Predicting_MBTI_using_BERT_algorithm.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q3IU8qa2V-3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing the necessary libraries\n",
        "import torch\n",
        "import pandas as pd\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from torch.utils.data import TensorDataset\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.notebook import tqdm\n",
        "!pip install transformers\n",
        "from transformers import BertTokenizer\n",
        "from transformers import BertForSequenceClassification\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYEagFlmJfCF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing the dataset into a DataFrame\n",
        "df = pd.read_csv('mbti_1.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55HVKcBlJfCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "8dff5351-70aa-4dcd-dc89-eece27c1a052"
      },
      "source": [
        "#showing the first 5 rows of the dataset\n",
        "df.head()"
      ],
      "execution_count": 3,
      "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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP,   I enjoyed our conversation the o...</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",
              "    </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...\n",
              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...\n",
              "4  ENTJ  'You're fired.|||That's another silly misconce..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dla2Z4AsVDDh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "c62d712c-cffe-4fdc-ac27-20e52464e7dd"
      },
      "source": [
        "#showing dataset infos\n",
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8675 entries, 0 to 8674\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   type    8675 non-null   object\n",
            " 1   posts   8675 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 135.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0vvG5XgJfCL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "60d8c6e2-a94c-4967-c2a1-9b9da0864501"
      },
      "source": [
        "#showing the posts of the first user in the dataset\n",
        "df.posts.values[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "\"'http://www.youtube.com/watch?v=qsXHcwe3krw|||http://41.media.tumblr.com/tumblr_lfouy03PMA1qa1rooo1_500.jpg|||enfp and intj moments  https://www.youtube.com/watch?v=iz7lE1g4XM4  sportscenter not top ten plays  https://www.youtube.com/watch?v=uCdfze1etec  pranks|||What has been the most life-changing experience in your life?|||http://www.youtube.com/watch?v=vXZeYwwRDw8   http://www.youtube.com/watch?v=u8ejam5DP3E  On repeat for most of today.|||May the PerC Experience immerse you.|||The last thing my INFJ friend posted on his facebook before committing suicide the next day. Rest in peace~   http://vimeo.com/22842206|||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...|||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 ...|||Welcome and stuff.|||http://playeressence.com/wp-content/uploads/2013/08/RED-red-the-pokemon-master-32560474-450-338.jpg  Game. Set. Match.|||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...|||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...|||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...|||Dear ENFP:  What were your favorite video games growing up and what are your now, current favorite video games? :cool:|||https://www.youtube.com/watch?v=QyPqT8umzmY|||It appears to be too late. :sad:|||There's someone out there for everyone.|||Wait... I thought confidence was a good thing.|||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...|||Yo entp ladies... if you're into a complimentary personality,well, hey.|||... when your main social outlet is xbox live conversations and even then you verbally fatigue quickly.|||http://www.youtube.com/watch?v=gDhy7rdfm14  I really dig the part from 1:46 to 2:50|||http://www.youtube.com/watch?v=msqXffgh7b8|||Banned because this thread requires it of me.|||Get high in backyard, roast and eat marshmellows in backyard while conversing over something intellectual, followed by massages and kisses.|||http://www.youtube.com/watch?v=Mw7eoU3BMbE|||http://www.youtube.com/watch?v=4V2uYORhQOk|||http://www.youtube.com/watch?v=SlVmgFQQ0TI|||Banned for too many b's in that sentence. How could you! Think of the B!|||Banned for watching movies in the corner with the dunces.|||Banned because Health class clearly taught you nothing about peer pressure.|||Banned for a whole host of reasons!|||http://www.youtube.com/watch?v=IRcrv41hgz4|||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...|||a pokemon world  an infj society  everyone becomes an optimist|||49142|||http://www.youtube.com/watch?v=ZRCEq_JFeFM|||http://discovermagazine.com/2012/jul-aug/20-things-you-didnt-know-about-deserts/desert.jpg|||http://oyster.ignimgs.com/mediawiki/apis.ign.com/pokemon-silver-version/d/dd/Ditto.gif|||http://www.serebii.net/potw-dp/Scizor.jpg|||Not all artists are artists because they draw. It's the idea that counts in forming something of your own... like a signature.|||Welcome to the robot ranks, person who downed my self-esteem cuz I'm not an avid signature artist like herself. :proud:|||Banned for taking all the room under my bed. Ya gotta learn to share with the roaches.|||http://www.youtube.com/watch?v=w8IgImn57aQ|||Banned for being too much of a thundering, grumbling kind of storm... yep.|||Ahh... old high school music I haven't heard in ages.   http://www.youtube.com/watch?v=dcCRUPCdB1w|||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...|||I like this person's mentality. He's a confirmed INTJ by the way. http://www.youtube.com/watch?v=hGKLI-GEc6M|||Move to the Denver area and start a new life for myself.'\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqruuMXiJfCO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "843a4069-a1fd-46ea-be7c-7ec5087cd4ce"
      },
      "source": [
        "#showing the number of users per personality type\n",
