tweets-sentiment-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np \n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_submisson = pd.read_csv(\"data/test.csv\", index_col=\"Id\")\n",
    "tweets = pd.read_csv(\"data/train.csv\", index_col=\"Id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Category</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>635769805279248384</th>\n",
       "      <td>negative</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635930169241374720</th>\n",
       "      <td>neutral</td>\n",
       "      <td>IOS 9 App Transport Security. Mm need to check...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635950258682523648</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Mar if you have an iOS device, you should down...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636030803433009153</th>\n",
       "      <td>negative</td>\n",
       "      <td>@jimmie_vanagon my phone does not run on lates...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636100906224848896</th>\n",
       "      <td>positive</td>\n",
       "      <td>Not sure how to start your publication on iOS?...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Category  \\\n",
       "Id                             \n",
       "635769805279248384  negative   \n",
       "635930169241374720   neutral   \n",
       "635950258682523648   neutral   \n",
       "636030803433009153  negative   \n",
       "636100906224848896  positive   \n",
       "\n",
       "                                                                Tweet  \n",
       "Id                                                                     \n",
       "635769805279248384                                      Not Available  \n",
       "635930169241374720  IOS 9 App Transport Security. Mm need to check...  \n",
       "635950258682523648  Mar if you have an iOS device, you should down...  \n",
       "636030803433009153  @jimmie_vanagon my phone does not run on lates...  \n",
       "636100906224848896  Not sure how to start your publication on iOS?...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.head()"
   ]
  },
  {
   "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>Tweet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>628949369883000832</th>\n",
       "      <td>dear @Microsoft the newOoffice for Mac is grea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628976607420645377</th>\n",
       "      <td>@Microsoft how about you make a system that do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629023169169518592</th>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629179223232479232</th>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629186282179153920</th>\n",
       "      <td>If I make a game as a #windows10 Universal App...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                Tweet\n",
       "Id                                                                   \n",
       "628949369883000832  dear @Microsoft the newOoffice for Mac is grea...\n",
       "628976607420645377  @Microsoft how about you make a system that do...\n",
       "629023169169518592                                      Not Available\n",
       "629179223232479232                                      Not Available\n",
       "629186282179153920  If I make a game as a #windows10 Universal App..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_submisson.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Category</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5969</td>\n",
       "      <td>5969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>positive</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2888</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category          Tweet\n",
       "count       5969           5969\n",
       "unique         3           5417\n",
       "top     positive  Not Available\n",
       "freq        2888            548"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive    2888\n",
      "neutral     2125\n",
      "negative     956\n",
      "Name: Category, dtype: int64\n",
      "\n",
      "positive    0.483833\n",
      "neutral     0.356006\n",
      "negative    0.160161\n",
      "Name: Category, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(tweets[\"Category\"].value_counts())\n",
    "print()\n",
    "print(tweets[\"Category\"].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    5969\n",
      "Name: Category, dtype: int64\n",
      "False    5969\n",
      "Name: Tweet, dtype: int64\n",
      "\n",
      "Not available 548\n"
     ]
    }
   ],
   "source": [
    "print(tweets[\"Category\"].isna().value_counts())\n",
    "print(tweets[\"Tweet\"].isna().value_counts())\n",
    "print(\"\\nNot available\", len(tweets[tweets[\"Tweet\"] == \"Not Available\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive    289\n",
      "neutral     172\n",
      "negative     87\n",
      "Name: Category, dtype: int64\n",
      "\n",
      "positive    0.527372\n",
      "neutral     0.313869\n",
      "negative    0.158759\n",
      "Name: Category, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(tweets[tweets[\"Tweet\"] == \"Not Available\"][\"Category\"].value_counts())\n",
    "print()\n",
    "print(tweets[tweets[\"Tweet\"] == \"Not Available\"][\"Category\"].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@jimmie_vanagon my phone does not run on latest IOS which may account for problem the other day .. time it was replaced\n",
      "@user my phone does not run on latest IOS which may account for problem the other day .. time it was replaced\n",
      " jimmie vanagon my phone does not run on latest     which may account for problem the other day    time it was replaced\n"
     ]
    }
   ],
   "source": [
    "def replace_pattern(txt, pattern, replaced_text=\"\"):\n",
    "    r = re.findall(pattern, txt)\n",
    "    for i in set(r):\n",
    "        txt = txt.replace(i, replaced_text)\n",
    "    return txt    \n",
    "\n",
    "tweets.iloc[3][\"Tweet\"]\n",
    "\n",
    "print(tweets.iloc[3][\"Tweet\"])\n",
    "print(replace_pattern(tweets.iloc[3][\"Tweet\"], r\"@[\\w]*\", \"@user\"))\n",
    "print(replace_pattern(tweets.iloc[3][\"Tweet\"], r\"[^a-z]\", \" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_hashtag(text):\n",
    "    pattern = r\"#[\\w]*\"\n",
    "    r = re.findall(pattern, text)\n",
    "    hashtags = []\n",
    "    for i in r:\n",
    "        hashtags.append(i[1:])\n",
    "        text = re.sub(i, i[1:], text)\n",
    "    return text, hashtags\n",
    "    \n",
    "#     hashatags \n",
    "# delete_hashtag(tweets[0, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(nltk.corpus.stopwords.words('english')) \n",
    "\n",
    "def remove_stopwords(tokens):\n",
    "    return [token for token in tokens if (not token in stop_words and len(token) > 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[':)']\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "RE_EMOTICONS = re.compile(\"(:-?\\))|(:p)|(:d+)|(:-?\\()|(:/)|(;-?\\))|(<3)|(=\\))|(\\)-?:)|(:'\\()|(8\\))\")\n",
    "text = \"Look @Qualcomm I found the 1st #Snapdragon Phone in my stuff from #Toshiba and @Microsoft. Still Working :)\"\n",
