Memology_english.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Memology - DataScience & Memes\n",
    "![](http://orig07.deviantart.net/3e62/f/2015/267/4/1/meme_team_logo_png_smol_by_kuiwi-d9ass0h.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "from sklearn.learning_curve import validation_curve,learning_curve\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "import seaborn as sn\n",
    "from pylab import rcParams      \n",
    "rcParams['figure.figsize'] = 8, 8\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, train_test_split, RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Part 1. Dataset and features description "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TL;DR about the project**\n",
    "\n",
    "I have collected (parsed) data from [knowyourmeme.com](http://knowyourmeme.com/) about 11679 memes, carefully placed on 369 pages. My crawling from page to page automatically was not really the best looking idea from a server point of view, so I got banned by IP. To avoid misunderstandings between my parser and server I had to search far and wide for the methods to bypass the security. As a result I came across TOR which dinamically updates the IP address for every new request and which I managed to connect to with Python. You may find more about parser in the [repository](https://github.com/DmitrySerg/memology)\n",
    "\n",
    "The project itself is dedicated to the greatest achievement of humanity - memes. I will try to forecast the popularity of a meme based on its text description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes = pd.read_csv('/Users/dmitrys/Desktop/DataProjects/KnowYourMemes/all_data_memes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.drop(['Unnamed: 0.1', 'Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes = memes.drop_duplicates(\"name\") # some memes were in duplicate due to the site structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11679 entries, 0 to 11678\n",
      "Data columns (total 10 columns):\n",
      "name        11679 non-null object\n",
      "added       11679 non-null object\n",
      "views       11679 non-null int64\n",
      "comments    11679 non-null object\n",
      "status      11679 non-null object\n",
      "year        11679 non-null object\n",
      "tags        11679 non-null object\n",
      "about       6832 non-null object\n",
      "origin      4819 non-null object\n",
      "spread      947 non-null object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 912.5+ KB\n"
     ]
    }
   ],
   "source": [
    "memes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, let's have a look at variables:\n",
    "\n",
    "- name - the name of the meme\n",
    "- added - date when it was added to the site\n",
    "- views - amount of views on the site \n",
    "- comments - amount of comments on the site\n",
    "- status - categorical variable, status is given to a meme depending on the 'approval' of one by the community \n",
    "- year - year of the first appearance of the origin\n",
    "- tags - tags given to meme\n",
    "- about - text description \n",
    "- origin - description of the origin \n",
    "- spread - where has it spread "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Last Time The Cubs Won the World Series</td>\n",
       "      <td>Nov 03, 2016 at 11:22AM EDT</td>\n",
       "      <td>17157</td>\n",
       "      <td>60</td>\n",
       "      <td>Submission</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Last Time the Cubs Won the World Series refers...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jokes and fascination with the Cubs’ had small...</td>\n",
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       "      <th>1</th>\n",
       "      <td>PenorCut's Fiverr Videos</td>\n",
       "      <td>Jul 20, 2015 at 07:54PM EDT</td>\n",
       "      <td>36629</td>\n",
       "      <td>39</td>\n",
       "      <td>Submission</td>\n",
       "      <td>Researching</td>\n",
       "      <td>YouTube</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AmourShipping</td>\n",
       "      <td>Nov 02, 2016 at 03:01PM EDT</td>\n",
       "      <td>32293</td>\n",
       "      <td>81</td>\n",
       "      <td>Submission</td>\n",
       "      <td>Pairing</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Amourshipping refers to the romantic pairing o...</td>\n",
       "      <td>On September 12th, 2013, a discussion thread f...</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                      name                        added  \\\n",
       "0  Last Time The Cubs Won the World Series  Nov 03, 2016 at 11:22AM EDT   \n",
       "1                 PenorCut's Fiverr Videos  Jul 20, 2015 at 07:54PM EDT   \n",
       "2                            AmourShipping  Nov 02, 2016 at 03:01PM EDT   \n",
       "\n",
       "   views comments      status         year     tags  \\\n",
       "0  17157       60  Submission       Sports  Chicago   \n",
       "1  36629       39  Submission  Researching  YouTube   \n",
       "2  32293       81  Submission      Pairing  Unknown   \n",
       "\n",
       "                                               about  \\\n",
       "0  Last Time the Cubs Won the World Series refers...   \n",
       "1                                                NaN   \n",
       "2  Amourshipping refers to the romantic pairing o...   \n",
       "\n",
       "                                              origin  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  On September 12th, 2013, a discussion thread f...   \n",
       "\n",
       "                                              spread  \n",
       "0  Jokes and fascination with the Cubs’ had small...  \n",
       "1                                                NaN  \n",
       "2                                                NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>added</th>\n",
       "      <th>views</th>\n",
       "      <th>comments</th>\n",
       "      <th>status</th>\n",
       "      <th>year</th>\n",
       "      <th>tags</th>\n",
       "      <th>about</th>\n",
       "      <th>origin</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11676</th>\n",
       "      <td>Cannon Penis</td>\n",
       "      <td>Oct 11, 2010 at 06:07PM EDT</td>\n",
       "      <td>20202</td>\n",
       "      <td>14</td>\n",
       "      <td>Submission</td>\n",
       "      <td>NSFW</td>\n",
       "      <td>YouTube</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>11677</th>\n",
       "      <td>Gaben</td>\n",
       "      <td>Oct 11, 2010 at 01:22PM EDT</td>\n",
       "      <td>479780</td>\n",
       "      <td>49</td>\n",
       "      <td>Submission</td>\n",
       "      <td>2010</td>\n",
       "      <td>tf2, stop, newell, control, wait, gabe, gaben,...</td>\n",
       "      <td>Gaben is a nickname given to Gabe Newell, the ...</td>\n",
       "      <td>The soundclip of Gabe Newell saying “gaben” ca...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11678</th>\n",
       "      <td>alex smith fail</td>\n",
       "      <td>Oct 11, 2010 at 06:00AM EDT</td>\n",
       "      <td>2730</td>\n",
       "      <td>14</td>\n",
       "      <td>Deadpool</td>\n",
       "      <td>2010</td>\n",
       "      <td>alex smith, 49ers, sf, san francisco, fail.qb,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                  name                        added   views comments  \\\n",
       "11676     Cannon Penis  Oct 11, 2010 at 06:07PM EDT   20202       14   \n",
       "11677            Gaben  Oct 11, 2010 at 01:22PM EDT  479780       49   \n",
       "11678  alex smith fail  Oct 11, 2010 at 06:00AM EDT    2730       14   \n",
       "\n",
       "           status  year                                               tags  \\\n",
       "11676  Submission  NSFW                                            YouTube   \n",
       "11677  Submission  2010  tf2, stop, newell, control, wait, gabe, gaben,...   \n",
       "11678    Deadpool  2010  alex smith, 49ers, sf, san francisco, fail.qb,...   \n",
       "\n",
       "                                                   about  \\\n",
       "11676                                                NaN   \n",
       "11677  Gaben is a nickname given to Gabe Newell, the ...   \n",
       "11678                                                NaN   \n",
       "\n",
       "                                                  origin spread  \n",
       "11676                                                NaN    NaN  \n",
       "11677  The soundclip of Gabe Newell saying “gaben” ca...    NaN  \n",
       "11678                                                NaN    NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Part  2. Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The first thing to do, of course, is to find the most popular meme of all times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5408</th>\n",
       "      <td>Doge</td>\n",
       "      <td>Jul 24, 2013 at 04:29PM EDT</td>\n",
       "      <td>11551088</td>\n",
       "      <td>893</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>Character</td>\n",
       "      <td>Tumblr</td>\n",
       "      <td>Doge is a slang term for “dog” that is primari...</td>\n",
       "      <td>The use of the misspelled word “doge” to refer...</td>\n",
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      "text/plain": [
       "      name                        added     views comments     status  \\\n",
       "5408  Doge  Jul 24, 2013 at 04:29PM EDT  11551088      893  Confirmed   \n",
       "\n",
       "           year    tags                                              about  \\\n",
       "5408  Character  Tumblr  Doge is a slang term for “dog” that is primari...   \n",
       "\n",
       "                                                 origin  \\\n",
