Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corpus creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import a corpus from the library *nltk*, and we transform it to a DataFrame which is the format we would normally get. We pick 4 categories for simplicity.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adventure</td>\n",
       "      <td>Dan Morgan told himself he would forget Ann Tu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adventure</td>\n",
       "      <td>Gavin paused wearily. ``You can't stay here wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adventure</td>\n",
       "      <td>The sentry was not dead. He was, in fact, show...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adventure</td>\n",
       "      <td>``So it wasn't the earthquake that made him re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adventure</td>\n",
       "      <td>She was carrying a quirt, and she started to r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       class                                               text\n",
       "0  adventure  Dan Morgan told himself he would forget Ann Tu...\n",
       "1  adventure  Gavin paused wearily. ``You can't stay here wi...\n",
       "2  adventure  The sentry was not dead. He was, in fact, show...\n",
       "3  adventure  ``So it wasn't the earthquake that made him re...\n",
       "4  adventure  She was carrying a quirt, and she started to r..."
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "import pandas as pd\n",
    "from nltk.corpus import brown\n",
    "from nltk.tokenize.moses import MosesDetokenizer\n",
    "import importlib\n",
    "import text_processing\n",
    "import warnings\n",
    "\n",
    "# Ommit warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Categorías\n",
    "categorias = ['adventure','editorial','fiction','news']\n",
    "\n",
    "y = []\n",
    "corpus = []\n",
    "for category in categorias:\n",
    "    for fileid in brown.fileids(category):\n",
    "        text = brown.words(fileid)\n",
    "        text = MosesDetokenizer().detokenize(text,return_str=True)\n",
    "        corpus.append(text)\n",
    "        y.append(category)\n",
    "        \n",
    "corpus = pd.DataFrame({'class':y, 'text':corpus})\n",
    "corpus.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corpus processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the *text_processor* function from our module we will perform several tasks on the corpus: \n",
    "- Preprocessing\n",
    "- Stop words treatement\n",
    "- Steeming\n",
    "- BOW (Bag Of Words) transformation\n",
    "- Variable reduction trough PCA and variance filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "    }\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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dan morgan told himself he would forget ann tu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gavin paused wearily you ca nt stay here with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the sentry was not dead he was in fact showing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>so it was nt the earthquake that made him retu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>she was carrying a quirt and she started to ra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text\n",
       "0  dan morgan told himself he would forget ann tu...\n",
       "1  gavin paused wearily you ca nt stay here with ...\n",
       "2  the sentry was not dead he was in fact showing...\n",
       "3  so it was nt the earthquake that made him retu...\n",
       "4  she was carrying a quirt and she started to ra..."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATM = text_processing.text_processor(df = corpus, topic = 'class', text = 'text')\n",
