在 scikit-learn 中结合递归特征消除和网格搜索

Question

我试图在 scikit-learn 中结合递归特征消除和网格搜索。正如您从下面的代码（有效）中看到的那样，我能够从网格搜索中获得最佳估计量，然后将该估计量传递给 RFECV。但是，我宁愿先进行 RFECV，然后再进行网格搜索。问题是，当我将选择器 ?from RFECV 传递给网格搜索时，它不接受：

ValueError：估计器 RFECV 的参数引导程序无效

是否可以从 RFECV 获取选择器并将其直接传递给 RandomizedSearchCV，或者这在程序上不是正确的做法？

from sklearn.datasets import make_classification
from sklearn.feature_selection import RFECV
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import randint as sp_randint

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000, n_features=25, n_informative=5, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, random_state=0)

grid = {"max_depth": [3, None],
        "min_samples_split": sp_randint(1, 11),
        "min_samples_leaf": sp_randint(1, 11),
        "bootstrap": [True, False],
        "criterion": ["gini", "entropy"]}

estimator = RandomForestClassifierCoef()
clf = RandomizedSearchCV(estimator, param_distributions=grid, cv=7)
clf.fit(X, y)
estimator = clf.best_estimator_

selector = RFECV(estimator, step=1, cv=4)
selector.fit(X, y)
selector.grid_scores_

Answer 1

最好的方法是使用此 SO 答案中的方法将 RFECV 嵌套在随机搜索中。一些示例代码，基于问题代码和上面提到的SO答案：

from sklearn.datasets import make_classification
from sklearn.feature_selection import RFECV
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import randint as sp_randint

# Build a classification task using 5 informative features
X, y = make_classification(n_samples=1000, n_features=25, n_informative=5, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, random_state=0)

grid = {"estimator__max_depth": [3, None],
        "estimator__min_samples_split": sp_randint(1, 11),
        "estimator__min_samples_leaf": sp_randint(1, 11),
        "estimator__bootstrap": [True, False],
        "estimator__criterion": ["gini", "entropy"]}

estimator = RandomForestClassifier()
selector = RFECV(estimator, step=1, cv=4)
clf = RandomizedSearchCV(selector, param_distributions=grid, cv=7)
clf.fit(X, y)
print(clf.grid_scores_)
print(clf.best_estimator_.n_features_)