{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 3 - Web Scraping for Reddit & Predicting Comments\n",
    "\n",
    "_Marco Tavora_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-23T19:28:02.619411Z",
     "start_time": "2017-10-23T19:28:02.600856Z"
    }
   },
   "source": [
    "## Problem Statement\n",
    "\n",
    "Determine which characteristics of a post on Reddit contribute most to the overall interaction as measured by number of comments.\n",
    "\n",
    "\n",
    "## Preamble\n",
    "\n",
    "In this project, we practiced some essential skills:\n",
    "\n",
    "- Collecting data by scraping a website using the Python package `requests` and using the Python library `BeautifulSoup` which efficiently extracts HTML code. We scraped the 'hot' threads as listed on the [Reddit homepage](https://www.reddit.com/) (see figure below) and acquired the following pieces of information about each thread:\n",
    "\n",
    "   - The title of the thread\n",
    "   - subreddit that the thread corresponds to\n",
    "   - The length of time it has been up on Reddit\n",
    "   - The number of comments on the thread\n",
    "\n",
    "- Using Natural Language Processing (NLP) techniques to preprocess the data. NLP, in a nutshell, is \"how to transform text data and convert it to features that enable us to build models.\" These techniques include:\n",
    "\n",
    "    - Tokenization (splitting text into pieces based on given patterns)\n",
    "    - Removing stopwords \n",
    "    - Stemming (returns the base form of the word)\n",
    "    - Lemmatization (return the word's *lemma*)\n",
    "\n",
    "- After the step above we obtain *numerical* features which allow for algebraic computations. We then build a `RandomForestClassifier` and use it to classify each post according to the corresponding number of comments associated with it. More concretely the model predicts whether or not a given Reddit post will have above or below the _median_ number of comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='redditpage.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Importing\n",
    "\n",
    "We first need to  `import`  the necessary packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" # see the value of multiple statements at once.\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfVectorizer, TfidfTransformer\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import json\n",
    "import datetime\n",
    "import csv\n",
    "import time\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "a948d79c-5527-4c0d-ab23-f5d43ce72056"
   },
   "source": [
    "## 2) Scraping\n",
    "\n",
    "The sub-items below are:\n",
    "\n",
    "2.1) Describe scraping strategy\n",
    "\n",
    "2.2) I write four functions to extract the items above, namely, thread title, its subreddit, the time it has been up and the number of comments\n",
    "\n",
    "2.3) I then write a function that finds the last `id` on the page, and stores it.\n",
    "\n",
    "2.4) I use the functions above to parse out the 4 fields - title, time, subreddit, and number of comments. \n",
    "\n",
    "2.5) I then create a `Dataframe` from the results with those 4 columns.\n",
    "\n",
    "2.6) I save the results into  `csv` file\n",
    "\n",
    "### 2.1) General Strategy\n",
    "\n",
    "The general strategy is:\n",
    "\n",
    "- Use the `requests` Python packages to make a `.get` request (the object `res` is a `Response` object):   \n",
    "\n",
    "        res = requests.get(URL,headers={\"user-agent\":'mt'})\n",
    "     \n",
    "- Create a BeautifulSoup object from the HTML\n",
    "\n",
    "        soup = BeautifulSoup(res.content,\"lxml\")\n",
    "\n",
    "- We then use `.extract` to see the page structure:\n",
    "\n",
    "        soup.extract\n",
    "\n",
    "The page has the following structure:\n",
    "\n",
    "- The thread title is within an `<a>` tag with the attribute `data-event-action=\"title\"`.\n",
    "- The time since the thread was created is within a `<time>` tag with attribute `class=\"live-timestamp\"`.\n",
    "- The subreddit is within an `<a>` tag with the attribute `class=\"subreddit hover may-blank\"`.\n",
    "- The number of comments is within an `<a>` tag with the attribute data-event-action=\"comments\"`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2) We first write four functions to extract the items above, namely, thread title, its subreddit, the time it has been up and the number of comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_title_from_result(result,num=25):\n",
    "    titles = []\n",
    "    title = result.find_all('a', {'data-event-action':'title'})\n",
    "    for i in title:\n",
    "        titles.append(i.text)\n",
    "    return titles\n",
    "\n",
    "def extract_time_from_result(result,num=25):\n",
    "    times = []\n",
    "    time = result.find_all('time', {'class':'live-timestamp'})\n",
    "    for i in time:\n",
    "        times.append(i.text)\n",
    "    return times\n",
    "\n",
    "def extract_subreddit_from_result(result,num=25):\n",
    "    subreddits = []\n",
    "    subreddit = result.find_all('a', {'class':'subreddit hover may-blank'})\n",
    "    for i in subreddit:\n",
    "        subreddits.append(i.string)\n",
    "    return subreddits\n",
    "\n",
    "def extract_num_from_result(result,num=25):\n",
    "    nums_lst = []\n",
    "    nums = result.find_all('a', {'data-event-action': 'comments'})\n",
    "    for i in nums:\n",
    "        nums_lst.append(i.string)\n",
    "    return nums_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3) We then write a function that finds the last `id` on the page, and stores it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_urls(n=25):\n",
    "    j=0   # counting loops\n",
    "    titles = []\n",
    "    times = []\n",
    "    subreddits = []\n",
    "    nums = []\n",
    "    URLS = []\n",
    "    URL = \"http://www.reddit.com\"\n",
    "    \n",
    "    for _ in range(n):\n",
    "        \n",
    "        res = requests.get(URL, headers={\"user-agent\":'mt'})\n",
    "        soup = BeautifulSoup(res.content,\"lxml\")\n",
    "        \n",
    "        titles.extend(extract_title_from_result(soup))\n",
    "        times.extend(extract_time_from_result(soup))\n",
    "        subreddits.extend(extract_subreddit_from_result(soup))\n",
    "        nums.extend(extract_num_from_result(soup))         \n",
    "\n",
    "        URL = soup.find('span',{'class':'next-button'}).find('a')['href']\n",
    "        URLS.append(URL)\n",
    "        j+=1\n",
    "        print(j)\n",
    "        time.sleep(3)\n",
    "        \n",
    "    return titles, times, subreddits, nums, URLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4) I now use the functions above to parse out the 4 fields - title, time, subreddit, and number of comments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles, times, subreddits, nums, URLS = get_urls(50) \n",
    "\n",
    "print(len(titles))\n",
    "print(len(times))\n",
    "print(len(subreddits))\n",
    "print(len(nums))\n",
    "print(len(URLS))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5) I then create a `Dataframe` from the results with those 4 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns = ['titles', 'times', 'subreddits', 'nums'])\n",
    "df.titles = titles\n",
    "df.times = times\n",
    "df.subreddits = subreddits\n",
    "df.nums = nums\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6) Saving results as a `.csv` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('reddit_df_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "04563b69-f7b6-466f-9d65-fc62c9ddee6a"
   },
   "source": [
    "## 3) Data cleaning, preprocessing and exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "243e949e-2742-40af-872e-fec475fd306c"
   },
   "source": [
    "### The steps are:\n",
    "\n",
    "-Loading in the data of scraped results\n",
    "\n",
    "- Writing a function to perform some necessary EDA steps. Among other things this function drops duplicates (a substantial part of the data is duplicated)\n",
    "\n",
    "- Removing the `hours ago` strings in the column `times` and converting the result to integers. We do this by splitting `df['times']` to obtain lists where the first element of the list is the number of hours, which is what we are after. We then pick the first element and convert to an integer.\n",
    "\n",
    "- We then remove the string `comments` from the column `num` using the same code as the previous cell. We use`value_counts` first, to check for errors or entries with a peculiar shape. We find that there are four entries equal to \"comment\" merely. We drop them.\n",
    "\n",
    "- We remove `r/` from `subreddits.`\n",
    "\n",
    "- We export the new `DataFrame` into a `csv` after the EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>titles</th>\n",
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       "      <th>0</th>\n",
       "      <td>I've lost a lot of weight, a lot of sadness, a...</td>\n",
       "      <td>1 hour ago</td>\n",
       "      <td>r/pics</td>\n",
       "      <td>1449 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dick Winters and his Easy Company (HBO's Band ...</td>\n",
       "      <td>3 hours ago</td>\n",
       "      <td>r/ColorizedHistory</td>\n",
       "      <td>174 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JJ slowly looking more like George Lucas</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>r/StarWars</td>\n",
       "      <td>246 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pink Floyd’s album “The Dark Side of The Moon”...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/Music</td>\n",
       "      <td>638 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>She's officially the little girl's best babysi...</td>\n",
       "      <td>3 hours ago</td>\n",
       "      <td>r/HumansBeingBros</td>\n",
       "      <td>91 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Obviously not a virus</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/gaming</td>\n",
       "      <td>199 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CaT BrUtAllY StRaNgLEs DoGGo</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/PeopleFuckingDying</td>\n",
       "      <td>105 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[IMAGE] These stairs count the calories that y...</td>\n",
       "      <td>7 hours ago</td>\n",
       "      <td>r/GetMotivated</td>\n",
       "      <td>600 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Obese lady knows more about cancer than Cancer...</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>r/insanepeoplefacebook</td>\n",
       "      <td>202 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This fucking ad</td>\n",
       "      <td>7 hours ago</td>\n",
       "      <td>r/assholedesign</td>\n",
       "      <td>245 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[WP] You're a powerful dragon that lived next ...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/WritingPrompts</td>\n",
       "      <td>129 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Technically murdered by Numbers, but too good ...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/MurderedByWords</td>\n",
       "      <td>32 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>If you wore a VR headset linked to a camera dr...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/Showerthoughts</td>\n",
       "      <td>220 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dozens of mayors sign letter supporting resolu...</td>\n",
       "      <td>9 hours ago</td>\n",
       "      <td>r/technology</td>\n",
       "      <td>441 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TIL the FBI mounted a 4-year-long undercover s...</td>\n",
       "      <td>12 hours ago</td>\n",
       "      <td>r/todayilearned</td>\n",
       "      <td>4402 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>“Excuse me sir, but I do believe you’ve droppe...</td>\n",
       "      <td>9 hours ago</td>\n",
       "      <td>r/BikiniBottomTwitter</td>\n",
       "      <td>87 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dad was a little too slow this time</td>\n",
       "      <td>7 hours ago</td>\n",
       "      <td>r/DadReflexes</td>\n",
       "      <td>201 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Yellow things.. hmm..</td>\n",
       "      <td>9 hours ago</td>\n",
       "      <td>r/oldpeoplefacebook</td>\n",
       "      <td>192 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Poverty comes at a high price</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>r/LateStageCapitalism</td>\n",
       "      <td>18 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Digit doesn't like Spaghetti</td>\n",
       "      <td>8 hours ago</td>\n",
       "      <td>r/catpranks</td>\n",
       "      <td>94 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Therapy dogs waiting to welcome the Parkland k...</td>\n",
       "      <td>11 hours ago</td>\n",
       "      <td>r/aww</td>\n",
       "      <td>1791 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Octopus running is 🔥</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>r/NatureIsFuckingLit</td>\n",
       "      <td>59 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>My god</td>\n",
       "      <td>9 hours ago</td>\n",
       "      <td>r/ATBGE</td>\n",