    "print([\"\".join(emot) for emot in RE_EMOTICONS.findall(text)])\n",
    "text = \"Not Available\"\n",
    "print([\"\".join(emot) for emot in RE_EMOTICONS.findall(text)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(data):\n",
    "    data[\"PreprocessedTweet\"] = None\n",
    "    data[\"Hashtags\"] = None\n",
    "    data[\"Emoticons\"] = None\n",
    "    data[\"Tokens\"] = None\n",
    "\n",
    "    for index, row in data.iterrows():\n",
    "        tweet = row[\"Tweet\"]\n",
    "        prepared_tweet = tweet.lower().strip()\n",
    "        prepared_tweet = replace_pattern(prepared_tweet, r\"http[\\w:/.]*\")\n",
    "        emots = [\"\".join(emot) for emot in RE_EMOTICONS.findall(prepared_tweet)]\n",
    "\n",
    "        prepared_tweet = replace_pattern(prepared_tweet, r\"@[\\w]*\")\n",
    "        prepared_tweet, hashtags = remove_hashtag(prepared_tweet)\n",
    "        prepared_tweet = replace_pattern(prepared_tweet, r\"[^a-z]\", \" \")\n",
    "\n",
    "        tokens = nltk.word_tokenize(prepared_tweet)\n",
    "        tokens = remove_stopwords(tokens)\n",
    "\n",
    "        row[\"PreprocessedTweet\"] = prepared_tweet\n",
    "        row[\"Hashtags\"] = hashtags\n",
    "        row[\"Emoticons\"] = emots\n",
    "        row[\"Tokens\"] = tokens + emots\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Category</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>PreprocessedTweet</th>\n",
       "      <th>Hashtags</th>\n",
       "      <th>Emoticons</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>635769805279248384</th>\n",
       "      <td>negative</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>not available</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[available]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635930169241374720</th>\n",
       "      <td>neutral</td>\n",
       "      <td>IOS 9 App Transport Security. Mm need to check...</td>\n",
       "      <td>ios   app transport security  mm need to check...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[ios, app, transport, security, need, check, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635950258682523648</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Mar if you have an iOS device, you should down...</td>\n",
       "      <td>mar if you have an ios device  you should down...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[mar, ios, device, download, app]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636030803433009153</th>\n",
       "      <td>negative</td>\n",
       "      <td>@jimmie_vanagon my phone does not run on lates...</td>\n",
       "      <td>my phone does not run on latest ios which may...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[phone, run, latest, ios, may, account, proble...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636100906224848896</th>\n",
       "      <td>positive</td>\n",
       "      <td>Not sure how to start your publication on iOS?...</td>\n",
       "      <td>not sure how to start your publication on ios ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[sure, start, publication, ios, live, helping,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636176272947744772</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Two Dollar Tuesday is here with Forklift 2, Qu...</td>\n",
       "      <td>two dollar tuesday is here with forklift    qu...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[two, dollar, tuesday, forklift, quickkey, ios...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636276311560859648</th>\n",
       "      <td>neutral</td>\n",
       "      <td>If you're not already signed up to test my iOS...</td>\n",
       "      <td>if you re not already signed up to test my ios...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[already, signed, test, ios, game, nows, chance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636302400546975744</th>\n",
       "      <td>neutral</td>\n",
       "      <td>YouTube Gaming Officially Launches On Web, And...</td>\n",
       "      <td>youtube gaming officially launches on web  and...</td>\n",
       "      <td>[webseries]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[youtube, gaming, officially, launches, web, a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636356154151575552</th>\n",
       "      <td>neutral</td>\n",
       "      <td>YouTube Gaming Launches Tomorrow with iOS and ...</td>\n",
       "      <td>youtube gaming launches tomorrow with ios and ...</td>\n",
       "      <td>[ios, game]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[youtube, gaming, launches, tomorrow, ios, and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636360240921972736</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@astrill Yashan from BBC @bbcchinese the VPN a...</td>\n",
       "      <td>yashan from bbc  the vpn access on ios may be...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[yashan, bbc, vpn, access, ios, may, limited, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Category  \\\n",
       "Id                             \n",
       "635769805279248384  negative   \n",
       "635930169241374720   neutral   \n",
       "635950258682523648   neutral   \n",
       "636030803433009153  negative   \n",
       "636100906224848896  positive   \n",
       "636176272947744772   neutral   \n",
       "636276311560859648   neutral   \n",
       "636302400546975744   neutral   \n",
       "636356154151575552   neutral   \n",
       "636360240921972736   neutral   \n",
       "\n",
       "                                                                Tweet  \\\n",
       "Id                                                                      \n",
       "635769805279248384                                      Not Available   \n",
       "635930169241374720  IOS 9 App Transport Security. Mm need to check...   \n",
       "635950258682523648  Mar if you have an iOS device, you should down...   \n",
       "636030803433009153  @jimmie_vanagon my phone does not run on lates...   \n",
       "636100906224848896  Not sure how to start your publication on iOS?...   \n",
       "636176272947744772  Two Dollar Tuesday is here with Forklift 2, Qu...   \n",
       "636276311560859648  If you're not already signed up to test my iOS...   \n",
       "636302400546975744  YouTube Gaming Officially Launches On Web, And...   \n",
       "636356154151575552  YouTube Gaming Launches Tomorrow with iOS and ...   \n",
       "636360240921972736  @astrill Yashan from BBC @bbcchinese the VPN a...   \n",
       "\n",
       "                                                    PreprocessedTweet  \\\n",
       "Id                                                                      \n",
       "635769805279248384                                      not available   \n",
       "635930169241374720  ios   app transport security  mm need to check...   \n",
       "635950258682523648  mar if you have an ios device  you should down...   \n",
       "636030803433009153   my phone does not run on latest ios which may...   \n",
       "636100906224848896  not sure how to start your publication on ios ...   \n",
