如何使用 scikit-learn 对文本对进行分类？

Question

我已经阅读了许多关于这个主题的不同博客，但一直未能找到明确的解决方案。我有以下情况：

我有一个标签为 1 或 -1 的文本对列表。
对于每个文本对，我希望特征按以下方式串联：f () = tfidf(t1) "concat" tfidf(t2)

关于如何做同样的事情有什么建议吗？我有以下代码，但它给出了一个错误：

    count_vect = TfidfVectorizer(analyzer=u'char', ngram_range=ngram_range)
    X0_train_counts = count_vect.fit_transform([x[0] for x in training_documents])
    X1_train_counts = count_vect.fit_transform([x[1] for x in training_documents])
    combined_features = FeatureUnion([("x0", X0_train_counts), ("x1", X1_train_counts)])
    clf = LinearSVC().fit(combined_features, training_target)
    average_training_accuracy += clf.score(combined_features, training_target)

这是我得到的错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
scoreEdgesUsingClassifier(None, pos, neg, 1,ngram_range=(2,5), max_size=1000000, test_size=100000)

 scoreEdgesUsingClassifier(unc, pos, neg, number_of_iterations, ngram_range, max_size, test_size)
 X0_train_counts = count_vect.fit_transform([x[0] for x in training_documents])
 X1_train_counts = count_vect.fit_transform([x[1] for x in training_documents])
 combined_features = FeatureUnion([("x0", X0_train_counts), ("x1", X1_train_counts)])
 print "Done transforming, now training classifier"

lib/python2.7/site-packages/sklearn/pipeline.pyc in __init__(self, transformer_list, n_jobs, transformer_weights)
616         self.n_jobs = n_jobs
617         self.transformer_weights = transformer_weights
--> 618         self._validate_transformers()
619 
620     def get_params(self, deep=True):

lib/python2.7/site-packages/sklearn/pipeline.pyc in _validate_transformers(self)
660                 raise TypeError("All estimators should implement fit and "
661                                 "transform. '%s' (type %s) doesn't" %
--> 662                                 (t, type(t)))
663 
664     def _iter(self):

TypeError: All estimators should implement fit and transform. '  (0, 49025) 0.0575144797079

 (254741, 38401)    0.184394443164
 (254741, 201747)   0.186080393768
 (254741, 179231)   0.195062580945
 (254741, 156925)   0.211367771299
 (254741, 90026)    0.202458920022' (type <class 'scipy.sparse.csr.csr_matrix'>) doesn't

更新

解决方法如下：

    count_vect = TfidfVectorizer(analyzer=u'char', ngram_range=ngram_range)
    training_docs_combined = [x[0] for x in training_documents] + [x[1] for x in training_documents]        
    X_train_counts = count_vect.fit_transform(training_docs_combined)
    concat_features  = hstack((X_train_counts[0:len(training_docs_combined) / 2 ], X_train_counts[len (training_docs_combined) / 2:]))

    clf = LinearSVC().fit(concat_features, training_target)
    average_training_accuracy += clf.score(concat_features, training_target)

Answer 1

来自 scikit-learn 的

FeatureUnion 作为输入估计器，而不是数据数组。

您可以将生成的 X0_train_counts、X1_train_counts 数组简单地与 scipy.sparse.hstack 连接起来，或者创建 TfidfVectorizer 的两个独立实例，将 FeatureUnion 应用到它们，然后调用 fit_transform 方法。

如何使用 scikit-learn 对文本对进行分类？

How to classify text pairs using scikit-learn?

python

machine-learning

tf-idf

scikit-learn

text-classification