category_encoders/hashing.py
def transform(self, X, override_return_df=False):
        """Perform the transformation to new categorical data.

        Parameters
        ----------

        X : array-like, shape = [n_samples, n_features]

        Returns
        -------

        p : array, shape = [n_samples, n_numeric + N]
            Transformed values with encoding applied.

        """

        if self._dim is None:
            raise ValueError('Must train encoder before it can be used to transform data.')

        # first check the type
        X = util.convert_input(X)

        # then make sure that it is the right size
        if X.shape[1] != self._dim:
            raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim, ))

        if not self.cols:
            return X

        X = self.hashing_trick(X, hashing_method=self.hash_method, N=self.n_components, cols=self.cols)

        if self.drop_invariant:
            for col in self.drop_cols:
                X.drop(col, 1, inplace=True)

        if self.return_df or override_return_df:
            return X
        else:
            return X.values
def transform(self, X, override_return_df=False):
        """Perform the transformation to new categorical data.

        Parameters
        ----------

        X : array-like, shape = [n_samples, n_features]

        Returns
        -------

        p : array, shape = [n_samples, n_numeric + N]
            Transformed values with encoding applied.

        """

        if self._dim is None:
            raise ValueError('Must train encoder before it can be used to transform data.')

        # first check the type
        X = util.convert_input(X)

        # then make sure that it is the right size
        if X.shape[1] != self._dim:
            raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim, ))

        if not self.cols:
            return X

        X = self.hashing_trick(X, hashing_method=self.hash_method, N=self.n_components, cols=self.cols)

        if self.drop_invariant:
            for col in self.drop_cols:
                X.drop(col, 1, inplace=True)

        if self.return_df or override_return_df:
            return X
        else:
            return X.values
Perform the transformation to new categorical data.

        Parameters
        ----------

        X : array-like, shape = [n_samples, n_features]

        Returns
        -------

        p : array, shape = [n_samples, n_numeric + N]
            Transformed values with encoding applied.