ -0,0 +1,240 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Core meta model interfaces and configurations. + +Provides the fundamental building blocks for implementing meta models in OpenSTEF. +These mixins establish contracts that ensure consistent behavior across different meta model types +while ensuring full compatability with regular Forecasters. +""" + +import logging +from abc import abstractmethod +from typing import override + +import pandas as pd +from pydantic import field_validator + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_core.types import Quantile +from openstef_models.models.forecasting.forecaster import ( + Forecaster, + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearForecaster, + GBLinearForecasterConfig, + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMForecasterConfig, LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import ( + LGBMLinearForecaster, + LGBMLinearForecasterConfig, + LGBMLinearHyperParams, +) +from openstef_models.models.forecasting.xgboost_forecaster import ( + XGBoostForecaster, + XGBoostForecasterConfig, + XGBoostHyperParams, +) + +logger = logging.getLogger(__name__) + + +BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster +BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams +BaseLearnerConfig = ( + LGBMForecasterConfig | LGBMLinearForecasterConfig | XGBoostForecasterConfig | GBLinearForecasterConfig +) + + +class FinalLearner: + """Combines base learner predictions for each quantile into final predictions.""" + + @abstractmethod + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + """Fit the final learner using base learner predictions. + + Args: + base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner + """ + raise NotImplementedError("Subclasses must implement the fit method.") + + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + """Generate final predictions based on base learner predictions. + + Args: + base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner + predictions. + + Returns: + ForecastDataset containing the final predictions. + """ + raise NotImplementedError("Subclasses must implement the predict method.") + + @property + @abstractmethod + def is_fitted(self) -> bool: + """Indicates whether the final learner has been fitted.""" + raise NotImplementedError("Subclasses must implement the is_fitted property.") + + +class MetaHyperParams(HyperParams): + """Hyperparameters for Stacked LGBM GBLinear Regressor.""" + + base_hyperparams: list[BaseLearnerHyperParams] + + @field_validator("base_hyperparams", mode="after") + @classmethod + def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: + hp_classes = [type(hp) for hp in v] + if not len(hp_classes) == len(set(hp_classes)): + raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") + return v + + +class MetaForecaster(Forecaster): + """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + + _config: ForecasterConfig + _base_learners: list[BaseLearner] + _final_learner: FinalLearner + + def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: + """Initialize base learners based on provided hyperparameters. + + Returns: + list[Forecaster]: List of initialized base learner forecasters. + """ + base_learners: list[BaseLearner] = [] + horizons = self.config.horizons + quantiles = self.config.quantiles + + for hyperparams in base_hyperparams: + forecaster_cls = hyperparams.forecaster_class() + config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) + if "hyperparams" in forecaster_cls.Config.model_fields: + config = config.model_copy(update={"hyperparams": hyperparams}) + + base_learners.append(config.forecaster_from_config()) + + return base_learners + + @property + @override + def is_fitted(self) -> bool: + return all(x.is_fitted for x in self._base_learners) and self._final_learner.is_fitted + + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + """Fit the Hybrid model to the training data. + + Args: + data: Training data in the expected ForecastInputDataset format. + data_val: Validation data for tuning the model (optional, not used in this implementation). + + """ + # Fit base learners + [x.fit(data=data, data_val=data_val) for x in self._base_learners] + + # Reset forecast start date to ensure we predict on the full dataset + full_dataset = ForecastInputDataset( + data=data.data, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.index[0], + ) + + base_predictions = self._predict_base_learners(data=full_dataset) + + quantile_datasets = self._prepare_input_final_learner( + base_predictions=base_predictions, quantiles=self._config.quantiles, target_series=data.target_series + ) + + self._final_learner.fit( + base_learner_predictions=quantile_datasets, + ) + + self._is_fitted = True + + def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLearner], ForecastDataset]: + """Generate predictions from base learners. + + Args: + data: Input data for prediction. + + Returns: + DataFrame containing base learner predictions. + """ + base_predictions: dict[type[BaseLearner], ForecastDataset] = {} + for learner in self._base_learners: + preds = learner.predict(data=data) + base_predictions[learner.__class__] = preds + + return base_predictions + + @staticmethod + def _prepare_input_final_learner( + quantiles: list[Quantile], + base_predictions: dict[type[BaseLearner], ForecastDataset], + target_series: pd.Series, + ) -> dict[Quantile, ForecastInputDataset]: + """Prepare input data for the final learner based on base learner predictions. + + Args: + quantiles: List of quantiles to prepare data for. + base_predictions: Predictions from base learners. + target_series: Actual target series for reference. + + Returns: + dictionary mapping Quantiles to ForecastInputDatasets. + """ + predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} + sample_interval = base_predictions[next(iter(base_predictions))].sample_interval + target_name = str(target_series.name) + + for q in quantiles: + df = pd.DataFrame({ + learner.__name__: preds.data[Quantile(q).format()] for learner, preds in base_predictions.items() + }) + df[target_name] = target_series + + predictions_quantiles[q] = ForecastInputDataset( + data=df, + sample_interval=sample_interval, + target_column=target_name, + forecast_start=df.index[0], + ) + + return predictions_quantiles + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + base_predictions = self._predict_base_learners(data=data) + + final_learner_input = self._prepare_input_final_learner( + quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series + ) + + return self._final_learner.predict(base_learner_predictions=final_learner_input) + + +__all__ = [ + "BaseLearner", + "BaseLearnerConfig", + "BaseLearnerHyperParams", + "FinalLearner", + "MetaForecaster", +] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py new file mode 100644 index 000000000..73debe3c7 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py @@ -0,0 +1,161 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). + +Provides method that attempts to combine the advantages of a linear model (Extraplolation) +and tree-based model (Non-linear patterns). This is acieved by training two base learners, +followed by a small linear model that regresses on the baselearners' predictions. +The implementation is based on sklearn's StackingRegressor. +""" + +import logging +from typing import override + +import pandas as pd +from pydantic import Field, field_validator + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_core.types import Quantile +from openstef_models.models.forecasting.forecaster import ( + Forecaster, + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.meta.meta_forecaster import ( + BaseLearner, + BaseLearnerHyperParams, + FinalLearner, + MetaForecaster, +) + +logger = logging.getLogger(__name__) + + +class StackingFinalLearner(FinalLearner): + """Combines base learner predictions per quantile into final predictions using a regression approach.""" + + def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: + """Initialize the Stacking final learner. + + Args: + forecaster: The forecaster model to be used as the final learner. + feature_adders: Placeholder for future feature adders (not yet implemented). + """ + # Feature adders placeholder for future use + if feature_adders is not None: + raise NotImplementedError("Feature adders are not yet implemented.") + + # Split forecaster per quantile + self.quantiles = forecaster.config.quantiles + models: list[Forecaster] = [] + for q in self.quantiles: + config = forecaster.config.model_copy( + update={ + "quantiles": [q], + } + ) + model = forecaster.__class__(config=config) + models.append(model) + self.models = models + + @override + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + for i, q in enumerate(self.quantiles): + self.models[i].fit(data=base_learner_predictions[q], data_val=None) + + @override + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions = [ + self.models[i].predict(data=base_learner_predictions[q]).data for i, q in enumerate(self.quantiles) + ] + + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + return ForecastDataset( + data=df, + sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + ) + + @property + def is_fitted(self) -> bool: + """Check the StackingFinalLearner is fitted.""" + return all(x.is_fitted for x in self.models) + + +class StackingHyperParams(HyperParams): + """Hyperparameters for Stacked LGBM GBLinear Regressor.""" + + base_hyperparams: list[BaseLearnerHyperParams] = Field( + default=[LGBMHyperParams(), GBLinearHyperParams()], + description="List of hyperparameter configurations for base learners. " + "Defaults to [LGBMHyperParams, GBLinearHyperParams].", + ) + + final_hyperparams: BaseLearnerHyperParams = Field( + default=GBLinearHyperParams(), + description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", + ) + + use_classifier: bool = Field( + default=True, + description="Whether to use sample weights when fitting base and final learners. Defaults to False.", + ) + + add_rolling_accuracy_features: bool = Field( + default=False, + description="Whether to add rolling accuracy features from base learners as additional features " + "to the final learner. Defaults to False.", + ) + + @field_validator("base_hyperparams", mode="after") + @classmethod + def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: + hp_classes = [type(hp) for hp in v] + if not len(hp_classes) == len(set(hp_classes)): + raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") + return v + + +class StackingForecasterConfig(ForecasterConfig): + """Configuration for Hybrid-based forecasting models.""" + + hyperparams: StackingHyperParams = StackingHyperParams() + + verbosity: bool = Field( + default=True, + description="Enable verbose output from the Hybrid model (True/False).", + ) + + +class StackingForecaster(MetaForecaster): + """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + + Config = StackingForecasterConfig + HyperParams = StackingHyperParams + + def __init__(self, config: StackingForecasterConfig) -> None: + """Initialize the Hybrid forecaster.""" + self._config = config + + self._base_learners: list[BaseLearner] = self._init_base_learners( + base_hyperparams=config.hyperparams.base_hyperparams + ) + + final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] + self._final_learner = StackingFinalLearner(forecaster=final_forecaster) + + +__all__ = ["StackingFinalLearner", "StackingForecaster", "StackingForecasterConfig", "StackingHyperParams"] diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 1a33b4622..dd124b414 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -30,9 +30,10 @@ from openstef_models.models import ForecastingModel from openstef_models.models.forecasting.flatliner_forecaster import FlatlinerForecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster -from openstef_models.models.forecasting.hybrid_forecaster import HybridForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster +from openstef_models.models.forecasting.meta.learned_weights_forecaster import LearnedWeightsForecaster +from openstef_models.models.forecasting.meta.stacking_forecaster import StackingForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, Imputer, NaNDropper, SampleWeighter, Scaler @@ -100,7 +101,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_id: ModelIdentifier = Field(description="Unique identifier for the forecasting model.") # Model configuration - model: Literal["xgboost", "gblinear", "flatliner", "hybrid", "lgbm", "lgbmlinear"] = Field( + model: Literal["xgboost", "gblinear", "flatliner", "stacking", "learned_weights", "lgbm", "lgbmlinear"] = Field( description="Type of forecasting model to use." ) # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( @@ -136,9 +137,14 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Hyperparameters for LightGBM forecaster.", ) - hybrid_hyperparams: HybridForecaster.HyperParams = Field( - default=HybridForecaster.HyperParams(), - description="Hyperparameters for Hybrid forecaster.", + stacking_hyperparams: StackingForecaster.HyperParams = Field( + default=StackingForecaster.HyperParams(), + description="Hyperparameters for Stacking forecaster.", + ) + + learned_weights_hyperparams: LearnedWeightsForecaster.HyperParams = Field( + default=LearnedWeightsForecaster.HyperParams(), + description="Hyperparameters for Learned Weights forecaster.", ) location: LocationConfig = Field( @@ -202,7 +208,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) sample_weight_exponent: float = Field( default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "hybrid", "xgboost"} + if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "xgboost"} else 0.0, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " @@ -301,9 +307,10 @@ def create_forecasting_workflow( LagsAdder( history_available=config.predict_history, horizons=config.horizons, - add_trivial_lags=config.model not in {"gblinear", "hybrid"}, # GBLinear uses only 7day lag. + add_trivial_lags=config.model + not in {"gblinear", "stacking", "learned_weights"}, # GBLinear uses only 7day lag. target_column=config.target_column, - custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "hybrid"} else [], + custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "learned_weights"} else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, @@ -425,7 +432,29 @@ def create_forecasting_workflow( postprocessing = [ ConfidenceIntervalApplicator(quantiles=config.quantiles), ] - elif config.model == "hybrid": + elif config.model == "learned_weights": + preprocessing = [ + *checks, + *feature_adders, + *feature_standardizers, + Imputer( + selection=Exclude(config.target_column), + imputation_strategy="mean", + fill_future_values=Include(config.energy_price_column), + ), + NaNDropper( + selection=Exclude(config.target_column), + ), + ] + forecaster = LearnedWeightsForecaster( + config=LearnedWeightsForecaster.Config( + quantiles=config.quantiles, + horizons=config.horizons, + hyperparams=config.learned_weights_hyperparams, + ) + ) + postprocessing = [QuantileSorter()] + elif config.model == "stacking": preprocessing = [ *checks, *feature_adders, @@ -439,11 +468,11 @@ def create_forecasting_workflow( selection=Exclude(config.target_column), ), ] - forecaster = HybridForecaster( - config=HybridForecaster.Config( + forecaster = StackingForecaster( + config=StackingForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, - hyperparams=config.hybrid_hyperparams, + hyperparams=config.stacking_hyperparams, ) ) postprocessing = [QuantileSorter()] diff --git a/packages/openstef-models/tests/unit/estimators/__init__.py b/packages/openstef-models/tests/unit/estimators/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/packages/openstef-models/tests/unit/estimators/test_hybrid.py b/packages/openstef-models/tests/unit/estimators/test_hybrid.py deleted file mode 100644 index 4c8a1ac97..000000000 --- a/packages/openstef-models/tests/unit/estimators/test_hybrid.py +++ /dev/null @@ -1,43 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -import pandas as pd -import pytest -from numpy.random import default_rng - -from openstef_models.estimators.hybrid import HybridQuantileRegressor - - -@pytest.fixture -def dataset() -> tuple[pd.DataFrame, pd.Series]: - n_samples = 100 - n_features = 5 - rng = default_rng() - X = pd.DataFrame(rng.random((n_samples, n_features))) - y = pd.Series(rng.random(n_samples)) - return X, y - - -def test_init_sets_quantiles_and_models(): - quantiles = [0.1, 0.5, 0.9] - model = HybridQuantileRegressor(quantiles=quantiles) - assert model.quantiles == quantiles - assert len(model._models) == len(quantiles) - - -def test_fit_and_predict_shape(dataset: tuple[pd.DataFrame, pd.Series]): - quantiles = [0.1, 0.5, 0.9] - X, y = dataset[0], dataset[1] - model = HybridQuantileRegressor(quantiles=quantiles) - model.fit(X, y) - preds = model.predict(X) - assert preds.shape == (X.shape[0], len(quantiles)) - - -def test_is_fitted(dataset: tuple[pd.DataFrame, pd.Series]): - quantiles = [0.1, 0.5, 0.9] - X, y = dataset[0], dataset[1] - model = HybridQuantileRegressor(quantiles=quantiles) - model.fit(X, y) - assert model.is_fitted diff --git a/packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py new file mode 100644 index 000000000..f227d1977 --- /dev/null +++ b/packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py @@ -0,0 +1,105 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_models.models.forecasting.meta.learned_weights_forecaster import ( + LearnedWeightsForecaster, + LearnedWeightsForecasterConfig, + LearnedWeightsHyperParams, +) + + +@pytest.fixture +def base_config() -> LearnedWeightsForecasterConfig: + """Base configuration for LearnedWeights forecaster tests.""" + + params = LearnedWeightsHyperParams() + return LearnedWeightsForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=params, + verbosity=False, + ) + + +def test_learned_weights_forecaster_fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LearnedWeightsForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = LearnedWeightsForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +def test_learned_weights_forecaster_predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LearnedWeightsForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = LearnedWeightsForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError, match="LearnedWeightsForecaster"): + forecaster.predict(sample_forecast_input_dataset) + + +def test_learned_weights_forecaster_with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: LearnedWeightsForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = LearnedWeightsForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = LearnedWeightsForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py similarity index 77% rename from packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py rename to packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py index 4e36e125d..416f36ab9 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py @@ -9,19 +9,19 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.hybrid_forecaster import ( - HybridForecaster, - HybridForecasterConfig, - HybridHyperParams, +from openstef_models.models.forecasting.meta.stacking_forecaster import ( + StackingForecaster, + StackingForecasterConfig, + StackingHyperParams, ) @pytest.fixture -def base_config() -> HybridForecasterConfig: - """Base configuration for Hybrid forecaster tests.""" +def base_config() -> StackingForecasterConfig: + """Base configuration for Stacking forecaster tests.""" - params = HybridHyperParams() - return HybridForecasterConfig( + params = StackingHyperParams() + return StackingForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], hyperparams=params, @@ -29,14 +29,14 @@ def base_config() -> HybridForecasterConfig: ) -def test_hybrid_forecaster_fit_predict( +def test_stacking_forecaster_fit_predict( sample_forecast_input_dataset: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: StackingForecasterConfig, ): """Test basic fit and predict workflow with comprehensive output validation.""" # Arrange expected_quantiles = base_config.quantiles - forecaster = HybridForecaster(config=base_config) + forecaster = StackingForecaster(config=base_config) # Act forecaster.fit(sample_forecast_input_dataset) @@ -56,26 +56,26 @@ def test_hybrid_forecaster_fit_predict( assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" -def test_hybrid_forecaster_predict_not_fitted_raises_error( +def test_stacking_forecaster_predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: StackingForecasterConfig, ): """Test that predict() raises NotFittedError when called before fit().""" # Arrange - forecaster = HybridForecaster(config=base_config) + forecaster = StackingForecaster(config=base_config) # Act & Assert - with pytest.raises(NotFittedError, match="HybridForecaster"): + with pytest.raises(NotFittedError, match="StackingForecaster"): forecaster.predict(sample_forecast_input_dataset) -def test_hybrid_forecaster_with_sample_weights( +def test_stacking_forecaster_with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: StackingForecasterConfig, ): """Test that forecaster works with sample weights and produces different results.""" # Arrange - forecaster_with_weights = HybridForecaster(config=base_config) + forecaster_with_weights = StackingForecaster(config=base_config) # Create dataset without weights for comparison data_without_weights = ForecastInputDataset( @@ -84,7 +84,7 @@ def test_hybrid_forecaster_with_sample_weights( target_column=sample_dataset_with_weights.target_column, forecast_start=sample_dataset_with_weights.forecast_start, ) - forecaster_without_weights = HybridForecaster(config=base_config) + forecaster_without_weights = StackingForecaster(config=base_config) # Act forecaster_with_weights.fit(sample_dataset_with_weights) From 72b1ca7ffa02d9c194913220d342ffba9dc44add Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 21 Nov 2025 20:04:43 +0100 Subject: [PATCH 26/72] fix merge issue Signed-off-by: Lars van Someren --- .../src/openstef_models/presets/forecasting_workflow.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 6616904b2..99cbea0ac 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -241,8 +241,6 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob mlflow_storage: MLFlowStorage | None = Field( default_factory=MLFlowStorage, description="Configuration for MLflow experiment tracking and model storage.", - default_factory=MLFlowStorage, - description="Configuration for MLflow experiment tracking and model storage.", ) model_reuse_enable: bool = Field( @@ -252,15 +250,11 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_reuse_max_age: timedelta = Field( default=timedelta(days=7), description="Maximum age of a model to be considered for reuse.", - default=timedelta(days=7), - description="Maximum age of a model to be considered for reuse.", ) model_selection_enable: bool = Field( default=True, description="Whether to enable automatic model selection based on performance.", - default=True, - description="Whether to enable automatic model selection based on performance.", ) model_selection_metric: tuple[QuantileOrGlobal, str, MetricDirection] = Field( default=(Q(0.5), "R2", "higher_is_better"), @@ -316,7 +310,7 @@ def create_forecasting_workflow( add_trivial_lags=config.model not in {"gblinear", "stacking", "learned_weights"}, # GBLinear uses only 7day lag. target_column=config.target_column, - custom_lags=[timedelta(days=7)] if config.model in {"gblinear","stacking" "learned_weights"} else [], + custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "stackinglearned_weights"} else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, From 553e2fdf1ac148293a9dd37f80d17283a4117831 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 21 Nov 2025 20:08:39 +0100 Subject: [PATCH 27/72] Fixed type Issues Signed-off-by: Lars van Someren --- .../benchmarks/liander_2024_benchmark_xgboost_gblinear.py | 2 +- .../src/openstef_models/models/forecasting/meta/__init__.py | 5 +++++ .../tests/unit/models/forecasting/meta/__init__.py | 0 3 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 packages/openstef-models/tests/unit/models/forecasting/meta/__init__.py diff --git a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py index 4ff925cce..63ad9baff 100644 --- a/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py +++ b/examples/benchmarks/liander_2024_benchmark_xgboost_gblinear.py @@ -45,7 +45,7 @@ BENCHMARK_RESULTS_PATH_XGBOOST = OUTPUT_PATH / "XGBoost" BENCHMARK_RESULTS_PATH_GBLINEAR = OUTPUT_PATH / "GBLinear" -N_PROCESSES = 1 # Amount of parallel processes to use for the benchmark +N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark # Model configuration diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py b/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py index 9ef8b6fdf..996e37d1a 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py @@ -1,3 +1,8 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" + from .meta_forecaster import FinalLearner, MetaForecaster, MetaHyperParams __all__ = [ diff --git a/packages/openstef-models/tests/unit/models/forecasting/meta/__init__.py b/packages/openstef-models/tests/unit/models/forecasting/meta/__init__.py new file mode 100644 index 000000000..e69de29bb From f873f892105327b383474791ab21a1026d4c6965 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 24 Nov 2025 11:59:07 +0100 Subject: [PATCH 28/72] Introduced openstef_metalearning Signed-off-by: Lars van Someren --- packages/openstef-metalearning/README.md | 0 packages/openstef-metalearning/pyproject.toml | 15 +++++ .../src/openstef_metalearning/__init__.py | 13 ++++ .../openstef_metalearning/models}/__init__.py | 0 .../models}/learned_weights_forecaster.py | 14 ++-- .../models}/meta_forecaster.py | 0 .../models}/stacking_forecaster.py | 12 ++-- .../general/distribution_transform.py | 65 +++++++++++++++++++ pyproject.toml | 4 ++ uv.lock | 18 +++++ 10 files changed, 128 insertions(+), 13 deletions(-) create mode 100644 packages/openstef-metalearning/README.md create mode 100644 packages/openstef-metalearning/pyproject.toml create mode 100644 packages/openstef-metalearning/src/openstef_metalearning/__init__.py rename packages/{openstef-models/src/openstef_models/models/forecasting/meta => openstef-metalearning/src/openstef_metalearning/models}/__init__.py (100%) rename packages/{openstef-models/src/openstef_models/models/forecasting/meta => openstef-metalearning/src/openstef_metalearning/models}/learned_weights_forecaster.py (98%) rename packages/{openstef-models/src/openstef_models/models/forecasting/meta => openstef-metalearning/src/openstef_metalearning/models}/meta_forecaster.py (100%) rename packages/{openstef-models/src/openstef_models/models/forecasting/meta => openstef-metalearning/src/openstef_metalearning/models}/stacking_forecaster.py (98%) create mode 100644 packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py diff --git a/packages/openstef-metalearning/README.md b/packages/openstef-metalearning/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-metalearning/pyproject.toml b/packages/openstef-metalearning/pyproject.toml new file mode 100644 index 000000000..31352ea30 --- /dev/null +++ b/packages/openstef-metalearning/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "openstef-metalearning" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = ["openstef-core", "openstef-models"] + +[tool.uv.sources] +openstef-models = { workspace = true } +openstef-core = { workspace = true } + + +[tool.hatch.build.targets.wheel] +packages = ["src/openstef_metalearning"] diff --git a/packages/openstef-metalearning/src/openstef_metalearning/__init__.py b/packages/openstef-metalearning/src/openstef_metalearning/__init__.py new file mode 100644 index 000000000..e659c6c12 --- /dev/null +++ b/packages/openstef-metalearning/src/openstef_metalearning/__init__.py @@ -0,0 +1,13 @@ +# SPDX-FileCopyrightText: 2017-2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Core models for OpenSTEF.""" + +import logging + +# Set up logging configuration +root_logger = logging.getLogger(name=__name__) +if not root_logger.handlers: + root_logger.addHandler(logging.NullHandler()) + +__all__ = [] diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py b/packages/openstef-metalearning/src/openstef_metalearning/models/__init__.py similarity index 100% rename from packages/openstef-models/src/openstef_models/models/forecasting/meta/__init__.py rename to packages/openstef-metalearning/src/openstef_metalearning/models/__init__.py diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py b/packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py similarity index 98% rename from packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py rename to packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py index 62d00a488..f8d22f12b 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/meta/learned_weights_forecaster.py +++ b/packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py @@ -21,6 +21,13 @@ NotFittedError, ) from openstef_core.types import Quantile +from openstef_metalearning.models.meta_forecaster import ( + BaseLearner, + BaseLearnerHyperParams, + FinalLearner, + MetaForecaster, + MetaHyperParams, +) from openstef_models.models.forecasting.forecaster import ( ForecasterConfig, ) @@ -28,13 +35,6 @@ GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -from openstef_models.models.forecasting.meta.meta_forecaster import ( - BaseLearner, - BaseLearnerHyperParams, - FinalLearner, - MetaForecaster, - MetaHyperParams, -) logger = logging.getLogger(__name__) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py b/packages/openstef-metalearning/src/openstef_metalearning/models/meta_forecaster.py similarity index 100% rename from packages/openstef-models/src/openstef_models/models/forecasting/meta/meta_forecaster.py rename to packages/openstef-metalearning/src/openstef_metalearning/models/meta_forecaster.py diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py b/packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py similarity index 98% rename from packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py rename to packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py index 73debe3c7..70130ff07 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/meta/stacking_forecaster.py +++ b/packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py @@ -21,6 +21,12 @@ ) from openstef_core.mixins import HyperParams from openstef_core.types import Quantile +from openstef_metalearning.models.meta_forecaster import ( + BaseLearner, + BaseLearnerHyperParams, + FinalLearner, + MetaForecaster, +) from openstef_models.models.forecasting.forecaster import ( Forecaster, ForecasterConfig, @@ -29,12 +35,6 @@ GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -from openstef_models.models.forecasting.meta.meta_forecaster import ( - BaseLearner, - BaseLearnerHyperParams, - FinalLearner, - MetaForecaster, -) logger = logging.getLogger(__name__) diff --git a/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py b/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py new file mode 100644 index 000000000..8e93da672 --- /dev/null +++ b/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py @@ -0,0 +1,65 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Transform for clipping feature values to observed ranges. + +This module provides functionality to clip feature values to their observed +minimum and maximum ranges during training, preventing out-of-range values +during inference and improving model robustness. +""" + +from typing import Literal, override + +import pandas as pd +from pydantic import Field, PrivateAttr + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.transforms import TimeSeriesTransform +from openstef_models.utils.feature_selection import FeatureSelection + +type ClipMode = Literal["minmax", "standard"] + + +class DistributionTransform(BaseConfig, TimeSeriesTransform): + """Transform dataframe to (robust) percentage of min-max of training data. + + Useful to determine whether datadrift has occured. + Can be used as a feature for learning sample weights in meta models. + """ + + robust_threshold: float = Field( + default=2.0, + description="Percentage of observations to ignore when determing percentage. (Single sided)", + ) + + _feature_mins: pd.Series = PrivateAttr(default_factory=pd.Series) + _feature_maxs: pd.Series = PrivateAttr(default_factory=pd.Series) + _is_fitted: bool = PrivateAttr(default=False) + + @property + @override + def is_fitted(self) -> bool: + return self._is_fitted + + @override + def fit(self, data: TimeSeriesDataset) -> None: + self._feature_mins = data.data.min(axis=0) + self._feature_maxs = data.data.max(axis=0) + self._is_fitted = True + + @override + def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: + if not self._is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Apply min-max scaling to each feature based on fitted min and max + transformed_data = (data.data - self._feature_mins) / (self._feature_maxs - self._feature_mins) + + return TimeSeriesDataset(data=transformed_data, sample_interval=data.sample_interval) + + @override + def features_added(self) -> list[str]: + return [] diff --git a/pyproject.toml b/pyproject.toml index 87ef62841..1ef0cea1c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,7 @@ optional-dependencies.beam = [ ] optional-dependencies.models = [ "openstef-models[xgb-cpu]", + "openstef-metalearning", ] urls.Documentation = "https://openstef.github.io/openstef/index.html" urls.Homepage = "https://lfenergy.org/projects/openstef/" @@ -77,6 +78,7 @@ openstef-beam = { workspace = true } openstef-models = { workspace = true } openstef-docs = { workspace = true } openstef-core = { workspace = true } +openstef-metalearning = { workspace = true } microsoft-python-type-stubs = { git = "git+https://github.com/microsoft/python-type-stubs.git" } [tool.uv.workspace] @@ -85,6 +87,7 @@ members = [ "packages/openstef-beam", "docs", "packages/openstef-core", + "packages/openstef-metalearning", ] [tool.ruff] @@ -190,6 +193,7 @@ source = [ "packages/openstef-beam/src", "packages/openstef-models/src", "packages/openstef-core/src", + "packages/openstef-metalearning/src", ] omit = [ "tests/*", diff --git a/uv.lock b/uv.lock index 013babc38..520b8f9d9 100644 --- a/uv.lock +++ b/uv.lock @@ -8,6 +8,7 @@ members = [ "openstef-beam", "openstef-core", "openstef-docs", + "openstef-metalearning", "openstef-models", ] @@ -2124,6 +2125,7 @@ beam = [ { name = "openstef-beam" }, ] models = [ + { name = "openstef-metalearning" }, { name = "openstef-models", extra = ["xgb-cpu"] }, ] @@ -2155,6 +2157,7 @@ requires-dist = [ { name = "openstef-beam", extras = ["all"], marker = "extra == 'all'", editable = "packages/openstef-beam" }, { name = "openstef-core", editable = "packages/openstef-core" }, { name = "openstef-core", marker = "extra == 'all'", editable = "packages/openstef-core" }, + { name = "openstef-metalearning", marker = "extra == 'models'", editable = "packages/openstef-metalearning" }, { name = "openstef-models", extras = ["xgb-cpu"], editable = "packages/openstef-models" }, { name = "openstef-models", extras = ["xgb-cpu"], marker = "extra == 'all'", editable = "packages/openstef-models" }, { name = "openstef-models", extras = ["xgb-cpu"], marker = "extra == 'models'", editable = "packages/openstef-models" }, @@ -2276,6 +2279,21 @@ requires-dist = [ { name = "sphinx-pyproject", specifier = ">=0.3.0" }, ] +[[package]] +name = "openstef-metalearning" +version = "0.1.0" +source = { editable = "packages/openstef-metalearning" } +dependencies = [ + { name = "openstef-core" }, + { name = "openstef-models" }, +] + +[package.metadata] +requires-dist = [ + { name = "openstef-core", editable = "packages/openstef-core" }, + { name = "openstef-models", editable = "packages/openstef-models" }, +] + [[package]] name = "openstef-models" version = "0.0.0" From 3338be1b40d7e4b8720ab06240c269ef601db7cf Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 24 Nov 2025 21:05:22 +0100 Subject: [PATCH 29/72] ResidualForecaster + refactoring Signed-off-by: Lars van Someren --- .../README.md | 0 .../pyproject.toml | 4 +- .../src/openstef_meta}/__init__.py | 0 .../src/openstef_meta/framework}/__init__.py | 8 +- .../openstef_meta/framework/base_learner.py | 26 ++ .../openstef_meta/framework/final_learner.py | 54 +++ .../framework}/meta_forecaster.py | 103 ++---- .../src/openstef_meta/models/__init__.py | 24 ++ .../models/learned_weights_forecaster.py | 109 ++++--- .../models/residual_forecaster.py | 235 +++++++++++++ .../models/stacking_forecaster.py | 16 +- .../src/openstef_meta/utils}/__init__.py | 0 .../src/openstef_meta/utils/pinball_errors.py | 22 ++ .../openstef-meta/tests/models/__init__.py | 0 .../openstef-meta/tests/models/conftest.py | 59 ++++ .../test_learned_weights_forecaster.py | 2 +- .../tests/models/test_residual_forecaster.py} | 77 +++-- .../tests/models}/test_stacking_forecaster.py | 2 +- .../models/forecasting/hybrid_forecaster.py | 308 ------------------ pyproject.toml | 8 +- uv.lock | 12 +- 21 files changed, 600 insertions(+), 469 deletions(-) rename packages/{openstef-metalearning => openstef-meta}/README.md (100%) rename packages/{openstef-metalearning => openstef-meta}/pyproject.toml (80%) rename packages/{openstef-metalearning/src/openstef_metalearning => openstef-meta/src/openstef_meta}/__init__.py (100%) rename packages/{openstef-metalearning/src/openstef_metalearning/models => openstef-meta/src/openstef_meta/framework}/__init__.py (55%) create mode 100644 packages/openstef-meta/src/openstef_meta/framework/base_learner.py create mode 100644 packages/openstef-meta/src/openstef_meta/framework/final_learner.py rename packages/{openstef-metalearning/src/openstef_metalearning/models => openstef-meta/src/openstef_meta/framework}/meta_forecaster.py (64%) create mode 100644 packages/openstef-meta/src/openstef_meta/models/__init__.py rename packages/{openstef-metalearning/src/openstef_metalearning => openstef-meta/src/openstef_meta}/models/learned_weights_forecaster.py (66%) create mode 100644 packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py rename packages/{openstef-metalearning/src/openstef_metalearning => openstef-meta/src/openstef_meta}/models/stacking_forecaster.py (92%) rename packages/{openstef-models/tests/unit/models/forecasting/meta => openstef-meta/src/openstef_meta/utils}/__init__.py (100%) create mode 100644 packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py create mode 100644 packages/openstef-meta/tests/models/__init__.py create mode 100644 packages/openstef-meta/tests/models/conftest.py rename packages/{openstef-models/tests/unit/models/forecasting/meta => openstef-meta/tests/models}/test_learned_weights_forecaster.py (98%) rename packages/{openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py => openstef-meta/tests/models/test_residual_forecaster.py} (56%) rename packages/{openstef-models/tests/unit/models/forecasting/meta => openstef-meta/tests/models}/test_stacking_forecaster.py (98%) delete mode 100644 packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py diff --git a/packages/openstef-metalearning/README.md b/packages/openstef-meta/README.md similarity index 100% rename from packages/openstef-metalearning/README.md rename to packages/openstef-meta/README.md diff --git a/packages/openstef-metalearning/pyproject.toml b/packages/openstef-meta/pyproject.toml similarity index 80% rename from packages/openstef-metalearning/pyproject.toml rename to packages/openstef-meta/pyproject.toml index 31352ea30..a91b25359 100644 --- a/packages/openstef-metalearning/pyproject.toml +++ b/packages/openstef-meta/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "openstef-metalearning" +name = "openstef-meta" version = "0.1.0" description = "Add your description here" readme = "README.md" @@ -12,4 +12,4 @@ openstef-core = { workspace = true } [tool.hatch.build.targets.wheel] -packages = ["src/openstef_metalearning"] +packages = ["src/openstef_meta"] diff --git a/packages/openstef-metalearning/src/openstef_metalearning/__init__.py b/packages/openstef-meta/src/openstef_meta/__init__.py similarity index 100% rename from packages/openstef-metalearning/src/openstef_metalearning/__init__.py rename to packages/openstef-meta/src/openstef_meta/__init__.py diff --git a/packages/openstef-metalearning/src/openstef_metalearning/models/__init__.py b/packages/openstef-meta/src/openstef_meta/framework/__init__.py similarity index 55% rename from packages/openstef-metalearning/src/openstef_metalearning/models/__init__.py rename to packages/openstef-meta/src/openstef_meta/framework/__init__.py index 996e37d1a..e64377d16 100644 --- a/packages/openstef-metalearning/src/openstef_metalearning/models/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/framework/__init__.py @@ -3,10 +3,14 @@ # SPDX-License-Identifier: MPL-2.0 """This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" -from .meta_forecaster import FinalLearner, MetaForecaster, MetaHyperParams +from .base_learner import BaseLearner, BaseLearnerHyperParams +from .final_learner import FinalLearner, FinalLearnerHyperParams +from .meta_forecaster import MetaForecaster __all__ = [ + "BaseLearner", + "BaseLearnerHyperParams", "FinalLearner", + "FinalLearnerHyperParams", "MetaForecaster", - "MetaHyperParams", ] diff --git a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py new file mode 100644 index 000000000..36688b419 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py @@ -0,0 +1,26 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Core meta model interfaces and configurations. + +Provides the fundamental building blocks for implementing meta models in OpenSTEF. +These mixins establish contracts that ensure consistent behavior across different meta model types +while ensuring full compatability with regular Forecasters. +""" + +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearForecaster, + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import ( + LGBMLinearForecaster, + LGBMLinearHyperParams, +) +from openstef_models.models.forecasting.xgboost_forecaster import ( + XGBoostForecaster, + XGBoostHyperParams, +) + +BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster +BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py new file mode 100644 index 000000000..26567e156 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -0,0 +1,54 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Core meta model interfaces and configurations. + +Provides the fundamental building blocks for implementing meta models in OpenSTEF. +These mixins establish contracts that ensure consistent behavior across different meta model types +while ensuring full compatability with regular Forecasters. +""" + +from abc import ABC, abstractmethod + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.mixins import HyperParams +from openstef_core.types import Quantile + + +class FinalLearnerHyperParams(HyperParams): + """Hyperparameters for the Final Learner.""" + + +class FinalLearnerConfig: + """Configuration for the Final Learner.""" + + +class FinalLearner(ABC): + """Combines base learner predictions for each quantile into final predictions.""" + + @abstractmethod + def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + """Fit the final learner using base learner predictions. + + Args: + base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner + """ + raise NotImplementedError("Subclasses must implement the fit method.") + + def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + """Generate final predictions based on base learner predictions. + + Args: + base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner + predictions. + + Returns: + ForecastDataset containing the final predictions. + """ + raise NotImplementedError("Subclasses must implement the predict method.") + + @property + @abstractmethod + def is_fitted(self) -> bool: + """Indicates whether the final learner has been fitted.""" + raise NotImplementedError("Subclasses must implement the is_fitted property.") diff --git a/packages/openstef-metalearning/src/openstef_metalearning/models/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py similarity index 64% rename from packages/openstef-metalearning/src/openstef_metalearning/models/meta_forecaster.py rename to packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 07b58501f..5b69e3153 100644 --- a/packages/openstef-metalearning/src/openstef_metalearning/models/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -9,110 +9,45 @@ """ import logging -from abc import abstractmethod from typing import override import pandas as pd -from pydantic import field_validator from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, ) -from openstef_core.mixins import HyperParams from openstef_core.types import Quantile +from openstef_meta.framework.base_learner import ( + BaseLearner, + BaseLearnerHyperParams, +) +from openstef_meta.framework.final_learner import FinalLearner from openstef_models.models.forecasting.forecaster import ( Forecaster, ForecasterConfig, ) -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearForecaster, - GBLinearForecasterConfig, - GBLinearHyperParams, -) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMForecasterConfig, LGBMHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import ( - LGBMLinearForecaster, - LGBMLinearForecasterConfig, - LGBMLinearHyperParams, -) -from openstef_models.models.forecasting.xgboost_forecaster import ( - XGBoostForecaster, - XGBoostForecasterConfig, - XGBoostHyperParams, -) logger = logging.getLogger(__name__) -BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster -BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams -BaseLearnerConfig = ( - LGBMForecasterConfig | LGBMLinearForecasterConfig | XGBoostForecasterConfig | GBLinearForecasterConfig -) - - -class FinalLearner: - """Combines base learner predictions for each quantile into final predictions.""" - - @abstractmethod - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - """Fit the final learner using base learner predictions. - - Args: - base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner - """ - raise NotImplementedError("Subclasses must implement the fit method.") - - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - """Generate final predictions based on base learner predictions. - - Args: - base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner - predictions. - - Returns: - ForecastDataset containing the final predictions. - """ - raise NotImplementedError("Subclasses must implement the predict method.") - - @property - @abstractmethod - def is_fitted(self) -> bool: - """Indicates whether the final learner has been fitted.""" - raise NotImplementedError("Subclasses must implement the is_fitted property.") - - -class MetaHyperParams(HyperParams): - """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - - base_hyperparams: list[BaseLearnerHyperParams] - - @field_validator("base_hyperparams", mode="after") - @classmethod - def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: - hp_classes = [type(hp) for hp in v] - if not len(hp_classes) == len(set(hp_classes)): - raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") - return v - - class MetaForecaster(Forecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + """Abstract class for Meta forecasters combining multiple models.""" _config: ForecasterConfig - _base_learners: list[BaseLearner] - _final_learner: FinalLearner - def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: + @staticmethod + def _init_base_learners( + config: ForecasterConfig, base_hyperparams: list[BaseLearnerHyperParams] + ) -> list[BaseLearner]: """Initialize base learners based on provided hyperparameters. Returns: list[Forecaster]: List of initialized base learner forecasters. """ base_learners: list[BaseLearner] = [] - horizons = self.config.horizons - quantiles = self.config.quantiles + horizons = config.horizons + quantiles = config.quantiles for hyperparams in base_hyperparams: forecaster_cls = hyperparams.forecaster_class() @@ -124,6 +59,19 @@ def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> return base_learners + @property + @override + def config(self) -> ForecasterConfig: + return self._config + + +class EnsembleForecaster(MetaForecaster): + """Abstract class for Meta forecasters combining multiple base learners and a final learner.""" + + _config: ForecasterConfig + _base_learners: list[BaseLearner] + _final_learner: FinalLearner + @property @override def is_fitted(self) -> bool: @@ -233,7 +181,6 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: __all__ = [ "BaseLearner", - "BaseLearnerConfig", "BaseLearnerHyperParams", "FinalLearner", "MetaForecaster", diff --git a/packages/openstef-meta/src/openstef_meta/models/__init__.py b/packages/openstef-meta/src/openstef_meta/models/__init__.py new file mode 100644 index 000000000..614543150 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/__init__.py @@ -0,0 +1,24 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" + +from .learned_weights_forecaster import ( + LearnedWeightsForecaster, + LearnedWeightsForecasterConfig, + LearnedWeightsHyperParams, +) +from .residual_forecaster import ResidualForecaster, ResidualForecasterConfig, ResidualHyperParams +from .stacking_forecaster import StackingForecaster, StackingForecasterConfig, StackingHyperParams + +__all__ = [ + "LearnedWeightsForecaster", + "LearnedWeightsForecasterConfig", + "LearnedWeightsHyperParams", + "ResidualForecaster", + "ResidualForecasterConfig", + "ResidualHyperParams", + "StackingForecaster", + "StackingForecasterConfig", + "StackingHyperParams", +] diff --git a/packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py similarity index 66% rename from packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py rename to packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index f8d22f12b..d606a79f5 100644 --- a/packages/openstef-metalearning/src/openstef_metalearning/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -10,24 +10,30 @@ """ import logging +from abc import abstractmethod from typing import override import pandas as pd from lightgbm import LGBMClassifier from pydantic import Field +from sklearn.linear_model import LinearRegression +from xgboost import XGBClassifier from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( NotFittedError, ) +from openstef_core.mixins import HyperParams from openstef_core.types import Quantile -from openstef_metalearning.models.meta_forecaster import ( +from openstef_meta.framework.base_learner import ( BaseLearner, BaseLearnerHyperParams, - FinalLearner, - MetaForecaster, - MetaHyperParams, ) +from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.framework.meta_forecaster import ( + EnsembleForecaster, +) +from openstef_meta.utils.pinball_errors import calculate_pinball_errors from openstef_models.models.forecasting.forecaster import ( ForecasterConfig, ) @@ -39,28 +45,16 @@ logger = logging.getLogger(__name__) -def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: - """Calculate pinball loss for given true and predicted values. - - Args: - y_true: True values as a pandas Series. - y_pred: Predicted values as a pandas Series. - alpha: Quantile value. - - Returns: - A pandas Series containing the pinball loss for each sample. - """ - diff = y_true - y_pred - sign = (diff >= 0).astype(float) - return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff +Classifier = LGBMClassifier | XGBClassifier | LinearRegression class LearnedWeightsFinalLearner(FinalLearner): """Combines base learner predictions with a classification approach to determine which base learner to use.""" + @abstractmethod def __init__(self, quantiles: list[Quantile]) -> None: self.quantiles = quantiles - self.models = [LGBMClassifier(class_weight="balanced", n_estimators=20) for _ in quantiles] + self.models: list[Classifier] = [] self._is_fitted = False @override @@ -125,7 +119,43 @@ def is_fitted(self) -> bool: return self._is_fitted -class LearnedWeightsHyperParams(MetaHyperParams): +class LGBMFinalLearner(LearnedWeightsFinalLearner): + """Final learner using only LGBM as base learners.""" + + def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: + self.quantiles = quantiles + self.models = [LGBMClassifier(class_weight="balanced", n_estimators=n_estimators) for _ in quantiles] + self._is_fitted = False + + +class RandomForestFinalLearner(LearnedWeightsFinalLearner): + def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: + self.quantiles = quantiles + self.models = [ + LGBMClassifier(boosting_type="rf", class_weight="balanced", n_estimators=n_estimators) for _ in quantiles + ] + self._is_fitted = False + + +class XGBFinalLearner(LearnedWeightsFinalLearner): + """Final learner using only XGBoost as base learners.""" + + def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: + self.quantiles = quantiles + self.models = [XGBClassifier(class_weight="balanced", n_estimators=n_estimators) for _ in quantiles] + self._is_fitted = False + + +class LogisticRegressionFinalLearner(LearnedWeightsFinalLearner): + """Final learner using only Logistic Regression as base learners.""" + + def __init__(self, quantiles: list[Quantile]) -> None: + self.quantiles = quantiles + self.models = [LinearRegression() for _ in quantiles] + self._is_fitted = False + + +class LearnedWeightsHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" base_hyperparams: list[BaseLearnerHyperParams] = Field( @@ -134,20 +164,9 @@ class LearnedWeightsHyperParams(MetaHyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_hyperparams: BaseLearnerHyperParams = Field( - default=GBLinearHyperParams(), - description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", - ) - - use_classifier: bool = Field( - default=True, - description="Whether to use sample weights when fitting base and final learners. Defaults to False.", - ) - - add_rolling_accuracy_features: bool = Field( - default=False, - description="Whether to add rolling accuracy features from base learners as additional features " - "to the final learner. Defaults to False.", + final_learner: type[LearnedWeightsFinalLearner] = Field( + default=LGBMFinalLearner, + description="Type of final learner to use. Defaults to LearnedWeightsFinalLearner.", ) @@ -162,7 +181,7 @@ class LearnedWeightsForecasterConfig(ForecasterConfig): ) -class LearnedWeightsForecaster(MetaForecaster): +class LearnedWeightsForecaster(EnsembleForecaster): """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" Config = LearnedWeightsForecasterConfig @@ -173,11 +192,17 @@ def __init__(self, config: LearnedWeightsForecasterConfig) -> None: self._config = config self._base_learners: list[BaseLearner] = self._init_base_learners( - base_hyperparams=config.hyperparams.base_hyperparams + config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = LearnedWeightsFinalLearner(quantiles=config.quantiles) - - # TODO(@Lars800): #745: Make forecaster Explainable - - -__all__ = ["LearnedWeightsForecaster", "LearnedWeightsForecasterConfig", "LearnedWeightsHyperParams"] + self._final_learner = config.hyperparams.final_learner(quantiles=config.quantiles) + + +__all__ = [ + "LGBMFinalLearner", + "LearnedWeightsForecaster", + "LearnedWeightsForecasterConfig", + "LearnedWeightsHyperParams", + "LogisticRegressionFinalLearner", + "RandomForestFinalLearner", + "XGBFinalLearner", +] diff --git a/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py new file mode 100644 index 000000000..4c0de156b --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py @@ -0,0 +1,235 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). + +Provides method that attempts to combine the advantages of a linear model (Extraplolation) +and tree-based model (Non-linear patterns). This is acieved by training two base learners, +followed by a small linear model that regresses on the baselearners' predictions. +The implementation is based on sklearn's ResidualRegressor. +""" + +import logging +from typing import override + +import pandas as pd +from pydantic import Field + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.exceptions import ( + NotFittedError, +) +from openstef_core.mixins import HyperParams +from openstef_core.types import Quantile +from openstef_meta.framework.base_learner import ( + BaseLearner, + BaseLearnerHyperParams, +) +from openstef_meta.framework.meta_forecaster import ( + MetaForecaster, +) +from openstef_models.models.forecasting.forecaster import ( + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams + +logger = logging.getLogger(__name__) + + +class ResidualHyperParams(HyperParams): + """Hyperparameters for Stacked LGBM GBLinear Regressor.""" + + primary_hyperparams: BaseLearnerHyperParams = Field( + default=GBLinearHyperParams(), + description="Primary model hyperparams. Defaults to GBLinearHyperParams.", + ) + + secondary_hyperparams: BaseLearnerHyperParams = Field( + default=LGBMHyperParams(), + description="Hyperparameters for the final learner. Defaults to LGBMHyperparams.", + ) + + +class ResidualForecasterConfig(ForecasterConfig): + """Configuration for Hybrid-based forecasting models.""" + + hyperparams: ResidualHyperParams = ResidualHyperParams() + + verbosity: bool = Field( + default=True, + description="Enable verbose output from the Hybrid model (True/False).", + ) + + +class ResidualForecaster(MetaForecaster): + """MetaForecaster that implements residual modeling. + + It takes in a primary forecaster and a residual forecaster. The primary forecaster makes initial predictions, + and the residual forecaster models the residuals (errors) of the primary forecaster to improve overall accuracy. + """ + + Config = ResidualForecasterConfig + HyperParams = ResidualHyperParams + + def __init__(self, config: ResidualForecasterConfig) -> None: + """Initialize the Hybrid forecaster.""" + self._config = config + + self._primary_model: BaseLearner = self._init_base_learners( + config=config, base_hyperparams=[config.hyperparams.primary_hyperparams] + )[0] + + self._secondary_model: list[BaseLearner] = self._init_secondary_model( + hyperparams=config.hyperparams.secondary_hyperparams + ) + self._is_fitted = False + + def _init_secondary_model(self, hyperparams: BaseLearnerHyperParams) -> list[BaseLearner]: + """Initialize secondary model for residual forecasting. + + Returns: + list[Forecaster]: List containing the initialized secondary model forecaster. + """ + models: list[BaseLearner] = [] + + for q in self.config.quantiles: + config = self._config.model_copy(update={"quantiles": [q]}) + secondary_model = self._init_base_learners(config=config, base_hyperparams=[hyperparams])[0] + models.append(secondary_model) + + return models + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + """Fit the Hybrid model to the training data. + + Args: + data: Training data in the expected ForecastInputDataset format. + data_val: Validation data for tuning the model (optional, not used in this implementation). + + """ + # Fit primary model + self._primary_model.fit(data=data, data_val=data_val) + + # Reset forecast start date to ensure we predict on the full dataset + full_dataset = ForecastInputDataset( + data=data.data, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.index[0], + ) + + secondary_input = self._prepare_secondary_input( + quantiles=self.config.quantiles, + base_predictions=self._primary_model.predict(data=full_dataset), + data=data, + ) + # Predict primary model on validation data if provided + if data_val is not None: + full_val_dataset = ForecastInputDataset( + data=data_val.data, + sample_interval=data_val.sample_interval, + target_column=data_val.target_column, + forecast_start=data_val.index[0], + ) + + secondary_val_input = self._prepare_secondary_input( + quantiles=self.config.quantiles, + base_predictions=self._primary_model.predict(data=full_val_dataset), + data=data_val, + ) + # Fit secondary model on residuals + [ + self._secondary_model[i].fit(data=secondary_input[q], data_val=secondary_val_input[q]) + for i, q in enumerate(secondary_input) + ] + + else: + # Fit secondary model on residuals + [ + self._secondary_model[i].fit(data=secondary_input[q], data_val=None) + for i, q in enumerate(secondary_input) + ] + + self._is_fitted = True + + @property + @override + def is_fitted(self) -> bool: + """Check the ResidualForecaster is fitted.""" + return self._is_fitted + + @staticmethod + def _prepare_secondary_input( + quantiles: list[Quantile], + base_predictions: ForecastDataset, + data: ForecastInputDataset, + ) -> dict[Quantile, ForecastInputDataset]: + """Adjust target series to be residuals for secondary model training. + + Args: + quantiles: List of quantiles to prepare data for. + base_predictions: Predictions from the primary model. + data: Original input data. + + Returns: + dict[Quantile, ForecastInputDataset]: Prepared datasets for each quantile. + """ + predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} + sample_interval = data.sample_interval + for q in quantiles: + predictions = base_predictions.data[q.format()] + df = data.data.copy() + df[data.target_column] = data.target_series - predictions + predictions_quantiles[q] = ForecastInputDataset( + data=df, + sample_interval=sample_interval, + target_column=data.target_column, + forecast_start=df.index[0], + ) + + return predictions_quantiles + + def _predict_secodary_model(self, data: ForecastInputDataset) -> ForecastDataset: + predictions: dict[str, pd.Series] = {} + for model in self._secondary_model: + pred = model.predict(data=data) + q = model.config.quantiles[0].format() + predictions[q] = pred.data[q] + + return ForecastDataset( + data=pd.DataFrame(predictions), + sample_interval=data.sample_interval, + ) + + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + """Generate predictions using the ResidualForecaster model. + + Args: + data: Input data for prediction. + + Returns: + ForecastDataset containing the predictions. + + Raises: + NotFittedError: If the ResidualForecaster instance is not fitted yet. + """ + if not self.is_fitted: + raise NotFittedError("The ResidualForecaster instance is not fitted yet. Call 'fit' first.") + + primary_predictions = self._primary_model.predict(data=data).data + + secondary_predictions = self._predict_secodary_model(data=data).data + + final_predictions = primary_predictions + secondary_predictions + + return ForecastDataset( + data=final_predictions, + sample_interval=data.sample_interval, + ) + + +__all__ = ["ResidualForecaster", "ResidualForecasterConfig", "ResidualHyperParams"] diff --git a/packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py similarity index 92% rename from packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py rename to packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 70130ff07..f48ae7988 100644 --- a/packages/openstef-metalearning/src/openstef_metalearning/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -21,11 +21,13 @@ ) from openstef_core.mixins import HyperParams from openstef_core.types import Quantile -from openstef_metalearning.models.meta_forecaster import ( +from openstef_meta.framework.base_learner import ( BaseLearner, BaseLearnerHyperParams, - FinalLearner, - MetaForecaster, +) +from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.framework.meta_forecaster import ( + EnsembleForecaster, ) from openstef_models.models.forecasting.forecaster import ( Forecaster, @@ -140,7 +142,7 @@ class StackingForecasterConfig(ForecasterConfig): ) -class StackingForecaster(MetaForecaster): +class StackingForecaster(EnsembleForecaster): """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" Config = StackingForecasterConfig @@ -151,10 +153,12 @@ def __init__(self, config: StackingForecasterConfig) -> None: self._config = config self._base_learners: list[BaseLearner] = self._init_base_learners( - base_hyperparams=config.hyperparams.base_hyperparams + config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] + final_forecaster = self._init_base_learners( + config=config, base_hyperparams=[config.hyperparams.final_hyperparams] + )[0] self._final_learner = StackingFinalLearner(forecaster=final_forecaster) diff --git a/packages/openstef-models/tests/unit/models/forecasting/meta/__init__.py b/packages/openstef-meta/src/openstef_meta/utils/__init__.py similarity index 100% rename from packages/openstef-models/tests/unit/models/forecasting/meta/__init__.py rename to packages/openstef-meta/src/openstef_meta/utils/__init__.py diff --git a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py new file mode 100644 index 000000000..b11d4c7b8 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py @@ -0,0 +1,22 @@ +"""Utility functions for calculating pinball loss errors. + +This module provides a function to compute the pinball loss for quantile regression. +""" + +import pandas as pd + + +def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: + """Calculate pinball loss for given true and predicted values. + + Args: + y_true: True values as a pandas Series. + y_pred: Predicted values as a pandas Series. + alpha: Quantile value. + + Returns: + A pandas Series containing the pinball loss for each sample. + """ + diff = y_true - y_pred + sign = (diff >= 0).astype(float) + return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff diff --git a/packages/openstef-meta/tests/models/__init__.py b/packages/openstef-meta/tests/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/models/conftest.py b/packages/openstef-meta/tests/models/conftest.py new file mode 100644 index 000000000..968e68d8c --- /dev/null +++ b/packages/openstef-meta/tests/models/conftest.py @@ -0,0 +1,59 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import datetime, timedelta + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets import ForecastInputDataset + + +@pytest.fixture +def sample_forecast_input_dataset() -> ForecastInputDataset: + """Create sample input dataset for forecaster training and prediction.""" + rng = np.random.default_rng(42) + num_samples = 14 + start_date = datetime.fromisoformat("2025-01-01T00:00:00") + + feature_1 = rng.normal(loc=0, scale=1, size=num_samples) + feature_2 = rng.normal(loc=0, scale=1, size=num_samples) + feature_3 = rng.uniform(low=-1, high=1, size=num_samples) + + return ForecastInputDataset( + data=pd.DataFrame( + { + "load": (feature_1 + feature_2 + feature_3) / 3, + "feature1": feature_1, + "feature2": feature_2, + "feature3": feature_3, + }, + index=pd.date_range(start=start_date, periods=num_samples, freq="1d"), + ), + sample_interval=timedelta(days=1), + target_column="load", + forecast_start=start_date + timedelta(days=num_samples // 2), + ) + + +@pytest.fixture +def sample_dataset_with_weights(sample_forecast_input_dataset: ForecastInputDataset) -> ForecastInputDataset: + """Create sample dataset with sample weights by adding weights to the base dataset.""" + rng = np.random.default_rng(42) + num_samples = len(sample_forecast_input_dataset.data) + + # Create varied sample weights (some high, some low) + sample_weights = rng.uniform(low=0.1, high=2.0, size=num_samples) + + # Add sample weights to existing data + data_with_weights = sample_forecast_input_dataset.data.copy() + data_with_weights["sample_weight"] = sample_weights + + return ForecastInputDataset( + data=data_with_weights, + sample_interval=sample_forecast_input_dataset.sample_interval, + target_column=sample_forecast_input_dataset.target_column, + forecast_start=sample_forecast_input_dataset.forecast_start, + ) diff --git a/packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py similarity index 98% rename from packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py rename to packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index f227d1977..4abdcee3c 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/meta/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -9,7 +9,7 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.meta.learned_weights_forecaster import ( +from openstef_meta.models.learned_weights_forecaster import ( LearnedWeightsForecaster, LearnedWeightsForecasterConfig, LearnedWeightsHyperParams, diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py b/packages/openstef-meta/tests/models/test_residual_forecaster.py similarity index 56% rename from packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py rename to packages/openstef-meta/tests/models/test_residual_forecaster.py index 4e36e125d..eba0d8d2a 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_hybrid_forecaster.py +++ b/packages/openstef-meta/tests/models/test_residual_forecaster.py @@ -9,19 +9,56 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.hybrid_forecaster import ( - HybridForecaster, - HybridForecasterConfig, - HybridHyperParams, +from openstef_meta.framework.base_learner import BaseLearnerHyperParams +from openstef_meta.models.residual_forecaster import ( + ResidualForecaster, + ResidualForecasterConfig, + ResidualHyperParams, ) +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams + + +@pytest.fixture(params=["gblinear", "lgbmlinear"]) +def primary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: + """Fixture to provide different primary models types.""" + learner_type = request.param + if learner_type == "gblinear": + return GBLinearHyperParams() + if learner_type == "lgbm": + return LGBMHyperParams() + if learner_type == "lgbmlinear": + return LGBMLinearHyperParams() + return XGBoostHyperParams() + + +@pytest.fixture(params=["gblinear", "lgbm", "lgbmlinear", "xgboost"]) +def secondary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: + """Fixture to provide different secondary models types.""" + learner_type = request.param + if learner_type == "gblinear": + return GBLinearHyperParams() + if learner_type == "lgbm": + return LGBMHyperParams() + if learner_type == "lgbmlinear": + return LGBMLinearHyperParams() + return XGBoostHyperParams() @pytest.fixture -def base_config() -> HybridForecasterConfig: - """Base configuration for Hybrid forecaster tests.""" - - params = HybridHyperParams() - return HybridForecasterConfig( +def base_config( + primary_model: BaseLearnerHyperParams, + secondary_model: BaseLearnerHyperParams, +) -> ResidualForecasterConfig: + """Base configuration for Residual forecaster tests.""" + + params = ResidualHyperParams( + primary_hyperparams=primary_model, + secondary_hyperparams=secondary_model, + ) + return ResidualForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], hyperparams=params, @@ -29,14 +66,14 @@ def base_config() -> HybridForecasterConfig: ) -def test_hybrid_forecaster_fit_predict( +def test_residual_forecaster_fit_predict( sample_forecast_input_dataset: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: ResidualForecasterConfig, ): """Test basic fit and predict workflow with comprehensive output validation.""" # Arrange expected_quantiles = base_config.quantiles - forecaster = HybridForecaster(config=base_config) + forecaster = ResidualForecaster(config=base_config) # Act forecaster.fit(sample_forecast_input_dataset) @@ -56,26 +93,26 @@ def test_hybrid_forecaster_fit_predict( assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" -def test_hybrid_forecaster_predict_not_fitted_raises_error( +def test_residual_forecaster_predict_not_fitted_raises_error( sample_forecast_input_dataset: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: ResidualForecasterConfig, ): """Test that predict() raises NotFittedError when called before fit().""" # Arrange - forecaster = HybridForecaster(config=base_config) + forecaster = ResidualForecaster(config=base_config) # Act & Assert - with pytest.raises(NotFittedError, match="HybridForecaster"): + with pytest.raises(NotFittedError, match="ResidualForecaster"): forecaster.predict(sample_forecast_input_dataset) -def test_hybrid_forecaster_with_sample_weights( +def test_residual_forecaster_with_sample_weights( sample_dataset_with_weights: ForecastInputDataset, - base_config: HybridForecasterConfig, + base_config: ResidualForecasterConfig, ): """Test that forecaster works with sample weights and produces different results.""" # Arrange - forecaster_with_weights = HybridForecaster(config=base_config) + forecaster_with_weights = ResidualForecaster(config=base_config) # Create dataset without weights for comparison data_without_weights = ForecastInputDataset( @@ -84,7 +121,7 @@ def test_hybrid_forecaster_with_sample_weights( target_column=sample_dataset_with_weights.target_column, forecast_start=sample_dataset_with_weights.forecast_start, ) - forecaster_without_weights = HybridForecaster(config=base_config) + forecaster_without_weights = ResidualForecaster(config=base_config) # Act forecaster_with_weights.fit(sample_dataset_with_weights) diff --git a/packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py b/packages/openstef-meta/tests/models/test_stacking_forecaster.py similarity index 98% rename from packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py rename to packages/openstef-meta/tests/models/test_stacking_forecaster.py index 416f36ab9..e8543f055 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/meta/test_stacking_forecaster.py +++ b/packages/openstef-meta/tests/models/test_stacking_forecaster.py @@ -9,7 +9,7 @@ from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q -from openstef_models.models.forecasting.meta.stacking_forecaster import ( +from openstef_meta.models.stacking_forecaster import ( StackingForecaster, StackingForecasterConfig, StackingHyperParams, diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py deleted file mode 100644 index 2b4b72573..000000000 --- a/packages/openstef-models/src/openstef_models/models/forecasting/hybrid_forecaster.py +++ /dev/null @@ -1,308 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). - -Provides method that attempts to combine the advantages of a linear model (Extraplolation) -and tree-based model (Non-linear patterns). This is acieved by training two base learners, -followed by a small linear model that regresses on the baselearners' predictions. -The implementation is based on sklearn's StackingRegressor. -""" - -import logging -from abc import abstractmethod -from typing import override - -import pandas as pd -from pydantic import Field, field_validator - -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.exceptions import ( - NotFittedError, -) -from openstef_core.mixins import HyperParams -from openstef_core.types import Quantile -from openstef_models.models.forecasting.forecaster import ( - Forecaster, - ForecasterConfig, -) -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearForecaster, - GBLinearForecasterConfig, - GBLinearHyperParams, -) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMForecasterConfig, LGBMHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import ( - LGBMLinearForecaster, - LGBMLinearForecasterConfig, - LGBMLinearHyperParams, -) -from openstef_models.models.forecasting.xgboost_forecaster import ( - XGBoostForecaster, - XGBoostForecasterConfig, - XGBoostHyperParams, -) - -logger = logging.getLogger(__name__) - - -BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster -BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams -BaseLearnerConfig = ( - LGBMForecasterConfig | LGBMLinearForecasterConfig | XGBoostForecasterConfig | GBLinearForecasterConfig -) - - -class FinalLearner: - """Combines base learner predictions for each quantile into final predictions.""" - - @abstractmethod - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - raise NotImplementedError("Subclasses must implement the fit method.") - - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - raise NotImplementedError("Subclasses must implement the predict method.") - - @property - @abstractmethod - def is_fitted(self) -> bool: - raise NotImplementedError("Subclasses must implement the is_fitted property.") - - -class FinalForecaster(FinalLearner): - """Combines base learner predictions for each quantile into final predictions.""" - - def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: - # Feature adders placeholder for future use - if feature_adders is not None: - raise NotImplementedError("Feature adders are not yet implemented.") - - # Split forecaster per quantile - self.quantiles = forecaster.config.quantiles - models: list[Forecaster] = [] - for q in self.quantiles: - config = forecaster.config.model_copy( - update={ - "quantiles": [q], - } - ) - model = forecaster.__class__(config=config) - models.append(model) - self.models = models - - @override - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: - for i, q in enumerate(self.quantiles): - self.models[i].fit(data=base_learner_predictions[q], data_val=None) - - @override - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions = [ - self.models[i].predict(data=base_learner_predictions[q]).data for i, q in enumerate(self.quantiles) - ] - - # Concatenate predictions along columns to form a DataFrame with quantile columns - df = pd.concat(predictions, axis=1) - - return ForecastDataset( - data=df, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, - ) - - @property - def is_fitted(self) -> bool: - return all(x.is_fitted for x in self.models) - - -class HybridHyperParams(HyperParams): - """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - - base_hyperparams: list[BaseLearnerHyperParams] = Field( - default=[LGBMHyperParams(), GBLinearHyperParams()], - description="List of hyperparameter configurations for base learners. " - "Defaults to [LGBMHyperParams, GBLinearHyperParams].", - ) - - final_hyperparams: BaseLearnerHyperParams = Field( - default=GBLinearHyperParams(), - description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", - ) - - add_rolling_accuracy_features: bool = Field( - default=False, - description="Whether to add rolling accuracy features from base learners as additional features " - "to the final learner. Defaults to False.", - ) - - @field_validator("base_hyperparams", mode="after") - @classmethod - def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: - hp_classes = [type(hp) for hp in v] - if not len(hp_classes) == len(set(hp_classes)): - raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") - return v - - -class HybridForecasterConfig(ForecasterConfig): - """Configuration for Hybrid-based forecasting models.""" - - hyperparams: HybridHyperParams = HybridHyperParams() - - verbosity: bool = Field( - default=True, - description="Enable verbose output from the Hybrid model (True/False).", - ) - - -class HybridForecaster(Forecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" - - Config = HybridForecasterConfig - HyperParams = HybridHyperParams - - _config: HybridForecasterConfig - - def __init__(self, config: HybridForecasterConfig) -> None: - """Initialize the Hybrid forecaster.""" - self._config = config - - self._base_learners: list[BaseLearner] = self._init_base_learners( - base_hyperparams=config.hyperparams.base_hyperparams - ) - final_forecaster = self._init_base_learners(base_hyperparams=[config.hyperparams.final_hyperparams])[0] - self._final_learner = FinalForecaster(forecaster=final_forecaster) - - def _init_base_learners(self, base_hyperparams: list[BaseLearnerHyperParams]) -> list[BaseLearner]: - """Initialize base learners based on provided hyperparameters. - - Returns: - list[Forecaster]: List of initialized base learner forecasters. - """ - base_learners: list[BaseLearner] = [] - horizons = self.config.horizons - quantiles = self.config.quantiles - - for hyperparams in base_hyperparams: - forecaster_cls = hyperparams.forecaster_class() - config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) - if "hyperparams" in forecaster_cls.Config.model_fields: - config = config.model_copy(update={"hyperparams": hyperparams}) - - base_learners.append(config.forecaster_from_config()) - - return base_learners - - @property - @override - def is_fitted(self) -> bool: - return all(x.is_fitted for x in self._base_learners) - - @property - @override - def config(self) -> ForecasterConfig: - return self._config - - @override - def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - """Fit the Hybrid model to the training data. - - Args: - data: Training data in the expected ForecastInputDataset format. - data_val: Validation data for tuning the model (optional, not used in this implementation). - - """ - # Fit base learners - [x.fit(data=data, data_val=data_val) for x in self._base_learners] - - # Reset forecast start date to ensure we predict on the full dataset - full_dataset = ForecastInputDataset( - data=data.data, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.index[0], - ) - - base_predictions = self._predict_base_learners(data=full_dataset) - - quantile_datasets = self._prepare_input_final_learner( - base_predictions=base_predictions, quantiles=self._config.quantiles, target_series=data.target_series - ) - - self._final_learner.fit( - base_learner_predictions=quantile_datasets, - ) - - self._is_fitted = True - - def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLearner], ForecastDataset]: - """Generate predictions from base learners. - - Args: - data: Input data for prediction. - - Returns: - DataFrame containing base learner predictions. - """ - base_predictions: dict[type[BaseLearner], ForecastDataset] = {} - for learner in self._base_learners: - preds = learner.predict(data=data) - base_predictions[learner.__class__] = preds - - return base_predictions - - @staticmethod - def _prepare_input_final_learner( - quantiles: list[Quantile], - base_predictions: dict[type[BaseLearner], ForecastDataset], - target_series: pd.Series, - ) -> dict[Quantile, ForecastInputDataset]: - """Prepare input data for the final learner based on base learner predictions. - - Args: - quantiles: List of quantiles to prepare data for. - base_predictions: Predictions from base learners. - target_series: Actual target series for reference. - - Returns: - dictionary mapping quantile strings to DataFrames of base learner predictions. - """ - predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} - sample_interval = base_predictions[next(iter(base_predictions))].sample_interval - target_name = str(target_series.name) - - for q in quantiles: - df = pd.DataFrame({ - learner.