(self, param_grid, grid_search_args)
Set up the grid search parameters to be used later for sklearn.model_selection.GridSearchCV
        param_grid is a series with the hyperparameters. The series can have multiple rows.
        grid_search_args is a string with the arguments for the call to GridSearchCV.
Set up the grid search parameters to be used later for sklearn.model_selection.GridSearchCV
        param_grid is a series with the hyperparameters. The series can have multiple rows.
        grid_search_args is a string with the arguments for the call to GridSearchCV.
Set up the grid search parameters to be used later for sklearn . model_selection . GridSearchCV param_grid is a series with the hyperparameters . The series can have multiple rows . grid_search_args is a string with the arguments for the call to GridSearchCV .
def _set_grid_params(self, param_grid, grid_search_args):
        """
        Set up the grid search parameters to be used later for sklearn.model_selection.GridSearchCV
        param_grid is a series with the hyperparameters. The series can have multiple rows.
        grid_search_args is a string with the arguments for the call to GridSearchCV.
        """
        
        # If key word arguments for the grid search are included in the request, get the parameters and values
        if len(grid_search_args) > 0:
            # Transform the string of arguments into a dictionary
            grid_search_args = utils.get_kwargs(grid_search_args)
            
            # Get the metric parameters, converting values to the correct data type
            self.model.grid_search_args = utils.get_kwargs_by_type(grid_search_args)

            # The refit parameter must be True, so this is ignored if passed in the arguments
            self.model.grid_search_args["refit"] = True
        else:
            self.model.grid_search_args = {}
        
        # If key word arguments for the grid search are included in the request, get the parameters and values
        if len(param_grid) > 0:
            # Transform the parameter grid dataframe into a list of dictionaries
            self.model.param_grid = list(param_grid.apply(utils.get_kwargs).apply(utils.get_kwargs_by_type))
        else:
            err = "An empty string is not a valid input for the param_grid argument"
            raise Exception(err)
        
        # Debug information is printed to the terminal and logs if the paramater debug = true
        if self.model.debug:
            self._print_log(9)
def _set_grid_params ( self , param_grid , grid_search_args ) : # If key word arguments for the grid search are included in the request, get the parameters and values if len ( grid_search_args ) > 0 : # Transform the string of arguments into a dictionary grid_search_args = utils . get_kwargs ( grid_search_args ) # Get the metric parameters, converting values to the correct data type self . model . grid_search_args = utils . get_kwargs_by_type ( grid_search_args ) # The refit parameter must be True, so this is ignored if passed in the arguments self . model . grid_search_args [ "refit" ] = True else : self . model . grid_search_args = { } # If key word arguments for the grid search are included in the request, get the parameters and values if len ( param_grid ) > 0 : # Transform the parameter grid dataframe into a list of dictionaries self . model . param_grid = list ( param_grid . apply ( utils . get_kwargs ) . apply ( utils . get_kwargs_by_type ) ) else : err = "An empty string is not a valid input for the param_grid argument" raise Exception ( err ) # Debug information is printed to the terminal and logs if the paramater debug = true if self . model . debug : self . _print_log ( 9 )