opposite_of_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",
        "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": [
        "##Load dataset"
      ],
      "metadata": {
        "id": "sI_9x4gjUpPf"
      }
    },
    {
      "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.shape)\n",
        "print(data.describe().T)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMS5k6uwZWXe",
        "outputId": "b91af857-9115-4476-9120-8df12415195b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768, 9)\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": "code",
      "source": [
        "\n",
        "d = preprocessing.normalize(data, axis=0)\n",
        "scaled_df = pd.DataFrame(d, columns = [data.columns])\n",
        "scaled_df.head()\n",
        "print(scaled_df.describe().T)\n",
        "print(type(scaled_df.describe().T))\n",
        "\n",
        "# norm_data = preprocessing.normalize(Insurance_Data, axis=0)\n",
        "# norm_df = pd.DataFrame(norm_data,columns=[Insurance_Data.columns])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wL0aVajyr8hI",
        "outputId": "d0c585e5-79c8-4a20-ba1f-a450cbee9cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          count      mean       std       min       25%  \\\n",
            "Pregnancies               768.0  0.027146  0.023789  0.000000  0.007060   \n",
            "Glucose                   768.0  0.035007  0.008756  0.012659  0.028698   \n",
            "BloodPressure             768.0  0.035588  0.005968  0.011821  0.031522   \n",
            "SkinThickness             768.0  0.033932  0.012283  0.008927  0.026191   \n",
            "Insulin                   768.0  0.028399  0.022277  0.003351  0.019098   \n",
            "BMI                       768.0  0.035302  0.007479  0.019799  0.029916   \n",
            "DiabetesPedigreeFunction  768.0  0.029538  0.020740  0.004883  0.015258   \n",
            "Age                       768.0  0.034021  0.012036  0.021493  0.024563   \n",
            "Outcome                   768.0  0.021316  0.029134  0.000000  0.000000   \n",
            "\n",
            "                               50%       75%       max  \n",
            "Pregnancies               0.021180  0.042360  0.120019  \n",
            "Glucose                   0.033660  0.040349  0.057251  \n",
            "BloodPressure             0.035463  0.039403  0.060090  \n",
            "SkinThickness             0.029333  0.040811  0.126259  \n",
            "Insulin                   0.019098  0.030454  0.202471  \n",
            "BMI                       0.034811  0.039815  0.072995  \n",
            "DiabetesPedigreeFunction  0.023317  0.039201  0.151484  \n",
            "Age                       0.029680  0.041962  0.082900  \n",
            "Outcome                   0.000000  0.061085  0.061085  \n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df.hist(column=\"Glucose\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "BwA_Ijybq_5S",
        "outputId": "c78d8354-f609-40f1-fb26-48e5f8382863"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': \"('Glucose',)\"}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "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": 469
        },
        "id": "Ko05EB2mrF-F",
        "outputId": "7c064ab9-a091-48aa-bb1c-5b482d0a26de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': \"('BloodPressure',)\"}>]], 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": [
        "X = scaled_df.iloc[:,0:8].to_numpy()\n",
        "# dataset.iloc[:, 4]\n",
        "print(X)\n",
        "y = data.iloc[:,8].to_numpy()\n",
        "print(y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h--Q06U4sIM1",
        "outputId": "d85dc278-54bd-4e7f-f253-3a46cbdca33c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.04235974 0.04257898 0.03546267 ... 0.03655192 0.03924821 0.05117289]\n",
            " [0.00705996 0.02445414 0.03250745 ... 0.02893694 0.02197149 0.03172719]\n",
            " [0.05647966 0.05264833 0.03152238 ... 0.02534702 0.04206507 0.03275065]\n",
            " ...\n",
            " [0.03529979 0.03481119 0.03546267 ... 0.0285018  0.01533622 0.03070373]\n",
            " [0.00705996 0.03624967 0.02955223 ... 0.03274443 0.02184629 0.04810252]\n",
            " [0.00705996 0.02675571 0.0344776  ... 0.03307079 0.019718   0.02353953]]\n",
            "[1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0\n",
            " 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0\n",
            " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
            " 1 0 0 1 1 1 0 0 0 1 0 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
            " 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0\n",
            " 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1\n",
            " 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0\n",
            " 1 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0\n",
            " 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1\n",
            " 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1\n",
            " 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 0 1 0 1\n",
