gensimNltkSpacy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/rajendra/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk; nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "# Gensim\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# spacy for lemmatization\n",
    "import spacy\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Enable logging for gensim - optional\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NLTK Stop words\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rec.autos' 'comp.sys.mac.hardware' 'rec.motorcycles' 'misc.forsale'\n",
      " 'comp.os.ms-windows.misc' 'alt.atheism' 'comp.graphics'\n",
      " 'rec.sport.baseball' 'rec.sport.hockey' 'sci.electronics' 'sci.space'\n",
      " 'talk.politics.misc' 'sci.med' 'talk.politics.mideast'\n",
      " 'soc.religion.christian' 'comp.windows.x' 'comp.sys.ibm.pc.hardware'\n",
      " 'talk.politics.guns' 'talk.religion.misc' 'sci.crypt']\n"
     ]
    },
    {
     "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>content</th>\n",
       "      <th>target</th>\n",
       "      <th>target_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: lerxst@wam.umd.edu (where's my thing)\\nS...</td>\n",
       "      <td>7</td>\n",
       "      <td>rec.autos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: guykuo@carson.u.washington.edu (Guy Kuo)...</td>\n",
       "      <td>4</td>\n",
       "      <td>comp.sys.mac.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>From: irwin@cmptrc.lonestar.org (Irwin Arnstei...</td>\n",
       "      <td>8</td>\n",
       "      <td>rec.motorcycles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>From: tchen@magnus.acs.ohio-state.edu (Tsung-K...</td>\n",
       "      <td>6</td>\n",
       "      <td>misc.forsale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>From: dabl2@nlm.nih.gov (Don A.B. Lindbergh)\\n...</td>\n",
       "      <td>2</td>\n",
       "      <td>comp.os.ms-windows.misc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                content  target  \\\n",
       "0     From: lerxst@wam.umd.edu (where's my thing)\\nS...       7   \n",
       "1     From: guykuo@carson.u.washington.edu (Guy Kuo)...       4   \n",
       "10    From: irwin@cmptrc.lonestar.org (Irwin Arnstei...       8   \n",
       "100   From: tchen@magnus.acs.ohio-state.edu (Tsung-K...       6   \n",
       "1000  From: dabl2@nlm.nih.gov (Don A.B. Lindbergh)\\n...       2   \n",
       "\n",
       "                 target_names  \n",
       "0                   rec.autos  \n",
       "1       comp.sys.mac.hardware  \n",
       "10            rec.motorcycles  \n",
       "100              misc.forsale  \n",
       "1000  comp.os.ms-windows.misc  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "df = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json')\n",
    "print(df.target_names.unique())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['From: (wheres my thing) Subject: WHAT car is this!? Nntp-Posting-Host: '\n",
      " 'rac3.wam.umd.edu Organization: University of Maryland, College Park Lines: '\n",
      " '15 I was wondering if anyone out there could enlighten me on this car I saw '\n",
      " 'the other day. It was a 2-door sports car, looked to be from the late 60s/ '\n",
      " 'early 70s. It was called a Bricklin. The doors were really small. In '\n",
      " 'addition, the front bumper was separate from the rest of the body. This is '\n",
      " 'all I know. If anyone can tellme a model name, engine specs, years of '\n",
      " 'production, where this car is made, history, or whatever info you have on '\n",
      " 'this funky looking car, please e-mail. Thanks, - IL ---- brought to you by '\n",
      " 'your neighborhood Lerxst ---- ']\n"
     ]
    }
   ],
   "source": [
    "# Convert to list\n",
    "data = df.content.values.tolist()\n",
    "\n",
    "# Remove Emails\n",
    "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]\n",
    "\n",
    "# Remove new line characters\n",
    "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
    "\n",
    "# Remove distracting single quotes\n",
    "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
    "\n",
    "pprint(data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp', 'posting', 'host', 'rac', 'wam', 'umd', 'edu', 'organization', 'university', 'of', 'maryland', 'college', 'park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front', 'bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']]\n"
     ]
    }