        "df.type.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INFP    1832\n",
              "INFJ    1470\n",
              "INTP    1304\n",
              "INTJ    1091\n",
              "ENTP     685\n",
              "ENFP     675\n",
              "ISTP     337\n",
              "ISFP     271\n",
              "ENTJ     231\n",
              "ISTJ     205\n",
              "ENFJ     190\n",
              "ISFJ     166\n",
              "ESTP      89\n",
              "ESFP      48\n",
              "ESFJ      42\n",
              "ESTJ      39\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5Jrf6k8hgdJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "e0a35c71-f537-41a3-ccd1-08cee9dfc17c"
      },
      "source": [
        "#visualizing the number of users per personality type using a histogram\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df.type)\n",
        "plt.xlabel('Types count');"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "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": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOC3VSkO5Pyz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "41168d8f-e8da-4a9a-9d71-a76627b57330"
      },
      "source": [
        "#removing URLs and punctuation from dataset\n",
        "for index in df.index.values:\n",
        "  df.posts.iloc[index] = ' '.join(df.posts.iloc[index].split('|||'))\n",
        "  df.posts.iloc[index] = re.sub(r\"http\\S+\",\"\",df.posts.iloc[index])\n",
        "  df.posts.iloc[index] = re.sub(r\"[-/@.?!_,:;()|0-9]\",\"\",df.posts.iloc[index])\n",
        "  df.posts.iloc[index] = ' '.join(df.posts.iloc[index].split('  '))\n",
        "df.head(10)\n",
        "\n",
        "\"\"\"\n",
        "saving the clean dataset into a csv file\n",
        "df.to_csv('mbti_clean_dataset.csv',index=False)\n",
        "\"\"\""
      ],
      "execution_count": 8,
      "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>' enfp and intj moments  sportscenter not top ...</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   Of course to which I say I know th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP  I enjoyed our conversation the oth...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired That's another silly misconcepti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>' Science is not perfect No scientist claims t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>'No I can't draw on my own nails haha Those we...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'I tend to build up a collection of things on ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>I'm not sure that's a good question The distin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>INTP</td>\n",
              "      <td>' I'm in this position where I have to actuall...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type                                              posts\n",
              "0  INFJ  ' enfp and intj moments  sportscenter not top ...\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...\n",
              "2  INTP  'Good one   Of course to which I say I know th...\n",
              "3  INTJ  'Dear INTP  I enjoyed our conversation the oth...\n",
              "4  ENTJ  'You're fired That's another silly misconcepti...\n",
              "5  INTJ  ' Science is not perfect No scientist claims t...\n",
              "6  INFJ  'No I can't draw on my own nails haha Those we...\n",
              "7  INTJ  'I tend to build up a collection of things on ...\n",
              "8  INFJ  I'm not sure that's a good question The distin...\n",
              "9  INTP  ' I'm in this position where I have to actuall..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpJ5p2PsJfCV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "055e0b47-6d53-4a94-a7e8-91da63933afd"
      },
      "source": [
        "#identifying the different classes of users in the dataset\n",
        "labels = df.type.unique()\n",
        "labels"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP',\n",
              "       'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEPiKr5dJfCY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "b33a3f01-f63c-4429-d960-2ee9b6fbeb05"
      },
      "source": [
        "#mapping personality types with their numberical representation\n",
        "labels2 = []\n",
        "label_rep = {}\n",
        "for index,labels in enumerate(labels):\n",
        "    label_rep[labels] = index\n",
        "    labels2.append(labels)\n",
        "labels2"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['INFJ',\n",
              " 'ENTP',\n",
              " 'INTP',\n",
              " 'INTJ',\n",
              " 'ENTJ',\n",
              " 'ENFJ',\n",
              " 'INFP',\n",
              " 'ENFP',\n",
              " 'ISFP',\n",
              " 'ISTP',\n",
              " 'ISFJ',\n",
              " 'ISTJ',\n",
              " 'ESTP',\n",
              " 'ESFP',\n",
              " 'ESTJ',\n",
              " 'ESFJ']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH96mi22JfCa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e25c8650-c9ef-4229-a5d1-518e79d80d73"
      },
      "source": [
        "label_rep"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ENFJ': 5,\n",
              " 'ENFP': 7,\n",
              " 'ENTJ': 4,\n",
              " 'ENTP': 1,\n",
              " 'ESFJ': 15,\n",
              " 'ESFP': 13,\n",
              " 'ESTJ': 14,\n",
              " 'ESTP': 12,\n",
              " 'INFJ': 0,\n",
              " 'INFP': 6,\n",
              " 'INTJ': 3,\n",
              " 'INTP': 2,\n",
              " 'ISFJ': 10,\n",
              " 'ISFP': 8,\n",
              " 'ISTJ': 11,\n",
              " 'ISTP': 9}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQFvj8sQJfCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "fb923cf9-1b02-4ef1-c582-f8aece9314f1"
      },
      "source": [
        "#replacing each personality type with its numerical representation\n",
        "df['label'] = df.type.replace(label_rep)\n",
        "df.head(10)"
      ],
      "execution_count": 12,
