NLP_MeyersBriggs_PredictiveModel.ipynb

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  "metadata": {
    "colab": {
      "name": "NLP_MeyersBriggs_PredictiveModel.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyNIa5wwr6gxlrYPrP23EK8X",
      "include_colab_link": true
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  "cells": [
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      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kvinne-anc/NLP-Meyers-Briggs-Project/blob/main/NLP_MeyersBriggs_PredictiveModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K08W580ApVVS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f43ea1ba-76f4-40f2-c792-9d99d3105d11"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style(\"whitegrid\"); \n",
        "plt.rcParams['figure.dpi'] = 360\n",
        "from wordcloud import WordCloud \n",
        "import spacy  \n",
        "from spacy.lang.en import English  \n",
        "nlp = English()  \n",
        "from collections import Counter \n",
        "!pip install category_encoders==2.*\n",
        "!pip install pandas-profiling==2.*\n",
        "!pip install nltk\n",
        "import zipfile\n",
        "import shutil\n",
        "from glob import glob\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sys\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "import category_encoders as ce\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import time\n",
        "from nltk.corpus import stopwords \n",
        "from nltk import word_tokenize\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from bs4 import BeautifulSoup as soup\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords \n",
        "from nltk import word_tokenize\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize\n",
        "!pip install num2words\n",
        "import num2words\n",
        "from nltk import ne_chunk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "Successfully installed confuse-1.3.0 htmlmin-0.1.12 imagehash-4.1.0 pandas-profiling-2.9.0 phik-0.10.0 tangled-up-in-unicode-0.0.6 tqdm-4.51.0 visions-0.5.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tqdm"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.15.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "Collecting num2words\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/eb/a2/ea800689730732e27711c41beed4b2a129b34974435bdc450377ec407738/num2words-0.5.10-py3-none-any.whl (101kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 2.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.6/dist-packages (from num2words) (0.6.2)\n",
            "Installing collected packages: num2words\n",
            "Successfully installed num2words-0.5.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig2pjATMt6_x"
      },
      "source": [
        "import json\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UghpTf7JrOq6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "3d8f151c-7e74-4540-97f5-c56b9f757e98"
      },
      "source": [
        "!unzip /content/MeyersBriggsML.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/MeyersBriggsML.zip\n",
            "  inflating: mbti_1.csv              \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4T9mtqXrWzC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "outputId": "309ddc1e-4912-4d76-8d7e-6ef44b5486c3"
      },
      "source": [
        "MB = pd.read_csv('/content/mbti_1.csv')\n",
        "MB.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>type</th>\n",
              "      <th>posts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>'http://www.youtube.com/watch?v=qsXHcwe3krw|||...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTP</td>\n",
              "      <td>'I'm finding the lack of me in these posts ver...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'Good one  _____   https://www.youtube.com/wat...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type                                              posts\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...\n",
              "2  INTP  'Good one  _____   https://www.youtube.com/wat..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeQzcw01rZbr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "47a0cf22-fa26-4e95-8a52-134efd2f9f04"
      },
      "source": [
        "[p.split('|||') for p in MB.head(2).posts.values]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[\"'http://www.youtube.com/watch?v=qsXHcwe3krw\",\n",
              "  'http://41.media.tumblr.com/tumblr_lfouy03PMA1qa1rooo1_500.jpg',\n",
              "  'enfp and intj moments  https://www.youtube.com/watch?v=iz7lE1g4XM4  sportscenter not top ten plays  https://www.youtube.com/watch?v=uCdfze1etec  pranks',\n",
              "  'What has been the most life-changing experience in your life?',\n",
              "  'http://www.youtube.com/watch?v=vXZeYwwRDw8   http://www.youtube.com/watch?v=u8ejam5DP3E  On repeat for most of today.',\n",
              "  'May the PerC Experience immerse you.',\n",
              "  'The last thing my INFJ friend posted on his facebook before committing suicide the next day. Rest in peace~   http://vimeo.com/22842206',\n",
              "  \"Hello ENFJ7. Sorry to hear of your distress. It's only natural for a relationship to not be perfection all the time in every moment of existence. Try to figure the hard times as times of growth, as...\",\n",
              "  '84389  84390  http://wallpaperpassion.com/upload/23700/friendship-boy-and-girl-wallpaper.jpg  http://assets.dornob.com/wp-content/uploads/2010/04/round-home-design.jpg ...',\n",
              "  'Welcome and stuff.',\n",
              "  'http://playeressence.com/wp-content/uploads/2013/08/RED-red-the-pokemon-master-32560474-450-338.jpg  Game. Set. Match.',\n",
              "  \"Prozac, wellbrutin, at least thirty minutes of moving your legs (and I don't mean moving them while sitting in your same desk chair), weed in moderation (maybe try edibles as a healthier alternative...\",\n",
              "  \"Basically come up with three items you've determined that each type (or whichever types you want to do) would more than likely use, given each types' cognitive functions and whatnot, when left by...\",\n",
              "  'All things in moderation.  Sims is indeed a video game, and a good one at that. Note: a good one at that is somewhat subjective in that I am not completely promoting the death of any given Sim...',\n",
              "  'Dear ENFP:  What were your favorite video games growing up and what are your now, current favorite video games? :cool:',\n",
              "  'https://www.youtube.com/watch?v=QyPqT8umzmY',\n",
              "  'It appears to be too late. :sad:',\n",
              "  \"There's someone out there for everyone.\",\n",
              "  'Wait... I thought confidence was a good thing.',\n",
              "  \"I just cherish the time of solitude b/c i revel within my inner world more whereas most other time i'd be workin... just enjoy the me time while you can. Don't worry, people will always be around to...\",\n",
              "  \"Yo entp ladies... if you're into a complimentary personality,well, hey.\",\n",
              "  '... when your main social outlet is xbox live conversations and even then you verbally fatigue quickly.',\n",
              "  'http://www.youtube.com/watch?v=gDhy7rdfm14  I really dig the part from 1:46 to 2:50',\n",
              "  'http://www.youtube.com/watch?v=msqXffgh7b8',\n",
              "  'Banned because this thread requires it of me.',\n",
              "  'Get high in backyard, roast and eat marshmellows in backyard while conversing over something intellectual, followed by massages and kisses.',\n",
              "  'http://www.youtube.com/watch?v=Mw7eoU3BMbE',\n",
              "  'http://www.youtube.com/watch?v=4V2uYORhQOk',\n",
              "  'http://www.youtube.com/watch?v=SlVmgFQQ0TI',\n",
              "  \"Banned for too many b's in that sentence. How could you! Think of the B!\",\n",
              "  'Banned for watching movies in the corner with the dunces.',\n",
              "  'Banned because Health class clearly taught you nothing about peer pressure.',\n",
              "  'Banned for a whole host of reasons!',\n",
              "  'http://www.youtube.com/watch?v=IRcrv41hgz4',\n",
              "  \"1) Two baby deer on left and right munching on a beetle in the middle.  2) Using their own blood, two cavemen diary today's latest happenings on their designated cave diary wall.  3) I see it as...\",\n",
              "  'a pokemon world  an infj society  everyone becomes an optimist',\n",
              "  '49142',\n",
              "  'http://www.youtube.com/watch?v=ZRCEq_JFeFM',\n",
              "  'http://discovermagazine.com/2012/jul-aug/20-things-you-didnt-know-about-deserts/desert.jpg',\n",
              "  'http://oyster.ignimgs.com/mediawiki/apis.ign.com/pokemon-silver-version/d/dd/Ditto.gif',\n",
              "  'http://www.serebii.net/potw-dp/Scizor.jpg',\n",
              "  \"Not all artists are artists because they draw. It's the idea that counts in forming something of your own... like a signature.\",\n",
              "  \"Welcome to the robot ranks, person who downed my self-esteem cuz I'm not an avid signature artist like herself. :proud:\",\n",
              "  'Banned for taking all the room under my bed. Ya gotta learn to share with the roaches.',\n",
              "  'http://www.youtube.com/watch?v=w8IgImn57aQ',\n",
              "  'Banned for being too much of a thundering, grumbling kind of storm... yep.',\n",
              "  \"Ahh... old high school music I haven't heard in ages.   http://www.youtube.com/watch?v=dcCRUPCdB1w\",\n",
              "  \"I failed a public speaking class a few years ago and I've sort of learned what I could do better were I to be in that position again. A big part of my failure was just overloading myself with too...\",\n",
              "  \"I like this person's mentality. He's a confirmed INTJ by the way. http://www.youtube.com/watch?v=hGKLI-GEc6M\",\n",
              "  \"Move to the Denver area and start a new life for myself.'\"],\n",
              " [\"'I'm finding the lack of me in these posts very alarming.\",\n",
              "  \"Sex can be boring if it's in the same position often. For example me and my girlfriend are currently in an environment where we have to creatively use cowgirl and missionary. There isn't enough...\",\n",
              "  \"Giving new meaning to 'Game' theory.\",\n",
              "  \"Hello *ENTP Grin*  That's all it takes. Than we converse and they do most of the flirting while I acknowledge their presence and return their words with smooth wordplay and more cheeky grins.\",\n",
              "  'This + Lack of Balance and Hand Eye Coordination.',\n",
              "  \"Real IQ test I score 127. Internet IQ tests are funny. I score 140s or higher.  Now, like the former responses of this thread I will mention that I don't believe in the IQ test. Before you banish...\",\n",
              "  \"You know you're an ENTP when you vanish from a site for a year and a half, return, and find people are still commenting on your posts and liking your ideas/thoughts. You know you're an ENTP when you...\",\n",
