{"id":3080,"date":"2015-04-13T16:15:09","date_gmt":"2015-04-13T13:15:09","guid":{"rendered":"http:\/\/www.webcodegeeks.com\/?p=3080"},"modified":"2015-04-11T16:16:50","modified_gmt":"2015-04-11T13:16:50","slug":"python-simplifying-creation-stop-word-list-defaultdict","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/python-simplifying-creation-stop-word-list-defaultdict\/","title":{"rendered":"Python: Simplifying the creation of a stop word list with defaultdict"},"content":{"rendered":"<p>I\u2019ve been playing around with topics models again and recently read a <a href=\"http:\/\/www.perseus.tufts.edu\/publications\/02-jocch-mimno.pdf\">paper by David Mimno<\/a> which suggested the following heuristic for working out which words should go onto the stop list:<\/p>\n<blockquote><p>A good heuristic for identifying such words is to remove those that occur in more than 5-10% of documents (most common) and those that occur fewer than 5-10 times in the entire corpus (least common).<\/p><\/blockquote>\n<p>I decided to try this out on the <a href=\"https:\/\/github.com\/mneedham\/neo4j-himym\/blob\/master\/data\/import\/sentences.csv\">HIMYM dataset<\/a> that I\u2019ve been working on over the last couple of months.<\/p>\n<p>I started out with the following code to build a dictionary of words, their total occurrences and the episodes they\u2019d been used in:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">import csv\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom collections import defaultdict\r\n\r\nepisodes = defaultdict(str)\r\nwith open('sentences.csv', 'r') as file:\r\n    reader = csv.reader(file, delimiter = ',')\r\n    reader.next()\r\n    for row in reader:\r\n        episodes&#x5B;row&#x5B;1]] += row&#x5B;4]\r\n\r\nvectorizer = CountVectorizer(analyzer='word', min_df = 0, stop_words = 'english')\r\nmatrix = vectorizer.fit_transform(episodes.values())\r\nfeatures = vectorizer.get_feature_names()\r\n\r\nwords = {}\r\nfor doc_id, doc in enumerate(matrix.todense()):\r\n    for word_id, score in enumerate(doc.tolist()&#x5B;0]):\r\n        word = features&#x5B;word_id]\r\n        if not words.get(word):\r\n            words&#x5B;word] = {}\r\n\r\n        if not words&#x5B;word].get('score'):\r\n            words&#x5B;word]&#x5B;'score'] = 0\r\n        words&#x5B;word]&#x5B;'score'] += score\r\n\r\n        if not words&#x5B;word].get('episodes'):\r\n            words&#x5B;word]&#x5B;'episodes'] = set()\r\n\r\n        if score &gt; 0:\r\n            words&#x5B;word]&#x5B;'episodes'].add(doc_id)<\/pre>\n<p>This works fine but the code inside the last for block is ugly and most of it is handling the case when parts of a dictionary aren\u2019t yet initialised which is defaultdict territory. You\u2019ll notice I am using defaultdict in the first part of the code but not yet the second as I\u2019d struggled to get it working.<\/p>\n<p>This was my first attempt to make the \u2018words\u2019 variable based on it:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">&gt;&gt;&gt; words = defaultdict({})\r\nTraceback (most recent call last):\r\n  File '&lt;stdin&gt;', line 1, in &lt;module&gt;\r\nTypeError: first argument must be callable<\/pre>\n<p>We can see why this doesn\u2019t work if we try to evaluate \u2018{}\u2019 as a function which is what defaultdict does internally:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">&gt;&gt;&gt; {}()\r\nTraceback (most recent call last):\r\n  File '&lt;stdin&gt;', line 1, in &lt;module&gt;\r\nTypeError: 'dict' object is not callable<\/pre>\n<p>Instead what we need is to pass in \u2018dict&#8217;:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">&gt;&gt;&gt; dict()\r\n{}\r\n\r\n&gt;&gt;&gt; words = defaultdict(dict)\r\n\r\n&gt;&gt;&gt; words\r\ndefaultdict(&lt;type 'dict'&gt;, {})<\/pre>\n<p>That simplifies the first bit of the loop:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">words = defaultdict(dict)\r\nfor doc_id, doc in enumerate(matrix.todense()):\r\n    for word_id, score in enumerate(doc.tolist()&#x5B;0]):\r\n        word = features&#x5B;word_id]\r\n        if not words&#x5B;word].get('score'):\r\n            words&#x5B;word]&#x5B;'score'] = 0\r\n        words&#x5B;word]&#x5B;'score'] += score\r\n\r\n        if not words&#x5B;word].get('episodes'):\r\n            words&#x5B;word]&#x5B;'episodes'] = set()\r\n\r\n        if score &gt; 0:\r\n            words&#x5B;word]&#x5B;'episodes'].add(doc_id)<\/pre>\n<p>We\u2019ve still got a couple of other places to simplify though which we can do by defining a <a href=\"http:\/\/pymotw.com\/2\/collections\/defaultdict.html\">custom function<\/a> and passing that into defaultdict:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">def default_dict_function():\r\n   return {'score': 0, 'episodes': set()}\r\n\r\n&gt;&gt;&gt; words = defaultdict(default_dict_function)\r\n\r\n&gt;&gt;&gt; words\r\ndefaultdict(&lt;function default_dict_function at 0x10963fcf8&gt;, {})<\/pre>\n<p>And here\u2019s the final product:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">def default_dict_function():\r\n   return {'score': 0, 'episodes': set()}\r\nwords = defaultdict(default_dict_function)\r\n\r\nfor doc_id, doc in enumerate(matrix.todense()):\r\n    for word_id, score in enumerate(doc.tolist()&#x5B;0]):\r\n        word = features&#x5B;word_id]\r\n        words&#x5B;word]&#x5B;'score'] += score\r\n        if score &gt; 0:\r\n            words&#x5B;word]&#x5B;'episodes'].add(doc_id)<\/pre>\n<p>After this we can write out the words to our stop list:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">with open('stop_words.txt', 'w') as file:\r\n    writer = csv.writer(file, delimiter = ',')\r\n    for word, value in words.iteritems():\r\n        # appears in &gt; 10% of episodes\r\n        if len(value&#x5B;'episodes']) &gt; int(len(episodes) \/ 10):\r\n            writer.writerow(&#x5B;word.encode('utf-8')])\r\n\r\n        # less than 10 occurences\r\n        if value&#x5B;'score'] &lt; 10:\r\n            writer.writerow(&#x5B;word.encode('utf-8')])<\/pre>\n<div class=\"attribution\">\n<table>\n<tbody>\n<tr>\n<td><span class=\"reference\">Reference: <\/span><\/td>\n<td><a href=\"http:\/\/www.markhneedham.com\/blog\/2015\/03\/22\/python-simplifying-the-creation-of-a-stop-word-list-with-defaultdict\/\">Python: Simplifying the creation of a stop word list with defaultdict<\/a> from our <a href=\"http:\/\/www.webcodegeeks.com\/wcg\/\">WCG partner<\/a> Mark Needham at the <a href=\"http:\/\/www.markhneedham.com\/blog\/\">Mark Needham Blog<\/a> blog.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>I\u2019ve been playing around with topics models again and recently read a paper by David Mimno which suggested the following heuristic for working out which words should go onto the stop list: A good heuristic for identifying such words is to remove those that occur in more than 5-10% of documents (most common) and those &hellip;<\/p>\n","protected":false},"author":48,"featured_media":1651,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[],"class_list":["post-3080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - 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