sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import utils\n",
    "import importlib\n",
    "importlib.reload(utils)\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re, string, collections, bcolz, pickle, os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import TruncatedSVD, PCA\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this notebook is to train a sentiment analysis classifier given the games reviews in the dataset. The model must classify text reviews in positive or negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/reviews.csv', lineterminator='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127699, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's 127699 games reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>text</th>\n",
       "      <th>critic</th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>it makes a terrible first impression, but  ult...</td>\n",
       "      <td>Gaming Nexus</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>#KILLALLZOMBIES</td>\n",
       "      <td>PlayStation 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.0</td>\n",
       "      <td>brings some fresh and inventive ideas to the ...</td>\n",
       "      <td>Playstation Universe</td>\n",
       "      <td>2014-11-17</td>\n",
       "      <td>#KILLALLZOMBIES</td>\n",
       "      <td>PlayStation 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>a fun main mode of play that can get quite add...</td>\n",
       "      <td>Gamers' Temple</td>\n",
       "      <td>2015-02-16</td>\n",
       "      <td>#KILLALLZOMBIES</td>\n",
       "      <td>PlayStation 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>simply put,  feels like half of a game.</td>\n",
       "      <td>DarkStation</td>\n",
       "      <td>2014-12-04</td>\n",
       "      <td>#KILLALLZOMBIES</td>\n",
       "      <td>PlayStation 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>combined with the price and lack of cross-buy,...</td>\n",
       "      <td>Worth Playing</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>#KILLALLZOMBIES</td>\n",
       "      <td>PlayStation 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score                                               text  \\\n",
       "0   74.0  it makes a terrible first impression, but  ult...   \n",
       "1   65.0   brings some fresh and inventive ideas to the ...   \n",
       "2   60.0  a fun main mode of play that can get quite add...   \n",
       "3   60.0            simply put,  feels like half of a game.   \n",
       "4   60.0  combined with the price and lack of cross-buy,...   \n",
       "\n",
       "                 critic        date            title       platform  \n",
       "0          Gaming Nexus  2014-11-18  #KILLALLZOMBIES  PlayStation 4  \n",
       "1  Playstation Universe  2014-11-17  #KILLALLZOMBIES  PlayStation 4  \n",
       "2        Gamers' Temple  2015-02-16  #KILLALLZOMBIES  PlayStation 4  \n",
       "3           DarkStation  2014-12-04  #KILLALLZOMBIES  PlayStation 4  \n",
       "4         Worth Playing  2014-11-30  #KILLALLZOMBIES  PlayStation 4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's keep just text and score columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[['text', 'score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>it makes a terrible first impression, but  ult...</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>brings some fresh and inventive ideas to the ...</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a fun main mode of play that can get quite add...</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>simply put,  feels like half of a game.</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>combined with the price and lack of cross-buy,...</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  score\n",
       "0  it makes a terrible first impression, but  ult...   74.0\n",
       "1   brings some fresh and inventive ideas to the ...   65.0\n",
       "2  a fun main mode of play that can get quite add...   60.0\n",
       "3            simply put,  feels like half of a game.   60.0\n",
       "4  combined with the price and lack of cross-buy,...   60.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data have 127699 reviews but we will keep just the most polarized ones. The filter is the next: <br>Positive: score > 85 <br/>Negative: score < 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reviews with score greater than 85: 43518\n",
      "reviews with score less than 50: 20428\n"
     ]
    }
   ],
   "source": [
    "print(f'reviews with score greater than 85: {len(df.loc[df[\"score\"] > 85])}')\n",
    "print(f'reviews with score less than 50: {len(df.loc[df[\"score\"] < 50])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos = df.loc[df['score'] > 85, 'text'].copy().reset_index(drop=True)\n",
    "neg = df.loc[df['score'] < 50, 'text'].copy().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43518, 20428)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pos), len(neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print some positive reviews examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it's sad that more people can't or don't appreciate a mesmerizing and beautiful game like this.  just because a game doesn't make a use for every button on the controller or over complicate a game shouldn't knock its score.  i do my review on my youtube channel under the same user name and even recorded with an hd pvr, and using expensive video editing software, the hd video review i made doesn't even begin to give the sheer beauty of the game justice.\r",
