Sklearn 管道：在 ColumnTransformer 中的 OneHotEncode 之后获取特征名称
Sklearn Pipeline: Get feature names after OneHotEncode In ColumnTransformer
我想在安装管道后获取功能名称。
categorical_features = ['brand', 'category_name', 'sub_category']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
numeric_features = ['num1', 'num2', 'num3', 'num4']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
然后
clf = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', GradientBoostingRegressor())])
与 pandas 数据框拟合后，我可以从
获得特征重要性
clf.steps[1][1].feature_importances_
我尝试了 clf.steps[0][1].get_feature_names()，但出现错误
AttributeError: Transformer num (type Pipeline) does not provide get_feature_names.
如何从中获取特征名称？
您可以使用以下代码段访问 feature_names：
clf.named_steps['preprocessor'].transformers_[1][1]\
.named_steps['onehot'].get_feature_names(categorical_features)
使用 sklearn >= 0.21 版本，我们可以使它更简单：
clf['preprocessor'].transformers_[1][1]\
['onehot'].get_feature_names(categorical_features)
可重现的例子：
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
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]})
numeric_features = ['num1']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
categorical_features = ['brand', 'category']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
clf = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', LinearRegression())])
clf.fit(df.drop('target', 1), df['target'])
clf.named_steps['preprocessor'].transformers_[1][1]\
.named_steps['onehot'].get_feature_names(categorical_features)
# ['brand_NaN' 'brand_aaaa' 'brand_asdfasdf' 'brand_sadfds' 'category_as'
# 'category_asdf' 'category_asdfas' 'category_asfa']
编辑：实际上彼得的评论答案在 ColumnTransformer doc:
The order of the columns in the transformed feature matrix follows the order of how the columns are specified in the transformers list. Columns of the original feature matrix that are not specified are dropped from the resulting transformed feature matrix, unless specified in the passthrough keyword. Those columns specified with passthrough are added at the right to the output of the transformers.
为了用 Paul 在他的评论中提出的问题来完成 Venkatachalam 的回答，ColumnTransformer 中出现的特征名称的顺序。get_feature_names() 方法取决于 ColumnTransformer 中 steps 变量的声明顺序实例化。
我找不到任何文档，所以我只是玩了下面的玩具示例，这让我理解了其中的逻辑。
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import RobustScaler
class testEstimator(BaseEstimator,TransformerMixin):
def __init__(self,string):
self.string = string
def fit(self,X):
return self
def transform(self,X):
return np.full(X.shape, self.string).reshape(-1,1)
def get_feature_names(self):
return self.string
transformers = [('first_transformer',testEstimator('A'),1), ('second_transformer',testEstimator('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('scaler',RobustScaler()), ('transformer', column_transformer)]
pipeline = Pipeline(steps)
dt_test = np.zeros((1000,2))
pipeline.fit_transform(dt_test)
for name,step in pipeline.named_steps.items():
if hasattr(step, 'get_feature_names'):
print(step.get_feature_names())
为了有一个更有代表性的例子，我添加了一个 RobustScaler 并将 ColumnTransformer 嵌套在一个 Pipeline 上。顺便说一下，您会发现我的版本 Venkatachalam 的方法来获取步骤的功能名称循环。您可以通过使用列表理解解压名称将其变成一个稍微有用的变量：
[i for i in v.get_feature_names() for k, v in pipeline.named_steps.items() if hasattr(v,'get_feature_names')]
所以尝试使用 dt_test 和估计器来确定特征名称是如何构建的，以及它是如何在 get_feature_names() 中连接起来的。
这是另一个使用输入列输出 2 列的转换器示例：
class testEstimator3(BaseEstimator,TransformerMixin):
def __init__(self,string):
self.string = string
def fit(self,X):
self.unique = np.unique(X)[0]
return self
def transform(self,X):
return np.concatenate((X.reshape(-1,1), np.full(X.shape,self.string).reshape(-1,1)), axis = 1)
def get_feature_names(self):
return list((self.unique,self.string))
dt_test2 = np.concatenate((np.full((1000,1),'A'),np.full((1000,1),'B')), axis = 1)
transformers = [('first_transformer',testEstimator3('A'),1), ('second_transformer',testEstimator3('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('transformer', column_transformer)]
pipeline = Pipeline(steps)
pipeline.fit_transform(dt_test2)
for step in pipeline.steps:
if hasattr(step[1], 'get_feature_names'):
print(step[1].get_feature_names())
如果您正在寻找如何访问连续管道之后的列名，最后一个管道是 ColumnTransformer，您可以按照以下示例访问它们：
在full_pipeline中有两条管道gender和relevent_experience
full_pipeline = ColumnTransformer([
("gender", gender_encoder, ["gender"]),
("relevent_experience", relevent_experience_encoder, ["relevent_experience"]),
])
gender 管道如下所示：
gender_encoder = Pipeline([
('imputer', SimpleImputer(strategy='most_frequent')),
("cat", OneHotEncoder())
])
拟合 full_pipeline 后，您可以使用以下代码段访问列名称
full_pipeline.transformers_[0][1][1].get_feature_names()
在我的例子中，输出是：
array(['x0_Female', 'x0_Male', 'x0_Other'], dtype=object)