    "ATM.corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dan morgan told would forget ann turner well r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gavin paused wearily ca nt stay late said dawn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sentry dead fact showing signs reviving carryi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nt earthquake made return village dammit nt wa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carrying quirt started raise let fall dangle w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text\n",
       "0  dan morgan told would forget ann turner well r...\n",
       "1  gavin paused wearily ca nt stay late said dawn...\n",
       "2  sentry dead fact showing signs reviving carryi...\n",
       "3  nt earthquake made return village dammit nt wa...\n",
       "4  carrying quirt started raise let fall dangle w..."
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATM.stop_words()\n",
    "ATM.corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dan morgan told would forget ann turner well r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gavin paus wearili ca nt stay late said dawn c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sentri dead fact show sign reviv carri enfield...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nt earthquak made return villag dammit nt want...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>carri quirt start rais let fall dangl wrist sa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text\n",
       "0  dan morgan told would forget ann turner well r...\n",
       "1  gavin paus wearili ca nt stay late said dawn c...\n",
       "2  sentri dead fact show sign reviv carri enfield...\n",
       "3  nt earthquak made return villag dammit nt want...\n",
       "4  carri quirt start rais let fall dangl wrist sa..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATM.steemer()\n",
    "ATM.corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATM.corpus_to_bag_of_words()\n",
    "ATM.bow_corpus.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OTLnFheX/AQAAAMy0xFQN3P1XkmyKNi7pjwtV1Fwxsvz/LQ9s0qtXHRd1OQAAAABK0BGt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\nPbbXGOpYpTKaqDttlUqlsgCQamNWBZq+bPfbLm2kUJoNgM9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bdd5d60f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zvS4NAAAAOC/cXBvjyrHmDv34+X36+YYqtXX16pKibN16aZGuv6BQKUl+r8sD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bde671f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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azG4zszvM7I7YkBckbZa0UdKvJH2xx9KiX7j+rFIFfKbfMdsGAAAAdCnQ1QDn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\nAEBSCEecVu88EJuJq9XSLXU63Bru2B/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bdd4e2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AABiTCG3YL7MnVejuy0/Vp885XH944VW95dqH9djyLVGXBQAAAIw5hDbst3jM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\nQleS9iLg+lxxUqzh3FE3UBknqD/ZLjlgWoFYjVy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GPbB1+ioiaXxjy+dRGQbbZ0q6iYG/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12be244aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ATM.plot_count_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zzo0Q24DgXUbOOTdSrAKCjyE451x+sQoIGfPrEJxzLp9YBYT0oLcQnHMun1gFhEG/dYVz\nzuUVq4CQ9ltXOOdcXrEKCIPmXUbOOZdPvALCoJFMCPDrEJxzLlusAkI6Y5SHz0DwFoJzzo0Uq4CQ\nMaO4KEFRQj6G4JxzWWIVENIZIyGRKkp4C8E557LEKiAMDhpFCVHiAcE550aJVUBIZ4JBZW8hOOfc\naAUFBElXStooqU3SrTnSJekLYfozki4I55dKekLSbyStk/TxyDL1kh6U9GL4t27iqpVbxiIBwccQ\nnHNuhDEDgqQkcDtwFbAcuFbS8qxsVwGt4etG4I5wfh9wmZmdC5wHXCnpkjDtVuAhM2sFHgqnJ1U6\nE3QZpZLeQnDOuWyFtBAuAtrMbJOZ9QP3Aiuz8qwE7rHAY0CtpOZw+nCYpzh8WWSZr4fvvw689UQq\nUojBTCZsIST9OgTnnMtSSEBoAbZHpneE8wrKIykp6WlgD/CgmT0e5mkys/bw/S6gaZxlH7fBjHcZ\nOedcPpM+qGxmg2Z2HjAPuEjS2TnyGEdbDiNIulHSWklrOzo6TqgsQwGhJJmgP+2P0HTOuahCAsJO\nYH5kel44b1x5zKwTeBi4Mpy1W1IzQPh3T64PN7O7zGyFma1obGwsoLj5DY8h+FlGzjk3SiEBYQ3Q\nKmmxpBRwDbAqK88q4PrwbKNLgC4za5fUKKkWQFIZcAXwfGSZG8L3NwA/PMG6jMm7jJxzLr+isTKY\nWVrSLcADQBK428zWSbopTL8TWA1cDbQBPcC7wsWbga+HZyolgPvM7Edh2qeA+yS9B9gK/P7EVSu3\n4YDgZxk559woYwYEADNbTXDQj867M/LegJtzLPcMcH6ede4DLh9PYU+UX5jmnHP5xepK5cGMUZRI\neEBwzrkcYhcQEj6G4JxzOcUuIAxdqewXpjnn3EixCghDYwh+t1PnnBstVgEhkzGSOtplZJbzWjjn\nnIulWAWEdCZDMhl0GZkFLQbnnHOBWAWEwciVyoCPIzjnXESsAkL0OgTAxxGccy4iVgEhOoYAHhCc\ncy4qVgEhnTGKwjEE8IDgnHNRsQoIg9ldRoN+C2znnBsSr4BgQZdRSVES8EFl55yLildAGDSSiQQl\nPobgnHOjxCogDI8heEBwzrlRYhUQBs1IRM8y8hvcOefcsHgFhMjN7cBbCM45FxWbgGBmo88y8oDg\nnHPDYhMQBsP7FhUlvMvIOedyiU9ACO9smoh0Gflpp845d1R8AkKkheCnnTrn3GixCQhDt7r2MQTn\nnMutoIAg6UpJGyW1Sbo1R7okfSFMf0bSBeH8+ZIelrRe0jpJH4os8zFJOyU9Hb6unrhqjZbJFRB8\nDME554YVjZVBUhK4HbgC2AGskbTKzNZHsl0FtIavi4E7wr9p4MNm9mtJVcCTkh6MLHubmX1m4qqT\nXzo6qOynnTrn3CiFtBAuAtrMbJOZ9QP3Aiuz8qwE7rHAY0CtpGYzazezXwOY2SFgA9AygeUv2OBw\nCyFBUTJBQh4QnHMuqpCA0AJsj0zvYPRBfcw8khYB5wOPR2Z/IOxiultSXa4Pl3SjpLWS1nZ0dBRQ\n3NyOBoRgeui5ys455wInZVBZUiXwPeBPzOxgOPsO4DTgPKAd+GyuZc3sLjNbYWYrGhsbj7sM0RYC\nQCqZ8BaCc85FFBIQdgLzI9PzwnkF5ZFUTBAMvmlm3x/KYGa7zWzQzDLAlwi6piZNdAwBIFWU9OsQ\nnHMuopCAsAZolbRYUgq4BliVlWcVcH14ttElQJeZtUsS8BVgg5l9LrqApObI5NuA5467FgUYzAQH\n/2QYEEqKvIXgnHNRY55lZGZpSbcADwBJ4G4zWyfppjD9TmA1cDXQBvQA7woXfw1wHfCspKfDeX9h\nZquBT0s6DzBgC/C+CatVDkPDBcnhFoKPITjnXNSYAQEgPICvzpp3Z+S9ATfnWO5RQHnWed24SnqC\n0lkthGAMwR+h6ZxzQ2JzpfLgqDEE7zJyzrmo2AWEhHcZOedcTrELCEUjuow8IDjn3JDYBIToze3A\nu4yccy5bbALC8IVpOnraqV+H4JxzR8UuIBQlfQzBOedyiV1AGL51hXcZOefcCLEJCNm3rvArlZ1z\nbqTYBITh004VOcvIu4ycc25Y7ALCiDEEbyE459yw2ASEUbeu8IDgnHMjxCYgZGzkaaepZJJ0xoZb\nDs45F3exCQjpwZEXplWUJAHoOjIwZWVyzrnpJDYBIXsMYemcKgCebz+YdxnnnIuT2ASEdNaVysua\nqwFY7wHBOeeAGAWE4TGEsMuoobKE2VUlHhCccy4Um4AwNIZQlDha5WXN1WxoPzRVRXLOuWklNgFh\n+NYVyaMPcFs+t5q2PYf89FPnnCNOASHrtFMIWggDg0bbnsNTVSznnJs24hMQsp6HALC8OTjTyMcR\nnHOuwIAg6UpJGyW1Sbo1R7okfSFMf0bSBeH8+ZIelrRe0jpJH4osUy/pQUkvhn/rJq5aox0dQzga\nEBY3VFJanGCDBwTnnBs7IEhKArcDVwHLgWslLc/KdhXQGr5uBO4I56eBD5vZcuAS4ObIsrcCD5lZ\nK/BQOD1pBsNbVyQiASGZEEubqlj/sgcE55wrpIVwEdBmZpvMrB+4F1iZlWclcI8FHgNqJTWbWbuZ\n/RrAzA4BG4CWyDJfD99/HXjrCdblmAbNRrQOhiyfW82GXQcx81tYOOfirZCA0AJsj0zv4OhBveA8\nkhYB5wOPh7OazKw9fL8LaMr14ZJulLRW0tqOjo4CiptbOmMjxg+GLGuuprNngPau3uNet3POnQpO\nyqCypErge8CfmNmo/hkLfp7n/IluZneZ2QozW9HY2HjcZRgczNNCCK9Y9nEE51zcFRIQdgLzI9Pz\nwnkF5ZFUTBAMvmlm34/k2S2pOczTDOwZX9HHZ9BsxPjBkDOHbmHh4wjOuZgrJCCsAVolLZaUAq4B\nVmXlWQVcH55tdAnQZWbtkgR8BdhgZp/LscwN4fsbgB8edy0KMJjJ3UKoLCni9MYK1m49MJkf75xz\n096YAcHM0sAtwAMEg8L3mdk6STdJuinMthrYBLQBXwLeH85/DXAdcJmkp8PX1WHap4ArJL0IvDGc\nnjTBGELu6r6utZHHN++jd2BwMovgnHPTWlEhmcxsNcFBPzrvzsh7A27OsdyjwOif5UHaPuDy8RT2\nRGQyRjJP+Hv9GQ187b+3sGbLfl7XevzjFM45N5PF5krldMZG3Ngu6pLTZpFKJnjkheM/i8k552a6\n2ASEwTynnQKUp4pYsaiOR17Ye5JL5Zxz00dsAkK+6xCGvP6MRjbuPsQuvx7BORdTsQkImbECQjh2\n8MiL3m3knIun2ASEdCaT87TTIcuaq2isKvFxBOdcbMUmIBxrDAFAEq9rbeDRtr3Dt8p2zrk48YAQ\n8dolDXT2DPDiHn+spnMufmITEMYaVAZYOid4YI4/Qc05F0exCQj5bl0RdXpjJZIHBOdcPMUqICR0\n7IBQWpxkXl2ZBwTnXCzFKiAUJY8dEACWNFZ6QHDOxVJsAsKxbm4XtWR2JZv3dvuZRs652IlNQBjM\nGAU0EDi9sZK+dIadB45MfqGcc24aiVdAKLCFANDW4aeeOufiJVYBYayzjCASEHwcwTkXM7EJCOlM\nhmQBfUa15SkaKlO8tKf7JJTKOeemj9gEhIxBcozTToec1lhJW4e3EJxz8RKbgDDWze2ilswOTj0N\nHgTnnHPxEJuAMDg49q0rhixprKTryAB7D/dPcqmcc276iE1AKOReRkOGBpZf8m4j51yMxCYgZKzw\ngHC6n2nknIuhggKCpCslbZTUJunWHOmS9IUw/RlJF0TS7pa0R9JzWct8TNJOSU+Hr6tPvDr5pQs8\n7RRgbk0p5amkBwTnXKyMGRAkJYHbgauA5cC1kpZnZbsKaA1fNwJ3RNK+BlyZZ/W3mdl54Wv1OMs+\nLoODRqLAgCCJ1tmVbGg/OJlFcs65aaWQFsJFQJuZbTKzfuBeYGVWnpXAPRZ4DKiV1AxgZo8A+yey\n0Mdj0ApvIQBcsLCOp7d30p/OTGKpnHNu+igkILQA2yPTO8J5482TywfCLqa7JdXlyiDpRklrJa3t\n6Dj+5x0XenO7IRcvrqcvneHZnV3H/ZnOOTeTTOWg8h3AacB5QDvw2VyZzOwuM1thZisaGxuP+8MK\nvXXFkAsX1QPwxOYpb9w459xJUUhA2AnMj0zPC+eNN88IZrbbzAbNLAN8iaBralKYWfCAnHEEhFmV\nJZzeWMGaLR4QnHPxUEhAWAO0SlosKQVcA6zKyrMKuD482+gSoMvM2o+10qExhtDbgOfy5T1RQ482\nGE8LAeCixbNYs2W/PxvBORcLYwYEM