LDAmodelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReturnTokens(object):\n",
    "    def __init__(self, dir_path):\n",
    "        self.dir_path = dir_path\n",
    "\n",
    "    def __iter__(self):\n",
    "        for file_name in os.listdir(self.dir_path):\n",
    "            if file_name[-4:]==\".txt\":\n",
    "                with open(os.path.join(self.dir_path, file_name), encoding='utf-8') as fp:\n",
    "                    text = fp.read()\n",
    "                    yield(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up dependencies and load the data\n",
    "Note: I set up two docs to play with different ways to tokenize/limit the tokens\n",
    "Also, there is a path to the Vowpal Wabbit binary/executable that will have to be changed (I don't know if we will use it but it might be useful to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "unable to import 'smart_open.gcs', disabling that module\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.wrappers import LdaVowpalWabbit\n",
    "from gensim.parsing.preprocessing import preprocess_string\n",
    "from gensim import corpora\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim.parsing.preprocessing import preprocess_string, strip_multiple_whitespaces, strip_numeric, remove_stopwords,strip_short, stem_text, strip_punctuation\n",
    "import gensim\n",
    "from pprint import pprint\n",
    "\n",
    "from smart_open import smart_open\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to SQL...\n"
     ]
    }
   ],
   "source": [
    "import pymysql\n",
    "\n",
    "print(\"Connecting to SQL...\")\n",
    "query = \"\"\"\n",
    "    SELECT\n",
    "        body as text\n",
    "    FROM clean_docs\n",
    "    WHERE\n",
    "        body IS NOT NULL\n",
    "        AND body != ''\n",
    "    ;\n",
    "    \"\"\"\n",
    "con = pymysql.connect(\n",
    "    host=\"127.0.0.1\",\n",
    "    port=3306,\n",
    "    user='dbuser',\n",
    "    password='dbuserdbuser',\n",
    "    db='PDB',\n",
    "    charset='utf8mb4',\n",
    "    cursorclass=pymysql.cursors.DictCursor\n",
    ")\n",
    "cur = con.cursor()\n",
    "cur.execute(query)\n",
    "result = cur.fetchall()\n",
    "\n",
    "docs = []\n",
    "for item in result:\n",
    "    docs.append(item['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenizing data\n",
    "## Once using NLTK\n",
    "## And once with gensim built in features\n",
    "Note: With the gensim built in features, I ignore stemming for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize the documents.\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "\n",
    "# Split the documents into tokens.\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "\n",
    "for idx in range(len(docs)):\n",
    "    docs[idx] = docs[idx].lower()  # Convert to lowercase.\n",
    "    docs[idx] = tokenizer.tokenize(docs[idx])  # Split into words.\n",
    "\n",
    "# Remove numbers, but not words that contain numbers.\n",
    "docs = [[token for token in doc if not token.isnumeric()] for doc in docs]\n",
    "\n",
    "# Remove words that are only one character.\n",
    "docs = [[token for token in doc if len(token) > 1] for doc in docs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize documents.\n",
    "from gensim.parsing.preprocessing import preprocess_string, strip_multiple_whitespaces, strip_numeric, remove_stopwords, strip_short, stem_text, strip_punctuation\n",
    "\n",
    "CUSTOM_FILTERS = [lambda x: x.lower(), strip_punctuation, strip_multiple_whitespaces, strip_numeric, remove_stopwords, strip_short]\n",
    "docs = [preprocess_string(doc, CUSTOM_FILTERS) for doc in docs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to only keep NOUNS, ADJ, VERB, ADV\n",
    "from spacy.lang.en import English\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    nlp = English()\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ not in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = lemmatization(docs, allowed_postags=['NOUN','ADJ', 'VERB', 'ADV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet\n",
    "def get_wordnet_pos(word):\n",
    "    \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n",
    "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "    tag_dict = {\"J\": wordnet.ADJ,\n",
    "                \"N\": wordnet.NOUN,\n",
    "                \"V\": wordnet.VERB,\n",
    "                \"R\": wordnet.ADV}\n",
    "\n",
    "    return tag_dict.get(tag, wordnet.NOUN)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/carriehaykellar/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Lemmatize the documents.\n",