__name__: preds.data[Quantile(q).format()] for learner, preds in base_predictions.items() - }) - df[target_name] = target_series - - predictions_quantiles[q] = ForecastInputDataset( - data=df, - sample_interval=sample_interval, - target_column=target_name, - forecast_start=df.index[0], - ) - - return predictions_quantiles - - @override - def predict(self, data: ForecastInputDataset) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - base_predictions = self._predict_base_learners(data=data) - - final_learner_input = self._prepare_input_final_learner( - quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series - ) - - return self._final_learner.predict(base_learner_predictions=final_learner_input) - - # TODO(@Lars800): #745: Make forecaster Explainable - - -__all__ = ["HybridForecaster", "HybridForecasterConfig", "HybridHyperParams"] diff --git a/pyproject.toml b/pyproject.toml index 1ef0cea1c..17df056fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ optional-dependencies.beam = [ ] optional-dependencies.models = [ "openstef-models[xgb-cpu]", - "openstef-metalearning", + "openstef-meta", ] urls.Documentation = "https://openstef.github.io/openstef/index.html" urls.Homepage = "https://lfenergy.org/projects/openstef/" @@ -78,7 +78,7 @@ openstef-beam = { workspace = true } openstef-models = { workspace = true } openstef-docs = { workspace = true } openstef-core = { workspace = true } -openstef-metalearning = { workspace = true } +openstef-meta = { workspace = true } microsoft-python-type-stubs = { git = "git+https://github.com/microsoft/python-type-stubs.git" } [tool.uv.workspace] @@ -87,7 +87,7 @@ members = [ "packages/openstef-beam", "docs", "packages/openstef-core", - "packages/openstef-metalearning", + "packages/openstef-meta", ] [tool.ruff] @@ -193,7 +193,7 @@ source = [ "packages/openstef-beam/src", "packages/openstef-models/src", "packages/openstef-core/src", - "packages/openstef-metalearning/src", + "packages/openstef-meta/src", ] omit = [ "tests/*", diff --git a/uv.lock b/uv.lock index 520b8f9d9..4e2691b89 100644 --- a/uv.lock +++ b/uv.lock @@ -8,7 +8,7 @@ members = [ "openstef-beam", "openstef-core", "openstef-docs", - "openstef-metalearning", + "openstef-meta", "openstef-models", ] @@ -2119,13 +2119,15 @@ all = [ { name = "openstef-beam", extra = ["all"] }, { name = "openstef-core" }, { name = "openstef-models", extra = ["xgb-cpu"] }, + { name = "openstef-meta" }, + ] beam = [ { name = "huggingface-hub" }, { name = "openstef-beam" }, ] models = [ - { name = "openstef-metalearning" }, + { name = "openstef-meta" }, { name = "openstef-models", extra = ["xgb-cpu"] }, ] @@ -2157,7 +2159,7 @@ requires-dist = [ { name = "openstef-beam", extras = ["all"], marker = "extra == 'all'", editable = "packages/openstef-beam" }, { name = "openstef-core", editable = "packages/openstef-core" }, { name = "openstef-core", marker = "extra == 'all'", editable = "packages/openstef-core" }, - { name = "openstef-metalearning", marker = "extra == 'models'", editable = "packages/openstef-metalearning" }, + { name = "openstef-meta", marker = "extra == 'models'", editable = "packages/openstef-meta" }, { name = "openstef-models", extras = ["xgb-cpu"], editable = "packages/openstef-models" }, { name = "openstef-models", extras = ["xgb-cpu"], marker = "extra == 'all'", editable = "packages/openstef-models" }, { name = "openstef-models", extras = ["xgb-cpu"], marker = "extra == 'models'", editable = "packages/openstef-models" }, @@ -2280,9 +2282,9 @@ requires-dist = [ ] [[package]] -name = "openstef-metalearning" +name = "openstef-meta" version = "0.1.0" -source = { editable = "packages/openstef-metalearning" } +source = { editable = "packages/openstef-meta" } dependencies = [ { name = "openstef-core" }, { name = "openstef-models" }, From 114189838023b66b3c61e390bd998719d381c511 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 24 Nov 2025 22:13:55 +0100 Subject: [PATCH 30/72] Testing and fixes on Learned Weights Forecaster Signed-off-by: Lars van Someren --- .../models/learned_weights_forecaster.py | 190 +++++++++++++++--- .../models/test_learned_weights_forecaster.py | 47 ++++- 2 files changed, 208 insertions(+), 29 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index d606a79f5..576b1a586 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -11,12 +11,12 @@ import logging from abc import abstractmethod -from typing import override +from typing import override, Literal, Self import pandas as pd from lightgbm import LGBMClassifier from pydantic import Field -from sklearn.linear_model import LinearRegression +from sklearn.linear_model import LogisticRegression from xgboost import XGBClassifier from openstef_core.datasets import ForecastDataset, ForecastInputDataset @@ -45,14 +45,32 @@ logger = logging.getLogger(__name__) -Classifier = LGBMClassifier | XGBClassifier | LinearRegression +# Base classes for Learned Weights Final Learner +Classifier = LGBMClassifier | XGBClassifier | LogisticRegression -class LearnedWeightsFinalLearner(FinalLearner): + +class LWFLHyperParams(HyperParams): + """Hyperparameters for Learned Weights Final Learner.""" + + @property + @abstractmethod + def learner(self) -> type["WeightsLearner"]: + """Returns the classifier to be used as final learner.""" + raise NotImplementedError("Subclasses must implement the 'estimator' property.") + + @classmethod + def learner_from_params(cls, quantiles: list[Quantile], hyperparams: Self) -> "WeightsLearner": + """Initialize the final learner from hyperparameters.""" + instance = cls() + return instance.learner(quantiles=quantiles, hyperparams=hyperparams) + + +class WeightsLearner(FinalLearner): """Combines base learner predictions with a classification approach to determine which base learner to use.""" @abstractmethod - def __init__(self, quantiles: list[Quantile]) -> None: + def __init__(self, quantiles: list[Quantile], hyperparams: LWFLHyperParams) -> None: self.quantiles = quantiles self.models: list[Classifier] = [] self._is_fitted = False @@ -119,42 +137,152 @@ def is_fitted(self) -> bool: return self._is_fitted -class LGBMFinalLearner(LearnedWeightsFinalLearner): - """Final learner using only LGBM as base learners.""" +# Final learner implementations using different classifiers +# 1 LGBM Classifier + + +class LGBMLearnerHyperParams(LWFLHyperParams): + """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" + + n_estimators: int = Field( + default=20, + description="Number of estimators for the LGBM Classifier. Defaults to 20.", + ) + + n_leaves: int = Field( + default=31, + description="Number of leaves for the LGBM Classifier. Defaults to 31.", + ) + + @property + @override + def learner(self) -> type["LGBMLearner"]: + """Returns the LGBMLearner""" + return LGBMLearner + - def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: +class LGBMLearner(WeightsLearner): + """Final learner with LGBM Classifier.""" + + HyperParams = LGBMLearnerHyperParams + + def __init__( + self, + quantiles: list[Quantile], + hyperparams: LGBMLearnerHyperParams, + ) -> None: self.quantiles = quantiles - self.models = [LGBMClassifier(class_weight="balanced", n_estimators=n_estimators) for _ in quantiles] + self.models = [ + LGBMClassifier( + class_weight="balanced", + n_estimators=hyperparams.n_estimators, + num_leaves=hyperparams.n_leaves, + ) + for _ in quantiles + ] self._is_fitted = False -class RandomForestFinalLearner(LearnedWeightsFinalLearner): - def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: +# 1 RandomForest Classifier +class RFLearnerHyperParams(LWFLHyperParams): + """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" + + n_estimators: int = Field( + default=20, + description="Number of estimators for the LGBM Classifier. Defaults to 20.", + ) + + n_leaves: int = Field( + default=31, + description="Number of leaves for the LGBM Classifier. Defaults to 31.", + ) + + @property + def learner(self) -> type["RandomForestLearner"]: + """Returns the LGBMClassifier to be used as final learner.""" + return RandomForestLearner + + +class RandomForestLearner(WeightsLearner): + """Final learner using only Random Forest as base learners.""" + + def __init__(self, quantiles: list[Quantile], hyperparams: RFLearnerHyperParams) -> None: + """Initialize RandomForestLearner.""" self.quantiles = quantiles self.models = [ - LGBMClassifier(boosting_type="rf", class_weight="balanced", n_estimators=n_estimators) for _ in quantiles + LGBMClassifier(boosting_type="rf", class_weight="balanced", n_estimators=hyperparams.n_estimators) + for _ in quantiles ] self._is_fitted = False -class XGBFinalLearner(LearnedWeightsFinalLearner): +# 3 XGB Classifier +class XGBLearnerHyperParams(LWFLHyperParams): + """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" + + n_estimators: int = Field( + default=20, + description="Number of estimators for the LGBM Classifier. Defaults to 20.", + ) + + @property + def learner(self) -> type["XGBLearner"]: + """Returns the LGBMClassifier to be used as final learner.""" + return XGBLearner + + +class XGBLearner(WeightsLearner): """Final learner using only XGBoost as base learners.""" - def __init__(self, quantiles: list[Quantile], n_estimators: int = 20) -> None: + def __init__(self, quantiles: list[Quantile], hyperparams: XGBLearnerHyperParams) -> None: self.quantiles = quantiles - self.models = [XGBClassifier(class_weight="balanced", n_estimators=n_estimators) for _ in quantiles] + self.models = [XGBClassifier(class_weight="balanced", n_estimators=hyperparams.n_estimators) for _ in quantiles] self._is_fitted = False -class LogisticRegressionFinalLearner(LearnedWeightsFinalLearner): +# 4 Logistic Regression Classifier +class LogisticLearnerHyperParams(LWFLHyperParams): + """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" + + fit_intercept: bool = Field( + default=True, + description="Whether to calculate the intercept for this model. Defaults to True.", + ) + + penalty: Literal["l1", "l2", "elasticnet"] = Field( + default="l2", + description="Specify the norm used in the penalization. Defaults to 'l2'.", + ) + + c: float = Field( + default=1.0, + description="Inverse of regularization strength; must be a positive float. Defaults to 1.0.", + ) + + @property + def learner(self) -> type["LogisticLearner"]: + """Returns the LGBMClassifier to be used as final learner.""" + return LogisticLearner + + +class LogisticLearner(WeightsLearner): """Final learner using only Logistic Regression as base learners.""" - def __init__(self, quantiles: list[Quantile]) -> None: + def __init__(self, quantiles: list[Quantile], hyperparams: LogisticLearnerHyperParams) -> None: self.quantiles = quantiles - self.models = [LinearRegression() for _ in quantiles] + self.models = [ + LogisticRegression( + class_weight="balanced", + fit_intercept=hyperparams.fit_intercept, + penalty=hyperparams.penalty, + C=hyperparams.c, + ) + for _ in quantiles + ] self._is_fitted = False +# Assembly classes class LearnedWeightsHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" @@ -164,16 +292,16 @@ class LearnedWeightsHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_learner: type[LearnedWeightsFinalLearner] = Field( - default=LGBMFinalLearner, - description="Type of final learner to use. Defaults to LearnedWeightsFinalLearner.", + final_hyperparams: LWFLHyperParams = Field( + default=LGBMLearnerHyperParams(), + description="Hyperparameters for the final learner. Defaults to LGBMLearnerHyperParams.", ) class LearnedWeightsForecasterConfig(ForecasterConfig): """Configuration for Hybrid-based forecasting models.""" - hyperparams: LearnedWeightsHyperParams = LearnedWeightsHyperParams() + hyperparams: LearnedWeightsHyperParams verbosity: bool = Field( default=True, @@ -194,15 +322,23 @@ def __init__(self, config: LearnedWeightsForecasterConfig) -> None: self._base_learners: list[BaseLearner] = self._init_base_learners( config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = config.hyperparams.final_learner(quantiles=config.quantiles) + self._final_learner = config.hyperparams.final_hyperparams.learner_from_params( + quantiles=config.quantiles, + hyperparams=config.hyperparams.final_hyperparams, + ) __all__ = [ - "LGBMFinalLearner", + "LGBMLearner", + "LGBMLearnerHyperParams", "LearnedWeightsForecaster", "LearnedWeightsForecasterConfig", "LearnedWeightsHyperParams", - "LogisticRegressionFinalLearner", - "RandomForestFinalLearner", - "XGBFinalLearner", + "LogisticLearner", + "LogisticLearnerHyperParams", + "RFLearnerHyperParams", + "RandomForestLearner", + "WeightsLearner", + "XGBLearner", + "XGBLearnerHyperParams", ] diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index 4abdcee3c..ba172bc6d 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -13,14 +13,39 @@ LearnedWeightsForecaster, LearnedWeightsForecasterConfig, LearnedWeightsHyperParams, + LGBMLearner, + LGBMLearnerHyperParams, + LogisticLearner, + LogisticLearnerHyperParams, + LWFLHyperParams, + RandomForestLearner, + RFLearnerHyperParams, + WeightsLearner, + XGBLearner, + XGBLearnerHyperParams, ) +@pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) +def final_hyperparams(request: pytest.FixtureRequest) -> LWFLHyperParams: + """Fixture to provide different primary models types.""" + learner_type = request.param + if learner_type == "rf": + return RFLearnerHyperParams() + if learner_type == "lgbm": + return LGBMLearnerHyperParams() + if learner_type == "xgboost": + return XGBLearnerHyperParams() + return LogisticLearnerHyperParams() + + @pytest.fixture -def base_config() -> LearnedWeightsForecasterConfig: +def base_config(final_hyperparams: LWFLHyperParams) -> LearnedWeightsForecasterConfig: """Base configuration for LearnedWeights forecaster tests.""" - params = LearnedWeightsHyperParams() + params = LearnedWeightsHyperParams( + final_hyperparams=final_hyperparams, + ) return LearnedWeightsForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))], @@ -29,6 +54,24 @@ def base_config() -> LearnedWeightsForecasterConfig: ) +def test_final_learner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): + """Test that the final learner corresponds to the specified hyperparameters.""" + forecaster = LearnedWeightsForecaster(config=base_config) + final_learner = forecaster._final_learner + + mapping: dict[type[LWFLHyperParams], type[WeightsLearner]] = { + RFLearnerHyperParams: RandomForestLearner, + LGBMLearnerHyperParams: LGBMLearner, + XGBLearnerHyperParams: XGBLearner, + LogisticLearnerHyperParams: LogisticLearner, + } + expected_learner_type = mapping[type(base_config.hyperparams.final_hyperparams)] + + assert isinstance(final_learner, expected_learner_type), ( + f"Final learner type {type(final_learner)} does not match expected type {expected_learner_type}" + ) + + def test_learned_weights_forecaster_fit_predict( sample_forecast_input_dataset: ForecastInputDataset, base_config: LearnedWeightsForecasterConfig, From 976a2fc0cb0cf5dd516c2c6905ed7f11b4efc453 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 25 Nov 2025 13:27:49 +0100 Subject: [PATCH 31/72] FinalLearner PreProcessor Signed-off-by: Lars van Someren --- .../openstef_meta/framework/final_learner.py | 65 ++++++++- .../framework/meta_forecaster.py | 29 +++- .../models/learned_weights_forecaster.py | 136 ++++++++++++++---- .../models/test_learned_weights_forecaster.py | 21 +++ 4 files changed, 212 insertions(+), 39 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index 26567e156..8f2c58424 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -10,14 +10,24 @@ from abc import ABC, abstractmethod -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.mixins import HyperParams +from pydantic import ConfigDict, Field + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset +from openstef_core.mixins import HyperParams, TransformPipeline +from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile class FinalLearnerHyperParams(HyperParams): """Hyperparameters for the Final Learner.""" + model_config = ConfigDict(arbitrary_types_allowed=True) + + feature_adders: list[TimeSeriesTransform] = Field( + default=[], + description="Additional features to add to the base learner predictions before fitting the final learner.", + ) + class FinalLearnerConfig: """Configuration for the Final Learner.""" @@ -26,29 +36,76 @@ class FinalLearnerConfig: class FinalLearner(ABC): """Combines base learner predictions for each quantile into final predictions.""" + def __init__(self, quantiles: list[Quantile], hyperparams: FinalLearnerHyperParams) -> None: + """Initialize the Final Learner.""" + self.quantiles = quantiles + self.hyperparams = hyperparams + self.final_learner_processing: TransformPipeline[TimeSeriesDataset] = TransformPipeline( + transforms=hyperparams.feature_adders + ) + self._is_fitted: bool = False + @abstractmethod - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + def fit( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> None: """Fit the final learner using base learner predictions. Args: base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner + predictions. + additional_features: Optional ForecastInputDataset containing additional features for the final learner. """ raise NotImplementedError("Subclasses must implement the fit method.") - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + def predict( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> ForecastDataset: """Generate final predictions based on base learner predictions. Args: base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner predictions. + additional_features: Optional ForecastInputDataset containing additional features for the final learner. Returns: ForecastDataset containing the final predictions. """ raise NotImplementedError("Subclasses must implement the predict method.") + def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset: + """Calculate additional features for the final learner. + + Args: + data: Input TimeSeriesDataset to calculate features on. + + Returns: + TimeSeriesDataset with additional features. + """ + data_ts = TimeSeriesDataset( + data=data.data, + sample_interval=data.sample_interval, + ) + data_transformed = self.final_learner_processing.transform(data_ts) + + return ForecastInputDataset( + data=data_transformed.data, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.forecast_start, + ) + @property @abstractmethod def is_fitted(self) -> bool: """Indicates whether the final learner has been fitted.""" raise NotImplementedError("Subclasses must implement the is_fitted property.") + + @property + def has_features(self) -> bool: + """Indicates whether the final learner uses additional features.""" + return len(self.final_learner_processing.transforms) > 0 diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 5b69e3153..3af6329d7 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -104,12 +104,20 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) + if self._final_learner.has_features: + features = self._final_learner.calculate_features(data=full_dataset) + else: + features = None + quantile_datasets = self._prepare_input_final_learner( - base_predictions=base_predictions, quantiles=self._config.quantiles, target_series=data.target_series + base_predictions=base_predictions, + quantiles=self._config.quantiles, + target_series=data.target_series, ) self._final_learner.fit( base_learner_predictions=quantile_datasets, + additional_features=features, ) self._is_fitted = True @@ -170,18 +178,31 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) - base_predictions = self._predict_base_learners(data=data) + full_dataset = ForecastInputDataset( + data=data.data, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.index[0], + ) + + base_predictions = self._predict_base_learners(data=full_dataset) final_learner_input = self._prepare_input_final_learner( quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series ) - return self._final_learner.predict(base_learner_predictions=final_learner_input) + if self._final_learner.has_features: + additional_features = self._final_learner.calculate_features(data=data) + else: + additional_features = None + + return self._final_learner.predict( + base_learner_predictions=final_learner_input, additional_features=additional_features + ) __all__ = [ "BaseLearner", "BaseLearnerHyperParams", - "FinalLearner", "MetaForecaster", ] diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 576b1a586..255e25dd0 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -11,12 +11,15 @@ import logging from abc import abstractmethod -from typing import override, Literal, Self +from typing import Literal, Self, override import pandas as pd from lightgbm import LGBMClassifier from pydantic import Field +from sklearn.dummy import DummyClassifier from sklearn.linear_model import LogisticRegression +from sklearn.preprocessing import LabelEncoder +from sklearn.utils.class_weight import compute_sample_weight # type: ignore from xgboost import XGBClassifier from openstef_core.datasets import ForecastDataset, ForecastInputDataset @@ -29,7 +32,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -47,10 +50,10 @@ # Base classes for Learned Weights Final Learner -Classifier = LGBMClassifier | XGBClassifier | LogisticRegression +Classifier = LGBMClassifier | XGBClassifier | LogisticRegression | DummyClassifier -class LWFLHyperParams(HyperParams): +class LWFLHyperParams(FinalLearnerHyperParams): """Hyperparameters for Learned Weights Final Learner.""" @property @@ -61,7 +64,11 @@ def learner(self) -> type["WeightsLearner"]: @classmethod def learner_from_params(cls, quantiles: list[Quantile], hyperparams: Self) -> "WeightsLearner": - """Initialize the final learner from hyperparameters.""" + """Initialize the final learner from hyperparameters. + + Returns: + WeightsLearner: An instance of the WeightsLearner initialized with the provided hyperparameters. + """ instance = cls() return instance.learner(quantiles=quantiles, hyperparams=hyperparams) @@ -69,23 +76,55 @@ def learner_from_params(cls, quantiles: list[Quantile], hyperparams: Self) -> "W class WeightsLearner(FinalLearner): """Combines base learner predictions with a classification approach to determine which base learner to use.""" - @abstractmethod def __init__(self, quantiles: list[Quantile], hyperparams: LWFLHyperParams) -> None: - self.quantiles = quantiles + """Initialize WeightsLearner.""" + super().__init__(quantiles=quantiles, hyperparams=hyperparams) self.models: list[Classifier] = [] + self._label_encoder = LabelEncoder() + self._is_fitted = False @override - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + def fit( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> None: + for i, q in enumerate(self.quantiles): - pred = base_learner_predictions[q].data.drop(columns=[base_learner_predictions[q].target_column]) + base_predictions = base_learner_predictions[q].data.drop( + columns=[base_learner_predictions[q].target_column] + ) + labels = self._prepare_classification_data( quantile=q, target=base_learner_predictions[q].target_series, - predictions=pred, + predictions=base_predictions, ) - self.models[i].fit(X=pred, y=labels) # type: ignore + if additional_features is not None: + df = pd.concat( + [base_predictions, additional_features.data], + axis=1, + ) + else: + df = base_predictions + + if len(labels.unique()) == 1: + msg = f"""Final learner for quantile {q.format()} has less than 2 classes in the target. + Switching to dummy classifier """ + logger.warning(msg=msg) + self.models[i] = DummyClassifier(strategy="most_frequent") + + if i == 0: + # Fit label encoder only once + self._label_encoder.fit(labels) + labels = self._label_encoder.transform(labels) + + # Balance classes + weights = compute_sample_weight("balanced", labels) + + self.models[i].fit(X=df, y=labels, sample_weight=weights) # type: ignore self._is_fitted = True @staticmethod @@ -105,25 +144,38 @@ def column_pinball_losses(preds: pd.Series) -> pd.Series: # For each sample, select the base learner with the lowest pinball loss return pinball_losses.idxmin(axis=1) - def _calculate_sample_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: + def _calculate_model_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: model = self.models[self.quantiles.index(quantile)] return model.predict_proba(X=base_predictions) # type: ignore - def _generate_predictions_quantile(self, base_predictions: ForecastInputDataset, quantile: Quantile) -> pd.Series: - df = base_predictions.data.drop(columns=[base_predictions.target_column]) - weights = self._calculate_sample_weights_quantile(base_predictions=df, quantile=quantile) + def _generate_predictions_quantile( + self, + base_predictions: ForecastInputDataset, + additional_features: ForecastInputDataset | None, + quantile: Quantile, + ) -> pd.Series: + base_df = base_predictions.data.drop(columns=[base_predictions.target_column]) + df = pd.concat([base_df, additional_features.data], axis=1) if additional_features is not None else base_df + + weights = self._calculate_model_weights_quantile(base_predictions=df, quantile=quantile) - return df.mul(weights).sum(axis=1) + return base_df.mul(weights).sum(axis=1) @override - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + def predict( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) # Generate predictions predictions = pd.DataFrame({ - Quantile(q).format(): self._generate_predictions_quantile(base_predictions=data, quantile=q) + Quantile(q).format(): self._generate_predictions_quantile( + base_predictions=data, quantile=q, additional_features=additional_features + ) for q, data in base_learner_predictions.items() }) @@ -133,14 +185,13 @@ def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset] ) @property + @override def is_fitted(self) -> bool: return self._is_fitted # Final learner implementations using different classifiers # 1 LGBM Classifier - - class LGBMLearnerHyperParams(LWFLHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" @@ -157,7 +208,7 @@ class LGBMLearnerHyperParams(LWFLHyperParams): @property @override def learner(self) -> type["LGBMLearner"]: - """Returns the LGBMLearner""" + """Returns the LGBMLearner.""" return LGBMLearner @@ -171,7 +222,8 @@ def __init__( quantiles: list[Quantile], hyperparams: LGBMLearnerHyperParams, ) -> None: - self.quantiles = quantiles + """Initialize LGBMLearner.""" + super().__init__(quantiles=quantiles, hyperparams=hyperparams) self.models = [ LGBMClassifier( class_weight="balanced", @@ -180,7 +232,6 @@ def __init__( ) for _ in quantiles ] - self._is_fitted = False # 1 RandomForest Classifier @@ -197,6 +248,21 @@ class RFLearnerHyperParams(LWFLHyperParams): description="Number of leaves for the LGBM Classifier. Defaults to 31.", ) + bagging_freq: int = Field( + default=1, + description="Frequency for bagging in the Random Forest. Defaults to 1.", + ) + + bagging_fraction: float = Field( + default=0.8, + description="Fraction of data to be used for each iteration of the Random Forest. Defaults to 0.8.", + ) + + feature_fraction: float = Field( + default=1, + description="Fraction of features to be used for each iteration of the Random Forest. Defaults to 1.", + ) + @property def learner(self) -> type["RandomForestLearner"]: """Returns the LGBMClassifier to be used as final learner.""" @@ -208,12 +274,20 @@ class RandomForestLearner(WeightsLearner): def __init__(self, quantiles: list[Quantile], hyperparams: RFLearnerHyperParams) -> None: """Initialize RandomForestLearner.""" - self.quantiles = quantiles + super().__init__(quantiles=quantiles, hyperparams=hyperparams) + self.models = [ - LGBMClassifier(boosting_type="rf", class_weight="balanced", n_estimators=hyperparams.n_estimators) + LGBMClassifier( + boosting_type="rf", + class_weight="balanced", + n_estimators=hyperparams.n_estimators, + bagging_freq=hyperparams.bagging_freq, + bagging_fraction=hyperparams.bagging_fraction, + feature_fraction=hyperparams.feature_fraction, + num_leaves=hyperparams.n_leaves, + ) for _ in quantiles ] - self._is_fitted = False # 3 XGB Classifier @@ -235,9 +309,9 @@ class XGBLearner(WeightsLearner): """Final learner using only XGBoost as base learners.""" def __init__(self, quantiles: list[Quantile], hyperparams: XGBLearnerHyperParams) -> None: - self.quantiles = quantiles - self.models = [XGBClassifier(class_weight="balanced", n_estimators=hyperparams.n_estimators) for _ in quantiles] - self._is_fitted = False + """Initialize XGBLearner.""" + super().__init__(quantiles=quantiles, hyperparams=hyperparams) + self.models = [XGBClassifier(n_estimators=hyperparams.n_estimators) for _ in quantiles] # 4 Logistic Regression Classifier @@ -269,7 +343,8 @@ class LogisticLearner(WeightsLearner): """Final learner using only Logistic Regression as base learners.""" def __init__(self, quantiles: list[Quantile], hyperparams: LogisticLearnerHyperParams) -> None: - self.quantiles = quantiles + """Initialize LogisticLearner.""" + super().__init__(quantiles=quantiles, hyperparams=hyperparams) self.models = [ LogisticRegression( class_weight="balanced", @@ -279,7 +354,6 @@ def __init__(self, quantiles: list[Quantile], hyperparams: LogisticLearnerHyperP ) for _ in quantiles ] - self._is_fitted = False # Assembly classes diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index ba172bc6d..af2504a55 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -24,6 +24,7 @@ XGBLearner, XGBLearnerHyperParams, ) +from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder @pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) @@ -146,3 +147,23 @@ def test_learned_weights_forecaster_with_sample_weights( # (This is a statistical test - with different weights, predictions should differ) differences = (result_with_weights.data - result_without_weights.data).abs() assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_learned_weights_forecaster_with_additional_features( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LearnedWeightsForecasterConfig, +): + """Test that forecaster works with additional features for the final learner.""" + # Arrange + # Add a simple feature adder that adds a constant feature + + base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + forecaster = LearnedWeightsForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" From 82795d9c00c2e69337c513fc5a74740a7f7a670f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 25 Nov 2025 13:38:52 +0100 Subject: [PATCH 32/72] Fixed benchmark references Signed-off-by: Lars van Someren --- .../presets/forecasting_workflow.py | 46 +++++++++++++++---- 1 file changed, 38 insertions(+), 8 deletions(-) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 99cbea0ac..df0f2c59c 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -25,6 +25,9 @@ from openstef_core.base_model import BaseConfig from openstef_core.mixins import TransformPipeline from openstef_core.types import LeadTime, Q, Quantile, QuantileOrGlobal +from openstef_meta.models.learned_weights_forecaster import LearnedWeightsForecaster +from openstef_meta.models.stacking_forecaster import StackingForecaster +from openstef_meta.models.residual_forecaster import ResidualForecaster from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback from openstef_models.mixins import ModelIdentifier from openstef_models.models import ForecastingModel @@ -32,8 +35,6 @@ from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster -from openstef_models.models.forecasting.meta.learned_weights_forecaster import LearnedWeightsForecaster -from openstef_models.models.forecasting.meta.stacking_forecaster import StackingForecaster from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster from openstef_models.transforms.energy_domain import WindPowerFeatureAdder from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, Imputer, NaNDropper, SampleWeighter, Scaler @@ -101,9 +102,9 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_id: ModelIdentifier = Field(description="Unique identifier for the forecasting model.") # Model configuration - model: Literal["xgboost", "gblinear", "flatliner", "stacking", "learned_weights", "lgbm", "lgbmlinear"] = Field( - description="Type of forecasting model to use." - ) # TODO(#652): Implement median forecaster + model: Literal[ + "xgboost", "gblinear", "flatliner", "stacking", "residual", "learned_weights", "lgbm", "lgbmlinear" + ] = Field(description="Type of forecasting model to use.") # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( default=[Q(0.5)], description="List of quantiles to predict for probabilistic forecasting.", @@ -137,6 +138,11 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Hyperparameters for LightGBM forecaster.", ) + residual_hyperparams: ResidualForecaster.HyperParams = Field( + default=ResidualForecaster.HyperParams(), + description="Hyperparameters for Residual forecaster.", + ) + stacking_hyperparams: StackingForecaster.HyperParams = Field( default=StackingForecaster.HyperParams(), description="Hyperparameters for Stacking forecaster.", @@ -208,7 +214,7 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob ) sample_weight_exponent: float = Field( default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "xgboost"} + if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "residual", "xgboost"} else 0.0, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " @@ -308,9 +314,11 @@ def create_forecasting_workflow( history_available=config.predict_history, horizons=config.horizons, add_trivial_lags=config.model - not in {"gblinear", "stacking", "learned_weights"}, # GBLinear uses only 7day lag. + not in {"gblinear", "residual", "stacking", "learned_weights"}, # GBLinear uses only 7day lag. target_column=config.target_column, - custom_lags=[timedelta(days=7)] if config.model in {"gblinear", "stackinglearned_weights"} else [], + custom_lags=[timedelta(days=7)] + if config.model in {"gblinear", "residual", "stacking", "learned_weights"} + else [], ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, @@ -430,6 +438,28 @@ def create_forecasting_workflow( ConfidenceIntervalApplicator(quantiles=config.quantiles), ] elif config.model == "learned_weights": + preprocessing = [ + *checks, + *feature_adders, + *feature_standardizers, + Imputer( + selection=Exclude(config.target_column), + imputation_strategy="mean", + fill_future_values=Include(config.energy_price_column), + ), + NaNDropper( + selection=Exclude(config.target_column), + ), + ] + forecaster = ResidualForecaster( + config=ResidualForecaster.Config( + quantiles=config.quantiles, + horizons=config.horizons, + hyperparams=config.residual_hyperparams, + ) + ) + postprocessing = [QuantileSorter()] + elif config.model == "residual": preprocessing = [ *checks, *feature_adders, From 140fe26e295c06065b936f49627ac4749af3be1b Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 25 Nov 2025 17:08:44 +0100 Subject: [PATCH 33/72] Added additional Feature logic to StackingForecaster Signed-off-by: Lars van Someren --- .../openstef_meta/framework/final_learner.py | 4 - .../models/stacking_forecaster.py | 123 ++++++++++++++---- .../tests/models/test_stacking_forecaster.py | 31 +++++ 3 files changed, 126 insertions(+), 32 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index 8f2c58424..dc92cc1c1 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -29,10 +29,6 @@ class FinalLearnerHyperParams(HyperParams): ) -class FinalLearnerConfig: - """Configuration for the Final Learner.""" - - class FinalLearner(ABC): """Combines base learner predictions for each quantile into final predictions.""" diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index f48ae7988..2af0ff395 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -20,12 +20,13 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_core.types import Quantile +from openstef_core.transforms import TimeSeriesTransform +from openstef_core.types import LeadTime, Quantile from openstef_meta.framework.base_learner import ( BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -41,47 +42,114 @@ logger = logging.getLogger(__name__) +class StackingFinalLearnerHyperParams(FinalLearnerHyperParams): + """HyperParams for Stacking Final Learner.""" + + feature_adders: list[TimeSeriesTransform] = Field( + default=[], + description="Additional features to add to the base learner predictions before fitting the final learner.", + ) + + forecaster_hyperparams: BaseLearnerHyperParams = Field( + default=GBLinearHyperParams(), + description="", + ) + + class StackingFinalLearner(FinalLearner): """Combines base learner predictions per quantile into final predictions using a regression approach.""" - def __init__(self, forecaster: Forecaster, feature_adders: None = None) -> None: + def __init__( + self, quantiles: list[Quantile], hyperparams: StackingFinalLearnerHyperParams, horizon: LeadTime + ) -> None: """Initialize the Stacking final learner. Args: - forecaster: The forecaster model to be used as the final learner. - feature_adders: Placeholder for future feature adders (not yet implemented). + quantiles: List of quantiles to predict. + hyperparams: Hyperparameters for the final learner. + horizon: Forecast horizon for which to create the final learner. """ - # Feature adders placeholder for future use - if feature_adders is not None: - raise NotImplementedError("Feature adders are not yet implemented.") + super().__init__(quantiles=quantiles, hyperparams=hyperparams) + + forecaster_hyperparams: BaseLearnerHyperParams = hyperparams.forecaster_hyperparams # Split forecaster per quantile - self.quantiles = forecaster.config.quantiles models: list[Forecaster] = [] for q in self.quantiles: - config = forecaster.config.model_copy( - update={ - "quantiles": [q], - } - ) - model = forecaster.__class__(config=config) + forecaster_cls = forecaster_hyperparams.forecaster_class() + config = forecaster_cls.Config(horizons=[horizon], quantiles=[q]) + if "hyperparams" in forecaster_cls.Config.model_fields: + config = config.model_copy(update={"hyperparams": forecaster_hyperparams}) + + model = config.forecaster_from_config() models.append(model) self.models = models + @staticmethod + def _combine_datasets( + data: ForecastInputDataset, additional_features: ForecastInputDataset + ) -> ForecastInputDataset: + """Combine base learner predictions with additional features for final learner input. + + Args: + data: ForecastInputDataset containing base learner predictions. + additional_features: ForecastInputDataset containing additional features. + + Returns: + ForecastInputDataset with combined features. + """ + additional_df = additional_features.data.loc[ + :, [col for col in additional_features.data.columns if col not in data.data.columns] + ] + # Merge on index to combine datasets + combined_df = data.data.join(additional_df) + + return ForecastInputDataset( + data=combined_df, + sample_interval=data.sample_interval, + forecast_start=data.forecast_start, + ) + @override - def fit(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> None: + def fit( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> None: + for i, q in enumerate(self.quantiles): - self.models[i].fit(data=base_learner_predictions[q], data_val=None) + if additional_features is not None: + data = self._combine_datasets( + data=base_learner_predictions[q], + additional_features=additional_features, + ) + else: + data = base_learner_predictions[q] + + self.models[i].fit(data=data, data_val=None) @override - def predict(self, base_learner_predictions: dict[Quantile, ForecastInputDataset]) -> ForecastDataset: + def predict( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) # Generate predictions - predictions = [ - self.models[i].predict(data=base_learner_predictions[q]).data for i, q in enumerate(self.quantiles) - ] + predictions: list[pd.DataFrame] = [] + for i, q in enumerate(self.quantiles): + if additional_features is not None: + data = self._combine_datasets( + data=base_learner_predictions[q], + additional_features=additional_features, + ) + else: + data = base_learner_predictions[q] + + p = self.models[i].predict(data=data).data + predictions.append(p) # Concatenate predictions along columns to form a DataFrame with quantile columns df = pd.concat(predictions, axis=1) @@ -106,9 +174,9 @@ class StackingHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_hyperparams: BaseLearnerHyperParams = Field( - default=GBLinearHyperParams(), - description="Hyperparameters for the final learner. Defaults to GBLinearHyperParams.", + final_hyperparams: StackingFinalLearnerHyperParams = Field( + default=StackingFinalLearnerHyperParams(), + description="Hyperparameters for the final learner.", ) use_classifier: bool = Field( @@ -156,10 +224,9 @@ def __init__(self, config: StackingForecasterConfig) -> None: config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - final_forecaster = self._init_base_learners( - config=config, base_hyperparams=[config.hyperparams.final_hyperparams] - )[0] - self._final_learner = StackingFinalLearner(forecaster=final_forecaster) + self._final_learner = StackingFinalLearner( + quantiles=config.quantiles, hyperparams=config.hyperparams.final_hyperparams, horizon=config.max_horizon + ) __all__ = ["StackingFinalLearner", "StackingForecaster", "StackingForecasterConfig", "StackingHyperParams"] diff --git a/packages/openstef-meta/tests/models/test_stacking_forecaster.py b/packages/openstef-meta/tests/models/test_stacking_forecaster.py index e8543f055..9eccde9b9 100644 --- a/packages/openstef-meta/tests/models/test_stacking_forecaster.py +++ b/packages/openstef-meta/tests/models/test_stacking_forecaster.py @@ -14,6 +14,7 @@ StackingForecasterConfig, StackingHyperParams, ) +from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder @pytest.fixture @@ -103,3 +104,33 @@ def test_stacking_forecaster_with_sample_weights( # (This is a statistical test - with different weights, predictions should differ) differences = (result_with_weights.data - result_without_weights.data).abs() assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_stacking_forecaster_with_additional_features( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: StackingForecasterConfig, +): + """Test that forecaster works with additional features for the final learner.""" + + base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + + # Arrange + expected_quantiles = base_config.quantiles + forecaster = StackingForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" From c053ea52f84accbbf35382ed4a409e47beebf2db Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 25 Nov 2025 17:13:26 +0100 Subject: [PATCH 34/72] added example to openstef Meta Signed-off-by: Lars van Someren --- .../examples/liander_2024_residual.py | 154 ++++++++++++++++++ .../presets/forecasting_workflow.py | 2 +- 2 files changed, 155 insertions(+), 1 deletion(-) create mode 100644 packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py diff --git a/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py b/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py new file mode 100644 index 000000000..3448697ac --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py @@ -0,0 +1,154 @@ +"""Liander 2024 Benchmark Example. + +==================================== + +This example demonstrates how to set up and run the Liander 2024 STEF benchmark using OpenSTEF BEAM. +The benchmark will evaluate XGBoost and GBLinear models on the dataset from HuggingFace. +""" + +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +import os +import time + +os.environ["OMP_NUM_THREADS"] = "1" # Set OMP_NUM_THREADS to 1 to avoid issues with parallel execution and xgboost +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +import logging +import multiprocessing +from datetime import timedelta +from pathlib import Path + +from pydantic_extra_types.coordinate import Coordinate +from pydantic_extra_types.country import CountryAlpha2 + +from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig, OpenSTEF4BacktestForecaster +from openstef_beam.benchmarking.benchmark_pipeline import BenchmarkContext +from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024Category, create_liander2024_benchmark_runner +from openstef_beam.benchmarking.callbacks.strict_execution_callback import StrictExecutionCallback +from openstef_beam.benchmarking.models.benchmark_target import BenchmarkTarget +from openstef_beam.benchmarking.storage.local_storage import LocalBenchmarkStorage +from openstef_core.types import LeadTime, Q +from openstef_models.integrations.mlflow.mlflow_storage import MLFlowStorage +from openstef_models.presets import ( + ForecastingWorkflowConfig, + create_forecasting_workflow, +) +from openstef_models.presets.forecasting_workflow import LocationConfig +from openstef_models.workflows import CustomForecastingWorkflow + +logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") + +OUTPUT_PATH = Path("./benchmark_results") + +N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark + +model = "residual" # Can be "stacking", "learned_weights" or "residual" + +# Model configuration +FORECAST_HORIZONS = [LeadTime.from_string("PT36H")] # Forecast horizon(s) +PREDICTION_QUANTILES = [ + Q(0.05), + Q(0.1), + Q(0.3), + Q(0.5), + Q(0.7), + Q(0.9), + Q(0.95), +] # Quantiles for probabilistic forecasts + +BENCHMARK_FILTER: list[Liander2024Category] | None = None + +USE_MLFLOW_STORAGE = False + +if USE_MLFLOW_STORAGE: + storage = MLFlowStorage( + tracking_uri=str(OUTPUT_PATH / "mlflow_artifacts"), + local_artifacts_path=OUTPUT_PATH / "mlflow_tracking_artifacts", + ) +else: + storage = None + +common_config = ForecastingWorkflowConfig( + model_id="common_model_", + model=model, + horizons=FORECAST_HORIZONS, + quantiles=PREDICTION_QUANTILES, + model_reuse_enable=False, + mlflow_storage=None, + radiation_column="shortwave_radiation", + rolling_aggregate_features=["mean", "median", "max", "min"], + wind_speed_column="wind_speed_80m", + pressure_column="surface_pressure", + temperature_column="temperature_2m", + relative_humidity_column="relative_humidity_2m", + energy_price_column="EPEX_NL", +) + + +# Create the backtest configuration +backtest_config = BacktestForecasterConfig( + requires_training=True, + predict_length=timedelta(days=7), + predict_min_length=timedelta(minutes=15), + predict_context_length=timedelta(days=14), # Context needed for lag features + predict_context_min_coverage=0.5, + training_context_length=timedelta(days=90), # Three months of training data + training_context_min_coverage=0.5, + predict_sample_interval=timedelta(minutes=15), +) + + +def _target_forecaster_factory( + context: BenchmarkContext, + target: BenchmarkTarget, +) -> OpenSTEF4BacktestForecaster: + # Factory function that creates a forecaster for a given target. + prefix = context.run_name + base_config = common_config + + def _create_workflow() -> CustomForecastingWorkflow: + # Create a new workflow instance with fresh model. + return create_forecasting_workflow( + config=base_config.model_copy( + update={ + "model_id": f"{prefix}_{target.name}", + "location": LocationConfig( + name=target.name, + description=target.description, + coordinate=Coordinate( + latitude=target.latitude, + longitude=target.longitude, + ), + country_code=CountryAlpha2("NL"), + ), + } + ) + ) + + return OpenSTEF4BacktestForecaster( + config=backtest_config, + workflow_factory=_create_workflow, + debug=False, + cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", + ) + + +if __name__ == "__main__": + start_time = time.time() + create_liander2024_benchmark_runner( + storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), + data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), # adjust path as needed + callbacks=[StrictExecutionCallback()], + ).run( + forecaster_factory=_target_forecaster_factory, + run_name=model, + n_processes=N_PROCESSES, + filter_args=BENCHMARK_FILTER, + ) + + end_time = time.time() + print(f"Benchmark completed in {end_time - start_time:.2f} seconds.") diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index df0f2c59c..37dc5bbdb 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -26,8 +26,8 @@ from openstef_core.mixins import TransformPipeline from openstef_core.types import LeadTime, Q, Quantile, QuantileOrGlobal from openstef_meta.models.learned_weights_forecaster import LearnedWeightsForecaster -from openstef_meta.models.stacking_forecaster import StackingForecaster from openstef_meta.models.residual_forecaster import ResidualForecaster +from openstef_meta.models.stacking_forecaster import StackingForecaster from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback from openstef_models.mixins import ModelIdentifier from openstef_models.models import ForecastingModel From 1d5d97d9c9b28fb77295a3e60024ff045fb222ee Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 25 Nov 2025 21:34:53 +0100 Subject: [PATCH 35/72] RulesForecaster with dummy features Signed-off-by: Lars van Someren --- .../src/openstef_meta/__init__.py | 2 +- .../openstef_meta/models/rules_forecaster.py | 198 ++++++++++++++++++ .../src/openstef_meta/utils/__init__.py | 11 + .../src/openstef_meta/utils/decision_tree.py | 138 ++++++++++++ .../tests/models/test_rules_forecaster.py | 136 ++++++++++++ .../openstef-meta/tests/utils/__init__.py | 0 .../tests/utils/test_decision_tree.py | 41 ++++ 7 files changed, 525 insertions(+), 1 deletion(-) create mode 100644 packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py create mode 100644 packages/openstef-meta/src/openstef_meta/utils/decision_tree.py create mode 100644 packages/openstef-meta/tests/models/test_rules_forecaster.py create mode 100644 packages/openstef-meta/tests/utils/__init__.py create mode 100644 packages/openstef-meta/tests/utils/test_decision_tree.py diff --git a/packages/openstef-meta/src/openstef_meta/__init__.py b/packages/openstef-meta/src/openstef_meta/__init__.py index e659c6c12..ff5902981 100644 --- a/packages/openstef-meta/src/openstef_meta/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/__init__.py @@ -1,7 +1,7 @@ # SPDX-FileCopyrightText: 2017-2025 Contributors to the OpenSTEF project # # SPDX-License-Identifier: MPL-2.0 -"""Core models for OpenSTEF.""" +"""Meta models for OpenSTEF.""" import logging diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py new file mode 100644 index 000000000..4211b9e18 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -0,0 +1,198 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Rules-based Meta Forecaster Module.""" + +import logging +from typing import override + +import pandas as pd +from pydantic import Field, field_validator +from pydantic_extra_types.country import CountryAlpha2 + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.mixins import HyperParams +from openstef_core.transforms import TimeSeriesTransform +from openstef_core.types import Quantile +from openstef_meta.framework.base_learner import ( + BaseLearner, + BaseLearnerHyperParams, +) +from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams +from openstef_meta.framework.meta_forecaster import ( + EnsembleForecaster, +) +from openstef_meta.utils.decision_tree import Decision, DecisionTree, Rule +from openstef_models.models.forecasting.forecaster import ( + ForecasterConfig, +) +from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearHyperParams, +) +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.transforms.time_domain import HolidayFeatureAdder + +logger = logging.getLogger(__name__) + + +class RulesLearnerHyperParams(FinalLearnerHyperParams): + """HyperParams for Stacking Final Learner.""" + + feature_adders: list[TimeSeriesTransform] = Field( + default=[], + description="Additional features to add to the final learner.", + ) + + decision_tree: DecisionTree = Field( + description="Decision tree defining the rules for the final learner.", + ) + + @field_validator("feature_adders", mode="after") + @classmethod + def _check_not_empty(cls, v: list[TimeSeriesTransform]) -> list[TimeSeriesTransform]: + if v == []: + raise ValueError("RulesForecaster requires at least one feature adder.") + return v + + +class RulesLearner(FinalLearner): + """Combines base learner predictions per quantile into final predictions using a regression approach.""" + + def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperParams) -> None: + """Initialize the Rules Learner. + + Args: + quantiles: List of quantiles to predict. + hyperparams: Hyperparameters for the final learner. + horizon: Forecast horizon for which to create the final learner. + """ + super().__init__(quantiles=quantiles, hyperparams=hyperparams) + + self.tree = hyperparams.decision_tree + self.feature_adders = hyperparams.feature_adders + + @override + def fit( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> None: + # No fitting needed for rule-based final learner + # Check that additional features are provided + if additional_features is None: + raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") + + def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: + """Predict using the decision tree rules. + + Args: + data: DataFrame containing the additional features. + columns: Expected columns for the output DataFrame. + + Returns: + DataFrame with predictions for each quantile. + """ + predictions = data.apply(self.tree.get_decision, axis=1) + + return pd.get_dummies(predictions).reindex(columns=columns) + + @override + def predict( + self, + base_learner_predictions: dict[Quantile, ForecastInputDataset], + additional_features: ForecastInputDataset | None, + ) -> ForecastDataset: + if additional_features is None: + raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") + + decisions = self._predict_tree( + additional_features.data, columns=base_learner_predictions[self.quantiles[0]].data.columns + ) + + # Generate predictions + predictions: list[pd.DataFrame] = [] + for q, data in base_learner_predictions.items(): + preds = data.data * decisions + predictions.append(preds.sum(axis=1).to_frame(name=Quantile(q).format())) + + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + return ForecastDataset( + data=df, + sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + ) + + @property + def is_fitted(self) -> bool: + """Check the Rules Final Learner is fitted.""" + return True + + +class RulesForecasterHyperParams(HyperParams): + """Hyperparameters for Rules Forecaster.""" + + base_hyperparams: list[BaseLearnerHyperParams] = Field( + default=[LGBMHyperParams(), GBLinearHyperParams()], + description="List of hyperparameter configurations for base learners. " + "Defaults to [LGBMHyperParams, GBLinearHyperParams].", + ) + + final_hyperparams: RulesLearnerHyperParams = Field( + description="Hyperparameters for the final learner.", + default=RulesLearnerHyperParams( + decision_tree=DecisionTree(nodes=[Decision(idx=0, decision="LGBMForecaster")], outcomes={"LGBMForecaster"}), + feature_adders=[HolidayFeatureAdder(country_code=CountryAlpha2("NL"))], + ), + ) + + @field_validator("base_hyperparams", mode="after") + @classmethod + def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: + hp_classes = [type(hp) for hp in v] + if not len(hp_classes) == len(set(hp_classes)): + raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") + return v + + +class RulesForecasterConfig(ForecasterConfig): + """Configuration for Hybrid-based forecasting models.""" + + hyperparams: RulesForecasterHyperParams = Field( + default=RulesForecasterHyperParams(), + description="Hyperparameters for the Hybrid forecaster.", + ) + + verbosity: bool = Field( + default=True, + description="Enable verbose output from the Hybrid model (True/False).", + ) + + +class RulesForecaster(EnsembleForecaster): + """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + + Config = RulesForecasterConfig + HyperParams = RulesForecasterHyperParams + + def __init__(self, config: RulesForecasterConfig) -> None: + """Initialize the Hybrid forecaster.""" + self._config = config + + self._base_learners: list[BaseLearner] = self._init_base_learners( + config=config, base_hyperparams=config.hyperparams.base_hyperparams + ) + + self._final_learner = RulesLearner( + quantiles=config.quantiles, + hyperparams=config.hyperparams.final_hyperparams, + ) + + +__all__ = [ + "RulesForecaster", + "RulesForecasterConfig", + "RulesForecasterHyperParams", + "RulesLearner", + "RulesLearnerHyperParams", +] diff --git a/packages/openstef-meta/src/openstef_meta/utils/__init__.py b/packages/openstef-meta/src/openstef_meta/utils/__init__.py index e69de29bb..8b8144daa 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/utils/__init__.py @@ -0,0 +1,11 @@ +"""Utility functions and classes for OpenSTEF Meta.""" + +from .decision_tree import Decision, DecisionTree, Rule +from .pinball_errors import calculate_pinball_errors + +__all__ = [ + "Decision", + "DecisionTree", + "Rule", + "calculate_pinball_errors", +] diff --git a/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py b/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py new file mode 100644 index 000000000..c5d49852a --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py @@ -0,0 +1,138 @@ +from typing import Literal + +import pandas as pd +from pydantic import BaseModel, Field, model_validator + + +class Node(BaseModel): + """A node in the decision tree, either a rule or a decision.""" + + idx: int = Field( + description="Index of the rule in the decision tree.", + ) + + +class Rule(Node): + """A single rule in the decision tree.""" + + idx: int = Field( + description="Index of the decision in the decision tree.", + ) + + rule_type: Literal["greater_than", "less_than"] = Field( + ..., + description="Type of the rule to apply.", + ) + feature_name: str = Field( + ..., + description="Name of the feature to which the rule applies.", + ) + + threshold: float | int = Field( + ..., + description="Threshold value for the rule.", + ) + + next_true: int = Field( + ..., + description="Index of the next rule if the condition is true.", + ) + + next_false: int = Field( + ..., + description="Index of the next rule if the condition is false.", + ) + + +class Decision(Node): + """A leaf decision in the decision tree.""" + + idx: int = Field( + description="Index of the decision in the decision tree.", + ) + + decision: str = Field( + ..., + description="The prediction value at this leaf.", + ) + + +class DecisionTree(BaseModel): + """A simple decision tree defined by a list of rules.""" + + nodes: list[Node] = Field( + ..., + description="List of rules that define the decision tree.", + ) + + outcomes: set[str] = Field( + ..., + description="Set of possible outcomes from the decision tree.", + ) + + @model_validator(mode="after") + def validate_tree_structure(self) -> "DecisionTree": + """Validate that the tree structure is correct. + + Raises: + ValueError: If tree is not built correctly. + + Returns: + The validated DecisionTree instance. + """ + node_idx = {node.idx for node in self.nodes} + if node_idx != set(range(len(self.nodes))): + raise ValueError("Rule indices must be consecutive starting from 0.") + + for node in self.nodes: + if isinstance(node, Rule): + if node.next_true not in node_idx: + msg = f"next_true index {node.next_true} not found in nodes." + raise ValueError(msg) + if node.next_false not in node_idx: + msg = f"next_false index {node.next_false} not found in nodes." + raise ValueError(msg) + if isinstance(node, Decision) and node.decision not in self.outcomes: + msg = f"Decision '{node.decision}' not in defined outcomes {self.outcomes}." + raise ValueError(msg) + + return self + + def get_decision(self, row: pd.Series) -> str: + """Get decision from the decision tree based on input features. + + Args: + row: Series containing feature values. + + Returns: + The decision outcome as a string. + + Raises: + ValueError: If the tree structure is invalid. + TypeError: If a node type is invalid. + """ + current_idx = 0 + while True: + current_node = self.nodes[current_idx] + if isinstance(current_node, Decision): + return current_node.decision + if isinstance(current_node, Rule): + feature_value = row[current_node.feature_name] + if current_node.rule_type == "greater_than": + if feature_value > current_node.threshold: + current_idx = current_node.next_true + else: + current_idx = current_node.next_false + elif current_node.rule_type == "less_than": + if feature_value < current_node.threshold: + current_idx = current_node.next_true + else: + current_idx = current_node.next_false + else: + msg = f"Invalid rule type '{current_node.rule_type}' at index {current_idx}." + raise ValueError(msg) + else: + msg = f"Invalid node type at index {current_idx}." + raise TypeError(msg) + + __all__ = ["Node", "Rule", "Decision", "DecisionTree"] diff --git a/packages/openstef-meta/tests/models/test_rules_forecaster.py b/packages/openstef-meta/tests/models/test_rules_forecaster.py new file mode 100644 index 000000000..434f0c6c2 --- /dev/null +++ b/packages/openstef-meta/tests/models/test_rules_forecaster.py @@ -0,0 +1,136 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_meta.models.rules_forecaster import ( + RulesForecaster, + RulesForecasterConfig, + RulesForecasterHyperParams, +) +from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder + + +@pytest.fixture +def base_config() -> RulesForecasterConfig: + """Base configuration for Rules forecaster tests.""" + + params = RulesForecasterHyperParams() + return RulesForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=params, + verbosity=False, + ) + + +def test_rules_forecaster_fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: RulesForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = RulesForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +def test_rules_forecaster_predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: RulesForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = RulesForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError, match="RulesForecaster"): + forecaster.predict(sample_forecast_input_dataset) + + +def test_rules_forecaster_with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: RulesForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = RulesForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = RulesForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_rules_forecaster_with_additional_features( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: RulesForecasterConfig, +): + """Test that forecaster works with additional features for the final learner.""" + + base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + + # Arrange + expected_quantiles = base_config.quantiles + forecaster = RulesForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/utils/__init__.py b/packages/openstef-meta/tests/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/utils/test_decision_tree.py b/packages/openstef-meta/tests/utils/test_decision_tree.py new file mode 100644 index 000000000..11298e9d5 --- /dev/null +++ b/packages/openstef-meta/tests/utils/test_decision_tree.py @@ -0,0 +1,41 @@ +import pandas as pd +import pytest + +from openstef_meta.utils.decision_tree import Decision, DecisionTree, Node, Rule + + +@pytest.fixture +def sample_dataset() -> pd.DataFrame: + data = { + "feature_1": [1, 2, 3, 4, 5], + "feature_2": [10, 20, 30, 40, 50], + } + return pd.DataFrame(data) + + +@pytest.fixture +def simple_decision_tree() -> DecisionTree: + nodes: list[Node] = [ + Rule( + idx=0, + rule_type="less_than", + feature_name="feature_1", + threshold=3, + next_true=1, + next_false=2, + ), + Decision(idx=1, decision="Class_A"), + Decision(idx=2, decision="Class_B"), + ] + return DecisionTree(nodes=nodes, outcomes={"Class_A", "Class_B"}) + + +def test_decision_tree_prediction(sample_dataset: pd.DataFrame, simple_decision_tree: DecisionTree): + + decisions = sample_dataset.apply(simple_decision_tree.get_decision, axis=1) + + expected_decisions = pd.Series( + ["Class_A", "Class_A", "Class_B", "Class_B", "Class_B"], + ) + + pd.testing.assert_series_equal(decisions, expected_decisions) From 100494cbbd079636d755da731527227855991e5b Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 26 Nov 2025 10:55:24 +0100 Subject: [PATCH 36/72] Updated feature specification Signed-off-by: Lars van Someren --- .../openstef_meta/framework/final_learner.py | 34 ++++++++-- .../framework/meta_forecaster.py | 1 + .../src/openstef_meta/transforms/selector.py | 50 ++++++++++++++ .../presets/forecasting_workflow.py | 12 ++-- .../general/distribution_transform.py | 65 ------------------- 5 files changed, 84 insertions(+), 78 deletions(-) create mode 100644 packages/openstef-meta/src/openstef_meta/transforms/selector.py delete mode 100644 packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index dc92cc1c1..f26105aa1 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -9,13 +9,17 @@ """ from abc import ABC, abstractmethod +from collections.abc import Sequence from pydantic import ConfigDict, Field from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset from openstef_core.mixins import HyperParams, TransformPipeline from openstef_core.transforms import TimeSeriesTransform +from openstef_models.transforms.general.scaler import Scaler from openstef_core.types import Quantile +from openstef_models.utils.feature_selection import FeatureSelection +from openstef_meta.transforms.selector import Selector class FinalLearnerHyperParams(HyperParams): @@ -23,8 +27,28 @@ class FinalLearnerHyperParams(HyperParams): model_config = ConfigDict(arbitrary_types_allowed=True) - feature_adders: list[TimeSeriesTransform] = Field( - default=[], + feature_adders: Sequence[TimeSeriesTransform] = Field( + default=[ + Selector( + selection=FeatureSelection( + include={ + "temperature_2m", + "relative_humidity_2m", + "surface_pressure", + "cloud_cover", + "wind_speed_10m", + "wind_speed_80m", + "wind_direction_10m", + "shortwave_radiation", + "direct_radiation", + "diffuse_radiation", + "direct_normal_irradiance", + "load", + } + ), + ), + Scaler(method="standard"), + ], description="Additional features to add to the base learner predictions before fitting the final learner.", ) @@ -82,11 +106,7 @@ def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset Returns: TimeSeriesDataset with additional features. """ - data_ts = TimeSeriesDataset( - data=data.data, - sample_interval=data.sample_interval, - ) - data_transformed = self.final_learner_processing.transform(data_ts) + data_transformed = self.final_learner_processing.transform(data) return ForecastInputDataset( data=data_transformed.data, diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 3af6329d7..670eeb1bc 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -105,6 +105,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) if self._final_learner.has_features: + self._final_learner.final_learner_processing.fit(data=full_dataset) features = self._final_learner.calculate_features(data=full_dataset) else: features = None diff --git a/packages/openstef-meta/src/openstef_meta/transforms/selector.py b/packages/openstef-meta/src/openstef_meta/transforms/selector.py new file mode 100644 index 000000000..75eb4e321 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/transforms/selector.py @@ -0,0 +1,50 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Transform for dropping rows containing NaN values. + +This module provides functionality to drop rows containing NaN values in selected +columns, useful for data cleaning and ensuring complete cases for model training. +""" + +from typing import override + +from pydantic import Field + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.datasets.validated_datasets import ForecastInputDataset +from openstef_core.transforms import TimeSeriesTransform +from openstef_models.utils.feature_selection import FeatureSelection + + +class Selector(BaseConfig, TimeSeriesTransform): + """Selects features based on FeatureSelection.""" + + selection: FeatureSelection = Field( + default=FeatureSelection.ALL, + description="Features to check for NaN values. Rows with NaN in any selected column are dropped.", + ) + + @override + def fit(self, data: TimeSeriesDataset) -> None: + if ( + isinstance(data, ForecastInputDataset) + and self.selection.include is not None + and (data.target_column not in self.selection.include) + ): + self.selection.include.add(data.target_column) + + @override + def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: + + features = self.selection.resolve(data.feature_names) + + transformed_data = data.data.drop(columns=[col for col in data.feature_names if col not in features]) + + return data.copy_with(data=transformed_data, is_sorted=True) + + @override + def features_added(self) -> list[str]: + return [] diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 37dc5bbdb..ba0e279f5 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -451,11 +451,11 @@ def create_forecasting_workflow( selection=Exclude(config.target_column), ), ] - forecaster = ResidualForecaster( - config=ResidualForecaster.Config( + forecaster = LearnedWeightsForecaster( + config=LearnedWeightsForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, - hyperparams=config.residual_hyperparams, + hyperparams=config.learned_weights_hyperparams, ) ) postprocessing = [QuantileSorter()] @@ -473,11 +473,11 @@ def create_forecasting_workflow( selection=Exclude(config.target_column), ), ] - forecaster = LearnedWeightsForecaster( - config=LearnedWeightsForecaster.Config( + forecaster = ResidualForecaster( + config=ResidualForecaster.Config( quantiles=config.quantiles, horizons=config.horizons, - hyperparams=config.learned_weights_hyperparams, + hyperparams=config.residual_hyperparams, ) ) postprocessing = [QuantileSorter()] diff --git a/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py b/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py deleted file mode 100644 index 8e93da672..000000000 --- a/packages/openstef-models/src/openstef_models/transforms/general/distribution_transform.py +++ /dev/null @@ -1,65 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""Transform for clipping feature values to observed ranges. - -This module provides functionality to clip feature values to their observed -minimum and maximum ranges during training, preventing out-of-range values -during inference and improving model robustness. -""" - -from typing import Literal, override - -import pandas as pd -from pydantic import Field, PrivateAttr - -from openstef_core.base_model import BaseConfig -from openstef_core.datasets import TimeSeriesDataset -from openstef_core.exceptions import NotFittedError -from openstef_core.transforms import TimeSeriesTransform -from openstef_models.utils.feature_selection import FeatureSelection - -type ClipMode = Literal["minmax", "standard"] - - -class DistributionTransform(BaseConfig, TimeSeriesTransform): - """Transform dataframe to (robust) percentage of min-max of training data. - - Useful to determine whether datadrift has occured. - Can be used as a feature for learning sample weights in meta models. - """ - - robust_threshold: float = Field( - default=2.0, - description="Percentage of observations to ignore when determing percentage. (Single sided)", - ) - - _feature_mins: pd.Series = PrivateAttr(default_factory=pd.Series) - _feature_maxs: pd.Series = PrivateAttr(default_factory=pd.Series) - _is_fitted: bool = PrivateAttr(default=False) - - @property - @override - def is_fitted(self) -> bool: - return self._is_fitted - - @override - def fit(self, data: TimeSeriesDataset) -> None: - self._feature_mins = data.data.min(axis=0) - self._feature_maxs = data.data.max(axis=0) - self._is_fitted = True - - @override - def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: - if not self._is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Apply min-max scaling to each feature based on fitted min and max - transformed_data = (data.data - self._feature_mins) / (self._feature_maxs - self._feature_mins) - - return TimeSeriesDataset(data=transformed_data, sample_interval=data.sample_interval) - - @override - def features_added(self) -> list[str]: - return [] From d8d10a1f42a3592cbb5c52b50809f4926c9198dc Mon Sep 17 00:00:00 2001 From: floriangoethals Date: Wed, 26 Nov 2025 14:30:21 +0100 Subject: [PATCH 37/72] entered flagger feature in new architecture --- .../openstef_meta/framework/final_learner.py | 3 +- .../framework/meta_forecaster.py | 1 + .../presets/forecasting_workflow.py | 4 +- .../transforms/general/__init__.py | 2 + .../transforms/general/flag_features_bound.py | 105 ++++++++++++++++++ .../general/test_flag_features_bound.py | 64 +++++++++++ 6 files changed, 176 insertions(+), 3 deletions(-) create mode 100644 packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py create mode 100644 packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index dc92cc1c1..22fcf4d7a 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -16,6 +16,7 @@ from openstef_core.mixins import HyperParams, TransformPipeline from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile +from openstef_models.transforms.general import Flagger class FinalLearnerHyperParams(HyperParams): @@ -24,7 +25,7 @@ class FinalLearnerHyperParams(HyperParams): model_config = ConfigDict(arbitrary_types_allowed=True) feature_adders: list[TimeSeriesTransform] = Field( - default=[], + default=[Flagger()], description="Additional features to add to the base learner predictions before fitting the final learner.", ) diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 3af6329d7..c5ba7a33b 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -105,6 +105,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) if self._final_learner.has_features: + self._final_learner.final_learner_processing.fit(full_dataset) features = self._final_learner.calculate_features(data=full_dataset) else: features = None diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 37dc5bbdb..8c4c7e611 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -437,7 +437,7 @@ def create_forecasting_workflow( postprocessing = [ ConfidenceIntervalApplicator(quantiles=config.quantiles), ] - elif config.model == "learned_weights": + elif config.model == "residual": preprocessing = [ *checks, *feature_adders, @@ -459,7 +459,7 @@ def create_forecasting_workflow( ) ) postprocessing = [QuantileSorter()] - elif config.model == "residual": + elif config.model == "learned_weights": preprocessing = [ *checks, *feature_adders, diff --git a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py index 79e59f58b..57a5b6187 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py @@ -17,6 +17,7 @@ from openstef_models.transforms.general.nan_dropper import NaNDropper from openstef_models.transforms.general.sample_weighter import SampleWeighter from openstef_models.transforms.general.scaler import Scaler +from openstef_models.transforms.general.flag_features_bound import Flagger __all__ = [ "Clipper", @@ -26,4 +27,5 @@ "NaNDropper", "SampleWeighter", "Scaler", + "Flagger" ] diff --git a/packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py b/packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py new file mode 100644 index 000000000..6f4a4df3a --- /dev/null +++ b/packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py @@ -0,0 +1,105 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Transform for clipping feature values to observed ranges. + +This module provides functionality to clip feature values to their observed +minimum and maximum ranges during training, preventing out-of-range values +during inference and improving model robustness. +""" + +from typing import Literal, override + +import pandas as pd +from pydantic import Field, PrivateAttr + +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.transforms import TimeSeriesTransform +from openstef_models.utils.feature_selection import FeatureSelection + + + + + +class Flagger(BaseConfig, TimeSeriesTransform): + """Transform that flags specified features to their observed min and max values. + + This transform flags the peaks for the metalearner to know when to expect outliers and + extrapolate from its training set. + + + Example: + >>> import pandas as pd + >>> from datetime import timedelta + >>> from openstef_core.datasets import TimeSeriesDataset + >>> from openstef_models.transforms.general import Clipper + >>> + >>> # Create sample training dataset + >>> training_data = pd.DataFrame({ + ... 'load': [100, 120, 110, 130, 125], + ... 'temperature': [20, 22, 21, 23, 24] + ... }, index=pd.date_range('2025-01-01', periods=5, freq='1h')) + >>> training_dataset = TimeSeriesDataset(training_data, timedelta(hours=1)) + >>> test_data = pd.DataFrame({ + ... 'load': [90, 140, 115], + ... 'temperature': [19, 25, 22] + ... }, index=pd.date_range('2025-01-06', periods=3, + ... freq='1h')) + >>> test_dataset = TimeSeriesDataset(test_data, timedelta(hours=1)) + >>> # Initialize and apply transform + >>> clipper = Clipper(selection=FeatureSelection(include=['load', 'temperature']), mode='minmax') + >>> clipper.fit(training_dataset) + >>> transformed_dataset = clipper.transform(test_dataset) + >>> clipper._feature_mins.to_dict() + {'load': 100, 'temperature': 20} + >>> clipper._feature_maxs.to_dict() + {'load': 130, 'temperature': 24} + >>> transformed_dataset.data['load'].tolist() + [100, 130, 115] + >>> transformed_dataset.data['temperature'].tolist() + [20, 24, 22] + + """ + + selection: FeatureSelection = Field(default=FeatureSelection.ALL, description="Features to flag.") + + _feature_mins: pd.Series = PrivateAttr(default_factory=pd.Series) + _feature_maxs: pd.Series = PrivateAttr(default_factory=pd.Series) + _is_fitted: bool = PrivateAttr(default=False) + @property + @override + def is_fitted(self) -> bool: + return self._is_fitted + + @override + def fit(self, data: TimeSeriesDataset) -> None: + features = self.selection.resolve(data.feature_names) + self._feature_mins = data.data.reindex(features, axis=1).min() + self._feature_maxs = data.data.reindex(features, axis=1).max() + self._is_fitted = True + + @override + def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: + if not self._is_fitted: + raise NotFittedError(self.__class__.__name__) + + features = self.selection.resolve(data.feature_names) + transformed_data = data.data.copy(deep=False).loc[:, features] + + # compute min & max of the features + min_aligned = self._feature_mins.reindex(features) + max_aligned = self._feature_maxs.reindex(features) + + outside = (transformed_data[features] <= min_aligned) | (transformed_data[features] >= max_aligned) + transformed_data = (~outside).astype(int) + + + + return TimeSeriesDataset(data=transformed_data, sample_interval=data.sample_interval) + + @override + def features_added(self) -> list[str]: + return [] diff --git a/packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py b/packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py new file mode 100644 index 000000000..62f31ef97 --- /dev/null +++ b/packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py @@ -0,0 +1,64 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pandas as pd +import pytest + +from openstef_core.datasets import TimeSeriesDataset +from openstef_core.exceptions import NotFittedError +from openstef_models.transforms.general import Flagger +from openstef_models.utils.feature_selection import FeatureSelection + + +@pytest.fixture +def train_dataset() -> TimeSeriesDataset: + """Training dataset with three features A, B, C.""" + return TimeSeriesDataset( + data=pd.DataFrame( + {"A": [1.0, 2.0, 3.0], "B": [1.0, 2.0, 3.0], "C": [1.0, 2.0, 3.0]}, + index=pd.date_range("2025-01-01", periods=3, freq="1h"), + ), + sample_interval=timedelta(hours=1), + ) + + +@pytest.fixture +def test_dataset() -> TimeSeriesDataset: + """Test dataset with values outside training ranges.""" + return TimeSeriesDataset( + data=pd.DataFrame( + {"A": [2, 2], "B": [0.0, 2.0], "C": [1, 4]}, + index=pd.date_range("2025-01-06", periods=2, freq="1h"), + ), + sample_interval=timedelta(hours=1), + ) + + + +def test_flagger__fit_transform( + train_dataset: TimeSeriesDataset, + test_dataset: TimeSeriesDataset, + ): + """Test fit and transform flags correctly leaves other columns unchanged.""" + # Arrange + flagger = Flagger(selection=FeatureSelection(include={"A", "B", "C"})) + + # Act + flagger.fit(train_dataset) + transformed_dataset = flagger.transform(test_dataset) + + # Assert + # Column C should remain unchanged + expected_df = pd.DataFrame( + { + "A": [1,1], + "B": [0,1], + "C": [0,0], # Unchanged + }, + index=test_dataset.index, + ) + pd.testing.assert_frame_equal(transformed_dataset.data, expected_df) + assert transformed_dataset.sample_interval == test_dataset.sample_interval From 797eee7328ddaf617287efb36326a81946acfc8c Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 26 Nov 2025 14:48:02 +0100 Subject: [PATCH 38/72] Fix sample weights Signed-off-by: Lars van Someren --- .../openstef_meta/framework/final_learner.py | 49 ++++++++++--------- .../framework/meta_forecaster.py | 1 + .../models/learned_weights_forecaster.py | 5 +- .../openstef_meta/models/rules_forecaster.py | 4 ++ 4 files changed, 35 insertions(+), 24 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index f26105aa1..4d0d7b719 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -11,15 +11,36 @@ from abc import ABC, abstractmethod from collections.abc import Sequence +import pandas as pd from pydantic import ConfigDict, Field from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset from openstef_core.mixins import HyperParams, TransformPipeline from openstef_core.transforms import TimeSeriesTransform -from openstef_models.transforms.general.scaler import Scaler from openstef_core.types import Quantile -from openstef_models.utils.feature_selection import FeatureSelection from openstef_meta.transforms.selector import Selector +from openstef_models.utils.feature_selection import FeatureSelection + +WEATHER_FEATURES = { + "temperature_2m", + "relative_humidity_2m", + "surface_pressure", + "cloud_cover", + "wind_speed_10m", + "wind_speed_80m", + "wind_direction_10m", + "shortwave_radiation", + "direct_radiation", + "diffuse_radiation", + "direct_normal_irradiance", + "load", +} + +SELECTOR = ( + Selector( + selection=FeatureSelection.NONE, + ), +) class FinalLearnerHyperParams(HyperParams): @@ -28,27 +49,7 @@ class FinalLearnerHyperParams(HyperParams): model_config = ConfigDict(arbitrary_types_allowed=True) feature_adders: Sequence[TimeSeriesTransform] = Field( - default=[ - Selector( - selection=FeatureSelection( - include={ - "temperature_2m", - "relative_humidity_2m", - "surface_pressure", - "cloud_cover", - "wind_speed_10m", - "wind_speed_80m", - "wind_direction_10m", - "shortwave_radiation", - "direct_radiation", - "diffuse_radiation", - "direct_normal_irradiance", - "load", - } - ), - ), - Scaler(method="standard"), - ], + default=[], description="Additional features to add to the base learner predictions before fitting the final learner.", ) @@ -70,6 +71,7 @@ def fit( self, base_learner_predictions: dict[Quantile, ForecastInputDataset], additional_features: ForecastInputDataset | None, + sample_weights: pd.Series | None = None, ) -> None: """Fit the final learner using base learner predictions. @@ -77,6 +79,7 @@ def fit( base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner predictions. additional_features: Optional ForecastInputDataset containing additional features for the final learner. + sample_weights: Optional series of sample weights for fitting. """ raise NotImplementedError("Subclasses must implement the fit method.") diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 670eeb1bc..85aaeb9b1 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -119,6 +119,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None self._final_learner.fit( base_learner_predictions=quantile_datasets, additional_features=features, + sample_weights=data.data.loc[:, data.sample_weight_column], ) self._is_fitted = True diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 255e25dd0..43e1ba567 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -89,6 +89,7 @@ def fit( self, base_learner_predictions: dict[Quantile, ForecastInputDataset], additional_features: ForecastInputDataset | None, + sample_weights: pd.Series | None = None, ) -> None: for i, q in enumerate(self.quantiles): @@ -121,8 +122,10 @@ def fit( self._label_encoder.fit(labels) labels = self._label_encoder.transform(labels) - # Balance classes + # Balance classes, adjust with sample weights weights = compute_sample_weight("balanced", labels) + if sample_weights is not None: + weights *= sample_weights self.models[i].fit(X=df, y=labels, sample_weight=weights) # type: ignore self._is_fitted = True diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index 4211b9e18..52033cbc5 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -76,12 +76,16 @@ def fit( self, base_learner_predictions: dict[Quantile, ForecastInputDataset], additional_features: ForecastInputDataset | None, + sample_weights: pd.Series | None = None, ) -> None: # No fitting needed for rule-based final learner # Check that additional features are provided if additional_features is None: raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") + if sample_weights is not None: + logger.warning("Sample weights are ignored in RulesLearner.fit method.") + def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: """Predict using the decision tree rules. From 88c68654ddb702713915673a4a31d34745357e02 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 27 Nov 2025 09:15:15 +0100 Subject: [PATCH 39/72] Fixes Signed-off-by: Lars van Someren --- .../src/openstef_meta/framework/final_learner.py | 12 +++++------- .../models/learned_weights_forecaster.py | 7 +++++-- .../openstef_models/presets/forecasting_workflow.py | 4 ++-- 3 files changed, 12 insertions(+), 11 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index eab95f1ca..c509dc05a 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -19,6 +19,7 @@ from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile from openstef_meta.transforms.selector import Selector +from openstef_models.transforms.general import Flagger from openstef_models.utils.feature_selection import FeatureSelection WEATHER_FEATURES = { @@ -36,12 +37,9 @@ "load", } -SELECTOR = ( - Selector( - selection=FeatureSelection.NONE, - ), +SELECTOR = Selector( + selection=FeatureSelection(include=WEATHER_FEATURES), ) -from openstef_models.transforms.general import Flagger class FinalLearnerHyperParams(HyperParams): @@ -49,8 +47,8 @@ class FinalLearnerHyperParams(HyperParams): model_config = ConfigDict(arbitrary_types_allowed=True) - feature_adders: list[TimeSeriesTransform] = Field( - default=[Flagger()], + feature_adders: Sequence[TimeSeriesTransform] = Field( + default=[SELECTOR], description="Additional features to add to the base learner predictions before fitting the final learner.", ) diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 43e1ba567..bf0150644 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -44,6 +44,8 @@ GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams logger = logging.getLogger(__name__) @@ -199,7 +201,7 @@ class LGBMLearnerHyperParams(LWFLHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" n_estimators: int = Field( - default=20, + default=200, description="Number of estimators for the LGBM Classifier. Defaults to 20.", ) @@ -232,6 +234,7 @@ def __init__( class_weight="balanced", n_estimators=hyperparams.n_estimators, num_leaves=hyperparams.n_leaves, + n_jobs=1, ) for _ in quantiles ] @@ -242,7 +245,7 @@ class RFLearnerHyperParams(LWFLHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( - default=20, + default=200, description="Number of estimators for the LGBM Classifier. Defaults to 20.", ) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 189f18df2..ba0e279f5 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -437,7 +437,7 @@ def create_forecasting_workflow( postprocessing = [ ConfidenceIntervalApplicator(quantiles=config.quantiles), ] - elif config.model == "residual": + elif config.model == "learned_weights": preprocessing = [ *checks, *feature_adders, @@ -459,7 +459,7 @@ def create_forecasting_workflow( ) ) postprocessing = [QuantileSorter()] - elif config.model == "learned_weights": + elif config.model == "residual": preprocessing = [ *checks, *feature_adders, From c6749b3ad9182b0ede8d372dd1606979720d5759 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 27 Nov 2025 15:25:43 +0100 Subject: [PATCH 40/72] PR compliant Signed-off-by: Lars van Someren --- packages/openstef-meta/pyproject.toml | 4 ++ .../src/openstef_meta/examples/__init__.py | 5 +++ .../examples/liander_2024_residual.py | 6 ++- .../openstef_meta/framework/final_learner.py | 1 - .../framework/meta_forecaster.py | 18 ++++++++- .../models/learned_weights_forecaster.py | 11 ++--- .../models/residual_forecaster.py | 4 +- .../openstef_meta/models/rules_forecaster.py | 6 +-- .../models/stacking_forecaster.py | 17 ++------ .../src/openstef_meta/transforms/__init__.py | 11 +++++ .../transforms}/flag_features_bound.py | 40 ++++++++----------- .../src/openstef_meta/utils/__init__.py | 4 ++ .../src/openstef_meta/utils/datasets.py | 0 .../src/openstef_meta/utils/decision_tree.py | 5 +++ .../src/openstef_meta/utils/pinball_errors.py | 4 ++ .../models/test_learned_weights_forecaster.py | 4 +- .../tests/transforms/__init__.py | 0 .../transforms}/test_flag_features_bound.py | 12 +++--- .../tests/utils/test_decision_tree.py | 4 ++ .../transforms/general/__init__.py | 2 - 20 files changed, 97 insertions(+), 61 deletions(-) create mode 100644 packages/openstef-meta/src/openstef_meta/examples/__init__.py create mode 100644 packages/openstef-meta/src/openstef_meta/transforms/__init__.py rename packages/{openstef-models/src/openstef_models/transforms/general => openstef-meta/src/openstef_meta/transforms}/flag_features_bound.py (79%) create mode 100644 packages/openstef-meta/src/openstef_meta/utils/datasets.py create mode 100644 packages/openstef-meta/tests/transforms/__init__.py rename packages/{openstef-models/tests/unit/transforms/general => openstef-meta/tests/transforms}/test_flag_features_bound.py (89%) diff --git a/packages/openstef-meta/pyproject.toml b/packages/openstef-meta/pyproject.toml index a91b25359..838ef8ee9 100644 --- a/packages/openstef-meta/pyproject.toml +++ b/packages/openstef-meta/pyproject.toml @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + [project] name = "openstef-meta" version = "0.1.0" diff --git a/packages/openstef-meta/src/openstef_meta/examples/__init__.py b/packages/openstef-meta/src/openstef_meta/examples/__init__.py new file mode 100644 index 000000000..765b7c107 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/examples/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Examples for OpenSTEF Meta.""" diff --git a/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py b/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py index 3448697ac..a8a42b113 100644 --- a/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py +++ b/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py @@ -42,6 +42,8 @@ logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") +logger = logging.getLogger(__name__) + OUTPUT_PATH = Path("./benchmark_results") N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark @@ -139,6 +141,7 @@ def _create_workflow() -> CustomForecastingWorkflow: if __name__ == "__main__": start_time = time.time() + create_liander2024_benchmark_runner( storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), # adjust path as needed @@ -151,4 +154,5 @@ def _create_workflow() -> CustomForecastingWorkflow: ) end_time = time.time() - print(f"Benchmark completed in {end_time - start_time:.2f} seconds.") + msg = f"Benchmark completed in {end_time - start_time:.2f} seconds." + logger.info(msg) diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index c509dc05a..cdf208309 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -19,7 +19,6 @@ from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile from openstef_meta.transforms.selector import Selector -from openstef_models.transforms.general import Flagger from openstef_models.utils.feature_selection import FeatureSelection WEATHER_FEATURES = { diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 928ac5651..56eb34681 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -64,6 +64,18 @@ def _init_base_learners( def config(self) -> ForecasterConfig: return self._config + @property + def feature_importances(self) -> pd.DataFrame: + """Placeholder for feature importances across base learners and final learner.""" + raise NotImplementedError("Feature importances are not implemented for EnsembleForecaster.") + # TODO(#745): Make MetaForecaster explainable + + @property + def model_contributions(self) -> pd.DataFrame: + """Placeholder for model contributions across base learners and final learner.""" + raise NotImplementedError("Model contributions are not implemented for EnsembleForecaster.") + # TODO(#745): Make MetaForecaster explainable + class EnsembleForecaster(MetaForecaster): """Abstract class for Meta forecasters combining multiple base learners and a final learner.""" @@ -116,10 +128,14 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None target_series=data.target_series, ) + sample_weights = None + if data.sample_weight_column in data.data.columns: + sample_weights = data.data.loc[:, data.sample_weight_column] + self._final_learner.fit( base_learner_predictions=quantile_datasets, additional_features=features, - sample_weights=data.data.loc[:, data.sample_weight_column], + sample_weights=sample_weights, ) self._is_fitted = True diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index bf0150644..df26edb12 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -44,8 +44,6 @@ GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams logger = logging.getLogger(__name__) @@ -93,6 +91,12 @@ def fit( additional_features: ForecastInputDataset | None, sample_weights: pd.Series | None = None, ) -> None: + q0 = self.quantiles[0] + base_learners_map = set(base_learner_predictions[q0].data.columns).difference({ + base_learner_predictions[q0].target_column, + base_learner_predictions[q0].sample_weight_column, + }) + self._label_encoder.fit(list(base_learners_map)) for i, q in enumerate(self.quantiles): base_predictions = base_learner_predictions[q].data.drop( @@ -119,9 +123,6 @@ def fit( logger.warning(msg=msg) self.models[i] = DummyClassifier(strategy="most_frequent") - if i == 0: - # Fit label encoder only once - self._label_encoder.fit(labels) labels = self._label_encoder.transform(labels) # Balance classes, adjust with sample weights diff --git a/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py index 4c0de156b..8905d8f57 100644 --- a/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py @@ -94,7 +94,7 @@ def _init_secondary_model(self, hyperparams: BaseLearnerHyperParams) -> list[Bas list[Forecaster]: List containing the initialized secondary model forecaster. """ models: list[BaseLearner] = [] - + # Different datasets per quantile, so we need a model per quantile for q in self.config.quantiles: config = self._config.model_copy(update={"quantiles": [q]}) secondary_model = self._init_base_learners(config=config, base_hyperparams=[hyperparams])[0] @@ -114,7 +114,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None # Fit primary model self._primary_model.fit(data=data, data_val=data_val) - # Reset forecast start date to ensure we predict on the full dataset + # Reset forecast start date to ensure we fit on the full training set full_dataset = ForecastInputDataset( data=data.data, sample_interval=data.sample_interval, diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index 52033cbc5..1389c28da 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -4,6 +4,7 @@ """Rules-based Meta Forecaster Module.""" import logging +from collections.abc import Sequence from typing import override import pandas as pd @@ -22,7 +23,7 @@ from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) -from openstef_meta.utils.decision_tree import Decision, DecisionTree, Rule +from openstef_meta.utils.decision_tree import Decision, DecisionTree from openstef_models.models.forecasting.forecaster import ( ForecasterConfig, ) @@ -38,7 +39,7 @@ class RulesLearnerHyperParams(FinalLearnerHyperParams): """HyperParams for Stacking Final Learner.""" - feature_adders: list[TimeSeriesTransform] = Field( + feature_adders: Sequence[TimeSeriesTransform] = Field( default=[], description="Additional features to add to the final learner.", ) @@ -64,7 +65,6 @@ def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperPara Args: quantiles: List of quantiles to predict. hyperparams: Hyperparameters for the final learner. - horizon: Forecast horizon for which to create the final learner. """ super().__init__(quantiles=quantiles, hyperparams=hyperparams) diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 2af0ff395..045fe2988 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -10,6 +10,7 @@ """ import logging +from collections.abc import Sequence from typing import override import pandas as pd @@ -45,14 +46,14 @@ class StackingFinalLearnerHyperParams(FinalLearnerHyperParams): """HyperParams for Stacking Final Learner.""" - feature_adders: list[TimeSeriesTransform] = Field( + feature_adders: Sequence[TimeSeriesTransform] = Field( default=[], description="Additional features to add to the base learner predictions before fitting the final learner.", ) forecaster_hyperparams: BaseLearnerHyperParams = Field( default=GBLinearHyperParams(), - description="", + description="Forecaster hyperparameters for the final learner. Defaults to GBLinearHyperParams.", ) @@ -115,6 +116,7 @@ def fit( self, base_learner_predictions: dict[Quantile, ForecastInputDataset], additional_features: ForecastInputDataset | None, + sample_weights: pd.Series | None = None, ) -> None: for i, q in enumerate(self.quantiles): @@ -179,17 +181,6 @@ class StackingHyperParams(HyperParams): description="Hyperparameters for the final learner.", ) - use_classifier: bool = Field( - default=True, - description="Whether to use sample weights when fitting base and final learners. Defaults to False.", - ) - - add_rolling_accuracy_features: bool = Field( - default=False, - description="Whether to add rolling accuracy features from base learners as additional features " - "to the final learner. Defaults to False.", - ) - @field_validator("base_hyperparams", mode="after") @classmethod def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: diff --git a/packages/openstef-meta/src/openstef_meta/transforms/__init__.py b/packages/openstef-meta/src/openstef_meta/transforms/__init__.py new file mode 100644 index 000000000..e551ace37 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/transforms/__init__.py @@ -0,0 +1,11 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Module for OpenSTEF Meta Transforms.""" + +from openstef_meta.transforms.flag_features_bound import Flagger + +__all__ = [ + "Flagger", +] diff --git a/packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py b/packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py similarity index 79% rename from packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py rename to packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py index 6f4a4df3a..0d5fcd379 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/flag_features_bound.py +++ b/packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py @@ -9,7 +9,7 @@ during inference and improving model robustness. """ -from typing import Literal, override +from typing import override import pandas as pd from pydantic import Field, PrivateAttr @@ -21,46 +21,39 @@ from openstef_models.utils.feature_selection import FeatureSelection - - - class Flagger(BaseConfig, TimeSeriesTransform): """Transform that flags specified features to their observed min and max values. - This transform flags the peaks for the metalearner to know when to expect outliers and + This transform flags the peaks for the metalearner to know when to expect outliers and extrapolate from its training set. - + Example: >>> import pandas as pd >>> from datetime import timedelta >>> from openstef_core.datasets import TimeSeriesDataset - >>> from openstef_models.transforms.general import Clipper - >>> + >>> from openstef_meta.transforms import Flagger + >>> from openstef_models.utils.feature_selection import FeatureSelection >>> # Create sample training dataset >>> training_data = pd.DataFrame({ - ... 'load': [100, 120, 110, 130, 125], - ... 'temperature': [20, 22, 21, 23, 24] - ... }, index=pd.date_range('2025-01-01', periods=5, freq='1h')) + ... 'load': [100, 90, 110], + ... 'temperature': [19, 20, 21] + ... }, index=pd.date_range('2025-01-01', periods=3, freq='1h')) >>> training_dataset = TimeSeriesDataset(training_data, timedelta(hours=1)) >>> test_data = pd.DataFrame({ - ... 'load': [90, 140, 115], - ... 'temperature': [19, 25, 22] + ... 'load': [90, 140, 100], + ... 'temperature': [18, 20, 22] ... }, index=pd.date_range('2025-01-06', periods=3, ... freq='1h')) >>> test_dataset = TimeSeriesDataset(test_data, timedelta(hours=1)) >>> # Initialize and apply transform - >>> clipper = Clipper(selection=FeatureSelection(include=['load', 'temperature']), mode='minmax') - >>> clipper.fit(training_dataset) - >>> transformed_dataset = clipper.transform(test_dataset) - >>> clipper._feature_mins.to_dict() - {'load': 100, 'temperature': 20} - >>> clipper._feature_maxs.to_dict() - {'load': 130, 'temperature': 24} + >>> flagger = Flagger(selection=FeatureSelection(include=['load', 'temperature'])) + >>> flagger.fit(training_dataset) + >>> transformed_dataset = flagger.transform(test_dataset) >>> transformed_dataset.data['load'].tolist() - [100, 130, 115] + [0, 0, 1] >>> transformed_dataset.data['temperature'].tolist() - [20, 24, 22] + [0, 1, 0] """ @@ -69,6 +62,7 @@ class Flagger(BaseConfig, TimeSeriesTransform): _feature_mins: pd.Series = PrivateAttr(default_factory=pd.Series) _feature_maxs: pd.Series = PrivateAttr(default_factory=pd.Series) _is_fitted: bool = PrivateAttr(default=False) + @property @override def is_fitted(self) -> bool: @@ -95,8 +89,6 @@ def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: outside = (transformed_data[features] <= min_aligned) | (transformed_data[features] >= max_aligned) transformed_data = (~outside).astype(int) - - return TimeSeriesDataset(data=transformed_data, sample_interval=data.sample_interval) diff --git a/packages/openstef-meta/src/openstef_meta/utils/__init__.py b/packages/openstef-meta/src/openstef_meta/utils/__init__.py index 8b8144daa..a6b9e93a4 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/utils/__init__.