       "      <td>248 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Oops! My cat accidentally jumped and landed it...</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>r/OopsDidntMeanTo</td>\n",
       "      <td>130 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Man..Talk about the greatest character develop...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/TheLastAirbender</td>\n",
       "      <td>79 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>North East India must be on every nature lover...</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>r/travel</td>\n",
       "      <td>60 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Canada Makes National Parks Permanently Free F...</td>\n",
       "      <td>12 hours ago</td>\n",
       "      <td>r/worldnews</td>\n",
       "      <td>1102 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Olive Garden employees who have had to cut som...</td>\n",
       "      <td>11 hours ago</td>\n",
       "      <td>r/AskReddit</td>\n",
       "      <td>2080 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Snek is now borns</td>\n",
       "      <td>7 hours ago</td>\n",
       "      <td>r/Sneks</td>\n",
       "      <td>33 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>On the fence.</td>\n",
       "      <td>7 hours ago</td>\n",
       "      <td>r/ChildrenFallingOver</td>\n",
       "      <td>96 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>r/The_Donald is imploding, following Trump's p...</td>\n",
       "      <td>12 hours ago</td>\n",
       "      <td>r/SubredditDrama</td>\n",
       "      <td>4781 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>I’ve worked my ass off to lose more than a foo...</td>\n",
       "      <td>10 hours ago</td>\n",
       "      <td>r/pics</td>\n",
       "      <td>1956 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Who's a Good Boy?</td>\n",
       "      <td>10 hours ago</td>\n",
       "      <td>r/comics</td>\n",
       "      <td>196 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>\"Don't touch my fries!\" repost r/gifs</td>\n",
       "      <td>3 hours ago</td>\n",
       "      <td>r/BetterEveryLoop</td>\n",
       "      <td>23 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Her stuffed toy is all she needs to have the c...</td>\n",
       "      <td>11 hours ago</td>\n",
       "      <td>r/MadeMeSmile</td>\n",
       "      <td>218 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>THERE IS NO CAROL!</td>\n",
       "      <td>12 hours ago</td>\n",
       "      <td>r/IASIP</td>\n",
       "      <td>113 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>User explains how China's economy is set up fo...</td>\n",
       "      <td>11 hours ago</td>\n",
       "      <td>r/bestof</td>\n",
       "      <td>903 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>I had tears in my eyes when my waitress handed...</td>\n",
       "      <td>13 hours ago</td>\n",
       "      <td>r/pics</td>\n",
       "      <td>1252 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Hello Mr. Krabs</td>\n",
       "      <td>12 hours ago</td>\n",
       "      <td>r/evilbuildings</td>\n",
       "      <td>197 comments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>China bans Animal Farm by George Orwell</td>\n",
       "      <td>13 hours ago</td>\n",
       "      <td>r/books</td>\n",
       "      <td>1498 comments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               titles         times  \\\n",
       "0   I've lost a lot of weight, a lot of sadness, a...    1 hour ago   \n",
       "1   Dick Winters and his Easy Company (HBO's Band ...   3 hours ago   \n",
       "2            JJ slowly looking more like George Lucas   5 hours ago   \n",
       "3   Pink Floyd’s album “The Dark Side of The Moon”...   6 hours ago   \n",
       "4   She's officially the little girl's best babysi...   3 hours ago   \n",
       "5                               Obviously not a virus   6 hours ago   \n",
       "6                        CaT BrUtAllY StRaNgLEs DoGGo   6 hours ago   \n",
       "7   [IMAGE] These stairs count the calories that y...   7 hours ago   \n",
       "8   Obese lady knows more about cancer than Cancer...   2 hours ago   \n",
       "9                                     This fucking ad   7 hours ago   \n",
       "10  [WP] You're a powerful dragon that lived next ...   6 hours ago   \n",
       "11  Technically murdered by Numbers, but too good ...   6 hours ago   \n",
       "12  If you wore a VR headset linked to a camera dr...   6 hours ago   \n",
       "13  Dozens of mayors sign letter supporting resolu...   9 hours ago   \n",
       "14  TIL the FBI mounted a 4-year-long undercover s...  12 hours ago   \n",
       "15  “Excuse me sir, but I do believe you’ve droppe...   9 hours ago   \n",
       "16                Dad was a little too slow this time   7 hours ago   \n",
       "17                              Yellow things.. hmm..   9 hours ago   \n",
       "18                      Poverty comes at a high price   5 hours ago   \n",
       "19                       Digit doesn't like Spaghetti   8 hours ago   \n",
       "20  Therapy dogs waiting to welcome the Parkland k...  11 hours ago   \n",
       "21                               Octopus running is 🔥   5 hours ago   \n",
       "22                                             My god   9 hours ago   \n",
       "23  Oops! My cat accidentally jumped and landed it...   5 hours ago   \n",
       "24  Man..Talk about the greatest character develop...   6 hours ago   \n",
       "25  North East India must be on every nature lover...   6 hours ago   \n",
       "26  Canada Makes National Parks Permanently Free F...  12 hours ago   \n",
       "27  Olive Garden employees who have had to cut som...  11 hours ago   \n",
       "28                                  Snek is now borns   7 hours ago   \n",
       "29                                      On the fence.   7 hours ago   \n",
       "30  r/The_Donald is imploding, following Trump's p...  12 hours ago   \n",
       "31  I’ve worked my ass off to lose more than a foo...  10 hours ago   \n",
       "32                                  Who's a Good Boy?  10 hours ago   \n",
       "33              \"Don't touch my fries!\" repost r/gifs   3 hours ago   \n",
       "34  Her stuffed toy is all she needs to have the c...  11 hours ago   \n",
       "35                                 THERE IS NO CAROL!  12 hours ago   \n",
       "36  User explains how China's economy is set up fo...  11 hours ago   \n",
       "37  I had tears in my eyes when my waitress handed...  13 hours ago   \n",
       "38                                    Hello Mr. Krabs  12 hours ago   \n",
       "39            China bans Animal Farm by George Orwell  13 hours ago   \n",
       "\n",
       "                subreddits           nums  \n",
       "0                   r/pics  1449 comments  \n",
       "1       r/ColorizedHistory   174 comments  \n",
       "2               r/StarWars   246 comments  \n",
       "3                  r/Music   638 comments  \n",
       "4        r/HumansBeingBros    91 comments  \n",
       "5                 r/gaming   199 comments  \n",
       "6     r/PeopleFuckingDying   105 comments  \n",
       "7           r/GetMotivated   600 comments  \n",
       "8   r/insanepeoplefacebook   202 comments  \n",
       "9          r/assholedesign   245 comments  \n",
       "10        r/WritingPrompts   129 comments  \n",
       "11       r/MurderedByWords    32 comments  \n",
       "12        r/Showerthoughts   220 comments  \n",
       "13            r/technology   441 comments  \n",
       "14         r/todayilearned  4402 comments  \n",
       "15   r/BikiniBottomTwitter    87 comments  \n",
       "16           r/DadReflexes   201 comments  \n",
       "17     r/oldpeoplefacebook   192 comments  \n",
       "18   r/LateStageCapitalism    18 comments  \n",
       "19             r/catpranks    94 comments  \n",
       "20                   r/aww  1791 comments  \n",
       "21    r/NatureIsFuckingLit    59 comments  \n",
       "22                 r/ATBGE   248 comments  \n",
       "23       r/OopsDidntMeanTo   130 comments  \n",
       "24      r/TheLastAirbender    79 comments  \n",
       "25                r/travel    60 comments  \n",
       "26             r/worldnews  1102 comments  \n",
       "27             r/AskReddit  2080 comments  \n",
       "28                 r/Sneks    33 comments  \n",
       "29   r/ChildrenFallingOver    96 comments  \n",
       "30        r/SubredditDrama  4781 comments  \n",
       "31                  r/pics  1956 comments  \n",
       "32                r/comics   196 comments  \n",
       "33       r/BetterEveryLoop    23 comments  \n",
       "34           r/MadeMeSmile   218 comments  \n",
       "35                 r/IASIP   113 comments  \n",
       "36                r/bestof   903 comments  \n",
       "37                  r/pics  1252 comments  \n",
       "38         r/evilbuildings   197 comments  \n",
       "39                 r/books  1498 comments  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('reddit_df_1.csv',index_col=0)\n",
    "df.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "focus": false,
    "id": "588f9845-6143-4bcc-bfd1-85d45b79303d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many missing values there are:\n",
      "\n",
      "titles        0\n",
      "times         0\n",
      "subreddits    0\n",
      "nums          0\n",
      "dtype: int64\n",
      "\n",
      "Which are the data types:\n",
      "\n",
      "titles        object\n",
      "times         object\n",
      "subreddits    object\n",
      "nums          object\n",
      "dtype: object\n",
      "\n",
      "Shape with duplicates: (1250, 4)\n",
      "How many duplicates of ['titles', 'subreddits']: 734\n",
      "Now how many duplicates of ['titles', 'subreddits']: 0\n",
      "Shape without duplicates: (516, 4)\n",
      "titles : 514\n",
      "times : 26\n",
      "subreddits : 299\n",
      "nums : 293\n"
     ]
    }
   ],
   "source": [
    "def eda(df,cols):\n",
    "    print(\"How many missing values there are:\")\n",
    "    print(\"\")\n",
    "    print(df.isnull().sum())\n",
    "    print(\"\")\n",
    "    print(\"Which are the data types:\")\n",
    "    print(\"\")\n",
    "    print(df.dtypes)\n",
    "    print(\"\")\n",
    "    print(\"Shape with duplicates:\",df.shape)\n",
    "    print (\"How many duplicates of {}:\".format(cols),df[cols].duplicated().sum())\n",
    "    df.drop_duplicates(subset=cols, keep='last', inplace=True) # drop duplicates\n",
    "    print (\"Now how many duplicates of {}:\".format(cols),df[cols].duplicated().sum())\n",
    "    print (\"Shape without duplicates:\",df.shape)\n",
    "    for col in df:\n",
    "        print (col,\":\",df[col].nunique())  #checking for unique entries\n",
    "        \n",
    "eda(df,['titles','subreddits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many 'comment' entries there are: comment    4\n",
      "Name: nums, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['times'] = [int(x[0]) for x in df['times'].str.split(' ')]   # Dropping 'hours ago' string\n",
    "print(\"How many 'comment' entries there are:\", df[df['nums'] == 'comment']['nums'].value_counts())\n",
    "df = df[df['nums'] != 'comment']                                # Dropping entries equal to 'comment' from nums column\n",
    "df['nums'] = [int(x[0]) for x in df['nums'].str.split(' ')]     # Dropping 'comments' from nums column\n",
    "df['subreddits'] = df['subreddits'].str[2:]                     # Dropping the 'r/' from subreddits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>187</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Dandrews practices his arithmetic</td>\n",
       "      <td>7</td>\n",
       "      <td>melbourne</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Downtown Vancouver [OC][4898 x 3265]</td>\n",
       "      <td>6</td>\n",
       "      <td>CityPorn</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>My mom took this photo in Yosemite at the vall...</td>\n",
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       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>So this happened today</td>\n",
       "      <td>16</td>\n",
       "      <td>FortNiteBR</td>\n",
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       "      <th>297</th>\n",
       "      <td>Here are a few of the many Forbidden no dancin...</td>\n",
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       "      <td>FortNiteBR</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>The new British 10p, will celebrate British li...</td>\n",
       "      <td>6</td>\n",
       "      <td>CasualUK</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>T_D is imploding with Trumpgret over Trump’s s...</td>\n",
       "      <td>10</td>\n",
       "      <td>Trumpgret</td>\n",
       "      <td>149</td>\n",
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       "      <td>White Beard vs...</td>\n",
       "      <td>13</td>\n",
       "      <td>OnePiece</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>baby kangaroo</td>\n",
       "      <td>18</td>\n",
       "      <td>aww</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Pre-testosterone vs 4 years on it. I’m the hap...</td>\n",
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       "      <td>lgbt</td>\n",
       "      <td>133</td>\n",
