h2o-py/h2o/model/model_base.py
def varimp(self, use_pandas=False):
        """
        Pretty print the variable importances, or return them in a list.

        :param use_pandas: If True, then the variable importances will be returned as a pandas data frame.

        :returns: A list or Pandas DataFrame.
        """
        model = self._model_json["output"]
        if self.algo=='glm' or "variable_importances" in list(model.keys()) and model["variable_importances"]:
            if self.algo=='glm':
                tempvals = model["standardized_coefficient_magnitudes"].cell_values
                maxVal = 0
                sum=0
                for item in tempvals:
                    sum=sum+item[1]
                    if item[1]>maxVal:
                        maxVal = item[1]
                vals = []
                for item in tempvals:
                    tempT = (item[0], item[1], item[1]/maxVal, item[1]/sum)
                    vals.append(tempT)
                header = ["variable", "relative_importance", "scaled_importance", "percentage"]
            else:
                vals = model["variable_importances"].cell_values
                header = model["variable_importances"].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 variable importances")
def varimp(self, use_pandas=False):
        model = self._model_json["output"]
        if self.algo=='glm' or "variable_importances" in list(model.keys()) and model["variable_importances"]:
            if self.algo=='glm':
                tempvals = model["standardized_coefficient_magnitudes"].cell_values
                maxVal = 0
                sum=0
                for item in tempvals:
                    sum=sum+item[1]
                    if item[1]>maxVal:
                        maxVal = item[1]
                vals = []
                for item in tempvals:
                    tempT = (item[0], item[1], item[1]/maxVal, item[1]/sum)
                    vals.append(tempT)
                header = ["variable", "relative_importance", "scaled_importance", "percentage"]
            else:
                vals = model["variable_importances"].cell_values
                header = model["variable_importances"].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 variable importances")
def varimp ( self , use_pandas = False ) : model = self . _model_json [ "output" ] if self . algo == 'glm' or "variable_importances" in list ( model . keys ( ) ) and model [ "variable_importances" ] : if self . algo == 'glm' : tempvals = model [ "standardized_coefficient_magnitudes" ] . cell_values maxVal = 0 sum = 0 for item in tempvals : sum = sum + item [ 1 ] if item [ 1 ] > maxVal : maxVal = item [ 1 ] vals = [ ] for item in tempvals : tempT = ( item [ 0 ] , item [ 1 ] , item [ 1 ] / maxVal , item [ 1 ] / sum ) vals . append ( tempT ) header = [ "variable" , "relative_importance" , "scaled_importance" , "percentage" ] else : vals = model [ "variable_importances" ] . cell_values header = model [ "variable_importances" ] . 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 variable importances" )
Pretty print the variable importances, or return them in a list.

        :param use_pandas: If True, then the variable importances will be returned as a pandas data frame.

        :returns: A list or Pandas DataFrame.
Pretty print the variable importances or return them in a list .