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Utility functions and classes for OpenSTEF Meta.""" from .decision_tree import Decision, DecisionTree, Rule diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py b/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py index c5d49852a..8e3940dfa 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py +++ b/packages/openstef-meta/src/openstef_meta/utils/decision_tree.py @@ -1,3 +1,8 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""A simple decision tree implementation for making decisions based on feature rules.""" + from typing import Literal import pandas as pd diff --git a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py index b11d4c7b8..5fe1166d0 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py +++ b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Utility functions for calculating pinball loss errors. This module provides a function to compute the pinball loss for quantile regression. diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index af2504a55..53d08e89f 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -135,7 +135,7 @@ def test_learned_weights_forecaster_with_sample_weights( forecaster_without_weights.fit(data_without_weights) # Predict using data without sample_weight column (since that's used for training, not prediction) - result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_with_weights = forecaster_with_weights.predict(sample_dataset_with_weights) result_without_weights = forecaster_without_weights.predict(data_without_weights) # Assert @@ -157,7 +157,7 @@ def test_learned_weights_forecaster_with_additional_features( # Arrange # Add a simple feature adder that adds a constant feature - base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore forecaster = LearnedWeightsForecaster(config=base_config) # Act diff --git a/packages/openstef-meta/tests/transforms/__init__.py b/packages/openstef-meta/tests/transforms/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py b/packages/openstef-meta/tests/transforms/test_flag_features_bound.py similarity index 89% rename from packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py rename to packages/openstef-meta/tests/transforms/test_flag_features_bound.py index 62f31ef97..dc0d3ea80 100644 --- a/packages/openstef-models/tests/unit/transforms/general/test_flag_features_bound.py +++ b/packages/openstef-meta/tests/transforms/test_flag_features_bound.py @@ -8,8 +8,7 @@ import pytest from openstef_core.datasets import TimeSeriesDataset -from openstef_core.exceptions import NotFittedError -from openstef_models.transforms.general import Flagger +from openstef_meta.transforms import Flagger from openstef_models.utils.feature_selection import FeatureSelection @@ -37,11 +36,10 @@ def test_dataset() -> TimeSeriesDataset: ) - def test_flagger__fit_transform( train_dataset: TimeSeriesDataset, test_dataset: TimeSeriesDataset, - ): +): """Test fit and transform flags correctly leaves other columns unchanged.""" # Arrange flagger = Flagger(selection=FeatureSelection(include={"A", "B", "C"})) @@ -54,9 +52,9 @@ def test_flagger__fit_transform( # Column C should remain unchanged expected_df = pd.DataFrame( { - "A": [1,1], - "B": [0,1], - "C": [0,0], # Unchanged + "A": [1, 1], + "B": [0, 1], + "C": [0, 0], # Unchanged }, index=test_dataset.index, ) diff --git a/packages/openstef-meta/tests/utils/test_decision_tree.py b/packages/openstef-meta/tests/utils/test_decision_tree.py index 11298e9d5..f40bdb220 100644 --- a/packages/openstef-meta/tests/utils/test_decision_tree.py +++ b/packages/openstef-meta/tests/utils/test_decision_tree.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + import pandas as pd import pytest diff --git a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py index 57a5b6187..79e59f58b 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py @@ -17,7 +17,6 @@ from openstef_models.transforms.general.nan_dropper import NaNDropper from openstef_models.transforms.general.sample_weighter import SampleWeighter from openstef_models.transforms.general.scaler import Scaler -from openstef_models.transforms.general.flag_features_bound import Flagger __all__ = [ "Clipper", @@ -27,5 +26,4 @@ "NaNDropper", "SampleWeighter", "Scaler", - "Flagger" ] From 4b3aff81e8110bf95f873536417f7ad02701c3c1 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 28 Nov 2025 15:09:13 +0100 Subject: [PATCH 41/72] Ensemble Forecast Dataset Signed-off-by: Lars van Someren --- .../openstef_meta/framework/base_learner.py | 9 + .../openstef_meta/framework/final_learner.py | 35 ++- .../framework/meta_forecaster.py | 65 +----- .../models/learned_weights_forecaster.py | 118 +++++----- .../openstef_meta/models/rules_forecaster.py | 17 +- .../models/stacking_forecaster.py | 17 +- .../src/openstef_meta/utils/datasets.py | 213 ++++++++++++++++++ .../src/openstef_meta/utils/pinball_errors.py | 14 +- .../models/test_learned_weights_forecaster.py | 6 +- .../tests/models/test_rules_forecaster.py | 2 +- .../tests/utils/test_datasets.py | 115 ++++++++++ .../models/forecasting/gblinear_forecaster.py | 2 +- 12 files changed, 468 insertions(+), 145 deletions(-) create mode 100644 packages/openstef-meta/tests/utils/test_datasets.py diff --git a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py index 36688b419..67c559468 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py @@ -8,6 +8,9 @@ while ensuring full compatability with regular Forecasters. """ +from typing import Any, Literal, Self, override + +from openstef_core.base_model import PydanticStringPrimitive from openstef_models.models.forecasting.gblinear_forecaster import ( GBLinearForecaster, GBLinearHyperParams, @@ -24,3 +27,9 @@ BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams +BaseLearnerNames = Literal[ + "LGBMForecaster", + "LGBMLinearForecaster", + "XGBoostForecaster", + "GBLinearForecaster", +] diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index cdf208309..9feee7add 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -19,6 +19,7 @@ from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile from openstef_meta.transforms.selector import Selector +from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.utils.feature_selection import FeatureSelection WEATHER_FEATURES = { @@ -47,7 +48,7 @@ class FinalLearnerHyperParams(HyperParams): model_config = ConfigDict(arbitrary_types_allowed=True) feature_adders: Sequence[TimeSeriesTransform] = Field( - default=[SELECTOR], + default=[], description="Additional features to add to the base learner predictions before fitting the final learner.", ) @@ -67,15 +68,14 @@ def __init__(self, quantiles: list[Quantile], hyperparams: FinalLearnerHyperPara @abstractmethod def fit( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, sample_weights: pd.Series | None = None, ) -> None: """Fit the final learner using base learner predictions. Args: - base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner - predictions. + base_predictions: EnsembleForecastDataset additional_features: Optional ForecastInputDataset containing additional features for the final learner. sample_weights: Optional series of sample weights for fitting. """ @@ -83,14 +83,13 @@ def fit( def predict( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, ) -> ForecastDataset: """Generate final predictions based on base learner predictions. Args: - base_learner_predictions: Dictionary mapping Quantiles to ForecastInputDatasets containing base learner - predictions. + base_predictions: EnsembleForecastDataset containing base learner predictions. additional_features: Optional ForecastInputDataset containing additional features for the final learner. Returns: @@ -116,6 +115,28 @@ def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset forecast_start=data.forecast_start, ) + @staticmethod + def _prepare_input_data( + dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None + ) -> pd.DataFrame: + """Prepare input data by combining base predictions with additional features if provided. + + Args: + dataset: ForecastInputDataset containing base predictions. + additional_features: Optional ForecastInputDataset containing additional features. + + Returns: + pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. + """ + df = dataset.input_data(start=dataset.index[0]) + if additional_features is not None: + df_a = additional_features.input_data(start=dataset.index[0]) + df = pd.concat( + [df, df_a], + axis=1, + ) + return df + @property @abstractmethod def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 56eb34681..9d998ec9e 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -9,7 +9,7 @@ """ import logging -from typing import override +from typing import cast, override import pandas as pd @@ -17,12 +17,13 @@ from openstef_core.exceptions import ( NotFittedError, ) -from openstef_core.types import Quantile from openstef_meta.framework.base_learner import ( BaseLearner, BaseLearnerHyperParams, + BaseLearnerNames, ) from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.models.forecasting.forecaster import ( Forecaster, ForecasterConfig, @@ -122,25 +123,19 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None else: features = None - quantile_datasets = self._prepare_input_final_learner( - base_predictions=base_predictions, - quantiles=self._config.quantiles, - target_series=data.target_series, - ) - sample_weights = None if data.sample_weight_column in data.data.columns: sample_weights = data.data.loc[:, data.sample_weight_column] self._final_learner.fit( - base_learner_predictions=quantile_datasets, + base_predictions=base_predictions, additional_features=features, sample_weights=sample_weights, ) self._is_fitted = True - def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLearner], ForecastDataset]: + def _predict_base_learners(self, data: ForecastInputDataset) -> EnsembleForecastDataset: """Generate predictions from base learners. Args: @@ -149,47 +144,13 @@ def _predict_base_learners(self, data: ForecastInputDataset) -> dict[type[BaseLe Returns: DataFrame containing base learner predictions. """ - base_predictions: dict[type[BaseLearner], ForecastDataset] = {} + base_predictions: dict[BaseLearnerNames, ForecastDataset] = {} for learner in self._base_learners: preds = learner.predict(data=data) - base_predictions[learner.__class__] = preds + name = cast(BaseLearnerNames, learner.__class__.__name__) + base_predictions[name] = preds - return base_predictions - - @staticmethod - def _prepare_input_final_learner( - quantiles: list[Quantile], - base_predictions: dict[type[BaseLearner], ForecastDataset], - target_series: pd.Series, - ) -> dict[Quantile, ForecastInputDataset]: - """Prepare input data for the final learner based on base learner predictions. - - Args: - quantiles: List of quantiles to prepare data for. - base_predictions: Predictions from base learners. - target_series: Actual target series for reference. - - Returns: - dictionary mapping Quantiles to ForecastInputDatasets. - """ - predictions_quantiles: dict[Quantile, ForecastInputDataset] = {} - sample_interval = base_predictions[next(iter(base_predictions))].sample_interval - target_name = str(target_series.name) - - for q in quantiles: - df = pd.DataFrame({ - learner.__name__: preds.data[Quantile(q).format()] for learner, preds in base_predictions.items() - }) - df[target_name] = target_series - - predictions_quantiles[q] = ForecastInputDataset( - data=df, - sample_interval=sample_interval, - target_column=target_name, - forecast_start=df.index[0], - ) - - return predictions_quantiles + return EnsembleForecastDataset.from_forecast_datasets(base_predictions, target_series=data.target_series) @override def predict(self, data: ForecastInputDataset) -> ForecastDataset: @@ -205,18 +166,12 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: base_predictions = self._predict_base_learners(data=full_dataset) - final_learner_input = self._prepare_input_final_learner( - quantiles=self._config.quantiles, base_predictions=base_predictions, target_series=data.target_series - ) - if self._final_learner.has_features: additional_features = self._final_learner.calculate_features(data=data) else: additional_features = None - return self._final_learner.predict( - base_learner_predictions=final_learner_input, additional_features=additional_features - ) + return self._final_learner.predict(base_predictions=base_predictions, additional_features=additional_features) __all__ = [ diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index df26edb12..15e667deb 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -32,11 +32,10 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams +from openstef_meta.framework.final_learner import EnsembleForecastDataset, FinalLearner, FinalLearnerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) -from openstef_meta.utils.pinball_errors import calculate_pinball_errors from openstef_models.models.forecasting.forecaster import ( ForecasterConfig, ) @@ -87,42 +86,22 @@ def __init__(self, quantiles: list[Quantile], hyperparams: LWFLHyperParams) -> N @override def fit( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, sample_weights: pd.Series | None = None, ) -> None: - q0 = self.quantiles[0] - base_learners_map = set(base_learner_predictions[q0].data.columns).difference({ - base_learner_predictions[q0].target_column, - base_learner_predictions[q0].sample_weight_column, - }) - self._label_encoder.fit(list(base_learners_map)) - for i, q in enumerate(self.quantiles): - base_predictions = base_learner_predictions[q].data.drop( - columns=[base_learner_predictions[q].target_column] - ) + self._label_encoder.fit(base_predictions.model_names) - labels = self._prepare_classification_data( - quantile=q, - target=base_learner_predictions[q].target_series, - predictions=base_predictions, + for i, q in enumerate(self.quantiles): + # Data preparation + dataset = base_predictions.select_quantile_classification(quantile=q) + input_data = self._prepare_input_data( + dataset=dataset, + additional_features=additional_features, ) - - if additional_features is not None: - df = pd.concat( - [base_predictions, additional_features.data], - axis=1, - ) - else: - df = base_predictions - - if len(labels.unique()) == 1: - msg = f"""Final learner for quantile {q.format()} has less than 2 classes in the target. - Switching to dummy classifier """ - logger.warning(msg=msg) - self.models[i] = DummyClassifier(strategy="most_frequent") - + labels = dataset.target_series + self._validate_labels(labels=labels, model_index=i) labels = self._label_encoder.transform(labels) # Balance classes, adjust with sample weights @@ -130,48 +109,62 @@ def fit( if sample_weights is not None: weights *= sample_weights - self.models[i].fit(X=df, y=labels, sample_weight=weights) # type: ignore + self.models[i].fit(X=input_data, y=labels, sample_weight=weights) # type: ignore self._is_fitted = True @staticmethod - def _prepare_classification_data(quantile: Quantile, target: pd.Series, predictions: pd.DataFrame) -> pd.Series: - """Selects base learner with lowest error for each sample as target for classification. + def _prepare_input_data( + dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None + ) -> pd.DataFrame: + """Prepare input data by combining base predictions with additional features if provided. + + Args: + dataset: ForecastInputDataset containing base predictions. + additional_features: Optional ForecastInputDataset containing additional features. Returns: - pd.Series: Series indicating the base learner with the lowest pinball loss for each sample. + pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. """ + df = dataset.input_data(start=dataset.index[0]) + if additional_features is not None: + df_a = additional_features.input_data(start=dataset.index[0]) + df = pd.concat( + [df, df_a], + axis=1, + ) + return df - # Calculate pinball loss for each base learner - def column_pinball_losses(preds: pd.Series) -> pd.Series: - return calculate_pinball_errors(y_true=target, y_pred=preds, alpha=quantile) - - pinball_losses = predictions.apply(column_pinball_losses) - - # For each sample, select the base learner with the lowest pinball loss - return pinball_losses.idxmin(axis=1) - - def _calculate_model_weights_quantile(self, base_predictions: pd.DataFrame, quantile: Quantile) -> pd.DataFrame: - model = self.models[self.quantiles.index(quantile)] + def _validate_labels(self, labels: pd.Series, model_index: int) -> None: + if len(labels.unique()) == 1: + msg = f"""Final learner for quantile {self.quantiles[model_index].format()} has less than 2 classes in the target. + Switching to dummy classifier """ + logger.warning(msg=msg) + self.models[model_index] = DummyClassifier(strategy="most_frequent") + def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_index: int) -> pd.DataFrame: + model = self.models[model_index] return model.predict_proba(X=base_predictions) # type: ignore def _generate_predictions_quantile( self, - base_predictions: ForecastInputDataset, + dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None, - quantile: Quantile, + model_index: int, ) -> pd.Series: - base_df = base_predictions.data.drop(columns=[base_predictions.target_column]) - df = pd.concat([base_df, additional_features.data], axis=1) if additional_features is not None else base_df - weights = self._calculate_model_weights_quantile(base_predictions=df, quantile=quantile) + input_data = self._prepare_input_data( + dataset=dataset, + additional_features=additional_features, + ) + + weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) - return base_df.mul(weights).sum(axis=1) + return dataset.input_data().mul(weights).sum(axis=1) @override def predict( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, ) -> ForecastDataset: if not self.is_fitted: @@ -180,14 +173,21 @@ def predict( # Generate predictions predictions = pd.DataFrame({ Quantile(q).format(): self._generate_predictions_quantile( - base_predictions=data, quantile=q, additional_features=additional_features + dataset=base_predictions.select_quantile(quantile=Quantile(q)), + additional_features=additional_features, + model_index=i, ) - for q, data in base_learner_predictions.items() + for i, q in enumerate(self.quantiles) }) + target_series = base_predictions.target_series + if target_series is not None: + predictions[base_predictions.target_column] = target_series return ForecastDataset( data=predictions, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + sample_interval=base_predictions.sample_interval, + target_column=base_predictions.target_column, + forecast_start=base_predictions.forecast_start, ) @property @@ -202,7 +202,7 @@ class LGBMLearnerHyperParams(LWFLHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" n_estimators: int = Field( - default=200, + default=20, description="Number of estimators for the LGBM Classifier. Defaults to 20.", ) @@ -246,7 +246,7 @@ class RFLearnerHyperParams(LWFLHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( - default=200, + default=20, description="Number of estimators for the LGBM Classifier. Defaults to 20.", ) diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index 1389c28da..b4059da58 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -7,6 +7,7 @@ from collections.abc import Sequence from typing import override +from openstef_meta.utils.datasets import EnsembleForecastDataset import pandas as pd from pydantic import Field, field_validator from pydantic_extra_types.country import CountryAlpha2 @@ -74,7 +75,7 @@ def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperPara @override def fit( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, sample_weights: pd.Series | None = None, ) -> None: @@ -103,28 +104,30 @@ def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: @override def predict( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, ) -> ForecastDataset: if additional_features is None: raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") decisions = self._predict_tree( - additional_features.data, columns=base_learner_predictions[self.quantiles[0]].data.columns + additional_features.data, columns=base_predictions.select_quantile(quantile=self.quantiles[0]).data.columns ) # Generate predictions predictions: list[pd.DataFrame] = [] - for q, data in base_learner_predictions.items(): - preds = data.data * decisions - predictions.append(preds.sum(axis=1).to_frame(name=Quantile(q).format())) + for q in self.quantiles: + dataset = base_predictions.select_quantile(quantile=q) + preds = dataset.input_data().multiply(decisions).sum(axis=1) + + predictions.append(preds.to_frame(name=Quantile(q).format())) # Concatenate predictions along columns to form a DataFrame with quantile columns df = pd.concat(predictions, axis=1) return ForecastDataset( data=df, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + sample_interval=base_predictions.sample_interval, ) @property diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 045fe2988..4b2dfc88f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -17,6 +17,7 @@ from pydantic import Field, field_validator from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_core.exceptions import ( NotFittedError, ) @@ -114,26 +115,27 @@ def _combine_datasets( @override def fit( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, sample_weights: pd.Series | None = None, ) -> None: for i, q in enumerate(self.quantiles): if additional_features is not None: + dataset = base_predictions.select_quantile(quantile=q) data = self._combine_datasets( - data=base_learner_predictions[q], + data=dataset, additional_features=additional_features, ) else: - data = base_learner_predictions[q] + data = base_predictions.select_quantile(quantile=q) self.models[i].fit(data=data, data_val=None) @override def predict( self, - base_learner_predictions: dict[Quantile, ForecastInputDataset], + base_predictions: EnsembleForecastDataset, additional_features: ForecastInputDataset | None, ) -> ForecastDataset: if not self.is_fitted: @@ -144,12 +146,11 @@ def predict( for i, q in enumerate(self.quantiles): if additional_features is not None: data = self._combine_datasets( - data=base_learner_predictions[q], + data=base_predictions.select_quantile(quantile=q), additional_features=additional_features, ) else: - data = base_learner_predictions[q] - + data = base_predictions.select_quantile(quantile=q) p = self.models[i].predict(data=data).data predictions.append(p) @@ -158,7 +159,7 @@ def predict( return ForecastDataset( data=df, - sample_interval=base_learner_predictions[self.quantiles[0]].sample_interval, + sample_interval=base_predictions.sample_interval, ) @property diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index e69de29bb..f42c41716 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -0,0 +1,213 @@ +from datetime import datetime, timedelta +from typing import Self, cast, override + +import pandas as pd + +from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset +from openstef_core.types import Quantile +from openstef_meta.framework.base_learner import BaseLearnerNames +from openstef_meta.utils.pinball_errors import calculate_pinball_errors + +DEFAULT_TARGET_COLUMN = {Quantile(0.5): "load"} + + +class EnsembleForecastDataset(TimeSeriesDataset): + """First stage output format for ensemble forecasters.""" + + forecast_start: datetime + quantiles: list[Quantile] + model_names: list[BaseLearnerNames] + target_column: str + + @override + def __init__( + self, + data: pd.DataFrame, + sample_interval: timedelta = timedelta(minutes=15), + forecast_start: datetime | None = None, + target_column: str = "load", + *, + horizon_column: str = "horizon", + available_at_column: str = "available_at", + ) -> None: + if "forecast_start" in data.attrs: + self.forecast_start = datetime.fromisoformat(data.attrs["forecast_start"]) + else: + self.forecast_start = forecast_start if forecast_start is not None else data.index.min().to_pydatetime() + self.target_column = data.attrs.get("target_column", target_column) + + super().__init__( + data=data, + sample_interval=sample_interval, + horizon_column=horizon_column, + available_at_column=available_at_column, + ) + quantile_feature_names = [col for col in self.feature_names if col != target_column] + + self.model_names, self.quantiles = self.get_learner_and_quantile(pd.Index(quantile_feature_names)) + n_cols = len(self.model_names) * len(self.quantiles) + if len(data.columns) not in {n_cols + 1, n_cols}: + raise ValueError("Data columns do not match the expected number based on base learners and quantiles.") + + @property + def target_series(self) -> pd.Series | None: + """Return the target series if available.""" + if self.target_column in self.data.columns: + return self.data[self.target_column] + return None + + @staticmethod + def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[BaseLearnerNames], list[Quantile]]: + """Extract base learner names and quantiles from feature names. + + Args: + feature_names: Index of feature names in the dataset. + + Returns: + Tuple containing a list of base learner names and a list of quantiles. + + Raises: + ValueError: If an invalid base learner name is found in a feature name. + """ + all_base_learners = BaseLearnerNames.__args__ + + base_learners: set[BaseLearnerNames] = set() + quantiles: set[Quantile] = set() + + for feature_name in feature_names: + learner_part, quantile_part = feature_name.split("_", maxsplit=1) + if learner_part not in all_base_learners or not Quantile.is_valid_quantile_string(quantile_part): + msg = f"Invalid base learner name in feature: {feature_name}" + raise ValueError(msg) + + base_learners.add(cast(BaseLearnerNames, learner_part)) + quantiles.add(Quantile.parse(quantile_part)) + + return list(base_learners), list(quantiles) + + @staticmethod + def get_quantile_feature_name(feature_name: str) -> tuple[BaseLearnerNames, Quantile]: + """Generate the feature name for a given base learner and quantile. + + Args: + feature_name: Feature name string in the format "BaseLearner_Quantile". + + Returns: + Tuple containing the base learner name and Quantile object. + """ + learner_part, quantile_part = feature_name.split("_", maxsplit=1) + return cast(BaseLearnerNames, learner_part), Quantile.parse(quantile_part) + + @classmethod + def from_forecast_datasets( + cls, + datasets: dict[BaseLearnerNames, ForecastDataset], + target_series: pd.Series | None = None, + sample_weights: pd.Series | None = None, + ) -> Self: + """Create an EnsembleForecastDataset from multiple ForecastDatasets. + + Args: + datasets: Dict of ForecastDatasets to combine. + target_series: Optional target series to include in the dataset. + sample_weights: Optional sample weights series to include in the dataset. + + Returns: + EnsembleForecastDataset combining all input datasets. + """ + ds1 = next(iter(datasets.values())) + additional_columns: dict[str, pd.Series] = {} + if isinstance(ds1.target_series, pd.Series): + additional_columns[ds1.target_column] = ds1.target_series + elif target_series is not None: + additional_columns[ds1.target_column] = target_series + + sample_weight_column = "sample_weight" + if sample_weights is not None: + additional_columns[sample_weight_column] = sample_weights + + combined_data = pd.DataFrame({ + f"{learner}_{q.format()}": ds.data[q.format()] for learner, ds in datasets.items() for q in ds.quantiles + }).assign(**additional_columns) + + return cls( + data=combined_data, + sample_interval=ds1.sample_interval, + forecast_start=ds1.forecast_start, + target_column=ds1.target_column, + ) + + @staticmethod + def _prepare_classification(data: pd.DataFrame, target: pd.Series, quantile: Quantile) -> pd.Series: + """Prepare data for classification tasks by converting quantile columns to binary indicators. + + Args: + data: DataFrame containing quantile predictions. + target: Series containing true target values. + quantile: Quantile for which to prepare classification data. + + Returns: + Series with categorical indicators of best-performing base learners. + """ + + # Calculate pinball loss for each base learner + def column_pinball_losses(preds: pd.Series) -> pd.Series: + return calculate_pinball_errors(y_true=target, y_pred=preds, quantile=quantile) + + pinball_losses = data.apply(column_pinball_losses) + + return pinball_losses.idxmin(axis=1) + + def select_quantile_classification(self, quantile: Quantile) -> ForecastInputDataset: + """Select classification target for a specific quantile. + + Args: + quantile: Quantile to select. + + Returns: + Series containing binary indicators of best-performing base learners for the specified quantile. + + Raises: + ValueError: If the target column is not found in the dataset. + """ + if self.target_column not in self.data.columns: + msg = f"Target column '{self.target_column}' not found in dataset." + raise ValueError(msg) + + selected_columns = [f"{learner}_{quantile.format()}" for learner in self.model_names] + prediction_data = self.data[selected_columns].copy() + prediction_data.columns = self.model_names + + target = self._prepare_classification( + data=prediction_data, + target=self.data[self.target_column], + quantile=quantile, + ) + prediction_data[self.target_column] = target + return ForecastInputDataset( + data=prediction_data, + sample_interval=self.sample_interval, + target_column=self.target_column, + forecast_start=self.forecast_start, + ) + + def select_quantile(self, quantile: Quantile) -> ForecastInputDataset: + """Select data for a specific quantile. + + Args: + quantile: Quantile to select. + + Returns: + ForecastInputDataset containing base predictions for the specified quantile. + """ + selected_columns = [f"{learner}_{quantile.format()}" for learner in self.model_names] + selected_columns.append(self.target_column) + prediction_data = self.data[selected_columns].copy() + prediction_data.columns = [*self.model_names, self.target_column] + + return ForecastInputDataset( + data=prediction_data, + sample_interval=self.sample_interval, + target_column=self.target_column, + forecast_start=self.forecast_start, + ) diff --git a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py index 5fe1166d0..85f29fd9c 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py +++ b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py @@ -7,10 +7,11 @@ This module provides a function to compute the pinball loss for quantile regression. """ +import numpy as np import pandas as pd -def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) -> pd.Series: +def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, quantile: float) -> pd.Series: """Calculate pinball loss for given true and predicted values. Args: @@ -21,6 +22,11 @@ def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, alpha: float) Returns: A pandas Series containing the pinball loss for each sample. """ - diff = y_true - y_pred - sign = (diff >= 0).astype(float) - return alpha * sign * diff - (1 - alpha) * (1 - sign) * diff + errors = y_true - y_pred + pinball_loss = np.where( + errors >= 0, + quantile * errors, # Under-prediction + (quantile - 1) * errors, # Over-prediction + ) + + return pd.Series(pinball_loss, index=y_true.index) diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index 53d08e89f..5ca7b3003 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -91,9 +91,9 @@ def test_learned_weights_forecaster_fit_predict( assert forecaster.is_fitted, "Model should be fitted after calling fit()" # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" + required_columns = [q.format() for q in expected_quantiles] + assert all(col in result.data.columns for col in required_columns), ( + f"Expected columns {required_columns}, got {list(result.data.columns)}" ) # Forecast data quality diff --git a/packages/openstef-meta/tests/models/test_rules_forecaster.py b/packages/openstef-meta/tests/models/test_rules_forecaster.py index 434f0c6c2..0dfaba4e5 100644 --- a/packages/openstef-meta/tests/models/test_rules_forecaster.py +++ b/packages/openstef-meta/tests/models/test_rules_forecaster.py @@ -112,7 +112,7 @@ def test_rules_forecaster_with_additional_features( ): """Test that forecaster works with additional features for the final learner.""" - base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore # Arrange expected_quantiles = base_config.quantiles diff --git a/packages/openstef-meta/tests/utils/test_datasets.py b/packages/openstef-meta/tests/utils/test_datasets.py new file mode 100644 index 000000000..43b6cf319 --- /dev/null +++ b/packages/openstef-meta/tests/utils/test_datasets.py @@ -0,0 +1,115 @@ +from collections.abc import Callable +from datetime import timedelta + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset +from openstef_core.types import Quantile +from openstef_meta.framework.base_learner import BaseLearnerNames +from openstef_meta.utils.datasets import EnsembleForecastDataset + + +@pytest.fixture +def simple_dataset() -> TimeSeriesDataset: + return TimeSeriesDataset( + data=pd.DataFrame( + data={ + "available_at": pd.to_datetime([ + "2023-01-01T09:50:00", # lead time = 10:00 - 09:50 = +10min + "2023-01-01T10:55:00", # lead time = 11:00 - 10:55 = +5min + "2023-01-01T12:10:00", # lead time = 12:00 - 12:10 = -10min + "2023-01-01T13:20:00", # lead time = 13:00 - 13:20 = -20min + "2023-01-01T14:15:00", # lead time = 14:00 - 14:15 = -15min + "2023-01-01T14:30:00", # lead time = 14:00 - 14:30 = -30min + ]), + "value1": [10, 20, 30, 40, 50, 55], # 55 should override 50 for 14:00 + }, + index=pd.to_datetime([ + "2023-01-01T10:00:00", + "2023-01-01T11:00:00", + "2023-01-01T12:00:00", + "2023-01-01T13:00:00", + # Duplicate timestamp with different availability + "2023-01-01T14:00:00", + "2023-01-01T14:00:00", + ]), + ), + sample_interval=timedelta(hours=1), + ) + + +@pytest.fixture +def forecast_dataset_factory() -> Callable[[], ForecastDataset]: + def _make() -> ForecastDataset: + rng = np.random.default_rng() + df = pd.DataFrame( + data={ + "quantile_P10": [90, 180, 270], + "quantile_P50": [100, 200, 300], + "quantile_P90": [110, 220, 330], + "load": [100, 200, 300], + }, + index=pd.to_datetime([ + "2023-01-01T10:00:00", + "2023-01-01T11:00:00", + "2023-01-01T12:00:00", + ]), + ) + df += rng.normal(0, 1, df.shape) # Add slight noise to avoid perfect predictions + + df["available_at"] = pd.to_datetime([ + "2023-01-01T09:50:00", + "2023-01-01T10:55:00", + "2023-01-01T12:10:00", + ]) + + return ForecastDataset( + data=df, + sample_interval=timedelta(hours=1), + target_column="load", + ) + + return _make + + +@pytest.fixture +def base_learner_output( + forecast_dataset_factory: Callable[[], ForecastDataset], +) -> dict[BaseLearnerNames, ForecastDataset]: + + return { + "GBLinearForecaster": forecast_dataset_factory(), + "LGBMForecaster": forecast_dataset_factory(), + } + + +@pytest.fixture +def ensemble_dataset(base_learner_output: dict[BaseLearnerNames, ForecastDataset]) -> EnsembleForecastDataset: + return EnsembleForecastDataset.from_forecast_datasets(base_learner_output) + + +def test_from_ensemble_output(ensemble_dataset: EnsembleForecastDataset): + + assert isinstance(ensemble_dataset, EnsembleForecastDataset) + assert ensemble_dataset.data.shape == (3, 7) # 3 timestamps, 2 learners * 3 quantiles + target + assert set(ensemble_dataset.model_names) == {"GBLinearForecaster", "LGBMForecaster"} + assert set(ensemble_dataset.quantiles) == {Quantile(0.1), Quantile(0.5), Quantile(0.9)} + + +def test_select_quantile(ensemble_dataset: EnsembleForecastDataset): + + dataset = ensemble_dataset.select_quantile(Quantile(0.5)) + + assert isinstance(dataset, ForecastInputDataset) + assert dataset.data.shape == (3, 3) # 3 timestamps, 2 learners * 1 quantiles + target + + +def test_select_quantile_classification(ensemble_dataset: EnsembleForecastDataset): + + dataset = ensemble_dataset.select_quantile_classification(Quantile(0.5)) + + assert isinstance(dataset, ForecastInputDataset) + assert dataset.data.shape == (3, 3) # 3 timestamps, 2 learners * 1 quantiles + target + assert all(dataset.target_series.apply(lambda x: x in BaseLearnerNames.__args__)) # type: ignore diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 92c3981a3..4fccf2825 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -272,7 +272,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None raise InputValidationError("The input data is empty after dropping NaN values.") # Fit the scalers - self._target_scaler.fit(data.target_series.to_frame()) + self._target_scaler.fit(data.target_series.to_frame().to_numpy()) # Prepare training data input_data, target, sample_weight = self._prepare_fit_input(data) From 719ea5cb547e95c54b3010b1cebdcdda4b0c623f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 28 Nov 2025 15:14:28 +0100 Subject: [PATCH 42/72] Make PR compliant Signed-off-by: Lars van Someren --- .../src/openstef_meta/framework/base_learner.py | 3 +-- .../openstef_meta/models/learned_weights_forecaster.py | 3 ++- .../src/openstef_meta/models/rules_forecaster.py | 2 +- .../src/openstef_meta/models/stacking_forecaster.py | 2 +- .../openstef-meta/src/openstef_meta/utils/datasets.py | 10 ++++++++++ .../src/openstef_meta/utils/pinball_errors.py | 2 +- packages/openstef-meta/tests/utils/test_datasets.py | 4 ++++ 7 files changed, 20 insertions(+), 6 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py index 67c559468..96cda27f5 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py @@ -8,9 +8,8 @@ while ensuring full compatability with regular Forecasters. """ -from typing import Any, Literal, Self, override +from typing import Literal -from openstef_core.base_model import PydanticStringPrimitive from openstef_models.models.forecasting.gblinear_forecaster import ( GBLinearForecaster, GBLinearHyperParams, diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 15e667deb..4097296d7 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -136,7 +136,8 @@ def _prepare_input_data( def _validate_labels(self, labels: pd.Series, model_index: int) -> None: if len(labels.unique()) == 1: - msg = f"""Final learner for quantile {self.quantiles[model_index].format()} has less than 2 classes in the target. + msg = f"""Final learner for quantile {self.quantiles[model_index].format()} has + less than 2 classes in the target. Switching to dummy classifier """ logger.warning(msg=msg) self.models[model_index] = DummyClassifier(strategy="most_frequent") diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index b4059da58..658b08957 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -7,7 +7,6 @@ from collections.abc import Sequence from typing import override -from openstef_meta.utils.datasets import EnsembleForecastDataset import pandas as pd from pydantic import Field, field_validator from pydantic_extra_types.country import CountryAlpha2 @@ -24,6 +23,7 @@ from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) +from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_meta.utils.decision_tree import Decision, DecisionTree from openstef_models.models.forecasting.forecaster import ( ForecasterConfig, diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 4b2dfc88f..2afa2dfc0 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -17,7 +17,6 @@ from pydantic import Field, field_validator from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_core.exceptions import ( NotFittedError, ) @@ -32,6 +31,7 @@ from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) +from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.models.forecasting.forecaster import ( Forecaster, ForecasterConfig, diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index f42c41716..41186152d 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -1,3 +1,13 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Ensemble Forecast Dataset. + +Validated dataset for ensemble forecasters first stage output. +Implements methods to select quantile-specific ForecastInputDatasets for final learners. +Also supports constructing classifation targets based on pinball loss. +""" + from datetime import datetime, timedelta from typing import Self, cast, override diff --git a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py index 85f29fd9c..08e1c7704 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py +++ b/packages/openstef-meta/src/openstef_meta/utils/pinball_errors.py @@ -17,7 +17,7 @@ def calculate_pinball_errors(y_true: pd.Series, y_pred: pd.Series, quantile: flo Args: y_true: True values as a pandas Series. y_pred: Predicted values as a pandas Series. - alpha: Quantile value. + quantile: Quantile value. Returns: A pandas Series containing the pinball loss for each sample. diff --git a/packages/openstef-meta/tests/utils/test_datasets.py b/packages/openstef-meta/tests/utils/test_datasets.py index 43b6cf319..045aecd13 100644 --- a/packages/openstef-meta/tests/utils/test_datasets.py +++ b/packages/openstef-meta/tests/utils/test_datasets.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + from collections.abc import Callable from datetime import timedelta From e3a587cf10ff9aaf063c22fa8c89735ee2d2a138 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 28 Nov 2025 15:43:45 +0100 Subject: [PATCH 43/72] fixed toml Signed-off-by: Lars van Someren --- packages/openstef-meta/pyproject.toml | 32 +++++++++++++++++++++------ uv.lock | 6 ++--- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/packages/openstef-meta/pyproject.toml b/packages/openstef-meta/pyproject.toml index 838ef8ee9..e013b1b94 100644 --- a/packages/openstef-meta/pyproject.toml +++ b/packages/openstef-meta/pyproject.toml @@ -4,16 +4,34 @@ [project] name = "openstef-meta" -version = "0.1.0" -description = "Add your description here" +version = "0.0.0" +description = "Meta models for OpenSTEF" readme = "README.md" -requires-python = ">=3.12" -dependencies = ["openstef-core", "openstef-models"] +keywords = ["energy", "forecasting", "machinelearning"] +license = "MPL-2.0" +authors = [ + { name = "Alliander N.V", email = "short.term.energy.forecasts@alliander.com" }, +] +requires-python = ">=3.12,<4.0" +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", +] -[tool.uv.sources] -openstef-models = { workspace = true } -openstef-core = { workspace = true } +dependencies = [ + "openstef-beam>=4.0.0.dev0,<5", + "openstef-core>=4.0.0.dev0,<5", + "openstef-models>=4.0.0.dev0,<5", +] +urls.Documentation = "https://openstef.github.io/openstef/index.html" +urls.Homepage = "https://lfenergy.org/projects/openstef/" +urls.Issues = "https://github.com/OpenSTEF/openstef/issues" +urls.Repository = "https://github.com/OpenSTEF/openstef" [tool.hatch.build.targets.wheel] packages = ["src/openstef_meta"] diff --git a/uv.lock b/uv.lock index 4e2691b89..5fc862810 100644 --- a/uv.lock +++ b/uv.lock @@ -2119,8 +2119,6 @@ all = [ { name = "openstef-beam", extra = ["all"] }, { name = "openstef-core" }, { name = "openstef-models", extra = ["xgb-cpu"] }, - { name = "openstef-meta" }, - ] beam = [ { name = "huggingface-hub" }, @@ -2283,15 +2281,17 @@ requires-dist = [ [[package]] name = "openstef-meta" -version = "0.1.0" +version = "0.0.0" source = { editable = "packages/openstef-meta" } dependencies = [ + { name = "openstef-beam" }, { name = "openstef-core" }, { name = "openstef-models" }, ] [package.metadata] requires-dist = [ + { name = "openstef-beam", editable = "packages/openstef-beam" }, { name = "openstef-core", editable = "packages/openstef-core" }, { name = "openstef-models", editable = "packages/openstef-models" }, ] From 308d7c8db6f825a81aab8d1aa6c5e462c59766a0 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Fri, 28 Nov 2025 15:47:43 +0100 Subject: [PATCH 44/72] Really fixed the TOML Signed-off-by: Lars van Someren --- packages/openstef-meta/pyproject.toml | 24 ++++++++++++------------ packages/openstef-models/pyproject.toml | 8 ++++---- pyproject.toml | 2 +- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/packages/openstef-meta/pyproject.toml b/packages/openstef-meta/pyproject.toml index e013b1b94..0f620e63b 100644 --- a/packages/openstef-meta/pyproject.toml +++ b/packages/openstef-meta/pyproject.toml @@ -7,25 +7,25 @@ name = "openstef-meta" version = "0.0.0" description = "Meta models for OpenSTEF" readme = "README.md" -keywords = ["energy", "forecasting", "machinelearning"] +keywords = [ "energy", "forecasting", "machinelearning" ] license = "MPL-2.0" authors = [ - { name = "Alliander N.V", email = "short.term.energy.forecasts@alliander.com" }, + { name = "Alliander N.V", email = "short.term.energy.forecasts@alliander.com" }, ] requires-python = ">=3.12,<4.0" classifiers = [ - "Development Status :: 5 - Production/Stable", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Programming Language :: Python :: 3.14", + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", ] dependencies = [ - "openstef-beam>=4.0.0.dev0,<5", - "openstef-core>=4.0.0.dev0,<5", - "openstef-models>=4.0.0.dev0,<5", + "openstef-beam>=4.0.0.dev0,<5", + "openstef-core>=4.0.0.dev0,<5", + "openstef-models>=4.0.0.dev0,<5", ] urls.Documentation = "https://openstef.github.io/openstef/index.html" @@ -34,4 +34,4 @@ urls.Issues = "https://github.com/OpenSTEF/openstef/issues" urls.Repository = "https://github.com/OpenSTEF/openstef" [tool.hatch.build.targets.wheel] -packages = ["src/openstef_meta"] +packages = [ "src/openstef_meta" ] diff --git a/packages/openstef-models/pyproject.toml b/packages/openstef-models/pyproject.toml index c12b3dacc..2b6f727bf 100644 --- a/packages/openstef-models/pyproject.toml +++ b/packages/openstef-models/pyproject.toml @@ -5,14 +5,14 @@ [build-system] build-backend = "hatchling.build" -requires = ["hatchling"] +requires = [ "hatchling" ] [project] name = "openstef-models" version = "0.0.0" description = "Core models for OpenSTEF" readme = "README.md" -keywords = ["energy", "forecasting", "machinelearning"] +keywords = [ "energy", "forecasting", "machinelearning" ] license = "MPL-2.0" authors = [ { name = "Alliander N.V", email = "short.term.energy.forecasts@alliander.com" }, @@ -45,7 +45,7 @@ optional-dependencies.xgb-cpu = [ "xgboost-cpu>=3,<4; sys_platform=='linux' or sys_platform=='win32'", ] -optional-dependencies.xgb-gpu = ["xgboost>=3,<4"] +optional-dependencies.xgb-gpu = [ "xgboost>=3,<4" ] urls.Documentation = "https://openstef.github.io/openstef/index.html" urls.Homepage = "https://lfenergy.org/projects/openstef/" @@ -53,4 +53,4 @@ urls.Issues = "https://github.com/OpenSTEF/openstef/issues" urls.Repository = "https://github.com/OpenSTEF/openstef" [tool.hatch.build.targets.wheel] -packages = ["src/openstef_models"] +packages = [ "src/openstef_models" ] diff --git a/pyproject.toml b/pyproject.toml index 17df056fa..66d397e87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,8 +41,8 @@ optional-dependencies.beam = [ "openstef-beam", ] optional-dependencies.models = [ - "openstef-models[xgb-cpu]", "openstef-meta", + "openstef-models[xgb-cpu]", ] urls.Documentation = "https://openstef.github.io/openstef/index.html" urls.Homepage = "https://lfenergy.org/projects/openstef/" From 460548b64d8da67c2aa8ee90931e8787b7343fed Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 1 Dec 2025 16:27:59 +0100 Subject: [PATCH 45/72] Renamed FinalLearner to Forecast Combiner. Eliminated redundant classes Signed-off-by: Lars van Someren --- .../src/openstef_meta/framework/__init__.py | 6 +- .../openstef_meta/framework/final_learner.py | 46 ++-- .../framework/meta_forecaster.py | 26 ++- .../models/learned_weights_forecaster.py | 215 +++++++----------- .../openstef_meta/models/rules_forecaster.py | 10 +- .../models/stacking_forecaster.py | 18 +- .../models/test_learned_weights_forecaster.py | 62 ++--- 7 files changed, 158 insertions(+), 225 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/framework/__init__.py b/packages/openstef-meta/src/openstef_meta/framework/__init__.py index e64377d16..64fa31259 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/framework/__init__.py @@ -4,13 +4,13 @@ """This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" from .base_learner import BaseLearner, BaseLearnerHyperParams -from .final_learner import FinalLearner, FinalLearnerHyperParams +from .final_learner import ForecastCombiner, ForecastCombinerHyperParams from .meta_forecaster import MetaForecaster __all__ = [ "BaseLearner", "BaseLearnerHyperParams", - "FinalLearner", - "FinalLearnerHyperParams", + "ForecastCombiner", + "ForecastCombinerHyperParams", "MetaForecaster", ] diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py index 9feee7add..1ed90a1ad 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/final_learner.py @@ -8,41 +8,26 @@ while ensuring full compatability with regular Forecasters. """ -from abc import ABC, abstractmethod +from abc import abstractmethod from collections.abc import Sequence import pandas as pd from pydantic import ConfigDict, Field from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset -from openstef_core.mixins import HyperParams, TransformPipeline +from openstef_core.mixins import HyperParams, Predictor, TransformPipeline from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import Quantile from openstef_meta.transforms.selector import Selector from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.utils.feature_selection import FeatureSelection -WEATHER_FEATURES = { - "temperature_2m", - "relative_humidity_2m", - "surface_pressure", - "cloud_cover", - "wind_speed_10m", - "wind_speed_80m", - "wind_direction_10m", - "shortwave_radiation", - "direct_radiation", - "diffuse_radiation", - "direct_normal_irradiance", - "load", -} - SELECTOR = Selector( - selection=FeatureSelection(include=WEATHER_FEATURES), + selection=FeatureSelection(include=None), ) -class FinalLearnerHyperParams(HyperParams): +class ForecastCombinerHyperParams(HyperParams): """Hyperparameters for the Final Learner.""" model_config = ConfigDict(arbitrary_types_allowed=True) @@ -53,10 +38,10 @@ class FinalLearnerHyperParams(HyperParams): ) -class FinalLearner(ABC): +class ForecastCombiner(Predictor[EnsembleForecastDataset, ForecastDataset]): """Combines base learner predictions for each quantile into final predictions.""" - def __init__(self, quantiles: list[Quantile], hyperparams: FinalLearnerHyperParams) -> None: + def __init__(self, quantiles: list[Quantile], hyperparams: ForecastCombinerHyperParams) -> None: """Initialize the Final Learner.""" self.quantiles = quantiles self.hyperparams = hyperparams @@ -68,14 +53,16 @@ def __init__(self, quantiles: list[Quantile], hyperparams: FinalLearnerHyperPara @abstractmethod def fit( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, sample_weights: pd.Series | None = None, ) -> None: """Fit the final learner using base learner predictions. Args: - base_predictions: EnsembleForecastDataset + data: EnsembleForecastDataset + data_val: Optional EnsembleForecastDataset for validation during fitting. Will be ignored additional_features: Optional ForecastInputDataset containing additional features for the final learner. sample_weights: Optional series of sample weights for fitting. """ @@ -83,13 +70,14 @@ def fit( def predict( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, ) -> ForecastDataset: """Generate final predictions based on base learner predictions. Args: - base_predictions: EnsembleForecastDataset containing base learner predictions. + data: EnsembleForecastDataset containing base learner predictions. + data_val: Optional EnsembleForecastDataset for validation during prediction. Will be ignored additional_features: Optional ForecastInputDataset containing additional features for the final learner. Returns: @@ -101,10 +89,10 @@ def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset """Calculate additional features for the final learner. Args: - data: Input TimeSeriesDataset to calculate features on. + data: Input ForecastInputDataset to calculate features on. Returns: - TimeSeriesDataset with additional features. + ForecastInputDataset with additional features. """ data_transformed = self.final_learner_processing.transform(data) diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 9d998ec9e..04d0042e9 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -22,7 +22,7 @@ BaseLearnerHyperParams, BaseLearnerNames, ) -from openstef_meta.framework.final_learner import FinalLearner +from openstef_meta.framework.final_learner import ForecastCombiner from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.models.forecasting.forecaster import ( Forecaster, @@ -83,12 +83,12 @@ class EnsembleForecaster(MetaForecaster): _config: ForecasterConfig _base_learners: list[BaseLearner] - _final_learner: FinalLearner + _forecast_combiner: ForecastCombiner @property @override def is_fitted(self) -> bool: - return all(x.is_fitted for x in self._base_learners) and self._final_learner.is_fitted + return all(x.is_fitted for x in self._base_learners) and self._forecast_combiner.is_fitted @property @override @@ -117,9 +117,9 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) - if self._final_learner.has_features: - self._final_learner.final_learner_processing.fit(full_dataset) - features = self._final_learner.calculate_features(data=full_dataset) + if self._forecast_combiner.has_features: + self._forecast_combiner.final_learner_processing.fit(full_dataset) + features = self._forecast_combiner.calculate_features(data=full_dataset) else: features = None @@ -127,8 +127,9 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None if data.sample_weight_column in data.data.columns: sample_weights = data.data.loc[:, data.sample_weight_column] - self._final_learner.fit( - base_predictions=base_predictions, + self._forecast_combiner.fit( + data=base_predictions, + data_val=None, # TODO ADD validation dataset support additional_features=features, sample_weights=sample_weights, ) @@ -166,12 +167,15 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: base_predictions = self._predict_base_learners(data=full_dataset) - if self._final_learner.has_features: - additional_features = self._final_learner.calculate_features(data=data) + if self._forecast_combiner.has_features: + additional_features = self._forecast_combiner.calculate_features(data=data) else: additional_features = None - return self._final_learner.predict(base_predictions=base_predictions, additional_features=additional_features) + return self._forecast_combiner.predict( + data=base_predictions, + additional_features=additional_features, + ) __all__ = [ diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 4097296d7..96132c302 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -11,7 +11,7 @@ import logging from abc import abstractmethod -from typing import Literal, Self, override +from typing import Literal, override import pandas as pd from lightgbm import LGBMClassifier @@ -32,7 +32,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import EnsembleForecastDataset, FinalLearner, FinalLearnerHyperParams +from openstef_meta.framework.final_learner import EnsembleForecastDataset, ForecastCombiner, ForecastCombinerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -50,52 +50,51 @@ # Base classes for Learned Weights Final Learner Classifier = LGBMClassifier | XGBClassifier | LogisticRegression | DummyClassifier +ClassifierNames = Literal["lgbm", "xgb", "logistic_regression", "dummy"] -class LWFLHyperParams(FinalLearnerHyperParams): +class WeightsCombinerHyperParams(ForecastCombinerHyperParams): """Hyperparameters for Learned Weights Final Learner.""" - @property @abstractmethod - def learner(self) -> type["WeightsLearner"]: - """Returns the classifier to be used as final learner.""" - raise NotImplementedError("Subclasses must implement the 'estimator' property.") - - @classmethod - def learner_from_params(cls, quantiles: list[Quantile], hyperparams: Self) -> "WeightsLearner": - """Initialize the final learner from hyperparameters. + def get_classifier(self) -> Classifier: + """Initialize the classifier from hyperparameters. Returns: - WeightsLearner: An instance of the WeightsLearner initialized with the provided hyperparameters. + Classifier: An instance of the classifier initialized with the provided hyperparameters. """ - instance = cls() - return instance.learner(quantiles=quantiles, hyperparams=hyperparams) + raise NotImplementedError("Subclasses must implement the 'get_classifier' method.") -class WeightsLearner(FinalLearner): +class WeightsCombiner(ForecastCombiner): """Combines base learner predictions with a classification approach to determine which base learner to use.""" - def __init__(self, quantiles: list[Quantile], hyperparams: LWFLHyperParams) -> None: - """Initialize WeightsLearner.""" + model_type: ClassifierNames = Field( + default="lgbm", description="Type of classifier to use for combining base learner predictions." + ) + + def __init__(self, quantiles: list[Quantile], hyperparams: WeightsCombinerHyperParams) -> None: + """Initialize WeightsCombiner.""" super().__init__(quantiles=quantiles, hyperparams=hyperparams) - self.models: list[Classifier] = [] - self._label_encoder = LabelEncoder() + self.models: list[Classifier] = [hyperparams.get_classifier() for _ in self.quantiles] + self._label_encoder = LabelEncoder() self._is_fitted = False @override def fit( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, sample_weights: pd.Series | None = None, ) -> None: - self._label_encoder.fit(base_predictions.model_names) + self._label_encoder.fit(data.model_names) for i, q in enumerate(self.quantiles): # Data preparation - dataset = base_predictions.select_quantile_classification(quantile=q) + dataset = data.select_quantile_classification(quantile=q) input_data = self._prepare_input_data( dataset=dataset, additional_features=additional_features, @@ -165,8 +164,8 @@ def _generate_predictions_quantile( @override def predict( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, ) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) @@ -174,21 +173,21 @@ def predict( # Generate predictions predictions = pd.DataFrame({ Quantile(q).format(): self._generate_predictions_quantile( - dataset=base_predictions.select_quantile(quantile=Quantile(q)), + dataset=data.select_quantile(quantile=Quantile(q)), additional_features=additional_features, model_index=i, ) for i, q in enumerate(self.quantiles) }) - target_series = base_predictions.target_series + target_series = data.target_series if target_series is not None: - predictions[base_predictions.target_column] = target_series + predictions[data.target_column] = target_series return ForecastDataset( data=predictions, - sample_interval=base_predictions.sample_interval, - target_column=base_predictions.target_column, - forecast_start=base_predictions.forecast_start, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.forecast_start, ) @property @@ -199,7 +198,7 @@ def is_fitted(self) -> bool: # Final learner implementations using different classifiers # 1 LGBM Classifier -class LGBMLearnerHyperParams(LWFLHyperParams): +class LGBMCombinerHyperParams(WeightsCombinerHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" n_estimators: int = Field( @@ -212,38 +211,18 @@ class LGBMLearnerHyperParams(LWFLHyperParams): description="Number of leaves for the LGBM Classifier. Defaults to 31.", ) - @property @override - def learner(self) -> type["LGBMLearner"]: - """Returns the LGBMLearner.""" - return LGBMLearner - - -class LGBMLearner(WeightsLearner): - """Final learner with LGBM Classifier.""" - - HyperParams = LGBMLearnerHyperParams - - def __init__( - self, - quantiles: list[Quantile], - hyperparams: LGBMLearnerHyperParams, - ) -> None: - """Initialize LGBMLearner.""" - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - self.models = [ - LGBMClassifier( - class_weight="balanced", - n_estimators=hyperparams.n_estimators, - num_leaves=hyperparams.n_leaves, - n_jobs=1, - ) - for _ in quantiles - ] + def get_classifier(self) -> LGBMClassifier: + """Returns the LGBM Classifier.""" + return LGBMClassifier( + class_weight="balanced", + n_estimators=self.n_estimators, + num_leaves=self.n_leaves, + n_jobs=1, + ) -# 1 RandomForest Classifier -class RFLearnerHyperParams(LWFLHyperParams): +class RFCombinerHyperParams(WeightsCombinerHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( @@ -271,35 +250,22 @@ class RFLearnerHyperParams(LWFLHyperParams): description="Fraction of features to be used for each iteration of the Random Forest. Defaults to 1.", ) - @property - def learner(self) -> type["RandomForestLearner"]: - """Returns the LGBMClassifier to be used as final learner.""" - return RandomForestLearner - - -class RandomForestLearner(WeightsLearner): - """Final learner using only Random Forest as base learners.""" - - def __init__(self, quantiles: list[Quantile], hyperparams: RFLearnerHyperParams) -> None: - """Initialize RandomForestLearner.""" - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - - self.models = [ - LGBMClassifier( - boosting_type="rf", - class_weight="balanced", - n_estimators=hyperparams.n_estimators, - bagging_freq=hyperparams.bagging_freq, - bagging_fraction=hyperparams.bagging_fraction, - feature_fraction=hyperparams.feature_fraction, - num_leaves=hyperparams.n_leaves, - ) - for _ in quantiles - ] + @override + def get_classifier(self) -> LGBMClassifier: + """Returns the Random Forest LGBMClassifier.""" + return LGBMClassifier( + boosting_type="rf", + class_weight="balanced", + n_estimators=self.n_estimators, + bagging_freq=self.bagging_freq, + bagging_fraction=self.bagging_fraction, + feature_fraction=self.feature_fraction, + num_leaves=self.n_leaves, + ) # 3 XGB Classifier -class XGBLearnerHyperParams(LWFLHyperParams): +class XGBCombinerHyperParams(WeightsCombinerHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( @@ -307,23 +273,13 @@ class XGBLearnerHyperParams(LWFLHyperParams): description="Number of estimators for the LGBM Classifier. Defaults to 20.", ) - @property - def learner(self) -> type["XGBLearner"]: - """Returns the LGBMClassifier to be used as final learner.""" - return XGBLearner - - -class XGBLearner(WeightsLearner): - """Final learner using only XGBoost as base learners.""" - - def __init__(self, quantiles: list[Quantile], hyperparams: XGBLearnerHyperParams) -> None: - """Initialize XGBLearner.""" - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - self.models = [XGBClassifier(n_estimators=hyperparams.n_estimators) for _ in quantiles] + @override + def get_classifier(self) -> XGBClassifier: + """Returns the XGBClassifier.""" + return XGBClassifier(n_estimators=self.n_estimators) -# 4 Logistic Regression Classifier -class LogisticLearnerHyperParams(LWFLHyperParams): +class LogisticCombinerHyperParams(WeightsCombinerHyperParams): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" fit_intercept: bool = Field( @@ -341,30 +297,17 @@ class LogisticLearnerHyperParams(LWFLHyperParams): description="Inverse of regularization strength; must be a positive float. Defaults to 1.0.", ) - @property - def learner(self) -> type["LogisticLearner"]: - """Returns the LGBMClassifier to be used as final learner.""" - return LogisticLearner - - -class LogisticLearner(WeightsLearner): - """Final learner using only Logistic Regression as base learners.""" - - def __init__(self, quantiles: list[Quantile], hyperparams: LogisticLearnerHyperParams) -> None: - """Initialize LogisticLearner.""" - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - self.models = [ - LogisticRegression( - class_weight="balanced", - fit_intercept=hyperparams.fit_intercept, - penalty=hyperparams.penalty, - C=hyperparams.c, - ) - for _ in quantiles - ] + @override + def get_classifier(self) -> LogisticRegression: + """Returns the LogisticRegression.""" + return LogisticRegression( + class_weight="balanced", + fit_intercept=self.fit_intercept, + penalty=self.penalty, + C=self.c, + ) -# Assembly classes class LearnedWeightsHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" @@ -374,8 +317,8 @@ class LearnedWeightsHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_hyperparams: LWFLHyperParams = Field( - default=LGBMLearnerHyperParams(), + combiner_hyperparams: WeightsCombinerHyperParams = Field( + default=LGBMCombinerHyperParams(), description="Hyperparameters for the final learner. Defaults to LGBMLearnerHyperParams.", ) @@ -404,23 +347,19 @@ def __init__(self, config: LearnedWeightsForecasterConfig) -> None: self._base_learners: list[BaseLearner] = self._init_base_learners( config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = config.hyperparams.final_hyperparams.learner_from_params( - quantiles=config.quantiles, - hyperparams=config.hyperparams.final_hyperparams, + self._forecast_combiner = WeightsCombiner( + quantiles=config.quantiles, hyperparams=config.hyperparams.combiner_hyperparams ) __all__ = [ - "LGBMLearner", - "LGBMLearnerHyperParams", + "LGBMCombinerHyperParams", "LearnedWeightsForecaster", "LearnedWeightsForecasterConfig", "LearnedWeightsHyperParams", - "LogisticLearner", - "LogisticLearnerHyperParams", - "RFLearnerHyperParams", - "RandomForestLearner", - "WeightsLearner", - "XGBLearner", - "XGBLearnerHyperParams", + "LogisticCombinerHyperParams", + "RFCombinerHyperParams", + "WeightsCombiner", + "WeightsCombinerHyperParams", + "XGBCombinerHyperParams", ] diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index 658b08957..97174a69f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -19,7 +19,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams +from openstef_meta.framework.final_learner import ForecastCombiner, ForecastCombinerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -37,7 +37,7 @@ logger = logging.getLogger(__name__) -class RulesLearnerHyperParams(FinalLearnerHyperParams): +class RulesLearnerHyperParams(ForecastCombinerHyperParams): """HyperParams for Stacking Final Learner.""" feature_adders: Sequence[TimeSeriesTransform] = Field( @@ -57,7 +57,7 @@ def _check_not_empty(cls, v: list[TimeSeriesTransform]) -> list[TimeSeriesTransf return v -class RulesLearner(FinalLearner): +class RulesLearner(ForecastCombiner): """Combines base learner predictions per quantile into final predictions using a regression approach.""" def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperParams) -> None: @@ -82,7 +82,7 @@ def fit( # No fitting needed for rule-based final learner # Check that additional features are provided if additional_features is None: - raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") + raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") if sample_weights is not None: logger.warning("Sample weights are ignored in RulesLearner.fit method.") @@ -108,7 +108,7 @@ def predict( additional_features: ForecastInputDataset | None, ) -> ForecastDataset: if additional_features is None: - raise ValueError("Additional features must be provided for RulesFinalLearner prediction.") + raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") decisions = self._predict_tree( additional_features.data, columns=base_predictions.select_quantile(quantile=self.quantiles[0]).data.columns diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 2afa2dfc0..17bb47ded 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -27,7 +27,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import FinalLearner, FinalLearnerHyperParams +from openstef_meta.framework.final_learner import ForecastCombiner, ForecastCombinerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -44,7 +44,7 @@ logger = logging.getLogger(__name__) -class StackingFinalLearnerHyperParams(FinalLearnerHyperParams): +class StackingForecastCombinerHyperParams(ForecastCombinerHyperParams): """HyperParams for Stacking Final Learner.""" feature_adders: Sequence[TimeSeriesTransform] = Field( @@ -58,11 +58,11 @@ class StackingFinalLearnerHyperParams(FinalLearnerHyperParams): ) -class StackingFinalLearner(FinalLearner): +class StackingForecastCombiner(ForecastCombiner): """Combines base learner predictions per quantile into final predictions using a regression approach.""" def __init__( - self, quantiles: list[Quantile], hyperparams: StackingFinalLearnerHyperParams, horizon: LeadTime + self, quantiles: list[Quantile], hyperparams: StackingForecastCombinerHyperParams, horizon: LeadTime ) -> None: """Initialize the Stacking final learner. @@ -164,7 +164,7 @@ def predict( @property def is_fitted(self) -> bool: - """Check the StackingFinalLearner is fitted.""" + """Check the StackingForecastCombiner is fitted.""" return all(x.is_fitted for x in self.models) @@ -177,8 +177,8 @@ class StackingHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_hyperparams: StackingFinalLearnerHyperParams = Field( - default=StackingFinalLearnerHyperParams(), + final_hyperparams: StackingForecastCombinerHyperParams = Field( + default=StackingForecastCombinerHyperParams(), description="Hyperparameters for the final learner.", ) @@ -216,9 +216,9 @@ def __init__(self, config: StackingForecasterConfig) -> None: config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = StackingFinalLearner( + self._final_learner = StackingForecastCombiner( quantiles=config.quantiles, hyperparams=config.hyperparams.final_hyperparams, horizon=config.max_horizon ) -__all__ = ["StackingFinalLearner", "StackingForecaster", "StackingForecasterConfig", "StackingHyperParams"] +__all__ = ["StackingForecastCombiner", "StackingForecaster", "StackingForecasterConfig", "StackingHyperParams"] diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index 5ca7b3003..ad00a393f 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -5,47 +5,47 @@ from datetime import timedelta import pytest +from lightgbm import LGBMClassifier +from sklearn.linear_model import LogisticRegression +from xgboost import XGBClassifier from openstef_core.datasets import ForecastInputDataset from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q from openstef_meta.models.learned_weights_forecaster import ( + Classifier, LearnedWeightsForecaster, LearnedWeightsForecasterConfig, LearnedWeightsHyperParams, - LGBMLearner, - LGBMLearnerHyperParams, - LogisticLearner, - LogisticLearnerHyperParams, - LWFLHyperParams, - RandomForestLearner, - RFLearnerHyperParams, - WeightsLearner, - XGBLearner, - XGBLearnerHyperParams, + LGBMCombinerHyperParams, + LogisticCombinerHyperParams, + RFCombinerHyperParams, + WeightsCombiner, + WeightsCombinerHyperParams, + XGBCombinerHyperParams, ) from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder @pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) -def final_hyperparams(request: pytest.FixtureRequest) -> LWFLHyperParams: +def combiner_hyperparams(request: pytest.FixtureRequest) -> WeightsCombinerHyperParams: """Fixture to provide different primary models types.""" learner_type = request.param if learner_type == "rf": - return RFLearnerHyperParams() + return RFCombinerHyperParams() if learner_type == "lgbm": - return LGBMLearnerHyperParams() + return LGBMCombinerHyperParams() if learner_type == "xgboost": - return XGBLearnerHyperParams() - return LogisticLearnerHyperParams() + return XGBCombinerHyperParams() + return LogisticCombinerHyperParams() @pytest.fixture -def base_config(final_hyperparams: LWFLHyperParams) -> LearnedWeightsForecasterConfig: +def base_config(combiner_hyperparams: WeightsCombinerHyperParams) -> LearnedWeightsForecasterConfig: """Base configuration for LearnedWeights forecaster tests.""" params = LearnedWeightsHyperParams( - final_hyperparams=final_hyperparams, + combiner_hyperparams=combiner_hyperparams, ) return LearnedWeightsForecasterConfig( quantiles=[Q(0.1), Q(0.5), Q(0.9)], @@ -55,21 +55,23 @@ def base_config(final_hyperparams: LWFLHyperParams) -> LearnedWeightsForecasterC ) -def test_final_learner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): - """Test that the final learner corresponds to the specified hyperparameters.""" +def test_forecast_combiner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): + """Test that the forecast combiner learner corresponds to the specified hyperparameters.""" forecaster = LearnedWeightsForecaster(config=base_config) - final_learner = forecaster._final_learner - - mapping: dict[type[LWFLHyperParams], type[WeightsLearner]] = { - RFLearnerHyperParams: RandomForestLearner, - LGBMLearnerHyperParams: LGBMLearner, - XGBLearnerHyperParams: XGBLearner, - LogisticLearnerHyperParams: LogisticLearner, + forecast_combiner = forecaster._forecast_combiner + assert isinstance(forecast_combiner, WeightsCombiner) + classifier = forecast_combiner.models[0] + + mapping: dict[type[WeightsCombinerHyperParams], type[Classifier]] = { + RFCombinerHyperParams: LGBMClassifier, + LGBMCombinerHyperParams: LGBMClassifier, + XGBCombinerHyperParams: XGBClassifier, + LogisticCombinerHyperParams: LogisticRegression, } - expected_learner_type = mapping[type(base_config.hyperparams.final_hyperparams)] + expected_type = mapping[type(base_config.hyperparams.combiner_hyperparams)] - assert isinstance(final_learner, expected_learner_type), ( - f"Final learner type {type(final_learner)} does not match expected type {expected_learner_type}" + assert isinstance(classifier, expected_type), ( + f"Final learner type {type(forecast_combiner)} does not match expected type {expected_type}" ) @@ -157,7 +159,7 @@ def test_learned_weights_forecaster_with_additional_features( # Arrange # Add a simple feature adder that adds a constant feature - base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore + base_config.hyperparams.combiner_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore forecaster = LearnedWeightsForecaster(config=base_config) # Act From b2fca54752c3dcd660c1bd63b4c22be492860b28 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 1 Dec 2025 17:08:09 +0100 Subject: [PATCH 46/72] fixed small issues Signed-off-by: Lars van Someren --- .../src/openstef_meta/framework/__init__.py | 2 +- ...{final_learner.py => forecast_combiner.py} | 6 ++-- .../framework/meta_forecaster.py | 4 +-- .../models/learned_weights_forecaster.py | 6 +++- .../openstef_meta/models/rules_forecaster.py | 19 +++++----- .../models/stacking_forecaster.py | 35 ++++++++++--------- .../tests/models/test_stacking_forecaster.py | 2 +- 7 files changed, 40 insertions(+), 34 deletions(-) rename packages/openstef-meta/src/openstef_meta/framework/{final_learner.py => forecast_combiner.py} (95%) diff --git a/packages/openstef-meta/src/openstef_meta/framework/__init__.py b/packages/openstef-meta/src/openstef_meta/framework/__init__.py index 64fa31259..bd120bc0e 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/framework/__init__.py @@ -4,7 +4,7 @@ """This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" from .base_learner import BaseLearner, BaseLearnerHyperParams -from .final_learner import ForecastCombiner, ForecastCombinerHyperParams +from .forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams from .meta_forecaster import MetaForecaster __all__ = [ diff --git a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py b/packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py similarity index 95% rename from packages/openstef-meta/src/openstef_meta/framework/final_learner.py rename to packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py index 1ed90a1ad..2a1146d63 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/final_learner.py +++ b/packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py @@ -45,7 +45,7 @@ def __init__(self, quantiles: list[Quantile], hyperparams: ForecastCombinerHyper """Initialize the Final Learner.""" self.quantiles = quantiles self.hyperparams = hyperparams - self.final_learner_processing: TransformPipeline[TimeSeriesDataset] = TransformPipeline( + self.pre_processing: TransformPipeline[TimeSeriesDataset] = TransformPipeline( transforms=hyperparams.feature_adders ) self._is_fitted: bool = False @@ -94,7 +94,7 @@ def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset Returns: ForecastInputDataset with additional features. """ - data_transformed = self.final_learner_processing.transform(data) + data_transformed = self.pre_processing.transform(data) return ForecastInputDataset( data=data_transformed.data, @@ -134,4 +134,4 @@ def is_fitted(self) -> bool: @property def has_features(self) -> bool: """Indicates whether the final learner uses additional features.""" - return len(self.final_learner_processing.transforms) > 0 + return len(self.pre_processing.transforms) > 0 diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 04d0042e9..49acd1c1f 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -22,7 +22,7 @@ BaseLearnerHyperParams, BaseLearnerNames, ) -from openstef_meta.framework.final_learner import ForecastCombiner +from openstef_meta.framework.forecast_combiner import ForecastCombiner from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.models.forecasting.forecaster import ( Forecaster, @@ -118,7 +118,7 @@ def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None base_predictions = self._predict_base_learners(data=full_dataset) if self._forecast_combiner.has_features: - self._forecast_combiner.final_learner_processing.fit(full_dataset) + self._forecast_combiner.pre_processing.fit(full_dataset) features = self._forecast_combiner.calculate_features(data=full_dataset) else: features = None diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py index 96132c302..1f4837038 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py @@ -32,7 +32,11 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import EnsembleForecastDataset, ForecastCombiner, ForecastCombinerHyperParams +from openstef_meta.framework.forecast_combiner import ( + EnsembleForecastDataset, + ForecastCombiner, + ForecastCombinerHyperParams, +) from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py index 97174a69f..d83d586f6 100644 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py @@ -19,7 +19,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import ForecastCombiner, ForecastCombinerHyperParams +from openstef_meta.framework.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -75,8 +75,9 @@ def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperPara @override def fit( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, sample_weights: pd.Series | None = None, ) -> None: # No fitting needed for rule-based final learner @@ -104,20 +105,20 @@ def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: @override def predict( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, ) -> ForecastDataset: if additional_features is None: raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") decisions = self._predict_tree( - additional_features.data, columns=base_predictions.select_quantile(quantile=self.quantiles[0]).data.columns + additional_features.data, columns=data.select_quantile(quantile=self.quantiles[0]).data.columns ) # Generate predictions predictions: list[pd.DataFrame] = [] for q in self.quantiles: - dataset = base_predictions.select_quantile(quantile=q) + dataset = data.select_quantile(quantile=q) preds = dataset.input_data().multiply(decisions).sum(axis=1) predictions.append(preds.to_frame(name=Quantile(q).format())) @@ -127,7 +128,7 @@ def predict( return ForecastDataset( data=df, - sample_interval=base_predictions.sample_interval, + sample_interval=data.sample_interval, ) @property @@ -190,7 +191,7 @@ def __init__(self, config: RulesForecasterConfig) -> None: config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = RulesLearner( + self._forecast_combiner = RulesLearner( quantiles=config.quantiles, hyperparams=config.hyperparams.final_hyperparams, ) diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py index 17bb47ded..7dd3b2220 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py @@ -27,7 +27,7 @@ BaseLearner, BaseLearnerHyperParams, ) -from openstef_meta.framework.final_learner import ForecastCombiner, ForecastCombinerHyperParams +from openstef_meta.framework.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams from openstef_meta.framework.meta_forecaster import ( EnsembleForecaster, ) @@ -115,28 +115,29 @@ def _combine_datasets( @override def fit( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, sample_weights: pd.Series | None = None, ) -> None: for i, q in enumerate(self.quantiles): if additional_features is not None: - dataset = base_predictions.select_quantile(quantile=q) - data = self._combine_datasets( + dataset = data.select_quantile(quantile=q) + input_data = self._combine_datasets( data=dataset, additional_features=additional_features, ) else: - data = base_predictions.select_quantile(quantile=q) + input_data = data.select_quantile(quantile=q) - self.models[i].fit(data=data, data_val=None) + self.models[i].fit(data=input_data, data_val=None) @override def predict( self, - base_predictions: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, ) -> ForecastDataset: if not self.is_fitted: raise NotFittedError(self.__class__.__name__) @@ -145,13 +146,13 @@ def predict( predictions: list[pd.DataFrame] = [] for i, q in enumerate(self.quantiles): if additional_features is not None: - data = self._combine_datasets( - data=base_predictions.select_quantile(quantile=q), + input_data = self._combine_datasets( + data=data.select_quantile(quantile=q), additional_features=additional_features, ) else: - data = base_predictions.select_quantile(quantile=q) - p = self.models[i].predict(data=data).data + input_data = data.select_quantile(quantile=q) + p = self.models[i].predict(data=input_data).data predictions.append(p) # Concatenate predictions along columns to form a DataFrame with quantile columns @@ -159,7 +160,7 @@ def predict( return ForecastDataset( data=df, - sample_interval=base_predictions.sample_interval, + sample_interval=data.sample_interval, ) @property @@ -177,7 +178,7 @@ class StackingHyperParams(HyperParams): "Defaults to [LGBMHyperParams, GBLinearHyperParams].", ) - final_hyperparams: StackingForecastCombinerHyperParams = Field( + combiner_hyperparams: StackingForecastCombinerHyperParams = Field( default=StackingForecastCombinerHyperParams(), description="Hyperparameters for the final learner.", ) @@ -216,8 +217,8 @@ def __init__(self, config: StackingForecasterConfig) -> None: config=config, base_hyperparams=config.hyperparams.base_hyperparams ) - self._final_learner = StackingForecastCombiner( - quantiles=config.quantiles, hyperparams=config.hyperparams.final_hyperparams, horizon=config.max_horizon + self._forecast_combiner = StackingForecastCombiner( + quantiles=config.quantiles, hyperparams=config.hyperparams.combiner_hyperparams, horizon=config.max_horizon ) diff --git a/packages/openstef-meta/tests/models/test_stacking_forecaster.py b/packages/openstef-meta/tests/models/test_stacking_forecaster.py index 9eccde9b9..33a956d8d 100644 --- a/packages/openstef-meta/tests/models/test_stacking_forecaster.py +++ b/packages/openstef-meta/tests/models/test_stacking_forecaster.py @@ -112,7 +112,7 @@ def test_stacking_forecaster_with_additional_features( ): """Test that forecaster works with additional features for the final learner.""" - base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) + base_config.hyperparams.combiner_hyperparams.feature_adders = [CyclicFeaturesAdder()] # Arrange expected_quantiles = base_config.quantiles From ddef9f3949641f0e1ac96a43d44bc310d6dfe2ac Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 2 Dec 2025 15:34:34 +0100 Subject: [PATCH 47/72] Major Refactor, Working Version Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 162 ++++++ .../src/openstef_meta/framework/__init__.py | 2 +- .../openstef_meta/framework/base_learner.py | 34 -- .../framework/meta_forecaster.py | 185 ------- .../src/openstef_meta/models/__init__.py | 24 - .../models/ensemble_forecasting_model.py | 479 ++++++++++++++++++ .../models/forecast_combiners/__init__.py | 28 + .../forecast_combiners}/forecast_combiner.py | 103 ++-- .../learned_weights_combiner.py} | 370 +++++++------- .../forecast_combiners/rules_combiner.py | 154 ++++++ .../stacking_combiner.py} | 161 +++--- .../models/forecasting/__init__.py | 12 + .../{ => forecasting}/residual_forecaster.py | 43 +- .../openstef_meta/models/rules_forecaster.py | 206 -------- .../src/openstef_meta/presets/__init__.py | 5 + .../presets/forecasting_workflow.py | 453 +++++++++++++++++ .../src/openstef_meta/utils/datasets.py | 39 +- .../openstef-meta/test_forecasting_model.py | 274 ++++++++++ .../models/test_learned_weights_forecaster.py | 342 ++++++------- .../tests/models/test_residual_forecaster.py | 284 +++++------ .../tests/models/test_rules_forecaster.py | 272 +++++----- .../tests/models/test_stacking_forecaster.py | 272 +++++----- .../presets/forecasting_workflow.py | 59 +-- .../workflows/custom_forecasting_workflow.py | 3 +- 24 files changed, 2519 insertions(+), 1447 deletions(-) create mode 100644 examples/benchmarks/liander_2024_ensemble.py delete mode 100644 packages/openstef-meta/src/openstef_meta/framework/base_learner.py delete mode 100644 packages/openstef-meta/src/openstef_meta/models/__init__.py create mode 100644 packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py create mode 100644 packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py rename packages/openstef-meta/src/openstef_meta/{framework => models/forecast_combiners}/forecast_combiner.py (63%) rename packages/openstef-meta/src/openstef_meta/models/{learned_weights_forecaster.py => forecast_combiners/learned_weights_combiner.py} (72%) create mode 100644 packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py rename packages/openstef-meta/src/openstef_meta/models/{stacking_forecaster.py => forecast_combiners/stacking_combiner.py} (54%) create mode 100644 packages/openstef-meta/src/openstef_meta/models/forecasting/__init__.py rename packages/openstef-meta/src/openstef_meta/models/{ => forecasting}/residual_forecaster.py (85%) delete mode 100644 packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py create mode 100644 packages/openstef-meta/src/openstef_meta/presets/__init__.py create mode 100644 packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py create mode 100644 packages/openstef-meta/test_forecasting_model.py diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py new file mode 100644 index 000000000..d3c990ad2 --- /dev/null +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -0,0 +1,162 @@ +"""Liander 2024 Benchmark Example. + +==================================== + +This example demonstrates how to set up and run the Liander 2024 STEF benchmark using OpenSTEF BEAM. +The benchmark will evaluate XGBoost and GBLinear models on the dataset from HuggingFace. +""" + +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +import os +import time + +os.environ["OMP_NUM_THREADS"] = "1" # Set OMP_NUM_THREADS to 1 to avoid issues with parallel execution and xgboost +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +import logging +import multiprocessing +from datetime import timedelta +from pathlib import Path + +from pydantic_extra_types.coordinate import Coordinate +from pydantic_extra_types.country import CountryAlpha2 + +from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig, OpenSTEF4BacktestForecaster +from openstef_beam.benchmarking.benchmark_pipeline import BenchmarkContext +from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024Category, create_liander2024_benchmark_runner +from openstef_beam.benchmarking.callbacks.strict_execution_callback import StrictExecutionCallback +from openstef_beam.benchmarking.models.benchmark_target import BenchmarkTarget +from openstef_beam.benchmarking.storage.local_storage import LocalBenchmarkStorage +from openstef_core.types import LeadTime, Q +from openstef_meta.presets import ( + EnsembleWorkflowConfig, + create_ensemble_workflow, +) +from openstef_models.integrations.mlflow.mlflow_storage import MLFlowStorage +from openstef_models.presets.forecasting_workflow import LocationConfig +from openstef_models.workflows import CustomForecastingWorkflow + +logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") + +OUTPUT_PATH = Path("./benchmark_results") + +N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark + +ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" +base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" +combiner_model = ( + "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner +) + +model = "Ensemble_" + "_".join(base_models) + "_" + ensemble_type + "_" + combiner_model + +# Model configuration +FORECAST_HORIZONS = [LeadTime.from_string("PT36H")] # Forecast horizon(s) +PREDICTION_QUANTILES = [ + Q(0.05), + Q(0.1), + Q(0.3), + Q(0.5), + Q(0.7), + Q(0.9), + Q(0.95), +] # Quantiles for probabilistic forecasts + +BENCHMARK_FILTER: list[Liander2024Category] | None = None + +USE_MLFLOW_STORAGE = False + +if USE_MLFLOW_STORAGE: + storage = MLFlowStorage( + tracking_uri=str(OUTPUT_PATH / "mlflow_artifacts"), + local_artifacts_path=OUTPUT_PATH / "mlflow_tracking_artifacts", + ) +else: + storage = None + +common_config = EnsembleWorkflowConfig( + model_id="common_model_", + ensemble_type=ensemble_type, + base_models=base_models, # type: ignore + combiner_model=combiner_model, + horizons=FORECAST_HORIZONS, + quantiles=PREDICTION_QUANTILES, + model_reuse_enable=False, + mlflow_storage=None, + radiation_column="shortwave_radiation", + rolling_aggregate_features=["mean", "median", "max", "min"], + wind_speed_column="wind_speed_80m", + pressure_column="surface_pressure", + temperature_column="temperature_2m", + relative_humidity_column="relative_humidity_2m", + energy_price_column="EPEX_NL", +) + + +# Create the backtest configuration +backtest_config = BacktestForecasterConfig( + requires_training=True, + predict_length=timedelta(days=7), + predict_min_length=timedelta(minutes=15), + predict_context_length=timedelta(days=14), # Context needed for lag features + predict_context_min_coverage=0.5, + training_context_length=timedelta(days=90), # Three months of training data + training_context_min_coverage=0.5, + predict_sample_interval=timedelta(minutes=15), +) + + +def _target_forecaster_factory( + context: BenchmarkContext, + target: BenchmarkTarget, +) -> OpenSTEF4BacktestForecaster: + # Factory function that creates a forecaster for a given target. + prefix = context.run_name + base_config = common_config + + def _create_workflow() -> CustomForecastingWorkflow: + # Create a new workflow instance with fresh model. + return create_ensemble_workflow( + config=base_config.model_copy( + update={ + "model_id": f"{prefix}_{target.name}", + "location": LocationConfig( + name=target.name, + description=target.description, + coordinate=Coordinate( + latitude=target.latitude, + longitude=target.longitude, + ), + country_code=CountryAlpha2("NL"), + ), + } + ) + ) + + return OpenSTEF4BacktestForecaster( + config=backtest_config, + workflow_factory=_create_workflow, + debug=False, + cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", + ) + + +if __name__ == "__main__": + start_time = time.time() + create_liander2024_benchmark_runner( + storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), + data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), + callbacks=[StrictExecutionCallback()], + ).run( + forecaster_factory=_target_forecaster_factory, + run_name=model, + n_processes=N_PROCESSES, + filter_args=BENCHMARK_FILTER, + ) + + end_time = time.time() + print(f"Benchmark completed in {end_time - start_time:.2f} seconds.") diff --git a/packages/openstef-meta/src/openstef_meta/framework/__init__.py b/packages/openstef-meta/src/openstef_meta/framework/__init__.py index bd120bc0e..0775b88f1 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/framework/__init__.py @@ -4,7 +4,7 @@ """This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" from .base_learner import BaseLearner, BaseLearnerHyperParams -from .forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams +from ..models.combiners.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams from .meta_forecaster import MetaForecaster __all__ = [ diff --git a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py b/packages/openstef-meta/src/openstef_meta/framework/base_learner.py deleted file mode 100644 index 96cda27f5..000000000 --- a/packages/openstef-meta/src/openstef_meta/framework/base_learner.py +++ /dev/null @@ -1,34 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Core meta model interfaces and configurations. - -Provides the fundamental building blocks for implementing meta models in OpenSTEF. -These mixins establish contracts that ensure consistent behavior across different meta model types -while ensuring full compatability with regular Forecasters. -""" - -from typing import Literal - -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearForecaster, - GBLinearHyperParams, -) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import ( - LGBMLinearForecaster, - LGBMLinearHyperParams, -) -from openstef_models.models.forecasting.xgboost_forecaster import ( - XGBoostForecaster, - XGBoostHyperParams, -) - -BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster -BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams -BaseLearnerNames = Literal[ - "LGBMForecaster", - "LGBMLinearForecaster", - "XGBoostForecaster", - "GBLinearForecaster", -] diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py index 49acd1c1f..e69de29bb 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py @@ -1,185 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Core meta model interfaces and configurations. - -Provides the fundamental building blocks for implementing meta models in OpenSTEF. -These mixins establish contracts that ensure consistent behavior across different meta model types -while ensuring full compatability with regular Forecasters. -""" - -import logging -from typing import cast, override - -import pandas as pd - -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.exceptions import ( - NotFittedError, -) -from openstef_meta.framework.base_learner import ( - BaseLearner, - BaseLearnerHyperParams, - BaseLearnerNames, -) -from openstef_meta.framework.forecast_combiner import ForecastCombiner -from openstef_meta.utils.datasets import EnsembleForecastDataset -from openstef_models.models.forecasting.forecaster import ( - Forecaster, - ForecasterConfig, -) - -logger = logging.getLogger(__name__) - - -class MetaForecaster(Forecaster): - """Abstract class for Meta forecasters combining multiple models.""" - - _config: ForecasterConfig - - @staticmethod - def _init_base_learners( - config: ForecasterConfig, base_hyperparams: list[BaseLearnerHyperParams] - ) -> list[BaseLearner]: - """Initialize base learners based on provided hyperparameters. - - Returns: - list[Forecaster]: List of initialized base learner forecasters. - """ - base_learners: list[BaseLearner] = [] - horizons = config.horizons - quantiles = config.quantiles - - for hyperparams in base_hyperparams: - forecaster_cls = hyperparams.forecaster_class() - config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) - if "hyperparams" in forecaster_cls.Config.model_fields: - config = config.model_copy(update={"hyperparams": hyperparams}) - - base_learners.append(config.forecaster_from_config()) - - return base_learners - - @property - @override - def config(self) -> ForecasterConfig: - return self._config - - @property - def feature_importances(self) -> pd.DataFrame: - """Placeholder for feature importances across base learners and final learner.""" - raise NotImplementedError("Feature importances are not implemented for EnsembleForecaster.") - # TODO(#745): Make MetaForecaster explainable - - @property - def model_contributions(self) -> pd.DataFrame: - """Placeholder for model contributions across base learners and final learner.""" - raise NotImplementedError("Model contributions are not implemented for EnsembleForecaster.") - # TODO(#745): Make MetaForecaster explainable - - -class EnsembleForecaster(MetaForecaster): - """Abstract class for Meta forecasters combining multiple base learners and a final learner.""" - - _config: ForecasterConfig - _base_learners: list[BaseLearner] - _forecast_combiner: ForecastCombiner - - @property - @override - def is_fitted(self) -> bool: - return all(x.is_fitted for x in self._base_learners) and self._forecast_combiner.is_fitted - - @property - @override - def config(self) -> ForecasterConfig: - return self._config - - @override - def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: - """Fit the Hybrid model to the training data. - - Args: - data: Training data in the expected ForecastInputDataset format. - data_val: Validation data for tuning the model (optional, not used in this implementation). - - """ - # Fit base learners - [x.fit(data=data, data_val=data_val) for x in self._base_learners] - - # Reset forecast start date to ensure we predict on the full dataset - full_dataset = ForecastInputDataset( - data=data.data, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.index[0], - ) - - base_predictions = self._predict_base_learners(data=full_dataset) - - if self._forecast_combiner.has_features: - self._forecast_combiner.pre_processing.fit(full_dataset) - features = self._forecast_combiner.calculate_features(data=full_dataset) - else: - features = None - - sample_weights = None - if data.sample_weight_column in data.data.columns: - sample_weights = data.data.loc[:, data.sample_weight_column] - - self._forecast_combiner.fit( - data=base_predictions, - data_val=None, # TODO ADD validation dataset support - additional_features=features, - sample_weights=sample_weights, - ) - - self._is_fitted = True - - def _predict_base_learners(self, data: ForecastInputDataset) -> EnsembleForecastDataset: - """Generate predictions from base learners. - - Args: - data: Input data for prediction. - - Returns: - DataFrame containing base learner predictions. - """ - base_predictions: dict[BaseLearnerNames, ForecastDataset] = {} - for learner in self._base_learners: - preds = learner.predict(data=data) - name = cast(BaseLearnerNames, learner.