在 Python 3 中使用 Pipeline 获取 RFE 的支持和排名属性
Get support and ranking attributes for RFE using Pipeline in Python 3
我目前拥有的代码如下，它运行良好。但是，我想为每个测试的功能数量打印以下 RFE 属性："rfe.support_[i]", "rfe.ranking_[i]" 和 所选要素的名称 因为“i”指的是索引，第一个属性 returns True or False（是否选择了列）和第二个 returns 他们各自的排名。
换句话说，我想打印每个 RFE 中考虑的列，并且它们不会保留为抽象的东西。
# Explore the number of selected features for RFE
from numpy import mean
from numpy import std
from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from category_encoders import OneHotEncoder
from sklearn.compose import ColumnTransformer
# Get the dataset
def get_dataset(df, target):
X, y = df.drop(columns = target), df[[target]].values.flatten()
return X, y
# Get a list of models to evaluate
def get_models(list_num_cols, list_cat_cols):
num_transformer = Pipeline(steps = [('num_imputer', SimpleImputer(strategy = 'median'))])
cat_transformer = Pipeline(steps = [('cat_imputer', SimpleImputer(strategy = 'most_frequent')),
('one-hot-encoder', OneHotEncoder())])
preprocessor = ColumnTransformer(transformers = [('num', num_transformer, list_num_cols),
('cat', cat_transformer, list_cat_cols)])
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(steps = [('preprocessor', preprocessor),
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)])
return models
# Evaluate a give model using cross-validation
def evaluate_model(model, X, y):
cv = RepeatedKFold(n_splits = 10, n_repeats = 3, random_state = 7)
scores = cross_val_score(model, X, y, scoring = 'neg_mean_absolute_error', cv = cv,
n_jobs = -1, error_score = 'raise')
return scores
# Define the dataset
X, y = get_dataset(my_df, 'my_target') # It begins here
# Get the models to evaluate
models = get_models(X.select_dtypes(include = 'number').columns.tolist(),
X.select_dtypes(include = 'object').columns.tolist())
# Evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
scores = evaluate_model(model, X, y)
results.append(scores)
names.append(name)
print('%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
以下是返回错误：
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_[0] # Returns: AttributeError: 'RFE' object has no attribute 'support_'
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].ranking_[0] # Returns: AttributeError: 'RFE' object has no attribute 'ranking_'
重点是您没有明确地安装'DecisionTreeRegressor_2'管道。
的确，尽管 cross_val_score already takes care of fitting the estimator as you might see here，cross_val_score 并不像 .fit() 方法那样 return 估计器实例。因此您无法访问 RFE 实例属性。
这是您设置中的玩具示例：
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_regression
X, y = make_regression()
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(
[
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)
])
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_ # this does not work
相反，您可能会看到，在拟合模型后，您将能够访问 support_ 和 ranking_ 属性：
models['DecisionTreeRegressor_2'].fit(X,y)
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_ # this works
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].ranking_ # this works
我回答了问题。我发布它以防它可以帮助某人。它包括使用“cross_validate”而不是“cross_val_score”和选项“return_estimator = True”，以便能够检索不同折叠和 RFE 中的管道，并访问他们按索引。然后你可以使用“named_steps”。
# Explore the number of selected features for RFE
from numpy import mean
from numpy import std
from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from category_encoders import OneHotEncoder
from sklearn.compose import ColumnTransformer
# Get the dataset
def get_dataset(df, target):
X, y = df.drop(columns = target), df[[target]].values.flatten()
return X, y
# Get a list of models to evaluate
def get_models(list_num_cols, list_cat_cols):
num_transformer = Pipeline(steps = [('num_imputer', SimpleImputer(strategy = 'median'))])
cat_transformer = Pipeline(steps = [('cat_imputer', SimpleImputer(strategy = 'most_frequent')),
('one-hot-encoder', OneHotEncoder())])
preprocessor = ColumnTransformer(transformers = [('num', num_transformer, list_num_cols),
('cat', cat_transformer, list_cat_cols)])
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(steps = [('preprocessor', preprocessor),
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)])
return models
# Evaluate a give model using cross-validation
def evaluate_model(model, X, y):
cv = RepeatedKFold(n_splits = 10, n_repeats = 3, random_state = 7)
output = cross_validate(model, X, y, scoring = 'neg_mean_absolute_error', cv = cv,
n_jobs = -1, error_score = 'raise', return_estimator = True)
return output
# Define the dataset
X, y = get_dataset(my_df, 'my_target') # It begins here
# Get the models to evaluate
models = get_models(X.select_dtypes(include = 'number').columns.tolist(),
X.select_dtypes(include = 'object').columns.tolist())
# Evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
output = evaluate_model(model, X, y)
results.append(output['test_score'])
names.append(name)
print('%s %.3f (%.3f)' % (name, mean(output['test_score']), std(output['test_score'])))
print(output)
print(output['estimator'][0].named_steps['s_dtr'].support_)
print(output['estimator'][0].named_steps['s_dtr'].ranking_)
print(output['estimator'][0].named_steps['s_dtr'].support_[2])
print(output['estimator'][0].named_steps['s_dtr'].ranking_[2])