              "  'http://img188.imageshack.us/img188/6422/6020d1f9da6944a6b71bbe6.jpg',\n",
              "  'http://img.adultdvdtalk.com/813a0c6243814cab84c51',\n",
              "  'I over think things sometimes. I go by the old Sherlock Holmes quote.  Perhaps, when a man has special knowledge and special powers like my  own, it rather encourages him to seek a complex...',\n",
              "  'cheshirewolf.tumblr.com  So is I :D',\n",
              "  '400,000+  post',\n",
              "  \"Not really; I've never thought of E/I or J/P as real functions.  I judge myself on what I use. I use Ne and Ti as my dominates. Fe for emotions and rarely Si. I also use Ni due to me strength...\",\n",
              "  'You know though. That was ingenious. After saying it I really want to try it and see what happens with me playing a first person shooter in the back while we drive around. I want to see the look on...',\n",
              "  \"out of all of them the rock paper one is the best. It makes me lol.  You guys are lucky :D I'm really high up on the tumblr system.\",\n",
              "  \"So did you hear about that new first person shooter game? I've been rocking the hell out of the soundtrack on my auto sound equipment that will shake the heavens. We managed to put a couple PS3's in...\",\n",
              "  'No; The way he connected things was very Ne. Ne dominates are just as aware of their environments as Se dominates.  Example: Shawn Spencer or Patrick Jane; Both ENTPs.',\n",
              "  \"Well charlie I will be the first to admit I do get jealous like you do. I chalk it up to my 4w3 heart mixed with my dominate 7w8. 7s and 8s both like to be noticed. 4's like to be known (not the same...\",\n",
              "  \";D I'll upload the same clip with the mic away from my mouth. Than you won't hear anything.  Ninja Assassin style but with splatter.\",\n",
              "  'Tik Tok is a really great song. As long as you can mental block out the singer. I love the beat it makes me bounce.',\n",
              "  'drop.io v1swck0  :D Mic really close to my mouth and smokin aces: assassins ball playing in the background.',\n",
              "  \"Sociable =/= extrovert; I'm an extrovert and I'm not sociable. :)\",\n",
              "  \"Sherlock in the movie was an ENTP. Normally he's played as a EXTJ. In the books he's an ESTJ.  As I said. The movie looked good except for it being called sherlock holmes.\",\n",
              "  'http://i817.photobucket.com/albums/zz96/kamioo/Dirtywinch.png',\n",
              "  \"Oh, I never had fear of kissing a guy. I will kiss an animal too. So there was nothing to vanish. Just personal taste and me not liking it.  The guy I kissed didn't know me. It was one of those...\",\n",
              "  \"Sounds pretty much like my area and what I'm going through right now trying to figure out which way I want to take my life. I want to do so many things. The biggest problem is that I know if I don't...\",\n",
              "  ';D I was operating under the impression that you were female. I never looked at your boxy. Okay, I help out my gay friends all the time and one of them has developed a little crush on me. I get red...',\n",
              "  \"T_T You just described me  and I'm living the worst nightmare. I'm trapped in one place with one one around. Only dull woods. If I was a serial killer this would be the perfect place but sadly I'm...\",\n",
              "  'TBH, and biased, sounds like a shadowed INFP. I think maybe he was hurt and turned ESTJ. I can tell because he has some of the typical INFP traits left over.',\n",
              "  \"*Checks list* I'm sorry. It seems that you have came at a bad time. We've already reached our quota of INFJs. However, being you're female and I like females I will make you a deal. I will kick one...\",\n",
              "  \"I'm ANTP (Leaning toward E). I'm easy for both ENTPs and INTPs to identify with. :)\",\n",
              "  \"I also imagine ENTP's interrogations would go a little bit like Jack's from 24 except more mechanical. Rigging up shock treatment equipment in an abandoned building out of an old car batty, jumper...\",\n",
              "  \"It was a compliment :) Trust me. I'm just as psychopathic :D except I have emoticons. They're just weird ones. Like laughing when I get hurt or at people running themselves over with their lawn mower...\",\n",
              "  'http://i817.photobucket.com/albums/zz96/kamioo/Thunderstorm.pnghttp://i817.photobucket.com/albums/zz96/kamioo/Thunderstormbw.png http://i817.photobucket.com/albums/zz96/kamioo/Cosmicstorm.png',\n",
              "  \"No. It's like a theme for where I live and that is why I know it by heart.   http://www.youtube.com/watch?v=j5W73HaVQBg\",\n",
              "  \"and I usual don't leave until the thing ends. But in the mean time. In between times. You work your thing. I'll work mine :D  ;D I'm the MBP; Pleasure to meet you.\",\n",
              "  'Damn, need to trust my instincts more I would have been closer I was going to say INFP.',\n",
              "  'EXFP? Leaning toward S with the way she responded.  :D My friends, even my gay and lesbian ones, always come to me for advice.',\n",
              "  \"I bow to my entp masters ENTPs are so great. If it wasn't for ENTPs I wouldn't have been able to build what I'm building  Duck Duck  Duck  Shotgun\",\n",
              "  'What? Me? I never do that >.> <.<',\n",
              "  \"Because its hard to be sad about losing someone you like when you knew you were right and give yourself a big pat on the back because you're awesome and always correct.\",\n",
              "  \"Oh, you don't have to tell me that most of them are stupid. I know this. That is why I play with them and it makes me laugh. :D As I'm going to take Neuropsychology and I have a few psychologist...\",\n",
              "  \":D I'm a Nightowl. I wake up between 6-7pm and stay awake till 10-11:30am.\",\n",
              "  'Personal opinion backed by theory would suggest that INTPs are the most socially difficult. While INTJs can be socially indifferent but they will also use social situations if the the need arises....',\n",
              "  \"Personal stocks that I have on my desktop that I've downloaded from random stock sites and stock photobuckets.\",\n",
              "  \"I'll tell you when I open photoshop.  :) Glad you like it static.\",\n",
              "  ':D Thanks.',\n",
              "  'http://i817.photobucket.com/albums/zz96/kamioo/Deathgrip.png http://i817.photobucket.com/albums/zz96/kamioo/Deathgripbw.png  Made for a friend. Several hours of work. I constructed every line by...',\n",
              "  \":) Static: http://i817.photobucket.com/albums/zz96/kamioo/Statickitten.png  I'll have to get to your avatar later if one of my fellow teammates doesn't.\",\n",
              "  \"Psychologist don't keep me around long enough to diagnosis me. I like to toy with them. What I have diagnosis myself with and had a few psychologist friends (+ a few other friends) tell me I have is...'\"]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vu1GTD4iO-j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "b20c0526-cd3e-4932-b8db-91261ec59cc4"
      },
      "source": [
        "view_type = MB['type'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(13,5))\n",
        "sns.barplot(view_type.index, view_type.values, alpha=0.8)\n",
        "plt.ylabel('Frequency', fontsize=12)\n",
        "plt.xlabel('Personality', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1luqYejzyj1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "2febaee7-87a6-4328-b618-594b3921e33b"
      },
      "source": [
        "def get_types(row):\n",
        "    t=row['type']\n",
        "\n",
        "    I = 0; N = 0\n",
        "    T = 0; J = 0\n",
        "    \n",
        "    if t[0] == 'I': I = 1\n",
        "    elif t[0] == 'E': I = 0\n",
        "    else: print('I-E incorrect')\n",
        "        \n",
        "    if t[1] == 'N': N = 1\n",
        "    elif t[1] == 'S': N = 0\n",
        "    else: print('N-S incorrect')\n",
        "        \n",
        "    if t[2] == 'T': T = 1\n",
        "    elif t[2] == 'F': T = 0\n",
        "    else: print('T-F incorrect')\n",
        "        \n",
        "    if t[3] == 'J': J = 1\n",
        "    elif t[3] == 'P': J = 0\n",
        "    else: print('J-P incorrect')\n",
        "    return pd.Series( {'IE':I, 'NS':N , 'TF': T, 'JP': J }) \n",
        "\n",
        "MB = MB.join(MB.apply (lambda row: get_types (row),axis=1))\n",
        "MB.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>type</th>\n",
              "      <th>posts</th>\n",
              "      <th>IE</th>\n",
              "      <th>NS</th>\n",
              "      <th>TF</th>\n",
              "      <th>JP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>'http://www.youtube.com/watch?v=qsXHcwe3krw|||...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTP</td>\n",
              "      <td>'I'm finding the lack of me in these posts ver...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'Good one  _____   https://www.youtube.com/wat...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP,   I enjoyed our conversation the o...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired.|||That's another silly misconce...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type                                              posts  IE  NS  TF  JP\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...   1   1   0   1\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...   0   1   1   0\n",
              "2  INTP  'Good one  _____   https://www.youtube.com/wat...   1   1   1   0\n",
              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...   1   1   1   1\n",
              "4  ENTJ  'You're fired.|||That's another silly misconce...   0   1   1   1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8CgFwqM0D1x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "65f32abe-5e84-4671-ad90-db3fb5964653"
      },
      "source": [
        "print (\"Introversion (I) /  Extroversion (E):\\t\", MB['IE'].value_counts()[0], \" / \", MB['IE'].value_counts()[1])\n",
        "print (\"Intuition (N) – Sensing (S):\\t\\t\", MB['NS'].value_counts()[0], \" / \", MB['NS'].value_counts()[1])\n",
        "print (\"Thinking (T) – Feeling (F):\\t\\t\", MB['TF'].value_counts()[0], \" / \", MB['TF'].value_counts()[1])\n",
        "print (\"Judging (J) – Perceiving (P):\\t\\t\", MB['JP'].value_counts()[0], \" / \", MB['JP'].value_counts()[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Introversion (I) /  Extroversion (E):\t 1999  /  6676\n",
            "Intuition (N) – Sensing (S):\t\t 1197  /  7478\n",
            "Thinking (T) – Feeling (F):\t\t 4694  /  3981\n",
            "Judging (J) – Perceiving (P):\t\t 5241  /  3434\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bW_D1VkL0D55"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8I2p1hcti8h5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "00c691a9-0d06-41af-f3aa-a078e8c02428"
      },
      "source": [
        "Letter = {'I':0, 'E':1, 'N':0, 'S':1, 'F':0, 'T':1, 'J':0, 'P':1}\n",
        "Pairs = [{0:'I', 1:'E'}, {0:'N', 1:'S'}, {0:'F', 1:'T'}, {0:'J', 1:'P'}]\n",
        "\n",
        "def translate_personality(personality):\n",