      "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>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>' enfp and intj moments  sportscenter not top ...</td>\n",
              "      <td>0</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'Good one   Of course to which I say I know th...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP  I enjoyed our conversation the oth...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired That's another silly misconcepti...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>' Science is not perfect No scientist claims t...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>'No I can't draw on my own nails haha Those we...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'I tend to build up a collection of things on ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>I'm not sure that's a good question The distin...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>INTP</td>\n",
              "      <td>' I'm in this position where I have to actuall...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type                                              posts  label\n",
              "0  INFJ  ' enfp and intj moments  sportscenter not top ...      0\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...      1\n",
              "2  INTP  'Good one   Of course to which I say I know th...      2\n",
              "3  INTJ  'Dear INTP  I enjoyed our conversation the oth...      3\n",
              "4  ENTJ  'You're fired That's another silly misconcepti...      4\n",
              "5  INTJ  ' Science is not perfect No scientist claims t...      3\n",
              "6  INFJ  'No I can't draw on my own nails haha Those we...      0\n",
              "7  INTJ  'I tend to build up a collection of things on ...      3\n",
              "8  INFJ  I'm not sure that's a good question The distin...      0\n",
              "9  INTP  ' I'm in this position where I have to actuall...      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWNJRXczJfCk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Splitting the dataset into training (85% of the data) and test (15% of the data) sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(df.index.values, df.label.values, test_size=0.15, random_state=17, stratify=df.label.values)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYjNyPs9JfCn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating a new column \"data_type\"\n",
        "df['data_type'] = ['not_set']*df.shape[0]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_lCQ-PjJfCs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#identifying the rows belonging to both training and test sets\n",
        "df.loc[x_train,'data_type'] = 'train'\n",
        "df.loc[x_test,'data_type'] = 'test'"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ejMUankJfCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "b37574ef-63b2-4ef8-fefe-392c4aca0e68"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 16,
      "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>label</th>\n",
              "      <th>data_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>' enfp and intj moments  sportscenter not top ...</td>\n",
              "      <td>0</td>\n",
              "      <td>train</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>1</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'Good one   Of course to which I say I know th...</td>\n",
              "      <td>2</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP  I enjoyed our conversation the oth...</td>\n",
              "      <td>3</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired That's another silly misconcepti...</td>\n",
              "      <td>4</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>' Science is not perfect No scientist claims t...</td>\n",
              "      <td>3</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>'No I can't draw on my own nails haha Those we...</td>\n",
              "      <td>0</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'I tend to build up a collection of things on ...</td>\n",
              "      <td>3</td>\n",
              "      <td>test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>I'm not sure that's a good question The distin...</td>\n",
              "      <td>0</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>INTP</td>\n",
              "      <td>' I'm in this position where I have to actuall...</td>\n",
              "      <td>2</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type                                              posts  label data_type\n",
              "0  INFJ  ' enfp and intj moments  sportscenter not top ...      0     train\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...      1     train\n",
              "2  INTP  'Good one   Of course to which I say I know th...      2     train\n",
              "3  INTJ  'Dear INTP  I enjoyed our conversation the oth...      3     train\n",
              "4  ENTJ  'You're fired That's another silly misconcepti...      4      test\n",
              "5  INTJ  ' Science is not perfect No scientist claims t...      3     train\n",
              "6  INFJ  'No I can't draw on my own nails haha Those we...      0      test\n",
              "7  INTJ  'I tend to build up a collection of things on ...      3      test\n",
              "8  INFJ  I'm not sure that's a good question The distin...      0     train\n",
              "9  INTP  ' I'm in this position where I have to actuall...      2     train"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkU4Vq0lJfCz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fe600c27-7786-4efe-fca1-3477bfba12de"
      },
      "source": [
        "#showing the amount of data reserved for training and test per personality type\n",
        "df.groupby(['type','label','data_type']).count()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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></th>\n",
              "      <th></th>\n",
