Add flag parameters for topic diff

gensim/models/ldamodel.py

@@ -971,16 +971,18 @@ def get_term_topics(self, word_id, minimum_probability=None):
 
         return values
 
-    def diff(self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10, normed=True):
+    def diff(self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10, diagonal=False, annotation=True, normed=True):
         """
         Calculate difference topic2topic between two Lda models
         `other` instances of `LdaMulticore` or `LdaModel`
         `distance` is function that will be applied to calculate difference between any topic pair.
         Available values: `kullback_leibler`, `hellinger` and `jaccard`
         `num_words` is quantity of most relevant words that used if distance == `jaccard` (also used for annotation)
         `n_ann_terms` is max quantity of words in intersection/symmetric difference between topics (used for annotation)
+        `diagonal` set to True if the difference is required only between the identical topic no.s (returns diagonal of diff matrix)
+        `annotation` whether the intersection or difference of words between two topics should be returned
         Returns a matrix Z with shape (m1.num_topics, m2.num_topics), where Z[i][j] - difference between topic_i and topic_j
-        and matrix annotation with shape (m1.num_topics, m2.num_topics, 2, None),
+        and matrix annotation (if True) with shape (m1.num_topics, m2.num_topics, 2, None),
         where:
 
             annotation[i][j] = [[`int_1`, `int_2`, ...], [`diff_1`, `diff_2`, ...]] and
@@ -1013,35 +1015,49 @@ def diff(self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10
         distance_func = distances[distance]
         d1, d2 = self.state.get_lambda(), other.state.get_lambda()
         t1_size, t2_size = d1.shape[0], d2.shape[0]
+        annotation_terms = None
 
         fst_topics = [{w for (w, _) in self.show_topic(topic, topn=num_words)} for topic in xrange(t1_size)]
         snd_topics = [{w for (w, _) in other.show_topic(topic, topn=num_words)} for topic in xrange(t2_size)]
 
         if distance == "jaccard":
             d1, d2 = fst_topics, snd_topics
 
-        z = np.zeros((t1_size, t2_size))
-        for topic1 in range(t1_size):
-            for topic2 in range(t2_size):
-                z[topic1][topic2] = distance_func(d1[topic1], d2[topic2])
-
-        if normed:
-            if np.abs(np.max(z)) > 1e-8:
-                z /= np.max(z)
-
-        annotation = [[None] * t1_size for _ in range(t2_size)]
+        if diagonal:
+            assert t1_size == t2_size, "Both input models should have same no. of topics, as the diagonal will only be valid in a square matrix"
+            # initialize z and annotation array
+            z = np.zeros(t1_size)
+            if annotation:
+                annotation_terms = np.zeros(t1_size, dtype=list)
+        else:
+            # initialize z and annotation matrix
+            z = np.zeros((t1_size, t2_size))
+            if annotation:
+                annotation_terms = np.zeros((t1_size, t2_size), dtype=list)
+
+        # iterate over each cell in the initialized z and annotation
+        for topic in np.ndindex(z.shape):
+            topic1 = topic[0]
+            if diagonal:
+                topic2 = topic1
+            else:
+                topic2 = topic[1]
 
-        for topic1 in range(t1_size):
-            for topic2 in range(t2_size):
+            z[topic] = distance_func(d1[topic1], d2[topic2])
+            if annotation:
                 pos_tokens = fst_topics[topic1] & snd_topics[topic2]
                 neg_tokens = fst_topics[topic1].symmetric_difference(snd_topics[topic2])
 
                 pos_tokens = sample(pos_tokens, min(len(pos_tokens), n_ann_terms))
                 neg_tokens = sample(neg_tokens, min(len(neg_tokens), n_ann_terms))
 
-                annotation[topic1][topic2] = [pos_tokens, neg_tokens]
+                annotation_terms[topic] = [pos_tokens, neg_tokens]
+
+        if normed:
+            if np.abs(np.max(z)) > 1e-8:
+                z /= np.max(z)
 
-        return z, annotation
+        return z, annotation_terms
 
     def __getitem__(self, bow, eps=None):
         """

gensim/test/test_tmdiff.py

@@ -4,6 +4,7 @@
 # Copyright (C) 2016 Radim Rehurek <radimrehurek@seznam.cz>
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+import logging
 import unittest
 import numpy as np
 
@@ -31,14 +32,22 @@ def setUp(self):
         self.model = LdaModel(corpus=self.corpus, id2word=self.dictionary, num_topics=self.num_topics, passes=10)
 
     def testBasic(self):
+        # test for matrix case
         mdiff, annotation = self.model.diff(self.model, n_ann_terms=self.n_ann_terms)
 
         self.assertEqual(mdiff.shape, (self.num_topics, self.num_topics))
         self.assertEquals(len(annotation), self.num_topics)
         self.assertEquals(len(annotation[0]), self.num_topics)
 
+        # test for diagonal case
+        mdiff, annotation = self.model.diff(self.model, n_ann_terms=self.n_ann_terms, diagonal=True)
+
+        self.assertEqual(mdiff.shape, (self.num_topics,))
+        self.assertEquals(len(annotation), self.num_topics)
+
     def testIdentity(self):
         for dist_name in ["hellinger", "kullback_leibler", "jaccard"]:
+            # test for matrix case
             mdiff, annotation = self.model.diff(self.model, n_ann_terms=self.n_ann_terms, distance=dist_name)
 
             for row in annotation:
@@ -51,6 +60,23 @@ def testIdentity(self):
             if dist_name == "jaccard":
                 self.assertTrue(np.allclose(mdiff, np.zeros(mdiff.shape, dtype=mdiff.dtype)))
 
+            # test for diagonal case
+            mdiff, annotation = self.model.diff(self.model, n_ann_terms=self.n_ann_terms, distance=dist_name, diagonal=True)
+
+            for (int_tokens, diff_tokens) in annotation:
+                self.assertEquals(diff_tokens, [])
+                self.assertEquals(len(int_tokens), self.n_ann_terms)
+
+            self.assertTrue(np.allclose(mdiff, np.zeros(mdiff.shape, dtype=mdiff.dtype)))
+
+            if dist_name == "jaccard":
+                self.assertTrue(np.allclose(mdiff, np.zeros(mdiff.shape, dtype=mdiff.dtype)))
+
     def testInput(self):
         self.assertRaises(ValueError, self.model.diff, self.model, n_ann_terms=self.n_ann_terms, distance='something')
         self.assertRaises(ValueError, self.model.diff, [], n_ann_terms=self.n_ann_terms, distance='something')
+
+
+if __name__ == '__main__':
+    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
+    unittest.main()