        "    # transform mbti to binary vector\n",
        "    \n",
        "    return [Letter[l] for l in personality]\n",
        "\n",
        "def translate_back(personality):\n",
        "    # transform binary vector to mbti personality\n",
        "    \n",
        "    s = \"\"\n",
        "    for i, l in enumerate(personality):\n",
        "        s += Pairs[i][l]\n",
        "    return s\n",
        "\n",
        "# Check ...\n",
        "M = MB.head(4)\n",
        "list_personality_bin = np.array([translate_personality(p) for p in M.type])\n",
        "print(\"Binarize MBTI list: \\n%s\" % list_personality_bin)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Binarize MBTI list: \n",
            "[[0 0 0 0]\n",
            " [1 0 1 1]\n",
            " [0 0 1 1]\n",
            " [0 0 1 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCENIv5BuP_l"
      },
      "source": [
        "#The code below does not appear to have worked "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os-OfGMkrnJe"
      },
      "source": [
        "MB['posts'] = [soup(text).get_text() for text in MB['posts']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "066rHWhvroPM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "7960582f-4897-43b9-bdc4-e55fbf989843"
      },
      "source": [
        "MB['posts']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n",
              "1       'I'm finding the lack of me in these posts ver...\n",
              "2       'Good one  _____   https://www.youtube.com/wat...\n",
              "3       'Dear INTP,   I enjoyed our conversation the o...\n",
              "4       'You're fired.|||That's another silly misconce...\n",
              "                              ...                        \n",
              "8670    'https://www.youtube.com/watch?v=t8edHB_h908||...\n",
              "8671    'So...if this thread already exists someplace ...\n",
              "8672    'So many questions when i do these things.  I ...\n",
              "8673    'I am very conflicted right now when it comes ...\n",
              "8674    'It has been too long since I have been on per...\n",
              "Name: posts, Length: 8675, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdbkIGA-j6Qa"
      },
      "source": [
        "types = ['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP',\n",
        "       'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ']\n",
        "  \n",
        "types = [x.lower() for x in types]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_drgVUrMj6TL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "6bce72ae-3697-45df-8e6c-db409838090c"
      },
      "source": [
        "stemmer = PorterStemmer()\n",
        "lemmatiser = WordNetLemmatizer()\n",
        "\n",
        "cachedStopWords = stopwords.words(\"english\")\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5_S25L2j6Wl"
      },
      "source": [
        "def pre_process_data(MB, remove_stop_words=True, remove_mbti_profiles=True):\n",
        "\n",
        "    list_personality = []\n",
        "    list_posts = []\n",
        "    len_data = len(MB)\n",
        "    i=0\n",
        "    \n",
        "        \n",
        "    for row in MB.iterrows():\n",
        "        i+=1\n",
        "        if (i % 500 == 0 or i == 1 or i == len_data):\n",
        "            print(\"%s of %s rows\" % (i, len_data))\n",
        "\n",
        "        ##### Remove and clean comments\n",
        "        posts = row[1].posts\n",
        "        temp = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', posts)\n",
        "        temp = re.sub(\"[^a-zA-Z]\", \" \", temp)\n",
        "        temp = re.sub(' +', ' ', temp).lower()\n",
        "        if remove_stop_words:\n",
        "            temp = \" \".join([lemmatiser.lemmatize(w) for w in temp.split(' ') if w not in cachedStopWords])\n",
        "        else:\n",
        "            temp = \" \".join([lemmatiser.lemmatize(w) for w in temp.split(' ')])\n",
        "            \n",
        "        if remove_mbti_profiles:\n",
        "            for t in types:\n",
        "                temp = temp.replace(t,\"\")\n",
        "\n",
        "        type_labelized = translate_personality(row[1].type)\n",
        "        list_personality.append(type_labelized)\n",
        "        list_posts.append(temp)\n",
        "\n",
        "    list_posts = np.array(list_posts)\n",
        "    list_personality = np.array(list_personality)\n",
        "    return list_posts, list_personality"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOkV0LYzkozg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "bb72ac82-e200-4c62-c12d-9bc73b9e8f51"
      },
      "source": [
        "list_posts, list_personality = pre_process_data(MB, remove_stop_words=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 of 8675 rows\n",
            "500 of 8675 rows\n",
            "1000 of 8675 rows\n",
            "1500 of 8675 rows\n",
            "2000 of 8675 rows\n",
            "2500 of 8675 rows\n",
            "3000 of 8675 rows\n",
            "3500 of 8675 rows\n",
            "4000 of 8675 rows\n",
            "4500 of 8675 rows\n",
            "5000 of 8675 rows\n",
            "5500 of 8675 rows\n",
            "6000 of 8675 rows\n",
            "6500 of 8675 rows\n",
            "7000 of 8675 rows\n",
            "7500 of 8675 rows\n",
            "8000 of 8675 rows\n",
            "8500 of 8675 rows\n",
            "8675 of 8675 rows\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuEh6qO0mfWh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "5fe4a15c-ac5e-4da0-bcfb-755ce15821ce"
      },
      "source": [
        "list_posts[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'   moment sportscenter top ten play prank life changing experience life repeat today may perc experience immerse last thing  friend posted facebook committing suicide next day rest peace hello  sorry hear distress natural relationship perfection time every moment existence try figure hard time time growth welcome stuff game set match prozac wellbrutin least thirty minute moving leg mean moving sitting desk chair weed moderation maybe try edible healthier alternative basically come three item determined type whichever type want would likely use given type cognitive function whatnot left thing moderation sims indeed video game good one note good one somewhat subjective completely promoting death given sim dear  favorite video game growing current favorite video game cool appears late sad someone everyone wait thought confidence good thing cherish time solitude b c revel within inner world whereas time workin enjoy time worry people always around yo  lady complimentary personality well hey main social outlet xbox live conversation even verbally fatigue quickly really dig part banned thread requires get high backyard roast eat marshmellows backyard conversing something intellectual followed massage kiss banned many b sentence could think b banned watching movie corner dunce banned health class clearly taught nothing peer pressure banned whole host reason two baby deer left right munching beetle middle using blood two caveman diary today latest happening designated cave diary wall see pokemon world  society everyone becomes optimist artist artist draw idea count forming something like signature welcome robot rank person downed self esteem cuz avid signature artist like proud banned taking room bed ya gotta learn share roach banned much thundering grumbling kind storm yep ahh old high school music heard age failed public speaking class year ago sort learned could better position big part failure overloading like person mentality confirmed  way move denver area start new life '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWVXuDp7mfaH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f0f1ea4-4693-4238-a598-f313cf860762"
      },
      "source": [
        "list_personality[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDMyKHAvmke7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "f989a4ca-b233-4229-ca03-c55c37ee3bed"
      },
      "source": [
        "nltk.download('punkt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4U3WkJBpnbSY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1d9936b1-e100-48f2-ad1a-8c53e0e420c9"
      },
      "source": [
        "type(list_posts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DueqRtMYo-Gg"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from nltk.tokenize import word_tokenize\n",
        "from sklearn.feature_extraction.text import TfidfTransformer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkKOZMk_o-J9"
      },
      "source": [
        "CV = CountVectorizer(analyzer=\"word\", \n",
        "                             max_features=1500, \n",
        "                             tokenizer=None,    \n",
        "                             preprocessor=None, \n",
        "                             stop_words=None,  \n",
        "                             max_df=0.7,\n",
        "                             min_df=0.1) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TL9mbBFpebh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "11545eb3-b7d7-4974-9d3e-272182632d23"
      },
      "source": [
        "print(\"CountVectorizer...\")\n",
        "X_CV = CV.fit_transform(list_posts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CountVectorizer...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWLDZ0GKpee7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1a1f763-83ba-4cce-8535-6221b15d8689"
      },
      "source": [
        "TF = TfidfTransformer()\n",
        "print(\"Tf-idf...\")\n",
        "X_TF =  TF.fit_transform(X_CV).toarray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tf-idf...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBcYp_TerNBK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e8c0ad7b-78aa-4488-9dac-47f0473b50f6"
      },
      "source": [
        "words = list(enumerate(CV.get_feature_names()))\n",
        "words"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 'ability'),\n",
              " (1, 'able'),\n",
              " (2, 'absolutely'),\n",
              " (3, 'accept'),\n",
              " (4, 'accurate'),\n",
              " (5, 'across'),\n",
              " (6, 'act'),\n",
              " (7, 'action'),\n",
              " (8, 'actual'),\n",
              " (9, 'actually'),\n",
              " (10, 'add'),\n",
              " (11, 'admit'),\n",
              " (12, 'advice'),\n",
              " (13, 'afraid'),\n",
              " (14, 'age'),\n",
              " (15, 'ago'),\n",
              " (16, 'agree'),\n",
              " (17, 'ah'),\n",
              " (18, 'almost'),\n",
              " (19, 'alone'),\n",
              " (20, 'along'),\n",
              " (21, 'already'),\n",
              " (22, 'although'),\n",
              " (23, 'amazing'),\n",
              " (24, 'amount'),\n",
              " (25, 'angry'),\n",
              " (26, 'animal'),\n",
              " (27, 'annoying'),\n",
              " (28, 'another'),\n",
              " (29, 'answer'),\n",
              " (30, 'anxiety'),\n",
              " (31, 'anymore'),\n",
              " (32, 'anyone'),\n",
              " (33, 'anything'),\n",
              " (34, 'anyway'),\n",
              " (35, 'apparently'),\n",
              " (36, 'appreciate'),\n",
              " (37, 'approach'),\n",
              " (38, 'area'),\n",
              " (39, 'argument'),\n",
              " (40, 'around'),\n",
              " (41, 'art'),\n",
              " (42, 'ask'),\n",
              " (43, 'asked'),\n",
              " (44, 'asking'),\n",
              " (45, 'aspect'),\n",
              " (46, 'assume'),\n",
              " (47, 'attention'),\n",
              " (48, 'attracted'),\n",
              " (49, 'avatar'),\n",
              " (50, 'avoid'),\n",
              " (51, 'aware'),\n",
              " (52, 'away'),\n",
              " (53, 'awesome'),\n",
              " (54, 'awkward'),\n",
              " (55, 'back'),\n",
              " (56, 'bad'),\n",
              " (57, 'based'),\n",
              " (58, 'basically'),\n",
              " (59, 'beautiful'),\n",
              " (60, 'become'),\n",
              " (61, 'bed'),\n",
              " (62, 'behavior'),\n",
              " (63, 'behind'),\n",
              " (64, 'belief'),\n",
              " (65, 'believe'),\n",
              " (66, 'best'),\n",
              " (67, 'better'),\n",
              " (68, 'big'),\n",
              " (69, 'bit'),\n",
              " (70, 'black'),\n",
              " (71, 'blue'),\n",
              " (72, 'body'),\n",
              " (73, 'book'),\n",
              " (74, 'bored'),\n",
              " (75, 'boring'),\n",
              " (76, 'born'),\n",
              " (77, 'bother'),\n",
              " (78, 'boy'),\n",
              " (79, 'boyfriend'),\n",
              " (80, 'brain'),\n",
              " (81, 'break'),\n",
              " (82, 'bring'),\n",
              " (83, 'brother'),\n",
              " (84, 'buy'),\n",
              " (85, 'call'),\n",
              " (86, 'called'),\n",
              " (87, 'came'),\n",
              " (88, 'cannot'),\n",
              " (89, 'car'),\n",
              " (90, 'care'),\n",
              " (91, 'career'),\n",
              " (92, 'case'),\n",
              " (93, 'cat'),\n",
              " (94, 'cause'),\n",
              " (95, 'certain'),\n",
              " (96, 'certainly'),\n",
              " (97, 'chance'),\n",
              " (98, 'change'),\n",
              " (99, 'changed'),\n",
              " (100, 'character'),\n",
              " (101, 'check'),\n",
              " (102, 'child'),\n",
              " (103, 'choice'),\n",
              " (104, 'choose'),\n",
              " (105, 'class'),\n",
              " (106, 'clear'),\n",
              " (107, 'clearly'),\n",
              " (108, 'close'),\n",
              " (109, 'cognitive'),\n",
              " (110, 'cold'),\n",
              " (111, 'college'),\n",
              " (112, 'color'),\n",
              " (113, 'come'),\n",
              " (114, 'comfortable'),\n",
              " (115, 'coming'),\n",
              " (116, 'comment'),\n",
              " (117, 'common'),\n",
              " (118, 'company'),\n",
              " (119, 'complete'),\n",
              " (120, 'completely'),\n",
              " (121, 'computer'),\n",
              " (122, 'concept'),\n",
              " (123, 'confused'),\n",
              " (124, 'connection'),\n",
              " (125, 'consider'),\n",
              " (126, 'considered'),\n",
              " (127, 'considering'),\n",
              " (128, 'constantly'),\n",
              " (129, 'contact'),\n",
              " (130, 'control'),\n",
              " (131, 'conversation'),\n",
              " (132, 'cool'),\n",
              " (133, 'correct'),\n",
              " (134, 'count'),\n",
              " (135, 'country'),\n",
              " (136, 'couple'),\n",
              " (137, 'course'),\n",
              " (138, 'crazy'),\n",
              " (139, 'create'),\n",
              " (140, 'creative'),\n",
              " (141, 'cry'),\n",
              " (142, 'curious'),\n",
              " (143, 'current'),\n",
              " (144, 'currently'),\n",
              " (145, 'cut'),\n",
              " (146, 'cute'),\n",
              " (147, 'dad'),\n",
              " (148, 'damn'),\n",
              " (149, 'dark'),\n",
              " (150, 'date'),\n",
              " (151, 'dating'),\n",
              " (152, 'day'),\n",
              " (153, 'dead'),\n",
              " (154, 'deal'),\n",
              " (155, 'dear'),\n",
              " (156, 'death'),\n",
              " (157, 'debate'),\n",
              " (158, 'decide'),\n",
              " (159, 'decided'),\n",
              " (160, 'decision'),\n",
              " (161, 'deep'),\n",
              " (162, 'definitely'),\n",
              " (163, 'degree'),\n",
              " (164, 'depends'),\n",
              " (165, 'depressed'),\n",
              " (166, 'depression'),\n",
              " (167, 'describe'),\n",
              " (168, 'description'),\n",
              " (169, 'desire'),\n",
              " (170, 'detail'),\n",
              " (171, 'die'),\n",
              " (172, 'difference'),\n",
              " (173, 'different'),\n",
              " (174, 'difficult'),\n",
              " (175, 'discussion'),\n",
              " (176, 'dislike'),\n",
              " (177, 'dog'),\n",
              " (178, 'dominant'),\n",
              " (179, 'done'),\n",
              " (180, 'dont'),\n",
              " (181, 'doubt'),\n",
              " (182, 'dream'),\n",
              " (183, 'drink'),\n",
              " (184, 'drive'),\n",
              " (185, 'due'),\n",
              " (186, 'early'),\n",
              " (187, 'easier'),\n",
              " (188, 'easily'),\n",
              " (189, 'easy'),\n",
              " (190, 'eat'),\n",
              " (191, 'edit'),\n",
              " (192, 'effect'),\n",
              " (193, 'effort'),\n",
              " (194, 'either'),\n",
              " (195, 'else'),\n",
              " (196, 'emotion'),\n",
              " (197, 'emotional'),\n",
              " (198, 'emotionally'),\n",
              " (199, 'end'),\n",
              " (200, 'ended'),\n",
              " (201, 'energy'),\n",
              " (202, 'english'),\n",
              " (203, 'enjoy'),\n",
              " (204, 'enneagram'),\n",
              " (205, 'enough'),\n",
              " (206, 'entire'),\n",
              " (207, 'especially'),\n",
              " (208, 'etc'),\n",
              " (209, 'ever'),\n",
              " (210, 'every'),\n",
              " (211, 'everyone'),\n",
              " (212, 'everything'),\n",
              " (213, 'evil'),\n",
              " (214, 'ex'),\n",
              " (215, 'exact'),\n",
              " (216, 'exactly'),\n",
              " (217, 'example'),\n",
              " (218, 'except'),\n",
              " (219, 'exist'),\n",
              " (220, 'expect'),\n",
              " (221, 'experience'),\n",
              " (222, 'explain'),\n",
              " (223, 'express'),\n",
              " (224, 'extremely'),\n",
              " (225, 'extrovert'),\n",
              " (226, 'extroverted'),\n",
              " (227, 'eye'),\n",
              " (228, 'face'),\n",
              " (229, 'fact'),\n",
              " (230, 'fair'),\n",
              " (231, 'fairly'),\n",
              " (232, 'fall'),\n",
              " (233, 'family'),\n",
              " (234, 'fan'),\n",
              " (235, 'far'),\n",
              " (236, 'fast'),\n",
              " (237, 'father'),\n",
              " (238, 'favorite'),\n",
              " (239, 'fe'),\n",
              " (240, 'fear'),\n",
              " (241, 'feeling'),\n",
              " (242, 'fellow'),\n",
              " (243, 'felt'),\n",
              " (244, 'female'),\n",
              " (245, 'fi'),\n",
              " (246, 'fight'),\n",
              " (247, 'figure'),\n",
              " (248, 'finally'),\n",
              " (249, 'finding'),\n",
              " (250, 'fine'),\n",
              " (251, 'first'),\n",
              " (252, 'fit'),\n",
              " (253, 'focus'),\n",
              " (254, 'follow'),\n",
              " (255, 'food'),\n",
              " (256, 'forget'),\n",
              " (257, 'form'),\n",
              " (258, 'forum'),\n",
              " (259, 'found'),\n",
              " (260, 'four'),\n",
              " (261, 'free'),\n",
              " (262, 'friendship'),\n",
              " (263, 'front'),\n",
              " (264, 'fuck'),\n",
              " (265, 'full'),\n",
              " (266, 'fun'),\n",
              " (267, 'function'),\n",
              " (268, 'funny'),\n",
              " (269, 'future'),\n",
              " (270, 'game'),\n",
              " (271, 'gave'),\n",
              " (272, 'general'),\n",
              " (273, 'generally'),\n",
              " (274, 'getting'),\n",
              " (275, 'girl'),\n",
              " (276, 'give'),\n",
              " (277, 'given'),\n",
              " (278, 'giving'),\n",
              " (279, 'glad'),\n",
              " (280, 'goal'),\n",
              " (281, 'god'),\n",
              " (282, 'going'),\n",
              " (283, 'gone'),\n",
              " (284, 'gonna'),\n",
              " (285, 'got'),\n",
              " (286, 'gotten'),\n",
              " (287, 'great'),\n",
              " (288, 'group'),\n",
              " (289, 'guess'),\n",
              " (290, 'guy'),\n",
              " (291, 'haha'),\n",
              " (292, 'hair'),\n",
              " (293, 'half'),\n",
              " (294, 'hand'),\n",
              " (295, 'hang'),\n",
              " (296, 'happen'),\n",
              " (297, 'happened'),\n",
              " (298, 'happens'),\n",
              " (299, 'happy'),\n",
              " (300, 'hard'),\n",
              " (301, 'hate'),\n",
              " (302, 'head'),\n",
              " (303, 'healthy'),\n",
              " (304, 'hear'),\n",
              " (305, 'heard'),\n",
              " (306, 'heart'),\n",
              " (307, 'hell'),\n",
              " (308, 'hello'),\n",
              " (309, 'help'),\n",
              " (310, 'helpful'),\n",
              " (311, 'hey'),\n",
              " (312, 'hi'),\n",
              " (313, 'high'),\n",
              " (314, 'highly'),\n",
              " (315, 'history'),\n",
              " (316, 'hit'),\n",
              " (317, 'hmm'),\n",
              " (318, 'hold'),\n",
              " (319, 'home'),\n",
              " (320, 'honest'),\n",
              " (321, 'honestly'),\n",
              " (322, 'hope'),\n",
              " (323, 'hot'),\n",
              " (324, 'hour'),\n",
              " (325, 'house'),\n",
              " (326, 'however'),\n",
              " (327, 'hug'),\n",
              " (328, 'huge'),\n",
              " (329, 'human'),\n",
              " (330, 'humor'),\n",
              " (331, 'hurt'),\n",
              " (332, 'idea'),\n",
              " (333, 'ideal'),\n",
              " (334, 'im'),\n",
              " (335, 'image'),\n",
              " (336, 'imagine'),\n",
              " (337, 'important'),\n",
              " (338, 'individual'),\n",
              " (339, 'information'),\n",
              " (340, 'inside'),\n",
              " (341, 'instead'),\n",
              " (342, 'intelligence'),\n",
              " (343, 'intelligent'),\n",
