simple_2D__pima_heatmap.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NaNfPBNxrNwd"
      },
      "outputs": [],
      "source": [
        "from numpy import loadtxt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Loading the dataset"
      ],
      "metadata": {
        "id": "epYVNdJN1ww3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('diabetes.csv', delimiter=',')\n",
        "features = [\"Pregnancies\",\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]\n",
        "data[[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]] = data[[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]].replace(0,data.mean(axis=0))\n",
        "outcome = data.iloc[:,8]\n",
        "print(data)\n",
        "print(data.describe().T)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMS5k6uwZWXe",
        "outputId": "bfca9b88-36f8-4895-a566-150f6c87c263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
            "0              6    148.0           72.0      35.000000   79.799479  33.6   \n",
            "1              1     85.0           66.0      29.000000   79.799479  26.6   \n",
            "2              8    183.0           64.0      20.536458   79.799479  23.3   \n",
            "3              1     89.0           66.0      23.000000   94.000000  28.1   \n",
            "4              0    137.0           40.0      35.000000  168.000000  43.1   \n",
            "..           ...      ...            ...            ...         ...   ...   \n",
            "763           10    101.0           76.0      48.000000  180.000000  32.9   \n",
            "764            2    122.0           70.0      27.000000   79.799479  36.8   \n",
            "765            5    121.0           72.0      23.000000  112.000000  26.2   \n",
            "766            1    126.0           60.0      20.536458   79.799479  30.1   \n",
            "767            1     93.0           70.0      31.000000   79.799479  30.4   \n",
            "\n",
            "     DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                       0.627   50        1  \n",
            "1                       0.351   31        0  \n",
            "2                       0.672   32        1  \n",
            "3                       0.167   21        0  \n",
            "4                       2.288   33        1  \n",
            "..                        ...  ...      ...  \n",
            "763                     0.171   63        0  \n",
            "764                     0.340   27        0  \n",
            "765                     0.245   30        0  \n",
            "766                     0.349   47        1  \n",
            "767                     0.315   23        0  \n",
            "\n",
            "[768 rows x 9 columns]\n",
