gensim_topic_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import pandas as pd\n",
    "import nltk\n",
    "\n",
    "#nltk.download('stopwords')\n",
    "#nltk.download('averaged_perceptron_tagger')\n",
    "#nltk.download('punkt')\n",
    "#nltk.download('universal_tagset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           PRE comprehend_topic_data/\r\n",
      "                           PRE comprehend_topic_results/\r\n",
      "                           PRE ecommerce_marketbasket/\r\n",
      "                           PRE ecommerce_recommender/\r\n",
      "                           PRE movie_lens/\r\n",
      "                           PRE netflix/\r\n",
      "2019-02-10 00:27:12          0 \r\n",
      "2019-02-22 05:21:06      56866 WorkdayClasses.csv\r\n"
     ]
    }
   ],
   "source": [
    "!aws s3 ls s3://ml-personalization/data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class Name</th>\n",
       "      <th>Description Text</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Coaching Habit</td>\n",
       "      <td>Coaching can help our people to perform at the...</td>\n",
       "      <td>Workday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Meaningful Feedback</td>\n",
       "      <td>Feedback is a catalyst for career growth and c...</td>\n",
       "      <td>Workday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Client Introductions and Meetings</td>\n",
       "      <td>Even the most experienced consultant must prep...</td>\n",
       "      <td>Workday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ladder of Inference &amp; The Drama Triangle</td>\n",
       "      <td>What you say and how you say it is a part of w...</td>\n",
       "      <td>Workday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Listening with Purpose</td>\n",
       "      <td>\"Most people do not listen with the intent to ...</td>\n",
       "      <td>Workday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Class Name  \\\n",
       "0                            Coaching Habit   \n",
       "1                       Meaningful Feedback   \n",
       "2         Client Introductions and Meetings   \n",
       "3  Ladder of Inference & The Drama Triangle   \n",
       "4                    Listening with Purpose   \n",
       "\n",
       "                                    Description Text   Source  \n",
       "0  Coaching can help our people to perform at the...  Workday  \n",
       "1  Feedback is a catalyst for career growth and c...  Workday  \n",
       "2  Even the most experienced consultant must prep...  Workday  \n",
       "3  What you say and how you say it is a part of w...  Workday  \n",
       "4  \"Most people do not listen with the intent to ...  Workday  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_data = pd.read_csv(\"s3://ml-personalization/data/WorkdayClasses.csv\")\n",
    "class_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[('Coaching', 'Coaching', ['VBG']), ('can', 'can', ['MD']), ('help', 'help', ['VB']), ('our', 'our', ['PRP$']), ('people', 'people', ['NNS']), ('to', 'to', ['TO']), ('perform', 'perform', ['VB']), ('at', 'at', ['IN']), ('their', 'their', ['PRP$']), ('best', 'best', ['JJS']), (',', ',', [',']), ('learn', 'learn', ['VBP']), ('from', 'from', ['IN']), ('their', 'their', ['PRP$']), ('own', 'own', ['JJ']), ('experiences', 'experience', ['NNS']), (',', ',', [',']), ('and', 'and', ['CC']), ('drive', 'drive', ['JJ']), ('empowerment', 'empowerment', ['NN']), ('and', 'and', ['CC']), ('engagement', 'engagement', ['NN']), ('.', '.', ['.'])], [('Yet', 'Yet', ['RB']), (',', ',', [',']), ('we', 'we', ['PRP']), ('regularly', 'regularly', ['VBP']), ('fall', 'fall', ['DT']), ('back', 'back', ['RB']), ('on', 'on', ['IN']), ('old', 'old', ['JJ']), ('habits', 'habit', ['NNS']), ('of', 'of', ['IN']), ('telling', 'tell', ['VBG']), ('instead', 'instead', ['RB']), ('of', 'of', ['IN']), ('asking', 'ask', ['VBG']), (',', ',', [',']), ('mentoring', 'mentor', ['VBG']), ('instead', 'instead', ['RB']), ('of', 'of', ['IN']), ('coaching', 'coaching', ['NN']), (',', ',', [',']), ('and', 'and', ['CC']), ('directing', 'direct', ['VBG']), ('instead', 'instead', ['RB']), ('of', 'of', ['IN']), ('leading', 'lead', ['VBG']), ('.', '.', ['.'])], [('In', 'In', ['IN']), ('this', 'this', ['DT']), ('class', 