              " (344, 'interest'),\n",
              " (345, 'interested'),\n",
              " (346, 'interesting'),\n",
              " (347, 'internet'),\n",
              " (348, 'introvert'),\n",
              " (349, 'introverted'),\n",
              " (350, 'intuition'),\n",
              " (351, 'intuitive'),\n",
              " (352, 'issue'),\n",
              " (353, 'job'),\n",
              " (354, 'joke'),\n",
              " (355, 'keep'),\n",
              " (356, 'kid'),\n",
              " (357, 'kill'),\n",
              " (358, 'kind'),\n",
              " (359, 'kinda'),\n",
              " (360, 'knew'),\n",
              " (361, 'knowing'),\n",
              " (362, 'knowledge'),\n",
              " (363, 'known'),\n",
              " (364, 'lack'),\n",
              " (365, 'language'),\n",
              " (366, 'last'),\n",
              " (367, 'late'),\n",
              " (368, 'lately'),\n",
              " (369, 'later'),\n",
              " (370, 'laugh'),\n",
              " (371, 'laughing'),\n",
              " (372, 'lazy'),\n",
              " (373, 'le'),\n",
              " (374, 'lead'),\n",
              " (375, 'learn'),\n",
              " (376, 'learned'),\n",
              " (377, 'learning'),\n",
              " (378, 'least'),\n",
              " (379, 'leave'),\n",
              " (380, 'left'),\n",
              " (381, 'let'),\n",
              " (382, 'level'),\n",
              " (383, 'lie'),\n",
              " (384, 'light'),\n",
              " (385, 'liked'),\n",
              " (386, 'likely'),\n",
              " (387, 'line'),\n",
              " (388, 'link'),\n",
              " (389, 'list'),\n",
              " (390, 'listen'),\n",
              " (391, 'listening'),\n",
              " (392, 'literally'),\n",
              " (393, 'little'),\n",
              " (394, 'live'),\n",
              " (395, 'living'),\n",
              " (396, 'logic'),\n",
              " (397, 'logical'),\n",
              " (398, 'lol'),\n",
              " (399, 'long'),\n",
              " (400, 'longer'),\n",
              " (401, 'look'),\n",
              " (402, 'looked'),\n",
              " (403, 'looking'),\n",
              " (404, 'lose'),\n",
              " (405, 'lost'),\n",
              " (406, 'loved'),\n",
              " (407, 'low'),\n",
              " (408, 'made'),\n",
              " (409, 'main'),\n",
              " (410, 'major'),\n",
              " (411, 'making'),\n",
              " (412, 'male'),\n",
              " (413, 'man'),\n",
              " (414, 'many'),\n",
              " (415, 'math'),\n",
              " (416, 'matter'),\n",
              " (417, 'may'),\n",
              " (418, 'maybe'),\n",
              " (419, 'mbti'),\n",
              " (420, 'mean'),\n",
              " (421, 'meaning'),\n",
              " (422, 'meant'),\n",
              " (423, 'meet'),\n",
              " (424, 'memory'),\n",
              " (425, 'men'),\n",
              " (426, 'mental'),\n",
              " (427, 'mention'),\n",
              " (428, 'mentioned'),\n",
              " (429, 'message'),\n",
              " (430, 'met'),\n",
              " (431, 'middle'),\n",
              " (432, 'might'),\n",
              " (433, 'mind'),\n",
              " (434, 'mine'),\n",
              " (435, 'minute'),\n",
              " (436, 'miss'),\n",
              " (437, 'mistake'),\n",
              " (438, 'mom'),\n",
              " (439, 'moment'),\n",
              " (440, 'money'),\n",
              " (441, 'month'),\n",
              " (442, 'mood'),\n",
              " (443, 'morning'),\n",
              " (444, 'mostly'),\n",
              " (445, 'mother'),\n",
              " (446, 'move'),\n",
              " (447, 'movie'),\n",
              " (448, 'music'),\n",
              " (449, 'must'),\n",
              " (450, 'name'),\n",
              " (451, 'natural'),\n",
              " (452, 'naturally'),\n",
              " (453, 'nature'),\n",
              " (454, 'ne'),\n",
              " (455, 'necessarily'),\n",
              " (456, 'needed'),\n",
              " (457, 'negative'),\n",
              " (458, 'new'),\n",
              " (459, 'next'),\n",
              " (460, 'ni'),\n",
              " (461, 'nice'),\n",
              " (462, 'night'),\n",
              " (463, 'non'),\n",
              " (464, 'none'),\n",
              " (465, 'normal'),\n",
              " (466, 'note'),\n",
              " (467, 'nothing'),\n",
              " (468, 'notice'),\n",
              " (469, 'noticed'),\n",
              " (470, 'nt'),\n",
              " (471, 'number'),\n",
              " (472, 'obvious'),\n",
              " (473, 'obviously'),\n",
              " (474, 'often'),\n",
              " (475, 'oh'),\n",
              " (476, 'ok'),\n",
              " (477, 'okay'),\n",
              " (478, 'old'),\n",
              " (479, 'older'),\n",
              " (480, 'online'),\n",
              " (481, 'op'),\n",
              " (482, 'open'),\n",
              " (483, 'opinion'),\n",
              " (484, 'opposite'),\n",
              " (485, 'option'),\n",
              " (486, 'order'),\n",
              " (487, 'original'),\n",
              " (488, 'others'),\n",
              " (489, 'otherwise'),\n",
              " (490, 'outside'),\n",
              " (491, 'page'),\n",
              " (492, 'pain'),\n",
              " (493, 'parent'),\n",
              " (494, 'part'),\n",
              " (495, 'particular'),\n",
              " (496, 'particularly'),\n",
              " (497, 'partner'),\n",
              " (498, 'party'),\n",
              " (499, 'past'),\n",
              " (500, 'pay'),\n",
              " (501, 'perc'),\n",
              " (502, 'perfect'),\n",
              " (503, 'perhaps'),\n",
              " (504, 'person'),\n",
              " (505, 'personal'),\n",
              " (506, 'personality'),\n",
              " (507, 'personally'),\n",
              " (508, 'perspective'),\n",
              " (509, 'phone'),\n",
              " (510, 'physical'),\n",
              " (511, 'pick'),\n",
              " (512, 'picture'),\n",
              " (513, 'piece'),\n",
              " (514, 'place'),\n",
              " (515, 'plan'),\n",
              " (516, 'play'),\n",
              " (517, 'playing'),\n",
              " (518, 'please'),\n",
              " (519, 'plus'),\n",
              " (520, 'point'),\n",
              " (521, 'positive'),\n",
              " (522, 'possibility'),\n",
              " (523, 'possible'),\n",
              " (524, 'possibly'),\n",
              " (525, 'post'),\n",
              " (526, 'posted'),\n",
              " (527, 'posting'),\n",
              " (528, 'power'),\n",
              " (529, 'prefer'),\n",
              " (530, 'pretty'),\n",
              " (531, 'probably'),\n",
              " (532, 'problem'),\n",
              " (533, 'process'),\n",
              " (534, 'project'),\n",
              " (535, 'proud'),\n",
              " (536, 'public'),\n",
              " (537, 'purpose'),\n",
              " (538, 'put'),\n",
              " (539, 'putting'),\n",
              " (540, 'quality'),\n",
              " (541, 'question'),\n",
              " (542, 'quickly'),\n",
              " (543, 'quiet'),\n",
              " (544, 'quite'),\n",
              " (545, 'quote'),\n",
              " (546, 'random'),\n",
              " (547, 'rarely'),\n",
              " (548, 'rather'),\n",
              " (549, 'read'),\n",
              " (550, 'reading'),\n",
              " (551, 'real'),\n",
              " (552, 'reality'),\n",
              " (553, 'realize'),\n",
              " (554, 'realized'),\n",
              " (555, 'reason'),\n",
              " (556, 'recently'),\n",
              " (557, 'red'),\n",
              " (558, 'relate'),\n",
              " (559, 'related'),\n",
              " (560, 'relationship'),\n",
              " (561, 'religion'),\n",
              " (562, 'remember'),\n",
              " (563, 'reply'),\n",
              " (564, 'research'),\n",
              " (565, 'respect'),\n",
              " (566, 'response'),\n",
              " (567, 'rest'),\n",
              " (568, 'result'),\n",
              " (569, 'romantic'),\n",
              " (570, 'room'),\n",
              " (571, 'rule'),\n",
              " (572, 'run'),\n",
              " (573, 'sad'),\n",
              " (574, 'said'),\n",
              " (575, 'saw'),\n",
              " (576, 'saying'),\n",
              " (577, 'school'),\n",
              " (578, 'science'),\n",
              " (579, 'se'),\n",
              " (580, 'second'),\n",
              " (581, 'seeing'),\n",
              " (582, 'seem'),\n",
              " (583, 'seemed'),\n",
              " (584, 'seems'),\n",
              " (585, 'seen'),\n",
              " (586, 'self'),\n",
              " (587, 'sense'),\n",
              " (588, 'sensitive'),\n",
              " (589, 'series'),\n",
              " (590, 'serious'),\n",
              " (591, 'seriously'),\n",
              " (592, 'set'),\n",
              " (593, 'several'),\n",
              " (594, 'sex'),\n",
              " (595, 'share'),\n",
              " (596, 'shit'),\n",
              " (597, 'short'),\n",
              " (598, 'show'),\n",
              " (599, 'shy'),\n",
              " (600, 'si'),\n",
              " (601, 'side'),\n",
              " (602, 'sign'),\n",
              " (603, 'similar'),\n",
              " (604, 'simple'),\n",
              " (605, 'simply'),\n",
              " (606, 'since'),\n",
              " (607, 'single'),\n",
              " (608, 'sister'),\n",
              " (609, 'sit'),\n",
              " (610, 'site'),\n",
              " (611, 'situation'),\n",
              " (612, 'skill'),\n",
              " (613, 'sleep'),\n",
              " (614, 'small'),\n",
              " (615, 'smart'),\n",
              " (616, 'smile'),\n",
              " (617, 'social'),\n",
              " (618, 'society'),\n",
              " (619, 'somehow'),\n",
              " (620, 'sometimes'),\n",
              " (621, 'somewhat'),\n",
              " (622, 'somewhere'),\n",
              " (623, 'song'),\n",
              " (624, 'soon'),\n",
              " (625, 'sorry'),\n",
              " (626, 'sort'),\n",
              " (627, 'soul'),\n",
              " (628, 'sound'),\n",
              " (629, 'space'),\n",
              " (630, 'speak'),\n",
              " (631, 'speaking'),\n",
              " (632, 'special'),\n",
              " (633, 'specific'),\n",
              " (634, 'spend'),\n",
              " (635, 'spent'),\n",
              " (636, 'stand'),\n",
              " (637, 'start'),\n",
              " (638, 'started'),\n",
              " (639, 'starting'),\n",
              " (640, 'state'),\n",
              " (641, 'statement'),\n",
              " (642, 'stay'),\n",
              " (643, 'step'),\n",
              " (644, 'stereotype'),\n",
              " (645, 'stick'),\n",
              " (646, 'still'),\n",
              " (647, 'stop'),\n",
              " (648, 'story'),\n",
              " (649, 'straight'),\n",
              " (650, 'strange'),\n",
              " (651, 'stress'),\n",
              " (652, 'strong'),\n",
              " (653, 'stuck'),\n",