0sDtwAPABuA+8xsnaSbJN0UZlsNbALaCH7tv39oeUnfBn4F\nLJW0Q9J7wqRPS3pW0jPA/wD+dKIqlS2dCQaGC70OYcjFi+s51Jvm+V1+tpFz7tRXVEim8JTQ1Vnz\n7oy8N+DmPMtem2f+dYUX88QM/cIfb0C4cHEwjrBm837Omlsz4eVyzrnpJBZXKg8FhPF2GbXUltFS\nW8YTPo7gnIuBWAWE8bYQIOg2emLzfr/zqXPulBeLgJA+gYBw4eJ69h7u56UOf2COc+7UFouAkDmB\ngHDp0kZSyQRf/q9NE10s55ybVmIRENLHOYYA0FxTxjsuWch9a7fTtufQRBfNOeemjVgEhKNjCMdX\n3VsuW0JFqoi///HGiSyWc85NK7EICEfHEI5v+fqKFDddejoPrt/NWj/jyDl3iopFQDjRFgLAu1+z\nmNlVJfz1D9fR3ZeeqKI559y0EauAcDxjCEPKUkn+7nfO4fldB/njb/7ab4vtnDvlxCIgDN26IqHj\nDwgAly9r4m/fdg6PvNDBn333N8NnLznn3KmgoFtXzHRhPDihFsKQay5awL7ufv7hgY0kJf7+d19B\n8fEOTjjn3DQSi4AwfHO75IkHBID3X3o6gxnjcw++wN7ufu54+wVUlMRiUzrnTmGx+Gk7PKh8gl1G\nQyTxwctb+dTvnMOjL3ZwzV2Psaurd0LW7ZxzUyVWAWEiuoyirrloAV+6fgWbOg7zP7/4KE9tOzCh\n63fOuZMpVgHheG5dMZbLlzXx/fe/htLiBH9w12P8+Xef4XtP7qC968iEf5Zzzk2mWHR8D9+6YoLG\nELItnVPFqptfy8f+Yx33P9fOd9ZuJ5kQ73v9aXzw8lZKi5OT8rnOOTeRYhEQhloIJ3ra6bHUVaT4\n/DXnk8kYG3cf4iuPbuaff/4S9z+3i7+4ehmXnzl7XM90ds65ky1WXUZFJ3ClcqESCbGsuZrP/N65\nfOM9F5Mx4733rOWNn/sF9/xqC3sO+eCzc256ikUL4USeh3AiXtvawE//9xtY/Ww7X3l0M3/9w3X8\n9Q/Xce78Wl65oI6m6hLm1JRyRlMVS2ZX+vUMzrkpFYuAMJmDymMpTiZYeV4Lbzl3LuvbD/KzDXt4\n6Pk9fPuJbRwZGBzOlypK8IqWGi5bNps3LmuidXYlmsQuLuecy1ZQQJB0JfB5IAl82cw+lZWuMP1q\noAd4p5n9Oky7G3gzsMfMzo4sUw98B1gEbAF+38wm5bzNQZu6gDBEEmfNreGsuTV84PJWzIzDfWle\n7uxlQ/tB1r3cxa827ePTP97Ip3+8kdMbK3jLuS1cvmw2s6tKqC1PkSryFoRzbvKMGRAkJYHbgSuA\nHcAaSavMbH0k21VAa/i6GLgj/AvwNeCLwD1Zq74VeMjMPiXp1nD6z4+/KvkNhlcqT/R1CCdCElWl\nxSydU8zSOVW89fwWAHZ19fLght38x29e5rafvsBtP31heJmW2jLOaKrkFfNqefslC5hdVTpVxXfO\nnYIKaSFcBLSZ2SYASfcCK4FoQFgJ3GPBk+gfk1QrqdnM2s3sEUmLcqx3JXBp+P7rwM+ZpICQHpz6\nFkKh5tSUct0lC7nukoW0dx3hqW2d7OvuZ9/hPjbv7WbjrkP84oUXufMXL/GHFy/g91fM54ymqhlR\nN+fc9FZIQGgBtkemd3D01/+x8rQA7cdYb5OZDaXvAppyZZJ0I3AjwIIFCwoo7mhTOYZwIpprymg+\np2zU/C17u7n94Tbu+dVWvvrLLZQVJ1nWXMXsqlLqKlI0VqaYU1PG3NpSLlpcT3kqFkNFzrkTNC2O\nFGZmknJ+59cfAAAQ00lEQVTeS9rM7gLuAlixYsVx3W96aAxhOnUZnYhFDRX8w++dy//+rTN4fNN+\nnt7eyYb2g7zUcZgDW/vZ191PWGVKixNcduZsfmv5HC5aXM/c2tEBxjnnoLCAsBOYH5meF84bb55s\nu4e6lSQ1A3sKKMtxmakthLE015Tx1vNbhscfhgwMZug41Memjm4eWLeL+59rZ/WzuwCYV1fGefNr\nOXdeLUvnVFFfkaKmrJim6lIftHYu5goJCGuAVkmLCQ7y1wB/mJVnFXBLOL5wMdAV6Q7KZxVwA/Cp\n8O8Px1Pw8ZhJYwgToTiZYG5tGXNry3htawMfe8tZbGg/yBOb97N2636e2tbJj54Z+e9JJsT8ujKW\nzK7iDWc0cPmyJm9NOBczYwYEM0tLugV4gOC007vNbJ2km8L0O4HVBKecthGcdvquoeUlfZtg8LhB\n0g7g/5rZVwgCwX2S3gNsBX5/IisWlZkGp51OpWRCnN1Sw9ktNbz7tYsB2Hs4aEF09vRzoKefHQeO\nsGlvN8/u6OKnG3bzVz9cx/z6MpY2VbN0TiVnNFXROju4gM5bEs6dmgoaQzCz1QQH/ei8OyPvDbg5\nz7LX5pm/D7i84JKegPRJvHXFTNFQWUJDZcmo+WbGSx3d/Oz53fxmRxcv7DrEwxv3DHe7VZYUcenS\nRq5Y3sRZc2tYUF/uAcK5U8S0GFSebMM3t/Pj1pgksWR2JUtmVw7P609nglNedx/iVy/t5cH1u4e7\nnJIJMbuqhNLiJKXFSZY3V/Pa1llcsKCO2vIUlSVFsW2ZOTfTxCogeAvh+KSKEiydU8XSOVW85dy5\nfOKtxvqXD/LinkNs6uhm98Fe+tIZDvUO8NDzu/ner3cMLyvBsjnVXL5sNq86bRYl4a3Am6pLaKkt\n89tzODeNxCIgpIdvfz3FBTlFJBPinHk1nDOvZlRaJmOsbz/I+vaDHDwyQGfPAE9s2c/tD7fxTz9r\nG5F3VkWKc8Mzns5bUMuS2ZXUlBVTkUp6oHBuCsQiIAxmMiQT8oPMSZCIDGBHdfb08+zOLjIWjFNs\n39/Db3Z08ZvtnTy8cc/wdRMQXC8yqzLF7KpSmmtKaW0KBrUvXOTXUTg3mWIRENIZ837sKVZbnuJ1\nrY0j5l0X/j3UO8AzO7rYvr+HriMDdB4ZYO+hPjoO9/FSx2Eeev7ooHbr7Epes6SBZc1VtDZV0VhZ\nQlkqSWVJkT+ZzrkTFIuAkMnYKXOV8qmoqrSY1yxpyJven87Qtucw//3SXn7xQgf3rtlG70BmVL7a\n8mKaa8poqS2luSa4DqOlroyW2jIWzSpnVo6zqpxzR8UiIHgLYWZLFSVYPrea5XOr+aPXncZgxthx\noIcXdx/mQE8/RwYGOdSbpr3rCO2dvezs7GXNlgN0HRkYsZ5ZFSnOaKri/AW1XLi4nnNaaqgvT/mj\nTZ0LxSIgDHpAOKUkE2LhrAoWzqo4Zr7geRNH2BledPfCrkNs2HWQux7ZxD///CUgONGgrjzF3Noy\nFtSXM7e2lMqSYipKksyuLmXxrArm1ZVRVVpEkT/Rzp3iYhEQ6spTLBrj4OFOPZUlRZzRVMUZTVX8\nj8j8nv40T23r5IXdh9jf3c/ew/3s7DzC+vaDPPT87pzdUQAlRQnqK4Lg0VxTSkvYJTW3JvgbBI7i\nk1M55yaBzI7rBqJTYsWKFbZ27dqpLoY7xaUHM3T3DdJ+8Ahb9vaws/MI3X1puvvSdBzuC7uljrCr\nq5f+wZHBo7GqhNbZlZw5p5oLF9Vx4eL6nFeEO3cySXrSzFaMlS8WLQTnxqMomaCmPEFNeTFnzqnO\nmy+TMfZ29/FyZy87DvSwbX8Pmzq6eXHPYb71xFbu/uVmABbNKue8+bWc3VJDY1UJjVUlzK8rp6W2\nzMcv3LTiAcG545RIiNlVpcyuKuW8+bUj0vrTGZ7d2cWaLft5atsB/vulffzg6ZdH5CktTrBoVgXz\n6sppqS2lpjxFcUKUFidZOqeKc+fVUlPuXVDu5PGA4NwkSBUleOXCOl65sA4ILsbrOjLA3sN97DnY\nx9b9PbTtOczmvd1s39/DY5v2cbgvPWo9DZUllKeSlKeSnD67knNaaljeXM2iWRXMrS31gW43oTwg\nOHcSSKK2PEVteYols6t4dY48ZkY6Y/T0DbLu5S6e2t7JjgM99A5kwov3OvnPyHMskgkxr66MhbMq\naKkto7IkSVmqiPJUktKiBJWlxZzeWEFrUxWVJf5Vd2PzvcS5aUISxUlRU57g1UsaeHWOi/UOdPez\ncfchtu3vYeu+brbuC8Yu1r/cRXffIEcGBnOuu6EyRV15iobKEhbOKmdxQ9BVVVteTE1ZMdWlxVSW\nFlHtp9fGmgcE52aQuooUl5w2i0tOm5UzPZMxetOD9A5k6DoyQNuew2zcdZCdnb3s7+6j41AfD67f\nzb7u/pzLlxQluGBBHRefVs+C+nKqS4upq0ixuKGCuvJivx/YKc4DgnOnkERClKeKKE9BfXggv2J5\n06h8XT0DtB88QldPcO+oQ71pDvUOsG1/D09s3s/nH3qR7DPSa8qKmVWZoqQoSWVJkjPnVHPOvBqW\nNlXRXFPKrMoSvwB0hvOA4FwM1ZQXH/MMpsN9afYe6uNgbzAQvnlvD5v3HqazZ4C+dIaungH+/amd\n/OtjW4eXKYqOadSVUV+eoq4ixaJZ5ZzRVOWn2c4AHhCcc6NUlhSNORCdyRib9nazZW837Qd7ebnz\nyPDYxrM7u+js6ScTaWWUFieYX1fOgvpy5tSU0lBZwuzqkuAmhLXB7UESEqlkglrvnpoSHhCcc8cl\nkRj9uNWoTMboPDLA5r2HeWH3YV7ac5ht+4NB8Ke2d7I/zzgGBKfbntNSTWtTFU3VpTRVl7C4oYLT\nGyv9NueTyAOCc25SJBKiviJFfUU9r1xYPyp9YDDD3sN9vNx5hB0HjtDTP4hZcK+pDe2HeHZnJ798\naR/96aO3B5FgdlUJ1aXFVJUGz8BIFSWoLi1m+dxqzmmp4YymKhoqU97COA4FBQRJVwKfB5LAl83s\nU1npCtOvBnqAd5rZr4+1rKSPAe8FOsLV/IWZrT7RCjnnZobiZILmmjKaa8p45cLceYYu6Gvv6mVT\nRzdtew6zs7OHQ71pDvYO0DuQ4XBfmhd3H2bVb45eCV5VWsTCWeXUhdd+zKsr45yWGs6aW83c2jKK\n/dTanMYMCJKSwO3AFcAOYI2kVWa2PpLtKqA1fF0M3AFcXMCyt5nZZyasNs65U0r0gr5lzfnvKwWw\nv7ufdS93DV8Bvm1/D509A2zf38P9z7YPP1tdglkVJcyrK+O0hgoWNVRQV5GiqqSIhsoSzmyuiu0N\nCQtpIVwEtJnZJgBJ9wIrgWhAWAncY8GtUx+TVCupGVhUwLLOOXfC6iuCx7RmP6oVoHdgkBd2H2JD\n+0Hau3rZ1dXL9gM9/GrTPr7/1M5R+RurSphbU0pteYqasuD5GOWpIhY1VHDBglrOnFN9Sp5iW0hA\naAG2R6Z3ELQCxsrTUsCyH5B0PbAW+LCZHcj+cEk3AjcCLFiwoIDiOufcSKXFSV4xr5ZXzKsdldYb\nPnHvcF+a9vC5GM/vOkTHoT46e/rZuq+b7v5BDvemh68ETxUlqC0rprqsmOaa0vC5G5XMrS1jTnUp\nLXVllKdm3hDtVJb4DuBvAAv/fhZ4d3YmM7sLuAuC5yGczAI65059pcVJSouTNFYFZzLlumUIBOMZ\n2/cf4dfbDrC+/SBdPQMc7B1gx4EjfOOxrfRlDX4vnlXBmc1V1FekqEgVUV8RdHudNbd62j7fu5CA\nsBOYH5meF84rJE9xvmXNbPfQTElfAn5UcKmdc+4kk8SCWeUsmFXOW89vGZE2mDF2HjhCe9cRdh3s\nZcveHta3d7Gh/RAHjwzQ3Z8e8SS+mrLi4Ue21lcEYyRnz63hDUsbp/RGhIV88hqgVdJigoP5NcAf\nZuVZBdwSjhFcDHSZWbukjnzLSmo2s6FbN74NeO6Ea+Occ1MgmTgaLPLp7OlnfftB1r98cPimhJs6\nunlyayedPf2kM0YqmeBVp8/i4tPqeeWCOs6cU011WdFJO4V2zIBgZmlJtwAPEJw6ereZrZN0U5h+\nJ7Ca4JTTNoLTTt91rGXDVX9a0nkEXUZbgPdNZMWcc246qS1P8erTG3j16aO7pAYzxpNbD/DAul08\nvHEPv/hxx3BaeSrJnJpS/vZt5+S9qeFE8WcqO+fcNLO/u5+nth1gU0c3uw4GZ0V98PJWls6pOq71\n+TOVnXNuhqqvSHH5siYuX3ZyP9cv13POOQd4QHDOORfygOCccw7wgOCccy7kAcE55xzgAcE551zI\nA4JzzjnAA4JzzrnQjLpSObw30tbjXLwB2DuBxZlKXpfp61Sqj9dlejqeuiw0s9EPisgyowLCiZC0\ntpBLt2cCr8v0dSrVx+syPU1mXbzLyDnnHOABwTnnXChOAeGuqS7ABPK6TF+nUn28LtPTpNUlNmMI\nzjnnji1OLQTnnHPH4AHBOeccEJOAIOlKSRsltUm6darLMx6S5kt6WNJ6SeskfSicXy/pQUkvhn/r\nprqshZKUlPSUpB+F0zOyLpJqJX1X0vOSNkh61Qyuy5+G+9dzkr4tqXSm1EXS3ZL2SHouMi9v2SV9\nJDwWbJT0pqkpdX556vMP4X72jKR/l1QbSZuw+pzyAUFSErgduApYDlwrafnUlmpc0sCHzWw5cAlw\nc1j+W4GHzKwVeCicnik+BGyITM/Uunwe+LGZnQmcS1CnGVcXSS3AB4EVZnY2wfPPr2Hm1OVrwJVZ\n83KWPfzuXAOcFS7zz+ExYjr5GqPr8yBwtpm9AngB+AhMfH1O+YAAXAS0mdkmM+sH7gVWTnGZCmZm\n7Wb26/D9IYKDTgtBHb4eZvs68NapKeH4SJoH/Dbw5cjsGVcXSTXA64GvAJhZv5l1MgPrEioCyiQV\nAeXAy8yQupjZI8D+rNn5yr4SuNfM+sxsM9BGcIyYNnLVx8x+YmbpcPIxYF74fkLrE4eA0AJsj0zv\nCOfNOJIWAecDjwNNZtYeJu0CmqaoWOP1j8D/ATKReTOxLouBDuCrYffXlyVVMAPrYmY7gc8A24B2\noMvMfsIMrEtEvrKfCseDdwP3h+8ntD5xCAinBEmVwPeAPzGzg9E0C84dnvbnD0t6M7DHzJ7Ml2em\n1IXgF/UFwB1mdj7QTVaXykypS9i/vpIgyM0FKiS9I5pnptQll5lc9mySPkrQjfzNyVh/HALCTmB+\nZHpeOG/GkFRMEAy+aWbfD2fvltQcpjcDe6aqfOPwGuAtkrYQdN1dJukbzMy67AB2mNnj4fR3CQLE\nTKzLG4HNZtZhZgPA94FXMzPrMiRf2Wfs8UDSO4E3A2+3oxeQTWh94hAQ1gCtkhZLShEMwKya4jIV\nTJII+qk3mNnnIkmrgBvC9zcAPzzZZRsvM/uImc0zs0UE/4efmdk7mJl12QVsl7Q0nHU5sJ4ZWBeC\nrqJLJJWH+9vlBGNVM7EuQ/KVfRVwjaQSSYuBVuCJKSjfuEi6kqCr9S1m1hNJmtj6mNkp/wKuJhiZ\nfwn46FSXZ5xlfy1Bc/cZ4OnwdTUwi+DsiReBnwL1U13WcdbrUuBH4fsZWRfgPGBt+L/5AVA3g+vy\nceB54DngX4GSmVIX4NsEYx8DBC239xyr7MBHw2PBRuCqqS5/gfVpIxgrGDoG3DkZ9fFbVzjnnAPi\n0WXknHOuAB4QnHPOAR4QnHPOhTwgOOecAzwgOOecC3lAcM45B3hAcM45F/r/pjfT+To+knYAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12be2774240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ATM.filter_variance()\n",