            " 0 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1\n",
            " 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1\n",
            " 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0\n",
            " 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0\n",
            " 1 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0\n",
            " 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 0\n",
            " 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1\n",
            " 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1\n",
            " 1 0 0 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "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": "zxD1_LrqXB2g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Train model"
      ],
      "metadata": {
        "id": "0kF_aaSmUuY7"
      }
    },
    {
      "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": "1400783e-7036-4223-869a-dad1efa33915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 75.79\n",
            "8/8 [==============================] - 0s 2ms/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] => 1 (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": [
        "## MAX - MIN"
      ],
      "metadata": {
        "id": "UaUqe6awUyvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testX = np.copy(x_test)\n",
        "max = np.max(testX,axis=0)\n",
        "print(max)\n",
        "\n",
        "min = np.min(testX,axis=0)\n",
        "print(min)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhCcrXoX-Gac",
        "outputId": "3c27d7d9-54c4-4509-93d7-3d25f01a2b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.12001927 0.05696377 0.05319401 0.08034653 0.17805938 0.06461858\n",
            " 0.15148431 0.07061859]\n",
            "[0.         0.01639866 0.02364178 0.01020273 0.00335058 0.0209956\n",
            " 0.00532073 0.02149261]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# #pregnancies\n",
        "# X_preg_max = np.copy(testX)\n",
        "# for i in range(len(X)):\n",
        "#     X_preg_max[i][0] = max[0]\n",
        "# _, accuracy = model.evaluate(X_preg_max, y, verbose=0)\n",
        "# print('Accuracy: %.2f' % (accuracy*100))\n",
        "# predictions_preg_max = model.predict(X_preg_max) \n",
        "\n",
        "# # reshape = predictions_preg_max.reshape(1,-1)\n",
        "# # print(reshape)\n",
        "# for i in range(5):\n",
        "# \tprint('%s => %s (expected %d)' % (X_preg_max[i].tolist(), predictions_preg_max[i], y[i]))\n"
      ],
      "metadata": {
        "id": "C7yXQmZk1TZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_max = []\n",
        "for j in range(8):\n",
        "  X_max = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_max[i][j] = max[j]\n",
        "  _, accuracy = model.evaluate(X_max, y_test, verbose=0)\n",
        "  predictions_sub_max = model.predict(X_max) \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",
        "print(prediction_max)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ys84h-5SKsBL",
        "outputId": "b90a9612-fe17-403f-d23a-6192ec2bef88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 3ms/step\n",
            "8/8 [==============================] - 0s 3ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.5319893  0.883704   0.19691484 ... 0.6542538  0.78293675 0.31541765]\n",
            " [0.83535826 0.88871074 0.49683887 ... 0.9005494  0.90779614 0.5083649 ]\n",
            " [0.43628806 0.69718534 0.1023619  ... 0.57469034 0.59211403 0.25003254]\n",
            " ...\n",
            " [0.34232086 0.56664103 0.05625768 ... 0.52467936 0.35963362 0.16251789]\n",
            " [0.3490364  0.7810644  0.08100341 ... 0.48073423 0.5168763  0.20471938]\n",
            " [0.92383945 0.90343684 0.68061453 ... 0.89562523 0.9218821  0.87095976]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# min = X.min()\n",
        "# #save a copy\n",
        "# X_preg_min = testX\n",
        "# for i in range(len(X)):\n",
        "#     X_preg_min.iloc[i][1] = min[0]\n",
        "# _, accuracy = model.evaluate(X_preg_min, y, verbose=0)\n",
        "# predictions_preg_min = model.predict(X_preg_min) \n",
        "# for i in range(5):\n",
        "# \tprint('%s => %s (expected %d)' % (X_preg_min.iloc[i].tolist(), predictions_preg_min[i], y.iloc[i]))\n"
      ],
      "metadata": {
        "id": "RtjwT1sY7b9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_min = []\n",
        "for j in range(8):\n",
        "  X_min = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_min[i][j] = min[j]\n",
        "  _, accuracy = model.evaluate(X_min, y_test, verbose=0)\n",
        "  predictions_sub_min = model.predict(X_min) \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",
        "print(prediction_min)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AJG2egdRhHM",
        "outputId": "e66898bd-b22b-4a47-e49c-c29237e5c18b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.10666633 0.07938994 0.21300815 ... 0.09604245 0.20916905 0.126393  ]\n",
            " [0.34766978 0.08563883 0.52152604 ... 0.37633127 0.47906202 0.24511085]\n",
