我在Pipeline流程中使用自定义类进行predict时遇到了问题，每个管道步骤都有一个自定义类。

class MyFeatureSelector():    def __init__(self, features=5, method='pca'):        self.features = features        self.method = method    def fit(self, X, Y):        return self    def transform(self, X, Y=None):        try:            if self.features < X.shape[1]:                if self.method == 'pca':                    selector = PCA(n_components=self.features)                elif self.method == 'rfe':                    selector = RFE(estimator=LinearRegression(n_jobs=-1),                                   n_features_to_select=self.features,                                   step=1)                selector.fit(X, Y)                return selector.transform(X)        except Exception as err:            print('MyFeatureSelector.transform(): {}'.format(err))        return X    def fit_transform(self, X, Y=None):        self.fit(X, Y)        return self.transform(X, Y)model = Pipeline([    ("DATA_CLEANER", MyDataCleaner(demo='', mode='strict')),    ("DATA_ENCODING", MyEncoder(encoder_name='code')),    ("FEATURE_SELECTION", MyFeatureSelector(features=15, method='rfe')),    ("HUBER_MODELLING", HuberRegressor())])

因此，以上代码在这里运行得很好：

 model.fit(X, _Y)

但在这里我遇到了错误

 prediction = model.predict(XT)

错误：形状（672,107）和（15,）不匹配：107（维度1）!= 15（维度0）

调试显示问题出在这里：selector.fit(X, Y)，因为在predict()步骤中创建了MyFeatureSelector的新实例，此时Y不存在。

我哪里做错了？

回答：

下面是工作版本的代码：

class MyFeatureSelector():    def __init__(self, features=5, method='pca'):        self.features = features        self.method = method        self.selector = None        self.init_selector()    def init_selector():        if self.method == 'pca':            self.selector = PCA(n_components=self.features)        elif self.method == 'rfe':        self.selector = RFE(estimator=LinearRegression(n_jobs=-1),                               n_features_to_select=self.features,                               step=1)    def fit(self, X, Y):       return self    def transform(self, X, Y=None):        try:            if self.features < X.shape[1]:                if Y is not None:                    self.selector.fit(X, Y)                return selector.transform(X)        except Exception as err:            print('MyFeatureSelector.transform(): {}'.format(err))       return Xdef fit_transform(self, X, Y=None):    self.fit(X, Y)    return self.transform(X, Y)