              " (654, 'study'),\n",
              " (655, 'stuff'),\n",
              " (656, 'stupid'),\n",
              " (657, 'style'),\n",
              " (658, 'subject'),\n",
              " (659, 'suck'),\n",
              " (660, 'super'),\n",
              " (661, 'suppose'),\n",
              " (662, 'supposed'),\n",
              " (663, 'sure'),\n",
              " (664, 'surprised'),\n",
              " (665, 'sweet'),\n",
              " (666, 'system'),\n",
              " (667, 'take'),\n",
              " (668, 'taken'),\n",
              " (669, 'taking'),\n",
              " (670, 'talk'),\n",
              " (671, 'talking'),\n",
              " (672, 'te'),\n",
              " (673, 'teacher'),\n",
              " (674, 'tell'),\n",
              " (675, 'telling'),\n",
              " (676, 'tend'),\n",
              " (677, 'tendency'),\n",
              " (678, 'term'),\n",
              " (679, 'terrible'),\n",
              " (680, 'test'),\n",
              " (681, 'text'),\n",
              " (682, 'th'),\n",
              " (683, 'thank'),\n",
              " (684, 'thanks'),\n",
              " (685, 'theory'),\n",
              " (686, 'thinking'),\n",
              " (687, 'though'),\n",
              " (688, 'thread'),\n",
              " (689, 'three'),\n",
              " (690, 'ti'),\n",
              " (691, 'tired'),\n",
              " (692, 'title'),\n",
              " (693, 'today'),\n",
              " (694, 'together'),\n",
              " (695, 'told'),\n",
              " (696, 'tongue'),\n",
              " (697, 'took'),\n",
              " (698, 'top'),\n",
              " (699, 'topic'),\n",
              " (700, 'totally'),\n",
              " (701, 'touch'),\n",
              " (702, 'towards'),\n",
              " (703, 'trait'),\n",
              " (704, 'tried'),\n",
              " (705, 'trouble'),\n",
              " (706, 'true'),\n",
              " (707, 'truly'),\n",
              " (708, 'trust'),\n",
              " (709, 'truth'),\n",
              " (710, 'try'),\n",
              " (711, 'trying'),\n",
              " (712, 'turn'),\n",
              " (713, 'tv'),\n",
              " (714, 'two'),\n",
              " (715, 'typing'),\n",
              " (716, 'understand'),\n",
              " (717, 'understanding'),\n",
              " (718, 'unfortunately'),\n",
              " (719, 'unless'),\n",
              " (720, 'upon'),\n",
              " (721, 'use'),\n",
              " (722, 'used'),\n",
              " (723, 'useful'),\n",
              " (724, 'user'),\n",
              " (725, 'using'),\n",
              " (726, 'usually'),\n",
              " (727, 'value'),\n",
              " (728, 'video'),\n",
              " (729, 'view'),\n",
              " (730, 'voice'),\n",
              " (731, 'wait'),\n",
              " (732, 'walk'),\n",
              " (733, 'wanted'),\n",
              " (734, 'wanting'),\n",
              " (735, 'watch'),\n",
              " (736, 'watched'),\n",
              " (737, 'watching'),\n",
              " (738, 'water'),\n",
              " (739, 'wear'),\n",
              " (740, 'week'),\n",
              " (741, 'weird'),\n",
              " (742, 'welcome'),\n",
              " (743, 'went'),\n",
              " (744, 'whatever'),\n",
              " (745, 'whenever'),\n",
              " (746, 'whether'),\n",
              " (747, 'white'),\n",
              " (748, 'whole'),\n",
              " (749, 'wink'),\n",
              " (750, 'wish'),\n",
              " (751, 'within'),\n",
              " (752, 'without'),\n",
              " (753, 'woman'),\n",
              " (754, 'wonder'),\n",
              " (755, 'wondering'),\n",
              " (756, 'word'),\n",
              " (757, 'work'),\n",
              " (758, 'worked'),\n",
              " (759, 'working'),\n",
              " (760, 'world'),\n",
              " (761, 'worry'),\n",
              " (762, 'worse'),\n",
              " (763, 'worst'),\n",
              " (764, 'worth'),\n",
              " (765, 'wow'),\n",
              " (766, 'write'),\n",
              " (767, 'writing'),\n",
              " (768, 'written'),\n",
              " (769, 'wrong'),\n",
              " (770, 'wrote'),\n",
              " (771, 'xd'),\n",
              " (772, 'yeah'),\n",
              " (773, 'year'),\n",
              " (774, 'yes'),\n",
              " (775, 'yesterday'),\n",
              " (776, 'yet'),\n",
              " (777, 'young'),\n",
              " (778, 'younger')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME3kSNbGsUfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4eeaa4bc-442e-43d5-fd64-e1a21ea84a72"
      },
      "source": [
        "X_TF.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8675, 779)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfmD_zb8tIgO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "04306711-48cd-4819-8b05-5ba70d47d964"
      },
      "source": [
        "print(\"X: Posts in tf-idf representation \\n* 1st row:\\n%s\" % X_TF[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: Posts in tf-idf representation \n",
            "* 1st row:\n",
            "[0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.08066234 0.06835878 0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.10318938 0.         0.04997299 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.08633404 0.\n",
            " 0.         0.10514588 0.         0.         0.         0.\n",
            " 0.         0.05216476 0.06843631 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.16417594 0.         0.10672825\n",
            " 0.         0.09861277 0.         0.         0.         0.04627579\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.07216453 0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.07304477\n",
            " 0.07435754 0.         0.10561952 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.10568773\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.04812683 0.         0.         0.09959232\n",
            " 0.09454365 0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.09540846 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.06889407\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.05607002 0.11168362 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.12047471\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.1568894  0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.08336823 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.07001458 0.         0.\n",
            " 0.29040689 0.         0.         0.         0.         0.\n",
            " 0.         0.18298708 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.05493701 0.         0.         0.         0.07795336 0.08276461\n",
            " 0.         0.         0.09094207 0.         0.         0.07952721\n",
            " 0.         0.12979153 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.05365645 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.05283637 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.05859915 0.09425227 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.07867047 0.0935585  0.\n",
            " 0.06138228 0.         0.16936371 0.         0.         0.\n",
            " 0.         0.         0.08001119 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.0668135  0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.09861277 0.         0.         0.         0.\n",
            " 0.04960674 0.         0.         0.05766498 0.05135388 0.\n",
            " 0.04681397 0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.0983655\n",
            " 0.         0.         0.         0.09432487 0.         0.\n",
            " 0.         0.15002735 0.         0.         0.         0.\n",
            " 0.         0.         0.08749628 0.07560037 0.07004949 0.\n",
            " 0.         0.09953556 0.         0.         0.         0.\n",
            " 0.         0.         0.05639988 0.07988561 0.         0.\n",
            " 0.         0.         0.         0.         0.09702441 0.06201558\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.06738859 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.11491475 0.         0.         0.\n",
            " 0.         0.         0.         0.1052468  0.         0.\n",
            " 0.09059315 0.         0.05941657 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.07252636 0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.08925744 0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.09798449 0.10599647 0.         0.         0.\n",
            " 0.         0.         0.10701133 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.05715628 0.         0.\n",
            " 0.         0.         0.05666305 0.         0.         0.\n",
            " 0.         0.         0.         0.09676259 0.         0.\n",
            " 0.08851729 0.         0.         0.08356023 0.         0.\n",
            " 0.         0.06110565 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.06449958 0.\n",
            " 0.         0.         0.         0.         0.09246409 0.\n",
            " 0.         0.08237386 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.06824835\n",
            " 0.09782253 0.         0.         0.10191596 0.         0.\n",
            " 0.         0.06462782 0.07332632 0.         0.         0.\n",
            " 0.         0.09897323 0.         0.         0.         0.\n",
            " 0.         0.0621804  0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.06614459 0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.07921868 0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.0510967  0.08801286\n",
            " 0.         0.         0.         0.14991925 0.         0.\n",
            " 0.         0.         0.09351118 0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.09887522 0.         0.         0.\n",
            " 0.10588076 0.         0.         0.         0.         0.\n",
            " 0.         0.05785777 0.         0.         0.         0.08288446\n",
            " 0.         0.         0.23475081 0.         0.         0.08988713\n",
            " 0.         0.         0.         0.         0.         0.08604974\n",
            " 0.         0.         0.         0.         0.16106855 0.\n",
            " 0.         0.         0.         0.         0.07015443 0.\n",
            " 0.         0.101432   0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.10887834 0.09336964\n",
            " 0.         0.         0.         0.         0.         0.\n",
            " 0.         0.         0.         0.         0.         0.04591431\n",
            " 0.         0.         0.         0.         0.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iGFQUkMtIc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "543c9ddd-25a9-4efc-90ee-8ff4dc4f88b6"
      },
      "source": [
        "type_pairs = [ \"IE: Introversion (I) / Extroversion (E)\", \"NS: Intuition (N) – Sensing (S)\", \n",
        "                   \"FT: Feeling (F) - Thinking (T)\", \"JP: Judging (J) – Perceiving (P)\"  ]\n",
        "\n",
        "for l in range(len(type_pairs)):\n",
        "    print(type_pairs[l])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E)\n",