Scikit-Learn 1.0 现在有新功能来跟踪功能名称。
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
# SimpleImputer does not have get_feature_names_out, so we need to add it
# manually. This should be fixed in Scikit-Learn 1.0.1: all transformers will
# have this method.
# g
SimpleImputer.get_feature_names_out = (lambda self, names=None:
self.feature_names_in_)
num_pipeline = make_pipeline(SimpleImputer(), StandardScaler())
transformer = make_column_transformer(
(num_pipeline, ["age", "height"]),
(OneHotEncoder(), ["city"]))
pipeline = make_pipeline(transformer, LinearRegression())
df = pd.DataFrame({"city": ["Rabat", "Tokyo", "Paris", "Auckland"],
"age": [32, 65, 18, 24],
"height": [172, 163, 169, 190],
"weight": [65, 62, 54, 95]},
index=["Alice", "Bunji", "Cécile", "Dave"])
pipeline.fit(df, df["weight"])
## get pipeline feature names
pipeline[:-1].get_feature_names_out()
## specify feature names as your columns
pd.DataFrame(pipeline[:-1].transform(df),
columns=pipeline[:-1].get_feature_names_out(),
index=df.index)
我想在安装管道后获取功能名称。
categorical_features = ['brand', 'category_name', 'sub_category']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
numeric_features = ['num1', 'num2', 'num3', 'num4']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
然后
clf = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', GradientBoostingRegressor())])
与 pandas 数据框拟合后，我可以从
获得特征重要性clf.steps[1][1].feature_importances_
我尝试了 clf.steps[0][1].get_feature_names()，但出现错误
AttributeError: Transformer num (type Pipeline) does not provide get_feature_names.
如何从中获取特征名称？
您可以使用以下代码段访问 feature_names：
clf.named_steps['preprocessor'].transformers_[1][1]\
.named_steps['onehot'].get_feature_names(categorical_features)
使用 sklearn >= 0.21 版本，我们可以使它更简单：
clf['preprocessor'].transformers_[1][1]\
['onehot'].get_feature_names(categorical_features)
可重现的例子：
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
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]})
numeric_features = ['num1']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
categorical_features = ['brand', 'category']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
clf = Pipeline(steps=[('preprocessor', preprocessor),
('regressor', LinearRegression())])
clf.fit(df.drop('target', 1), df['target'])
clf.named_steps['preprocessor'].transformers_[1][1]\
.named_steps['onehot'].get_feature_names(categorical_features)
# ['brand_NaN' 'brand_aaaa' 'brand_asdfasdf' 'brand_sadfds' 'category_as'
# 'category_asdf' 'category_asdfas' 'category_asfa']
编辑：实际上彼得的评论答案在 ColumnTransformer doc:
The order of the columns in the transformed feature matrix follows the order of how the columns are specified in the transformers list. Columns of the original feature matrix that are not specified are dropped from the resulting transformed feature matrix, unless specified in the passthrough keyword. Those columns specified with passthrough are added at the right to the output of the transformers.
