More flexibility in setting anchor strength (fixes #16)

README.md

@@ -127,6 +127,12 @@ topic_model.fit(X, anchors=[[0, 2], 1], anchor_strength=2)
 
 anchors the features of columns 0 and 2 to the first topic, and feature 1 to the second topic.
 
+Different anchor strengths can be specified for each topic, and further for each word in a topic. The below example uses different anchor strengths for "dog" and "cat" in the first topic, and the same anchor strength for "apple" and "pear" in the second:
+
+```python
+topic_model.fit(X, words=words, anchors=[['dog','cat'], ['apple','pear'], 'building'], anchor_strength=[[2, 2.5], 2, 1.5])
+```
+
 ### Anchoring Strategies
 
 In our TACL paper, we explore several anchoring strategies:

corextopic/corextopic.py

@@ -198,8 +198,18 @@ def fit_transform(self, X, y=None, anchors=None, anchor_strength=1, words=None,
             if anchors is not None:
                 for a in flatten(anchors):
                     self.alpha[:, a] = 0
-                for ia, a in enumerate(anchors):
-                    self.alpha[ia, a] = anchor_strength
+                if type(anchor_strength) != list:
+                    for ia, a in enumerate(anchors):
+                        self.alpha[ia, a] = anchor_strength
+                else:
+                    assert len(anchors) == len(anchor_strength), 'Number of topics and number of anchor strengths do not match'
+                    for ia, (a, a_s) in enumerate(zip(anchors, anchor_strength)):
+                        if type(a_s) == list:
+                            if len(a_s) == 1:
+                                a_s = a_s[0]
+                            else:
+                                assert len(a_s) == len(a), 'Number of anchor strengths does not match number of seeds for topic number ' + str(ia + 1) 
+                        self.alpha[ia, a] = a_s
 
             p_y_given_x, _, log_z = self.calculate_latent(X, self.theta)
 

corextopic/example/corex_topic_example.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "**Author:** [Ryan J. Gallagher](http://ryanjgallagher.github.io/)  \n",
     "\n",
-    "**Last updated:** 07/21/2018"
+    "**Last updated:** 05/22/2020"
    ]
   },
   {
@@ -936,6 +936,50 @@
     "**Note:** If you do not specify the column labels through `words`, then you can still anchor by specifying the column indices of the features you wish to anchor on. You may also specify anchors using a mix of strings and indices if desired."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The anchor strength can either be set globally for all topics and words, as above, or independently for each topic. \n",
+    "In the latter case, each word can be given its own strength or the same value can be used for each word in the topic.\n",
+    "\n",
+    "In the example below, the second topic anchoring \"nasa\" and \"politics\" weights \"politics\" more highly than \"nasa\", while the third topic weights them equally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anchor_strengths = [6, [6, 7], 7, 6]\n",
+    "\n",
+    "anchored_topic_model = ct.Corex(n_hidden=50, seed=2)\n",
+    "anchored_topic_model.fit(doc_word, words=words, anchors=anchor_words, anchor_strength=anchor_strengths);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: nasa,space,orbit,technology,shuttle,launch,development,moon,commercial,mission\n",
+      "1: nasa,politics,gov,ames,jsc,comprehensive,arc,lewis,larc,shafer\n",
+      "2: news,nasa,insisting,pasadena,advertising,edwards,hal,llnl,part1,cfv\n",
+      "3: war,israel,armenians,israeli,armenian,jews,soldiers,military,killed,argic\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(len(anchor_words)):\n",
+    "    topic_words,_ = zip(*anchored_topic_model.get_topics(topic=n))\n",
+    "    print('{}: '.format(n) + ','.join(topic_words))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},