            "NS: Intuition (N) – Sensing (S)\n",
            "FT: Feeling (F) - Thinking (T)\n",
            "JP: Judging (J) – Perceiving (P)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGY5lA7ktIjc"
      },
      "source": [
        "#print(\"MBTI 1st row: %s\" % translate_back(list_personality[0]))\n",
        "#print(\"Y: Binarized MBTI 1st row: %s\" % list_personality[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dj_vfWuhwLr-"
      },
      "source": [
        "from numpy import loadtxt\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bduw8Sz9wLvW"
      },
      "source": [
        "X = X_TF"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKg5avND8RkR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "3cab756a-63fe-4acc-c338-a364f0268fbe"
      },
      "source": [
        "list_personality"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0],\n",
              "       [1, 0, 1, 1],\n",
              "       [0, 0, 1, 1],\n",
              "       ...,\n",
              "       [0, 0, 1, 1],\n",
              "       [0, 0, 0, 1],\n",
              "       [0, 0, 0, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lbxYZH23uvT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "d5a729c7-6bb4-4891-cf3b-72fff9e09da5"
      },
      "source": [
        "for l in range(len(type_pairs)):\n",
        "    print(\"%s ...\" % (type_pairs[l]))\n",
        "    \n",
        "    # Let's train type indicator individually\n",
        "    Y = list_personality[:,l]\n",
        "\n",
        "    # split data into train and test sets\n",
        "    seed = 7\n",
        "    test_size = 0.33\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
        "\n",
        "    # fit model on training data\n",
        "    model = XGBClassifier()\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # make predictions for test data\n",
        "    y_pred = model.predict(X_test)\n",
        "    predictions = [round(value) for value in y_pred]\n",
        "    # evaluate predictions\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    print(\"* %s Accuracy: %.2f%%\" % (type_pairs[l], accuracy * 100.0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E) ...\n",
            "* IE: Introversion (I) / Extroversion (E) Accuracy: 78.66%\n",
            "NS: Intuition (N) – Sensing (S) ...\n",
            "* NS: Intuition (N) – Sensing (S) Accuracy: 86.03%\n",
            "FT: Feeling (F) - Thinking (T) ...\n",
            "* FT: Feeling (F) - Thinking (T) Accuracy: 72.23%\n",
            "JP: Judging (J) – Perceiving (P) ...\n",
            "* JP: Judging (J) – Perceiving (P) Accuracy: 66.12%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7zF9mFErRAV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNBA-pM2xRqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fdf9b8fe-e7f4-4992-a1da-5de215fd52e6"
      },
      "source": [
        "for l in range(len(type_pairs)):\n",
        "    print(\"%s ...\" % (type_pairs[l]))\n",
        "    \n",
        "    Y = list_personality[:,l]\n",
        "\n",
        "    # split data into train and test sets\n",
        "    seed = 7\n",
        "    test_size = 0.33\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
        "\n",
        "    # fit model on training data\n",
        "    model = XGBClassifier()\n",
        "    eval_set = [(X_test, y_test)]\n",
        "    model.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\"logloss\", eval_set=eval_set, verbose=True)\n",
        "\n",
        "    # make predictions for test data\n",
        "    y_pred = model.predict(X_test)\n",
        "    predictions = [round(value) for value in y_pred]\n",
        "    # evaluate predictions\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    print(\"* %s Accuracy: %.2f%%\" % (type_pairs[l], accuracy * 100.0))\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E) ...\n",
            "[0]\tvalidation_0-logloss:0.66318\n",
            "Will train until validation_0-logloss hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-logloss:0.638035\n",
            "[2]\tvalidation_0-logloss:0.617298\n",
            "[3]\tvalidation_0-logloss:0.600237\n",
            "[4]\tvalidation_0-logloss:0.586077\n",
            "[5]\tvalidation_0-logloss:0.57407\n",
            "[6]\tvalidation_0-logloss:0.564219\n",
            "[7]\tvalidation_0-logloss:0.556033\n",
            "[8]\tvalidation_0-logloss:0.548668\n",
            "[9]\tvalidation_0-logloss:0.542554\n",
            "[10]\tvalidation_0-logloss:0.537249\n",
            "[11]\tvalidation_0-logloss:0.532953\n",
            "[12]\tvalidation_0-logloss:0.529022\n",
            "[13]\tvalidation_0-logloss:0.525587\n",
            "[14]\tvalidation_0-logloss:0.522417\n",
            "[15]\tvalidation_0-logloss:0.520099\n",
            "[16]\tvalidation_0-logloss:0.518165\n",
            "[17]\tvalidation_0-logloss:0.516207\n",
            "[18]\tvalidation_0-logloss:0.514738\n",
            "[19]\tvalidation_0-logloss:0.512806\n",
            "[20]\tvalidation_0-logloss:0.511295\n",
            "[21]\tvalidation_0-logloss:0.510344\n",
            "[22]\tvalidation_0-logloss:0.508716\n",
            "[23]\tvalidation_0-logloss:0.507551\n",
            "[24]\tvalidation_0-logloss:0.506879\n",
            "[25]\tvalidation_0-logloss:0.505897\n",
            "[26]\tvalidation_0-logloss:0.504674\n",
            "[27]\tvalidation_0-logloss:0.50414\n",
            "[28]\tvalidation_0-logloss:0.503541\n",
            "[29]\tvalidation_0-logloss:0.503272\n",
            "[30]\tvalidation_0-logloss:0.503029\n",
            "[31]\tvalidation_0-logloss:0.502899\n",
            "[32]\tvalidation_0-logloss:0.502302\n",
            "[33]\tvalidation_0-logloss:0.501705\n",
            "[34]\tvalidation_0-logloss:0.501893\n",
            "[35]\tvalidation_0-logloss:0.50121\n",
            "[36]\tvalidation_0-logloss:0.501021\n",
            "[37]\tvalidation_0-logloss:0.50046\n",
            "[38]\tvalidation_0-logloss:0.500152\n",
            "[39]\tvalidation_0-logloss:0.499938\n",
            "[40]\tvalidation_0-logloss:0.499622\n",
            "[41]\tvalidation_0-logloss:0.499289\n",
            "[42]\tvalidation_0-logloss:0.499245\n",
            "[43]\tvalidation_0-logloss:0.498854\n",
            "[44]\tvalidation_0-logloss:0.497997\n",
            "[45]\tvalidation_0-logloss:0.497702\n",
            "[46]\tvalidation_0-logloss:0.497786\n",
            "[47]\tvalidation_0-logloss:0.497143\n",
            "[48]\tvalidation_0-logloss:0.496424\n",
            "[49]\tvalidation_0-logloss:0.49625\n",
            "[50]\tvalidation_0-logloss:0.496112\n",
            "[51]\tvalidation_0-logloss:0.495531\n",
            "[52]\tvalidation_0-logloss:0.495293\n",
            "[53]\tvalidation_0-logloss:0.494884\n",
            "[54]\tvalidation_0-logloss:0.494193\n",
            "[55]\tvalidation_0-logloss:0.493911\n",
            "[56]\tvalidation_0-logloss:0.493688\n",
            "[57]\tvalidation_0-logloss:0.493486\n",
            "[58]\tvalidation_0-logloss:0.493114\n",
            "[59]\tvalidation_0-logloss:0.492951\n",
            "[60]\tvalidation_0-logloss:0.493005\n",
            "[61]\tvalidation_0-logloss:0.492808\n",
            "[62]\tvalidation_0-logloss:0.492584\n",
            "[63]\tvalidation_0-logloss:0.492661\n",
            "[64]\tvalidation_0-logloss:0.492202\n",
            "[65]\tvalidation_0-logloss:0.491937\n",
            "[66]\tvalidation_0-logloss:0.49184\n",
            "[67]\tvalidation_0-logloss:0.491921\n",
            "[68]\tvalidation_0-logloss:0.491514\n",
            "[69]\tvalidation_0-logloss:0.491449\n",
            "[70]\tvalidation_0-logloss:0.491958\n",
            "[71]\tvalidation_0-logloss:0.49171\n",
            "[72]\tvalidation_0-logloss:0.491449\n",
            "[73]\tvalidation_0-logloss:0.491246\n",
            "[74]\tvalidation_0-logloss:0.491059\n",
            "[75]\tvalidation_0-logloss:0.491027\n",
            "[76]\tvalidation_0-logloss:0.491114\n",
            "[77]\tvalidation_0-logloss:0.491\n",
            "[78]\tvalidation_0-logloss:0.491195\n",
            "[79]\tvalidation_0-logloss:0.491016\n",
            "[80]\tvalidation_0-logloss:0.490998\n",
            "[81]\tvalidation_0-logloss:0.4909\n",
            "[82]\tvalidation_0-logloss:0.490669\n",
            "[83]\tvalidation_0-logloss:0.490629\n",
            "[84]\tvalidation_0-logloss:0.490242\n",
            "[85]\tvalidation_0-logloss:0.489994\n",
            "[86]\tvalidation_0-logloss:0.489641\n",
            "[87]\tvalidation_0-logloss:0.489834\n",
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            "[98]\tvalidation_0-logloss:0.489162\n",
            "[99]\tvalidation_0-logloss:0.489687\n",
            "* IE: Introversion (I) / Extroversion (E) Accuracy: 78.83%\n",
            "NS: Intuition (N) – Sensing (S) ...\n",
            "[0]\tvalidation_0-logloss:0.642848\n",
            "Will train until validation_0-logloss hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-logloss:0.602162\n",
            "[2]\tvalidation_0-logloss:0.568719\n",
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            "[98]\tvalidation_0-logloss:0.376833\n",
            "[99]\tvalidation_0-logloss:0.376755\n",
            "* NS: Intuition (N) – Sensing (S) Accuracy: 86.03%\n",
            "FT: Feeling (F) - Thinking (T) ...\n",
            "[0]\tvalidation_0-logloss:0.686387\n",
            "Will train until validation_0-logloss hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-logloss:0.681184\n",
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            "[98]\tvalidation_0-logloss:0.558121\n",
            "[99]\tvalidation_0-logloss:0.557828\n",
            "* FT: Feeling (F) - Thinking (T) Accuracy: 72.23%\n",
            "JP: Judging (J) – Perceiving (P) ...\n",
            "[0]\tvalidation_0-logloss:0.684418\n",
            "Will train until validation_0-logloss hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-logloss:0.676968\n",
            "[2]\tvalidation_0-logloss:0.670395\n",
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            "[98]\tvalidation_0-logloss:0.619651\n",
            "[99]\tvalidation_0-logloss:0.61952\n",
            "* JP: Judging (J) – Perceiving (P) Accuracy: 66.12%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RIkISZBxRts",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0147c788-0555-4a7f-8ee4-6ad4e42ee7f0"
      },
      "source": [
        "from xgboost import plot_importance\n",
        "\n",
        "y = list_personality[:,0]\n",
        "\n",
        "model = XGBClassifier()\n",
        "model.fit(X, y)\n",
        "\n",
        "ax = plot_importance(model, max_num_features=25)\n",
        "\n",
        "fig = ax.figure\n",
        "fig.set_size_inches(15, 20)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQloPmk23zLc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "3cbff092-4812-4f34-aa79-7f3441570b99"
      },
      "source": [
        "features = sorted(list(enumerate(model.feature_importances_)), key=lambda x: x[1], reverse=True)\n",
        "for f in features[0:25]:\n",
        "    print(\"%d\\t%f\\t%s\" % (f[0],f[1],CV.get_feature_names()[f[0]]))\n",
        "    \n",
        "# Save xgb_params for late discussuin\n",