'class', ['NN']), (',', ',', [',']), ('we', 'we', ['PRP']), ('’', '’', ['VBP']), ('ll', 'll', ['JJ']), ('delineate', 'delineate', ['VBP']), ('the', 'the', ['DT']), ('difference', 'difference', ['NN']), ('between', 'between', ['IN']), ('mentoring', 'mentor', ['VBG']), ('and', 'and', ['CC']), ('coaching', 'coaching', ['NN']), (',', ',', [',']), ('introduce', 'introduce', ['VB']), ('a', 'a', ['DT']), ('simple', 'simple', ['JJ']), ('frameworks', 'framework', ['NN']), ('for', 'for', ['IN']), ('a', 'a', ['DT']), ('10', '10', ['CD']), ('minutes', 'minute', ['NNS']), ('coaching', 'coach', ['VBG']), ('conversation', 'conversation', ['NN']), (',', ',', [',']), ('and', 'and', ['CC']), ('establish', 'establish', ['VB']), ('a', 'a', ['DT']), ('new', 'new', ['JJ']), ('habit', 'habit', ['NN']), ('to', 'to', ['TO']), ('reinforce', 'reinforce', ['VB']), ('regular', 'regular', ['JJ']), ('coaching', 'coaching', ['NN']), ('conversations', 'conversation', ['NNS']), ('with', 'with', ['IN']), ('your', 'your', ['PRP$']), ('team', 'team', ['NN']), ('.', '.', ['.'])], [('At', 'At', ['IN']), ('the', 'the', ['DT']), ('end', 'end', ['NN']), ('of', 'of', ['IN']), ('this', 'this', ['DT']), ('course', 'course', ['NN']), (',', ',', [',']), ('participants', 'participant', ['NNS']), ('will', 'will', ['MD']), ('be', 'be', ['VB']), ('able', 'able', ['JJ']), ('to', 'to', ['TO']), (':', ':', [':']), ('Determine', 'Determine', ['VB']), ('when', 'when', ['WRB']), ('and', 'and', ['CC']), ('how', 'how', ['WRB']), ('to', 'to', ['TO']), ('use', 'use', ['VB']), ('coaching', 'coach', ['VBG']), ('questions', 'question', ['NNS']), ('to', 'to', ['TO']), ('support', 'support', ['VB']), ('others', 'others', ['NNS']), ('development', 'development', ['NN']), ('and', 'and', ['CC']), ('performance', 'performance', ['NN']), (',', ',', [',']), ('Facilitate', 'Facilitate', ['NNP']), ('a', 'a', ['DT']), ('coaching', 'coach', ['VBG']), ('conversation', 'conversation', ['NN']), ('in', 'in', ['IN']), ('10', '10', ['CD']), ('minutes', 'minute', ['NNS']), ('or', 'or', ['CC']), ('less', 'less', ['JJR']), (',', ',', [',']), ('Effectively', 'Effectively', ['RB']), ('establish', 'establish', ['VB']), ('a', 'a', ['DT']), ('new', 'new', ['JJ']), ('habit', 'habit', ['NN'])]]\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "class Splitter(object):\n",
    "    \"\"\"\n",
    "    split the document into sentences and tokenize each sentence\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.splitter = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "        self.tokenizer = nltk.tokenize.TreebankWordTokenizer()\n",
    "\n",
    "    def split(self,text):\n",
    "        \"\"\"\n",
    "        out : ['What', 'can', 'I', 'say', 'about', 'this', 'place', '.']\n",
    "        \"\"\"\n",
    "        # split into single sentence\n",
    "        sentences = self.splitter.tokenize(text)\n",
    "        # tokenization in each sentences\n",
    "        tokens = [self.tokenizer.tokenize(sent) for sent in sentences]\n",
    "        return tokens\n",
    "\n",
    "\n",
    "class LemmatizationWithPOSTagger(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def get_wordnet_pos(self,treebank_tag):\n",
    "        \"\"\"\n",
    "        return WORDNET POS compliance to WORDENT lemmatization (a,n,r,v) \n",
    "        \"\"\"\n",
    "        if treebank_tag.startswith('J'):\n",
    "            return wordnet.ADJ\n",
    "        elif treebank_tag.startswith('V'):\n",
    "            return wordnet.VERB\n",
    "        elif treebank_tag.startswith('N'):\n",
    "            return wordnet.NOUN\n",
    "        elif treebank_tag.startswith('R'):\n",
    "            return wordnet.ADV\n",
    "        else:\n",
    "            # As default pos in lemmatization is Noun\n",
    "            return wordnet.NOUN\n",
    "\n",
    "    def pos_tag(self,tokens):\n",
    "        # find the pos tagginf for each tokens [('What', 'WP'), ('can', 'MD'), ('I', 'PRP') ....\n",
    "        pos_tokens = [nltk.pos_tag(token) for token in tokens]\n",
    "\n",
    "        # lemmatization using pos tagg   \n",
    "        # convert into feature set of [('What', 'What', ['WP']), ('can', 'can', ['MD']), ... ie [original WORD, Lemmatized word, POS tag]\n",
    "        pos_tokens = [ [(word, lemmatizer.lemmatize(word,self.get_wordnet_pos(pos_tag)), [pos_tag]) for (word,pos_tag) in pos] for pos in pos_tokens]\n",
