Merge pull request #642 from jskda/pull_request.unit_test_fixes

Pull request.unit test fixes

nltk/tag/__init__.py

@@ -53,7 +53,7 @@ of ``None``.
 We evaluate a tagger on data that was not seen during training:
 
     >>> tagger.evaluate(brown.tagged_sents(categories='news')[500:600])
-    0.734...
+    0.73...
 
 For more information, please consult chapter 5 of the NLTK Book.
 """

nltk/test/classify.doctest

@@ -13,11 +13,11 @@ implement the `ClassifyI` interface:
     >>> import nltk
     >>> nltk.usage(nltk.classify.ClassifierI)
     ClassifierI supports the following operations:
-      - self.batch_classify(featuresets)
-      - self.batch_prob_classify(featuresets)
       - self.classify(featureset)
+      - self.classify_many(featuresets)
       - self.labels()
       - self.prob_classify(featureset)
+      - self.prob_classify_many(featuresets)
 
 NLTK defines several classifier classes:
 
@@ -62,9 +62,9 @@ Test the Naive Bayes classifier:
     >>> classifier = nltk.classify.NaiveBayesClassifier.train(train)
     >>> sorted(classifier.labels())
     ['x', 'y']
-    >>> classifier.batch_classify(test)
+    >>> classifier.classify_many(test)
     ['y', 'x', 'y', 'x']
-    >>> for pdist in classifier.batch_prob_classify(test):
+    >>> for pdist in classifier.prob_classify_many(test):
     ...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
     0.3104 0.6896
     0.5746 0.4254
@@ -92,9 +92,9 @@ Test the Decision Tree classifier:
       a=1? ................................................ y
     c=1? .................................................. y
     <BLANKLINE>
-    >>> classifier.batch_classify(test)
+    >>> classifier.classify_many(test)
     ['y', 'y', 'y', 'x']
-    >>> for pdist in classifier.batch_prob_classify(test):
+    >>> for pdist in classifier.prob_classify_many(test):
     ...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
     Traceback (most recent call last):
       . . .
@@ -113,10 +113,10 @@ Test SklearnClassifier, which requires the scikit-learn package.
     >>> classif = SklearnClassifier(BernoulliNB()).train(train_data)
     >>> test_data = [{"a": 3, "b": 2, "c": 1},
     ...              {"a": 0, "b": 3, "c": 7}]
-    >>> classif.batch_classify(test_data)
+    >>> classif.classify_many(test_data)
     ['ham', 'spam']
     >>> classif = SklearnClassifier(SVC(), sparse=False).train(train_data)
-    >>> classif.batch_classify(test_data)
+    >>> classif.classify_many(test_data)
     ['ham', 'spam']
 
 Test the Maximum Entropy classifier training algorithms; they should all
@@ -179,7 +179,7 @@ Regression tests for TypedMaxentFeatureEncoding
     >>> classifier = maxent.MaxentClassifier.train(
     ...     train, bernoulli=False, encoding=encoding, trace=0)
 
-    >>> classifier.batch_classify(test)
+    >>> classifier.classify_many(test)
     ['y', 'x']