python:如何从 feature_importances 中获取真实的特征名称_编程设计

我正在使用 Python 的 sklearn 随机森林 (ensemble.RandomForestClassifier) 进行分类，并使用 feature_importances_ 为分类器寻找重要特征.现在我的代码是:

for trip in database: 
    venue_feature_start.append(Counter(trip['POI'])) 
# Counter(trip['POI']) is like Counter({'school':1, 'hospital':1, 'bus station':2}),actually key is the feature 
 
feat_loc_vectorizer = DictVectorizer() 
feat_loc_vectorizer.fit(venue_feature_start) 
feat_loc_orig_mat = feat_loc_vectorizer.transform(venue_feature_start) 
 
orig_tfidf = TfidfTransformer() 
orig_ven_feat = orig_tfidf.fit_transform(feat_loc_orig_mat.tocsr()) 
 
# so DictVectorizer() and TfidfTransformer() help me to phrase the features and for each instance, the feature dimension is 580, which means that there are 580 venue types  
 
data = orig_ven_feat.tocsr() 
 
le = LabelEncoder()  
labels = le.fit_transform(labels_raw) 
if "Unlabelled" in labels_raw: 
    unlabelled_int = int(le.transform(["Unlabelled"])) 
else: 
    unlabelled_int = -1 
 
valid_rows_idx = np.where(labels!=unlabelled_int)[0]   
labels = labels[valid_rows_idx] 
user_ids = np.asarray(user_ids_raw) 
# user_ids is for cross validation, labels is for classification  
 
clf = ensemble.RandomForestClassifier(n_estimators = 50) 
cv_indices = LeavePUsersOut(user_ids[valid_rows_idx], n_folds = 10)                       
data = data[valid_rows_idx,:].toarray() 
for train_ind, test_ind in cv_indices: 
    train_data = data[train_ind,:] 
    test_data = data[test_ind,:] 
    labels_train = labels[train_ind] 
    labels_test = labels[test_ind] 
 
    print ("Training classifier...") 
    clf.fit(train_data,labels_train) 
    importances = clf.feature_importances_

现在的问题是，当我使用 feature_importances 时，我得到一个维度为 580(与特征维度相同)的数组，我想知道前 20 个重要特征(前 20 个重要场所)

我认为至少我应该知道的是 重要性 中最大的 20 个数字的索引，但我不知道:

如何根据重要性
获取前 20 名的指数
因为我使用了 Dictvectorizer 和 TfidfTransformer，所以我不知道如何将索引与真实的地点名称(“学校”、“家”……)相匹配

有什么想法可以帮助我吗？非常感谢!

请您参考如下方法:

要获得每个特征名称的重要性，只需一起遍历列名称和 feature_importances(它们相互映射):

for feat, importance in zip(df.columns, clf.feature_importances_): 
    print 'feature: {f}, importance: {i}'.format(f=feat, i=importance)