    "        return pos_tokens\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "splitter = Splitter()\n",
    "lemmatization_using_pos_tagger = LemmatizationWithPOSTagger()\n",
    "\n",
    "#step 1 split document into sentence followed by tokenization\n",
    "tokens = class_data['Description Text'].apply(lambda x: splitter.split(x))\n",
    "\n",
    "#step 2 lemmatization using pos tagger \n",
    "lemma_pos_token = tokens.apply(lambda x: lemmatization_using_pos_tagger.pos_tag(x))\n",
    "print(lemma_pos_token[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coaching can help our people to perform at their best , learn from their own experience , and drive empowerment and engagement . Yet , we regularly fall back on old habit of tell instead of ask , mentor instead of coaching , and direct instead of lead . In this class , we ’ ll delineate the difference between mentor and coaching , introduce a simple framework for a 10 minute coach conversation , and establish a new habit to reinforce regular coaching conversation with your team . At the end of this course , participant will be able to : Determine when and how to use coach question to support others development and performance , Facilitate a coach conversation in 10 minute or less , Effectively establish a new habit'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating and testing lemmatizing function\n",
    "\n",
    "def lemmatize_to_string(value):\n",
    "    string_list = []\n",
    "    for i in range(len(value)):\n",
    "        string_list.extend([x[1] for x in value[i]])\n",
    "    \n",
    "    \n",
    "    return \" \".join(string_list)\n",
    "        \n",
    "lemmatize_to_string(lemma_pos_token[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see the above lemmatizing approach isn't perfect, applying to all rows\n",
    "\n",
    "lemmatized_descriptions = lemma_pos_token.apply(lambda x: lemmatize_to_string(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converting to lower case...\n",
      "converting to lower case...\n",
      "removing English stopwords...\n",
      "removing English stopwords...\n",
      "stemming words...\n",
      "\n",
      "\n",
      "Coaching can help our people to perform at their best, learn from their own experiences, and drive empowerment and engagement. Yet, we regularly fall back on old habits of telling instead of asking, mentoring instead of coaching, and directing instead of leading. In this class, we’ll delineate the difference between mentoring and coaching, introduce a simple frameworks for a 10 minutes coaching conversation, and establish a new habit to reinforce regular coaching conversations with your team. At the end of this course, participants will be able to: Determine when and how to use coaching questions to support others development and performance, Facilitate a coaching conversation in 10 minutes or less, Effectively establish a new habit\n",
      "\n",
      "coaching help people perform best learn experience drive empowerment engagement yet regularly fall back old habit tell instead ask mentor instead coaching direct instead lead class delineate difference mentor coaching introduce simple framework minute coach conversation establish new habit reinforce regular coaching conversation team end course participant able determine use coach question support others development performance facilitate coach conversation minute less effectively establish new habit\n",
      "\n",
      "coach help peopl perform best learn experi drive empower engag yet regularli fall back old habit tell instead ask mentor instead coach direct instead lead class delin differ mentor coach introduc simpl framework minut coach convers establish new habit reinforc regular coach convers team end cours particip abl determin use coach question support other develop perform facilit coach convers minut less effect establish new habit\n"
     ]
    }
   ],
   "source": [
    "# basic text preparation for NLP\n",
    "\n",
    "# converting to lowercase\n",
    "class_data['Lemma_Processed'] = preprocessing.lowercase(lemmatized_descriptions)\n",
    "class_data['Stem_Processed'] = preprocessing.lowercase(class_data['Description Text'])\n",
    "\n",
    "# stripping punctuation\n",
    "class_data['Lemma_Processed'] = preprocessing.strip_punctuation(class_data['Lemma_Processed'])\n",
