topic modelling using NMF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from gensim import corpora,models\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from gensim.parsing.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
    "from nltk.stem.porter import *\n",
    "import sklearn\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/abcnews-date-text.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1186018, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publish_date</th>\n",
       "      <th>headline_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aba decides against community broadcasting lic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20030219</td>\n",
       "      <td>act fire witnesses must be aware of defamation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20030219</td>\n",
       "      <td>a g calls for infrastructure protection summit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz staff in aust strike for pay rise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz strike to affect australian travellers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date                                      headline_text\n",
       "0      20030219  aba decides against community broadcasting lic...\n",
       "1      20030219     act fire witnesses must be aware of defamation\n",
       "2      20030219     a g calls for infrastructure protection summit\n",
       "3      20030219           air nz staff in aust strike for pay rise\n",
       "4      20030219      air nz strike to affect australian travellers"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = data.publish_date.apply(lambda x:pd.to_datetime(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2003-02-19\n",
       "1   2003-02-19\n",
       "2   2003-02-19\n",
       "3   2003-02-19\n",
       "4   2003-02-19\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2003-02-19\n",
       "1   2003-02-19\n",
       "2   2003-02-19\n",
       "3   2003-02-19\n",
       "4   2003-02-19\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_freq = data['date'].apply(lambda x : x.year)\n",
    "year_freq = year_freq.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    91928\n",
       "2012    89008\n",
       "2014    81531\n",
       "2008    79991\n",
       "2011    77778\n",
       "2015    77290\n",
       "2007    77174\n",
       "2009    76444\n",
       "2010    74931\n",
       "2005    73124\n",
       "2004    72674\n",
       "2006    66901\n",
       "2003    63997\n",
       "2016    61574\n",
       "2017    47511\n",
       "2018    40102\n",
       "2019    34060\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(8,7))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of tweets\")\n",
    "sns.set()\n",
    "sns.lineplot(x = year_freq.index,y = year_freq.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig,ax = plt.subplots()\n",
    "d = pd.DataFrame(data={\"year\":year_freq.index,\"frequency\":year_freq.values})\n",
    "ax.barh(width = 'frequency', y= 'year',data = d)\n",
    "ax.set_yticks(d['year'])\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(10)\n",
    "ax.set_xlabel(\"Number of headlines\")\n",
    "ax.set_ylabel(\"Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Preprocessing using NLTK and GENSIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publish_date</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aba decides against community broadcasting lic...</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20030219</td>\n",
       "      <td>act fire witnesses must be aware of defamation</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20030219</td>\n",