   ],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/gensim/models/phrases.py:494: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp_posting_host', 'rac_wam_umd_edu', 'organization', 'university', 'of', 'maryland_college_park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front_bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']\n"
     ]
    }
   ],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "# See trigram example\n",
    "print(trigram_mod[bigram_mod[data_words[0]]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "\n",
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\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_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['where', 's', 'thing', 'car', 'nntp_post', 'host', 'rac_wam', 'umd', 'organization', 'university', 'maryland_college', 'park', 'line', 'wonder', 'anyone', 'could', 'enlighten', 'car', 'see', 'day', 'door', 'sport', 'car', 'look', 'late', 'early', 'call', 'bricklin', 'door', 'really', 'small', 'addition', 'front_bumper', 'separate', 'rest', 'body', 'know', 'anyone', 'tellme', 'model', 'name', 'engine', 'spec', 'year', 'production', 'car', 'make', 'history', 'whatev', 'info', 'funky', 'look', 'car', 'mail', 'thank', 'bring', 'neighborhood', 'lerxst']]\n"
     ]
    }
   ],
   "source": [
    "# Remove Stop Words\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 5), (7, 1), (8, 1), (9, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 2), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1), (42, 1), (43, 1), (44, 1), (45, 1), (46, 1), (47, 1), (48, 1), (49, 1), (50, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'addition'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('addition', 1),\n",
       "  ('anyone', 2),\n",
       "  ('body', 1),\n",
       "  ('bricklin', 1),\n",
       "  ('bring', 1),\n",
       "  ('call', 1),\n",
       "  ('car', 5),\n",
       "  ('could', 1),\n",
       "  ('day', 1),\n",
       "  ('door', 2),\n",
       "  ('early', 1),\n",
       "  ('engine', 1),\n",
       "  ('enlighten', 1),\n",
       "  ('front_bumper', 1),\n",
       "  ('funky', 1),\n",
       "  ('history', 1),\n",
       "  ('host', 1),\n",
       "  ('info', 1),\n",
       "  ('know', 1),\n",
       "  ('late', 1),\n",
       "  ('lerxst', 1),\n",
       "  ('line', 1),\n",
       "  ('look', 2),\n",
       "  ('mail', 1),\n",
       "  ('make', 1),\n",
       "  ('maryland_college', 1),\n",
       "  ('model', 1),\n",
       "  ('name', 1),\n",
       "  ('neighborhood', 1),\n",
       "  ('nntp_post', 1),\n",
       "  ('organization', 1),\n",
       "  ('park', 1),\n",
       "  ('production', 1),\n",
       "  ('rac_wam', 1),\n",
       "  ('really', 1),\n",
       "  ('rest', 1),\n",
       "  ('s', 1),\n",
       "  ('see', 1),\n",
       "  ('separate', 1),\n",
       "  ('small', 1),\n",
       "  ('spec', 1),\n",
       "  ('sport', 1),\n",
       "  ('tellme', 1),\n",
       "  ('thank', 1),\n",
       "  ('thing', 1),\n",
       "  ('umd', 1),\n",
       "  ('university', 1),\n",
       "  ('whatev', 1),\n",
       "  ('where', 1),\n",
       "  ('wonder', 1),\n",
       "  ('year', 1)]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=20, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "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=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download File: http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
    "mallet_path = 'path/to/mallet-2.0.8/bin/mallet' # update this path\n",
    "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=20, id2word=id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show Topics\n",
    "pprint(ldamallet.show_topics(formatted=False))\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_ldamallet = coherence_model_ldamallet.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_ldamallet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can take a long time to run.\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show graph\n",
    "limit=40; start=2; step=6;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the coherence scores\n",
    "for m, cv in zip(x, coherence_values):\n",
    "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the model and print the topics\n",
    "optimal_model = model_list[3]\n",
    "model_topics = optimal_model.show_topics(formatted=False)\n",
    "pprint(optimal_model.print_topics(num_words=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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