__class__.__name__) - base_predictions[name] = preds - - return EnsembleForecastDataset.from_forecast_datasets(base_predictions, target_series=data.target_series) - - @override - def predict(self, data: ForecastInputDataset) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - full_dataset = ForecastInputDataset( - data=data.data, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.index[0], - ) - - base_predictions = self._predict_base_learners(data=full_dataset) - - if self._forecast_combiner.has_features: - additional_features = self._forecast_combiner.calculate_features(data=data) - else: - additional_features = None - - return self._forecast_combiner.predict( - data=base_predictions, - additional_features=additional_features, - ) - - -__all__ = [ - "BaseLearner", - "BaseLearnerHyperParams", - "MetaForecaster", -] diff --git a/packages/openstef-meta/src/openstef_meta/models/__init__.py b/packages/openstef-meta/src/openstef_meta/models/__init__.py deleted file mode 100644 index 614543150..000000000 --- a/packages/openstef-meta/src/openstef_meta/models/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" - -from .learned_weights_forecaster import ( - LearnedWeightsForecaster, - LearnedWeightsForecasterConfig, - LearnedWeightsHyperParams, -) -from .residual_forecaster import ResidualForecaster, ResidualForecasterConfig, ResidualHyperParams -from .stacking_forecaster import StackingForecaster, StackingForecasterConfig, StackingHyperParams - -__all__ = [ - "LearnedWeightsForecaster", - "LearnedWeightsForecasterConfig", - "LearnedWeightsHyperParams", - "ResidualForecaster", - "ResidualForecasterConfig", - "ResidualHyperParams", - "StackingForecaster", - "StackingForecasterConfig", - "StackingHyperParams", -] diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py new file mode 100644 index 000000000..c7eaf3c1f --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -0,0 +1,479 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""High-level forecasting model that orchestrates the complete prediction pipeline. + +Combines feature engineering, forecasting, and postprocessing into a unified interface. +Handles both single-horizon and multi-horizon forecasters while providing consistent +data transformation and validation. +""" + +import logging +from datetime import datetime, timedelta +from functools import partial +from typing import cast, override + +import pandas as pd +from pydantic import Field, PrivateAttr + +from openstef_beam.evaluation import EvaluationConfig, EvaluationPipeline, SubsetMetric +from openstef_beam.evaluation.metric_providers import MetricProvider, ObservedProbabilityProvider, R2Provider +from openstef_core.base_model import BaseModel +from openstef_core.datasets import ( + ForecastDataset, + ForecastInputDataset, + TimeSeriesDataset, +) +from openstef_core.datasets.timeseries_dataset import validate_horizons_present +from openstef_core.exceptions import NotFittedError +from openstef_core.mixins import Predictor, TransformPipeline +from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner +from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_models.models.forecasting import Forecaster +from openstef_models.models.forecasting.forecaster import ForecasterConfig +from openstef_models.models.forecasting_model import ModelFitResult +from openstef_models.utils.data_split import DataSplitter + + +class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]): + """Complete forecasting pipeline combining preprocessing, prediction, and postprocessing. + + Orchestrates the full forecasting workflow by managing feature engineering, + model training/prediction, and result postprocessing. Automatically handles + the differences between single-horizon and multi-horizon forecasters while + ensuring data consistency and validation throughout the pipeline. + + Invariants: + - fit() must be called before predict() + - Forecaster and preprocessing horizons must match during initialization + + Important: + The `cutoff_history` parameter is crucial when using lag-based features in + preprocessing. For example, a lag-14 transformation creates NaN values for + the first 14 days of data. Set `cutoff_history` to exclude these incomplete + rows from training. You must configure this manually based on your preprocessing + pipeline since lags cannot be automatically inferred from the transforms. + + Example: + Basic forecasting workflow: + + >>> from openstef_models.models.forecasting.constant_median_forecaster import ( + ... ConstantMedianForecaster, ConstantMedianForecasterConfig + ... ) + >>> from openstef_core.types import LeadTime + >>> + >>> # Note: This is a conceptual example showing the API structure + >>> # Real usage requires implemented forecaster classes + >>> forecaster = ConstantMedianForecaster( + ... config=ConstantMedianForecasterConfig(horizons=[LeadTime.from_string("PT36H")]) + ... ) + >>> # Create and train model + >>> model = ForecastingModel( + ... forecaster=forecaster, + ... cutoff_history=timedelta(days=14), # Match your maximum lag in preprocessing + ... ) + >>> model.fit(training_data) # doctest: +SKIP + >>> + >>> # Generate forecasts + >>> forecasts = model.predict(new_data) # doctest: +SKIP + """ + + # Forecasting components + common_preprocessing: TransformPipeline[TimeSeriesDataset] = Field( + default_factory=TransformPipeline[TimeSeriesDataset], + description="Feature engineering pipeline for transforming raw input data into model-ready features.", + exclude=True, + ) + + model_specific_preprocessing: dict[str, TransformPipeline[TimeSeriesDataset]] = Field( + default_factory=dict, + description="Feature engineering pipeline for transforming raw input data into model-ready features.", + exclude=True, + ) + + forecasters: dict[str, Forecaster] = Field( + default=..., + description="Underlying forecasting algorithm, either single-horizon or multi-horizon.", + exclude=True, + ) + + combiner: ForecastCombiner = Field( + default=..., + description="Combiner to aggregate forecasts from multiple forecasters if applicable.", + exclude=True, + ) + + combiner_preprocessing: TransformPipeline[TimeSeriesDataset] = Field( + default_factory=TransformPipeline[TimeSeriesDataset], + description="Feature engineering for the forecast combiner.", + exclude=True, + ) + + postprocessing: TransformPipeline[ForecastDataset] = Field( + default_factory=TransformPipeline[ForecastDataset], + description="Postprocessing pipeline for transforming model outputs into final forecasts.", + exclude=True, + ) + target_column: str = Field( + default="load", + description="Name of the target variable column in datasets.", + ) + data_splitter: DataSplitter = Field( + default_factory=DataSplitter, + description="Data splitting strategy for train/validation/test sets.", + ) + cutoff_history: timedelta = Field( + default=timedelta(days=0), + description="Amount of historical data to exclude from training and prediction due to incomplete features " + "from lag-based preprocessing. When using lag transforms (e.g., lag-14), the first N days contain NaN values. " + "Set this to match your maximum lag duration (e.g., timedelta(days=14)). " + "Default of 0 assumes no invalid rows are created by preprocessing.", + ) + # Evaluation + evaluation_metrics: list[MetricProvider] = Field( + default_factory=lambda: [R2Provider(), ObservedProbabilityProvider()], + description="List of metric providers for evaluating model score.", + ) + # Metadata + tags: dict[str, str] = Field( + default_factory=dict, + description="Optional metadata tags for the model.", + ) + + _logger: logging.Logger = PrivateAttr(default=logging.getLogger(__name__)) + + @property + def config(self) -> list[ForecasterConfig]: + """Returns the configuration of the underlying forecaster.""" + return [x.config for x in self.forecasters.values()] + + @property + @override + def is_fitted(self) -> bool: + return all(f.is_fitted for f in self.forecasters.values()) and self.combiner.is_fitted + + @property + def forecaster_names(self) -> list[str]: + """Returns the names of the underlying forecasters.""" + return list(self.forecasters.keys()) + + @override + def fit( + self, + data: TimeSeriesDataset, + data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, + ) -> ModelFitResult: + """Train the forecasting model on the provided dataset. + + Fits the preprocessing pipeline and underlying forecaster. Handles both + single-horizon and multi-horizon forecasters appropriately. + + The data splitting follows this sequence: + 1. Split test set from full data (using test_splitter) + 2. Split validation from remaining train+val data (using val_splitter) + 3. Train on the final training set + + Args: + data: Historical time series data with features and target values. + data_val: Optional validation data. If provided, splitters are ignored for validation. + data_test: Optional test data. If provided, splitters are ignored for test. + + Returns: + FitResult containing training details and metrics. + """ + # Fit the feature engineering transforms + self.common_preprocessing.fit(data=data) + + # Fit predict forecasters + ensemble_predictions = self._preprocess_fit_forecasters( + data=data, + data_val=data_val, + data_test=data_test, + ) + + if data_val is not None: + ensemble_predictions_val = self._predict_forecasters( + data=self.prepare_input(data=data_val), + ) + else: + ensemble_predictions_val = None + + if len(self.combiner_preprocessing.transforms) > 0: + combiner_data = self.prepare_input(data=data) + self.combiner_preprocessing.fit(combiner_data) + combiner_data = self.combiner_preprocessing.transform(combiner_data) + features = ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + else: + features = None + + self.combiner.fit( + data=ensemble_predictions, + data_val=ensemble_predictions_val, + additional_features=features, + ) + + # Prepare input datasets for metrics calculation + input_data_train = self.prepare_input(data=data) + input_data_val = self.prepare_input(data=data_val) if data_val else None + input_data_test = self.prepare_input(data=data_test) if data_test else None + + metrics_train = self._predict_and_score(input_data=input_data_train) + metrics_val = self._predict_and_score(input_data=input_data_val) if input_data_val else None + metrics_test = self._predict_and_score(input_data=input_data_test) if input_data_test else None + metrics_full = self.score(data=data) + + return ModelFitResult( + input_dataset=data, + input_data_train=input_data_train, + input_data_val=input_data_val, + input_data_test=input_data_test, + metrics_train=metrics_train, + metrics_val=metrics_val, + metrics_test=metrics_test, + metrics_full=metrics_full, + ) + + def _preprocess_fit_forecasters( + self, + data: TimeSeriesDataset, + data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, + ) -> EnsembleForecastDataset: + + predictions_raw: dict[str, ForecastDataset] = {} + + for name, forecaster in self.forecasters.items(): + validate_horizons_present(data, forecaster.config.horizons) + + # Transform and split input data + input_data_train = self.prepare_input(data=data, forecaster_name=name) + input_data_val = self.prepare_input(data=data_val, forecaster_name=name) if data_val else None + input_data_test = self.prepare_input(data=data_test, forecaster_name=name) if data_test else None + + # Drop target column nan's from training data. One can not train on missing targets. + target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] + input_data_train = input_data_train.pipe_pandas(target_dropna) + input_data_val = input_data_val.pipe_pandas(target_dropna) if input_data_val else None + input_data_test = input_data_test.pipe_pandas(target_dropna) if input_data_test else None + + # Transform the input data to a valid forecast input and split into train/val/test + input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( + data=input_data_train, + data_val=input_data_val, + data_test=input_data_test, + target_column=self.target_column, + ) + + # Fit the model + forecaster.fit(data=input_data_train, data_val=input_data_val) + predictions_raw[name] = self.forecasters[name].predict(data=input_data_train) + + return EnsembleForecastDataset.from_forecast_datasets( + predictions_raw, target_series=data.data[self.target_column] + ) + + def _predict_forecasters(self, data: TimeSeriesDataset) -> EnsembleForecastDataset: + """Generate predictions from base learners. + + Args: + data: Input data for prediction. + + Returns: + DataFrame containing base learner predictions. + """ + base_predictions: dict[str, ForecastDataset] = {} + for name, forecaster in self.forecasters.items(): + forecaster_data = self.prepare_input(data, forecaster_name=name) + preds = forecaster.predict(data=forecaster_data) + base_predictions[name] = preds + + return EnsembleForecastDataset.from_forecast_datasets( + base_predictions, target_series=data.data[self.target_column] + ) + + @override + def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: + """Generate forecasts using the trained model. + + Transforms input data through the preprocessing pipeline, generates predictions + using the underlying forecaster, and applies postprocessing transformations. + + Args: + data: Input time series data for generating forecasts. + forecast_start: Starting time for forecasts. If None, uses data end time. + + Returns: + Processed forecast dataset with predictions and uncertainty estimates. + + Raises: + NotFittedError: If the model hasn't been trained yet. + """ + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Transform the input data to a valid forecast input + input_data = self.prepare_input(data=data, forecast_start=forecast_start) + + # Generate predictions + raw_predictions = self._predict(input_data=input_data) + + return self.postprocessing.transform(data=raw_predictions) + + def prepare_input( + self, + data: TimeSeriesDataset, + forecaster_name: str | None = None, + forecast_start: datetime | None = None, + ) -> ForecastInputDataset: + """Prepare input data for forecasting by applying preprocessing and filtering. + + Transforms raw time series data through the preprocessing pipeline, restores + the target column, and filters out incomplete historical data to ensure + training quality. + + Args: + data: Raw time series dataset to prepare for forecasting. + forecast_start: Optional start time for forecasts. If provided and earlier + than the cutoff time, overrides the cutoff for data filtering. + + Returns: + Processed forecast input dataset ready for model prediction. + """ + # Transform and restore target column + data = self.common_preprocessing.transform(data=data) + + # Apply model-specific preprocessing if available + if forecaster_name in self.model_specific_preprocessing: + self.model_specific_preprocessing[forecaster_name].fit(data=data) + data = self.model_specific_preprocessing[forecaster_name].transform(data=data) + + input_data = restore_target(dataset=data, original_dataset=data, target_column=self.target_column) + + # Cut away input history to avoid training on incomplete data + input_data_start = cast("pd.Series[pd.Timestamp]", input_data.index).min().to_pydatetime() + input_data_cutoff = input_data_start + self.cutoff_history + if forecast_start is not None and forecast_start < input_data_cutoff: + input_data_cutoff = forecast_start + self._logger.warning( + "Forecast start %s is after input data start + cutoff history %s. Using forecast start as cutoff.", + forecast_start, + input_data_cutoff, + ) + input_data = input_data.filter_by_range(start=input_data_cutoff) + + return ForecastInputDataset.from_timeseries( + dataset=input_data, + target_column=self.target_column, + forecast_start=forecast_start, + ) + + def _predict_and_score(self, input_data: ForecastInputDataset) -> SubsetMetric: + prediction_raw = self._predict(input_data=input_data) + prediction = self.postprocessing.transform(data=prediction_raw) + return self._calculate_score(prediction=prediction) + + def _predict(self, input_data: ForecastInputDataset) -> ForecastDataset: + + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + ensemble_predictions = self._predict_forecasters(data=input_data) + + additional_features = ( + ForecastInputDataset.from_timeseries( + self.combiner_preprocessing.transform(data=input_data), target_column=self.target_column + ) + if len(self.combiner_preprocessing.transforms) > 0 + else None + ) + + # Predict and restore target column + prediction = self.combiner.predict( + data=ensemble_predictions, + additional_features=additional_features, + ) + return restore_target(dataset=prediction, original_dataset=input_data, target_column=self.target_column) + + def score( + self, + data: TimeSeriesDataset, + ) -> SubsetMetric: + """Evaluate model performance on the provided dataset. + + Generates predictions for the dataset and calculates evaluation metrics + by comparing against ground truth values. Uses the configured evaluation + metrics to assess forecast quality at the maximum forecast horizon. + + Args: + data: Time series dataset containing both features and target values + for evaluation. + + Returns: + Evaluation metrics including configured providers (e.g., R2, observed + probability) computed at the maximum forecast horizon. + """ + prediction = self.predict(data=data) + + return self._calculate_score(prediction=prediction) + + def _calculate_score(self, prediction: ForecastDataset) -> SubsetMetric: + if prediction.target_series is None: + raise ValueError("Prediction dataset must contain target series for scoring.") + + # We need to make sure there are no NaNs in the target label for metric calculation + prediction = prediction.pipe_pandas(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownArgumentType, reportUnknownMemberType] + + pipeline = EvaluationPipeline( + # Needs only one horizon since we are using only a single prediction step + # If a more comprehensive test is needed, a backtest should be run. + config=EvaluationConfig(available_ats=[], lead_times=[self.config[0].max_horizon]), + quantiles=self.combiner.config.quantiles, + # Similarly windowed metrics are not relevant for single predictions. + window_metric_providers=[], + global_metric_providers=self.evaluation_metrics, + ) + + evaluation_result = pipeline.run_for_subset( + filtering=self.combiner.config.max_horizon, + predictions=prediction, + ) + global_metric = evaluation_result.get_global_metric() + if not global_metric: + return SubsetMetric( + window="global", + timestamp=prediction.forecast_start, + metrics={}, + ) + + return global_metric + + +def restore_target[T: TimeSeriesDataset]( + dataset: T, + original_dataset: TimeSeriesDataset, + target_column: str, +) -> T: + """Restore the target column from the original dataset to the given dataset. + + Maps target values from the original dataset to the dataset using index alignment. + Ensures the target column is present in the dataset for downstream processing. + + Args: + dataset: Dataset to modify by adding the target column. + original_dataset: Source dataset containing the target values. + target_column: Name of the target column to restore. + + Returns: + Dataset with the target column restored from the original dataset. + """ + target_series = original_dataset.select_features([target_column]).select_version().data[target_column] + + def _transform_restore_target(df: pd.DataFrame) -> pd.DataFrame: + return df.assign(**{str(target_series.name): df.index.map(target_series)}) # pyright: ignore[reportUnknownMemberType] + + return dataset.pipe_pandas(_transform_restore_target) + + +__all__ = ["EnsembleForecastingModel", "ModelFitResult", "restore_target"] diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py new file mode 100644 index 000000000..db4917778 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py @@ -0,0 +1,28 @@ +"""Forecast Combiners.""" + +from .forecast_combiner import ForecastCombiner, ForecastCombinerConfig +from .learned_weights_combiner import ( + LGBMCombinerHyperParams, + LogisticCombinerHyperParams, + RFCombinerHyperParams, + WeightsCombiner, + WeightsCombinerConfig, + XGBCombinerHyperParams, +) +from .rules_combiner import RulesCombiner, RulesCombinerConfig +from .stacking_combiner import StackingCombiner, StackingCombinerConfig + +__all__ = [ + "ForecastCombiner", + "ForecastCombinerConfig", + "LGBMCombinerHyperParams", + "LogisticCombinerHyperParams", + "RFCombinerHyperParams", + "RulesCombiner", + "RulesCombinerConfig", + "StackingCombiner", + "StackingCombinerConfig", + "WeightsCombiner", + "WeightsCombinerConfig", + "XGBCombinerHyperParams", +] diff --git a/packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py similarity index 63% rename from packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py rename to packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index 2a1146d63..8a12027d9 100644 --- a/packages/openstef-meta/src/openstef_meta/framework/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -9,15 +9,15 @@ """ from abc import abstractmethod -from collections.abc import Sequence +from typing import Self import pandas as pd from pydantic import ConfigDict, Field -from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset -from openstef_core.mixins import HyperParams, Predictor, TransformPipeline -from openstef_core.transforms import TimeSeriesTransform -from openstef_core.types import Quantile +from openstef_core.base_model import BaseConfig +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.mixins import HyperParams, Predictor +from openstef_core.types import LeadTime, Quantile from openstef_meta.transforms.selector import Selector from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.utils.feature_selection import FeatureSelection @@ -27,28 +27,74 @@ ) -class ForecastCombinerHyperParams(HyperParams): +class ForecastCombinerConfig(BaseConfig): """Hyperparameters for the Final Learner.""" model_config = ConfigDict(arbitrary_types_allowed=True) - feature_adders: Sequence[TimeSeriesTransform] = Field( - default=[], - description="Additional features to add to the base learner predictions before fitting the final learner.", + hyperparams: HyperParams = Field( + description="Hyperparameters for the final learner.", ) + quantiles: list[Quantile] = Field( + default=[Quantile(0.5)], + description=( + "Probability levels for uncertainty estimation. Each quantile represents a confidence level " + "(e.g., 0.1 = 10th percentile, 0.5 = median, 0.9 = 90th percentile). " + "Models must generate predictions for all specified quantiles." + ), + min_length=1, + ) + + horizons: list[LeadTime] = Field( + default=..., + description=( + "Lead times for predictions, accounting for data availability and versioning cutoffs. " + "Each horizon defines how far ahead the model should predict." + ), + min_length=1, + ) + + @property + def max_horizon(self) -> LeadTime: + """Returns the maximum lead time (horizon) from the configured horizons. + + Useful for determining the furthest prediction distance required by the model. + This is commonly used for data preparation and validation logic. + + Returns: + The maximum lead time. + """ + return max(self.horizons) + + def with_horizon(self, horizon: LeadTime) -> Self: + """Create a new configuration with a different horizon. + + Useful for creating multiple forecaster instances for different prediction + horizons from a single base configuration. + + Args: + horizon: The new lead time to use for predictions. + + Returns: + New configuration instance with the specified horizon. + """ + return self.model_copy(update={"horizons": [horizon]}) + + @classmethod + def combiner_class(cls) -> type["ForecastCombiner"]: + """Get the associated Forecaster class for this configuration. + + Returns: + The Forecaster class that uses this configuration. + """ + raise NotImplementedError("Subclasses must implement combiner_class") + class ForecastCombiner(Predictor[EnsembleForecastDataset, ForecastDataset]): """Combines base learner predictions for each quantile into final predictions.""" - def __init__(self, quantiles: list[Quantile], hyperparams: ForecastCombinerHyperParams) -> None: - """Initialize the Final Learner.""" - self.quantiles = quantiles - self.hyperparams = hyperparams - self.pre_processing: TransformPipeline[TimeSeriesDataset] = TransformPipeline( - transforms=hyperparams.feature_adders - ) - self._is_fitted: bool = False + config: ForecastCombinerConfig @abstractmethod def fit( @@ -85,24 +131,6 @@ def predict( """ raise NotImplementedError("Subclasses must implement the predict method.") - def calculate_features(self, data: ForecastInputDataset) -> ForecastInputDataset: - """Calculate additional features for the final learner. - - Args: - data: Input ForecastInputDataset to calculate features on. - - Returns: - ForecastInputDataset with additional features. - """ - data_transformed = self.pre_processing.transform(data) - - return ForecastInputDataset( - data=data_transformed.data, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.forecast_start, - ) - @staticmethod def _prepare_input_data( dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None @@ -130,8 +158,3 @@ def _prepare_input_data( def is_fitted(self) -> bool: """Indicates whether the final learner has been fitted.""" raise NotImplementedError("Subclasses must implement the is_fitted property.") - - @property - def has_features(self) -> bool: - """Indicates whether the final learner uses additional features.""" - return len(self.pre_processing.transforms) > 0 diff --git a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py similarity index 72% rename from packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py rename to packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 1f4837038..31f39e095 100644 --- a/packages/openstef-meta/src/openstef_meta/models/learned_weights_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -26,27 +26,13 @@ from openstef_core.exceptions import ( NotFittedError, ) -from openstef_core.mixins import HyperParams -from openstef_core.types import Quantile -from openstef_meta.framework.base_learner import ( - BaseLearner, - BaseLearnerHyperParams, -) -from openstef_meta.framework.forecast_combiner import ( - EnsembleForecastDataset, +from openstef_core.mixins.predictor import HyperParams +from openstef_core.types import LeadTime, Quantile +from openstef_meta.models.forecast_combiners.forecast_combiner import ( ForecastCombiner, - ForecastCombinerHyperParams, -) -from openstef_meta.framework.meta_forecaster import ( - EnsembleForecaster, -) -from openstef_models.models.forecasting.forecaster import ( - ForecasterConfig, -) -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearHyperParams, + ForecastCombinerConfig, ) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_meta.utils.datasets import EnsembleForecastDataset logger = logging.getLogger(__name__) @@ -57,152 +43,17 @@ ClassifierNames = Literal["lgbm", "xgb", "logistic_regression", "dummy"] -class WeightsCombinerHyperParams(ForecastCombinerHyperParams): - """Hyperparameters for Learned Weights Final Learner.""" +class ClassifierParamsMixin: + """Hyperparameters for the Final Learner.""" @abstractmethod def get_classifier(self) -> Classifier: - """Initialize the classifier from hyperparameters. - - Returns: - Classifier: An instance of the classifier initialized with the provided hyperparameters. - """ - raise NotImplementedError("Subclasses must implement the 'get_classifier' method.") - - -class WeightsCombiner(ForecastCombiner): - """Combines base learner predictions with a classification approach to determine which base learner to use.""" - - model_type: ClassifierNames = Field( - default="lgbm", description="Type of classifier to use for combining base learner predictions." - ) - - def __init__(self, quantiles: list[Quantile], hyperparams: WeightsCombinerHyperParams) -> None: - """Initialize WeightsCombiner.""" - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - self.models: list[Classifier] = [hyperparams.get_classifier() for _ in self.quantiles] - - self._label_encoder = LabelEncoder() - self._is_fitted = False - - @override - def fit( - self, - data: EnsembleForecastDataset, - data_val: EnsembleForecastDataset | None = None, - additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, - ) -> None: - - self._label_encoder.fit(data.model_names) - - for i, q in enumerate(self.quantiles): - # Data preparation - dataset = data.select_quantile_classification(quantile=q) - input_data = self._prepare_input_data( - dataset=dataset, - additional_features=additional_features, - ) - labels = dataset.target_series - self._validate_labels(labels=labels, model_index=i) - labels = self._label_encoder.transform(labels) - - # Balance classes, adjust with sample weights - weights = compute_sample_weight("balanced", labels) - if sample_weights is not None: - weights *= sample_weights - - self.models[i].fit(X=input_data, y=labels, sample_weight=weights) # type: ignore - self._is_fitted = True - - @staticmethod - def _prepare_input_data( - dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None - ) -> pd.DataFrame: - """Prepare input data by combining base predictions with additional features if provided. + """Returns the classifier instance.""" + msg = "Subclasses must implement get_classifier method." + raise NotImplementedError(msg) - Args: - dataset: ForecastInputDataset containing base predictions. - additional_features: Optional ForecastInputDataset containing additional features. - Returns: - pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. - """ - df = dataset.input_data(start=dataset.index[0]) - if additional_features is not None: - df_a = additional_features.input_data(start=dataset.index[0]) - df = pd.concat( - [df, df_a], - axis=1, - ) - return df - - def _validate_labels(self, labels: pd.Series, model_index: int) -> None: - if len(labels.unique()) == 1: - msg = f"""Final learner for quantile {self.quantiles[model_index].format()} has - less than 2 classes in the target. - Switching to dummy classifier """ - logger.warning(msg=msg) - self.models[model_index] = DummyClassifier(strategy="most_frequent") - - def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_index: int) -> pd.DataFrame: - model = self.models[model_index] - return model.predict_proba(X=base_predictions) # type: ignore - - def _generate_predictions_quantile( - self, - dataset: ForecastInputDataset, - additional_features: ForecastInputDataset | None, - model_index: int, - ) -> pd.Series: - - input_data = self._prepare_input_data( - dataset=dataset, - additional_features=additional_features, - ) - - weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) - - return dataset.input_data().mul(weights).sum(axis=1) - - @override - def predict( - self, - data: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None = None, - ) -> ForecastDataset: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions = pd.DataFrame({ - Quantile(q).format(): self._generate_predictions_quantile( - dataset=data.select_quantile(quantile=Quantile(q)), - additional_features=additional_features, - model_index=i, - ) - for i, q in enumerate(self.quantiles) - }) - target_series = data.target_series - if target_series is not None: - predictions[data.target_column] = target_series - - return ForecastDataset( - data=predictions, - sample_interval=data.sample_interval, - target_column=data.target_column, - forecast_start=data.forecast_start, - ) - - @property - @override - def is_fitted(self) -> bool: - return self._is_fitted - - -# Final learner implementations using different classifiers -# 1 LGBM Classifier -class LGBMCombinerHyperParams(WeightsCombinerHyperParams): +class LGBMCombinerHyperParams(HyperParams, ClassifierParamsMixin): """Hyperparameters for Learned Weights Final Learner with LGBM Classifier.""" n_estimators: int = Field( @@ -226,7 +77,7 @@ def get_classifier(self) -> LGBMClassifier: ) -class RFCombinerHyperParams(WeightsCombinerHyperParams): +class RFCombinerHyperParams(HyperParams, ClassifierParamsMixin): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( @@ -269,7 +120,7 @@ def get_classifier(self) -> LGBMClassifier: # 3 XGB Classifier -class XGBCombinerHyperParams(WeightsCombinerHyperParams): +class XGBCombinerHyperParams(HyperParams, ClassifierParamsMixin): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" n_estimators: int = Field( @@ -283,7 +134,7 @@ def get_classifier(self) -> XGBClassifier: return XGBClassifier(n_estimators=self.n_estimators) -class LogisticCombinerHyperParams(WeightsCombinerHyperParams): +class LogisticCombinerHyperParams(HyperParams, ClassifierParamsMixin): """Hyperparameters for Learned Weights Final Learner with LGBM Random Forest Classifier.""" fit_intercept: bool = Field( @@ -312,58 +163,189 @@ def get_classifier(self) -> LogisticRegression: ) -class LearnedWeightsHyperParams(HyperParams): - """Hyperparameters for Stacked LGBM GBLinear Regressor.""" +class WeightsCombinerConfig(ForecastCombinerConfig): + """Configuration for WeightsCombiner.""" + + hyperparams: HyperParams = Field( + default=LGBMCombinerHyperParams(), + description="Hyperparameters for the Weights Combiner.", + ) - base_hyperparams: list[BaseLearnerHyperParams] = Field( - default=[LGBMHyperParams(), GBLinearHyperParams()], - description="List of hyperparameter configurations for base learners. " - "Defaults to [LGBMHyperParams, GBLinearHyperParams].", + quantiles: list[Quantile] = Field( + default=[Quantile(0.5)], + description=( + "Probability levels for uncertainty estimation. Each quantile represents a confidence level " + "(e.g., 0.1 = 10th percentile, 0.5 = median, 0.9 = 90th percentile). " + "Models must generate predictions for all specified quantiles." + ), + min_length=1, ) - combiner_hyperparams: WeightsCombinerHyperParams = Field( - default=LGBMCombinerHyperParams(), - description="Hyperparameters for the final learner. Defaults to LGBMLearnerHyperParams.", + horizons: list[LeadTime] = Field( + default=..., + description=( + "Lead times for predictions, accounting for data availability and versioning cutoffs. " + "Each horizon defines how far ahead the model should predict." + ), + min_length=1, ) + @property + def get_classifier(self) -> Classifier: + """Returns the classifier instance from hyperparameters. + + Returns: + Classifier instance. -class LearnedWeightsForecasterConfig(ForecasterConfig): - """Configuration for Hybrid-based forecasting models.""" + Raises: + TypeError: If hyperparams do not implement ClassifierParamsMixin. + """ + if not isinstance(self.hyperparams, ClassifierParamsMixin): + msg = "hyperparams must implement ClassifierParamsMixin to get classifier." + raise TypeError(msg) + return self.hyperparams.get_classifier() - hyperparams: LearnedWeightsHyperParams - verbosity: bool = Field( - default=True, - description="Enable verbose output from the Hybrid model (True/False).", - ) +class WeightsCombiner(ForecastCombiner): + """Combines base learner predictions with a classification approach to determine which base learner to use.""" + Config = WeightsCombinerConfig + LGBMHyperParams = LGBMCombinerHyperParams + RFHyperParams = RFCombinerHyperParams + XGBHyperParams = XGBCombinerHyperParams + LogisticHyperParams = LogisticCombinerHyperParams + + def __init__(self, config: WeightsCombinerConfig) -> None: + """Initialize the Weigths Combiner.""" + self.quantiles = config.quantiles + self.config = config + self.hyperparams = config.hyperparams + self._is_fitted: bool = False + self._is_fitted = False + self._label_encoder = LabelEncoder() + + # Initialize a classifier per quantile + self.models: list[Classifier] = [config.get_classifier for _ in self.quantiles] + + @override + def fit( + self, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, + sample_weights: pd.Series | None = None, + ) -> None: + + self._label_encoder.fit(data.forecaster_names) + + for i, q in enumerate(self.quantiles): + # Data preparation + dataset = data.select_quantile_classification(quantile=q) + input_data = self._prepare_input_data( + dataset=dataset, + additional_features=additional_features, + ) + labels = dataset.target_series + self._validate_labels(labels=labels, model_index=i) + labels = self._label_encoder.transform(labels) + + # Balance classes, adjust with sample weights + weights = compute_sample_weight("balanced", labels) + if sample_weights is not None: + weights *= sample_weights + + self.models[i].fit(X=input_data, y=labels, sample_weight=weights) # type: ignore + self._is_fitted = True + + @staticmethod + def _prepare_input_data( + dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None + ) -> pd.DataFrame: + """Prepare input data by combining base predictions with additional features if provided. + + Args: + dataset: ForecastInputDataset containing base predictions. + additional_features: Optional ForecastInputDataset containing additional features. + + Returns: + pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. + """ + df = dataset.input_data(start=dataset.index[0]) + if additional_features is not None: + df_a = additional_features.input_data(start=dataset.index[0]) + df = pd.concat( + [df, df_a], + axis=1, + ) + return df -class LearnedWeightsForecaster(EnsembleForecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" + def _validate_labels(self, labels: pd.Series, model_index: int) -> None: + if len(labels.unique()) == 1: + msg = f"""Final learner for quantile {self.quantiles[model_index].format()} has + less than 2 classes in the target. + Switching to dummy classifier """ + logger.warning(msg=msg) + self.models[model_index] = DummyClassifier(strategy="most_frequent") - Config = LearnedWeightsForecasterConfig - HyperParams = LearnedWeightsHyperParams + def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_index: int) -> pd.DataFrame: + model = self.models[model_index] + return model.predict_proba(X=base_predictions) # type: ignore - def __init__(self, config: LearnedWeightsForecasterConfig) -> None: - """Initialize the LearnedWeightsForecaster.""" - self._config = config + def _generate_predictions_quantile( + self, + dataset: ForecastInputDataset, + additional_features: ForecastInputDataset | None, + model_index: int, + ) -> pd.Series: - self._base_learners: list[BaseLearner] = self._init_base_learners( - config=config, base_hyperparams=config.hyperparams.base_hyperparams + input_data = self._prepare_input_data( + dataset=dataset, + additional_features=additional_features, ) - self._forecast_combiner = WeightsCombiner( - quantiles=config.quantiles, hyperparams=config.hyperparams.combiner_hyperparams + + weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) + + return dataset.input_data().mul(weights).sum(axis=1) + + @override + def predict( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> ForecastDataset: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions = pd.DataFrame({ + Quantile(q).format(): self._generate_predictions_quantile( + dataset=data.select_quantile(quantile=Quantile(q)), + additional_features=additional_features, + model_index=i, + ) + for i, q in enumerate(self.quantiles) + }) + target_series = data.target_series + if target_series is not None: + predictions[data.target_column] = target_series + + return ForecastDataset( + data=predictions, + sample_interval=data.sample_interval, + target_column=data.target_column, + forecast_start=data.forecast_start, ) + @property + @override + def is_fitted(self) -> bool: + return self._is_fitted + __all__ = [ "LGBMCombinerHyperParams", - "LearnedWeightsForecaster", - "LearnedWeightsForecasterConfig", - "LearnedWeightsHyperParams", "LogisticCombinerHyperParams", "RFCombinerHyperParams", "WeightsCombiner", - "WeightsCombinerHyperParams", "XGBCombinerHyperParams", ] diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py new file mode 100644 index 000000000..a030b3df5 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py @@ -0,0 +1,154 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""Rules-based Meta Forecaster Module.""" + +import logging +from typing import cast, override + +import pandas as pd +from pydantic import Field, field_validator + +from openstef_core.datasets import ForecastDataset, ForecastInputDataset +from openstef_core.mixins import HyperParams +from openstef_core.types import LeadTime, Quantile +from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner, ForecastCombinerConfig +from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_meta.utils.decision_tree import Decision, DecisionTree + +logger = logging.getLogger(__name__) + + +class RulesLearnerHyperParams(HyperParams): + """HyperParams for Stacking Final Learner.""" + + decision_tree: DecisionTree = Field( + description="Decision tree defining the rules for the final learner.", + default=DecisionTree( + nodes=[Decision(idx=0, decision="LGBMForecaster")], + outcomes={"LGBMForecaster"}, + ), + ) + + +class RulesCombinerConfig(ForecastCombinerConfig): + """Configuration for Rules-based Forecast Combiner.""" + + hyperparams: HyperParams = Field( + description="Hyperparameters for the Rules-based final learner.", + default=RulesLearnerHyperParams(), + ) + + quantiles: list[Quantile] = Field( + default=[Quantile(0.5)], + description=( + "Probability levels for uncertainty estimation. Each quantile represents a confidence level " + "(e.g., 0.1 = 10th percentile, 0.5 = median, 0.9 = 90th percentile). " + "Models must generate predictions for all specified quantiles." + ), + min_length=1, + ) + + horizons: list[LeadTime] = Field( + default=..., + description=( + "Lead times for predictions, accounting for data availability and versioning cutoffs. " + "Each horizon defines how far ahead the model should predict." + ), + min_length=1, + ) + + @field_validator("hyperparams", mode="after") + @staticmethod + def _validate_hyperparams(v: HyperParams) -> HyperParams: + if not isinstance(v, RulesLearnerHyperParams): + raise TypeError("hyperparams must be an instance of RulesLearnerHyperParams.") + return v + + +class RulesCombiner(ForecastCombiner): + """Combines base learner predictions per quantile into final predictions using a regression approach.""" + + Config = RulesCombinerConfig + + def __init__(self, config: RulesCombinerConfig) -> None: + """Initialize the Rules Learner. + + Args: + config: Configuration for the Rules Combiner. + """ + hyperparams = cast(RulesLearnerHyperParams, config.hyperparams) + self.tree = hyperparams.decision_tree + self.quantiles = config.quantiles + self.config = config + + @override + def fit( + self, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, + sample_weights: pd.Series | None = None, + ) -> None: + # No fitting needed for rule-based final learner + # Check that additional features are provided + if additional_features is None: + raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") + + if sample_weights is not None: + logger.warning("Sample weights are ignored in RulesLearner.fit method.") + + def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: + """Predict using the decision tree rules. + + Args: + data: DataFrame containing the additional features. + columns: Expected columns for the output DataFrame. + + Returns: + DataFrame with predictions for each quantile. + """ + predictions = data.apply(self.tree.get_decision, axis=1) + + return pd.get_dummies(predictions).reindex(columns=columns) + + @override + def predict( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> ForecastDataset: + if additional_features is None: + raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") + + decisions = self._predict_tree( + additional_features.data, columns=data.select_quantile(quantile=self.quantiles[0]).data.columns + ) + + # Generate predictions + predictions: list[pd.DataFrame] = [] + for q in self.quantiles: + dataset = data.select_quantile(quantile=q) + preds = dataset.input_data().multiply(decisions).sum(axis=1) + + predictions.append(preds.to_frame(name=Quantile(q).format())) + + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + return ForecastDataset( + data=df, + sample_interval=data.sample_interval, + ) + + @property + def is_fitted(self) -> bool: + """Check the Rules Final Learner is fitted.""" + return True + + +__all__ = [ + "RulesCombiner", + "RulesCombinerConfig", + "RulesLearnerHyperParams", +] diff --git a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py similarity index 54% rename from packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py rename to packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index 7dd3b2220..b8f4ebad5 100644 --- a/packages/openstef-meta/src/openstef_meta/models/stacking_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -10,8 +10,7 @@ """ import logging -from collections.abc import Sequence -from typing import override +from typing import TYPE_CHECKING, cast, override import pandas as pd from pydantic import Field, field_validator @@ -21,69 +20,105 @@ NotFittedError, ) from openstef_core.mixins import HyperParams -from openstef_core.transforms import TimeSeriesTransform from openstef_core.types import LeadTime, Quantile -from openstef_meta.framework.base_learner import ( - BaseLearner, - BaseLearnerHyperParams, -) -from openstef_meta.framework.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams -from openstef_meta.framework.meta_forecaster import ( - EnsembleForecaster, -) +from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner, ForecastCombinerConfig from openstef_meta.utils.datasets import EnsembleForecastDataset -from openstef_models.models.forecasting.forecaster import ( - Forecaster, - ForecasterConfig, -) from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearForecaster, GBLinearHyperParams, ) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams + +if TYPE_CHECKING: + from openstef_models.models.forecasting.forecaster import Forecaster logger = logging.getLogger(__name__) +ForecasterHyperParams = GBLinearHyperParams | LGBMHyperParams +ForecasterType = GBLinearForecaster | LGBMForecaster + -class StackingForecastCombinerHyperParams(ForecastCombinerHyperParams): - """HyperParams for Stacking Final Learner.""" +class StackingCombinerConfig(ForecastCombinerConfig): + """Configuration for the Stacking final learner.""" - feature_adders: Sequence[TimeSeriesTransform] = Field( - default=[], - description="Additional features to add to the base learner predictions before fitting the final learner.", + hyperparams: HyperParams = Field( + description="Hyperparameters for the Stacking Combiner.", ) - forecaster_hyperparams: BaseLearnerHyperParams = Field( - default=GBLinearHyperParams(), - description="Forecaster hyperparameters for the final learner. Defaults to GBLinearHyperParams.", + quantiles: list[Quantile] = Field( + default=[Quantile(0.5)], + description=( + "Probability levels for uncertainty estimation. Each quantile represents a confidence level " + "(e.g., 0.1 = 10th percentile, 0.5 = median, 0.9 = 90th percentile). " + "Models must generate predictions for all specified quantiles." + ), + min_length=1, ) + horizons: list[LeadTime] = Field( + default=..., + description=( + "Lead times for predictions, accounting for data availability and versioning cutoffs. " + "Each horizon defines how far ahead the model should predict." + ), + min_length=1, + ) -class StackingForecastCombiner(ForecastCombiner): + @field_validator("hyperparams", mode="after") + @staticmethod + def validate_forecaster( + v: HyperParams, + ) -> HyperParams: + """Validate that the forecaster class is set in the hyperparameters. + + Args: + v: Hyperparameters to validate. + + Returns: + Validated hyperparameters. + + Raises: + ValueError: If the forecaster class is not set. + """ + if not hasattr(v, "forecaster_class"): + raise ValueError("forecaster_class must be set in hyperparameters for StackingCombinerConfig.") + return v + + +class StackingCombiner(ForecastCombiner): """Combines base learner predictions per quantile into final predictions using a regression approach.""" + Config = StackingCombinerConfig + LGBMHyperParams = LGBMHyperParams + GBLinearHyperParams = GBLinearHyperParams + def __init__( - self, quantiles: list[Quantile], hyperparams: StackingForecastCombinerHyperParams, horizon: LeadTime + self, + config: StackingCombinerConfig, ) -> None: """Initialize the Stacking final learner. Args: - quantiles: List of quantiles to predict. - hyperparams: Hyperparameters for the final learner. - horizon: Forecast horizon for which to create the final learner. + config: Configuration for the Stacking combiner. """ - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - - forecaster_hyperparams: BaseLearnerHyperParams = hyperparams.forecaster_hyperparams + forecaster_hyperparams = cast(ForecasterHyperParams, config.hyperparams) + self.quantiles = config.quantiles + self.config = config + self.hyperparams = forecaster_hyperparams + self._is_fitted: bool = False # Split forecaster per quantile models: list[Forecaster] = [] for q in self.quantiles: forecaster_cls = forecaster_hyperparams.forecaster_class() - config = forecaster_cls.Config(horizons=[horizon], quantiles=[q]) + forecaster_config = forecaster_cls.Config( + horizons=[config.max_horizon], + quantiles=[q], + ) if "hyperparams" in forecaster_cls.Config.model_fields: - config = config.model_copy(update={"hyperparams": forecaster_hyperparams}) + forecaster_config = forecaster_config.model_copy(update={"hyperparams": forecaster_hyperparams}) - model = config.forecaster_from_config() + model = forecaster_config.forecaster_from_config() models.append(model) self.models = models @@ -167,59 +202,3 @@ def predict( def is_fitted(self) -> bool: """Check the StackingForecastCombiner is fitted.""" return all(x.is_fitted for x in self.models) - - -class StackingHyperParams(HyperParams): - """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - - base_hyperparams: list[BaseLearnerHyperParams] = Field( - default=[LGBMHyperParams(), GBLinearHyperParams()], - description="List of hyperparameter configurations for base learners. " - "Defaults to [LGBMHyperParams, GBLinearHyperParams].", - ) - - combiner_hyperparams: StackingForecastCombinerHyperParams = Field( - default=StackingForecastCombinerHyperParams(), - description="Hyperparameters for the final learner.", - ) - - @field_validator("base_hyperparams", mode="after") - @classmethod - def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: - hp_classes = [type(hp) for hp in v] - if not len(hp_classes) == len(set(hp_classes)): - raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") - return v - - -class StackingForecasterConfig(ForecasterConfig): - """Configuration for Hybrid-based forecasting models.""" - - hyperparams: StackingHyperParams = StackingHyperParams() - - verbosity: bool = Field( - default=True, - description="Enable verbose output from the Hybrid model (True/False).", - ) - - -class StackingForecaster(EnsembleForecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" - - Config = StackingForecasterConfig - HyperParams = StackingHyperParams - - def __init__(self, config: StackingForecasterConfig) -> None: - """Initialize the Hybrid forecaster.""" - self._config = config - - self._base_learners: list[BaseLearner] = self._init_base_learners( - config=config, base_hyperparams=config.hyperparams.base_hyperparams - ) - - self._forecast_combiner = StackingForecastCombiner( - quantiles=config.quantiles, hyperparams=config.hyperparams.combiner_hyperparams, horizon=config.max_horizon - ) - - -__all__ = ["StackingForecastCombiner", "StackingForecaster", "StackingForecasterConfig", "StackingHyperParams"] diff --git a/packages/openstef-meta/src/openstef_meta/models/forecasting/__init__.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/__init__.py new file mode 100644 index 000000000..fce9bcb92 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/__init__.py @@ -0,0 +1,12 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 +"""This module provides meta-forecasting models.""" + +from .residual_forecaster import ResidualForecaster, ResidualForecasterConfig, ResidualHyperParams + +__all__ = [ + "ResidualForecaster", + "ResidualForecasterConfig", + "ResidualHyperParams", +] diff --git a/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py similarity index 85% rename from packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py rename to packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py index 8905d8f57..efd8f50bc 100644 --- a/packages/openstef-meta/src/openstef_meta/models/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py @@ -12,6 +12,8 @@ import logging from typing import override +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster, LGBMLinearHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster, XGBoostHyperParams import pandas as pd from pydantic import Field @@ -21,23 +23,23 @@ ) from openstef_core.mixins import HyperParams from openstef_core.types import Quantile -from openstef_meta.framework.base_learner import ( - BaseLearner, - BaseLearnerHyperParams, -) -from openstef_meta.framework.meta_forecaster import ( - MetaForecaster, -) + + from openstef_models.models.forecasting.forecaster import ( + Forecaster, ForecasterConfig, ) from openstef_models.models.forecasting.gblinear_forecaster import ( + GBLinearForecaster, GBLinearHyperParams, ) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams logger = logging.getLogger(__name__) +BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster +BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams + class ResidualHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" @@ -64,7 +66,7 @@ class ResidualForecasterConfig(ForecasterConfig): ) -class ResidualForecaster(MetaForecaster): +class ResidualForecaster(Forecaster): """MetaForecaster that implements residual modeling. It takes in a primary forecaster and a residual forecaster. The primary forecaster makes initial predictions, @@ -102,6 +104,29 @@ def _init_secondary_model(self, hyperparams: BaseLearnerHyperParams) -> list[Bas return models + @staticmethod + def _init_base_learners( + config: ForecasterConfig, base_hyperparams: list[BaseLearnerHyperParams] + ) -> list[BaseLearner]: + """Initialize base learners based on provided hyperparameters. + + Returns: + list[Forecaster]: List of initialized base learner forecasters. + """ + base_learners: list[BaseLearner] = [] + horizons = config.horizons + quantiles = config.quantiles + + for hyperparams in base_hyperparams: + forecaster_cls = hyperparams.forecaster_class() + config = forecaster_cls.Config(horizons=horizons, quantiles=quantiles) + if "hyperparams" in forecaster_cls.Config.model_fields: + config = config.model_copy(update={"hyperparams": hyperparams}) + + base_learners.append(config.forecaster_from_config()) + + return base_learners + @override def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: """Fit the Hybrid model to the training data. diff --git a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py deleted file mode 100644 index d83d586f6..000000000 --- a/packages/openstef-meta/src/openstef_meta/models/rules_forecaster.py +++ /dev/null @@ -1,206 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Rules-based Meta Forecaster Module.""" - -import logging -from collections.abc import Sequence -from typing import override - -import pandas as pd -from pydantic import Field, field_validator -from pydantic_extra_types.country import CountryAlpha2 - -from openstef_core.datasets import ForecastDataset, ForecastInputDataset -from openstef_core.mixins import HyperParams -from openstef_core.transforms import TimeSeriesTransform -from openstef_core.types import Quantile -from openstef_meta.framework.base_learner import ( - BaseLearner, - BaseLearnerHyperParams, -) -from openstef_meta.framework.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams -from openstef_meta.framework.meta_forecaster import ( - EnsembleForecaster, -) -from openstef_meta.utils.datasets import EnsembleForecastDataset -from openstef_meta.utils.decision_tree import Decision, DecisionTree -from openstef_models.models.forecasting.forecaster import ( - ForecasterConfig, -) -from openstef_models.models.forecasting.gblinear_forecaster import ( - GBLinearHyperParams, -) -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -from openstef_models.transforms.time_domain import HolidayFeatureAdder - -logger = logging.getLogger(__name__) - - -class RulesLearnerHyperParams(ForecastCombinerHyperParams): - """HyperParams for Stacking Final Learner.""" - - feature_adders: Sequence[TimeSeriesTransform] = Field( - default=[], - description="Additional features to add to the final learner.", - ) - - decision_tree: DecisionTree = Field( - description="Decision tree defining the rules for the final learner.", - ) - - @field_validator("feature_adders", mode="after") - @classmethod - def _check_not_empty(cls, v: list[TimeSeriesTransform]) -> list[TimeSeriesTransform]: - if v == []: - raise ValueError("RulesForecaster requires at least one feature adder.") - return v - - -class RulesLearner(ForecastCombiner): - """Combines base learner predictions per quantile into final predictions using a regression approach.""" - - def __init__(self, quantiles: list[Quantile], hyperparams: RulesLearnerHyperParams) -> None: - """Initialize the Rules Learner. - - Args: - quantiles: List of quantiles to predict. - hyperparams: Hyperparameters for the final learner. - """ - super().__init__(quantiles=quantiles, hyperparams=hyperparams) - - self.tree = hyperparams.decision_tree - self.feature_adders = hyperparams.feature_adders - - @override - def fit( - self, - data: EnsembleForecastDataset, - data_val: EnsembleForecastDataset | None = None, - additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, - ) -> None: - # No fitting needed for rule-based final learner - # Check that additional features are provided - if additional_features is None: - raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") - - if sample_weights is not None: - logger.warning("Sample weights are ignored in RulesLearner.fit method.") - - def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: - """Predict using the decision tree rules. - - Args: - data: DataFrame containing the additional features. - columns: Expected columns for the output DataFrame. - - Returns: - DataFrame with predictions for each quantile. - """ - predictions = data.apply(self.tree.get_decision, axis=1) - - return pd.get_dummies(predictions).reindex(columns=columns) - - @override - def predict( - self, - data: EnsembleForecastDataset, - additional_features: ForecastInputDataset | None = None, - ) -> ForecastDataset: - if additional_features is None: - raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") - - decisions = self._predict_tree( - additional_features.data, columns=data.select_quantile(quantile=self.quantiles[0]).data.columns - ) - - # Generate predictions - predictions: list[pd.DataFrame] = [] - for q in self.quantiles: - dataset = data.select_quantile(quantile=q) - preds = dataset.input_data().multiply(decisions).sum(axis=1) - - predictions.append(preds.to_frame(name=Quantile(q).format())) - - # Concatenate predictions along columns to form a DataFrame with quantile columns - df = pd.concat(predictions, axis=1) - - return ForecastDataset( - data=df, - sample_interval=data.sample_interval, - ) - - @property - def is_fitted(self) -> bool: - """Check the Rules Final Learner is fitted.""" - return True - - -class RulesForecasterHyperParams(HyperParams): - """Hyperparameters for Rules Forecaster.""" - - base_hyperparams: list[BaseLearnerHyperParams] = Field( - default=[LGBMHyperParams(), GBLinearHyperParams()], - description="List of hyperparameter configurations for base learners. " - "Defaults to [LGBMHyperParams, GBLinearHyperParams].", - ) - - final_hyperparams: RulesLearnerHyperParams = Field( - description="Hyperparameters for the final learner.", - default=RulesLearnerHyperParams( - decision_tree=DecisionTree(nodes=[Decision(idx=0, decision="LGBMForecaster")], outcomes={"LGBMForecaster"}), - feature_adders=[HolidayFeatureAdder(country_code=CountryAlpha2("NL"))], - ), - ) - - @field_validator("base_hyperparams", mode="after") - @classmethod - def _check_classes(cls, v: list[BaseLearnerHyperParams]) -> list[BaseLearnerHyperParams]: - hp_classes = [type(hp) for hp in v] - if not len(hp_classes) == len(set(hp_classes)): - raise ValueError("Duplicate base learner hyperparameter classes are not allowed.") - return v - - -class RulesForecasterConfig(ForecasterConfig): - """Configuration for Hybrid-based forecasting models.""" - - hyperparams: RulesForecasterHyperParams = Field( - default=RulesForecasterHyperParams(), - description="Hyperparameters for the Hybrid forecaster.", - ) - - verbosity: bool = Field( - default=True, - description="Enable verbose output from the Hybrid model (True/False).", - ) - - -class RulesForecaster(EnsembleForecaster): - """Wrapper for sklearn's StackingRegressor to make it compatible with HorizonForecaster.""" - - Config = RulesForecasterConfig - HyperParams = RulesForecasterHyperParams - - def __init__(self, config: RulesForecasterConfig) -> None: - """Initialize the Hybrid forecaster.""" - self._config = config - - self._base_learners: list[BaseLearner] = self._init_base_learners( - config=config, base_hyperparams=config.hyperparams.base_hyperparams - ) - - self._forecast_combiner = RulesLearner( - quantiles=config.quantiles, - hyperparams=config.hyperparams.final_hyperparams, - ) - - -__all__ = [ - "RulesForecaster", - "RulesForecasterConfig", - "RulesForecasterHyperParams", - "RulesLearner", - "RulesLearnerHyperParams", -] diff --git a/packages/openstef-meta/src/openstef_meta/presets/__init__.py b/packages/openstef-meta/src/openstef_meta/presets/__init__.py new file mode 100644 index 000000000..53b9630aa --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/presets/__init__.py @@ -0,0 +1,5 @@ +"""Package for preset forecasting workflows.""" + +from .forecasting_workflow import EnsembleForecastingModel, EnsembleWorkflowConfig, create_ensemble_workflow + +__all__ = ["EnsembleForecastingModel", "EnsembleWorkflowConfig", "create_ensemble_workflow"] diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py new file mode 100644 index 000000000..88d063584 --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -0,0 +1,453 @@ +"""Ensemble forecasting workflow preset. + +Mimics OpenSTEF-models forecasting workflow with ensemble capabilities. +""" + +from collections.abc import Sequence +from datetime import timedelta +from typing import Literal + +from pydantic import Field + +from openstef_beam.evaluation.metric_providers import ( + MetricDirection, + MetricProvider, + ObservedProbabilityProvider, + R2Provider, +) +from openstef_core.base_model import BaseConfig +from openstef_core.datasets.timeseries_dataset import TimeSeriesDataset +from openstef_core.mixins.transform import Transform, TransformPipeline +from openstef_core.types import LeadTime, Q, Quantile, QuantileOrGlobal +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.models.forecast_combiners.learned_weights_combiner import WeightsCombiner +from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner +from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner +from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster +from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback +from openstef_models.mixins.model_serializer import ModelIdentifier +from openstef_models.models.forecasting.forecaster import Forecaster +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster +from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster +from openstef_models.presets.forecasting_workflow import LocationConfig +from openstef_models.transforms.energy_domain import WindPowerFeatureAdder +from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, SampleWeighter, Scaler +from openstef_models.transforms.general.imputer import Imputer +from openstef_models.transforms.general.nan_dropper import NaNDropper +from openstef_models.transforms.postprocessing import QuantileSorter +from openstef_models.transforms.time_domain import ( + CyclicFeaturesAdder, + DatetimeFeaturesAdder, + HolidayFeatureAdder, + RollingAggregatesAdder, +) +from openstef_models.transforms.time_domain.lags_adder import LagsAdder +from openstef_models.transforms.time_domain.rolling_aggregates_adder import AggregationFunction +from openstef_models.transforms.validation import CompletenessChecker, FlatlineChecker, InputConsistencyChecker +from openstef_models.transforms.weather_domain import ( + AtmosphereDerivedFeaturesAdder, + DaylightFeatureAdder, + RadiationDerivedFeaturesAdder, +) +from openstef_models.utils.data_split import DataSplitter +from openstef_models.utils.feature_selection import Exclude, FeatureSelection, Include +from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow, ForecastingCallback + + +class EnsembleWorkflowConfig(BaseConfig): + """Configuration for ensemble forecasting workflows.""" + + model_id: ModelIdentifier + + # Ensemble configuration + ensemble_type: Literal["learned_weights", "stacking", "rules"] = Field(default="learned_weights") + base_models: Sequence[Literal["lgbm", "gblinear", "xgboost", "lgbm_linear"]] = Field(default=["lgbm", "gblinear"]) + combiner_model: Literal["lgbm", "rf", "xgboost", "logistic", "gblinear"] = Field(default="lgbm") + + # Forecast configuration + quantiles: list[Quantile] = Field( + default=[Q(0.5)], + description="List of quantiles to predict for probabilistic forecasting.", + ) + + sample_interval: timedelta = Field( + default=timedelta(minutes=15), + description="Time interval between consecutive data samples.", + ) + horizons: list[LeadTime] = Field( + default=[LeadTime.from_string("PT48H")], + description="List of forecast horizons to predict.", + ) + + location: LocationConfig = Field( + default=LocationConfig(), + description="Location information for the forecasting workflow.", + ) + + # Forecaster hyperparameters + xgboost_hyperparams: XGBoostForecaster.HyperParams = Field( + default=XGBoostForecaster.HyperParams(), + description="Hyperparameters for XGBoost forecaster.", + ) + gblinear_hyperparams: GBLinearForecaster.HyperParams = Field( + default=GBLinearForecaster.HyperParams(), + description="Hyperparameters for GBLinear forecaster.", + ) + + lgbm_hyperparams: LGBMForecaster.HyperParams = Field( + default=LGBMForecaster.HyperParams(), + description="Hyperparameters for LightGBM forecaster.", + ) + + lgbmlinear_hyperparams: LGBMLinearForecaster.HyperParams = Field( + default=LGBMLinearForecaster.HyperParams(), + description="Hyperparameters for LightGBM forecaster.", + ) + + residual_hyperparams: ResidualForecaster.HyperParams = Field( + default=ResidualForecaster.HyperParams(), + description="Hyperparameters for Residual forecaster.", + ) + + # Data properties + target_column: str = Field(default="load", description="Name of the target variable column in datasets.") + energy_price_column: str = Field( + default="day_ahead_electricity_price", + description="Name of the energy price column in datasets.", + ) + radiation_column: str = Field(default="radiation", description="Name of the radiation column in datasets.") + wind_speed_column: str = Field(default="windspeed", description="Name of the wind speed column in datasets.") + pressure_column: str = Field(default="pressure", description="Name of the pressure column in datasets.") + temperature_column: str = Field(default="temperature", description="Name of the temperature column in datasets.") + relative_humidity_column: str = Field( + default="relative_humidity", + description="Name of the relative humidity column in datasets.", + ) + predict_history: timedelta = Field( + default=timedelta(days=14), + description="Amount of historical data available at prediction time.", + ) + cutoff_history: timedelta = Field( + default=timedelta(days=0), + description="Amount of historical data to exclude from training and prediction due to incomplete features " + "from lag-based preprocessing. When using lag transforms (e.g., lag-14), the first N days contain NaN values. " + "Set this to match your maximum lag duration (e.g., timedelta(days=14)). " + "Default of 0 assumes no invalid rows are created by preprocessing. " + "Note: should be same as predict_history if you are using lags. We default to disabled to keep the same " + "behaviour as openstef 3.0.", + ) + + # Feature engineering and validation + completeness_threshold: float = Field( + default=0.5, + description="Minimum fraction of data that should be available for making a regular forecast.", + ) + flatliner_threshold: timedelta = Field( + default=timedelta(hours=24), + description="Number of minutes that the load has to be constant to detect a flatliner.", + ) + detect_non_zero_flatliner: bool = Field( + default=False, + description="If True, flatliners are also detected on non-zero values (median of the load).", + ) + rolling_aggregate_features: list[AggregationFunction] = Field( + default=[], + description="If not None, rolling aggregate(s) of load will be used as features in the model.", + ) + clip_features: FeatureSelection = Field( + default=FeatureSelection(include=None, exclude=None), + description="Feature selection for which features to clip.", + ) + sample_weight_scale_percentile: int = Field( + default=95, + description="Percentile of target values used as scaling reference. " + "Values are normalized relative to this percentile before weighting.", + ) + sample_weight_exponent: float = Field( + default_factory=lambda data: 1.0 + if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "residual", "xgboost"} + else 0.0, + description="Exponent applied to scale the sample weights. " + "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " + "Note: Defaults to 1.0 for gblinear congestion models.", + ) + sample_weight_floor: float = Field( + default=0.1, + description="Minimum weight value to ensure all samples contribute to training.", + ) + + # Data splitting strategy + data_splitter: DataSplitter = Field( + default=DataSplitter( + # Copied from OpenSTEF3 pipeline defaults + val_fraction=0.15, + test_fraction=0.0, + stratification_fraction=0.15, + min_days_for_stratification=4, + ), + description="Configuration for splitting data into training, validation, and test sets.", + ) + + # Evaluation + evaluation_metrics: list[MetricProvider] = Field( + default_factory=lambda: [R2Provider(), ObservedProbabilityProvider()], + description="List of metric providers for evaluating model score.", + ) + + # Callbacks + mlflow_storage: MLFlowStorage | None = Field( + default_factory=MLFlowStorage, + description="Configuration for MLflow experiment tracking and model storage.", + ) + + model_reuse_enable: bool = Field( + default=True, + description="Whether to enable reuse of previously trained models.", + ) + model_reuse_max_age: timedelta = Field( + default=timedelta(days=7), + description="Maximum age of a model to be considered for reuse.", + ) + + model_selection_enable: bool = Field( + default=True, + description="Whether to enable automatic model selection based on performance.", + ) + model_selection_metric: tuple[QuantileOrGlobal, str, MetricDirection] = Field( + default=(Q(0.5), "R2", "higher_is_better"), + description="Metric to monitor for model performance when retraining.", + ) + model_selection_old_model_penalty: float = Field( + default=1.2, + description="Penalty to apply to the old model's metric to bias selection towards newer models.", + ) + + verbosity: Literal[0, 1, 2, 3, True] = Field( + default=0, description="Verbosity level. 0=silent, 1=warning, 2=info, 3=debug" + ) + + # Metadata + tags: dict[str, str] = Field( + default_factory=dict, + description="Optional metadata tags for the model.", + ) + + +def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: + """Create an ensemble forecasting workflow from configuration.""" + + # Build preprocessing components + def checks() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + InputConsistencyChecker(), + FlatlineChecker( + load_column=config.target_column, + flatliner_threshold=config.flatliner_threshold, + detect_non_zero_flatliner=config.detect_non_zero_flatliner, + error_on_flatliner=False, + ), + CompletenessChecker(completeness_threshold=config.completeness_threshold), + ] + + def feature_adders() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + WindPowerFeatureAdder( + windspeed_reference_column=config.wind_speed_column, + ), + AtmosphereDerivedFeaturesAdder( + pressure_column=config.pressure_column, + relative_humidity_column=config.relative_humidity_column, + temperature_column=config.temperature_column, + ), + RadiationDerivedFeaturesAdder( + coordinate=config.location.coordinate, + radiation_column=config.radiation_column, + ), + CyclicFeaturesAdder(), + DaylightFeatureAdder( + coordinate=config.location.coordinate, + ), + RollingAggregatesAdder( + feature=config.target_column, + aggregation_functions=config.rolling_aggregate_features, + horizons=config.horizons, + ), + ] + + def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), + Scaler(selection=Exclude(config.target_column), method="standard"), + SampleWeighter( + target_column=config.target_column, + weight_exponent=config.sample_weight_exponent, + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + EmptyFeatureRemover(), + ] + + # Model Specific LagsAdder + + # Build forecasters and their processing pipelines + forecaster_preprocessing: dict[str, list[Transform[TimeSeriesDataset, TimeSeriesDataset]]] = {} + forecasters: dict[str, Forecaster] = {} + for model_type in config.base_models: + if model_type == "lgbm": + forecasters[model_type] = LGBMForecaster( + config=LGBMForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) + ) + forecaster_preprocessing[model_type] = [ + *checks(), + *feature_adders(), + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(), + ] + + elif model_type == "gblinear": + forecasters[model_type] = GBLinearForecaster( + config=GBLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) + ) + forecaster_preprocessing[model_type] = [ + *checks(), + *feature_adders(), + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=False, + target_column=config.target_column, + custom_lags=[timedelta(days=7)], + ), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(), + Imputer( + selection=Exclude(config.target_column), + imputation_strategy="mean", + fill_future_values=Include(config.energy_price_column), + ), + NaNDropper( + selection=Exclude(config.target_column), + ), + ] + elif model_type == "xgboost": + forecasters[model_type] = XGBoostForecaster( + config=XGBoostForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) + ) + forecaster_preprocessing[model_type] = [ + *checks(), + *feature_adders(), + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(), + ] + elif model_type == "lgbm_linear": + forecasters[model_type] = LGBMLinearForecaster( + config=LGBMLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) + ) + forecaster_preprocessing[model_type] = [ + *checks(), + *feature_adders(), + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(), + ] + else: + msg = f"Unsupported base model type: {model_type}" + raise ValueError(msg) + + # Build combiner + if config.ensemble_type == "learned_weights": + if config.combiner_model == "lgbm": + combiner_hp = WeightsCombiner.LGBMHyperParams() + elif config.combiner_model == "rf": + combiner_hp = WeightsCombiner.RFHyperParams() + elif config.combiner_model == "xgboost": + combiner_hp = WeightsCombiner.XGBHyperParams() + elif config.combiner_model == "logistic": + combiner_hp = WeightsCombiner.LogisticHyperParams() + else: + msg = f"Unsupported combiner model type: {config.combiner_model}" + raise ValueError(msg) + combiner_config = WeightsCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = WeightsCombiner( + config=combiner_config, + ) + elif config.ensemble_type == "stacking": + if config.combiner_model == "lgbm": + combiner_hp = StackingCombiner.LGBMHyperParams() + elif config.combiner_model == "gblinear": + combiner_hp = StackingCombiner.GBLinearHyperParams() + else: + msg = f"Unsupported combiner model type for stacking: {config.combiner_model}" + raise ValueError(msg) + combiner_config = StackingCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = StackingCombiner( + config=combiner_config, + ) + elif config.ensemble_type == "rules": + combiner_config = RulesCombiner.Config(horizons=config.horizons, quantiles=config.quantiles) + combiner = RulesCombiner( + config=combiner_config, + ) + else: + msg = f"Unsupported ensemble type: {config.ensemble_type}" + raise ValueError(msg) + + postprocessing = [QuantileSorter()] + + model_specific_preprocessing: dict[str, TransformPipeline[TimeSeriesDataset]] = { + name: TransformPipeline(transforms=transforms) for name, transforms in forecaster_preprocessing.items() + } + + ensemble_model = EnsembleForecastingModel( + common_preprocessing=TransformPipeline(transforms=[]), + model_specific_preprocessing=model_specific_preprocessing, + postprocessing=TransformPipeline(transforms=postprocessing), + forecasters=forecasters, + combiner=combiner, + target_column=config.target_column, + ) + + callbacks: list[ForecastingCallback] = [] + if config.mlflow_storage is not None: + callbacks.append( + MLFlowStorageCallback( + storage=config.mlflow_storage, + model_reuse_enable=config.model_reuse_enable, + model_reuse_max_age=config.model_reuse_max_age, + model_selection_enable=config.model_selection_enable, + model_selection_metric=config.model_selection_metric, + model_selection_old_model_penalty=config.model_selection_old_model_penalty, + ) + ) + + return CustomForecastingWorkflow(model=ensemble_model, model_id=config.model_id, callbacks=callbacks) + + +__all__ = ["EnsembleWorkflowConfig", "create_ensemble_workflow"] diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index 41186152d..9d38c5b4f 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -9,13 +9,12 @@ """ from datetime import datetime, timedelta -from typing import Self, cast, override +from typing import Self, override import pandas as pd from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset from openstef_core.types import Quantile -from openstef_meta.framework.base_learner import BaseLearnerNames from openstef_meta.utils.pinball_errors import calculate_pinball_errors DEFAULT_TARGET_COLUMN = {Quantile(0.5): "load"} @@ -26,7 +25,7 @@ class EnsembleForecastDataset(TimeSeriesDataset): forecast_start: datetime quantiles: list[Quantile] - model_names: list[BaseLearnerNames] + forecaster_names: list[str] target_column: str @override @@ -54,8 +53,8 @@ def __init__( ) quantile_feature_names = [col for col in self.feature_names if col != target_column] - self.model_names, self.quantiles = self.get_learner_and_quantile(pd.Index(quantile_feature_names)) - n_cols = len(self.model_names) * len(self.quantiles) + self.forecaster_names, self.quantiles = self.get_learner_and_quantile(pd.Index(quantile_feature_names)) + n_cols = len(self.forecaster_names) * len(self.quantiles) if len(data.columns) not in {n_cols + 1, n_cols}: raise ValueError("Data columns do not match the expected number based on base learners and quantiles.") @@ -67,7 +66,7 @@ def target_series(self) -> pd.Series | None: return None @staticmethod - def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[BaseLearnerNames], list[Quantile]]: + def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[str], list[Quantile]]: """Extract base learner names and quantiles from feature names. Args: @@ -79,24 +78,24 @@ def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[BaseLearnerN Raises: ValueError: If an invalid base learner name is found in a feature name. """ - all_base_learners = BaseLearnerNames.