scikit-learn · glemaitre · Aug 13, 2019 · Aug 1, 2019 · Aug 1, 2019 · Aug 1, 2019
diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py
@@ -213,13 +213,13 @@ def fit(self, X, y=None, **params):
         return self
 
     def transform(self, X):
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
         H = self.components_
         W, _, self.n_iter_ = self._fit_transform(X, H=H, update_H=False)
         return W
 
     def inverse_transform(self, W):
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
         return np.dot(W, self.components_)
 
     def fit_transform(self, X, y=None, W=None, H=None):

diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
@@ -1381,7 +1381,7 @@ the correct interface more easily.
       ...     def predict(self, X):
       ...
       ...         # Check is fit had been called
-      ...         check_is_fitted(self, ['X_', 'y_'])
+      ...         check_is_fitted(self)
       ...
       ...         # Input validation
       ...         X = check_array(X)

diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -216,7 +216,7 @@ def predict_proba(self, X):
         C : array, shape (n_samples, n_classes)
             The predicted probas.
         """
-        check_is_fitted(self, ["classes_", "calibrated_classifiers_"])
+        check_is_fitted(self)
         X = check_array(X, accept_sparse=['csc', 'csr', 'coo'],
                         force_all_finite=False)
         # Compute the arithmetic mean of the predictions of the calibrated
@@ -244,7 +244,7 @@ def predict(self, X):
         C : array, shape (n_samples,)
             The predicted class.
         """
-        check_is_fitted(self, ["classes_", "calibrated_classifiers_"])
+        check_is_fitted(self)
         return self.classes_[np.argmax(self.predict_proba(X), axis=1)]
 
 

diff --git a/sklearn/cluster/_feature_agglomeration.py b/sklearn/cluster/_feature_agglomeration.py
@@ -36,7 +36,7 @@ def transform(self, X):
         Y : array, shape = [n_samples, n_clusters] or [n_clusters]
             The pooled values for each feature cluster.
         """
-        check_is_fitted(self, "labels_")
+        check_is_fitted(self)
 
         X = check_array(X)
         if len(self.labels_) != X.shape[1]:
@@ -71,7 +71,7 @@ def inverse_transform(self, Xred):
             A vector of size n_samples with the values of Xred assigned to
             each of the cluster of samples.
         """
-        check_is_fitted(self, "labels_")
+        check_is_fitted(self)
 
         unil, inverse = np.unique(self.labels_, return_inverse=True)
         return Xred[..., inverse]
diff --git a/sklearn/cluster/affinity_propagation_.py b/sklearn/cluster/affinity_propagation_.py
@@ -407,7 +407,7 @@ def predict(self, X):
         labels : ndarray, shape (n_samples,)
             Cluster labels.
         """
-        check_is_fitted(self, "cluster_centers_indices_")
+        check_is_fitted(self)
         if not hasattr(self, "cluster_centers_"):
             raise ValueError("Predict method is not supported when "
                              "affinity='precomputed'.")

diff --git a/sklearn/cluster/birch.py b/sklearn/cluster/birch.py
@@ -534,8 +534,7 @@ def partial_fit(self, X=None, y=None):
             return self._fit(X)
 
     def _check_fit(self, X):
-        check_is_fitted(self, ['subcluster_centers_', 'partial_fit_'],
-                        all_or_any=any)
+        check_is_fitted(self)
 
         if (hasattr(self, 'subcluster_centers_') and
                 X.shape[1] != self.subcluster_centers_.shape[1]):
@@ -583,7 +582,7 @@ def transform(self, X):
         X_trans : {array-like, sparse matrix}, shape (n_samples, n_clusters)
             Transformed data.
         """
-        check_is_fitted(self, 'subcluster_centers_')
+        check_is_fitted(self)
         return euclidean_distances(X, self.subcluster_centers_)
 
     def _global_clustering(self, X=None):

diff --git a/sklearn/cluster/k_means_.py b/sklearn/cluster/k_means_.py
@@ -1033,7 +1033,7 @@ def transform(self, X):
         X_new : array, shape [n_samples, k]
             X transformed in the new space.
         """
-        check_is_fitted(self, 'cluster_centers_')
+        check_is_fitted(self)
 