    "class_data['Stem_Processed'] = preprocessing.strip_punctuation(class_data['Stem_Processed'])\n",
    "\n",
    "# removing stopwords\n",
    "class_data['Lemma_Processed'] = preprocessing.remove_english_stopwords(class_data['Lemma_Processed'])\n",
    "class_data['Stem_Processed'] = preprocessing.remove_english_stopwords(class_data['Stem_Processed'])\n",
    "\n",
    "# stemming\n",
    "class_data['Stem_Processed'] = preprocessing.stem(class_data['Stem_Processed'])\n",
    "\n",
    "print()\n",
    "print()\n",
    "print(class_data['Description Text'][0])\n",
    "print()\n",
    "print(class_data['Lemma_Processed'][0])\n",
    "print()\n",
    "print(class_data['Stem_Processed'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluating model ranges to determine best topic model\n",
    "\n",
    "import time\n",
    "from collections import defaultdict\n",
    "\n",
    "grid = defaultdict(list)\n",
    "parameter_list = range(2,8)\n",
    "eval_columns = ['Number_Of_Topics', 'Lemma_Coherence', 'Stem_Coherence']\n",
    "\n",
    "# getting kfold splits\n",
    "\n",
    "from sklearn.model_selection import KFold # import KFold\n",
    "kf = KFold(n_splits=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building topic models for 2 topics\n",
      "Building topic models for 3 topics\n",
      "Building topic models for 4 topics\n",
      "Building topic models for 5 topics\n",
      "Building topic models for 6 topics\n",
      "Building topic models for 7 topics\n",
      "Building topic models for 2 topics\n",
      "Building topic models for 3 topics\n",
      "Building topic models for 4 topics\n",
      "Building topic models for 5 topics\n",
      "Building topic models for 6 topics\n",
      "Building topic models for 7 topics\n",
      "Building topic models for 2 topics\n",
      "Building topic models for 3 topics\n",
      "Building topic models for 4 topics\n",
      "Building topic models for 5 topics\n",
      "Building topic models for 6 topics\n",
      "Building topic models for 7 topics\n",
      "Building topic models for 2 topics\n",
      "Building topic models for 3 topics\n",
      "Building topic models for 4 topics\n",
      "Building topic models for 5 topics\n",
      "Building topic models for 6 topics\n",
      "Building topic models for 7 topics\n",
      "Building topic models for 2 topics\n",
      "Building topic models for 3 topics\n",
      "Building topic models for 4 topics\n",
      "Building topic models for 5 topics\n",
      "Building topic models for 6 topics\n",
      "Building topic models for 7 topics\n",
      "    Number_Of_Topics  Lemma_Coherence  Stem_Coherence\n",
      "0                2.0         0.257453        0.292505\n",
      "1                3.0         0.306590        0.321425\n",
      "2                4.0         0.315804        0.310355\n",
      "3                5.0         0.355308        0.326782\n",
      "4                6.0         0.368670        0.282336\n",
      "5                7.0         0.361800        0.302446\n",
      "6                2.0         0.328493        0.319380\n",
      "7                3.0         0.300471        0.320795\n",
      "8                4.0         0.315152        0.309896\n",
      "9                5.0         0.308019        0.370406\n",
      "10               6.0         0.301616        0.422639\n",
      "11               7.0         0.312004        0.359877\n",
      "12               2.0         0.324097        0.439151\n",
      "13               3.0         0.310698        0.318621\n",
      "14               4.0         0.324104        0.382143\n",
      "15               5.0         0.303354        0.372749\n",
      "16               6.0         0.323872        0.356766\n",
      "17               7.0         0.324771        0.324734\n",
      "18               2.0         0.358195        0.297947\n",
      "19               3.0         0.345887        0.351043\n",
      "20               4.0         0.366832        0.330227\n",
      "21               5.0         0.387611        0.400210\n",
      "22               6.0         0.390209        0.470276\n",
      "23               7.0         0.346332        0.474466\n",
      "24               2.0         0.378726        0.360205\n",