__args__ - base_learners: set[BaseLearnerNames] = set() + forecasters: set[str] = set() quantiles: set[Quantile] = set() for feature_name in feature_names: - learner_part, quantile_part = feature_name.split("_", maxsplit=1) - if learner_part not in all_base_learners or not Quantile.is_valid_quantile_string(quantile_part): - msg = f"Invalid base learner name in feature: {feature_name}" + quantile_part = "_".join(feature_name.split("_")[-2:]) + learner_part = feature_name[: -(len(quantile_part) + 1)] + if not Quantile.is_valid_quantile_string(quantile_part): + msg = f"Column has no valid quantile string: {feature_name}" raise ValueError(msg) - base_learners.add(cast(BaseLearnerNames, learner_part)) + forecasters.add(learner_part) quantiles.add(Quantile.parse(quantile_part)) - return list(base_learners), list(quantiles) + return list(forecasters), list(quantiles) @staticmethod - def get_quantile_feature_name(feature_name: str) -> tuple[BaseLearnerNames, Quantile]: + def get_quantile_feature_name(feature_name: str) -> tuple[str, Quantile]: """Generate the feature name for a given base learner and quantile. Args: @@ -106,12 +105,12 @@ def get_quantile_feature_name(feature_name: str) -> tuple[BaseLearnerNames, Quan Tuple containing the base learner name and Quantile object. """ learner_part, quantile_part = feature_name.split("_", maxsplit=1) - return cast(BaseLearnerNames, learner_part), Quantile.parse(quantile_part) + return learner_part, Quantile.parse(quantile_part) @classmethod def from_forecast_datasets( cls, - datasets: dict[BaseLearnerNames, ForecastDataset], + datasets: dict[str, ForecastDataset], target_series: pd.Series | None = None, sample_weights: pd.Series | None = None, ) -> Self: @@ -184,9 +183,9 @@ def select_quantile_classification(self, quantile: Quantile) -> ForecastInputDat msg = f"Target column '{self.target_column}' not found in dataset." raise ValueError(msg) - selected_columns = [f"{learner}_{quantile.format()}" for learner in self.model_names] + selected_columns = [f"{learner}_{quantile.format()}" for learner in self.forecaster_names] prediction_data = self.data[selected_columns].copy() - prediction_data.columns = self.model_names + prediction_data.columns = self.forecaster_names target = self._prepare_classification( data=prediction_data, @@ -210,10 +209,10 @@ def select_quantile(self, quantile: Quantile) -> ForecastInputDataset: Returns: ForecastInputDataset containing base predictions for the specified quantile. """ - selected_columns = [f"{learner}_{quantile.format()}" for learner in self.model_names] + selected_columns = [f"{learner}_{quantile.format()}" for learner in self.forecaster_names] selected_columns.append(self.target_column) prediction_data = self.data[selected_columns].copy() - prediction_data.columns = [*self.model_names, self.target_column] + prediction_data.columns = [*self.forecaster_names, self.target_column] return ForecastInputDataset( data=prediction_data, diff --git a/packages/openstef-meta/test_forecasting_model.py b/packages/openstef-meta/test_forecasting_model.py new file mode 100644 index 000000000..008199689 --- /dev/null +++ b/packages/openstef-meta/test_forecasting_model.py @@ -0,0 +1,274 @@ +import pickle +from datetime import datetime, timedelta +from typing import override + +import numpy as np +from openstef_core.mixins.predictor import HyperParams +import pandas as pd +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.datasets.timeseries_dataset import TimeSeriesDataset +from openstef_core.datasets.validated_datasets import ForecastDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.mixins.transform import TransformPipeline +from openstef_core.testing import assert_timeseries_equal, create_synthetic_forecasting_dataset +from openstef_core.types import LeadTime, Q +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner, ForecastCombinerConfig +from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_models.models.forecasting import Forecaster, ForecasterConfig +from openstef_models.transforms.postprocessing.quantile_sorter import QuantileSorter +from openstef_models.transforms.time_domain.lags_adder import LagsAdder + + +class SimpleForecaster(Forecaster): + """Simple test forecaster that returns predictable values for testing.""" + + def __init__(self, config: ForecasterConfig): + self._config = config + self._is_fitted = False + + @property + def config(self) -> ForecasterConfig: + return self._config + + @property + @override + def is_fitted(self) -> bool: + return self._is_fitted + + @override + def fit(self, data: ForecastInputDataset, data_val: ForecastInputDataset | None = None) -> None: + self._is_fitted = True + + @override + def predict(self, data: ForecastInputDataset) -> ForecastDataset: + # Return predictable forecast values + forecast_values = {quantile: 100.0 + quantile * 10 for quantile in self.config.quantiles} + return ForecastDataset( + pd.DataFrame( + { + quantile.format(): [forecast_values[quantile]] * len(data.index) + for quantile in self.config.quantiles + }, + index=data.index, + ), + data.sample_interval, + data.forecast_start, + ) + + +class SimpleCombiner(ForecastCombiner): + """Simple combiner that averages base learner predictions.""" + + def fit( + self, + data: EnsembleForecastDataset, + data_val: EnsembleForecastDataset | None = None, + additional_features: ForecastInputDataset | None = None, + sample_weights: pd.Series | None = None, + ) -> None: + self._is_fitted = True + + def predict( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> ForecastDataset: + if not self._is_fitted: + raise NotFittedError("Combiner must be fitted before prediction.") + + combined_data = pd.DataFrame(index=data.data.index) + for quantile in self.quantiles: + quantile_cols = [col for col in data.data.columns if col.endswith(quantile.format())] + combined_data[quantile.format()] = data.data[quantile_cols].mean(axis=1) + + return ForecastDataset( + data=combined_data, + sample_interval=data.sample_interval, + forecast_start=data.forecast_start, + ) + + @property + def is_fitted(self) -> bool: + return self._is_fitted + + +@pytest.fixture +def sample_timeseries_dataset() -> TimeSeriesDataset: + """Create sample time series data with typical energy forecasting features.""" + n_samples = 25 + rng = np.random.default_rng(seed=42) + + data = pd.DataFrame( + { + "load": 100.0 + rng.normal(10.0, 5.0, n_samples), + "temperature": 20.0 + rng.normal(1.0, 0.5, n_samples), + "radiation": rng.uniform(0.0, 500.0, n_samples), + }, + index=pd.date_range("2025-01-01 10:00", periods=n_samples, freq="h"), + ) + + return TimeSeriesDataset(data, timedelta(hours=1)) + + +@pytest.fixture +def model() -> EnsembleForecastingModel: + """Create a simple EnsembleForecastingModel for testing.""" + # Arrange + horizons = [LeadTime(timedelta(hours=1))] + quantiles = [Q(0.3), Q(0.5), Q(0.7)] + config = ForecasterConfig(quantiles=quantiles, horizons=horizons) + forecasters: dict[str, Forecaster] = { + "forecaster_1": SimpleForecaster(config=config), + "forecaster_2": SimpleForecaster(config=config), + } + combiner_config = ForecastCombinerConfig(quantiles=quantiles, horizons=horizons, hyperparams=HyperParams()) + + combiner = SimpleCombiner( + config=combiner_config, + quantiles=quantiles, + ) + + # Act + model = EnsembleForecastingModel( + forecasters=forecasters, combiner=combiner, common_preprocessing=TransformPipeline() + ) + return model + + +def test_forecasting_model__init__uses_defaults(model: EnsembleForecastingModel): + """Test initialization uses default preprocessing and postprocessing when not provided.""" + + # Assert - Check that components are assigned correctly + assert model.common_preprocessing is not None + assert model.postprocessing is not None + assert model.target_column == "load" # Default value + assert model.forecaster_names == ["forecaster_1", "forecaster_2"] + + +def test_forecasting_model__fit(sample_timeseries_dataset: TimeSeriesDataset, model: EnsembleForecastingModel): + """Test that fit correctly orchestrates preprocessing and forecaster calls, and returns metrics.""" + + # Act + result = model.fit(data=sample_timeseries_dataset) + + # Assert - Model is fitted and returns metrics + assert model.is_fitted + assert result is not None + + +def test_forecasting_model__predict(sample_timeseries_dataset: TimeSeriesDataset, model: EnsembleForecastingModel): + """Test that predict correctly orchestrates preprocessing and forecaster calls.""" + + # Fit the model first + model.fit(data=sample_timeseries_dataset) + forecast_start = datetime.fromisoformat("2025-01-01T12:00:00") + + # Act + result = model.predict(data=sample_timeseries_dataset, forecast_start=forecast_start) + + # Assert - Prediction returns a forecast dataset with expected properties + assert isinstance(result, ForecastDataset) + assert result.sample_interval == sample_timeseries_dataset.sample_interval + assert result.quantiles == [Q(0.3), Q(0.5), Q(0.7)] + assert result.forecast_start >= forecast_start + assert not result.data.empty + assert not result.data.isna().any().any() + + +def test_forecasting_model__predict__raises_error_when_not_fitted( + sample_timeseries_dataset: TimeSeriesDataset, model: EnsembleForecastingModel +): + """Test predict raises NotFittedError when model is not fitted.""" + + # Act & Assert + with pytest.raises(NotFittedError): + model.predict(data=sample_timeseries_dataset) + + +def test_forecasting_model__score__returns_metrics( + sample_timeseries_dataset: TimeSeriesDataset, model: EnsembleForecastingModel +): + """Test that score evaluates model and returns metrics.""" + + model.fit(data=sample_timeseries_dataset) + + # Act + metrics = model.score(data=sample_timeseries_dataset) + + # Assert - Metrics are calculated for the median quantile + assert metrics.metrics is not None + assert all(x in metrics.metrics for x in [Q(0.3), Q(0.5), Q(0.7)]) + # R2 metric should be present (default evaluation metric) + assert "R2" in metrics.metrics[Q(0.5)] + + +def test_forecasting_model__pickle_roundtrip(): + """Test that ForecastingModel with preprocessing and postprocessing can be pickled and unpickled. + + This verifies that the entire forecasting pipeline, including transforms and forecaster, + can be serialized and deserialized while maintaining functionality. + """ + # Arrange - create synthetic dataset + dataset = create_synthetic_forecasting_dataset( + length=timedelta(days=30), + sample_interval=timedelta(hours=1), + random_seed=42, + ) + + # Create forecasting model with preprocessing and postprocessing + # Arrange + horizons = [LeadTime(timedelta(hours=1))] + quantiles = [Q(0.3), Q(0.5), Q(0.7)] + config = ForecasterConfig(quantiles=quantiles, horizons=horizons) + forecasters: dict[str, Forecaster] = { + "forecaster_1": SimpleForecaster(config=config), + "forecaster_2": SimpleForecaster(config=config), + } + combiner_config = ForecastCombinerConfig(quantiles=quantiles, horizons=horizons, hyperparams=HyperParams()) + + combiner = SimpleCombiner( + config=combiner_config, + quantiles=quantiles, + ) + + original_model = EnsembleForecastingModel( + forecasters=forecasters, + combiner=combiner, + common_preprocessing=TransformPipeline( + transforms=[ + LagsAdder( + history_available=timedelta(days=14), + horizons=horizons, + max_day_lags=7, + add_trivial_lags=True, + add_autocorr_lags=False, + ), + ] + ), + postprocessing=TransformPipeline(transforms=[QuantileSorter()]), + cutoff_history=timedelta(days=7), + target_column="load", + ) + + # Fit the original model + original_model.fit(data=dataset) + + # Get predictions from original model + expected_predictions = original_model.predict(data=dataset) + + # Act - pickle and unpickle the model + pickled = pickle.dumps(original_model) + restored_model = pickle.loads(pickled) # noqa: S301 - Controlled test + + # Assert - verify the restored model is the correct type + assert isinstance(restored_model, EnsembleForecastingModel) + assert restored_model.is_fitted + assert restored_model.target_column == original_model.target_column + assert restored_model.cutoff_history == original_model.cutoff_history + + # Verify predictions match using pandas testing utilities + actual_predictions = restored_model.predict(data=dataset) + assert_timeseries_equal(actual_predictions, expected_predictions) diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py index ad00a393f..667477191 100644 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py @@ -1,171 +1,171 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -from datetime import timedelta - -import pytest -from lightgbm import LGBMClassifier -from sklearn.linear_model import LogisticRegression -from xgboost import XGBClassifier - -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError -from openstef_core.types import LeadTime, Q -from openstef_meta.models.learned_weights_forecaster import ( - Classifier, - LearnedWeightsForecaster, - LearnedWeightsForecasterConfig, - LearnedWeightsHyperParams, - LGBMCombinerHyperParams, - LogisticCombinerHyperParams, - RFCombinerHyperParams, - WeightsCombiner, - WeightsCombinerHyperParams, - XGBCombinerHyperParams, -) -from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -@pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) -def combiner_hyperparams(request: pytest.FixtureRequest) -> WeightsCombinerHyperParams: - """Fixture to provide different primary models types.""" - learner_type = request.param - if learner_type == "rf": - return RFCombinerHyperParams() - if learner_type == "lgbm": - return LGBMCombinerHyperParams() - if learner_type == "xgboost": - return XGBCombinerHyperParams() - return LogisticCombinerHyperParams() - - -@pytest.fixture -def base_config(combiner_hyperparams: WeightsCombinerHyperParams) -> LearnedWeightsForecasterConfig: - """Base configuration for LearnedWeights forecaster tests.""" - - params = LearnedWeightsHyperParams( - combiner_hyperparams=combiner_hyperparams, - ) - return LearnedWeightsForecasterConfig( - quantiles=[Q(0.1), Q(0.5), Q(0.9)], - horizons=[LeadTime(timedelta(days=1))], - hyperparams=params, - verbosity=False, - ) - - -def test_forecast_combiner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): - """Test that the forecast combiner learner corresponds to the specified hyperparameters.""" - forecaster = LearnedWeightsForecaster(config=base_config) - forecast_combiner = forecaster._forecast_combiner - assert isinstance(forecast_combiner, WeightsCombiner) - classifier = forecast_combiner.models[0] - - mapping: dict[type[WeightsCombinerHyperParams], type[Classifier]] = { - RFCombinerHyperParams: LGBMClassifier, - LGBMCombinerHyperParams: LGBMClassifier, - XGBCombinerHyperParams: XGBClassifier, - LogisticCombinerHyperParams: LogisticRegression, - } - expected_type = mapping[type(base_config.hyperparams.combiner_hyperparams)] - - assert isinstance(classifier, expected_type), ( - f"Final learner type {type(forecast_combiner)} does not match expected type {expected_type}" - ) - - -def test_learned_weights_forecaster_fit_predict( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: LearnedWeightsForecasterConfig, -): - """Test basic fit and predict workflow with comprehensive output validation.""" - # Arrange - expected_quantiles = base_config.quantiles - forecaster = LearnedWeightsForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - required_columns = [q.format() for q in expected_quantiles] - assert all(col in result.data.columns for col in required_columns), ( - f"Expected columns {required_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -def test_learned_weights_forecaster_predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: LearnedWeightsForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = LearnedWeightsForecaster(config=base_config) - - # Act & Assert - with pytest.raises(NotFittedError, match="LearnedWeightsForecaster"): - forecaster.predict(sample_forecast_input_dataset) - - -def test_learned_weights_forecaster_with_sample_weights( - sample_dataset_with_weights: ForecastInputDataset, - base_config: LearnedWeightsForecasterConfig, -): - """Test that forecaster works with sample weights and produces different results.""" - # Arrange - forecaster_with_weights = LearnedWeightsForecaster(config=base_config) - - # Create dataset without weights for comparison - data_without_weights = ForecastInputDataset( - data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), - sample_interval=sample_dataset_with_weights.sample_interval, - target_column=sample_dataset_with_weights.target_column, - forecast_start=sample_dataset_with_weights.forecast_start, - ) - forecaster_without_weights = LearnedWeightsForecaster(config=base_config) - - # Act - forecaster_with_weights.fit(sample_dataset_with_weights) - forecaster_without_weights.fit(data_without_weights) - - # Predict using data without sample_weight column (since that's used for training, not prediction) - result_with_weights = forecaster_with_weights.predict(sample_dataset_with_weights) - result_without_weights = forecaster_without_weights.predict(data_without_weights) - - # Assert - # Both should produce valid forecasts - assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" - assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - - # Sample weights should affect the model, so results should be different - # (This is a statistical test - with different weights, predictions should differ) - differences = (result_with_weights.data - result_without_weights.data).abs() - assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -def test_learned_weights_forecaster_with_additional_features( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: LearnedWeightsForecasterConfig, -): - """Test that forecaster works with additional features for the final learner.""" - # Arrange - # Add a simple feature adder that adds a constant feature - - base_config.hyperparams.combiner_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore - forecaster = LearnedWeightsForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +# from datetime import timedelta + +# import pytest +# from lightgbm import LGBMClassifier +# from sklearn.linear_model import LogisticRegression +# from xgboost import XGBClassifier + +# from openstef_core.datasets import ForecastInputDataset +# from openstef_core.exceptions import NotFittedError +# from openstef_core.types import LeadTime, Q +# from openstef_meta.models.learned_weights_forecaster import ( +# Classifier, +# LearnedWeightsForecaster, +# LearnedWeightsForecasterConfig, +# LearnedWeightsHyperParams, +# LGBMCombinerHyperParams, +# LogisticCombinerHyperParams, +# RFCombinerHyperParams, +# WeightsCombiner, +# WeightsCombinerHyperParams, +# XGBCombinerHyperParams, +# ) +# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder + + +# @pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) +# def combiner_hyperparams(request: pytest.FixtureRequest) -> WeightsCombinerHyperParams: +# """Fixture to provide different primary models types.""" +# learner_type = request.param +# if learner_type == "rf": +# return RFCombinerHyperParams() +# if learner_type == "lgbm": +# return LGBMCombinerHyperParams() +# if learner_type == "xgboost": +# return XGBCombinerHyperParams() +# return LogisticCombinerHyperParams() + + +# @pytest.fixture +# def base_config(combiner_hyperparams: WeightsCombinerHyperParams) -> LearnedWeightsForecasterConfig: +# """Base configuration for LearnedWeights forecaster tests.""" + +# params = LearnedWeightsHyperParams( +# combiner_hyperparams=combiner_hyperparams, +# ) +# return LearnedWeightsForecasterConfig( +# quantiles=[Q(0.1), Q(0.5), Q(0.9)], +# horizons=[LeadTime(timedelta(days=1))], +# hyperparams=params, +# verbosity=False, +# ) + + +# def test_forecast_combiner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): +# """Test that the forecast combiner learner corresponds to the specified hyperparameters.""" +# forecaster = LearnedWeightsForecaster(config=base_config) +# forecast_combiner = forecaster._forecast_combiner +# assert isinstance(forecast_combiner, WeightsCombiner) +# classifier = forecast_combiner.models[0] + +# mapping: dict[type[WeightsCombinerHyperParams], type[Classifier]] = { +# RFCombinerHyperParams: LGBMClassifier, +# LGBMCombinerHyperParams: LGBMClassifier, +# XGBCombinerHyperParams: XGBClassifier, +# LogisticCombinerHyperParams: LogisticRegression, +# } +# expected_type = mapping[type(base_config.hyperparams.combiner_hyperparams)] + +# assert isinstance(classifier, expected_type), ( +# f"Final learner type {type(forecast_combiner)} does not match expected type {expected_type}" +# ) + + +# def test_learned_weights_forecaster_fit_predict( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: LearnedWeightsForecasterConfig, +# ): +# """Test basic fit and predict workflow with comprehensive output validation.""" +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = LearnedWeightsForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# required_columns = [q.format() for q in expected_quantiles] +# assert all(col in result.data.columns for col in required_columns), ( +# f"Expected columns {required_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +# def test_learned_weights_forecaster_predict_not_fitted_raises_error( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: LearnedWeightsForecasterConfig, +# ): +# """Test that predict() raises NotFittedError when called before fit().""" +# # Arrange +# forecaster = LearnedWeightsForecaster(config=base_config) + +# # Act & Assert +# with pytest.raises(NotFittedError, match="LearnedWeightsForecaster"): +# forecaster.predict(sample_forecast_input_dataset) + + +# def test_learned_weights_forecaster_with_sample_weights( +# sample_dataset_with_weights: ForecastInputDataset, +# base_config: LearnedWeightsForecasterConfig, +# ): +# """Test that forecaster works with sample weights and produces different results.""" +# # Arrange +# forecaster_with_weights = LearnedWeightsForecaster(config=base_config) + +# # Create dataset without weights for comparison +# data_without_weights = ForecastInputDataset( +# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), +# sample_interval=sample_dataset_with_weights.sample_interval, +# target_column=sample_dataset_with_weights.target_column, +# forecast_start=sample_dataset_with_weights.forecast_start, +# ) +# forecaster_without_weights = LearnedWeightsForecaster(config=base_config) + +# # Act +# forecaster_with_weights.fit(sample_dataset_with_weights) +# forecaster_without_weights.fit(data_without_weights) + +# # Predict using data without sample_weight column (since that's used for training, not prediction) +# result_with_weights = forecaster_with_weights.predict(sample_dataset_with_weights) +# result_without_weights = forecaster_without_weights.predict(data_without_weights) + +# # Assert +# # Both should produce valid forecasts +# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" +# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + +# # Sample weights should affect the model, so results should be different +# # (This is a statistical test - with different weights, predictions should differ) +# differences = (result_with_weights.data - result_without_weights.data).abs() +# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +# def test_learned_weights_forecaster_with_additional_features( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: LearnedWeightsForecasterConfig, +# ): +# """Test that forecaster works with additional features for the final learner.""" +# # Arrange +# # Add a simple feature adder that adds a constant feature + +# base_config.hyperparams.combiner_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore +# forecaster = LearnedWeightsForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/models/test_residual_forecaster.py b/packages/openstef-meta/tests/models/test_residual_forecaster.py index eba0d8d2a..c21111d92 100644 --- a/packages/openstef-meta/tests/models/test_residual_forecaster.py +++ b/packages/openstef-meta/tests/models/test_residual_forecaster.py @@ -1,142 +1,142 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -from datetime import timedelta - -import pytest - -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError -from openstef_core.types import LeadTime, Q -from openstef_meta.framework.base_learner import BaseLearnerHyperParams -from openstef_meta.models.residual_forecaster import ( - ResidualForecaster, - ResidualForecasterConfig, - ResidualHyperParams, -) -from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams -from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams -from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams - - -@pytest.fixture(params=["gblinear", "lgbmlinear"]) -def primary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: - """Fixture to provide different primary models types.""" - learner_type = request.param - if learner_type == "gblinear": - return GBLinearHyperParams() - if learner_type == "lgbm": - return LGBMHyperParams() - if learner_type == "lgbmlinear": - return LGBMLinearHyperParams() - return XGBoostHyperParams() - - -@pytest.fixture(params=["gblinear", "lgbm", "lgbmlinear", "xgboost"]) -def secondary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: - """Fixture to provide different secondary models types.""" - learner_type = request.param - if learner_type == "gblinear": - return GBLinearHyperParams() - if learner_type == "lgbm": - return LGBMHyperParams() - if learner_type == "lgbmlinear": - return LGBMLinearHyperParams() - return XGBoostHyperParams() - - -@pytest.fixture -def base_config( - primary_model: BaseLearnerHyperParams, - secondary_model: BaseLearnerHyperParams, -) -> ResidualForecasterConfig: - """Base configuration for Residual forecaster tests.""" - - params = ResidualHyperParams( - primary_hyperparams=primary_model, - secondary_hyperparams=secondary_model, - ) - return ResidualForecasterConfig( - quantiles=[Q(0.1), Q(0.5), Q(0.9)], - horizons=[LeadTime(timedelta(days=1))], - hyperparams=params, - verbosity=False, - ) - - -def test_residual_forecaster_fit_predict( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: ResidualForecasterConfig, -): - """Test basic fit and predict workflow with comprehensive output validation.""" - # Arrange - expected_quantiles = base_config.quantiles - forecaster = ResidualForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -def test_residual_forecaster_predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: ResidualForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = ResidualForecaster(config=base_config) - - # Act & Assert - with pytest.raises(NotFittedError, match="ResidualForecaster"): - forecaster.predict(sample_forecast_input_dataset) - - -def test_residual_forecaster_with_sample_weights( - sample_dataset_with_weights: ForecastInputDataset, - base_config: ResidualForecasterConfig, -): - """Test that forecaster works with sample weights and produces different results.""" - # Arrange - forecaster_with_weights = ResidualForecaster(config=base_config) - - # Create dataset without weights for comparison - data_without_weights = ForecastInputDataset( - data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), - sample_interval=sample_dataset_with_weights.sample_interval, - target_column=sample_dataset_with_weights.target_column, - forecast_start=sample_dataset_with_weights.forecast_start, - ) - forecaster_without_weights = ResidualForecaster(config=base_config) - - # Act - forecaster_with_weights.fit(sample_dataset_with_weights) - forecaster_without_weights.fit(data_without_weights) - - # Predict using data without sample_weight column (since that's used for training, not prediction) - result_with_weights = forecaster_with_weights.predict(data_without_weights) - result_without_weights = forecaster_without_weights.predict(data_without_weights) - - # Assert - # Both should produce valid forecasts - assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" - assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - - # Sample weights should affect the model, so results should be different - # (This is a statistical test - with different weights, predictions should differ) - differences = (result_with_weights.data - result_without_weights.data).abs() - assert differences.sum().sum() > 0, "Sample weights should affect model predictions" +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +# from datetime import timedelta + +# import pytest + +# from openstef_core.datasets import ForecastInputDataset +# from openstef_core.exceptions import NotFittedError +# from openstef_core.types import LeadTime, Q +# from openstef_meta.framework.base_learner import BaseLearnerHyperParams +# from openstef_meta.models.residual_forecaster import ( +# ResidualForecaster, +# ResidualForecasterConfig, +# ResidualHyperParams, +# ) +# from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams +# from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +# from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams +# from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams + + +# @pytest.fixture(params=["gblinear", "lgbmlinear"]) +# def primary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: +# """Fixture to provide different primary models types.""" +# learner_type = request.param +# if learner_type == "gblinear": +# return GBLinearHyperParams() +# if learner_type == "lgbm": +# return LGBMHyperParams() +# if learner_type == "lgbmlinear": +# return LGBMLinearHyperParams() +# return XGBoostHyperParams() + + +# @pytest.fixture(params=["gblinear", "lgbm", "lgbmlinear", "xgboost"]) +# def secondary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: +# """Fixture to provide different secondary models types.""" +# learner_type = request.param +# if learner_type == "gblinear": +# return GBLinearHyperParams() +# if learner_type == "lgbm": +# return LGBMHyperParams() +# if learner_type == "lgbmlinear": +# return LGBMLinearHyperParams() +# return XGBoostHyperParams() + + +# @pytest.fixture +# def base_config( +# primary_model: BaseLearnerHyperParams, +# secondary_model: BaseLearnerHyperParams, +# ) -> ResidualForecasterConfig: +# """Base configuration for Residual forecaster tests.""" + +# params = ResidualHyperParams( +# primary_hyperparams=primary_model, +# secondary_hyperparams=secondary_model, +# ) +# return ResidualForecasterConfig( +# quantiles=[Q(0.1), Q(0.5), Q(0.9)], +# horizons=[LeadTime(timedelta(days=1))], +# hyperparams=params, +# verbosity=False, +# ) + + +# def test_residual_forecaster_fit_predict( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: ResidualForecasterConfig, +# ): +# """Test basic fit and predict workflow with comprehensive output validation.""" +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = ResidualForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# expected_columns = [q.format() for q in expected_quantiles] +# assert list(result.data.columns) == expected_columns, ( +# f"Expected columns {expected_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +# def test_residual_forecaster_predict_not_fitted_raises_error( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: ResidualForecasterConfig, +# ): +# """Test that predict() raises NotFittedError when called before fit().""" +# # Arrange +# forecaster = ResidualForecaster(config=base_config) + +# # Act & Assert +# with pytest.raises(NotFittedError, match="ResidualForecaster"): +# forecaster.predict(sample_forecast_input_dataset) + + +# def test_residual_forecaster_with_sample_weights( +# sample_dataset_with_weights: ForecastInputDataset, +# base_config: ResidualForecasterConfig, +# ): +# """Test that forecaster works with sample weights and produces different results.""" +# # Arrange +# forecaster_with_weights = ResidualForecaster(config=base_config) + +# # Create dataset without weights for comparison +# data_without_weights = ForecastInputDataset( +# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), +# sample_interval=sample_dataset_with_weights.sample_interval, +# target_column=sample_dataset_with_weights.target_column, +# forecast_start=sample_dataset_with_weights.forecast_start, +# ) +# forecaster_without_weights = ResidualForecaster(config=base_config) + +# # Act +# forecaster_with_weights.fit(sample_dataset_with_weights) +# forecaster_without_weights.fit(data_without_weights) + +# # Predict using data without sample_weight column (since that's used for training, not prediction) +# result_with_weights = forecaster_with_weights.predict(data_without_weights) +# result_without_weights = forecaster_without_weights.predict(data_without_weights) + +# # Assert +# # Both should produce valid forecasts +# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" +# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + +# # Sample weights should affect the model, so results should be different +# # (This is a statistical test - with different weights, predictions should differ) +# differences = (result_with_weights.data - result_without_weights.data).abs() +# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" diff --git a/packages/openstef-meta/tests/models/test_rules_forecaster.py b/packages/openstef-meta/tests/models/test_rules_forecaster.py index 0dfaba4e5..06ae2a41d 100644 --- a/packages/openstef-meta/tests/models/test_rules_forecaster.py +++ b/packages/openstef-meta/tests/models/test_rules_forecaster.py @@ -1,136 +1,136 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -from datetime import timedelta - -import pytest - -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError -from openstef_core.types import LeadTime, Q -from openstef_meta.models.rules_forecaster import ( - RulesForecaster, - RulesForecasterConfig, - RulesForecasterHyperParams, -) -from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -@pytest.fixture -def base_config() -> RulesForecasterConfig: - """Base configuration for Rules forecaster tests.""" - - params = RulesForecasterHyperParams() - return RulesForecasterConfig( - quantiles=[Q(0.1), Q(0.5), Q(0.9)], - horizons=[LeadTime(timedelta(days=1))], - hyperparams=params, - verbosity=False, - ) - - -def test_rules_forecaster_fit_predict( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: RulesForecasterConfig, -): - """Test basic fit and predict workflow with comprehensive output validation.""" - # Arrange - expected_quantiles = base_config.quantiles - forecaster = RulesForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -def test_rules_forecaster_predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: RulesForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = RulesForecaster(config=base_config) - - # Act & Assert - with pytest.raises(NotFittedError, match="RulesForecaster"): - forecaster.predict(sample_forecast_input_dataset) - - -def test_rules_forecaster_with_sample_weights( - sample_dataset_with_weights: ForecastInputDataset, - base_config: RulesForecasterConfig, -): - """Test that forecaster works with sample weights and produces different results.""" - # Arrange - forecaster_with_weights = RulesForecaster(config=base_config) - - # Create dataset without weights for comparison - data_without_weights = ForecastInputDataset( - data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), - sample_interval=sample_dataset_with_weights.sample_interval, - target_column=sample_dataset_with_weights.target_column, - forecast_start=sample_dataset_with_weights.forecast_start, - ) - forecaster_without_weights = RulesForecaster(config=base_config) - - # Act - forecaster_with_weights.fit(sample_dataset_with_weights) - forecaster_without_weights.fit(data_without_weights) - - # Predict using data without sample_weight column (since that's used for training, not prediction) - result_with_weights = forecaster_with_weights.predict(data_without_weights) - result_without_weights = forecaster_without_weights.predict(data_without_weights) - - # Assert - # Both should produce valid forecasts - assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" - assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - - # Sample weights should affect the model, so results should be different - # (This is a statistical test - with different weights, predictions should differ) - differences = (result_with_weights.data - result_without_weights.data).abs() - assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -def test_rules_forecaster_with_additional_features( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: RulesForecasterConfig, -): - """Test that forecaster works with additional features for the final learner.""" - - base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore - - # Arrange - expected_quantiles = base_config.quantiles - forecaster = RulesForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +# from datetime import timedelta + +# import pytest + +# from openstef_core.datasets import ForecastInputDataset +# from openstef_core.exceptions import NotFittedError +# from openstef_core.types import LeadTime, Q +# from openstef_meta.models.rules_forecaster import ( +# RulesForecaster, +# RulesForecasterConfig, +# RulesForecasterHyperParams, +# ) +# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder + + +# @pytest.fixture +# def base_config() -> RulesForecasterConfig: +# """Base configuration for Rules forecaster tests.""" + +# params = RulesForecasterHyperParams() +# return RulesForecasterConfig( +# quantiles=[Q(0.1), Q(0.5), Q(0.9)], +# horizons=[LeadTime(timedelta(days=1))], +# hyperparams=params, +# verbosity=False, +# ) + + +# def test_rules_forecaster_fit_predict( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: RulesForecasterConfig, +# ): +# """Test basic fit and predict workflow with comprehensive output validation.""" +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = RulesForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# expected_columns = [q.format() for q in expected_quantiles] +# assert list(result.data.columns) == expected_columns, ( +# f"Expected columns {expected_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +# def test_rules_forecaster_predict_not_fitted_raises_error( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: RulesForecasterConfig, +# ): +# """Test that predict() raises NotFittedError when called before fit().""" +# # Arrange +# forecaster = RulesForecaster(config=base_config) + +# # Act & Assert +# with pytest.raises(NotFittedError, match="RulesForecaster"): +# forecaster.predict(sample_forecast_input_dataset) + + +# def test_rules_forecaster_with_sample_weights( +# sample_dataset_with_weights: ForecastInputDataset, +# base_config: RulesForecasterConfig, +# ): +# """Test that forecaster works with sample weights and produces different results.""" +# # Arrange +# forecaster_with_weights = RulesForecaster(config=base_config) + +# # Create dataset without weights for comparison +# data_without_weights = ForecastInputDataset( +# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), +# sample_interval=sample_dataset_with_weights.sample_interval, +# target_column=sample_dataset_with_weights.target_column, +# forecast_start=sample_dataset_with_weights.forecast_start, +# ) +# forecaster_without_weights = RulesForecaster(config=base_config) + +# # Act +# forecaster_with_weights.fit(sample_dataset_with_weights) +# forecaster_without_weights.fit(data_without_weights) + +# # Predict using data without sample_weight column (since that's used for training, not prediction) +# result_with_weights = forecaster_with_weights.predict(data_without_weights) +# result_without_weights = forecaster_without_weights.predict(data_without_weights) + +# # Assert +# # Both should produce valid forecasts +# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" +# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + +# # Sample weights should affect the model, so results should be different +# # (This is a statistical test - with different weights, predictions should differ) +# differences = (result_with_weights.data - result_without_weights.data).abs() +# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +# def test_rules_forecaster_with_additional_features( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: RulesForecasterConfig, +# ): +# """Test that forecaster works with additional features for the final learner.""" + +# base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore + +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = RulesForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# expected_columns = [q.format() for q in expected_quantiles] +# assert list(result.data.columns) == expected_columns, ( +# f"Expected columns {expected_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/models/test_stacking_forecaster.py b/packages/openstef-meta/tests/models/test_stacking_forecaster.py index 33a956d8d..fac92e7cc 100644 --- a/packages/openstef-meta/tests/models/test_stacking_forecaster.py +++ b/packages/openstef-meta/tests/models/test_stacking_forecaster.py @@ -1,136 +1,136 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -from datetime import timedelta - -import pytest - -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError -from openstef_core.types import LeadTime, Q -from openstef_meta.models.stacking_forecaster import ( - StackingForecaster, - StackingForecasterConfig, - StackingHyperParams, -) -from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -@pytest.fixture -def base_config() -> StackingForecasterConfig: - """Base configuration for Stacking forecaster tests.""" - - params = StackingHyperParams() - return StackingForecasterConfig( - quantiles=[Q(0.1), Q(0.5), Q(0.9)], - horizons=[LeadTime(timedelta(days=1))], - hyperparams=params, - verbosity=False, - ) - - -def test_stacking_forecaster_fit_predict( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: StackingForecasterConfig, -): - """Test basic fit and predict workflow with comprehensive output validation.""" - # Arrange - expected_quantiles = base_config.quantiles - forecaster = StackingForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -def test_stacking_forecaster_predict_not_fitted_raises_error( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: StackingForecasterConfig, -): - """Test that predict() raises NotFittedError when called before fit().""" - # Arrange - forecaster = StackingForecaster(config=base_config) - - # Act & Assert - with pytest.raises(NotFittedError, match="StackingForecaster"): - forecaster.predict(sample_forecast_input_dataset) - - -def test_stacking_forecaster_with_sample_weights( - sample_dataset_with_weights: ForecastInputDataset, - base_config: StackingForecasterConfig, -): - """Test that forecaster works with sample weights and produces different results.""" - # Arrange - forecaster_with_weights = StackingForecaster(config=base_config) - - # Create dataset without weights for comparison - data_without_weights = ForecastInputDataset( - data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), - sample_interval=sample_dataset_with_weights.sample_interval, - target_column=sample_dataset_with_weights.target_column, - forecast_start=sample_dataset_with_weights.forecast_start, - ) - forecaster_without_weights = StackingForecaster(config=base_config) - - # Act - forecaster_with_weights.fit(sample_dataset_with_weights) - forecaster_without_weights.fit(data_without_weights) - - # Predict using data without sample_weight column (since that's used for training, not prediction) - result_with_weights = forecaster_with_weights.predict(data_without_weights) - result_without_weights = forecaster_without_weights.predict(data_without_weights) - - # Assert - # Both should produce valid forecasts - assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" - assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - - # Sample weights should affect the model, so results should be different - # (This is a statistical test - with different weights, predictions should differ) - differences = (result_with_weights.data - result_without_weights.data).abs() - assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -def test_stacking_forecaster_with_additional_features( - sample_forecast_input_dataset: ForecastInputDataset, - base_config: StackingForecasterConfig, -): - """Test that forecaster works with additional features for the final learner.""" - - base_config.hyperparams.combiner_hyperparams.feature_adders = [CyclicFeaturesAdder()] - - # Arrange - expected_quantiles = base_config.quantiles - forecaster = StackingForecaster(config=base_config) - - # Act - forecaster.fit(sample_forecast_input_dataset) - result = forecaster.predict(sample_forecast_input_dataset) - - # Assert - # Basic functionality - assert forecaster.is_fitted, "Model should be fitted after calling fit()" - - # Check that necessary quantiles are present - expected_columns = [q.format() for q in expected_quantiles] - assert list(result.data.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.data.columns)}" - ) - - # Forecast data quality - assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +# from datetime import timedelta + +# import pytest + +# from openstef_core.datasets import ForecastInputDataset +# from openstef_core.exceptions import NotFittedError +# from openstef_core.types import LeadTime, Q +# from openstef_meta.models.stacking_forecaster import ( +# StackingForecaster, +# StackingForecasterConfig, +# StackingHyperParams, +# ) +# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder + + +# @pytest.fixture +# def base_config() -> StackingForecasterConfig: +# """Base configuration for Stacking forecaster tests.""" + +# params = StackingHyperParams() +# return StackingForecasterConfig( +# quantiles=[Q(0.1), Q(0.5), Q(0.9)], +# horizons=[LeadTime(timedelta(days=1))], +# hyperparams=params, +# verbosity=False, +# ) + + +# def test_stacking_forecaster_fit_predict( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: StackingForecasterConfig, +# ): +# """Test basic fit and predict workflow with comprehensive output validation.""" +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = StackingForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# expected_columns = [q.format() for q in expected_quantiles] +# assert list(result.data.columns) == expected_columns, ( +# f"Expected columns {expected_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +# def test_stacking_forecaster_predict_not_fitted_raises_error( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: StackingForecasterConfig, +# ): +# """Test that predict() raises NotFittedError when called before fit().""" +# # Arrange +# forecaster = StackingForecaster(config=base_config) + +# # Act & Assert +# with pytest.raises(NotFittedError, match="StackingForecaster"): +# forecaster.predict(sample_forecast_input_dataset) + + +# def test_stacking_forecaster_with_sample_weights( +# sample_dataset_with_weights: ForecastInputDataset, +# base_config: StackingForecasterConfig, +# ): +# """Test that forecaster works with sample weights and produces different results.""" +# # Arrange +# forecaster_with_weights = StackingForecaster(config=base_config) + +# # Create dataset without weights for comparison +# data_without_weights = ForecastInputDataset( +# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), +# sample_interval=sample_dataset_with_weights.sample_interval, +# target_column=sample_dataset_with_weights.target_column, +# forecast_start=sample_dataset_with_weights.forecast_start, +# ) +# forecaster_without_weights = StackingForecaster(config=base_config) + +# # Act +# forecaster_with_weights.fit(sample_dataset_with_weights) +# forecaster_without_weights.fit(data_without_weights) + +# # Predict using data without sample_weight column (since that's used for training, not prediction) +# result_with_weights = forecaster_with_weights.predict(data_without_weights) +# result_without_weights = forecaster_without_weights.predict(data_without_weights) + +# # Assert +# # Both should produce valid forecasts +# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" +# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + +# # Sample weights should affect the model, so results should be different +# # (This is a statistical test - with different weights, predictions should differ) +# differences = (result_with_weights.data - result_without_weights.data).abs() +# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +# def test_stacking_forecaster_with_additional_features( +# sample_forecast_input_dataset: ForecastInputDataset, +# base_config: StackingForecasterConfig, +# ): +# """Test that forecaster works with additional features for the final learner.""" + +# base_config.hyperparams.combiner_hyperparams.feature_adders = [CyclicFeaturesAdder()] + +# # Arrange +# expected_quantiles = base_config.quantiles +# forecaster = StackingForecaster(config=base_config) + +# # Act +# forecaster.fit(sample_forecast_input_dataset) +# result = forecaster.predict(sample_forecast_input_dataset) + +# # Assert +# # Basic functionality +# assert forecaster.is_fitted, "Model should be fitted after calling fit()" + +# # Check that necessary quantiles are present +# expected_columns = [q.format() for q in expected_quantiles] +# assert list(result.data.columns) == expected_columns, ( +# f"Expected columns {expected_columns}, got {list(result.data.columns)}" +# ) + +# # Forecast data quality +# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index ba0e279f5..7d5769f0e 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -25,9 +25,7 @@ from openstef_core.base_model import BaseConfig from openstef_core.mixins import TransformPipeline from openstef_core.types import LeadTime, Q, Quantile, QuantileOrGlobal -from openstef_meta.models.learned_weights_forecaster import LearnedWeightsForecaster -from openstef_meta.models.residual_forecaster import ResidualForecaster -from openstef_meta.models.stacking_forecaster import StackingForecaster +from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback from openstef_models.mixins import ModelIdentifier from openstef_models.models import ForecastingModel @@ -143,16 +141,6 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob description="Hyperparameters for Residual forecaster.", ) - stacking_hyperparams: StackingForecaster.HyperParams = Field( - default=StackingForecaster.HyperParams(), - description="Hyperparameters for Stacking forecaster.", - ) - - learned_weights_hyperparams: LearnedWeightsForecaster.HyperParams = Field( - default=LearnedWeightsForecaster.HyperParams(), - description="Hyperparameters for Learned Weights forecaster.", - ) - location: LocationConfig = Field( default=LocationConfig(), description="Location information for the forecasting workflow.", @@ -437,28 +425,7 @@ def create_forecasting_workflow( postprocessing = [ ConfidenceIntervalApplicator(quantiles=config.quantiles), ] - elif config.model == "learned_weights": - preprocessing = [ - *checks, - *feature_adders, - *feature_standardizers, - Imputer( - selection=Exclude(config.target_column), - imputation_strategy="mean", - fill_future_values=Include(config.energy_price_column), - ), - NaNDropper( - selection=Exclude(config.target_column), - ), - ] - forecaster = LearnedWeightsForecaster( - config=LearnedWeightsForecaster.Config( - quantiles=config.quantiles, - horizons=config.horizons, - hyperparams=config.learned_weights_hyperparams, - ) - ) - postprocessing = [QuantileSorter()] + elif config.model == "residual": preprocessing = [ *checks, @@ -481,28 +448,6 @@ def create_forecasting_workflow( ) ) postprocessing = [QuantileSorter()] - elif config.model == "stacking": - preprocessing = [ - *checks, - *feature_adders, - *feature_standardizers, - Imputer( - selection=Exclude(config.target_column), - imputation_strategy="mean", - fill_future_values=Include(config.energy_price_column), - ), - NaNDropper( - selection=Exclude(config.target_column), - ), - ] - forecaster = StackingForecaster( - config=StackingForecaster.Config( - quantiles=config.quantiles, - horizons=config.horizons, - hyperparams=config.stacking_hyperparams, - ) - ) - postprocessing = [QuantileSorter()] else: msg = f"Unsupported model type: {config.model}" raise ValueError(msg) diff --git a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py index a740ac7c0..542d00448 100644 --- a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py @@ -18,6 +18,7 @@ from openstef_core.datasets import TimeSeriesDataset, VersionedTimeSeriesDataset from openstef_core.datasets.validated_datasets import ForecastDataset from openstef_core.exceptions import NotFittedError, SkipFitting +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel from openstef_models.mixins import ModelIdentifier, PredictorCallback from openstef_models.mixins.callbacks import WorkflowContext from openstef_models.models.forecasting_model import ForecastingModel, ModelFitResult @@ -117,7 +118,7 @@ class CustomForecastingWorkflow(BaseModel): ... ) # doctest: +SKIP """ - model: ForecastingModel = Field(description="The forecasting model to use.") + model: ForecastingModel | EnsembleForecastingModel = Field(description="The forecasting model to use.") callbacks: list[ForecastingCallback] = Field( default_factory=list[ForecastingCallback], description="List of callbacks to execute during workflow events." ) From 9c6de7daca20af7dc402f8a27bdc2ab08135df26 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 3 Dec 2025 13:13:18 +0100 Subject: [PATCH 48/72] Fixed tests Signed-off-by: Lars van Someren --- .../src/openstef_meta/framework/__init__.py | 16 -- .../framework/meta_forecaster.py | 0 .../src/openstef_meta/mixins/__init__.py | 1 + .../src/openstef_meta/mixins/contributions.py | 16 ++ .../models/ensemble_forecasting_model.py | 84 ++++----- .../forecast_combiners/forecast_combiner.py | 27 ++- .../models/forecasting/residual_forecaster.py | 37 ++-- .../models/forecast_combiners/conftest.py | 57 ++++++ .../test_learned_weights_combiner.py | 95 ++++++++++ .../forecast_combiners/test_rules_combiner.py | 64 +++++++ .../test_stacking_combiner.py | 85 +++++++++ .../forecasting/test_residual_forecaster.py | 142 +++++++++++++++ .../test_ensemble_forecasting_model.py} | 9 +- .../models/test_learned_weights_forecaster.py | 171 ------------------ .../tests/models/test_residual_forecaster.py | 142 --------------- .../tests/models/test_rules_forecaster.py | 136 -------------- .../tests/models/test_stacking_forecaster.py | 136 -------------- .../tests/utils/test_datasets.py | 18 +- .../openstef_models/estimators/__init__.py | 7 - .../src/openstef_models/estimators/hybrid.py | 146 --------------- 20 files changed, 549 insertions(+), 840 deletions(-) delete mode 100644 packages/openstef-meta/src/openstef_meta/framework/__init__.py delete mode 100644 packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py create mode 100644 packages/openstef-meta/src/openstef_meta/mixins/__init__.py create mode 100644 packages/openstef-meta/src/openstef_meta/mixins/contributions.py create mode 100644 packages/openstef-meta/tests/models/forecast_combiners/conftest.py create mode 100644 packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py create mode 100644 packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py create mode 100644 packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py create mode 100644 packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py rename packages/openstef-meta/{test_forecasting_model.py => tests/models/test_ensemble_forecasting_model.py} (98%) delete mode 100644 packages/openstef-meta/tests/models/test_learned_weights_forecaster.py delete mode 100644 packages/openstef-meta/tests/models/test_residual_forecaster.py delete mode 100644 packages/openstef-meta/tests/models/test_rules_forecaster.py delete mode 100644 packages/openstef-meta/tests/models/test_stacking_forecaster.py delete mode 100644 packages/openstef-models/src/openstef_models/estimators/__init__.py delete mode 100644 packages/openstef-models/src/openstef_models/estimators/hybrid.py diff --git a/packages/openstef-meta/src/openstef_meta/framework/__init__.py b/packages/openstef-meta/src/openstef_meta/framework/__init__.py deleted file mode 100644 index 0775b88f1..000000000 --- a/packages/openstef-meta/src/openstef_meta/framework/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""This module provides meta-forecasting models and related hyperparameters for the OpenSTEF project.""" - -from .base_learner import BaseLearner, BaseLearnerHyperParams -from ..models.combiners.forecast_combiner import ForecastCombiner, ForecastCombinerHyperParams -from .meta_forecaster import MetaForecaster - -__all__ = [ - "BaseLearner", - "BaseLearnerHyperParams", - "ForecastCombiner", - "ForecastCombinerHyperParams", - "MetaForecaster", -] diff --git a/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py b/packages/openstef-meta/src/openstef_meta/framework/meta_forecaster.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py new file mode 100644 index 000000000..71d67869d --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py @@ -0,0 +1 @@ +"""Mixins for OpenSTEF-Meta package.""" diff --git a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py new file mode 100644 index 000000000..9fb68377c --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py @@ -0,0 +1,16 @@ +"""ExplainableMetaForecaster Mixin.""" + +from abc import ABC, abstractmethod + +import pandas as pd + +from openstef_core.datasets import ForecastInputDataset + + +class ContributionsMixin(ABC): + """Mixin class for models that support contribution analysis.""" + + @abstractmethod + def predict_contributions(self, X: ForecastInputDataset) -> pd.DataFrame: + """Get feature contributions for the given input data X.""" + raise NotImplementedError("This method should be implemented by subclasses.") diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index c7eaf3c1f..5ff81cd8e 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -215,20 +215,17 @@ def fit( ) # Prepare input datasets for metrics calculation - input_data_train = self.prepare_input(data=data) - input_data_val = self.prepare_input(data=data_val) if data_val else None - input_data_test = self.prepare_input(data=data_test) if data_test else None - metrics_train = self._predict_and_score(input_data=input_data_train) - metrics_val = self._predict_and_score(input_data=input_data_val) if input_data_val else None - metrics_test = self._predict_and_score(input_data=input_data_test) if input_data_test else None + metrics_train = self._predict_and_score(data=data) + metrics_val = self._predict_and_score(data=data_val) if data_val else None + metrics_test = self._predict_and_score(data=data_test) if data_test else None metrics_full = self.score(data=data) return ModelFitResult( input_dataset=data, - input_data_train=input_data_train, - input_data_val=input_data_val, - input_data_test=input_data_test, + input_data_train=ForecastInputDataset.from_timeseries(data), + input_data_val=ForecastInputDataset.from_timeseries(data_val) if data_val else None, + input_data_test=ForecastInputDataset.from_timeseries(data_test) if data_test else None, metrics_train=metrics_train, metrics_val=metrics_val, metrics_test=metrics_test, @@ -274,53 +271,29 @@ def _preprocess_fit_forecasters( predictions_raw, target_series=data.data[self.target_column] ) - def _predict_forecasters(self, data: TimeSeriesDataset) -> EnsembleForecastDataset: + def _predict_forecasters( + self, data: TimeSeriesDataset, forecast_start: datetime | None = None + ) -> EnsembleForecastDataset: """Generate predictions from base learners. Args: data: Input data for prediction. + forecast_start: Optional start time for forecasts. Returns: DataFrame containing base learner predictions. """ base_predictions: dict[str, ForecastDataset] = {} for name, forecaster in self.forecasters.items(): - forecaster_data = self.prepare_input(data, forecaster_name=name) - preds = forecaster.predict(data=forecaster_data) + forecaster_data = self.prepare_input(data, forecaster_name=name, forecast_start=forecast_start) + preds_raw = forecaster.predict(data=forecaster_data) + preds = self.postprocessing.transform(data=preds_raw) base_predictions[name] = preds return EnsembleForecastDataset.from_forecast_datasets( base_predictions, target_series=data.data[self.target_column] ) - @override - def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: - """Generate forecasts using the trained model. - - Transforms input data through the preprocessing pipeline, generates predictions - using the underlying forecaster, and applies postprocessing transformations. - - Args: - data: Input time series data for generating forecasts. - forecast_start: Starting time for forecasts. If None, uses data end time. - - Returns: - Processed forecast dataset with predictions and uncertainty estimates. - - Raises: - NotFittedError: If the model hasn't been trained yet. - """ - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Transform the input data to a valid forecast input - input_data = self.prepare_input(data=data, forecast_start=forecast_start) - - # Generate predictions - raw_predictions = self._predict(input_data=input_data) - - return self.postprocessing.transform(data=raw_predictions) - def prepare_input( self, data: TimeSeriesDataset, @@ -369,21 +342,33 @@ def prepare_input( forecast_start=forecast_start, ) - def _predict_and_score(self, input_data: ForecastInputDataset) -> SubsetMetric: - prediction_raw = self._predict(input_data=input_data) - prediction = self.postprocessing.transform(data=prediction_raw) + def _predict_and_score(self, data: TimeSeriesDataset) -> SubsetMetric: + prediction = self.predict(data) return self._calculate_score(prediction=prediction) - def _predict(self, input_data: ForecastInputDataset) -> ForecastDataset: + def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: + """Generate forecasts for the provided dataset. + + Args: + data: Input time series dataset for prediction. + forecast_start: Optional start time for forecasts. + Returns: + ForecastDataset containing the generated forecasts. + + Raises: + NotFittedError: If the model has not been fitted yet. + """ if not self.is_fitted: raise NotFittedError(self.__class__.__name__) - ensemble_predictions = self._predict_forecasters(data=input_data) + ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) additional_features = ( ForecastInputDataset.from_timeseries( - self.combiner_preprocessing.transform(data=input_data), target_column=self.target_column + self.combiner_preprocessing.transform(data=data), + target_column=self.target_column, + forecast_start=forecast_start, ) if len(self.combiner_preprocessing.transforms) > 0 else None @@ -394,7 +379,8 @@ def _predict(self, input_data: ForecastInputDataset) -> ForecastDataset: data=ensemble_predictions, additional_features=additional_features, ) - return restore_target(dataset=prediction, original_dataset=input_data, target_column=self.target_column) + + return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) def score( self, @@ -429,14 +415,14 @@ def _calculate_score(self, prediction: ForecastDataset) -> SubsetMetric: # Needs only one horizon since we are using only a single prediction step # If a more comprehensive test is needed, a backtest should be run. config=EvaluationConfig(available_ats=[], lead_times=[self.config[0].max_horizon]), - quantiles=self.combiner.config.quantiles, + quantiles=self.config[0].quantiles, # Similarly windowed metrics are not relevant for single predictions. window_metric_providers=[], global_metric_providers=self.evaluation_metrics, ) evaluation_result = pipeline.run_for_subset( - filtering=self.combiner.config.max_horizon, + filtering=self.config[0].max_horizon, predictions=prediction, ) global_metric = evaluation_result.get_global_metric() diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index 8a12027d9..09b4e9017 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -81,15 +81,6 @@ def with_horizon(self, horizon: LeadTime) -> Self: """ return self.model_copy(update={"horizons": [horizon]}) - @classmethod - def combiner_class(cls) -> type["ForecastCombiner"]: - """Get the associated Forecaster class for this configuration. - - Returns: - The Forecaster class that uses this configuration. - """ - raise NotImplementedError("Subclasses must implement combiner_class") - class ForecastCombiner(Predictor[EnsembleForecastDataset, ForecastDataset]): """Combines base learner predictions for each quantile into final predictions.""" @@ -158,3 +149,21 @@ def _prepare_input_data( def is_fitted(self) -> bool: """Indicates whether the final learner has been fitted.""" raise NotImplementedError("Subclasses must implement the is_fitted property.") + + @abstractmethod + def predict_contributions( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> pd.DataFrame: + """Generate final predictions based on base learner predictions. + + Args: + data: EnsembleForecastDataset containing base learner predictions. + data_val: Optional EnsembleForecastDataset for validation during prediction. Will be ignored + additional_features: Optional ForecastInputDataset containing additional features for the final learner. + + Returns: + ForecastDataset containing the final contributions. + """ + raise NotImplementedError("Subclasses must implement the predict method.") diff --git a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py index efd8f50bc..96cb8911f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py @@ -12,8 +12,6 @@ import logging from typing import override -from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster, LGBMLinearHyperParams -from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster, XGBoostHyperParams import pandas as pd from pydantic import Field @@ -23,8 +21,6 @@ ) from openstef_core.mixins import HyperParams from openstef_core.types import Quantile - - from openstef_models.models.forecasting.forecaster import ( Forecaster, ForecasterConfig, @@ -34,22 +30,24 @@ GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster, LGBMLinearHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostForecaster, XGBoostHyperParams logger = logging.getLogger(__name__) -BaseLearner = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster -BaseLearnerHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams +ResidualBaseForecaster = LGBMForecaster | LGBMLinearForecaster | XGBoostForecaster | GBLinearForecaster +ResidualBaseForecasterHyperParams = LGBMHyperParams | LGBMLinearHyperParams | XGBoostHyperParams | GBLinearHyperParams class ResidualHyperParams(HyperParams): """Hyperparameters for Stacked LGBM GBLinear Regressor.""" - primary_hyperparams: BaseLearnerHyperParams = Field( + primary_hyperparams: ResidualBaseForecasterHyperParams = Field( default=GBLinearHyperParams(), description="Primary model hyperparams. Defaults to GBLinearHyperParams.", ) - secondary_hyperparams: BaseLearnerHyperParams = Field( + secondary_hyperparams: ResidualBaseForecasterHyperParams = Field( default=LGBMHyperParams(), description="Hyperparameters for the final learner. Defaults to LGBMHyperparams.", ) @@ -80,22 +78,22 @@ def __init__(self, config: ResidualForecasterConfig) -> None: """Initialize the Hybrid forecaster.""" self._config = config - self._primary_model: BaseLearner = self._init_base_learners( + self._primary_model: ResidualBaseForecaster = self._init_base_learners( config=config, base_hyperparams=[config.hyperparams.primary_hyperparams] )[0] - self._secondary_model: list[BaseLearner] = self._init_secondary_model( + self._secondary_model: list[ResidualBaseForecaster] = self._init_secondary_model( hyperparams=config.hyperparams.secondary_hyperparams ) self._is_fitted = False - def _init_secondary_model(self, hyperparams: BaseLearnerHyperParams) -> list[BaseLearner]: + def _init_secondary_model(self, hyperparams: ResidualBaseForecasterHyperParams) -> list[ResidualBaseForecaster]: """Initialize secondary model for residual forecasting. Returns: list[Forecaster]: List containing the initialized secondary model forecaster. """ - models: list[BaseLearner] = [] + models: list[ResidualBaseForecaster] = [] # Different datasets per quantile, so we need a model per quantile for q in self.config.quantiles: config = self._config.model_copy(update={"quantiles": [q]}) @@ -106,14 +104,14 @@ def _init_secondary_model(self, hyperparams: BaseLearnerHyperParams) -> list[Bas @staticmethod def _init_base_learners( - config: ForecasterConfig, base_hyperparams: list[BaseLearnerHyperParams] - ) -> list[BaseLearner]: + config: ForecasterConfig, base_hyperparams: list[ResidualBaseForecasterHyperParams] + ) -> list[ResidualBaseForecaster]: """Initialize base learners based on provided hyperparameters. Returns: list[Forecaster]: List of initialized base learner forecasters. """ - base_learners: list[BaseLearner] = [] + base_learners: list[ResidualBaseForecaster] = [] horizons = config.horizons quantiles = config.quantiles @@ -256,5 +254,14 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + @property + def config(self) -> ResidualForecasterConfig: + """Get the configuration of the ResidualForecaster. + + Returns: + ResidualForecasterConfig: The configuration of the forecaster. + """ + return self._config + __all__ = ["ResidualForecaster", "ResidualForecasterConfig", "ResidualHyperParams"] diff --git a/packages/openstef-meta/tests/models/forecast_combiners/conftest.py b/packages/openstef-meta/tests/models/forecast_combiners/conftest.py new file mode 100644 index 000000000..c80385a07 --- /dev/null +++ b/packages/openstef-meta/tests/models/forecast_combiners/conftest.py @@ -0,0 +1,57 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from collections.abc import Callable +from datetime import timedelta + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets.validated_datasets import ForecastDataset +from openstef_meta.utils.datasets import EnsembleForecastDataset + + +@pytest.fixture +def forecast_dataset_factory() -> Callable[[], ForecastDataset]: + def _make() -> ForecastDataset: + rng = np.random.default_rng() + df = pd.DataFrame( + data={ + "quantile_P10": [90, 180, 270], + "quantile_P50": [100, 200, 300], + "quantile_P90": [110, 220, 330], + "load": [100, 200, 300], + }, + index=pd.to_datetime([ + "2023-01-01T10:00:00", + "2023-01-01T11:00:00", + "2023-01-01T12:00:00", + ]), + ) + df += rng.normal(0, 1, df.shape) # Add slight noise to avoid perfect predictions + + df["available_at"] = pd.to_datetime([ + "2023-01-01T09:50:00", + "2023-01-01T10:55:00", + "2023-01-01T12:10:00", + ]) + + return ForecastDataset( + data=df, + sample_interval=timedelta(hours=1), + target_column="load", + ) + + return _make + + +@pytest.fixture +def ensemble_dataset(forecast_dataset_factory: Callable[[], ForecastDataset]) -> EnsembleForecastDataset: + base_learner_output = { + "GBLinearForecaster": forecast_dataset_factory(), + "LGBMForecaster": forecast_dataset_factory(), + } + + return EnsembleForecastDataset.from_forecast_datasets(base_learner_output) diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py new file mode 100644 index 000000000..ac7a4c380 --- /dev/null +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py @@ -0,0 +1,95 @@ +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_meta.models.forecast_combiners.learned_weights_combiner import ( + WeightsCombiner, + WeightsCombinerConfig, +) +from openstef_meta.utils.datasets import EnsembleForecastDataset + + +@pytest.fixture(params=["lgbm", "xgboost", "rf", "logistic"]) +def classifier(request: pytest.FixtureRequest) -> str: + """Fixture to provide different classifier types for LearnedWeightsCombiner tests.""" + return request.param + + +@pytest.fixture +def config(classifier: str) -> WeightsCombinerConfig: + """Fixture to create WeightsCombinerConfig based on the classifier type.""" + if classifier == "lgbm": + hp = WeightsCombiner.LGBMHyperParams(n_leaves=5, n_estimators=10) + elif classifier == "xgboost": + hp = WeightsCombiner.XGBHyperParams(n_estimators=10) + elif classifier == "rf": + hp = WeightsCombiner.RFHyperParams(n_estimators=10, n_leaves=5) + elif classifier == "logistic": + hp = WeightsCombiner.LogisticHyperParams() + else: + msg = f"Unsupported classifier type: {classifier}" + raise ValueError(msg) + + return WeightsCombiner.Config( + hyperparams=hp, quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))] + ) + + +@pytest.fixture +def forecaster(config: WeightsCombinerConfig) -> WeightsCombiner: + return WeightsCombiner(config) + + +def test_initialization(forecaster: WeightsCombiner): + assert isinstance(forecaster, WeightsCombiner) + + +def test_quantile_weights_combiner__fit_predict( + ensemble_dataset: EnsembleForecastDataset, + config: WeightsCombinerConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = config.quantiles + forecaster = WeightsCombiner(config=config) + + # Act + forecaster.fit(ensemble_dataset) + result = forecaster.predict(ensemble_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + expected_columns.append("load") + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + # Since forecast is deterministic with fixed random seed, check value spread (vectorized) + # All quantiles should have some variation (not all identical values) + stds = result.data.std() + assert (stds > 0).all(), f"All columns should have variation, got stds: {dict(stds)}" + + +def test_weights_combiner_not_fitted_error( + ensemble_dataset: EnsembleForecastDataset, + config: WeightsCombinerConfig, +): + """Test that NotFittedError is raised when predicting before fitting.""" + # Arrange + forecaster = WeightsCombiner(config=config) + # Act & Assert + with pytest.raises(NotFittedError): + forecaster.predict(ensemble_dataset) diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py new file mode 100644 index 000000000..6ee938ef4 --- /dev/null +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py @@ -0,0 +1,64 @@ +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_meta.models.forecast_combiners.rules_combiner import ( + RulesCombiner, + RulesCombinerConfig, +) +from openstef_meta.utils.datasets import EnsembleForecastDataset + + +@pytest.fixture +def config() -> RulesCombinerConfig: + """Fixture to create RulesCombinerConfig.""" + return RulesCombiner.Config( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + ) + + +@pytest.fixture +def forecaster(config: RulesCombinerConfig) -> RulesCombiner: + return RulesCombiner(config=config) + + +def test_initialization(forecaster: RulesCombiner): + assert isinstance(forecaster, RulesCombiner) + + +def test_quantile_weights_combiner__fit_predict( + ensemble_dataset: EnsembleForecastDataset, + config: RulesCombinerConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = config.quantiles + forecaster = RulesCombiner(config=config) + additional_features = ensemble_dataset.select_quantile(Q(0.5)) + additional_features.data = additional_features.data.drop(columns=additional_features.target_column) + additional_features.data.columns = ["feature1", "feature2"] + + # Act + forecaster.fit(ensemble_dataset, additional_features=additional_features) + result = forecaster.predict(ensemble_dataset, additional_features=additional_features) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py new file mode 100644 index 000000000..abcd9f66c --- /dev/null +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py @@ -0,0 +1,85 @@ +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_meta.models.forecast_combiners.stacking_combiner import ( + StackingCombiner, + StackingCombinerConfig, +) +from openstef_meta.utils.datasets import EnsembleForecastDataset + + +@pytest.fixture(params=["lgbm", "gblinear"]) +def regressor(request: pytest.FixtureRequest) -> str: + """Fixture to provide different classifier types for LearnedWeightsCombiner tests.""" + return request.param + + +@pytest.fixture +def config(regressor: str) -> StackingCombinerConfig: + """Fixture to create StackingCombinerConfig based on the classifier type.""" + if regressor == "lgbm": + hp = StackingCombiner.LGBMHyperParams(num_leaves=5, n_estimators=10) + elif regressor == "gblinear": + hp = StackingCombiner.GBLinearHyperParams(n_steps=10) + else: + msg = f"Unsupported regressor type: {regressor}" + raise ValueError(msg) + + return StackingCombiner.Config( + hyperparams=hp, quantiles=[Q(0.1), Q(0.5), Q(0.9)], horizons=[LeadTime(timedelta(days=1))] + ) + + +@pytest.fixture +def forecaster(config: StackingCombinerConfig) -> StackingCombiner: + return StackingCombiner(config) + + +def test_initialization(forecaster: StackingCombiner): + assert isinstance(forecaster, StackingCombiner) + + +def test_quantile_weights_combiner__fit_predict( + ensemble_dataset: EnsembleForecastDataset, + config: StackingCombinerConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = config.quantiles + forecaster = StackingCombiner(config=config) + + # Act + forecaster.fit(ensemble_dataset) + result = forecaster.predict(ensemble_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +def test_stacking_combiner_not_fitted_error( + ensemble_dataset: EnsembleForecastDataset, + config: StackingCombinerConfig, +): + """Test that NotFittedError is raised when predicting before fitting.""" + # Arrange + forecaster = StackingCombiner(config=config) + # Act & Assert + with pytest.raises(NotFittedError): + forecaster.predict(ensemble_dataset) diff --git a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py new file mode 100644 index 000000000..37f30ab2d --- /dev/null +++ b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py @@ -0,0 +1,142 @@ +# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# # +# # SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta + +import pytest + +from openstef_core.datasets import ForecastInputDataset +from openstef_core.exceptions import NotFittedError +from openstef_core.types import LeadTime, Q +from openstef_meta.models.forecasting.residual_forecaster import ( + ResidualBaseForecasterHyperParams, + ResidualForecaster, + ResidualForecasterConfig, + ResidualHyperParams, +) +from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams +from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams +from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams +from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams + + +@pytest.fixture(params=["gblinear", "lgbmlinear"]) +def primary_model(request: pytest.FixtureRequest) -> ResidualBaseForecasterHyperParams: + """Fixture to provide different primary models types.""" + learner_type = request.param + if learner_type == "gblinear": + return GBLinearHyperParams() + if learner_type == "lgbm": + return LGBMHyperParams() + if learner_type == "lgbmlinear": + return LGBMLinearHyperParams() + return XGBoostHyperParams() + + +@pytest.fixture(params=["gblinear", "lgbm", "lgbmlinear", "xgboost"]) +def secondary_model(request: pytest.FixtureRequest) -> ResidualBaseForecasterHyperParams: + """Fixture to provide different secondary models types.""" + learner_type = request.param + if learner_type == "gblinear": + return GBLinearHyperParams() + if learner_type == "lgbm": + return LGBMHyperParams() + if learner_type == "lgbmlinear": + return LGBMLinearHyperParams() + return XGBoostHyperParams() + + +@pytest.fixture +def base_config( + primary_model: ResidualBaseForecasterHyperParams, + secondary_model: ResidualBaseForecasterHyperParams, +) -> ResidualForecasterConfig: + """Base configuration for Residual forecaster tests.""" + + params = ResidualHyperParams( + primary_hyperparams=primary_model, + secondary_hyperparams=secondary_model, + ) + return ResidualForecasterConfig( + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime(timedelta(days=1))], + hyperparams=params, + verbosity=False, + ) + + +def test_residual_forecaster_fit_predict( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: ResidualForecasterConfig, +): + """Test basic fit and predict workflow with comprehensive output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = ResidualForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict(sample_forecast_input_dataset) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + expected_columns = [q.format() for q in expected_quantiles] + assert list(result.data.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.data.columns)}" + ) + + # Forecast data quality + assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" + + +def test_residual_forecaster_predict_not_fitted_raises_error( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: ResidualForecasterConfig, +): + """Test that predict() raises NotFittedError when called before fit().""" + # Arrange + forecaster = ResidualForecaster(config=base_config) + + # Act & Assert + with pytest.raises(NotFittedError, match="ResidualForecaster"): + forecaster.predict(sample_forecast_input_dataset) + + +def test_residual_forecaster_with_sample_weights( + sample_dataset_with_weights: ForecastInputDataset, + base_config: ResidualForecasterConfig, +): + """Test that forecaster works with sample weights and produces different results.""" + # Arrange + forecaster_with_weights = ResidualForecaster(config=base_config) + + # Create dataset without weights for comparison + data_without_weights = ForecastInputDataset( + data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), + sample_interval=sample_dataset_with_weights.sample_interval, + target_column=sample_dataset_with_weights.target_column, + forecast_start=sample_dataset_with_weights.forecast_start, + ) + forecaster_without_weights = ResidualForecaster(config=base_config) + + # Act + forecaster_with_weights.fit(sample_dataset_with_weights) + forecaster_without_weights.fit(data_without_weights) + + # Predict using data without sample_weight column (since that's used for training, not prediction) + result_with_weights = forecaster_with_weights.predict(data_without_weights) + result_without_weights = forecaster_without_weights.predict(data_without_weights) + + # Assert + # Both should produce valid forecasts + assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" + assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" + + # Sample weights should affect the model, so results should be different + # (This is a statistical test - with different weights, predictions should differ) + differences = (result_with_weights.data - result_without_weights.data).abs() + assert differences.sum().sum() > 0, "Sample weights should affect model predictions" diff --git a/packages/openstef-meta/test_forecasting_model.py b/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py similarity index 98% rename from packages/openstef-meta/test_forecasting_model.py rename to packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py index 008199689..126163bd9 100644 --- a/packages/openstef-meta/test_forecasting_model.py +++ b/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py @@ -3,7 +3,6 @@ from typing import override import numpy as np -from openstef_core.mixins.predictor import HyperParams import pandas as pd import pytest @@ -11,6 +10,7 @@ from openstef_core.datasets.timeseries_dataset import TimeSeriesDataset from openstef_core.datasets.validated_datasets import ForecastDataset from openstef_core.exceptions import NotFittedError +from openstef_core.mixins.predictor import HyperParams from openstef_core.mixins.transform import TransformPipeline from openstef_core.testing import assert_timeseries_equal, create_synthetic_forecasting_dataset from openstef_core.types import LeadTime, Q @@ -62,6 +62,11 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: class SimpleCombiner(ForecastCombiner): """Simple combiner that averages base learner predictions.""" + def __init__(self, config: ForecastCombinerConfig): + self._config = config + self._is_fitted = False + self.quantiles = config.quantiles + def fit( self, data: EnsembleForecastDataset, @@ -128,7 +133,6 @@ def model() -> EnsembleForecastingModel: combiner = SimpleCombiner( config=combiner_config, - quantiles=quantiles, ) # Act @@ -231,7 +235,6 @@ def test_forecasting_model__pickle_roundtrip(): combiner = SimpleCombiner( config=combiner_config, - quantiles=quantiles, ) original_model = EnsembleForecastingModel( diff --git a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py b/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py deleted file mode 100644 index 667477191..000000000 --- a/packages/openstef-meta/tests/models/test_learned_weights_forecaster.py +++ /dev/null @@ -1,171 +0,0 @@ -# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# # -# # SPDX-License-Identifier: MPL-2.0 - -# from datetime import timedelta - -# import pytest -# from lightgbm import LGBMClassifier -# from sklearn.linear_model import LogisticRegression -# from xgboost import XGBClassifier - -# from openstef_core.datasets import ForecastInputDataset -# from openstef_core.exceptions import NotFittedError -# from openstef_core.types import LeadTime, Q -# from openstef_meta.models.learned_weights_forecaster import ( -# Classifier, -# LearnedWeightsForecaster, -# LearnedWeightsForecasterConfig, -# LearnedWeightsHyperParams, -# LGBMCombinerHyperParams, -# LogisticCombinerHyperParams, -# RFCombinerHyperParams, -# WeightsCombiner, -# WeightsCombinerHyperParams, -# XGBCombinerHyperParams, -# ) -# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -# @pytest.fixture(params=["rf", "lgbm", "xgboost", "logistic"]) -# def combiner_hyperparams(request: pytest.FixtureRequest) -> WeightsCombinerHyperParams: -# """Fixture to provide different primary models types.""" -# learner_type = request.param -# if learner_type == "rf": -# return RFCombinerHyperParams() -# if learner_type == "lgbm": -# return LGBMCombinerHyperParams() -# if learner_type == "xgboost": -# return XGBCombinerHyperParams() -# return LogisticCombinerHyperParams() - - -# @pytest.fixture -# def base_config(combiner_hyperparams: WeightsCombinerHyperParams) -> LearnedWeightsForecasterConfig: -# """Base configuration for LearnedWeights forecaster tests.""" - -# params = LearnedWeightsHyperParams( -# combiner_hyperparams=combiner_hyperparams, -# ) -# return LearnedWeightsForecasterConfig( -# quantiles=[Q(0.1), Q(0.5), Q(0.9)], -# horizons=[LeadTime(timedelta(days=1))], -# hyperparams=params, -# verbosity=False, -# ) - - -# def test_forecast_combiner_corresponds_to_hyperparams(base_config: LearnedWeightsForecasterConfig): -# """Test that the forecast combiner learner corresponds to the specified hyperparameters.""" -# forecaster = LearnedWeightsForecaster(config=base_config) -# forecast_combiner = forecaster._forecast_combiner -# assert isinstance(forecast_combiner, WeightsCombiner) -# classifier = forecast_combiner.models[0] - -# mapping: dict[type[WeightsCombinerHyperParams], type[Classifier]] = { -# RFCombinerHyperParams: LGBMClassifier, -# LGBMCombinerHyperParams: LGBMClassifier, -# XGBCombinerHyperParams: XGBClassifier, -# LogisticCombinerHyperParams: LogisticRegression, -# } -# expected_type = mapping[type(base_config.hyperparams.combiner_hyperparams)] - -# assert isinstance(classifier, expected_type), ( -# f"Final learner type {type(forecast_combiner)} does not match expected type {expected_type}" -# ) - - -# def test_learned_weights_forecaster_fit_predict( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: LearnedWeightsForecasterConfig, -# ): -# """Test basic fit and predict workflow with comprehensive output validation.""" -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = LearnedWeightsForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# required_columns = [q.format() for q in expected_quantiles] -# assert all(col in result.data.columns for col in required_columns), ( -# f"Expected columns {required_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -# def test_learned_weights_forecaster_predict_not_fitted_raises_error( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: LearnedWeightsForecasterConfig, -# ): -# """Test that predict() raises NotFittedError when called before fit().""" -# # Arrange -# forecaster = LearnedWeightsForecaster(config=base_config) - -# # Act & Assert -# with pytest.raises(NotFittedError, match="LearnedWeightsForecaster"): -# forecaster.predict(sample_forecast_input_dataset) - - -# def test_learned_weights_forecaster_with_sample_weights( -# sample_dataset_with_weights: ForecastInputDataset, -# base_config: LearnedWeightsForecasterConfig, -# ): -# """Test that forecaster works with sample weights and produces different results.""" -# # Arrange -# forecaster_with_weights = LearnedWeightsForecaster(config=base_config) - -# # Create dataset without weights for comparison -# data_without_weights = ForecastInputDataset( -# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), -# sample_interval=sample_dataset_with_weights.sample_interval, -# target_column=sample_dataset_with_weights.target_column, -# forecast_start=sample_dataset_with_weights.forecast_start, -# ) -# forecaster_without_weights = LearnedWeightsForecaster(config=base_config) - -# # Act -# forecaster_with_weights.fit(sample_dataset_with_weights) -# forecaster_without_weights.fit(data_without_weights) - -# # Predict using data without sample_weight column (since that's used for training, not prediction) -# result_with_weights = forecaster_with_weights.predict(sample_dataset_with_weights) -# result_without_weights = forecaster_without_weights.predict(data_without_weights) - -# # Assert -# # Both should produce valid forecasts -# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" -# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - -# # Sample weights should affect the model, so results should be different -# # (This is a statistical test - with different weights, predictions should differ) -# differences = (result_with_weights.data - result_without_weights.data).abs() -# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -# def test_learned_weights_forecaster_with_additional_features( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: LearnedWeightsForecasterConfig, -# ): -# """Test that forecaster works with additional features for the final learner.""" -# # Arrange -# # Add a simple feature adder that adds a constant feature - -# base_config.hyperparams.combiner_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore -# forecaster = LearnedWeightsForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/models/test_residual_forecaster.py b/packages/openstef-meta/tests/models/test_residual_forecaster.py deleted file mode 100644 index c21111d92..000000000 --- a/packages/openstef-meta/tests/models/test_residual_forecaster.py +++ /dev/null @@ -1,142 +0,0 @@ -# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# # -# # SPDX-License-Identifier: MPL-2.0 - -# from datetime import timedelta - -# import pytest - -# from openstef_core.datasets import ForecastInputDataset -# from openstef_core.exceptions import NotFittedError -# from openstef_core.types import LeadTime, Q -# from openstef_meta.framework.base_learner import BaseLearnerHyperParams -# from openstef_meta.models.residual_forecaster import ( -# ResidualForecaster, -# ResidualForecasterConfig, -# ResidualHyperParams, -# ) -# from openstef_models.models.forecasting.gblinear_forecaster import GBLinearHyperParams -# from openstef_models.models.forecasting.lgbm_forecaster import LGBMHyperParams -# from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearHyperParams -# from openstef_models.models.forecasting.xgboost_forecaster import XGBoostHyperParams - - -# @pytest.fixture(params=["gblinear", "lgbmlinear"]) -# def primary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: -# """Fixture to provide different primary models types.""" -# learner_type = request.param -# if learner_type == "gblinear": -# return GBLinearHyperParams() -# if learner_type == "lgbm": -# return LGBMHyperParams() -# if learner_type == "lgbmlinear": -# return LGBMLinearHyperParams() -# return XGBoostHyperParams() - - -# @pytest.fixture(params=["gblinear", "lgbm", "lgbmlinear", "xgboost"]) -# def secondary_model(request: pytest.FixtureRequest) -> BaseLearnerHyperParams: -# """Fixture to provide different secondary models types.""" -# learner_type = request.param -# if learner_type == "gblinear": -# return GBLinearHyperParams() -# if learner_type == "lgbm": -# return LGBMHyperParams() -# if learner_type == "lgbmlinear": -# return LGBMLinearHyperParams() -# return XGBoostHyperParams() - - -# @pytest.fixture -# def base_config( -# primary_model: BaseLearnerHyperParams, -# secondary_model: BaseLearnerHyperParams, -# ) -> ResidualForecasterConfig: -# """Base configuration for Residual forecaster tests.""" - -# params = ResidualHyperParams( -# primary_hyperparams=primary_model, -# secondary_hyperparams=secondary_model, -# ) -# return ResidualForecasterConfig( -# quantiles=[Q(0.1), Q(0.5), Q(0.9)], -# horizons=[LeadTime(timedelta(days=1))], -# hyperparams=params, -# verbosity=False, -# ) - - -# def test_residual_forecaster_fit_predict( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: ResidualForecasterConfig, -# ): -# """Test basic fit and predict workflow with comprehensive output validation.""" -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = ResidualForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# expected_columns = [q.format() for q in expected_quantiles] -# assert list(result.data.columns) == expected_columns, ( -# f"Expected columns {expected_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -# def test_residual_forecaster_predict_not_fitted_raises_error( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: ResidualForecasterConfig, -# ): -# """Test that predict() raises NotFittedError when called before fit().""" -# # Arrange -# forecaster = ResidualForecaster(config=base_config) - -# # Act & Assert -# with pytest.raises(NotFittedError, match="ResidualForecaster"): -# forecaster.predict(sample_forecast_input_dataset) - - -# def test_residual_forecaster_with_sample_weights( -# sample_dataset_with_weights: ForecastInputDataset, -# base_config: ResidualForecasterConfig, -# ): -# """Test that forecaster works with sample weights and produces different results.""" -# # Arrange -# forecaster_with_weights = ResidualForecaster(config=base_config) - -# # Create dataset without weights for comparison -# data_without_weights = ForecastInputDataset( -# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), -# sample_interval=sample_dataset_with_weights.sample_interval, -# target_column=sample_dataset_with_weights.target_column, -# forecast_start=sample_dataset_with_weights.forecast_start, -# ) -# forecaster_without_weights = ResidualForecaster(config=base_config) - -# # Act -# forecaster_with_weights.fit(sample_dataset_with_weights) -# forecaster_without_weights.fit(data_without_weights) - -# # Predict using data without sample_weight column (since that's used for training, not prediction) -# result_with_weights = forecaster_with_weights.predict(data_without_weights) -# result_without_weights = forecaster_without_weights.predict(data_without_weights) - -# # Assert -# # Both should produce valid forecasts -# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" -# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - -# # Sample weights should affect the model, so results should be different -# # (This is a statistical test - with different weights, predictions should differ) -# differences = (result_with_weights.data - result_without_weights.data).abs() -# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" diff --git a/packages/openstef-meta/tests/models/test_rules_forecaster.py b/packages/openstef-meta/tests/models/test_rules_forecaster.py deleted file mode 100644 index 06ae2a41d..000000000 --- a/packages/openstef-meta/tests/models/test_rules_forecaster.py +++ /dev/null @@ -1,136 +0,0 @@ -# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# # -# # SPDX-License-Identifier: MPL-2.0 - -# from datetime import timedelta - -# import pytest - -# from openstef_core.datasets import ForecastInputDataset -# from openstef_core.exceptions import NotFittedError -# from openstef_core.types import LeadTime, Q -# from openstef_meta.models.rules_forecaster import ( -# RulesForecaster, -# RulesForecasterConfig, -# RulesForecasterHyperParams, -# ) -# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -# @pytest.fixture -# def base_config() -> RulesForecasterConfig: -# """Base configuration for Rules forecaster tests.""" - -# params = RulesForecasterHyperParams() -# return RulesForecasterConfig( -# quantiles=[Q(0.1), Q(0.5), Q(0.9)], -# horizons=[LeadTime(timedelta(days=1))], -# hyperparams=params, -# verbosity=False, -# ) - - -# def test_rules_forecaster_fit_predict( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: RulesForecasterConfig, -# ): -# """Test basic fit and predict workflow with comprehensive output validation.""" -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = RulesForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# expected_columns = [q.format() for q in expected_quantiles] -# assert list(result.data.columns) == expected_columns, ( -# f"Expected columns {expected_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -# def test_rules_forecaster_predict_not_fitted_raises_error( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: RulesForecasterConfig, -# ): -# """Test that predict() raises NotFittedError when called before fit().""" -# # Arrange -# forecaster = RulesForecaster(config=base_config) - -# # Act & Assert -# with pytest.raises(NotFittedError, match="RulesForecaster"): -# forecaster.predict(sample_forecast_input_dataset) - - -# def test_rules_forecaster_with_sample_weights( -# sample_dataset_with_weights: ForecastInputDataset, -# base_config: RulesForecasterConfig, -# ): -# """Test that forecaster works with sample weights and produces different results.""" -# # Arrange -# forecaster_with_weights = RulesForecaster(config=base_config) - -# # Create dataset without weights for comparison -# data_without_weights = ForecastInputDataset( -# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), -# sample_interval=sample_dataset_with_weights.sample_interval, -# target_column=sample_dataset_with_weights.target_column, -# forecast_start=sample_dataset_with_weights.forecast_start, -# ) -# forecaster_without_weights = RulesForecaster(config=base_config) - -# # Act -# forecaster_with_weights.fit(sample_dataset_with_weights) -# forecaster_without_weights.fit(data_without_weights) - -# # Predict using data without sample_weight column (since that's used for training, not prediction) -# result_with_weights = forecaster_with_weights.predict(data_without_weights) -# result_without_weights = forecaster_without_weights.predict(data_without_weights) - -# # Assert -# # Both should produce valid forecasts -# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" -# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - -# # Sample weights should affect the model, so results should be different -# # (This is a statistical test - with different weights, predictions should differ) -# differences = (result_with_weights.data - result_without_weights.data).abs() -# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -# def test_rules_forecaster_with_additional_features( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: RulesForecasterConfig, -# ): -# """Test that forecaster works with additional features for the final learner.""" - -# base_config.hyperparams.final_hyperparams.feature_adders.append(CyclicFeaturesAdder()) # type: ignore - -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = RulesForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# expected_columns = [q.format() for q in expected_quantiles] -# assert list(result.data.columns) == expected_columns, ( -# f"Expected columns {expected_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/models/test_stacking_forecaster.py b/packages/openstef-meta/tests/models/test_stacking_forecaster.py deleted file mode 100644 index fac92e7cc..000000000 --- a/packages/openstef-meta/tests/models/test_stacking_forecaster.py +++ /dev/null @@ -1,136 +0,0 @@ -# # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# # -# # SPDX-License-Identifier: MPL-2.0 - -# from datetime import timedelta - -# import pytest - -# from openstef_core.datasets import ForecastInputDataset -# from openstef_core.exceptions import NotFittedError -# from openstef_core.types import LeadTime, Q -# from openstef_meta.models.stacking_forecaster import ( -# StackingForecaster, -# StackingForecasterConfig, -# StackingHyperParams, -# ) -# from openstef_models.transforms.time_domain.cyclic_features_adder import CyclicFeaturesAdder - - -# @pytest.fixture -# def base_config() -> StackingForecasterConfig: -# """Base configuration for Stacking forecaster tests.""" - -# params = StackingHyperParams() -# return StackingForecasterConfig( -# quantiles=[Q(0.1), Q(0.5), Q(0.9)], -# horizons=[LeadTime(timedelta(days=1))], -# hyperparams=params, -# verbosity=False, -# ) - - -# def test_stacking_forecaster_fit_predict( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: StackingForecasterConfig, -# ): -# """Test basic fit and predict workflow with comprehensive output validation.""" -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = StackingForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# expected_columns = [q.format() for q in expected_quantiles] -# assert list(result.data.columns) == expected_columns, ( -# f"Expected columns {expected_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" - - -# def test_stacking_forecaster_predict_not_fitted_raises_error( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: StackingForecasterConfig, -# ): -# """Test that predict() raises NotFittedError when called before fit().""" -# # Arrange -# forecaster = StackingForecaster(config=base_config) - -# # Act & Assert -# with pytest.raises(NotFittedError, match="StackingForecaster"): -# forecaster.predict(sample_forecast_input_dataset) - - -# def test_stacking_forecaster_with_sample_weights( -# sample_dataset_with_weights: ForecastInputDataset, -# base_config: StackingForecasterConfig, -# ): -# """Test that forecaster works with sample weights and produces different results.""" -# # Arrange -# forecaster_with_weights = StackingForecaster(config=base_config) - -# # Create dataset without weights for comparison -# data_without_weights = ForecastInputDataset( -# data=sample_dataset_with_weights.data.drop(columns=["sample_weight"]), -# sample_interval=sample_dataset_with_weights.sample_interval, -# target_column=sample_dataset_with_weights.target_column, -# forecast_start=sample_dataset_with_weights.forecast_start, -# ) -# forecaster_without_weights = StackingForecaster(config=base_config) - -# # Act -# forecaster_with_weights.fit(sample_dataset_with_weights) -# forecaster_without_weights.fit(data_without_weights) - -# # Predict using data without sample_weight column (since that's used for training, not prediction) -# result_with_weights = forecaster_with_weights.predict(data_without_weights) -# result_without_weights = forecaster_without_weights.predict(data_without_weights) - -# # Assert -# # Both should produce valid forecasts -# assert not result_with_weights.data.isna().any().any(), "Weighted forecast should not contain NaN values" -# assert not result_without_weights.data.isna().any().any(), "Unweighted forecast should not contain NaN values" - -# # Sample weights should affect the model, so results should be different -# # (This is a statistical test - with different weights, predictions should differ) -# differences = (result_with_weights.data - result_without_weights.data).abs() -# assert differences.sum().sum() > 0, "Sample weights should affect model predictions" - - -# def test_stacking_forecaster_with_additional_features( -# sample_forecast_input_dataset: ForecastInputDataset, -# base_config: StackingForecasterConfig, -# ): -# """Test that forecaster works with additional features for the final learner.""" - -# base_config.hyperparams.combiner_hyperparams.feature_adders = [CyclicFeaturesAdder()] - -# # Arrange -# expected_quantiles = base_config.quantiles -# forecaster = StackingForecaster(config=base_config) - -# # Act -# forecaster.fit(sample_forecast_input_dataset) -# result = forecaster.predict(sample_forecast_input_dataset) - -# # Assert -# # Basic functionality -# assert forecaster.is_fitted, "Model should be fitted after calling fit()" - -# # Check that necessary quantiles are present -# expected_columns = [q.format() for q in expected_quantiles] -# assert list(result.data.columns) == expected_columns, ( -# f"Expected columns {expected_columns}, got {list(result.data.columns)}" -# ) - -# # Forecast data quality -# assert not result.data.isna().any().any(), "Forecast should not contain NaN or None values" diff --git a/packages/openstef-meta/tests/utils/test_datasets.py b/packages/openstef-meta/tests/utils/test_datasets.py index 045aecd13..efb64f3ea 100644 --- a/packages/openstef-meta/tests/utils/test_datasets.py +++ b/packages/openstef-meta/tests/utils/test_datasets.py @@ -11,7 +11,6 @@ from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset from openstef_core.types import Quantile -from openstef_meta.framework.base_learner import BaseLearnerNames from openstef_meta.utils.datasets import EnsembleForecastDataset @@ -79,26 +78,25 @@ def _make() -> ForecastDataset: @pytest.fixture -def base_learner_output( +def base_predictions( forecast_dataset_factory: Callable[[], ForecastDataset], -) -> dict[BaseLearnerNames, ForecastDataset]: - +) -> dict[str, ForecastDataset]: return { - "GBLinearForecaster": forecast_dataset_factory(), - "LGBMForecaster": forecast_dataset_factory(), + "model_1": forecast_dataset_factory(), + "model_2": forecast_dataset_factory(), } @pytest.fixture -def ensemble_dataset(base_learner_output: dict[BaseLearnerNames, ForecastDataset]) -> EnsembleForecastDataset: - return EnsembleForecastDataset.from_forecast_datasets(base_learner_output) +def ensemble_dataset(base_predictions: dict[str, ForecastDataset]) -> EnsembleForecastDataset: + return EnsembleForecastDataset.from_forecast_datasets(base_predictions) def test_from_ensemble_output(ensemble_dataset: EnsembleForecastDataset): assert isinstance(ensemble_dataset, EnsembleForecastDataset) assert ensemble_dataset.data.shape == (3, 7) # 3 timestamps, 2 learners * 3 quantiles + target - assert set(ensemble_dataset.model_names) == {"GBLinearForecaster", "LGBMForecaster"} + assert set(ensemble_dataset.forecaster_names) == {"model_1", "model_2"} assert set(ensemble_dataset.quantiles) == {Quantile(0.1), Quantile(0.5), Quantile(0.9)} @@ -116,4 +114,4 @@ def test_select_quantile_classification(ensemble_dataset: EnsembleForecastDatase assert isinstance(dataset, ForecastInputDataset) assert dataset.data.shape == (3, 3) # 3 timestamps, 2 learners * 1 quantiles + target - assert all(dataset.target_series.apply(lambda x: x in BaseLearnerNames.