带OHE的ColumnTransformer&amp；管道-执行ct后，OHE编码字段是保留还是删除？

ColumnTransformer amp; Pipeline with OHE - Is the OHE encoded field retained or removed after ct is performed?(带OHE的ColumnTransformeramp；管道-执行ct后，OHE编码字段是保留还是删除？)

问题描述

CT上的单据：

remainder{‘drop’, ‘passthrough’} or estimator, default=’drop’

By default, only the specified columns in transformers are transformed and combined in the output, and the non-specified columns are dropped. (default of 'drop'). By specifying remainder='passthrough', all remaining columns that were not specified in transformers will be automatically passed through. This subset of columns is concatenated with the output of the transformers. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform. Note that using this feature requires that the DataFrame columns input at fit and transform have identical order.

我认为这个余数=与正在进行OneFire编码的字段无关。我想知道OHE领域是怎样的(例如。"CatX")正在处理中。

当我执行独立的CT转换时，我看到‘CATx’没有出现在输出中。

ct = ColumnTransformer(transformers=[('OHE',ohe,ohe_col)],remainder='passthrough')

ct = ColumnTransformer(transformers=[('OHE',ohe,ohe_col),('OHE1',ohe,ohe_col)],
remainder='passthrough')

然后我试着把这个放进管道里，我试着做了两次CT。这就是令人困惑的部分。它过去了。这告诉我第一个CT1将‘CATx’传递给了CT2。

ct = ColumnTransformer(transformers=[('OHE',ohe,ohe_col)],remainder='passthrough')    
Pipeline([('ct1',ct),('ct2',ct)('model',v)])

问题：

1. 使用管道时，谁控制CT是否在退出时传递‘CATx’？
2. 使用管道时，如果正在传递"CATx"，模型将无法处理它吗？

我希望我的问题是清楚的。感谢您提前回复。

推荐答案

ColumnTransformer用于通过指定的转换来转换数组或数据帧的列。这意味着经过调整后，您将不再拥有原始的专栏。

import pandas as pd
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.preprocessing import OneHotEncoder

X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
              'title': ['His Last Bow', 'How Watson Learned the Trick', 'A Moveable Feast', 'The Grapes of Wrath'],
              'expert_rating': [5, 3, 4, 5],
              'user_rating': [4, 5, 4, 3]})

ct = ColumnTransformer(transformers=[
    ('ohe', OneHotEncoder(), make_column_selector(pattern='city'))], 
    remainder='passthrough', verbose_feature_names_out=False)

pd.DataFrame(ct.fit_transform(X), columns=ct.get_feature_names_out())

ct_d = ColumnTransformer(transformers=[
    ('ohe1', OneHotEncoder(), make_column_selector(pattern='city')),
    ('ohe2', OneHotEncoder(), make_column_selector(pattern='city'))], 
    remainder='passthrough', verbose_feature_names_out=True)

pd.DataFrame(ct_d.fit_transform(X), columns=ct_d.get_feature_names_out())

from sklearn.pipeline import Pipeline

pipe_new = Pipeline([('ohe', OneHotEncoder())])
pd.DataFrame(pipe_new.fit_transform(pd.DataFrame(X['city'])).toarray())

ct = ColumnTransformer(transformers=[
    ('ohe', OneHotEncoder(), [0])], remainder='passthrough', verbose_feature_names_out=False)

pipe = Pipeline([
    ('ct1', ct),
    ('ct2', ct)
])

pd.DataFrame(pipe.fit_transform(X))