            " [0.07518598 0.02764399 0.11180345 ... 0.07051739 0.09503799 0.09476782]\n",
            " ...\n",
            " [0.05184029 0.02080186 0.0617392  ... 0.05836041 0.03750047 0.05743587]\n",
            " [0.05331927 0.03757798 0.08866955 ... 0.04941219 0.06894286 0.07478713]\n",
            " [0.66079956 0.1252322  0.7016986  ... 0.3314158  0.5250973  0.67942876]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pregreant difference\n",
        "result = np.subtract(prediction_max, prediction_min)\n",
        "print(result.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9DQhFrpIFNu",
        "outputId": "eee67ac9-71fa-4968-96c6-7b21a99414c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(231, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# heatmap_diff = pd.DataFrame(result, columns = ['Column_A','Column_B','Column_C'])\n",
        "heatmap_diff = pd.DataFrame(result, columns = [data.columns[0:8]])\n",
        "print(heatmap_diff)\n",
        "print(heatmap_diff.describe().T)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEmKcoPYI5Vh",
        "outputId": "7b839dff-d2eb-4b1c-d3e9-4ebdb7fc8692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Pregnancies   Glucose BloodPressure SkinThickness   Insulin       BMI  \\\n",
            "0      0.425323  0.804314     -0.016093      0.035950  0.105906  0.558211   \n",
            "1      0.487688  0.803072     -0.024687      0.053736  0.036035  0.524218   \n",
            "2      0.361102  0.669541     -0.009442      0.021747  0.108916  0.504173   \n",
            "3      0.431702  0.782128     -0.014091      0.030890  0.123943  0.514085   \n",
            "4      0.265150  0.650866     -0.006592      0.015791  0.100652  0.494936   \n",
            "..          ...       ...           ...           ...       ...       ...   \n",
            "226    0.467560  0.802704     -0.024688      0.053844  0.036108  0.607337   \n",
            "227    0.509254  0.774827     -0.024446      0.053509  0.059670  0.616757   \n",
            "228    0.290481  0.545839     -0.005482      0.012988  0.087262  0.466319   \n",
            "229    0.295717  0.743486     -0.007666      0.017969  0.108189  0.431322   \n",
            "230    0.263040  0.778205     -0.021084      0.046546  0.034878  0.564209   \n",
            "\n",
            "    DiabetesPedigreeFunction       Age  \n",
            "0                   0.573768  0.189025  \n",
            "1                   0.428734  0.263254  \n",
            "2                   0.497076  0.155265  \n",
            "3                   0.572881  0.198565  \n",
            "4                   0.399827  0.118328  \n",
            "..                       ...       ...  \n",
            "226                 0.490544  0.281736  \n",
            "227                 0.549994  0.280826  \n",
            "228                 0.322133  0.105082  \n",
            "229                 0.447933  0.129932  \n",
            "230                 0.396785  0.191531  \n",
            "\n",
            "[231 rows x 8 columns]\n",
            "                          count      mean       std       min       25%  \\\n",
            "Pregnancies               231.0  0.375697  0.128758  0.017373  0.290429   \n",
            "Glucose                   231.0  0.731983  0.087257  0.243444  0.702894   \n",
            "BloodPressure             231.0 -0.015562  0.007294 -0.024695 -0.022652   \n",
            "SkinThickness             231.0  0.035073  0.014541  0.003502  0.022248   \n",
            "Insulin                   231.0  0.071437  0.039819 -0.002461  0.031573   \n",
            "BMI                       231.0  0.482183  0.135762  0.047463  0.417722   \n",
            "DiabetesPedigreeFunction  231.0  0.418701  0.149859  0.046668  0.334133   \n",
            "Age                       231.0  0.196125  0.073274  0.014050  0.139100   \n",
            "\n",
            "                               50%       75%       max  \n",
            "Pregnancies               0.418404  0.487639  0.510440  \n",
            "Glucose                   0.764407  0.792277  0.805210  \n",
            "BloodPressure            -0.016996 -0.009629 -0.000083  \n",
            "SkinThickness             0.037539  0.049744  0.053849  \n",
            "Insulin                   0.079565  0.106730  0.137963  \n",
            "BMI                       0.516390  0.594390  0.616875  \n",
            "DiabetesPedigreeFunction  0.472285  0.543885  0.589218  \n",
            "Age                       0.208879  0.261765  0.281736  \n"
          ]
        }
      ]
    },
    {
      "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",
        "\n",
        "plt.xlabel('patient',fontsize=22)\n",
        "plt.ylabel('difference',fontsize=22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YZsseRoWJxV0",
        "outputId": "e218274a-61b1-4ebc-c3e2-b7a52cff27a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(83.25, 0.5, 'difference')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Outlier Detection"
      ],
      "metadata": {
        "id": "TgrDbVNRVI7Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####changed!"