       "5408  The use of the misspelled word “doge” to refer...   \n",
       "\n",
       "                                                 spread  \n",
       "5408  On October 28th, 2010, a photo of Kabosu was s...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "memes[memes.views==memes.views.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this is Doge!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://i0.kym-cdn.com/entries/icons/original/000/013/564/doge.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date added\n",
    "\n",
    "Since we are having a great variable `added`, it would be great to play with it a little, transform into datetime format and get hour, weekday, month and year out of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes['Timestamp'] = memes.added.apply(lambda x: pd.to_datetime(x))\n",
    "memes['Year'] = memes.Timestamp.apply(lambda x: x.year)\n",
    "memes['Month'] = memes.Timestamp.apply(lambda x: x.month)\n",
    "memes['Day'] = memes.Timestamp.apply(lambda x: x.day)\n",
    "memes['Hour'] = memes.Timestamp.apply(lambda x: x.hour)\n",
    "memes['Weekday'] = memes.Timestamp.apply(lambda x: x.weekday())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will also need a variable accounting for the amount of days passed since the addition to the site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes['days_from_creation'] = (memes.Timestamp.max() - memes.Timestamp)/np.timedelta64(1, 'D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's possible to plot some histograms for newly created variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A+EqSxyR5+3B/dmfiV5Nsz9CZSHJzrXW6M/ErSV45Y9sXL1HdAMAaUms9Kcm3W2sfqbW+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\nFkYPDAAAAGDxBBgAAADA4gkwAAAAgMU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11713e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index =0 \n",
    "f, axarr = plt.subplots(2, 2, figsize=(15,10))\n",
    "times = ['Month', 'Day', 'Hour', 'Weekday']\n",
    "bins = [12, 31, 24, 7]\n",
    "index = 0\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axarr[i, j].hist(memes[times[index]], bins = bins[index])\n",
    "        axarr[i, j].set_title('Histogram of {}'.format(times[index]))\n",
    "        index += 1\n",
    "\n",
    "f.subplots_adjust(hspace=0.2)\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh boy what do we have here:\n",
    "- We have a visible drop in September on the month histogram, (probably main audience of the site has classes starting in this month)\n",
    "- Day has nothing special, the distribution is pretty much uniform\n",
    "- Hour histogram is quite illustrative - during the night activity goes down and then grows, reaching its peak right after the school ends\n",
    "- Weekday distribution shows a significant drop during weekends, I wonder why is that so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117575e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.Year.value_counts().plot(kind='bar', figsize=(10, 5))\n",
    "plt.title(\"Histogram of year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012                  21.9%\n",
       "2011                  20.3%\n",
       "2015                  14.7%\n",
       "2014    13.900000000000002%\n",
       "2016                  13.4%\n",
       "2013                  12.3%\n",
       "2010    3.5999999999999996%\n",
       "Name: Year, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.Year.value_counts(normalize=True).apply(lambda x: \"{}%\".format(str(round(x, 3)*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like 2011 and 2012 were a golden era for memes, 42% (what a coincidence) of all memes on the site were added during these two years\n",
    "\n",
    "# Year of the first appearance of the origin\n",
    "\n",
    "Let's have a look at the second variable connected with year - year of the first meme appearance, `year`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011           1670\n",
       "2012           1238\n",
       "Unknown         881\n",
       "2010            597\n",
       "2013            581\n",
       "NSFW            541\n",
       "2014            529\n",
       "2016            406\n",
       "Researching     383\n",
       "2015            343\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.year.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, some tags found their way into this variable, we'll have to clean them, simultaneously adding those values to the `tag` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clearYear(year):\n",
    "    if not year.isdigit():\n",
    "        return np.NaN, ' '+year\n",
    "    else:\n",
    "        return int(year), ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years, tags = zip(*memes.year.apply(lambda x: clearYear(x)))\n",
    "memes.year = years\n",
    "memes.tags += tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.tags[memes.tags.isin(['none...', 'none', 'Unknown', 'none... Unknown', 'None'])] = 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7218.000000\n",
       "mean     2007.279163\n",
       "std        40.585179\n",
       "min      1000.000000\n",
       "25%      2009.000000\n",
       "50%      2011.000000\n",
       "75%      2013.000000\n",
       "max      2916.000000\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect! Now that the variable is clean, we can see that the minimal value is 1000 and maximum - 2196, which looks like a misprint of '2016'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AR4E7gY312ALgiohYDqwHVgFrgS0Rsaj/2JKkbvX6EwifBh6olxdQzdbPBR6tx7YDlwLH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117127710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.year[(memes.year>2000)&(memes.year<2020)].hist(bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding some bounds for the plot allows us to prove once again that 2011 and 2012 were the origin years for most of memes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can have a look at the most \"historical\" memes, that had their origin before, say, year 1500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Tutankhamun\n",
      "Year: 1341.0\n",
      "About:\n",
      "Tutankhamun (lit. translation, Living Image of Amun) was an ancient Egyptian pharaoh who rose to power and control of Egypt at the age of 10 and ruled for approximately 8 years before his death in 1323 B.C. at the age of 18 or 19 years old. Because of this, as well as the circumstances surrounding the excavation of his tomb lead by Howard Carter in 1922, and what has been discovered since his tomb’s discovery, he has become famous around the world for being the subject of one of the world’s most in-depth excavations and ongoing archaeological mysteries.\n",
      "==================================================================\n",
      "\n",
      "Name: Foxes\n",
      "Year: 1000.0\n",
      "About:\n",
      "Foxes are omnivorous members of the Canidae family, a lineage of carnivorous mammals that also includes dogs, wolves and jackals. Foxes are found on every continent except Antarctica, with the most common species being the red fox (Vulpes vulpes) and its 47 recognized sub-species.\n",
      "==================================================================\n",
      "\n",
      "Name: Katana\n",
      "Year: 1392.0\n",
      "About:\n",
      "Katana are traditional Japanese swords originally wielded by samurai in feudal Japan. Online, the weapons are often associated with White enthusiasts of Japanese culture, who are mockingly referred to as neckbeards and weeaboos.\n",
      "==================================================================\n",
      "\n",
      "Name: Anglophile\n",
      "Year: 1066.0\n",
      "About:\n",
      "Anglophile is a word meaning a non-English person who admires England and English people and their culture. The term Anglophile is commonly used incorrectly to refer to a person who admires Great Britain or the British isles (known officially as a Britophile). Anglophiles are known for having romanticised views of England and the United Kingdom and some have been known to prefer English culture over that of their own indigenous culture, similar to Japanophiles or Weeaboos.\n",
      "==================================================================\n",
      "\n",
      "Name: Vitruvian Man\n",
      "Year: 1490.0\n",
      "About:\n",
      "Vitruvian Man, also known as the “Canon of Proportions,” is an illustration of a man inside of a circle and square in two superimposed positions. Created by the Italian Renaissance artist Leonardo da Vinci circa 1490, the drawing is based on the ideal geometric human proportions as described in the treatise De Architectura by the Roman architect Vitruvius.\n",
      "==================================================================\n",
      "\n",
      "Name: Pascal's Triangle\n",
      "Year: 1100.0\n",
      "About:\n",
      "Pascal’s Triangleis a triangular formation that is bordered by a series of ones with each interior row being equal to the sum of the two interior rows above. The array illustrates the mathematical concept of binomial coefficients.\n",
      "==================================================================\n",
      "\n",
      "Name: Chocolate\n",
      "Year: 1100.0\n",
      "About:\n",
      "Chocolate is a food item known for its sweet taste, its tendency to melt in a hot environment and its all-round delicious nature. Chocolate is typically produced using cocoa solids, sugar and other fats. Milk chocolate, as the name would suggest, is created using milk and is the most common type of chocolate available. In modern culture, chocolate is often lauded as being an incredible food in similar vein to Bacon (which leads to unexpected results when you combine the two)\n",
      "==================================================================\n",
      "\n",
      "Name: V Sign\n",
