random_features_helper.py 文件源码

def compute_preprocessor(self,method):
        self.data={}
        if method=='none':
            self.data=self.orig_data
        elif method=='min_max':
            transform=preprocessing.MinMaxScaler()
            self.data['X_train']=transform.fit_transform(self.orig_data['X_train'])
            self.data['X_val']=transform.transform(self.orig_data['X_val'])
            self.data['X_test']=transform.transform(self.orig_data['X_test'])
        elif method=='scaled':
            self.data['X_train']=preprocessing.scale(self.orig_data['X_train'])
            self.data['X_val']=preprocessing.scale(self.orig_data['X_val'])
            self.data['X_test']=preprocessing.scale(self.orig_data['X_test'])
        elif method=='normalized':
            self.data['X_train']=preprocessing.normalize(self.orig_data['X_train'])
            self.data['X_val']=preprocessing.normalize(self.orig_data['X_val'])
            self.data['X_test']=preprocessing.normalize(self.orig_data['X_test'])
        self.data['y_train']=self.orig_data['y_train']
        self.data['y_val']=self.orig_data['y_val']
        self.data['y_test']=self.orig_data['y_test']