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       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Centralia, PA (underground coal mine fire sinc...</td>\n",
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       "      <td>AbandonedPorn</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>Talented sister</td>\n",
       "      <td>20</td>\n",
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       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>A Minecraft Voxelart piece I recently finished...</td>\n",
       "      <td>20</td>\n",
       "      <td>gaming</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                titles  times      subreddits  \\\n",
       "178                                    rainy afternoon     11         raining   \n",
       "180                   Well this tweet took a wild turn     19          trashy   \n",
       "181                             Sweet ki..........nvm!      3  yesyesyesyesno   \n",
       "187         Two drunk gentlemen try to pass each other     17  StoppedWorking   \n",
       "192                                  Minute of silence      4           gwent   \n",
       "196                  Dandrews practices his arithmetic      7       melbourne   \n",
       "272               Downtown Vancouver [OC][4898 x 3265]      6        CityPorn   \n",
       "292  My mom took this photo in Yosemite at the vall...     17       EarthPorn   \n",
       "294                             So this happened today     16      FortNiteBR   \n",
       "295                        Maximum Height Impulse Test     12      FortNiteBR   \n",
       "296  Stave church that combines peaked Christian ar...     19   evilbuildings   \n",
       "297  Here are a few of the many Forbidden no dancin...      1      FortNiteBR   \n",
       "298  The new British 10p, will celebrate British li...      6        CasualUK   \n",
       "299  T_D is imploding with Trumpgret over Trump’s s...     10       Trumpgret   \n",
       "300                                  White Beard vs...     13        OnePiece   \n",
       "301                                      baby kangaroo     18             aww   \n",
       "302  Pre-testosterone vs 4 years on it. I’m the hap...     11            lgbt   \n",
       "303  Centralia, PA (underground coal mine fire sinc...     10   AbandonedPorn   \n",
       "304                                    Talented sister     20  wholesomememes   \n",
       "305  A Minecraft Voxelart piece I recently finished...     20          gaming   \n",
       "\n",
       "     nums  \n",
       "178    10  \n",
       "180  1242  \n",
       "181     5  \n",
       "187   102  \n",
       "192    45  \n",
       "196    63  \n",
       "272     5  \n",
       "292    34  \n",
       "294    89  \n",
       "295    70  \n",
       "296   165  \n",
       "297    33  \n",
       "298   201  \n",
       "299   149  \n",
       "300   138  \n",
       "301    49  \n",
       "302   133  \n",
       "303    46  \n",
       "304   225  \n",
       "305  1109  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('reddit_df_eda_1.csv')   # exporting the DataFrame after the EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "c7631f51-07f2-4c79-a093-3e9bc7849a48"
   },
   "source": [
    "## 4) Creating a new binary variable\n",
    "\n",
    "We want to predict a binary variable - whether the number of comments was low or high. For that we:\n",
    "- Compute the median number of comments and create a new binary variable that is true when the number of comments is high (above the median).\n",
    "- We convert this into a _binary_ classification problem, by predicting two classes, high vs low number of comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median is: 84.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(512, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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      "text/plain": [
       "                                         titles  times      subreddits  nums  \\\n",
       "178                             rainy afternoon     11         raining    10   \n",
       "180            Well this tweet took a wild turn     19          trashy  1242   \n",
       "181                      Sweet ki..........nvm!      3  yesyesyesyesno     5   \n",
       "187  Two drunk gentlemen try to pass each other     17  StoppedWorking   102   \n",
       "192                           Minute of silence      4           gwent    45   \n",
       "\n",
       "     binary  \n",
       "178       0  \n",
       "180       1  \n",
       "181       0  \n",
       "187       1  \n",
       "192       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('reddit_df_eda_1.csv',index_col=0)\n",
    "print(\"The median is:\",np.median(df['nums']))\n",
    "df['binary'] = df['nums'].apply(lambda x: 1 if x >= np.median(df['nums']) else 0)\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('df_with_binary_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the baseline accuracy for this model?\n",
    "\n",
    "\n",
    "The baseline accuracy is the accuracy we would get if we always predict that the number of comments is larger than the median: \n",
    "\n",
    "$$ \\text{baseline} = \\frac{n_{\\text{above median}}}{n_{\\text{total}}}$$\n",
    "\n",
    "Since we are using the median as a criterion for classification, the baseline should be 0.5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy is 0.5\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('df_with_binary_1.csv')\n",
    "baseline = np.mean(df['binary'])\n",
    "print(\"The baseline accuracy is\",baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "4fb29de2-5b98-474c-a4ad-5170b72b9aea"
   },
   "source": [
    "## 5) Model building I\n",
    "\n",
    "We now create a `RandomForestClassifier` model to classify into high or low the number of comments. As a warm-up, we start by only using the `subreddits` as a feature. We first need to create dummy variables for the subreddits, which are in text format, to build our predictors (and drop one of the dummy columns):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 297)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <th>binary</th>\n",
       "      <th>4PanelCringe</th>\n",
       "      <th>ACPocketCamp</th>\n",
       "      <th>AFL</th>\n",
       "      <th>ATBGE</th>\n",
       "      <th>AbandonedPorn</th>\n",
       "      <th>AccidentalWesAnderson</th>\n",
       "      <th>Android</th>\n",
       "      <th>AnimalsBeingBros</th>\n",
       "      <th>AnimalsBeingDerps</th>\n",
       "      <th>...</th>\n",
       "      <th>videos</th>\n",
       "      <th>wallstreetbets</th>\n",
       "      <th>warriors</th>\n",
       "      <th>westworld</th>\n",
       "      <th>wholesomememes</th>\n",
       "      <th>woof_irl</th>\n",
       "      <th>worldnews</th>\n",
       "      <th>yesyesyesyesno</th>\n",
       "      <th>youtubehaiku</th>\n",
       "      <th>zelda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 297 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     binary  4PanelCringe  ACPocketCamp  AFL  ATBGE  AbandonedPorn  \\\n",
       "178       0             0             0    0      0              0   \n",
       "180       1             0             0    0      0              0   \n",
       "181       0             0             0    0      0              0   \n",
       "187       1             0             0    0      0              0   \n",
       "192       0             0             0    0      0              0   \n",
       "\n",
       "     AccidentalWesAnderson  Android  AnimalsBeingBros  AnimalsBeingDerps  \\\n",
       "178                      0        0                 0                  0   \n",
       "180                      0        0                 0                  0   \n",
       "181                      0        0                 0                  0   \n",
       "187                      0        0                 0                  0   \n",
       "192                      0        0                 0                  0   \n",
       "\n",
       "     ...    videos  wallstreetbets  warriors  westworld  wholesomememes  \\\n",
       "178  ...         0               0         0          0               0   \n",
       "180  ...         0               0         0          0               0   \n",
       "181  ...         0               0         0          0               0   \n",
       "187  ...         0               0         0          0               0   \n",
       "192  ...         0               0         0          0               0   \n",
       "\n",
       "     woof_irl  worldnews  yesyesyesyesno  youtubehaiku  zelda  \n",
       "178         0          0               0             0      0  \n",
       "180         0          0               0             0      0  \n",
       "181         0          0               1             0      0  \n",
       "187         0          0               0             0      0  \n",
       "192         0          0               0             0      0  \n",
       "\n",
       "[5 rows x 297 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subred = pd.concat([df['binary'],pd.get_dummies(df['subreddits'], drop_first = True)], axis = 1)\n",
    "df_subred.shape\n",
    "df_subred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subred.to_csv('subred_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A simple case:\n",
    "\n",
    "In the rest of the notebook I will perform a grid search to find optimal hyperparameters. Before that, however, for simplicity and purposes of illustration, I will consider a simple example with some already chosen parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now train a Random Forest Classifier using the following function `rfTTscore` (the name stands for \"random forest train/test score\"). It does the following:\n",
    "- Separates the data into predictors and target\n",
    "- Splits the data into training and testing sets\n",
    "- Instantiates a `RandomForestClassifier` and train it (i.e. fit it) on the training data\n",
    "- Now, applying the model, which was fitted *using the training data*, to the remaining data i.e. the testing data, we evaluate the accuracy of the model using the mean accuracy score `.score`:\n",
    "\n",
    "$$ {\\text{mean accuracy score}} = \\frac{{{\\text{number of correct predictions}}}}{{{\\text{number of observations}}}}$$\n",
    "- We print out the class predictions\n",
    "- We then print out the predicting probabilities\n",
    "- The following lines build a confusion matrix\n",
    "- Finally, the main features are printed out with the corresponding bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean accuracy score is: 0.51\n",
      "\n",
      "Is the prediction smaller (S) or larger (L) than the median:\n",
      "\n",
      "['S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S']\n",
      "\n",
      "What were the probabilities of the each result above:\n",
      "\n",
      "Probabilities that the number of comments is smaller than the media for each observation are:\n",
      "\n",
      "[('S', 0.57), ('S', 0.42), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.4), ('S', 0.57), ('S', 0.5), ('S', 0.57), ('S', 0.25), ('S', 0.57), ('S', 0.54), ('S', 0.59), ('S', 0.46), ('S', 0.57), ('S', 0.47), ('S', 0.4), ('S', 0.42), ('S', 0.22), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.25), ('S', 0.23), ('S', 0.66), ('S', 0.4), ('S', 0.59), ('S', 0.25), ('S', 0.57), ('S', 0.25), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.59), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.65), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.58), ('S', 0.58), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.66), ('S', 0.5), ('S', 0.57), ('S', 0.22), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.5), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.47), ('S', 0.57), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.25), ('S', 0.57), ('S', 0.62), ('S', 0.57), ('S', 0.6), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.58), ('S', 0.58), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.32), ('S', 0.57), ('S', 0.6), ('S', 0.66), ('S', 0.42), ('S', 0.22), ('S', 0.59), ('S', 0.57), ('S', 0.57), ('S', 0.62), ('S', 0.57), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.25), ('S', 0.58), ('S', 0.55), ('S', 0.57), ('S', 0.25), ('S', 0.57), ('S', 0.57), ('S', 0.59), ('S', 0.57), ('S', 0.4), ('S', 0.68), ('S', 0.53), ('S', 0.57), ('S', 0.22), ('S', 0.64), ('S', 0.57), ('S', 0.57), ('S', 0.47), ('S', 0.57), ('S', 0.52), ('S', 0.68), ('S', 0.66), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.22), ('S', 0.25), ('S', 0.25), ('S', 0.57), ('S', 0.18), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.58), ('S', 0.58), ('S', 0.53), ('S', 0.4), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.59), ('S', 0.47), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.57), ('S', 0.58), ('S', 0.57), ('S', 0.57), ('S', 0.18), ('S', 0.57)]\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "Predicted Values   0   1\n",
      "Actual Values           \n",
      "0                 61  13\n",
      "1                 62  18\n",
      "Features and their importance:\n",