            "                          count        mean        std     min        25%  \\\n",
            "Pregnancies               768.0    3.845052   3.369578   0.000   1.000000   \n",
            "Glucose                   768.0  121.681605  30.436016  44.000  99.750000   \n",
            "BloodPressure             768.0   72.254807  12.115932  24.000  64.000000   \n",
            "SkinThickness             768.0   26.606479   9.631241   7.000  20.536458   \n",
            "Insulin                   768.0  118.660163  93.080358  14.000  79.799479   \n",
            "BMI                       768.0   32.450805   6.875374  18.200  27.500000   \n",
            "DiabetesPedigreeFunction  768.0    0.471876   0.331329   0.078   0.243750   \n",
            "Age                       768.0   33.240885  11.760232  21.000  24.000000   \n",
            "Outcome                   768.0    0.348958   0.476951   0.000   0.000000   \n",
            "\n",
            "                                 50%        75%     max  \n",
            "Pregnancies                 3.000000    6.00000   17.00  \n",
            "Glucose                   117.000000  140.25000  199.00  \n",
            "BloodPressure              72.000000   80.00000  122.00  \n",
            "SkinThickness              23.000000   32.00000   99.00  \n",
            "Insulin                    79.799479  127.25000  846.00  \n",
            "BMI                        32.000000   36.60000   67.10  \n",
            "DiabetesPedigreeFunction    0.372500    0.62625    2.42  \n",
            "Age                        29.000000   41.00000   81.00  \n",
            "Outcome                     0.000000    1.00000    1.00  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try to use the softUmput from GitHub open resource but it didn't work"
      ],
      "metadata": {
        "id": "DPM7uLJU2Gch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from soft_impute import SoftImpute\n",
        "# clf = SoftImpute(J=1, lambda_=2.0)\n",
        "# X_test = data.to_numpy().copy()\n",
        "# fit = clf.fit(X_test)\n",
        "# imputed = clf.predict(X_test)\n",
        "# k = imputed[:,0]\n",
        "# plt.hist(k,bins = [0,2,4,6,8,10,12,14,16]) \n",
        "# plt.title(\"histogram\") \n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "AFosUCVwt_Ia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Normalization"
      ],
      "metadata": {
        "id": "g7Z5JfZ_2UB5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = preprocessing.normalize(data.iloc[:,0:8], axis=0)\n",
        "scaled_df = pd.DataFrame(d, columns = features)\n",
        "scaled_df = pd.concat([scaled_df, outcome], axis=1)\n"
      ],
      "metadata": {
        "id": "wL0aVajyr8hI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df.hist(column=\"Glucose\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "BwA_Ijybq_5S",