      " this is a game perfectly balancing your left and right hand to the patterns you behold on the screen.  with all the vibrant colors and elegant patterns produced before you however, the magnificence of the art displayed before you in crisp smooth fashion can easily take you mind off of concentrating on what you are trying to do.  it simply is that beautiful, and in my opinion, if the ps3 hit game flower was a beautiful horse, then  is \"the black stallion\", an even more colorful, beautiful, and artistic masterpiece.\r",
      " is it a simple game?  yes.  however that's the brilliance of the game.  it's overly simple for anyone to pick up and enjoy as well as admire, even those non-gaming friends and family members, but it's difficult to master.  it is like trying to perfectly do two different things with both sides of your brain, right and left, where you pat one head with your left hand and rub your belly with your right hand, and then on command, you switch... all while being distracted by a true work of magnificent art dancing gracefully in front of you.\r",
      " the music in the game perfectly matches the pace and art of the game, smoothly taking you from a graceful slow dance to a emotional, fast and dramatic beat depending on where you are in the levels.  beating each increasingly difficult level rewards the player with seeing the two fish/birds that they were controlling mesh into a single flying dragons which can gracefully be flown to collect glowing gems in a big room and then draw a glowing track of lights with the flight of your dragon as if to set it's flight into a glowing piece of art for all to see.\r",
      " for what it is,  should be an award winning downloadable simple game that everyone can appreciate and enjoy.  to compare this game and knock it points because it doesn't compete against a bigger, completely different game like destiny is both unfair and foolish.  it is a masterfully simple and beautiful game that does what it intended to do perfectly with a few single flaw which will likely be patched soon (i show this flaw in my video).  i give this game a 9.5 out of 10 and rated it up based on the simplicity of its brilliance alone.  a rare gem in an overly complicated gaming world that sadly many people can't stop and appreciate and will take for granted.  it's much like someone ignoring a beautiful sunset right in front of them to text their 50th meaningless conversation of the day on their smart phone instead.\n",
      "\n",
      "\n",
      "a really fun and addictive time online, single player, or with friends. if you enjoy rpg games this game is a must have. sure it is not perfect in graphics but most definitely makes up for that everywhere else. tons of items, tons of character builds, ton of options. i only have one complaint, blizzard had failed to control hackers (modders) on the console version. so if you run into a game with one of these hackers it can cause a minor annoyance. almost a perfect game for me.\n",
      "\n",
      "\n",
      "aside from some extremely minor hiccups in matchmaking that appear to have been quickly resolved, halo 5 has been rock solid, and has also seen regular playlist updates and tweaks to address community concerns.\n",
      "\n",
      "\n",
      " is a story-driven masterpiece. even though you will be able to finish it within 4-6 hours, it will leave you breathless and blown away.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    print(''.join(pos[np.random.randint(0, len(pos))]))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print some negative reviews examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this game annoys me. the heroes/villains that are created can act about 4 different ways, giving them no definitive character. the combat is repetitive and there is no fun in buying new weapons. they all look stupid, as does the armour. missions are as follows:\r",
      "go somewhere\r",
      "fight 10-25 enemies the same way\r",
      "steal/administer something\r",
      "put something somwhere\r",
      "fight a crappy boss\r",
      "i dislike this game. it could have been so much better.\n",
      "\n",
      "\n",
      "constantly having the batmobile shoehorned into mission, some of which are pure **** in terms of mechanics, ultimately ruined this game for me. compare to arkham city, this was a huge disappointment.\n",
      "\n",
      "\n",
      "i do not recommend it. and the people who give it a score of 10, are activision workers and they themselves make the votes .... 0/10 this is called destiny 1.5 is a fraud\n",
      "\n",
      "\n",
      " is a mess - an altogether stress-inducing experience that i wouldn't wish upon anyone. i only hope the sequel proves to be a better experience.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    print(''.join(neg[np.random.randint(0, len(neg))]))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must add the labels: 0 for negative reviews, 1 for positive reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neg = pd.concat([pd.DataFrame(neg), pd.DataFrame(np.zeros(neg.shape), columns=['class'])], 1)\n",
