如何在 python 的 sklearn 中使用 gridsearchcv 执行特征选择

Question

我使用recursive feature elimination with cross validation (rfecv) 作为randomforest classifier 的功能选择器，如下所示。

X = df[[my_features]] #all my features
y = df['gold_standard'] #labels

clf = RandomForestClassifier(random_state = 42, class_weight="balanced")
rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(10), scoring='roc_auc')
rfecv.fit(X,y)

print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[rfecv.support_])

我也在执行GridSearchCV如下调整RandomForestClassifier的超参数如下。

X = df[[my_features]] #all my features
y = df['gold_standard'] #labels

x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0)

rfc = RandomForestClassifier(random_state=42, class_weight = 'balanced')
param_grid = { 
    'n_estimators': [200, 500],
    'max_features': ['auto', 'sqrt', 'log2'],
    'max_depth' : [4,5,6,7,8],
    'criterion' :['gini', 'entropy']
}
k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= k_fold, scoring = 'roc_auc')
CV_rfc.fit(x_train, y_train)
print(CV_rfc.best_params_)
print(CV_rfc.best_score_)
print(CV_rfc.best_estimator_)

pred = CV_rfc.predict_proba(x_test)[:,1]
print(roc_auc_score(y_test, pred))

但是，我不清楚如何将特征选择 (rfecv) 与 GridSearchCV 合并。

编辑：

当我运行@Gambit 建议的答案时，出现以下错误：

ValueError: Invalid parameter criterion for estimator RFECV(cv=StratifiedKFold(n_splits=10, random_state=None, shuffle=False),
   estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced',
            criterion='gini', max_depth=None, max_features='auto',
            max_leaf_nodes=None, min_impurity_decrease=0.0,
            min_impurity_split=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators='warn', n_jobs=None, oob_score=False,
            random_state=42, verbose=0, warm_start=False),
   min_features_to_select=1, n_jobs=None, scoring='roc_auc', step=1,
   verbose=0). Check the list of available parameters with `estimator.get_params().keys()`.

我可以通过在param_grid 参数列表中使用estimator__ 来解决上述问题。

---

我现在的问题是如何使用x_test 中选定的特征和参数来验证模型是否可以正常处理看不见的数据。如何获取best features 并使用optimal hyperparameters 进行训练？

如果需要，我很乐意提供更多详细信息。

Answer 1

您只需将递归特征消除估计器直接传递给GridSearchCV 对象。像这样的东西应该可以工作

X = df[my_features] #all my features
y = df['gold_standard'] #labels

clf = RandomForestClassifier(random_state = 42, class_weight="balanced")
rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(10), scoring='auc_roc')

param_grid = { 
    'n_estimators': [200, 500],
    'max_features': ['auto', 'sqrt', 'log2'],
    'max_depth' : [4,5,6,7,8],
    'criterion' :['gini', 'entropy']
}
k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)

#------------- Just pass your RFECV object as estimator here directly --------#

CV_rfc = GridSearchCV(estimator=rfecv, param_grid=param_grid, cv= k_fold, scoring = 'roc_auc')


CV_rfc.fit(x_train, y_train)
print(CV_rfc.best_params_)
print(CV_rfc.best_score_)
print(CV_rfc.best_estimator_)

Answer 2

你可以做你想做的事，在你想传递给估计器的参数名称前加上'estimator__'。

X = df[[my_features]]
y = df[gold_standard]

clf = RandomForestClassifier(random_state=0, class_weight="balanced")
rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(3), scoring='roc_auc')

param_grid = { 
    'estimator__n_estimators': [200, 500],
    'estimator__max_features': ['auto', 'sqrt', 'log2'],
    'estimator__max_depth' : [4,5,6,7,8],
    'estimator__criterion' :['gini', 'entropy']
}
k_fold = StratifiedKFold(n_splits=3, shuffle=True, random_state=0)

CV_rfc = GridSearchCV(estimator=rfecv, param_grid=param_grid, cv= k_fold, scoring = 'roc_auc')

X_train, X_test, y_train, y_test = train_test_split(X, y)

CV_rfc.fit(X_train, y_train)

我制作的虚假数据的输出：

{'estimator__n_estimators': 200, 'estimator__max_depth': 6, 'estimator__criterion': 'entropy', 'estimator__max_features': 'auto'}
0.5653035605690997
RFECV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),
   estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced',
            criterion='entropy', max_depth=6, max_features='auto',
            max_leaf_nodes=None, min_impurity_decrease=0.0,
            min_impurity_split=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=200, n_jobs=None, oob_score=False, random_state=0,
            verbose=0, warm_start=False),
   min_features_to_select=1, n_jobs=None, scoring='roc_auc', step=1,
   verbose=0)

Answer 3

基本上，您希望在使用递归特征消除（使用交叉验证）进行特征选择之后微调分类器的超参数（使用交叉验证）。

管道对象正是用于组装数据转换和应用估算器的目的。

也许您可以使用不同的模型（GradientBoostingClassifier 等）进行最终分类。可以采用以下方法：

from sklearn.datasets import load_breast_cancer
from sklearn.feature_selection import RFECV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size=0.33, 
                                                    random_state=42)


from sklearn.pipeline import Pipeline

#this is the classifier used for feature selection
clf_featr_sele = RandomForestClassifier(n_estimators=30, 
                                        random_state=42,
                                        class_weight="balanced") 
rfecv = RFECV(estimator=clf_featr_sele, 
              step=1, 
              cv=5, 
              scoring = 'roc_auc')

#you can have different classifier for your final classifier
clf = RandomForestClassifier(n_estimators=10, 
                             random_state=42,
                             class_weight="balanced") 
CV_rfc = GridSearchCV(clf, 
                      param_grid={'max_depth':[2,3]},
                      cv= 5, scoring = 'roc_auc')

pipeline  = Pipeline([('feature_sele',rfecv),
                      ('clf_cv',CV_rfc)])

pipeline.fit(X_train, y_train)
pipeline.predict(X_test)

现在，您可以将此管道（包括特征选择）应用于测试数据。