我目前拥有的代码如下，它运行良好。但是，我想为每个测试的功能数量打印以下 RFE 属性："rfe.support_[i]", "rfe.ranking_[i]" 和 所选要素的名称 因为“i”指的是索引，第一个属性 returns True or False（是否选择了列）和第二个 returns 他们各自的排名。
换句话说，我想打印每个 RFE 中考虑的列，并且它们不会保留为抽象的东西。
# Explore the number of selected features for RFE
from numpy import mean
from numpy import std
from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from category_encoders import OneHotEncoder
from sklearn.compose import ColumnTransformer
# Get the dataset
def get_dataset(df, target):
X, y = df.drop(columns = target), df[[target]].values.flatten()
return X, y
# Get a list of models to evaluate
def get_models(list_num_cols, list_cat_cols):
num_transformer = Pipeline(steps = [('num_imputer', SimpleImputer(strategy = 'median'))])
cat_transformer = Pipeline(steps = [('cat_imputer', SimpleImputer(strategy = 'most_frequent')),
('one-hot-encoder', OneHotEncoder())])
preprocessor = ColumnTransformer(transformers = [('num', num_transformer, list_num_cols),
('cat', cat_transformer, list_cat_cols)])
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(steps = [('preprocessor', preprocessor),
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)])
return models
# Evaluate a give model using cross-validation
def evaluate_model(model, X, y):
cv = RepeatedKFold(n_splits = 10, n_repeats = 3, random_state = 7)
scores = cross_val_score(model, X, y, scoring = 'neg_mean_absolute_error', cv = cv,
n_jobs = -1, error_score = 'raise')
return scores
# Define the dataset
X, y = get_dataset(my_df, 'my_target') # It begins here
# Get the models to evaluate
models = get_models(X.select_dtypes(include = 'number').columns.tolist(),
X.select_dtypes(include = 'object').columns.tolist())
# Evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
scores = evaluate_model(model, X, y)
results.append(scores)
names.append(name)
print('%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
以下是返回错误：
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_[0] # Returns: AttributeError: 'RFE' object has no attribute 'support_'
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].ranking_[0] # Returns: AttributeError: 'RFE' object has no attribute 'ranking_'
重点是您没有明确地安装'DecisionTreeRegressor_2'管道。
的确，尽管 cross_val_score already takes care of fitting the estimator as you might see here，cross_val_score 并不像 .fit() 方法那样 return 估计器实例。因此您无法访问 RFE 实例属性。
这是您设置中的玩具示例：
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_regression
X, y = make_regression()
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(
[
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)
])
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_ # this does not work
相反，您可能会看到，在拟合模型后，您将能够访问 support_ 和 ranking_ 属性：
models['DecisionTreeRegressor_2'].fit(X,y)
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].support_ # this works
models['DecisionTreeRegressor_2'].named_steps['s_dtr'].ranking_ # this works
我回答了问题。我发布它以防它可以帮助某人。它包括使用“cross_validate”而不是“cross_val_score”和选项“return_estimator = True”，以便能够检索不同折叠和 RFE 中的管道，并访问他们按索引。然后你可以使用“named_steps”。
# Explore the number of selected features for RFE
from numpy import mean
from numpy import std
from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from category_encoders import OneHotEncoder
from sklearn.compose import ColumnTransformer
# Get the dataset
def get_dataset(df, target):
X, y = df.drop(columns = target), df[[target]].values.flatten()
return X, y
# Get a list of models to evaluate
def get_models(list_num_cols, list_cat_cols):
num_transformer = Pipeline(steps = [('num_imputer', SimpleImputer(strategy = 'median'))])
cat_transformer = Pipeline(steps = [('cat_imputer', SimpleImputer(strategy = 'most_frequent')),
('one-hot-encoder', OneHotEncoder())])
preprocessor = ColumnTransformer(transformers = [('num', num_transformer, list_num_cols),
('cat', cat_transformer, list_cat_cols)])
models = dict()
for i in range(2, 4):
rfe_dtr = RFE(estimator = DecisionTreeRegressor(), n_features_to_select = i)
model_dtr = DecisionTreeRegressor()
models['DecisionTreeRegressor_' + str(i)] = Pipeline(steps = [('preprocessor', preprocessor),
('s_dtr', rfe_dtr),
('m_dtr', model_dtr)])
return models
# Evaluate a give model using cross-validation
def evaluate_model(model, X, y):
cv = RepeatedKFold(n_splits = 10, n_repeats = 3, random_state = 7)
output = cross_validate(model, X, y, scoring = 'neg_mean_absolute_error', cv = cv,
n_jobs = -1, error_score = 'raise', return_estimator = True)
return output
# Define the dataset
X, y = get_dataset(my_df, 'my_target') # It begins here
# Get the models to evaluate
models = get_models(X.select_dtypes(include = 'number').columns.tolist(),
X.select_dtypes(include = 'object').columns.tolist())
# Evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
output = evaluate_model(model, X, y)
results.append(output['test_score'])
names.append(name)
print('%s %.3f (%.3f)' % (name, mean(output['test_score']), std(output['test_score'])))
print(output)
print(output['estimator'][0].named_steps['s_dtr'].support_)
print(output['estimator'][0].named_steps['s_dtr'].ranking_)
print(output['estimator'][0].named_steps['s_dtr'].support_[2])
print(output['estimator'][0].named_steps['s_dtr'].ranking_[2])