我正在编写一个类，用于在不同的缩放器之间切换。以下代码“有效”（但不切换缩放器）：

from sklearn.preprocessing import StandardScaler, MinMaxScalerclass CustomTransformer(StandardScaler, MinMaxScaler):    def __init__(self, which,with_std=True,with_mean=True, feature_range=(0,1)):        self.which = which        self.with_mean = with_mean        self.with_std = with_std        self.feature_range = feature_range        if which=="standard":            self = StandardScaler.__init__(self)        else:            self = MinMaxScaler.__init__(self)X = [[1,2,3],[3,4,5],[6,7,8]]ct = CustomTransformer(which="standard")    ct.fit_transform(X)array([[-1.13554995, -1.13554995, -1.13554995],       [-0.16222142, -0.16222142, -0.16222142],       [ 1.29777137,  1.29777137,  1.29777137]])ct = CustomTransformer(which="")ct.fit_transform(X)array([[-1.13554995, -1.13554995, -1.13554995],       [-0.16222142, -0.16222142, -0.16222142],       [ 1.29777137,  1.29777137,  1.29777137]])

所以我的问题更像是理论性的：

在scikit-learn中，如何正确地实现条件多类继承并切换缩放器？

回答：

这“仅仅”是有效的：

from sklearn.base import TransformerMixinfrom sklearn.preprocessing import StandardScaler, MinMaxScalerX = [[1,2,3],[3,4,5],[6,7,8]]class CustomTransformer(TransformerMixin):    def __init__(self, condition,with_mean=True, with_std=True, feature_range=(0,1), **kwargs):        self.condition = condition        if condition:            self.scaler = StandardScaler(with_mean=with_mean, with_std=with_std, **kwargs)        else:            self.scaler = MinMaxScaler(feature_range=feature_range, **kwargs)    def fit(self, X):        return self.scaler.fit(X)    def transform(self, X):        return self.scaler.transform(X)    def get_params(self):        d = self.scaler.get_params()        d['condition'] = self.condition        return d

ct = CustomTransformer(False, feature_range=(0,.1))ct.fit_transform(X)array([[0.  , 0.  , 0.  ],       [0.04, 0.04, 0.04],       [0.1 , 0.1 , 0.1 ]])

ct = CustomTransformer(True, feature_range=(0,.1))ct.fit_transform(X)array([[-1.13554995, -1.13554995, -1.13554995],       [-0.16222142, -0.16222142, -0.16222142],       [ 1.29777137,  1.29777137,  1.29777137]])

现在这个CustomTransformer可以通过.get_params()被GridSearchCV访问：

from sklearn.model_selection import GridSearchCVgs = GridSearchCV(ct, param_grid={})gs.get_params(){'cv': None, 'error_score': nan, 'estimator__copy': True, 'estimator__with_mean': True, 'estimator__with_std': True, 'estimator__condition': True, 'estimator': <__main__.CustomTransformer at 0x7fbd8d3aa9d0>, 'iid': 'deprecated', 'n_jobs': None, 'param_grid': {}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': False, 'scoring': None, 'verbose': 0}