    "ATM.token_cluster(n_clusters = 300)\n",
    "ATM.token_PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.27322338,  0.27453399,  0.02269392, ...,  0.01432648,\n",
       "         0.01376316, -0.01663805],\n",
       "       [ 0.21312755,  0.25457254, -0.10414664, ..., -0.00936547,\n",
       "         0.00238513, -0.00718032],\n",
       "       [ 0.15519688,  0.1519959 , -0.079707  , ...,  0.00107165,\n",
       "         0.01243804,  0.00442105],\n",
       "       ...,\n",
       "       [ 0.30950403, -0.06029534, -0.05409209, ..., -0.05649224,\n",
       "        -0.02021379,  0.02219675],\n",
       "       [ 0.20892799, -0.09269462, -0.03125705, ..., -0.00897335,\n",
       "         0.00463271,  0.01247202],\n",
       "       [ 0.20823086, -0.10055526,  0.03921134, ..., -0.02491222,\n",
       "        -0.1071738 , -0.01901273]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATM.bow_corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corpus translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "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</th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adventure</td>\n",
       "      <td>dan morgan told himself he would forget ann tu...</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adventure</td>\n",
       "      <td>gavin paused wearily you ca nt stay here with ...</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adventure</td>\n",
       "      <td>the sentry was not dead he was in fact showing...</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adventure</td>\n",
       "      <td>so it was nt the earthquake that made him retu...</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adventure</td>\n",
       "      <td>she was carrying a quirt and she started to ra...</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       class                                               text language\n",
       "0  adventure  dan morgan told himself he would forget ann tu...       en\n",
       "1  adventure  gavin paused wearily you ca nt stay here with ...       en\n",
       "2  adventure  the sentry was not dead he was in fact showing...       en\n",
       "3  adventure  so it was nt the earthquake that made him retu...       en\n",
       "4  adventure  she was carrying a quirt and she started to ra...       en"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(text_processing)\n",
    "my_trans = text_processing.translator(df = corpus, text = 'text', topic = 'class', pre_process=True)\n",
    "my_trans.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *translator* object makes some preprocessing by default. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan Morgan told himself he would forget Ann Tu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavin paused wearily. ``You can't stay here wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The sentry was not dead. He was, in fact, show...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>``So it wasn't the earthquake that made him re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>She was carrying a quirt, and she started to r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text\n",
       "0  Dan Morgan told himself he would forget Ann Tu...\n",
       "1  Gavin paused wearily. ``You can't stay here wi...\n",
       "2  The sentry was not dead. He was, in fact, show...\n",
       "3  ``So it wasn't the earthquake that made him re...\n",
       "4  She was carrying a quirt, and she started to r..."
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_trans.corpus.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the language can be detected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    en\n",