       "636176272947744772  two dollar tuesday is here with forklift    qu...   \n",
       "636276311560859648  if you re not already signed up to test my ios...   \n",
       "636302400546975744  youtube gaming officially launches on web  and...   \n",
       "636356154151575552  youtube gaming launches tomorrow with ios and ...   \n",
       "636360240921972736   yashan from bbc  the vpn access on ios may be...   \n",
       "\n",
       "                       Hashtags Emoticons  \\\n",
       "Id                                          \n",
       "635769805279248384           []        []   \n",
       "635930169241374720           []        []   \n",
       "635950258682523648           []        []   \n",
       "636030803433009153           []        []   \n",
       "636100906224848896           []        []   \n",
       "636176272947744772           []        []   \n",
       "636276311560859648           []        []   \n",
       "636302400546975744  [webseries]        []   \n",
       "636356154151575552  [ios, game]        []   \n",
       "636360240921972736           []        []   \n",
       "\n",
       "                                                               Tokens  \n",
       "Id                                                                     \n",
       "635769805279248384                                        [available]  \n",
       "635930169241374720  [ios, app, transport, security, need, check, p...  \n",
       "635950258682523648                  [mar, ios, device, download, app]  \n",
       "636030803433009153  [phone, run, latest, ios, may, account, proble...  \n",
       "636100906224848896  [sure, start, publication, ios, live, helping,...  \n",
       "636176272947744772  [two, dollar, tuesday, forklift, quickkey, ios...  \n",
       "636276311560859648   [already, signed, test, ios, game, nows, chance]  \n",
       "636302400546975744  [youtube, gaming, officially, launches, web, a...  \n",
       "636356154151575552  [youtube, gaming, launches, tomorrow, ios, and...  \n",
       "636360240921972736  [yashan, bbc, vpn, access, ios, may, limited, ...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets = prepare_data(tweets)\n",
    "tweets.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Tweet</th>\n",
       "      <th>PreprocessedTweet</th>\n",
       "      <th>Hashtags</th>\n",
       "      <th>Emoticons</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>628949369883000832</th>\n",
       "      <td>dear @Microsoft the newOoffice for Mac is grea...</td>\n",
       "      <td>dear  the newooffice for mac is great and all ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[dear, newooffice, mac, great, lync, update, mon]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628976607420645377</th>\n",
       "      <td>@Microsoft how about you make a system that do...</td>\n",
       "      <td>how about you make a system that doesn t eat ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[make, system, eat, friggin, discs, time, happ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629023169169518592</th>\n",
       "      <td>Not Available</td>\n",
       "      <td>not available</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[available]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629179223232479232</th>\n",
       "      <td>Not Available</td>\n",
       "      <td>not available</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[available]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629186282179153920</th>\n",
       "      <td>If I make a game as a #windows10 Universal App...</td>\n",
       "      <td>if i make a game as a windows   universal app ...</td>\n",
       "      <td>[windows10, xboxone]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[make, game, windows, universal, app, xboxone,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                Tweet  \\\n",
       "Id                                                                      \n",
       "628949369883000832  dear @Microsoft the newOoffice for Mac is grea...   \n",
       "628976607420645377  @Microsoft how about you make a system that do...   \n",
       "629023169169518592                                      Not Available   \n",
       "629179223232479232                                      Not Available   \n",
       "629186282179153920  If I make a game as a #windows10 Universal App...   \n",
       "\n",
       "                                                    PreprocessedTweet  \\\n",
       "Id                                                                      \n",
       "628949369883000832  dear  the newooffice for mac is great and all ...   \n",
       "628976607420645377   how about you make a system that doesn t eat ...   \n",
       "629023169169518592                                      not available   \n",
       "629179223232479232                                      not available   \n",
       "629186282179153920  if i make a game as a windows   universal app ...   \n",
       "\n",
       "                                Hashtags Emoticons  \\\n",
       "Id                                                   \n",
       "628949369883000832                    []        []   \n",
       "628976607420645377                    []        []   \n",
       "629023169169518592                    []        []   \n",
       "629179223232479232                    []        []   \n",
       "629186282179153920  [windows10, xboxone]        []   \n",
       "\n",
       "                                                               Tokens  \n",
       "Id                                                                     \n",
       "628949369883000832  [dear, newooffice, mac, great, lync, update, mon]  \n",
       "628976607420645377  [make, system, eat, friggin, discs, time, happ...  \n",
       "629023169169518592                                        [available]  \n",
       "629179223232479232                                        [available]  \n",
       "629186282179153920  [make, game, windows, universal, app, xboxone,...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_submisson = prepare_data(tweets_submisson)\n",
    "tweets_submisson.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'available': 619,\n",
       " 'ios': 4763,\n",
       " 'app': 403,\n",
       " 'transport': 9610,\n",
       " 'security': 8161,\n",
       " 'need': 6196,\n",
       " 'check': 1562,\n",
       " 'party': 6694,\n",
       " 'network': 6222,\n",
       " 'pod': 6953,\n",
       " 'supports': 9089,\n",
       " 'mar': 5627,\n",
       " 'device': 2479,\n",
       " 'download': 2709,\n",
       " 'phone': 6832,\n",
       " 'run': 7949,\n",
       " 'latest': 5184,\n",
       " 'may': 5725,\n",
       " 'account': 55,\n",
       " 'problem': 7160,\n",
       " 'day': 2286,\n",
       " 'time': 9457,\n",
       " 'replaced': 7648,\n",
       " 'sure': 9095,\n",
       " 'start': 8825,\n",
       " 'publication': 7276,\n",
       " 'live': 5377,\n",
       " 'helping': 4237,\n",
       " 'ask': 509,\n",
       " 'anything': 386,\n",
       " 'sessions': 8244,\n",
       " 'today': 9487,\n",
