(self, column_name, raw_column_data)
return ColumnTransformInfo(
            column_name=column_name, column_type='discrete', transform=ohe,
            transform_aux=None,
            output_info=[SpanInfo(num_categories, 'softmax')],
            output_dimensions=num_categories)
Fit one hot encoder for discrete column.
Fit one hot encoder for discrete column.
Fit one hot encoder for discrete column .
def _fit_discrete(self, column_name, raw_column_data):
        """Fit one hot encoder for discrete column."""
        ohe = OneHotEncodingTransformer()
        fit_data = pd.DataFrame(raw_column_data, columns=[column_name])

        ohe.fit(fit_data, column_name)
        num_categories = len(ohe.dummies)

        return ColumnTransformInfo(
            column_name=column_name, column_type='discrete', transform=ohe,
            transform_aux=None,
            output_info=[SpanInfo(num_categories, 'softmax')],
            output_dimensions=num_categories)
def _fit_discrete ( self , column_name , raw_column_data ) : ohe = OneHotEncodingTransformer ( ) fit_data = pd . DataFrame ( raw_column_data , columns = [ column_name ] ) ohe . fit ( fit_data , column_name ) num_categories = len ( ohe . dummies ) return ColumnTransformInfo ( column_name = column_name , column_type = 'discrete' , transform = ohe , transform_aux = None , output_info = [ SpanInfo ( num_categories , 'softmax' ) ] , output_dimensions = num_categories )