为了用 Paul 在他的评论中提出的问题来完成 Venkatachalam 的回答，ColumnTransformer 中出现的特征名称的顺序。get_feature_names() 方法取决于 ColumnTransformer 中 steps 变量的声明顺序实例化。
我找不到任何文档，所以我只是玩了下面的玩具示例，这让我理解了其中的逻辑。
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import RobustScaler
class testEstimator(BaseEstimator,TransformerMixin):
def __init__(self,string):
self.string = string
def fit(self,X):
return self
def transform(self,X):
return np.full(X.shape, self.string).reshape(-1,1)
def get_feature_names(self):
return self.string
transformers = [('first_transformer',testEstimator('A'),1), ('second_transformer',testEstimator('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('scaler',RobustScaler()), ('transformer', column_transformer)]
pipeline = Pipeline(steps)
dt_test = np.zeros((1000,2))
pipeline.fit_transform(dt_test)
for name,step in pipeline.named_steps.items():
if hasattr(step, 'get_feature_names'):
print(step.get_feature_names())
为了有一个更有代表性的例子，我添加了一个 RobustScaler 并将 ColumnTransformer 嵌套在一个 Pipeline 上。顺便说一下，您会发现我的版本 Venkatachalam 的方法来获取步骤的功能名称循环。您可以通过使用列表理解解压名称将其变成一个稍微有用的变量：
[i for i in v.get_feature_names() for k, v in pipeline.named_steps.items() if hasattr(v,'get_feature_names')]
所以尝试使用 dt_test 和估计器来确定特征名称是如何构建的，以及它是如何在 get_feature_names() 中连接起来的。 这是另一个使用输入列输出 2 列的转换器示例：
class testEstimator3(BaseEstimator,TransformerMixin):
def __init__(self,string):
self.string = string
def fit(self,X):
self.unique = np.unique(X)[0]
return self
def transform(self,X):
return np.concatenate((X.reshape(-1,1), np.full(X.shape,self.string).reshape(-1,1)), axis = 1)
def get_feature_names(self):
return list((self.unique,self.string))
dt_test2 = np.concatenate((np.full((1000,1),'A'),np.full((1000,1),'B')), axis = 1)
transformers = [('first_transformer',testEstimator3('A'),1), ('second_transformer',testEstimator3('B'),0)]
column_transformer = ColumnTransformer(transformers)
steps = [('transformer', column_transformer)]
pipeline = Pipeline(steps)
pipeline.fit_transform(dt_test2)
for step in pipeline.steps:
if hasattr(step[1], 'get_feature_names'):
print(step[1].get_feature_names())
如果您正在寻找如何访问连续管道之后的列名，最后一个管道是 ColumnTransformer，您可以按照以下示例访问它们：
在full_pipeline中有两条管道gender和relevent_experience
full_pipeline = ColumnTransformer([
("gender", gender_encoder, ["gender"]),
("relevent_experience", relevent_experience_encoder, ["relevent_experience"]),
])
gender 管道如下所示：
gender_encoder = Pipeline([
('imputer', SimpleImputer(strategy='most_frequent')),
("cat", OneHotEncoder())
])
拟合 full_pipeline 后，您可以使用以下代码段访问列名称
full_pipeline.transformers_[0][1][1].get_feature_names()
在我的例子中，输出是：
array(['x0_Female', 'x0_Male', 'x0_Other'], dtype=object)
Scikit-Learn 1.0 现在有新功能来跟踪功能名称。
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
# SimpleImputer does not have get_feature_names_out, so we need to add it
# manually. This should be fixed in Scikit-Learn 1.0.1: all transformers will
# have this method.
# g
SimpleImputer.get_feature_names_out = (lambda self, names=None:
self.feature_names_in_)
num_pipeline = make_pipeline(SimpleImputer(), StandardScaler())
transformer = make_column_transformer(
(num_pipeline, ["age", "height"]),
(OneHotEncoder(), ["city"]))
pipeline = make_pipeline(transformer, LinearRegression())
df = pd.DataFrame({"city": ["Rabat", "Tokyo", "Paris", "Auckland"],
"age": [32, 65, 18, 24],
"height": [172, 163, 169, 190],
"weight": [65, 62, 54, 95]},
index=["Alice", "Bunji", "Cécile", "Dave"])
pipeline.fit(df, df["weight"])
## get pipeline feature names
pipeline[:-1].get_feature_names_out()
## specify feature names as your columns
pd.DataFrame(pipeline[:-1].transform(df),
columns=pipeline[:-1].get_feature_names_out(),
index=df.index)