        "outputId": "9299957e-fa8b-45fc-db5e-05580334a65f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Glucose'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df.hist(column=\"BloodPressure\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "Ko05EB2mrF-F",
        "outputId": "67693c49-cd97-4482-b9fe-f79cfb81ffdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'BloodPressure'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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srCxNmzatzHh2drbf6SMAAGCvkpIS3XbbbSosLJTL5brg3Es+gpKZmamtW7f6hRPppwByRvv27dW4cWP16dNHu3fvVvPmzS/psSZPnqwJEyb4louKitSkSROlp6f77aDH41FOTo769u2r8PDwS3osVAw9r3oV7Xm7rBU/O8c2W7Mygl1CGTzXqx49D45A9v3MGZCLcUkBZfz48VqyZInWr1+vK6644oJzu3btKknatWuXmjdvroSEBG3cuNFvTn5+viSd930rTqdTTqezzHh4eHi5zTvfOAKHnle9i+25u9RRBdVULpufSzzXqx49D45A9L0i26vQZcbGGI0fP17vvfee1qxZo+Tk5J+9z5YtWyRJjRs3liSlpqbq66+/VkFBgW9OTk6OXC6X2rRpU5FyAABADVWhIyiZmZnKzs7WBx98oHr16ikvL0+SVL9+fdWpU0e7d+9Wdna2BgwYoAYNGuirr77Sgw8+qB49euiaa66RJKWnp6tNmzYaMWKEZs6cqby8PD3++OPKzMws9ygJAACofSp0BOWFF15QYWGhevXqpcaNG/tub775piQpIiJCq1atUnp6ulq1aqWHHnpIQ4cO1eLFi33bCA0N1ZIlSxQaGqrU1FTdcccdGjlypN/npgAAgNqtQkdQfu6CnyZNmmjdunU/u52kpCQtW7asIg8NAABqEb6LBwAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKwTFuwCAOBczSYtDXYJZThDjWamSO2yVshd6iizfu8fBgahKqDm4ggKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUqFFBmzJiha6+9VvXq1VNcXJyGDBmiHTt2+M05efKkMjMz1aBBA9WtW1dDhw5Vfn6+35z9+/dr4MCBioqKUlxcnCZOnKjTp0//8r0BAAA1QoUCyrp165SZmakNGzYoJydHHo9H6enpOnHihG/Ogw8+qMWLF+vtt9/WunXrdPDgQd18882+9aWlpRo4cKBOnTqlTz/9VK+88ooWLFigJ554ovL2CgAAVGthFZm8fPlyv+UFCxYoLi5OmzdvVo8ePVRYWKiXXnpJ2dnZuv766yVJ8+fPV+vWrbVhwwZ169ZNK1eu1Pbt27Vq1SrFx8erY8eOevLJJ/Xoo48qKytLERERlbd3AACgWqpQQDlXYWGhJCk2NlaStHnzZnk8HqWlpfnmtGrVSk2bNlVubq66deum3NxctW/fXvHx8b45GRkZuueee7Rt2zZ16tSpzOO43W653W7fclFRkSTJ4/HI4/H4xs/8fPYYAoueV72K9twZagJZTq3hDDF+/56L34HKx+tLcASy7xXZ5iUHFK/XqwceeEC/+tWv1K5dO0lSXl6eIiIiFBMT4zc3Pj5eeXl5vjlnh5Mz68+sK8+MGTM0bdq0MuMrV65UVFRUmfGcnJwK7w9+GXpe9S625zNTAlxILfNkF2+548uWLaviSmoPXl+CIxB9Lykpuei5lxxQMjMztXXrVn388ceXuomLNnnyZE2YMMG3XFRUpCZNmig9PV0ul8s37vF4lJOTo759+yo8PDzgdYGeB0NFe94ua0UVVFXzOUOMnuzi1ZRNIXJ7HWXWb83KCEJVNRuvL8ERyL6fOQNyMS4poIwfP15LlizR+vXrdcUVV/jGExISdOrUKR07dszvKEp+fr4SEhJ8czZu3Oi3vTNX+ZyZcy6n0ymn01lmPDw8vNzmnW8cgUPPq97F9txdWvaPKS6d2+sot6c8/wOH15fgCETfK7K9Cl3FY4zR+PHj9d5772nNmjVKTk72W9+5c2eFh4dr9erVvrEdO3Zo//79Sk1NlSSlpqbq66+/VkFBgW9OTk6OXC6X2rRpU5FyAABADVWhIyiZmZnKzs7WBx98oHr16vneM1K/fn3VqVNH9evX15gxYzRhwgTFxsbK5XLpd7/7nVJTU9WtWzdJUnp6utq0aaMRI0Zo5syZysvL0+OPP67MzMxyj5IAAIDap0IB5YUXXpAk9erVy298/vz5Gj16tCTpueeeU0hIiIYOHSq3262MjAz9+c9/9s0NDQ3VkiVLdM899yg1NVXR0dEaNWqUpk+f/sv2BAAA1BgVCijG/PzlipGRkZozZ47mzJlz3jlJSUm84x0AAJwX38UDAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFinwgFl/fr1GjRokBITE+VwOPT+++/7rR89erQcDoffrV+/fn5zjh49qttvv10ul0sxMTEaM2aMiouLf9GOAACAmqPCAeXEiRPq0KGD5syZc945/fr106FDh3y3N954w2/97bffrm3btiknJ0dLlizR+vXrNW7cuIpXDwAAaqSwit6hf//+6t+//wXnOJ1OJSQklLvum2++0fLly/X555+rS5cukqTZs2drwIABeuaZZ5SYmFjRkgAAQA1T4YByMdauXau4uDhddtlluv766/XUU0+pQYMGkqTc3FzFxMT4wokkpaWlKSQkRJ999pluuummMttzu91yu92+5aKiIkmSx+ORx+PxjZ/5+ewxBBY9r3oV7bkz1ASynFrDGWL8/j0XvwOVj9eX4Ahk3yuyzUoPKP369dPNN9+s5ORk7d69W4899pj69++v3NxchYaGKi8vT3Fxcf5FhIUpNjZWeXl55W5zxowZmjZtWpnxlStXKioqqsx4Tk5O5ewMLho9r3oX2/OZKQEupJZ5sou33PFly5ZVcSW1B68vwRGIvpeUlFz03EoPKLfeeqvv5/bt2+uaa65R8+bNtXbtWvXp0+eStjl58mRNmDDBt1xUVKQmTZooPT1dLpfLN+7xeJSTk6O+ffsqPDz80ncCF42eV72K9rxd1ooqqKrmc4YYPdnFqymbQuT2Osqs35qVEYSqajZeX4IjkH0/cwbkYgTkFM/ZrrzySjVs2FC7du1Snz59lJCQoIKCAr85p0+f1tGjR8/7vhWn0ymn01lmPDw8vNzmnW8cgUPPq97F9txdWvaPKS6d2+sot6c8/wOH15fgCETfK7K9gH8OyoEDB3TkyBE1btxYkpSamqpjx45p8+bNvjlr1qyR1+tV165dA10OAACoBip8BKW4uFi7du3yLe/Zs0dbtmxRbGysYmNjNW3aNA0dOlQJCQnavXu3HnnkEbVo0UIZGT8d/mzdurX69eunsWPHau7cufJ4PBo/frxuvfVWruABAACSLuEIyqZNm9SpUyd16tRJkjRhwgR16tRJTzzxhEJDQ/XVV19p8ODBatmypcaMGaPOnTvro48+8jtF8/rrr6tVq1bq06ePBgwYoO7du2vevHmVt1cAAKBaq/ARlF69esmY81+2uGLFz78hLzY2VtnZ2RV9aAAAUEvwXTwAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWCcs2AUA1UmzSUuDXYKcoUYzU6R2WSvkLnUEuxwACAiOoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrhAW7AACoCZpNWhrsEips7x8GBrsE4Lw4ggIAAKxDQAEAANapcEBZv369Bg0apMTERDkcDr3//vt+640xeuKJJ9S4cWPVqVNHaWlp2rlzp9+co0eP6vbbb5fL5VJMTIzGjBmj4uLiX7QjAACg5qhwQDlx4oQ6dOigOXPmlLt+5syZ+tOf/qS5c+fqs88+U3R0tDIyMnTy5EnfnNtvv13btm1TTk6OlixZovXr12vcuHGXvhcAAKBGqfCbZPv376/+/fuXu84Yo1mzZunxxx/XjTfeKEl69dVXFR8fr/fff1+33nqrvvnmGy1fvlyff/65unTpIkmaPXu2BgwYoGeeeUaJiYlltut2u+V2u33LRUVFkiSPxyOPx+MbP/Pz2WMIrNrWc2eoCXYJcoYYv39RNWpi323/va1try+2CGTfK7JNhzHmkn/bHA6H3nvvPQ0ZMkSS9N1336l58+b68ssv1bFjR9+8nj17qmPHjnr++ef18ssv66GHHtL//d//+dafPn1akZGRevvtt3XTTTeVeZysrCxNmzatzHh2draioqIutXwAAFCFSkpKdNttt6mwsFAul+uCcyv1MuO8vDxJUnx8vN94fHy8b11eXp7i4uL8iwgLU2xsrG/OuSZPnqwJEyb4louKitSkSROlp6f77aDH41FOTo769u2r8PDwStknXFht63m7rBXBLkHOEKMnu3g1ZVOI3F5HsMupNWpi37dmZQS7hAuqba8vtghk38+cAbkY1eJzUJxOp5xOZ5nx8PDwcpt3vnEETm3pubvUnj9Mbq/Dqnpqi5rU9+ryO1tbXl9sE4i+V2R7lXqZcUJCgiQpPz/fbzw/P9+3LiEhQQUFBX7rT58+raNHj/rmAACA2q1SA0pycrISEhK0evVq31hRUZE+++wzpaamSpJSU1N17Ngxbd682TdnzZo18nq96tq1a2WWAwAAqqkKn+IpLi7Wrl27fMt79uzRli1bFBsbq6ZNm+qBBx7QU089pauuukrJycmaMmWKEhMTfW+kbd26tfr166exY8dq7ty58ng8Gj9+vG699dZyr+ABAAC1T4UDyqZNm9S7d2/f8pk3r44aNUoLFizQI488ohMnTmjcuHE6duyYunfvruXLlysyMtJ3n9dff13jx49Xnz59FBISoqFDh+pPf/pTJewOAACoCSocUHr16qULXZnscDg0ffp0TZ8+/bxzYmNjlZ2dXdGHBgAAtQTfxQMAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWKfSA0pWVpYcDoffrVWrVr71J0+eVGZmpho0aKC6detq6NChys/Pr+wyAABANRaQIyht27bVoUOHfLePP/7Yt+7BBx/U4sWL9fbbb2vdunU6ePCgbr755kCUAQAAqqmwgGw0LEwJCQllxgsLC/XSSy8pOztb119/vSRp/vz5at26tTZs2KBu3boFohwAAFDNBCSg7Ny5U4mJiYqMjFRqaqpmzJihpk2bavPmzfJ4PEpLS/PNbdWqlZo2barc3NzzBhS32y232+1bLioqkiR5PB55PB7f+Jmfzx5DYNW2njtDTbBLkDPE+P2LqlET+277721te32xRSD7XpFtOowxlfrb9uGHH6q4uFhXX321Dh06pGnTpumHH37Q1q1btXjxYt15551+YUOSUlJS1Lt3bz399NPlbjMrK0vTpk0rM56dna2oqKjKLB8AAARISUmJbrvtNhUWFsrlcl1wbqUHlHMdO3ZMSUlJevbZZ1WnTp1LCijlHUFp0qSJDh8+7LeDHo9HOTk56tu3r8LDwwOzQ/BT23reLmtFsEuQM8ToyS5eTdkUIrfXEexyao2a2PetWRnBLuGCatvriy0C2feioiI1bNjwogJKQE7xnC0mJkYtW7bUrl271LdvX506dUrHjh1TTEyMb05+fn6571k5w+l0yul0lhkPDw8vt3nnG0fg1Jaeu0vt+cPk9jqsqqe2qEl9ry6/s7Xl9cU2geh7RbYX8M9BKS4u1u7du9W4cWN17txZ4eHhWr16tW/9jh07tH//fqWmpga6FAAAUE1U+hGUhx9+WIMGDVJSUpIOHjyoqVOnKjQ0VMOHD1f9+vU1ZswYTZgwQbGxsXK5XPrd736n1NRUruABAAA+lR5QDhw4oOHDh+vIkSNq1KiRunfvrg0bNqhRo0aSpOeee04hISEaOnSo3G63MjIy9Oc//7myywAAANVYpQeUhQsXXnB9ZGSk5syZozlz5lT2QwMAgBqC7+IBAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ1K/zZj4GI1m7Q02CUAACzFERQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrhAW7AABAcDSbtDTYJVyQM9RoZorULmuF3KUOSdLePwwMclWoKhxBAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADr8FH3AIBqw/aP5y8PH89/aTiCAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYJ6gf1DZnzhz98Y9/VF5enjp06KDZs2crJSUlmCVJ4oOAAAAItqAdQXnzzTc1YcIETZ06VV988YU6dOigjIwMFRQUBKskAABgiaAdQXn22Wc1duxY3XnnnZKkuXPnaunSpXr55Zc1adKkYJVVbQXrqI8z1GhmitQua4XcpY6g1AAANqtuR+XPvK4HW1ACyqlTp7R582ZNnjzZNxYSEqK0tDTl5uaWme92u+V2u33LhYWFkqSjR4/K4/H4xj0ej0pKSnTkyBGFh4dfcn1hp09c8n1rmzCvUUmJV2GeEJV6CShVgZ4HB32vevQ8OM70/Zf+LS3P8ePHJUnGmJ+vo1If+SIdPnxYpaWlio+P9xuPj4/Xt99+W2b+jBkzNG3atDLjycnJAasRF++2YBdQC9Hz4KDvVY+eB0eg+378+HHVr1//gnOqxbcZT548WRMmTPAte71eHT16VA0aNJDD8f+n6qKiIjVp0kTff/+9XC5XMEqtdeh51aPnwUHfqx49D45A9t0Yo+PHjysxMfFn5wYloDRs2FChoaHKz8/3G8/Pz1dCQkKZ+U6nU06n028sJibmvNt3uVw8masYPa969Dw46HvVo+fBEai+/9yRkzOCchVPRESEOnfurNWrV/vGvF6vVq9erdTU1GCUBAAALBK0UzwTJkzQqFGj1KVLF6WkpGjWrFk6ceKE76oeAABQewUtoPzmN7/Rjz/+qCeeeEJ5eXnq2LGjli9fXuaNsxXhdDo1derUMqeDEDj0vOrR8+Cg71WPngeHLX13mIu51gcAAKAK8V08AADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsY3VAmTNnjpo1a6bIyEh17dpVGzduvOD8t99+W61atVJkZKTat2+vZcuW+a1/9913lZ6e7vuI/C1btgSw+uqrMvvu8Xj06KOPqn379oqOjlZiYqJGjhypgwcPBno3qpXKfq5nZWWpVatWio6O1mWXXaa0tDR99tlngdyFaqeye3623/72t3I4HJo1a1YlV139VXbfR48eLYfD4Xfr169fIHeh2gnEc/2bb77R4MGDVb9+fUVHR+vaa6/V/v37K7dwY6mFCxeaiIgI8/LLL5tt27aZsWPHmpiYGJOfn1/u/E8++cSEhoaamTNnmu3bt5vHH3/chIeHm6+//to359VXXzXTpk0zf/nLX4wk8+WXX1bR3lQfld33Y8eOmbS0NPPmm2+ab7/91uTm5pqUlBTTuXPnqtwtqwXiuf7666+bnJwcs3v3brN161YzZswY43K5TEFBQVXtltUC0fMz3n33XdOhQweTmJhonnvuuQDvSfUSiL6PGjXK9OvXzxw6dMh3O3r0aFXtkvUC0fNdu3aZ2NhYM3HiRPPFF1+YXbt2mQ8++OC827xU1gaUlJQUk5mZ6VsuLS01iYmJZsaMGeXOHzZsmBk4cKDfWNeuXc3dd99dZu6ePXsIKOcRyL6fsXHjRiPJ7Nu3r3KKruaqoueFhYVGklm1alXlFF3NBarnBw4cMJdffrnZunWrSUpKIqCcIxB9HzVqlLnxxhsDUm9NEIie/+Y3vzF33HFHYAo+i5WneE6dOqXNmzcrLS3NNxYSEqK0tDTl5uaWe5/c3Fy/+ZKUkZFx3vkoq6r6XlhYKIfDccEvfKwtqqLnp06d0rx581S/fn