Python 如何使用列转换器获取功能名称

只是尝试获取所有功能名称：

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder,StandardScaler
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.linear_model import LinearRegression

df = pd.DataFrame({'brand'      : ['aaaa', 'asdfasdf', 'sadfds', 'NaN'],
                   'category'   : ['asdf','asfa','asdfas','as'], 
                   'num1'       : [1, 1, 0, 0] ,
                   'target'     : [0.2,0.11,1.34,1.123]})



train_continuous_cols = df.select_dtypes(include=["int64","float64"]).columns.tolist()
train_categorical_cols = df.select_dtypes(include=["object"]).columns.tolist()


preprocess = make_column_transformer( 
    (StandardScaler(),train_continuous_cols),
    (OneHotEncoder(), train_categorical_cols)
)
df= preprocess.fit_transform(df)

获取此错误：

preprocess.get_feature_names()

---

我怎样才能解决它？在线示例使用管道，我试图避免这种情况

我假设您正在寻找访问transformer结果的方法，它生成一个numpy数组

ColumnTransformer具有一个名为：`

从文件中：

因此，不幸的是，这只提供了有关变压器本身及其应用到的列的信息，但不提供结果数据位置的信息，以下情况除外：

注：变换后的特征矩阵中的列顺序如下： 列在变压器列表中的指定顺序

因此，我们知道输出列的顺序与transformers列表中指定列的顺序相同。另外，对于transformer步骤，我们还知道它们产生了多少列，因为StandardScaler（）产生的列数与原始数据相同，OneHotEncoder（）产生的列数等于类别数

transformers_ : list
   The collection of fitted transformers as tuples of
   (name, fitted_transformer, column). `fitted_transformer` can be an
   estimator, 'drop', or 'passthrough'. In case there were no columns
   selected, this will be the unfitted transformer.
   If there are remaining columns, the final element is a tuple of the
   form:
   ('remainder', transformer, remaining_columns) corresponding to the
   ``remainder`` parameter. If there are remaining columns, then
   ``len(transformers_)==len(transformers)+1``, otherwise
   ``len(transformers_)==len(transformers)``.

这将产生以下输出：

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder,StandardScaler
from sklearn.compose import ColumnTransformer, make_column_transformer

df = pd.DataFrame({'brand'      : ['aaaa', 'asdfasdf', 'sadfds', 'NaN'],
                   'category'   : ['asdf','asfa','asdfas','asd'],
                   'num1'       : [1, 1, 0, 0] ,
                   'target'     : [0.2,0.11,1.34,1.123]})

train_continuous_cols = df.select_dtypes(include=["int64","float64"]).columns.tolist()
train_categorical_cols = df.select_dtypes(include=["object"]).columns.tolist()
# get n_categories for categorical features
n_categories = [df[x].nunique() for x in train_categorical_cols]

preprocess = make_column_transformer(
    (StandardScaler(),train_continuous_cols),
    (OneHotEncoder(), train_categorical_cols)
)
preprocessed_df = preprocess.fit_transform(df)
# the scaler yield 1 column each
indexes_scaler = list(range(0,len(train_continuous_cols)))
# the encoder yields a number of columns equal to the number of categories in the data
cum_index_encoder = [0] + list(np.cumsum(n_categories))

# the encoder indexes come after the scaler indexes
start_index_encoder = indexes_scaler[-1]+1
indexes_encoder = [x + start_index_encoder for x in cum_index_encoder]
# get both lower and uper bound of index
index_pairs= zip (indexes_encoder[:-1],indexes_encoder[1:])

转化2个连续Col，形成具有以下形状的df： （4,2）

改造后的立柱品牌产生了形状为： （4,4）

转换后的列类别生成了一个具有以下形状的df： （4,4）

---

伙计们，我是通过反复试验才发现这一点的：col_names=train_continuous_cols+preprocess.transformers[1][1]。get_feature_names（train_categorical_cols）。tolist（）当我尝试这样做时，我得到的是AttributeError:'ColumnTransformer'对象没有属性'transformers'

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder,StandardScaler
from sklearn.compose import ColumnTransformer, make_column_transformer

df = pd.DataFrame({'brand'      : ['aaaa', 'asdfasdf', 'sadfds', 'NaN'],
                   'category'   : ['asdf','asfa','asdfas','asd'],
                   'num1'       : [1, 1, 0, 0] ,
                   'target'     : [0.2,0.11,1.34,1.123]})

train_continuous_cols = df.select_dtypes(include=["int64","float64"]).columns.tolist()
train_categorical_cols = df.select_dtypes(include=["object"]).columns.tolist()
# get n_categories for categorical features
n_categories = [df[x].nunique() for x in train_categorical_cols]

preprocess = make_column_transformer(
    (StandardScaler(),train_continuous_cols),
    (OneHotEncoder(), train_categorical_cols)
)
preprocessed_df = preprocess.fit_transform(df)
# the scaler yield 1 column each
indexes_scaler = list(range(0,len(train_continuous_cols)))
# the encoder yields a number of columns equal to the number of categories in the data
cum_index_encoder = [0] + list(np.cumsum(n_categories))

# the encoder indexes come after the scaler indexes
start_index_encoder = indexes_scaler[-1]+1
indexes_encoder = [x + start_index_encoder for x in cum_index_encoder]
# get both lower and uper bound of index
index_pairs= zip (indexes_encoder[:-1],indexes_encoder[1:])

print ('Transformed {} continious cols resulting in a df with shape:'.format(len(train_continuous_cols)))
print (preprocessed_df[: , indexes_scaler].shape)

for column, (start_id, end_id) in zip (train_categorical_cols,index_pairs):
    print('Transformed column {} resulted in a df with shape:'.format(column))
    print(preprocessed_df[:, start_id:end_id].shape)