       "      <td>a g calls for infrastructure protection summit</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz staff in aust strike for pay rise</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz strike to affect australian travellers</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date                                      headline_text       date\n",
       "0      20030219  aba decides against community broadcasting lic... 2003-02-19\n",
       "1      20030219     act fire witnesses must be aware of defamation 2003-02-19\n",
       "2      20030219     a g calls for infrastructure protection summit 2003-02-19\n",
       "3      20030219           air nz staff in aust strike for pay rise 2003-02-19\n",
       "4      20030219      air nz strike to affect australian travellers 2003-02-19"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['publish_date'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aba decides against community broadcasting lic...\n",
       "1       act fire witnesses must be aware of defamation\n",
       "2       a g calls for infrastructure protection summit\n",
       "3             air nz staff in aust strike for pay rise\n",
       "4        air nz strike to affect australian travellers\n",
       "Name: headline_text, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headline_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aba decides against community broadcasting lic...\n",
       "1       act fire witnesses must be aware of defamation\n",
       "2       a g calls for infrastructure protection summit\n",
       "3             air nz staff in aust strike for pay rise\n",
       "4        air nz strike to affect australian travellers\n",
       "Name: headline_text, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headline_text.apply(lambda x:x.lower()).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_preprocess(x,stemmer,word2cnt={}):\n",
    "        \n",
    "    x = simple_preprocess(x)\n",
    "    \n",
    "    x = [word for word in x if word not in STOPWORDS]\n",
    "    \n",
    "    x = [word for word in x if len(word)>3]\n",
    "\n",
    "    x = [stemmer.stem(WordNetLemmatizer().lemmatize(word,pos='v')) for word in x]\n",
    "    \n",
    "    for word in x:\n",
    "        word2cnt[word] = word2cnt.get(word,0) + 1\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before preprocessing\n",
      " aba decides against community broadcasting licence\n",
      "after preprocessing\n",
      " ['decid', 'commun', 'broadcast', 'licenc']\n"
     ]
    }
   ],
   "source": [
    "sample = data.iloc[0,0]\n",
    "print(\"before preprocessing\\n\",sample)\n",
    "print(\"after preprocessing\\n\",text_preprocess(sample,stemmer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2cnt = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['token'] = data.headline_text.apply(lambda x: text_preprocess(x,stemmer,word2cnt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [decid, commun, broadcast, licenc]\n",
       "1                        [wit, awar, defam]\n",
       "2    [call, infrastructur, protect, summit]\n",
       "3               [staff, aust, strike, rise]\n",
       "4      [strike, affect, australian, travel]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.token.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Dictionary for corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = corpora.Dictionary(data.token,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 broadcast\n",
      "1 commun\n",
      "2 decid\n",
      "3 licenc\n",
      "4 awar\n",
      "5 defam\n",
      "6 wit\n",
      "7 call\n",
      "8 infrastructur\n",
      "9 protect\n"
     ]
    }
   ],
   "source": [
    "for word,index in list(dictionary.iteritems())[:10]:\n",
    "    print(word,index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2c = list(word2cnt.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67259"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(w2c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2c.sort(key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [word for word,_ in w2c[:15]]\n",