       " 'friday': 3626,\n",
       " 'two': 9757,\n",
       " 'dollar': 2658,\n",
       " 'tuesday': 9709,\n",
       " 'forklift': 3552,\n",
       " 'quickkey': 7345,\n",
       " 'suite': 9036,\n",
       " 'pages': 6628,\n",
       " 'already': 259,\n",
       " 'signed': 8421,\n",
       " 'test': 9322,\n",
       " 'game': 3724,\n",
       " 'nows': 6355,\n",
       " 'chance': 1515,\n",
       " 'youtube': 10471,\n",
       " 'gaming': 3734,\n",
       " 'officially': 6440,\n",
       " 'launches': 5193,\n",
       " 'web': 10164,\n",
       " 'android': 320,\n",
       " 'august': 589,\n",
       " 'finally': 3389,\n",
       " 'going': 3892,\n",
       " 'webseries': 10170,\n",
       " 'tomorrow': 9515,\n",
       " 'apps': 440,\n",
       " 'head': 4180,\n",
       " 'twitch': 9753,\n",
       " 'yashan': 10440,\n",
       " 'bbc': 775,\n",
       " 'vpn': 10064,\n",
       " 'access': 47,\n",
       " 'limited': 5339,\n",
       " 'china': 1605,\n",
       " 'military': 5886,\n",
       " 'parade': 6661,\n",
       " 'chat': 1553,\n",
       " 'parkrun': 6677,\n",
       " 'downloaded': 2711,\n",
       " 'great': 3978,\n",
       " 'easier': 2844,\n",
       " 'info': 4622,\n",
       " 'amp': 296,\n",
       " 'ready': 7461,\n",
       " 'saturday': 8031,\n",
       " 'devices': 2480,\n",
       " 'know': 5093,\n",
       " 'got': 3926,\n",
       " 'project': 7196,\n",
       " 'want': 10102,\n",
       " 'work': 10349,\n",
       " 'help': 4233,\n",
       " 'swift': 9138,\n",
       " 'excuse': 3128,\n",
       " 'hang': 4118,\n",
       " 'devs': 2488,\n",
       " 'sat': 8021,\n",
       " 'met': 5829,\n",
       " 'developer': 2473,\n",
       " 'crossskyhigh': 2153,\n",
       " 'try': 9696,\n",
       " 'indiedev': 4606,\n",
       " 'best': 873,\n",
       " 'way': 10146,\n",
       " 'get': 3802,\n",
       " 'audio': 585,\n",
       " 'recordings': 7514,\n",
       " 'jailbreaking': 4833,\n",
       " 'option': 6514,\n",
       " 'five': 3430,\n",
       " 'free': 3606,\n",
       " 'games': 3731,\n",
       " 'edition': 2874,\n",
       " 'week': 10181,\n",
       " 'news': 6239,\n",
       " 'lchbuzz': 5218,\n",
       " 'looks': 5439,\n",
       " 'interesting': 4707,\n",
       " 'always': 270,\n",
       " 'ended': 2976,\n",
       " 'back': 665,\n",
       " 'keyboard': 5022,\n",
       " 'glitches': 3864,\n",
       " 'ones': 6472,\n",
       " 'see': 8162,\n",
       " 'eyes': 3192,\n",
       " 'real': 7463,\n",
       " 'people': 6770,\n",
       " 'reporter': 7660,\n",
       " 'fresco': 3616,\n",
       " 'coming': 1827,\n",
       " 'september': 8223,\n",
       " 'siri': 8463,\n",
       " 'knows': 5097,\n",
       " 'apple': 417,\n",
       " 'event': 3083,\n",
       " 'giveusahint': 3848,\n",
       " 'build': 1211,\n",
       " 'might': 5872,\n",
       " 'last': 5175,\n",
       " 'till': 9455,\n",
       " 'come': 1815,\n",
       " 'many': 5625,\n",
       " 'years': 10449,\n",
       " 'hundred': 4441,\n",
       " 'photo': 6836,\n",
       " 'albums': 223,\n",
       " 'appear': 408,\n",
       " 'right': 7806,\n",
       " 'beat': 794,\n",
       " 'fairly': 3224,\n",
       " 'decent': 2328,\n",
       " 'even': 3081,\n",
       " 'though': 9397,\n",
       " 'differences': 2523,\n",
       " 'features': 3307,\n",
       " 'still': 8876,\n",
       " 'make': 5582,\n",
       " 'lean': 5231,\n",
       " 'little': 5375,\n",
       " 'towards': 9565,\n",
       " 'huawei': 4414,\n",
       " 'watch': 10130,\n",
       " 'pre': 7059,\n",
       " 'order': 6521,\n",
       " 'suggests': 9031,\n",
       " 'wear': 10157,\n",
       " 'iphone': 4768,\n",
       " 'owners': 6607,\n",
       " 'use': 9904,\n",
       " 'soo': 8652,\n",
       " 'speedenews': 8716,\n",
       " 'rumor': 7944,\n",
       " 'soon': 8653,\n",
       " 'close': 1721,\n",
       " 'working': 10354,\n",
       " 'near': 6186,\n",
       " 'rhode': 7786,\n",
       " 'island': 4787,\n",
       " 'lyft': 5529,\n",
       " 'let': 5285,\n",
       " 'wish': 10308,\n",
       " 'customfoldericons': 2211,\n",
       " 'worked': 10350,\n",
       " 'received': 7495,\n",
       " 'december': 2327,\n",
       " 'checked': 1563,\n",
       " 'old': 6451,\n",
       " 'ipod': 4770,\n",
       " 'gen': 3778,\n",
       " 'upgraded': 9878,\n",
       " 'randomly': 7406,\n",
       " 'open': 6486,\n",
       " 'twitter': 9754,\n",
       " 'please': 6931,\n",
       " 'stop': 8899,\n",
       " 'walking': 10090,\n",
       " 'one': 6468,\n",
       " 'month': 6003,\n",
       " 'late': 5180,\n",
       " 'update': 9872,\n",
       " 'annonced': 346,\n",
       " 'july': 4950,\n",
       " 'wait': 10076,\n",
       " 'heard': 4195,\n",
       " 'said': 7984,\n",
       " 'mcpe': 5744,\n",
       " 'cant': 1358,\n",
       " ':)': 4,\n",
       " 'october': 6420,\n",
       " 'users': 9908,\n",
       " 'follow': 3509,\n",
       " 'guess': 4039,\n",
       " 'female': 3329,\n",
       " 'visiting': 10034,\n",
       " 'site': 8474,\n",
       " 'disposable': 2595,\n",
       " 'income': 4586,\n",
       " 'loving': 5481,\n",
       " 'new': 6231,\n",
       " 'mobile': 5957,\n",
       " 'love': 5471,\n",
       " 'sept': 8222,\n",
       " 'comparable': 1855,\n",
       " 'believe': 838,\n",
       " 'wrong': 10393,\n",
       " 'google': 3912,\n",
       " 'expands': 3142,\n",
       " 'smartwatch': 8542,\n",
       " 'platform': 6915,\n",
       " 'monday': 5986,\n",
       " 'rolled': 7870,\n",
       " 'harmony': 4144,\n",
       " 'yes': 10454,\n",
       " 'asked': 510,\n",
       " 'attendant': 564,\n",
       " 'print': 7144,\n",
       " 'paper': 6658,\n",
       " 'pass': 6697,\n",
       " 'delta': 2387,\n",
       " 'lost': 5454,\n",
       " 'login': 5419,\n",
       " 'annoying': 355,\n",
       " 'companies': 1853,\n",
       " 'announced': 348,\n",
       " 'partnership': 6692,\n",
       " 'aimed': 198,\n",
       " 'making': 5589,\n",
       " 'compatible': 1861,\n",
       " 'zeroes': 10495,\n",
       " 'short': 8363,\n",
       " 'video': 9992,\n",
       " 'enterprises': 3014,\n",
       " 'nintendo': 6280,\n",
       " 'fans': 3254,\n",
       " 'sound': 8668,\n",
       " 'crazy': 2110,\n",
       " 'say': 8046,\n",
       " 'kirby': 5063,\n",
       " 'rainbow': 7386,\n",
       " 'curse': 2205,\n",
       " 'canvas': 1361,\n",
       " 'ever': 3087,\n",
       " 'focused': 3496,\n",
       " 'enjoyed': 3000,\n",
       " 'incredible': 4593,\n",
       " 'success': 9006,\n",
       " 'focus': 3495,\n",
       " 'pick': 6849,\n",
       " 'brains': 1114,\n",
       " 'looking': 5438,\n",
       " 'dev': 2471,\n",
       " 'orks': 6539,\n",
       " 'tell': 9291,\n",
       " 'gunning': 4056,\n",
       " 'release': 7593,\n",
       " 'herald': 4243,\n",
       " 'oblivion': 6404,\n",
       " 'add': 98,\n",
       " 'type': 9760,\n",
       " 'copy': 2024,\n",
       " 'paste': 6707,\n",
       " 'webroot': 10169,\n",
       " 'jailbroken': 4834,\n",
       " 'reconsider': 7510,\n",
       " 'touch': 9548,\n",
       " 'ofc': 6430,\n",
       " 'downgrade': 2708,\n",
       " 'signded': 8420,\n",
       " 'supposed': 9091,\n",
       " 'managing': 5609,\n",
