personality_development_SOC.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "_DNFSOVn4Pvq"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "from scipy.spatial.distance import cdist\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "# k_dic = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "3zHqlFcH7bgJ"
      },
      "outputs": [],
      "source": [
        "def k_means(k, data1, noIter=1000):\n",
        "    data = data1.values\n",
        "    # print(data)\n",
        "    n = len(data)\n",
        "    index = np.random.randint(0, n, size= (k,1))\n",
        "    centroids = []\n",
        "    \n",
        "    for x in index:\n",
        "        centroids.extend(data[x][:])\n",
        "    \n",
        "    centroids = np.array(centroids)\n",
        "    print(centroids)\n",
        "    \n",
        "#   Instead of looping through all points and finding the distance from each centroid we can do this    \n",
        "    distances = cdist(data, centroids ,'euclidean')\n",
        "    \n",
        "#   Using the distance to find the closest centroid    \n",
        "    points_ = [] \n",
        "    for distance in distances:\n",
        "        temp = min(distance)\n",
        "        res = [i for i, j in enumerate(distance) if j == temp]\n",
        "        points_.append(res[0])\n",
        "    \n",
        "    points = np.array(points_)\n",
        "#     a shorter way of find the minimum distance from each centroid \n",
        "#     points = np.array([np.argmin(i) for i in distances])\n",
        "\n",
        "    for _ in range(noIter):\n",
        "        centroids = []\n",
        "        \n",
        "        for index in range(k):\n",
        "            # data[points == index] gives us all the points that are closest to the centroid (labelled as index)\n",
        "            # .mean() is used to find the mean of the points, axis=0 means along the column, i.e., the points get \n",
        "            # added\n",
        "            # print(data[points == index])\n",
        "            temp_cent = data[points==index].mean(axis=0) \n",
        "            centroids.append(temp_cent)\n",
        "            \n",
        "        centroids = np.array(centroids)\n",
        "        distances = cdist(data, centroids ,'euclidean')\n",
        "        points_=[]\n",
        "        for distance in distances:\n",
        "            temp = min(distance)\n",
        "            res = [i for i, j in enumerate(distance) if j == temp]\n",
        "            points_.append(res[0])\n",
        "        points = np.array(points_) \n",
        "    return points, centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "kDI951KQJFFL"
      },
      "outputs": [
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              "         EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  \\\n",
              "0         4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0  ...   \n",
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              "4         3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0  ...   \n",
              "...       ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
              "1015336   4.0   2.0   4.0   3.0   4.0   3.0   3.0   3.0   3.0    3.0  ...   \n",
              "1015337   4.0   3.0   4.0   3.0   3.0   3.0   4.0   4.0   3.0    3.0  ...   \n",
              "1015338   4.0   2.0   4.0   3.0   5.0   1.0   4.0   2.0   4.0    4.0  ...   \n",
              "1015339   2.0   4.0   3.0   4.0   2.0   2.0   1.0   4.0   2.0    4.0  ...   \n",
              "1015340   4.0   2.0   4.0   2.0   4.0   1.0   4.0   2.0   4.0    4.0  ...   \n",
              "\n",
              "                    dateload  screenw  screenh  introelapse  testelapse  \\\n",
              "0        2016-03-03 02:01:01    768.0   1024.0          9.0       234.0   \n",
              "1        2016-03-03 02:01:20   1360.0    768.0         12.0       179.0   \n",
              "2        2016-03-03 02:01:56   1366.0    768.0          3.0       186.0   \n",
              "3        2016-03-03 02:02:02   1920.0   1200.0        186.0       219.0   \n",
              "4        2016-03-03 02:02:57   1366.0    768.0          8.0       315.0   \n",
              "...                      ...      ...      ...          ...         ...   \n",
              "1015336  2018-11-08 12:04:58   1920.0   1080.0          3.0       160.0   \n",
              "1015337  2018-11-08 12:07:18   1920.0   1080.0          3.0       122.0   \n",
