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Support ScalarizedOutcomeConstraint in _prepare_p_feasible #4856

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64 changes: 63 additions & 1 deletion ax/analysis/tests/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,15 +10,22 @@
import numpy as np
import pandas as pd
from ax.analysis.plotly.utils import STALE_FAIL_REASON, truncate_label
from ax.analysis.utils import _relativize_df_with_sq, prepare_arm_data
from ax.analysis.utils import (
_get_scalarized_constraint_mean_and_sem,
_prepare_p_feasible,
_relativize_df_with_sq,
prepare_arm_data,
)
from ax.api.client import Client
from ax.api.configs import RangeParameterConfig
from ax.core.arm import Arm
from ax.core.batch_trial import BatchTrial
from ax.core.data import relativize_dataframe
from ax.core.experiment import Experiment
from ax.core.metric import Metric
from ax.core.outcome_constraint import OutcomeConstraint, ScalarizedOutcomeConstraint
from ax.core.trial_status import TrialStatus # noqa
from ax.core.types import ComparisonOp
from ax.exceptions.core import UserInputError
from ax.utils.common.testutils import TestCase
from ax.utils.testing.core_stubs import get_offline_experiments, get_online_experiments
Expand Down Expand Up @@ -865,3 +872,58 @@ def test_offline(self) -> None:
trial_index=trial_index,
additional_arms=additional_arms,
)

def test_scalarized_constraints(self) -> None:
df = pd.DataFrame(
{
"trial_index": [0, 0],
"arm_name": ["arm1", "arm2"],
"m1_mean": [5.0, 15.0],
"m1_sem": [1.0, 1.0],
"m2_mean": [5.0, 15.0],
"m2_sem": [1.0, 1.0],
"regular_mean": [8.0, 12.0],
"regular_sem": [0.5, 0.5],
}
)

scalarized_constraint = ScalarizedOutcomeConstraint(
metrics=[Metric(name="m1"), Metric(name="m2")],
weights=[1.0, 1.0],
op=ComparisonOp.LEQ,
bound=25.0,
relative=False,
)

# Helper math: mean = w1*m1 + w2*m2, SEM = sqrt(w1^2*s1^2 + w2^2*s2^2)
mean, sem = _get_scalarized_constraint_mean_and_sem(df, scalarized_constraint)
np.testing.assert_array_almost_equal(mean, [10.0, 30.0])
np.testing.assert_array_almost_equal(sem, [np.sqrt(2), np.sqrt(2)])

# Missing metric returns NaN mean and zero SEM
missing_constraint = ScalarizedOutcomeConstraint(
metrics=[Metric(name="m1"), Metric(name="missing")],
weights=[1.0, 1.0],
op=ComparisonOp.LEQ,
bound=10.0,
)
mean, sem = _get_scalarized_constraint_mean_and_sem(df, missing_constraint)
self.assertTrue(np.all(np.isnan(mean)))
np.testing.assert_array_equal(sem, np.zeros(2))

# p_feasible with mixed regular + scalarized constraints
regular_constraint = OutcomeConstraint(
metric=Metric(name="regular"),
op=ComparisonOp.LEQ,
bound=10.0,
relative=False,
)
p_feasible = _prepare_p_feasible(
df=df,
status_quo_df=None,
outcome_constraints=[regular_constraint, scalarized_constraint],
)
self.assertFalse(p_feasible.isna().any())
# arm1 (regular=8, scalarized=10) more feasible than
# arm2 (regular=12, scalarized=30)
self.assertGreater(p_feasible.iloc[0], p_feasible.iloc[1])
123 changes: 82 additions & 41 deletions ax/analysis/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from typing import Sequence

import numpy as np
import numpy.typing as npt
import pandas as pd
import torch
from ax.adapter.base import Adapter
Expand Down Expand Up @@ -540,6 +541,54 @@ def _extract_generation_node_name(trial: BaseTrial, arm: Arm) -> str:
return Keys.UNKNOWN_GENERATION_NODE.value


def _get_scalarized_constraint_mean_and_sem(
df: pd.DataFrame,
constraint: ScalarizedOutcomeConstraint,
) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]:
"""
Compute the combined mean and SEM for a ScalarizedOutcomeConstraint.

For independent random variables:
combined_mean = sum(weight_i * mean_i)
combined_sem = sqrt(sum((weight_i * sem_i)^2))

Args:
df: DataFrame with "{metric_name}_mean" and "{metric_name}_sem" columns.
constraint: The ScalarizedOutcomeConstraint.

