Add NLTKClassifier wrapper class

This makes it easier to add more classifiers from nltk.classify

text/classifiers.py

@@ -134,6 +134,31 @@ def extract_features(self, text):
         except TypeError:
             return self.feature_extractor(text)
 
+
+class NLTKClassifier(BaseClassifier):
+
+    """An abstract class that wraps around the nltk.classify module."""
+
+    nltk_class = None # This must be a class within nltk.classify
+
+    @cached_property
+    def classifier(self):
+        '''The classifier.'''
+        self.train_features = [(self.extract_features(d), c) for d, c in self.train_set]
+        try:
+            return self.nltk_class.train(self.train_features)
+        except AttributeError: # nltk_class has not been defined
+            raise ValueError("NLTKClassifier must have a nltk_class"
+                            " variable that is not None.")
+
+    def classify(self, text):
+        '''Classifies the text.
+
+        :param text: A string of text.
+        '''
+        text_features = self.extract_features(text)
+        return self.classifier.classify(text_features)
+
     def accuracy(self, test_set, format=None):
         '''Compute the accuracy on a test set.
 
@@ -150,8 +175,25 @@ def accuracy(self, test_set, format=None):
         test_features = [(self.extract_features(d), c) for d, c in test_data]
         return nltk.classify.accuracy(self.classifier, test_features)
 
+    def update(self, new_data):
+        '''Update the classifier with new training data and re-trains the
+        classifier.
+
+        :param new_data: New data as a list of tuples of the form
+            ``(text, label)``.
+        '''
+        self.train_set += new_data
+        self.train_features = [(self.extract_features(d), c)
+                                for d, c in self.train_set]
+        try:
+            self.classifier = self.nltk_class.train(self.train_features)
+        except AttributeError: # Descendant has not defined nltk_class
+            raise ValueError("NLTKClassifier must have a nltk_class"
+                            " variable that is not None.")
+        return True
+
 
-class NaiveBayesClassifier(BaseClassifier):
+class NaiveBayesClassifier(NLTKClassifier):
 
     '''A classifier based on the Naive Bayes algorithm, as implemented in
     NLTK.
@@ -164,19 +206,7 @@ class NaiveBayesClassifier(BaseClassifier):
     .. versionadded:: 0.6.0
     '''
 
-    @cached_property
-    def classifier(self):
-        '''The Naive Bayes classifier.'''
-        self.train_features = [(self.extract_features(d), c) for d, c in self.train_set]
-        return nltk.classify.NaiveBayesClassifier.train(self.train_features)
-
-    def classify(self, text):
-        '''Classifies the text.
-
-        :param text: A string of text.
-        '''
-        text_features = self.extract_features(text)
-        return self.classifier.classify(text_features)
+    nltk_class = nltk.classify.NaiveBayesClassifier
 
     def prob_classify(self, text):
         '''Return the label probability distribution for classifying a string
@@ -209,15 +239,3 @@ def show_informative_features(self, *args, **kwargs):
         '''
         return self.classifier.show_most_informative_features(*args, **kwargs)
 
-    def update(self, new_data):
-        '''Update the classifier with new training data and re-trains the
-        classifier.
-
-        :param new_data: New data as a list of tuples of the form
-            ``(text, label)``.
-        '''
-        self.train_set += new_data
-        self.train_features = [(self.extract_features(d), c)
-                                for d, c in self.train_set]
-        self.classifier = nltk.classify.NaiveBayesClassifier.train(self.train_features)
-        return True