      "Year: 1415.0\n",
      "About:\n",
      "V Sign is a hand gesture in which the index and middle fingers are raised and parted, while the other fingers are clenched. The sign is used in multiple cultures with various meanings.\n",
      "==================================================================\n",
      "\n",
      "Name: The Last Supper Parodies\n",
      "Year: 1495.0\n",
      "About:\n",
      "The Last Supper Parodies are photoshopped or redrawn images that play off of Leonardo da Vinci’s[1] painting depicting the last supper of Jesus and his twelve disciples.\n",
      "==================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ancient = memes[memes.year<1500]\n",
    "ancient.dropna(subset=['about'], inplace=True)\n",
    "for i in ancient.index.tolist():\n",
    "    print(\"Name: {}\".format(ancient.loc[i, \"name\"]))\n",
    "    print(\"Year: {}\".format(ancient.loc[i, \"year\"]))\n",
    "    print(\"About:\\n{}\".format(ancient.loc[i, \"about\"]))\n",
    "    print(\"==================================================================\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neat, meme creators found inspiration in so many historical events and artifacts, even Vitruvian Man and The Last Supper have their own memes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amount of views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113826198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.views[memes.views>0].hist(bins=10)\n",
    "plt.title(\"Hist of Views\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skewed to the left, logarithm will cure that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4DXgKuCCltCEi9gOuAQ4AdgDvSCk9OAfPQ5I0iWlH9BHxIeAKYEnbtA5Yk1JaCQwBp0bE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\nQS9Jlft/OhEoKQ6ZamQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175db438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.views[memes.views>0].apply(np.log).hist(bins=10)\n",
    "plt.title(\"Hist of log Views\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popularity\n",
    "\n",
    "Metrics of popularity will be the estimated number of views per day. To calculate it, simply divide the amount of views by the days passed since publication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes['average_views'] = memes.views/memes.days_from_creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# most rescent meme, at the time of parsing the data exactly 0 days have passed \n",
    "# since its publication, so we'll have to clean it by replacing average with fact views\n",
    "memes.average_views[memes.name==\"#DrunkHillary\"] = 2568 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5PxkR64FjgG3AqTnnqyNiN+BSYA/gEeDNOef752AckqQpdJ3ZR8T7gYuAZe2ms4ENOefV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\n8bhzzolfq6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185416a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(memes.average_views.apply(np.log))\n",
    "plt.title(\"Hist of log average views\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have a look at the most popular!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meme name: \n",
      "Harambe the Gorilla\n",
      "Average views (per day) 14441.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Dat Boi\n",
      "Average views (per day) 13750.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Ken Bone\n",
      "Average views (per day) 11085.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Doge\n",
      "Average views (per day) 9611.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Describe Yourself in 3 Fictional Characters\n",
      "Average views (per day) 7528.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Hugh Mungus\n",
      "Average views (per day) 6872.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "( ͡° ͜ʖ ͡°) / Lenny Face\n",
      "Average views (per day) 6747.0\n",
      "==============\n",
      "\n"
     ]
    }
   ],
   "source": [
    "popular = memes.sort_values(by='average_views', ascending=False)\n",
    "for i in popular.index.tolist()[:7]:\n",
    "    print('Meme name: ')\n",
    "    print(popular.loc[i,'name'])\n",
    "    print(\"Average views (per day) {}\".format(round(popular.loc[i,'average_views'])))\n",
    "    print('==============\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Harambe](http://knowyourmeme.com/memes/harambe-the-gorilla) is in the lead - damn ninjas were cutting their onions too fast. Doge is among the top as well!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9      1 Comment\">\\n<a href=\"/memes/sombra#comments\" ...\n",
       "10     1 Comment\">\\n<a href=\"/memes/subcultures/pokem...\n",
       "36     1 Comment\">\\n<a href=\"/memes/cajama#comments\" ...\n",
       "56     1 Comment\">\\n<a href=\"/memes/spinnyboiz#commen...\n",
       "65     1 Comment\">\\n<a href=\"/memes/finally-i-can-exp...\n",
       "71     1 Comment\">\\n<a href=\"/memes/mr-hudson#comment...\n",
       "117    1 Comment\">\\n<a href=\"/memes/wallace-and-gromi...\n",
       "118    1 Comment\">\\n<a href=\"/memes/worm-on-a-string#...\n",
       "120    1 Comment\">\\n<a href=\"/memes/slondermon#commen...\n",
       "122    1 Comment\">\\n<a href=\"/memes/harambe-s-son-and...\n",
       "Name: comments, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memes.comments[~memes.comments.apply(lambda x: x.isdigit())][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Needs some cleaning again - some HTML tags are still present "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.comments[~memes.comments.apply(lambda x: x.isdigit())] = 1\n",
    "memes.comments = memes.comments.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117127160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.comments.hist()\n",
    "plt.title(\"Hist of comments\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about most commented memes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes['average_comments'] = memes.comments/memes.days_from_creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.average_comments[memes.name == \"#DrunkHillary\"] = memes.comments[memes.name == \"#DrunkHillary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meme name: \n",
      "#SpiritCooking\n",
      "Average comments (per day) 50.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "#DrunkHillary\n",
      "Average comments (per day) 29.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "AmourShipping\n",
      "Average comments (per day) 16.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "#ScienceMustFall\n",
      "Average comments (per day) 16.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Last Time The Cubs Won the World Series\n",
      "Average comments (per day) 15.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "Vote From Home\n",
      "Average comments (per day) 14.0\n",
      "==============\n",
      "\n",
      "Meme name: \n",
      "\"On November 9th...\"\n",
      "Average comments (per day) 12.0\n",
      "==============\n",
      "\n"
     ]
    }
   ],
   "source": [
    "popular = memes.sort_values(by='average_comments', ascending=False)\n",
    "for i in popular.index.tolist()[:7]:\n",
    "    print('Meme name: ')\n",
    "    print(popular.loc[i,'name'])\n",
    "    print(\"Average comments (per day) {}\".format(round(popular.loc[i,'average_comments'])))\n",
    "    print('==============\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fiIhZwL3AxsCawBnAC4F/BV4EbNT1tV9EfAl4IrAW8MnM/HJEHAM8izYQv5GZH42I2cAh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\nsKk7zvATwSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137f34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "memes.status.value_counts().plot(kind='barh')\n",
    "plt.title(\"Hist of Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each meme has one of 3 status:\n",
    "- Submission - recently added and has not yet gained popularity in the community\n",
    "- Deadpool - it's dead and nobody cares any longer\n",
    "- Confirmed - actively used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.snowball import RussianStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stop = stopwords.words('english')\n",
    "stemmer = nltk.stem.snowball.EnglishStemmer('english')\n",
    "\n",
    "def data_Stemmer(text):\n",
    "    return ' '.join([stemmer.stem(word) for word in text.split(' ')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modelling purposes, concatenate all the available text fields in one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes[\"description\"] = memes.tags.fillna('')+' '+memes.about.fillna(\"\")+' '+memes.origin.fillna(\"\")+ \" \" + memes.spread.fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memes.description = memes.description.apply(lambda x: x.lower().replace('unknown', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = memes.description[memes.description!=\"   \"]\n",
    "y = memes.average_views[memes.description!=\"   \"]\n",
    "status = memes.status[memes.description!=\"   \"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "status = le.fit_transform(status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For TF-IDF I'll try to use bigrams, and to keep the vocabulary relatively small, let's limit the minimal frequency of word in the document by 0.1%. That means the model will drop words and bigrams that can be found in less than 0.1% of all documents. As a result, the amount of unique words in vocabulary drops from 363K to only 6.4K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer\n",
    "tf_vectorizer = TfidfVectorizer(stop_words=stop, ngram_range=(1, 2), min_df=0.001, norm='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11154, 6456)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_vector = tf_vectorizer.fit_transform(X)\n",