       "1    en\n",
       "2    en\n",
       "3    en\n",
       "4    en\n",
       "Name: language, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_trans.language_detect().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the language of origin detected we can translate the texts to our will. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Dan Morgan se dijo a sí mismo que olvidaría a ...\n",
       "1      Gavin se detuvo cansadamente, no puedes quedar...\n",
       "2      el centinela no estaba muerto, de hecho estaba...\n",
       "3      así que no fue el terremoto lo que lo hizo reg...\n",
       "4      ella llevaba una bata y comenzó a levantarla, ...\n",
       "5      tal era mi estado de ánimo que no cuestioné la...\n",
       "6      el gorro duro plano era pequeño pero se lo met...\n",
       "7      si percibía alguna preocupación inusual por pa...\n",
       "8      milagrosamente ella encontró exactamente la de...\n",
       "9      el equipo de brannon conocido como el slash-b ...\n",
       "10     el autor de las notas anónimas parecía saberlo...\n",
       "11     cuando habían pasado varios minutos y no había...\n",
       "12     por encima de su hombro podía ver la sonrisa s...\n",
       "13     aunque no era más grande que manantiales holan...\n",
       "14     a principios de noviembre las nubes se alzaron...\n",
       "15     sobre el traqueteo de los guardabarros zumband...\n",
       "16     La guié hasta el diván. Apagué la televisión. ...\n",
       "17     Demasiadas personas piensan que el propósito p...\n",
       "18     mejor escribir una postal para walter, abrió e...\n",
       "19     estaban al oeste de la sabina, pero solo Dios ...\n",
       "20     El aceite de azufre y la copra son el tipo de ...\n",
       "21     Supongo que se pasó la mañana preparándose, lu...\n",
       "22     luego golpeó con calma y cuidado los cinco dis...\n",
       "23     ahora a la mañana siguiente estaban anclados e...\n",
       "24     Temprano, ese día, matsuo vio a un marino que ...\n",
       "25     Las sillas retrocedieron y los clientes abando...\n",
       "26     durante varios meses, Jack Carter era un enorm...\n",
       "27     en el borde de la divertida multitud de espect...\n",
       "28     Bastardos diría que todo lo que hice fue darle...\n",
       "29     la sesión de asamblea trajo mucho bien la asam...\n",
       "                             ...                        \n",
       "99     si los cardenales prestan atención al gen mauc...\n",
       "100    las noticias románticas se refieren a la señor...\n",
       "101    después de permanecer cerrado durante siete me...\n",
       "102    una noche en Nueva Orleans es la alegría plane...\n",
       "103    el ferrocarril de Baltimore y Ohio anunció aye...\n",
       "104    londres feb 9 secretos vitales del primer subm...\n",
       "105    st johns mich 19 de abril un jurado de siete h...\n",
       "106    El consejo de administración de la universidad...\n",
       "107    Salem especial por segundo mes consecutivo, el...\n",
       "108    alrededor de 70 contribuyentes de la providenc...\n",
       "109    asilomar marzo 26 extensos programas de fumiga...\n",
       "110    probablemente la cosa más popular que ha golpe...\n",
       "111    santa barbara el movimiento de recuperación ac...\n",
       "112    El vendedor de maquinaria agrícola LGB se incl...\n",
       "113    greer garson, estrella de fama mundial de la p...\n",
       "114    una galleta con relleno de caramelo y glaseado...\n",
       "115    La sala bonanza de escape del hotel tiene una ...\n",
       "116    Enrique Jordá, director de orquesta y director...\n",
       "117    por fin la casa blanca va a obtener algunos mu...\n",
