我使用recursive feature elimination with cross validation (rfecv)
作为randomforest classifier
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_])
我也按照以下方式执行GridSearchCV
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,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estimator__
参数列表中使用param_grid
来解决上述问题。
我现在的问题是如何使用x_test
中选定的功能和参数来验证模型是否能够正常使用看不见的数据。我怎样才能获得best features
并用optimal hyperparameters
进行训练?
如果需要,我很乐意提供更多细节。
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。
也许你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。
您只需将Recursive Feature Elimination Estimator直接传递到GridSearchCV
对象中即可。这样的事情应该有效
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_)
您可以通过使用'estimator__'
为要传递给估算器的参数名称添加前缀来执行所需操作。
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)
我做的假数据输出:
{'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)