    "X_train_vector.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30% of the dataset is reserved for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtr, Xval, ytr, yval  = train_test_split(X_train_vector, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variable (average views per day), I'll divide into five groups by quantile to shift the task from regression to classification with multiple classes. Simultaneously we are having a naive baseline - if all objects have the same class, the resulting accuracy will be 1/5 = 20%. Another good point - no class disbalance for us!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ytr = pd.qcut(ytr, q=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76.442, 11084.963]    1562\n",
       "[0.085, 1.518]         1562\n",
       "(25.185, 76.442]       1561\n",
       "(6.795, 25.185]        1561\n",
       "(1.518, 6.795]         1561\n",
       "Name: average_views, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, now we have neat groups for memes:\n",
    "- totally dead - less than 1 view per day \n",
    "- unpopular - 1 to 6 views \n",
    "- average - 6 to 24 \n",
    "- popular - 24 to 74 \n",
    "- viral - from 74 and higher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For future needs, save the existing intervals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intervals = ytr.unique().categories.get_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since I cannot perform the same quantile transformation on the test set (groups will not be equal), I need to have a function for test values transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def quantile(y):\n",
    "    for i in range(len(intervals)):\n",
    "        value = json.loads(intervals[i].replace(\"(\", \"[\"))[1]\n",
    "        if y<=value:\n",
    "            return intervals[i]\n",
    "            break\n",
    "    return intervals[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yval = yval.apply(quantile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, with the help of `LabelEncoder` prepare target variables for both train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ytr = le.fit_transform(ytr)\n",
    "yval = le.fit_transform(yval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training two models - logistic regression and random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5, random_state=7, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_val_predict_proba(estimator, X_train, y_train, skf):\n",
    "    \n",
    "    prediction = np.zeros([y_train.shape[0], len(np.unique(y_train))])\n",
    "    for train_idx, test_idx in skf.split(X_train, y_train):\n",
    "        X_train_part, X_test = X_train[train_idx, :], X_train[test_idx,:]\n",
    "        y_train_part, y_test = y_train[train_idx], y_train[test_idx]\n",
    "        \n",
    "        estimator.fit(X_train_part, y_train_part)\n",
    "        \n",
    "        prediction[test_idx, :] = estimator.predict_proba(X_test)\n",
    "        \n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logit_sgd = SGDClassifier(loss='log', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(random_state=42, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logit_sgd_oof_pred = cross_val_predict_proba(logit_sgd, Xtr, ytr, skf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_oof_pred = cross_val_predict_proba(rf, Xtr, ytr, skf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_accuracy_score(predictions, y):\n",
    "    return round(accuracy_score(map(np.argmax, predictions), y), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.421"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accuracy_score(logit_sgd_oof_pred, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.384"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accuracy_score(rf_oof_pred, ytr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, we have noce results here, logistic regression on cross-validation has shown accurace that beats the baseline more than twice (2.3 times to be precise), and forest gave 2 times increase. Now train models on the whole train set to check feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logit_sgd.fit(Xtr, ytr)\n",
    "logit_sgd_valid_pred = logit_sgd.predict_proba(Xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_coefficients(coef, feature_names, title, plot_num, n_top_features=25):\n",
    "    interesting_coefficients = np.argsort(coef)[-n_top_features:]\n",
    "    \n",
    "    plt.figure(figsize=(15, 15))\n",
    "    plt.subplot(510+plot_num+1)\n",
    "    colors = [\"blue\" for c in coef[interesting_coefficients]]\n",
    "    plt.bar(np.arange(n_top_features), coef[interesting_coefficients], color=colors)\n",
    "    feature_names = np.array(feature_names)\n",
    "    plt.xticks(np.arange(1, 1 + n_top_features), \n",
    "               feature_names[interesting_coefficients], rotation=60, ha=\"right\", fontsize=14);\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These graphs show top-25 words that most significantly increase the probability of certain popularity class attribution (at least that's what logistic regression thinks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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31UG/gi7aRMSBKFk4A5V+vgws2FD7vQ5GB80izlF9PTN3K98biUbpmy7tG4Rm\ni14qn8d9pd2d0fG5W0RcU3cyAFDKX/ZFidDf0bl5PBrpewV1Ri9H5YbHoBK7Jt0CEBHnoJv5FMC2\nmZlodLwTDW6YUZkJOQvNnK0dfeXYf0Hv04FoFu2/dd9YM3NkpSO+ERrMGVF5/Ww0Gns5cADw2Trb\n7x9L+eMiaJb70Sxl0Jl5fvn+cPRenRvaEKrWwaXsK6/8FBrhvDYzX87Mk1Di/gQ6Ln/Y5DWrxPIK\ncAq6bxwKbBURf4iItcrrA7UGrIuSMlD562GZ+SSauXgdzXQ2pnJczAlclpnPl+v3v1AlxEiUHCxV\nd9u9zlNJSgajUfh/hnY8/Ta6h16Vmb1kcem6YyhxzI0GWLeMiCNQEvSrzHwwM5/JzBPR7P81wAUR\ncXuTCWsZAL4U3bcODq2THJmZ30ODOiOB2ZoY/C33rEOATdA1ewM00DoNGjQ4Gt3H9kODkN/MBsrr\nKjNWs0TEPqG1w3OhSYH9UDXEmRExVZOD4PCuwZTPoxma+cv3/4veiy+g6/choequJr2C+pkfycwX\nyj3/MDT4NwK4KrSBRpNuRzOIa6NZ1g0z87aSqK+MKusaU7lmzQLclqVSLDP/nJkHoXvYaig/mX9s\nf+9El6BFX0nMPKgztxIqgXkb/cNnyszj0YeyHHpTmoxnKBrRmhHtiPZOIpaZr5SRpjXQRaTutueL\niBkq7T2POjrrVL43MjP/gRKTi9CB+2bdsVQ8AnwEzdi8IzO7pf3bUcJ6YWhjk1pExBSh3dh6N9dB\nqLRz7tB6iu1QR/yNVEnfQ6isrHaVk3VVVJO9SkQcFpUFq5k5IjNXBhYtF/0mk+beOsBtI2KPiBhe\nOqG3oc7nzsDBdY/8ldmaQZn5fGZegkY+j0advN5/P8zMH2bmdzLzsGy4vLCUGFyCzteb0blyW3l5\nHbRWciDK2S5AM9tfBXbvnQup3VdvQTOJx0F9C/AjYseIGF7+PANaszA3cE5ErNj7ucx8MzP3Q2U7\np9UZw2jciWa1Vytt9Y7B59G6tF1RZ+yqJjqh5Xz9K7BJ7/0p338FbaByHypzrH2grzKjOktELI1G\nXGdAMwObow7pCWUkep7R/6bxiqG6EcYCaMZ7moi4EZgsMw8vL79cYmtsbUm/4+zPaNOYldD1e+dy\nftwA3Ig653U7NyIOjYgZSp/iajSreTlwAtqEoLc5xRuMQ2drXGTmY2iA4GJU9bAmOj6XrfzM05m5\nXXn9usz3VNATAAAgAElEQVR8sYlYKu31ythuQPe2n0TEFmggfEM061y73r0xM5/IzF1Refpb6N76\nNkoOj8zMMzPz4My8vok4Kvf19VH/5o+oX3UN8DVUNbUKsEedfZvRiYiPAgcDXwS+GRFLhtYrjszM\np9C9ff4yi9RUDCuh92FnYM9QhdRkmfnPzPwl8ENUnXJRze32rptzRsTawNKZuQ2wfGZuBiwX2jzw\nOuDpMqAxEB4GVg+t96/6OZpVPC8z/zC2v2yiXYMWEZcD/8rMzSPiY8A3UBndVGgU5dvZ4NqzEkNv\nRGUG4JOoA7o8mq3YPcex3vR9tH8TWhOwLXBOGQ1eEtVrP45qkO8oo4LfBr6amYs3HNNgNAPweTQK\n/LtexyYifo9GZG9GB+s65UZUR7sroff9MWDHzLy8fP9baCRpMnRh7aJRlpOApTLznjraf4+45kcz\nvCuimbvLgfNz4Bbb74jKto5Gs3lzoVmBXwEXZwPrvSpt9wZTPopGFxdCHf6ZUGd8HbSm5Cp0g619\n1LESw3CUkMyUmWdHxIyZ+Z+IWBCNxi8KTJ0qSxgQ5VzZCh2fz6HNa37RUFsLo07/V1LrNaZGHZvV\nUCnhcNTpPap63YqBWWw/JZpZ3QiVGZ6HOmCTowGnzdDAz3kl/r+O+jeNVwzzoc7WH1GZ6R/KcbMs\nSpS/mJnPRo3rdyv3j8GohKuDNphaA12vTwWGlq93AfbJzN/U0fZo4jgUnQeB3vMt0Oj8L1BythYw\nR2YuWXcMJY7eGrjZgY3R53EkGoR9ApVxvVAS2RuBT2fmvTW2PzsaDFgMbYxybmaeFNqM7FPAiMy8\nLiKWQeWGF6KNU26KmtcJxrvXRs6NBnI2Q8fHZcDVmflEXe2NIZaP9x84i4hPoXN2WXTf3Tcb2HSq\nev0JlY+tjmYwz4+IddHSlhnQ8XBDqiy3dtXzPvrW786NSuemRAnaS6gfODWaTWpyILwa27bouB2B\nlrhcX1e/aiza7qK+3FPAkqj09B7ggjKTVvs9JN69odJv0Ez7kZl5WGjDlCGoHPpANKt2zUC9HyW+\nI9A16wq0GdvfQrs0XwqslJn3j+3vmigTtJJw/AK4KDNPCC2SnB+VKD2IbmZrZuY/G2q/d4BMgTbh\n6C3s7+1ytDka3Tkf2LPOC3e/OKYFvodOzj8C38/M35XO2P7oYnYf2oZ0ZrSTzZUNxTI5Wsw9GHWs\njkFJ89Xl+zOh9+nj6AS6B9g6M6+rqf3Jyu/fDr3/NwDfycxHQxtVbI6SxpFolOMXmXlgHW1XYqhu\ndvFxNN09WWbeXBLnHdCMxSPAJWWEqTFlNHp/4G+ZeVqZ7d0QrcOaGR0bl2XmrxuO4w60WPYltEvh\nleVC+nmUuK6LPquram63/26JM6IdlfYu585LqExoK1QKe12qzLB2lUTxY2hQZSl0zt6NRuh3L3H8\nAfhWNrBBSGj96ROhtZnbo1LTy1E529fQtWsoOmcPayp5r7wX06HNHno7s+2CktW/osRgGBr9/Gxo\ni/M/AGtl5p9qjmfKzHwltN7oOJQs348qAeYA7szMLetMzkq7vevF8SgBWA+9/4+gxHlJtF7yF73P\nrq62KzH0PosPoQX1F/XuEWUUeEOUvC+CynROzMwH605I+sV0DvBKZm5ROja7o+vpveh8fQt9JrWv\nFy3XpaVQMrok2jr7sMy8vby+NirZnhJdO/dschCjdEI/gY7HedF7MTeaXbwcuDEb3Gk0tGHMrWiw\nYK9UlU7vtbnRzMh1mfn9htrvHZ+Ho8/kLuA3WTafCG3SsR56Xy7OzD2biKMSz5ZooPMGdK68VXnt\no5RZ5sx8pIlzpN+A41xoIOm10HriA9Fgzu/QzOt12eDmdKHdTY8AtszM50Llnl9HZfFvoHvbSVnz\n7paVAaWz0D39yygxXA71/acCfpSZtc7ajUVc86F7x5Ro0mRJdK4OQdUgd2bm5uPyOye6Eseit4PP\ntqXztya6iO6PMuYZ0GhkIyo36cPR6NFxqFxrNTSqtmn53qbo4Gkqjhcyc2+0luqfwI0RcTZaINlL\nSK5FsxbrNJWcFUehOt8z0G4626AL2d/R2oEb0PvzFprRGVxXcgbv1Knfj8pQjkElGA+Hdhe6JjM3\nQOsFNkOJ64/qansU9kPHwWXAGRHxINpZalO06cDH0YLnpu2GkrGvRsRHMvP11CMXdkQzaEugEdDa\nRd86vG3QxXP9zFy9cgzuhEa59kCz3bUmZ0UvhoPQDOrywNGl43k9OjaXzMzvZuaxDSZng8tNdQo0\niHQAOheOLf+tmpnfLfFNRTOlW1Q6+ENRUrgr6pR/NDOPRJ/J79HsxQJNxFB5L6ZBO411I+K3oR0U\nT0FJ0eloZHQfYMNy4z8CrVEb7+Qs+jaAWCC0luaCUDXCNGjA4Gp0nXoVlRBtPb5tjkpJzqZGm/Wc\nWhLVE9DjWW5C97m9ys/Wmpz1zs9KB/Ik1Ln6WujBxKSqELZAFQdzZeaOJTkb1EDHs7eufCF0L7u1\nxPD7zFwbnScPovN2bzQQV6syM/JfNFjzCipLehNtKHVyqMT0V2ggZTV0fELNz+GqHJ8roGPxAtS/\n+VNmboj6HXOg+8xO0Wz58TPoPrIE8KcoywiK/6B7+/lNNV6uFbOh/szXgU0qydkqaE3xZagPeGwT\nMVSOze+jqqAl0VrI2yNiteh77u0TqbX/vfVHTSVns6Jj4hrg6dAGR69n5lYoWe3tXjh1ne33i2Vo\naWMFtInSZJn51zLovS99g0xjvSHG2CrJ2UxoUOuM1Lrl76Dz4Vm0Sch3y/22MdFXZrlQGVS6BFVw\nHYTuZfujc+dktH5ynAeUJpoZtMpo445opuQsVAbyPHBmZv4xtIh7MzRT1MiTxCsnyW7At9CB8QBa\n77Q+ypYfyMx7I2LRzLy7iThGE1uvrHB+dJAc1dQoeL92D0TJ6C9L2zNQkrXMvKPyc2uizumUwPbZ\nTLnOA+jC9TDqhO6ERpp2zcxT6m6v0m7v+FwMlXB+FZWZToFmJzZDN9mDIuITaN3kS3WPyveLaT30\n718YXTAuQmtpXi2vLw48klqE30gcEbE3MG9mfrN83Xumz1aoo9FJrZFsRJkZ+DWQmXl8OW83RrM0\nt6Nkdals8HmFlRG/E9DA0Tcy88lyHHwfJdG7pXaJazSGytfTopLC9dD5eAMayBkBLFL3LFX/OCLi\nfLTG7WSUFK2AOlunZGUNSegxAFuiQadNssZSlYi4F80iX4+u5dOjZGlIZv5nVHHX1Xa/3/1TdL28\nHK05+0xm3hfa+fc49OiH+2pu83Lg