    "counts = [count for _,count in w2c[:15]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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np6uqqkoJCQl68803NWPGDA0YMED79+/XtGnT9MEHH/zmdr799lutXr1axcXFioyM1F133aW1a9eqtLRUmzdvVn5+vu6++25FRkY2054BaGkuZklFT16G8WKRSd3IxYxMzJoiE5cqjnv37tXgwYPl5+cnPz8/vfnmm4qIiNCAAQMkST169NBll12m77///je3ExoaKj8/PwUFBalt27YqLi7W3r17NXLkSHl5eclutysjI6M5dglAC3XixIUtOmi3B17wazwdmdSNXMzIxOzcTLy8bAoKCmjwdl3qULWPj49sNlvtz0eOHJFhGGc9xzAM1dTUyGaz1T5WVVV11nNatWpV++dfnnfutn/44QdVVlY2xW4AAAB4JJcqjiEhIcrMzFRVVZXKyso0ffp02Ww2ZWZmSpL279+vkydPqnPnzmrbtq2+++47SdK2bdssbXvTpk0yDEP5+fkaO3YsxREAAOACuNSh6jvvvFMHDhxQfHy8HA6HkpKSFBoaqkceeURpaWny9fVVWlqa/Pz8NGbMGE2fPl1DhgxR7969Zbfbf3PbCQkJWrhwoWJjYyVJ8+fPV0BAw7+yBQAAaClsxrnHgnFBxi/M1PFTZc4eA4ALSU+N4xzHRkAmdSMXMzIxaxHnOAIAAMB1URwBAABgCcURAAAAllAcAQAAYIlLXVXtjlamDHD2CABcTHlFtbNHAIAmQXFsoPz8EjkcXJj+C65sMyMTMzIBAPfEoWoAAABYQnEEAACAJRRHAAAAWEJxBAAAgCVcHNNAjbF8j6ex2wOdPYLLIRMzV82kvKJaxUUsIwoAdaE4NhBrVQOeJT01TlzvDQB141A1AAAALKE4AgAAwBKKIwAAACyhOAIAAMASiiMAAAAsoTj+11dffaVFixY5ewwAAACXRXH8r++++075+fnOHgMAAMBlecx9HFNTU/XBBx+oXbt2stvt6t+/vwzD0CuvvCKbzaauXbtq/vz5evfdd/XDDz9o/vz5kqQnn3xSHTt21KuvvqrS0lItX75c9913n5P3BgAAwPV4RHHMysrS559/rvfff19lZWUaOnSoQkND9frrr2vt2rVq166dHn30US1btkwTJ07UX/7yF82dO1deXl7KzMzU22+/rTZt2uizzz6jNAJw2qo2rrqajjORSd3IxYxMzJoiE48ojnv27NHgwYPl5+cnPz8/RUZGymazKSIiQu3atZMkjRo1SnPmzNHs2bPVpUsXZWdny9fXV9dff73sdruT9wCAKzlxovnXjrHbA53yvq6MTOpGLmZkYnZuJl5etkZZJtkjiqOXl5ccDsdZvzv3Z8MwVF1dLUmKi4vTpk2b5OvrqyFDhjTbnAAAAO7MIy6O+fOf/6zMzExVVlaqpKREH330kYqKipSVlaXCwkJJ0tq1axUaGipJuuOOO7R3717t3r1bd955pyTJ29u7tlgCAADAzCOKY3h4uG699VYNHTpUkyZNUvv27XXDDTfo//2//6fExEQNGjRIRUVFmj59uiTJ399fPXv2VLdu3XTppZdKkrp3764vv/xSixcvduauAAAAuCyPOFS9b98+XXfddcrIyFBVVZVGjRqlG264QV26dNGIESPqfM0TTzxx1s/XX3+9tm7d2hzjAgAAuCWPKI7XX3+9li1bpldeeUWGYegvf/mLunTp4uyxAAAAPIpHFMe2bdtq5cqVzh4DAADAo3nEOY4AAABoehRHAAAAWEJxBAAAgCUecY6jM61MGeDsEQA0ovIK7ucKAOdzQcWxsrJSJ0+e1JVXXtlU87id/PwSORyGs8dwGSz7ZEYmZmQCAO6p3kPVW7du1YIFC1RSUqJBgwYpLi5Or732WnPMBgAAABdSb3FcsWKFRo4cqczMTPXo0UPbt2/Xxo0bm2M2AAAAuJB6i6NhGAoODtaePXsUFhamgIAAGQaHZgEAAFqaes9x9PLy0qZNm/Txxx9r9uzZ2rFjh2w2W3PM5haCggKcPYLLsdsDnT2CyyETM1fNpLyiWsVFZc4eAwBcUr3Fcfbs2Vq2bJkefPBB2e12LV++XPPmzWuO2dzC+IWZOn6Kf2QAT5GeGicu2wGAutVbHG+99Va9+uqrtT+vWbOmKecBAACAi6q3OE6ePLnO37/44ouNPgwAAABcV73FceDAgbV/rqqq0gcffKCbb765SYcCAACA66m3OA4dOtT0c2JiYpMNBAAAANd0wWtVG4ah48ePN8UsAAAAcGEXfI7jwYMH1atXryYbCAAAAK7pgs5xtNlsGjNmjG6//fYmHcoZ1q5dq9atWysmJsbZowAAALikeg9VDx06VOHh4QoICFBgYKB69uwpb2/v5pitWX3xxReqrKx09hgAAAAuq95vHLdu3aq5c+cqODhYNTU1mjdvnp599ln17t27Oeark2EYWrx4sbZt2yZvb2+NGjVKYWFh+utf/6rCwkK1bt1a8+bNU/fu3ZWcnKxevXopPj5ekhQcHKxvv/1WaWlp2r9/v44dO6YxY8YoKytLn376qex2u/r27eu0fQMAAHBV9RbHJUuW6I033lBwcLAk6X//93+VkpKi9957r8mHO58tW7boiy++UHp6uqqqqpSQkKA333xTM2bM0IABA7R//35NmzZNH3zwwW9up7KyUps2bZL0n/3q1asXpRGA05ZDdNVlGJ2JTOpGLmZkYtYUmdRbHP39/WtLoyR17drV6WtV7927V4MHD5afn5/8/Pz05ptvKiIiQgMGDJAk9ejRQ5dddpm+//7739xO9+7dm2NcAG7mxInmX3TQbg90yvu6MjKpG7mYkYnZuZl4edkUFBTQ4O3We45jWFiYXnrpJZWWlqqiokJvv/22OnfurNOnT6uwsLDBA1wMHx+fs8rrkSNHZBjGWc8xDEM1NTWy2Wy1j1VVVZ31HH9//6YfFgAAwEPU+43jyy+/rJqaGj3zzDNn/X7jxo2y2Wz65ptvmmy48wkJCdHrr7+u0aNHq7q6WtOnT5fNZlNmZmbtoeqTJ0+qc+fOatu2rb777jtJ0rZt2867TW9vb9XU1DTXLgAAALideovj22+/7XJLDN555506cOCA4uPj5XA4lJSUpNDQUD3yyCNKS0uTr6+v0tLS5OfnpzFjxmj69OkaMmSIevfuLbvdXuc2//znP+uZZ55RYGCgBg0a1Mx7BAAA4PpsxrnHeM8xePBgbd68ubnmcTvjF2bq+KkyZ48BoJGkp8ZxjqOLIJO6kYsZmZg57RzH4OBgpaen6+jRoyosLKz9HwAAAFqWeg9Vf/jhh9qyZctZv3PWuY0AAABwnnqL49dff90ccwAAAMDF1VscHQ6HVq5cqZ07d6q6ulq33XabJk+eLB+fel8KAAAAD1LvOY6pqan69NNPNW7cON1zzz3at2+fnnrqqeaYDQAAAC6k3q8Nd+3apXfffVe+vr6SpPDwcMXGxjb5YO5iZcoAZ48AoBGVV1Q7ewQAcFn1FkfDMGpLoyT5+fmd9XNLl59fIofjN+9o1KJwSwQzMjEjEwBwT/Uequ7SpYsef/xx/fjjjzpy5IieeOIJ/eEPf2iO2