__args__)) # type: ignore + assert all(dataset.target_series.apply(lambda x: x in {"model_1", "model_2"})) # type: ignore diff --git a/packages/openstef-models/src/openstef_models/estimators/__init__.py b/packages/openstef-models/src/openstef_models/estimators/__init__.py deleted file mode 100644 index 07a4cbc99..000000000 --- a/packages/openstef-models/src/openstef_models/estimators/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""Custom estimators for multi quantiles.""" - -__all__ = [] diff --git a/packages/openstef-models/src/openstef_models/estimators/hybrid.py b/packages/openstef-models/src/openstef_models/estimators/hybrid.py deleted file mode 100644 index 1660d8707..000000000 --- a/packages/openstef-models/src/openstef_models/estimators/hybrid.py +++ /dev/null @@ -1,146 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 -"""Hybrid quantile regression estimators for multi-quantile forecasting. - -This module provides the HybridQuantileRegressor class, which combines LightGBM and linear models -using stacking for robust multi-quantile regression, including serialization utilities. -""" - -import numpy as np -import numpy.typing as npt -import pandas as pd -from lightgbm import LGBMRegressor -from sklearn.ensemble import StackingRegressor -from sklearn.linear_model import QuantileRegressor -from xgboost import XGBRegressor - - -class HybridQuantileRegressor: - """Custom Hybrid regressor for multi-quantile estimation using sample weights.""" - - def __init__( # noqa: D107, PLR0913, PLR0917 - self, - quantiles: list[float], - lgbm_n_estimators: int = 100, - lgbm_learning_rate: float = 0.1, - lgbm_max_depth: int = -1, - lgbm_min_child_weight: float = 1.0, - ligntgbm_min_child_samples: int = 1, - lgbm_min_data_in_leaf: int = 20, - lgbm_min_data_in_bin: int = 10, - lgbm_reg_alpha: float = 0.0, - lgbm_reg_lambda: float = 0.0, - lgbm_num_leaves: int = 31, - lgbm_max_bin: int = 255, - lgbm_colsample_by_tree: float = 1.0, - gblinear_n_steps: int = 100, - gblinear_learning_rate: float = 0.15, - gblinear_reg_alpha: float = 0.0001, - gblinear_reg_lambda: float = 0, - gblinear_feature_selector: str = "shuffle", - gblinear_updater: str = "shotgun", - ): - self.quantiles = quantiles - - self._models: list[StackingRegressor] = [] - - for q in quantiles: - lgbm_model = LGBMRegressor( - objective="quantile", - alpha=q, - min_child_samples=ligntgbm_min_child_samples, - n_estimators=lgbm_n_estimators, - learning_rate=lgbm_learning_rate, - max_depth=lgbm_max_depth, - min_child_weight=lgbm_min_child_weight, - min_data_in_leaf=lgbm_min_data_in_leaf, - min_data_in_bin=lgbm_min_data_in_bin, - reg_alpha=lgbm_reg_alpha, - reg_lambda=lgbm_reg_lambda, - num_leaves=lgbm_num_leaves, - max_bin=lgbm_max_bin, - colsample_bytree=lgbm_colsample_by_tree, - verbosity=-1, - linear_tree=False, - ) - - linear = XGBRegressor( - booster="gblinear", - # Core parameters for forecasting - objective="reg:quantileerror", - n_estimators=gblinear_n_steps, - learning_rate=gblinear_learning_rate, - # Regularization parameters - reg_alpha=gblinear_reg_alpha, - reg_lambda=gblinear_reg_lambda, - # Boosting structure control - feature_selector=gblinear_feature_selector, - updater=gblinear_updater, - quantile_alpha=q, - ) - - final_estimator = QuantileRegressor(quantile=q) - - self._models.append( - StackingRegressor( - estimators=[("lgbm", lgbm_model), ("gblinear", linear)], # type: ignore - final_estimator=final_estimator, - verbose=3, - passthrough=False, - n_jobs=None, - cv=1, - ) - ) - self.is_fitted: bool = False - self.feature_names: list[str] = [] - - @staticmethod - def _prepare_input(X: npt.NDArray[np.floating] | pd.DataFrame) -> pd.DataFrame: - """Prepare input data by handling missing values. - - Args: - X: Input features as a DataFrame or ndarray. - - Returns: - A DataFrame with missing values handled. - """ - return pd.DataFrame(X).ffill().fillna(0) # type: ignore[reportUnknownMemberType] - - def fit( - self, - X: npt.NDArray[np.floating] | pd.DataFrame, - y: npt.NDArray[np.floating] | pd.Series, - sample_weight: npt.NDArray[np.floating] | pd.Series | None = None, - feature_name: list[str] | None = None, - ) -> None: - """Fit the multi-quantile regressor. - - Args: - X: Input features as a DataFrame. - y: Target values as a 2D array where each column corresponds to a quantile. - sample_weight: Sample weights for training data. - feature_name: List of feature names. - """ - self.feature_names = feature_name if feature_name is not None else [] - - for model in self._models: - model.fit( - X=self._prepare_input(X), # type: ignore - y=y, - sample_weight=sample_weight, - ) - self.is_fitted = True - - def predict(self, X: npt.NDArray[np.floating] | pd.DataFrame) -> npt.NDArray[np.floating]: - """Predict quantiles for the input features. - - Args: - X: Input features as a DataFrame. - - Returns: - - A 2D array where each column corresponds to predicted quantiles. - """ # noqa: D412 - X = X.ffill().fillna(0) # type: ignore - return np.column_stack([model.predict(X=X) for model in self._models]) # type: ignore From e18ce5ab23cdeea1a973bee72139a811d736cc72 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 3 Dec 2025 13:29:20 +0100 Subject: [PATCH 49/72] Prepared TODOs for Florian Signed-off-by: Lars van Someren --- .../models/ensemble_forecasting_model.py | 34 +++++++++++++ .../learned_weights_combiner.py | 40 +++++++++++++++ .../forecast_combiners/rules_combiner.py | 28 +++++++++++ .../forecast_combiners/stacking_combiner.py | 49 +++++++++++++++++++ .../forecast_combiners/test_rules_combiner.py | 2 - 5 files changed, 151 insertions(+), 2 deletions(-) diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 5ff81cd8e..7eab3d74f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -308,6 +308,7 @@ def prepare_input( Args: data: Raw time series dataset to prepare for forecasting. + forecaster_name: Optional name of the forecaster for model-specific preprocessing. forecast_start: Optional start time for forecasts. If provided and earlier than the cutoff time, overrides the cutoff for data filtering. @@ -382,6 +383,39 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) + def predict_contributions(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> pd.DataFrame: + """Generate forecasts for the provided dataset. + + Args: + data: Input time series dataset for prediction. + forecast_start: Optional start time for forecasts. + + Returns: + ForecastDataset containing the generated forecasts. + + Raises: + NotFittedError: If the model has not been fitted yet. + """ + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) + + additional_features = ( + ForecastInputDataset.from_timeseries( + self.combiner_preprocessing.transform(data=data), + target_column=self.target_column, + forecast_start=forecast_start, + ) + if len(self.combiner_preprocessing.transforms) > 0 + else None + ) + + return self.combiner.predict_contributions( + data=ensemble_predictions, + additional_features=additional_features, + ) + def score( self, data: TimeSeriesDataset, diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 31f39e095..98c1767a3 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -336,6 +336,46 @@ def predict( forecast_start=data.forecast_start, ) + @override + def predict_contributions( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> pd.DataFrame: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + contributions = pd.DataFrame({ + Quantile(q).format(): self._generate_contributions_quantile( + dataset=data.select_quantile(quantile=Quantile(q)), + additional_features=additional_features, + model_index=i, + ) + for i, q in enumerate(self.quantiles) + }) + target_series = data.target_series + if target_series is not None: + contributions[data.target_column] = target_series + + return contributions + + def _generate_contributions_quantile( + self, + dataset: ForecastInputDataset, + additional_features: ForecastInputDataset | None, + model_index: int, + ) -> pd.DataFrame: + # TODO: FLORIAN Update content + # input_data = self._prepare_input_data( + # dataset=dataset, + # additional_features=additional_features, + # ) + + # weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) + + return pd.DataFrame() + @property @override def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py index a030b3df5..965997cde 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py @@ -141,6 +141,34 @@ def predict( sample_interval=data.sample_interval, ) + @override + def predict_contributions( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> pd.DataFrame: + if additional_features is None: + raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") + + decisions = self._predict_tree( + additional_features.data, columns=data.select_quantile(quantile=self.quantiles[0]).data.columns + ) + + # Generate predictions + predictions: list[pd.DataFrame] = [] + for q in self.quantiles: + dataset = data.select_quantile(quantile=q) + preds = dataset.input_data().multiply(decisions).sum(axis=1) + + predictions.append(preds.to_frame(name=Quantile(q).format())) + + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + # TODO FLORIAN return only Decision datadrame + + return df + @property def is_fitted(self) -> bool: """Check the Rules Final Learner is fitted.""" diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index b8f4ebad5..3155d44bc 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -198,6 +198,55 @@ def predict( sample_interval=data.sample_interval, ) + @override + def predict_contributions( + self, + data: EnsembleForecastDataset, + additional_features: ForecastInputDataset | None = None, + ) -> pd.DataFrame: + if not self.is_fitted: + raise NotFittedError(self.__class__.__name__) + + # Generate predictions + predictions: list[pd.DataFrame] = [] + for i, q in enumerate(self.quantiles): + if additional_features is not None: + input_data = self._combine_datasets( + data=data.select_quantile(quantile=q), + additional_features=additional_features, + ) + else: + input_data = data.select_quantile(quantile=q) + p = self.models[i].predict(data=input_data).data + predictions.append(p) + # Concatenate predictions along columns to form a DataFrame with quantile columns + df = pd.concat(predictions, axis=1) + + contributions = pd.DataFrame() + for q in self.quantiles: + # Extract base predictions for this quantile + # TODO Florian implement contributions extraction per quantile + + return pd.DataFrame() # Placeholder for actual implementation + + @staticmethod + def contributions_from_predictions( + base_predictions: pd.DataFrame, + final_predictions: pd.Series, + ) -> pd.DataFrame: + """Extract contributions from predictions DataFrame. + + Args: + predictions: DataFrame containing predictions. + + Returns: + DataFrame with contributions per base learner. + """ + # TODO Florian implement contributions extraction + # abs(final_predictions) / sum(abs(base_predictions), axis=1) + + return pd.DataFrame() # Placeholder for actual implementation + @property def is_fitted(self) -> bool: """Check the StackingForecastCombiner is fitted.""" diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py index 6ee938ef4..aa08bf59a 100644 --- a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py @@ -6,8 +6,6 @@ import pytest -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q from openstef_meta.models.forecast_combiners.rules_combiner import ( RulesCombiner, From ece5d1863f85b1cc36111d63714b4b76d5d20482 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 3 Dec 2025 13:31:43 +0100 Subject: [PATCH 50/72] Small fix Signed-off-by: Lars van Someren --- .../models/forecast_combiners/stacking_combiner.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index 3155d44bc..a38123898 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -226,8 +226,9 @@ def predict_contributions( for q in self.quantiles: # Extract base predictions for this quantile # TODO Florian implement contributions extraction per quantile + pass - return pd.DataFrame() # Placeholder for actual implementation + return contributions # Placeholder for actual implementation @staticmethod def contributions_from_predictions( From c33ce9354abf3d08a90b59d419009822b5a29ae0 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 3 Dec 2025 14:15:06 +0100 Subject: [PATCH 51/72] Made PR Compliant Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 1 - .../openstef4_backtest_forecaster.py | 6 + .../src/openstef_meta/mixins/__init__.py | 4 + .../src/openstef_meta/mixins/contributions.py | 4 + .../src/openstef_meta/models/__init__.py | 5 + .../models/ensemble_forecasting_model.py | 15 +- .../models/forecast_combiners/__init__.py | 4 + .../src/openstef_meta/presets/__init__.py | 4 + .../presets/forecasting_workflow.py | 168 +++++++++--------- .../src/openstef_meta/utils/datasets.py | 1 - .../tests/{ => unit}/models/__init__.py | 0 .../tests/{ => unit}/models/conftest.py | 0 .../models/forecast_combiners}/__init__.py | 0 .../models/forecast_combiners/conftest.py | 0 .../test_learned_weights_combiner.py | 0 .../forecast_combiners/test_rules_combiner.py | 2 - .../test_stacking_combiner.py | 0 .../models/forecasting}/__init__.py | 0 .../forecasting/test_residual_forecaster.py | 0 .../models/test_ensemble_forecasting_model.py | 9 +- .../tests/unit/transforms/__init__.py | 0 .../transforms/test_flag_features_bound.py | 0 .../tests/unit/utils/__init__.py | 0 .../tests/{ => unit}/utils/test_datasets.py | 0 .../{ => unit}/utils/test_decision_tree.py | 0 .../mlflow/mlflow_storage_callback.py | 8 +- 26 files changed, 138 insertions(+), 93 deletions(-) create mode 100644 packages/openstef-meta/src/openstef_meta/models/__init__.py rename packages/openstef-meta/tests/{ => unit}/models/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/conftest.py (100%) rename packages/openstef-meta/tests/{transforms => unit/models/forecast_combiners}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/conftest.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_learned_weights_combiner.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_rules_combiner.py (95%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_stacking_combiner.py (100%) rename packages/openstef-meta/tests/{utils => unit/models/forecasting}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecasting/test_residual_forecaster.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/test_ensemble_forecasting_model.py (97%) create mode 100644 packages/openstef-meta/tests/unit/transforms/__init__.py rename packages/openstef-meta/tests/{ => unit}/transforms/test_flag_features_bound.py (100%) create mode 100644 packages/openstef-meta/tests/unit/utils/__init__.py rename packages/openstef-meta/tests/{ => unit}/utils/test_datasets.py (100%) rename packages/openstef-meta/tests/{ => unit}/utils/test_decision_tree.py (100%) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index d3c990ad2..6e58f7998 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -18,7 +18,6 @@ os.environ["MKL_NUM_THREADS"] = "1" import logging -import multiprocessing from datetime import timedelta from pathlib import Path diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 56dad935f..d8a303007 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -17,6 +17,7 @@ from openstef_core.datasets import TimeSeriesDataset from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow @@ -58,6 +59,11 @@ def quantiles(self) -> list[Q]: if self._workflow is None: self._workflow = self.workflow_factory() # Extract quantiles from the workflow's model + + if isinstance(self._workflow.model, EnsembleForecastingModel): + # Assuming all ensemble members have the same quantiles + name = self._workflow.model.forecaster_names[0] + return self._workflow.model.forecasters[name].config.quantiles return self._workflow.model.forecaster.config.quantiles @override diff --git a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py index 71d67869d..90a57a257 100644 --- a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py @@ -1 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Mixins for OpenSTEF-Meta package.""" diff --git a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py index 9fb68377c..f00c185b3 100644 --- a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py +++ b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """ExplainableMetaForecaster Mixin.""" from abc import ABC, abstractmethod diff --git a/packages/openstef-meta/src/openstef_meta/models/__init__.py b/packages/openstef-meta/src/openstef_meta/models/__init__.py new file mode 100644 index 000000000..13175057c --- /dev/null +++ b/packages/openstef-meta/src/openstef_meta/models/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Meta Forecasting models.""" diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 5ff81cd8e..a56c8b3e7 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -61,16 +61,24 @@ class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastD >>> from openstef_models.models.forecasting.constant_median_forecaster import ( ... ConstantMedianForecaster, ConstantMedianForecasterConfig ... ) + >>> from openstef_meta.models.forecast_combiners.learned_weights_combiner import WeightsCombiner >>> from openstef_core.types import LeadTime >>> >>> # Note: This is a conceptual example showing the API structure >>> # Real usage requires implemented forecaster classes - >>> forecaster = ConstantMedianForecaster( + >>> forecaster_1 = ConstantMedianForecaster( ... config=ConstantMedianForecasterConfig(horizons=[LeadTime.from_string("PT36H")]) ... ) + >>> forecaster_2 = ConstantMedianForecaster( + ... config=ConstantMedianForecasterConfig(horizons=[LeadTime.from_string("PT36H")]) + ... ) + >>> combiner_config = WeightsCombiner.Config( + ... horizons=[LeadTime.from_string("PT36H")], + ... ) >>> # Create and train model - >>> model = ForecastingModel( - ... forecaster=forecaster, + >>> model = EnsembleForecastingModel( + ... forecasters={"constant_median": forecaster_1, "constant_median_2": forecaster_2}, + ... combiner=WeightsCombiner(config=combiner_config), ... cutoff_history=timedelta(days=14), # Match your maximum lag in preprocessing ... ) >>> model.fit(training_data) # doctest: +SKIP @@ -308,6 +316,7 @@ def prepare_input( Args: data: Raw time series dataset to prepare for forecasting. + forecaster_name: Name of the forecaster for model-specific preprocessing. forecast_start: Optional start time for forecasts. If provided and earlier than the cutoff time, overrides the cutoff for data filtering. diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py index db4917778..56a4cadff 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Forecast Combiners.""" from .forecast_combiner import ForecastCombiner, ForecastCombinerConfig diff --git a/packages/openstef-meta/src/openstef_meta/presets/__init__.py b/packages/openstef-meta/src/openstef_meta/presets/__init__.py index 53b9630aa..ad62320c2 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/presets/__init__.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Package for preset forecasting workflows.""" from .forecasting_workflow import EnsembleForecastingModel, EnsembleWorkflowConfig, create_ensemble_workflow diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 88d063584..1087f2c5d 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Ensemble forecasting workflow preset. Mimics OpenSTEF-models forecasting workflow with ensemble capabilities. @@ -5,7 +9,7 @@ from collections.abc import Sequence from datetime import timedelta -from typing import Literal +from typing import TYPE_CHECKING, Literal from pydantic import Field @@ -24,9 +28,8 @@ from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster -from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback +from openstef_models.integrations.mlflow import MLFlowStorage from openstef_models.mixins.model_serializer import ModelIdentifier -from openstef_models.models.forecasting.forecaster import Forecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster @@ -55,6 +58,9 @@ from openstef_models.utils.feature_selection import Exclude, FeatureSelection, Include from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow, ForecastingCallback +if TYPE_CHECKING: + from openstef_models.models.forecasting.forecaster import Forecaster + class EnsembleWorkflowConfig(BaseConfig): """Configuration for ensemble forecasting workflows.""" @@ -235,62 +241,72 @@ class EnsembleWorkflowConfig(BaseConfig): ) -def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: - """Create an ensemble forecasting workflow from configuration.""" - - # Build preprocessing components - def checks() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - InputConsistencyChecker(), - FlatlineChecker( - load_column=config.target_column, - flatliner_threshold=config.flatliner_threshold, - detect_non_zero_flatliner=config.detect_non_zero_flatliner, - error_on_flatliner=False, - ), - CompletenessChecker(completeness_threshold=config.completeness_threshold), - ] - - def feature_adders() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - WindPowerFeatureAdder( - windspeed_reference_column=config.wind_speed_column, - ), - AtmosphereDerivedFeaturesAdder( - pressure_column=config.pressure_column, - relative_humidity_column=config.relative_humidity_column, - temperature_column=config.temperature_column, - ), - RadiationDerivedFeaturesAdder( - coordinate=config.location.coordinate, - radiation_column=config.radiation_column, - ), - CyclicFeaturesAdder(), - DaylightFeatureAdder( - coordinate=config.location.coordinate, - ), - RollingAggregatesAdder( - feature=config.target_column, - aggregation_functions=config.rolling_aggregate_features, - horizons=config.horizons, - ), - ] - - def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), - Scaler(selection=Exclude(config.target_column), method="standard"), - SampleWeighter( - target_column=config.target_column, - weight_exponent=config.sample_weight_exponent, - weight_floor=config.sample_weight_floor, - weight_scale_percentile=config.sample_weight_scale_percentile, - ), - EmptyFeatureRemover(), - ] - - # Model Specific LagsAdder +# Build preprocessing components +def checks(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + InputConsistencyChecker(), + FlatlineChecker( + load_column=config.target_column, + flatliner_threshold=config.flatliner_threshold, + detect_non_zero_flatliner=config.detect_non_zero_flatliner, + error_on_flatliner=False, + ), + CompletenessChecker(completeness_threshold=config.completeness_threshold), + ] + +def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + WindPowerFeatureAdder( + windspeed_reference_column=config.wind_speed_column, + ), + AtmosphereDerivedFeaturesAdder( + pressure_column=config.pressure_column, + relative_humidity_column=config.relative_humidity_column, + temperature_column=config.temperature_column, + ), + RadiationDerivedFeaturesAdder( + coordinate=config.location.coordinate, + radiation_column=config.radiation_column, + ), + CyclicFeaturesAdder(), + DaylightFeatureAdder( + coordinate=config.location.coordinate, + ), + RollingAggregatesAdder( + feature=config.target_column, + aggregation_functions=config.rolling_aggregate_features, + horizons=config.horizons, + ), + ] + + +def feature_standardizers(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), + Scaler(selection=Exclude(config.target_column), method="standard"), + SampleWeighter( + target_column=config.target_column, + weight_exponent=config.sample_weight_exponent, + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + EmptyFeatureRemover(), + ] + + +def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: # noqa: C901, PLR0912, PLR0915 + """Create an ensemble forecasting workflow from configuration. + + Args: + config (EnsembleWorkflowConfig): Configuration for the ensemble workflow. + + Returns: + CustomForecastingWorkflow: Configured ensemble forecasting workflow. + + Raises: + ValueError: If an unsupported base model or combiner type is specified. + """ # Build forecasters and their processing pipelines forecaster_preprocessing: dict[str, list[Transform[TimeSeriesDataset, TimeSeriesDataset]]] = {} forecasters: dict[str, Forecaster] = {} @@ -300,8 +316,8 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=LGBMForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), + *checks(config), + *feature_adders(config), LagsAdder( history_available=config.predict_history, horizons=config.horizons, @@ -310,7 +326,7 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), + *feature_standardizers(config), ] elif model_type == "gblinear": @@ -318,8 +334,8 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=GBLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), + *checks(config), + *feature_adders(config), LagsAdder( history_available=config.predict_history, horizons=config.horizons, @@ -329,7 +345,7 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), + *feature_standardizers(config), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", @@ -344,8 +360,8 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=XGBoostForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), + *checks(config), + *feature_adders(config), LagsAdder( history_available=config.predict_history, horizons=config.horizons, @@ -354,15 +370,15 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), + *feature_standardizers(config), ] elif model_type == "lgbm_linear": forecasters[model_type] = LGBMLinearForecaster( config=LGBMLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), + *checks(config), + *feature_adders(config), LagsAdder( history_available=config.predict_history, horizons=config.horizons, @@ -371,7 +387,7 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), + *feature_standardizers(config), ] else: msg = f"Unsupported base model type: {model_type}" @@ -435,17 +451,7 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas ) callbacks: list[ForecastingCallback] = [] - if config.mlflow_storage is not None: - callbacks.append( - MLFlowStorageCallback( - storage=config.mlflow_storage, - model_reuse_enable=config.model_reuse_enable, - model_reuse_max_age=config.model_reuse_max_age, - model_selection_enable=config.model_selection_enable, - model_selection_metric=config.model_selection_metric, - model_selection_old_model_penalty=config.model_selection_old_model_penalty, - ) - ) + # TODO(Egor): Implement MLFlow for OpenSTEF-meta # noqa: TD003 return CustomForecastingWorkflow(model=ensemble_model, model_id=config.model_id, callbacks=callbacks) diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index 9d38c5b4f..e0bba9265 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -78,7 +78,6 @@ def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[str], list[Q Raises: ValueError: If an invalid base learner name is found in a feature name. """ - forecasters: set[str] = set() quantiles: set[Quantile] = set() diff --git a/packages/openstef-meta/tests/models/__init__.py b/packages/openstef-meta/tests/unit/models/__init__.py similarity index 100% rename from packages/openstef-meta/tests/models/__init__.py rename to packages/openstef-meta/tests/unit/models/__init__.py diff --git a/packages/openstef-meta/tests/models/conftest.py b/packages/openstef-meta/tests/unit/models/conftest.py similarity index 100% rename from packages/openstef-meta/tests/models/conftest.py rename to packages/openstef-meta/tests/unit/models/conftest.py diff --git a/packages/openstef-meta/tests/transforms/__init__.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/__init__.py similarity index 100% rename from packages/openstef-meta/tests/transforms/__init__.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/__init__.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/conftest.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/conftest.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/conftest.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/conftest.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_learned_weights_combiner.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_learned_weights_combiner.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_rules_combiner.py similarity index 95% rename from packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_rules_combiner.py index 6ee938ef4..aa08bf59a 100644 --- a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py +++ b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_rules_combiner.py @@ -6,8 +6,6 @@ import pytest -from openstef_core.datasets import ForecastInputDataset -from openstef_core.exceptions import NotFittedError from openstef_core.types import LeadTime, Q from openstef_meta.models.forecast_combiners.rules_combiner import ( RulesCombiner, diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py diff --git a/packages/openstef-meta/tests/utils/__init__.py b/packages/openstef-meta/tests/unit/models/forecasting/__init__.py similarity index 100% rename from packages/openstef-meta/tests/utils/__init__.py rename to packages/openstef-meta/tests/unit/models/forecasting/__init__.py diff --git a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py b/packages/openstef-meta/tests/unit/models/forecasting/test_residual_forecaster.py similarity index 100% rename from packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py rename to packages/openstef-meta/tests/unit/models/forecasting/test_residual_forecaster.py diff --git a/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py similarity index 97% rename from packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py rename to packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py index 126163bd9..33b78cfc9 100644 --- a/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py +++ b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py @@ -1,4 +1,8 @@ -import pickle +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +import pickle # noqa: S403 - Controlled test from datetime import datetime, timedelta from typing import override @@ -136,10 +140,9 @@ def model() -> EnsembleForecastingModel: ) # Act - model = EnsembleForecastingModel( + return EnsembleForecastingModel( forecasters=forecasters, combiner=combiner, common_preprocessing=TransformPipeline() ) - return model def test_forecasting_model__init__uses_defaults(model: EnsembleForecastingModel): diff --git a/packages/openstef-meta/tests/unit/transforms/__init__.py b/packages/openstef-meta/tests/unit/transforms/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/transforms/test_flag_features_bound.py b/packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py similarity index 100% rename from packages/openstef-meta/tests/transforms/test_flag_features_bound.py rename to packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py diff --git a/packages/openstef-meta/tests/unit/utils/__init__.py b/packages/openstef-meta/tests/unit/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/utils/test_datasets.py b/packages/openstef-meta/tests/unit/utils/test_datasets.py similarity index 100% rename from packages/openstef-meta/tests/utils/test_datasets.py rename to packages/openstef-meta/tests/unit/utils/test_datasets.py diff --git a/packages/openstef-meta/tests/utils/test_decision_tree.py b/packages/openstef-meta/tests/unit/utils/test_decision_tree.py similarity index 100% rename from packages/openstef-meta/tests/utils/test_decision_tree.py rename to packages/openstef-meta/tests/unit/utils/test_decision_tree.py diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index fd59cd600..6d8ee425f 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -101,7 +101,7 @@ def on_fit_end( run = self.storage.create_run( model_id=context.workflow.model_id, tags=context.workflow.model.tags, - hyperparams=context.workflow.model.forecaster.hyperparams, + hyperparams=context.workflow.model.forecaster.hyperparams, # type: ignore TODO Make MLFlow compatible with OpenSTEF Meta ) run_id: str = run.info.run_id self._logger.info("Created MLflow run %s for model %s", run_id, context.workflow.model_id) @@ -114,7 +114,11 @@ def on_fit_end( self._logger.info("Stored training data at %s for run %s", data_path, run_id) # Store feature importance plot if enabled - if self.store_feature_importance_plot and isinstance(context.workflow.model.forecaster, ExplainableForecaster): + if ( + self.store_feature_importance_plot + and isinstance(context.workflow.model, ForecastingModel) + and isinstance(context.workflow.model.forecaster, ExplainableForecaster) + ): fig = context.workflow.model.forecaster.plot_feature_importances() fig.write_html(data_path / "feature_importances.html") # pyright: ignore[reportUnknownMemberType] From eb775e41c2d661f025cc97268f5bbdbf4abe148f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 4 Dec 2025 11:40:44 +0100 Subject: [PATCH 52/72] BugFix Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 3 ++ .../models/ensemble_forecasting_model.py | 32 ++++++++----- .../forecast_combiners/forecast_combiner.py | 22 --------- .../learned_weights_combiner.py | 14 +++--- .../presets/forecasting_workflow.py | 47 ++++++++++++++----- .../src/openstef_meta/utils/datasets.py | 37 +++++++++++++++ 6 files changed, 103 insertions(+), 52 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 6e58f7998..0f7248183 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -18,6 +18,7 @@ os.environ["MKL_NUM_THREADS"] = "1" import logging +import multiprocessing from datetime import timedelta from pathlib import Path @@ -93,6 +94,8 @@ temperature_column="temperature_2m", relative_humidity_column="relative_humidity_2m", energy_price_column="EPEX_NL", + forecast_combiner_sample_weight_exponent=1, + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 1, "xgboost": 0, "lgbm_linear": 0}, ) diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index a56c8b3e7..1c6f82bb0 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -35,6 +35,8 @@ from openstef_models.models.forecasting_model import ModelFitResult from openstef_models.utils.data_split import DataSplitter +logger = logging.getLogger(__name__) + class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]): """Complete forecasting pipeline combining preprocessing, prediction, and postprocessing. @@ -191,6 +193,15 @@ def fit( Returns: FitResult containing training details and metrics. """ + + # Transform the input data to a valid forecast input and split into train/val/test + data, data_val, data_test = self.data_splitter.split_dataset( + data=data, + data_val=data_val, + data_test=data_test, + target_column=self.target_column, + ) + # Fit the feature engineering transforms self.common_preprocessing.fit(data=data) @@ -249,27 +260,24 @@ def _preprocess_fit_forecasters( predictions_raw: dict[str, ForecastDataset] = {} + if data_test is not None: + logger.info("Data test provided during fit, but will be ignored for MetaForecating") + for name, forecaster in self.forecasters.items(): validate_horizons_present(data, forecaster.config.horizons) # Transform and split input data - input_data_train = self.prepare_input(data=data, forecaster_name=name) - input_data_val = self.prepare_input(data=data_val, forecaster_name=name) if data_val else None - input_data_test = self.prepare_input(data=data_test, forecaster_name=name) if data_test else None + input_data_train = self.prepare_input(data=data, forecaster_name=name, forecast_start=data.index[0]) + input_data_val = ( + self.prepare_input(data=data_val, forecaster_name=name, forecast_start=data_val.index[0]) + if data_val + else None + ) # Drop target column nan's from training data. One can not train on missing targets. target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] input_data_train = input_data_train.pipe_pandas(target_dropna) input_data_val = input_data_val.pipe_pandas(target_dropna) if input_data_val else None - input_data_test = input_data_test.pipe_pandas(target_dropna) if input_data_test else None - - # Transform the input data to a valid forecast input and split into train/val/test - input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( - data=input_data_train, - data_val=input_data_val, - data_test=input_data_test, - target_column=self.target_column, - ) # Fit the model forecaster.fit(data=input_data_train, data_val=input_data_val) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index 09b4e9017..b1df023f6 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -122,28 +122,6 @@ def predict( """ raise NotImplementedError("Subclasses must implement the predict method.") - @staticmethod - def _prepare_input_data( - dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None - ) -> pd.DataFrame: - """Prepare input data by combining base predictions with additional features if provided. - - Args: - dataset: ForecastInputDataset containing base predictions. - additional_features: Optional ForecastInputDataset containing additional features. - - Returns: - pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. - """ - df = dataset.input_data(start=dataset.index[0]) - if additional_features is not None: - df_a = additional_features.input_data(start=dataset.index[0]) - df = pd.concat( - [df, df_a], - axis=1, - ) - return df - @property @abstractmethod def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 31f39e095..cdda00c1c 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -32,7 +32,7 @@ ForecastCombiner, ForecastCombinerConfig, ) -from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_meta.utils.datasets import EnsembleForecastDataset, combine_forecast_input_datasets logger = logging.getLogger(__name__) @@ -241,18 +241,17 @@ def fit( for i, q in enumerate(self.quantiles): # Data preparation dataset = data.select_quantile_classification(quantile=q) - input_data = self._prepare_input_data( + combined_data = combine_forecast_input_datasets( dataset=dataset, - additional_features=additional_features, + other=additional_features, ) - labels = dataset.target_series + input_data = combined_data.input_data() + labels = combined_data.target_series self._validate_labels(labels=labels, model_index=i) labels = self._label_encoder.transform(labels) # Balance classes, adjust with sample weights - weights = compute_sample_weight("balanced", labels) - if sample_weights is not None: - weights *= sample_weights + weights = compute_sample_weight("balanced", labels) * combined_data.sample_weight_series self.models[i].fit(X=input_data, y=labels, sample_weight=weights) # type: ignore self._is_fitted = True @@ -276,6 +275,7 @@ def _prepare_input_data( df = pd.concat( [df, df_a], axis=1, + join="inner", ) return df diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 1087f2c5d..011cce2b0 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -11,6 +11,7 @@ from datetime import timedelta from typing import TYPE_CHECKING, Literal +from openstef_meta.transforms.selector import Selector from pydantic import Field from openstef_beam.evaluation.metric_providers import ( @@ -83,7 +84,7 @@ class EnsembleWorkflowConfig(BaseConfig): description="Time interval between consecutive data samples.", ) horizons: list[LeadTime] = Field( - default=[LeadTime.from_string("PT48H")], + default=[LeadTime.from_string("PT36H")], description="List of forecast horizons to predict.", ) @@ -171,14 +172,19 @@ class EnsembleWorkflowConfig(BaseConfig): description="Percentile of target values used as scaling reference. " "Values are normalized relative to this percentile before weighting.", ) - sample_weight_exponent: float = Field( - default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "residual", "xgboost"} - else 0.0, + forecaster_sample_weight_exponent: dict[str, float] = Field( + default={"gblinear": 1.0, "lgbm": 0, "xgboost": 0, "lgbm_linear": 0}, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " "Note: Defaults to 1.0 for gblinear congestion models.", ) + + forecast_combiner_sample_weight_exponent: float = Field( + default=0, + description="Exponent applied to scale the sample weights for the forecast combiner model. " + "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values.", + ) + sample_weight_floor: float = Field( default=0.1, description="Minimum weight value to ensure all samples contribute to training.", @@ -281,13 +287,15 @@ def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesD ] -def feature_standardizers(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: +def feature_standardizers( + config: EnsembleWorkflowConfig, model_type: str +) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: return [ Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), Scaler(selection=Exclude(config.target_column), method="standard"), SampleWeighter( target_column=config.target_column, - weight_exponent=config.sample_weight_exponent, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], weight_floor=config.sample_weight_floor, weight_scale_percentile=config.sample_weight_scale_percentile, ), @@ -326,7 +334,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config), + *feature_standardizers(config, model_type), ] elif model_type == "gblinear": @@ -345,7 +353,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config), + *feature_standardizers(config, model_type), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", @@ -370,7 +378,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config), + *feature_standardizers(config, model_type), ] elif model_type == "lgbm_linear": forecasters[model_type] = LGBMLinearForecaster( @@ -387,7 +395,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin ), HolidayFeatureAdder(country_code=config.location.country_code), DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config), + *feature_standardizers(config, model_type), ] else: msg = f"Unsupported base model type: {model_type}" @@ -441,13 +449,30 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin name: TransformPipeline(transforms=transforms) for name, transforms in forecaster_preprocessing.items() } + if config.forecast_combiner_sample_weight_exponent != 0: + combiner_transforms = [ + SampleWeighter( + target_column=config.target_column, + weight_exponent=config.forecast_combiner_sample_weight_exponent, + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + Selector(selection=Include("sample_weight", config.target_column)), + ] + else: + combiner_transforms = [] + + combiner_preprocessing: TransformPipeline[TimeSeriesDataset] = TransformPipeline(transforms=combiner_transforms) + ensemble_model = EnsembleForecastingModel( common_preprocessing=TransformPipeline(transforms=[]), model_specific_preprocessing=model_specific_preprocessing, + combiner_preprocessing=combiner_preprocessing, postprocessing=TransformPipeline(transforms=postprocessing), forecasters=forecasters, combiner=combiner, target_column=config.target_column, + data_splitter=config.data_splitter, ) callbacks: list[ForecastingCallback] = [] diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index e0bba9265..cb6dbdad2 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -20,6 +20,43 @@ DEFAULT_TARGET_COLUMN = {Quantile(0.5): "load"} +def combine_forecast_input_datasets( + dataset: ForecastInputDataset, other: ForecastInputDataset | None, join: str = "inner" +) -> ForecastInputDataset: + """Combine multiple TimeSeriesDatasets into a single dataset. + + Args: + dataset: First ForecastInputDataset. + other: Second ForecastInputDataset or None. + join: Type of join to perform on the datasets. Defaults to "inner". + + Returns: + Combined ForecastDataset. + """ + if not isinstance(other, ForecastInputDataset): + return dataset + if join != "inner": + raise NotImplementedError("Only 'inner' join is currently supported.") + df_other = other.data + if dataset.target_column in df_other.columns: + df_other = df_other.drop(columns=[dataset.target_column]) + + df_one = dataset.data + df = pd.concat( + [df_one, df_other], + axis=1, + join="inner", + ) + + return ForecastInputDataset( + data=df, + sample_interval=dataset.sample_interval, + target_column=dataset.target_column, + sample_weight_column=dataset.sample_weight_column, + forecast_start=dataset.forecast_start, + ) + + class EnsembleForecastDataset(TimeSeriesDataset): """First stage output format for ensemble forecasters.""" From e212448dbcdad7dd5e024d49d69cf9a7532d7af2 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 4 Dec 2025 12:38:24 +0100 Subject: [PATCH 53/72] fixes Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 4 +- .../models/ensemble_forecasting_model.py | 93 +++++++++++----- .../presets/forecasting_workflow.py | 101 ++++++++++-------- .../src/openstef_meta/transforms/selector.py | 2 +- 4 files changed, 121 insertions(+), 79 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 0f7248183..6d8931099 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,7 +44,7 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 11 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" @@ -95,7 +95,7 @@ relative_humidity_column="relative_humidity_2m", energy_price_column="EPEX_NL", forecast_combiner_sample_weight_exponent=1, - forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 1, "xgboost": 0, "lgbm_linear": 0}, + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 0, "xgboost": 0, "lgbm_linear": 0}, ) diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 1c6f82bb0..a6298088b 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -194,6 +194,16 @@ def fit( FitResult containing training details and metrics. """ + score_data = data.copy_with(data=data.data) + # Fit the feature engineering transforms + self.common_preprocessing.fit(data=data) + data = self.common_preprocessing.transform(data=data) + + if data_val is not None: + data_val = self.common_preprocessing.transform(data=data_val) + if data_test is not None: + data_test = self.common_preprocessing.transform(data=data_test) + # Transform the input data to a valid forecast input and split into train/val/test data, data_val, data_test = self.data_splitter.split_dataset( data=data, @@ -202,9 +212,6 @@ def fit( target_column=self.target_column, ) - # Fit the feature engineering transforms - self.common_preprocessing.fit(data=data) - # Fit predict forecasters ensemble_predictions = self._preprocess_fit_forecasters( data=data, @@ -219,13 +226,7 @@ def fit( else: ensemble_predictions_val = None - if len(self.combiner_preprocessing.transforms) > 0: - combiner_data = self.prepare_input(data=data) - self.combiner_preprocessing.fit(combiner_data) - combiner_data = self.combiner_preprocessing.transform(combiner_data) - features = ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) - else: - features = None + features = self._fit_transform_combiner_data(data=data) self.combiner.fit( data=ensemble_predictions, @@ -234,11 +235,36 @@ def fit( ) # Prepare input datasets for metrics calculation + metrics_train = self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions, additional_features=features + ) + if data_val is not None: + features_val = self._transform_combiner_data(data=data_val) + metrics_val = ( + self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions_val, additional_features=features_val + ) + if ensemble_predictions_val + else None + ) + else: + metrics_val = None - metrics_train = self._predict_and_score(data=data) - metrics_val = self._predict_and_score(data=data_val) if data_val else None - metrics_test = self._predict_and_score(data=data_test) if data_test else None - metrics_full = self.score(data=data) + if data_test is not None: + features_test = self._transform_combiner_data(data=data_test) + ensemble_predictions_test = self._predict_forecasters( + data=self.prepare_input(data=data_test), + ) + metrics_test = ( + self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions_test, additional_features=features_test + ) + if ensemble_predictions_test + else None + ) + else: + metrics_test = None + metrics_full = self.score(data=score_data) return ModelFitResult( input_dataset=data, @@ -251,6 +277,21 @@ def fit( metrics_full=metrics_full, ) + def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: + if len(self.combiner_preprocessing.transforms) == 0: + return None + combiner_data = self.prepare_input(data=data) + combiner_data = self.combiner_preprocessing.transform(combiner_data) + return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + + def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: + if len(self.combiner_preprocessing.transforms) == 0: + return None + combiner_data = self.prepare_input(data=data) + self.combiner_preprocessing.fit(combiner_data) + combiner_data = self.combiner_preprocessing.transform(combiner_data) + return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + def _preprocess_fit_forecasters( self, data: TimeSeriesDataset, @@ -331,9 +372,6 @@ def prepare_input( Returns: Processed forecast input dataset ready for model prediction. """ - # Transform and restore target column - data = self.common_preprocessing.transform(data=data) - # Apply model-specific preprocessing if available if forecaster_name in self.model_specific_preprocessing: self.model_specific_preprocessing[forecaster_name].fit(data=data) @@ -359,8 +397,10 @@ def prepare_input( forecast_start=forecast_start, ) - def _predict_and_score(self, data: TimeSeriesDataset) -> SubsetMetric: - prediction = self.predict(data) + def _predict_combiner_and_score( + self, ensemble_dataset: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None + ) -> SubsetMetric: + prediction = self.combiner.predict(ensemble_dataset, additional_features=additional_features) return self._calculate_score(prediction=prediction) def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: @@ -379,22 +419,17 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non if not self.is_fitted: raise NotFittedError(self.__class__.__name__) + # Common preprocessing + data = self.common_preprocessing.transform(data=data) + ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) - additional_features = ( - ForecastInputDataset.from_timeseries( - self.combiner_preprocessing.transform(data=data), - target_column=self.target_column, - forecast_start=forecast_start, - ) - if len(self.combiner_preprocessing.transforms) > 0 - else None - ) + features = self._transform_combiner_data(data=data) # Predict and restore target column prediction = self.combiner.predict( data=ensemble_predictions, - additional_features=additional_features, + additional_features=features, ) return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 011cce2b0..ee60e67bb 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -263,6 +263,12 @@ def checks(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: return [ + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ), WindPowerFeatureAdder( windspeed_reference_column=config.wind_speed_column, ), @@ -287,18 +293,10 @@ def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesD ] -def feature_standardizers( - config: EnsembleWorkflowConfig, model_type: str -) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: +def feature_standardizers(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: return [ Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), Scaler(selection=Exclude(config.target_column), method="standard"), - SampleWeighter( - target_column=config.target_column, - weight_exponent=config.forecaster_sample_weight_exponent[model_type], - weight_floor=config.sample_weight_floor, - weight_scale_percentile=config.sample_weight_scale_percentile, - ), EmptyFeatureRemover(), ] @@ -315,6 +313,17 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin Raises: ValueError: If an unsupported base model or combiner type is specified. """ + # Common preprocessing + common_preprocessing = TransformPipeline( + transforms=[ + *checks(config), + *feature_adders(config), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(config), + ] + ) + # Build forecasters and their processing pipelines forecaster_preprocessing: dict[str, list[Transform[TimeSeriesDataset, TimeSeriesDataset]]] = {} forecasters: dict[str, Forecaster] = {} @@ -324,17 +333,12 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin config=LGBMForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(config), - *feature_adders(config), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config, model_type), ] elif model_type == "gblinear": @@ -342,18 +346,31 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin config=GBLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(config), - *feature_adders(config), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=False, + SampleWeighter( target_column=config.target_column, - custom_lags=[timedelta(days=7)], + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + Selector( + selection=FeatureSelection( + exclude={ + "load_lag_P14D", + "load_lag_P13D", + "load_lag_P12D", + "load_lag_P11D", + "load_lag_P10D", + "load_lag_P9D", + "load_lag_P8D", + "load_lag_P7D", + "load_lag_P6D", + "load_lag_P5D", + "load_lag_P4D", + "load_lag_P3D", + "load_lag_P2D", + } + ) ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config, model_type), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", @@ -368,34 +385,24 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin config=XGBoostForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(config), - *feature_adders(config), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config, model_type), ] elif model_type == "lgbm_linear": forecasters[model_type] = LGBMLinearForecaster( config=LGBMLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(config), - *feature_adders(config), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(config, model_type), ] else: msg = f"Unsupported base model type: {model_type}" @@ -465,7 +472,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin combiner_preprocessing: TransformPipeline[TimeSeriesDataset] = TransformPipeline(transforms=combiner_transforms) ensemble_model = EnsembleForecastingModel( - common_preprocessing=TransformPipeline(transforms=[]), + common_preprocessing=common_preprocessing, model_specific_preprocessing=model_specific_preprocessing, combiner_preprocessing=combiner_preprocessing, postprocessing=TransformPipeline(transforms=postprocessing), diff --git a/packages/openstef-meta/src/openstef_meta/transforms/selector.py b/packages/openstef-meta/src/openstef_meta/transforms/selector.py index 75eb4e321..e4c5d343b 100644 --- a/packages/openstef-meta/src/openstef_meta/transforms/selector.py +++ b/packages/openstef-meta/src/openstef_meta/transforms/selector.py @@ -24,7 +24,7 @@ class Selector(BaseConfig, TimeSeriesTransform): selection: FeatureSelection = Field( default=FeatureSelection.ALL, - description="Features to check for NaN values. Rows with NaN in any selected column are dropped.", + description="Feature selection for efficient model specific preprocessing.x", ) @override From b44fd928ff9ddf5eab552fa33ebb2f46742843c0 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 4 Dec 2025 14:39:31 +0100 Subject: [PATCH 54/72] bug fixes Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 6 ++--- .../presets/forecasting_workflow.py | 27 +++++++++++++++++-- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 6d8931099..be9053a75 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,9 +44,9 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 11 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" +ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner @@ -95,7 +95,7 @@ relative_humidity_column="relative_humidity_2m", energy_price_column="EPEX_NL", forecast_combiner_sample_weight_exponent=1, - forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 0, "xgboost": 0, "lgbm_linear": 0}, + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 1, "xgboost": 0, "lgbm_linear": 0}, ) diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index ee60e67bb..6ad2b78d7 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -354,7 +354,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin ), Selector( selection=FeatureSelection( - exclude={ + exclude={ # Fix hardcoded lag features should be replaced by a LagsAdder classmethod "load_lag_P14D", "load_lag_P13D", "load_lag_P12D", @@ -362,7 +362,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin "load_lag_P10D", "load_lag_P9D", "load_lag_P8D", - "load_lag_P7D", + # "load_lag_P7D", # Keep 7D lag for weekly seasonality "load_lag_P6D", "load_lag_P5D", "load_lag_P4D", @@ -371,6 +371,29 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin } ) ), + Selector( # Fix hardcoded holiday features should be replaced by a HolidayFeatureAdder classmethod + selection=FeatureSelection( + exclude={ + "is_ascension_day", + "is_christmas_day", + "is_easter_monday", + "is_easter_sunday", + "is_good_friday", + "is_holiday", + "is_king_s_day", + "is_liberation_day", + "is_new_year_s_day", + "is_second_day_of_christmas", + "is_sunday", + "is_week_day", + "is_weekend_day", + "is_whit_monday", + "is_whit_sunday", + "month_of_year", + "quarter_of_year", + } + ) + ), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", From 51579d0a7dc95cbd0a730aa075403469f67a1469 Mon Sep 17 00:00:00 2001 From: floriangoethals Date: Thu, 4 Dec 2025 15:38:19 +0100 Subject: [PATCH 55/72] added learned weights contributions --- examples/benchmarks/liander_2024_ensemble.py | 3 +- .../openstef4_backtest_forecaster.py | 15 +++++- .../learned_weights_combiner.py | 47 ++++++++++++++----- .../forecast_combiners/stacking_combiner.py | 43 +++++++---------- 4 files changed, 67 insertions(+), 41 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index d3c990ad2..b490a800c 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -141,6 +141,7 @@ def _create_workflow() -> CustomForecastingWorkflow: config=backtest_config, workflow_factory=_create_workflow, debug=False, + contributions=True, cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", ) @@ -149,7 +150,7 @@ def _create_workflow() -> CustomForecastingWorkflow: start_time = time.time() create_liander2024_benchmark_runner( storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), - data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), + data_dir=Path("local_data/liander2024-energy-forecasting-benchmark"), callbacks=[StrictExecutionCallback()], ).run( forecaster_factory=_target_forecaster_factory, diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 56dad935f..8e7554b24 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -18,6 +18,7 @@ from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): @@ -40,6 +41,10 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): default=False, description="When True, saves intermediate input data for debugging", ) + contributions: bool = Field( + default=False, + description="When True, saves intermediate input data for explainability", + ) _workflow: CustomForecastingWorkflow | None = PrivateAttr(default=None) _is_flatliner_detected: bool = PrivateAttr(default=False) @@ -50,6 +55,8 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): def model_post_init(self, context: Any) -> None: if self.debug: self.cache_dir.mkdir(parents=True, exist_ok=True) + if self.contributions: + self.cache_dir.mkdir(parents=True, exist_ok=True) @property @override @@ -58,7 +65,7 @@ def quantiles(self) -> list[Q]: if self._workflow is None: self._workflow = self.workflow_factory() # Extract quantiles from the workflow's model - return self._workflow.model.forecaster.config.quantiles + return self._workflow.model.forecaster.config.quantiles # type: ignore @override def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: @@ -69,6 +76,7 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: training_data = data.get_window( start=data.horizon - self.config.training_context_length, end=data.horizon, available_before=data.horizon ) + if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") @@ -91,6 +99,7 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: path=self.cache_dir / f"debug_{id_str}_prepared_training.parquet" ) + @override def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDataset | None: if self._is_flatliner_detected: @@ -121,6 +130,10 @@ def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDatas predict_data.to_parquet(path=self.cache_dir / f"debug_{id_str}_predict.parquet") forecast.to_parquet(path=self.cache_dir / f"debug_{id_str}_forecast.parquet") + if self.contributions and isinstance(self._workflow.model, EnsembleForecastingModel): + contr_str = data.horizon.strftime("%Y%m%d%H%M%S") + contributions = self._workflow.model.predict_contributions(predict_data) + contributions.to_parquet(path=self.cache_dir / f"contrib_{contr_str}_predict.parquet") return forecast diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 98c1767a3..8d4a237a4 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -289,7 +289,10 @@ def _validate_labels(self, labels: pd.Series, model_index: int) -> None: def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_index: int) -> pd.DataFrame: model = self.models[model_index] - return model.predict_proba(X=base_predictions) # type: ignore + weights_array = model.predict_proba(base_predictions.to_numpy()) # type: ignore + + return pd.DataFrame(weights_array, index=base_predictions.index, columns=self._label_encoder.classes_) # type: ignore + def _generate_predictions_quantile( self, @@ -346,14 +349,17 @@ def predict_contributions( raise NotFittedError(self.__class__.__name__) # Generate predictions - contributions = pd.DataFrame({ - Quantile(q).format(): self._generate_contributions_quantile( + contribution_list = [ + self._generate_contributions_quantile( dataset=data.select_quantile(quantile=Quantile(q)), additional_features=additional_features, model_index=i, ) for i, q in enumerate(self.quantiles) - }) + ] + + contributions = pd.concat(contribution_list, axis=1) + target_series = data.target_series if target_series is not None: contributions[data.target_column] = target_series @@ -366,15 +372,30 @@ def _generate_contributions_quantile( additional_features: ForecastInputDataset | None, model_index: int, ) -> pd.DataFrame: - # TODO: FLORIAN Update content - # input_data = self._prepare_input_data( - # dataset=dataset, - # additional_features=additional_features, - # ) - - # weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) - - return pd.DataFrame() + input_data = self._prepare_input_data( + dataset=dataset, + additional_features=additional_features, + ) + weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) + weights.columns = [f"{col}_{Quantile(self.quantiles[model_index]).format()}" for col in weights.columns] + return weights + + + # def _generate_contributions_quantile( + # self, + # dataset: ForecastInputDataset, + # additional_features: ForecastInputDataset | None, + # model_index: int, + # ) -> pd.DataFrame: + # # TODO: FLORIAN Update content + # # input_data = self._prepare_input_data( + # # dataset=dataset, + # # additional_features=additional_features, + # # ) + + # # weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) + + # return pd.DataFrame() @property @override diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index a38123898..beaa556a8 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -204,37 +204,30 @@ def predict_contributions( data: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None, ) -> pd.DataFrame: - if not self.is_fitted: - raise NotFittedError(self.__class__.__name__) - - # Generate predictions - predictions: list[pd.DataFrame] = [] - for i, q in enumerate(self.quantiles): - if additional_features is not None: - input_data = self._combine_datasets( - data=data.select_quantile(quantile=q), - additional_features=additional_features, - ) - else: - input_data = data.select_quantile(quantile=q) - p = self.models[i].predict(data=input_data).data - predictions.append(p) # Concatenate predictions along columns to form a DataFrame with quantile columns - df = pd.concat(predictions, axis=1) - - contributions = pd.DataFrame() - for q in self.quantiles: + predictions = self.predict(data=data, additional_features = additional_features).data + contributions = {} + + for i, q in enumerate(self.quantiles): # Extract base predictions for this quantile # TODO Florian implement contributions extraction per quantile - pass - - return contributions # Placeholder for actual implementation + quantile_label = Quantile(q).format() + final_prediction = predictions.loc[:,quantile_label] + base_predictions = data.select_quantile(q).data + contributions[quantile_label] = self.contributions_from_predictions( + base_predictions=base_predictions, + #Final_prediction taken as the biggest value + final_predictions=final_prediction + ) + contributions = pd.DataFrame(contributions) + + return pd.DataFrame(contributions) # Placeholder for actual implementation @staticmethod def contributions_from_predictions( base_predictions: pd.DataFrame, final_predictions: pd.Series, - ) -> pd.DataFrame: + ) -> pd.Series: """Extract contributions from predictions DataFrame. Args: @@ -244,9 +237,7 @@ def contributions_from_predictions( DataFrame with contributions per base learner. """ # TODO Florian implement contributions extraction - # abs(final_predictions) / sum(abs(base_predictions), axis=1) - - return pd.DataFrame() # Placeholder for actual implementation + return final_predictions.abs()/base_predictions.abs().sum(axis=1) # Placeholder for actual implementation @property def is_fitted(self) -> bool: From 2899baf0e3513e4d582e81eb8464f9674414fff4 Mon Sep 17 00:00:00 2001 From: lars800 Date: Fri, 5 Dec 2025 17:11:37 +0100 Subject: [PATCH 56/72] Added Feature Contributions Residual Forecaster and Stacking Forecaster can now predict model contributions. Regular forecasters (EXCEPT LGBM Linear) can predict feature contributions --- .../openstef4_backtest_forecaster.py | 4 +- .../learned_weights_combiner.py | 18 ------ .../forecast_combiners/stacking_combiner.py | 56 ++++++----------- .../models/forecasting/residual_forecaster.py | 61 ++++++++++++++++++- .../models/forecast_combiners/conftest.py | 8 ++- .../test_stacking_combiner.py | 23 ++++++- .../forecasting/test_residual_forecaster.py | 35 +++++++++++ .../openstef_models/explainability/mixins.py | 13 ++++ .../models/forecasting/gblinear_forecaster.py | 44 +++++++++++++ .../models/forecasting/lgbm_forecaster.py | 35 +++++++++++ .../forecasting/lgbmlinear_forecaster.py | 37 +++++++++++ .../models/forecasting/xgboost_forecaster.py | 45 ++++++++++++++ .../forecasting/test_gblinear_forecaster.py | 32 ++++++++++ .../forecasting/test_lgbm_forecaster.py | 31 ++++++++++ .../forecasting/test_xgboost_forecaster.py | 33 +++++++++- 15 files changed, 410 insertions(+), 65 deletions(-) diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 8e7554b24..5ae961632 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -65,7 +65,7 @@ def quantiles(self) -> list[Q]: if self._workflow is None: self._workflow = self.workflow_factory() # Extract quantiles from the workflow's model - return self._workflow.model.forecaster.config.quantiles # type: ignore + return self._workflow.model.forecaster.config.quantiles # type: ignore @override def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: @@ -76,7 +76,6 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: training_data = data.get_window( start=data.horizon - self.config.training_context_length, end=data.horizon, available_before=data.horizon ) - if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") @@ -99,7 +98,6 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: path=self.cache_dir / f"debug_{id_str}_prepared_training.parquet" ) - @override def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDataset | None: if self._is_flatliner_detected: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 8d4a237a4..acf366661 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -292,7 +292,6 @@ def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_ weights_array = model.predict_proba(base_predictions.to_numpy()) # type: ignore return pd.DataFrame(weights_array, index=base_predictions.index, columns=self._label_encoder.classes_) # type: ignore - def _generate_predictions_quantile( self, @@ -379,23 +378,6 @@ def _generate_contributions_quantile( weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) weights.columns = [f"{col}_{Quantile(self.quantiles[model_index]).format()}" for col in weights.columns] return weights - - - # def _generate_contributions_quantile( - # self, - # dataset: ForecastInputDataset, - # additional_features: ForecastInputDataset | None, - # model_index: int, - # ) -> pd.DataFrame: - # # TODO: FLORIAN Update content - # # input_data = self._prepare_input_data( - # # dataset=dataset, - # # additional_features=additional_features, - # # ) - - # # weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) - - # return pd.DataFrame() @property @override diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index beaa556a8..1814ef49b 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -10,7 +10,7 @@ """ import logging -from typing import TYPE_CHECKING, cast, override +from typing import cast, override import pandas as pd from pydantic import Field, field_validator @@ -23,15 +23,13 @@ from openstef_core.types import LeadTime, Quantile from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner, ForecastCombinerConfig from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_models.models.forecasting.forecaster import Forecaster from openstef_models.models.forecasting.gblinear_forecaster import ( GBLinearForecaster, GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams -if TYPE_CHECKING: - from openstef_models.models.forecasting.forecaster import Forecaster - logger = logging.getLogger(__name__) ForecasterHyperParams = GBLinearHyperParams | LGBMHyperParams @@ -204,40 +202,26 @@ def predict_contributions( data: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None, ) -> pd.DataFrame: - # Concatenate predictions along columns to form a DataFrame with quantile columns - predictions = self.predict(data=data, additional_features = additional_features).data - contributions = {} - - for i, q in enumerate(self.quantiles): - # Extract base predictions for this quantile - # TODO Florian implement contributions extraction per quantile - quantile_label = Quantile(q).format() - final_prediction = predictions.loc[:,quantile_label] - base_predictions = data.select_quantile(q).data - contributions[quantile_label] = self.contributions_from_predictions( - base_predictions=base_predictions, - #Final_prediction taken as the biggest value - final_predictions=final_prediction - ) - contributions = pd.DataFrame(contributions) - - return pd.DataFrame(contributions) # Placeholder for actual implementation - @staticmethod - def contributions_from_predictions( - base_predictions: pd.DataFrame, - final_predictions: pd.Series, - ) -> pd.Series: - """Extract contributions from predictions DataFrame. - - Args: - predictions: DataFrame containing predictions. + # Generate predictions + predictions: list[pd.DataFrame] = [] + for i, q in enumerate(self.quantiles): + if additional_features is not None: + input_data = self._combine_datasets( + data=data.select_quantile(quantile=q), + additional_features=additional_features, + ) + else: + input_data = data.select_quantile(quantile=q) + p = self.predict_contributions_quantile( + model=self.models[i], + data=input_data, + ) + p.columns = [f"{col}_{Quantile(self.quantiles[i]).format()}" for col in p.columns] + predictions.append(p) - Returns: - DataFrame with contributions per base learner. - """ - # TODO Florian implement contributions extraction - return final_predictions.abs()/base_predictions.abs().sum(axis=1) # Placeholder for actual implementation + # Concatenate predictions along columns to form a DataFrame with quantile columns + return pd.concat(predictions, axis=1) @property def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py index 96cb8911f..be9100a1a 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py @@ -13,7 +13,7 @@ from typing import override import pandas as pd -from pydantic import Field +from pydantic import Field, model_validator from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.exceptions import ( @@ -52,6 +52,30 @@ class ResidualHyperParams(HyperParams): description="Hyperparameters for the final learner. Defaults to LGBMHyperparams.", ) + primary_name: str = Field( + default="primary_model", + description="Name identifier for the primary model.", + ) + + secondary_name: str = Field( + default="secondary_model", + description="Name identifier for the secondary model.", + ) + + @model_validator(mode="after") + def validate_names(self) -> "ResidualHyperParams": + """Validate that primary and secondary names are not the same. + + Raises: + ValueError: If primary and secondary names are the same. + + Returns: + ResidualHyperParams: The validated hyperparameters. + """ + if self.primary_name == self.secondary_name: + raise ValueError("Primary and secondary model names must be different.") + return self + class ResidualForecasterConfig(ForecasterConfig): """Configuration for Hybrid-based forecasting models.""" @@ -85,6 +109,8 @@ def __init__(self, config: ResidualForecasterConfig) -> None: self._secondary_model: list[ResidualBaseForecaster] = self._init_secondary_model( hyperparams=config.hyperparams.secondary_hyperparams ) + self.primary_name = config.hyperparams.primary_name + self.secondary_name = config.hyperparams.secondary_name self._is_fitted = False def _init_secondary_model(self, hyperparams: ResidualBaseForecasterHyperParams) -> list[ResidualBaseForecaster]: @@ -254,6 +280,39 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Generate prediction contributions using the ResidualForecaster model. + + Args: + data: Input data for prediction contributions. + + Returns: + pd.DataFrame containing the prediction contributions. + """ + primary_predictions = self._primary_model.predict(data=data).data + + secondary_predictions = self._predict_secodary_model(data=data).data + + if not scale: + primary_contributions = primary_predictions + primary_name = self._primary_model.__class__.__name__ + primary_contributions.columns = [f"{primary_name}_{q}" for q in primary_contributions.columns] + + secondary_contributions = secondary_predictions + secondary_name = self._secondary_model[0].__class__.__name__ + secondary_contributions.columns = [f"{secondary_name}_{q}" for q in secondary_contributions.columns] + + return pd.concat([primary_contributions, secondary_contributions], axis=1) + + primary_contributions = primary_predictions.abs() / (primary_predictions.abs() + secondary_predictions.abs()) + primary_contributions.columns = [f"{self.primary_name}_{q}" for q in primary_contributions.columns] + + secondary_contributions = secondary_predictions.abs() / ( + primary_predictions.abs() + secondary_predictions.abs()) + secondary_contributions.columns = [f"{self.secondary_name}_{q}" for q in secondary_contributions.columns] + + return pd.concat([primary_contributions, secondary_contributions], axis=1) + @property def config(self) -> ResidualForecasterConfig: """Get the configuration of the ResidualForecaster. diff --git a/packages/openstef-meta/tests/models/forecast_combiners/conftest.py b/packages/openstef-meta/tests/models/forecast_combiners/conftest.py index c80385a07..cf4edb982 100644 --- a/packages/openstef-meta/tests/models/forecast_combiners/conftest.py +++ b/packages/openstef-meta/tests/models/forecast_combiners/conftest.py @@ -17,11 +17,13 @@ def forecast_dataset_factory() -> Callable[[], ForecastDataset]: def _make() -> ForecastDataset: rng = np.random.default_rng() + coef = rng.normal(0, 1, 3) + df = pd.DataFrame( data={ - "quantile_P10": [90, 180, 270], - "quantile_P50": [100, 200, 300], - "quantile_P90": [110, 220, 330], + "quantile_P10": np.array([1, 2, 3]) * coef[0], + "quantile_P50": np.array([1, 2, 3]) * coef[1], + "quantile_P90": np.array([1, 2, 3]) * coef[2], "load": [100, 200, 300], }, index=pd.to_datetime([ diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py index abcd9f66c..4235df532 100644 --- a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py @@ -3,7 +3,7 @@ # # SPDX-License-Identifier: MPL-2.0 from datetime import timedelta - +import pandas as pd import pytest from openstef_core.exceptions import NotFittedError @@ -17,13 +17,13 @@ @pytest.fixture(params=["lgbm", "gblinear"]) def regressor(request: pytest.FixtureRequest) -> str: - """Fixture to provide different classifier types for LearnedWeightsCombiner tests.""" + """Fixture to provide different regressor types for Stacking tests.""" return request.param @pytest.fixture def config(regressor: str) -> StackingCombinerConfig: - """Fixture to create StackingCombinerConfig based on the classifier type.""" + """Fixture to create StackingCombinerConfig based on the regressor type.""" if regressor == "lgbm": hp = StackingCombiner.LGBMHyperParams(num_leaves=5, n_estimators=10) elif regressor == "gblinear": @@ -83,3 +83,20 @@ def test_stacking_combiner_not_fitted_error( # Act & Assert with pytest.raises(NotFittedError): forecaster.predict(ensemble_dataset) + + +def test_stacking_combiner_predict_contributions( + ensemble_dataset: EnsembleForecastDataset, + config: StackingCombinerConfig, +): + """Test that predict_contributions method returns contributions with correct shape.""" + # Arrange + forecaster = StackingCombiner(config=config) + forecaster.fit(ensemble_dataset) + + # Act + contributions = forecaster.predict_contributions(ensemble_dataset) + + # Assert + assert isinstance(contributions, pd.DataFrame), "Contributions should be returned as a DataFrame." + assert len(contributions.columns) == len(ensemble_dataset.quantiles) * len(ensemble_dataset.forecaster_names) diff --git a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py index 37f30ab2d..9e80abc9d 100644 --- a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py +++ b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py @@ -4,6 +4,7 @@ from datetime import timedelta +import pandas as pd import pytest from openstef_core.datasets import ForecastInputDataset @@ -140,3 +141,37 @@ def test_residual_forecaster_with_sample_weights( # (This is a statistical test - with different weights, predictions should differ) differences = (result_with_weights.data - result_without_weights.data).abs() assert differences.sum().sum() > 0, "Sample weights should affect model predictions" + + +def test_residual_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: ResidualForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = ResidualForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + base_models = [ + forecaster.primary_name, + forecaster.secondary_name + ] + expected_columns = [f"{col}_{q.format()}" for col in base_models for q in expected_quantiles] + assert sorted(result.columns) == sorted(expected_columns), ( + f"Expected columns {expected_columns}, got {list(result.columns)}" + ) + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + assert all(abs(col_sums - 1.0) < 1e-6), f"Contributions for quantile {q.format()} should sum to 1.0" diff --git a/packages/openstef-models/src/openstef_models/explainability/mixins.py b/packages/openstef-models/src/openstef_models/explainability/mixins.py index 9969b4993..e9c6b45f3 100644 --- a/packages/openstef-models/src/openstef_models/explainability/mixins.py +++ b/packages/openstef-models/src/openstef_models/explainability/mixins.py @@ -13,6 +13,7 @@ import pandas as pd import plotly.graph_objects as go +from openstef_core.datasets.validated_datasets import ForecastInputDataset from openstef_core.types import Q, Quantile from openstef_models.explainability.plotters.feature_importance_plotter import FeatureImportancePlotter @@ -44,6 +45,18 @@ def feature_importances(self) -> pd.DataFrame: """ raise NotImplementedError + @abstractmethod + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + + Returns: + DataFrame with contributions per base learner. + """ + raise NotImplementedError + def plot_feature_importances(self, quantile: Quantile = Q(0.5)) -> go.Figure: """Create interactive treemap visualization of feature importances. diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 4fccf2825..6fc0c30d6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -326,6 +326,50 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per base learner. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + xgb_input: xgb.DMatrix = xgb.DMatrix(data=input_data) + + # Generate predictions + booster = self._gblinear_model.get_booster() + predictions_array: np.ndarray = booster.predict(xgb_input, pred_contribs=True, strict_shape=True)[:, :, :-1] + + # Remove last column + contribs = predictions_array / np.sum(predictions_array, axis=-1, keepdims=True) + + # Flatten to 2D array, name columns accordingly + contribs = contribs.reshape(contribs.shape[0], -1) + df = pd.DataFrame( + data=contribs, + index=input_data.index, + columns=[ + f"{feature}_{quantile.format()}" + for feature in input_data.columns + for quantile in self.config.quantiles + + ], + ) + + if scale: + # Scale contributions so that they sum to 1.0 per quantile and are positive + for q in self.config.quantiles: + quantile_cols = [col for col in df.columns if col.endswith(f"_{q.format()}")] + row_sums = df[quantile_cols].abs().sum(axis=1) + df[quantile_cols] = df[quantile_cols].abs().div(row_sums, axis=0) + + # Construct DataFrame with appropriate quantile columns + return df + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 03c667b00..84852aaaa 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -312,6 +312,41 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per base learner. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + + contributions: list[pd.DataFrame] = [] + + for i, quantile in enumerate(self.config.quantiles): + # Get model for specific quantile + model: LGBMRegressor = self._lgbm_model.models[i] # type: ignore + + # Generate contributions using LightGBM's built-in method, and remove bias term + contribs_quantile: np.ndarray[float] = model.predict(input_data, pred_contrib=True)[:, :-1] # type: ignore + + if scale: + # Scale contributions so that they sum to 1.0 per quantile + contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) + + contributions.append(pd.DataFrame( + data=contribs_quantile, + index=input_data.index, + columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], + )) + + # Construct DataFrame + return pd.concat(contributions, axis=1) + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index eace689fb..e39e60bb5 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -314,6 +314,43 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per base learner. + """ + raise NotImplementedError("predict_contributions is not yet implemented for LGBMLinearForecaster") + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + + contributions: list[pd.DataFrame] = [] + + for i, quantile in enumerate(self.config.quantiles): + # Get model for specific quantile + model: LGBMRegressor = self._lgbmlinear_model.models[i] # type: ignore + + # Generate contributions NOT AVAILABLE FOR LGBM with linear_trees=true + contribs_quantile: np.ndarray[float] = model.predict(input_data, pred_contrib=True)[:, :-1] # type: ignore + + if scale: + # Scale contributions so that they sum to 1.0 per quantile + contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) + + contributions.append(pd.DataFrame( + data=contribs_quantile, + index=input_data.index, + columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], + )) + + # Construct DataFrame + return pd.concat(contributions, axis=1) + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index 2c673c68b..6843df2fa 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -420,6 +420,51 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> pd.DataFrame: + """Get feature contributions for each prediction. + + Args: + data: Input dataset for which to compute feature contributions. + scale: If True, scale contributions to sum to 1.0 per quantile. + + Returns: + DataFrame with contributions per base learner. + """ + # Get input features for prediction + input_data: pd.DataFrame = data.input_data(start=data.forecast_start) + xgb_input: xgb.DMatrix = xgb.DMatrix(data=input_data) + + # Generate predictions + booster = self._xgboost_model.get_booster() + predictions_array: np.ndarray = booster.predict(xgb_input, pred_contribs=True, strict_shape=True)[:, :, :-1] + + # Remove last column + contribs = predictions_array / np.sum(predictions_array, axis=-1, keepdims=True) + + # Flatten to 2D array, name columns accordingly + contribs = contribs.reshape(contribs.shape[0], -1) + + df = pd.DataFrame( + data=contribs, + index=input_data.index, + columns=[ + f"{feature}_{quantile.format()}" + for feature in input_data.columns + for quantile in self.config.quantiles + + ], + ) + + if scale: + # Scale contributions so that they sum to 1.0 per quantile and are positive + for q in self.config.quantiles: + quantile_cols = [col for col in df.columns if col.endswith(f"_{q.format()}")] + row_sums = df[quantile_cols].abs().sum(axis=1) + df[quantile_cols] = df[quantile_cols].abs().div(row_sums, axis=0) + + # Construct DataFrame with appropriate quantile columns + return df + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py index 1eba577f5..b260d1b4e 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py @@ -4,6 +4,7 @@ from datetime import timedelta +import numpy as np import pandas as pd import pytest @@ -132,3 +133,34 @@ def test_gblinear_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +def test_gblinear_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: GBLinearForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = GBLinearForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + input_features = sample_forecast_input_dataset.input_data().columns + expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] + assert list(result.