6N57HRGfQSVrn0Hl0CeiZzUOSJlWJa4z0b3kHjSo+NfKawO1\nffmNqITwkHKOro4G/GZEO9CekZkvNh1PRDyMOuKXAd1UeecMKBm5ACWI/5eZl9Xc7v/8u0LrpzdF\n97VXUGd0UWCxzJy3zvZHEc8yaAbv69X+VEmc/g9dM37ScAyD0b/5vMy8oHwOp6ANSy5AAzl/aWpW\nucTQu3b+Cg0mbYYGj3ZFSzd+lJk/Lz/7qQG4hh+EHlz+CqrGuD4rj1GKiKWzzDw3EQNKkqdFA61b\noMTwUHRfuQRVotzZRPv9Yvk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fQuvgTomI9Zo4JvrFMzOaQXwY3bsOKoNNb2bm39Bup/M3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235e0950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ljhxiyCWOHGLIJY4cYsgljhxiyCWOHGLIKY7JmZoErVvxCzPrDTxvZksCY4ANgSs784tG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107e4950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b4mhDDG2Jow0xtCWONsTw9nG0IYa2xNGGGNoSRxtiaEscbYihLXG0IYa2xNGGGNoSRxti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\nJZ1ceieOBx6XtENdQVFD2b0ywhiI+4uWA/6MTSK2A46VdEWzr5+0Fw1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d35f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YMzMzMzOzydkUE/qGiNgF2BT4T/PhmJmZmZmZTb4mZgbsYWCdpgMxMzMzMzOb3E0wAcvM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\nLsYVytcOjYjt6j7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70NqfpmKolkpchRqD4yPi86mtxtdHozPPoe3Ha2+Yon8b6SHo5v1z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\nOPsC4RERo/H8niWxlv5xfL8shys+53Ye6qq5p6SSJPhPbPi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X6sChEXEjcKikJ8sA0ZOx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e08a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rhSYkJR41MjMzM5t2TcoI2l+BjYFzBry+KPBAZj4LEBG3AKsBl1QaoU0TmjJqZGZmZmY2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\nGlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e369b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titles = [\"Very unpopular\", \"Unpopular\", 'Average', 'Popular', 'Very popular']\n",
    "for i in range(5):\n",
    "    visualize_coefficients(logit_sgd.coef_[i,:], tf_vectorizer.get_feature_names(),\n",
    "                      title=titles[i], plot_num=i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some findings\n",
    "\n",
    "Well, it turned out to be quite fascinating. The most popular memes had words like 'obama', 'islam', 'money' in their text descriptions. That's quite logical - politics, religion and eonomy, nothing can beat those topics, not even kittens. \n",
    "\n",
    "Second place (popular) has politics again - we have 'trump', 'politics', 'activism' among the main words. And pokemon, main summer trend!\n",
    "\n",
    "Average popularity is all about hobbies and animals - 'animal', 'pig', 'dogs', 'health', 'literature', 'videogame', 'fashion', 'american football'. Those are themes that do not get that much of attention usually but are always spoken about nevertheless. \n",
    "\n",
    "Unpopular themes have themes like 'researching', 'photograph', 'virtual', 'advertisment', 'professional' and, surprisingly, politics again. Maybe in this group it was mentioned not in the context of the widely discussed news like USA president elections but some more local and moderate. \n",
    "\n",
    "Finally, the long forgotten memes - once popular and now practically dead 'team fortress', 'playstation', some words about nationalities, 'frensh', 'dutch' and 'russian' turned out to be the most boring in terms of memes. We can also see pokemon here, written without it's signature style accent, probably, those were some fraud memes and reuploads that rarely gain any attention. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's have a closer look at random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_params_dist_grid = {\n",
    "    'max_depth': np.arange(5,14, 2),\n",
    "    'max_features': np.linspace(0.2, 0.5, 3),\n",
    "    'min_samples_leaf': np.arange(3,10,2)\n",
    "}\n",
    "rf_params_fixed = {\n",
    "    'random_state': 7,\n",
    "    'n_estimators': 100,\n",
    "    'n_jobs': -1\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of combinations: 60\n"
     ]
    }
   ],
   "source": [
    "combinations = np.prod([len(i) for i in rf_params_dist_grid.values()])\n",
    "print('Total number of combinations: {}'.format(combinations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_grid = RandomizedSearchCV(\n",
    "    estimator = RandomForestClassifier(**rf_params_fixed),\n",
    "    param_distributions = rf_params_dist_grid,\n",
    "    cv = skf,\n",
    "    scoring='accuracy',\n",
    "    n_iter = 10,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:  4.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 33s, sys: 9.87 s, total: 8min 42s\n",
      "Wall time: 4min 40s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=7, shuffle=True),\n",
       "          error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=-1, oob_score=False, random_state=7,\n",
       "            verbose=0, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'max_features': array([ 0.2 ,  0.35,  0.5 ]), 'max_depth': array([ 5,  7,  9, 11, 13]), 'min_samples_leaf': array([3, 5, 7, 9])},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='accuracy', verbose=True)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "rf_grid.fit(Xtr, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=13, max_features=0.20000000000000001,\n",
       "            max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "            min_samples_leaf=9, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=False, random_state=7, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_with_std(x, data, **kwargs):\n",
    "        mu, std = data.mean(1), data.std(1)\n",
    "        lines = plt.plot(x, mu, '-', **kwargs)\n",
    "        plt.fill_between(x, mu - std, mu + std, edgecolor='none',\n",
    "                         facecolor=lines[0].get_color(), alpha=0.2)\n",
    "        \n",
    "def plot_learning_curve(clf, X, y, scoring, cv=5):\n",
    "\n",
    "    train_sizes = np.linspace(0.05, 1, 20)\n",
    "    n_train, val_train, val_test = learning_curve(clf,\n",
    "                                                  X, y, train_sizes, cv=cv,\n",
    "                                                  scoring=scoring, n_jobs = -1)\n",
    "    plot_with_std(n_train, val_train, label='training scores', c='green')\n",
    "    plot_with_std(n_train, val_test, label='validation scores', c='red')\n",
    "    plt.xlabel('Training Set Size'); plt.ylabel(scoring)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oiHVgGAYl3hIqfBWUektznnPoYDpiHTy79Vlu/8fttEZbOW/ceSxesJgyX9mgvD/Qb8LJ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\nnahCUhRFUXSiCklRFEXRiX8AvO+Jile/05EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103e2ba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(rf_grid.best_estimator_,Xtr, ytr, scoring='accuracy', cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a nice pace accuracy on validation is climbing up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.408\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy: {}'.format(round(rf_grid.best_score_, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost up to logit in quality! Now train on all data with the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=13, max_features=0.20000000000000001,\n",
       "            max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "            min_samples_leaf=9, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=True, random_state=7, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=7, oob_score=True, n_estimators=100, n_jobs=-1, **rf_grid.best_params_)\n",
    "rfc.fit(Xtr, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.409\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy: {}'.format(round(rfc.oob_score_, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = tf_vectorizer.get_feature_names()\n",
    "importance = rfc.feature_importances_\n",
    "\n",
    "feature_weights = pd.DataFrame(importance, index = names, columns = ['importance'])\n",
    "feature_weights.sort_values(by=['importance'], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v2ZyRiYnBJ65em+2r1szjyAAAYN8gMAJg6RkbzeTGDQObJmc5bcU5\nmxKBEQAAWJIGAAAAQMkMIwCYI0vgAABYagRGADBXlsABALDEWJIGAAAAQEFgBAAAAEBBYAQAAABA\nwR5GALCPsck2AADzTWAEAPsam2wDADDPLEkDAAAAoCAwAgAAAKAgMAIAAACgYA8jAGBWNtkGANj/\nCIwAgNnZZBsAYL8jMAIAFpWZZjSZzQQAsPcIjACAxWWGGU1mMwEA7D0CIwBgv2V/JgCAwQRGAMD+\nay/vzySgAgD2FQIjAIC9xQbiAMA+4mELPQAAAAAAFheBEQAAAAAFgREAAAAABYERAAAAAAWBEQAA\nAAAFgREAAAAAheULPQAAAObHyPi2ZGx0YNv4ls0ZmZgYfOLqtdm+as08jgwAWOwERgAAS9XYaCY3\nbhjYNDnLaSvO2ZQIjABgv2ZJGgAAAAAFgREAAAAABYERAAAAAAWBEQAAAAAFm14DALBHeCobACwd\nAiMAAPaMvfxUNgEVAMwfgREAAPsmARUAzBuBEQAADGMvB1QAsJAERgAAsAiZ0QTAQhIYAQDAYrTE\nl9wJxAAWN4ERAACw95fcLZJATDgFMJjACAAAWPpmCKjsPwUwmMAIAABgD7LcDlgKBEYAAAB70iJZ\nbpcIqIDdJzACAADYly3xgGqp14PFSmAEAADA8Jb4Bul7vR4sUgIjAAAAWCBmULFYCYwAAABgoZhB\nxSIlMAIAAADmxVKfQTVTvaUwW0tgBAAAAMyPpT6DaoZ6S2HDeYER8H/ZO+swu6rr/X+S4O7uUCgO\nwTXB3eENrsGtOIW0UKxYcW9xSmERXArFpWgp7sWKS3ErBPj98a6Te+bmzmSSzAz98tvv8+TJXDv7\n7H32XvKutdcuKCgoKCgoKCgoKCgo+F9HD5NhvUf4FwUFBQUFBQUFBQUFBQUFBQUFP2sUwqigoKCg\noKCgoKCgoKCgoKCgoA0KYVRQUFBQUFBQUFBQUFBQUFBQ0AaFMCooKCgoKCgoKCgoKCgoKCgoaINC\nGBUUFBQUFBQUFBQUFBQUFBQUtEEhjAoKCgoKCgoKCgoKCgoKCgoK2qAQRgUFBQUFBQUFBQUFBQUF\nBQUFbVAIo4KCgoKCgoKCgoKCgoKCgoKCNiiEUUFBQUFBQUFBQUFBQUFBQUFBGxTCqKCgoKCgoKCg\noKCgoKCgoKCgDUbrzJckjQGcBmwIfAOcGBHHtfPd+YEzgfmBZ4GdI+Iftc8FHAlMA9wKbB8RH4xK\nJwoKCgoKCgoKCgoKCgoKCgoKug6dzTA6HlgMWB7YERiUxE8bSBoHuAm4H+gL3AfcKGnc/HwR4ALg\nsLzeBMBFo9aFgoKCgoKCgoKCgoKCgoKCgoKuxHAJoySBBgK/iojHI+J64FhgtxZf3xj4NiL2jYgX\nImIv4FNgQH6+GzA4Ii6OiKeBLYFVJM3SFZ0pKCgoKCgoKCgoKCgoKCgoKBh1dCbDaH5gDODvtffu\nAxaR1Kvpu4s1fY98vUT+vThwT/VBRLwJvF77vKCgoKCgoKCgoKCgoKCgoKDgJ0ZnCKOpgY8i4tva\ne+9hEmmKFt99u+m994DpOvl5QUFBQUFBQUFBQUFBQUFBQcFPjM4QRuMA/216r3o9Zie/O2YnPy8o\nKCgoKCgoKCgoKCgoKCgo+InR68cff+zwC5I2BM6IiClq7/0SeAaYMiI+rL1/A/BsROxfe+9oYJ6I\nWFPSF4Ai4qba5w/iukbHd3AbHd9kQUFBQUFBQUFBQUFBQUFBQcHIoLncEACjdeKHbwETSxotIobk\ne1PhzKCPWnx3qqb3pgLe6eTn