DOC: Remove docstring which are not useful

doc/whats_new/v0.0.4.rst

@@ -78,6 +78,15 @@ Maintenance
 - Remove deprecated parameters in 0.2 - :issue:`331` by :user:`Guillaume
   Lemaitre <glemaitre>`.
 
+- Make some modules private.
+  :issue:`452` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+Documentation
+.............
+
+- Remove some docstring which are not necessary.
+  :issue:`454` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Deprecation
 ...........
 

imblearn/base.py

@@ -95,7 +95,8 @@ def fit_sample(self, X, y):
 
     @abstractmethod
     def _sample(self, X, y):
-        """Resample the dataset.
+        """Base method defined in each sampler to defined the sampling
+        strategy.
 
         Parameters
         ----------

imblearn/combine/_smote_enn.py

@@ -149,26 +149,6 @@ def fit(self, X, y):
         return self
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new)
-            The corresponding label of `X_resampled`
-
-        """
         self._validate_estimator()
 
         X_res, y_res = self.smote_.fit_sample(X, y)

imblearn/combine/_smote_tomek.py

@@ -157,26 +157,6 @@ def fit(self, X, y):
         return self
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        """
         self._validate_estimator()
 
         X_res, y_res = self.smote_.fit_sample(X, y)

imblearn/ensemble/_balance_cascade.py

@@ -152,30 +152,6 @@ def _validate_estimator(self):
         self.logger.debug(self.estimator_)
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_subset, n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_subset, n_samples_new)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_subset, n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
         self._validate_estimator()
 
         random_state = check_random_state(self.random_state)

imblearn/ensemble/_easy_ensemble.py

@@ -102,31 +102,6 @@ def __init__(self,
         self.n_subsets = n_subsets
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_subset, n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_subset, n_samples_new)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_subset, n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
-
         random_state = check_random_state(self.random_state)
 
         X_resampled = []

imblearn/over_sampling/_adasyn.py

@@ -107,27 +107,6 @@ def _validate_estimator(self):
         self.nn_.set_params(**{'n_jobs': self.n_jobs})
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-
-        """
         self._validate_estimator()
         random_state = check_random_state(self.random_state)
 

imblearn/over_sampling/_random_over_sampler.py

@@ -89,26 +89,6 @@ def _check_X_y(X, y):
         return X, y, binarize_y
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        """
         random_state = check_random_state(self.random_state)
         target_stats = Counter(y)
 

imblearn/under_sampling/_prototype_generation/_cluster_centroids.py

@@ -136,26 +136,6 @@ def _generate_sample(self, X, y, centroids, target_class):
         return X_new, y_new
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        """
         self._validate_estimator()
 
         if self.voting == 'auto':

imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py

@@ -129,30 +129,6 @@ def _validate_estimator(self):
                              ' Got {} instead.'.format(type(self.n_neighbors)))
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
         self._validate_estimator()
 
         random_state = check_random_state(self.random_state)

imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py

@@ -139,30 +139,6 @@ def _validate_estimator(self):
             raise NotImplementedError
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
         self._validate_estimator()
 
         idx_under = np.empty((0, ), dtype=int)
@@ -328,31 +304,6 @@ def _validate_estimator(self):
             ratio=self.ratio)
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
-
         self._validate_estimator()
 
         X_, y_ = X, y
@@ -539,30 +490,6 @@ def _validate_estimator(self):
             ratio=self.ratio)
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-        Returns
-        -------
-        X_resampled : {ndarray, sparse matrix}, shape \
-(n_samples_new, n_features)
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
         self._validate_estimator()
 
         X_, y_ = X, y

imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py

@@ -126,26 +126,6 @@ def _validate_estimator(self):
                 type(self.estimator)))
 
     def _sample(self, X, y):
-        """Resample the dataset.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Matrix containing the data which have to be sampled.
-
-        y : array-like, shape (n_samples,)
-            Corresponding label for each sample in X.
-
-            The array containing the resampled data.
-
-        y_resampled : ndarray, shape (n_samples_new,)
-            The corresponding label of `X_resampled`
-
-        idx_under : ndarray, shape (n_samples, )
-            If `return_indices` is `True`, a boolean array will be returned
-            containing the which samples have been selected.
-
-        """
         self._validate_estimator()
 
         target_stats = Counter(y)