打印k值位于GridSearchCV的param_网格内的SelectKBest的功能名称

Question

我在param_网格中尝试了PCA的SelectKBest和n_分量的k参数组合。我可以使用下面的代码打印k值和n_组件。我将发布整个代码，这样您就可以知道这些特性是从哪个列表中提取的

#THE FIRST FEATURE HAS TO BE THE LABEL

featurelist = ['poi', 'exercised_stock_options', 'expenses', 'from_messages', 
           'from_poi_to_this_person', 'from_this_person_to_poi', 'other', 
           'restricted_stock', 'salary', 'shared_receipt_with_poi', 
           'to_messages', 'total_payments', 'total_stock_value', 
           'ratio_from_poi', 'ratio_to_poi']

enronml = pd.DataFrame(enron[['poi', 'exercised_stock_options', 'expenses', 'from_messages', 
           'from_poi_to_this_person', 'from_this_person_to_poi', 'other', 
           'restricted_stock', 'salary', 'shared_receipt_with_poi', 
           'to_messages', 'total_payments', 'total_stock_value', 
           'ratio_from_poi', 'ratio_to_poi']].copy())


enronml = enronml.to_dict(orient="index")
dataset = enronml

#featureFormat, takes the dictionary as the dataset, converts the first 
feature in featurelist into label

data = featureFormat(dataset, featurelist, sort_keys = True)
labels, features = targetFeatureSplit(data)

from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import GaussianNB

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, labels, 
test_size=0.20, random_state=0)


pca = PCA()
gnba = GaussianNB()
steps = [('scaler', MinMaxScaler()),
     ('best', SelectKBest()),
     ('pca', pca),
     ('gnba', gnba)]

pipeline = Pipeline(steps)

parameters = [    
{
'best__k':[3],
'pca__n_components': [1,2]
},
{
'best__k':[4],
'pca__n_components': [1,2,3]
},
{
'best__k':[5],
'pca__n_components': [1,2,3,4]
},
]

cv = StratifiedShuffleSplit(test_size=0.2, random_state=42)
gnbawithpca = GridSearchCV(pipeline, param_grid = parameters, cv=cv, 
scoring="f1")
gnbawithpca.fit(X_train,y_train)

means = gnbawithpca.cv_results_['mean_test_score']
stds = gnbawithpca.cv_results_['std_test_score']


for mean, std, params in zip(means, stds, 
gnbawithpca.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r"
          % (mean, std * 2, params))

我能得到这样的结果

^{pr2}$

我想知道选择了哪些功能，例如，当best_k=5时，我想知道这5个功能的名称。

Answer 1

已解决

定义要在GridSearchCV中使用的管道时，请为每个步骤命名：

steps = [('scaler', MinMaxScaler()),
     ('best', SelectKBest()),
     ('pca', pca),
     ('gnba', gnba)]

pipeline = Pipeline(steps)

你这么做有两个原因：

因此，您可以在参数网格中定义参数（需要名称来标识为哪个步骤定义参数）。在

因此您可以从GridSearchCV对象访问步骤的属性（这将回答您的问题）。在

^{pr2}$