116NCh8oqvpgLVc6/XqxEjRmjixIlq27ZtYIqvxgL5XF+7dq3i4uJ09dVX65577tGRI0cqfweqoUD03Ov1aunSpWrZsqUyMjIUFxenrl276v3336/0+q0MKIcPH1ZpaWmZj72Pj49XXl5euffJy8ur0HyUVRV9P3nypB599FENHz6cbydVYHu+ZMkS1a1bV5GRkXruueeUk5Ojhg0bVu4OVEOB6vnTTz+tsLAw3XfffZVfdA0QqL7369dPr776qlavXq2nn35a69atU//+/VVaWlr5O1HNBKLnBQUFKi4u1h/+8Af169dPK1eu1E033aSbb75Z69atq9T6g/ZdPKh9PB6Phg0bJmOMXnjhhWCXU+P17t1bW7Zs0eHDh/WXv/xFw4YN02effaa4uLhgl1bjbN68Wc8//7y++OILORyOYJdTq9x6662+n9u3b69rrrlGzZs319q1a9WnT58gVlYzeb1eSdKNN96oBx98UJLUsWNHffrpp5o7d6569uxZaY9l5RGUhg0bKjQ0VPn5+X7j+fn5SkhIKPc+CQkJFZqPsgLZ9zPhZN++fcrJyeHoyf8nkD2Pjo5WixYt1K1bN7300ksKCwvTSy+9VLk7UA0FoucfffSRCgoK1LRpU4WFhSksLEz79u3TQw89pGbNmgVkP6qbqnpdv/LKK9WwYUPt2rXrlxddzQWi5w0bNlRYWJjatGnjN6d169aVfhWPlQElIiJCnTt31urVq31jXq9Xq1evVmpqarn3SU1N9ZsvSTk5Oeedj7IC1fcz4WTnzp1atWqVGjRoEJgdqIaq8rnu9Xrldrt/edHVXCB6PmLECH311VfasmWL75aYmKiJEydqxYoVgduZaqSqnusHDhzQkSNH1Lhx48opvBoLRM8jIiJ07bXXaseOHX5z/vWvfykpKalydyDgb8O9RAsXLjROp9MsWLDAbN++3YwbN87ExMSYvLw8Y4wxI0aMMJMmTfLN/+STT0xYWJh55plnzDfffGOmTp1a5tKoI0eOmC+//NIsXbrUSDILFy40X375pTl06FCV75+tKrvvp06dMoMHDzZXXHGF2bJli9+lgG63Oyj7aJvK7nlxcbGZPHmyyc3NNXv37jWbNm0yd955p3E6nWbr1q1B2UfbBOL15VxcxVNWZff9+PHj5uGHHza5ublmz549ZtWqVeY//uM/zFVXXWVOnjwZlH20TSCe6++++64JDw838+bNMzt37jSzZ882oaGh5qOPPqrU2q0NKMYYM3v2bNO0aVMTERFhUlJSzIYNG3zrevbsaUaNGuU3/6233jItW7Y0ERERpm3btmbp0qV+6+fPn28klblNnTq1Cvam+qjMvp+5pLu82z/+8Y8q2iP7VWbP//3vf5ubbrrJJCYmmoiICNO4cWMzePBgs3HjxqranWqhsl9fzkVAKV9l9r2kpMSkp6ebRo0amfDwcJOUlGTGjh3r++OLnwTiuf7SSy+ZFi1amMjISNOhQwfz/vvvV3rdDmOMqdxjMgAAAL+Mle9BAQAAtRsBBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACs8/8AyqD8Jbc3wyYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = scaled_df.iloc[:,0:8].to_numpy()\n",
        "y = data.iloc[:,8].to_numpy()\n"
      ],
      "metadata": {
        "id": "h--Q06U4sIM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Split the dataset and train the model"
      ],
      "metadata": {
        "id": "6XO56w692or3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state=5)"
      ],
      "metadata": {
        "id": "l8GdTk4yavg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_df = pd.DataFrame(x_train,columns = features)\n",
        "x_test_df = pd.DataFrame(x_test,columns = features)"
      ],
      "metadata": {
        "id": "IKIxyqAQ2-j1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(12,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), input_shape=(8,), activation='relu'))\n",
        "model.add(Dense(8,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), activation='relu'))\n",
        "model.add(Dense(1,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "MF9Y7uwkv8La"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=150, batch_size=10, verbose=0)\n",
        "# evaluate the keras model\n",
        "# test DS\n",
        "#training error\n",