       " 'macs': 5543,\n",
       " 'book': 1055,\n",
       " 'read': 7454,\n",
       " 'really': 7473,\n",
       " 'well': 10198,\n",
       " 'done': 2676,\n",
       " 'thanks': 9347,\n",
       " 'keep': 4998,\n",
       " 'confirmed': 1915,\n",
       " 'incompatible': 4587,\n",
       " 'healthkit': 4191,\n",
       " 'announcing': 353,\n",
       " 'lee': 5246,\n",
       " 'currently': 2203,\n",
       " 'operating': 6494,\n",
       " 'put': 7313,\n",
       " 'upload': 9882,\n",
       " 'wed': 10173,\n",
       " 'night': 6267,\n",
       " 'pacific': 6614,\n",
       " 'join': 4914,\n",
       " 'blast': 978,\n",
       " 'founder': 3569,\n",
       " 'kander': 4974,\n",
       " 'codigames': 1760,\n",
       " 'like': 5324,\n",
       " 'arpgs': 475,\n",
       " 'dungeon': 2808,\n",
       " 'legends': 5257,\n",
       " 'arrive': 480,\n",
       " 'gamedev': 3728,\n",
       " ':d': 7,\n",
       " 'idea': 4489,\n",
       " 'vulnerabilities': 10068,\n",
       " 'rarely': 7421,\n",
       " 'first': 3420,\n",
       " 'lumino': 5514,\n",
       " 'city': 1667,\n",
       " 'models': 5963,\n",
       " 'til': 9454,\n",
       " 'sunday': 9054,\n",
       " 'certain': 1492,\n",
       " 'far': 3257,\n",
       " 'versions': 9977,\n",
       " 'change': 1519,\n",
       " 'future': 3697,\n",
       " 'favorite': 3288,\n",
       " 'harbaugh': 4132,\n",
       " 'autocorrects': 609,\n",
       " 'garbage': 3739,\n",
       " 'moto': 6043,\n",
       " 'submitted': 8993,\n",
       " 'end': 2974,\n",
       " 'cleaned': 1694,\n",
       " 'bugs': 1210,\n",
       " 'next': 6246,\n",
       " 'pixel': 6890,\n",
       " 'steam': 8855,\n",
       " 'lot': 5455,\n",
       " 'improvements': 4565,\n",
       " 'released': 7594,\n",
       " 'thursday': 9430,\n",
       " 'kxlu': 5126,\n",
       " 'listen': 5362,\n",
       " 'using': 9911,\n",
       " 'vodafone': 10048,\n",
       " 'smart': 8536,\n",
       " 'ultra': 9786,\n",
       " 'since': 8446,\n",
       " 'finding': 3395,\n",
       " 'faults': 3280,\n",
       " 'think': 9384,\n",
       " 'meet': 5769,\n",
       " 'endless': 2979,\n",
       " 'runner': 7950,\n",
       " 'race': 7363,\n",
       " 'sun': 9049,\n",
       " 'macworld': 5544,\n",
       " 'launch': 5191,\n",
       " 'binaries': 933,\n",
       " 'windows': 10288,\n",
       " 'phones': 6833,\n",
       " 'tablets': 9176,\n",
       " 'btw': 1195,\n",
       " 'others': 6554,\n",
       " 'fair': 3221,\n",
       " 'number': 6369,\n",
       " 'cataloguing': 1432,\n",
       " 'issues': 4797,\n",
       " 'music': 6110,\n",
       " 'symptom': 9157,\n",
       " 'hopefully': 4374,\n",
       " 'means': 5755,\n",
       " 'around': 473,\n",
       " 'along': 254,\n",
       " 'look': 5436,\n",
       " 'version': 9976,\n",
       " 'likely': 5328,\n",
       " 'speed': 8715,\n",
       " 'sharing': 8294,\n",
       " 'automate': 611,\n",
       " 'things': 9382,\n",
       " 'workflow': 10353,\n",
       " 'solution': 8629,\n",
       " 'thank': 9345,\n",
       " 'actually': 91,\n",
       " 'articlesection': 492,\n",
       " 'classpagepandroid': 1688,\n",
       " 'hey': 4258,\n",
       " 'tommaso': 9511,\n",
       " 'date': 2271,\n",
       " 'answer': 364,\n",
       " 'thats': 9351,\n",
       " 'school': 8071,\n",
       " 'starts': 8828,\n",
       " 'crowdviz': 2157,\n",
       " 'releases': 7595,\n",
       " 'labor': 5132,\n",
       " 'unites': 9836,\n",
       " 'blind': 992,\n",
       " 'sighted': 8415,\n",
       " 'assistants': 532,\n",
       " 'minecraft': 5907,\n",
       " 'dont': 2682,\n",
       " 'cause': 1444,\n",
       " 'left': 5249,\n",
       " 'quite': 7352,\n",
       " 'funny': 3690,\n",
       " 'developers': 2474,\n",
       " 'taking': 9195,\n",
       " 'advantage': 133,\n",
       " 'ugmonk': 9776,\n",
       " 'anniversary': 343,\n",
       " 'set': 8245,\n",
       " 'links': 5355,\n",
       " 'fine': 3397,\n",
       " 'times': 9459,\n",
       " 'click': 1706,\n",
       " 'safari': 7978,\n",
       " 'cancel': 1340,\n",
       " 'works': 10356,\n",
       " 'official': 6439,\n",
       " 'hehe': 4216,\n",
       " 'yay': 10441,\n",
       " 'happy': 4130,\n",
       " ':p': 8,\n",
       " 'correct': 2040,\n",
       " 'tech': 9265,\n",
       " 'star': 8815,\n",
       " 'wars': 10121,\n",
       " 'uprising': 9888,\n",
       " 'canon': 1355,\n",
       " 'wondering': 10334,\n",
       " 'mean': 5751,\n",
       " 'awesome': 641,\n",
       " 'ride': 7798,\n",
       " 'csrracing': 2177,\n",
       " 'car': 1374,\n",
       " 'iphones': 4769,\n",
       " 'show': 8377,\n",
       " 'server': 8237,\n",
       " 'smile': 8550,\n",
       " '=)': 11,\n",
       " 'give': 3844,\n",
       " 'hint': 4288,\n",
       " 'keynote': 5023,\n",
       " 'operate': 6493,\n",
       " 'anymore': 383,\n",
       " 'tune': 9715,\n",
       " 'big': 906,\n",
       " 'fun': 3675,\n",
       " 'continuity': 1986,\n",
       " 'require': 7678,\n",
       " 'separate': 8220,\n",
       " 'pair': 6632,\n",
       " 'share': 8289,\n",
       " 'inbox': 4572,\n",
       " 'pretty': 7119,\n",
       " 'animation': 337,\n",
       " 'thew': 9376,\n",
       " 'doesnt': 2649,\n",
       " ':(': 3,\n",
       " 'would': 10371,\n",
       " 'fifa': 3358,\n",
       " 'console': 1951,\n",
       " 'stopped': 8903,\n",
       " 'playing': 6922,\n",
       " 'wan': 10099,\n",
       " 'announcement': 349,\n",
       " 'browser': 1183,\n",
       " 'starting': 8827,\n",
       " 'pst': 7268,\n",
       " 'attn': 572,\n",
       " 'beginning': 824,\n",
       " 'oct': 6419,\n",
       " 'munzee': 6101,\n",
       " 'also': 261,\n",
       " 'review': 7765,\n",
       " 'includes': 4581,\n",
       " 'bug': 1208,\n",
       " 'fixes': 3434,\n",
       " 'launching': 5194,\n",
       " 'tower': 9566,\n",
       " 'dwellers': 2825,\n",
       " 'gold': 3893,\n",
       " 'tandem': 9211,\n",
       " 'bangkok': 711,\n",
       " 'market': 5649,\n",
       " 'rate': 7425,\n",
       " 'client': 1708,\n",
       " 'larges': 5171,\n",
       " 'legit': 5263,\n",
       " 'running': 7953,\n",
       " 'sick': 8402,\n",
       " 'product': 7176,\n",
       " 'retina': 7737,\n",
       " 'movable': 6059,\n",
       " 'challenge': 1505,\n",
       " 'movband': 6060,\n",
       " 'spirit': 8728,\n",
       " 'fest': 3337,\n",
       " 'move': 6061,\n",
       " 'step': 8860,\n",
       " 'closer': 1723,\n",
       " 'ipad': 4766,\n",
       " 'another': 363,\n",
       " 'world': 10358,\n",
       " 'guessing': 4041,\n",
       " 'possibly': 7021,\n",
       " 'woods': 10339,\n",
       " 'club': 1734,\n",
       " 'room': 7885,\n",
       " 'stands': 8813,\n",
       " 'survey': 9108,\n",
       " 'question': 7340,\n",
       " 'ahead': 190,\n",
       " 'announcements': 350,\n",
       " 'price': 7129,\n",
       " 'inch': 4576,\n",
       " 'stylus': 8989,\n",
       " 'cover': 2081,\n",
       " 'excited': 3118,\n",
       " 'pro': 7156,\n",
       " 'wanting': 10104,\n",
       " 'true': 9678,\n",
       " 'announces': 352,\n",
       " 'huge': 4424,\n",
       " 'draw': 2726,\n",
       " 'dropping': 2771,\n",
       " 'november': 6351,\n",