      ],
      "metadata": {
        "id": "mSWF7opJbV_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "row_num_double = heatmap_diff[(heatmap_diff.iloc[:,1] < 0.55)].index \n",
        "print(row_num_double.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48iNFgvZVu9N",
        "outputId": "359e01f5-0be7-48a1-b39b-5316fd59dc16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 45  47  81  90 103 105 120 170 187 228]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sort = heatmap_diff[heatmap_diff.index.isin(row_num_double)]\n",
        "print(sort)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnh8PfwtWgYr",
        "outputId": "3e542780-bd52-4b42-c43e-77bc32a7952e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Pregnancies   Glucose BloodPressure SkinThickness   Insulin       BMI  \\\n",
            "45     0.017373  0.243444     -0.000083      0.007991  0.006773  0.089994   \n",
            "47     0.336446  0.544752     -0.006684      0.016085  0.100621  0.452119   \n",
            "81     0.106641  0.337205     -0.001644      0.012293  0.008568  0.071375   \n",
            "90     0.043036  0.541143     -0.001806      0.012573  0.009451  0.082640   \n",
            "103    0.155713  0.530494     -0.002251      0.016997  0.017034  0.171526   \n",
            "105    0.357924  0.534397     -0.010138      0.022840  0.006212  0.385250   \n",
            "120    0.186234  0.452452     -0.002573      0.015598  0.018897  0.384574   \n",
            "170    0.233456  0.488471     -0.004022      0.010490  0.075555  0.397396   \n",
            "187    0.025698  0.425100     -0.001214      0.009001  0.007338  0.047463   \n",
            "228    0.290481  0.545839     -0.005482      0.012988  0.087262  0.466319   \n",
            "\n",
            "    DiabetesPedigreeFunction       Age  \n",
            "45                  0.083882  0.014050  \n",
            "47                  0.439211  0.125427  \n",
            "81                  0.046668  0.030946  \n",
            "90                  0.078999  0.033577  \n",
            "103                 0.128986  0.062698  \n",
            "105                 0.156512  0.108036  \n",
            "120                 0.084230  0.049906  \n",
            "170                 0.340770  0.070278  \n",
            "187                 0.097030  0.018877  \n",
            "228                 0.322133  0.105082  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filter = pd.DataFrame(x_test[heatmap_diff.index.isin(row_num_double)],columns = features)\n",
        "print(filter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6FD-Gx9Wi_5",
        "outputId": "73a56551-c255-4655-c0bc-9db8b5eeb106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
            "0     0.000000  0.051785       0.038418       0.080347  0.003351  0.064619   \n",
            "1     0.007060  0.031934       0.030537       0.016579  0.043558  0.026109   \n",
            "2     0.120019  0.046894       0.035463       0.052289  0.027283  0.044493   \n",
            "3     0.042360  0.052648       0.046298       0.026191  0.019098  0.044384   \n",
            "4     0.070600  0.042579       0.041373       0.061216  0.056721  0.040903   \n",
            "5     0.091779  0.036250       0.044328       0.026191  0.019098  0.047213   \n",
            "6     0.077660  0.038839       0.034037       0.026191  0.019098  0.056895   \n",
            "7     0.007060  0.028770       0.036448       0.015304  0.011009  0.021213   \n",
            "8     0.028240  0.056676       0.034478       0.049738  0.178059  0.039924   \n",
            "9     0.007060  0.029633       0.039403       0.014029  0.019625  0.021104   \n",
            "\n",
            "   DiabetesPedigreeFunction       Age  \n",
            "0                  0.151484  0.025586  \n",
            "1                  0.008638  0.023540  \n",
            "2                  0.051142  0.048103  \n",
            "3                  0.091454  0.046056  \n",
            "4                  0.062659  0.052196  \n",
            "5                  0.036494  0.042985  \n",
            "6                  0.036181  0.040938  \n",
            "7                  0.009327  0.028657  \n",
            "8                  0.145788  0.031727  \n",
            "9                  0.030735  0.022516  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.stats.api as sms\n",
        "interval = filter.apply(lambda x: sms.DescrStatsW(x).tconfint_mean(), axis=0)\n",
        "print(interval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iO5f80I9Wu3B",
        "outputId": "a7949d91-e1f1-4ced-c87f-63c5267feeda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
            "0     0.014863  0.034370       0.034582       0.020588  0.003196  0.030341   \n",
            "1     0.075505  0.048832       0.041575       0.053027  0.076184  0.051030   \n",
            "\n",
            "   DiabetesPedigreeFunction       Age  \n",
            "0                  0.025504  0.028321  \n",
            "1                  0.099276  0.044140  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_df.hist(column=\"Pregnancies\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "fnjSfJqKhGAX",
        "outputId": "6de399b6-1da5-4b10-e391-0c6394a381af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Pregnancies'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "filter.hist(column=\"Pregnancies\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "gs_AvJCgg9Ad",
        "outputId": "50c27e76-6c0c-407b-8e79-7e7f3861580e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Pregnancies'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": [
        "filter.hist(column=\"Glucose\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "px52dVJagyfL",
        "outputId": "73bb8050-a659-4113-c78e-46c018825873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Glucose'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "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": [
        "x_train_df.hist(column=\"Glucose\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "oH6E4W-OWw_c",
        "outputId": "00814e7d-8e69-4875-f524-13e0f50233bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Glucose'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": [
        "import statsmodels.stats.api as sms\n",
        "confid = filter.apply(lambda x: sms.DescrStatsW(x).tconfint_mean())\n",
        "confindence_interval= confid.to_numpy()\n",
        "low_bd = confindence_interval[0]\n",
        "high_bd = confindence_interval[1]\n",
        "print(low_bd)\n",
        "print(high_bd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3tDauCZXQ5T",
        "outputId": "dca02fcc-03f7-4cfb-f7fa-9ff30692717f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.01486269 0.03436979 0.03458172 0.02058772 0.00319599 0.03034115\n",
            " 0.0255042  0.02832091]\n",
            "[0.07550476 0.04883184 0.04157491 0.05302739 0.07618396 0.0510304\n",
            " 0.09927631 0.0441399 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####changed!"