QAAAOBC6i2ODz/8sIqKijR69GiNHDlSBQUFmj9/fnPMBgAAABdS76HqgIAADRs2TE8++aQKCwv1z3/+U23btm2O2QAAAOBC6i2OS5Ys0RdffKFVq1apvLxcL730kg4ePKj777+/OeZzeY2xfI+nsdsDnT2CyyETM2dlUl5RreIilgkFgIthaeWY9957T5LUsWNHvfHGG4qPj6c4/hdrVQPuJT01TlyWAwAXp95zHKuqqs66itrX11c2m61JhwIAAIDrqfcbx549e2rGjBkaPny4bDabNmzYoFtuuaU5ZgMAAIALqbc4zp8/X0uXLtUTTzwhHx8f9enTR1OmTGmO2QAAAOBC6i2OrVu3VnJycnPMAgAAABdW7zmOAAAAgERxBAAAgEVuWRy3b9+uV1555YJek5ycrPXr1ysvL08TJ05soskAAAA8V73nOJaVlWnLli06ffq0DMOo/f0999zTpIP9lgMHDlz0azt06KCXX365EacBAABoGeotjrNmzdJPP/2kP/zhD41y/8bq6mo98sgjysnJ0cmTJxUcHKwZM2Zo4sSJysrKkiSlpaVJkiZPnqy5c+cqJydHkpSQkKCePXtqzZo1kqQrr7xSR48e1f79+3Xs2DGNHTtWN954o5YsWaLy8nIVFRVpzpw5ioyMrH3/3NxcJSUlKSsrSwcPHtSCBQtUWlqqgoICTZo0SWPGjGnwPgIAAHiieovjt99+q02bNsnHp96nWrJv3z75+vrq7bfflsPh0Lhx47Rjx47zPvf06dPasGGD8vLylJqaqpEjR2r06NGSpGHDhiktLU2VlZXatGmTJOmBBx7QwoUL1alTJ33yySd6/PHHzyqOv7Zu3Trdf//96tOnj44cOaLY2FiKI9ACuOoSkK46lzORSd3IxYxMzJoik3rbYMeOHRv1DUNCQtS2bVutXr1a33//vQ4fPqzS0tI6n9u5c2cdOnRI48ePV1hYmGbNmlXn87p3717750WLFmn79u3asmWLvvzyS505c+a8syQnJ2vXrl1asWKFDh48eN45AHiWEydcb9FBuz3QJedyJjKpG7mYkYnZuZl4edkUFBTQ4O3WWxz/8Ic/KCkpSX379pW/v3/t7y/2HMcPP/xQS5cuVVJSkuLj43Xq1ClJOuv8yerqavn4+Khdu3bKyMjQ7t27tWPHDg0dOlQZGRmmbf56roSEBIWGhio0NFR9+vTRQw89dN5Zpk+frjZt2igiIkJRUVF6//33L2qfAAAAWoJ6i+OZM2d07bXX6scff2yUN/zkk080ePBgDRs2TEeOHFF2drZuueUWFRYWqqCgQAEBAdq1a5ciIiL04Ycf6h//+IeeffZZ9e3bV5988omOHTsmb29vVVRUmLZdWFiow4cP680335Sfn58WL16smpqa886ye/dubd68WR06dNDq1aslSTU1NfL29m6UfQUAAPAk9RbHJ554olHfcMSIEXrooYeUkZEhX19f9ezZUwUFBZowYYKGDx+ujh07qlu3bpKksLAwZWZmKjo6Wq1atVJsbKyCg4NVVFSk2bNn64orrjhr223bttXw4cMVHR0tHx8f9e7dW+Xl5ec9BD116lQlJCSoVatW6tKli6666irl5ubq2muvbdR9BgAA8AQ249fHiH9l2rRpeu655zRkyJA6X5ient6kg7mL8QszdfxUmbPHAGBRemqcS54LxTlaZmRSN3IxIxOzZj/H8ZebZM+fP7/BbwIAAAD3d97iePPNN0uSevXq1WzDAAAAwHW55ZKDAAAAaH4URwAAAFhyQcWxsrJSR48ebapZAAAA4MLqvR3P1q1b9emnn+rBBx9UbGysiouLNWXKFI0bN6455nN5K1MGOHsEABegvKLa2SMAgNuqtziuWLFCjz32mDIzM9WjRw/97W9/U1JSEsXxv/LzS+Rw1HlHoxaJWyKYkYkZmQCAe6r3ULVhGAoODtaePXsUFhamgIAAnefWjwAAAPBg9RZHLy8vbdq0SR9//LFuu+027dixQzabrTlmAwAAgAuptzjOnj1ba9eu1YMPPii73a7ly5dr3rx5zTEbAAAAXMh5lxwEAFdUXlGt4iLPW+aT8z7NyKRu5GJGJmbNvuRgYmLibx6Sfv311xv85p6AtaqB5pWeGif+eQAA5zhvcRw7dqyk/9yOp6SkRMOGDZO3t7c2btyoNm3aNNuAAAAAcA3nLY4DBw6UJK1cuVJr1qyRl9d/TocMDw/XqFGjmmc6AAAAuIx6L445deqUKioqan8+c+aMTp8+3aRDAQAAwPXUewPwmJgYjRw5UnfeeacMw9CWLVs0cuTI5pgNAAAALqTe4nj//fera9eu+vTTTyVJycnJ6tevX5MPBgAAANdS76Hq4cOHKzIyUikpKUpJSbno0vj111//5v0ft2/frldeeeWitv1reXl5mjhxYoO3AwAAgLPV+43jJZdcop9//lkdO3Zs0Bt169ZN3bp1O+/jBw4caND2f9GhQwe9/PLLjbItAAAA/J96i2NZWZnuuOMOdezYUa1bt679fXp6+gW9UXZ2tpYtWybpPyXy888/V0FBgVJSUnTVVZn+E/EAACAASURBVFdpzZo1kqQrr7xSgwYN0t/+9jfl5OSopqZGEydOVExMjNavX6/33ntPhYWF6tmzp7Zt26aPPvpIvr6+OnjwoB566CG98MILSkpKUlZWlk6ePKm//vWv+vnnn2Wz2TRjxgx16dJFcXFx2rVrlySpb9++mjNnjqKiorRixQp5e3trwoQJF7RvAAAALUG9xbEplhesqqrS22+/raysLD333HNav369Ro8eLUkaNmyYFi9erK5du+qpp55SSUmJRo8erVtuuUXSfw5Fb9q0ST4+Pjp+/Lg+/vhjRUREKCMjQ7GxsWe9z2OPPaZhw4bpjjvu0PHjx5WQkKANGzbod7/7nQ4ePChvb2/V1NTos88+U1RUlHbt2qVHH3200fcXAADAE9RbHHv16qUvv/xSu3btUlVVlW677Tb16tWrQW/at29fSVLnzp1VWFhoenzPnj0qLy/Xu+++K0kqLS1VTk6OJOmPf/yjfHz+M3ZsbKwyMjIUERGhzZs3a9WqVaqqqjprO99//72WLl0qSaqurtaRI0cUFhamTz75RD4+PkpKSlJGRoaKi4t18uRJderUqUH7BqDp2e2Bzh6hSXjqfjUEmdSNXMzIxKwpMqm3OG7YsEFLlizRgAEDZBiGZsyYoalTpzboljytWrWSpPMuaehwOLRo0SJ17dpVknTy5ElddtllSk9Pl7+/f+3z7rjjDj355JPau3evfve736lDhw7Kzc09azuvvfaa2rZtK0k6fvy4goKCVFNTo2XLlsnPz0/Tpk3T5s2blZ6erttvv/2i9wlA8/HENWlZa9eMTOpGLmZkYtZUa1XXe1X1q6++qnXr1mnevHlKSUnRO++80yTrVHt7e6u6ulqS1Lt3b7311luS/lP2YmNjdezYMdNr/Pz81LdvXz3++OOmw9S/bOfNN9+UJH333XcaMmSIysrK1LVrVx06dEiHDx9Wp06dFBoaquXLlysiIqLR9wsAAMBT1FscHQ6H2rdvX/tzhw4dapcfbEwhISFKT0/XqlWrNGXKFJWXlysmJkbjxo3TzJkzdc0119T5uri4OP373/+uXSLx11JSUvTll19qyJAhevDBB/X0008rICBANptNf/rTn2oPS/fu3VslJSUKCQlp9P0CAADwFDbDMIzfekJSUpKSkpIUGRkpSdq2bZtee+01rVq1qlkGdHXjF2bq+KkyZ48BtBjpqXEeeUiKQ21mZFI3cjEjE7OmOlRd7zmO8+fP1/33368FCxbIZrPJx8dHzz//fIPfGAAAAO7lvMWxpKREAQEB6ty5s7Zs2aLDhw+rpqZGN9xwQ+1VzQAAAGg5ztsA+/Tpoz/96U8KDw9XeHg4t6kBAABo4c5bHHfs2KFPP/1Un3zyid544w15eXmpX79+Cg8PV69eveTr69uccwIAAMDJzlscL7/8ckVFRSkqKkqS9NNPP2nPnj1atGiRfvjhB+3bt6/ZhgQAAIDz1Xuy4pEjR5SVlaXdu3frX//6l7p27dqgm397mpUpA5w9AtCilFdUO3sEAGixzlsclyxZoqysLJ05c0Z9+/ZVQkKC+vTpU7vqC/4jP79EDsdv3tGoReGWCGZkYkYmAOCezlscV6xYof79+2vSpEnq0aNHc84EAAAAF3Te4rhlyxZt375dqampOnz4sG677TaFh4fr9ttvV0BAw28gCQAAAPdy3rUDr7vuOt1zzz1atWqVMjIydPvtt2vr1q2Kjo7WPffc05wzAgAAwAVYWnT66NGjKigoUGVlpXx9fZtkrWoAAAC4tvMeql61apWys7O1d+9eXXbZZQoLC9Pw4cMVGhoqf3//5pzRpTXGuo+exm4PdPYILodMzKxmUl5RreIi1oMHAFfwmzcADwsL00MPPaTrrruuGUdyL+MXZur4Kf5RA5pKemqcuP4aAFzDeYvj3//+9+acAwAAAC6OkxUBAABgCcURAAAAllAcAQAAYAnFEQAAAJa0iOKYnZ2txMTEs343b948ff3112c9lpiYqOzsbGeMCAAA4PJaRHGsy2OPPaZu3bo5ewwAAAC3cd7b8Xiq1157Tdu2bVNZWZlmzpzp7HEAAADcRosqjuvXr1dmZqZeeuklTZ482dnjALCopay801L280KQSd3IxYxMzJoikxZTHA8ePKj58+frmWee0aWXXurscQBcgBMnPH/tGLs9sEXs54Ugk7qRixmZmJ2biZeXrVGWSW4x5zheeumlSktL09NPP63S0lJnjwMAAOB2WkxxvOqqq9S/f3/16tVLS5cudfY4AAAAbqfFHKr+xaxZsxQTE6OysjJnjwIAAOBWWkRxDA0NVWhoqCSpXbt22r17t+lxSVq1alWzzwYAAOAuWsyhagAAADQMxREAAACWUBwBAABgCcURAAAAllAcAQAAYEmLuKq6Ka1MGeDsEQCPVl5R7ewRAAD/RXFsoPz8EjkchrPHcBks+2RGJmZkAgDuiUPVAAAAsITiCAAAAEsojgAAALCEcxwbKCgowNkjuBy7PdDZI7gcMvk/XOwCAO6L4thA4xdm6vipMmePAbiN9NQ4Z48AALhIHKoGAACAJRRHAAAAWEJxBAAAgCUURwAAAFhCcQQAAIAlFEcAAABYQnH8ryNHjmju3LnOHgMAAMBlURz/6+jRozpy5IizxwAAAHBZbnED8OzsbC1atEgOh0NXXXWVWrdurZycHNXU1GjixImKiYnR+vXr9dFHHyk/P18nTpxQRESEkpOTZbPZ9OKLL+of//iHvL29ddttt2nmzJk6duyYJkyYoHbt2snf318nT55Ubm6uHn30UT388MPO3mUAAACX4xbFUZIOHz6s7du3a8WKFWrfvr2eeuoplZSUaPTo0brlllskSZ9//rk2btyoNm3aKCkpSVu3blWrVq2UlZWld999V76+vpo6darWrFmjfv366dChQ/r73/+u3//+98rOztayZcsojUAzYRlGMzIxI5O6kYsZmZg1RSZuUxyvv/56BQYGas+ePSovL9e7774rSSotLVVOTo4k6Y477tAVV1whSYqKitKnn36qVq1aKTo6WpdccokkadiwYdqwYYP69eunoKAg/f73v3fODgEt3IkTxc4ewaXY7YFkcg4yqRu5mJGJ2bmZeHnZFBQU0ODtuk1x9Pf3lyQ5HA4tWrRIXbt2lSSdPHlSl112mdLT0+Xt7V37fIfDIW9vbzkcDtO2qqurz9omAAAA6ud2F8f07t1bb731liTp+PHjio2N1bFjxyRJu3btUnFxsSoqKpSRkaGwsDD17t1bGRkZKi8vV3V1td5991317t3btF1vb+/aQgkAAAAztyuOU6ZMUXl5uWJiYjRu3DjNnDlT11xzjSTp8ssv18SJExUbG6vw8HD17dtXERERCg8P17BhwxQdHa0rr7xSY8eONW23U6dOKi4u1syZM5t7lwAAANyCzTAMw9lDNIb169frs88+05NPPtms7zt+YaaOnypr1vcE3Fl6apwkznE8F+domZFJ3cjFjEzMmuocR7f7xhEAAADO4TYXx9QnPj5e8fHxzh4DAADAY/GNIwAAACyhOAIAAMASiiMAAAAs8ZhzHJ1lZcoAZ48AuJXyimr5t+KvHgBwR/zt3UD5+SVyODzijkaNglsimJGJmT9rygKAW+JQNQAAACyhOAIAAMASiiMAAAAs4RzHBmqM5Xs8jZ3z10xaaiblFdUqLmJJTgDwFBTHBmKtauD80lPjxGVBAOA5OFQNAAAASyiOAAAAsITiCAAAAEsojgAAALCE4ggAAABL3LY4Jicna/369Rf8uqVLl+qf//ynJCkxMVHZ2dmNPRoAAIBHctvieLH27t2rmpoaZ48BAADgdtymOBqGoSeeeEIDBw5UYmKifvzxR0nShg0bNHToUMXFxWnu3LmqqKiQJL3xxhsaMWKEYmJiNHToUH3//ffasGGDDhw4oJSUFH377beSpHfeeUdDhw7VHXfcoaysLKftHwAAgKtzm+L4wQcf6F//+pfef/99Pffcc/rxxx9VVlamtWvXas2aNdq4caOCgoK0cuVKlZSUaNu2bVq1apXef/99hYeHa/Xq1frLX/6im2++WQsXLlRwcLAkKTAwUO+9955SUlL0/PPPO3kvAQAAXJfbrBzz2WefacCAAfL19dXll1+usLAwGYahH374QSNHjpQkVVVV6Y9//KMCAgKUmpqqjIwMHT58WLt27dJNN91U53YjIyMlSTfeeKNOnTrVbPsDtBTnW26xpS7D+FvIxIxM6kYuZmRi1hSZuE1xtNlsMgyj9mcfHx/V1NRo8ODBSklJkSSdOXNGNTU1OnbsmBITEzV27FiFhYXpiiuu0DfffFPndr29vWu3D6DxnThhXnTQbg+s8/ctGZmYkUndyMWMTMzOzcTLy6agoIAGb9dtDlX36dNHmzdvVmVlpU6fPq1du3ZJkrZu3ar8/HwZhqFHHnlEr732mr7++mtde+21uvvuu9WtWzdt27at9oIYb29vLo4BAAC4CG7zjWNkZKS+/vprxcTE6IorrlCnTp0UGBioKVOmaNy4cXI4HLrppps0adIkVVdX66233lJUVJQMw1BISIhycnIkSX379tXDDz+sp556ysl7BAAA4F5sxq+P/+KCjV+YqeOnypw9BuCS0lPjOFRtEZmYkUndyMWMTMxa/KFqAAAAOBfFEQAAAJZQHAEAAGAJxREAAACWUBwBAABgidvcjsdVrUwZ4OwRAJdVXlHt7BEAAI2I4thA+fklcji4o9EvuCWCGZkAADwFh6oBAABgCcURAAAAllAcAQAAYAnFEQAAAJZwcUwDNca6j57Gbg909ggupyVmUl5RreIi1nEHAE9CcWyg8QszdfwU/zgC50pPjRPXkgOAZ+FQNQAAACyhOAIAAMASiiMAAAAsoTgCAADAEoojAAAALHHp4rh9+3a98sorF/Sa5ORkrV+/Xnl5eZo4cWITTQYAANDyuPTteA4cOHDRr+3QoYNefvnlRpwGAACgZWuy4lhdXa1HHnlEOTk5OnnypIKDgzVjxgxNnDhRWVlZkqS0tDRJ0uTJkzV37lzl5ORIkhISEtSzZ0+tWbNGknTllVfq6NGj2r9/v44dO6axY8fqxhtv1JIlS1ReXq6ioiLNmTNHkZGRte+fm5urpKQkZWVl6eDBg1qwYIFKS0tVUFCgSZMmacyYMUpLS1NeXp5++OEH/fTTTxoxYoTuu+++pooEAADArTVZcdy3b598fX319ttvy+FwaNy4cdqxY8d5n3v69Glt2LBBeXl5Sk1N1ciRIzV69GhJ0rBhw5SWlqbKykpt2rRJkvTAAw9o4cKF6tSpkz755BM9/vjjZxXHX1u3bp3uv/9+9enTR0eOHFFsbKzGjBkjSfr222+1evVqFRcXKzIyUnfddZfatGnTBIkALc9vrZjTElfTqQ+ZmJFJ3cjFjEzMmiKTJiuOISEhatu2rVavXq3vv/9ehw8fVmlpaZ3P7dy5sw4dOqTx48crLCxMs2bNqvN53bt3r/3zokWLtH37dm3ZskVffvmlzpw5c95ZkpOTtWvXLq1YsUIHDx48a47Q0FD5+fkpKChIbdu2VXFxMcURaCQnTtS9dozdHnjex1oqMjEjk7qRixmZmJ2biZeXrVGWSW6yi2M+/PBDPfTQQ/L391d8fLxCQkIkSYZh1D6nurpaktSuXTtlZGRo7NixOnTokIYOHaqioiLTNv39/Wv/nJCQoK+++ko333yzJk+e/JuzTJ8+XVu3blWnTp00ffr0sx5r1apV7Z9tNttZ8wEAAOD/NFlx/OSTTzR48GANGzZMbdq0UXZ2tgIDA1VYWKiCggJVVlZq165dkv5TMmfOnKnw8HClpKSodevWOnbsmLy9vWvL5a8VFhbq8OHDmjZtmsLCwvThhx+qpqbmvLPs3r1bDzzwgCIjI7Vz505J+s3nAwAAwKzJDlWPGDFCDz30kDIyMuTr66uePXuqoKBAEyZM0PDhw9WxY0d169ZNkhQWFqbMzExFR0erVatWio2NVXBwsIqKijR79mxdccUVZ227bdu2Gj58uKKjo+Xj46PevXurvLz8vIfCp06dqoSEBLVq1UpdunTRVVddpdzc3KbadQAAAI9kMzg22yDjF2bq+KkyZ48BuJz01DjOcbwAZGJGJnUjFzMyMXO7cxwBAADgWSiOAAAAsITiCAAAAEsojgAAALCE4ggAAABLmux2PC3FypQBzh4BcEnlFeZ7sAIA3BvFsYHy80vkcHBHo19wSwQzMgEAeAoOVQMAAMASiiMAAAAsoTgCAADAEs5xbKDGWL7H09jtgc4eweV4ciblFdUqLmLZTQBoCSiODcRa1Wjp0lPjxKU/ANAycKgaAAAAllAcAQAAYAnFEQAAAJZQHAEAAGAJxREAAACWUBwBAABgiUcUxzlz5uiOO+7Q+++/7+xRAAAAPJZH3Mfxvffe01dffSU/Pz9njwIAAOCx3L44Tp48WYZhaMSIEerRo4e++eYbnT59Wu3bt9eSJUt0xRVXKD09XcuXL5fNZlO3bt20YMECVVZW6m9/+5tycnJUU1OjiRMnKiYmxtm7AwAA4LJshmEYzh6ioYKDg5WZmanFixfrueeek5eXl2bNmqUuXbooOjpaQ4cO1fr169WxY0fNnDlTAwcO1P79+9W+fXslJSWppKREo0eP1vLly3X11Vdf0HuzcgxauvTUOGePAABoJm7/jeMvrr32Ws2ePVvr1q3ToUOHtH//fl1zzTXat2+fevbsqY4dO0qSFi1aJEl64YUXVF5ernfffVeSVFpaqpycnAsujgCkEycubNFBuz3wgl/j6cjEjEzqRi5mZGJ2biZeXjYFBQU0eLseUxwPHDigGTNm6O6779bAgQPl5eUlwzDk4+Mjm81W+7yCggJJksPh0KJFi9S1a1dJ0smTJ3XZZZc5ZXYAAAB34BFXVUvS3r171atXL40ZM0bXXXedPvroI9XU1Khbt27av3+/Tpw4IUl6/PHH9eGHH6p379566623JEnHjx9XbGysjh075sxdAAAAcGke841jVFSUpkyZoiFDhkiSbr75ZuXm5qpDhw6aN2+exo8fL4fDoR49eig+Pl5lZWV65JFHFBMTo5qaGs2cOVPXXHONk/cCAADAdXlEcfz2228lSevWravz8UGDBmnQoEFn/S4gIECLFy9u8tkAAAA8hcccqgYAAEDTojgCAADAEoojAAAALKE4AgAAwBKKIwAAACzxiKuqnWllygBnjwA4VXlFtbNHAAA0E4pjA+Xnl8jhcPvlvhsNyz6ZkQkAwFNwqBoAAACWUBwBAABgCcURAAAAlnCOYwMFBQU4ewSXY7cHOnsEl+PJmZRXVKu4qMzZYwAAmgHFsYHGL8zU8VP8o4mWKz01Tlz6AwAtA4eqAQAAYAnFEQAAAJZQHAEAAGAJxREAAACWUBwBAABgCcURAAAAlrTo4piWlqa0tDRnjwEAAOAWWnRxBAAAgHVudQNwwzC0ePFibdu2Td7e3ho1apS2bdumyy67TDk5OXr22Wf1+eefa+PGjSorK5Ovr69SU1N1ww036KmnntLu3bvl5eWlyMhITZkyRZL01VdfafTo0crLy1N8fLymTp3q5L0EAABwTW5VHLds2aIvvvhC6enpqqqqUkJCgioqKtSrVy8tW7ZMJSUleuqpp7Rq1Sr5+/vrueee0+rVq3Xvvfdq586dysjIUFlZmebMmaOKigpJUn5+vtasWaOSkhL1799f99xzjwICWEYQuBAXs6SiJy/DeLHIxIxM6kYuZmRi1hSZuFVx3Lt3rwYPHiw/Pz/5+flp48aNSkxMVPfu3SVJAQEBSk1NVUZGhg4fPqxdu3bppptuUocOHdSqVSuNHj1aEREReuihh9SqVStJUt++feXn56fLL79c7dq10+nTpymOwAU6ceLCFh202wMv+DWejkzMyKRu5GJGJmbnZuLlZVNQUMP7jVud4+jj4yObzVb7c25urkpLS+Xv7y9JOnbsmEaNGqXi4mKFhYVp6NChMgxDPj4+WrdunaZNm6bCwkKNHj1ahw4dqt3mL2w2mwzDaN6dAgAAcBNuVRxDQkKUmZmpqqoqlZWVacKECcrLy6t9/Ouvv9a1116ru+++W926ddO2bdtUU1Ojf/3rXxo7dqxCQkI0e/ZsderUqbY4AgAAwBq3OlR955136sCBA4qPj5fD4VBSUpI2b95c+/htt92mt956S1FRUTIMQyEhIcrJydEf//hH9ejRQzExMbrkkkvUs2dPhYWF6X//93+duDcAAADuxWZwbLZBxi/M1PFTZc4eA3Ca9NQ4znFsBGRiRiZ1IxczMjHjHEcAAAA4FcURAAAAllAcAQAAYAnFEQAAAJZQHAEAAGCJW92OxxWtTBng7BEApyqvqHb2CACAZkJxbKD8/BI5HNzR6BfcEsGMTAAAnoJD1QAAALCE4ggAAABLKI4AAACwhOIIAAAASyiOAAAAsITiCAAAAEsojgAAALCE4ggAAABLKI4AAACwhOIIAAAASyiOAAAAsITiCAAAAEsojgAAALCE4ggAAABLKI4AAACwhOIIAAAAS3ycPYC78/KyOXsEl0MmZmRiRiZmZGJGJnUjFzMyMft1Jo2Vj80wDKNRtgQAAACPxqFqAAAAWEJxBAAAgCUURwAAAFhCcQQAAIAlFEcAAABYQnEEAACAJRRHAAAAWEJxBAAAgCUURwAAAFhCcQQAAIAlFMeLkJ6erqioKA0YMECrV6929jhNLjExUdHR0YqLi1NcXJy+/PLL82awZ88eDRkyRAMGDNCSJUtqf//NN98oPj5eAwcO1Lx581RdXe2MXWmwkpISxcTEKDc3V9KF7+/Ro0d11113adCgQbrvvvt05swZSVJRUZEmTZqkwYMH66677tKJEyeaf+cu0rmZzJkzRwMGDKj9vGzdulVS42Xl6pYtW6bo6GhFR0fr6aeflsTnRKo7l5b+WXnuuecUFRWl6OhovfLKK5L4rNSVSUv/nPziqaeeUnJysqTG+zxUVlZq5syZGjx4sIYOHap///vf9Q9i4IL8/PPPRkREhHHq1CnjzJkzxpAhQ4ycnBxnj9VkHA6HcfvttxtVVVW1vztfBmVlZUa/fv2MH3/80aiqqjLuvfde46OPPjIMwzCio6ONffv2GYZhGHPmzDFWr17tlP1piP379xsxMTFG165djSNHjlzU/k6aNMl4//33DcMwjGXLlhlPP/20YRiG8eijjxorVqwwDMMw3nvvPWPatGnNvXsX5dxMDMMwYmJijLy8vLOe15hZubLdu3cbo0aNMioqKozKykojKSnJSE9Pb/Gfk7pyyczMbNGflezsbGP06NFGVVWVUVZWZkRERBjffPNNi/6s1JXJv//97xb9OfnFnj17jNDQUGP27NmGYTTe5+Hvf/+7MX/+fMMwDOOzzz4zRowYUe8sfON4gfbs2aPevXurbdu2at26tQYOHKgtW7Y4e6wm8/3330uS7r33XsXGxuqNN944bwZfffWVrr32Wl199dXy8fHRkCFDtGXLFv30008qLy9Xjx49JEnx8fFumdnatWv18MMPq3379pJ0wftbVVWlvXv3auDAgWf9XpI++ugjDRkyRJIUExOjnTt3qqqqygl7eWHOzaSsrExHjx7V3LlzNWTIEC1dulQOh6NRs3JldrtdycnJ8vPzk6+vrzp16qTDhw+3+M9JXbkcPXq0RX9WevXqpddff10+Pj7Kz89XTU2NioqKWvRnpa5M/P39W/TnRJIKCwu1ZMkSTZ48WZIa9fPw0UcfKTY2VpIUEhKigoICHT169DfnoTheoOPHj8tut9f+3L59e+Xl5TlxoqZVVFSkPn366Pnnn9err76qNWvW6OjRo3VmcL5szv293W53y8wee+wx3XrrrbU/X+j+njp1SgEBAfLx8Tnr9+duy8fHRwEBASooKGiO3WqQczM5efKkevfurccff1xr167VP//5T73zzjuNmpUr69y5c+1f5ocPH9bmzZtls9la/Oekrlz69u3boj8rkuTr66ulS5cqOjpaffr04e8UmTOprq5u8Z+Tv/71r3rwwQfVpk0bSeZ/exryeahrWz///PNvzkNxvEAOh0M2m632Z8MwzvrZ0/zP//yPnn76aQUGBuryyy/X8OHDtXTp0jozOF82nprZhe5vXft9vhwMw5CXl/v953n11Vfr+eefV/v27XXJJZcoMTFRO3bsaNKsXFFOTo7uvfde/f/27jQkqvaN4/h3sHHKlB4L2rSoaIEU2ywqRMtQX2hZUWSFKUQRRRZtWLSRmZESWlAEkZjYQptDZhm0ENSULRBYGRQtGlJZabbpbM+LHobsrzb22KN//X1eec7x3HPdFxeHa+4znLNu3Tr69eunOvnHj3kZNGiQagVISkrCYrFQUVHB8+fPVSvUz4nFYunQdXLixAn69OnDhAkTXPtash5+PsedOvn/qKI2pHfv3vV+ZPz27VvXbbr26M6dO1gsFte20+nEz8+vwRw0lpuf91dWVraLnDV3vt27d6empga73V7v/+H7t+XKykoAbDYbnz9/5q+//voPZ9MyHj9+TFFRkWvb6XTSqVOnFs1VW3f37l0SExNZvXo1M2bMUJ384+e8dPRaefr0KY8ePQKgS5cuREZGcuvWrQ5dKw3lpLCwsEPXSWFhIdevXyc2NpY9e/Zw+fJlTp482WL10KtXL968efM/YzVFjWMzTZw4EYvFwvv37/n69SsXL14kNDS0tcP6Y2pqati1axe1tbV8+vSJM2fOkJ6e3mAORowYwbNnz3jx4gV2u52CggJCQ0Px8/PDZDJx9+5dAMxmc7vIWXPnazQaCQ4OprCwEID8/HxXHsLCwsjPzwe+XyiCg4MxGo2tM7F/wel0smPHDqqrq7FarRw/fpyIiIgWzVVbVlFRwbJly8jIyCA6OhpQnUDDWuUu/wAABZ1JREFUeenotVJeXs7GjRupq6ujrq6OS5cuERcX16FrpaGcjB07tkPXSXZ2NgUFBZjNZpKSkggPDyctLa3F6iEsLAyz2Qx8XygymUz07du3yZgMTqfT+acm3F6dPXuWAwcOYLVamTVrFosWLWrtkP6ozMxMioqKcDgczJs3j4SEhEZzYLFYSEtLo7a2lrCwMNavX4/BYKC0tJSNGzfy6dMnAgICSEtLw9PTs5Vn9nvCw8M5fPgw/v7+zZ7vq1evSE5O5t27d/Tp04fdu3fTrVs3qqqqSE5OpqysDB8fHzIyMvD392/tqbrtx5zk5eWRl5eHzWYjMjKSNWvWAM2vjcZy1ZZt376dU6dO0b9/f9e+uLg4BgwY0KHrpLG8OByODlsrAHv37uX8+fN4eHgQGRnJ8uXLO/w1paGcdORryo9Onz5NcXExO3fubLF6qK2tZfPmzZSUlODp6cn27dsJCAhoMg41jiIiIiLiFt2qFhERERG3qHEUEREREbeocRQRERERt6hxFBERERG3qHEUEREREbd0au0ARETammHDhjF06NB6b1AIDAwkNTW1FaNqvqNHj1JTU8PixYtbOxQRaSf0OB4RkZ8MGzYMi8VC9+7dWzsUEZE2RSuOIiLNEBgYyJQpUygtLSUjIwMvLy9SU1OpqqrCbrcTHx/PrFmzAMjKyuLs2bP4+voSHBxMSUkJubm5JCcnM2TIEBYuXAhQb/v169ds27aNiooKrFYr0dHRLFmyhPLychITEwkLC+P+/ft8/PiRtWvXEhERgc1mIz09natXr+Lh4cGoUaPYsmULBw4c4MOHD2zevLnRcW02GykpKdy7dw+j0Yi/vz9paWl07dq1NdMsIm2UGkcRkQYkJCTUu1V96NAhevTogdVqZfLkyWRlZWGz2YiNjWXXrl0EBARQU1PDnDlzGDx4MG/evOHixYvk5+djMplYunSpW5+7du1aEhMTCQ8Pp7a2lkWLFtG/f3+CgoIoKysjJCSETZs2UVRUxI4dO4iIiODIkSM8ePAAs9mMp6cnq1atcr127Ffj9uzZk+LiYgoLCzEYDKSnp/P48WNGjx7dovkUkfZBjaOISANycnIavVUdHBwMwPPnz3n58iUbNmxwHfv27RsPHz7kyZMnRERE4O3tDcCcOXPIyclp8jO/fPnC7du3qa6uJisry7WvtLSUoKAg17tlAYYPH05VVRUAN27cIDY2ls6dOwPfXxMK31/f9qtxQ0JC8PDwYPbs2YSEhBAVFUVQUFDzEyYiHYIaRxGRZvLy8gLAbrfj4+OD2Wx2HausrMTHx4fMzEx+/Am50Wh0/W0wGOods1qtADgcDpxOJ8eOHaNLly4AvH//HpPJxIcPHzAaja5VUIPB4Dq/U6f6l/LKykocDodru6lxu3btitls5t69e9y8eZOVK1eycOFC5s+f/++SJCLtkh7HIyLymwYOHEjnzp1djWNFRQUxMTGUlJQwadIkLly4QHV1NQ6Hg/z8fNd5vr6+lJSUAPD69WuKi4sB8Pb2ZuTIkWRnZwPw8eNH5s6dy6VLl5qMY8KECRQUFFBXV4fD4WDr1q2cO3fOdbypca9cuUJiYiKjRo1i+fLlTJ8+3RWbiMjPtOIoIvKbPD092bdvH6mpqRw8eBCbzcaKFSsYM2YMAAsWLGDevHmYTCb8/Pxc58XHx7NmzRqioqLw9/dn/PjxrmMZGRmkpKQwdepU6urqiImJYdq0aZSXlzcaR1xcHK9evWLmzJk4nU7GjRtHfHw8+/fv/+W4druda9euERMTg5eXF926dSMlJeUPZEtE2gM9jkdE5D9w4cIF8vLyyM3Nbe1QRER+m25Vi4iIiIhbtOIoIiIiIm7RiqOIiIiIuEWNo4iIiIi4RY2jiIiIiLhFjaOIiIiIuEWNo4iIiIi45W/Rpb/7o6gXcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig,ax = plt.subplots()\n",
