base.py 文件源码

def _evaluate(self, individual, X, y, cv=3):
        """ Evaluate method

        Parameters
        ----------
        individual: list [n_features]
                The input individual to be evaluated

        Return
        ----------
        Score of the individual : turple( cross_val_score, feature score)
        """
        # Select Features
        features = list(compress(range(len(individual)), individual))
        train = np.reshape([X[:, i] for i in features],
                           [len(features), len(X)]).T

        if train.shape[1] == 0:
            return 0,1,

        # Applying K-Fold Cross Validation
        accuracies = cross_val_score(estimator=clone(self.estimator), X=train, 
                                     y=y, cv=cv, 
                                     scoring=self.cv_metric_function)

        if self.features_metric_function == None :
            feature_score = pow(sum(individual)/(len(individual)*5), 2)
        else:
            feature_score = self.features_metric_function(individual)

        return accuracies.mean() - accuracies.std(), feature_score