        "_, accuracy = model.evaluate(x_train, y_train, verbose=0)\n",
        "print('Accuracy: %.2f' % (accuracy*100))\n",
        "\n",
        "predictions = (model.predict(x_test) > 0.5).astype(int)\n",
        "\n",
        "# summarize the first 5 cases\n",
        "for i in range(5):\n",
        "\tprint('%s => %d (expected %d)' % (x_test[i].tolist(), predictions[i], y_test[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49QiuqqWwBGZ",
        "outputId": "2485545f-946a-4d72-bd09-09e868513847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 76.91\n",
            "8/8 [==============================] - 0s 4ms/step\n",
            "[0.042359742733119723, 0.026468013734211867, 0.030537300807401756, 0.04081093651464417, 0.030155216874819788, 0.034811354622936566, 0.005320730020922629, 0.047079059916829195] => 0 (expected 0)\n",
            "[0.0352997856109331, 0.037975845792564855, 0.039402968783744205, 0.02619100303991568, 0.019098179371178546, 0.029154509496709377, 0.011643009222254223, 0.07061858987524379] => 0 (expected 0)\n",
            "[0.021179871366559862, 0.030495754954635412, 0.03546267190536978, 0.02619100303991568, 0.019098179371178546, 0.028066654664742608, 0.012957542521540988, 0.02763336125553018] => 0 (expected 0)\n",
            "[0.028239828488746482, 0.02848188434442364, 0.033492523466182575, 0.048462987111139944, 0.019098179371178546, 0.03568163848850998, 0.009076539447456249, 0.03377410820120355] => 0 (expected 0)\n",
            "[0.028239828488746482, 0.027618796940047166, 0.02758207814862094, 0.021680810023404714, 0.011727028784652139, 0.02262738050490877, 0.021282920083690517, 0.026609903431251285] => 0 (expected 0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Class contrastive analysis: Max Max - Min Min"
      ],
      "metadata": {
        "id": "vNfF4viMHi5R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def max_calcuation(arr):\n",
        "  max = np.max(arr, axis =0)\n",
        "  return max"
      ],
      "metadata": {
        "id": "HFuAxBWzF_iN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def min_calcuation(arr):\n",
        "  min = np.min(arr, axis =0)\n",
        "  return min"
      ],
      "metadata": {
        "id": "Cu4jkMyIG8-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def max_matrix(arr,model):\n",
        "  prediction_max = []\n",
        "  for i in range(len(arr)):\n",
        "    row_index = 0\n",
        "    X_single= np.full((28,8),arr[i])\n",
        "    for m in range(8):\n",
        "      for j in range(m+1,8):\n",
        "       X_single[row_index][m] = max[m]\n",
        "       X_single[row_index][j] = max[j]\n",
        "       row_index +=1\n",
        "    predictions_sub_max = model.predict(X_single).reshape((1,28))\n",
        "    if len(prediction_max) == 0:\n",