      "\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AIMnRwFuBf1xVPwLMAP8myTrg/cCrqupkRldwv5rkKODngI3Dc/3Wsh6VJOkxTSQ6VbUNeOrwPs6pwHeBr45t8hLgj4dtdwA7huUvAk4CbkiyHbgAOBZ4HnBHVX1p2O7y4Tm+DzwIvDfJzwMPzDVPks1JZpLM7Hng3iU8UknSuEm+p3M1o/dw/gGjK5/Zao5lAT5eVb/4/y1MTptrB1X1cJLTgbOB84A3AGfNsd0WYAvAoU87fq79SpKWwCQ/vXYFoxCcyyhA464DXg2QZBNwyrD8s8CPJXnOsO6wJM8FbgWm9i4Hfhn41PB+zxFV9RHgjcCccZIk9ZjYlU5V7UqyAfhGVX0rydTY6j8A3pdkB7Ad+PzwmL9OciHwP/d+UAB4a1V9KcmvAFclWcvo/aF3A0cBfz685xPgTQ2HJkmax0Q/Mj286b/3/p3ApuH+bkZXQXM95hPAC+dYfg3w/FmLvwWcvkTjSpIWacX8z6GSpAOf0ZEktTE6kqQ2RkeS1MboSJLaGB1JUhujI0lqY3QkSW2MjiSpjdGRJLUxOpKkNivm11WvFCc//QhmLnvFpMeQpAOSVzqSpDZGR5LUxuhIktoYHUlSG6MjSWpjdCRJbYyOJKmN0ZEktTE6kqQ2RkeS1MboSJLaGB1JUhujI0lqY3QkSW2MjiSpjdGRJLVJVU16hhUlyX3AbZOeY4U6Grh70kOsYJ6f+Xlu5negnJtjq+op+9rI3xz6aLdV1fSkh1iJksx4bubn+Zmf52Z+B9u58eU1SVIboyNJamN0Hm3LpAdYwTw3j83zMz/PzfwOqnPjBwkkSW280pEktTmoopPkJ5PcluT2JJfMsf7QJFcO6z+XZGps3ZuH5bcl+Sedc3fY33OT5MeTbE2yc/jzrO7Zl9ti/t4M65+Z5P4kF3fN3GmR31enJPlMkl3D36F1nbMvt0V8Xx2S5PLhnNyS5M3dsy+bqjoobsAa4MvAs4AnADcBJ83a5vXAu4f75wFXDvdPGrY/FDhueJ41kz6mFXJung8cM9zfBHxj0sezUs7N2Po/Ba4CLp708ayk88Pof9nYAZw6fP1kv6/+7tycD1wx3D8MuBOYmvQxLcXtYLrSOR24vaq+UlV/C1wBnDNrm3OAy4f7VwNnJ8mw/Iqqeqiq7gBuH57vQLHf56aqtlXVN4flu4B1SQ5tmbrHYv7ekOSfAV9hdG4ORIs5Pz8B7KiqmwCq6m+qak/T3B0Wc24KeGKStcB64G+B7/eMvbwOpug8Hfja2NdfH5bNuU1VPQzcy+i/vhby2NVsMedm3CuBbVX10DLNOQn7fW6SPBH4D8DbGuaclMX83XkuUEn+KskXkvz7hnk7LebcXA38APgW8FXgd6vqnuUeuMPB9BMJMsey2R/dm2+bhTx2NVvMuRmtTDYC72D0X68HksWcm7cBv19V9w8XPgeixZyftcAZwAuBB4BrkmytqmuWdsSJWcy5OR3YAxwDPAm4Psn/rqqvLO2I/Q6mK52vA/9w7OtnAN+cb5vhsvYI4J4FPnY1W8y5IckzgD8D/kVVfXnZp+21mHPzj4DfSXIn8EbgN5K8YbkHbrbY76tPVdXdVfUA8BHgR5Z94j6LOTfnA39ZVT+squ8ANwAHxI/KOZiicyNwfJLjkjyB0Zt2H5q1zYeAC4b75wKfqNE7eR8Czhs+aXIccDzw+aa5O+z3uUlyJPAXwJur6oa2ifvs97mpqjOraqqqpoD/DPx2Vf23rsGbLOb76q+AU5IcNvyD+1Lgi01zd1jMufkqcFZGngi8CLi1ae7lNelPMnTegJ8GvsToEyVvGZb9R+Bnh/vrGH3K6HZGUXnW2GPfMjzuNuCnJn0sK+XcAG9l9Nrz9rHbUyd9PCvh3Mx6jks5AD+9ttjzA/wSow9Z3Az8zqSPZaWcG+DwYfkuRiH+d5M+lqW6+RMJJEltDqaX1yRJE2Z0JEltjI4kqY3RkSS1MTqSpDZGR5LUxuhIktoYHUlSm/8L6coG6ESPkqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19ae1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rfTTscore(df,target_col,test_size,n_estimators,max_depth):\n",
    "    \n",
    "    X = df.drop(target_col, axis=1)   # define predictors\n",
    "    y = df[target_col]                # defines target\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                        y, test_size = test_size, random_state=42) # train/test split\n",
    "\n",
    "    rf = RandomForestClassifier(max_depth = max_depth, n_estimators = n_estimators)    #instantiates model\n",
    "    rf.fit(X_train,y_train)      # fit the model to the training data\n",
    "    \n",
    "    # .score returns the mean accuracy on the test data\n",
    "    \n",
    "    print(\"The mean accuracy score is:\",round(rf.score(X_test,y_test),2))\n",
    "    print(\"\")\n",
    "    print(\"Is the prediction smaller (S) or larger (L) than the median:\\n\")\n",
    "    preds = rf.predict(X_test)\n",
    "    print(['S' if p == 0 else 'L' for p in rf.predict(X_test)])\n",
    "    print(\"\")\n",
    "    print(\"What were the probabilities of the each result above:\\n\")\n",
    "    print(\"Probabilities that the number of comments is smaller than the media for each observation are:\\n\")\n",
    "    print([('S',round(p[0],2)) if p[0] > p[1] else ('S',round(p[0],2)) for p in rf.predict_proba(X_test)])\n",
    "    print(\"\")\n",
    "    print(\"Confusion Matrix:\\n\")\n",
    "    print(pd.crosstab(pd.concat([X_test,y_test],axis=1)['binary'], preds, rownames=['Actual Values'], colnames=['Predicted Values']))\n",
    "    print('Features and their importance:\\n')\n",
    "    feature_importances = pd.Series(rf.feature_importances_, index=X.columns).sort_values().tail(5)\n",
    "    print(feature_importances.plot(kind=\"barh\", figsize=(6,6)))\n",
    "    return \n",
    "\n",
    "rfTTscore(df_subred,'binary',0.3,25,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments:\n",
    "\n",
    "- Using the subreddits only as predictors, the mean accuracy score is mediocre, slighly above the baseline value. \n",
    "- The probabilities used for classifying the preditions are close to $50\\%$.\n",
    "- The main features are`pics`, and `gaming`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) Model building II\n",
    "\n",
    "I will now proceed with `GridSearchCV` on the training data (following lesson 4.07). The `GridSearchCV` looks for optimal hyperparameters. The function below does the following:\n",
    "- Separates the data into predictors and target\n",
    "- Splits the data into training and testing sets\n",
    "- Defines a set of hyperparameters `rf_params` which are related to the random forest classification model (see scikitLearn documentation for more details)\n",
    "- Instantiates `GridSearchCV`. The latter does the following (this is taken from `scikitLearn` docs):\n",
    "  - Searches over specified parameter values of ranges for an estimator, in this case a `RandomForestClassifier`.\n",
    "  - Implements a `fit` and a `score` method.\n",
    "  - The parameters of the estimator are optimized by cross-validated grid-search over a parameter grid.\n",
    "- The `RandomForestClassifier` is fitted to the training data\n",
    "- The best parameters obtained by fitting `GridSearchCV` on the training are printed\n",
    "- A `RandomForestClassifier` with optimized hyperparameters is instantiated and trained on the training data\n",
    "- The mean accuracy score is evaluated in the testing data\n",
    "- We print out the class predictions\n",
    "- We then print out the predicting probabilities\n",
    "- The following lines build a confusion matrix\n",
    "- Finally, the main features are printed out with the corresponding bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining ranges for the hyperparameters to be scanned by the grid search\n",
    "n_estimators = list(range(20,220,10))\n",
    "max_depth = list(range(2, 22, 2)) + [None]\n",
    "\n",
    "def rfscore2(df,target_col,test_size,n_estimators,max_depth):\n",
    "    \n",
    "    X = df.drop(target_col, axis=1)   # predictors\n",
    "    y = df[target_col]                # target\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                        y, test_size = test_size, random_state=42) # TT split\n",
    "    rf_params = {\n",
    "             'n_estimators':n_estimators,\n",
    "             'max_depth':max_depth}   # parameters for grid search\n",
    "    rf_gs = GridSearchCV(RandomForestClassifier(), rf_params, cv=5, verbose=1, n_jobs=-1)\n",
    "    rf_gs.fit(X_train,y_train) # training the random forest with all possible parameters\n",
    "    print('GridSearch results')\n",
    "    print('The best parameters on the training data are:\\n',rf_gs.best_params_) # printing the best parameters\n",
    "    max_depth_best = rf_gs.best_params_['max_depth']      # getting the best max_depth\n",
    "    n_estimators_best = rf_gs.best_params_['n_estimators']  # getting the best n_estimators\n",
    "    print(\"best max_depth:\",max_depth_best)\n",
    "    print(\"best n_estimators:\",n_estimators_best)\n",
    "    best_rf_gs = RandomForestClassifier(max_depth=max_depth_best,n_estimators=n_estimators_best) # instantiate the best model\n",
    "    best_rf_gs.fit(X_train,y_train)  # fitting the best model\n",
    "    best_rf_score = best_rf_gs.score(X_test,y_test) \n",
    "    print (\"best score is:\",round(best_rf_score,2))\n",
    "    print(\"Is the prediction smaller (S) or larger (L) than the median:\\n\")\n",
    "    preds = best_rf_gs.predict(X_test)\n",
    "    print(['S' if p == 0 else 'L' for p in best_rf_gs.predict(X_test)])\n",
    "    print(\"\")\n",
    "    print(\"What were the probabilities of the each result above:\\n\")\n",
    "    print(\"Probabilities that the number of comments is smaller than the media for each observation are:\\n\")\n",
    "    print([('S',round(p[0],2)) if p[0] > p[1] else ('S',round(p[0],2)) for p in best_rf_gs.predict_proba(X_test)])\n",
    "    print(\"\")\n",
    "    print(\"Confusion Matrix:\\n\")\n",
    "    print(pd.crosstab(pd.concat([X_test,y_test],axis=1)['binary'], preds, rownames=['Actual Values'], colnames=['Predicted Values']))\n",
    "    print('Features and their importance:\\n')\n",
    "    feature_importances = pd.Series(best_rf_gs.feature_importances_, index=X.columns).sort_values().tail(5)\n",
    "    print(feature_importances.plot(kind=\"barh\", figsize=(6,6)))\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 110 candidates, totalling 550 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  76 tasks      | elapsed:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done 317 tasks      | elapsed:   27.3s\n",
      "[Parallel(n_jobs=-1)]: Done 550 out of 550 | elapsed:   49.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearch results\n",
      "The best parameters on the training data are:\n",
      " {'max_depth': 8, 'n_estimators': 100}\n",
      "best max_depth: 8\n",
      "best n_estimators: 100\n",
      "best score is: 0.53\n",
      "Is the prediction smaller (S) or larger (L) than the median:\n",
      "\n",
      "['S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S']\n",
      "\n",
      "What were the probabilities of the each result above:\n",
      "\n",
      "Probabilities that the number of comments is smaller than the media for each observation are:\n",
      "\n",
      "[('S', 0.53), ('S', 0.44), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.45), ('S', 0.53), ('S', 0.51), ('S', 0.53), ('S', 0.36), ('S', 0.53), ('S', 0.51), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.51), ('S', 0.45), ('S', 0.44), ('S', 0.31), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.36), ('S', 0.48), ('S', 0.57), ('S', 0.45), ('S', 0.52), ('S', 0.36), ('S', 0.53), ('S', 0.36), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.54), ('S', 0.57), ('S', 0.53), ('S', 0.53), ('S', 0.59), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.56), ('S', 0.48), ('S', 0.53), ('S', 0.31), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.48), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.52), ('S', 0.56), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.36), ('S', 0.53), ('S', 0.56), ('S', 0.53), ('S', 0.54), ('S', 0.54), ('S', 0.53), ('S', 0.53), ('S', 0.54), ('S', 0.54), ('S', 0.57), ('S', 0.53), ('S', 0.53), ('S', 0.54), ('S', 0.53), ('S', 0.53), ('S', 0.48), ('S', 0.53), ('S', 0.52), ('S', 0.57), ('S', 0.44), ('S', 0.31), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.56), ('S', 0.53), ('S', 0.56), ('S', 0.53), ('S', 0.53), ('S', 0.38), ('S', 0.54), ('S', 0.52), ('S', 0.53), ('S', 0.36), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.45), ('S', 0.61), ('S', 0.48), ('S', 0.53), ('S', 0.31), ('S', 0.56), ('S', 0.53), ('S', 0.53), ('S', 0.49), ('S', 0.53), ('S', 0.53), ('S', 0.61), ('S', 0.57), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.31), ('S', 0.36), ('S', 0.36), ('S', 0.53), ('S', 0.37), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.54), ('S', 0.48), ('S', 0.45), ('S', 0.53), ('S', 0.53), ('S', 0.52), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.52), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.53), ('S', 0.37), ('S', 0.53)]\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "Predicted Values   0   1\n",
      "Actual Values           \n",
      "0                 62  12\n",
      "1                 61  19\n",
      "Features and their importance:\n",
      "\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19c73518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfscore2(df_subred,'binary',0.3,n_estimators,max_depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments:\n",
    "\n",
    "- Again, using the subreddits only as predictors, the mean accuracy score is only slighly above the baseline value.\n",
    "- Again, the probabilities used for classifying the predictions are close to $50\\%$.\n",
    "- The main features continue to be `pics`, and `gaming`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) Model building III\n",
    "\n",
    "The following function is essentially the same as the one above but:\n",
    "- The parameter space is larger\n",
    "- The fitting procedure is different (the reason will be discussed later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rfscore_3(df,target_col,cv):\n",
    "    \n",
    "    X = df.drop(target_col, axis=1)   # predictors\n",
    "    y = df[target_col]                # target\n",
    "    \n",
    "    rf_params = {\n",
    "             'n_estimators':list(range(20,220,10)),\n",
    "             'min_samples_split':list(range(2, 11, 2)),\n",
    "             'max_depth':list(range(2, 22, 2)) + [None]}\n",
    "    \n",
    "    rf_gs = GridSearchCV(RandomForestClassifier(), rf_params, cv=cv, verbose=1, n_jobs=-1)\n",
    "    rf_gs.fit(X,y) # training the random forest with all possible parameters\n",
    "    print('GridSearch results')\n",
    "    print('The best parameters on the training data are:\\n',rf_gs.best_params_) # printing the best parameters\n",
    "    max_depth_best = rf_gs.best_params_['max_depth']      # getting the best max_depth\n",
    "    n_estimators_best = rf_gs.best_params_['n_estimators']  # getting the best n_estimators\n",
    "    print(\"best max_depth:\",max_depth_best)\n",
    "    print(\"best n_estimators:\",n_estimators_best)\n",
    "    best_rf_gs = RandomForestClassifier(max_depth=max_depth_best,n_estimators=n_estimators_best) # instantiate the best model\n",
    "    best_rf_gs.fit(X,y)  # fitting the best model\n",
    "    best_rf_score = best_rf_gs.score(X,y) \n",
    "    print (\"best score is:\",round(best_rf_score,2))\n",