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "docs = [[lemmatizer.lemmatize(token, get_wordnet_pos(token)) for token in doc] for doc in docs]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tagged = [[nltk.pos_tag(token) for token in doc] for doc in docs]\n",
    "tagged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute bigrams.\n",
    "from gensim.models import Phrases\n",
    "\n",
    "# Add bigrams and trigrams to docs (only ones that appear 20 times or more).\n",
    "bigram = Phrases(docs, min_count=20)\n",
    "for idx in range(len(docs)):\n",
    "    for token in bigram[docs[idx]]:\n",
    "        if '_' in token:\n",
    "            # Token is a bigram, add to document.\n",
    "            docs[idx].append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rare and common tokens.\n",
    "from gensim.corpora import Dictionary\n",
    "\n",
    "# Create a dictionary representation of the documents.\n",
    "dictionary = Dictionary(docs)\n",
    "\n",
    "# Filter out words that occur less than 50 documents, or more than 40% of the documents.\n",
    "dictionary.filter_extremes(no_below=50, no_above=0.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bag-of-words representation of the documents.\n",
    "corpus = [dictionary.doc2bow(doc) for doc in docs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 5016\n",
      "Number of documents: 5010\n"
     ]
    }
   ],
   "source": [
    "#Making sure the documents and number of tokens seem okay\n",
    "print('Number of unique tokens: %d' % len(dictionary))\n",
    "print('Number of documents: %d' % len(corpus))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running with Gensium LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running LDA Gensium...\n"
     ]
    }
   ],
   "source": [
    "print(\"Running LDA Gensium...\")\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=20, id2word=dictionary, eta='auto', alpha='asymmetric', passes=20, per_word_topics=True, eval_every=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics_per_document = lda_model.get_document_topics(corpus,per_word_topics=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  0.16793247],\n",
       " ...]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_topics_per_document = []\n",
    "for t in topics_per_document:\n",
    "    list_topics_per_document.append([item for lt in t for item in lt])\n",
    "\n",
    "list_topics_per_document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics_per_document = pd.DataFrame(data=list_topics_per_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.152214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031269</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.049211</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.083600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.065483</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.492628</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5010 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0         1    2         3     4         5     6         7     8   \\\n",
       "0      0  0.085037  1.0  0.137922   3.0  0.121501  11.0  0.402289  12.0   \n",
       "1      1  0.188264  5.0  0.242467   6.0  0.165554  12.0  0.095233  17.0   \n",
       "2      0  0.142166  1.0  0.183988   7.0  0.125987   8.0  0.065542   9.0   \n",
       "3      4  0.014155  5.0  0.041651   6.0  0.054087   7.0  0.111524  15.0   \n",
       "4      0  0.120961  5.0  0.247828   6.0  0.079094  14.0  0.550415   NaN   \n",
       "...   ..       ...  ...       ...   ...       ...   ...       ...   ...   \n",
       "5005   5  0.081483  6.0  0.366744   8.0  0.027917  11.0  0.190710  12.0   \n",
       "5006   6  0.048306  7.0  0.663529  11.0  0.223745  17.0  0.060682   NaN   \n",
       "5007   0  0.077557  7.0  0.737540   8.0  0.062596  12.0  0.037025  17.0   \n",
       "5008   6  0.066857  7.0  0.702879  15.0  0.075581  17.0  0.152214   NaN   \n",
       "5009   0  0.031269  3.0  0.049211   5.0  0.083600   6.0  0.065483  11.0   \n",
       "\n",
       "            9   ...  30  31  32  33  34  35  36  37  38  39  \n",
       "0     0.027714  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "1     0.307223  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "2     0.033774  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "3     0.607133  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "4          NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "...        ...  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