              "      <th>posts</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th>label</th>\n",
              "      <th>data_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ENFJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
              "      <th>test</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ENFP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
              "      <th>test</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ENTJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
              "      <th>test</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ENTP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>test</th>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ESFJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">15</th>\n",
              "      <th>test</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ESFP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
              "      <th>test</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ESTJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
              "      <th>test</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ESTP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
              "      <th>test</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">INFJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
              "      <th>test</th>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>1249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">INFP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
              "      <th>test</th>\n",
              "      <td>275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>1557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">INTJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>test</th>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">INTP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>test</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>1108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ISFJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
              "      <th>test</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ISFP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
              "      <th>test</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ISTJ</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
              "      <th>test</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">ISTP</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
              "      <th>test</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      posts\n",
              "type label data_type       \n",
              "ENFJ 5     test          28\n",
              "           train        162\n",
              "ENFP 7     test         101\n",
              "           train        574\n",
              "ENTJ 4     test          35\n",
              "           train        196\n",
              "ENTP 1     test         103\n",
              "           train        582\n",
              "ESFJ 15    test           6\n",
              "           train         36\n",
              "ESFP 13    test           7\n",
              "           train         41\n",
              "ESTJ 14    test           6\n",
              "           train         33\n",
              "ESTP 12    test          13\n",
              "           train         76\n",
              "INFJ 0     test         221\n",
              "           train       1249\n",
              "INFP 6     test         275\n",
              "           train       1557\n",
              "INTJ 3     test         164\n",
              "           train        927\n",
              "INTP 2     test         196\n",
              "           train       1108\n",
              "ISFJ 10    test          25\n",
              "           train        141\n",
              "ISFP 8     test          41\n",
              "           train        230\n",
              "ISTJ 11    test          31\n",
              "           train        174\n",
              "ISTP 9     test          50\n",
              "           train        287"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnXnlic8bmrS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4e5f36254f134aef8a58ef907765a9d0",
            "7d456a6af1d84e2a845d228138feb2fb",
            "65adee19b4344f5ba3bd06a8bf865e21",
            "5c117aeb3f124e959434b862fb12b06c",
            "6e94a2260b90450e8888c1c55c98552d",
            "48b3cccb74a24c389052b43dd492aa4f",
            "d85135e4a4d442da9561547821dfd06a",
            "0387104626014402a96841394de7bb61"
          ]
        },
        "outputId": "6da5c453-d758-47f0-baf0-c67560ab12f6"
      },
      "source": [
        "#creating a BERT tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', \n",
        "                                          do_lower_case=True)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e5f36254f134aef8a58ef907765a9d0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztbkIkXXbqOP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#encoding the data using our tokenizer\n",
        "encoded_data_train = tokenizer.batch_encode_plus(\n",
        "    df[df.data_type=='train'].posts.values, \n",
        "    add_special_tokens=True, \n",
        "    return_attention_mask=True, \n",
        "    pad_to_max_length=True, \n",
        "    max_length=256, \n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "encoded_data_test = tokenizer.batch_encode_plus(\n",
        "    df[df.data_type=='test'].posts.values, \n",
        "    add_special_tokens=True, \n",
        "    return_attention_mask=True, \n",
        "    pad_to_max_length=True, \n",
        "    max_length=256, \n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3Kin1vmbu2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "80e812f9-c68c-4e52-db1a-7968bb72e186"
      },
      "source": [
        "#showing an example of data tokenized and encoded by BERT tokenizer\n",