      "25               3.0         0.327686        0.363964\n",
      "26               4.0         0.397977        0.419771\n",
      "27               5.0         0.371169        0.405231\n",
      "28               6.0         0.363816        0.389686\n",
      "29               7.0         0.370402        0.364636\n"
     ]
    }
   ],
   "source": [
    "# evaluating kfold splits\n",
    "\n",
    "from gensim import corpora, models\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "import re\n",
    "import warnings\n",
    "\n",
    "eval_df = pd.DataFrame(columns=eval_columns)\n",
    "\n",
    "for train_index, test_index in kf.split(class_data):\n",
    "    \n",
    "    # getting train/test split for the fold\n",
    "    train_data = class_data.iloc[train_index]\n",
    "    test_data = class_data.iloc[test_index]\n",
    "    \n",
    "    # creating text lists\n",
    "    stemmed_train_text = []\n",
    "    lemma_train_text = []\n",
    "    stemmed_test_text = []\n",
    "    lemma_test_text = []\n",
    "    \n",
    "    for row in train_data.iterrows():\n",
    "        stem_val = str(row[1]['Stem_Processed'])\n",
    "        lemma_val = str(row[1]['Lemma_Processed'])\n",
    "        stemmed_train_text.append(re.split(r'\\s+', stem_val))\n",
    "        lemma_train_text.append(re.split(r'\\s+', lemma_val))\n",
    "        \n",
    "    for row in test_data.iterrows():\n",
    "        stem_val = str(row[1]['Stem_Processed'])\n",
    "        lemma_val = str(row[1]['Lemma_Processed'])\n",
    "        stemmed_test_text.append(re.split(r'\\s+', stem_val))\n",
    "        lemma_test_text.append(re.split(r'\\s+', lemma_val))\n",
    "        \n",
    "    # creating dictionaries\n",
    "    stem_dictionary = corpora.Dictionary(stemmed_train_text)\n",
    "    lemma_dictionary = corpora.Dictionary(lemma_train_text)\n",
    "    \n",
    "    # creating corpuses\n",
    "    stem_corpus = [stem_dictionary.doc2bow(stem) for stem in stemmed_train_text]\n",
    "    lemma_corpus = [lemma_dictionary.doc2bow(lemma) for lemma in lemma_train_text]\n",
    "    \n",
    "    # building models for parameter values\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        \n",
    "        for param in parameter_list:\n",
    "            \n",
    "            # building models\n",
    "            print(\"Building topic models for {0} topics\".format(str(param)))\n",
    "            start_time = time.time()\n",
    "            stem_model = models.ldamodel.LdaModel(corpus=stem_corpus, id2word=stem_dictionary, num_topics=param, passes=10, random_state = 1)\n",
    "            lemma_model = models.ldamodel.LdaModel(corpus=lemma_corpus, id2word=lemma_dictionary, num_topics=param, passes=10, random_state = 1)\n",
    "            elapsed = time.time() - start_time\n",
    "            #print(\"Elapsed time: %s\" % elapsed)\n",
    "            \n",
    "            # getting coherence values\n",
    "            stem_cm = CoherenceModel(model=stem_model, texts=stemmed_train_text, dictionary=stem_dictionary, coherence='c_v')\n",
    "            lemma_cm = CoherenceModel(model=lemma_model, texts=lemma_train_text, dictionary=lemma_dictionary, coherence='c_v')\n",
    "            stem_coherence = stem_cm.get_coherence()\n",
    "            lemma_coherence = lemma_cm.get_coherence()\n",
    "        \n",
    "            # adding to eval data frame\n",
    "            eval_df.loc[len(eval_df)] = [param, lemma_coherence, stem_coherence]\n",
    "\n",
    "print(eval_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_Of_Topics</th>\n",
       "      <th>Lemma_Coherence</th>\n",
       "      <th>Stem_Coherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.292505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.306590</td>\n",
       "      <td>0.321425</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.315804</td>\n",
       "      <td>0.310355</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.355308</td>\n",
       "      <td>0.326782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.368670</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "   Number_Of_Topics  Lemma_Coherence  Stem_Coherence\n",
       "0               2.0         0.257453        0.292505\n",
       "1               3.0         0.306590        0.321425\n",
       "2               4.0         0.315804        0.310355\n",
