pre_processing_abstracts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/rgomes/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "#Importing the dependencies\n",
    "import re, string, unicodedata, itertools, random, os, glob, math, time, json\n",
    "import pickle\n",
    "import nltk\n",
    "from nltk import SnowballStemmer\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "from scipy.spatial import distance_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer, CountVectorizer\n",
    "\n",
    "from __future__ import print_function\n",
    "print(__doc__)\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "plt.rcParams['figure.figsize'] = (24, 9)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_non_ascii(words):\n",
    "    \"\"\"Remove non-ASCII characters from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
    "        new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def to_lowercase(words):\n",
    "    \"\"\"Convert all characters to lowercase from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = word.lower()\n",
    "        new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def remove_punctuation(words):\n",
    "    \"\"\"Remove punctuation from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = re.sub(r'[^\\w\\s]', '', word)\n",
    "        if new_word != '':\n",
    "            new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def replace_numbers(words):\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = re.sub(r'[0-9]', '', word)\n",
    "        if new_word != '':\n",
    "            new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def remove_stopwords(words):\n",
    "    \"\"\"Remove stop words from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        if word not in stopwords.words('english'):\n",
    "            new_words.append(word)\n",
    "    return new_words\n",
    "\n",
    "def stemming(words):\n",
    "    \"\"\"Reduce words to radical\"\"\"\n",
    "    stemmer = SnowballStemmer('english')\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = stemmer.stem(word)\n",
    "        new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def trimAndClear(words):    \n",
    "    \"\"\"Remove words with only 2 letters, whitespace and empty\"\"\"\n",
    "    new_words = map(lambda w : w.strip(), words)\n",
    "    new_words = list(filter(lambda w: w != '' and len(w) > 2, new_words))\n",
    "    return new_words\n",
    "\n",
    "def normalize(words):\n",
    "    words = remove_non_ascii(words)\n",
    "    words = to_lowercase(words)\n",
    "    words = remove_punctuation(words)\n",
    "    words = replace_numbers(words)\n",
    "    words = remove_stopwords(words)\n",
    "    words = stemming(words)\n",
    "    words = trimAndClear(words)\n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ./20_news/Process+Mining+Abstracts.txt\n",
      "Saving 5746 unique words\n",
      "saved...\n",
      "Saving 1387 documents\n",
      "saved...\n"
     ]
    }
   ],
   "source": [
    "filename = './Process+Mining+Abstracts.txt'\n",
    "\n",
    "def loadCorpus():\n",
    "    voc = set()\n",
    "    docs = []\n",
    "       \n",
    "    with open(filename, 'rb') as f:\n",
    "        print('Loading', filename)\n",
    "\n",
    "        for line in f:\n",
    "            # readline\n",
    "            line = line.decode(\"utf-8\", \"ignore\")\n",
    "            \n",
    "            # read the email\n",
    "            tokens = line.split(' ')\n",
    "            \n",
    "            words = normalize(tokens)\n",
    "            if len(words) > 1:\n",
    "                voc.update(words)\n",
    "                text = ' '.join(words)\n",
    "                docs.append(text)\n",
    "\n",
    "    # save as pickle\n",
    "    print('Saving', len(voc), 'unique words')\n",
    "    tokens_file = open('./representations/tokens_vector_abstracts.pkl', 'wb')\n",
    "    pickle.dump(list(voc), tokens_file)\n",
    "    print('saved...')\n",
    "    \n",
    "    print('Saving', len(docs), 'documents')\n",
    "    d_file = open('./representations/documents_abstracts.pkl', 'wb')\n",
