prevent print_topics from truncating features

mglearn/tools.py

@@ -98,19 +98,44 @@ def print_topics(topics, feature_names, sorting, topics_per_chunk=6,
         these_topics = topics[i: i + topics_per_chunk]
         # maybe we have less than topics_per_chunk left
         len_this_chunk = len(these_topics)
-        # print topic headers
-        print(("topic {:<8}" * len_this_chunk).format(*these_topics))
-        print(("-------- {0:<5}" * len_this_chunk).format(""))
+        #generate list of sorted features and their lengths
+        row = []
+        for i in range(n_words):
+            row.append(feature_names[sorting[these_topics, i]])
+        topic_words = np.array(row).T
+        #get max feature length for each topic
+        max_feat_len = []
+        for t in topic_words:
+            max_feat_len.append(len(max(t, key = len)))
+        #generate space between strings equal to 1+len(longest string in topic)
+        result = [None]*len(these_topics)*2
+        result[::2] = these_topics
+        nums = np.array([(x - 5) for x in max_feat_len])
+        nums[nums < 0] = 0 #prevents spaces of negative length
+        result[1::2] = [str(x) for x in nums]
+        print(("topic {:<{}} " * len_this_chunk).format(*result))
+
+        #generate space between strings equal to 1+len(longest string in topic)
+        result = [None]*len(these_topics)*2
+        result[::2] = ['']*len(these_topics)
+        nums = np.array([(x - 8) for x in max_feat_len])
+        nums[nums < 0] = 0 #prevents spaces of negative length
+        result[1::2] = [str(x) for x in nums]
+        print(("-------- {:<{}} " * len_this_chunk).format(*result))
+
         # print top n_words frequent words
         for i in range(n_words):
+            #generate space between strings 
+            result = [None]*len(these_topics)*2
+            result[::2] = feature_names[sorting[these_topics, i]]
+            result[1::2] = [str(x+2) for x in max_feat_len]
             try:
-                print(("{:<14}" * len_this_chunk).format(
-                    *feature_names[sorting[these_topics, i]]))
+                print(("{:<{}}" * len_this_chunk).format(*result))
             except:
                 pass
         print("\n")
 
-
+        
 def get_tree(tree, **kwargs):
     try:
         # python3