       "3               5.0         0.355308        0.326782\n",
       "4               6.0         0.368670        0.282336"
      ]
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     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Lemma_Coherence</th>\n",
       "      <th>Stem_Coherence</th>\n",
       "      <th>diff</th>\n",
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       "    <tr>\n",
       "      <th>Number_Of_Topics</th>\n",
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       "      <td>0.329393</td>\n",
       "      <td>0.341838</td>\n",
       "      <td>-0.012445</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.318266</td>\n",
       "      <td>0.335170</td>\n",
       "      <td>-0.016903</td>\n",
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       "      <th>4.0</th>\n",
       "      <td>0.343974</td>\n",
       "      <td>0.350478</td>\n",
       "      <td>-0.006504</td>\n",
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       "      <th>5.0</th>\n",
       "      <td>0.345092</td>\n",
       "      <td>0.375076</td>\n",
       "      <td>-0.029983</td>\n",
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       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.349637</td>\n",
       "      <td>0.384341</td>\n",
       "      <td>-0.034704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.343062</td>\n",
       "      <td>0.365232</td>\n",
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      "text/plain": [
       "                  Lemma_Coherence  Stem_Coherence      diff\n",
       "Number_Of_Topics                                           \n",
       "2.0                      0.329393        0.341838 -0.012445\n",
       "3.0                      0.318266        0.335170 -0.016903\n",
       "4.0                      0.343974        0.350478 -0.006504\n",
       "5.0                      0.345092        0.375076 -0.029983\n",
       "6.0                      0.349637        0.384341 -0.034704\n",
       "7.0                      0.343062        0.365232 -0.022170"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_eval_df = eval_df \\\n",
    ".groupby('Number_Of_Topics')[['Lemma_Coherence', 'Stem_Coherence']] \\\n",
    ".agg('mean')\n",
    "\n",
    "avg_eval_df['diff'] = avg_eval_df['Lemma_Coherence'] - avg_eval_df['Stem_Coherence']\n",
    "avg_eval_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmed_text = []\n",
    "\n",
    "for row in class_data.iterrows():\n",
    "    stem_val = str(row[1]['Stem_Processed'])\n",
    "    stemmed_text.append(re.split(r'\\s+', stem_val))\n",
    "        \n",
    "# creating dictionaries\n",
    "stem_dictionary = corpora.Dictionary(stemmed_text)\n",
    "\n",
    "# creating corpuses\n",
    "stem_corpus = [stem_dictionary.doc2bow(stem) for stem in stemmed_text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model\n",
    "model2 = models.ldamodel.LdaModel(corpus=stem_corpus, id2word=stem_dictionary, num_topics=2, passes=50, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model Anny\n",
    "model6 = models.ldamodel.LdaModel(corpus=stem_corpus, id2word=stem_dictionary, num_topics=6, passes=50, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model Anny\n",
    "model7 = models.ldamodel.LdaModel(corpus=stem_corpus, id2word=stem_dictionary, num_topics=7, passes=50, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim as gensimvis\n",
    "\n",
    "vis_data = gensimvis.prepare(model7, stem_corpus, stem_dictionary)\n",
    "#pyLDAvis.display(vis_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
       "<div id=\"ldavis_el301381400956603015529646476980\"></div>\n",
       "<script type=\"text/javascript\">\n",
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       "\n",
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       "       new LDAvis(\"#\" + \"ldavis_el301381400956603015529646476980\", ldavis_el301381400956603015529646476980_data);\n",
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       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el301381400956603015529646476980\", ldavis_el301381400956603015529646476980_data);\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 18,
     "metadata": {},
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   "source": [
    "pyLDAvis.display(vis_data)"