7aHlzRcUFBQUFBQUFBQUFBQUFBQUdD06syXtceBbYMnae8sAj0bE\nD03ffbDpewBLAQ/UPl+6+kDS9MD0+X5BQUFBQUFBQUFBQUFBQUFBwf8AhrslDUDSmZgk2gYXrr4I\n2C4irpQ0JfBpRHwjaXzgJSCAM4EdgI2B2SLiS0mLA3cBuwEPAScBX0XEWl3es4KCgoKCgoKCgoKC\ngoKCgoKCkUJnMowA9gYeAW4HzgAOjYgr87N3AAFExOfAGjir6FFgCWC1iPgyP38Q2B4YBPwd+BjY\nuis6UlBQUFBQUFBQUFBQUFBQUFDQNehUhlFBQUFBQUFBQUFBQUFBQUFBwf8/6GyGUUFBQUFBQUFB\nQUFBQUFBQUHB/ycohFFBQUFBQUFBQUFBQUFBQUFBQRsUwqigoKCgoKDgJ4ek3pLG/qnv4+cASb1+\n6nsoKCgoKCgo+L+PQhgV/OwhaTRJ/SR1+3yX1EfSBPXX3d1mT0PSRD0xlgVdC0ljSLpU0to93O7P\ncq5IGl3SMZJ2zdc/y372FCTNBfwV2Oinvpf/65A0OjB9/j1aD7U5oaRNJE2Wr382hFUSmadIOiRf\n/+z0+v8PyFOde7K9ohMKCnoQKavH/anv4+eIIsw6AUm9fk7GTzN+zn1L7AzcCczenY1I2hN4AbhW\n0rmSxoiI77u5zR5dw5ImBw4Bpuih9npcRuV67zGHoCfWn6R9gY+AWYHnuru9bLOXpN4R8UO+7pEx\n7UF5Nh4wIbC7pLEj4oceepY/V739MjAW0E/STNC9z1LSBJJWyr9/NmOaY3YR1nlExJAeanpz4HBg\nhWz353SiSi/gP8BBksaNiO//P7CbfjbItf5H4OiefG6V7st7KCRjwf8MelLn9dSakzQF8Edgg55o\nL9v8/0YP/J8zknpa6EoaLSJ+jIgfeypDpbvbaGpvtJ407Hoq2llHRJwK/AvYQ9KY3dGGpI2AXYBB\nwDXA0sAO3dFWttdbUq+6QdJD+AwYAOwlaU5Jc3dHIxVp09y/7hbOSXD8mA7BVJIm6ca2+kD3OlaS\nJpH0KrArsFlELBYRL/XAOPbKcfxB0sySLgcW6+42q3a7s51sq3dEfAycDXwOHJYfdSfB0RuGcUK6\nfT105/Vr7YwVEf8FjgLmA1aHbicd5gDOljTRTyBHuw05ZtMAM0vaELpX79bm4B8xGb26pFmbPuvK\n9no3X7ebicXeGfg5FXgWr3noxrXe4h6OrZ5lN12/d/7/s3R+IuIz4D2cdddjGbaS5pB0Td5DtwYP\newo9HEzr05NrvYP76Fb50tPkTVMgb9JuaGPB/L932vE/5utVJK3c1e3V0AeTRSv1UNCpp/3nHg1m\nN6PXjz/+3wgCVQ+9NvHmBr6NiJd6oO0xsEMwHvAIcEVEfNXFbfyU/RsdO5SvAq9ExFPVPXXXYpC0\nBfBf4NWIeKSb2uhTKWlJawGDgRUj4t4uuHabsZEUwI8RMaAz3+8qSFoeR3RfA66MiI+6uo1sZ+j8\nlLQdcCYwGrBCRNzZxW3Vldn0mHh7HrguIj7vyrY6uIc9gWOA9SPipi6+dvPc2QaYE3gkIq7oyuun\no/gscFZEnNCZ++kqpBG0ITAQ+BjYNSI+7Op2sq3RqkwKSRMCqwI3R8Sn3dBWfXx/CawJ7An0i4hX\nuno8c+31qq2JvsBvI2LdrmqjE22OHhHfdVd7tXaXBw4EvsZ9fKIb5+eMwGnApMAQYIOI+KCr22nR\n7kQR8UkXXatZlowWEUMkXQLMBcwYEZO2+m5XoUleD8CBkysi4rRuaKu+9uYCJsby7dMqw6+r116t\nvVmBfsCfgLki4vnuJqjT8doNWADYOyLu7uLrD0NC9yTkLWKjA291o605ZkT8V9LEwLnAF8BeEfGf\nLm6nTwaa6uuhH5YxAyLi2a5sr6ntSYFtsJ30VES8nu9353pYAvgEeKE75k9TWwvg7NPngM96KCg0\nHTAl8HQGM7pjPOtzZRLgq4j4pquuP5y2x8Hr4Qdgn4h4t4uuOw/wJLBmZTvnWC4JnALsA1w+qpmv\nrZ6FpNWBa4HHgfMj4oxRaWME7mU3HDj8V0T8vZvaqK+HcYAhEfFtd7TVHv5PEEZNAzUVcCXwS+Ab\n4CA8+bplkUnqD1yGtxq9DkwGDIyIt7uwjZ+yf2sD5+MMnG+A6YBjgfO6w0GQtBBwOfAt8AGO8h4H\n/DGjQF0OSX/A/TseZ//sltkBo3LNaSPirWR7R8dbAJ6MiCPy892xk/x6VxBULdofA0c8NwZuBFbB\nQvKiiLiwrohGsZ2hpFvtvduAZYAHI6LfqLbRQdub4j6+gxX3PyNilS5uo804pROyC7AQni83duPa\nmwpnVKyJDaFlgB2Bc0fl2TXNzV7AfsByEbFyfr4z8Cnw74i4bxS7UbXZB/ihyXldBTgUZzss1lUG\nSQf30AvYGugPzA38JiL+2k1tjQmchcnaZ4GV8drbujvayzYXAfYC3sDPdJWIuLWr1no7bfYFDsBO\n1sPALRHxWle3KWlJXL/ofmASYBG89fW4rlx/TUGEcYF7gAWBqyJiwzrx2NWQVOm68bA+umZUyej6\nWq/1qw/WBYcCvwVuj4i9W8nyroKk8bEzMARnh70I/Ko7CL8khM8HlgLexYb6dRFxbHcQOKlrT8Pk\n9x3A+sAdEbFiV7bTot3ZgHOwLlo7Iu7urrWe628nbJP9DbgzIr7tRpJxNOB0TOx/hImO87pTnkla\nAddHWwg4IyLO74Y2RgMmjYj38vXiOCNt04h4pqvbyzZ2AP4A/AOo6meeHBEXddH1m0nphYELgLGB\ncYFbsM1yT1fLmCQULwTmweTU+8DVEXFqN86TPliWbYLXw3vApRFxVlethxZBvTOANbCOvSQiruzK\nsWxh524MbIF3DOwPvNtV/p6ksbC8XCoi5pQ0HpbT72Ni6uouaqeN7pMPy7gc++njYV/90Ih4tBvl\n2CLAFcBXeJ4sjgPNp3YVId08DyT9Dgdh/46TBC7vThuwjv/pLWlqm9XQW9J6eMvPE9hQvw7vmV+u\nK9pSU1pgvl7dtxD9ImJLYDvswI4yfoL+Nad2jg1sCxwd3qrSD3gUG+pLd0GbrebXQOC+iJgr2zsu\n/3U58SBpfElXAisC3wF3YSWw2ihedxB2FomI79OhGR1YQNKkkh7Hwnh34G+S9kwhOrLttUoXnxuY\nF1g+IjYF+gJPAftLmj66oJ5KCtnKCVlV0maS5khDeV5gKUnrj0obVTtNr/tKOgmnlm6fba0NLC1p\n21Ftr2pTLba8YSJlBrx96qWI+KadeTyi7fXJ/3vnvx0wMTsRjlb3A34DHMko1NpqMTeHAM8A30o6\nUNKDWIbtB9wuaR+NYoHAVFbfpxybQ1J1/7cDVwNTAzPmd7slnTaJjXexPJsMmB/YSNI0+flIr4V2\nfrs2jpitjiNmxwArSFojf9OlulXSusDNwIfYKX8DRwe7LTtA0pa4Ds6H2KlbGbhqVNpsR8/2wsR3\nAGthwu90rAP7j9zdD9Nutf2zkmejAz9iZ24wMG1+PmRU5WY77S+BHfG3sbP1Dq51129k22te6/le\nb2AcnHH6bH6+p7Kenrpha1qSp+dguXU9NtoXBNaRa3uNtKHeztgMxDX0Fs9/Z+PaNMuNqlPQzrrt\nh22wfpiw3Rnrok06+M3Itj++pBnzef0LE2Pf0tjO2x1OzwHADcCXmEw5hdx2142ZHL/DWVMDcGbM\nG8BlkmYbVXlW6fba62kkPQachAmVBYCNJf1iFNtps10qHeOLgcflLAsi4kHsvC6d3+lqvTA5sCmw\nc0QsFxELYef1YEnzdsH1ezeRRdVuizvw2tsNbwM6f1QJjnbGprr+QsAS2Dk/WdL83egg74nX24rY\nnrgTOE1S/65aDzWyaE5M7E+X/48BXCBp/OiCOmmVvdlEFo2G7c7VgPci4o2uIItq/uw3mMCcUtJe\nEfEFtm1nwpnDo4xm3Zdtf4flyMXZ/tR4e/RYXaAX2tsKthXWefNFxHLA3sDywB6j0l6t3d51myXt\niOWAI4DxgUMkzdoV/l5n8D9NGNUW1ULYoNwj/z8/Ih6PiJ2xYbSxvHVlpJCCrqq3MUHNuPwBWAmY\nMw27I7GwvFPSQXKBrZF2Rrqrfy0c8Kp/P6ahXGF8nOFzo6QpJQ3GmSqHAs+kchhhVEZpjmcfSYtJ\nGlvSRDhz6hz5xKYzcQT0eODhUWivvcVcGZU7RsSfImIN4GTg13KK5MhiBWBROf2xwonAepgougVn\ni/TDz/Qg7FyOEJqFfZPQWxqYGZNEhFOQL8BO8/4tvj8i7Q6trZPG69+xEN4L+LOkmSPiRVyz4ig5\nPXJk2qlIm+b7/AgLxTWA13LuPoCVwBFyavmIttVG1kWjTtGUko6WtJ+k5SPirWznORxJHiWHvNlR\njYgfatdbAZgscptWRByJFeqoEIyt5ub9ONNoQ5yNtiSOzu8C7MsI1haSNKvy6HNlHa0cx5uBm4Dr\nJV0GLIq3b9yNSZVRruPQinBIbATcFRHL5DrfFvgFo1ALp9X8rMnWNXFG4dMZOf4DJjwOyfZGllDp\n0/S6am8Z3L89IuJgLFu+l3RYq9+Napv53irAERGxa0Tsh3XRAspC0SPTTk3PDiUucnxXAv4TEUMi\n4musZz8EVk2naJRIv5rRtYOkf2AHeaeIOAc7yJNK+lV+vTvsooXxfNkpHPl/HjtCc+Fgw8igWuur\nwdDx/QFv9V4Ibyk8EwdK7pV0BaNGRrdXa2MG/Pz2j4jrImJv4AS8/kco2CVpDUlzqVGjr83aS7m4\nJXBCRLyKidvf4VIB741czzoMIIB17Tt4u/4bmBw7EtsSXUbY5lp+HsuRv0paFbgUuBXoL2mGKrg4\nktcfRkbImWGrAPulvbkaDnRtIWmd/E6XOiLy9qlNcLbBg5jgXB87eyNlA9auXa9BWNUfXAJnz6+Y\nwbVN8famLUahnV61IMnckuZKx3ggfobHyWUXwIHfBSpdOdKda7Rdr8M5KXaMb8z7uBWv8YOAd0d2\nrlSo7lfSITkf5wWWxRlMH0TEYBzU/i+240eYFGtFauT7E2F/6LBwxtaGwME4K/SLUelXi3tYWdL+\n+XJrYHBE/DMi/h4Rh2DidqS32LbyUdJG+yewLnBQRPwR2y0vkmQII1gnra5XqzFNfTu/pCPl0hzj\n4WDT3Thza6SITEmLSpoh/2624/+FEwEOlwmbE/O9ripG3Ub3wdDDHX4dEQ9HxNPYD1sBz9eRRpNM\nGaP2/vjYR3k9GlnJ52MydXmNYH1XOdg6Z/5dkW8/SJpH0p3AbdguuiT1+q9xGZnD87vdvl3sf44w\narGo+mOBex9OWW+Ohv8OM3r9O2s0SxpYf5g1Y/IPwEPALZJOlcmVX2Gj7g6cPjsBNmTXwsphhB5U\nT/QPs9V14qbq39HA5ZK2SYdvPCx4DwVewgxt37yfQZiQGGFEo5bIMliJ/AXXUvgEE1S7Y+djLmDV\niNgfOBrXcRkhNC3mKjpXGeBT4ZNNhj6fiNgLb3nYemScLDljYTac9rhnTXk/ip2QE4D3I+K/EfFN\nKmbOzEsAACAASURBVIE3SPKhozblFPTq7141Yb+kpDMlHSZpubzGN1gAz1a7xBPAY8D0Kcw60596\nhGwouVHr187YkZscz0OloQ6OGkyJ61+NMGrPbTJJv5W0taS+EfEa3qr1bxwNrHAMnqO/HpF2Usgv\nKWnqfN0r/98Yz/t5sFI5TNKZ4ToRt+VvhhbvG8k+VmtvU0nXSTpBLsZ3Ll5n48jb4CrsiuXKIiPa\nVou5OUbew4d4T/lseCvFtxHxVUScizMe1uxsH7ONnUhDoyb7jsbrrB827N7EjuqXeP3PqkYB3hE1\nKKvnVS+kPXbT1xbEkVXyvi7Ga3K5StaPqONTm5+zp8G8HiahwNtNq6ypXjnGlwMTStppZPtZmy9z\nSpq6Nr7LAE9X1801ciBwoFwTZ6SikWobwZpZPgHqx3xvCeBBSUtL+hfOMFoDeLvF+Le6dmWMNuuh\ng3FE+uDUf2DDeZLanP0Ay9O1SYNvVAwiSaNJOgvrtbNx9k1V7+0ZvO18T0kT5liOkm0kaT45EFRh\nOeC+NP6ewxHCzbDzM0v+ptPPr2mt/0qNDKLeWP8/HhHPyls6RsN6fax8b2T0Xl0fTS9pKTm41huv\nhU9wPaEKZ2L7cg1Jk41A/87Fsr9POuPzS9pD0vzABOHo9VuYSPwbzho5CTsG/eRtR6Oy1ueQdHza\niAvnx58D00TEl7XrngV8J0e6R4mwzd/vgJ2pHTChcReWm32B8zCpsknea6dJByXx1rTOJ1fDsZwd\nZ/Hdkdf+OiL+hkmx3+d7oxqdn07SvrKDOU54q8a/gfnkwxBuzL6uBMyjdJpGBjWCYxDwqrwNdDFg\n7CQdCNcJvAnoK2mknMmcmxNLug5vBbtF0okR8SXOOngUOKMm34aMCtmXfRpP0hnAxZLWVyNT5DMc\nLHwQ2zPzYZk2CNtnI9JGr6b/++VYroFtznHw1qJ6QOsVHDxcU9IkI0qKNdm5x0raSdJU6S+8BWwo\n6T7sCx2a97KOpEXr9zoySNtzXRzYmjDt3jpJXT2v44GZlIG4EZTVfWryZVK57iHh7chX48B25bO8\nn/3cRLaFO505IhNel1ev87djSjoXb3VbCwdHTg1nFB2NiY3++d0RlWF3AIeqsSVsWkkD5OSGH/A2\nu7doEG2/BraR61GNNNrRfT+mjvqs1o+TcVBmTY1A0EnS5vIWXaCNTDkYeEy24w/JPn4BfKzGgSvf\n4Mz6iTGR25n+VPd0GU6mmCD7M3bKwpPwen4V67lqTT+NgwtLqIdOe/2fI4xy4k2hrHAeEXfhB7MY\n3st6D46KV9+/HTOlm9IJwkHS0th4+7j23liSzsHOzoH4Ac2P0++fwc7IjHkPW0TEhnjsvsrfd1p4\ndGf/0oAbjB26OnEzh6QLMOH1DV7Ae0TEK3jRrQlsGRGbRMTLWIDtSlsDsNOQ9AtJ1WkifXEqYpWi\ndxJOQ947vM3vATmSsBqOSI4Qaou5VXTuyezLPGqbWXU23urUIQMsR2wmrr3uhY8OfgUL3nFpkCXf\n5Hvf4udZx0NVW9FOhkUK0fslbZ1vVVuYDsFbGX7AY3kIjm7ehB2DZau+pWE3MSbKvuyob3nt32NF\nT/3e5G1fr0paE2/VqAqrLQ+sLZM766WT/FtMtPSTt62N105bY0papuk5IG97eRVHcbcF/ijpwIi4\nHDt1KyiJtDTGDsCn3Y1IuvVEuC5Y5QBUGVHr4T3Oa0bEWtjI2zGfxV9wdGezbHtkM0Ymk2s+nYJJ\nlAGY+Jocr/fP8Zwi27kBr/cj1MEJbZ2cm7vVfvIbYKbIgnw1h+Exkvhor4+SxpU0X778MttZTdIW\nsuM4OXY6doqINzHJtwme92NhIvxJYKAcbeqUEZRrGFJPVY6LpGNxRuKfJS2V33kP+EZts88uyvta\nTa0z2eptTaM8vaOudCsjAZMnBwDX5bq4BphF0kq1674DjAnsLWnijuaMTELtLKmqOVHP6LsXG/+3\nSdo+P74bUH6vuu6dWI8dk69H2HDOZzG7pHswSfqYGpHxh3AByWuwA7lY3schOKOrXUjaBz/zaptX\nnxzjB/O3L+GAwWCZlH0MmBVnO1R4BMufLZXkU2eg1tuuxsfzfFCS+IfiLZnjY7LjYlyv4sj8/kgR\nAGnk/SXv/TpJJ+dHd2AH/F5sGC4cEX/B20MPhvYd83bW+mY01vo45FrPufENzoquTi37FKfNz5zf\nGW6Wn6QZJE2bf/fOudlHzgp+FBNCf8NbWz/DMnzZSv4n4XcfDpSs1FH/so3qmW2AswJXzLX3EN6y\ndDVwbq7Nx7F8+Q8wS0Scgp/vQIYlsjsNSXtj4nJWLKcvkrQr3rI4haRN0vH7EQcu/gP8RtJknRnT\nDtodHc+DkyPiRizv1sSytg+eO09g8rtv/qZd2z11zgXpePxQI9nnz3V+E7Y1Zsw+VDXY6sTXJZjA\n7Tuy/crrrYsdm92wTj0gP/oEB9fGBOaJiN/gQN5BePw7e/3m7a1LSboQ25JrY/v2WaC32m5Buwbb\nSeurE9s0m/WVpBWxHHwf64ZjMOG8cUT8OyKqLTP7YPt3DY1CnZG0dwbjDJ+xsd22VThD6wccYFo9\nInZJYmw2XA+xU5nKasqmzfU+AdYxe2B/4S68Hfo9vNWn0svfYV9oCMMGvDvT9piSzsaZdLPjubKe\nvFX+CbyunwZmjYg/YYd5O6w/RpXQvALL/gvDmbtjYZmztBywqZ7XFzjwO/2Itlmzq0/Geu9aOXgB\n1gnfYae/2jp1J5Z3p49gW0/gcauToBtjmThXRMyH19dakvpFxC3Z/9Pq9zk81NbLltgmWUzSXsDL\nONvlduCk8IEjhwDbyhl4V2L/9thO9mdE7dwqK6cqPP8+lmOLYaJluGOZMmJ9MphUrQt5d9FmOJjx\nAPbnfsTZYFsA09au/SLeSTPcbMn0wS7Il1tiuVVlcJ+CAwefRcRueB1eACwi6Zfp39+T/w7K/nVr\nHaOfnDCqhI4ajHYlJC6WVG2VuBQXbX0PT/DZ1PaI0UE0HIN2hb/MAN4XEZtFxNtqZGFMhh/SthFx\nLVYwv8BGyITYKF8Bb+UYT440T0hGfIdjCHV7/yTNJmmPcNHo/VPwVZ/tho3F8YFlwim5J2KhsQh2\n+L/FWSkz5M/WxIt+uKc6qDUrvRkZQcKG1xbAZnLG0RV421RfNbaFrYqV0I2daG8Yp0jDRufuxkb5\ndNgp35u2ZNvUWPD/Si22/khaXdKb2MB5QtJGkibN5/wtVtzXYaduI0kzprC9GwvfA+RMoDHSGJwX\nE1kd4f38zm45T4fIRNpSwNYRsSs2gD7GW87+g5XKZjhbagyZ2Z8Opyx25KxOKelh7LhVpxj0lrMa\nrsWE6g7Zn9eBhSV9l33bKn8XkpaNiFOxgrsZK/Fmw2pL+cSeyXPc5sr3q7EZAOwbEStHxLJYof5W\nJonOxgbE0O1VEXEZFsidOhq3pjgG473v9+FnNgbOXLgt1899eA5tjTOqHsRrYAGZIBguMdyO7FkT\n+B4bO7tgMndJfGrKg1jYLy6fDlVhN0xSD3Pc6UjMzWpN94qITyStkN8fks7dfHgedYTFgK0kTZ5G\nwHnY4T4Wp6J/iQ3GNXIcj8KGwjp4TnyB1+M0pMMwHJnZJ+XWgfIJN5XBtYBMZi6PCYyF8VyZERub\nS9N26+fr2ABcneFvCZ0WOEu1I9bzuusD60XEqhGxeF5vP2ykX5e/mTCvMTZ2nL/BUdCOsBpeq19k\nW6PL0eiDsUO+NiYXTpJPZ7oSGD/HpcIkWIdvr05GI1s4WLNj/fMclp1H4vUFNpzfwVtHjg2fDDo9\n1kXDFHRM42r2bCMw6XwsDDVG58XG8ZIRcRg2lifBc+RqPG67S1oir7E8jt6/ikmJTqEWKFk6xw6c\nTTsvJiKOx+N5NdZxZ4ZPIz0Pz/N7gX2bx2p4SGNzMbwOF8Dp47vL2wDuws/1soj4XUR8LEezF8JR\n2FZOaUdr/Tsaa/0h2q71ybHMmQvYLCLWxvNqLkn7daIffbCMb96SOxDroxXxWjsSP8NF8bbXRXAU\nu8JYmMxaXUk+tYfqmSWh/VfsgC+cbS2JbYiVcUDvHizf3gtvXwTbYxPk+8Pr3zDPVc6CWh/YPCLW\ni4iVMKmxFw46nYHrmMyXemsc7CC9jXVYpyET8DtIWlauN/gdDiZMLukUTFo9jsd1Mvwc/4izEOrE\nYHuYET+fw7