         X = self._check_test_data(X)
         return self._transform(X)
@@ -1063,7 +1063,7 @@ def predict(self, X, sample_weight=None):
         labels : array, shape [n_samples,]
             Index of the cluster each sample belongs to.
         """
-        check_is_fitted(self, 'cluster_centers_')
+        check_is_fitted(self)
 
         X = self._check_test_data(X)
         x_squared_norms = row_norms(X, squared=True)
@@ -1090,7 +1090,7 @@ def score(self, X, y=None, sample_weight=None):
         score : float
             Opposite of the value of X on the K-means objective.
         """
-        check_is_fitted(self, 'cluster_centers_')
+        check_is_fitted(self)
 
         X = self._check_test_data(X)
         x_squared_norms = row_norms(X, squared=True)
@@ -1733,7 +1733,7 @@ def predict(self, X, sample_weight=None):
         labels : array, shape [n_samples,]
             Index of the cluster each sample belongs to.
         """
-        check_is_fitted(self, 'cluster_centers_')
+        check_is_fitted(self)
 
         X = self._check_test_data(X)
         return self._labels_inertia_minibatch(X, sample_weight)[0]
diff --git a/sklearn/cluster/mean_shift_.py b/sklearn/cluster/mean_shift_.py
@@ -435,6 +435,6 @@ def predict(self, X):
         labels : array, shape [n_samples,]
             Index of the cluster each sample belongs to.
         """
-        check_is_fitted(self, "cluster_centers_")
+        check_is_fitted(self)
 
         return pairwise_distances_argmin(X, self.cluster_centers_)
diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
@@ -342,7 +342,7 @@ def get_feature_names(self):
         feature_names : list of strings
             Names of the features produced by transform.
         """
-        check_is_fitted(self, 'transformers_')
+        check_is_fitted(self)
         feature_names = []
         for name, trans, _, _ in self._iter(fitted=True):
             if trans == 'drop':
@@ -550,7 +550,7 @@ def transform(self, X):
             sparse matrices.
 
         """
-        check_is_fitted(self, 'transformers_')
+        check_is_fitted(self)
         X = _check_X(X)
         if hasattr(X, "columns"):
             X_feature_names = np.asarray(X.columns)

diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py
@@ -221,7 +221,7 @@ def predict(self, X):
             Predicted values.
 
         """
-        check_is_fitted(self, "regressor_")
+        check_is_fitted(self)
         pred = self.regressor_.predict(X)
         if pred.ndim == 1:
             pred_trans = self.transformer_.inverse_transform(

diff --git a/sklearn/covariance/elliptic_envelope.py b/sklearn/covariance/elliptic_envelope.py
@@ -147,7 +147,7 @@ def decision_function(self, X):
             compatibility with other outlier detection algorithms.
 
         """
-        check_is_fitted(self, 'offset_')
+        check_is_fitted(self)
         negative_mahal_dist = self.score_samples(X)
         return negative_mahal_dist - self.offset_
 
@@ -163,7 +163,7 @@ def score_samples(self, X):
         negative_mahal_distances : array-like, shape (n_samples, )
             Opposite of the Mahalanobis distances.
         """
-        check_is_fitted(self, 'offset_')
+        check_is_fitted(self)
         return -self.mahalanobis(X)
 
     def predict(self, X):