    "pos = pd.concat([pd.DataFrame(pos), pd.DataFrame(np.ones(pos.shape), columns=['class'])], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean, standard deviation and max length of negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(681.7165654983356, 823.7599066187882, 14736)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lens = neg['text'].str.len()\n",
    "lens.mean(), lens.std(), lens.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAFpCAYAAAC4ZG/7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngGANAAADCNYAADDA/wf0IVE0Hw4N4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f478145a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens.hist(figsize=(12, 6), bins=25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reviews with more than 5000 characters are dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "long_reviews = neg.loc[neg['text'].str.len() > 5000].index\n",
    "neg.drop(long_reviews, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean, standard deviation and max length of positive reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(586.1869801001884, 738.0401469961486, 25554)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lens = pos['text'].str.len()\n",
    "lens.mean(), lens.std(), lens.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAGAAwRoAAAYQrAEAYADBGgAABhCsAQBgAMEaAAAGEKwBAGAAwRoAAAYQrAEAYADBGgAA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\n/h8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4783804b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens.hist(figsize=(12, 6), bins=25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "long_reviews = pos.loc[pos['text'].str.len() > 5000].index\n",
    "pos.drop(long_reviews, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is desirable to have a balanced dataset (similar quantity of positive and negative instances). So we will pick a random subset of the positive instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rand = np.random.permutation(pos.shape[0])\n",
    "pos = pos.iloc[rand[:neg.shape[0]]].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20419, 2), (20419, 2))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg.shape, pos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We concatenate positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>guys i got this game 2 days ago and i found it...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a resounding meh. less than even. this game ha...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>well, its out. i am a massive fan of the tony ...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a very good game that could have been a lot be...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>simply horrible. dlc full of glitches, lack of...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  class\n",
       "0  guys i got this game 2 days ago and i found it...    1.0\n",
       "1  a resounding meh. less than even. this game ha...    0.0\n",
       "2  well, its out. i am a massive fan of the tony ...    0.0\n",
       "3  a very good game that could have been a lot be...    1.0\n",
       "4  simply horrible. dlc full of glitches, lack of...    0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([pos, neg]).sample(frac=1).reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40838, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Split data into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['text'].values, df['class'].values, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32670, 8168, 32670, 8168)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "re_tok = re.compile(f'([{string.punctuation}“”¨«»®´·º½¾¿¡§£₤‘’])')\n",
    "def tokenize(s): return re_tok.sub(r' \\1 ', s).split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = CountVectorizer(tokenizer=tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_train = vect.fit_transform(X_train)\n",
    "tf_test = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train term frequency contains 32675 documents and 45424 tokens. Each row represents a document and each column how many times that token appears on the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<32670x45094 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 2560062 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance, the first document have just 65 of the 45424 possible tokens. That's why the matrix is stored as a sparse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x45094 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 65 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45094"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = vect.get_feature_names()\n",
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['awful', 'awfull', 'awfully', 'awfulness', 'awful—the']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[4000: 4005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"on paper, then,  is an exciting proposition that bundles the promises of aspirational game design with the raw power and fun associated with motorbikes. unfortunately, those promises are broken and the resulting game falls flat. unless you're so enamoured with two-wheeled machines that you simply can't help but pick yourself up a copy, you should wait for a new contender to try its hand at delivering a biking game of this scope.\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First review's tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w0 = set([o for o in X_train[0].split(' ')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'',\n",
