scikit-learn
diff --git a/‎sklearn/utils/estimator_checks.py
Lines changed: 14 additions & 14 deletions b/‎sklearn/utils/estimator_checks.py
Lines changed: 14 additions & 14 deletions
@@ -301,11 +301,11 @@ def _set_check_estimator_ids(obj):
     Parameters
     ----------
     obj : estimator or function
-        Items generated by `check_estimator`
+        Items generated by `check_estimator`.
 
     Returns
     -------
-    id : string or None
+    id : str or None
 
     See also
     --------
@@ -327,7 +327,7 @@ def _set_check_estimator_ids(obj):
 
 
 def _construct_instance(Estimator):
-    """Construct Estimator instance if possible"""
+    """Construct Estimator instance if possible."""
     required_parameters = getattr(Estimator, "_required_parameters", [])
     if len(required_parameters):
         if required_parameters in (["estimator"], ["base_estimator"]):
@@ -346,7 +346,7 @@ def _construct_instance(Estimator):
 
 def _mark_xfail_checks(estimator, check, pytest):
     """Mark (estimator, check) pairs with xfail according to the
-    _xfail_checks_ tag"""
+    _xfail_checks_ tag."""
     xfail_checks = estimator._get_tags()['_xfail_checks'] or {}
     check_name = _set_check_estimator_ids(check)
 
@@ -462,7 +462,7 @@ def check_estimator(Estimator, generate_only=False):
            Passing a class was deprecated in version 0.23, and support for
            classes was removed in 0.24.
 
-    generate_only : bool, optional (default=False)
+    generate_only : bool, default=False
         When `False`, checks are evaluated when `check_estimator` is called.
         When `True`, `check_estimator` returns a generator that yields
         (estimator, check) tuples. The check is run by calling
@@ -597,7 +597,7 @@ def _set_checking_parameters(estimator):
 
 
 class _NotAnArray:
-    """An object that is convertible to an array
+    """An object that is convertible to an array.
 
     Parameters
     ----------
@@ -663,12 +663,12 @@ def _pairwise_estimator_convert_X(X, estimator, kernel=linear_kernel):
 
 
 def _generate_sparse_matrix(X_csr):
-    """Generate sparse matrices with {32,64}bit indices of diverse format
+    """Generate sparse matrices with {32,64}bit indices of diverse format.
 
         Parameters
         ----------
         X_csr: CSR Matrix
-            Input matrix in CSR format
+            Input matrix in CSR format.
 
         Returns
         -------
@@ -1498,7 +1498,7 @@ def check_estimators_nan_inf(name, estimator_orig):
 
 @ignore_warnings
 def check_nonsquare_error(name, estimator_orig):
-    """Test that error is thrown when non-square data provided"""
+    """Test that error is thrown when non-square data provided."""
 
     X, y = make_blobs(n_samples=20, n_features=10)
     estimator = clone(estimator_orig)
@@ -1511,7 +1511,7 @@ def check_nonsquare_error(name, estimator_orig):
 
 @ignore_warnings
 def check_estimators_pickle(name, estimator_orig):
-    """Test that we can pickle all estimators"""
+    """Test that we can pickle all estimators."""
     check_methods = ["predict", "transform", "decision_function",
                      "predict_proba"]
 
@@ -1722,7 +1722,7 @@ def check_clustering(name, clusterer_orig, readonly_memmap=False):
 
 @ignore_warnings(category=FutureWarning)
 def check_clusterer_compute_labels_predict(name, clusterer_orig):
-    """Check that predict is invariant of compute_labels"""
+    """Check that predict is invariant of compute_labels."""
     X, y = make_blobs(n_samples=20, random_state=0)
     clusterer = clone(clusterer_orig)
     set_random_state(clusterer)
@@ -2027,7 +2027,7 @@ def check_classifiers_multilabel_representation_invariance(name,
 @ignore_warnings(category=FutureWarning)
 def check_estimators_fit_returns_self(name, estimator_orig,
                                       readonly_memmap=False):
-    """Check if self is returned when calling fit"""
+    """Check if self is returned when calling fit."""
     X, y = make_blobs(random_state=0, n_samples=21)
     # some want non-negative input
     X -= X.min()
@@ -2423,7 +2423,7 @@ def check_estimators_overwrite_params(name, estimator_orig):
 
 @ignore_warnings(category=FutureWarning)
 def check_no_attributes_set_in_init(name, estimator_orig):
-    """Check setting during init. """
+    """Check setting during init."""
     estimator = clone(estimator_orig)
     if hasattr(type(estimator).__init__, "deprecated_original"):
         return
@@ -2570,7 +2570,7 @@ def check_parameters_default_constructible(name, Estimator):
 
         try:
             def param_filter(p):
-                """Identify hyper parameters of an estimator"""
+                """Identify hyper parameters of an estimator."""
                 return (p.name != 'self' and
                         p.kind != p.VAR_KEYWORD and
                         p.kind != p.VAR_POSITIONAL)