        "encoded_data_test"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  101,  1005,  2017,  ...,  2026, 13148,   102],\n",
              "        [  101,  1005,  2053,  ...,  2016,  2515,   102],\n",
              "        [  101,  1005,  1045,  ...,  4426,  2079,   102],\n",
              "        ...,\n",
              "        [  101,  1005,  1045,  ..., 10047,  5457,   102],\n",
              "        [  101,  1005,  2023,  ...,  3798,  1997,   102],\n",
              "        [  101,  1005,  2009,  ...,  1055,  2035,   102]]), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1,  ..., 1, 1, 1],\n",
              "        [1, 1, 1,  ..., 1, 1, 1],\n",
              "        [1, 1, 1,  ..., 1, 1, 1],\n",
              "        ...,\n",
              "        [1, 1, 1,  ..., 1, 1, 1],\n",
              "        [1, 1, 1,  ..., 1, 1, 1],\n",
              "        [1, 1, 1,  ..., 1, 1, 1]])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMF3O35WeAP8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#preparing inputs for BERT model\n",
        "input_ids_train = encoded_data_train['input_ids']\n",
        "attention_masks_train = encoded_data_train['attention_mask']\n",
        "labels_train = torch.tensor(df[df.data_type=='train'].label.values)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEQvSrPweFuZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_ids_test = encoded_data_test['input_ids']\n",
        "attention_masks_test = encoded_data_test['attention_mask']\n",
        "labels_test = torch.tensor(df[df.data_type=='test'].label.values)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47_6VWONeHr-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8590cc32-7c18-4f01-9672-480dcf7e97bf"
      },
      "source": [
        "dataset_train = TensorDataset(input_ids_train,attention_masks_train,labels_train)\n",
        "dataset_test = TensorDataset(input_ids_test,attention_masks_test,labels_test)\n",
        "len(dataset_train)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-E3ECRVFePMq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114,
          "referenced_widgets": [
            "c9d8fe3db1864a78b66d2b6b5b34764c",
            "4da97c3413064137ada31d718d988892",
            "7ad4a121159340e389c7c96c8bc96540",
            "16b9a5bbfb01427cb3c661e341dbf014",
            "2df3eb69cf8b44889c6afa271c97cbb4",
            "5005f3a2cd7e4c4da890c53b5071b6db",
            "dcae4fb6322a47da98a5fbdc7cab4c03",
            "8663007a3b1849b384c0d8b7bcb2ef7f",
            "5ee9d490d3f844e18e794fde067ee320",
            "a4912015aa69468baabb2ea460df5465",
            "a5d9fb8eb46c4b439b62d4b21c2c50a8",
            "2501e39b8b80441c88f955d70264095c",
            "6745553360e240868f1b7746bce5fa6c",
            "4693c33adeb44a228fb5e8b132495a0a",
            "f879587b9b6c450da5f22548ebee361f",
            "46b1633c53ca41d2bfd7ebbbecb42007"
          ]
        },
        "outputId": "f1b0b2d5-fbbc-4ef7-906a-f43769fea873"
      },
      "source": [
        "#creating a BertForSequenceClassification model\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
        "                                                      num_labels=len(label_rep),\n",
        "                                                      output_attentions=False,\n",
        "                                                      output_hidden_states=False)\n",
        "\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9d8fe3db1864a78b66d2b6b5b34764c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ee9d490d3f844e18e794fde067ee320",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaNzrWUEf-HV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XkspjU2jP_I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining the batch size\n",
        "batch_size = 32\n",
        "#less for limited hardware ressources. Example : 4"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffr44FSylE1W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating dataloader to load the data while training\n",
        "dataloader_train = DataLoader(dataset_train, \n",
        "                              sampler=RandomSampler(dataset_train), \n",
        "                              batch_size=batch_size)\n",
        "\n",
        "dataloader_test = DataLoader(dataset_test, \n",
        "                                   sampler=SequentialSampler(dataset_test), \n",
        "                                   batch_size=batch_size)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiaYNIkPlGps",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from transformers import AdamW, get_linear_schedule_with_warmup"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFFWqOXfNTJ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "outputId": "a16ed733-569f-4a7b-8132-200fa360f6a0"
      },
      "source": [
        "\"\"\"\n",
        "Testing different learning rates for AdamW optimizer in one epoch\n",
        "The following values of learning rates were tested on this code\n",
        "Recommanded learning rate for BERT between 2e-5 and 5e-5\n",
        "\"\"\"\n",
        "learning_rate = ['2e-5','3e-5','5e-5','6e-5']\n",
        "accuracy = [0.28,0.32,0.45,0.35]\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.title('Testing different learning rates for AdamW optimizer in one epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Learning Rate')\n",
        "plt.legend()\n",
        "plt.plot(learning_rate,accuracy)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff76f059128>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmfeSuknsOcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating an AdamW optimizer\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr=5e-5, \n",
        "                  eps=1e-8)\n",
        "#recommanded epsilon : 1e-8"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6uzp8NBsQ3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining the number of epochs\n",
        "epochs = 10"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvO3wPAusTQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating a scheduler to update the learning rate while training\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
