我阅读了许多关于这个主题的不同博客，但始终没有找到一个明确的解决方案。我有以下场景：
- 我有一组文本对，每对文本都标记为1或-1。
- 对于每对文本，我希望特征以如下方式连接：f () = tfidf(t1) “concat” tfidf(t2)
关于如何实现这个目标有什么建议吗？我有以下代码，但它会报错：
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)我得到的错误如下：
---------------------------------------------------------------------------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_jobs617 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
这是解决方案：
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)回答：
scikit-learn中的FeatureUnion接受的是估计器，而不是数据数组。
你可以使用scipy.sparse.hstack简单地连接X0_train_counts和X1_train_counts数组，或者创建两个独立的TfidfVectorizer实例，对它们应用FeatureUnion，然后调用fit_transform方法。