      ],
      "metadata": {
        "id": "8c_DxfsrbbQt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ref = x_train_df[ (x_train_df.iloc[:,1] > high_bd[1]) | (x_train_df.iloc[:,1] < low_bd[1])].index \n",
        "print(len(train_ref.values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcH8fnO5XaN5",
        "outputId": "4d94c4b7-d613-4ed3-f527-1d16e8726452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "330\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_clean_data = x_train_df[~x_train_df.index.isin(train_ref)]\n",
        "print(train_clean_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P73LbxxfXmY7",
        "outputId": "7eca4f12-68f3-4ff1-cee0-a6e56c62d6bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
            "1       0.070600  0.037113       0.037433       0.035710  0.029198  0.039054   \n",
            "2       0.028240  0.042579       0.029552       0.034434  0.076106  0.033615   \n",
            "3       0.091779  0.044017       0.043343       0.047188  0.033506  0.044167   \n",
            "4       0.007060  0.036825       0.023642       0.057390  0.046429  0.044058   \n",
            "6       0.042360  0.044305       0.038418       0.052289  0.033506  0.050150   \n",
            "..           ...       ...            ...            ...       ...       ...   \n",
            "526     0.042360  0.047470       0.033493       0.033159  0.040207  0.036552   \n",
            "529     0.063540  0.040277       0.046298       0.026191  0.019098  0.035573   \n",
            "530     0.077660  0.034523       0.039403       0.047188  0.035899  0.046016   \n",
            "532     0.028240  0.037113       0.042358       0.025507  0.064618  0.038184   \n",
            "535     0.042360  0.035962       0.038418       0.039536  0.019098  0.030025   \n",
            "\n",
            "     DiabetesPedigreeFunction       Age  \n",
            "1                    0.017527  0.039915  \n",
            "2                    0.009390  0.029680  \n",
            "3                    0.073489  0.039915  \n",
            "4                    0.038372  0.024563  \n",
            "6                    0.035743  0.027633  \n",
            "..                        ...       ...  \n",
            "526                  0.039499  0.050149  \n",
            "529                  0.045946  0.046056  \n",
            "530                  0.049139  0.049126  \n",
            "532                  0.014460  0.023540  \n",
            "535                  0.035367  0.050149  \n",
            "\n",
            "[207 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_df = pd.DataFrame(y_train)\n",
        "print(y_train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZJkvtzrXpNQ",
        "outputId": "7b06a0c4-71b5-4e4f-f2b9-685cbf2cb9b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     0\n",
            "0    0\n",
            "1    0\n",
            "2    1\n",
            "3    0\n",
            "4    1\n",
            "..  ..\n",
            "532  0\n",
            "533  1\n",
            "534  0\n",
            "535  1\n",
            "536  1\n",
            "\n",
            "[537 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_clean_data = y_train_df[~y_train_df.index.isin(train_ref)]\n",
        "print(y_train_clean_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvBPVCCuXzjV",
        "outputId": "70e1f63d-13d3-4dfe-a708-825812554798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     0\n",
            "1    0\n",
            "2    1\n",
            "3    0\n",
            "4    1\n",
            "6    0\n",
            "..  ..\n",
            "526  0\n",
            "529  1\n",
            "530  1\n",
            "532  0\n",
            "535  1\n",
            "\n",
            "[207 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Train new model"
      ],
      "metadata": {
        "id": "xgfKwhp-X60s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_clean = Sequential()\n",
        "\n",
        "model_clean.add(Dense(12,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), input_shape=(8,), activation='relu'))\n",
        "model_clean.add(Dense(8,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), activation='relu'))\n",
        "model_clean.add(Dense(1,kernel_initializer = tf.keras.initializers.glorot_uniform(seed=6), activation='sigmoid'))\n",
        "\n",
        "\n",
        "model_clean.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "BCTUgvQBX4AD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_clean.fit(train_clean_data, y_train_clean_data, epochs=150, batch_size=10, verbose=0)\n",
        "# evaluate the keras model\n",
        "# test DS\n",
        "#training error\n",
        "_, accuracy = model_clean.evaluate(train_clean_data, y_train_clean_data, verbose=0)\n",
        "print('Accuracy: %.2f' % (accuracy*100))\n",
        "\n",
        "predictions = (model_clean.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": "fMVQVay2X995",
        "outputId": "0ddeaf27-97f0-4870-c93d-9c2c1e2ef2aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 61.84\n",
            "8/8 [==============================] - 0s 2ms/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] => 1 (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": "code",
