(self, scorer, subset='train')
return metrics_df
Prepare score metrics using a spaCy scrorer 
        Returns a dataframe formatted for output
Prepare score metrics using a spaCy scrorer 
        Returns a dataframe formatted for output
Prepare score metrics using a spaCy scrorer Returns a dataframe formatted for output
def _prep_scores(self, scorer, subset='train'):
        """
        Prepare score metrics using a spaCy scrorer 
        Returns a dataframe formatted for output
        """
        columns = ["metric", "value"]

        # Prepare scorer metrics
        scores = {"Precision": scorer.scores["ents_p"], "Recall": scorer.scores["ents_r"], "F-score": scorer.scores["ents_f"]}
        metrics_df = pd.DataFrame([(k, v) for k, v in scores.items()], columns=columns)

        metrics_df.loc[:,'model'] = self.model
        metrics_df.loc[:,'subset'] = subset
        metrics_df = metrics_df[['model', 'subset'] + columns]

        return metrics_df
def _prep_scores ( self , scorer , subset = 'train' ) : columns = [ "metric" , "value" ] # Prepare scorer metrics scores = { "Precision" : scorer . scores [ "ents_p" ] , "Recall" : scorer . scores [ "ents_r" ] , "F-score" : scorer . scores [ "ents_f" ] } metrics_df = pd . DataFrame ( [ ( k , v ) for k , v in scores . items ( ) ] , columns = columns ) metrics_df . loc [ : , 'model' ] = self . model metrics_df . loc [ : , 'subset' ] = subset metrics_df = metrics_df [ [ 'model' , 'subset' ] + columns ] return metrics_df