              "1015338  2018-11-08 12:07:49   1920.0   1080.0          2.0       135.0   \n",
              "1015339  2018-11-08 12:08:34   1920.0   1080.0          6.0       212.0   \n",
              "1015340  2018-11-08 12:08:45   1920.0   1080.0          3.0       176.0   \n",
              "\n",
              "         endelapse  IPC  country  lat_appx_lots_of_err  long_appx_lots_of_err  \n",
              "0                6    1       GB               51.5448                 0.1991  \n",
              "1               11    1       MY                3.1698                101.706  \n",
              "2                7    1       GB               54.9119                -1.3833  \n",
              "3                7    1       GB                 51.75                  -1.25  \n",
              "4               17    2       KE                   1.0                   38.0  \n",
              "...            ...  ...      ...                   ...                    ...  \n",
              "1015336         10    2       US               39.9883               -75.2208  \n",
              "1015337          7    1       US                  38.0                  -97.0  \n",
              "1015338         12    6       US               36.1473                -86.777  \n",
              "1015339          8    1       US               34.1067              -117.8067  \n",
              "1015340          9    1       US                  38.0                  -97.0  \n",
              "\n",
              "[1015341 rows x 110 columns]"
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          },
          "metadata": {},
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      "source": [
        "df=pd.read_csv('data-final.csv', delimiter='\\t')\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [
        {
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              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78791</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78792</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78793</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78794</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78795</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78796</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78797</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78798</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9 rows × 50 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  OPN1  \\\n",
              "78790     1     5     1     5     1     3     1     5     1      4  ...     5   \n",
              "78791     2     4     3     2     3     4     3     4     4      5  ...     3   \n",
              "78792     5     3     2     2     4     1     4     4     3      3  ...     4   \n",
              "78793     3     3     2     5     3     3     3     5     2      5  ...     3   \n",
              "78794     1     3     3     3     1     4     5     5     1      5  ...     1   \n",
              "78795     3     3     3     3     3     3     3     3     3      3  ...     3   \n",
              "78796     5     1     5     1     5     1     5     2     5      2  ...     4   \n",
              "78797     3     1     2     5     1     2     1     5     1      5  ...     3   \n",
              "78798     3     1     3     3     4     2     2     3     2      4  ...     4   \n",
              "\n",
              "       OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  \n",
              "78790     2     4     2     4     2     3     5     5      4  \n",
              "78791     2     3     3     3     2     3     2     2      3  \n",
              "78792     2     5     2     5     1     5     4     4      5  \n",
              "78793     3     5     2     3     3     1     3     5      4  \n",
              "78794     3     3     2     3     4     1     1     3      2  \n",
              "78795     3     3     3     3     3     3     3     3      3  \n",
              "78796     2     5     3     5     2     5     3     5      5  \n",
              "78797     3     3     3     4     3     5     2     3      3  \n",
              "78798     1     4     1     4     2     5     4     5      2  \n",
              "\n",
              "[9 rows x 50 columns]"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = df[df.columns[0:50]]\n",
        "Y = X[0:1000000].fillna(3)\n",
        "Y = Y.replace(to_replace=0, value=1)\n",
        "df = X.head(600000)\n",
        "df = df.fillna(3)\n",
        "df = df.astype(int)\n",
        "df = df.replace(to_replace=0, value=1)\n",