Returns:
Tuple of (combined_mean, combined_sem) as numpy arrays.
If any component metric is missing, mean is NaN and sem is 0.
"""
n_rows = len(df)
combined_mean = np.zeros(n_rows)
combined_var = np.zeros(n_rows)
all_metrics_present = True

for metric, weight in constraint.metric_weights:
mean_col = f"{metric.name}_mean"
sem_col = f"{metric.name}_sem"

if mean_col in df.columns:
combined_mean += weight * df[mean_col].values
else:
all_metrics_present = False
break

if sem_col in df.columns:
metric_sem = df[sem_col].fillna(0).values
else:
metric_sem = np.zeros(n_rows)

combined_var += (weight**2) * (metric_sem**2)

if not all_metrics_present:
# Match existing pattern: mean=NaN, sem=0 for missing data
return np.full(n_rows, np.nan), np.zeros(n_rows)

return combined_mean, np.sqrt(combined_var)


def _prepare_p_feasible(
df: pd.DataFrame,
status_quo_df: pd.DataFrame | None,
Expand Down Expand Up @@ -571,34 +620,27 @@ def _prepare_p_feasible(
return pd.Series(np.ones(len(df)))

# If an arm is missing data for a metric leave the mean as NaN.
oc_names = []
for oc in outcome_constraints:
if isinstance(oc, ScalarizedOutcomeConstraint):
# take the str representation of the scalarized outcome constraint
oc_names.append(str(oc))
else:
oc_names.append(oc.metric.name)

assert len(oc_names) == len(outcome_constraints)

means = []
sigmas = []
for i, oc_name in enumerate(oc_names):
df_constraint = none_throws(rel_df if outcome_constraints[i].relative else df)
# TODO[T235432214]: currently we are leaving the mean as NaN if the constraint
# is on ScalarizedOutcomeConstraint but we should be able to calculate it by
# setting the mean to be weights * individual metrics and sem to be
# sqrt(sum((weights * individual_sems)^2)), assuming independence.
if f"{oc_name}_mean" in df_constraint.columns:
means.append(df_constraint[f"{oc_name}_mean"].tolist())
for oc in outcome_constraints:
df_constraint = none_throws(rel_df if oc.relative else df)

if isinstance(oc, ScalarizedOutcomeConstraint):
mean, sem = _get_scalarized_constraint_mean_and_sem(df_constraint, oc)
means.append(mean.tolist())
sigmas.append(sem.tolist())
else:
means.append([float("nan")] * len(df_constraint))
sigmas.append(
(df_constraint[f"{oc_name}_sem"].fillna(0)).tolist()
if f"{oc_name}_sem" in df_constraint.columns
else [0] * len(df)
)
metric_name = oc.metric.name
if f"{metric_name}_mean" in df_constraint.columns:
means.append(df_constraint[f"{metric_name}_mean"].tolist())
else:
means.append([float("nan")] * len(df_constraint))

sigmas.append(
(df_constraint[f"{metric_name}_sem"].fillna(0)).tolist()
if f"{metric_name}_sem" in df_constraint.columns
else [0] * len(df)
)

con_lower_inds = [
i
Expand Down Expand Up @@ -665,28 +707,27 @@ def _prepare_p_feasible_per_constraint(
if len(outcome_constraints) == 0:
return pd.DataFrame(index=df.index)

oc_names = []
for oc in outcome_constraints:
if isinstance(oc, ScalarizedOutcomeConstraint):
oc_names.append(str(oc))
else:
oc_names.append(oc.metric.name)

result_df = pd.DataFrame(index=df.index)
# Compute probability for each constraint individually
for oc_name, oc in zip(oc_names, outcome_constraints):
for oc in outcome_constraints:
df_constraint = none_throws(rel_df if oc.relative else df)

# Get mean and sigma for this constraint
if f"{oc_name}_mean" in df_constraint.columns:
mean = df_constraint[f"{oc_name}_mean"].values
if isinstance(oc, ScalarizedOutcomeConstraint):
mean, sigma = _get_scalarized_constraint_mean_and_sem(df_constraint, oc)
oc_display_name = str(oc)
else:
mean = np.nan * np.ones(len(df_constraint))
metric_name = oc.metric.name
oc_display_name = metric_name

if f"{oc_name}_sem" in df_constraint.columns:
sigma = df_constraint[f"{oc_name}_sem"].fillna(0).values
else:
sigma = np.zeros(len(df))
if f"{metric_name}_mean" in df_constraint.columns:
mean = df_constraint[f"{metric_name}_mean"].values
else:
mean = np.full(len(df_constraint), np.nan)

if f"{metric_name}_sem" in df_constraint.columns:
sigma = df_constraint[f"{metric_name}_sem"].fillna(0).values
else:
sigma = np.zeros(len(df))

# Convert to torch tensors (shape: [n_arms, 1])
mean_tensor = torch.tensor(mean, dtype=torch.double).unsqueeze(-1)
Expand All @@ -706,7 +747,7 @@ def _prepare_p_feasible_per_constraint(

# Convert back to numpy and store in result dataframe
prob = log_prob.exp().squeeze().numpy()
result_df[f"p_feasible_{oc_name}"] = prob
result_df[f"p_feasible_{oc_display_name}"] = prob

return result_df

Expand Down