如何在python中的sklearn中使用gridsearchcv进行特征选择
作者:互联网
我正在使用递归特征消除和交叉验证(rfecv)作为随机森林分类器的特征选择器,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_])
我也按如下方式执行GridSearchCV来调整RandomForestClassifier的超参数,如下所示.
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,我收到以下错误:
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中的选定功能和参数来验证模型是否能够正常使用看不见的数据.如何获得最佳功能并使用最佳超参数进行训练?
如果需要,我很乐意提供更多细节.
解决方法:
基本上,您希望在使用递归特征消除(使用交叉验证)进行特征选择后微调分类器的超参数(使用交叉验证).
管道对象正是为了组装数据转换和应用估计器的目的.
可能是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.
标签:grid-search,python,scikit-learn,data-science,machine-learning 来源: https://codeday.me/bug/20190927/1824334.html