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Conform with Numpy v2 requirements #1699

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Conform with Numpy v2 requirements #1699

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4 changes: 2 additions & 2 deletions src/evidently/legacy/metrics/recsys/pairwise_distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,11 +62,11 @@ def calculate(self, data: InputData) -> PairwiseDistanceResult:
all_items = all_items[all_items[prediction_name] <= self.k + 1]
all_items = all_items[[item_id.column_name] + self.item_features]
if current_train_data is not None:
if not np.in1d(self.item_features, current_train_data.columns).all(): # type: ignore[attr-defined]
if not np.isin(self.item_features, current_train_data.columns).all(): # type: ignore[attr-defined]
raise ValueError("current_train_data must contain item_features.")
all_items = pd.concat([all_items, current_train_data[[item_id.column_name] + self.item_features]])
if reference_train_data is not None:
if not np.in1d(self.item_features, reference_train_data.columns).all(): # type: ignore[attr-defined]
if not np.isin(self.item_features, reference_train_data.columns).all(): # type: ignore[attr-defined]
raise ValueError("reference_train_data must contain item_features.")
all_items = pd.concat([all_items, reference_train_data[[item_id.column_name] + self.item_features]])

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2 changes: 1 addition & 1 deletion tests/conftest.py
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Expand Up @@ -9,7 +9,7 @@
from evidently.pydantic_utils import PolymorphicModel

# for np.testing.assert_equal to work with ApproxValue
np.core.numeric.ScalarType = np.core.numeric.ScalarType + (ApproxValue,) # type: ignore[attr-defined]
np._core.numeric.ScalarType = np._core.numeric.ScalarType + (ApproxValue,) # type: ignore[attr-defined]


def smart_assert_equal(actual, expected, path=""):
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