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[MRG] Addresses test errors on numpy-dev #9683

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Merged
lesteve merged 1 commit into from
Sep 4, 2017
Merged

[MRG] Addresses test errors on numpy-dev #9683

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2 changes: 1 addition & 1 deletion sklearn/ensemble/gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,7 +153,7 @@ class ZeroEstimator(object):
"""An estimator that simply predicts zero. """

def fit(self, X, y, sample_weight=None):
if np.issubdtype(y.dtype, int):
if np.issubdtype(y.dtype, np.signedinteger):
# classification
self.n_classes = np.unique(y).shape[0]
if self.n_classes == 2:
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2 changes: 1 addition & 1 deletion sklearn/feature_extraction/text.py
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Expand Up @@ -1086,7 +1086,7 @@ def transform(self, X, copy=True):
-------
vectors : sparse matrix, [n_samples, n_features]
"""
if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float):
if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.floating):
# preserve float family dtype
X = sp.csr_matrix(X, copy=copy)
else:
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2 changes: 1 addition & 1 deletion sklearn/learning_curve.py
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Expand Up @@ -206,7 +206,7 @@ def _translate_train_sizes(train_sizes, n_max_training_samples):
n_ticks = train_sizes_abs.shape[0]
n_min_required_samples = np.min(train_sizes_abs)
n_max_required_samples = np.max(train_sizes_abs)
if np.issubdtype(train_sizes_abs.dtype, np.float):
if np.issubdtype(train_sizes_abs.dtype, np.floating):
if n_min_required_samples <= 0.0 or n_max_required_samples > 1.0:
raise ValueError("train_sizes has been interpreted as fractions "
"of the maximum number of training samples and "
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2 changes: 1 addition & 1 deletion sklearn/model_selection/_validation.py
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Expand Up @@ -1097,7 +1097,7 @@ def _translate_train_sizes(train_sizes, n_max_training_samples):
n_ticks = train_sizes_abs.shape[0]
n_min_required_samples = np.min(train_sizes_abs)
n_max_required_samples = np.max(train_sizes_abs)
if np.issubdtype(train_sizes_abs.dtype, np.float):
if np.issubdtype(train_sizes_abs.dtype, np.floating):
if n_min_required_samples <= 0.0 or n_max_required_samples > 1.0:
raise ValueError("train_sizes has been interpreted as fractions "
"of the maximum number of training samples and "
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2 changes: 1 addition & 1 deletion sklearn/utils/__init__.py
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Expand Up @@ -90,7 +90,7 @@ def safe_mask(X, mask):
mask
"""
mask = np.asarray(mask)
if np.issubdtype(mask.dtype, np.int):
if np.issubdtype(mask.dtype, np.signedinteger):
return mask

if hasattr(X, "toarray"):
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