    "    pickle.dump(list(docs), d_file)\n",
    "    print('saved...')\n",
    "    \n",
    "loadCorpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5746 tokens loaded\n",
      "1387 tokens loaded\n"
     ]
    }
   ],
   "source": [
    "data_f = open('./representations/tokens_vector_abstracts.pkl', 'rb')\n",
    "voc = pickle.load(data_f)\n",
    "print(len(voc), 'tokens loaded')\n",
    "\n",
    "data_f = open('./representations/documents_abstracts.pkl', 'rb')\n",
    "docs = pickle.load(data_f)\n",
    "print(len(docs), 'tokens loaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tf(term, document):\n",
    "    return freq(term, document)\n",
    "\n",
    "def freq(term, document):\n",
    "    return document.split().count(term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating tf matrix...\n",
      "(1387, 5746)\n",
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     ]
    }
   ],
   "source": [
    "tf_matrix = []\n",
    "\n",
    "print('calculating tf matrix...')\n",
    "for doc in docs:\n",
    "    \n",
    "    #print('The doc is \"' + doc + '\"')\n",
    "    tf_vector = [tf(word, doc) for word in voc]\n",
    "    #tf_vector_string = ', '.join(format(freq, 'd') for freq in tf_vector)\n",
    "    #print('The tf vector for Document %d is [%s]' % ((docs.index(doc)+1), tf_vector_string))\n",
    "    tf_matrix.append(tf_vector)\n",
    "    \n",
    "print(np.shape(tf_matrix))\n",
    "print(tf_matrix[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving tf matrix...\n",
      "saved\n"
     ]
    }
   ],
   "source": [
    "print('saving tf matrix...')\n",
    "np.savetxt('./representations/tf_matrix_abstracts.txt', tf_matrix)\n",
    "print('saved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def l2_normalizer(vec):\n",
    "    denom = np.sum([el**2 for el in vec])\n",
    "    return [(el / math.sqrt(denom)) for el in vec]\n",
    "\n",
    "doc_term_matrix_l2 = []\n",
    "for vec in tf_matrix:\n",
    "    doc_term_matrix_l2.append(l2_normalizer(vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "saving tf matrix normalized...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved\n"
     ]
    }
   ],
   "source": [
    "print(doc_term_matrix_l2[0])\n",
    "print('saving tf matrix normalized...')\n",
    "np.savetxt('./representations/tf_matrix_normalized_abstracts.txt', doc_term_matrix_l2)\n",
    "print('saved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numDocsContaining(word, doclist):\n",
    "    doccount = 0\n",
    "    for doc in doclist:\n",
    "        if freq(word, doc) > 0:\n",
    "            doccount +=1\n",
    "    return doccount \n",
    "\n",
    "def idf(word, doclist):\n",
    "    n_samples = len(doclist)\n",
    "    df = numDocsContaining(word, doclist)\n",
    "    return np.log(n_samples / 1+df)\n",
    "\n",
    "my_idf_vector = [idf(word, docs) for word in voc]\n",
    "#print(my_idf_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_idf_matrix(idf_vector):\n",
    "    idf_mat = np.zeros((len(idf_vector), len(idf_vector)))\n",
    "    np.fill_diagonal(idf_mat, idf_vector)\n",
    "    return idf_mat\n",
    "\n",
    "my_idf_matrix = build_idf_matrix(my_idf_vector)\n",
    "#print(my_idf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1387, 5746)\n",
      "saving tf matrix normalized...\n",
      "saved\n"
     ]
    }
   ],
   "source": [
    "doc_term_matrix_tfidf = []\n",
    "\n",
    "#performing tf-idf matrix multiplication\n",
    "for tf_vector in tf_matrix:\n",
    "    doc_term_matrix_tfidf.append(np.dot(tf_vector, my_idf_matrix))\n",
    "\n",
    "#normalizing\n",
    "doc_term_matrix_tfidf_l2 = []\n",
    "for tf_vector in doc_term_matrix_tfidf:\n",
    "    doc_term_matrix_tfidf_l2.append(l2_normalizer(tf_vector))\n",
    "                                    \n",
    "print(np.shape(np.matrix(doc_term_matrix_tfidf_l2)))\n",
    "print('saving tf matrix normalized...')\n",
    "np.savetxt('./representations/tfidf_matrix_abstracts.txt', doc_term_matrix_l2)\n",
    "print('saved')"
   ]
  }
 ],
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