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   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
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       "\n",
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       "       new LDAvis(\"#\" + \"ldavis_el301381400955664053289786769096\", ldavis_el301381400955664053289786769096_data);\n",
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       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el301381400955664053289786769096\", ldavis_el301381400955664053289786769096_data);\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data6 = gensimvis.prepare(model6, stem_corpus, stem_dictionary)\n",
    "pyLDAvis.display(vis_data6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
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\"hadoop\", \"hive\", \"ident\", \"includ\", \"includ\", \"introduc\", \"introduc\", \"key\", \"knowledg\", \"knowledg\", \"kubernet\", \"languag\", \"languag\", \"learn\", \"learn\", \"linux\", \"listen\", \"machin\", \"machin\", \"major\", \"manag\", \"manag\", \"manipul\", \"mapreduc\", \"materi\", \"model\", \"model\", \"move\", \"move\", \"need\", \"need\", \"network\", \"network\", \"neural\", \"neural\", \"new\", \"new\", \"nosql\", \"pass\", \"perform\", \"perform\", \"platform\", \"practic\", \"practic\", \"predict\", \"predict\", \"principl\", \"program\", \"program\", \"provid\", \"provid\", \"qualifi\", \"r\", \"r\", \"regress\", \"regress\", \"reproduc\", \"requisit\", \"scalabl\", \"secur\", \"secur\", \"servic\", \"servic\", \"slalom\", \"slalom\", \"spark\", \"specialti\", \"sql\", \"storag\", \"stori\", \"stream\", \"student\", \"student\", \"style\", \"success\", \"system\", \"system\", \"technolog\", \"text\", \"text\", \"tool\", \"tool\", \"understand\", \"understand\", \"use\", \"use\", \"valu\", \"visual\", \"visual\", \"way\", \"way\", \"week\", \"week\", \"wide\", \"without\", \"work\", \"work\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
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       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el301381400955665053281960165342\", ldavis_el301381400955665053281960165342_data);\n",
       "   }(LDAvis);\n",
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       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el301381400955665053281960165342\", ldavis_el301381400955665053281960165342_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el301381400955665053281960165342\", ldavis_el301381400955665053281960165342_data);\n",
       "            })\n",
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       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data2 = gensimvis.prepare(model2, stem_corpus, stem_dictionary)\n",
    "pyLDAvis.display(vis_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.046*\"data\" + 0.020*\"cours\" + 0.016*\"use\" + 0.016*\"learn\" + 0.015*\"big\"'),\n",
       " (1,\n",
       "  '0.044*\"aw\" + 0.037*\"cours\" + 0.017*\"exam\" + 0.015*\"certif\" + 0.012*\"provid\"'),\n",
       " (2,\n",
       "  '0.024*\"cours\" + 0.021*\"learn\" + 0.021*\"applic\" + 0.019*\"deep\" + 0.015*\"build\"'),\n",
       " (3,\n",
       "  '0.051*\"learn\" + 0.026*\"machin\" + 0.016*\"understand\" + 0.015*\"cours\" + 0.011*\"practic\"'),\n",
       " (4,\n",
       "  '0.019*\"use\" + 0.015*\"system\" + 0.014*\"perform\" + 0.013*\"feedback\" + 0.011*\"comput\"'),\n",
       " (5,\n",
       "  '0.037*\"cloud\" + 0.032*\"cours\" + 0.018*\"servic\" + 0.015*\"comput\" + 0.014*\"aw\"'),\n",
       " (6,\n",
       "  '0.048*\"googl\" + 0.043*\"data\" + 0.041*\"cloud\" + 0.023*\"platform\" + 0.021*\"cours\"')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model7.print_topics(num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing to file with single column for use with comprehend\n",
    "\n",
    "from io import StringIO\n",
    "s3_client = boto3.client('s3')\n",
    "\n",
    "output_data = class_data['Processed']\n",
    "output_data_buffer = StringIO(output_data.to_csv(header = False, index = False))\n",
    "s3_client.put_object(Bucket='ml-personalization', Key='data/comprehend_topic_data/comprehend_data.csv', Body=output_data_buffer.read(), StorageClass='STANDARD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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