FIX AttributeError related to n_features_ (fix #6)

Author: ZhiningLiu1998

imbalanced_ensemble/ensemble/_bagging.py

@@ -361,7 +361,7 @@ def _fit(self, X, y,
             sample_weight = _check_sample_weight(sample_weight, X, dtype=None)
 
         # Remap output
-        n_samples, self.n_features_ = X.shape
+        n_samples, self.n_features_in_ = X.shape
         self._n_samples = n_samples
         y = self._validate_y(y)
 
@@ -384,11 +384,11 @@ def _fit(self, X, y,
         if isinstance(self.max_features, numbers.Integral):
             max_features = self.max_features
         elif isinstance(self.max_features, float):
-            max_features = self.max_features * self.n_features_
+            max_features = self.max_features * self.n_features_in_
         else:
             raise ValueError("max_features must be int or float")
 
-        if not (0 < max_features <= self.n_features_):
+        if not (0 < max_features <= self.n_features_in_):
             raise ValueError("max_features must be in (0, n_features]")
 
         max_features = max(1, int(max_features))

imbalanced_ensemble/ensemble/base.py

@@ -479,8 +479,8 @@ def fit(self, X, y, *, sample_weight=None, **kwargs):
                 raise ValueError("sample_weight cannot contain negative weights")
 
         # Remap output
-        n_samples, self.n_features_ = X.shape
-        self.features_ = np.arange(self.n_features_)
+        n_samples, self.n_features_in_ = X.shape
+        self.features_ = np.arange(self.n_features_in_)
         self._n_samples = n_samples
         y = self._validate_y(y)
         self._encode_map = {c: np.where(self.classes_==c)[0][0] for c in self.classes_}
@@ -534,11 +534,11 @@ def predict_proba(self, X):
             X, accept_sparse=['csr', 'csc'], dtype=None,
             force_all_finite=False
         )
-        if self.n_features_ != X.shape[1]:
+        if self.n_features_in_ != X.shape[1]:
             raise ValueError("Number of features of the model must "
                              "match the input. Model n_features is {0} and "
                              "input n_features is {1}."
-                             "".format(self.n_features_, X.shape[1]))
+                             "".format(self.n_features_in_, X.shape[1]))
 
         # Parallel loop
         n_jobs, _, starts = _partition_estimators(self.n_estimators,

imbalanced_ensemble/ensemble/compatible/bagging_compatible.py

@@ -133,7 +133,7 @@ class CompatibleBaggingClassifier(ImbalancedEnsembleClassifierMixin,
     base_estimator_ : estimator
         The base estimator from which the ensemble is grown.
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when :meth:`fit` is performed.
 
     estimators_ : list of estimators
@@ -297,7 +297,7 @@ def fit(self, X, y,
             sample_weight = _check_sample_weight(sample_weight, X, dtype=None)
 
         # Remap output
-        n_samples, self.n_features_ = X.shape
+        n_samples, self.n_features_in_ = X.shape
         self._n_samples = n_samples
         y = self._validate_y(y)
 
@@ -320,11 +320,11 @@ def fit(self, X, y,
         if isinstance(self.max_features, numbers.Integral):
             max_features = self.max_features
         elif isinstance(self.max_features, float):
-            max_features = self.max_features * self.n_features_
+            max_features = self.max_features * self.n_features_in_
         else:
             raise ValueError("max_features must be int or float")
 
-        if not (0 < max_features <= self.n_features_):
+        if not (0 < max_features <= self.n_features_in_):
             raise ValueError("max_features must be in (0, n_features]")
 
         max_features = max(1, int(max_features))

imbalanced_ensemble/ensemble/over_sampling/over_bagging.py

@@ -116,7 +116,7 @@ class OverBaggingClassifier(ResampleBaggingClassifier):
     estimators_ : list of classifiers
         The collection of fitted sub-estimators.
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when :meth:`fit` is performed.
 
     estimators_ : list of estimators

imbalanced_ensemble/ensemble/over_sampling/smote_bagging.py

@@ -125,7 +125,7 @@ class SMOTEBaggingClassifier(ResampleBaggingClassifier):
     estimators_ : list of classifiers
         The collection of fitted sub-estimators.
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when :meth:`fit` is performed.
 
     estimators_ : list of estimators

imbalanced_ensemble/ensemble/under_sampling/balanced_random_forest.py

@@ -280,7 +280,7 @@ class labels (multi-output problem).
         The number of classes (single output problem), or a list containing the
         number of classes for each output (multi-output problem).
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when ``fit`` is performed.
 
     n_outputs_ : int
@@ -485,7 +485,7 @@ def fit(self, X, y,
             X.sort_indices()
 
         # Remap output
-        _, self.n_features_ = X.shape
+        _, self.n_features_in_ = X.shape
 
         y = np.atleast_1d(y)
         if y.ndim == 2 and y.shape[1] == 1:

imbalanced_ensemble/ensemble/under_sampling/easy_ensemble.py

@@ -118,7 +118,7 @@ class EasyEnsembleClassifier(ResampleBaggingClassifier):
     estimators_ : list of classifiers
         The collection of fitted sub-estimators.
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when :meth:`fit` is performed.
 
     estimators_ : list of estimators

imbalanced_ensemble/ensemble/under_sampling/under_bagging.py

@@ -114,7 +114,7 @@ class UnderBaggingClassifier(ResampleBaggingClassifier):
     estimators_ : list of classifiers
         The collection of fitted sub-estimators.
 
-    n_features_ : int
+    n_features_in_ : int
         The number of features when :meth:`fit` is performed.
 
     estimators_ : list of estimators

imbalanced_ensemble/utils/_validation_param.py

@@ -577,7 +577,7 @@ def check_visualizer_ensembles(ensembles:dict, eval_datasets_:dict, eval_metrics
             names_imbalanced_ensemble.append(name)
     names_sklearn_ensemble = list(set(ensembles.keys()) - set(names_imbalanced_ensemble))
 
-    # Raise error if not all ensembles have the same n_features_
+    # Raise error if not all ensembles have the same n_features_/n_features_in_
     n_features_fitted = _check_all_estimators_have_same_attribute(ensembles,
         attr_alias = ('n_features_', 'n_features_in_'))