def _fit_transform(self, X):
        """Assume X contains only categorical features.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)
            Dense array or sparse matrix.
        """
        X = self._matrix_adjust(X)

        X = check_array(
            X,
            accept_sparse='csc',
            force_all_finite=False,
            dtype=int
        )

        if X.min() < 0:
            raise ValueError("X needs to contain only non-negative integers.")

        n_samples, n_features = X.shape

        # Remember which values should not be replaced by the value 'other'
        if self.minimum_fraction is not None:
            do_not_replace_by_other = list()
            for column in range(X.shape[1]):
                do_not_replace_by_other.append(list())

                if sparse.issparse(X):
                    indptr_start = X.indptr[column]
                    indptr_end = X.indptr[column + 1]
                    unique = np.unique(X.data[indptr_start:indptr_end])
                    colsize = indptr_end - indptr_start
                else:
                    unique = np.unique(X[:, column])
                    colsize = X.shape[0]

                for unique_value in unique:
                    if np.isfinite(unique_value):
                        if sparse.issparse(X):
                            indptr_start = X.indptr[column]
                            indptr_end = X.indptr[column + 1]
                            count = np.nansum(unique_value ==
                                              X.data[indptr_start:indptr_end])
                        else:
                            count = np.nansum(unique_value == X[:, column])
                    else:
                        if sparse.issparse(X):
                            indptr_start = X.indptr[column]
                            indptr_end = X.indptr[column + 1]
                            count = np.nansum(~np.isfinite(
                                X.data[indptr_start:indptr_end]))
                        else:
                            count = np.nansum(~np.isfinite(X[:, column]))

                    fraction = float(count) / colsize
                    if fraction >= self.minimum_fraction:
                        do_not_replace_by_other[-1].append(unique_value)

                for unique_value in unique:
                    if unique_value not in do_not_replace_by_other[-1]:
                        if sparse.issparse(X):
                            indptr_start = X.indptr[column]
                            indptr_end = X.indptr[column + 1]
                            X.data[indptr_start:indptr_end][
                                X.data[indptr_start:indptr_end] ==
                                unique_value] = SPARSE_ENCODINGS['OTHER']
                        else:
                            X[:, column][X[:, column] == unique_value] = SPARSE_ENCODINGS['OTHER']

            self.do_not_replace_by_other_ = do_not_replace_by_other

        if sparse.issparse(X):
            n_values = X.max(axis=0).toarray().flatten() + len(SPARSE_ENCODINGS)
        else:
            n_values = np.max(X, axis=0) + len(SPARSE_ENCODINGS)

        self.n_values_ = n_values
        n_values = np.hstack([[0], n_values])
        indices = np.cumsum(n_values)
        self.feature_indices_ = indices

        if sparse.issparse(X):
            row_indices = X.indices
            column_indices = []
            for i in range(len(X.indptr) - 1):
                nbr = X.indptr[i+1] - X.indptr[i]
                column_indices_ = [indices[i]] * nbr
                column_indices_ += X.data[X.indptr[i]:X.indptr[i+1]]
                column_indices.extend(column_indices_)
            data = np.ones(X.data.size)
        else:
            column_indices = (X + indices[:-1]).ravel()
            row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
                                    n_features)
            data = np.ones(n_samples * n_features)

        out = sparse.coo_matrix((data, (row_indices, column_indices)),
                                shape=(n_samples, indices[-1]),
                                dtype=self.dtype).tocsc()

        mask = np.array(out.sum(axis=0)).ravel() != 0
        active_features = np.where(mask)[0]
        out = out[:, active_features]
        self.active_features_ = active_features
        return out.tocsr() if self.sparse else out.toarray()
Assume X contains only categorical features.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)
            Dense array or sparse matrix.