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MNT Adjust code after NEP 51 numpy scalar formatting changes #27042

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Merged
merged 7 commits into from
Aug 22, 2023
Merged

MNT Adjust code after NEP 51 numpy scalar formatting changes #27042

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4a61a39
MNT Adjust code after NEP 51 numpy scalar formatting changes
lesteve Aug 9, 2023
50c8879
[scipy-dev]
lesteve Aug 9, 2023
e439f6d
[scipy-dev]
lesteve Aug 9, 2023
6789108
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
lesteve Aug 18, 2023
4d17d1f
[scipy-dev] [azure parallel]
lesteve Aug 18, 2023
cba58a4
[scipy-dev] Use .item instead of .tolist
lesteve Aug 18, 2023
c9472a7
[scipy-dev] clean-up
lesteve Aug 18, 2023
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2 changes: 1 addition & 1 deletion sklearn/dummy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,7 +227,7 @@ def fit(self, X, y, sample_weight=None):
"The constant target value must be present in "
"the training data. You provided constant={}. "
"Possible values are: {}.".format(
self.constant, list(self.classes_[k])
self.constant, self.classes_[k].tolist()
)
)
raise ValueError(err_msg)
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4 changes: 2 additions & 2 deletions sklearn/impute/tests/test_impute.py
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Expand Up @@ -259,7 +259,7 @@ def test_imputation_median_special_cases():
@pytest.mark.parametrize("dtype", [None, object, str])
def test_imputation_mean_median_error_invalid_type(strategy, dtype):
X = np.array([["a", "b", 3], [4, "e", 6], ["g", "h", 9]], dtype=dtype)
msg = "non-numeric data:\ncould not convert string to float: '"
msg = "non-numeric data:\ncould not convert string to float:"
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I remember here why I removed the quote here. We reraise an error from numpy ... we could also rewrite the error message if we think this is really important.

scikit-learn/sklearn/impute/_base.py

Line 327 in 83760ab

raise new_ve from None 

In [1]: import numpy as np

In [2]: np.array(['a']).astype(np.float64)
--------------------------------------------------------------------------
ValueError                               Traceback (most recent call last)
Cell In[2], line 1
----> 1 np.array(['a']).astype(np.float64)

ValueError: could not convert string to float: np.str_('a')

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I think rewriting the error message will be confusing for the user. Not sure, but it somehow feels wrong to modify an exception that we don't generate.

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I remember here why I removed the quote here. We reraise an error from numpy ... we could also rewrite the error message if we think this is really important.

scikit-learn/sklearn/impute/_base.py

Line 327 in 83760ab

raise new_ve from None 

In [1]: import numpy as np

In [2]: np.array(['a']).astype(np.float64)
--------------------------------------------------------------------------
ValueError                               Traceback (most recent call last)
Cell In[2], line 1
----> 1 np.array(['a']).astype(np.float64)

ValueError: could not convert string to float: np.str_('a')

with pytest.raises(ValueError, match=msg):
imputer = SimpleImputer(strategy=strategy)
imputer.fit_transform(X)
Expand All @@ -272,7 +272,7 @@ def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
if type == "dataframe":
pd = pytest.importorskip("pandas")
X = pd.DataFrame(X)
msg = "non-numeric data:\ncould not convert string to float: '"
msg = "non-numeric data:\ncould not convert string to float:"
with pytest.raises(ValueError, match=msg):
imputer = SimpleImputer(strategy=strategy)
imputer.fit_transform(X)
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4 changes: 2 additions & 2 deletions sklearn/preprocessing/_encoders.py
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Expand Up @@ -774,8 +774,8 @@ def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx):
if infrequent_indices is not None and drop_idx in infrequent_indices:
categories = self.categories_[feature_idx]
raise ValueError(
f"Unable to drop category {categories[drop_idx]!r} from feature"
f" {feature_idx} because it is infrequent"
f"Unable to drop category {categories[drop_idx].item()!r} from"
f" feature {feature_idx} because it is infrequent"
)
return default_to_infrequent[drop_idx]

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4 changes: 3 additions & 1 deletion sklearn/utils/class_weight.py
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Expand Up @@ -74,8 +74,10 @@ def compute_class_weight(class_weight, *, classes, y):

n_weighted_classes = len(classes) - len(unweighted_classes)
if unweighted_classes and n_weighted_classes != len(class_weight):
unweighted_classes_user_friendly_str = np.array(unweighted_classes).tolist()
raise ValueError(
f"The classes, {unweighted_classes}, are not in class_weight"
f"The classes, {unweighted_classes_user_friendly_str}, are not in"
" class_weight"
)

return weight
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2 changes: 1 addition & 1 deletion sklearn/utils/validation.py
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Expand Up @@ -2271,7 +2271,7 @@ def _check_pos_label_consistency(pos_label, y_true):
or np.array_equal(classes, [1])
)
):
classes_repr = ", ".join(repr(c) for c in classes)
classes_repr = ", ".join([repr(c) for c in classes.tolist()])
raise ValueError(
f"y_true takes value in {{{classes_repr}}} and pos_label is not "
"specified: either make y_true take value in {0, 1} or "
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