    "    print(\"Is the prediction smaller (S) or larger (L) than the median:\\n\")\n",
    "    preds = best_rf_gs.predict(X)\n",
    "    print(['S' if p == 0 else 'L' for p in best_rf_gs.predict(X)])\n",
    "    print(\"\")\n",
    "    print(\"What were the probabilities of the each result above:\\n\")\n",
    "    print(\"Probabilities that the number of comments is smaller than the media for each observation are:\\n\")\n",
    "    print([('S',round(p[0],2)) if p[0] > p[1] else ('S',round(p[0],2)) for p in best_rf_gs.predict_proba(X)])\n",
    "    print(\"\")\n",
    "    print(\"Confusion Matrix:\\n\")\n",
    "    print(pd.crosstab(pd.concat([X,y],axis=1)['binary'], preds, rownames=['Actual Values'], colnames=['Predicted Values']))\n",
    "    print('Features and their importance:\\n')\n",
    "    feature_importances = pd.Series(best_rf_gs.feature_importances_, index=X.columns).sort_values().tail(5)\n",
    "    print(feature_importances.plot(kind=\"barh\", figsize=(6,6)))\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1100 candidates, totalling 5500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  75 tasks      | elapsed:    8.3s\n",
      "[Parallel(n_jobs=-1)]: Done 226 tasks      | elapsed:   35.0s\n",
      "[Parallel(n_jobs=-1)]: Done 476 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 826 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1276 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1826 tasks      | elapsed:  5.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2476 tasks      | elapsed:  7.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3226 tasks      | elapsed:  9.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4076 tasks      | elapsed: 11.5min\n",
      "[Parallel(n_jobs=-1)]: Done 5026 tasks      | elapsed: 14.0min\n",
      "[Parallel(n_jobs=-1)]: Done 5500 out of 5500 | elapsed: 16.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearch results\n",
      "The best parameters on the training data are:\n",
      " {'max_depth': 4, 'min_samples_split': 8, 'n_estimators': 180}\n",
      "best max_depth: 4\n",
      "best n_estimators: 180\n",
      "best score is: 0.72\n",
      "Is the prediction smaller (S) or larger (L) than the median:\n",
      "\n",
      "['S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'L']\n",
      "\n",
      "What were the probabilities of the each result above:\n",
      "\n",
      "Probabilities that the number of comments is smaller than the media for each observation are:\n",
      "\n",
      "[('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.53), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.49), ('S', 0.49), ('S', 0.49), ('S', 0.5), ('S', 0.48), ('S', 0.5), ('S', 0.54), ('S', 0.5), ('S', 0.52), ('S', 0.52), ('S', 0.4), ('S', 0.49), ('S', 0.5), ('S', 0.52), ('S', 0.5), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.54), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.49), ('S', 0.49), ('S', 0.54), ('S', 0.49), ('S', 0.53), ('S', 0.43), ('S', 0.43), ('S', 0.5), ('S', 0.51), ('S', 0.43), ('S', 0.49), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.46), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.4), ('S', 0.49), ('S', 0.51), ('S', 0.39), ('S', 0.49), ('S', 0.56), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.5), ('S', 0.5), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.39), ('S', 0.46), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.52), ('S', 0.5), ('S', 0.52), ('S', 0.53), ('S', 0.51), ('S', 0.54), ('S', 0.49), ('S', 0.51), ('S', 0.52), ('S', 0.5), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.5), ('S', 0.52), ('S', 0.52), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.52), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.54), ('S', 0.49), ('S', 0.45), ('S', 0.39), ('S', 0.49), ('S', 0.5), ('S', 0.49), ('S', 0.5), ('S', 0.52), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.52), ('S', 0.5), ('S', 0.52), ('S', 0.52), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.52), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.52), 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('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.54), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.49), ('S', 0.48), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.54), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.48), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.53), ('S', 0.39), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.49), ('S', 0.4), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.54), ('S', 0.51), ('S', 0.45), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.5), ('S', 0.52), ('S', 0.51), ('S', 0.5), ('S', 0.54), ('S', 0.5), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.52), ('S', 0.52), ('S', 0.46), ('S', 0.5), ('S', 0.48), ('S', 0.5), ('S', 0.39), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.39), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.43), ('S', 0.5), ('S', 0.46), ('S', 0.49), ('S', 0.4), ('S', 0.5), ('S', 0.49), ('S', 0.49), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.52), ('S', 0.5), ('S', 0.5), ('S', 0.5), ('S', 0.51), ('S', 0.49), ('S', 0.49), ('S', 0.51), ('S', 0.5)]\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "Predicted Values    0    1\n",
      "Actual Values             \n",
      "0                 231   25\n",
      "1                 117  139\n",
      "Features and their importance:\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a192dc2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfscore_3(df_subred,'binary',5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments:\n",
    "\n",
    "- The mean accuracy score improved substatially now.\n",
    "- The main features continue to be `pics`, and `gaming`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can compute the `cross_val_score` corresponding to the best model from the cell above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_score(df,target_col,cv,n_estimators,min_samples_split,max_depth):\n",
    "    X = df.drop(target_col,axis=1)\n",
    "    y = df[target_col]\n",
    "    rf = RandomForestClassifier(n_estimators=n_estimators_best,min_samples_split=min_samples_split_best,\n",
    "                                max_depth=max_depth_best)\n",
    "    s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)\n",
    "    return(\"{} Score is :{:0.3} ± {:0.3}\".format(\"Random Forest\", s.mean().round(3), s.std().round(3)))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Random Forest Score is :0.588 ± 0.023'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_best = {'max_depth': 4, 'min_samples_split': 8, 'n_estimators': 180}\n",
    "n_estimators_best = dict_best['n_estimators']\n",
    "min_samples_split_best = dict_best['min_samples_split']\n",
    "max_depth_best = dict_best['max_depth']\n",
    "cv_score(df_subred,'binary',5,n_estimators_best,min_samples_split_best,max_depth_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7)  Model Building IV: Including thread features\n",
    "\n",
    "I will now use CountVectorizer to create features based on the words in the thread titles. We will then combine this new table with the subreddits features table and build a new model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We first need to preprocess the text:\n",
    "\n",
    "- We take out stopwords (common words, adding no meaning such as for example, \"I\", \"am\")\n",
    "- Take out punctuation and spaces\n",
    "- Converts text to lower case\n",
    "- Use stemming (e.g. it converts the words scientific, scientist, science into scien). \n",
    "\n",
    "The function below does that [1]. A few words regarding this function are in order:\n",
    "- Using `PorterStemmer()` we groups words with same stems\n",
    "- Using `stopwords.words('english')` we exclude stop words (in English) such as:\n",
    "\n",
    "        ['i', 'me', 'my', 'myself', 'we',...]\n",
    "    \n",
    "- `maketrans()` creates a mapping table. We may create an empty mapping table, and then set the third argument of this function to all characters we want to remove during the translation process. For example:\n",
    "        \n",
    "        str.maketrans('', '', string.punctuation)\n",
    "        str.maketrans('', '', string.digits)\n",
    "\n",
    "- The function `translate()` maps a set of characters into another.\n",
    "- Using `text.lower().strip()` we removes spaces\n",
    "\n",
    "Some simple examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['o', 'n', 'e', '', 't', 'w', 'o', '', '', 'a', 'n', 'd', '', '']"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "words_1 = 'One,TWO,1and 3' \n",
    "w = [w.translate(str.maketrans(\n",
    "    '','',string.digits)).translate(\n",
    "    str.maketrans('','',\n",
    "                  string.punctuation)).lower().strip() \\\n",
    "     for w in words_1]                                     \n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The function we will use is given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "def cleaner(text):\n",
    "    stemmer = PorterStemmer()                                          \n",
    "    stop = stopwords.words('english')    \n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))   \n",
    "    text = text.translate(str.maketrans('', '', string.digits))        \n",
    "    text = text.lower().strip() \n",
    "    final_text = []\n",
    "    for w in text.split():\n",
    "        if w not in stop:\n",
    "            final_text.append(stemmer.stem(w.strip()))\n",
    "    return ' '.join(final_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recalling `df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titles</th>\n",
       "      <th>times</th>\n",
       "      <th>subreddits</th>\n",
       "      <th>nums</th>\n",
       "      <th>binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>rainy afternoon</td>\n",
       "      <td>11</td>\n",
       "      <td>raining</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Well this tweet took a wild turn</td>\n",
       "      <td>19</td>\n",
       "      <td>trashy</td>\n",
       "      <td>1242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Sweet ki..........nvm!</td>\n",
       "      <td>3</td>\n",
       "      <td>yesyesyesyesno</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Two drunk gentlemen try to pass each other</td>\n",
       "      <td>17</td>\n",
       "      <td>StoppedWorking</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Minute of silence</td>\n",
       "      <td>4</td>\n",
       "      <td>gwent</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         titles  times      subreddits  nums  \\\n",
       "178                             rainy afternoon     11         raining    10   \n",
       "180            Well this tweet took a wild turn     19          trashy  1242   \n",
       "181                      Sweet ki..........nvm!      3  yesyesyesyesno     5   \n",
       "187  Two drunk gentlemen try to pass each other     17  StoppedWorking   102   \n",
       "192                           Minute of silence      4           gwent    45   \n",
       "\n",
       "     binary  \n",
       "178       0  \n",
       "180       1  \n",
       "181       0  \n",
       "187       1  \n",
       "192       0  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('df_with_binary_1.csv',index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us consider only words that occured min_df times or more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1),\n",
       "        preprocessor=<function cleaner at 0x1a193ee1e0>, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Words that showed up at least 1 times:\n",
      "\n",
      "['abandon', 'abgeb', 'abomin', 'absolut', 'absolutelynotmeirl', 'accept', 'accident', 'accord', 'account', 'accus', 'across', 'acryl', 'act', 'actual', 'ad', 'adapt', 'addit', 'administration', 'admit', 'adolf', 'adopt', 'aeiou', 'af', 'afghanistan', 'africa', 'afternoon', 'age', 'agenda', 'agent', 'aglet', 'ago', 'air', 'aja', 'alberta', 'album', 'alex', 'aliv', 'alleg', 'alon', 'alp', 'alreadi', 'alright', 'also', 'altright', 'alway', 'ama', 'amaz', 'amnesti', 'among', 'amount', 'ampat', 'android', 'anim', 'animaux', 'ankl', 'anon', 'anoth', 'antivenom', 'anyon', 'anyth', 'apart', 'app', 'appar', 'appear', 'appl', 'appreci', 'approach', 'april', 'aquaria', 'aquarium', 'archer', 'architectur', 'arena', 'arent', 'arithmet', 'arm', 'around', 'artwork', 'ashley', 'ask', 'ass', 'assassin', 'assault', 'assaultstyl', 'asylum', 'atheist', 'attack', 'attempt', 'aunt', 'austin', 'australia', 'australian', 'autobiographi', 'autour', 'av', 'avatar', 'aveng', 'avoid', 'away', 'awhil', 'aww', 'baadddd', 'babi', 'babysitt', 'back', 'bacon', 'bacteria', 'bad', 'bag', 'ball', 'ban', 'band', 'banff', 'bank', 'bar', 'barackobama', 'barcelona', 'base', 'basic', 'bath', 'battl', 'battlefeel', 'battleground', 'bavarian', 'baymax', 'beach', 'beam', 'beard', 'beast', 'beat', 'beauti', 'beaver', 'becom', 'bed', 'beer', 'befriend', 'beg', 'behaviour', 'behind', 'believ', 'bell', 'belong', 'ben', 'bend', 'best', 'bet', 'better', 'beyonc', 'big', 'bigger', 'bill', 'billion', 'billionair', 'bimbo', 'binari', 'birth', 'birthday', 'black', 'blep', 'blew', 'blow', 'blue', 'boat', 'bodega', 'bodi', 'boi', 'bold', 'bone', 'book', 'born', 'boss', 'bot', 'bottl', 'bow', 'bowl', 'boy', 'boycott', 'boyfriend', 'brad', 'brain', 'brand', 'brat', 'brawl', 'brazil', 'bread', 'breadstick', 'breed', 'brick', 'brilliant', 'bring', 'britain', 'british', 'briton', 'broken', 'brooklyn', 'brother', 'brought', 'brows', 'brutal', 'bu', 'bubbl', 'bucket', 'bucki', 'buddi', 'bug', 'bulli', 'bun', 'burger', 'burn', 'busi', 'bust', 'butterfli', 'buy', 'ca', 'cabl', 'cafe', 'caiman', 'cake', 'calcul', 'california', 'call', 'calm', 'calori', 'came', 'camera', 'cameraman', 'campaign', 'campsit', 'can', 'canada', 'cancer', 'cant', 'canvass', 'canyon', 'cappi', 'captur', 'car', 'cardiac', 'care', 'career', 'caretak', 'carol', 'carolina', 'carri', 'carv', 'cat', 'catapult', 'catch', 'caught', 'caus', 'cave', 'celebr', 'censor', 'censorship', 'center', 'centralia', 'centuri', 'ceo', 'chair', 'challeng', 'chamber', 'chancellor', 'chang', 'channel', 'charact', 'charg', 'chase', 'cheap', 'check', 'checkmat', 'cheer', 'cheesi', 'chemo', 'chi', 'chicken', 'chief', 'childhood', 'children', 'chill', 'chime', 'china', 'chine', 'chocol', 'christian', 'church', 'circul', 'citi', 'clash', 'class', 'clay', 'clean', 'cleveland', 'climat', 'climb', 'close', 'cloth', 'clubhous', 'coach', 'coal', 'coastal', 'code', 'coffe', 'coliseum', 'collag', 'collect', 'collector', 'colleg', 'collegeeduc', 'color', 'colorblind', 'colosseum', 'combin', 'come', 'command', 'comment', 'commun', 'commut', 'compani', 'companion', 'comparison', 'complet', 'concept', 'concern', 'condit', 'confer', 'confid', 'confirm', 'confus', 'congress', 'connect', 'consequ', 'contest', 'continu', 'control', 'convert', 'cool', 'cop', 'cope', 'core', 'could', 'count', 'countless', 'countri', 'coupl', 'courag', 'cousin', 'cove', 'cozi', 'cracker', 'crash', 'crayola', 'crazi', 'credit', 'creed', 'creepi', 'crew', 'cri', 'cring', 'critic', 'crocodil', 'crusti', 'cryptocurr', 'cuck', 'cultur', 'cupcak', 'current', 'curri', 'custodi', 'custom', 'cut', 'cutter', 'cw', 'cycl', 'da', 'dad', 'dalton', 'damn', 'danc', 'dandrew', 'danger', 'dark', 'daughter', 'dave', 'day', 'de', 'dea', 'deagl', 'dealership', 'dear', 'death', 'debat', 'debut', 'decemb', 'decid', 'decis', 'deck', 'decomiss', 'decri', 'deep', 'defenc', 'deflect', 'delici', 'democrat', 'dermagraph', 'deserv', 'design', 'desir', 'desk', 'desktop', 'destroy', 'develop', 'devis', 'dicaprio', 'dick', 'didnt', 'die', 'diet', 'digit', 'directli', 'director', 'disagre', 'disappear', 'discord', 'discov', 'discuss', 'disgust', 'distast', 'dive', 'diver', 'do', 'dodger', 'doesnt', 'dog', 'dogcuzzi', 'doggo', 'domest', 'donald', 'done', 'donkey', 'dont', 'door', 'dorwel', 'dot', 'downtown', 'downvot', 'dozen', 'dpd', 'dr', 'drag', 'dragon', 'drama', 'drank', 'draw', 'dream', 'dress', 'drink', 'drive', 'drone', 'drop', 'drug', 'drummer', 'drunk', 'dude', 'due', 'dump', 'duti', 'eagl', 'ear', 'earli', 'earth', 'earthli', 'easi', 'east', 'eat', 'eaten', 'economi', 'edg', 'ef', 'effici', 'effort', 'egg', 'einstein', 'electr', 'elev', 'eleven', 'eli', 'elimin', 'els', 'emerald', 'emot', 'emperor', 'employe', 'enforc', 'engin', 'english', 'enjoy', 'enough', 'enter', 'entir', 'epa', 'ephraim', 'epic', 'episod', 'equal', 'eric', 'erzgebirg', 'escal', 'escola', 'et', 'euthanis', 'evan', 'even', 'event', 'ever', 'everi', 'everyon', 'everything', 'evolut', 'ex', 'exactli', 'excus', 'exercis', 'exist', 'expect', 'expens', 'experienc', 'explain', 'express', 'extend', 'eye', 'fabul', 'face', 'facebook', 'facharbeit', 'facial', 'fail', 'fairi', 'fake', 'famili', 'fan', 'fanart', 'far', 'farm', 'farmer', 'fart', 'fascist', 'fasten', 'father', 'faulti', 'favorit', 'favourit', 'fbi', 'februari', 'feedback', 'feel', 'feelsgoodman', 'feet', 'feetsi', 'feliciano', 'fell', 'fellow', 'femal', 'femin', 'fenc', 'ferm', 'ferri', 'fidget', 'field', 'fieri', 'fight', 'figur', 'fill', 'film', 'filthi', 'final', 'find', 'fine', 'finger', 'finish', 'finn', 'fire', 'firework', 'first', 'fish', 'flat', 'flew', 'flood', 'floof', 'floor', 'floyd', 'fogl', 'foi', 'follow', 'food', 'foot', 'footag', 'forbidden', 'forc', 'forest', 'forev', 'forlorn', 'form', 'former', 'fortnit', 'fortun', 'fought', 'found', 'four', 'fox', 'fragt', 'franklin', 'fre', 'free', 'fresh', 'fri', 'friend', 'front', 'frugal', 'fuck', 'fun', 'fur', 'furryirl', 'fursona', 'futur', 'fxx', 'fössta', 'ga', 'gain', 'game', 'gangtok', 'gap', 'garden', 'gari', 'gateway', 'gave', 'gay', 'gear', 'gem', 'gener', 'gentlemen', 'georg', 'germani', 'get', 'giant', 'gift', 'gina', 'girl', 'girlfriend', 'give', 'glass', 'glitch', 'glo', 'glömt', 'gm', 'go', 'goal', 'goalkeep', 'god', 'goe', 'good', 'googl', 'got', 'goteem', 'gotta', 'govern', 'grandfath', 'grandma', 'grandmoth', 'grandpar', 'grant', 'graviti', 'greasi', 'great', 'greatest', 'green', 'grenad', 'grey', 'grip', 'groov', 'group', 'grow', 'grown', 'gtr', 'guard', 'gud', 'guess', 'guest', 'guitar', 'gun', 'guy', 'gym', 'hack', 'hair', 'haircut', 'half', 'halitreph', 'halloween', 'ham', 'hamil', 'hand', 'handshak', 'happen', 'happi', 'happiest', 'happili', 'harass', 'harbor', 'harden', 'harder', 'harri', 'hasn', 'hate', 'haul', 'haven', 'havent', 'hawaii', 'hbo', 'he', 'head', 'headlin', 'headset', 'healthi', 'hear', 'heard', 'heart', 'heat', 'heater', 'heck', 'height', 'hello', 'help', 'here', 'hey', 'hick', 'hideo', 'high', 'hill', 'hit', 'hitch', 'hitler', 'hmm', 'hmmm', 'hockey', 'hold', 'hole', 'hollywood', 'holocaust', 'home', 'homeless', 'homemad', 'hometown', 'hope', 'hospit', 'hot', 'hour', 'hous', 'housewarm', 'huge', 'human', 'hut', 'hxi', 'hydetweet', 'hype', 'hyperx', 'ice', 'ich', 'idea', 'if', 'ifragmentix', 'ignor', 'illeg', 'im', 'imag', 'imagin', 'impact', 'impati', 'implod', 'impuls', 'impusl', 'inappropri', 'incident', 'incis', 'includ', 'increas', 'incred', 'india', 'indonesia', 'infant', 'infin', 'influenc', 'infowar', 'inhuman', 'innoc', 'insan', 'insid', 'inspect', 'inspir', 'instead', 'interdit', 'interest', 'internet', 'interview', 'introduc', 'invad', 'invest', 'investig', 'iphon', 'iq', 'iran', 'irl', 'ironi', 'island', 'isnt', 'it', 'itapth', 'item', 'ive', 'jalapeño', 'jame', 'jan', 'januari', 'japanes', 'jare', 'jason', 'jealou', 'jelli', 'jellyfish', 'jesu', 'jew', 'jimmi', 'jinp', 'jj', 'joast', 'job', 'johnson', 'johnston', 'jone', 'joust', 'joy', 'jump', 'justifi', 'kangaroo', 'keep', 'ken', 'keto', 'key', 'kick', 'kid', 'kill', 'killer', 'kim', 'king', 'kingdom', 'kinvm', 'kitti', 'klansman', 'klay', 'knew', 'kno', 'knock', 'know', 'known', 'kojima', 'kong', 'korea', 'krab', 'köpa', 'la', 'lack', 'ladi', 'ladybug', 'laid', 'lake', 'laker', 'land', 'landscap', 'laps', 'larg', 'largest', 'laser', 'last', 'late', 'latest', 'latrosisith', 'lazi', 'le', 'lead', 'leaf', 'leagu', 'learn', 'least', 'leav', 'lectur', 'left', 'leg', 'lego', 'lehrer', 'leichtenstein', 'leo', 'less', 'let', 'lethal', 'letter', 'liber', 'librari', 'life', 'lifelik', 'light', 'lightn', 'like', 'line', 'linemen', 'link', 'lion', 'list', 'littl', 'live', 'lmaoo', 'load', 'local', 'locat', 'logan', 'logo', 'lol', 'long', 'longer', 'longest', 'longtim', 'look', 'loos', 'loot', 'lord', 'lose', 'lost', 'lot', 'loudest', 'loudli', 'loung', 'lous', 'love', 'lover', 'lower', 'luca', 'machin', 'mad', 'made', 'maggi', 'magic', 'magma', 'magnet', 'main', 'maintain', 'make', 'maker', 'man', 'manag', 'mane', 'mang', 'mani', 'mantalk', 'march', 'marijuana', 'mario', 'mark', 'marri', 'marriag', 'marvel', 'mask', 'masspres', 'maui', 'maximum', 'may', 'mayor', 'mcdonald', 'mcferryfac', 'mcfuck', 'me', 'meal', 'mean', 'meet', 'mein', 'meirl', 'meirlmeirl', 'meme', 'men', 'mention', 'merch', 'messag', 'meta', 'metal', 'method', 'midafternoon', 'middl', 'midterm', 'might', 'mike', 'mile', 'milit', 'militari', 'milk', 'milki', 'million', 'milwauke', 'mine', 'minecraft', 'ministri', 'minnesota', 'minut', 'misconcept', 'miss', 'mod', 'model', 'mom', 'moment', 'momma', 'money', 'month', 'moon', 'morbidli', 'morn', 'moscow', 'mostli', 'mother', 'motif', 'motor', 'mount', 'mountain', 'mouth', 'movi', 'mph', 'mr', 'mrw', 'much', 'multipl', 'murder', 'music', 'musician', 'must', 'mycours', 'mysteri', 'nab', 'name', 'nasa', 'nation', 'natur', 'nearli', 'neatli', 'neckbeardi', 'ned', 'need', 'neg', 'neighborhood', 'neonazi', 'nest', 'net', 'neutral', 'never', 'new', 'newest', 'news', 'next', 'nfl', 'nh', 'ninenin', 'ninja', 'no', 'nobushi', 'nope', 'nordic', 'north', 'northern', 'notch', 'note', 'notsobrief', 'novacan', 'nra', 'nuclear', 'number', 'när', 'obama', 'obes', 'obvious', 'oc', 'ocean', 'octopu', 'ocx', 'odyssey', 'offens', 'offic', 'offici', 'often', 'og', 'oh', 'oil', 'old', 'oldest', 'oliv', 'olymp', 'one', 'onion', 'onli', 'oop', 'oper', 'oppon', 'opportun', 'optic', 'orang', 'order', 'oregon', 'orient', 'orwel', 'osaka', 'outplay', 'overtim', 'overwhelm', 'owner', 'oxid', 'pa', 'pace', 'paid', 'pain', 'paint', 'pant', 'panther', 'paper', 'parent', 'park', 'parkland', 'part', 'parti', 'partly', 'pass', 'past', 'pastel', 'pattern', 'peac', 'peak', 'pedal', 'pen', 'penguinz', 'penni', 'peopl', 'perfect', 'perman', 'person', 'pet', 'pewdiepi', 'phone', 'photo', 'pi', 'pic', 'pick', 'pictur', 'piec', 'pierc', 'pigment', 'pilot', 'pink', 'pirat', 'piss', 'pitch', 'pitt', 'pizza', 'place', 'play', 'player', 'playerscoachestrain', 'pleas', 'pm', 'poem', 'point', 'poke', 'polic', 'pool', 'poop', 'pop', 'popcorn', 'popper', 'portfolio', 'possess', 'post', 'poster', 'pot', 'potenti', 'poverti', 'power', 'ppl', 'practic', 'praetorian', 'prank', 'premier', 'prep', 'prequel', 'present', 'presid', 'press', 'pretestosteron', 'pretti', 'pretzel', 'price', 'princess', 'prison', 'probabl', 'process', 'produc', 'product', 'progun', 'protest', 'proud', 'psbattl', 'pubg', 'publish', 'puerto', 'pull', 'punch', 'pup', 'pupper', 'puppi', 'purchas', 'pure', 'purest', 'purpl', 'purpos', 'push', 'put', 'qual', 'qualifi', 'quarter', 'queen', 'quest', 'question', 'queu', 'quick', 'quietli', 'quot', 'rachel', 'raini', 'rais', 'raja', 'rake', 'rall', 'rand', 'raptor', 'raspberri', 'rcatprank', 'rd', 'reach', 'reaction', 'read', 'real', 'realilti', 'realiz', 'realli', 'reallif', 'rear', 'reason', 'receiv', 'recent', 'recept', 'recherch', 'recurs', 'red', 'reddit', 'redditor', 'reded', 'redneck', 'redondo', 'ref', 'regiment', 'rel', 'relat', 'relay', 'releas', 'religi', 'rememb', 'remov', 'rep', 'repair', 'repli', 'report', 'repost', 'republican', 'request', 'requir', 'reseach', 'research', 'resembl', 'resid', 'resign', 'resolut', 'restor', 'restrict', 'retir', 'retreat', 'reurop', 'review', 'reviv', 'rf', 'rgif', 'rico', 'ride', 'rider', 'rifl', 'right', 'riker', 'risk', 'rlatinopeopletwitt', 'rmalelivingspac', 'road', 'robert', 'rochest', 'rock', 'rocket', 'rodriguez', 'roger', 'roll', 'roller', 'roman', 'rome', 'ronni', 'room', 'rose', 'rover', 'royalti', 'rt', 'rthedonald', 'ruin', 'run', 'russiag', 'russian', 'rworldnew', 'réseau', 'sad', 'safe', 'safeti', 'said', 'salad', 'salti', 'sanchez', 'sasr', 'sat', 'save', 'saw', 'say', 'sayori', 'scam', 'scari', 'scenario', 'scene', 'sceneri', 'school', 'scienc', 'scientif', 'scob', 'scoreboard', 'screenshot', 'scroll', 'scrub', 'se', 'sea', 'seal', 'season', 'seat', 'sebastian', 'second', 'secret', 'seem', 'seen', 'seiz', 'sell', 'semest', 'semi', 'senat', 'send', 'sens', 'sent', 'separ', 'seri', 'serv', 'server', 'servic', 'session', 'set', 'setter', 'sew', 'sewer', 'sex', 'sexual', 'shake', 'shakespearean', 'share', 'shatter', 'shave', 'she', 'shed', 'shelter', 'shini', 'shinkansen', 'ship', 'shoot', 'short', 'shot', 'show', 'shown', 'showwithinashow', 'shrink', 'shut', 'sick', 'side', 'sidewalk', 'sign', 'silenc', 'sinc', 'singular', 'sink', 'sir', 'sister', 'six', 'size', 'skeleton', 'skeptic', 'sketch', 'skull', 'sleep', 'slow', 'slowli', 'small', 'smallest', 'smh', 'smile', 'snack', 'snek', 'snow', 'soccer', 'social', 'sold', 'soldier', 'solv', 'somebodi', 'someon', 'someth', 'sometim', 'son', 'soon', 'soooooo', 'sound', 'sourc', 'south', 'southern', 'space', 'spacex', 'spaghetti', 'spagoot', 'spainth', 'spam', 'speci', 'special', 'speech', 'speed', 'spend', 'spent', 'spici', 'spinner', 'spoil', 'sponsor', 'spooki', 'spot', 'spread', 'squirl', 'staff', 'stafford', 'stair', 'stan', 'stanc', 'stand', 'standards', 'stapleless', 'stapler', 'star', 'stare', 'start', 'starterpack', 'startup', 'state', 'station', 'stationoc', 'statu', 'stave', 'stay', 'steam', 'steel', 'step', 'steph', 'stephen', 'stick', 'still', 'stoke', 'stone', 'stop', 'storm', 'strang', 'stranger', 'strangl', 'stranz', 'straw', 'streak', 'street', 'strike', 'stuck', 'student', 'studi', 'stuf', 'stuff', 'stumbl', 'sua', 'suav', 'sub', 'subreddit', 'subscrib', 'substanti', 'subtitl', 'subway', 'success', 'suggest', 'summari', 'summer', 'sunday', 'sunris', 'super', 'superblood', 'supermass', 'supermodel', 'support', 'supremacist', 'sur', 'sure', 'surfac', 'surgeon', 'surgeri', 'surgicaldust', 'surpass', 'surreal', 'surrend', 'surveil', 'surviv', 'survivor', 'sweet', 'switch', 'sworn', 'syndrom', 'system', 'tabl', 'taboo', 'taika', 'take', 'talent', 'taliban', 'talk', 'tank', 'taquito', 'tarantino', 'tardigrad', 'td', 'tea', 'teach', 'teacher', 'team', 'teamth', 'tear', 'tech', 'technic', 'teeth', 'tehran', 'tell', 'temperatur', 'tesla', 'test', 'testifi', 'texa', 'text', 'th', 'thank', 'that', 'the', 'theatric', 'theori', 'therapi', 'there', 'thermal', 'theyr', 'thing', 'think', 'third', 'this', 'thompson', 'thoroughli', 'thought', 'thousand', 'three', 'throne', 'throughout', 'throw', 'ti', 'tick', 'ticket', 'tie', 'tifu', 'tiki', 'til', 'tim', 'time', 'tinder', 'tinderl', 'tini', 'tiniest', 'tip', 'toast', 'today', 'told', 'toler', 'tonight', 'took', 'toomeirlformeirl', 'tooth', 'top', 'tossdan', 'tot', 'total', 'touch', 'tough', 'tower', 'toy', 'trade', 'train', 'transport', 'transpos', 'trashpanda', 'trashtalk', 'treat', 'treatment', 'trebuchet', 'tree', 'trend', 'tri', 'trial', 'trick', 'trickshot', 'trilog', 'trip', 'trixi', 'trooper', 'troubl', 'truck', 'true', 'trump', 'trumpet', 'trumpgret', 'trust', 'truth', 'tube', 'tuesday', 'turiaf', 'turn', 'turnout', 'turtl', 'twain', 'tweet', 'twice', 'twin', 'twitter', 'two', 'tx', 'ubisoft', 'udiezlk', 'uk', 'ultim', 'ulyss', 'umbrella', 'uncl', 'underachiev', 'underappreci', 'undercov', 'underground', 'understand', 'uniform', 'union', 'univers', 'unknowingli', 'unlimit', 'updat', 'upon', 'upper', 'upset', 'upsid', 'upvot', 'us', 'usa', 'use', 'user', 'uss', 'vaccin', 'valley', 'vancouv', 'vase', 'vegan', 'venom', 'vermicelli', 'version', 'vet', 'video', 'view', 'vintag', 'viral', 'virtual', 'viru', 'visit', 'visitor', 'voorhe', 'vote', 'voxelart', 'vr', 'vs', 'waist', 'wait', 'waititi', 'waitress', 'wake', 'walk', 'wall', 'wallet', 'walmart', 'walton', 'wann', 'want', 'war', 'warf', 'warmer', 'warrior', 'washington', 'washroom', 'wasp', 'watch', 'water', 'way', 'wcgw', 'wdywt', 'wear', 'weather', 'weatherman', 'websit', 'week', 'weibo', 'weight', 'weiney', 'welcom', 'well', 'wenn', 'went', 'werent', 'wesley', 'westworld', 'wet', 'what', 'wheat', 'wheelchair', 'whichev', 'whistl', 'white', 'who', 'whole', 'wholesom', 'whose', 'widow', 'wife', 'wild', 'win', 'windmil', 'wing', 'winkleboss', 'winner', 'winter', 'without', 'wolf', 'wonder', 'wont', 'wood', 'wooden', 'woof', 'words', 'wore', 'work', 'worker', 'world', 'worst', 'worth', 'would', 'wp', 'wrote', 'wwii', 'wymyn', 'währenddessen', 'xi', 'xpost', 'ya', 'yeah', 'year', 'yearlong', 'yearold', 'yearsold', 'yellow', 'yep', 'yisss', 'york', 'yosemit', 'you', 'young', 'your', 'yourself', 'ypg', 'zip', 'ክቿሠነ', 'ክቿዪሀዐሁነ', 'ጌዪቿልጕጎክኗ'] \n",
