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[pre-commit.ci] pre-commit autoupdate #28

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3810c7a
[pre-commit.ci] pre-commit autoupdate
pre-commit-ci[bot] Jan 19, 2026
f2e4a95
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 19, 2026
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8 changes: 4 additions & 4 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,8 @@ repos:
hooks:
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: https://github.com/psf/black
rev: 25.1.0
- repo: https://github.com/psf/black-pre-commit-mirror
rev: 26.1.0
hooks:
- id: black
args: [--line-length=128, --extend-exclude=.ipynb, --verbose]
Expand All @@ -20,12 +20,12 @@ repos:
additional_dependencies: [pycodestyle>=2.11.0]
args: [--max-line-length=128, '--exclude=./.*,build,dist', '--ignore=E501,W503,E203,F841,E231,W604', --count, --statistics, --show-source]
- repo: https://github.com/pycqa/isort
rev: 6.0.1
rev: 7.0.0
hooks:
- id: isort
args: [--profile=black, --line-length=128]
- repo: https://github.com/kynan/nbstripout
rev: 0.8.1
rev: 0.9.0
hooks:
- id: nbstripout
- repo: https://github.com/nbQA-dev/nbQA
Expand Down
6 changes: 2 additions & 4 deletions benchmarks/train_crnn_cinc2020/trainer.py
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Expand Up @@ -245,17 +245,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
head_labels = all_labels[:head_num, ...]
head_labels_classes = [np.array(classes)[np.where(row)] for row in head_labels]
for n in range(head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
scalar prediction: {[round(n, 3) for n in head_scalar_preds[n].tolist()]}
binary prediction: {head_bin_preds[n].tolist()}
labels: {head_labels[n].astype(int).tolist()}
predicted classes: {head_preds_classes[n].tolist()}
label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

(
Expand Down
16 changes: 4 additions & 12 deletions benchmarks/train_crnn_cinc2021/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,14 +314,10 @@ def _train_test_split(self, train_ratio: float = 0.8, force_recompute: bool = Fa
)
train_file.write_text(json.dumps(train_set, ensure_ascii=False))
test_file.write_text(json.dumps(test_set, ensure_ascii=False))
print(
textwrap.dedent(
f"""
print(textwrap.dedent(f"""
train set saved to \n\042{str(train_file)}\042
test set saved to \n\042{str(test_file)}\042
"""
)
)
"""))
else:
train_set = json.loads(train_file.read_text())
test_set = json.loads(test_file.read_text())
Expand Down Expand Up @@ -362,16 +358,12 @@ def _check_train_test_split_validity(self, train_set: List[str], test_set: List[
test_classes = set(list_sum([self.reader.get_labels(rec, fmt="a") for rec in test_set]))
test_classes.intersection_update(all_classes)
is_valid = len(all_classes) == len(train_classes) == len(test_classes)
print(
textwrap.dedent(
f"""
print(textwrap.dedent(f"""
all_classes: {all_classes}
train_classes: {train_classes}
test_classes: {test_classes}
is_valid: {is_valid}
"""
)
)
"""))
return is_valid

def persistence(self) -> None:
Expand Down
6 changes: 2 additions & 4 deletions benchmarks/train_crnn_cinc2021/trainer.py
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Expand Up @@ -233,17 +233,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
head_labels = all_labels[:head_num, ...]
head_labels_classes = [np.array(classes)[np.where(row)] for row in head_labels]
for n in range(head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
scalar prediction: {[round(n, 3) for n in head_scalar_preds[n].tolist()]}
binary prediction: {head_bin_preds[n].tolist()}
labels: {head_labels[n].astype(int).tolist()}
predicted classes: {head_preds_classes[n].tolist()}
label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

(
Expand Down
6 changes: 2 additions & 4 deletions benchmarks/train_crnn_cinc2023/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -346,17 +346,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
]
log_head_num = min(log_head_num, len(head_scalar_preds))
for n in range(log_head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
cpc scalar prediction: {[round(item, 3) for item in head_scalar_preds[n].tolist()]}
cpc binary prediction: {head_bin_preds[n].tolist()}
cpc labels: {head_labels[n].astype(int).tolist()}
cpc predicted classes: {head_preds_classes[n].tolist()}
cpc label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

eval_res = compute_challenge_metrics(
Expand Down
42 changes: 14 additions & 28 deletions benchmarks/train_hybrid_cpsc2020/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,8 +162,7 @@ def train(