       "5005  0.021429  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "5006       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "5007  0.082232  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "5008       NaN  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "5009  0.492628  ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[5010 rows x 40 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topics_per_document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('mysql://dbuser:dbuserdbuser@localhost:3306/PDB', echo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics_per_document.to_sql('topics5_per_doc', con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics = lda_model.show_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.011*\"tha\" + 0.010*\"eee\" + 0.008*\"tan\" + 0.008*\"test\" + 0.008*\"far\" + 0.007*\"algeria\" + 0.005*\"guinea\" + 0.005*\"ben\"'),\n",
       " (1,\n",
       "  '0.017*\"lebanon\" + 0.014*\"continued\" + 0.013*\"continued_president\" + 0.012*\"syrian\" + 0.009*\"angola\" + 0.009*\"palestinian\" + 0.009*\"christian\" + 0.008*\"page\"'),\n",
       " (2,\n",
       "  '0.028*\"pakistan\" + 0.024*\"india\" + 0.017*\"indian\" + 0.014*\"pakistani\" + 0.011*\"page\" + 0.009*\"east\" + 0.006*\"bangladesh\" + 0.005*\"delhi\"'),\n",
       " (3,\n",
       "  '0.022*\"vietnamese\" + 0.013*\"north_vietnamese\" + 0.012*\"enemy\" + 0.011*\"page\" + 0.010*\"south_vietnam\" + 0.009*\"unit\" + 0.009*\"province\" + 0.009*\"lao\"'),\n",
       " (4,\n",
       "  '0.009*\"east\" + 0.008*\"corp\" + 0.008*\"major\" + 0.007*\"problem\" + 0.007*\"europe\" + 0.006*\"important\" + 0.006*\"ninh\" + 0.006*\"affair\"'),\n",
       " (5,\n",
       "  '0.042*\"israeli\" + 0.032*\"arab\" + 0.031*\"israel\" + 0.018*\"egyptian\" + 0.016*\"egypt\" + 0.015*\"fedayeen\" + 0.014*\"jordan\" + 0.013*\"syria\"'),\n",
       " (6,\n",
       "  '0.014*\"cuba\" + 0.010*\"cont\" + 0.008*\"president_secret\" + 0.007*\"cuban\" + 0.005*\"castro\" + 0.005*\"sox\" + 0.005*\"moscow\" + 0.004*\"chinese\"'),\n",
       " (7,\n",
       "  '0.011*\"oil\" + 0.009*\"page\" + 0.006*\"economic\" + 0.006*\"price\" + 0.006*\"european\" + 0.006*\"increase\" + 0.005*\"percent\" + 0.005*\"trade\"'),\n",
       " (8,\n",
       "  '0.014*\"souvanna\" + 0.013*\"lao\" + 0.010*\"berlin\" + 0.008*\"phoumi\" + 0.007*\"west\" + 0.006*\"action\" + 0.006*\"congo\" + 0.005*\"ambassador\"'),\n",
       " (9,\n",
       "  '0.017*\"talk\" + 0.016*\"hanoi\" + 0.016*\"vietnamese\" + 0.013*\"moscow\" + 0.010*\"statement\" + 0.008*\"north_vietnamese\" + 0.007*\"peking\" + 0.007*\"war\"'),\n",
       " (10,\n",
       "  '0.036*\"west\" + 0.028*\"german\" + 0.026*\"germany\" + 0.022*\"east\" + 0.015*\"west_germany\" + 0.014*\"berlin\" + 0.012*\"bonn\" + 0.012*\"west_german\"'),\n",
       " (11,\n",
       "  '0.143*\"lao\" + 0.047*\"plaine\" + 0.036*\"pathet\" + 0.035*\"de\" + 0.033*\"muong\" + 0.033*\"pathet_lao\" + 0.032*\"ban\" + 0.032*\"jarres\"'),\n",
       " (12,\n",
       "  '0.025*\"cambodia\" + 0.018*\"phnom\" + 0.018*\"penh\" + 0.017*\"phnom_penh\" + 0.016*\"cambodian\" + 0.015*\"page\" + 0.011*\"kompong\" + 0.010*\"lon\"'),\n",
       " (13,\n",
       "  '0.023*\"hanoi\" + 0.019*\"vietnamese\" + 0.015*\"north_vietnamese\" + 0.010*\"war\" + 0.008*\"special\" + 0.008*\"broadcast\" + 0.008*\"eye\" + 0.007*\"report_north\"'),\n",
       " (14,\n",
       "  '0.031*\"million\" + 0.024*\"ton\" + 0.018*\"grain\" + 0.013*\"percent\" + 0.011*\"crop\" + 0.011*\"harvest\" + 0.011*\"million_ton\" + 0.011*\"import\"'),\n",
       " (15,\n",
       "  '0.018*\"chinese\" + 0.016*\"missile\" + 0.009*\"peking\" + 0.008*\"photography\" + 0.007*\"construction\" + 0.007*\"launch\" + 0.007*\"mao\" + 0.006*\"submarine\"'),\n",
       " (16,\n",
       "  '0.009*\"prime\" + 0.008*\"page\" + 0.008*\"prime_minister\" + 0.006*\"election\" + 0.006*\"portugal\" + 0.006*\"cabinet\" + 0.005*\"socialist\" + 0.005*\"national\"'),\n",
       " (17,\n",
       "  '0.011*\"congo\" + 0.010*\"rebel\" + 0.009*\"cyprus\" + 0.009*\"boundary\" + 0.008*\"cont\" + 0.008*\"greek\" + 0.007*\"cypriot\" + 0.007*\"viet\"'),\n",
       " (18,\n",
       "  '0.015*\"turkish\" + 0.014*\"greek\" + 0.012*\"cyprus\" + 0.011*\"page\" + 0.009*\"cypriot\" + 0.009*\"egypt\" + 0.009*\"turkey\" + 0.007*\"egyptian\"'),\n",
       " (19,\n",