        "                                            num_warmup_steps = 0.1,\n",
        "                                            num_training_steps=len(dataloader_train)*epochs)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kf39NzjjtbAA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTOnAv5wvXMT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEZUyg18vmZB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining the global accuracy function\n",
        "def f1_score_func(preds, labels):\n",
        "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    return f1_score(labels_flat, preds_flat, average='weighted')"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RKERtDSxVJn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining the accuracy per class function\n",
        "def accuracy_per_class(preds, labels):\n",
        "    label_dict_inverse = {v: k for k, v in label_rep.items()}\n",
        "    \n",
        "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "\n",
        "    for label in np.unique(labels_flat):\n",
        "        y_preds = preds_flat[labels_flat==label]\n",
        "        y_true = labels_flat[labels_flat==label]\n",
        "        print(f'Class: {label_dict_inverse[label]}')\n",
        "        print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\\n')\n",
        "    return preds_flat, labels_flat"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVYlV3IlDUnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34bab5f2-5cb2-4e09-ee7a-a4c3184011ba"
      },
      "source": [
        "#Defining the device on which we're going to run the code\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "print(device)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3C-jgsW7sv-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Defining the evaluation function to get predictions and true types\n",
        "def evaluate(dataloader_test):\n",
        "\n",
        "    model.eval()\n",
        "    \n",
        "    loss_val_total = 0\n",
        "    predictions, true_vals = [], []\n",
        "    \n",
        "    for batch in dataloader_test:\n",
        "        \n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "        \n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels':         batch[2],\n",
        "                 }\n",
        "\n",
        "        with torch.no_grad():        \n",
        "            outputs = model(**inputs)\n",
        "            \n",
        "        loss = outputs[0]\n",
        "        logits = outputs[1]\n",
        "        loss_val_total += loss.item()\n",
        "\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        label_ids = inputs['labels'].cpu().numpy()\n",
        "        predictions.append(logits)\n",
        "        true_vals.append(label_ids)\n",
        "    \n",
        "    loss_val_avg = loss_val_total/len(dataloader_test) \n",
        "    \n",
        "    predictions = np.concatenate(predictions, axis=0)\n",
        "    true_vals = np.concatenate(true_vals, axis=0)\n",
        "            \n",
        "    return loss_val_avg, predictions, true_vals"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXNoaoU-HFAK",
        "colab_type": "code",
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          "height": 906,
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        },
        "outputId": "f26d6425-f2d9-463d-c1b8-5a5ae8965113"
      },
      "source": [
        "#Training \n",
        "\n",
        "import random\n",
        "seed_value = 17\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "torch.manual_seed(seed_value)\n",
        "torch.cuda.manual_seed_all(seed_value)\n",
        "f1_score_history = []\n",
        "f1_score_train_history = []\n",
        "train_loss_history = []\n",
        "train_test_history = []\n",
        "\n",
        "for epoch in tqdm(range(1, epochs+1)):\n",
        "    \n",
        "    model.train()\n",
        "    \n",
        "    loss_train_total = 0\n",
        "\n",
        "    progress_bar = tqdm(dataloader_train, desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n",
        "    for batch in progress_bar:\n",
        "\n",
        "        model.zero_grad()\n",
        "        \n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "        \n",
        "        inputs = {'input_ids':      batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels':         batch[2],\n",
        "                 }       \n",
        "\n",
        "        outputs = model(**inputs)\n",
        "        \n",
        "        loss = outputs[0]\n",
        "        loss_train_total += loss.item()\n",
        "        loss.backward()\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        \n",
        "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})\n",
        "         \n",
        "        \n",
        "    torch.save(model.state_dict(), f'finetuned_BERT_epoch_{epoch}.model')\n",
        "        \n",
        "    tqdm.write(f'\\nEpoch {epoch}')\n",
        "    \n",
        "    loss_train_avg = loss_train_total/len(dataloader_train)            \n",
        "    tqdm.write(f'Training loss: {loss_train_avg}')\n",
        "    \n",
        "    val_loss, predictions, true_vals = evaluate(dataloader_test)\n",
        "    tr_loss, predictions_train, true_vals_train = evaluate(dataloader_train)\n",
        "    val_f1 = f1_score_func(predictions, true_vals)\n",
        "    tr_f1 = f1_score_func(predictions_train, true_vals_train)\n",
        "    f1_score_history.append(val_f1)\n",
        "    f1_score_train_history.append(tr_f1)\n",
        "    tqdm.write(f'Test loss: {val_loss}')\n",
        "    train_test_history.append(val_loss)\n",
        "    tqdm.write(f'F1 Score test (Weighted): {val_f1}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 1', max=231.0, style=ProgressStyle(description_widt…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 1\n",
            "Training loss: 2.0688837075130246\n",