      "source": [
        "prediction_max_clean = []\n",
        "for j in range(8):\n",
        "  X_max = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_max[i][j] = max[j]\n",
        "  _, accuracy = model_clean.evaluate(X_max, y_test, verbose=0)\n",
        "  predictions_sub_max = model_clean.predict(X_max) \n",
        "  if len(prediction_max_clean) == 0:\n",
        "    prediction_max_clean = predictions_sub_max\n",
        "  else:\n",
        "    prediction_max_clean = np.concatenate((prediction_max_clean, predictions_sub_max), axis=1)\n",
        "print(prediction_max_clean)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2z5FL1Q4YECW",
        "outputId": "5f2874f8-ec98-4319-cc29-0f29a05165ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.76270777 0.48137105 0.4446046  ... 0.5823301  0.78751343 0.5347573 ]\n",
            " [0.8026249  0.56060255 0.5379104  ... 0.68736464 0.8018434  0.54269916]\n",
            " [0.72031975 0.36264402 0.33412012 ... 0.49418205 0.71864176 0.48448578]\n",
            " ...\n",
            " [0.7240752  0.33089525 0.30414793 ... 0.48885274 0.63701755 0.46552584]\n",
            " [0.75104535 0.43732524 0.39881593 ... 0.55660313 0.7498468  0.5529061 ]\n",
            " [0.7916976  0.30986875 0.2880467  ... 0.4052751  0.66972053 0.49177924]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_min_clean = []\n",
        "for j in range(8):\n",
        "  X_min = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_min[i][j] = min[j]\n",
        "  _, accuracy = model_clean.evaluate(X_min, y_test, verbose=0)\n",
        "  predictions_sub_min = model_clean.predict(X_min) \n",
        "  if len(prediction_min_clean) == 0:\n",
        "    prediction_min_clean = predictions_sub_min\n",
        "  else:\n",
        "    prediction_min_clean = np.concatenate((prediction_min_clean, predictions_sub_min), axis=1)\n",
        "print(prediction_min_clean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVR8enGDYm5D",
        "outputId": "7a53d7bd-9387-41b4-c02f-4fe017983943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.30567658 0.4429469  0.4548286  ... 0.39333642 0.45243928 0.3659583 ]\n",
            " [0.4125473  0.5222222  0.5481623  ... 0.50700235 0.5203356  0.3734054 ]\n",
            " [0.2729682  0.32770863 0.34337416 ... 0.3123988  0.31576076 0.3206196 ]\n",
            " ...\n",
            " [0.28633028 0.2991596  0.312962   ... 0.3078368  0.23695865 0.3051922 ]\n",
            " [0.3109463  0.39970732 0.40876186 ... 0.36859053 0.36065492 0.38309565]\n",
            " [0.27998358 0.30654824 0.3371552  ... 0.22868365 0.21651083 0.3016814 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_clean = np.subtract(prediction_max_clean, prediction_min_clean)\n",
        "heatmap_diff_clean = pd.DataFrame(result_clean, columns = [data.columns[0:8]])\n",
        "print(heatmap_diff_clean)\n",
        "print(heatmap_diff_clean.describe().T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IW3vaXUY-3C",
        "outputId": "9913bf72-8c3e-46f0-889b-9049aacda9a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Pregnancies   Glucose BloodPressure SkinThickness   Insulin       BMI  \\\n",
            "0      0.457031  0.038424     -0.010224     -0.029680 -0.054810  0.188994   \n",
            "1      0.390078  0.038380     -0.010252     -0.029830 -0.083005  0.180362   \n",
            "2      0.447352  0.034935     -0.009254     -0.026607 -0.066683  0.181783   \n",
            "3      0.447698  0.037091     -0.009833     -0.028613 -0.068226  0.184706   \n",
            "4      0.442536  0.036763     -0.009712     -0.028007 -0.054430  0.188347   \n",
            "..          ...       ...           ...           ...       ...       ...   \n",
            "226    0.389448  0.038605     -0.010319     -0.029908 -0.095289  0.189056   \n",
            "227    0.362343  0.037197     -0.009970     -0.029137 -0.081348  0.175133   \n",
            "228    0.437745  0.031736     -0.008814     -0.025034 -0.072655  0.181016   \n",
            "229    0.440099  0.037618     -0.009946     -0.028775 -0.056843  0.188013   \n",
            "230    0.511714  0.003321     -0.049109     -0.050338 -0.119763  0.176591   \n",
            "\n",
            "    DiabetesPedigreeFunction       Age  \n",
            "0                   0.335074  0.168799  \n",
            "1                   0.281508  0.169294  \n",
            "2                   0.402881  0.163866  \n",
            "3                   0.386710  0.167632  \n",
            "4                   0.395009  0.168699  \n",
            "..                       ...       ...  \n",
            "226                 0.355783  0.170443  \n",
            "227                 0.334127  0.152977  \n",
            "228                 0.400059  0.160334  \n",
            "229                 0.389192  0.169810  \n",
            "230                 0.453210  0.190098  \n",
            "\n",
            "[231 rows x 8 columns]\n",
            "                          count      mean       std       min       25%  \\\n",
            "Pregnancies               231.0  0.406246  0.058630  0.104707  0.381723   \n",
            "Glucose                   231.0  0.032748  0.008656 -0.008335  0.032403   \n",