# max_itr = n_epochs * n_train

msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
Starting training:
------------------
Epochs: {n_epochs}
Expand All @@ -174,8 +173,7 @@ def train(
Device: {device.type}
Optimizer: {config.train_optimizer}
-----------------------------------------
"""
)
""")
# print(msg) # in case no logger
if logger:
logger.info(msg)
Expand Down Expand Up @@ -351,20 +349,17 @@ def train(
scheduler.step()

if debug:
eval_train_msg = textwrap.dedent(
f"""
eval_train_msg = textwrap.dedent(f"""
train/auroc: {eval_train_res[0]}
train/auprc: {eval_train_res[1]}
train/accuracy: {eval_train_res[2]}
train/f_measure: {eval_train_res[3]}
train/f_beta_measure: {eval_train_res[4]}
train/g_beta_measure: {eval_train_res[5]}
"""
)
""")
else:
eval_train_msg = ""
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
Train epoch_{epoch + 1}:
--------------------
train/epoch_loss: {epoch_loss}{eval_train_msg}
Expand All @@ -375,8 +370,7 @@ def train(
test/f_beta_measure: {eval_res[4]}
test/g_beta_measure: {eval_res[5]}
---------------------------------
"""
)
""")
elif config.model_name == "seq_lab":
eval_res = evaluate_seq_lab(model, val_loader, config, device, debug)
model.train()
Expand All @@ -403,8 +397,7 @@ def train(
scheduler.step()

if debug:
eval_train_msg = textwrap.dedent(
f"""
eval_train_msg = textwrap.dedent(f"""
train/total_loss: {eval_train_res.total_loss}
train/spb_loss: {eval_train_res.spb_loss}
train/pvc_loss: {eval_train_res.pvc_loss}
Expand All @@ -414,12 +407,10 @@ def train(
train/pvc_fp: {eval_train_res.pvc_fp}
train/spb_fn: {eval_train_res.spb_fn}
train/pvc_fn: {eval_train_res.pvc_fn}
"""
)
""")
else:
eval_train_msg = ""
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
Train epoch_{epoch + 1}:
--------------------
train/epoch_loss: {epoch_loss}{eval_train_msg}
Expand All @@ -433,8 +424,7 @@ def train(
test/spb_fn: {eval_res.spb_fn}
test/pvc_fn: {eval_res.pvc_fn}
---------------------------------
"""
)
""")

# print(msg) # in case no logger
if logger:
Expand All @@ -460,12 +450,10 @@ def train(
print(msg)
break

msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
best challenge metric = {best_challenge_metric},
obtained at epoch {best_epoch}
"""
)
""")
if logger:
logger.info(msg)
else:
Expand Down Expand Up @@ -615,17 +603,15 @@ def evaluate_crnn(
head_labels = all_labels[:head_num, ...]
head_labels_classes = [np.array(classes)[np.where(row)] for row in head_labels]
for n in range(head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
scalar prediction: {[round(n, 3) for n in head_scalar_preds[n].tolist()]}
binary prediction: {head_bin_preds[n].tolist()}
labels: {head_labels[n].astype(int).tolist()}
predicted classes: {head_preds_classes[n].tolist()}
label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
if logger:
logger.info(msg)
else:
Expand Down
8 changes: 2 additions & 6 deletions benchmarks/train_hybrid_cpsc2021/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1183,14 +1183,10 @@ def _train_test_split(self, train_ratio: float = 0.8, force_recompute: bool = Fa

train_file.write_text(json.dumps(train_set, ensure_ascii=False))
test_file.write_text(json.dumps(test_set, ensure_ascii=False))
print(
nildent(
f"""
print(nildent(f"""
train set saved to \n\042{str(train_file)}\042
test set saved to \n\042{str(test_file)}\042
"""
)
)
"""))
else:
train_set = json.loads(train_file.read_text())
test_set = json.loads(test_file.read_text())
Expand Down
5 changes: 1 addition & 4 deletions benchmarks/train_mtl_cinc2022/inputs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -404,9 +404,7 @@ def from_waveform(self, waveform: Union[np.ndarray, torch.Tensor]) -> torch.Tens


class SpectralInput(_SpectralInput):
__doc__ = (
_SpectralInput.__doc__
+ """
__doc__ = _SpectralInput.__doc__ + """