            "Test loss: 1.7883009241848458\n",
            "F1 Score (Weighted): 0.4286282476007504\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch 2', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 2\n",
            "Training loss: 1.617831041286518\n",
            "Test loss: 1.5653505005487582\n",
            "F1 Score (Weighted): 0.5103480190822232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 3\n",
            "Training loss: 1.286478349398741\n",
            "Test loss: 1.6269895129087495\n",
            "F1 Score (Weighted): 0.5080979182526982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch 4', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 4\n",
            "Training loss: 0.9797707001368204\n",
            "Test loss: 1.6999986549703086\n",
            "F1 Score (Weighted): 0.5223945909537996\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch 5', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 5\n",
            "Training loss: 0.6720608310911046\n",
            "Test loss: 1.9276001453399658\n",
            "F1 Score (Weighted): 0.4980500266574749\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 6', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 6\n",
            "Training loss: 0.4465039252460777\n",
            "Test loss: 2.054774586747332\n",
            "F1 Score (Weighted): 0.5042282812251806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dcb3ba4067ef4a7b94b94a91a30592a2",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 7', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 7\n",
            "Training loss: 0.30711663553080004\n",
            "Test loss: 2.2394956117723046\n",
            "F1 Score (Weighted): 0.4906617941605253\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 8', max=231.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 8\n",
            "Training loss: 0.20699453421614386\n",
            "Test loss: 2.387341641798252\n",
            "F1 Score (Weighted): 0.49181760378450684\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 9\n",
            "Training loss: 0.13895735022051509\n",
            "Test loss: 2.4679925761571746\n",
            "F1 Score (Weighted): 0.5094793188183633\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch 10', max=231.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "Epoch 10\n",
            "Training loss: 0.10729642118855472\n",
            "Test loss: 2.4919467873689607\n",
            "F1 Score (Weighted): 0.5072270427770138\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkx5AjNQK-J9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "outputId": "b95b5749-6a15-4543-9611-edadebe4392e"
      },
      "source": [
        "#Plotting the training and test accuracy per epoch\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.plot(range(1,epochs+1),f1_score_train_history,label='Training loss')\n",
        "plt.plot(range(1,epochs+1),f1_score_history,label='Test loss')\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe93ceadac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsPQVhzwCobg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "00b9dc5a-fc4f-484a-910d-548703b2d0a5"
      },
      "source": [
        "#creating a BertForSequenceClassification model\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
        "                                                      num_labels=len(label_rep),\n",
        "                                                      output_attentions=False,\n",
        "                                                      output_hidden_states=False)\n",
        "\n",
        "model.to(device)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=16, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUuHyyNIQkyl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1853c09-95e4-4eb3-9622-7a71ce19065c"
      },
      "source": [
        "#Loading the model we saved in a file\n",
        "model.load_state_dict(torch.load('finetuned_BERT_epoch_4.model', map_location=torch.device('cuda')))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBx7V3tAQj74",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Getting the predictions and the types of the users in the test set\n",
        "_, predictions, true_vals = evaluate(dataloader_test)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCZ5S65gDVQH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "outputId": "9c0e9963-bf8e-48bf-aaa5-ff57d7fbc7c3"
      },
      "source": [
        "#Getting the accuracy per class of each personality type\n",
        "y_preds, y_true = accuracy_per_class(predictions, true_vals)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class: INFJ\n",
            "Accuracy: 111/221\n",
            "\n",
            "Class: ENTP\n",
            "Accuracy: 43/103\n",
            "\n",
            "Class: INTP\n",
            "Accuracy: 116/196\n",
            "\n",
            "Class: INTJ\n",
            "Accuracy: 95/164\n",
            "\n",
            "Class: ENTJ\n",
            "Accuracy: 18/35\n",
            "\n",
            "Class: ENFJ\n",
            "Accuracy: 12/28\n",
            "\n",
            "Class: INFP\n",
            "Accuracy: 175/275\n",
            "\n",
            "Class: ENFP\n",
            "Accuracy: 50/101\n",
            "\n",
            "Class: ISFP\n",
            "Accuracy: 16/41\n",
            "\n",
            "Class: ISTP\n",
            "Accuracy: 20/50\n",
            "\n",
            "Class: ISFJ\n",
            "Accuracy: 13/25\n",
            "\n",
            "Class: ISTJ\n",
            "Accuracy: 13/31\n",
            "\n",
            "Class: ESTP\n",
            "Accuracy: 3/13\n",
            "\n",
            "Class: ESFP\n",
            "Accuracy: 0/7\n",
            "\n",
            "Class: ESTJ\n",
            "Accuracy: 0/6\n",
            "\n",
            "Class: ESFJ\n",
            "Accuracy: 1/6\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkUBVjKbopgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "74b16577-154a-4f66-a8d1-dde76ef5d16d"
      },
      "source": [
