使用交叉验证执行递归特征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
产生错误的代码如下:
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
我知道我还应该对 LogisticRegression 分类器的参数执行 GridSearch,但我认为这不是错误的根源(或者是吗?)。
我应该提一下,我正在测试大约 50 个功能,并且几乎所有功能都是分类的(这就是我使用 DictVectorizer 对它们进行适当转换的原因)。
您可以给我的任何帮助或指导都非常受欢迎。谢谢!
编辑
以下是一些训练数据示例:
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']