models_classification.py 文件源码

def printReport(self, printConfusionMatrix, printModelParameters):
        # Print the metric determined in the previous function.

        print("\nModel Report")
        #Outpute the parameters used for modeling
        if printModelParameters:
            print('\nModel being built with the following parameters:')
            print(self.alg.get_params())

        if printConfusionMatrix:
            for key,data in self.dp.items():
                if key!='predict':
                    print("\nConfusion Matrix for %s data:"%key)
                    print(pd.crosstab(
                            data[self.datablock.target], 
                            self.predictions_class[key])
                    )
            print('Note: rows - actual; col - predicted')

        print("\nScoring Metric:")
        for key,data in self.dp.items():
            if key!='predict':
                name = '%s_%s'%(self.scoring_metric,key)
                print("\t%s (%s): %s" % 
                    (
                    self.scoring_metric,
                    key,
                    "{0:.3%}".format(self.classification_output[name])
                    )
                )

        print("\nCV Score for Scoring Metric (%s):"%self.scoring_metric)
        print("\tMean - %f | Std - %f" % (
            self.classification_output['CVScore_mean'],
            self.classification_output['CVScore_std'])
        )

        if self.additional_display_metrics:
            print("\nAdditional Scoring Metrics:")
            for metric in self.additional_display_metrics:
                for key,data in self.dp.items():
                    if key!='predict':
                        name = '%s_%s'%(metric,key)
                        print("\t%s (%s): %s" % (
                            metric,
                            key,
                            "{0:.3%}".format(
                                    self.classification_output[name])
                            )
                        )