From 8ef297de5080c860149b5d812790d29e3f09d66d Mon Sep 17 00:00:00 2001 From: "kumarashutosh.ee@gmail.com" Date: Mon, 4 Sep 2017 03:27:12 +0530 Subject: [PATCH] Fixes deprecation warning --- sklearn/ensemble/gradient_boosting.py | 2 +- sklearn/feature_extraction/text.py | 2 +- sklearn/learning_curve.py | 2 +- sklearn/model_selection/_validation.py | 2 +- sklearn/utils/__init__.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py index a72f25a5f7b9b..854f728c5638a 100644 --- a/sklearn/ensemble/gradient_boosting.py +++ b/sklearn/ensemble/gradient_boosting.py @@ -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: diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index fa7306ab9def5..417aeef2f8bc2 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -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: diff --git a/sklearn/learning_curve.py b/sklearn/learning_curve.py index cfe1aba4ea178..5571138d68d83 100644 --- a/sklearn/learning_curve.py +++ b/sklearn/learning_curve.py @@ -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 " diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py index 773f70fb7dba2..f337f3bf1bb57 100644 --- a/sklearn/model_selection/_validation.py +++ b/sklearn/model_selection/_validation.py @@ -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 " diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py index 4b2665cdd4f77..83e8a48a6625a 100644 --- a/sklearn/utils/__init__.py +++ b/sklearn/utils/__init__.py @@ -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"):