       "  '0.008*\"today\" + 0.005*\"army\" + 0.005*\"south_vietnam\" + 0.005*\"dominican\" + 0.005*\"union\" + 0.004*\"election\" + 0.004*\"strike\" + 0.004*\"come\"')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_model.print_topics(20, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vary num of topics\n",
    "import time\n",
    "models2a = []\n",
    "for k in [10, 20, 40, 60]:\n",
    "    t1 = time.time()\n",
    "    models2a.append(gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=k, id2word=dictionary, eta='auto', alpha='asymmetric', passes=2, per_word_topics=True))\n",
    "    print(k)\n",
    "    t2 = time.time()\n",
    "    print(\"Model time: \", t2-t1)\n",
    "for m in models2a:\n",
    "    t3 = time.time()\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.num_topics, \n",
    "              m.log_perplexity(corpus),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))\n",
    "    t4 = time.time()\n",
    "    print(\"Coherence Score Time: \", t4-t3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in models2a:\n",
    "    t3 = time.time()\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.passes, \n",
    "              m.log_perplexity(corpus),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))\n",
    "    t4 = time.time()\n",
    "    print(\"Coherence Score Time: \", t4-t3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vary num of passes\n",
    "import time\n",
    "models2a = []\n",
    "for k in [5, 10, 15, 20, 25,30]:\n",
    "    t1 = time.time()\n",
    "    models2a.append(gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=20, id2word=dictionary, eta='auto', alpha='asymmetric', passes=k, per_word_topics=True))\n",
    "    print(k)\n",
    "    t2 = time.time()\n",
    "    print(\"Model time: \", t2-t1)\n",
    "for m in models2a:\n",
    "    t3 = time.time()\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.passes, \n",
    "              m.log_perplexity(corpus),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))\n",
    "    t4 = time.time()\n",
    "    print(\"Coherence Score Time: \", t4-t3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in models2a:\n",
    "    t3 = time.time()\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.passes, \n",
    "              m.log_perplexity(corpus),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))\n",
    "    t4 = time.time()\n",
    "    print(\"Coherence Score Time: \", t4-t3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running with Mallet Wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Running LDA Mallet...\")\n",
    "mallet_path = '/Users/carriehaykellar/Downloads/mallet-2.0.8/bin/mallet'\n",
    "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=5, id2word=dictionary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ldamallet.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_topics_sentences(ldamodel, corpus, texts):\n",
    "    # Init output\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row in enumerate(ldamodel[corpus]):\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:  # => dominant topic\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "\n",
    "    # Add original text to the end of the output\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    \n",
    "    return(sent_topics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topic_sents_keywords = format_topics_sentences(ldamallet, corpus, docs)\n",
    "\n",
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "\n",
    "# Show\n",
    "df_dominant_topic.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changes list to string s.t. it can import into sql\n",
    "df_dominant_topic['Text'] = [df_dominant_topic['Text'].map(str, l) for l in df_dominant_topic['Text']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('mysql://dbuser:dbuserdbuser@localhost:3306/PDB', echo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dominant_topic.to_sql('topic_per_doc', con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "c_v, = plt.plot([10,20,40,60], [0.537, 0.578, 0.515, 0.478], 'b', label='20 passes')\n",
    "u_mass, = plt.plot([10, 20,40,60], [0.314, 0.382, 0.381, 0.377], 'r', label='1 pass')\n",
    "two, = plt.plot([10, 20,40,60], [0.451, 0.497, 0.467, 0.403], 'g', label='2 passes')\n",
    "plt.ylabel('Coherence')\n",
    "plt.legend(handles=[c_v,u_mass, two])\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "twenty_x = [10,20,40,60]\n",
    "twenty_y = [0.537, 0.578, 0.515, 0.478]\n",
    "one_x = [10, 20,40,60]\n",
    "one_y = [0.314, 0.382, 0.381, 0.377]\n",
    "two_x = [10, 20,40,60]\n",
    "two_y = [0.451, 0.497, 0.467, 0.403]\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(x=twenty_x, y=twenty_y,\n",