            "BloodPressure             231.0 -0.011463  0.008027 -0.056952 -0.010288   \n",
            "SkinThickness             231.0 -0.027922  0.007338 -0.058366 -0.029751   \n",
            "Insulin                   231.0 -0.071668  0.026290 -0.129790 -0.087697   \n",
            "BMI                       231.0  0.172553  0.026058  0.048256  0.169395   \n",
            "DiabetesPedigreeFunction  231.0  0.358340  0.071844  0.089582  0.322708   \n",
            "Age                       231.0  0.160459  0.021325  0.054189  0.158890   \n",
            "\n",
            "                               50%       75%       max  \n",
            "Pregnancies               0.420753  0.442464  0.531420  \n",
            "Glucose                   0.035896  0.037926  0.038646  \n",
            "BloodPressure            -0.009946 -0.009237  0.003350  \n",
            "SkinThickness            -0.028474 -0.026516  0.009550  \n",
            "Insulin                  -0.074070 -0.059235  0.020685  \n",
            "BMI                       0.180918  0.186543  0.220759  \n",
            "DiabetesPedigreeFunction  0.389943  0.405564  0.463373  \n",
            "Age                       0.166582  0.170060  0.198838  \n"
          ]
        }
      ]
    },
    {
      "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_clean,cmap = 'cool')\n",
        "\n",
        "plt.xlabel('patient',fontsize=22)\n",
        "plt.ylabel('difference',fontsize=22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GFmRkKTOZL1X",
        "outputId": "048821af-eb7e-468c-a943-75be8c49692a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(83.25, 0.5, 'difference')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Validation"
      ],
      "metadata": {
        "id": "ZEtngsH7bPMD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####changed!"
      ],
      "metadata": {
        "id": "_TQkvNG3bfis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "check_row_num_double= heatmap_diff_clean[(heatmap_diff_clean.iloc[:,1] < 0.55)].index \n",
        "print(check_row_num_double.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgp_nm8IbLu4",
        "outputId": "2f2c5394-1e10-4cb0-c2cb-9962c15f28ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
            "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
            "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
            "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
            "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
            " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
            " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
            " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(row_num_double)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4QqgTfbbiPf",
        "outputId": "31ff9366-46ef-4638-92d8-50472f6ecd76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int64Index([45, 47, 81, 90, 103, 105, 120, 170, 187, 228], dtype='int64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confidence interval MAX - MIN"
      ],
      "metadata": {
        "id": "tLXYLK_NVD7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.stats.api as sms\n",
        "x_test_df = pd.DataFrame(x_test, columns = [data.columns[0:8]])\n",
        "confid = x_test_df.apply(lambda x: sms.DescrStatsW(x).tconfint_mean())\n",
        "confindence_interval= confid.to_numpy()\n",
        "low_bd = confindence_interval[0]\n",
        "high_bd = confindence_interval[1]\n"
      ],
      "metadata": {
        "id": "t3cKEitNdHDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(low_bd)\n",
        "print(high_bd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-3F7osvpIDY",
        "outputId": "3e29db9a-8405-463d-acf9-e29ac0f61652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.02592853 0.03412377 0.0354592  0.03187318 0.02432258 0.0339663\n",
            " 0.02743483 0.03371798]\n",
            "[0.03201813 0.0364496  0.03692008 0.03512155 0.03054805 0.03595773\n",
            " 0.03338002 0.03696707]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(min)\n",
        "print(max)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiv2HwyApRhu",
        "outputId": "e5860879-84e9-488e-c41c-97a139ad7150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.         0.01639866 0.02364178 0.01020273 0.00335058 0.0209956\n",
            " 0.00532073 0.02149261]\n",
            "[0.12001927 0.05696377 0.05319401 0.08034653 0.17805938 0.06461858\n",
            " 0.15148431 0.07061859]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_high = []\n",
        "for j in range(8):\n",
        "  X_high = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_high[i][j] = high_bd[j]\n",
        "  _, accuracy = model.evaluate(X_high, y_test, verbose=0)\n",
        "  predictions_sub_high = model.predict(X_high) \n",
        "  if len(prediction_high) == 0:\n",
        "    prediction_high = predictions_sub_high\n",
        "  else:\n",
        "    prediction_high = np.concatenate((prediction_high, predictions_sub_high), axis=1)\n",
        "print(prediction_high)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zD72lvuIgWl2",