      "\n",
      "There are 1823 such words\n"
     ]
    }
   ],
   "source": [
    "min_df = 1 # Set to 1 to get more data points\n",
    "cvt = CountVectorizer(min_df=min_df, preprocessor=cleaner)\n",
    "cvt.fit(df[\"titles\"])\n",
    "print('Words that showed up at least {} times:\\n'.format(min_df))\n",
    "print(cvt.get_feature_names(),'\\n')\n",
    "print('There are {} such words'.format(len(cvt.get_feature_names())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying `cvt.transform` to a `Series` we obtain a sparse matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix is:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The row 30 is:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Matrix is:\\n')\n",
    "cvt.transform(df['titles']).todense()\n",
    "print(\"\")\n",
    "print('The row {} is:\\n'.format(30))\n",
    "cvt.transform(df['titles']).todense()[30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use just `cvt.fit_transform` for the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix is:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The row 30 is:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvt = CountVectorizer(min_df=min_df, preprocessor=cleaner)\n",
    "print('Matrix is:\\n')\n",
    "cvt.fit_transform(df[\"titles\"]).todense()\n",
    "print(\"\")\n",
    "print('The row {} is:\\n'.format(30))\n",
    "cvt.fit_transform(df[\"titles\"]).todense()[30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot histograms to find how many words are on each bin (the $x$-axis shows observed word frequencies above 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25241da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(cvt.transform(df[\"titles\"]).sum(axis=0),columns=cvt.get_feature_names()).T.hist()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Word frequencies ', fontsize=12);\n",
    "plt.ylabel('Number of words per bin (log scale)', fontsize=12);\n",
    "plt.title('Histogram of word frequencies above 3', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22fb37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(cvt.transform(df[\"titles\"]).sum(axis=0),columns=cvt.get_feature_names()).T.hist()\n",
    "plt.xlabel('Word frequencies ', fontsize=12);\n",
    "plt.ylabel('Number of words per bin', fontsize=12);\n",
    "plt.title('Histogram of word frequencies above 3', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us now build a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>abomin</th>\n",
       "      <th>absolut</th>\n",
       "      <th>absolutelynotmeirl</th>\n",
       "      <th>accept</th>\n",
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       "      <th>account</th>\n",
       "      <th>accus</th>\n",
       "      <th>...</th>\n",
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       "      <th>yourself</th>\n",
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       "      <th>ክቿዪሀዐሁነ</th>\n",
       "      <th>ጌዪቿልጕጎክኗ</th>\n",
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      ],
      "text/plain": [
       "   abandon  abgeb  abomin  absolut  absolutelynotmeirl  accept  accident  \\\n",
       "0        0      0       0        0                   0       0         0   \n",
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       "4        0      0       0        0                   0       0         0   \n",
       "\n",
       "   accord  account  accus    ...     yosemit  you  young  your  yourself  ypg  \\\n",
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       "1       0        0      0    ...           0    0      0     0         0    0   \n",
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       "4       0        0      0    ...           0    0      0     0         0    0   \n",
       "\n",
       "   zip  ክቿሠነ  ክቿዪሀዐሁነ  ጌዪቿልጕጎክኗ  \n",
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       "\n",
       "[5 rows x 1823 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(512, 1823)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvt = CountVectorizer(min_df=min_df, preprocessor=cleaner)\n",
    "X_title = cvt.fit_transform(df[\"titles\"])\n",
    "X_thread = pd.DataFrame(X_title.todense(), \n",
    "                        columns=cvt.get_feature_names())\n",
    "\n",
    "X_thread.head()\n",
    "X_thread.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us examine the top 5 occuring words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most common words are:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "get     17\n",
       "like    16\n",
       "love    15\n",
       "new     15\n",
       "year    15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The most common words are:\\n')\n",
    "X_thread.sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now join both datasets, the one about subreddits and the one about threads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   binary  4PanelCringe  ACPocketCamp  AFL  ATBGE  AbandonedPorn  \\\n",
       "0       0             0             0    0      0              0   \n",
       "1       1             0             0    0      0              0   \n",
       "2       0             0             0    0      0              0   \n",
       "3       1             0             0    0      0              0   \n",
       "4       0             0             0    0      0              0   \n",
       "\n",
       "   AccidentalWesAnderson  Android  AnimalsBeingBros  AnimalsBeingDerps  \\\n",
       "0                      0        0                 0                  0   \n",
       "1                      0        0                 0                  0   \n",
       "2                      0        0                 0                  0   \n",
       "3                      0        0                 0                  0   \n",
       "4                      0        0                 0                  0   \n",
       "\n",
       "     ...     yosemit  you  young  your  yourself  ypg  zip  ክቿሠነ  ክቿዪሀዐሁነ  \\\n",
       "0    ...           0    0      0     0         0    0    0     0        0   \n",
       "1    ...           0    0      0     0         0    0    0     0        0   \n",
       "2    ...           0    0      0     0         0    0    0     0        0   \n",
       "3    ...           0    0      0     0         0    0    0     0        0   \n",
       "4    ...           0    0      0     0         0    0    0     0        0   \n",
       "\n",
       "   ጌዪቿልጕጎክኗ  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 2120 columns]"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(512, 2120)"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subred = pd.read_csv('subred_1.csv',index_col=0)\n",
    "df_all = pd.concat([df_subred,X_thread],axis=1)\n",
    "df_all.head()\n",
    "df_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv('df_threads_and_subreddits_new.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us drop eventual duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 2120)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(512, 1080)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.shape\n",
    "df_all = df_all.T.drop_duplicates().T\n",
    "df_all.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We apply the last two functions steps again:\n",
    "- Apply `rfscore2`\n",
    "- Apply `rfscore_3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 220 candidates, totalling 1100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    4.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   29.4s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1100 out of 1100 | elapsed:  3.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearch results\n",
      "The best parameters on the training data are:\n",
      " {'max_depth': 10, 'n_estimators': 30}\n",
      "best max_depth: 10\n",
      "best n_estimators: 30\n",
      "best score is: 0.56\n",
      "Is the prediction smaller (S) or larger (L) than the median:\n",
      "\n",
      "['S', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S']\n",
      "\n",
      "What were the probabilities of the each result above:\n",
      "\n",
      "Probabilities that the number of comments is smaller than the media for each observation are:\n",
      "\n",
      "[('S', 0.54), ('S', 0.46), ('S', 0.57), ('S', 0.5), ('S', 0.54), ('S', 0.54), ('S', 0.5), ('S', 0.54), ('S', 0.44), ('S', 0.41), ('S', 0.52), ('S', 0.54), ('S', 0.54), ('S', 0.55), ('S', 0.54), ('S', 0.57), ('S', 0.54), ('S', 0.44), ('S', 0.46), ('S', 0.47), ('S', 0.54), ('S', 0.48), ('S', 0.54), ('S', 0.48), ('S', 0.5), ('S', 0.5), ('S', 0.54), ('S', 0.46), ('S', 0.57), ('S', 0.48), ('S', 0.55), ('S', 0.52), ('S', 0.5), ('S', 0.52), ('S', 0.52), ('S', 0.54), ('S', 0.54), ('S', 0.58), ('S', 0.57), ('S', 0.49), ('S', 0.59), ('S', 0.66), ('S', 0.57), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.55), ('S', 0.56), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.48), ('S', 0.48), ('S', 0.46), ('S', 0.54), ('S', 0.52), ('S', 0.54), ('S', 0.52), ('S', 0.46), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.57), ('S', 0.54), ('S', 0.48), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.52), ('S', 0.54), ('S', 0.53), ('S', 0.52), ('S', 0.54), ('S', 0.57), ('S', 0.55), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.57), ('S', 0.5), ('S', 0.54), ('S', 0.53), ('S', 0.55), ('S', 0.55), ('S', 0.46), ('S', 0.54), ('S', 0.5), ('S', 0.55), ('S', 0.46), ('S', 0.46), ('S', 0.54), ('S', 0.55), ('S', 0.54), ('S', 0.55), ('S', 0.52), ('S', 0.52), ('S', 0.54), ('S', 0.54), ('S', 0.35), ('S', 0.54), ('S', 0.52), ('S', 0.39), ('S', 0.52), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.44), ('S', 0.51), ('S', 0.52), ('S', 0.57), ('S', 0.52), ('S', 0.44), ('S', 0.58), ('S', 0.59), ('S', 0.54), ('S', 0.48), ('S', 0.56), ('S', 0.51), ('S', 0.51), ('S', 0.57), ('S', 0.49), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.5), ('S', 0.52), ('S', 0.54), ('S', 0.41), ('S', 0.54), ('S', 0.54), ('S', 0.54), ('S', 0.53), ('S', 0.5), ('S', 0.54), ('S', 0.46), ('S', 0.54), ('S', 0.59), ('S', 0.55), ('S', 0.54), ('S', 0.54), ('S', 0.52), ('S', 0.53), ('S', 0.54), ('S', 0.5), ('S', 0.53), ('S', 0.54), ('S', 0.52), ('S', 0.34), ('S', 0.54), ('S', 0.5), ('S', 0.39), ('S', 0.55)]\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "Predicted Values   0   1\n",
      "Actual Values           \n",
      "0                 63  11\n",
      "1                 56  24\n",
      "Features and their importance:\n",