    "                    mode='lines+markers',\n",
    "                    name='20 Passes'))\n",
    "fig.add_trace(go.Scatter(x=one_x, y=one_y,\n",
    "                    mode='lines+markers',\n",
    "                    name='1 Pass'))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=two_x, y=two_y,\n",
    "                    mode='lines+markers',\n",
    "                    name='2 Passes'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"/Users/carriehaykellar/Desktop/Pass_Coherence.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "twenty_x = [5,10, 15,20,25,30]\n",
    "twenty_y = [0.555, 0.571, 0.565, 0.598,0.551, 0.583]\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(x=twenty_x, y=twenty_y,\n",
    "                    mode='lines+markers',\n",
    "                    name=''))\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"/Users/carriehaykellar/Desktop/Topic_Coherence.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot([20,40,60,80,100], [0.549, 0.501, 0.443, 0.413, 0.310])\n",
    "plt.ylabel('c_v coherence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now varying number of passes/iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models2a = []\n",
    "for k in [5, 10, 15, 20, 25,30]:\n",
    "    models2a.append(gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=20, id2word=dictionary, eta='auto', alpha='asymmetric', passes=k, per_word_topics=True))\n",
    "    print(k)\n",
    "for m in models2a:\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.passes, \n",
    "              m.log_perplexity(corpus2),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in models2a:\n",
    "    coherence_model_lda = CoherenceModel(model=m, corpus=corpus, coherence='u_mass')\n",
    "    cm = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "    print('%3d %10.3f %10.3f %10.3f'%(m.passes, \n",
    "              m.log_perplexity(corpus),\n",
    "              coherence_model_lda.get_coherence(),\n",
    "              cm.get_coherence()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can also see the coherence per topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the model and print the topics\n",
    "optimal_model = models2a[2]\n",
    "model_topics = optimal_model.show_topics(formatted=False)\n",
    "#pprint(optimal_model.print_topics(num_words=7))\n",
    "top_topics = optimal_model.top_topics(corpus2, texts=docs2, coherence='c_v') \n",
    "\n",
    "a = [t[1] for t in top_topics]\n",
    "pprint(top_topics)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way to print the top tokens by topic\n",
    "pprint(optimal_model.print_topics(num_words=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the average top topic score (i.e., for each document the topic probability of the top topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = [sorted(optimal_model[corpus2][i][0],key=lambda x: (x[1]), reverse=True)[0][1]\n",
    "    for i in range(len(corpus2)) ]\n",
    "o2 = [sorted(optimal_model[corpus2][i][0],key=lambda x: (x[1]), reverse=True)[1][1]\n",
    "    for i in range(len(corpus2)) ]\n",
    "\n",
    "print(sum(o)/len(o))\n",
    "print(sum(o2)/len(o2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's do the same with 80 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the model and print the topics\n",
    "optimal_model80 = models2[3]\n",
    "model_topics80 = optimal_model80.show_topics(formatted=False)\n",
    "#pprint(optimal_model.print_topics(num_words=7))\n",
    "top_topics80 = optimal_model80.top_topics(corpus2, texts=docs2, coherence='c_v') \n",
    "\n",
    "a80 = [t[1] for t in top_topics80]\n",
    "pprint(top_topics80)\n",
    "print(a80)\n",
    "o80 = [sorted(optimal_model80[corpus2][i][0],key=lambda x: (x[1]), reverse=True)[0][1]\n",
    "    for i in range(len(corpus2)) ]\n",
    "o280 = [sorted(optimal_model80[corpus2][i][0],key=lambda x: (x[1]), reverse=True)[1][1]\n",
    "    for i in range(len(corpus2)) ]\n",
    "\n",
    "print(sum(o80)/len(o80))\n",
    "print(sum(o280)/len(o280))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the topics and tokens\n",
    "This is super-cool but I haven't compared models or anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim\n",
    "import pickle \n",
    "import pyLDAvis\n",
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "LDAvis_prepared = pyLDAvis.gensim.prepare(models2a[2], corpus2, dict2)\n",
    "LDAvis_prepared"
   ]
  }
 ],
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