        "df[78790:78799]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hA-yJl0sot_y",
        "outputId": "02ae543a-3a40-4f6f-f716-44eab421f02b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3 4 2 1 1 4 2 5 2 5 4 4 4 3 2 1 3 1 1 3 1 5 2 4 2 5 4 2 5 4 4 4 1 1\n",
            "  4 5 4 3 5 2 4 1 4 1 2 4 5 2]\n",
            " [1 4 2 5 5 5 1 4 1 5 4 2 5 5 4 3 4 2 4 5 2 5 1 4 3 5 2 4 4 4 3 5 5 3 1 5\n",
            "  3 3 4 3 3 2 4 1 4 1 5 3 4 5]\n",
            " [3 2 4 2 5 2 4 4 2 2 4 4 4 3 4 2 2 2 2 1 5 5 1 5 1 5 1 5 5 5 4 4 5 1 2 2\n",
            "  4 1 4 4 5 2 3 2 5 3 5 5 4 5]\n",
            " [1 4 1 5 4 2 1 4 2 5 4 5 5 2 2 1 4 4 5 4 2 1 5 4 2 4 5 4 1 3 1 5 4 5 1 5\n",
            "  1 5 1 1 5 1 5 1 5 1 4 2 5 5]\n",
            " [4 1 5 2 5 1 5 1 5 4 2 2 5 2 2 3 2 2 4 2 5 5 5 4 1 2 4 4 5 5 5 1 5 1 5 1\n",
            "  5 1 5 5 5 3 5 5 5 4 5 4 1 3]]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "k = 5\n",
        "points, centroids = k_means(k, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuO3dYIqpUpp",
        "outputId": "0fa084c1-b8db-4bf4-b7bf-55a849a5a19a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "600000\n"
          ]
        }
      ],
      "source": [
        "print(len(points))\n",
        "points=np.array(points)\n",
        "df['category']=points\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {},
      "outputs": [],
      "source": [
        "l = [0]*400000\n",
        "points = np.append(points, l)\n",
        "points.shape\n",
        "Y['category'] = points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4BqWJwJpy2X",
        "outputId": "f386fed6-3f13-477a-f0b4-909b92ac0c77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['EXT1', 'EXT2', 'EXT3', 'EXT4', 'EXT5', 'EXT6', 'EXT7', 'EXT8', 'EXT9', 'EXT10', 'EST1', 'EST2', 'EST3', 'EST4', 'EST5', 'EST6', 'EST7', 'EST8', 'EST9', 'EST10', 'AGR1', 'AGR2', 'AGR3', 'AGR4', 'AGR5', 'AGR6', 'AGR7', 'AGR8', 'AGR9', 'AGR10', 'CSN1', 'CSN2', 'CSN3', 'CSN4', 'CSN5', 'CSN6', 'CSN7', 'CSN8', 'CSN9', 'CSN10', 'OPN1', 'OPN2', 'OPN3', 'OPN4', 'OPN5', 'OPN6', 'OPN7', 'OPN8', 'OPN9', 'OPN10', 'category']\n"
          ]
        },
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .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>EXT1</th>\n",
              "      <th>EXT2</th>\n",
              "      <th>EXT3</th>\n",
              "      <th>EXT4</th>\n",
              "      <th>EXT5</th>\n",
              "      <th>EXT6</th>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "    </tr>\n",
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              "    <tr>\n",
              "      <th>599995</th>\n",
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              "      <td>...</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599997</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599998</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599999</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600000 rows × 51 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  \\\n",
              "0          4     1     5     2     5     1     5     2     4      1  ...   \n",
              "1          3     5     3     4     3     3     2     5     1      5  ...   \n",
              "2          2     3     4     4     3     2     1     3     2      5  ...   \n",
              "3          2     2     2     3     4     2     2     4     1      4  ...   \n",
              "4          3     3     3     3     5     3     3     5     3      4  ...   \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
              "599995     1     4     2     5     2     2     2     5     1      5  ...   \n",
              "599996     1     4     2     4     2     2     1     5     1      5  ...   \n",
              "599997     1     4     3     4     4     4     3     3     4      4  ...   \n",
              "599998     3     3     4     2     4     2     3     3     4      3  ...   \n",
              "599999     1     4     2     4     2     2     2     5     1      4  ...   \n",
              "\n",
              "        OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  category  \n",
              "0          1     4     1     4     1     5     3     4      5         2  \n",
              "1          2     4     2     3     1     4     2     5      3         1  \n",
              "2          1     2     1     4     2     5     3     4      4         1  \n",
              "3          2     5     2     3     1     4     4     3      3         0  \n",