diff --git a/sklearn/cross_decomposition/pls_.py b/sklearn/cross_decomposition/pls_.py
@@ -398,7 +398,7 @@ def transform(self, X, Y=None, copy=True):
         -------
         x_scores if Y is not given, (x_scores, y_scores) otherwise.
         """
-        check_is_fitted(self, 'x_mean_')
+        check_is_fitted(self)
         X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
         # Normalize
         X -= self.x_mean_
@@ -433,7 +433,7 @@ def predict(self, X, copy=True):
         This call requires the estimation of a p x q matrix, which may
         be an issue in high dimensional space.
         """
-        check_is_fitted(self, 'x_mean_')
+        check_is_fitted(self)
         X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
         # Normalize
         X -= self.x_mean_
@@ -872,7 +872,7 @@ def transform(self, X, Y=None):
             Target vectors, where n_samples is the number of samples and
             n_targets is the number of response variables.
         """
-        check_is_fitted(self, 'x_mean_')
+        check_is_fitted(self)
         X = check_array(X, dtype=np.float64)
         Xr = (X - self.x_mean_) / self.x_std_
         x_scores = np.dot(Xr, self.x_weights_)

diff --git a/sklearn/decomposition/base.py b/sklearn/decomposition/base.py
@@ -122,7 +122,7 @@ def transform(self, X):
         IncrementalPCA(batch_size=3, n_components=2)
         >>> ipca.transform(X) # doctest: +SKIP
         """
-        check_is_fitted(self, ['mean_', 'components_'], all_or_any=all)
+        check_is_fitted(self)
 
         X = check_array(X)
         if self.mean_ is not None:

diff --git a/sklearn/decomposition/dict_learning.py b/sklearn/decomposition/dict_learning.py
@@ -911,7 +911,7 @@ def transform(self, X):
             Transformed data
 
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         X = check_array(X)
 

diff --git a/sklearn/decomposition/factor_analysis.py b/sklearn/decomposition/factor_analysis.py
@@ -261,7 +261,7 @@ def transform(self, X):
         X_new : array-like, shape (n_samples, n_components)
             The latent variables of X.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         X = check_array(X)
         Ih = np.eye(len(self.components_))
@@ -285,7 +285,7 @@ def get_covariance(self):
         cov : array, shape (n_features, n_features)
             Estimated covariance of data.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         cov = np.dot(self.components_.T, self.components_)
         cov.flat[::len(cov) + 1] += self.noise_variance_  # modify diag inplace
@@ -299,7 +299,7 @@ def get_precision(self):
         precision : array, shape (n_features, n_features)
             Estimated precision of data.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         n_features = self.components_.shape[1]
 
@@ -333,7 +333,7 @@ def score_samples(self, X):
         ll : array, shape (n_samples,)
             Log-likelihood of each sample under the current model
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         Xr = X - self.mean_
         precision = self.get_precision()

diff --git a/sklearn/decomposition/fastica_.py b/sklearn/decomposition/fastica_.py
@@ -574,7 +574,7 @@ def transform(self, X, copy=True):
         -------
         X_new : array-like, shape (n_samples, n_components)
         """
-        check_is_fitted(self, 'mixing_')
+        check_is_fitted(self)
 
         X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
         if self.whiten:
@@ -597,7 +597,7 @@ def inverse_transform(self, X, copy=True):
         -------
         X_new : array-like, shape (n_samples, n_features)
         """
-        check_is_fitted(self, 'mixing_')
+        check_is_fitted(self)
 
         X = check_array(X, copy=(copy and self.whiten), dtype=FLOAT_DTYPES)
         X = np.dot(X, self.mixing_.T)

diff --git a/sklearn/decomposition/kernel_pca.py b/sklearn/decomposition/kernel_pca.py
@@ -319,7 +319,7 @@ def transform(self, X):
         -------
         X_new : array-like, shape (n_samples, n_components)
         """
-        check_is_fitted(self, 'X_fit_')
+        check_is_fitted(self)
 
         # Compute centered gram matrix between X and training data X_fit_
         K = self._centerer.transform(self._get_kernel(X, self.X_fit_))

diff --git a/sklearn/decomposition/nmf.py b/sklearn/decomposition/nmf.py
@@ -1312,7 +1312,7 @@ def transform(self, X):
         W : array, shape (n_samples, n_components)
             Transformed data
         """
-        check_is_fitted(self, 'n_components_')
+        check_is_fitted(self)
 