       " 'a',\n",
       " 'an',\n",
       " 'and',\n",
       " 'are',\n",
       " 'aspirational',\n",
       " 'associated',\n",
       " 'at',\n",
       " 'biking',\n",
       " 'broken',\n",
       " 'bundles',\n",
       " 'but',\n",
       " \"can't\",\n",
       " 'contender',\n",
       " 'copy,',\n",
       " 'delivering',\n",
       " 'design',\n",
       " 'enamoured',\n",
       " 'exciting',\n",
       " 'falls',\n",
       " 'flat.',\n",
       " 'for',\n",
       " 'fun',\n",
       " 'game',\n",
       " 'hand',\n",
       " 'help',\n",
       " 'is',\n",
       " 'its',\n",
       " 'machines',\n",
       " 'motorbikes.',\n",
       " 'new',\n",
       " 'of',\n",
       " 'on',\n",
       " 'paper,',\n",
       " 'pick',\n",
       " 'power',\n",
       " 'promises',\n",
       " 'proposition',\n",
       " 'raw',\n",
       " 'resulting',\n",
       " 'scope.',\n",
       " 'should',\n",
       " 'simply',\n",
       " 'so',\n",
       " 'that',\n",
       " 'the',\n",
       " 'then,',\n",
       " 'this',\n",
       " 'those',\n",
       " 'to',\n",
       " 'try',\n",
       " 'two-wheeled',\n",
       " 'unfortunately,',\n",
       " 'unless',\n",
       " 'up',\n",
       " 'wait',\n",
       " 'with',\n",
       " 'you',\n",
       " \"you're\",\n",
       " 'yourself'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41989"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.vocabulary_['unless']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_train[0, 41989]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we reduce our training data to 2 dimentions (using TruncatedSVD rather than PCA because we are working with sparse matrices) and then we plot it in order to  roughly see how classes are separated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svd = TruncatedSVD()\n",
    "reduced_tf_train = svd.fit_transform(tf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wA5ckSVLCDFySJEkJM3BJkiQlzMAlSZKUMAOXJElSwgxckiRJCTNwSZIkJczAJUmSlDAD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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4783918be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embeddings(reduced_tf_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First attempt is to use a simple Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = tf_train[y_train==1].sum(0) + 1\n",
    "q = tf_train[y_train==0].sum(0) + 1\n",
    "r = np.log((p/p.sum())/(q/q.sum()))\n",
    "b = np.log(len(p)/len(q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9038932419196866\n"
     ]
    }
   ],
   "source": [
    "pre_preds = tf_test @ r.T + b\n",
    "preds = pre_preds.T > 0\n",
    "acc = (preds==y_test).mean()\n",
    "print(f'Accuracy: {acc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than use r coefficients that we get trough Naive Bayes, let's learn them from data using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9275220372184133\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.2, dual=True)\n",
    "model.fit(tf_train, y_train)\n",
    "preds = model.predict(tf_test)\n",
    "acc = (preds==y_test).mean()\n",
    "print(f'Accuracy: {acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Jxmf5Ok0A4cvaruw7k+yVfuZe/XcM1dD6ZY6abYKHw+cN6/7JEnSk9Y2wJ7A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47825cb2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix(y_test, preds.T), classes=['Negative', 'Positive'], title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot most relevant words that the algorithm uses to classify a text in positive or negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coef_df = pd.DataFrame({'vocab': vocab, 'coef':model.coef_.reshape(-1)})\n",
    "pos_top10 = coef_df.sort_values('coef', ascending=False).reset_index(drop=True)[:10]\n",
    "neg_top10 = coef_df.sort_values('coef').reset_index(drop=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vuqtYkySHAYcBzNpizlgOkTR2ewJfqqp7AJJ8Zdj+O4BfAv+Y5Gzg7Fb+x8Dp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\nZJiZmVkW/wGe1sKC7akFPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f477dbe95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(8, 8))\n",
    "fig.subplots_adjust(wspace=0.8)\n",
    "pos_top10.sort_values('coef').plot.barh(legend=False, ax=axs[0])\n",
    "axs[0].set_yticklabels(pos_top10['vocab'].values.tolist()[::-1])\n",
    "axs[0].set_title('Positive');\n",
    "neg_top10.sort_values('coef', ascending=False).plot.barh(legend=False, ax=axs[1])\n",
    "axs[1].set_yticklabels(neg_top10['vocab'].values.tolist()[::-1])\n",
    "axs[1].set_title('Negative');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing 'ea' token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12893"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.vocabulary_['ea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_train = lil2(tf_train)\n",
    "tf_train.removecol(12893)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_test = lil2(tf_test)\n",
    "tf_test.removecol(12893)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9280117531831538\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.2, dual=True)\n",
    "model.fit(tf_train, y_train)\n",
    "preds = model.predict(tf_test)\n",
    "acc = (preds==y_test).mean()\n",