        "      prediction_max = predictions_sub_max\n",
        "    else:\n",
        "      prediction_max = np.concatenate((prediction_max, predictions_sub_max), axis=1)\n",
        "  prediction_max = prediction_max.reshape((231,28))\n",
        "  return prediction_max\n"
      ],
      "metadata": {
        "id": "4s54c1xfHZba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def min_matrix(arr,model):\n",
        "  prediction_min = []\n",
        "  for i in range(len(arr)):\n",
        "    row_index = 0\n",
        "    X_single= np.full((28,8),arr[i])\n",
        "    for m in range(8):\n",
        "      for j in range(m+1,8):\n",
        "       X_single[row_index][m] = min[m]\n",
        "       X_single[row_index][j] = min[j]\n",
        "       row_index +=1\n",
        "    predictions_sub_min = model.predict(X_single).reshape((1,28))\n",
        "    if len(prediction_min) == 0:\n",
        "      prediction_min = predictions_sub_min\n",
        "    else:\n",
        "      prediction_min = np.concatenate((prediction_min, predictions_sub_min), axis=1)\n",
        "  prediction_min = prediction_min.reshape((231,28))\n",
        "  return prediction_min"
      ],
      "metadata": {
        "id": "M-oLgwhdI9OJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testX = np.copy(x_test)\n",
        "max = max_calcuation(testX)\n",
        "min = min_calcuation(testX)\n",
        "# MaxMax = [[0 for x in range(28)] for y in range(len(x_test))]"
      ],
      "metadata": {
        "id": "yhCcrXoX-Gac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_max = max_matrix(x_test,model)\n",
        "prediction_min = min_matrix(x_test,model)\n",
        "result = np.subtract(prediction_max, prediction_min)\n",
        "heatmap_diff = pd.DataFrame(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToUK16L5IKLQ",
        "outputId": "43744076-df36-4e49-eb45-a55e93bfe195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.set(font_scale=1.5)\n",
        "\n",
        "# sns.heatmap(heatmap_diff,\n",
        "#             cmap='coolwarm',\n",
        "#             annot=True,\n",
        "#             fmt='.5g',\n",
        "#             vmax=1)\n",
        "# ax = sns.heatmap(heatmap_diff,cmap = sns.color_palette(\"vlag\", as_cmap=True), vmin=-1, vmax=1)\n",
        "ax = sns.heatmap(heatmap_diff,cmap = sns.color_palette(\"vlag\", as_cmap=True))\n",
        "# ax = sns.heatmap(heatmap_diff,cmap = 'cool')\n",
        "plt.xlabel('features',fontsize=22)\n",
        "plt.ylabel('patients',fontsize=22)\n",
        "plt.savefig('2d_heatmap.eps', format='eps')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "YZsseRoWJxV0",
        "outputId": "183ee93f-e0e6-468e-f631-0ba134ed33c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}