              "4          1     5     1     5     1     5     3     5      5         2  \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...    ...       ...  \n",
              "599995     4     1     3     4     5     5     2     4      4         1  \n",
              "599996     1     5     1     4     1     5     4     5      4         1  \n",
              "599997     1     5     1     4     1     5     3     3      4         0  \n",
              "599998     1     4     2     4     2     4     3     4      4         2  \n",
              "599999     1     4     1     4     2     5     4     5      4         1  \n",
              "\n",
              "[600000 rows x 51 columns]"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(list(df))\n",
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EXT1</th>\n",
              "      <th>EXT2</th>\n",
              "      <th>EXT3</th>\n",
              "      <th>EXT4</th>\n",
              "      <th>EXT5</th>\n",
              "      <th>EXT6</th>\n",
              "      <th>EXT7</th>\n",
              "      <th>EXT8</th>\n",
              "      <th>EXT9</th>\n",
              "      <th>EXT10</th>\n",
              "      <th>...</th>\n",
              "      <th>OPN1</th>\n",
              "      <th>OPN2</th>\n",
              "      <th>OPN3</th>\n",
              "      <th>OPN4</th>\n",
              "      <th>OPN5</th>\n",
              "      <th>OPN6</th>\n",
              "      <th>OPN7</th>\n",
              "      <th>OPN8</th>\n",
              "      <th>OPN9</th>\n",
              "      <th>OPN10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>309190</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>240953</th>\n",
              "      <td>5.0</td>\n",
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              "      <th>609687</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>757042</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>788110</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583790</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156125</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124439</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995561</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682921</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400000 rows × 50 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  \\\n",
              "309190   2.0   5.0   4.0   5.0   1.0   5.0   1.0   4.0   4.0    5.0  ...   \n",
              "240953   5.0   2.0   5.0   2.0   5.0   4.0   5.0   2.0   5.0    2.0  ...   \n",
              "609687   2.0   5.0   3.0   5.0   1.0   5.0   2.0   5.0   1.0    5.0  ...   \n",
              "757042   5.0   1.0   4.0   1.0   4.0   1.0   5.0   2.0   4.0    1.0  ...   \n",
              "788110   1.0   5.0   5.0   3.0   3.0   5.0   2.0   5.0   1.0    5.0  ...   \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
              "583790   2.0   1.0   4.0   4.0   3.0   1.0   4.0   4.0   3.0    4.0  ...   \n",
              "156125   1.0   5.0   3.0   5.0   3.0   5.0   3.0   5.0   1.0    5.0  ...   \n",
              "124439   3.0   2.0   4.0   2.0   4.0   2.0   5.0   3.0   4.0    2.0  ...   \n",
              "995561   1.0   1.0   5.0   3.0   1.0   1.0   2.0   5.0   2.0    5.0  ...   \n",
              "682921   3.0   5.0   2.0   5.0   3.0   3.0   3.0   2.0   3.0    4.0  ...   \n",
              "\n",
              "        OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  \n",
              "309190   5.0   2.0   4.0   3.0   2.0   2.0   5.0   5.0   4.0    2.0  \n",
              "240953   4.0   2.0   2.0   2.0   5.0   4.0   5.0   5.0   2.0    5.0  \n",
              "609687   3.0   4.0   3.0   4.0   2.0   2.0   4.0   2.0   4.0    3.0  \n",
              "757042   5.0   1.0   5.0   1.0   3.0   2.0   5.0   5.0   5.0    5.0  \n",
              "788110   4.0   2.0   5.0   2.0   5.0   1.0   5.0   1.0   4.0    5.0  \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  \n",
              "583790   5.0   1.0   5.0   5.0   4.0   5.0   4.0   4.0   5.0    5.0  \n",
              "156125   5.0   1.0   5.0   1.0   3.0   1.0   5.0   5.0   5.0    5.0  \n",
              "124439   4.0   3.0   3.0   4.0   3.0   3.0   4.0   4.0   4.0    3.0  \n",
              "995561   5.0   1.0   5.0   1.0   4.0   1.0   5.0   4.0   5.0    5.0  \n",
              "682921   4.0   3.0   5.0   3.0   5.0   1.0   5.0   3.0   4.0    5.0  \n",
              "\n",
              "[400000 rows x 50 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "309190    0\n",
              "240953    2\n",
              "609687    0\n",
