python:如何从feature_importances获取真正的功能名称

Question

我使用Python的sklearn随机林(ensemble.RandomForestClassifier)进行分类,并feature_importances_用于查找分类器的重要功能.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的数组(与特征维度相同),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来自重要数据的20个最大数字的指数,但我不知道:

1. 如何从顶部20指数的重要性有关

2. 因为我使用了Dictvectorizer和TfidfTransformer所以我不知道如何将索引与真实的场地名称('school','home',....)相匹配

有什么好主意帮我吗？非常感谢你!

Answer 1

要获得每个功能名称的重要性，只需将列名称和 feature_importances 一起迭代（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)

Answer 2

该feature_importances_方法以特征被馈送到算法的顺序返回相对重要性数字.因此,为了获得前20个功能,您需要将功能从最重要到最不重要的功能进行排序,例如:

importances = forest.feature_importances_
indices = numpy.argsort(importances)[-20:]

([-20:]因为你需要采用数组的最后20个元素,因为argsort按升序排序)