(self, X, y)
return self
Check inputs and statistics of the sampler.

        You should use ``fit_resample`` in all cases.

        Parameters
        ----------
        X : {array-like, dataframe, sparse matrix} of shape \
                (n_samples, n_features)
            Data array.

        y : array-like of shape (n_samples,)
            Target array.

        Returns
        -------
        self : object
            Return the instance itself.
Check inputs and statistics of the sampler.
Check inputs and statistics of the sampler .
def fit(self, X, y):
        """Check inputs and statistics of the sampler.

        You should use ``fit_resample`` in all cases.

        Parameters
        ----------
        X : {array-like, dataframe, sparse matrix} of shape \
                (n_samples, n_features)
            Data array.

        y : array-like of shape (n_samples,)
            Target array.

        Returns
        -------
        self : object
            Return the instance itself.
        """
        # we need to overwrite SamplerMixin.fit to bypass the validation
        if self.validate:
            check_classification_targets(y)
            X, y, _ = self._check_X_y(X, y, accept_sparse=self.accept_sparse)

        self.sampling_strategy_ = check_sampling_strategy(
            self.sampling_strategy, y, self._sampling_type
        )

        return self
def fit ( self , X , y ) : # we need to overwrite SamplerMixin.fit to bypass the validation if self . validate : check_classification_targets ( y ) X , y , _ = self . _check_X_y ( X , y , accept_sparse = self . accept_sparse ) self . sampling_strategy_ = check_sampling_strategy ( self . sampling_strategy , y , self . _sampling_type ) return self