TypeError:只有具有一个元素的整数数组才能转换为索引

Question

在使用交叉验证执行递归功能选择时，出现以下错误：

Traceback (most recent call last):
  File "/Users/.../srl/main.py", line 32, in <module>
    argident_sys.train_classifier()
  File "/Users/.../srl/identification.py", line 194, in train_classifier
    feat_selector.fit(train_argcands_feats,train_argcands_target)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 298, in fit
    ranking_ = rfe.fit(X[train], y[train]).ranking_
TypeError: only integer arrays with one element can be converted to an index

生成错误的代码如下：

def train_classifier(self):

    # Get the argument candidates
    argcands = self.get_argcands(self.reader)

    # Extract the necessary features from the argument candidates
    train_argcands_feats = []
    train_argcands_target = []

    for argcand in argcands:
        train_argcands_feats.append(self.extract_features(argcand))
        if argcand["info"]["label"] == "NULL":
            train_argcands_target.append("NULL")
        else:
            train_argcands_target.append("ARG")

    # Transform the features to the format required by the classifier
    self.feat_vectorizer = DictVectorizer()
    train_argcands_feats = self.feat_vectorizer.fit_transform(train_argcands_feats)

    # Transform the target labels to the format required by the classifier
    self.target_names = list(set(train_argcands_target))
    train_argcands_target = [self.target_names.index(target) for target in train_argcands_target]

    ## Train the appropriate supervised model      

    # Recursive Feature Elimination
    self.classifier = LogisticRegression()
    feat_selector = RFECV(estimator=self.classifier, step=1, cv=StratifiedKFold(train_argcands_target, 10))

    feat_selector.fit(train_argcands_feats,train_argcands_target)

    print feat_selector.n_features_
    print feat_selector.support_
    print feat_selector.ranking_
    print feat_selector.cv_scores_

    return

我知道我也应该对logisticsregression分类器的参数执行GridSearch，但我不认为这是错误的根源（或者是错误？）。

我应该提到的是，我正在测试大约50个特性，而且几乎所有的特性都是分类的（这就是为什么我使用DictVectorizer对它们进行适当转换的原因）。

你能给我的任何帮助或指导都是非常受欢迎的。谢谢！

编辑

以下是一些培训数据示例：

train_argcands_feats = [{'head_lemma': u'Bras\xedlia', 'head': u'Bras\xedlia', 'head_postag': u'PROP'}, {'head_lemma': u'Pesquisa_Datafolha', 'head': u'Pesquisa_Datafolha', 'head_postag': u'N'}, {'head_lemma': u'dado', 'head': u'dado', 'head_postag': u'N'}, {'head_lemma': u'postura', 'head': u'postura', 'head_postag': u'N'}, {'head_lemma': u'maioria', 'head': u'maioria', 'head_postag': u'N'}, {'head_lemma': u'querer', 'head': u'quer', 'head_postag': u'V-FIN'}, {'head_lemma': u'PT', 'head': u'PT', 'head_postag': u'PROP'}, {'head_lemma': u'participar', 'head': u'participando', 'head_postag': u'V-GER'}, {'head_lemma': u'surpreendente', 'head': u'supreendente', 'head_postag': u'ADJ'}, {'head_lemma': u'Bras\xedlia', 'head': u'Bras\xedlia', 'head_postag': u'PROP'}, {'head_lemma': u'Pesquisa_Datafolha', 'head': u'Pesquisa_Datafolha', 'head_postag': u'N'}, {'head_lemma': u'revelar', 'head': u'revela', 'head_postag': u'V-FIN'}, {'head_lemma': u'recusar', 'head': u'recusando', 'head_postag': u'V-GER'}, {'head_lemma': u'maioria', 'head': u'maioria', 'head_postag': u'N'}, {'head_lemma': u'PT', 'head': u'PT', 'head_postag': u'PROP'}, {'head_lemma': u'participar', 'head': u'participando', 'head_postag': u'V-GER'}, {'head_lemma': u'surpreendente', 'head': u'supreendente', 'head_postag': u'ADJ'}, {'head_lemma': u'Bras\xedlia', 'head': u'Bras\xedlia', 'head_postag': u'PROP'}, {'head_lemma': u'Pesquisa_Datafolha', 'head': u'Pesquisa_Datafolha', 'head_postag': u'N'}, {'head_lemma': u'revelar', 'head': u'revela', 'head_postag': u'V-FIN'}, {'head_lemma': u'governo', 'head': u'Governo', 'head_postag': u'N'}, {'head_lemma': u'de', 'head': u'de', 'head_postag': u'PRP'}, {'head_lemma': u'governo', 'head': u'Governo', 'head_postag': u'N'}, {'head_lemma': u'recusar', 'head': u'recusando', 'head_postag': u'V-GER'}, {'head_lemma': u'maioria', 'head': u'maioria', 'head_postag': u'N'}, {'head_lemma': u'querer', 'head': u'quer', 'head_postag': u'V-FIN'}, {'head_lemma': u'PT', 'head': u'PT', 'head_postag': u'PROP'}, {'head_lemma': u'surpreendente', 'head': u'supreendente', 'head_postag': u'ADJ'}, {'head_lemma': u'Bras\xedlia', 'head': u'Bras\xedlia', 'head_postag': u'PROP'}, {'head_lemma': u'Pesquisa_Datafolha', 'head': u'Pesquisa_Datafolha', 'head_postag': u'N'}, {'head_lemma': u'revelar', 'head': u'revela', 'head_postag': u'V-FIN'}, {'head_lemma': u'muito', 'head': u'Muitas', 'head_postag': u'PRON-DET'}, {'head_lemma': u'prioridade', 'head': u'prioridades', 'head_postag': u'N'}, {'head_lemma': u'com', 'head': u'com', 'head_postag': u'PRP'}, {'head_lemma': u'prioridade', 'head': u'prioridades', 'head_postag': u'N'}]

train_argcands_target = ['NULL', 'ARG', 'ARG', 'ARG', 'NULL', 'NULL', 'NULL', 'NULL', 'NULL', 'NULL', 'NULL', 'NULL', 'ARG', 'ARG', 'ARG', 'ARG', 'NULL', 'NULL', 'NULL', 'NULL', 'ARG', 'NULL', 'NULL', 'NULL', 'NULL', 'NULL', 'ARG', 'NULL', 'NULL', 'NULL', 'NULL', 'ARG', 'ARG', 'NULL', 'NULL']

Answer 1

如果还有人感兴趣

我用CountVectorizer在一些非常相似的东西上，它给了我同样的错误。我意识到矢量器给了我一个COO稀疏矩阵，它基本上是一个坐标列表。不能通过行索引访问COO矩阵中的元素。最好将其转换为按行索引的CSR矩阵（压缩稀疏行）。转换可以很容易地完成。不需要其他改变，这对我有效。

Answer 2

我终于解决了这个问题。必须做两件事：

1. train_argcands_target是一个列表，它必须是一个numpy数组。我很惊讶以前我直接使用估计器时效果很好。
2. 出于某种原因（我还不知道为什么），如果使用DictVectorizer创建的稀疏矩阵，它也不会工作。我不得不“手动”地将每个功能字典转换为一个只包含代表每个功能值的整数的功能数组。转换过程与我在目标值的代码中给出的过程类似。

感谢所有想帮忙的人！