              "757042    0\n",
              "788110    0\n",
              "         ..\n",
              "583790    0\n",
              "156125    3\n",
              "124439    0\n",
              "995561    0\n",
              "682921    0\n",
              "Name: category, Length: 400000, dtype: int32"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(Y[df.columns[0:50]], Y['category'], test_size=0.4, random_state=2)\n",
        "display(X_test)\n",
        "display(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [],
      "source": [
        "# the nn.Module is the PyTorch class we are inheriting from so that we can use the prebuild functionalities\n",
        "# inheritance is basically deriving the functions/ variables from your parent class (nn.Module) to a child class(Net)\n",
        "\n",
        "class NetModel(nn.Module):\n",
        "    # Init is the constructor for this python class\n",
        "    # The self is like the keyword this in c++, which basically tells the complier\n",
        "    # that we are refering to the current instance of the class\n",
        "    def __init__(self, input_dim, hidden1_dim, hidden2_dim, no_class):\n",
        "        super(NetModel, self).__init__()# this statement calls the constructor of our parent class which is nn.Module\n",
        "        self.linear1 = nn.Linear(input_dim, hidden1_dim)\n",
        "        self.linear2 = nn.Linear(hidden1_dim, hidden2_dim)\n",
        "        self.linear3 = nn.Linear(hidden2_dim, no_class)\n",
        "    # we define a function called forward for forward propagation\n",
        "    # Note: we have to name the function forward\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.linear1(x))\n",
        "        x = F.relu(self.linear2(x))\n",
        "        x = F.softmax(self.linear3(x), dim=1)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [],
      "source": [
        "# making an object for our model\n",
        "model = NetModel(X_test.shape[1], 30, 15, len(set(y_test)))\n",
        "\n",
        "# len(set(y_set)) gives us the no of labels which is 3 in this case"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [],
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func  = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train = Variable(torch.from_numpy(np.array(X_train))).float()\n",
        "y_train = Variable(torch.from_numpy(np.array(y_train))).long()\n",
        "X_test  = Variable(torch.from_numpy(np.array(X_test))).float()\n",
        "y_test  = Variable(torch.from_numpy(np.array(y_test))).long()\n",
        "\n",
        "# For complex models we will need to use Datasets and Dataloaders instead of Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "epochs = 400\n",
        "Loss = []\n",
        "\n",
        "for i in range(epochs):\n",
        "\n",
        "    # Dataloaders are used to split this whole data into batches, add padding, etc\n",
        "    y_pred = model(X_train)  \n",
        "    \n",
        "    # Calculating the loss\n",
        "    loss = loss_func(y_pred, y_train)\n",
        "    Loss.append(loss.item())\n",
        "    \n",
        "    # This calculates the gradient and updates the weights for our model\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    # print('At the end of ', i,' th iteration , values of arguments are : \\n', model.parameters )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {},
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting the loss\n",
        "\n",
        "plot, (ax1) = plt.subplots(1, figsize=(12, 6))\n",
        "\n",
        "ax1.plot(Loss)\n",
        "ax1.set_ylabel(\"train data losss\")\n",
        "\n",
        "ax1.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([1., 0., 1.,  ..., 1., 1., 1.])\n",
            "tensor([0, 0, 0,  ..., 0, 0, 0])\n"
          ]
        }
      ],
      "source": [
        "# we write no_grad as we are not going to calculate the gradient for the testing data\n",
        "with torch.no_grad():\n",
        "    y_pred = model(X_test)\n",
        "    correct = (torch.argmax(y_pred, dim=1) == y_test).float()\n",
        "    print(correct) # gives 0 when y_pred and y_test are not the same (as int/ float value of a bool false is 0)\n",
        "    \n",
        "    accuracy = correct.mean()\n",
        "print(torch.argmax(y_pred, dim=1))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "SoCWeek3Extended.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "88279d2366fe020547cde40dd65aa0e3aa662a6ec1f3ca12d88834876c85e1a6"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}