O9XilbrsnrVgGn0bDsfhRYWXaQK6dxFkwAjNSpq5LWlqP0c+As5QVw9tgWctbPxTgY\ne214GzjYRp0Yr/fhXb/NFsKaHfohDgQtCrwYjvi/ju3LbarfR8QL2Nlclw62pqmxXaqeddIXZ4Fs\nk/f/ePhEwPOAQZVdG97Gey3WLdMyEnVBUy8cioPX7wDLZnDrSmCApFmwDT86zuKdLOXIivh5v9mZ\ndqKRATOhpI0lLZD+xA54Do6f33sAz88VaDi2YBKnWg8d9aeVv7AgXuvzh0/+XBy4IBwgvBnrgA+i\nUWdnUuyMP9yZvrW4hxXVCDTthOf5pGlrfIpl9Vi0zZyfFK+Xh0aivUnkTLSFsK4bBOwgaevwidRX\nYmKnCip/jtfoYupEzU41tu/egjOMjk8bajQchL0lIl5W48TFPjRIm0Ox77dDXqtD8jTXQiWrr8K+\n7JFYRq+D5/gBwE7yScM34Wd4Tl5iL5wJuslw2hlhOzftn0q2V8Tn2XhObqLhZC7mGL4UEeuHT8Ac\nN9sbH+uGEyPizxHxe2CHtId2xb7ljsrMsRyH+3DQqL226j7YtXmvT2Fb7Ag5g3sQtg/HkDOfP8M2\ny3h4vhDe7XEZ8AtJ+w7bUtfiJyeMaoqvSrP6AU+yXwBHyk7zS8AEOZlvxUpvoNLZD6fnb4Fr/7Q8\n4aQu9GVG+0Aa9VDGxNkpk0i6FEc7L8dM5kzYGJkBL+x7cfriyRFx2/9I/zYDtpO0bvhY5wkk7Z3X\newY/59GjcST5ydjg3C581OdvsWC5S44A74dPPWjXSJeGKSI6Wv4/NjYOXg3Xo3g3Iv6c/doXs/QV\nM32npNuxor00hnPynHyCTfMxih1F58bFmRXfASfkmKyHlfeuOKrwTV6nIvTGwqmTf8TGxOVYyP46\n+3tSRJyaY3MDeURrfjYkIvbFxvN52PB9AhtBd7TTp8ogeRuz4ZB1UDCxtQBwVd73K3nvq9KIxt2F\nn+dtOKLfCwuQjjAzntPbRcRV8mkDP2CSby3sYD+b8+U4/Lw2xYJQETEvnrOVUzwAWCQiNqrNMeTt\nASfhufAmJqAulmvd9M97mB+4NR3NO3CW3/bAF+ETrv6Bs4yWqN3/0uF6P+2ipviq+TImXstj4HU8\nHhboF9Ew0BfCz/RQmcy9LH+3Rqu5l+30ruZOzVGdVo1C0gtlXz6VTyT8LZYBa8hRw/PxHNq4tt6f\nxyetvJTX64q5+V2uzYuAR+Q05aq2UZtjQLNPV0uqDOzp8HPZTM7kWxhvKb0dkwvj5v+n4Oc1dzgS\nOAs2OBfEiv1K7Jx0iJQpf46I/tE4znbxvMZewO5JVG6dP9klIi7BjsEOktZLw2E9LMvB2ws7qhXz\nPpaVN0m6T86amgkTYXdIWknSo3g+/RYTDYdiAvdxSdfgNf4i0D/vZxjU5srJEXFwjeRZARsLqwNn\nRcTdEbETDkocnv04GThR0m7ydqcdsdH0FzIzpj0iLg3IVjU0ZsAO8W4R8RA2iL+SNDN2OP6JyZNV\nZHJpIJZDT7Ro5pd5T7OEa70cCeyjBpGxMj6Z7ytJF+H5d0r26yNMBnyGaw88irdtHRERv4qIf7fq\nVzt9XVvSWzjC/6Ck0/Ba2A9H3tfG+v4aPLbbSloyIs7COuodfCx8p6J1cmblFzjgcS7wcUQ8l8bq\nJXi+fJL3s6OkQXKGwt54jt0EQ6P6o7LW98lbegavjZWicUrgv7DO6+z6WxBntOwiacf8aC18utST\nmBg7FMuBd3Ew4VVcu+U4OcNsZfwMB9VIgY7GsXLiBmKioR+eL18nkbQDDpY8j9fKJpIekDOp7sFz\n91nViDcNS8LVj7GeoPbRJFgnPCCpv1wkeSrsUL6J7YgHcPDtZrwuPgMWiIjTh9e3Wvs7YfJiN/xs\nB6Yt9SF+tgthXbojjVNylwzX5Lgfy5rh2e1PY4dwI0nzpUxYFB9dv0dEXIMj1d/geXMJzsI9WdIs\n8tbtlbE86xThUOvfZJLuxnrtZkwovh7eXnQFlhtHhbMNHsS69k8ykTkY2zOvNj+3ZtQIjslkUnqQ\npHmSCKpqAVbZZndiHbduzudJ5CyM97AD9nI7falvw5xf0oZy1tY/s39f03bbye7Yttpcja21f8Lr\n8iMaWeOdya6dXtJymAR9Cdshn9fk+8n52XZJFFQ1Te7EduBGwNnRiWLGNZmzC3au9wZukLRv3v+b\nOBOrCrCfgmXk5XLpjouzj5dGO1ujNey2yIlqH0+Ene5p5Zo0v8Hk7B+xTToY2FnSXWm33ILt3Vc6\nM5a1e5gj1/Uf8Zbo67DMuhTb2VXW+lVYlu8s6aq0ea7F9uLLLWRKvZREKxtjZkz6bho+Iewe7J/s\nJNfzOgTr4LXUCD49g8uhPDW8fuUcrebFr7Mf26U9eiFwT86lA/C4rQdMJmnHiHgOz52zZAK23RNC\n1ZagrUpWbI/ldD/g6Yh4P/u4Lw5SVEXSF5G0TkQ8gdfcRi3GsSvs3KH1XtUgJy/APmC7da/UyKKt\nEjx2AE6Vg/AT0DjVu8IXeZ9TYbulH/Zn78IJGZeHs2zbQ7MPVtlIu2LCf69wAsk52I7um/26Hvsq\ni8sJGGBdVAUauhU9Thhp2BMMesmnNLwgaYpwxOh5GoXNLs7XKwLjhYv73oQfVJXeSkQMjiwe2wq1\niX4InngzYsdncWzMzYMV3HjAohHxK0zynIH3/h+O2dP9ganCRad+0v7VDIcLcIR4wxTqS+BK/zul\nsrwSp1hOmNf6APgzsJCkjcIp3etiw/YiYMrwlqB2URP828kEwBlyps3XOYbf5ueVAN0LEwwKE22r\nYAb1VmD2iDimuY0WbQ5R56Nzi2BlPjMWkI9jJXoi8HBEVLVqmomoBfACvSAiXglHiQJYRZLy+1Wq\n4X15/8vIWx0rbI0Z4KsxKz1vGjLIEcrJan2qDJJfYoP8ObzPd2ps0PwbO8JnYKGwdH7nXOyY/Sbf\nOxXYMJyp0+46SPyAjd0l5bpWb0j6TTiisysmACbP+/sUk6T3A99GxEsy0fEZJsSIiM/ToG3GHNjh\n/n3O1XHxOhs3XCdhcvI4XezIPI8dlbuxITkDTsudF5NGFUk53GyDmuKrWP/B2HmZHdej+AgbC+Pj\noui7pYCeERNg44aJ2oPw8dBDFamc9n5StjNUWcsFW1/C6+1+OePgfLxtYWVMQJ+FMyfmwNH/17AB\ntCS1LJjK0OuCublsNTdzbfbHJOD3mBReJBo1qaq2f8Bz9/Z8fRGOrp2A59wqOLraD1gr5cmtmCy6\nv/Z8qqyC5yPi03BmxQ0tHtdQ1J7xx3LdmSMkrRjOxrokr1kVen8Az80l5Wj2rjhb41I8n07DxueD\nOF3/u1ZtJT7EGVCL48yFD3BkdUZsPEb+Pzd2tI4NkxhrYuPoZVy4cseI+EitT6Wsb3XrnY7LLXg7\n8M3ZxvfZboW98XxcOiKOw5kXAzBRtDx+JncCYyn36bcY03qB1n6SDpeLfI6BtyuNDlwj6RFMkt6B\nCYbVsOP5LF6Ht2FHcpcwAYyk7SWdn00NwYGHVeSttS/ieVFte30Ep8x/ln1cIfXsUnguPhXe7r1C\n9nO6aBAeLftVPcOaoTcVXrOn4Ll+IA7OXBURf4mIJSNidmyQHYHnz9OkvoqIsyNCEfGv4Tkjcm2W\nvTGZvismUH7Axn+FPbDM2yBcM6wiUk4ls0Mj4p683qiu9aUlLZOy6u/hCCjZr+9S57UMXDSPJ5bJ\nK2LCv2pnLGB+ectPkHVgcpxni4ht8/sz4gDXHyPiuLRpmttpznSbAhfm3jPX3hHUtnanQX8ZNtoH\nhI9EXwvroLGBXSNi89RFlTyeikadsfpBHNNJugrP+Z3lQxvGxTJuMCYSr8N65y08F8fG8nsLvD72\nioitwoGA9grx1/s3kbzFeBdgm/D2kJWA09OWuhp4Abg+HDCoxnuqvAeAwyPilGhBZKY8qWTnf7Et\n+yiuUwLe+jyPpD/IhE5gp/Qx7Accgcmq67CtNBcuNtyuA1lru1e2fzR2IJ/GsnQ/TF70z/t6Cdud\nC0paK5w1fTjWwetgW2mrcH294W7DkTMY/o3lxQAceJoa23hf4qBMtRbPwTbTsViv3ZpjtE+1/pqR\n8nIMefvQP7A++au8PTuwDb9KzrNKvw7Cjv7stevci+3S4R7AkON4An42Z2PbYDk8L4dm74UPWrgB\nk7rLR8RvsQ3/Bxw8nTZt/1ZttJFr2c9pMNm/U0Qsmn8/mV/ZD+v7ioB7AZO3v8VzZ2ysny5tr3/R\n2BbZV9Jf8Vbks+TA3DPZzzuzn32xbbgCLhlwKs7+vhMTZ1tExM7hGqEjsh1tH7zGZsc2+oXYaT8S\n+wory4TgN+EizZvjtbc4npub1uVLjuV8OCixXfbzu1zry6lR52/K7N8PcgmGS7Gc/CXOrv84X29B\nZqGFa6CeEhHtZqnUkTp9p7QnT8RlIibC/tzd2B98D+vxF7Pfh8hBoONxxky7ZF/eU0XQngwcJG+L\nehbrsm+x/ie/ezK2ZZS22/n42YKf3/pN49gVdu5S+VnzQTPXRsSG4SDWMH1T60Bab+yL9MMk6ji4\n5trYec0hmIR/ANul6+YYX4Xr9bbkB2po9sFekzQodfYhwH5ydvSxObbrp2yDRmLBDpJGj4j3whng\n7er2rkKvH3/s9sLaQ6FhozvTYEN7XOxkfYUNPPD++KrY9C046jQoIs6RWdndgEeH54DU2l4QG3Vz\nYAH4PI7Mvo+F3z75+SqRzq+cEXAijtS98r/UvzRQ6vuNt8FC/XwsJI7CW27Wx0J2MPBQKunqGlfg\nhXBgOB2u00gl+Rcs9C7HAv0ZnDm0OlbO84ePhq2+/yKOMA2IpmyiXOQ/NAmR5uMgd8LC7ZXs02WY\nvT4TG+1PYMb2+TTObsBZRGfn7ycBvkyDqrrm/pi8epuG0noB15h4Pr8zNXZClsURxR9TwA2Ra2sc\nismUTVvdd1MfnwKODGddVe/vgB2/P2MDdzngvIjYWa6ttQywSUQ8nt/fCDtEy9WMyxGCvHVoCWz0\n3I8N2Tki4n1Jr+B5uVv4WPlJsOO6FRaQq+P5vGNkhlbTtevr4GvslH2FCZpx8/ezhzMNrsKG6q7V\nXJeZ8zuAxSPiUXlL2G2t2uqgf6vhefEJJjC3jYh7JL2A18GWaawcjaNlv8WyYFfsKA+IiHfaufZO\nwFcRcVHVV7koa+D1dytezzfg+TQaNlJvxg7CuznGfbBReByu4/BYUztdPjdr125zAlH9fWhTL+iX\n2KA/DNeWODONKeSCpZNhufNV3scOWB58gjPTzsNG9JARMe5kR7wPVtqzRMTiKcPvx4TF+fm9WbBM\n+AxnHn0uZ6NNBfwtXKj2QEzGbZAGXZvjf2VSe3RsIK6IFf5i+dlT2f/lIok1SZtiAneuZjmWn1dF\nICfEcvyOMLnV5vP8+1ZsWG2FsxwuwY74UemAIGfjzB0+Mpl0cGeozYEzcJHgHamhaR1OSoMEeBlH\nzS6JiEGSBuD1/yx2IB/BBtA2ETFD6popMdH6aG3MZsEZMmOHsxaQ6/cMyGutg+XZbdhpfAkby9+G\nScfqPi/FzvGA6OTxvvX5W833/HsbnNUwSxpfyNlpN2Ldewk2yN7EROJR+NmvF86CGOYZdXAPAzFZ\n+F1EzJtG7OrYaJy/0qmSfo2Jo8UjyRNJv4i2GYT70U1rvRNjOUxf5YyPDbBh++uIuFuuU3EMlm/b\npK4YF5MM/0zDvqX+S4d0noj4W01m9sbBuOo5/Y7MLoqILyR9AJwSDtZVTsJVWIce1I78qtbejHiN\n3lA33mVi+UAccf4IF+g/MiKOlwNf8wJr1+b5Ktm/+SKDPsMbu6bPe+MtWVvh53MCXmtP4UDWRFg/\nnIHnwPrYNr0DZ05/iHV/u9HqpnU+M7YXHsCO1zFYp92Dddy6+dmz+d6J+CTSTeQC6VMCE0U7JEpH\nkDNCtsM6bvfs+2k4i/GQ1OXT4PFfGgdmh7Sy/ZquO4yuSr10KZ4HN+d7H+OsmgMlbY511p7hqHz1\nu5mxHnul2aZvbkeuv7Qylk1H4kzxszChtgcm/PbFZPSJtd99hu2j7bFsnxwTAi9GxKHDGcPN8prb\n4sDE4tiWPgYTKFeHt8QgEwKB58gB0cIhbnH9uqysz5uBNLLZ3m3xu1vxmtkkHEQcZswqn6SD57gS\nJmkuw/N/LpzBtxwmGefH8u/biHhP0vrYPpo1WgQJ27Oz22l7ekwunx4tnHmZfNwJEyf3Nn3W0lbK\nzybH9k2/iFhA3jJ+ArYl/43X/tN4XU2Pn+09EXGqnLm0PQ66/UPeonRcuCh7R31pnqejYxtoW6zX\n++DMz79ExH5yuYPrgYkj4tu0jw6hUb7i6OY28rrN/td02I/shQmnMXFJg6qe2z7hLN3qnq4FHouI\ng2ViagAmpmm6/26zc4czjvX5vzL2IV8Fjkm/58/Yd9gK18w7GNiosuXkQ3P2AtbsSD530H5HPtir\nwJ0Rsa3s7x2HZdm1+dvf4nIQJzCCtvWooEczjGoP51Ds9F+BFWO1/7HaAvMZNlpXxqzrjNgxmUOu\niP6f6CBarWFPIhsPb+PZEkfAH0zj8DzMmg/ATN4HuGbEwZKE2fqb8OL7n+pfNDIbqho8gzEhsyF2\nli7DE2rvMOv8J8xSzle71IU4EtNhxobar83yI3aufoXHcAk8xlfg6MQJcp0kMBv7N+wAVieGVdvY\nhkbA6w3UxnMieetge9G5q2g/Ovd67XofRWOLyzjp8G6Bia/RsUF1ECbcdq797h3sBA/BxAk0LBJS\nHQAAIABJREFUyLqnMTHQV7nfuD0llve6aNTIosSK2BHfET+/3XFl/77YWPiIZJxTcK6Kjb7hkpit\nIGc4zYizCO7HpMlreG6CndytsbFCOBvnAjw2LwIrhyOB3zRdd2hdAXkLzFRYqawI3Bg+oW6X/Prh\n+f9ZeF0sJkd+q+/fSqaKR8QNzW0Np38zZV+qlNbLaET9d8Nbq5YPb8E5Hm9TuRNHEffAzvowZJEa\nZMpZYbKoD40tb8Ip40dHxKPhCNiN6QxNm9+7J0wWrYQd1gDeCEf/H6u1021zs/a7lgomsqirfJrH\notix+DwiVstrrS5vpwEbSrPhzLYPw8dp74OdzHmA7SPigOxfewZXc423JSXtgeXIc3jeTSZp+xyj\nM2gUeSaN/uux4bJDvv0hjnr+QtLO+JleEUlGRIOsqY5Yv562R6xPrMZ+8KOwHFlQ0tRpCK1Liy20\nVV9qDuRc+d0Bte8ch9PtB+Zbu+Kspc3C6eHXY4evX+3SB2X7O+Xr2XHW2i7yIQcbYqe2+T7qcmh7\nTHxNl/LzBJwSP2dEXB4R/XAm3Tk5zv8CXlKjcO470XCi++CsoM0w+XpNjs0YOEjyPiZH38Dr6hK8\nlekdvBYXkjM75paz3xYA/hqdJIuyb5WT8ht8+til6SR+iSOp9dp0T2LncvMwCTcBnl+DsaG7VtTI\norx+e4cTbCFpHTkd/VJMhk0h1xj4Fht/N9Co2UA6eBMAv1Yj06Uii8bLMem2td4RKt2bf28i6XeS\nVghns62MndaN0zm9HZP/74XrwoEJj+lyHKr7bKX/2tQIkx3+v5InfyWOy74dka/3zjFbXQ7AjYcJ\nykea5Unz2gsTc9tVzmHqoyOwbfIZllnbY4d0rbSLBmF9tZxc124cnJU8mKZtSzVd0CGpmGPxESbQ\nt8I65gacubAj1rN74UDDEVh+fofJuhsiYsXhOSM1fXsGltdH4+DTvHiOHhMRX6YsngMYGI5IP4i3\n8f03r/NeRDwZnSSLJK0lZxiumW/tiWX2aHIh8B/wfB2TRs2Nt7P/E2O5RSvbr9bG0ELMapv9sBCe\n+w+l3XAGJt52lbR4eEvwy3juzlwbq1cj4rbI0g1N41i1U31/EeyEfx4RL0fEPzDxMx8m+67HxMcK\naluUf1kyOyuvuWH2v8PgXsqGrYEHI+Kx8LbW87Gjuk22t5Myaypl1mAcrPm69VXbIuxgjyXpGJz5\nvU5+9AHWc+/nvYye/28mF5/fFtudazddbyhZVPkkLfyv6rktDtwVEXtnv6pg+lx4zr+Jt7p+nfJm\nAJaNXzVdr5V+o+k7G0o6WVmbB28dnwbrhrrvMZOkweGMmE/wFtGpWvWxnXv4AMvtH+XAxyI4aLA+\nlle7Y537Nl7bo2O/E6zHpwSOlonUZWI4ZFF1P3Km1hL5+jtsx76Li00Pwfpjj5Szr+P5cbpMjF2C\nsw8XjRZkkZqybmTsi+fw0+Fg2hLZlyPSNh+ES7wsnmM7NvYxHs17fDEiDq/sy7xut9u5wxnHH+ST\nB7fHcnIivKvnz6mXT8Ik3/apR17IPh4ib/HbH6/VkSGLhueD7YrrKfbLOfEU3iJZ1WH6fUQc05Ft\n3R3oVsJIrdPbtsYCdHM8IZ7CTvGi2OB/HxMLT2Ph8RyOFDyTl+gomlNPnRtb3os+eURUW7+ewIqG\n/N4VeX3hhbsuJnLWxEXtLoiI3dsTTD9B/4YaRWocsXmhvK3s8+xjlU3xGI6qriQfD3spLhJXTcjK\nEVc0pYw3IxoRiXqq/S+Bd8KM9QbY0aqMopmxcJkQuFpOgb4GG2dH0HSKW7Rlm+v1B3rLp5T8DZNu\nYwD/kVn9VXGx54NwpPhmvMCulrQ7Jvpeof2aDTNigb1jeJ/2lnhf+GZYuM6qxrG64Od4S45ndYx1\ntX7upEFadYhw5HQ5efscabQsQxbTy7l6NX52x4RJjUE4Inc9zgCYH2eFfduiieEi7NxPj53v/nnt\nfXBBykXDx33ejYsOT5q/+VdEXBwRgyLi/uZrqnHk+fdyNGcZbNzvhkmmvnIRuUqZ7SZpjvDWtDMw\noXpv9m8HnFb9SXM7TW22V6Bvbax8ToiI1/KeB2c/bsXK5fAc+5tyDDbEYzp1ZMSyqa0+TfN0fmys\nHZRvfY2dq+rzMfP/ZbBz/TWuWXYRje1NB4ePf29Gj85NNbb1VKTNUniufYgJ74qgOQNnJK4qFyCs\nMka2lfQrmdQ5OyK2j4hVIrNOOkIlW2tjex+Nuic3YHl8BbC/nA58DD7O+qjaZa7GMqJydCbD0aJL\ncURvr/o4a9gj1p9j2CPWd5VPOvsLjaO+b81xnAsTnS37Unv9AI40zy+TU1djontKfHzqNhHxIjbg\ntpQ0N5ajvfABB9W20DexAXplvn4cG0srYX3RN1y/rc19SNpa0uVyZHALXBfq4zR2dsDy9Pj87nJY\nVu8nRz5PxQ7rMMGEdJInwvptVfkEkbeA1cLE0/7YoVo5n+vvgRkl7RDO9jwWr5vBeB1cGT65rNOQ\ntKBMvG6KScVx8fM8GBth69Xu9yvsFPfJuf4rHNFcOnwy6BfNTk6L9taX9C6em+fh5z9Otv1Ktgt2\nTH6P09frJ8n1B46OYbf4TM9PoIcqpPMxp1zb41Q8LremTfEZNuQXxls6n8RbGjaQ64mcgmX2qzRq\nhbVBTZ9/QKNG2O14O34fvI1utryXL3C5gF1kIvNivP5vwAGvp/A6HUb/RNtIeGUHvitpZknH5Jy9\nB6+bL2ry5nRsBw9MQuAQPPa3YBttKaxH2jy3jox0SRvI5FSFm7If0+EgxgA8zttgsnQdrLMmSd07\nAK+dQe210QIHYtt2MWwf3JNtvIgd8IPz3oRrz6wkkwab4TnTacjbUm7Hz2Ql7GCdgknRwzHRuDBA\neNv//Xg9VOTE/Thjc3i1Fuu27jFYZ68tE9MP47U4KZYlo+MMzX9iOUD+PwDPsbpdOWvO94FqbBuq\nHOVtgb/JQafKSZymdj+XYMduA0ywnIvJr3qB5Cci4tWafXIrsOrw+ptzbAxyK2bt93/Ac3RKbM/+\nofabcyJiYAy/DEHVx41xRvWqeK5cLWdaP4AJ/iqQNyTHbDmcSfsG1sOztHPv1T3XCeg5JU1TWyv9\n8cmUS8gZ1ntgn+sJTChPgzNF7sY6eQa8Fax57XVEFE2b/saf8G6SsyQdXrOrqxMpq2tODywqO/Bn\n5D00Z1s2by3v03QPj2Edvg4uK3JbRDyMfch5yRMIsV33LDC2nLk4PibiDoiIt6O286GpvVZ27gW4\ntuD0+fotvB52kzRTuIzAY/h5vorH+hdY7/8+XD/xK7XYThttg4YHYRtpc2wDfZrfqQ7f2UnS/OEM\nu3fx2j4Hy7tv6LhmXk/bucMEJ/E82Qpnzq2L5+iSOID3CA6SrCkTwptmO2tkH++JiD076F+76KQP\ndhcuqQB+tlPQ2Drf6eBaV6JLCSP5OPVJ8u9WBYrHIgt1RsTNEXEfNhCuAP4UEV9ExNaYad4ekyxj\nhVOBl4yIfaKDiE40mMu9sPA7ERcsPTON7pNwdHO52s9OxUpna+DtiPg1JowWiA4K6/5E/auEcvMR\nm2dJGpjK+T4c8eiLHabXceT4M8zaLiwfG94uVCvkm683kPQyNiQPkJ3hC7ECXwVH4c7Eqb6z5Bi8\ngZXS9pgsmjOd9T44cj1GsyDOPv5Y+7senduSro3O/QJvG7g/2/oWk3mvYmXVh9rpGeE03MdzvCeo\n3R8R8XpEHBR5ZHkH49pHjngcgAX7gHwuT5KRVjlS8wF2pBaUtGF4L/qSWGBtFBELp6M5qjgPG+4b\nZZ+vwGsE/NxWpnZCWUdIBdO8//587KhtixXVIvndi/CzrMjLc3FW0S44u2fqcCHHdpGGSUVkDpAx\nR378JTBmNLb0jJH/ryhvVasKMd+GFdBkEfFQ+NSH9vpXGUFzS3oIE0xv4n3vE+ffKI+Cryn/87Hy\n2x8bjuMCa4SjA+0Rfj06N2syZ5z8f6Icw17Y6F5T0mrh7aXXUDumFDtYD2GHZT6ZjOl0IUpwloi8\njxtsmIyXfakMkytxVsBh4cyGw/BJhNPmdz6JiANTwRMRD0TEmphkmSOGrcfW2SPWf5/fPwA7dMdh\n426edJ5b9aVNDTu8pj7Ez+srnGa9Pja+jpIzBn+HHYXtwtl85+LxXbe6SERcExEfqLF3fwtg44gY\nkLqtfg8zylv6dsUp4W9iw+gxOc1/F2xwboaJ3OWxcfIsdgCXxxkYJ9IOwmTgm1jHLIb1wZn52UVY\nRm8uabpw5tRxOJI6ZkQci2XBNsBM4TpsnUbaGI/iLIo/hNPgN8bPcV7saCzfpOcmB54MZzN8GxEf\nR8Sbahw60FGgZg68Do6LiBmxbJwT1576B5bVK0haOPXXE3h8h867iHgkIl5rNs7p4bXezto8EDu5\nk4VPAjwHnz44Tq6PN3AWzqwpI1fFdsY0ePxXjUbGUZu2avr8fVwnZHGcsfEmJmvmoa2OuQw7/RXh\nUmWk3oTXx7LRYstMtldlb9XtsmVwgdVtw8GJq4B11KhF8QZ+TovJRVlPx1uS98R205yRmXUjgHFw\njY/q1KNPsZ75Elg/vEXqPXwYxLfYARmM5UTlsHU6GCRnY2yNgyxPYgJjZRywWR47szvkunmMxhbj\nFfJ+rmtx2Y5QZRTNHhHCunAg3pJyGSYkNpW3BYJlxLiYXB497eDXOtm35TEBvAieK6dgmfqvcHHd\nLXDw8k9hJ+pFrJMHhYNtm+Ls0nqW0h7AOBFxQkR8LbXZhv0aJoD3zDl6Ms44W4wGTsdzf/OcGzfg\ngAW169QDoi/l3BteX3vhrLt+6fRX2/VGw47wEGxDrKThnDrYigiQt4/uivXe/BHRHxNvB2Ln/jRg\nLznbrgqQTUHjtOSNI+J3Hdx7ZQdOn6TNg9hnqDJF/o4J02sw4b5ozr298Xg/gnX2kbjOzRLhTLAR\nsSd2xjp8iohYNfs7UM6WPglnHe+nRgHvOXCm0yfhgOhKUfMZ0teZIP8eWhtHJjEPlbN+58Jk1KPk\naXL5vSvxNux15ODPaXidPozn8G0RcUF78qU2ppWdO7MaRbH3w7WellWDwLo+x7gKZv0qx3PtcH2p\n9SOiOpCk8jV+jBYEXNrJ5+JM54WxjL6QtoGYS7E8qfzkKpvrAWzHLxQdJyP0iO6r6fjm4OSXea35\naQTsH8bPchu5htCJmCfZGvhvuO7h6sBC4Z01o4rh+WDLy6fpPRQRfSOiZYH+nkKXEUYpnG7BjFmV\n+jiunMI9exof32D2vG6cfYYnYh81TuXZHhsIC+AsFaJ2+lK9zRZCcQmcwbIjeRw0PpmkOlHqRqws\nyes+hNnQDXCqKeEtYe2xvVWWT9W/LbN/Y3dD/5oLaDcfsdkvGkdsbiRpLjwBv8EK/G1sDC0habMU\nYPNE1sKpXXeM2t/11NIJsv1tsaJ+ABvmu4TrM7yNhcW/MUk2BKf4bwT8LuywP4MVx1fyqVlbA7en\n0d4q1bOnonOPAdfJ0aZqDn2PFchgrJgXlIvUVXgdp2KOkENcIZ2SvjgKfQw+UWZ8/MyWk7RUTXiP\nhiNmZ0gaKyLeCh/fem/Li4/c/XyOCY15sRMwCGdDDAxv9dmemiHUCWyCI1L98HOptoVNhCOeBykz\nlsi9v5LuxOTSkIj4a7RzuhS0dXZyfi4l6V9YER8NXCan9v4L+LC23io2/iC8Zt7DivRunOXz7+F1\nTM5YvAjPu79hJ78ytHfDc+ZrXHh+wtr9vofJq4fCUZ0NokWGVhO6ZW6m/Ji96b1eclTuFrJAajhb\nZXZ8Ws+D2ImrjmE9HRMfm8qZJUtHxHa4xsfu0UjDr7cxRb29Frf2OT5Na6Y0Qp7BRwZXxtHTeJ4O\nkDR7OEvlOWrGS/3aNUPr+ZqxNTSTARNS89DxEevn4q2L92HH8cWIuDAyZVwton41A+z7lJ3jp4L/\nS7Y3Vs2JGISf1a9TbxyL18NKmLCqisC3QTijc0Ic9f2vahHyGgbiQMVr0Ug5r06x2i3H90xM6kyJ\nZfsk4RMeN4yIpSPitlZOR62vE9GoA3MZdvC/k1SRbIdiknv5fH06jk5ekv14N7w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HEiSl6NBpb2ghL7pnSdS9x9oJmt4+pYWTdUGuda8tYzla3ehPxiX02/y7xUd6Gwp+iHyg3PTafo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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e005f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "feature_weights.head(40).plot(ax=ax,kind = 'bar', figsize=(20, 10), fontsize=14)\n",
    "fig.autofmt_xdate()\n",
    "plt.title(\"Random forest feature importance\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some reason the most important word is **shown**, it's hard to say without context why; **series** are most probably connected with memes, originated from various tv series; **image** and **video** are among top-10 as well. Presence of year in the description is also important, years from '2011' to '2016' are among top-40 words, could be because of the important events that took place between these years.\n",
    "\n",
    "Of course, we have main words from out logistic regression here - 'politics', 'funny', 'meme', 'twitter', 'youtube', etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we can have a look at confusion matrix and understand, where models struggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion(predictions, title):\n",
    "    array = confusion_matrix(yval, predictions)\n",
    "    df_cm = pd.DataFrame(array, index = [i for i in titles],\n",
    "                      columns = [i for i in titles])\n",
    "    plt.figure(figsize = (7,7))\n",
    "    sn.heatmap(df_cm, annot=True, fmt='g', cbar=False)\n",
    "    plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fw++bm0LKMcak4Ky2i7p8TKC8P3UalStV4r7bb2HHjp2YHtfSItyMay+/hGPa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f5168d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion(map(np.argmax, rfc.predict_proba(Xval)), \"Random forest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o4IFhL7Nm/QaCwSC3XnoRubt28djo10gMBmlQJ4NBl10S65i+El65glcnTeCh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\nIAiCoBD8AoGRqjM4+/68AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e405a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion(map(np.argmax, logit_sgd.predict_proba(Xval)), \"Logit regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like random forest is capable of good differentiating between very popular and [popular categories, while logit has better separation of unpopular and very unpopular memes. That means it's worth trying to blend their predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def best_linear_mix(target, pred1, pred2):\n",
    "    best_acc, best_w1 = 0, 0\n",
    "    best_pred = pred1\n",
    "    for w1 in np.linspace(0, 1, 100):\n",
    "        curr_pred = w1 * pred1 + (1 - w1) * pred2\n",
    "        curr_acc = accuracy_score(target, np.argmax(curr_pred, axis=1))\n",
    "        if curr_acc >  best_acc:\n",
    "            best_acc = curr_acc\n",
    "            best_pred = curr_pred\n",
    "            best_w1 = w1\n",
    "    return best_acc, best_w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mix_cv_acc, best_logit_weight = best_linear_mix(ytr, logit_sgd_oof_pred, rf_oof_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mix accuracy: 0.426\n",
      "Weight of logit in mix: 0.939\n"
     ]
    }
   ],
   "source": [
    "print('Mix accuracy: {}'.format(round(mix_cv_acc, 3)))\n",
    "print(\"Weight of logit in mix: {}\".format(round(best_logit_weight, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the quality increase, accuracy climbed up by 2%, even though random forest has only 8% of weight in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mix_valid_pred = best_logit_weight * logit_sgd_valid_pred + (1-best_logit_weight) * rfc.predict_proba(Xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mix accuracy on validation: 0.429\n"
     ]
    }
   ],
   "source": [
    "print('Mix accuracy on validation: {}'.format(get_accuracy_score(mix_valid_pred, yval)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad, quality on train and test is almost identical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJCA/hZRrjDkeSDbGHIIKSUREAuCnkC4HugM1gRuAqwJNJCIiCclPISUBNwEn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\nrjcC3rTWdopdHwecDXQHmgC1gIbA9db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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11de55410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion(map(np.argmax, mix_valid_pred), 'Mixed predictions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not much, but the diagonal of the matrix now has some more correct predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions and what was all of that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results**\n",
    "1. Two models are trained as well as their ensemble, which yields accuracy of 0.43, beating the baseline 2.3 times\n",
    "2. The models have most difficulties with differentiating of Very Unpopular, Unpopular and Average groups. Probably, that is because of the data quality, during pasring some memes, that were added recently, could get a biased popularity estimate (few days passed since their creation). It can be fixed by limiting the dataset by date so that each meme has a least one month of history, for example.\n",
    "3. Logistic regression allowed us to find some logical groups of words with common themes \n",
    "\n",
    "**But why?**\n",
    "1. For research purposes\n",
    "2. Of course, meme popularity predictions are hard to monetize but predicting the virality of your advertisement - that's where we're talking business. \n",
    "\n",
    "**What else can be done**\n",
    "1. More complex models and metadata. We still have lot's of features to be explored and added to the mosel, starting from dates and going as far as topic modelling over memes, why not.  \n",
    "2. Neural networks, of course. And lots of layers. And then some more. Dataset can be enriched with the most important meme part - the meme itself. Working with pictures can open so many opportunities. Besides, most memes are created with the help of templates and generators, so we can try to claster groups of pictures by their templates and use it for predictions. \n",
    "3. Finally, the most interesting part - our own neural network memegenerator. Having template examples, text descriptions and other cool stuff we can try to synthesize meme and its text. Oh boy, oh boy! \n",
    "\n",
    "To be continued..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**sorry for a long post, here is a research potato for you**\n",
    "![](http://www.cosplayisland.co.uk/files/costumes/855/41850/5643874030_530d7eba22.jpg)"
   ]
  }
 ],
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