       " 'woman': 10328,\n",
       " 'plane': 6906,\n",
       " 'flipped': 3464,\n",
       " 'photos': 6840,\n",
       " 'cat': 1431,\n",
       " 'whole': 10239,\n",
       " 'spiritanimal': 8729,\n",
       " 'reveals': 7759,\n",
       " 'soanxious': 8598,\n",
       " 'items': 4802,\n",
       " 'asap': 499,\n",
       " 'prediction': 7069,\n",
       " 'touchscreen': 9552,\n",
       " 'controller': 1994,\n",
       " 'applegenius': 420,\n",
       " 'stillnojobthough': 8877,\n",
       " 'giant': 3821,\n",
       " 'god': 3883,\n",
       " 'call': 1303,\n",
       " 'maxipad': 5723,\n",
       " 'appleevent': 419,\n",
       " 'capturing': 1373,\n",
       " 'covent': 2080,\n",
       " 'garden': 3742,\n",
       " 'store': 8907,\n",
       " 'limit': 5338,\n",
       " 'rude': 7931,\n",
       " 'unfollow': 9820,\n",
       " 'bitches': 952,\n",
       " 'following': 3513,\n",
       " 'bye': 1278,\n",
       " 'surprised': 9103,\n",
       " 'unveiled': 9866,\n",
       " 'mini': 5909,\n",
       " 'tea': 9249,\n",
       " 'sounds': 8671,\n",
       " 'good': 3904,\n",
       " 'hope': 4371,\n",
       " 'announce': 347,\n",
       " 'something': 8638,\n",
       " 'different': 2524,\n",
       " 'exciting': 3121,\n",
       " 'reading': 7457,\n",
       " 'prelude': 7078,\n",
       " 'wsj': 10395,\n",
       " 'bloomberg': 1012,\n",
       " 'nyt': 6391,\n",
       " 'traded': 9582,\n",
       " 'computer': 1883,\n",
       " 'uncle': 9797,\n",
       " 'tells': 9294,\n",
       " 'prices': 7130,\n",
       " 'drop': 2768,\n",
       " 'happening': 4125,\n",
       " 'expected': 3147,\n",
       " 'waking': 10083,\n",
       " 'thing': 9381,\n",
       " 'forward': 3561,\n",
       " 'gon': 3900,\n",
       " 'buy': 1268,\n",
       " 'stream': 8929,\n",
       " 'address': 105,\n",
       " 'able': 24,\n",
       " 'view': 9998,\n",
       " 'better': 881,\n",
       " 'introduce': 4736,\n",
       " 'large': 5168,\n",
       " 'enough': 3004,\n",
       " 'stand': 8809,\n",
       " 'wherever': 10219,\n",
       " 'snag': 8564,\n",
       " 'older': 6452,\n",
       " 'season': 8141,\n",
       " 'rbi': 7443,\n",
       " 'baseball': 741,\n",
       " 'yall': 10434,\n",
       " 'mad': 5545,\n",
       " 'paying': 6735,\n",
       " 'fix': 3432,\n",
       " 'broken': 1171,\n",
       " 'bigger': 912,\n",
       " 'problems': 7161,\n",
       " 'deal': 2302,\n",
       " 'goes': 3891,\n",
       " 'gesture': 3801,\n",
       " 'controlled': 1993,\n",
       " 'upgrades': 9879,\n",
       " 'impress': 4557,\n",
       " 'unveil': 9865,\n",
       " 'releasing': 7596,\n",
       " 'wednesday': 10175,\n",
       " 'earlier': 2835,\n",
       " 'tests': 9329,\n",
       " 'lab': 5129,\n",
       " 'dead': 2296,\n",
       " 'shit': 8341,\n",
       " 'highly': 4272,\n",
       " 'doubt': 2701,\n",
       " 'covered': 2083,\n",
       " 'curious': 2200,\n",
       " 'bring': 1150,\n",
       " 'checks': 1565,\n",
       " 'extra': 3183,\n",
       " 'widget': 10257,\n",
       " 'optimization': 6512,\n",
       " 'etc': 3066,\n",
       " 'moms': 5981,\n",
       " 'away': 638,\n",
       " 'worth': 10370,\n",
       " 'tweeting': 9745,\n",
       " 'kweent': 5125,\n",
       " 'anthonychat': 371,\n",
       " 'aapl': 14,\n",
       " 'prius': 7150,\n",
       " 'prepare': 7089,\n",
       " 'reminiscing': 7623,\n",
       " 'legacy': 5252,\n",
       " 'hardware': 4138,\n",
       " 'including': 4582,\n",
       " 'pumped': 7288,\n",
       " 'sundays': 9057,\n",
       " 'vlog': 10041,\n",
       " 'processing': 7167,\n",
       " 'exporting': 3173,\n",
       " 'fassbender': 3270,\n",
       " 'comes': 1820,\n",
       " 'seeing': 8165,\n",
       " 'deeper': 2347,\n",
       " 'integration': 4696,\n",
       " 'splitscreen': 8740,\n",
       " 'inc': 4573,\n",
       " 'accidentally': 51,\n",
       " 'sitting': 8477,\n",
       " 'long': 5429,\n",
       " 'stash': 8832,\n",
       " 'freezer': 3611,\n",
       " 'cool': 2010,\n",
       " 'whoops': 10243,\n",
       " 'straya': 8928,\n",
       " 'trying': 9699,\n",
       " 'contact': 1970,\n",
       " 'quantas': 7332,\n",
       " 'could': 2058,\n",
       " 'dad': 2230,\n",
       " 'dramatically': 2725,\n",
       " 'upset': 9890,\n",
       " 'reason': 7477,\n",
       " 'getting': 3807,\n",
       " 'hahah': 4077,\n",
       " 'blog': 1002,\n",
       " 'rumors': 7946,\n",
       " 'strong': 8956,\n",
       " 'remain': 7606,\n",
       " 'dubious': 2784,\n",
       " 'spec': 8705,\n",
       " 'noritz': 6315,\n",
       " 'submit': 8992,\n",
       " 'vid': 9990,\n",
       " 'explaining': 3161,\n",
       " 'win': 10284,\n",
       " 'noritzpro': 6316,\n",
       " 'dedicate': 2344,\n",
       " 'survive': 9111,\n",
       " 'break': 1126,\n",
       " 'size': 8480,\n",
       " 'cry': 2169,\n",
       " 'delivery': 2384,\n",
       " 'leave': 5240,\n",
       " 'absolute': 35,\n",
       " 'worst': 10369,\n",
       " 'uniting': 9837,\n",
       " 'postexistence': 7025,\n",
       " 'bandeau': 707,\n",
       " 'take': 9188,\n",
       " 'effect': 2884,\n",
       " 'actuary': 92,\n",
       " 'onwards': 6482,\n",
       " 'desktop': 2452,\n",
       " 'verge': 9970,\n",
       " 'larger': 5170,\n",
       " 'tonight': 9520,\n",
       " 'upgrade': 9877,\n",
       " 'adobe': 120,\n",
       " 'came': 1317,\n",
       " 'facebook': 3200,\n",
       " 'bought': 1090,\n",
       " 'instagram': 4675,\n",
       " 'wants': 10105,\n",
       " 'sell': 8190,\n",
       " 'updates': 9874,\n",
       " 'products': 7179,\n",
       " 'knew': 5084,\n",
       " 'dat': 2267,\n",
       " 'wud': 10400,\n",
       " 'ppl': 7037,\n",
       " 'chasing': 1552,\n",
       " 'harry': 4148,\n",
       " 'couldve': 2060,\n",
       " 'used': 9905,\n",
       " 'wasnt': 10125,\n",
       " 'harrys': 4150,\n",
       " 'fault': 3279,\n",
       " 'ran': 7403,\n",
       " 'whyd': 10250,\n",
       " 'pay': 6732,\n",
       " 'crack': 2090,\n",
       " 'wtf': 10398,\n",
       " 'literally': 5372,\n",
       " 'anxious': 381,\n",
       " 'abt': 37,\n",
       " 'disappoint': 2562,\n",
       " 'srsly': 8788,\n",
       " 'pupils': 7297,\n",
       " 'course': 2075,\n",
       " 'ideal': 4490,\n",
       " 'smashed': 8544,\n",
       " 'someone': 8636,\n",
       " 'hates': 4161,\n",
       " 'plus': 6945,\n",
       " 'morning': 6027,\n",
       " 'checking': 1564,\n",
       " 'highlights': 4271,\n",
       " 'memory': 5801,\n",
       " 'space': 8683,\n",
       " 'couple': 2072,\n",
       " 'hours': 4399,\n",
       " 'obsolescence': 6410,\n",
       " 'outpaced': 6571,\n",
       " 'pausing': 6728,\n",
       " 'year': 10447,\n",
       " 'yell': 10452,\n",
       " 'pandora': 6651,\n",
       " 'refuses': 7555,\n",
       " 'play': 6916,\n",
       " 'carpenters': 1398,\n",
       " 'superstar': 9077,\n",
       " 'anyone': 384,\n",
       " 'forget': 3545,\n",
       " 'est': 3061,\n",
       " 'charlotte': 1546,\n",
       " 'hebrew': 4208,\n",
       " 'crash': 2100,\n",
       " 'survival': 9109,\n",
       " 'phrases': 6841,\n",
       " 'mac': 5535,\n",
       " 'center': 1481,\n",
       " 'stage': 8802,\n",
       " 'top': 9529,\n",
