在scikit-learn中使用make_pipeline时，为什么会出现“管道的最后一步”错误？

如何解决在scikit-learn中使用make_pipeline时，为什么会出现“管道的最后一步”错误？

因此，我尝试使用make_pipeline中的scikit-learn清理数据（替换丢失的值，然后清理异常值，将编码函数应用于分类变量，最后添加随机森林回归器通过RandomForestRegressor。输入是DataFrame。最终，我想将其通过gridsearchcv来搜索回归器的最佳超参数。

为此，我构建了一些自定义类，它们按照建议的here继承了TransformerMixin类。这是我到目前为止所拥有的

from sklearn.pipeline import make_pipeline
from sklearn.base import TransformerMixin
import pandas as pd

class Cleaning(TransformerMixin):
    def __init__(self,column_labels):
        self.column_labels = column_labels
    def fit(self,X,y=None):
        return self
    def transform(self,X):
        """Given a dataframe X with predictors,clean it."""
        X_imputed,medians_X = median_imputer(X) # impute all missing numeric data with median
        
        quantiles_X = get_quantiles(X_imputed,self.column_labels)
        X_nooutliers,_ = replace_outliers(X_imputed,self.column_labels,medians_X,quantiles_X)
        return X_nooutliers

class Encoding(TransformerMixin):
    def __init__(self,encoder_list):
        self.encoder_list = encoder_list
    def fit(self,X):
        """Takes in dataframe X and applies encoding transformation to them"""
        return encode_data(self.encoder_list,X)

但是，当我运行以下代码行时，出现错误：

import category_encoders as ce

pipeline_cleaning = Cleaning(column_labels = train_labels)

OneHot_binary = ce.OneHotEncoder(cols = ['new_store']) 
OneHot = ce.OneHotEncoder(cols= ['transport_availability']) 
Count = ce.CountEncoder(cols = ['county'])
pipeline_encoding = Encoding([OneHot_binary,OneHot,Count])

baseline = RandomForestRegressor(n_estimators=500,random_state=12)
make_pipeline([pipeline_cleaning,pipeline_encoding,baseline])

错误是说Last step of Pipeline should implement fit or be the string 'passthrough'。我不明白为什么？

编辑：最后一行略有错别字，正确。传递给make_pipeline的列表中的第三个元素是回归器

解决方法

更改行：

make_pipeline([pipeline_cleaning,pipeline_encoding,baseline])

至（无列表）：

make_pipeline(pipeline_cleaning,baseline)
Pipeline(steps=[('cleaning',<__main__.Cleaning object at 0x7f617260c1d0>),('encoding',<__main__.Encoding object at 0x7f617260c278>),('randomforestregressor',RandomForestRegressor(n_estimators=500,random_state=12))])

你没事