h2o-py/h2o/model/dim_reduction.py
def varimp(self, use_pandas=False):
        """
        Return the Importance of components associcated with a pca model.

        use_pandas: ``bool``  (default: ``False``).
        """
        model = self._model_json["output"]
        if "importance" in list(model.keys()) and model["importance"]:
            vals = model["importance"].cell_values
            header = model["importance"].col_header
            if use_pandas and can_use_pandas():
                import pandas
                return pandas.DataFrame(vals, columns=header)
            else:
                return vals
        else:
            print("Warning: This model doesn't have importances of components.")
def varimp(self, use_pandas=False):
        model = self._model_json["output"]
        if "importance" in list(model.keys()) and model["importance"]:
            vals = model["importance"].cell_values
            header = model["importance"].col_header
            if use_pandas and can_use_pandas():
                import pandas
                return pandas.DataFrame(vals, columns=header)
            else:
                return vals
        else:
            print("Warning: This model doesn't have importances of components.")
def varimp ( self , use_pandas = False ) : model = self . _model_json [ "output" ] if "importance" in list ( model . keys ( ) ) and model [ "importance" ] : vals = model [ "importance" ] . cell_values header = model [ "importance" ] . col_header if use_pandas and can_use_pandas ( ) : import pandas return pandas . DataFrame ( vals , columns = header ) else : return vals else : print ( "Warning: This model doesn't have importances of components." )
Return the Importance of components associcated with a pca model.

        use_pandas: ``bool``  (default: ``False``).
Return the Importance of components associcated with a pca model .