    "d = pd.DataFrame(data={\"words\":words[::-1],\"counts\":counts[::-1]})\n",
    "ax.barh(width = 'counts', y= 'words',data = d)\n",
    "ax.set_yticks(d['words'])\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(10)\n",
    "ax.set_ylabel(\"Words in corpus\")\n",
    "ax.set_xlabel(\"Frequencies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NMF Modelling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Filter infrequent terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary.filter_extremes(no_below=100,no_above=0.1,keep_n=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Convert tokens to indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.token.apply(lambda x:dictionary.doc2bow(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [(0, 1), (1, 1), (2, 1), (3, 1)]\n",
       "1                [(4, 1), (5, 1), (6, 1)]\n",
       "2       [(7, 1), (8, 1), (9, 1), (10, 1)]\n",
       "3    [(11, 1), (12, 1), (13, 1), (14, 1)]\n",
       "4    [(14, 1), (15, 1), (16, 1), (17, 1)]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Split data to into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = sklearn.model_selection.train_test_split(df,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2                 [(7, 1), (8, 1), (9, 1), (10, 1)]\n",
       "5                       [(18, 1), (19, 1), (20, 1)]\n",
       "7     [(25, 1), (26, 1), (27, 1), (28, 1), (29, 1)]\n",
       "12                               [(41, 1), (42, 1)]\n",
       "13             [(43, 1), (44, 1), (45, 1), (46, 1)]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Build NMF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_topics = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import nmf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken for 20 epochs is 6.5680731972058615\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "passes = 20\n",
    "nmfModel = nmf.Nmf(train,num_topics=num_topics,passes=passes)\n",
    "print(\"time taken for {} epochs is {}\".format(passes, (time.time()-start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.35841745141450837),\n",
       " (2, 0.034326409005173995),\n",
       " (3, 0.5537606935053131),\n",
       " (4, 0.045632210674669375)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmfModel[test.to_list()[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "words in topic 1 :\n",
      " ['australia', 'death', 'report', 'council', 'rise', 'market', 'south', 'australian', 'rural', 'open']\n",
      "\n",
      "words in topic 2 :\n",
      " ['plan', 'charg', 'council', 'murder', 'water', 'drug', 'assault', 'child', 'woman', 'face']\n",
      "\n",
      "words in topic 3 :\n",
      " ['polic', 'investig', 'probe', 'miss', 'offic', 'search', 'shoot', 'arrest', 'crash', 'hunt']\n",
      "\n",
      "words in topic 4 :\n",
      " ['court', 'face', 'govt', 'kill', 'accus', 'crash', 'fund', 'urg', 'murder', 'water']\n",
      "\n",
      "words in topic 5 :\n",
      " ['say', 'need', 'minist', 'australian', 'govern', 'labor', 'union', 'health', 'chang', 'trump']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(num_topics):\n",
    "    topic = nmfModel.show_topic(i)\n",
    "    topic = [dictionary[int(x[0])] for x in topic]\n",
    "    print(\"words in topic {} :\\n {}\\n\".format(i+1,topic))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For various number of topic selection, it is observed that NMF does not learn the semantics in text as well as LDA. Words having similar meaning are classified into different topics. The training time for same number of epochs and number of topics is lesser for NMF in comparison to LDA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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