我在尝试为房屋数据创建转换管道

from sklearn.base import BaseEstimator, TransformerMixinrooms_ix, bedrooms_ix, population_ix, household_ix = 3,4,5,6class CombineAttributesAdder(BaseEstimator, TransformerMixin):    def __init__(self, add_bedrooms_per_room = True):        self.add_bedrooms_per_room = add_bedrooms_per_room    def fit(self, X, y=None):        return self    def transfrom(self, X, y=None):        rooms_per_househond = X[:,rooms_ix]/X[:,household_ix]        population_per_household = X[:,population_ix]/ X[:, household_ix]        if self.add_bedrooms_per_room:            bedrooms_per_room = X[:,bedrooms_ix]/X[:rooms_ix]            return np.c_[X, rooms_per_househond, population_per_household, bedrooms_per_room]        else:            return np.c_[X, rooms_per_househond, population_per_household]

我用于管道的代码如下：

from sklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScalerfrom imblearn.pipeline import make_pipelinenum_pipline = Pipeline([    ('imputer', SimpleImputer(missing_values=np.nan, strategy='median')),    ('attribs_adder', CombineAttributesAdder(add_bedrooms_per_room=False)),    ('stand_scaler', StandardScaler()),])housing_num_transform = num_pipline.fit_transform(housing_num)

它抛出了错误：

回答：

在你的CombineAttributesAdder类的主体中，你拼错了单词transform。方法定义应该如下所示：

class CombineAttributesAdder(BaseEstimator, TransformerMixin):    def __init__(self, add_bedrooms_per_room = True):        self.add_bedrooms_per_room = add_bedrooms_per_room    def fit(self, X, y=None):        return self    def transform(self, X, y=None):  # <-- 不是 'transfrom'        ...

对于每个转换器，Pipeline对象将调用fit_transform，这反过来将调用fit和transform方法。由于这个拼写错误，程序无法找到相应的方法并抛出错误。