       "118    Para un encuentro crucial, una de las pregunta...\n",
       "119    mis amigos biólogos me dicen que los mecanismo...\n",
       "120    sen john l Mcclellan de arkansas y rep david m...\n",
       "121    la nación la guerra de tres frentes en una ses...\n",
       "122    el torneo de maestros de golf demostró el lune...\n",
       "123    cuando mickey charles manto el hombre de múscu...\n",
       "124    en washington en el presidente electo john f k...\n",
       "125    un filósofo puede señalar que los problemas de...\n",
       "126    la presidencia hablando y escuchando aunque el...\n",
       "127    Los titulares de bonos de peaje están encontra...\n",
       "128    cada prestatario de la biblioteca o al menos a...\n",
       "Length: 129, dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_trans.translate(to_lang = 'es')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the *topic analysis* class we can easily perform unsupervised topic analysis of our corpus, using both LDA and LSI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import text_processing\n",
    "import importlib\n",
    "\n",
    "import nltk\n",
    "import pandas as pd\n",
    "from nltk.corpus import brown\n",
    "from nltk.tokenize.moses import MosesDetokenizer\n",
    "import importlib\n",
    "import text_processing\n",
    "import warnings\n",
    "\n",
    "# Ommit warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Categorías\n",
    "# categorias = ['adventure','editorial','fiction','news']\n",
    "categorias = ['news']\n",
    "y = []\n",
    "corpus = []\n",
    "for category in categorias:\n",
    "    for fileid in brown.fileids(category):\n",
    "        text = brown.words(fileid)\n",
    "        text = MosesDetokenizer().detokenize(text,return_str=True)\n",
    "        corpus.append(text)\n",
    "        y.append(category)\n",
    "        \n",
    "corpus = pd.DataFrame({'class':y, 'text':corpus})\n",
    "corpus.head()\n",
    "\n",
    "# Initialise the class, applying some preprocessing to the text\n",
    "ta = text_processing.topic_analysis(df = corpus, text = 'text')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.088*\"laos\" + 0.077*\"mantle\" + 0.075*\"sales\" + 0.074*\"jury\" + 0.074*\"dallas\"'),\n",
       " (1,\n",
       "  '-0.277*\"mantle\" + -0.200*\"maris\" + -0.170*\"game\" + -0.164*\"palmer\" + -0.145*\"runs\"'),\n",
       " (2,\n",
       "  '0.232*\"laos\" + 0.160*\"khrushchev\" + 0.112*\"nato\" + 0.103*\"communist\" + 0.095*\"premier\"'),\n",
       " (3,\n",
       "  '0.167*\"sales\" + 0.136*\"gin\" + -0.126*\"library\" + 0.120*\"shares\" + 0.114*\"dallas\"'),\n",
       " (4,\n",
       "  '0.176*\"gin\" + 0.171*\"dallas\" + 0.157*\"shares\" + -0.155*\"jury\" + -0.138*\"fulton\"')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LSI\n",
    "lsi = ta.LSI(no_below=1, no_above=0.3, num_topics=5)\n",
    "lsi.show_topics(num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.003*\"game\" + 0.003*\"laws\" + 0.003*\"anti-trust\" + 0.003*\"yards\" + 0.003*\"bonds\"'),\n",
       " (1,\n",
       "  '0.004*\"palmer\" + 0.004*\"player\" + 0.003*\"laos\" + 0.003*\"khrushchev\" + 0.002*\"republican\"'),\n",
       " (2,\n",
       "  '0.004*\"library\" + 0.004*\"congo\" + 0.003*\"libraries\" + 0.003*\"catholic\" + 0.003*\"belgians\"'),\n",
       " (3,\n",
       "  '0.003*\"mantle\" + 0.003*\"sales\" + 0.003*\"maris\" + 0.003*\"jury\" + 0.002*\"tax\"'),\n",
       " (4,\n",
       "  '0.002*\"senate\" + 0.002*\"texas\" + 0.002*\"tax\" + 0.002*\"portland\" + 0.002*\"democratic\"')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LDA\n",
    "lda = ta.LDA(no_below=1, no_above=0.3, num_topics=5)\n",
    "lda.show_topics(num_words=5)"
   ]
  }
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