       " 'gambling': 3723,\n",
       " 'timcook': 9456,\n",
       " 'accounting': 56,\n",
       " 'career': 1385,\n",
       " 'engage': 2989,\n",
       " 'kubusfair': 5115,\n",
       " 'pdt': 6744,\n",
       " 'find': 3394,\n",
       " 'videos': 9994,\n",
       " 'train': 9593,\n",
       " 'muh': 6092,\n",
       " 'pokeymanz': 6970,\n",
       " 'rip': 7818,\n",
       " 'marks': 5653,\n",
       " 'weeks': 10187,\n",
       " 'brings': 1152,\n",
       " 'redesigns': 7521,\n",
       " 'iwatch': 4812,\n",
       " 'decision': 2335,\n",
       " 'surface': 9097,\n",
       " 'pressure': 7112,\n",
       " 'sensitivity': 8213,\n",
       " 'castleberryisd': 1424,\n",
       " 'grade': 3945,\n",
       " 'deployment': 2422,\n",
       " 'students': 8968,\n",
       " 'flat': 3446,\n",
       " 'tiges': 9450,\n",
       " 'fanboy': 3249,\n",
       " 'excitement': 3120,\n",
       " 'graders': 3947,\n",
       " 'faces': 3204,\n",
       " 'seem': 8170,\n",
       " 'pros': 7223,\n",
       " 'guy': 4063,\n",
       " 'quarter': 7333,\n",
       " 'results': 7729,\n",
       " 'spoil': 8744,\n",
       " 'whys': 10252,\n",
       " 'flossin': 3477,\n",
       " 'original': 6536,\n",
       " 'macbook': 5536,\n",
       " 'money': 5992,\n",
       " 'lmao': 5386,\n",
       " 'christen': 1627,\n",
       " 'patio': 6718,\n",
       " 'coffee': 1762,\n",
       " 'redzone': 7530,\n",
       " 'split': 8738,\n",
       " 'ninja': 6279,\n",
       " 'tricks': 9640,\n",
       " 'microsoft': 5861,\n",
       " 'edge': 2866,\n",
       " 'recap': 7492,\n",
       " 'transloader': 9606,\n",
       " 'content': 1978,\n",
       " 'golf': 3897,\n",
       " 'dudes': 2789,\n",
       " 'pool': 6994,\n",
       " 'shall': 8281,\n",
       " 'immediately': 4536,\n",
       " 'sep': 8219,\n",
       " 'much': 6088,\n",
       " 'expect': 3143,\n",
       " 'ordered': 6522,\n",
       " 'probably': 7159,\n",
       " 'tears': 9262,\n",
       " 'blogchat': 1003,\n",
       " 'introduced': 4737,\n",
       " 'lose': 5448,\n",
       " 'numeracy': 6372,\n",
       " 'map': 5626,\n",
       " 'fits': 3428,\n",
       " 'bringing': 1151,\n",
       " 'alive': 243,\n",
       " 'digitally': 2529,\n",
       " 'search': 8139,\n",
       " 'appstore': 441,\n",
       " 'level': 5295,\n",
       " 'disruptive': 2603,\n",
       " 'hosting': 4391,\n",
       " 'annual': 356,\n",
       " 'splash': 8735,\n",
       " 'africapkenya': 162,\n",
       " 'yas': 10439,\n",
       " 'reveal': 7755,\n",
       " 'gamespot': 3733,\n",
       " 'choice': 1611,\n",
       " 'dragon': 2719,\n",
       " 'ball': 696,\n",
       " 'alex': 235,\n",
       " 'gibney': 3825,\n",
       " 'explains': 3162,\n",
       " 'steve': 8867,\n",
       " 'jobs': 4906,\n",
       " 'documentary': 2646,\n",
       " 'via': 9980,\n",
       " 'started': 8826,\n",
       " 'listening': 5365,\n",
       " 'later': 5182,\n",
       " 'nfl': 6249,\n",
       " 'hump': 4439,\n",
       " 'doctor': 2642,\n",
       " 'diagnosed': 2495,\n",
       " 'repetitive': 7646,\n",
       " 'strain': 8919,\n",
       " 'injury': 4639,\n",
       " 'known': 5096,\n",
       " 'thumb': 9426,\n",
       " 'social': 8603,\n",
       " 'media': 5762,\n",
       " 'cautionary': 1449,\n",
       " 'tale': 9198,\n",
       " 'details': 2464,\n",
       " 'adblockers': 97,\n",
       " 'onlineadvertisers': 6477,\n",
       " 'beyond': 891,\n",
       " 'mostly': 6037,\n",
       " 'bands': 708,\n",
       " 'relegious': 7597,\n",
       " 'requirements': 7679,\n",
       " 'needed': 6198,\n",
       " 'plan': 6905,\n",
       " 'suck': 9011,\n",
       " 'joke': 4920,\n",
       " 'saving': 8041,\n",
       " 'trading': 9584,\n",
       " 'street': 8934,\n",
       " 'biggest': 913,\n",
       " 'cons': 1936,\n",
       " 'despite': 2455,\n",
       " 'fact': 3207,\n",
       " 'constantly': 1957,\n",
       " 'dissing': 2608,\n",
       " 'everything': 3092,\n",
       " 'held': 4223,\n",
       " 'translation': 9605,\n",
       " 'internet': 4719,\n",
       " 'timing': 9461,\n",
       " 'smartphone': 8539,\n",
       " 'status': 8845,\n",
       " 'saying': 8048,\n",
       " 'buying': 1271,\n",
       " 'else': 2935,\n",
       " 'offer': 6432,\n",
       " 'honestly': 4349,\n",
       " 'dropped': 2770,\n",
       " 'beer': 813,\n",
       " 'cooler': 2011,\n",
       " 'beach': 781,\n",
       " 'yesterday': 10455,\n",
       " 'christmas': 1633,\n",
       " 'upcoming': 9871,\n",
       " 'sheffield': 8312,\n",
       " 'swfc': 9137,\n",
       " 'analyst': 306,\n",
       " 'force': 3533,\n",
       " 'newiphone': 6236,\n",
       " 'samsung': 8000,\n",
       " 'abandoned': 15,\n",
       " 'ordering': 6523,\n",
       " 'upgrading': 9880,\n",
       " 'camera': 1319,\n",
       " 'seriously': 8234,\n",
       " 'yet': 10456,\n",
       " 'photographyproblems': 6839,\n",
       " 'firstft': 3421,\n",
       " 'reign': 7570,\n",
       " 'queen': 7336,\n",
       " 'unveiling': 9867,\n",
       " 'sign': 8417,\n",
       " 'least': 5238,\n",
       " 'ago': 179,\n",
       " 'kids': 5037,\n",
       " 'kind': 5051,\n",
       " 'yeah': 10444,\n",
       " 'ievent': 4497,\n",
       " 'low': 5482,\n",
       " 'cost': 2049,\n",
       " 'replacement': 7649,\n",
       " 'model': 5961,\n",
       " 'aka': 212,\n",
       " 'peak': 6749,\n",
       " 'within': 10314,\n",
       " 'unique': 9832,\n",
       " 'mark': 5646,\n",
       " 'patterns': 6725,\n",
       " 'made': 5548,\n",
       " 'oil': 6446,\n",
       " 'fingertip': 3402,\n",
       " 'glass': 3858,\n",
       " 'screen': 8114,\n",
       " 'swanage': 9125,\n",
       " 'handed': 4108,\n",
       " 'town': 9567,\n",
       " 'hall': 4090,\n",
       " 'office': 6436,\n",
       " 'speak': 8700,\n",
       " 'business': 1256,\n",
       " 'insider': 4659,\n",
       " 'rumour': 7947,\n",
       " 'respond': 7712,\n",
       " 'voice': 10049,\n",
       " '):': 0,\n",
       " 'weighs': 10191,\n",
       " 'cup': 2195,\n",
       " 'nuts': 6376,\n",
       " 'generation': 3783,\n",
       " 'ithink': 4803,\n",
       " 'cuzzo': 2219,\n",
       " 'applewatch': 422,\n",
       " 'red': 7519,\n",
       " 'stoked': 8890,\n",
       " 'streaming': 8931,\n",
       " 'service': 8240,\n",
       " 'purchase': 7299,\n",
       " 'hoping': 4376,\n",
       " 'potential': 7029,\n",
       " 'seats': 8145,\n",
       " ...}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dummy(doc):\n",
    "    return doc\n",
    "\n",
    "bow_vectorizer = CountVectorizer(tokenizer=dummy, preprocessor=dummy)\n",
    "\n",
    "X_train_counts = bow_vectorizer.fit_transform(tweets['Tokens'])\n",
    "# print(vector.shape)\n",
    "bow_vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_clf_and_save_submission(clf, sub_file_name):\n",
    "    clf = clf.fit(X_train_counts, tweets['Category'])\n",
    "    \n",
    "    predicted = clf.predict(X_train_counts)\n",
    "    print(np.mean(predicted == tweets['Category']))\n",
    "    \n",
    "    X_test_counts = bow_vectorizer.transform(tweets_submisson['Tokens'])\n",
    "    test_predicted = clf.predict(X_test_counts)\n",
    "    test_predicted\n",
    "\n",
    "    submisson = pd.DataFrame(data={\n",