        "default_get_xgb_params = model.get_xgb_params()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "454\t0.009758\tne\n",
            "290\t0.007875\tguy\n",
            "266\t0.006692\tfun\n",
            "398\t0.006598\tlol\n",
            "311\t0.006342\they\n",
            "408\t0.006188\tmade\n",
            "448\t0.006078\tmusic\n",
            "760\t0.006072\tworld\n",
            "53\t0.006061\tawesome\n",
            "233\t0.006057\tfamily\n",
            "182\t0.005953\tdream\n",
            "543\t0.005832\tquiet\n",
            "180\t0.005827\tdont\n",
            "74\t0.005763\tbored\n",
            "391\t0.005704\tlistening\n",
            "26\t0.005660\tanimal\n",
            "270\t0.005552\tgame\n",
            "660\t0.005545\tsuper\n",
            "749\t0.005527\twink\n",
            "347\t0.005486\tinternet\n",
            "138\t0.005432\tcrazy\n",
            "470\t0.005395\tnt\n",
            "291\t0.005331\thaha\n",
            "264\t0.005330\tfuck\n",
            "674\t0.005288\ttell\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aJ2vCZP3zRx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "24246ced-40f9-4078-9fd7-9c3c4f3222aa"
      },
      "source": [
        "default_get_xgb_params = model.get_xgb_params()\n",
        "print (default_get_xgb_params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 1, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 3, 'min_child_weight': 1, 'missing': None, 'n_estimators': 100, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'subsample': 1, 'verbosity': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15Iy0xs13zO3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "31138392-dd77-415d-e1cc-cebcda81a351"
      },
      "source": [
        "param = {}\n",
        "\n",
        "param['n_estimators'] = 200\n",
        "param['max_depth'] = 2\n",
        "param['nthread'] = 8\n",
        "param['learning_rate'] = 0.2\n",
        "\n",
        "# Let's train type indicator individually\n",
        "for l in range(len(type_pairs)):\n",
        "    print(\"%s ...\" % (type_pairs[l]))\n",
        "    \n",
        "    Y = list_personality[:,l]\n",
        "\n",
        "    # split data into train and test sets\n",
        "    seed = 7\n",
        "    test_size = 0.33\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
        "\n",
        "    # fit model on training data\n",
        "    model = XGBClassifier(**param)\n",
        "    model.fit(X_train, y_train)\n",
        "    # make predictions for test data\n",
        "    y_pred = model.predict(X_test)\n",
        "    predictions = [round(value) for value in y_pred]\n",
        "    # evaluate predictions\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    print(\"* %s Accuracy: %.2f%%\" % (type_pairs[l], accuracy * 100.0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E) ...\n",
            "* IE: Introversion (I) / Extroversion (E) Accuracy: 78.90%\n",
            "NS: Intuition (N) – Sensing (S) ...\n",
            "* NS: Intuition (N) – Sensing (S) Accuracy: 86.17%\n",
            "FT: Feeling (F) - Thinking (T) ...\n",
            "* FT: Feeling (F) - Thinking (T) Accuracy: 74.05%\n",
            "JP: Judging (J) – Perceiving (P) ...\n",
            "* JP: Judging (J) – Perceiving (P) Accuracy: 65.81%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1A_eC8dmkig"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qM3wGHJdYNUc"
      },
      "source": [
        "from numpy import loadtxt\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import StratifiedKFold\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Alh8MvlQa7Z9"
      },
      "source": [
        "X = X_TF\n",
        "\n",
        "# setup parameters for xgboost\n",
        "param = {}\n",
        "param['n_estimators'] = 200\n",
        "param['max_depth'] = 2\n",
        "param['nthread'] = 8\n",
        "param['learning_rate'] = 0.2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFqyJY5Ia7dy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "7ab53799-acba-4e54-a32a-59a65548b0d6"
      },
      "source": [
        "# Let's train type indicator individually\n",
        "for l in range(len(type_pairs)):\n",
        "    print(\"%s ...\" % (type_pairs[l]))\n",
        "    \n",
        "    Y = list_personality[:,l]\n",
        "    model = XGBClassifier(**param)\n",
        "    # learning_rate = [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3]\n",
        "    # param_grid = dict(learning_rate=learning_rate)\n",
        "    \n",
        "    param_grid = {\n",
        "        'n_estimators' : [ 200, 300],\n",
        "        'learning_rate': [ 0.2, 0.3]\n",
        "        # 'learning_rate': [ 0.01, 0.1, 0.2, 0.3],\n",
        "        # 'max_depth': [2,3,4],\n",
        "    }\n",
        "    \n",
        "    \n",
        "    kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\n",
        "    grid_search = GridSearchCV(model, param_grid, scoring=\"neg_log_loss\", n_jobs=-1, cv=kfold)\n",
        "    grid_result = grid_search.fit(X, Y)\n",
        "\n",
        "    # summarize results\n",
        "    print(\"* Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "    means = grid_result.cv_results_['mean_test_score']\n",
        "    stds = grid_result.cv_results_['std_test_score']\n",
        "    params = grid_result.cv_results_['params']\n",
        "    for mean, stdev, param in zip(means, stds, params):\n",
        "        print(\"* %f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E) ...\n",
            "* Best: -0.497541 using {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.497541 (0.010130) with: {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.499836 (0.010853) with: {'learning_rate': 0.2, 'n_estimators': 300}\n",
            "* -0.504249 (0.009978) with: {'learning_rate': 0.3, 'n_estimators': 200}\n",
            "* -0.509720 (0.011784) with: {'learning_rate': 0.3, 'n_estimators': 300}\n",
            "NS: Intuition (N) – Sensing (S) ...\n",
            "* Best: -0.383073 using {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.383073 (0.009737) with: {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.393860 (0.010829) with: {'learning_rate': 0.2, 'n_estimators': 300}\n",
            "* -0.403081 (0.015213) with: {'learning_rate': 0.3, 'n_estimators': 200}\n",
            "* -0.425149 (0.015202) with: {'learning_rate': 0.3, 'n_estimators': 300}\n",
            "FT: Feeling (F) - Thinking (T) ...\n",
            "* Best: -0.525792 using {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.525792 (0.015644) with: {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.527687 (0.018638) with: {'learning_rate': 0.2, 'n_estimators': 300}\n",
            "* -0.539129 (0.022442) with: {'learning_rate': 0.3, 'n_estimators': 200}\n",
            "* -0.546775 (0.024071) with: {'learning_rate': 0.3, 'n_estimators': 300}\n",
            "JP: Judging (J) – Perceiving (P) ...\n",
            "* Best: -0.632055 using {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.632055 (0.009239) with: {'learning_rate': 0.2, 'n_estimators': 200}\n",
            "* -0.640074 (0.012337) with: {'learning_rate': 0.2, 'n_estimators': 300}\n",
            "* -0.652689 (0.015080) with: {'learning_rate': 0.3, 'n_estimators': 200}\n",
            "* -0.669889 (0.020823) with: {'learning_rate': 0.3, 'n_estimators': 300}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrRisaR4YNYX"
      },
      "source": [
        "#Prediction "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wULhPmCtr0VD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8449913b-6816-4c65-b6d7-897921d200fc"
      },
      "source": [
        "test_set = \"\"\"you can't even trust yourself sometimes\n",
        "I LOVE YOU SO MATCHA\n",
        "Take me back\n",
        "I know that you think that you're on your own. But just know that I am here, and I'll lead you home if you let me. \n",
        "energy (conservation) synchronicity space-time continuum causality\n",
        "GLOOM SLEEPER A VOID\n",
        "the part of my brain that holds back the bad thoughts\n",
        "The Marriage of the Sun and Moon: This takes place in a man when the heart and mind are joined in eternal union. It occurs when the positive and negative poles within are united, and from that union is made the Philosopher's Stone.\n",
        "TIME KILLS. Over enough time, all of us will reach our end. It is an inevitable fact, a mutable law of the universe. It is our responsibility to maximize our time. To make the most of it. To think long term so we may live long term. \n",
        "frequency = velocity / wavelength \"It really Hertz.\"\n",
        "cover your home in a dick welcome to the modern world esurance \"poor font choices\"\n",
        "OnlyPain\n",
        "Some angry guy with road rage just yelled out of his window, \"I'm gonna make your life a living hell!\" I yelled back, \"Thanks, but I'm not looking for a relationship right now.\"\n",
        "\"\"\"\n",
        "\n",
        "# The type is just a dummy so that the data prep fucntion can be reused\n",
        "mydata = pd.DataFrame(data={'type': ['ENFJ'], 'posts': [test_set]})\n",
        "\n",
        "test_set, dummy = pre_process_data(mydata, remove_stop_words=True)\n",
        "\n",
        "my_X_CV = CV.transform(test_set)\n",
        "my_X_TF =  TF.transform(my_X_CV).toarray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 of 1 rows\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZPTtYKrxAx5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "e2d9babb-7a4c-4e7c-9b6e-669659194ea3"
      },
      "source": [
        "param = {}\n",
        "param['n_estimators'] = 200\n",
        "param['max_depth'] = 2\n",
        "param['nthread'] = 8\n",
        "param['learning_rate'] = 0.2\n",
        "\n",
        "result = []\n",
        "# Let's train type indicator individually\n",
        "for l in range(len(type_pairs)):\n",
        "    print(\"%s ...\" % (type_pairs[l]))\n",
        "    \n",
        "    Y = list_personality[:,l]\n",
        "\n",
        "    # split data into train and test sets\n",
        "    seed = 7\n",
        "    test_size = 0.33\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
        "\n",
        "    # fit model on training data\n",
        "    model = XGBClassifier(**param)\n",
        "    model.fit(X_train, y_train)\n",
        "    \n",
        "    # make predictions for my  data\n",
        "    y_pred = model.predict(my_X_TF)\n",
        "    result.append(y_pred[0])\n",
        "    # print(\"* %s prediction: %s\" % (type_pairs[l], y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IE: Introversion (I) / Extroversion (E) ...\n",
            "NS: Intuition (N) – Sensing (S) ...\n",
            "FT: Feeling (F) - Thinking (T) ...\n",
            "JP: Judging (J) – Perceiving (P) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWc73beN0QlF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fb87a142-63a6-4638-b4be-2ead3ae83a7f"
      },
      "source": [
        "print(\"The result is: \", translate_back(result))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The result is:  INFP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXY9HJe2U5TE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}