        "outputId": "27c4ccea-814c-4abc-dea5-2aa6cef8799e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.17885749 0.44547108 0.20566104 ... 0.2219631  0.30799568 0.17244922]\n",
            " [0.49296275 0.46595055 0.51043946 ... 0.6183568  0.6074567  0.31864765]\n",
            " [0.12915163 0.19010845 0.10747043 ... 0.16923766 0.15018107 0.13102855]\n",
            " ...\n",
            " [0.09069254 0.11762914 0.0592171  ... 0.14267401 0.06152889 0.08068555]\n",
            " [0.09317099 0.26671457 0.08514713 ... 0.12247976 0.11079879 0.10428341]\n",
            " [0.78040135 0.48927802 0.6923238  ... 0.5640168  0.6504252  0.75324863]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_low = []\n",
        "for j in range(8):\n",
        "  X_low = np.copy(x_test)\n",
        "  for i in range(len(x_test)):\n",
        "    X_low[i][j] = low_bd[j]\n",
        "  _, accuracy = model.evaluate(X_low, y_test, verbose=0)\n",
        "  predictions_sub_low = model.predict(X_low) \n",
        "  if len(prediction_low) == 0:\n",
        "    prediction_low = predictions_sub_low\n",
        "  else:\n",
        "    prediction_low = np.concatenate((prediction_low, predictions_sub_low), axis=1)\n",
        "print(prediction_low)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_pWTlSXg5pa",
        "outputId": "3f362499-13f1-4afb-feea-68d37ed14d7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 3ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "8/8 [==============================] - 0s 2ms/step\n",
            "[[0.16267733 0.38275936 0.20646006 ... 0.2000907  0.28500354 0.16178893]\n",
            " [0.464439   0.40244824 0.5116599  ... 0.58688784 0.5808746  0.30225068]\n",
            " [0.11682832 0.15340103 0.10793985 ... 0.15154888 0.1366436  0.12254964]\n",
            " ...\n",
            " [0.08169463 0.09330423 0.05948979 ... 0.1273361  0.05546113 0.0751825 ]\n",
            " [0.08394992 0.21921934 0.08552842 ... 0.10903667 0.10039194 0.09734132]\n",
            " [0.7601812  0.42512727 0.6933632  ... 0.5314623  0.62495637 0.7387352 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_bd = np.subtract(prediction_high, prediction_low)\n",
        "print(result_bd.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fi29rK1ThJIP",
        "outputId": "4a99a911-a3e9-4f28-c7f8-009684133553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(231, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap_bd_diff = pd.DataFrame(result_bd, columns = [data.columns[0:8]])\n",
        "print(heatmap_bd_diff)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaS4g_KRhTpS",
        "outputId": "1910f452-ca9d-4ace-b0fc-f1b689120fad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Pregnancies   Glucose BloodPressure SkinThickness   Insulin       BMI  \\\n",
            "0      0.016180  0.062712     -0.000799      0.001630  0.000851  0.021872   \n",
            "1      0.028524  0.063502     -0.001220      0.002494  0.001287  0.031469   \n",
            "2      0.012323  0.036707     -0.000469      0.000978  0.000498  0.017689   \n",
            "3      0.016630  0.055422     -0.000700      0.001396  0.000746  0.018370   \n",
            "4      0.007962  0.034566     -0.000328      0.000709  0.000358  0.017081   \n",
            "..          ...       ...           ...           ...       ...       ...   \n",
            "226    0.028492  0.062826     -0.001221      0.002496  0.001287  0.032526   \n",
            "227    0.027057  0.063356     -0.001210      0.002472  0.001273  0.030466   \n",
            "228    0.008998  0.024325     -0.000273      0.000582  0.000286  0.015338   \n",
            "229    0.009221  0.047495     -0.000381      0.000807  0.000415  0.013443   \n",
            "230    0.020220  0.064151     -0.001039      0.002185  0.001132  0.032555   \n",
            "\n",
            "    DiabetesPedigreeFunction       Age  \n",
            "0                   0.022992  0.010660  \n",
            "1                   0.026582  0.016397  \n",
            "2                   0.013537  0.008479  \n",
            "3                   0.020063  0.011310  \n",
            "4                   0.008747  0.006262  \n",
            "..                       ...       ...  \n",
            "226                 0.027537  0.018813  \n",
            "227                 0.025918  0.019065  \n",
            "228                 0.006068  0.005503  \n",
            "229                 0.010407  0.006942  \n",
            "230                 0.025469  0.014513  \n",
            "\n",
            "[231 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "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_bd_diff,cmap = sns.color_palette(\"vlag\", as_cmap=True), vmin=-1, vmax=1)\n",
        "# ax = sns.heatmap(heatmap_bd_diff,cmap = sns.color_palette(\"vlag\", as_cmap=True))\n",
        "ax = sns.heatmap(heatmap_bd_diff,cmap = 'cool',vmin=0, vmax=0.7)\n",
        "\n",
        "plt.xlabel('patient',fontsize=22)\n",
        "plt.ylabel('difference',fontsize=22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "v6rWN2dDheqe",
        "outputId": "fac5c16b-f27f-4c46-87d1-a4a89dec84e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(83.25, 0.5, 'difference')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}