      "\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAFpCAYAAABgcnRFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAEw1JREFUeJzt3X2MZQd53/HvrzZ4bXaDA2skExevA7T2ri3sMhCcOIDcNiEgahK3UgKiNjLdunHSkspSSYMlU1UqRFXoC7RoaRpc5c0ldQRW4zQoresXwPWMvcuwtU2NvZYxUQiluDasXbF++sc9Ky7jWc8zO3fuGW++H2m0d84595xn787d75x75iVVhSRJHX9h7AEkSc8fRkOS1GY0JEltRkOS1GY0JEltRkOS1GY0JEltRkOS1GY0JEltRkOS1Hby2APM2s6dO2vXrl1jjyFJzytLS0vfqKoz1truhIvGrl27WFxcHHsMSXpeSfJIZztfnpIktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVLbCfej0b/+yBN87Or/OvYYkjRX13z80rkcxzMNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVLb6NFI8u+S7B57DknS2kb/gYVV9d6xZ5Ak9cztTCPJriT3J7khyReT/F6S05LcmmRh2OYtSe5JciDJHw/L3pRk//B2b5Id85pZkvT95n2m8ZeBq6rqziT/Hvj5oyuSnAF8AnhjVT2c5CXDqmuBa4b7bAeemvPMkqTBvK9pPFpVdw63fxO4ZGrdG4DbquphgKr65rD8TuDXkvx94PSq+u7KnSbZm2QxyeKTT31rE8eXpD/f5h2Neo73s8p6qupDwHuBU4EvJDl3lW32VdVCVS1s33b6LOeVJE2ZdzRekeTi4fbPAXdMrfs88KYk5wAcfXkqySurarmqPgwsAs+KhiRpPuYdjfuAK5J8EXgJ8G+PrqiqPwP2AjclOQDcOKx6X5IvDcsOA7fMeWZJ0mDeF8KfqaqrVyx789EbVXULK6JQVb84h7kkSQ2jf3OfJOn5Y25nGlV1CDh/XseTJM2eZxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqG/13hM/ay87ewTUfv3TsMSTphOSZhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSpzWhIktqMhiSp7eSxB5i1p750kPvOPW/sMaQt67z77xt7BD2PeaYhSWozGpKkNqMhSWozGpKkNqMhSWozGpKkNqMhSWozGpKkNqMhSWozGpKkNqMhSWozGpKkttGikeQPkpw+1vElSes32k+5raq3jnVsSdLxaUUjyXXAu4BHgW8AS8DjwF7ghcCDwLur6jtJPgkcBs4FzgbeA1wBXAzcVVVXDvs8BCwA24FbgDuAHwUeAy6rqsNJXgf8OvDtYf1PVdX5G/1LS5KOz5ovTyVZAC4HLgJ+hsl/9AA3VdXrquo1wH3AVVN3+0HgUuCXgJuBjwB7gAuSXLjKYV4NfKyq9gDfGo4H8BvA1VV1MXDkOWbcm2QxyeI3j3x3rb+SJOk4da5pXAJ8uqoOV9UTTCIAcH6S25MsMzkL2TN1n5urqoBl4E+rarmqngEOArtWOcbDVbV/uL0E7Bqud+yoqs8Ny3/7WANW1b6qWqiqhZecdML9XilJ2jI60cgxln8S+IWqugD4ILBtat3Tw5/PTN0++v5q/6tPb3Nk2OZYx5UkjaQTjTuAtyfZlmQ78LZh+Q7gT5K8gMmZxkxV1f8BnkjyhmHRz876GJKk9VnztZyqujvJZ4ADwCPAIpOL4NcBdw3LlplEZNauAj6R5NvArcNxJUkjyeTSwxobJdur6skkpwG3AXur6p5NH2447nD7/cCZVfUPnus+5287tT61a9dmjyY9b513/31jj6AtKMlSVS2stV33qvG+JLuZXLe4YR7BGLwtyS8zmfMR4Mo5HVeStIpWNKrqnZs9yDGOeyNw4xjHliQ9mz97SpLUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUdsL98olt5+/hvMXFsceQpBOSZxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpDajIUlqMxqSpLaTxx5g1g7+74NccMMFY48hbRnLVyyPPYJOIJ5pSJLajIYkqc1oSJLajIYkqc1oSJLajIYkqc1oSJLajIYkqc1oSJLajIYkqc1oSJLajIYkqc1oSJLajIYkqW2u0UhyXZL7k3w2ye8kuTbJrUkWhvU7kxwabl+Z5KYkf5jkfyX51XnOKkl6trn9Po0hDJcDFw3HvQdYWuNuFw7bPw08kORfV9WjmzqoJOmY5nmmcQnw6ao6XFVPADc37vPHVfV4VT0F/E/g7NU2SrI3yWKSxSNPHJnhyJKkafOMRo6x/LtTc2xbse7pqdtHOMaZUVXtq6qFqlo4acdJG5tSknRM84zGHcDbk2xLsh1427D8EPDa4fbfnOM8kqR1mls0qupu4DPAAeAmYBF4HPjnwN9L8jlg57zmkSStX6pqfgdLtlfVk0lOA24D9lbVPbM8xqnnnFqvuv5Vs9yl9Ly2fMXy2CPoeSDJUlUtrLXd3L56arAvyW4m1y5umHUwJEmba67RqKp3zvN4kqTZ8jvCJUltRkOS1GY0JEltRkOS1GY0JEltRkOS1GY0JEltRkOS1GY0JElt8/4xIptuz0v3sHjF4thjSNIJyTMNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktZ089gAz97V74foXjz2FNB/XPz72BPpzxjMNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVKb0ZAktRkNSVLbzKORZFeS+5J8IsnBJH+U5NQkr0zyh0mWktye5NwkJyV5KBOnJ3kmyRuH/dye5FVJ3pRk//B2b5Ids55ZktSzWWcarwY+VlV7gG8BlwP7gF+sqtcC1wL/pqqOAF8GdgOXAEvAjyc5BTirqh4ctr2mqi4Efhw4vEkzS5LWsFm/T+Phqto/3F4CdgE/CnwqydFtThn+vB14I3AO8M+AvwP8d+DuYf2dwK8l+S3gpqr66sqDJdkL7AV4xYuzcrUkaUY260zj6anbR4CXAN+qqgun3s4b1t/O5Azi9cAfAKcDbwZuA6iqDwHvBU4FvpDk3JUHq6p9VbVQVQtnnGY0JGmzzOtC+P8FHk7ytwCGaxivGdbdxeQs5JmqegrYD/xdJjEhySurarmqPgwsAs+KhiRpPub51VPvAq5KcgA4CFwGUFVPA48CXxi2ux3YASwP778vyZeG+x0GbpnjzJKkKamqsWeYqYWXn1SLe7ePPYY0H/6OcM1IkqWqWlhrO79PQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUtlm/T2M8L78Irl8cewpJOiF5piFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqS2k8ceYNaWH3ucXe//z2OPIa3q0IfeNvYI0oZ4piFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJajMakqQ2oyFJapvZT7lNch3wLuBR4BvAEvA4sBd4IfAg8O6q+k6STwKHgXOBs4H3AFcAFwN3VdWVwz5/AvggcArwFeA9VfXkrGaWJK3PTM40kiwAlwMXAT8DLAyrbqqq11XVa4D7gKum7vaDwKXALwE3Ax8B9gAXJLkwyU7gA8Bfq6q/AiwC/3AW80qSjs+szjQuAT5dVYcBktw8LD8/yT8FTge2A/9l6j43V1UlWQb+tKqWh/seBHYBZwG7gTuTwORs5fOrHTzJXiZnNJz0A2fM6K8kSVppVtHIMZZ/EnhHVR1IciXw5ql1Tw9/PjN1++j7JwNHgM9W1c+tdfCq2gfsAzjlzFfXegaXJPXN6kL4HcDbk2xLsh04+uvJdgB/kuQFTK53rMcXgB9L8iqAJKcl+UszmleSdBxmcqZRVXcn+QxwAHiEyfWHx4HrgLuGZctMItLd558NZye/k+SUYfEHgC/PYmZJ0vqlajav5iTZXlVPJjkNuA3YW1X3zGTn63DKma+uM6/4F/M+rNTi7wjXVpVkqaoW1tpuZl9yC+xLshvYBtwwRjAkSZtrZtGoqnfOal+SpK3J7wiXJLUZDUlSm9GQJLUZDUlSm9GQJLUZDUlSm9GQJLUZDUlSm9GQJLXN8seIbAkX/NCLWfTn+0jSpvBMQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW1GQ5LUZjQkSW2pqrFnmKkkTwAPjD3HKnYC3xh7iBW24kzgXOuxFWcC51qvrTDX2VV1xlobnXC/uQ94oKoWxh5ipSSLW22urTgTONd6bMWZwLnWa6vOtRpfnpIktRkNSVLbiRiNfWMPcAxbca6tOBM413psxZnAudZrq871LCfchXBJ0uY5Ec80JEmbZEtHI8lbkjyQ5MEk719l/SlJbhzW35Vk19S6Xx6WP5DkJ7v7HHGuQ0mWk+xPsjjPuZK8NMl/S/Jkko+uuM9rh7keTPKvkmQLzHTrsM/9w9vL1jPTBuf660mWhsdkKcmlU/fZ0GO1iXON+Xi9fuq4B5L8dHefI8002vNwav0rho/7a7v7nKuq2pJvwEnAV4AfBl4IHAB2r9jm54GPD7d/FrhxuL172P4U4JxhPyd19jnGXMO6Q8DOkR6vFwGXAFcDH11xn/8BXAwEuAX4qS0w063AwkiP1UXAy4fb5wOPzeKx2uS5xny8TgNOHm6fCXydyZf6b+i5uBkzjf08nFr/n4BPAdd29znPt618pvF64MGqeqiq/h/wu8BlK7a5DLhhuP17wF8dPru7DPjdqnq6qh4GHhz219nnGHPNwnHPVVXfrqo7gKemN05yJvADVfX5mnz0/gfgHWPONCMbmeveqvrasPwgsG34zHGjj9WmzLXO42/GXN+pqu8Oy7cBRy+ibvS5uBkzzcJG/n8gyTuAh5j8G65nn3OzlaPxQ8CjU+9/dVi26jbDB8HjwEuf476dfY4xF0w+cP9oeGlh7zpn2uhcz7XPr66xz3nPdNRvDC8hXHccLwPNaq7LgXur6mk2/lht1lxHjfZ4JfmRJAeBZeDqYf1Gn4ubMROM+DxM8iLgHwEfPI59zs1W/o7w1T6wV35GcKxtjrV8tUiu97OMzZgL4Meq6mvD682fTXJ/Vd02p7k2ss/nshkzAbyrqh5LsoPJqfy7mXxmP7e5kuwBPgz8xDr2OcZcMPLjVVV3AXuSnAfckOSW5j7nOlNVPcW4z8MPAh+pqidXdH0WH1szs5XPNL4K/MWp988CvnasbZKcDLwY+OZz3LezzzHm4uhLC1X1deD3Wf/LVhuZ67n2edYa+5z3TFTVY8OfTwC/zZwfqyRnMfk3+ttV9ZWp7TfyWG3WXKM/XlNz3Ad8m8k1l40+FzdjprGfhz8C/GqSQ8D7gH+c5Bea+5yfsS6mrPXG5CzoISYXjI9e/NmzYptr+P4LSv9xuL2H77/g/BCTi0lr7nOkuV4E7Bi2eRHwOeAt85prav2VPPui893AG/jexd23jjnTsM+dw+0XMHlN+Oo5/huePmx/+Sr7Pe7HarPm2gKP1zl87yLz2Uz+s9vZ2ecIM22J5+Gw/Hq+dyF8w/9vzfJtlIOu4x/grcCXmXzlwK8My/4J8DeG29uYfJXBg0y+cuWHp+77K8P9HmDqq1hW2+fYczH5qogDw9vBkeY6xOSznSeZfGaze1i+AHxp2OdHGb4hdKyZhifzEvDF4bH6lwxfgTaPuYAPMPnMdP/U28tm8Vhtxlxb4PF693Dc/cA9wDtm9Vyc9Uxsgefh1D6uZ4jGLB6rWb75HeGSpLatfE1DkrTFGA1JUpvRkCS1GQ1JUpvRkCS1GQ1JUpvRkCS1GQ1JUtv/BxJTK1pAdF78AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2533ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_estimators = list(range(20,220,10))\n",
    "max_depth = list(range(2, 22, 2)) + [None]\n",
    "\n",
    "rfscore2(df_all,'binary',0.3,n_estimators,max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1100 candidates, totalling 5500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    3.5s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   26.8s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2442 tasks      | elapsed:  6.3min\n",
      "[Parallel(n_jobs=-1)]: Done 3192 tasks      | elapsed:  8.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4042 tasks      | elapsed: 11.0min\n",
      "[Parallel(n_jobs=-1)]: Done 4992 tasks      | elapsed: 14.3min\n",
      "[Parallel(n_jobs=-1)]: Done 5500 out of 5500 | elapsed: 17.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearch results\n",
      "The best parameters on the training data are:\n",
      " {'max_depth': 4, 'min_samples_split': 6, 'n_estimators': 30}\n",
      "best max_depth: 4\n",
      "best n_estimators: 30\n",
      "best score is: 0.68\n",
      "Is the prediction smaller (S) or larger (L) than the median:\n",
      "\n",
      "['S', 'L', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'L', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S', 'S', 'S', 'S', 'L', 'S', 'S', 'S']\n",
      "\n",
      "What were the probabilities of the each result above:\n",
      "\n",
      "Probabilities that the number of comments is smaller than the media for each observation are:\n",
      "\n",
      "[('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.49), ('S', 0.53), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.49), ('S', 0.49), ('S', 0.51), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.46), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.46), ('S', 0.53), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.53), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.53), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.53), ('S', 0.44), ('S', 0.51), ('S', 0.62), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.53), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.54), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.46), ('S', 0.44), ('S', 0.51), ('S', 0.48), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.53), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.48), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.4), ('S', 0.47), ('S', 0.49), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.5), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.61), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.52), ('S', 0.54), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.52), ('S', 0.51), ('S', 0.56), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.61), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.44), ('S', 0.51), ('S', 0.49), ('S', 0.47), ('S', 0.44), ('S', 0.46), ('S', 0.47), ('S', 0.48), ('S', 0.46), ('S', 0.49), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.43), ('S', 0.49), ('S', 0.46), ('S', 0.49), ('S', 0.49), ('S', 0.49), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.47), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.44), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.49), ('S', 0.47), ('S', 0.44), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.47), ('S', 0.5), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.49), ('S', 0.47), ('S', 0.51), ('S', 0.46), ('S', 0.47), ('S', 0.49), ('S', 0.49), ('S', 0.51), ('S', 0.44), ('S', 0.55), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.46), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.49), ('S', 0.62), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.48), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.53), ('S', 0.51), ('S', 0.5), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.46), ('S', 0.49), ('S', 0.51), ('S', 0.47), ('S', 0.52), ('S', 0.52), ('S', 0.51), ('S', 0.41), ('S', 0.53), ('S', 0.38), ('S', 0.47), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.44), ('S', 0.54), ('S', 0.51), ('S', 0.5), ('S', 0.46), ('S', 0.56), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.53), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.42), ('S', 0.51), ('S', 0.46), ('S', 0.49), ('S', 0.52), ('S', 0.53), ('S', 0.61), ('S', 0.44), ('S', 0.51), ('S', 0.51), ('S', 0.62), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.52), ('S', 0.32), ('S', 0.52), ('S', 0.46), ('S', 0.47), ('S', 0.51), ('S', 0.42), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.59), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.49), ('S', 0.52), ('S', 0.54), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.42), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.48), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.46), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.44), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.52), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.52), ('S', 0.51), ('S', 0.53), ('S', 0.51), ('S', 0.52), ('S', 0.53), ('S', 0.42), ('S', 0.49), ('S', 0.49), ('S', 0.51), ('S', 0.49), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.37), ('S', 0.52), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.53), ('S', 0.52), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.48), ('S', 0.51), ('S', 0.52), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.47), ('S', 0.46), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.44), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.46), ('S', 0.44), ('S', 0.48), ('S', 0.5), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.47), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.51), ('S', 0.54), ('S', 0.49), ('S', 0.51), ('S', 0.51), ('S', 0.51)]\n",
      "\n",
      "Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Values    0    1\n",
      "Actual Values             \n",
      "0                 232   24\n",
      "1                 142  114\n",
      "Features and their importance:\n",
      "\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a244e9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfscore_3(df_all,'binary',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8) Model Building V:  Predicting comments using a logistic regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_all.drop('binary', axis=1)\n",
    "y = df_all['binary']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5324675324675324\n"
     ]
    }
   ],
   "source": [
    "y_pred_class = logreg.predict(X_test)\n",
    "from sklearn import metrics\n",
    "print(metrics.accuracy_score(y_test, y_pred_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_all.drop('binary', axis=1)\n",
    "y = df_all['binary']\n",
    "logregCV = LogisticRegressionCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression CV Score:\t0.568 ± 0.029\n"
     ]
    }
   ],
   "source": [
    "s_CV = cross_val_score(logregCV, X, y, cv=5, n_jobs=-1)\n",
    "print(\"{} Score:\\t{:0.3} ± {:0.3}\".format(\"Logistic Regression CV\", s_CV.mean().round(3), s_CV.std().round(3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executive Summary\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions:\n",
    "\n",
    "1) The accuracy score is quite low, just slighly higher than the baseline when only the subreddits are considered.\n",
    "\n",
    "2) With the parameters ranges in the `GridSearchCV` fixed, including words in the threads in addition to only subreddits dummies in the set of predictors increased the accuracy of the model compared with the case where only the latter was used, but only slighly.\n",
    "\n",
    "3) The most significant gain came when we included a new hyperparameter, namely, `'min_samples_split` in the model and kept threads' words and subredddits as predictors. The accuracy in this case was close to 0.7.\n",
    "\n",
    "4) The most common words in the threads are 'get','like' and 'love'.\n",
    "\n",
    "5) The most important features in our best model were 'game', 'guy', both from the threads. \n",
    "\n",
    "6) In a nutshell our best model was obtained considering both subredddits as dummies and words on the threads and keeping `max_depth`,`min_samples_split` and `n_estimators` as parameters to be scanned by the `GridSearchCV`\n",
    "\n",
    "7) The obvious next step is to increase the number of scraped pages. Because our data was limited, we could not set a minimum frequency to the words. Taking only words that occur more frequently can help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## References\n",
    "\n",
    "[1] https://github.com/myarolin/Natural_Language_Processing_with_Reddit"
   ]
  }
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