    "        'Id': tweets_submisson.index,\n",
    "        'Category': test_predicted,\n",
    "    })\n",
    "    # submisson\n",
    "    print(sub_file_name)\n",
    "    submisson.to_csv(sub_file_name, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8003015580499246\n",
      "submission_nb.csv\n"
     ]
    }
   ],
   "source": [
    "# clf = MultinomialNB().fit(X_train_counts, tweets['Category'])\n",
    "\n",
    "train_clf_and_save_submission(MultinomialNB(), \"submission_nb.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4838331378790417\n",
      "submission_svc.csv\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "train_clf_and_save_submission(SVC(gamma='auto', random_state=42), \"submission_svc.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8364885240408779\n",
      "submission_sgd.csv\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "train_clf_and_save_submission(SGDClassifier(loss='hinge', penalty='l2', alpha=1e-3, random_state=42, max_iter=5, tol=None), \"submission_sgd.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word2vec (overfitting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_len = int(len(tweets) * 0.1)\n",
    "tweets = tweets.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "# print(train_set_len)\n",
    "\n",
    "X_train = tweets.loc[train_set_len:, \"Tweet\"].values\n",
    "y_train = tweets.loc[train_set_len:, \"Category\"].values\n",
    "X_test = tweets.loc[:train_set_len, \"Tweet\"].values\n",
    "y_test = tweets.loc[:train_set_len, \"Category\"].values\n",
    "# X_train = tweets.loc[train_set_len:, \"Tokens\"].values\n",
    "# X_test = tweets.loc[:train_set_len, \"Tokens\"].values\n",
    "\n",
    "type(X_train)\n",
    "# [print(d + '\\n') for d in X_train[:5]]\n",
    "\n",
    "# def prepare_data_word2vec(data):\n",
    "#     processed_data = []\n",
    "#     for tweet in data:\n",
    "#         processed_tweet = replace_pattern(tweet, r\"@[\\w]*\", \"user\")\n",
    "#         processed_tweet = replace_pattern(processed_tweet, r\"http[\\w:/.]*\")\n",
    "#         processed_data.append(processed_tweet)\n",
    "#     return processed_data\n",
    "        \n",
    "# X_train = prepare_data_word2vec(X_train)\n",
    "# X_test = prepare_data_word2vec(X_test)\n",
    "\n",
    "# print(\"-\" * 120)    \n",
    "# [print(d + '\\n') for d in X_train[:5]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "def prepare_y(y):\n",
    "    LabelEncoder()\n",
    "    encoder = LabelEncoder()\n",
    "    encoder.fit(y)\n",
    "    encoded_y = encoder.transform(y)\n",
    "    return to_categorical(encoded_y)\n",
    "\n",
    "y_train = prepare_y(y_train)\n",
    "y_test = prepare_y(y_test)\n",
    "\n",
    "\n",
    "tokenizer_obj = Tokenizer()\n",
    "X = X_train + X_test\n",
    "tokenizer_obj.fit_on_texts(X)\n",
    "\n",
    "max_length = max([len(s.split()) for s in X])\n",
    "vocab_size = len(tokenizer_obj.word_index) + 1\n",
    "\n",
    "X_train_tokens = tokenizer_obj.texts_to_sequences(X_train)\n",
    "X_test_tokens = tokenizer_obj.texts_to_sequences(X_test)\n",
    "\n",
    "X_train_pad = pad_sequences(X_train_tokens, maxlen=max_length, padding=\"post\")\n",
    "X_test_pad = pad_sequences(X_test_tokens, maxlen=max_length, padding=\"post\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Embedding, LSTM, GRU, Embedding, Softmax\n",
    "\n",
    "EMBEDDING_DIM = 10\n",
    "UNITS = 16\n",
    "\n",
    "model = Sequential([\n",
    "    Embedding(vocab_size, EMBEDDING_DIM, input_length=max_length),\n",
    "    GRU(units=UNITS, dropout=0.2, recurrent_dropout=0.2),\n",
    "    Dense(3, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5373 samples, validate on 597 samples\n",
      "Epoch 1/25\n",
      "5373/5373 - 30s - loss: 1.0212 - accuracy: 0.4774 - val_loss: 1.0205 - val_accuracy: 0.4774\n",
      "Epoch 2/25\n",
      "5373/5373 - 23s - loss: 1.0143 - accuracy: 0.4846 - val_loss: 1.0221 - val_accuracy: 0.4774\n",
      "Epoch 3/25\n",
      "5373/5373 - 22s - loss: 1.0137 - accuracy: 0.4846 - val_loss: 1.0205 - val_accuracy: 0.4774\n",
      "Epoch 4/25\n",
      "5373/5373 - 22s - loss: 1.0001 - accuracy: 0.4856 - val_loss: 1.0183 - val_accuracy: 0.4774\n",
      "Epoch 5/25\n",
      "5373/5373 - 22s - loss: 0.9071 - accuracy: 0.5317 - val_loss: 1.0143 - val_accuracy: 0.4774\n",
      "Epoch 6/25\n",
      "5373/5373 - 24s - loss: 0.8237 - accuracy: 0.5680 - val_loss: 1.0176 - val_accuracy: 0.4774\n",
      "Epoch 7/25\n",
      "5373/5373 - 24s - loss: 0.7690 - accuracy: 0.5896 - val_loss: 1.0137 - val_accuracy: 0.4774\n",
      "Epoch 8/25\n",
      "5373/5373 - 23s - loss: 0.7162 - accuracy: 0.6159 - val_loss: 1.0147 - val_accuracy: 0.4774\n",
      "Epoch 9/25\n",
      "5373/5373 - 25s - loss: 0.6670 - accuracy: 0.6564 - val_loss: 1.0172 - val_accuracy: 0.4791\n",
      "Epoch 10/25\n",
      "5373/5373 - 25s - loss: 0.5980 - accuracy: 0.7095 - val_loss: 1.0193 - val_accuracy: 0.4791\n",
      "Epoch 11/25\n",
      "5373/5373 - 26s - loss: 0.5197 - accuracy: 0.7631 - val_loss: 1.0183 - val_accuracy: 0.4791\n",
      "Epoch 12/25\n",
      "5373/5373 - 24s - loss: 0.4473 - accuracy: 0.8079 - val_loss: 1.0173 - val_accuracy: 0.4791\n",
      "Epoch 13/25\n",
      "5373/5373 - 24s - loss: 0.3814 - accuracy: 0.8438 - val_loss: 1.0219 - val_accuracy: 0.4807\n",
      "Epoch 14/25\n",
      "5373/5373 - 25s - loss: 0.3490 - accuracy: 0.8561 - val_loss: 1.0239 - val_accuracy: 0.4908\n",
      "Epoch 15/25\n",
      "5373/5373 - 26s - loss: 0.2970 - accuracy: 0.8787 - val_loss: 1.0190 - val_accuracy: 0.4908\n",
      "Epoch 16/25\n",
      "5373/5373 - 43s - loss: 0.2823 - accuracy: 0.8857 - val_loss: 1.0151 - val_accuracy: 0.4874\n",
      "Epoch 17/25\n",
      "5373/5373 - 39s - loss: 0.2407 - accuracy: 0.9008 - val_loss: 1.0167 - val_accuracy: 0.4874\n",
      "Epoch 18/25\n",
      "5373/5373 - 30s - loss: 0.2213 - accuracy: 0.9123 - val_loss: 1.0170 - val_accuracy: 0.4774\n",
      "Epoch 19/25\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-84-49c9a3888ec0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test_pad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    122\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 86\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_counter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled_without_tracing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    485\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    488\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1821\u001b[0m     \u001b[1;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1822\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1823\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1825\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1141\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1222\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m   1225\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    512\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train_pad, y_train, batch_size=16, epochs=25, validation_data=(X_test_pad, y_test), verbose=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}