        "#Plotting the confusion matrix in order to identifiy the misclassified types\n",
        "from sklearn import metrics\n",
        "def show_confusion_matrix(confusion_matrix):\n",
        "  plt.figure(figsize=(20,10))\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
        "  plt.ylabel('True Type')\n",
        "  plt.xlabel('Predicted Type');\n",
        "\n",
        "cm = metrics.confusion_matrix(y_true, y_preds)\n",
        "df_cm = pd.DataFrame(cm, index=labels2, columns=labels2)\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbnD7o6GPvXk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "In the next step, we're going to try to understand the reason why certain personality types are being misclassified.\n",
        "We're gonna use INFP and INFJ personality types as an example.\n",
        "We're going to identify the vocabulary used in commun between these two types and calculate its percentage\n",
        "\"\"\"\n",
        "infj = df[df.type=='INFJ'].posts.values"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSe-vjJf2a38",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "infp = df[df.type=='INFP'].posts.values"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGwCsyj32iX-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d102e280-5c00-4940-fe01-a48ed1c603e2"
      },
      "source": [
        "infj_tokens = []\n",
        "for post in infj:\n",
        "  tokens = tokenizer.tokenize(post)\n",
        "  infj_tokens = infj_tokens + tokens\n",
        "infj_tokens = list(set(infj_tokens))\n",
        "len(infj_tokens)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20629"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqJy57Zq5zgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3cecaa0a-3f59-43c6-8339-64c235db69ef"
      },
      "source": [
        "infp_tokens = []\n",
        "for post in infp:\n",
        "  tokens = tokenizer.tokenize(post)\n",
        "  infp_tokens = infp_tokens + tokens\n",
        "infp_tokens = list(set(infp_tokens))\n",
        "len(infp_tokens)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21417"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oorA9QOS8UF5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def intersection(lst1, lst2): \n",
        "    lst3 = [value for value in lst1 if value in lst2] \n",
        "    return lst3 "
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjEj2C288zTI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inter = intersection(infj_tokens,infp_tokens)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YD1-Qw-384F6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bad1275c-c611-4a79-f9dd-1747b9c12739"
      },
      "source": [
        "len(inter)/len(infp_tokens)*100"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "88.691226595695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctMYcBNXD1Hh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1880fe06-f53f-4e5e-cd2a-6b1a68f5e05a"
      },
      "source": [
        "len(inter)/len(infj_tokens)*100"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92.07911192980755"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqwuafihqvN2",
        "colab_type": "text"
      },
      "source": [
        "The results obtained are realistic, since INFPs and INFJs share many personality traits according to psychologists."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YcBqbd-XrT4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Identifying the missclassifications in each personaity trait\n",
        "def translate(list1 , list2):\n",
        "  lst=[]\n",
        "  for x in list1:\n",
        "    if x in list2:\n",
        "      lst.append(0)\n",
        "    else:\n",
        "      lst.append(1)\n",
        "  return(lst)\n",
        "y_true_ie=translate(y_true,[0,6,3,2,10,8,11,9])\n",
        "y_preds_ie=translate(y_preds,[0,6,3,2,10,8,11,9])\n",
        "  \n",
        "y_true_sn=translate(y_true,[15,13,14,12,10,8,11,9])\n",
        "y_preds_sn=translate(y_preds,[15,13,14,12,10,8,11,9])\n",
        "\n",
        "y_true_tf=translate(y_true,[4,1,14,12,3,2,11,9])\n",
        "y_preds_tf=translate(y_preds,[4,1,14,12,3,2,11,9])\n",
        "\n",
        "y_true_jp=translate(y_true,[5,4,15,14,0,3,10,11])\n",
        "y_preds_jp=translate(y_preds,[5,4,15,14,0,3,10,11])"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kB0mWh40vVwg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "839ea116-5d0d-490f-a718-de87ea7440c1"
      },
      "source": [
        "#Plotting the confusion matrix for Introverts vs Extroverts\n",
        "cm = metrics.confusion_matrix(y_true_ie, y_preds_ie)\n",
        "df_cm = pd.DataFrame(cm, index=['Introvert','Extrovert'], columns=['Introvert','Extrovert'])\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhn-XEwTvakt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "01a7d9a7-3dd8-4aab-81d3-ff161bc56c1d"
      },
      "source": [
        "#Plotting the confusion matrix for Sensing vs Intuitive\n",
        "cm = metrics.confusion_matrix(y_true_sn, y_preds_sn)\n",
        "df_cm = pd.DataFrame(cm, index=['Sensing','Intuition'], columns=['Sensing','Intuition'])\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuvOWfuUve4O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "371851c3-335e-4447-902e-95d3bcfe41ee"
      },
      "source": [
        "#Plotting the confusion matrix for Thinking vs Feeling\n",
        "cm = metrics.confusion_matrix(y_true_tf, y_preds_tf)\n",
        "df_cm = pd.DataFrame(cm, index=['Thinking','Feeling'], columns=['Thinking','Feeling'])\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95Bhb2iEvi2z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "99d7a7e5-269d-4fed-eabb-e58956752c71"
      },
      "source": [
        "#Plotting the confusion matrix for Judging vs Perceiving\n",
        "cm = metrics.confusion_matrix(y_true_jp, y_preds_jp)\n",
        "df_cm = pd.DataFrame(cm, index=['Judging','Perceiving'], columns=['Judging','Perceiving'])\n",
        "show_confusion_matrix(df_cm)\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}