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.columns)}" + ) + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index, dtype=np.float32), atol=1e-10) diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py index b4fe1c989..886da0ce6 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_lgbm_forecaster.py @@ -146,4 +146,35 @@ def test_lgbm_forecaster__feature_importances( assert (feature_importances >= 0).all().all() +def test_lgbm_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: LGBMForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = LGBMForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + input_features = sample_forecast_input_dataset.input_data().columns + expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] + assert sorted(result.columns) == sorted(expected_columns), ( + f"Expected columns {expected_columns}, got {list(result.columns)}" + ) + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index), atol=1e-10) + + # TODO(@MvLieshout): Add tests on different loss functions # noqa: TD003 diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py index dd0e80058..bde85e36a 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py @@ -22,7 +22,7 @@ def base_config() -> XGBoostForecasterConfig: """Base configuration for XGBoost forecaster tests.""" return XGBoostForecasterConfig( horizons=[LeadTime(timedelta(days=1))], - quantiles=[Q(0.1), Q(0.5), Q(0.9)], + quantiles=[Q(0.1), Q(0.3), Q(0.5), Q(0.7), Q(0.9)], hyperparams=XGBoostHyperParams( n_estimators=10, # Small for fast tests ), @@ -167,3 +167,34 @@ def test_xgboost_forecaster__feature_importances( col_sums = feature_importances.sum(axis=0) pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=expected_columns), atol=1e-10) assert (feature_importances >= 0).all().all() + + +def test_xgboost_forecaster_predict_contributions( + sample_forecast_input_dataset: ForecastInputDataset, + base_config: XGBoostForecasterConfig, +): + """Test basic fit and predict workflow with output validation.""" + # Arrange + expected_quantiles = base_config.quantiles + forecaster = XGBoostForecaster(config=base_config) + + # Act + forecaster.fit(sample_forecast_input_dataset) + result = forecaster.predict_contributions(sample_forecast_input_dataset, scale=True) + + # Assert + # Basic functionality + assert forecaster.is_fitted, "Model should be fitted after calling fit()" + + # Check that necessary quantiles are present + input_features = sample_forecast_input_dataset.input_data().columns + expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] + assert list(result.columns) == expected_columns, ( + f"Expected columns {expected_columns}, got {list(result.columns)}" + ) + + # Contributions should sum to 1.0 per quantile + for q in expected_quantiles: + quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] + col_sums = result[quantile_cols].sum(axis=1) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index), atol=1e-10) From 6f88d726d3e9fc8f768c6b31cd66bdd0379ab3dc Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 8 Dec 2025 09:46:57 +0100 Subject: [PATCH 57/72] Bugfixes Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 4 +- .../models/ensemble_forecasting_model.py | 72 ++++++-------- .../presets/forecasting_workflow.py | 4 +- .../tests/regression/__init__.py | 0 .../test_ensemble_forecasting_model.py | 98 +++++++++++++++++++ 5 files changed, 132 insertions(+), 46 deletions(-) create mode 100644 packages/openstef-meta/tests/regression/__init__.py create mode 100644 packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index be9053a75..9e6e0237a 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,9 +44,9 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" +ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index a6298088b..8299d43a7 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -193,7 +193,6 @@ def fit( Returns: FitResult containing training details and metrics. """ - score_data = data.copy_with(data=data.data) # Fit the feature engineering transforms self.common_preprocessing.fit(data=data) @@ -204,16 +203,8 @@ def fit( if data_test is not None: data_test = self.common_preprocessing.transform(data=data_test) - # Transform the input data to a valid forecast input and split into train/val/test - data, data_val, data_test = self.data_splitter.split_dataset( - data=data, - data_val=data_val, - data_test=data_test, - target_column=self.target_column, - ) - - # Fit predict forecasters - ensemble_predictions = self._preprocess_fit_forecasters( + # Fit forecasters + ensemble_predictions = self._fit_forecasters( data=data, data_val=data_val, data_test=data_test, @@ -280,8 +271,7 @@ def fit( def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: if len(self.combiner_preprocessing.transforms) == 0: return None - combiner_data = self.prepare_input(data=data) - combiner_data = self.combiner_preprocessing.transform(combiner_data) + combiner_data = self.combiner_preprocessing.transform(data) return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: @@ -292,28 +282,32 @@ def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInput combiner_data = self.combiner_preprocessing.transform(combiner_data) return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) - def _preprocess_fit_forecasters( + def _fit_forecasters( self, data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, ) -> EnsembleForecastDataset: - predictions_raw: dict[str, ForecastDataset] = {} + predictions: dict[str, ForecastDataset] = {} if data_test is not None: logger.info("Data test provided during fit, but will be ignored for MetaForecating") for name, forecaster in self.forecasters.items(): validate_horizons_present(data, forecaster.config.horizons) + # Apply model-specific preprocessing if available - # Transform and split input data - input_data_train = self.prepare_input(data=data, forecaster_name=name, forecast_start=data.index[0]) - input_data_val = ( - self.prepare_input(data=data_val, forecaster_name=name, forecast_start=data_val.index[0]) - if data_val - else None - ) + if name in self.model_specific_preprocessing: + self.model_specific_preprocessing[name].fit(data=data) + data = self.model_specific_preprocessing[name].transform(data=data) + data_val = self.model_specific_preprocessing[name].transform(data=data_val) if data_val else None + + input_data_train = self.prepare_input(data=data, forecast_start=data.index[0]) + if data_val is not None: + input_data_val = self.prepare_input(data=data_val, forecast_start=data_val.index[0]) + else: + input_data_val = None # Drop target column nan's from training data. One can not train on missing targets. target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] @@ -322,11 +316,11 @@ def _preprocess_fit_forecasters( # Fit the model forecaster.fit(data=input_data_train, data_val=input_data_val) - predictions_raw[name] = self.forecasters[name].predict(data=input_data_train) + predictions_raw = self.forecasters[name].predict(data=input_data_train) - return EnsembleForecastDataset.from_forecast_datasets( - predictions_raw, target_series=data.data[self.target_column] - ) + predictions[name] = self.postprocessing.transform(data=predictions_raw) + + return EnsembleForecastDataset.from_forecast_datasets(predictions, target_series=data.data[self.target_column]) def _predict_forecasters( self, data: TimeSeriesDataset, forecast_start: datetime | None = None @@ -340,9 +334,16 @@ def _predict_forecasters( Returns: DataFrame containing base learner predictions. """ + data_common = self.common_preprocessing.transform(data=data) + base_predictions: dict[str, ForecastDataset] = {} for name, forecaster in self.forecasters.items(): - forecaster_data = self.prepare_input(data, forecaster_name=name, forecast_start=forecast_start) + forecaster_data = ( + self.model_specific_preprocessing[name].transform(data=data_common) + if name in self.model_specific_preprocessing + else data_common + ) + forecaster_data = self.prepare_input(forecaster_data, forecast_start=forecast_start) preds_raw = forecaster.predict(data=forecaster_data) preds = self.postprocessing.transform(data=preds_raw) base_predictions[name] = preds @@ -354,29 +355,18 @@ def _predict_forecasters( def prepare_input( self, data: TimeSeriesDataset, - forecaster_name: str | None = None, forecast_start: datetime | None = None, ) -> ForecastInputDataset: - """Prepare input data for forecasting by applying preprocessing and filtering. - - Transforms raw time series data through the preprocessing pipeline, restores - the target column, and filters out incomplete historical data to ensure - training quality. + """Prepare input data for forecastingfiltering. Args: data: Raw time series dataset to prepare for forecasting. - forecaster_name: Name of the forecaster for model-specific preprocessing. forecast_start: Optional start time for forecasts. If provided and earlier than the cutoff time, overrides the cutoff for data filtering. Returns: Processed forecast input dataset ready for model prediction. """ - # Apply model-specific preprocessing if available - if forecaster_name in self.model_specific_preprocessing: - self.model_specific_preprocessing[forecaster_name].fit(data=data) - data = self.model_specific_preprocessing[forecaster_name].transform(data=data) - input_data = restore_target(dataset=data, original_dataset=data, target_column=self.target_column) # Cut away input history to avoid training on incomplete data @@ -401,6 +391,7 @@ def _predict_combiner_and_score( self, ensemble_dataset: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None ) -> SubsetMetric: prediction = self.combiner.predict(ensemble_dataset, additional_features=additional_features) + prediction.data[ensemble_dataset.target_column] = ensemble_dataset.target_series return self._calculate_score(prediction=prediction) def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: @@ -419,9 +410,6 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non if not self.is_fitted: raise NotFittedError(self.__class__.__name__) - # Common preprocessing - data = self.common_preprocessing.transform(data=data) - ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) features = self._transform_combiner_data(data=data) diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 6ad2b78d7..0a565793a 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -11,7 +11,6 @@ from datetime import timedelta from typing import TYPE_CHECKING, Literal -from openstef_meta.transforms.selector import Selector from pydantic import Field from openstef_beam.evaluation.metric_providers import ( @@ -29,6 +28,7 @@ from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster +from openstef_meta.transforms.selector import Selector from openstef_models.integrations.mlflow import MLFlowStorage from openstef_models.mixins.model_serializer import ModelIdentifier from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster @@ -84,7 +84,7 @@ class EnsembleWorkflowConfig(BaseConfig): description="Time interval between consecutive data samples.", ) horizons: list[LeadTime] = Field( - default=[LeadTime.from_string("PT36H")], + default=[LeadTime.from_string("PT48H")], description="List of forecast horizons to predict.", ) diff --git a/packages/openstef-meta/tests/regression/__init__.py b/packages/openstef-meta/tests/regression/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py new file mode 100644 index 000000000..f3d156a13 --- /dev/null +++ b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py @@ -0,0 +1,98 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta +from typing import cast + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets.validated_datasets import TimeSeriesDataset +from openstef_core.types import LeadTime, Q +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.presets import EnsembleWorkflowConfig, create_ensemble_workflow +from openstef_models.models.forecasting_model import ForecastingModel +from openstef_models.presets import ForecastingWorkflowConfig, create_forecasting_workflow + + +@pytest.fixture +def sample_timeseries_dataset() -> TimeSeriesDataset: + """Create sample time series data with typical energy forecasting features.""" + n_samples = 25 + rng = np.random.default_rng(seed=42) + + data = pd.DataFrame( + { + "load": 100.0 + rng.normal(10.0, 5.0, n_samples), + "temperature": 20.0 + rng.normal(1.0, 0.5, n_samples), + "radiation": rng.uniform(0.0, 500.0, n_samples), + }, + index=pd.date_range("2025-01-01 10:00", periods=n_samples, freq="h", tz="UTC"), + ) + + return TimeSeriesDataset(data, timedelta(hours=1)) + + +@pytest.fixture +def config() -> EnsembleWorkflowConfig: + return EnsembleWorkflowConfig( + model_id="ensemble_model_", + ensemble_type="learned_weights", + base_models=["gblinear", "lgbm"], + combiner_model="lgbm", + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime.from_string("PT36H")], + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 0}, + ) + + +@pytest.fixture +def create_models( + config: EnsembleWorkflowConfig, +) -> tuple[EnsembleForecastingModel, dict[str, ForecastingModel]]: + + ensemble_model = cast(EnsembleForecastingModel, create_ensemble_workflow(config=config).model) + + base_models: dict[str, ForecastingModel] = {} + for forecaster_name in config.base_models: + model_config = ForecastingWorkflowConfig( + model_id=f"{forecaster_name}_model_", + model=forecaster_name, # type: ignore + quantiles=config.quantiles, + horizons=config.horizons, + sample_weight_exponent=config.forecaster_sample_weight_exponent[forecaster_name], + ) + base_model = create_forecasting_workflow(config=model_config).model + base_models[forecaster_name] = cast(ForecastingModel, base_model) + + return ensemble_model, base_models + + +def test_preprocessing( + sample_timeseries_dataset: TimeSeriesDataset, + create_models: tuple[EnsembleForecastingModel, dict[str, ForecastingModel]], +) -> None: + + ensemble_model, base_models = create_models + + ensemble_model.common_preprocessing.fit(data=sample_timeseries_dataset) + + # Check all base models + for name, model in base_models.items(): + # Ensemble model + common_ensemble = ensemble_model.common_preprocessing.transform(data=sample_timeseries_dataset) + ensemble_model.model_specific_preprocessing[name].fit(data=common_ensemble) + transformed_ensemble = ensemble_model.model_specific_preprocessing[name].transform(data=common_ensemble) + # Base model + model.preprocessing.fit(data=sample_timeseries_dataset) + transformed_base = model.preprocessing.transform(data=sample_timeseries_dataset) + # Compare + pd.testing.assert_frame_equal( + transformed_ensemble.data, + transformed_base.data, + check_dtype=False, + check_index_type=False, + check_column_type=False, + ) From 20edf2d808b4b5af5f8c76f1e8b06f07b30cac5d Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 8 Dec 2025 10:16:27 +0100 Subject: [PATCH 58/72] fixes Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 +- .../src/openstef_meta/mixins/__init__.py | 4 ++++ .../src/openstef_meta/mixins/contributions.py | 4 ++++ .../src/openstef_meta/models/__init__.py | 0 .../models/forecast_combiners/__init__.py | 4 ++++ .../forecast_combiners/stacking_combiner.py | 14 +++++++------- .../src/openstef_meta/presets/__init__.py | 4 ++++ .../presets/forecasting_workflow.py | 18 ++++++++++++++++-- .../src/openstef_meta/utils/datasets.py | 1 - .../models/forecast_combiners/__init__.py | 0 .../test_stacking_combiner.py | 1 + .../tests/models/forecasting/__init__.py | 0 .../forecasting/test_residual_forecaster.py | 6 +----- .../models/test_ensemble_forecasting_model.py | 9 ++++++--- .../openstef_models/explainability/mixins.py | 1 + .../mlflow/mlflow_storage_callback.py | 6 ++++++ .../models/forecasting/flatliner_forecaster.py | 12 ++++++++++++ 17 files changed, 67 insertions(+), 19 deletions(-) create mode 100644 packages/openstef-meta/src/openstef_meta/models/__init__.py create mode 100644 packages/openstef-meta/tests/models/forecast_combiners/__init__.py create mode 100644 packages/openstef-meta/tests/models/forecasting/__init__.py diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index b490a800c..28bc782cc 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,7 +44,7 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" diff --git a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py index 71d67869d..90a57a257 100644 --- a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py @@ -1 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Mixins for OpenSTEF-Meta package.""" diff --git a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py index 9fb68377c..f00c185b3 100644 --- a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py +++ b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """ExplainableMetaForecaster Mixin.""" from abc import ABC, abstractmethod diff --git a/packages/openstef-meta/src/openstef_meta/models/__init__.py b/packages/openstef-meta/src/openstef_meta/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py index db4917778..56a4cadff 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/__init__.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Forecast Combiners.""" from .forecast_combiner import ForecastCombiner, ForecastCombinerConfig diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index 1814ef49b..eb7b29424 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -12,6 +12,7 @@ import logging from typing import cast, override +from openstef_models.explainability.mixins import ExplainableForecaster import pandas as pd from pydantic import Field, field_validator @@ -203,7 +204,6 @@ def predict_contributions( additional_features: ForecastInputDataset | None = None, ) -> pd.DataFrame: - # Generate predictions predictions: list[pd.DataFrame] = [] for i, q in enumerate(self.quantiles): if additional_features is not None: @@ -213,14 +213,14 @@ def predict_contributions( ) else: input_data = data.select_quantile(quantile=q) - p = self.predict_contributions_quantile( - model=self.models[i], - data=input_data, - ) - p.columns = [f"{col}_{Quantile(self.quantiles[i]).format()}" for col in p.columns] + model = self.models[i] + if not isinstance(model, ExplainableForecaster): + raise NotImplementedError( + "Predicting contributions is only supported for ExplainableForecaster models." + ) + p = model.predict_contributions(data=input_data, scale=True) predictions.append(p) - # Concatenate predictions along columns to form a DataFrame with quantile columns return pd.concat(predictions, axis=1) @property diff --git a/packages/openstef-meta/src/openstef_meta/presets/__init__.py b/packages/openstef-meta/src/openstef_meta/presets/__init__.py index 53b9630aa..ad62320c2 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/presets/__init__.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Package for preset forecasting workflows.""" from .forecasting_workflow import EnsembleForecastingModel, EnsembleWorkflowConfig, create_ensemble_workflow diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 88d063584..c4e947d0a 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + """Ensemble forecasting workflow preset. Mimics OpenSTEF-models forecasting workflow with ensemble capabilities. @@ -235,8 +239,18 @@ class EnsembleWorkflowConfig(BaseConfig): ) -def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: - """Create an ensemble forecasting workflow from configuration.""" +def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: # noqa: C901, PLR0912, PLR0915 + """Create an ensemble forecasting workflow from configuration. + + Args: + config: Configuration for the ensemble forecasting workflow. + + Returns: + An instance of CustomForecastingWorkflow configured as an ensemble forecaster. + + Raises: + ValueError: If an unsupported base model or combiner type is specified. + """ # Build preprocessing components def checks() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index 9d38c5b4f..e0bba9265 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -78,7 +78,6 @@ def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[str], list[Q Raises: ValueError: If an invalid base learner name is found in a feature name. """ - forecasters: set[str] = set() quantiles: set[Quantile] = set() diff --git a/packages/openstef-meta/tests/models/forecast_combiners/__init__.py b/packages/openstef-meta/tests/models/forecast_combiners/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py index 4235df532..530018ab7 100644 --- a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py +++ b/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py @@ -3,6 +3,7 @@ # # SPDX-License-Identifier: MPL-2.0 from datetime import timedelta + import pandas as pd import pytest diff --git a/packages/openstef-meta/tests/models/forecasting/__init__.py b/packages/openstef-meta/tests/models/forecasting/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py index 9e80abc9d..0f319552e 100644 --- a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py +++ b/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py @@ -4,7 +4,6 @@ from datetime import timedelta -import pandas as pd import pytest from openstef_core.datasets import ForecastInputDataset @@ -161,10 +160,7 @@ def test_residual_forecaster_predict_contributions( assert forecaster.is_fitted, "Model should be fitted after calling fit()" # Check that necessary quantiles are present - base_models = [ - forecaster.primary_name, - forecaster.secondary_name - ] + base_models = [forecaster.primary_name, forecaster.secondary_name] expected_columns = [f"{col}_{q.format()}" for col in base_models for q in expected_quantiles] assert sorted(result.columns) == sorted(expected_columns), ( f"Expected columns {expected_columns}, got {list(result.columns)}" diff --git a/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py index 126163bd9..33b78cfc9 100644 --- a/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py +++ b/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py @@ -1,4 +1,8 @@ -import pickle +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +import pickle # noqa: S403 - Controlled test from datetime import datetime, timedelta from typing import override @@ -136,10 +140,9 @@ def model() -> EnsembleForecastingModel: ) # Act - model = EnsembleForecastingModel( + return EnsembleForecastingModel( forecasters=forecasters, combiner=combiner, common_preprocessing=TransformPipeline() ) - return model def test_forecasting_model__init__uses_defaults(model: EnsembleForecastingModel): diff --git a/packages/openstef-models/src/openstef_models/explainability/mixins.py b/packages/openstef-models/src/openstef_models/explainability/mixins.py index e9c6b45f3..b0fb6fab1 100644 --- a/packages/openstef-models/src/openstef_models/explainability/mixins.py +++ b/packages/openstef-models/src/openstef_models/explainability/mixins.py @@ -51,6 +51,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p Args: data: Input dataset for which to compute feature contributions. + scale: Whether to scale contributions to sum to the prediction value. Returns: DataFrame with contributions per base learner. diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index fd59cd600..7ba5222c3 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -22,6 +22,7 @@ from openstef_core.datasets.versioned_timeseries_dataset import VersionedTimeSeriesDataset from openstef_core.exceptions import ModelNotFoundError, SkipFitting from openstef_core.types import Q, QuantileOrGlobal +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel from openstef_models.explainability import ExplainableForecaster from openstef_models.integrations.mlflow.mlflow_storage import MLFlowStorage from openstef_models.mixins.callbacks import WorkflowContext @@ -97,6 +98,11 @@ def on_fit_end( if self.model_selection_enable: self._run_model_selection(workflow=context.workflow, result=result) + if isinstance(context.workflow.model, EnsembleForecastingModel): + raise NotImplementedError( + "MLFlowStorageCallback does not yet support EnsembleForecastingWorkflow model storage." + ) + # Create a new run run = self.storage.create_run( model_id=context.workflow.model_id, diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py index 51a5b5ed0..73f9d56b3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py @@ -102,3 +102,15 @@ def feature_importances(self) -> pd.DataFrame: index=["load"], columns=[quantile.format() for quantile in self.config.quantiles], ) + + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + + if scale: + pass + forecast_index = data.create_forecast_range(horizon=self.config.max_horizon) + + return pd.DataFrame( + data={quantile.format(): 0.0 for quantile in self.config.quantiles}, + index=forecast_index, + ) From 354b6f27d707b7b779568eba21dee9cad3a26f4d Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 8 Dec 2025 10:22:43 +0100 Subject: [PATCH 59/72] Squashed commit of the following: commit 6f88d726d3e9fc8f768c6b31cd66bdd0379ab3dc Author: Lars van Someren Date: Mon Dec 8 09:46:57 2025 +0100 Bugfixes Signed-off-by: Lars van Someren commit b44fd928ff9ddf5eab552fa33ebb2f46742843c0 Author: Lars van Someren Date: Thu Dec 4 14:39:31 2025 +0100 bug fixes Signed-off-by: Lars van Someren commit e212448dbcdad7dd5e024d49d69cf9a7532d7af2 Author: Lars van Someren Date: Thu Dec 4 12:38:24 2025 +0100 fixes Signed-off-by: Lars van Someren commit eb775e41c2d661f025cc97268f5bbdbf4abe148f Author: Lars van Someren Date: Thu Dec 4 11:40:44 2025 +0100 BugFix Signed-off-by: Lars van Someren commit c33ce9354abf3d08a90b59d419009822b5a29ae0 Author: Lars van Someren Date: Wed Dec 3 14:15:06 2025 +0100 Made PR Compliant Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 + .../openstef4_backtest_forecaster.py | 8 +- .../src/openstef_meta/models/__init__.py | 5 + .../models/ensemble_forecasting_model.py | 164 +++++++---- .../forecast_combiners/forecast_combiner.py | 22 -- .../learned_weights_combiner.py | 14 +- .../presets/forecasting_workflow.py | 267 ++++++++++-------- .../src/openstef_meta/transforms/selector.py | 2 +- .../src/openstef_meta/utils/datasets.py | 37 +++ .../tests/{models => regression}/__init__.py | 0 .../test_ensemble_forecasting_model.py | 98 +++++++ .../models}/__init__.py | 0 .../tests/{ => unit}/models/conftest.py | 0 .../models/forecast_combiners}/__init__.py | 0 .../models/forecast_combiners/conftest.py | 0 .../test_learned_weights_combiner.py | 0 .../forecast_combiners/test_rules_combiner.py | 0 .../test_stacking_combiner.py | 0 .../models/forecasting}/__init__.py | 0 .../forecasting/test_residual_forecaster.py | 0 .../models/test_ensemble_forecasting_model.py | 0 .../{utils => unit/transforms}/__init__.py | 0 .../transforms/test_flag_features_bound.py | 0 .../tests/unit/utils/__init__.py | 0 .../tests/{ => unit}/utils/test_datasets.py | 0 .../{ => unit}/utils/test_decision_tree.py | 0 .../mlflow/mlflow_storage_callback.py | 8 +- 27 files changed, 422 insertions(+), 205 deletions(-) rename packages/openstef-meta/tests/{models => regression}/__init__.py (100%) create mode 100644 packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py rename packages/openstef-meta/tests/{models/forecast_combiners => unit/models}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/conftest.py (100%) rename packages/openstef-meta/tests/{models/forecasting => unit/models/forecast_combiners}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/conftest.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_learned_weights_combiner.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_rules_combiner.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecast_combiners/test_stacking_combiner.py (100%) rename packages/openstef-meta/tests/{transforms => unit/models/forecasting}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/forecasting/test_residual_forecaster.py (100%) rename packages/openstef-meta/tests/{ => unit}/models/test_ensemble_forecasting_model.py (100%) rename packages/openstef-meta/tests/{utils => unit/transforms}/__init__.py (100%) rename packages/openstef-meta/tests/{ => unit}/transforms/test_flag_features_bound.py (100%) create mode 100644 packages/openstef-meta/tests/unit/utils/__init__.py rename packages/openstef-meta/tests/{ => unit}/utils/test_datasets.py (100%) rename packages/openstef-meta/tests/{ => unit}/utils/test_decision_tree.py (100%) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 28bc782cc..48630af70 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -94,6 +94,8 @@ temperature_column="temperature_2m", relative_humidity_column="relative_humidity_2m", energy_price_column="EPEX_NL", + forecast_combiner_sample_weight_exponent=1, + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 1, "xgboost": 0, "lgbm_linear": 0}, ) diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 5ae961632..966b06f10 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -17,6 +17,7 @@ from openstef_core.datasets import TimeSeriesDataset from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel @@ -65,7 +66,12 @@ def quantiles(self) -> list[Q]: if self._workflow is None: self._workflow = self.workflow_factory() # Extract quantiles from the workflow's model - return self._workflow.model.forecaster.config.quantiles # type: ignore + + if isinstance(self._workflow.model, EnsembleForecastingModel): + # Assuming all ensemble members have the same quantiles + name = self._workflow.model.forecaster_names[0] + return self._workflow.model.forecasters[name].config.quantiles + return self._workflow.model.forecaster.config.quantiles @override def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: diff --git a/packages/openstef-meta/src/openstef_meta/models/__init__.py b/packages/openstef-meta/src/openstef_meta/models/__init__.py index e69de29bb..13175057c 100644 --- a/packages/openstef-meta/src/openstef_meta/models/__init__.py +++ b/packages/openstef-meta/src/openstef_meta/models/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +"""Meta Forecasting models.""" diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 7eab3d74f..6ce21dcd7 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -35,6 +35,8 @@ from openstef_models.models.forecasting_model import ModelFitResult from openstef_models.utils.data_split import DataSplitter +logger = logging.getLogger(__name__) + class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]): """Complete forecasting pipeline combining preprocessing, prediction, and postprocessing. @@ -61,16 +63,24 @@ class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastD >>> from openstef_models.models.forecasting.constant_median_forecaster import ( ... ConstantMedianForecaster, ConstantMedianForecasterConfig ... ) + >>> from openstef_meta.models.forecast_combiners.learned_weights_combiner import WeightsCombiner >>> from openstef_core.types import LeadTime >>> >>> # Note: This is a conceptual example showing the API structure >>> # Real usage requires implemented forecaster classes - >>> forecaster = ConstantMedianForecaster( + >>> forecaster_1 = ConstantMedianForecaster( + ... config=ConstantMedianForecasterConfig(horizons=[LeadTime.from_string("PT36H")]) + ... ) + >>> forecaster_2 = ConstantMedianForecaster( ... config=ConstantMedianForecasterConfig(horizons=[LeadTime.from_string("PT36H")]) ... ) + >>> combiner_config = WeightsCombiner.Config( + ... horizons=[LeadTime.from_string("PT36H")], + ... ) >>> # Create and train model - >>> model = ForecastingModel( - ... forecaster=forecaster, + >>> model = EnsembleForecastingModel( + ... forecasters={"constant_median": forecaster_1, "constant_median_2": forecaster_2}, + ... combiner=WeightsCombiner(config=combiner_config), ... cutoff_history=timedelta(days=14), # Match your maximum lag in preprocessing ... ) >>> model.fit(training_data) # doctest: +SKIP @@ -183,11 +193,18 @@ def fit( Returns: FitResult containing training details and metrics. """ + score_data = data.copy_with(data=data.data) # Fit the feature engineering transforms self.common_preprocessing.fit(data=data) + data = self.common_preprocessing.transform(data=data) - # Fit predict forecasters - ensemble_predictions = self._preprocess_fit_forecasters( + if data_val is not None: + data_val = self.common_preprocessing.transform(data=data_val) + if data_test is not None: + data_test = self.common_preprocessing.transform(data=data_test) + + # Fit forecasters + ensemble_predictions = self._fit_forecasters( data=data, data_val=data_val, data_test=data_test, @@ -200,13 +217,7 @@ def fit( else: ensemble_predictions_val = None - if len(self.combiner_preprocessing.transforms) > 0: - combiner_data = self.prepare_input(data=data) - self.combiner_preprocessing.fit(combiner_data) - combiner_data = self.combiner_preprocessing.transform(combiner_data) - features = ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) - else: - features = None + features = self._fit_transform_combiner_data(data=data) self.combiner.fit( data=ensemble_predictions, @@ -215,11 +226,36 @@ def fit( ) # Prepare input datasets for metrics calculation + metrics_train = self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions, additional_features=features + ) + if data_val is not None: + features_val = self._transform_combiner_data(data=data_val) + metrics_val = ( + self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions_val, additional_features=features_val + ) + if ensemble_predictions_val + else None + ) + else: + metrics_val = None - metrics_train = self._predict_and_score(data=data) - metrics_val = self._predict_and_score(data=data_val) if data_val else None - metrics_test = self._predict_and_score(data=data_test) if data_test else None - metrics_full = self.score(data=data) + if data_test is not None: + features_test = self._transform_combiner_data(data=data_test) + ensemble_predictions_test = self._predict_forecasters( + data=self.prepare_input(data=data_test), + ) + metrics_test = ( + self._predict_combiner_and_score( + ensemble_dataset=ensemble_predictions_test, additional_features=features_test + ) + if ensemble_predictions_test + else None + ) + else: + metrics_test = None + metrics_full = self.score(data=score_data) return ModelFitResult( input_dataset=data, @@ -232,44 +268,59 @@ def fit( metrics_full=metrics_full, ) - def _preprocess_fit_forecasters( + def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: + if len(self.combiner_preprocessing.transforms) == 0: + return None + combiner_data = self.combiner_preprocessing.transform(data) + return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + + def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: + if len(self.combiner_preprocessing.transforms) == 0: + return None + combiner_data = self.prepare_input(data=data) + self.combiner_preprocessing.fit(combiner_data) + combiner_data = self.combiner_preprocessing.transform(combiner_data) + return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + + def _fit_forecasters( self, data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, ) -> EnsembleForecastDataset: - predictions_raw: dict[str, ForecastDataset] = {} + predictions: dict[str, ForecastDataset] = {} + + if data_test is not None: + logger.info("Data test provided during fit, but will be ignored for MetaForecating") for name, forecaster in self.forecasters.items(): validate_horizons_present(data, forecaster.config.horizons) + # Apply model-specific preprocessing if available - # Transform and split input data - input_data_train = self.prepare_input(data=data, forecaster_name=name) - input_data_val = self.prepare_input(data=data_val, forecaster_name=name) if data_val else None - input_data_test = self.prepare_input(data=data_test, forecaster_name=name) if data_test else None + if name in self.model_specific_preprocessing: + self.model_specific_preprocessing[name].fit(data=data) + data = self.model_specific_preprocessing[name].transform(data=data) + data_val = self.model_specific_preprocessing[name].transform(data=data_val) if data_val else None + + input_data_train = self.prepare_input(data=data, forecast_start=data.index[0]) + if data_val is not None: + input_data_val = self.prepare_input(data=data_val, forecast_start=data_val.index[0]) + else: + input_data_val = None # Drop target column nan's from training data. One can not train on missing targets. target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] input_data_train = input_data_train.pipe_pandas(target_dropna) input_data_val = input_data_val.pipe_pandas(target_dropna) if input_data_val else None - input_data_test = input_data_test.pipe_pandas(target_dropna) if input_data_test else None - - # Transform the input data to a valid forecast input and split into train/val/test - input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( - data=input_data_train, - data_val=input_data_val, - data_test=input_data_test, - target_column=self.target_column, - ) # Fit the model forecaster.fit(data=input_data_train, data_val=input_data_val) - predictions_raw[name] = self.forecasters[name].predict(data=input_data_train) + predictions_raw = self.forecasters[name].predict(data=input_data_train) - return EnsembleForecastDataset.from_forecast_datasets( - predictions_raw, target_series=data.data[self.target_column] - ) + predictions[name] = self.postprocessing.transform(data=predictions_raw) + + return EnsembleForecastDataset.from_forecast_datasets(predictions, target_series=data.data[self.target_column]) def _predict_forecasters( self, data: TimeSeriesDataset, forecast_start: datetime | None = None @@ -283,9 +334,16 @@ def _predict_forecasters( Returns: DataFrame containing base learner predictions. """ + data_common = self.common_preprocessing.transform(data=data) + base_predictions: dict[str, ForecastDataset] = {} for name, forecaster in self.forecasters.items(): - forecaster_data = self.prepare_input(data, forecaster_name=name, forecast_start=forecast_start) + forecaster_data = ( + self.model_specific_preprocessing[name].transform(data=data_common) + if name in self.model_specific_preprocessing + else data_common + ) + forecaster_data = self.prepare_input(forecaster_data, forecast_start=forecast_start) preds_raw = forecaster.predict(data=forecaster_data) preds = self.postprocessing.transform(data=preds_raw) base_predictions[name] = preds @@ -297,14 +355,9 @@ def _predict_forecasters( def prepare_input( self, data: TimeSeriesDataset, - forecaster_name: str | None = None, forecast_start: datetime | None = None, ) -> ForecastInputDataset: - """Prepare input data for forecasting by applying preprocessing and filtering. - - Transforms raw time series data through the preprocessing pipeline, restores - the target column, and filters out incomplete historical data to ensure - training quality. + """Prepare input data for forecastingfiltering. Args: data: Raw time series dataset to prepare for forecasting. @@ -315,14 +368,6 @@ def prepare_input( Returns: Processed forecast input dataset ready for model prediction. """ - # Transform and restore target column - data = self.common_preprocessing.transform(data=data) - - # Apply model-specific preprocessing if available - if forecaster_name in self.model_specific_preprocessing: - self.model_specific_preprocessing[forecaster_name].fit(data=data) - data = self.model_specific_preprocessing[forecaster_name].transform(data=data) - input_data = restore_target(dataset=data, original_dataset=data, target_column=self.target_column) # Cut away input history to avoid training on incomplete data @@ -343,8 +388,11 @@ def prepare_input( forecast_start=forecast_start, ) - def _predict_and_score(self, data: TimeSeriesDataset) -> SubsetMetric: - prediction = self.predict(data) + def _predict_combiner_and_score( + self, ensemble_dataset: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None + ) -> SubsetMetric: + prediction = self.combiner.predict(ensemble_dataset, additional_features=additional_features) + prediction.data[ensemble_dataset.target_column] = ensemble_dataset.target_series return self._calculate_score(prediction=prediction) def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: @@ -365,20 +413,12 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) - additional_features = ( - ForecastInputDataset.from_timeseries( - self.combiner_preprocessing.transform(data=data), - target_column=self.target_column, - forecast_start=forecast_start, - ) - if len(self.combiner_preprocessing.transforms) > 0 - else None - ) + features = self._transform_combiner_data(data=data) # Predict and restore target column prediction = self.combiner.predict( data=ensemble_predictions, - additional_features=additional_features, + additional_features=features, ) return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index 09b4e9017..b1df023f6 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -122,28 +122,6 @@ def predict( """ raise NotImplementedError("Subclasses must implement the predict method.") - @staticmethod - def _prepare_input_data( - dataset: ForecastInputDataset, additional_features: ForecastInputDataset | None - ) -> pd.DataFrame: - """Prepare input data by combining base predictions with additional features if provided. - - Args: - dataset: ForecastInputDataset containing base predictions. - additional_features: Optional ForecastInputDataset containing additional features. - - Returns: - pd.DataFrame: Combined DataFrame of base predictions and additional features if provided. - """ - df = dataset.input_data(start=dataset.index[0]) - if additional_features is not None: - df_a = additional_features.input_data(start=dataset.index[0]) - df = pd.concat( - [df, df_a], - axis=1, - ) - return df - @property @abstractmethod def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index acf366661..8ea079595 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -32,7 +32,7 @@ ForecastCombiner, ForecastCombinerConfig, ) -from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_meta.utils.datasets import EnsembleForecastDataset, combine_forecast_input_datasets logger = logging.getLogger(__name__) @@ -241,18 +241,17 @@ def fit( for i, q in enumerate(self.quantiles): # Data preparation dataset = data.select_quantile_classification(quantile=q) - input_data = self._prepare_input_data( + combined_data = combine_forecast_input_datasets( dataset=dataset, - additional_features=additional_features, + other=additional_features, ) - labels = dataset.target_series + input_data = combined_data.input_data() + labels = combined_data.target_series self._validate_labels(labels=labels, model_index=i) labels = self._label_encoder.transform(labels) # Balance classes, adjust with sample weights - weights = compute_sample_weight("balanced", labels) - if sample_weights is not None: - weights *= sample_weights + weights = compute_sample_weight("balanced", labels) * combined_data.sample_weight_series self.models[i].fit(X=input_data, y=labels, sample_weight=weights) # type: ignore self._is_fitted = True @@ -276,6 +275,7 @@ def _prepare_input_data( df = pd.concat( [df, df_a], axis=1, + join="inner", ) return df diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index c4e947d0a..0a565793a 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -9,7 +9,7 @@ from collections.abc import Sequence from datetime import timedelta -from typing import Literal +from typing import TYPE_CHECKING, Literal from pydantic import Field @@ -28,9 +28,9 @@ from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster -from openstef_models.integrations.mlflow import MLFlowStorage, MLFlowStorageCallback +from openstef_meta.transforms.selector import Selector +from openstef_models.integrations.mlflow import MLFlowStorage from openstef_models.mixins.model_serializer import ModelIdentifier -from openstef_models.models.forecasting.forecaster import Forecaster from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster from openstef_models.models.forecasting.lgbmlinear_forecaster import LGBMLinearForecaster @@ -59,6 +59,9 @@ from openstef_models.utils.feature_selection import Exclude, FeatureSelection, Include from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow, ForecastingCallback +if TYPE_CHECKING: + from openstef_models.models.forecasting.forecaster import Forecaster + class EnsembleWorkflowConfig(BaseConfig): """Configuration for ensemble forecasting workflows.""" @@ -169,14 +172,19 @@ class EnsembleWorkflowConfig(BaseConfig): description="Percentile of target values used as scaling reference. " "Values are normalized relative to this percentile before weighting.", ) - sample_weight_exponent: float = Field( - default_factory=lambda data: 1.0 - if data.get("model") in {"gblinear", "lgbmlinear", "lgbm", "learned_weights", "stacking", "residual", "xgboost"} - else 0.0, + forecaster_sample_weight_exponent: dict[str, float] = Field( + default={"gblinear": 1.0, "lgbm": 0, "xgboost": 0, "lgbm_linear": 0}, description="Exponent applied to scale the sample weights. " "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values. " "Note: Defaults to 1.0 for gblinear congestion models.", ) + + forecast_combiner_sample_weight_exponent: float = Field( + default=0, + description="Exponent applied to scale the sample weights for the forecast combiner model. " + "0=uniform weights, 1=linear scaling, >1=stronger emphasis on high values.", + ) + sample_weight_floor: float = Field( default=0.1, description="Minimum weight value to ensure all samples contribute to training.", @@ -239,71 +247,82 @@ class EnsembleWorkflowConfig(BaseConfig): ) +# Build preprocessing components +def checks(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + InputConsistencyChecker(), + FlatlineChecker( + load_column=config.target_column, + flatliner_threshold=config.flatliner_threshold, + detect_non_zero_flatliner=config.detect_non_zero_flatliner, + error_on_flatliner=False, + ), + CompletenessChecker(completeness_threshold=config.completeness_threshold), + ] + + +def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ), + WindPowerFeatureAdder( + windspeed_reference_column=config.wind_speed_column, + ), + AtmosphereDerivedFeaturesAdder( + pressure_column=config.pressure_column, + relative_humidity_column=config.relative_humidity_column, + temperature_column=config.temperature_column, + ), + RadiationDerivedFeaturesAdder( + coordinate=config.location.coordinate, + radiation_column=config.radiation_column, + ), + CyclicFeaturesAdder(), + DaylightFeatureAdder( + coordinate=config.location.coordinate, + ), + RollingAggregatesAdder( + feature=config.target_column, + aggregation_functions=config.rolling_aggregate_features, + horizons=config.horizons, + ), + ] + + +def feature_standardizers(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: + return [ + Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), + Scaler(selection=Exclude(config.target_column), method="standard"), + EmptyFeatureRemover(), + ] + + def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: # noqa: C901, PLR0912, PLR0915 """Create an ensemble forecasting workflow from configuration. Args: - config: Configuration for the ensemble forecasting workflow. + config (EnsembleWorkflowConfig): Configuration for the ensemble workflow. Returns: - An instance of CustomForecastingWorkflow configured as an ensemble forecaster. + CustomForecastingWorkflow: Configured ensemble forecasting workflow. Raises: ValueError: If an unsupported base model or combiner type is specified. """ - - # Build preprocessing components - def checks() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - InputConsistencyChecker(), - FlatlineChecker( - load_column=config.target_column, - flatliner_threshold=config.flatliner_threshold, - detect_non_zero_flatliner=config.detect_non_zero_flatliner, - error_on_flatliner=False, - ), - CompletenessChecker(completeness_threshold=config.completeness_threshold), - ] - - def feature_adders() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - WindPowerFeatureAdder( - windspeed_reference_column=config.wind_speed_column, - ), - AtmosphereDerivedFeaturesAdder( - pressure_column=config.pressure_column, - relative_humidity_column=config.relative_humidity_column, - temperature_column=config.temperature_column, - ), - RadiationDerivedFeaturesAdder( - coordinate=config.location.coordinate, - radiation_column=config.radiation_column, - ), - CyclicFeaturesAdder(), - DaylightFeatureAdder( - coordinate=config.location.coordinate, - ), - RollingAggregatesAdder( - feature=config.target_column, - aggregation_functions=config.rolling_aggregate_features, - horizons=config.horizons, - ), - ] - - def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), - Scaler(selection=Exclude(config.target_column), method="standard"), - SampleWeighter( - target_column=config.target_column, - weight_exponent=config.sample_weight_exponent, - weight_floor=config.sample_weight_floor, - weight_scale_percentile=config.sample_weight_scale_percentile, - ), - EmptyFeatureRemover(), + # Common preprocessing + common_preprocessing = TransformPipeline( + transforms=[ + *checks(config), + *feature_adders(config), + HolidayFeatureAdder(country_code=config.location.country_code), + DatetimeFeaturesAdder(onehot_encode=False), + *feature_standardizers(config), ] - - # Model Specific LagsAdder + ) # Build forecasters and their processing pipelines forecaster_preprocessing: dict[str, list[Transform[TimeSeriesDataset, TimeSeriesDataset]]] = {} @@ -314,17 +333,12 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=LGBMForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), ] elif model_type == "gblinear": @@ -332,18 +346,54 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=GBLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=False, + SampleWeighter( target_column=config.target_column, - custom_lags=[timedelta(days=7)], + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + Selector( + selection=FeatureSelection( + exclude={ # Fix hardcoded lag features should be replaced by a LagsAdder classmethod + "load_lag_P14D", + "load_lag_P13D", + "load_lag_P12D", + "load_lag_P11D", + "load_lag_P10D", + "load_lag_P9D", + "load_lag_P8D", + # "load_lag_P7D", # Keep 7D lag for weekly seasonality + "load_lag_P6D", + "load_lag_P5D", + "load_lag_P4D", + "load_lag_P3D", + "load_lag_P2D", + } + ) + ), + Selector( # Fix hardcoded holiday features should be replaced by a HolidayFeatureAdder classmethod + selection=FeatureSelection( + exclude={ + "is_ascension_day", + "is_christmas_day", + "is_easter_monday", + "is_easter_sunday", + "is_good_friday", + "is_holiday", + "is_king_s_day", + "is_liberation_day", + "is_new_year_s_day", + "is_second_day_of_christmas", + "is_sunday", + "is_week_day", + "is_weekend_day", + "is_whit_monday", + "is_whit_sunday", + "month_of_year", + "quarter_of_year", + } + ) ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", @@ -358,34 +408,24 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas config=XGBoostForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), ] elif model_type == "lgbm_linear": forecasters[model_type] = LGBMLinearForecaster( config=LGBMLinearForecaster.Config(quantiles=config.quantiles, horizons=config.horizons) ) forecaster_preprocessing[model_type] = [ - *checks(), - *feature_adders(), - LagsAdder( - history_available=config.predict_history, - horizons=config.horizons, - add_trivial_lags=True, + SampleWeighter( target_column=config.target_column, + weight_exponent=config.forecaster_sample_weight_exponent[model_type], + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, ), - HolidayFeatureAdder(country_code=config.location.country_code), - DatetimeFeaturesAdder(onehot_encode=False), - *feature_standardizers(), ] else: msg = f"Unsupported base model type: {model_type}" @@ -439,27 +479,34 @@ def feature_standardizers() -> list[Transform[TimeSeriesDataset, TimeSeriesDatas name: TransformPipeline(transforms=transforms) for name, transforms in forecaster_preprocessing.items() } + if config.forecast_combiner_sample_weight_exponent != 0: + combiner_transforms = [ + SampleWeighter( + target_column=config.target_column, + weight_exponent=config.forecast_combiner_sample_weight_exponent, + weight_floor=config.sample_weight_floor, + weight_scale_percentile=config.sample_weight_scale_percentile, + ), + Selector(selection=Include("sample_weight", config.target_column)), + ] + else: + combiner_transforms = [] + + combiner_preprocessing: TransformPipeline[TimeSeriesDataset] = TransformPipeline(transforms=combiner_transforms) + ensemble_model = EnsembleForecastingModel( - common_preprocessing=TransformPipeline(transforms=[]), + common_preprocessing=common_preprocessing, model_specific_preprocessing=model_specific_preprocessing, + combiner_preprocessing=combiner_preprocessing, postprocessing=TransformPipeline(transforms=postprocessing), forecasters=forecasters, combiner=combiner, target_column=config.target_column, + data_splitter=config.data_splitter, ) callbacks: list[ForecastingCallback] = [] - if config.mlflow_storage is not None: - callbacks.append( - MLFlowStorageCallback( - storage=config.mlflow_storage, - model_reuse_enable=config.model_reuse_enable, - model_reuse_max_age=config.model_reuse_max_age, - model_selection_enable=config.model_selection_enable, - model_selection_metric=config.model_selection_metric, - model_selection_old_model_penalty=config.model_selection_old_model_penalty, - ) - ) + # TODO(Egor): Implement MLFlow for OpenSTEF-meta # noqa: TD003 return CustomForecastingWorkflow(model=ensemble_model, model_id=config.model_id, callbacks=callbacks) diff --git a/packages/openstef-meta/src/openstef_meta/transforms/selector.py b/packages/openstef-meta/src/openstef_meta/transforms/selector.py index 75eb4e321..e4c5d343b 100644 --- a/packages/openstef-meta/src/openstef_meta/transforms/selector.py +++ b/packages/openstef-meta/src/openstef_meta/transforms/selector.py @@ -24,7 +24,7 @@ class Selector(BaseConfig, TimeSeriesTransform): selection: FeatureSelection = Field( default=FeatureSelection.ALL, - description="Features to check for NaN values. Rows with NaN in any selected column are dropped.", + description="Feature selection for efficient model specific preprocessing.x", ) @override diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index e0bba9265..cb6dbdad2 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -20,6 +20,43 @@ DEFAULT_TARGET_COLUMN = {Quantile(0.5): "load"} +def combine_forecast_input_datasets( + dataset: ForecastInputDataset, other: ForecastInputDataset | None, join: str = "inner" +) -> ForecastInputDataset: + """Combine multiple TimeSeriesDatasets into a single dataset. + + Args: + dataset: First ForecastInputDataset. + other: Second ForecastInputDataset or None. + join: Type of join to perform on the datasets. Defaults to "inner". + + Returns: + Combined ForecastDataset. + """ + if not isinstance(other, ForecastInputDataset): + return dataset + if join != "inner": + raise NotImplementedError("Only 'inner' join is currently supported.") + df_other = other.data + if dataset.target_column in df_other.columns: + df_other = df_other.drop(columns=[dataset.target_column]) + + df_one = dataset.data + df = pd.concat( + [df_one, df_other], + axis=1, + join="inner", + ) + + return ForecastInputDataset( + data=df, + sample_interval=dataset.sample_interval, + target_column=dataset.target_column, + sample_weight_column=dataset.sample_weight_column, + forecast_start=dataset.forecast_start, + ) + + class EnsembleForecastDataset(TimeSeriesDataset): """First stage output format for ensemble forecasters.""" diff --git a/packages/openstef-meta/tests/models/__init__.py b/packages/openstef-meta/tests/regression/__init__.py similarity index 100% rename from packages/openstef-meta/tests/models/__init__.py rename to packages/openstef-meta/tests/regression/__init__.py diff --git a/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py new file mode 100644 index 000000000..f3d156a13 --- /dev/null +++ b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py @@ -0,0 +1,98 @@ +# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project +# +# SPDX-License-Identifier: MPL-2.0 + +from datetime import timedelta +from typing import cast + +import numpy as np +import pandas as pd +import pytest + +from openstef_core.datasets.validated_datasets import TimeSeriesDataset +from openstef_core.types import LeadTime, Q +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.presets import EnsembleWorkflowConfig, create_ensemble_workflow +from openstef_models.models.forecasting_model import ForecastingModel +from openstef_models.presets import ForecastingWorkflowConfig, create_forecasting_workflow + + +@pytest.fixture +def sample_timeseries_dataset() -> TimeSeriesDataset: + """Create sample time series data with typical energy forecasting features.""" + n_samples = 25 + rng = np.random.default_rng(seed=42) + + data = pd.DataFrame( + { + "load": 100.0 + rng.normal(10.0, 5.0, n_samples), + "temperature": 20.0 + rng.normal(1.0, 0.5, n_samples), + "radiation": rng.uniform(0.0, 500.0, n_samples), + }, + index=pd.date_range("2025-01-01 10:00", periods=n_samples, freq="h", tz="UTC"), + ) + + return TimeSeriesDataset(data, timedelta(hours=1)) + + +@pytest.fixture +def config() -> EnsembleWorkflowConfig: + return EnsembleWorkflowConfig( + model_id="ensemble_model_", + ensemble_type="learned_weights", + base_models=["gblinear", "lgbm"], + combiner_model="lgbm", + quantiles=[Q(0.1), Q(0.5), Q(0.9)], + horizons=[LeadTime.from_string("PT36H")], + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 0}, + ) + + +@pytest.fixture +def create_models( + config: EnsembleWorkflowConfig, +) -> tuple[EnsembleForecastingModel, dict[str, ForecastingModel]]: + + ensemble_model = cast(EnsembleForecastingModel, create_ensemble_workflow(config=config).model) + + base_models: dict[str, ForecastingModel] = {} + for forecaster_name in config.base_models: + model_config = ForecastingWorkflowConfig( + model_id=f"{forecaster_name}_model_", + model=forecaster_name, # type: ignore + quantiles=config.quantiles, + horizons=config.horizons, + sample_weight_exponent=config.forecaster_sample_weight_exponent[forecaster_name], + ) + base_model = create_forecasting_workflow(config=model_config).model + base_models[forecaster_name] = cast(ForecastingModel, base_model) + + return ensemble_model, base_models + + +def test_preprocessing( + sample_timeseries_dataset: TimeSeriesDataset, + create_models: tuple[EnsembleForecastingModel, dict[str, ForecastingModel]], +) -> None: + + ensemble_model, base_models = create_models + + ensemble_model.common_preprocessing.fit(data=sample_timeseries_dataset) + + # Check all base models + for name, model in base_models.items(): + # Ensemble model + common_ensemble = ensemble_model.common_preprocessing.transform(data=sample_timeseries_dataset) + ensemble_model.model_specific_preprocessing[name].fit(data=common_ensemble) + transformed_ensemble = ensemble_model.model_specific_preprocessing[name].transform(data=common_ensemble) + # Base model + model.preprocessing.fit(data=sample_timeseries_dataset) + transformed_base = model.preprocessing.transform(data=sample_timeseries_dataset) + # Compare + pd.testing.assert_frame_equal( + transformed_ensemble.data, + transformed_base.data, + check_dtype=False, + check_index_type=False, + check_column_type=False, + ) diff --git a/packages/openstef-meta/tests/models/forecast_combiners/__init__.py b/packages/openstef-meta/tests/unit/models/__init__.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/__init__.py rename to packages/openstef-meta/tests/unit/models/__init__.py diff --git a/packages/openstef-meta/tests/models/conftest.py b/packages/openstef-meta/tests/unit/models/conftest.py similarity index 100% rename from packages/openstef-meta/tests/models/conftest.py rename to packages/openstef-meta/tests/unit/models/conftest.py diff --git a/packages/openstef-meta/tests/models/forecasting/__init__.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/__init__.py similarity index 100% rename from packages/openstef-meta/tests/models/forecasting/__init__.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/__init__.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/conftest.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/conftest.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/conftest.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/conftest.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_learned_weights_combiner.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/test_learned_weights_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_learned_weights_combiner.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_rules_combiner.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/test_rules_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_rules_combiner.py diff --git a/packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py similarity index 100% rename from packages/openstef-meta/tests/models/forecast_combiners/test_stacking_combiner.py rename to packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py diff --git a/packages/openstef-meta/tests/transforms/__init__.py b/packages/openstef-meta/tests/unit/models/forecasting/__init__.py similarity index 100% rename from packages/openstef-meta/tests/transforms/__init__.py rename to packages/openstef-meta/tests/unit/models/forecasting/__init__.py diff --git a/packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py b/packages/openstef-meta/tests/unit/models/forecasting/test_residual_forecaster.py similarity index 100% rename from packages/openstef-meta/tests/models/forecasting/test_residual_forecaster.py rename to packages/openstef-meta/tests/unit/models/forecasting/test_residual_forecaster.py diff --git a/packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py similarity index 100% rename from packages/openstef-meta/tests/models/test_ensemble_forecasting_model.py rename to packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py diff --git a/packages/openstef-meta/tests/utils/__init__.py b/packages/openstef-meta/tests/unit/transforms/__init__.py similarity index 100% rename from packages/openstef-meta/tests/utils/__init__.py rename to packages/openstef-meta/tests/unit/transforms/__init__.py diff --git a/packages/openstef-meta/tests/transforms/test_flag_features_bound.py b/packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py similarity index 100% rename from packages/openstef-meta/tests/transforms/test_flag_features_bound.py rename to packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py diff --git a/packages/openstef-meta/tests/unit/utils/__init__.py b/packages/openstef-meta/tests/unit/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/openstef-meta/tests/utils/test_datasets.py b/packages/openstef-meta/tests/unit/utils/test_datasets.py similarity index 100% rename from packages/openstef-meta/tests/utils/test_datasets.py rename to packages/openstef-meta/tests/unit/utils/test_datasets.py diff --git a/packages/openstef-meta/tests/utils/test_decision_tree.py b/packages/openstef-meta/tests/unit/utils/test_decision_tree.py similarity index 100% rename from packages/openstef-meta/tests/utils/test_decision_tree.py rename to packages/openstef-meta/tests/unit/utils/test_decision_tree.py diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index 7ba5222c3..fcaa4ed46 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -107,7 +107,7 @@ def on_fit_end( run = self.storage.create_run( model_id=context.workflow.model_id, tags=context.workflow.model.tags, - hyperparams=context.workflow.model.forecaster.hyperparams, + hyperparams=context.workflow.model.forecaster.hyperparams, # type: ignore TODO Make MLFlow compatible with OpenSTEF Meta ) run_id: str = run.info.run_id self._logger.info("Created MLflow run %s for model %s", run_id, context.workflow.model_id) @@ -120,7 +120,11 @@ def on_fit_end( self._logger.info("Stored training data at %s for run %s", data_path, run_id) # Store feature importance plot if enabled - if self.store_feature_importance_plot and isinstance(context.workflow.model.forecaster, ExplainableForecaster): + if ( + self.store_feature_importance_plot + and isinstance(context.workflow.model, ForecastingModel) + and isinstance(context.workflow.model.forecaster, ExplainableForecaster) + ): fig = context.workflow.model.forecaster.plot_feature_importances() fig.write_html(data_path / "feature_importances.html") # pyright: ignore[reportUnknownMemberType] From e6bc447c0f09fa1e17ed0d659c2aae2e11ebf4a8 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 8 Dec 2025 14:00:55 +0100 Subject: [PATCH 60/72] Fixes Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 6 +++--- .../openstef4_backtest_forecaster.py | 6 ++++-- .../models/ensemble_forecasting_model.py | 1 - .../forecast_combiners/stacking_combiner.py | 16 ++++++++++++---- .../mlflow/mlflow_storage_callback.py | 2 +- 5 files changed, 20 insertions(+), 11 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 48630af70..3678f6fc7 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,9 +44,9 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" +ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner @@ -152,7 +152,7 @@ def _create_workflow() -> CustomForecastingWorkflow: start_time = time.time() create_liander2024_benchmark_runner( storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), - data_dir=Path("local_data/liander2024-energy-forecasting-benchmark"), + data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), callbacks=[StrictExecutionCallback()], ).run( forecaster_factory=_target_forecaster_factory, diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 966b06f10..e19c6937f 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -9,6 +9,7 @@ from pathlib import Path from typing import Any, override +import pandas as pd from pydantic import Field, PrivateAttr from openstef_beam.backtesting.backtest_forecaster.mixins import BacktestForecasterConfig, BacktestForecasterMixin @@ -19,7 +20,6 @@ from openstef_core.types import Q from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel from openstef_models.workflows.custom_forecasting_workflow import CustomForecastingWorkflow -from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): @@ -137,7 +137,9 @@ def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDatas if self.contributions and isinstance(self._workflow.model, EnsembleForecastingModel): contr_str = data.horizon.strftime("%Y%m%d%H%M%S") contributions = self._workflow.model.predict_contributions(predict_data) - contributions.to_parquet(path=self.cache_dir / f"contrib_{contr_str}_predict.parquet") + df = pd.concat([contributions, forecast.data.drop(columns=["load"])]) + + df.to_parquet(path=self.cache_dir / f"contrib_{contr_str}_predict.parquet") return forecast diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 6ce21dcd7..eba82d91f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -361,7 +361,6 @@ def prepare_input( Args: data: Raw time series dataset to prepare for forecasting. - forecaster_name: Optional name of the forecaster for model-specific preprocessing. forecast_start: Optional start time for forecasts. If provided and earlier than the cutoff time, overrides the cutoff for data filtering. diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index eb7b29424..c4165197f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -10,9 +10,8 @@ """ import logging -from typing import cast, override +from typing import TYPE_CHECKING, cast, override -from openstef_models.explainability.mixins import ExplainableForecaster import pandas as pd from pydantic import Field, field_validator @@ -24,13 +23,16 @@ from openstef_core.types import LeadTime, Quantile from openstef_meta.models.forecast_combiners.forecast_combiner import ForecastCombiner, ForecastCombinerConfig from openstef_meta.utils.datasets import EnsembleForecastDataset -from openstef_models.models.forecasting.forecaster import Forecaster +from openstef_models.explainability.mixins import ExplainableForecaster from openstef_models.models.forecasting.gblinear_forecaster import ( GBLinearForecaster, GBLinearHyperParams, ) from openstef_models.models.forecasting.lgbm_forecaster import LGBMForecaster, LGBMHyperParams +if TYPE_CHECKING: + from openstef_models.models.forecasting.forecaster import Forecaster + logger = logging.getLogger(__name__) ForecasterHyperParams = GBLinearHyperParams | LGBMHyperParams @@ -221,7 +223,13 @@ def predict_contributions( p = model.predict_contributions(data=input_data, scale=True) predictions.append(p) - return pd.concat(predictions, axis=1) + contributions = pd.concat(predictions, axis=1) + + target_series = data.target_series + if target_series is not None: + contributions[data.target_column] = target_series + + return contributions @property def is_fitted(self) -> bool: diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index fcaa4ed46..beacf50f1 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -122,7 +122,7 @@ def on_fit_end( # Store feature importance plot if enabled if ( self.store_feature_importance_plot - and isinstance(context.workflow.model, ForecastingModel) + and isinstance(context.workflow.model, ForecastingModel) # type: ignore and isinstance(context.workflow.model.forecaster, ExplainableForecaster) ): fig = context.workflow.model.forecaster.plot_feature_importances() From bedf6af01bcff1b7af732ac2bf4231db7b26dfcc Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 8 Dec 2025 14:32:30 +0100 Subject: [PATCH 61/72] fixed tests Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 +- .../learned_weights_combiner.py | 6 +++++- .../models/forecast_combiners/rules_combiner.py | 6 +----- .../models/forecasting/residual_forecaster.py | 4 +++- .../test_stacking_combiner.py | 2 +- .../models/forecasting/base_case_forecaster.py | 17 +++++++++++++++++ .../forecasting/constant_median_forecaster.py | 17 +++++++++++++++++ .../models/forecasting/gblinear_forecaster.py | 5 +---- .../models/forecasting/lgbm_forecaster.py | 12 +++++++----- .../models/forecasting/lgbmlinear_forecaster.py | 12 +++++++----- .../models/forecasting/xgboost_forecaster.py | 5 +---- .../forecasting/test_gblinear_forecaster.py | 4 +--- .../forecasting/test_xgboost_forecaster.py | 6 ++---- 13 files changed, 64 insertions(+), 34 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 3678f6fc7..ed795eca7 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,7 +44,7 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 8ea079595..08f094b27 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -289,7 +289,11 @@ def _validate_labels(self, labels: pd.Series, model_index: int) -> None: def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_index: int) -> pd.DataFrame: model = self.models[model_index] - weights_array = model.predict_proba(base_predictions.to_numpy()) # type: ignore + if isinstance(model, DummyClassifier): + weights_array = pd.DataFrame(0, index=base_predictions.index, columns=self._label_encoder.classes_) + weights_array[self._label_encoder.classes_[0]] = 1.0 + else: + weights_array = model.predict_proba(base_predictions.to_numpy()) # type: ignore return pd.DataFrame(weights_array, index=base_predictions.index, columns=self._label_encoder.classes_) # type: ignore diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py index 965997cde..eb3dabcd2 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py @@ -163,11 +163,7 @@ def predict_contributions( predictions.append(preds.to_frame(name=Quantile(q).format())) # Concatenate predictions along columns to form a DataFrame with quantile columns - df = pd.concat(predictions, axis=1) - - # TODO FLORIAN return only Decision datadrame - - return df + return pd.concat(predictions, axis=1) @property def is_fitted(self) -> bool: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py index be9100a1a..79cc44cd5 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py @@ -285,6 +285,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = Tru Args: data: Input data for prediction contributions. + scale: Whether to scale contributions to sum to 1. Defaults to True. Returns: pd.DataFrame containing the prediction contributions. @@ -308,7 +309,8 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = Tru primary_contributions.columns = [f"{self.primary_name}_{q}" for q in primary_contributions.columns] secondary_contributions = secondary_predictions.abs() / ( - primary_predictions.abs() + secondary_predictions.abs()) + primary_predictions.abs() + secondary_predictions.abs() + ) secondary_contributions.columns = [f"{self.secondary_name}_{q}" for q in secondary_contributions.columns] return pd.concat([primary_contributions, secondary_contributions], axis=1) diff --git a/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py index 530018ab7..cb182e242 100644 --- a/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py +++ b/packages/openstef-meta/tests/unit/models/forecast_combiners/test_stacking_combiner.py @@ -100,4 +100,4 @@ def test_stacking_combiner_predict_contributions( # Assert assert isinstance(contributions, pd.DataFrame), "Contributions should be returned as a DataFrame." - assert len(contributions.columns) == len(ensemble_dataset.quantiles) * len(ensemble_dataset.forecaster_names) + assert len(contributions.columns) == (len(ensemble_dataset.quantiles) * len(ensemble_dataset.forecaster_names)) + 1 diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py index d7e01c965..324812a0a 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/base_case_forecaster.py @@ -189,6 +189,23 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: sample_interval=data.sample_interval, ) + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Generate feature contributions. + + Args: + data: The forecast input dataset containing target variable history. + scale: Whether to scale contributions to sum to 1. Defaults to True. + + Returns: + pd.DataFrame containing the prediction contributions. + """ + return pd.DataFrame( + data=1.0, + index=data.index, + columns=["load_" + quantile.format() for quantile in self.config.quantiles], + ) + @property @override def feature_importances(self) -> pd.DataFrame: diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py index 930881a55..e142f4711 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/constant_median_forecaster.py @@ -141,3 +141,20 @@ def feature_importances(self) -> pd.DataFrame: index=["load"], columns=[quantile.format() for quantile in self.config.quantiles], ) + + @override + def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: + """Generate feature contributions. + + Args: + data: The forecast input dataset containing target variable history. + scale: Whether to scale contributions to sum to 1. Defaults to True. + + Returns: + pd.DataFrame containing the prediction contributions. + """ + return pd.DataFrame( + data=1.0, + index=data.index, + columns=["load_" + quantile.format() for quantile in self.config.quantiles], + ) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 6fc0c30d6..9b230e060 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -353,10 +353,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = Tru data=contribs, index=input_data.index, columns=[ - f"{feature}_{quantile.format()}" - for feature in input_data.columns - for quantile in self.config.quantiles - + f"{feature}_{quantile.format()}" for feature in input_data.columns for quantile in self.config.quantiles ], ) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index 84852aaaa..ed3de0058 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -338,11 +338,13 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p # Scale contributions so that they sum to 1.0 per quantile contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) - contributions.append(pd.DataFrame( - data=contribs_quantile, - index=input_data.index, - columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], - )) + contributions.append( + pd.DataFrame( + data=contribs_quantile, + index=input_data.index, + columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], + ) + ) # Construct DataFrame return pd.concat(contributions, axis=1) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index e39e60bb5..b484c8a37 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -342,11 +342,13 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p # Scale contributions so that they sum to 1.0 per quantile contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) - contributions.append(pd.DataFrame( - data=contribs_quantile, - index=input_data.index, - columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], - )) + contributions.append( + pd.DataFrame( + data=contribs_quantile, + index=input_data.index, + columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], + ) + ) # Construct DataFrame return pd.concat(contributions, axis=1) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index 6843df2fa..1469309f6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -448,10 +448,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p data=contribs, index=input_data.index, columns=[ - f"{feature}_{quantile.format()}" - for feature in input_data.columns - for quantile in self.config.quantiles - + f"{feature}_{quantile.format()}" for feature in input_data.columns for quantile in self.config.quantiles ], ) diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py index b260d1b4e..f7766581f 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_gblinear_forecaster.py @@ -155,9 +155,7 @@ def test_gblinear_forecaster_predict_contributions( # Check that necessary quantiles are present input_features = sample_forecast_input_dataset.input_data().columns expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] - assert list(result.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.columns)}" - ) + assert list(result.columns) == expected_columns, f"Expected columns {expected_columns}, got {list(result.columns)}" # Contributions should sum to 1.0 per quantile for q in expected_quantiles: diff --git a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py index bde85e36a..e8bcfd1ec 100644 --- a/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py +++ b/packages/openstef-models/tests/unit/models/forecasting/test_xgboost_forecaster.py @@ -189,12 +189,10 @@ def test_xgboost_forecaster_predict_contributions( # Check that necessary quantiles are present input_features = sample_forecast_input_dataset.input_data().columns expected_columns = [f"{col}_{q.format()}" for col in input_features for q in expected_quantiles] - assert list(result.columns) == expected_columns, ( - f"Expected columns {expected_columns}, got {list(result.columns)}" - ) + assert list(result.columns) == expected_columns, f"Expected columns {expected_columns}, got {list(result.columns)}" # Contributions should sum to 1.0 per quantile for q in expected_quantiles: quantile_cols = [col for col in result.columns if col.endswith(f"_{q.format()}")] col_sums = result[quantile_cols].sum(axis=1) - pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index), atol=1e-10) + pd.testing.assert_series_equal(col_sums, pd.Series(1.0, index=result.index), atol=1e-10, check_dtype=False) From c9f135f54d9103cf31fd2945f4a0b38bd5bec33c Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 9 Dec 2025 10:41:06 +0100 Subject: [PATCH 62/72] small fix Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 4 ++-- .../backtest_forecaster/openstef4_backtest_forecaster.py | 4 ++-- .../models/forecast_combiners/learned_weights_combiner.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index ed795eca7..133b23c8a 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,9 +44,9 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 # multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" +ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index e19c6937f..874276089 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -136,8 +136,8 @@ def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDatas if self.contributions and isinstance(self._workflow.model, EnsembleForecastingModel): contr_str = data.horizon.strftime("%Y%m%d%H%M%S") - contributions = self._workflow.model.predict_contributions(predict_data) - df = pd.concat([contributions, forecast.data.drop(columns=["load"])]) + contributions = self._workflow.model.predict_contributions(predict_data, forecast_start=data.horizon) + df = pd.concat([contributions, forecast.data.drop(columns=["load"])], axis=1) df.to_parquet(path=self.cache_dir / f"contrib_{contr_str}_predict.parquet") return forecast diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 08f094b27..c421e4c73 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -293,7 +293,7 @@ def _predict_model_weights_quantile(self, base_predictions: pd.DataFrame, model_ weights_array = pd.DataFrame(0, index=base_predictions.index, columns=self._label_encoder.classes_) weights_array[self._label_encoder.classes_[0]] = 1.0 else: - weights_array = model.predict_proba(base_predictions.to_numpy()) # type: ignore + weights_array = model.predict_proba(base_predictions) # type: ignore return pd.DataFrame(weights_array, index=base_predictions.index, columns=self._label_encoder.classes_) # type: ignore From 845e384ca2fb64b33e01daaca3267f8d180b702f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 15 Dec 2025 13:00:30 +0100 Subject: [PATCH 63/72] Stacking Bugfix Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 12 +- .../models/ensemble_forecasting_model.py | 255 +++++++++--------- .../forecast_combiners/forecast_combiner.py | 2 - .../learned_weights_combiner.py | 1 - .../forecast_combiners/rules_combiner.py | 4 - .../forecast_combiners/stacking_combiner.py | 12 +- .../presets/forecasting_workflow.py | 17 +- 7 files changed, 154 insertions(+), 149 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 02fb9e3ee..23e2b4fcb 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,12 +44,12 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 if True else multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" +ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( - "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner + "gblinear" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner ) model = "Ensemble_" + "_".join(base_models) + "_" + ensemble_type + "_" + combiner_model @@ -94,8 +94,8 @@ temperature_column="temperature_2m", relative_humidity_column="relative_humidity_2m", energy_price_column="EPEX_NL", - forecast_combiner_sample_weight_exponent=1, - forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 1, "xgboost": 0, "lgbm_linear": 0}, + forecast_combiner_sample_weight_exponent=0, + forecaster_sample_weight_exponent={"gblinear": 1, "lgbm": 0, "xgboost": 0, "lgbm_linear": 0}, ) @@ -143,7 +143,7 @@ def _create_workflow() -> CustomForecastingWorkflow: config=backtest_config, workflow_factory=_create_workflow, debug=False, - contributions=True, + contributions=False, cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", ) diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 5f8824336..ce3c7df8f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -37,8 +37,6 @@ logger = logging.getLogger(__name__) -logger = logging.getLogger(__name__) - class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]): """Complete forecasting pipeline combining preprocessing, prediction, and postprocessing. @@ -195,16 +193,6 @@ def fit( Returns: FitResult containing training details and metrics. """ - score_data = data.copy_with(data=data.data) - # Fit the feature engineering transforms - self.common_preprocessing.fit(data=data) - data = self.common_preprocessing.transform(data=data) - - if data_val is not None: - data_val = self.common_preprocessing.transform(data=data_val) - if data_test is not None: - data_test = self.common_preprocessing.transform(data=data_test) - # Fit forecasters ensemble_predictions = self._fit_forecasters( data=data, @@ -212,62 +200,21 @@ def fit( data_test=data_test, ) - if data_val is not None: - ensemble_predictions_val = self._predict_forecasters( - data=self.prepare_input(data=data_val), - ) - else: - ensemble_predictions_val = None - - features = self._fit_transform_combiner_data(data=data) - - self.combiner.fit( - data=ensemble_predictions, - data_val=ensemble_predictions_val, - additional_features=features, + self._fit_combiner( + ensemble_dataset=ensemble_predictions, + original_data=data, ) - # Prepare input datasets for metrics calculation - metrics_train = self._predict_combiner_and_score( - ensemble_dataset=ensemble_predictions, additional_features=features - ) - if data_val is not None: - features_val = self._transform_combiner_data(data=data_val) - metrics_val = ( - self._predict_combiner_and_score( - ensemble_dataset=ensemble_predictions_val, additional_features=features_val - ) - if ensemble_predictions_val - else None - ) - else: - metrics_val = None - - if data_test is not None: - features_test = self._transform_combiner_data(data=data_test) - ensemble_predictions_test = self._predict_forecasters( - data=self.prepare_input(data=data_test), - ) - metrics_test = ( - self._predict_combiner_and_score( - ensemble_dataset=ensemble_predictions_test, additional_features=features_test - ) - if ensemble_predictions_test - else None - ) - else: - metrics_test = None - metrics_full = self.score(data=score_data) - + metrics = self.score(data=data) return ModelFitResult( input_dataset=data, input_data_train=ForecastInputDataset.from_timeseries(data), input_data_val=ForecastInputDataset.from_timeseries(data_val) if data_val else None, input_data_test=ForecastInputDataset.from_timeseries(data_test) if data_test else None, - metrics_train=metrics_train, - metrics_val=metrics_val, - metrics_test=metrics_test, - metrics_full=metrics_full, + metrics_train=metrics, + metrics_val=None, + metrics_test=None, + metrics_full=metrics, ) def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: @@ -279,9 +226,8 @@ def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputData def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: if len(self.combiner_preprocessing.transforms) == 0: return None - combiner_data = self.prepare_input(data=data) - self.combiner_preprocessing.fit(combiner_data) - combiner_data = self.combiner_preprocessing.transform(combiner_data) + self.combiner_preprocessing.fit(data=data) + combiner_data = self.combiner_preprocessing.transform(data) return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) def _fit_forecasters( @@ -292,71 +238,104 @@ def _fit_forecasters( ) -> EnsembleForecastDataset: predictions: dict[str, ForecastDataset] = {} - if data_test is not None: logger.info("Data test provided during fit, but will be ignored for MetaForecating") - for name, forecaster in self.forecasters.items(): - validate_horizons_present(data, forecaster.config.horizons) - # Apply model-specific preprocessing if available - - if name in self.model_specific_preprocessing: - self.model_specific_preprocessing[name].fit(data=data) - data = self.model_specific_preprocessing[name].transform(data=data) - data_val = self.model_specific_preprocessing[name].transform(data=data_val) if data_val else None - - input_data_train = self.prepare_input(data=data, forecast_start=data.index[0]) - if data_val is not None: - input_data_val = self.prepare_input(data=data_val, forecast_start=data_val.index[0]) - else: - input_data_val = None - - # Drop target column nan's from training data. One can not train on missing targets. - target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] - input_data_train = input_data_train.pipe_pandas(target_dropna) - input_data_val = input_data_val.pipe_pandas(target_dropna) if input_data_val else None - - # Fit the model - forecaster.fit(data=input_data_train, data_val=input_data_val) - predictions_raw = self.forecasters[name].predict(data=input_data_train) - - predictions[name] = self.postprocessing.transform(data=predictions_raw) + # Fit the feature engineering transforms + self.common_preprocessing.fit(data=data) + data_transformed = self.common_preprocessing.transform(data=data) + [ + self.model_specific_preprocessing[name].fit(data=data_transformed) + for name in self.model_specific_preprocessing + ] + logger.debug("Completed fitting preprocessing pipelines.") + + # Fit the forecasters + for name in self.forecasters: + logger.debug("Started fitting Forecaster '%s'.", name) + predictions[name] = self._fit_forecaster( + data=data, + data_val=data_val, + data_test=None, + forecaster_name=name, + ) return EnsembleForecastDataset.from_forecast_datasets(predictions, target_series=data.data[self.target_column]) - def _predict_forecasters( - self, data: TimeSeriesDataset, forecast_start: datetime | None = None - ) -> EnsembleForecastDataset: - """Generate predictions from base learners. + def _fit_forecaster( + self, + data: TimeSeriesDataset, + data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, + forecaster_name: str = "", + ) -> ForecastDataset: + """Train the forecaster on the provided dataset. Args: - data: Input data for prediction. - forecast_start: Optional start time for forecasts. + data: Historical time series data with features and target values. + data_val: Optional validation data. + data_test: Optional test data. + forecaster_name: Name of the forecaster to train. Returns: - DataFrame containing base learner predictions. + ForecastDataset containing the trained forecaster's predictions. """ - data_common = self.common_preprocessing.transform(data=data) - - base_predictions: dict[str, ForecastDataset] = {} - for name, forecaster in self.forecasters.items(): - forecaster_data = ( - self.model_specific_preprocessing[name].transform(data=data_common) - if name in self.model_specific_preprocessing - else data_common + forecaster = self.forecasters[forecaster_name] + validate_horizons_present(data, forecaster.config.horizons) + + # Transform and split input data + input_data_train = self.prepare_input(data=data, forecaster_name=forecaster_name) + input_data_val = self.prepare_input(data=data_val, forecaster_name=forecaster_name) if data_val else None + input_data_test = self.prepare_input(data=data_test, forecaster_name=forecaster_name) if data_test else None + + # Drop target column nan's from training data. One can not train on missing targets. + target_dropna = partial(pd.DataFrame.dropna, subset=[self.target_column]) # pyright: ignore[reportUnknownMemberType] + input_data_train = input_data_train.pipe_pandas(target_dropna) + input_data_val = input_data_val.pipe_pandas(target_dropna) if input_data_val else None + input_data_test = input_data_test.pipe_pandas(target_dropna) if input_data_test else None + + # Transform the input data to a valid forecast input and split into train/val/test + input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( + data=input_data_train, data_val=input_data_val, data_test=input_data_test, target_column=self.target_column + ) + logger.debug("Started fitting forecaster '%s'.", forecaster_name) + # Fit the model + forecaster.fit(data=input_data_train, data_val=input_data_val) + prediction = self._predict_forecaster(input_data=input_data_train, forecaster_name=forecaster_name) + logger.debug("Completed fitting forecaster '%s'.", forecaster_name) + + return ForecastDataset( + data=prediction.data, + sample_interval=prediction.sample_interval, + forecast_start=prediction.forecast_start, + ) + + def _predict_forecaster(self, input_data: ForecastInputDataset, forecaster_name: str) -> ForecastDataset: + # Predict and restore target column + prediction_raw = self.forecasters[forecaster_name].predict(data=input_data) + prediction = self.postprocessing.transform(prediction_raw) + return restore_target(dataset=prediction, original_dataset=input_data, target_column=self.target_column) + + def _predict_forecasters( + self, + data: TimeSeriesDataset, + forecast_start: datetime | None = None, + ) -> EnsembleForecastDataset: + predictions: dict[str, ForecastDataset] = {} + for name in self.forecasters: + logger.debug("Generating predictions for forecaster '%s'.", name) + input_data = self.prepare_input(data=data, forecast_start=forecast_start, forecaster_name=name) + predictions[name] = self._predict_forecaster( + input_data=input_data, + forecaster_name=name, ) - forecaster_data = self.prepare_input(forecaster_data, forecast_start=forecast_start) - preds_raw = forecaster.predict(data=forecaster_data) - preds = self.postprocessing.transform(data=preds_raw) - base_predictions[name] = preds - return EnsembleForecastDataset.from_forecast_datasets( - base_predictions, target_series=data.data[self.target_column] - ) + return EnsembleForecastDataset.from_forecast_datasets(predictions, target_series=data.data[self.target_column]) def prepare_input( self, data: TimeSeriesDataset, + forecaster_name: str = "", forecast_start: datetime | None = None, ) -> ForecastInputDataset: """Prepare input data for forecastingfiltering. @@ -365,12 +344,20 @@ def prepare_input( data: Raw time series dataset to prepare for forecasting. forecast_start: Optional start time for forecasts. If provided and earlier than the cutoff time, overrides the cutoff for data filtering. + forecaster_name: Name of the forecaster for which to prepare input data. Returns: Processed forecast input dataset ready for model prediction. """ + logger.debug("Preparing input data for forecaster '%s'.", forecaster_name) input_data = restore_target(dataset=data, original_dataset=data, target_column=self.target_column) + # Transform the data + input_data = self.common_preprocessing.transform(data=input_data) + if forecaster_name in self.model_specific_preprocessing: + logger.debug("Applying model-specific preprocessing for forecaster '%s'.", forecaster_name) + input_data = self.model_specific_preprocessing[forecaster_name].transform(data=input_data) + # Cut away input history to avoid training on incomplete data input_data_start = cast("pd.Series[pd.Timestamp]", input_data.index).min().to_pydatetime() input_data_cutoff = input_data_start + self.cutoff_history @@ -389,12 +376,28 @@ def prepare_input( forecast_start=forecast_start, ) - def _predict_combiner_and_score( - self, ensemble_dataset: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None - ) -> SubsetMetric: - prediction = self.combiner.predict(ensemble_dataset, additional_features=additional_features) + def _predict_combiner( + self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset + ) -> ForecastDataset: + logger.debug("Predicting combiner.") + features = self._transform_combiner_data(data=original_data) + prediction_raw = self.combiner.predict(ensemble_dataset, additional_features=features) + prediction = self.postprocessing.transform(prediction_raw) + prediction.data[ensemble_dataset.target_column] = ensemble_dataset.target_series - return self._calculate_score(prediction=prediction) + return prediction + + def _fit_combiner(self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset) -> None: + logger.debug("Fitting combiner.") + features = self._fit_transform_combiner_data(data=original_data) + self.combiner.fit(ensemble_dataset, additional_features=features) + + def _predict_contributions_combiner( + self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset + ) -> pd.DataFrame: + + features = self._transform_combiner_data(data=original_data) + return self.combiner.predict_contributions(ensemble_dataset, additional_features=features) def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: """Generate forecasts for the provided dataset. @@ -411,17 +414,15 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non """ if not self.is_fitted: raise NotFittedError(self.__class__.__name__) + logger.debug("Generating predictions.") ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) - features = self._transform_combiner_data(data=data) - # Predict and restore target column - prediction = self.combiner.predict( - data=ensemble_predictions, - additional_features=features, + prediction = self._predict_combiner( + ensemble_dataset=ensemble_predictions, + original_data=data, ) - return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) def predict_contributions(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> pd.DataFrame: @@ -442,11 +443,9 @@ def predict_contributions(self, data: TimeSeriesDataset, forecast_start: datetim ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) - features = self._transform_combiner_data(data=data) - - return self.combiner.predict_contributions( - data=ensemble_predictions, - additional_features=features, + return self._predict_contributions_combiner( + ensemble_dataset=ensemble_predictions, + original_data=data, ) def score( diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index b1df023f6..f0078d949 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -93,7 +93,6 @@ def fit( data: EnsembleForecastDataset, data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, ) -> None: """Fit the final learner using base learner predictions. @@ -101,7 +100,6 @@ def fit( data: EnsembleForecastDataset data_val: Optional EnsembleForecastDataset for validation during fitting. Will be ignored additional_features: Optional ForecastInputDataset containing additional features for the final learner. - sample_weights: Optional series of sample weights for fitting. """ raise NotImplementedError("Subclasses must implement the fit method.") diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index c421e4c73..1ee3c6c7a 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -233,7 +233,6 @@ def fit( data: EnsembleForecastDataset, data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, ) -> None: self._label_encoder.fit(data.forecaster_names) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py index eb3dabcd2..57c44be02 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py @@ -88,16 +88,12 @@ def fit( data: EnsembleForecastDataset, data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, ) -> None: # No fitting needed for rule-based final learner # Check that additional features are provided if additional_features is None: raise ValueError("Additional features must be provided for RulesForecastCombiner prediction.") - if sample_weights is not None: - logger.warning("Sample weights are ignored in RulesLearner.fit method.") - def _predict_tree(self, data: pd.DataFrame, columns: pd.Index) -> pd.DataFrame: """Predict using the decision tree rules. diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index c4165197f..8324ca607 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -10,6 +10,7 @@ """ import logging +from functools import partial from typing import TYPE_CHECKING, cast, override import pandas as pd @@ -154,7 +155,6 @@ def fit( data: EnsembleForecastDataset, data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, ) -> None: for i, q in enumerate(self.quantiles): @@ -167,6 +167,10 @@ def fit( else: input_data = data.select_quantile(quantile=q) + # Prepare input data by dropping rows with NaN target values + target_dropna = partial(pd.DataFrame.dropna, subset=[input_data.target_column]) # pyright: ignore[reportUnknownMemberType] + input_data = input_data.pipe_pandas(target_dropna) + self.models[i].fit(data=input_data, data_val=None) @override @@ -188,6 +192,12 @@ def predict( ) else: input_data = data.select_quantile(quantile=q) + + if isinstance(self.models[i], GBLinearForecaster): + feature_cols = [x for x in input_data.data.columns if x != data.target_column] + feature_dropna = partial(pd.DataFrame.dropna, subset=feature_cols) # pyright: ignore[reportUnknownMemberType] + input_data = input_data.pipe_pandas(feature_dropna) + p = self.models[i].predict(data=input_data).data predictions.append(p) diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 0a565793a..9505e9e7a 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -9,7 +9,7 @@ from collections.abc import Sequence from datetime import timedelta -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING, Literal, cast from pydantic import Field @@ -294,11 +294,14 @@ def feature_adders(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesD def feature_standardizers(config: EnsembleWorkflowConfig) -> list[Transform[TimeSeriesDataset, TimeSeriesDataset]]: - return [ - Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), - Scaler(selection=Exclude(config.target_column), method="standard"), - EmptyFeatureRemover(), - ] + return cast( + list[Transform[TimeSeriesDataset, TimeSeriesDataset]], + [ + Clipper(selection=Include(config.energy_price_column).combine(config.clip_features), mode="standard"), + Scaler(selection=Exclude(config.target_column), method="standard"), + EmptyFeatureRemover(), + ], + ) def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastingWorkflow: # noqa: C901, PLR0912, PLR0915 @@ -454,7 +457,7 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin if config.combiner_model == "lgbm": combiner_hp = StackingCombiner.LGBMHyperParams() elif config.combiner_model == "gblinear": - combiner_hp = StackingCombiner.GBLinearHyperParams() + combiner_hp = StackingCombiner.GBLinearHyperParams(reg_alpha=0.0, reg_lambda=0.0) else: msg = f"Unsupported combiner model type for stacking: {config.combiner_model}" raise ValueError(msg) From 780e012e1b97ccf63d2d138ac946d10991fd91c6 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 15 Dec 2025 13:40:46 +0100 Subject: [PATCH 64/72] Added hard Forecast Selection Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 6 ++--- .../learned_weights_combiner.py | 27 +++++++++++++++++++ .../mlflow/mlflow_storage_callback.py | 6 +---- 3 files changed, 31 insertions(+), 8 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 23e2b4fcb..f5ca1c5a6 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,12 +44,12 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 1 if True else multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 11 if True else multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark -ensemble_type = "stacking" # "stacking", "learned_weights" or "rules" +ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" combiner_model = ( - "gblinear" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner + "lgbm" # "lgbm", "xgboost", "rf" or "logistic" for learned weights combiner, gblinear for stacking combiner ) model = "Ensemble_" + "_".join(base_models) + "_" + ensemble_type + "_" + combiner_model diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 1ee3c6c7a..6d6025e30 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -66,6 +66,16 @@ class LGBMCombinerHyperParams(HyperParams, ClassifierParamsMixin): description="Number of leaves for the LGBM Classifier. Defaults to 31.", ) + reg_alpha: float = Field( + default=0.0, + description="L1 regularization term on weights. Defaults to 0.0.", + ) + + reg_lambda: float = Field( + default=0.0, + description="L2 regularization term on weights. Defaults to 0.0.", + ) + @override def get_classifier(self) -> LGBMClassifier: """Returns the LGBM Classifier.""" @@ -73,6 +83,8 @@ def get_classifier(self) -> LGBMClassifier: class_weight="balanced", n_estimators=self.n_estimators, num_leaves=self.n_leaves, + reg_alpha=self.reg_alpha, + reg_lambda=self.reg_lambda, n_jobs=1, ) @@ -190,6 +202,15 @@ class WeightsCombinerConfig(ForecastCombinerConfig): min_length=1, ) + hard_selection: bool = Field( + default=False, + description=( + "If True, the combiner will select the base model with the highest predicted probability " + "for each instance (hard selection). If False, it will use the predicted probabilities as " + "weights to combine base model predictions (soft selection)." + ), + ) + @property def get_classifier(self) -> Classifier: """Returns the classifier instance from hyperparameters. @@ -223,6 +244,7 @@ def __init__(self, config: WeightsCombinerConfig) -> None: self._is_fitted: bool = False self._is_fitted = False self._label_encoder = LabelEncoder() + self.hard_selection = config.hard_selection # Initialize a classifier per quantile self.models: list[Classifier] = [config.get_classifier for _ in self.quantiles] @@ -310,6 +332,11 @@ def _generate_predictions_quantile( weights = self._predict_model_weights_quantile(base_predictions=input_data, model_index=model_index) + if self.hard_selection: + # If selection mode is hard, set the max weight to 1 and others to 0 + # Edge case if max weights are equal, distribute equally + weights = (weights == weights.max(axis=1).to_frame().to_numpy()) / weights.sum(axis=1).to_frame().to_numpy() + return dataset.input_data().mul(weights).sum(axis=1) @override diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index fcaa4ed46..57673d270 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -120,11 +120,7 @@ def on_fit_end( self._logger.info("Stored training data at %s for run %s", data_path, run_id) # Store feature importance plot if enabled - if ( - self.store_feature_importance_plot - and isinstance(context.workflow.model, ForecastingModel) - and isinstance(context.workflow.model.forecaster, ExplainableForecaster) - ): + if self.store_feature_importance_plot and isinstance(context.workflow.model.forecaster, ExplainableForecaster): fig = context.workflow.model.forecaster.plot_feature_importances() fig.write_html(data_path / "feature_importances.html") # pyright: ignore[reportUnknownMemberType] From 682ae2fef555789c2875aab42be78c308af06eb8 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Mon, 15 Dec 2025 17:27:23 +0100 Subject: [PATCH 65/72] Improved data handling in EnsembleForecasting model, correct data splitting and Model Fit Result. Validation and test data can now be fully used Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 +- .../models/ensemble_forecasting_model.py | 279 +++++++++++++++--- .../src/openstef_meta/utils/datasets.py | 24 ++ .../workflows/custom_forecasting_workflow.py | 8 +- 4 files changed, 273 insertions(+), 40 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index f5ca1c5a6..6cfe6f901 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -44,7 +44,7 @@ OUTPUT_PATH = Path("./benchmark_results") -N_PROCESSES = 11 if True else multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark +N_PROCESSES = 1 if True else multiprocessing.cpu_count() # Amount of parallel processes to use for the benchmark ensemble_type = "learned_weights" # "stacking", "learned_weights" or "rules" base_models = ["lgbm", "gblinear"] # combination of "lgbm", "gblinear", "xgboost" and "lgbm_linear" diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index ce3c7df8f..9140c4443 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -38,6 +38,56 @@ logger = logging.getLogger(__name__) +class EnsembleModelFitResult(BaseModel): + forecaster_fit_results: dict[str, ModelFitResult] = Field(description="ModelFitResult for each base Forecaster") + + combiner_fit_result: ModelFitResult = Field(description="ModelFitResult for the ForecastCombiner") + + # Make compatible with ModelFitResult interface + @property + def input_dataset(self) -> EnsembleForecastDataset: + """Returns the input dataset used for fitting the combiner.""" + return cast( + "EnsembleForecastDataset", + self.combiner_fit_result.input_dataset, + ) + + @property + def input_data_train(self) -> ForecastInputDataset: + """Returns the training input data used for fitting the combiner.""" + return self.combiner_fit_result.input_data_train + + @property + def input_data_val(self) -> ForecastInputDataset | None: + """Returns the validation input data used for fitting the combiner.""" + return self.combiner_fit_result.input_data_val + + @property + def input_data_test(self) -> ForecastInputDataset | None: + """Returns the test input data used for fitting the combiner.""" + return self.combiner_fit_result.input_data_test + + @property + def metrics_train(self) -> SubsetMetric: + """Returns the full metrics calculated during combiner fitting.""" + return self.combiner_fit_result.metrics_train + + @property + def metrics_val(self) -> SubsetMetric | None: + """Returns the full metrics calculated during combiner fitting.""" + return self.combiner_fit_result.metrics_val + + @property + def metrics_test(self) -> SubsetMetric | None: + """Returns the full metrics calculated during combiner fitting.""" + return self.combiner_fit_result.metrics_test + + @property + def metrics_full(self) -> SubsetMetric: + """Returns the full metrics calculated during combiner fitting.""" + return self.combiner_fit_result.metrics_full + + class EnsembleForecastingModel(BaseModel, Predictor[TimeSeriesDataset, ForecastDataset]): """Complete forecasting pipeline combining preprocessing, prediction, and postprocessing. @@ -174,7 +224,7 @@ def fit( data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, - ) -> ModelFitResult: + ) -> EnsembleModelFitResult: """Train the forecasting model on the provided dataset. Fits the preprocessing pipeline and underlying forecaster. Handles both @@ -194,27 +244,49 @@ def fit( FitResult containing training details and metrics. """ # Fit forecasters - ensemble_predictions = self._fit_forecasters( + train_ensemble, val_ensemble, test_ensemble, forecaster_fit_results = self._fit_forecasters( data=data, data_val=data_val, data_test=data_test, ) - self._fit_combiner( - ensemble_dataset=ensemble_predictions, - original_data=data, + combiner_fit_result = self._fit_combiner( + train_ensemble_dataset=train_ensemble, + val_ensemble_dataset=val_ensemble, + test_ensemble_dataset=test_ensemble, + data=data, + data_val=data_val, + data_test=data_test, ) - metrics = self.score(data=data) - return ModelFitResult( - input_dataset=data, - input_data_train=ForecastInputDataset.from_timeseries(data), - input_data_val=ForecastInputDataset.from_timeseries(data_val) if data_val else None, - input_data_test=ForecastInputDataset.from_timeseries(data_test) if data_test else None, - metrics_train=metrics, - metrics_val=None, - metrics_test=None, - metrics_full=metrics, + return EnsembleModelFitResult( + forecaster_fit_results=forecaster_fit_results, + combiner_fit_result=combiner_fit_result, + ) + + @staticmethod + def _combine_datasets( + data: ForecastInputDataset, additional_features: ForecastInputDataset + ) -> ForecastInputDataset: + """Combine base learner predictions with additional features for final learner input. + + Args: + data: ForecastInputDataset containing base learner predictions. + additional_features: ForecastInputDataset containing additional features. + + Returns: + ForecastInputDataset with combined features. + """ + additional_df = additional_features.data.loc[ + :, [col for col in additional_features.data.columns if col not in data.data.columns] + ] + # Merge on index to combine datasets + combined_df = data.data.join(additional_df) + + return ForecastInputDataset( + data=combined_df, + sample_interval=data.sample_interval, + forecast_start=data.forecast_start, ) def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: @@ -223,21 +295,57 @@ def _transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputData combiner_data = self.combiner_preprocessing.transform(data) return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) - def _fit_transform_combiner_data(self, data: TimeSeriesDataset) -> ForecastInputDataset | None: + def _fit_prepare_combiner_data( + self, + data: TimeSeriesDataset, + data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, + ) -> tuple[ForecastInputDataset | None, ForecastInputDataset | None, ForecastInputDataset | None]: + if len(self.combiner_preprocessing.transforms) == 0: - return None + return None, None, None self.combiner_preprocessing.fit(data=data) - combiner_data = self.combiner_preprocessing.transform(data) - return ForecastInputDataset.from_timeseries(combiner_data, target_column=self.target_column) + + input_data_train = self.combiner_preprocessing.transform(data) + input_data_val = self.combiner_preprocessing.transform(data_val) if data_val else None + input_data_test = self.combiner_preprocessing.transform(data_test) if data_test else None + + input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( + data=input_data_train, data_val=input_data_val, data_test=input_data_test, target_column=self.target_column + ) + combiner_data = ForecastInputDataset.from_timeseries(input_data_train, target_column=self.target_column) + + combiner_data_val = ( + ForecastInputDataset.from_timeseries(input_data_val, target_column=self.target_column) + if input_data_val + else None + ) + + combiner_data_test = ( + ForecastInputDataset.from_timeseries(input_data_test, target_column=self.target_column) + if input_data_test + else None + ) + + return combiner_data, combiner_data_val, combiner_data_test def _fit_forecasters( self, data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, - ) -> EnsembleForecastDataset: + ) -> tuple[ + EnsembleForecastDataset, + EnsembleForecastDataset | None, + EnsembleForecastDataset | None, + dict[str, ModelFitResult], + ]: + + predictions_train: dict[str, ForecastDataset] = {} + predictions_val: dict[str, ForecastDataset | None] = {} + predictions_test: dict[str, ForecastDataset | None] = {} + results: dict[str, ModelFitResult] = {} - predictions: dict[str, ForecastDataset] = {} if data_test is not None: logger.info("Data test provided during fit, but will be ignored for MetaForecating") @@ -253,14 +361,36 @@ def _fit_forecasters( # Fit the forecasters for name in self.forecasters: logger.debug("Started fitting Forecaster '%s'.", name) - predictions[name] = self._fit_forecaster( - data=data, - data_val=data_val, - data_test=None, - forecaster_name=name, + predictions_train[name], predictions_val[name], predictions_test[name], results[name] = ( + self._fit_forecaster( + data=data, + data_val=data_val, + data_test=None, + forecaster_name=name, + ) ) - return EnsembleForecastDataset.from_forecast_datasets(predictions, target_series=data.data[self.target_column]) + train_ensemble = EnsembleForecastDataset.from_forecast_datasets( + predictions_train, target_series=data.data[self.target_column] + ) + + if all(isinstance(v, ForecastDataset) for v in predictions_val.values()): + val_ensemble = EnsembleForecastDataset.from_forecast_datasets( + {k: v for k, v in predictions_val.items() if v is not None}, + target_series=data.data[self.target_column], + ) + else: + val_ensemble = None + + if all(isinstance(v, ForecastDataset) for v in predictions_test.values()): + test_ensemble = EnsembleForecastDataset.from_forecast_datasets( + {k: v for k, v in predictions_test.items() if v is not None}, + target_series=data.data[self.target_column], + ) + else: + test_ensemble = None + + return train_ensemble, val_ensemble, test_ensemble, results def _fit_forecaster( self, @@ -268,7 +398,12 @@ def _fit_forecaster( data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, forecaster_name: str = "", - ) -> ForecastDataset: + ) -> tuple[ + ForecastDataset, + ForecastDataset | None, + ForecastDataset | None, + ModelFitResult, + ]: """Train the forecaster on the provided dataset. Args: @@ -298,18 +433,42 @@ def _fit_forecaster( input_data_train, input_data_val, input_data_test = self.data_splitter.split_dataset( data=input_data_train, data_val=input_data_val, data_test=input_data_test, target_column=self.target_column ) - logger.debug("Started fitting forecaster '%s'.", forecaster_name) + # Fit the model + logger.debug("Started fitting forecaster '%s'.", forecaster_name) forecaster.fit(data=input_data_train, data_val=input_data_val) - prediction = self._predict_forecaster(input_data=input_data_train, forecaster_name=forecaster_name) logger.debug("Completed fitting forecaster '%s'.", forecaster_name) - return ForecastDataset( - data=prediction.data, - sample_interval=prediction.sample_interval, - forecast_start=prediction.forecast_start, + prediction_train = self._predict_forecaster(input_data=input_data_train, forecaster_name=forecaster_name) + metrics_train = self._calculate_score(prediction=prediction_train) + + if input_data_val is not None: + prediction_val = self._predict_forecaster(input_data=input_data_val, forecaster_name=forecaster_name) + metrics_val = self._calculate_score(prediction=prediction_val) + else: + prediction_val = None + metrics_val = None + + if input_data_test is not None: + prediction_test = self._predict_forecaster(input_data=input_data_test, forecaster_name=forecaster_name) + metrics_test = self._calculate_score(prediction=prediction_test) + else: + prediction_test = None + metrics_test = None + + result = ModelFitResult( + input_dataset=input_data_train, + input_data_train=input_data_train, + input_data_val=input_data_val, + input_data_test=input_data_test, + metrics_train=metrics_train, + metrics_val=metrics_val, + metrics_test=metrics_test, + metrics_full=metrics_train, ) + return prediction_train, prediction_val, prediction_test, result + def _predict_forecaster(self, input_data: ForecastInputDataset, forecaster_name: str) -> ForecastDataset: # Predict and restore target column prediction_raw = self.forecasters[forecaster_name].predict(data=input_data) @@ -387,10 +546,56 @@ def _predict_combiner( prediction.data[ensemble_dataset.target_column] = ensemble_dataset.target_series return prediction - def _fit_combiner(self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset) -> None: + def _fit_combiner( + self, + data: TimeSeriesDataset, + train_ensemble_dataset: EnsembleForecastDataset, + data_val: TimeSeriesDataset | None = None, + data_test: TimeSeriesDataset | None = None, + val_ensemble_dataset: EnsembleForecastDataset | None = None, + test_ensemble_dataset: EnsembleForecastDataset | None = None, + ) -> ModelFitResult: + + features_train, features_val, features_test = self._fit_prepare_combiner_data( + data=data, data_val=data_val, data_test=data_test + ) + logger.debug("Fitting combiner.") - features = self._fit_transform_combiner_data(data=original_data) - self.combiner.fit(ensemble_dataset, additional_features=features) + self.combiner.fit( + data=train_ensemble_dataset, data_val=val_ensemble_dataset, additional_features=features_train + ) + + prediction_train = self.combiner.predict(train_ensemble_dataset, additional_features=features_train) + metrics_train = self._calculate_score(prediction=prediction_train) + + if val_ensemble_dataset is not None: + prediction_val = self.combiner.predict(val_ensemble_dataset, additional_features=features_val) + metrics_val = self._calculate_score(prediction=prediction_val) + else: + prediction_val = None + metrics_val = None + + if test_ensemble_dataset is not None: + prediction_test = self.combiner.predict(test_ensemble_dataset, additional_features=features_test) + metrics_test = self._calculate_score(prediction=prediction_test) + else: + prediction_test = None + metrics_test = None + + return ModelFitResult( + input_dataset=train_ensemble_dataset, + input_data_train=train_ensemble_dataset.select_quantile(quantile=self.config[0].quantiles[0]), + input_data_val=val_ensemble_dataset.select_quantile(quantile=self.config[0].quantiles[0]) + if val_ensemble_dataset + else None, + input_data_test=test_ensemble_dataset.select_quantile(quantile=self.config[0].quantiles[0]) + if test_ensemble_dataset + else None, + metrics_train=metrics_train, + metrics_val=metrics_val, + metrics_test=metrics_test, + metrics_full=metrics_train, + ) def _predict_contributions_combiner( self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index cb6dbdad2..d6dfda8a7 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -256,3 +256,27 @@ def select_quantile(self, quantile: Quantile) -> ForecastInputDataset: target_column=self.target_column, forecast_start=self.forecast_start, ) + + def select_forecaster(self, forecaster_name: str) -> ForecastDataset: + """Select data for a specific base learner across all quantiles. + + Args: + forecaster_name: Name of the base learner to select. + + Returns: + ForecastDataset containing predictions from the specified base learner. + """ + selected_columns = [ + f"{forecaster_name}_{q.format()}" for q in self.quantiles if f"{forecaster_name}_{q.format()}" in self.data + ] + prediction_data = self.data[selected_columns].copy() + prediction_data.columns = [q.format() for q in self.quantiles] + + prediction_data[self.target_column] = self.data[self.target_column] + + return ForecastDataset( + data=prediction_data, + sample_interval=self.sample_interval, + forecast_start=self.forecast_start, + target_column=self.target_column, + ) diff --git a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py index 542d00448..8b0f6c1b6 100644 --- a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py @@ -18,7 +18,7 @@ from openstef_core.datasets import TimeSeriesDataset, VersionedTimeSeriesDataset from openstef_core.datasets.validated_datasets import ForecastDataset from openstef_core.exceptions import NotFittedError, SkipFitting -from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel, EnsembleModelFitResult from openstef_models.mixins import ModelIdentifier, PredictorCallback from openstef_models.mixins.callbacks import WorkflowContext from openstef_models.models.forecasting_model import ForecastingModel, ModelFitResult @@ -131,7 +131,7 @@ def fit( data: TimeSeriesDataset, data_val: TimeSeriesDataset | None = None, data_test: TimeSeriesDataset | None = None, - ) -> ModelFitResult | None: + ) -> ModelFitResult | EnsembleModelFitResult | None: """Train the forecasting model with callback execution. Executes the complete training workflow including pre-fit callbacks, @@ -154,6 +154,10 @@ def fit( result = self.model.fit(data=data, data_val=data_val, data_test=data_test) + if isinstance(result, EnsembleModelFitResult): + self._logger.info("Discarding EnsembleModelFitResult for compatibility.") + result = result.combiner_fit_result + for callback in self.callbacks: callback.on_fit_end(context=context, result=result) except SkipFitting as e: From 619c271871df7c2bb88a171ce308b36b2d91bda2 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 16 Dec 2025 11:22:32 +0100 Subject: [PATCH 66/72] Migrated Flagger and Selector to OpenSTEF Models transforms Signed-off-by: Lars van Someren --- .../models/forecast_combiners/forecast_combiner.py | 2 +- .../src/openstef_meta/presets/forecasting_workflow.py | 2 +- .../src/openstef_meta/transforms/__init__.py | 11 ----------- .../unit/models/test_ensemble_forecasting_model.py | 1 - .../openstef-meta/tests/unit/transforms/__init__.py | 0 .../openstef_models/transforms/general/__init__.py | 4 ++++ .../openstef_models/transforms/general/flagger.py} | 8 ++++---- .../openstef_models/transforms/general}/selector.py | 0 .../tests/unit/transforms/general/test_flagger.py} | 2 +- 9 files changed, 11 insertions(+), 19 deletions(-) delete mode 100644 packages/openstef-meta/src/openstef_meta/transforms/__init__.py delete mode 100644 packages/openstef-meta/tests/unit/transforms/__init__.py rename packages/{openstef-meta/src/openstef_meta/transforms/flag_features_bound.py => openstef-models/src/openstef_models/transforms/general/flagger.py} (92%) rename packages/{openstef-meta/src/openstef_meta/transforms => openstef-models/src/openstef_models/transforms/general}/selector.py (100%) rename packages/{openstef-meta/tests/unit/transforms/test_flag_features_bound.py => openstef-models/tests/unit/transforms/general/test_flagger.py} (97%) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index f0078d949..1e5eb29dd 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -18,7 +18,7 @@ from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.mixins import HyperParams, Predictor from openstef_core.types import LeadTime, Quantile -from openstef_meta.transforms.selector import Selector +from openstef_models.transforms.general.selector import Selector from openstef_meta.utils.datasets import EnsembleForecastDataset from openstef_models.utils.feature_selection import FeatureSelection diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 9505e9e7a..9b018a279 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -28,7 +28,7 @@ from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster -from openstef_meta.transforms.selector import Selector +from openstef_models.transforms.general.selector import Selector from openstef_models.integrations.mlflow import MLFlowStorage from openstef_models.mixins.model_serializer import ModelIdentifier from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster diff --git a/packages/openstef-meta/src/openstef_meta/transforms/__init__.py b/packages/openstef-meta/src/openstef_meta/transforms/__init__.py deleted file mode 100644 index e551ace37..000000000 --- a/packages/openstef-meta/src/openstef_meta/transforms/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""Module for OpenSTEF Meta Transforms.""" - -from openstef_meta.transforms.flag_features_bound import Flagger - -__all__ = [ - "Flagger", -] diff --git a/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py index 33b78cfc9..b106eca1f 100644 --- a/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py +++ b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py @@ -76,7 +76,6 @@ def fit( data: EnsembleForecastDataset, data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, - sample_weights: pd.Series | None = None, ) -> None: self._is_fitted = True diff --git a/packages/openstef-meta/tests/unit/transforms/__init__.py b/packages/openstef-meta/tests/unit/transforms/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py index 79e59f58b..3f20c927e 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/__init__.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/__init__.py @@ -13,17 +13,21 @@ from openstef_models.transforms.general.empty_feature_remover import ( EmptyFeatureRemover, ) +from openstef_models.transforms.general.flagger import Flagger from openstef_models.transforms.general.imputer import Imputer from openstef_models.transforms.general.nan_dropper import NaNDropper from openstef_models.transforms.general.sample_weighter import SampleWeighter from openstef_models.transforms.general.scaler import Scaler +from openstef_models.transforms.general.selector import Selector __all__ = [ "Clipper", "DimensionalityReducer", "EmptyFeatureRemover", + "Flagger", "Imputer", "NaNDropper", "SampleWeighter", "Scaler", + "Selector", ] diff --git a/packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py b/packages/openstef-models/src/openstef_models/transforms/general/flagger.py similarity index 92% rename from packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py rename to packages/openstef-models/src/openstef_models/transforms/general/flagger.py index 0d5fcd379..5c3675148 100644 --- a/packages/openstef-meta/src/openstef_meta/transforms/flag_features_bound.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/flagger.py @@ -2,11 +2,11 @@ # # SPDX-License-Identifier: MPL-2.0 -"""Transform for clipping feature values to observed ranges. +"""Transform for flagging feature values inside or outside observed training ranges. This module provides functionality to clip feature values to their observed -minimum and maximum ranges during training, preventing out-of-range values -during inference and improving model robustness. +minimum and maximum ranges during training. It is useful to flag data drift and +can be used to inform forecast combiners which models might perform better. """ from typing import override @@ -32,7 +32,7 @@ class Flagger(BaseConfig, TimeSeriesTransform): >>> import pandas as pd >>> from datetime import timedelta >>> from openstef_core.datasets import TimeSeriesDataset - >>> from openstef_meta.transforms import Flagger + >>> from openstef_models.transforms.general import Flagger >>> from openstef_models.utils.feature_selection import FeatureSelection >>> # Create sample training dataset >>> training_data = pd.DataFrame({ diff --git a/packages/openstef-meta/src/openstef_meta/transforms/selector.py b/packages/openstef-models/src/openstef_models/transforms/general/selector.py similarity index 100% rename from packages/openstef-meta/src/openstef_meta/transforms/selector.py rename to packages/openstef-models/src/openstef_models/transforms/general/selector.py diff --git a/packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py b/packages/openstef-models/tests/unit/transforms/general/test_flagger.py similarity index 97% rename from packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py rename to packages/openstef-models/tests/unit/transforms/general/test_flagger.py index dc0d3ea80..b250099f4 100644 --- a/packages/openstef-meta/tests/unit/transforms/test_flag_features_bound.py +++ b/packages/openstef-models/tests/unit/transforms/general/test_flagger.py @@ -8,7 +8,7 @@ import pytest from openstef_core.datasets import TimeSeriesDataset -from openstef_meta.transforms import Flagger +from openstef_models.transforms.general import Flagger from openstef_models.utils.feature_selection import FeatureSelection From 3b6587a0a1d2c93d64e85574a5b52c60bcd60c1f Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 16 Dec 2025 12:06:50 +0100 Subject: [PATCH 67/72] Fixed restore target Forecast Combiner Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 +- .../models/ensemble_forecasting_model.py | 31 +++++++++++-------- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 6cfe6f901..f82f6ff24 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -40,7 +40,7 @@ from openstef_models.presets.forecasting_workflow import LocationConfig from openstef_models.workflows import CustomForecastingWorkflow -logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") +logging.basicConfig(level=logging.DEBUG, format="[%(asctime)s][%(levelname)s] %(message)s") OUTPUT_PATH = Path("./benchmark_results") diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 9140c4443..d216d159c 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -346,9 +346,6 @@ def _fit_forecasters( predictions_test: dict[str, ForecastDataset | None] = {} results: dict[str, ModelFitResult] = {} - if data_test is not None: - logger.info("Data test provided during fit, but will be ignored for MetaForecating") - # Fit the feature engineering transforms self.common_preprocessing.fit(data=data) data_transformed = self.common_preprocessing.transform(data=data) @@ -360,12 +357,12 @@ def _fit_forecasters( # Fit the forecasters for name in self.forecasters: - logger.debug("Started fitting Forecaster '%s'.", name) + logger.debug("Fitting Forecaster '%s'.", name) predictions_train[name], predictions_val[name], predictions_test[name], results[name] = ( self._fit_forecaster( data=data, data_val=data_val, - data_test=None, + data_test=data_test, forecaster_name=name, ) ) @@ -436,6 +433,7 @@ def _fit_forecaster( # Fit the model logger.debug("Started fitting forecaster '%s'.", forecaster_name) + logger.debug(input_data_train.data.head().iloc[:, :5]) forecaster.fit(data=input_data_train, data_val=input_data_val) logger.debug("Completed fitting forecaster '%s'.", forecaster_name) @@ -471,6 +469,8 @@ def _fit_forecaster( def _predict_forecaster(self, input_data: ForecastInputDataset, forecaster_name: str) -> ForecastDataset: # Predict and restore target column + logger.debug("Predicting forecaster '%s'.", forecaster_name) + logger.debug(input_data.data.head().iloc[:, :5]) prediction_raw = self.forecasters[forecaster_name].predict(data=input_data) prediction = self.postprocessing.transform(prediction_raw) return restore_target(dataset=prediction, original_dataset=input_data, target_column=self.target_column) @@ -535,16 +535,22 @@ def prepare_input( forecast_start=forecast_start, ) - def _predict_combiner( + def _predict_transform_combiner( self, ensemble_dataset: EnsembleForecastDataset, original_data: TimeSeriesDataset ) -> ForecastDataset: logger.debug("Predicting combiner.") features = self._transform_combiner_data(data=original_data) + + return self._predict_combiner(ensemble_dataset, features) + + def _predict_combiner( + self, ensemble_dataset: EnsembleForecastDataset, features: ForecastInputDataset | None + ) -> ForecastDataset: + logger.debug("Predicting combiner.") prediction_raw = self.combiner.predict(ensemble_dataset, additional_features=features) prediction = self.postprocessing.transform(prediction_raw) - prediction.data[ensemble_dataset.target_column] = ensemble_dataset.target_series - return prediction + return restore_target(dataset=prediction, original_dataset=ensemble_dataset, target_column=self.target_column) def _fit_combiner( self, @@ -565,18 +571,18 @@ def _fit_combiner( data=train_ensemble_dataset, data_val=val_ensemble_dataset, additional_features=features_train ) - prediction_train = self.combiner.predict(train_ensemble_dataset, additional_features=features_train) + prediction_train = self._predict_combiner(train_ensemble_dataset, features=features_train) metrics_train = self._calculate_score(prediction=prediction_train) if val_ensemble_dataset is not None: - prediction_val = self.combiner.predict(val_ensemble_dataset, additional_features=features_val) + prediction_val = self._predict_combiner(val_ensemble_dataset, features=features_val) metrics_val = self._calculate_score(prediction=prediction_val) else: prediction_val = None metrics_val = None if test_ensemble_dataset is not None: - prediction_test = self.combiner.predict(test_ensemble_dataset, additional_features=features_test) + prediction_test = self._predict_combiner(test_ensemble_dataset, features=features_test) metrics_test = self._calculate_score(prediction=prediction_test) else: prediction_test = None @@ -624,11 +630,10 @@ def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = Non ensemble_predictions = self._predict_forecasters(data=data, forecast_start=forecast_start) # Predict and restore target column - prediction = self._predict_combiner( + return self._predict_transform_combiner( ensemble_dataset=ensemble_predictions, original_data=data, ) - return restore_target(dataset=prediction, original_dataset=data, target_column=self.target_column) def predict_contributions(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> pd.DataFrame: """Generate forecasts for the provided dataset. From ede090879f182a72f271b67c4947a405440fead6 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 16 Dec 2025 12:10:27 +0100 Subject: [PATCH 68/72] Streamline logging statements, Fix quality Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 2 +- .../src/openstef_meta/models/ensemble_forecasting_model.py | 2 -- .../models/forecast_combiners/forecast_combiner.py | 2 +- .../src/openstef_meta/presets/forecasting_workflow.py | 2 +- 4 files changed, 3 insertions(+), 5 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index f82f6ff24..6cfe6f901 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -40,7 +40,7 @@ from openstef_models.presets.forecasting_workflow import LocationConfig from openstef_models.workflows import CustomForecastingWorkflow -logging.basicConfig(level=logging.DEBUG, format="[%(asctime)s][%(levelname)s] %(message)s") +logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") OUTPUT_PATH = Path("./benchmark_results") diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index d216d159c..b60c07a46 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -433,7 +433,6 @@ def _fit_forecaster( # Fit the model logger.debug("Started fitting forecaster '%s'.", forecaster_name) - logger.debug(input_data_train.data.head().iloc[:, :5]) forecaster.fit(data=input_data_train, data_val=input_data_val) logger.debug("Completed fitting forecaster '%s'.", forecaster_name) @@ -470,7 +469,6 @@ def _fit_forecaster( def _predict_forecaster(self, input_data: ForecastInputDataset, forecaster_name: str) -> ForecastDataset: # Predict and restore target column logger.debug("Predicting forecaster '%s'.", forecaster_name) - logger.debug(input_data.data.head().iloc[:, :5]) prediction_raw = self.forecasters[forecaster_name].predict(data=input_data) prediction = self.postprocessing.transform(prediction_raw) return restore_target(dataset=prediction, original_dataset=input_data, target_column=self.target_column) diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index 1e5eb29dd..d64614067 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -18,8 +18,8 @@ from openstef_core.datasets import ForecastDataset, ForecastInputDataset from openstef_core.mixins import HyperParams, Predictor from openstef_core.types import LeadTime, Quantile -from openstef_models.transforms.general.selector import Selector from openstef_meta.utils.datasets import EnsembleForecastDataset +from openstef_models.transforms.general.selector import Selector from openstef_models.utils.feature_selection import FeatureSelection SELECTOR = Selector( diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index 9b018a279..dbe34509b 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -28,7 +28,6 @@ from openstef_meta.models.forecast_combiners.rules_combiner import RulesCombiner from openstef_meta.models.forecast_combiners.stacking_combiner import StackingCombiner from openstef_meta.models.forecasting.residual_forecaster import ResidualForecaster -from openstef_models.transforms.general.selector import Selector from openstef_models.integrations.mlflow import MLFlowStorage from openstef_models.mixins.model_serializer import ModelIdentifier from openstef_models.models.forecasting.gblinear_forecaster import GBLinearForecaster @@ -40,6 +39,7 @@ from openstef_models.transforms.general import Clipper, EmptyFeatureRemover, SampleWeighter, Scaler from openstef_models.transforms.general.imputer import Imputer from openstef_models.transforms.general.nan_dropper import NaNDropper +from openstef_models.transforms.general.selector import Selector from openstef_models.transforms.postprocessing import QuantileSorter from openstef_models.transforms.time_domain import ( CyclicFeaturesAdder, From ab13581222680162e36dda5ffd514f2351d788a6 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 16 Dec 2025 14:09:48 +0100 Subject: [PATCH 69/72] Resolved comments, fixed bug Signed-off-by: Lars van Someren --- .../openstef4_backtest_forecaster.py | 7 +- .../src/openstef_meta/mixins/__init__.py | 5 - .../src/openstef_meta/mixins/contributions.py | 20 --- .../models/ensemble_forecasting_model.py | 9 +- .../forecast_combiners/forecast_combiner.py | 12 +- .../learned_weights_combiner.py | 19 ++- .../forecast_combiners/rules_combiner.py | 2 +- .../forecast_combiners/stacking_combiner.py | 14 +- .../models/forecasting/residual_forecaster.py | 11 +- .../presets/forecasting_workflow.py | 134 +++++++++--------- .../src/openstef_meta/utils/datasets.py | 26 ++-- .../test_ensemble_forecasting_model.py | 4 +- .../models/test_ensemble_forecasting_model.py | 2 +- .../openstef_models/explainability/mixins.py | 2 +- .../forecasting/flatliner_forecaster.py | 2 - .../models/forecasting/gblinear_forecaster.py | 2 +- .../models/forecasting/lgbm_forecaster.py | 2 +- .../forecasting/lgbmlinear_forecaster.py | 28 +--- .../models/forecasting/xgboost_forecaster.py | 2 +- .../presets/forecasting_workflow.py | 6 +- .../utils/multi_quantile_regressor.py | 8 +- .../workflows/custom_forecasting_workflow.py | 2 +- 22 files changed, 133 insertions(+), 186 deletions(-) delete mode 100644 packages/openstef-meta/src/openstef_meta/mixins/__init__.py delete mode 100644 packages/openstef-meta/src/openstef_meta/mixins/contributions.py diff --git a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py index 874276089..3c39c8846 100644 --- a/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py +++ b/packages/openstef-beam/src/openstef_beam/backtesting/backtest_forecaster/openstef4_backtest_forecaster.py @@ -44,7 +44,7 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): ) contributions: bool = Field( default=False, - description="When True, saves intermediate input data for explainability", + description="When True, saves base Forecaster prediction contributions for ensemble models in cache_dir", ) _workflow: CustomForecastingWorkflow | None = PrivateAttr(default=None) @@ -54,9 +54,7 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): @override def model_post_init(self, context: Any) -> None: - if self.debug: - self.cache_dir.mkdir(parents=True, exist_ok=True) - if self.contributions: + if self.debug or self.contributions: self.cache_dir.mkdir(parents=True, exist_ok=True) @property @@ -68,7 +66,6 @@ def quantiles(self) -> list[Q]: # Extract quantiles from the workflow's model if isinstance(self._workflow.model, EnsembleForecastingModel): - # Assuming all ensemble members have the same quantiles name = self._workflow.model.forecaster_names[0] return self._workflow.model.forecasters[name].config.quantiles return self._workflow.model.forecaster.config.quantiles diff --git a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py b/packages/openstef-meta/src/openstef_meta/mixins/__init__.py deleted file mode 100644 index 90a57a257..000000000 --- a/packages/openstef-meta/src/openstef_meta/mixins/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""Mixins for OpenSTEF-Meta package.""" diff --git a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py b/packages/openstef-meta/src/openstef_meta/mixins/contributions.py deleted file mode 100644 index f00c185b3..000000000 --- a/packages/openstef-meta/src/openstef_meta/mixins/contributions.py +++ /dev/null @@ -1,20 +0,0 @@ -# SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project -# -# SPDX-License-Identifier: MPL-2.0 - -"""ExplainableMetaForecaster Mixin.""" - -from abc import ABC, abstractmethod - -import pandas as pd - -from openstef_core.datasets import ForecastInputDataset - - -class ContributionsMixin(ABC): - """Mixin class for models that support contribution analysis.""" - - @abstractmethod - def predict_contributions(self, X: ForecastInputDataset) -> pd.DataFrame: - """Get feature contributions for the given input data X.""" - raise NotImplementedError("This method should be implemented by subclasses.") diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index b60c07a46..5c1d00bcd 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -268,10 +268,10 @@ def fit( def _combine_datasets( data: ForecastInputDataset, additional_features: ForecastInputDataset ) -> ForecastInputDataset: - """Combine base learner predictions with additional features for final learner input. + """Combine Forecaster learner predictions with additional features for ForecastCombiner input. Args: - data: ForecastInputDataset containing base learner predictions. + data: ForecastInputDataset containing base Forecaster predictions. additional_features: ForecastInputDataset containing additional features. Returns: @@ -507,13 +507,12 @@ def prepare_input( Processed forecast input dataset ready for model prediction. """ logger.debug("Preparing input data for forecaster '%s'.", forecaster_name) - input_data = restore_target(dataset=data, original_dataset=data, target_column=self.target_column) - # Transform the data - input_data = self.common_preprocessing.transform(data=input_data) + input_data = self.common_preprocessing.transform(data=data) if forecaster_name in self.model_specific_preprocessing: logger.debug("Applying model-specific preprocessing for forecaster '%s'.", forecaster_name) input_data = self.model_specific_preprocessing[forecaster_name].transform(data=input_data) + input_data = restore_target(dataset=input_data, original_dataset=data, target_column=self.target_column) # Cut away input history to avoid training on incomplete data input_data_start = cast("pd.Series[pd.Timestamp]", input_data.index).min().to_pydatetime() diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py index d64614067..a8cd4864f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/forecast_combiner.py @@ -83,7 +83,7 @@ def with_horizon(self, horizon: LeadTime) -> Self: class ForecastCombiner(Predictor[EnsembleForecastDataset, ForecastDataset]): - """Combines base learner predictions for each quantile into final predictions.""" + """Combines base Forecaster predictions for each quantile into final predictions.""" config: ForecastCombinerConfig @@ -94,7 +94,7 @@ def fit( data_val: EnsembleForecastDataset | None = None, additional_features: ForecastInputDataset | None = None, ) -> None: - """Fit the final learner using base learner predictions. + """Fit the final learner using base Forecaster predictions. Args: data: EnsembleForecastDataset @@ -108,10 +108,10 @@ def predict( data: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None, ) -> ForecastDataset: - """Generate final predictions based on base learner predictions. + """Generate final predictions based on base Forecaster predictions. Args: - data: EnsembleForecastDataset containing base learner predictions. + data: EnsembleForecastDataset containing base Forecaster predictions. data_val: Optional EnsembleForecastDataset for validation during prediction. Will be ignored additional_features: Optional ForecastInputDataset containing additional features for the final learner. @@ -132,10 +132,10 @@ def predict_contributions( data: EnsembleForecastDataset, additional_features: ForecastInputDataset | None = None, ) -> pd.DataFrame: - """Generate final predictions based on base learner predictions. + """Generate final predictions based on base Forecaster predictions. Args: - data: EnsembleForecastDataset containing base learner predictions. + data: EnsembleForecastDataset containing base Forecaster predictions. data_val: Optional EnsembleForecastDataset for validation during prediction. Will be ignored additional_features: Optional ForecastInputDataset containing additional features for the final learner. diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py index 6d6025e30..d2b0fac48 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/learned_weights_combiner.py @@ -1,12 +1,13 @@ # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project # # SPDX-License-Identifier: MPL-2.0 -"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). +"""Learned Weights Combiner. -Provides method that attempts to combine the advantages of a linear model (Extraplolation) -and tree-based model (Non-linear patterns). This is acieved by training two base learners, -followed by a small linear model that regresses on the baselearners' predictions. -The implementation is based on sklearn's StackingRegressor. +Forecast combiner that uses a classification approach to learn weights for base forecasters. +It is designed to efficiently combine predictions from multiple base forecasters by learning which +forecaster is likely to perform best under different conditions. The combiner can operate in two modes: +- Hard Selection: Selects the base forecaster with the highest predicted probability for each instance. +- Soft Selection: Uses the predicted probabilities as weights to combine base forecaster predictions. """ import logging @@ -228,7 +229,13 @@ def get_classifier(self) -> Classifier: class WeightsCombiner(ForecastCombiner): - """Combines base learner predictions with a classification approach to determine which base learner to use.""" + """Combines base Forecaster predictions with a classification approach. + + The classifier is used to predict model weights for each base forecaster. + Depending on the `hard_selection` parameter in the configuration, the combiner can either + select the base forecaster with the highest predicted probability (hard selection) or use + the predicted probabilities as weights to combine base forecaster predictions (soft selection). + """ Config = WeightsCombinerConfig LGBMHyperParams = LGBMCombinerHyperParams diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py index 57c44be02..93a12744f 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/rules_combiner.py @@ -67,7 +67,7 @@ def _validate_hyperparams(v: HyperParams) -> HyperParams: class RulesCombiner(ForecastCombiner): - """Combines base learner predictions per quantile into final predictions using a regression approach.""" + """Combines base Forecaster predictions per quantile into final predictions using hard-coded rules.""" Config = RulesCombinerConfig diff --git a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py index 8324ca607..d59811453 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecast_combiners/stacking_combiner.py @@ -1,12 +1,10 @@ # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project # # SPDX-License-Identifier: MPL-2.0 -"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). +"""Stacking Forecast Combiner. -Provides method that attempts to combine the advantages of a linear model (Extraplolation) -and tree-based model (Non-linear patterns). This is acieved by training two base learners, -followed by a small linear model that regresses on the baselearners' predictions. -The implementation is based on sklearn's StackingRegressor. +This module implements a Stacking Combiner that integrates predictions from multiple base Forecasters. +It uses a regression approach to combine the predictions for each quantile into final forecasts. """ import logging @@ -88,7 +86,7 @@ def validate_forecaster( class StackingCombiner(ForecastCombiner): - """Combines base learner predictions per quantile into final predictions using a regression approach.""" + """Combines base Forecaster predictions per quantile into final predictions using a regression approach.""" Config = StackingCombinerConfig LGBMHyperParams = LGBMHyperParams @@ -128,10 +126,10 @@ def __init__( def _combine_datasets( data: ForecastInputDataset, additional_features: ForecastInputDataset ) -> ForecastInputDataset: - """Combine base learner predictions with additional features for final learner input. + """Combine base Forecaster predictions with additional features for final learner input. Args: - data: ForecastInputDataset containing base learner predictions. + data: ForecastInputDataset containing base Forecaster predictions. additional_features: ForecastInputDataset containing additional features. Returns: diff --git a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py index 79cc44cd5..de44e003c 100644 --- a/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py +++ b/packages/openstef-meta/src/openstef_meta/models/forecasting/residual_forecaster.py @@ -1,12 +1,11 @@ # SPDX-FileCopyrightText: 2025 Contributors to the OpenSTEF project # # SPDX-License-Identifier: MPL-2.0 -"""Hybrid Forecaster (Stacked LightGBM + Linear Model Gradient Boosting). +"""Residual Forecaster. Provides method that attempts to combine the advantages of a linear model (Extraplolation) -and tree-based model (Non-linear patterns). This is acieved by training two base learners, -followed by a small linear model that regresses on the baselearners' predictions. -The implementation is based on sklearn's ResidualRegressor. +and tree-based model (Non-linear patterns). This is achieved by training a primary model, +typically linear, followed by a secondary model that learns to predict the residuals (errors) of the primary model. """ import logging @@ -132,10 +131,10 @@ def _init_secondary_model(self, hyperparams: ResidualBaseForecasterHyperParams) def _init_base_learners( config: ForecasterConfig, base_hyperparams: list[ResidualBaseForecasterHyperParams] ) -> list[ResidualBaseForecaster]: - """Initialize base learners based on provided hyperparameters. + """Initialize base Forecaster based on provided hyperparameters. Returns: - list[Forecaster]: List of initialized base learner forecasters. + list[Forecaster]: List of initialized base Forecaster forecasters. """ base_learners: list[ResidualBaseForecaster] = [] horizons = config.horizons diff --git a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py index dbe34509b..52568b3a1 100644 --- a/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py +++ b/packages/openstef-meta/src/openstef_meta/presets/forecasting_workflow.py @@ -355,48 +355,28 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin weight_floor=config.sample_weight_floor, weight_scale_percentile=config.sample_weight_scale_percentile, ), + # Remove lags Selector( selection=FeatureSelection( - exclude={ # Fix hardcoded lag features should be replaced by a LagsAdder classmethod - "load_lag_P14D", - "load_lag_P13D", - "load_lag_P12D", - "load_lag_P11D", - "load_lag_P10D", - "load_lag_P9D", - "load_lag_P8D", - # "load_lag_P7D", # Keep 7D lag for weekly seasonality - "load_lag_P6D", - "load_lag_P5D", - "load_lag_P4D", - "load_lag_P3D", - "load_lag_P2D", - } + exclude=set( + LagsAdder( + history_available=config.predict_history, + horizons=config.horizons, + add_trivial_lags=True, + target_column=config.target_column, + ).features_added() + ).difference({"load_lag_P7D"}) ) ), - Selector( # Fix hardcoded holiday features should be replaced by a HolidayFeatureAdder classmethod + # Remove holiday features to avoid linear dependencies + Selector( selection=FeatureSelection( - exclude={ - "is_ascension_day", - "is_christmas_day", - "is_easter_monday", - "is_easter_sunday", - "is_good_friday", - "is_holiday", - "is_king_s_day", - "is_liberation_day", - "is_new_year_s_day", - "is_second_day_of_christmas", - "is_sunday", - "is_week_day", - "is_weekend_day", - "is_whit_monday", - "is_whit_sunday", - "month_of_year", - "quarter_of_year", - } + exclude=set(HolidayFeatureAdder(country_code=config.location.country_code).features_added()) ) ), + Selector( + selection=FeatureSelection(exclude=set(DatetimeFeaturesAdder(onehot_encode=False).features_added())) + ), Imputer( selection=Exclude(config.target_column), imputation_strategy="mean", @@ -435,46 +415,64 @@ def create_ensemble_workflow(config: EnsembleWorkflowConfig) -> CustomForecastin raise ValueError(msg) # Build combiner - if config.ensemble_type == "learned_weights": - if config.combiner_model == "lgbm": + # Case: Ensemble type, combiner model + match (config.ensemble_type, config.combiner_model): + case ("learned_weights", "lgbm"): combiner_hp = WeightsCombiner.LGBMHyperParams() - elif config.combiner_model == "rf": + combiner_config = WeightsCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = WeightsCombiner( + config=combiner_config, + ) + case ("learned_weights", "rf"): combiner_hp = WeightsCombiner.RFHyperParams() - elif config.combiner_model == "xgboost": + combiner_config = WeightsCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = WeightsCombiner( + config=combiner_config, + ) + case ("learned_weights", "xgboost"): combiner_hp = WeightsCombiner.XGBHyperParams() - elif config.combiner_model == "logistic": + combiner_config = WeightsCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = WeightsCombiner( + config=combiner_config, + ) + case ("learned_weights", "logistic"): combiner_hp = WeightsCombiner.LogisticHyperParams() - else: - msg = f"Unsupported combiner model type: {config.combiner_model}" - raise ValueError(msg) - combiner_config = WeightsCombiner.Config( - hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles - ) - combiner = WeightsCombiner( - config=combiner_config, - ) - elif config.ensemble_type == "stacking": - if config.combiner_model == "lgbm": + combiner_config = WeightsCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = WeightsCombiner( + config=combiner_config, + ) + case ("stacking", "lgbm"): combiner_hp = StackingCombiner.LGBMHyperParams() - elif config.combiner_model == "gblinear": + combiner_config = StackingCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = StackingCombiner( + config=combiner_config, + ) + case ("stacking", "gblinear"): combiner_hp = StackingCombiner.GBLinearHyperParams(reg_alpha=0.0, reg_lambda=0.0) - else: - msg = f"Unsupported combiner model type for stacking: {config.combiner_model}" + combiner_config = StackingCombiner.Config( + hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles + ) + combiner = StackingCombiner( + config=combiner_config, + ) + case ("rules", _): + combiner_config = RulesCombiner.Config(horizons=config.horizons, quantiles=config.quantiles) + combiner = RulesCombiner( + config=combiner_config, + ) + case _: + msg = f"Unsupported ensemble and combiner combination: {config.ensemble_type}, {config.combiner_model}" raise ValueError(msg) - combiner_config = StackingCombiner.Config( - hyperparams=combiner_hp, horizons=config.horizons, quantiles=config.quantiles - ) - combiner = StackingCombiner( - config=combiner_config, - ) - elif config.ensemble_type == "rules": - combiner_config = RulesCombiner.Config(horizons=config.horizons, quantiles=config.quantiles) - combiner = RulesCombiner( - config=combiner_config, - ) - else: - msg = f"Unsupported ensemble type: {config.ensemble_type}" - raise ValueError(msg) postprocessing = [QuantileSorter()] diff --git a/packages/openstef-meta/src/openstef_meta/utils/datasets.py b/packages/openstef-meta/src/openstef_meta/utils/datasets.py index d6dfda8a7..e85c05b09 100644 --- a/packages/openstef-meta/src/openstef_meta/utils/datasets.py +++ b/packages/openstef-meta/src/openstef_meta/utils/datasets.py @@ -93,7 +93,7 @@ def __init__( self.forecaster_names, self.quantiles = self.get_learner_and_quantile(pd.Index(quantile_feature_names)) n_cols = len(self.forecaster_names) * len(self.quantiles) if len(data.columns) not in {n_cols + 1, n_cols}: - raise ValueError("Data columns do not match the expected number based on base learners and quantiles.") + raise ValueError("Data columns do not match the expected number based on base Forecasters and quantiles.") @property def target_series(self) -> pd.Series | None: @@ -104,16 +104,16 @@ def target_series(self) -> pd.Series | None: @staticmethod def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[str], list[Quantile]]: - """Extract base learner names and quantiles from feature names. + """Extract base Forecaster names and quantiles from feature names. Args: feature_names: Index of feature names in the dataset. Returns: - Tuple containing a list of base learner names and a list of quantiles. + Tuple containing a list of base Forecaster names and a list of quantiles. Raises: - ValueError: If an invalid base learner name is found in a feature name. + ValueError: If an invalid base Forecaster name is found in a feature name. """ forecasters: set[str] = set() quantiles: set[Quantile] = set() @@ -132,13 +132,13 @@ def get_learner_and_quantile(feature_names: pd.Index) -> tuple[list[str], list[Q @staticmethod def get_quantile_feature_name(feature_name: str) -> tuple[str, Quantile]: - """Generate the feature name for a given base learner and quantile. + """Generate the feature name for a given base Forecaster and quantile. Args: - feature_name: Feature name string in the format "BaseLearner_Quantile". + feature_name: Feature name string in the format "model_Quantile". Returns: - Tuple containing the base learner name and Quantile object. + Tuple containing the base Forecaster name and Quantile object. """ learner_part, quantile_part = feature_name.split("_", maxsplit=1) return learner_part, Quantile.parse(quantile_part) @@ -192,10 +192,10 @@ def _prepare_classification(data: pd.DataFrame, target: pd.Series, quantile: Qua quantile: Quantile for which to prepare classification data. Returns: - Series with categorical indicators of best-performing base learners. + Series with categorical indicators of best-performing base Forecasters. """ - # Calculate pinball loss for each base learner + # Calculate pinball loss for each base Forecaster def column_pinball_losses(preds: pd.Series) -> pd.Series: return calculate_pinball_errors(y_true=target, y_pred=preds, quantile=quantile) @@ -210,7 +210,7 @@ def select_quantile_classification(self, quantile: Quantile) -> ForecastInputDat quantile: Quantile to select. Returns: - Series containing binary indicators of best-performing base learners for the specified quantile. + Series containing binary indicators of best-performing base Forecasters for the specified quantile. Raises: ValueError: If the target column is not found in the dataset. @@ -258,13 +258,13 @@ def select_quantile(self, quantile: Quantile) -> ForecastInputDataset: ) def select_forecaster(self, forecaster_name: str) -> ForecastDataset: - """Select data for a specific base learner across all quantiles. + """Select data for a specific base Forecaster across all quantiles. Args: - forecaster_name: Name of the base learner to select. + forecaster_name: Name of the base Forecaster to select. Returns: - ForecastDataset containing predictions from the specified base learner. + ForecastDataset containing predictions from the specified base Forecaster. """ selected_columns = [ f"{forecaster_name}_{q.format()}" for q in self.quantiles if f"{forecaster_name}_{q.format()}" in self.data diff --git a/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py index f3d156a13..23835d6e7 100644 --- a/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py +++ b/packages/openstef-meta/tests/regression/test_ensemble_forecasting_model.py @@ -82,7 +82,9 @@ def test_preprocessing( # Check all base models for name, model in base_models.items(): # Ensemble model - common_ensemble = ensemble_model.common_preprocessing.transform(data=sample_timeseries_dataset) + common_ensemble = ensemble_model.common_preprocessing.transform( + data=sample_timeseries_dataset.copy_with(sample_timeseries_dataset.data) + ) ensemble_model.model_specific_preprocessing[name].fit(data=common_ensemble) transformed_ensemble = ensemble_model.model_specific_preprocessing[name].transform(data=common_ensemble) # Base model diff --git a/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py index b106eca1f..84f14cef7 100644 --- a/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py +++ b/packages/openstef-meta/tests/unit/models/test_ensemble_forecasting_model.py @@ -64,7 +64,7 @@ def predict(self, data: ForecastInputDataset) -> ForecastDataset: class SimpleCombiner(ForecastCombiner): - """Simple combiner that averages base learner predictions.""" + """Simple combiner that averages base Forecaster predictions.""" def __init__(self, config: ForecastCombinerConfig): self._config = config diff --git a/packages/openstef-models/src/openstef_models/explainability/mixins.py b/packages/openstef-models/src/openstef_models/explainability/mixins.py index b0fb6fab1..1e82fc413 100644 --- a/packages/openstef-models/src/openstef_models/explainability/mixins.py +++ b/packages/openstef-models/src/openstef_models/explainability/mixins.py @@ -54,7 +54,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p scale: Whether to scale contributions to sum to the prediction value. Returns: - DataFrame with contributions per base learner. + DataFrame with contributions per feature. """ raise NotImplementedError diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py index 73f9d56b3..a0afa77b6 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/flatliner_forecaster.py @@ -106,8 +106,6 @@ def feature_importances(self) -> pd.DataFrame: @override def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = True) -> pd.DataFrame: - if scale: - pass forecast_index = data.create_forecast_range(horizon=self.config.max_horizon) return pd.DataFrame( diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py index 9b230e060..60e7c0a73 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py @@ -334,7 +334,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool = Tru scale: If True, scale contributions to sum to 1.0 per quantile. Returns: - DataFrame with contributions per base learner. + DataFrame with contributions per feature. """ # Get input features for prediction input_data: pd.DataFrame = data.input_data(start=data.forecast_start) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py index ed3de0058..5868289d3 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbm_forecaster.py @@ -320,7 +320,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p scale: If True, scale contributions to sum to 1.0 per quantile. Returns: - DataFrame with contributions per base learner. + DataFrame with contributions per feature. """ # Get input features for prediction input_data: pd.DataFrame = data.input_data(start=data.forecast_start) diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py index b484c8a37..391bcceca 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/lgbmlinear_forecaster.py @@ -323,35 +323,9 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p scale: If True, scale contributions to sum to 1.0 per quantile. Returns: - DataFrame with contributions per base learner. + DataFrame with contributions per feature. """ raise NotImplementedError("predict_contributions is not yet implemented for LGBMLinearForecaster") - # Get input features for prediction - input_data: pd.DataFrame = data.input_data(start=data.forecast_start) - - contributions: list[pd.DataFrame] = [] - - for i, quantile in enumerate(self.config.quantiles): - # Get model for specific quantile - model: LGBMRegressor = self._lgbmlinear_model.models[i] # type: ignore - - # Generate contributions NOT AVAILABLE FOR LGBM with linear_trees=true - contribs_quantile: np.ndarray[float] = model.predict(input_data, pred_contrib=True)[:, :-1] # type: ignore - - if scale: - # Scale contributions so that they sum to 1.0 per quantile - contribs_quantile = np.abs(contribs_quantile) / np.sum(np.abs(contribs_quantile), axis=1, keepdims=True) - - contributions.append( - pd.DataFrame( - data=contribs_quantile, - index=input_data.index, - columns=[f"{feature}_{quantile.format()}" for feature in input_data.columns], - ) - ) - - # Construct DataFrame - return pd.concat(contributions, axis=1) @property @override diff --git a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py index 1469309f6..495fbde6c 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py @@ -428,7 +428,7 @@ def predict_contributions(self, data: ForecastInputDataset, *, scale: bool) -> p scale: If True, scale contributions to sum to 1.0 per quantile. Returns: - DataFrame with contributions per base learner. + DataFrame with contributions per feature. """ # Get input features for prediction input_data: pd.DataFrame = data.input_data(start=data.forecast_start) diff --git a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py index 7d5769f0e..39a415f60 100644 --- a/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/presets/forecasting_workflow.py @@ -100,9 +100,9 @@ class ForecastingWorkflowConfig(BaseConfig): # PredictionJob model_id: ModelIdentifier = Field(description="Unique identifier for the forecasting model.") # Model configuration - model: Literal[ - "xgboost", "gblinear", "flatliner", "stacking", "residual", "learned_weights", "lgbm", "lgbmlinear" - ] = Field(description="Type of forecasting model to use.") # TODO(#652): Implement median forecaster + model: Literal["xgboost", "gblinear", "flatliner", "residual", "lgbm", "lgbmlinear"] = Field( + description="Type of forecasting model to use." + ) # TODO(#652): Implement median forecaster quantiles: list[Quantile] = Field( default=[Q(0.5)], description="List of quantiles to predict for probabilistic forecasting.", diff --git a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py index 763932268..b95fbc28c 100644 --- a/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py +++ b/packages/openstef-models/src/openstef_models/utils/multi_quantile_regressor.py @@ -41,7 +41,7 @@ def __init__( base_learner: A scikit-learn compatible regressor class that supports quantile regression. quantile_param: The name of the parameter in base_learner that sets the quantile level. quantiles: List of quantiles to predict (e.g., [0.1, 0.5, 0.9]). - hyperparams: Dictionary of hyperparameters to pass to each base learner instance. + hyperparams: Dictionary of hyperparameters to pass to each estimator instance. """ self.quantiles = quantiles self.hyperparams = hyperparams @@ -56,7 +56,7 @@ def _init_model(self, q: float) -> BaseEstimator: base_learner = self.base_learner(**params) if self.quantile_param not in base_learner.get_params(): # type: ignore - msg = f"The base learner does not support the quantile parameter '{self.quantile_param}'." + msg = f"The base estimator does not support the quantile parameter '{self.quantile_param}'." raise ValueError(msg) return base_learner @@ -149,9 +149,9 @@ def models(self) -> list[BaseEstimator]: @property def has_feature_names(self) -> bool: - """Check if the base learners have feature names. + """Check if the base estimators have feature names. Returns: - True if the base learners have feature names, False otherwise. + True if the base estimators have feature names, False otherwise. """ return len(self.model_feature_names) > 0 diff --git a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py index 8b0f6c1b6..5aff3c938 100644 --- a/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py +++ b/packages/openstef-models/src/openstef_models/workflows/custom_forecasting_workflow.py @@ -155,7 +155,7 @@ def fit( result = self.model.fit(data=data, data_val=data_val, data_test=data_test) if isinstance(result, EnsembleModelFitResult): - self._logger.info("Discarding EnsembleModelFitResult for compatibility.") + self._logger.debug("Discarding EnsembleModelFitResult for compatibility.") result = result.combiner_fit_result for callback in self.callbacks: From b5a3737c487109d472e0473d999d17fb8e2cc215 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Tue, 16 Dec 2025 14:11:38 +0100 Subject: [PATCH 70/72] Moved example Signed-off-by: Lars van Someren --- .../examples => examples/benchmarks}/liander_2024_residual.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {packages/openstef-meta/src/openstef_meta/examples => examples/benchmarks}/liander_2024_residual.py (100%) diff --git a/packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py b/examples/benchmarks/liander_2024_residual.py similarity index 100% rename from packages/openstef-meta/src/openstef_meta/examples/liander_2024_residual.py rename to examples/benchmarks/liander_2024_residual.py From 297f186d7a9e4ff131804f9efee40f4ea6193a5e Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Wed, 17 Dec 2025 16:25:59 +0100 Subject: [PATCH 71/72] Integrated changes to beam structure Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_ensemble.py | 56 +++-------------- .../benchmarking/baselines/openstef4.py | 63 +++++++++---------- .../models/ensemble_forecasting_model.py | 4 +- 3 files changed, 43 insertions(+), 80 deletions(-) diff --git a/examples/benchmarks/liander_2024_ensemble.py b/examples/benchmarks/liander_2024_ensemble.py index 6cfe6f901..29ecb9b25 100644 --- a/examples/benchmarks/liander_2024_ensemble.py +++ b/examples/benchmarks/liander_2024_ensemble.py @@ -22,23 +22,18 @@ from datetime import timedelta from pathlib import Path -from pydantic_extra_types.coordinate import Coordinate -from pydantic_extra_types.country import CountryAlpha2 - -from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig, OpenSTEF4BacktestForecaster -from openstef_beam.benchmarking.benchmark_pipeline import BenchmarkContext +from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig +from openstef_beam.benchmarking.baselines import ( + create_openstef4_preset_backtest_forecaster, +) from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024Category, create_liander2024_benchmark_runner from openstef_beam.benchmarking.callbacks.strict_execution_callback import StrictExecutionCallback -from openstef_beam.benchmarking.models.benchmark_target import BenchmarkTarget from openstef_beam.benchmarking.storage.local_storage import LocalBenchmarkStorage from openstef_core.types import LeadTime, Q from openstef_meta.presets import ( EnsembleWorkflowConfig, - create_ensemble_workflow, ) from openstef_models.integrations.mlflow.mlflow_storage import MLFlowStorage -from openstef_models.presets.forecasting_workflow import LocationConfig -from openstef_models.workflows import CustomForecastingWorkflow logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") @@ -78,7 +73,7 @@ else: storage = None -common_config = EnsembleWorkflowConfig( +workflow_config = EnsembleWorkflowConfig( model_id="common_model_", ensemble_type=ensemble_type, base_models=base_models, # type: ignore @@ -112,42 +107,6 @@ ) -def _target_forecaster_factory( - context: BenchmarkContext, - target: BenchmarkTarget, -) -> OpenSTEF4BacktestForecaster: - # Factory function that creates a forecaster for a given target. - prefix = context.run_name - base_config = common_config - - def _create_workflow() -> CustomForecastingWorkflow: - # Create a new workflow instance with fresh model. - return create_ensemble_workflow( - config=base_config.model_copy( - update={ - "model_id": f"{prefix}_{target.name}", - "location": LocationConfig( - name=target.name, - description=target.description, - coordinate=Coordinate( - latitude=target.latitude, - longitude=target.longitude, - ), - country_code=CountryAlpha2("NL"), - ), - } - ) - ) - - return OpenSTEF4BacktestForecaster( - config=backtest_config, - workflow_factory=_create_workflow, - debug=False, - contributions=False, - cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", - ) - - if __name__ == "__main__": start_time = time.time() create_liander2024_benchmark_runner( @@ -155,7 +114,10 @@ def _create_workflow() -> CustomForecastingWorkflow: data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), callbacks=[StrictExecutionCallback()], ).run( - forecaster_factory=_target_forecaster_factory, + forecaster_factory=create_openstef4_preset_backtest_forecaster( + workflow_config=workflow_config, + cache_dir=OUTPUT_PATH / "cache", + ), run_name=model, n_processes=N_PROCESSES, filter_args=BENCHMARK_FILTER, diff --git a/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py b/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py index 82fc162cd..9019a2156 100644 --- a/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py +++ b/packages/openstef-beam/src/openstef_beam/benchmarking/baselines/openstef4.py @@ -32,6 +32,7 @@ from openstef_core.exceptions import FlatlinerDetectedError, NotFittedError from openstef_core.types import Q from openstef_meta.models.ensemble_forecasting_model import EnsembleForecastingModel +from openstef_meta.presets import EnsembleWorkflowConfig, create_ensemble_workflow from openstef_models.presets import ForecastingWorkflowConfig from openstef_models.workflows.custom_forecasting_workflow import ( CustomForecastingWorkflow, @@ -57,10 +58,8 @@ class OpenSTEF4BacktestForecaster(BaseModel, BacktestForecasterMixin): config: BacktestForecasterConfig = Field( description="Configuration for the backtest forecaster interface", ) - workflow_factory: Callable[[WorkflowCreationContext], CustomForecastingWorkflow] = ( - Field( - description="Factory function that creates a new CustomForecastingWorkflow instance", - ) + workflow_factory: Callable[[WorkflowCreationContext], CustomForecastingWorkflow] = Field( + description="Factory function that creates a new CustomForecastingWorkflow instance", ) cache_dir: Path = Field( description="Directory to use for caching model artifacts during backtesting", @@ -112,9 +111,7 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") - training_data.to_parquet( - path=self.cache_dir / f"debug_{id_str}_training.parquet" - ) + training_data.to_parquet(path=self.cache_dir / f"debug_{id_str}_training.parquet") try: # Use the workflow's fit method @@ -134,9 +131,7 @@ def fit(self, data: RestrictedHorizonVersionedTimeSeries) -> None: ) @override - def predict( - self, data: RestrictedHorizonVersionedTimeSeries - ) -> TimeSeriesDataset | None: + def predict(self, data: RestrictedHorizonVersionedTimeSeries) -> TimeSeriesDataset | None: if self._is_flatliner_detected: self._logger.info("Skipping prediction due to prior flatliner detection") return None @@ -147,8 +142,7 @@ def predict( # Extract the dataset including both historical context and forecast period predict_data = data.get_window( start=data.horizon - self.config.predict_context_length, - end=data.horizon - + self.config.predict_length, # Include the forecast period + end=data.horizon + self.config.predict_length, # Include the forecast period available_before=data.horizon, # Only use data available at prediction time (prevents lookahead bias) ) @@ -163,23 +157,13 @@ def predict( if self.debug: id_str = data.horizon.strftime("%Y%m%d%H%M%S") - predict_data.to_parquet( - path=self.cache_dir / f"debug_{id_str}_predict.parquet" - ) - forecast.to_parquet( - path=self.cache_dir / f"debug_{id_str}_forecast.parquet" - ) + predict_data.to_parquet(path=self.cache_dir / f"debug_{id_str}_predict.parquet") + forecast.to_parquet(path=self.cache_dir / f"debug_{id_str}_forecast.parquet") - if self.contributions and isinstance( - self._workflow.model, EnsembleForecastingModel - ): + if self.contributions and isinstance(self._workflow.model, EnsembleForecastingModel): contr_str = data.horizon.strftime("%Y%m%d%H%M%S") - contributions = self._workflow.model.predict_contributions( - predict_data, forecast_start=data.horizon - ) - df = pd.concat( - [contributions, forecast.data.drop(columns=["load"])], axis=1 - ) + contributions = self._workflow.model.predict_contributions(predict_data, forecast_start=data.horizon) + df = pd.concat([contributions, forecast.data.drop(columns=["load"])], axis=1) df.to_parquet(path=self.cache_dir / f"contrib_{contr_str}_predict.parquet") return forecast @@ -190,7 +174,7 @@ class OpenSTEF4PresetBacktestForecaster(OpenSTEF4BacktestForecaster): def _preset_target_forecaster_factory( - base_config: ForecastingWorkflowConfig, + base_config: ForecastingWorkflowConfig | EnsembleWorkflowConfig, backtest_config: BacktestForecasterConfig, cache_dir: Path, context: BenchmarkContext, @@ -204,6 +188,23 @@ def _preset_target_forecaster_factory( def _create_workflow(context: WorkflowCreationContext) -> CustomForecastingWorkflow: # Create a new workflow instance with fresh model. + if isinstance(base_config, EnsembleWorkflowConfig): + return create_ensemble_workflow( + config=base_config.model_copy( + update={ + "model_id": f"{prefix}_{target.name}", + "location": LocationConfig( + name=target.name, + description=target.description, + coordinate=Coordinate( + latitude=target.latitude, + longitude=target.longitude, + ), + ), + } + ) + ) + return create_forecasting_workflow( config=base_config.model_copy( update={ @@ -230,7 +231,7 @@ def _create_workflow(context: WorkflowCreationContext) -> CustomForecastingWorkf def create_openstef4_preset_backtest_forecaster( - workflow_config: ForecastingWorkflowConfig, + workflow_config: ForecastingWorkflowConfig | EnsembleWorkflowConfig, backtest_config: BacktestForecasterConfig | None = None, cache_dir: Path = Path("cache"), ) -> ForecasterFactory[BenchmarkTarget]: @@ -253,9 +254,7 @@ def create_openstef4_preset_backtest_forecaster( requires_training=True, predict_length=timedelta(days=7), predict_min_length=timedelta(minutes=15), - predict_context_length=timedelta( - days=14 - ), # Context needed for lag features + predict_context_length=timedelta(days=14), # Context needed for lag features predict_context_min_coverage=0.5, training_context_length=timedelta(days=90), # Three months of training data training_context_min_coverage=0.5, diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 5c1d00bcd..9299e879d 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -605,7 +605,9 @@ def _predict_contributions_combiner( ) -> pd.DataFrame: features = self._transform_combiner_data(data=original_data) - return self.combiner.predict_contributions(ensemble_dataset, additional_features=features) + predictions = self.combiner.predict_contributions(ensemble_dataset, additional_features=features) + predictions[ensemble_dataset.target_column] = ensemble_dataset.target_series + return predictions def predict(self, data: TimeSeriesDataset, forecast_start: datetime | None = None) -> ForecastDataset: """Generate forecasts for the provided dataset. From 0ac62c89f25f5085389f74d954d01d95b2459f74 Mon Sep 17 00:00:00 2001 From: Lars van Someren Date: Thu, 18 Dec 2025 09:44:05 +0100 Subject: [PATCH 72/72] make PR compliant Signed-off-by: Lars van Someren --- examples/benchmarks/liander_2024_residual.py | 55 +++------------- .../models/ensemble_forecasting_model.py | 2 +- .../mlflow/mlflow_storage_callback.py | 65 +++++-------------- .../models/forecasting_model.py | 2 +- .../transforms/general/selector.py | 4 +- 5 files changed, 28 insertions(+), 100 deletions(-) diff --git a/examples/benchmarks/liander_2024_residual.py b/examples/benchmarks/liander_2024_residual.py index a8a42b113..aecb3de9e 100644 --- a/examples/benchmarks/liander_2024_residual.py +++ b/examples/benchmarks/liander_2024_residual.py @@ -22,23 +22,18 @@ from datetime import timedelta from pathlib import Path -from pydantic_extra_types.coordinate import Coordinate -from pydantic_extra_types.country import CountryAlpha2 - -from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig, OpenSTEF4BacktestForecaster -from openstef_beam.benchmarking.benchmark_pipeline import BenchmarkContext +from openstef_beam.backtesting.backtest_forecaster import BacktestForecasterConfig +from openstef_beam.benchmarking.baselines import ( + create_openstef4_preset_backtest_forecaster, +) from openstef_beam.benchmarking.benchmarks.liander2024 import Liander2024Category, create_liander2024_benchmark_runner from openstef_beam.benchmarking.callbacks.strict_execution_callback import StrictExecutionCallback -from openstef_beam.benchmarking.models.benchmark_target import BenchmarkTarget from openstef_beam.benchmarking.storage.local_storage import LocalBenchmarkStorage from openstef_core.types import LeadTime, Q from openstef_models.integrations.mlflow.mlflow_storage import MLFlowStorage from openstef_models.presets import ( ForecastingWorkflowConfig, - create_forecasting_workflow, ) -from openstef_models.presets.forecasting_workflow import LocationConfig -from openstef_models.workflows import CustomForecastingWorkflow logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s] %(message)s") @@ -104,50 +99,18 @@ ) -def _target_forecaster_factory( - context: BenchmarkContext, - target: BenchmarkTarget, -) -> OpenSTEF4BacktestForecaster: - # Factory function that creates a forecaster for a given target. - prefix = context.run_name - base_config = common_config - - def _create_workflow() -> CustomForecastingWorkflow: - # Create a new workflow instance with fresh model. - return create_forecasting_workflow( - config=base_config.model_copy( - update={ - "model_id": f"{prefix}_{target.name}", - "location": LocationConfig( - name=target.name, - description=target.description, - coordinate=Coordinate( - latitude=target.latitude, - longitude=target.longitude, - ), - country_code=CountryAlpha2("NL"), - ), - } - ) - ) - - return OpenSTEF4BacktestForecaster( - config=backtest_config, - workflow_factory=_create_workflow, - debug=False, - cache_dir=OUTPUT_PATH / "cache" / f"{context.run_name}_{target.name}", - ) - - if __name__ == "__main__": start_time = time.time() + # Run for XGBoost model create_liander2024_benchmark_runner( storage=LocalBenchmarkStorage(base_path=OUTPUT_PATH / model), - data_dir=Path("../data/liander2024-energy-forecasting-benchmark"), # adjust path as needed callbacks=[StrictExecutionCallback()], ).run( - forecaster_factory=_target_forecaster_factory, + forecaster_factory=create_openstef4_preset_backtest_forecaster( + workflow_config=common_config, + cache_dir=OUTPUT_PATH / "cache", + ), run_name=model, n_processes=N_PROCESSES, filter_args=BENCHMARK_FILTER, diff --git a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py index 9299e879d..5394ab476 100644 --- a/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py +++ b/packages/openstef-meta/src/openstef_meta/models/ensemble_forecasting_model.py @@ -732,7 +732,7 @@ def restore_target[T: TimeSeriesDataset]( target_series = original_dataset.select_features([target_column]).select_version().data[target_column] def _transform_restore_target(df: pd.DataFrame) -> pd.DataFrame: - return df.assign(**{str(target_series.name): df.index.map(target_series)}) # pyright: ignore[reportUnknownMemberType] + return df.assign(**{str(target_series.name): df.index.map(target_series)}) # type: ignore return dataset.pipe_pandas(_transform_restore_target) diff --git a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py index 0d66be6e1..91be9fcab 100644 --- a/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py +++ b/packages/openstef-models/src/openstef_models/integrations/mlflow/mlflow_storage_callback.py @@ -93,9 +93,7 @@ def on_fit_start( now = datetime.now(tz=UTC) end_time_millis = cast(float | None, run.info.end_time) run_end_datetime = ( - datetime.fromtimestamp(end_time_millis / 1000, tz=UTC) - if end_time_millis is not None - else None + datetime.fromtimestamp(end_time_millis / 1000, tz=UTC) if end_time_millis is not None else None ) self._logger.info( "Found previous MLflow run %s for model %s ended at %s", @@ -103,10 +101,7 @@ def on_fit_start( context.workflow.model_id, run_end_datetime, ) - if ( - run_end_datetime is not None - and (now - run_end_datetime) <= self.model_reuse_max_age - ): + if run_end_datetime is not None and (now - run_end_datetime) <= self.model_reuse_max_age: raise SkipFitting("Model is recent enough, skipping re-fit.") @override @@ -132,23 +127,17 @@ def on_fit_end( experiment_tags=context.workflow.experiment_tags, ) run_id: str = run.info.run_id - self._logger.info( - "Created MLflow run %s for model %s", run_id, context.workflow.model_id - ) + self._logger.info("Created MLflow run %s for model %s", run_id, context.workflow.model_id) # Store the model input - run_path = self.storage.get_artifacts_path( - model_id=context.workflow.model_id, run_id=run_id - ) + run_path = self.storage.get_artifacts_path(model_id=context.workflow.model_id, run_id=run_id) data_path = run_path / self.storage.data_path data_path.mkdir(parents=True, exist_ok=True) result.input_dataset.to_parquet(path=data_path / "data.parquet") self._logger.info("Stored training data at %s for run %s", data_path, run_id) # Store feature importance plot if enabled - if self.store_feature_importance_plot and isinstance( - context.workflow.model.forecaster, ExplainableForecaster - ): + if self.store_feature_importance_plot and isinstance(context.workflow.model.forecaster, ExplainableForecaster): fig = context.workflow.model.forecaster.plot_feature_importances() fig.write_html(data_path / "feature_importances.html") # pyright: ignore[reportUnknownMemberType] @@ -166,17 +155,11 @@ def on_fit_end( if result.metrics_val is not None: metrics.update(_metrics_to_dict(metrics=result.metrics_val, prefix="val_")) if result.metrics_test is not None: - metrics.update( - _metrics_to_dict(metrics=result.metrics_test, prefix="test_") - ) + metrics.update(_metrics_to_dict(metrics=result.metrics_test, prefix="test_")) # Mark the run as finished - self.storage.finalize_run( - model_id=context.workflow.model_id, run_id=run_id, metrics=metrics - ) - self._logger.info( - "Stored MLflow run %s for model %s", run_id, context.workflow.model_id - ) + self.storage.finalize_run(model_id=context.workflow.model_id, run_id=run_id, metrics=metrics) + self._logger.info("Stored MLflow run %s for model %s", run_id, context.workflow.model_id) @override def on_predict_start( @@ -196,9 +179,7 @@ def on_predict_start( # Load the model from the latest run run_id: str = run.info.run_id - old_model = self.storage.load_run_model( - run_id=run_id, model_id=context.workflow.model_id - ) + old_model = self.storage.load_run_model(run_id=run_id, model_id=context.workflow.model_id) if not isinstance(old_model, ForecastingModel): self._logger.warning( @@ -214,9 +195,7 @@ def on_predict_start( context.workflow.model_id, ) - def _run_model_selection( - self, workflow: CustomForecastingWorkflow, result: ModelFitResult - ) -> None: + def _run_model_selection(self, workflow: CustomForecastingWorkflow, result: ModelFitResult) -> None: # Find the latest successful run for this model runs = self.storage.search_latest_runs(model_id=workflow.model_id) run = next(iter(runs), None) @@ -252,9 +231,7 @@ def _run_model_selection( if old_metrics is None: return - if self._check_is_new_model_better( - old_metrics=old_metrics, new_metrics=new_metrics - ): + if self._check_is_new_model_better(old_metrics=old_metrics, new_metrics=new_metrics): workflow.model = new_model else: workflow.model = old_model @@ -263,9 +240,7 @@ def _run_model_selection( self.model_selection_metric, run_id, ) - raise SkipFitting( - "New model did not improve monitored metric, skipping re-fit." - ) + raise SkipFitting("New model did not improve monitored metric, skipping re-fit.") def _try_load_model( self, @@ -273,9 +248,7 @@ def _try_load_model( workflow: CustomForecastingWorkflow, ) -> ForecastingModel | None: try: - old_model = self.storage.load_run_model( - run_id=run_id, model_id=workflow.model_id - ) + old_model = self.storage.load_run_model(run_id=run_id, model_id=workflow.model_id) except ModelNotFoundError: self._logger.warning( "Could not load model from previous run %s for model %s, skipping model selection", @@ -309,9 +282,7 @@ def _try_evaluate_model( ) return None - def _check_tags_compatible( - self, run_tags: dict[str, str], new_tags: dict[str, str], run_id: str - ) -> bool: + def _check_tags_compatible(self, run_tags: dict[str, str], new_tags: dict[str, str], run_id: str) -> bool: """Check if model tags are compatible, excluding mlflow.runName. Returns: @@ -363,13 +334,9 @@ def _check_is_new_model_better( ) match direction: - case "higher_is_better" if ( - new_metric >= old_metric / self.model_selection_old_model_penalty - ): + case "higher_is_better" if new_metric >= old_metric / self.model_selection_old_model_penalty: return True - case "lower_is_better" if ( - new_metric <= old_metric / self.model_selection_old_model_penalty - ): + case "lower_is_better" if new_metric <= old_metric / self.model_selection_old_model_penalty: return True case _: return False diff --git a/packages/openstef-models/src/openstef_models/models/forecasting_model.py b/packages/openstef-models/src/openstef_models/models/forecasting_model.py index f2de3c4b3..9acea87fa 100644 --- a/packages/openstef-models/src/openstef_models/models/forecasting_model.py +++ b/packages/openstef-models/src/openstef_models/models/forecasting_model.py @@ -381,7 +381,7 @@ def restore_target[T: TimeSeriesDataset]( target_series = original_dataset.select_features([target_column]).select_version().data[target_column] def _transform_restore_target(df: pd.DataFrame) -> pd.DataFrame: - return df.assign(**{str(target_series.name): df.index.map(target_series)}) # pyright: ignore[reportUnknownMemberType] + return df.assign(**{str(target_series.name): df.index.map(target_series)}) # type: ignore return dataset.pipe_pandas(_transform_restore_target) diff --git a/packages/openstef-models/src/openstef_models/transforms/general/selector.py b/packages/openstef-models/src/openstef_models/transforms/general/selector.py index 00afbd68a..38f7c68bc 100644 --- a/packages/openstef-models/src/openstef_models/transforms/general/selector.py +++ b/packages/openstef-models/src/openstef_models/transforms/general/selector.py @@ -74,9 +74,7 @@ def fit(self, data: TimeSeriesDataset) -> None: def transform(self, data: TimeSeriesDataset) -> TimeSeriesDataset: features = self.selection.resolve(data.feature_names) - transformed_data = data.data.drop( - columns=[col for col in data.feature_names if col not in features] - ) + transformed_data = data.data.drop(columns=[col for col in data.feature_names if col not in features]) return data.copy_with(data=transformed_data, is_sorted=True)