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fix: selectively ignore one warning instead of all warnings #235

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merged 2 commits into from
Apr 21, 2023
Merged

fix: selectively ignore one warning instead of all warnings #235

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f4a5104
fix: don't blindly ignore all warnings
lars-reimann Apr 21, 2023
37dd955
fix: selectively ignore `scikit-learn` warning about missing feature …
lars-reimann Apr 21, 2023
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10 changes: 0 additions & 10 deletions src/safeds/data/tabular/transformation/_label_encoder.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,5 @@
from __future__ import annotations

import warnings
from typing import Any

from sklearn.preprocessing import OrdinalEncoder as sk_OrdinalEncoder

from safeds.data.tabular.containers import Table
Expand All @@ -12,13 +9,6 @@
)


def warn(*_: Any, **__: Any) -> None:
pass


warnings.warn = warn


# noinspection PyProtectedMember
class LabelEncoder(InvertibleTableTransformer):
"""The LabelEncoder encodes one or more given columns into labels."""
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5 changes: 4 additions & 1 deletion src/safeds/ml/classical/_util_sklearn.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import warnings
from typing import Any

from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -84,7 +85,9 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
result_set.columns = dataset.column_names

try:
predicted_target_vector = model.predict(dataset_df.values)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="X does not have valid feature names")
predicted_target_vector = model.predict(dataset_df.values)
result_set[target_name] = predicted_target_vector
return Table(result_set).tag_columns(target_name=target_name, feature_names=feature_names)
except ValueError as exception:
Expand Down