         W, _, n_iter_ = non_negative_factorization(
             X=X, W=None, H=self.components_, n_components=self.n_components_,
@@ -1339,5 +1339,5 @@ def inverse_transform(self, W):
 
         .. versionadded:: 0.18
         """
-        check_is_fitted(self, 'n_components_')
+        check_is_fitted(self)
         return np.dot(W, self.components_)
diff --git a/sklearn/decomposition/online_lda.py b/sklearn/decomposition/online_lda.py
@@ -594,7 +594,7 @@ def _unnormalized_transform(self, X):
         doc_topic_distr : shape=(n_samples, n_components)
             Document topic distribution for X.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         # make sure feature size is the same in fitted model and in X
         X = self._check_non_neg_array(X, "LatentDirichletAllocation.transform")
@@ -748,7 +748,7 @@ def _perplexity_precomp_distr(self, X, doc_topic_distr=None,
         score : float
             Perplexity score.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         X = self._check_non_neg_array(X,
                                       "LatentDirichletAllocation.perplexity")

diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py
@@ -569,7 +569,7 @@ def score_samples(self, X):
         ll : array, shape (n_samples,)
             Log-likelihood of each sample under the current model
         """
-        check_is_fitted(self, 'mean_')
+        check_is_fitted(self)
 
         X = check_array(X)
         Xr = X - self.mean_

diff --git a/sklearn/decomposition/sparse_pca.py b/sklearn/decomposition/sparse_pca.py
@@ -221,7 +221,7 @@ def transform(self, X):
         X_new array, shape (n_samples, n_components)
             Transformed data.
         """
-        check_is_fitted(self, 'components_')
+        check_is_fitted(self)
 
         X = check_array(X)
         X = X - self.mean_

diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
@@ -505,7 +505,7 @@ def transform(self, X):
         if self.solver == 'lsqr':
             raise NotImplementedError("transform not implemented for 'lsqr' "
                                       "solver (use 'svd' or 'eigen').")
-        check_is_fitted(self, ['xbar_', 'scalings_'], all_or_any=any)
+        check_is_fitted(self)
 
         X = check_array(X)
         if self.solver == 'svd':
@@ -528,7 +528,7 @@ def predict_proba(self, X):
         C : array, shape (n_samples, n_classes)
             Estimated probabilities.
         """
-        check_is_fitted(self, 'classes_')
+        check_is_fitted(self)
 
         decision = self.decision_function(X)
         if self.classes_.size == 2:
@@ -704,7 +704,7 @@ def fit(self, X, y):
         return self
 
     def _decision_function(self, X):
-        check_is_fitted(self, 'classes_')
+        check_is_fitted(self)
 
         X = check_array(X)
         norm2 = []

diff --git a/sklearn/dummy.py b/sklearn/dummy.py
@@ -171,7 +171,7 @@ def predict(self, X):
         y : array, shape = [n_samples] or [n_samples, n_outputs]
             Predicted target values for X.
         """
-        check_is_fitted(self, 'classes_')
+        check_is_fitted(self)
 
         # numpy random_state expects Python int and not long as size argument
         # under Windows
@@ -249,7 +249,7 @@ def predict_proba(self, X):
             the model, where classes are ordered arithmetically, for each
             output.
         """
-        check_is_fitted(self, 'classes_')
+        check_is_fitted(self)
 
         # numpy random_state expects Python int and not long as size argument
         # under Windows
@@ -498,7 +498,7 @@ def predict(self, X, return_std=False):
         y_std : array, shape = [n_samples] or [n_samples, n_outputs]
             Standard deviation of predictive distribution of query points.
         """
-        check_is_fitted(self, "constant_")
+        check_is_fitted(self)
         n_samples = _num_samples(X)
 
         y = np.full((n_samples, self.n_outputs_), self.constant_,