    "print(f'Accuracy: {acc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try now with bigram tf-idf rather than unigram tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(strip_accents='unicode', tokenizer=tokenize, ngram_range=(1, 2), max_df=0.9, min_df=3, sublinear_tf=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfidf_train = vect.fit_transform(X_train)\n",
    "tfidf_test = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Division between classes is more clear now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svd = TruncatedSVD()\n",
    "reduced_tfidf_train = svd.fit_transform(tfidf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERESkI4YvIiIiIh0xfBERERHpiOGLiIiISEcMX0REREQ6YvgiIiIi0hHDFxEREZGOGL6I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\neX40RVPT8DpkBuxFkuIi82IMt73lSZUWotR9HXQ7XQylijx2PE1ubpz38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m8UBgbOu0ZF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c82821278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embeddings(reduced_tfidf_train, y_train, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = tfidf_train[y_train==1].sum(0) + 1\n",
    "q = tfidf_train[y_train==0].sum(0) + 1\n",
    "r = np.log((p/p.sum())/(q/q.sum()))\n",
    "b = np.log(len(p)/len(q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9483349657198825\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(C=30, dual=True)\n",
    "model.fit(tfidf_train, y_train)\n",
    "preds = model.predict(tfidf_test)\n",
    "acc = (preds==y_test).mean()\n",
    "print(f'Accuracy: {acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tx9tbsSRJ0hgSYNwYim76acg5N20hc0ZVPdZuFjaSJGlMG84iflOSbDDiPZEk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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c8859bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix(y_test, preds.T), classes=['Negative', 'Positive'], title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try now with a simple Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/martinpella/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers import Flatten, Dense, Dropout, Convolution1D, MaxPooling1D, SpatialDropout1D, Input, concatenate\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenize reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['tokenized'] = df['text'].apply(tokenize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def update_vocab_counter(row):\n",
    "    for word in row:\n",
    "        vocab_counter[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab_counter = collections.Counter()\n",
    "df['tokenized'].apply(update_vocab_counter);\n",
    "vocab = sorted(vocab_counter, key=vocab_counter.get, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49981"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word to index dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w2id = {w:i for i, w in enumerate(vocab)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Words to index transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform_to_ids(row):\n",
    "    return [w2id[w] for w in row]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['tokenized'] = df['tokenized'].apply(lambda x: transform_to_ids(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['tokenized'].values, df['class'].values, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxlen = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the inputs to the conv net must have a fixed size. We set 1000 as the max length and we fill with -1 all reviews smaller than 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = pad_sequences(X_train, maxlen=maxlen, value=-1)\n",
    "x_test = pad_sequences(X_test, maxlen=maxlen, value=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential([Embedding(input_dim=len(vocab), output_dim=32, input_length=maxlen),\n",
    "                    SpatialDropout1D(0.2),\n",
    "                    Dropout(0.25),\n",
    "                    Convolution1D(64, 5, padding='same', activation='relu'),\n",
    "                    Dropout(0.25),\n",
    "                    MaxPooling1D(),\n",
    "                    Flatten(),\n",
    "                    Dense(100, activation='relu'),\n",
    "                    Dropout(0.85),\n",
    "                    Dense(1, activation='sigmoid')])\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32670 samples, validate on 8168 samples\n",
      "Epoch 1/4\n",
      "32670/32670 [==============================] - 8s - loss: 0.4518 - acc: 0.7556 - val_loss: 0.1932 - val_acc: 0.9247\n",
      "Epoch 2/4\n",
      "32670/32670 [==============================] - 8s - loss: 0.1879 - acc: 0.9352 - val_loss: 0.1781 - val_acc: 0.9344\n",
      "Epoch 3/4\n",
      "32670/32670 [==============================] - 8s - loss: 0.1323 - acc: 0.9557 - val_loss: 0.1965 - val_acc: 0.9345\n",
      "Epoch 4/4\n",
      "32670/32670 [==============================] - 8s - loss: 0.1015 - acc: 0.9672 - val_loss: 0.2129 - val_acc: 0.9327\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f1bf5be7c50>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=4, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe finding best hyperparameters could lead to a better accuracy, but this more complex model is not better than our linear classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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