Concatenation of 3 different types of spectrograms:
- Spectrogram
Expand Down Expand Up @@ -444,7 +442,6 @@ class SpectralInput(_SpectralInput):
(32, 3, 224, 2308)

"""
)

__name__ = "SpectralInput"

Expand Down
12 changes: 4 additions & 8 deletions benchmarks/train_mtl_cinc2022/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -353,17 +353,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
]
log_head_num = min(log_head_num, len(head_scalar_preds))
for n in range(log_head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
murmur scalar prediction: {[round(item, 3) for item in head_scalar_preds[n].tolist()]}
murmur binary prediction: {head_bin_preds[n].tolist()}
murmur labels: {head_labels[n].astype(int).tolist()}
murmur predicted classes: {head_preds_classes[n].tolist()}
murmur label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)
if "outcome" in input_tensors:
head_scalar_preds = all_outputs[0].outcome_output.prob[:log_head_num]
Expand All @@ -382,17 +380,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
]
log_head_num = min(log_head_num, len(head_scalar_preds))
for n in range(log_head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
outcome scalar prediction: {[round(item, 3) for item in head_scalar_preds[n].tolist()]}
outcome binary prediction: {head_bin_preds[n].tolist()}
outcome labels: {head_labels[n].astype(int).tolist()}
outcome predicted classes: {head_preds_classes[n].tolist()}
outcome label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

eval_res = compute_challenge_metrics(
Expand Down
6 changes: 2 additions & 4 deletions docs/source/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,13 +216,11 @@ def setup(app):
]

# https://sphinxcontrib-bibtex.readthedocs.io/en/latest/usage.html#latex-backend-fails-with-citations-in-figure-captions
latex_elements = {
"preamble": r"""
latex_elements = {"preamble": r"""
% make phantomsection empty inside figures
\usepackage{etoolbox}
\AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}}
"""
}
"""}

man_pages = [(master_doc, project, f"{project} Documentation", [author], 1)]

Expand Down
6 changes: 2 additions & 4 deletions test/test_pipelines/test_crnn_cinc2021_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -621,17 +621,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
head_labels = all_labels[:head_num, ...]
head_labels_classes = [np.array(classes)[np.where(row)] for row in head_labels]
for n in range(head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
scalar prediction: {[round(n, 3) for n in head_scalar_preds[n].tolist()]}
binary prediction: {head_bin_preds[n].tolist()}
labels: {head_labels[n].astype(int).tolist()}
predicted classes: {head_preds_classes[n].tolist()}
label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

(
Expand Down
12 changes: 4 additions & 8 deletions test/test_pipelines/test_mtl_cinc2022_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3329,17 +3329,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
]
log_head_num = min(log_head_num, len(head_scalar_preds))
for n in range(log_head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
murmur scalar prediction: {[round(item, 3) for item in head_scalar_preds[n].tolist()]}
murmur binary prediction: {head_bin_preds[n].tolist()}
murmur labels: {head_labels[n].astype(int).tolist()}
murmur predicted classes: {head_preds_classes[n].tolist()}
murmur label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)
if "outcome" in input_tensors:
head_scalar_preds = all_outputs[0].outcome_output.prob[:log_head_num]
Expand All @@ -3358,17 +3356,15 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
]
log_head_num = min(log_head_num, len(head_scalar_preds))
for n in range(log_head_num):
msg = textwrap.dedent(
f"""
msg = textwrap.dedent(f"""
----------------------------------------------
outcome scalar prediction: {[round(item, 3) for item in head_scalar_preds[n].tolist()]}
outcome binary prediction: {head_bin_preds[n].tolist()}
outcome labels: {head_labels[n].astype(int).tolist()}
outcome predicted classes: {head_preds_classes[n].tolist()}
outcome label classes: {head_labels_classes[n].tolist()}
----------------------------------------------
"""
)
""")
self.log_manager.log_message(msg)

eval_res = compute_challenge_metrics(
Expand Down
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