George_Moody_Challenge_2023_CV_local.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload\n",
        "%reload_ext autoreload"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "q0sWcdDJkCO5"
      },
      "outputs": [],
      "source": [
        "from evaluate_model import *\n",
        "from team_code import *\n",
        "from run_model import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "quSp-rnhvG4T"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "\n",
        "font = {'size'   : 16}\n",
        "\n",
        "matplotlib.rc('font', **font)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "data_folder = \"./training_subset/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "patient_ids = find_data_folders(data_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "patient_metadata, recording_metadata, recording_data = load_challenge_data(data_folder, patient_ids[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "kf = KFold(n_splits=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "patient_ids = np.asarray(patient_ids)\n",
        "for i, (train_index, test_index) in enumerate(kf.split(np.unique(patient_ids))):\n",
        "    train_id, val_id = patient_ids[train_index], patient_ids[test_index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],\n",
              "      dtype=int64)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.where(np.isin(patient_ids,train_id))[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),\n",
              " array([0, 1, 2, 3]))"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "next(kf.split(patient_ids))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for i, (train_index, test_index) in enumerate(kf.split(patient_ids)):\n",
        "    train\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Patient: ICARE_0284\\nAge: 53\\nSex: Male\\nROSC: nan\\nOHCA: True\\nVFib: True\\nTTM: 33\\nOutcome: Good\\nCPC: 1\\n'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "patient_metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'ICARE_0284'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_patient_id(patient_metadata)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "[-2.5     -2.28125 -1.65625 ...  3.75     4.375    4.71875]\n",
            "[-1.3125  -0.90625 -1.0625  ... -4.8125  -4.59375 -4.71875]\n",
            "[-3.34375 -3.84375 -3.90625 ... -0.15625  0.125    0.5625 ]\n",
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            "[-0.84375 -1.53125 -1.875   ... -0.03125 -0.3125  -0.21875]\n",
            "[-1.3125  -1.46875 -0.8125  ... -1.84375 -1.75    -2.15625]\n",
            "[-3.4375  -2.1875  -1.65625 ...  1.34375  2.       2.65625]\n",
            "[0.8125  1.53125 2.5     ... 2.15625 2.9375  2.96875]\n",
            "[ 2.03125  2.4375   2.75    ... -1.71875 -1.75    -1.53125]\n",
            "[-5.6875  -4.53125 -5.      ... -4.8125  -3.5625  -3.53125]\n",
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            "[-6.1875  -6.96875 -8.25    ...  0.5      1.       1.6875 ]\n",
            "[4.15625 5.84375 5.3125  ... 2.25    1.875   0.8125 ]\n",
            "[4.      6.375   6.9375  ... 6.96875 8.21875 8.9375 ]\n",
            "[ -2.5625   -2.15625  -1.59375 ... -11.09375 -10.0625   -9.75   ]\n",
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            "[-2.53125 -3.03125 -3.5625  ... -5.65625 -4.90625 -4.125  ]\n",
            "[-3.65625 -3.4375  -4.21875 ... -0.125    0.5625   0.5    ]\n",
            "[  1.40625   2.3125    1.34375 ... -19.96875 -19.03125 -18.1875 ]\n",
            "[-1.96875 -2.09375 -3.15625 ... 20.25    20.09375 19.59375]\n",
            "[-0.375    0.375   -0.21875 ... -4.1875  -3.4375  -3.75   ]\n",
            "[-0.5625   0.9375   1.09375 ...  4.65625  5.3125   5.59375]\n",
            "[-4.4375  -3.15625 -3.      ...  0.03125  1.53125  1.96875]\n",
            "[-12.625   -12.78125 -13.78125 ... -14.65625 -13.59375 -12.34375]\n",
            "[ -0.6875    0.        0.375   ... -11.09375  -9.90625  -9.0625 ]\n",
            "[-3.46875 -2.      -2.96875 ...  1.625    2.25     2.625  ]\n",
            "[-6.15625 -6.125   -6.8125  ... -3.75    -4.09375 -3.9375 ]\n",
            "[-3.09375 -2.875   -2.96875 ...  2.40625  3.0625   1.6875 ]\n",
            "[-10.03125  -8.28125  -7.875   ...  -5.3125   -4.65625  -3.84375]\n",
            "[ 4.       5.3125   5.75    ... -4.4375  -5.15625 -5.90625]\n",
            "[ 5.03125  5.46875  4.71875 ... -6.28125 -5.46875 -4.4375 ]\n",
            "[-18.4375  -16.625   -16.28125 ...   5.9375    5.90625   5.625  ]\n",
            "[ 5.75     6.125    5.1875  ... -3.09375 -4.15625 -4.28125]\n",
            "[ 11.5625   12.3125   11.21875 ... -12.0625  -10.34375 -10.78125]\n",
            "[ 0.1875   0.21875  0.96875 ... -8.375   -6.5     -5.40625]\n",
            "[-6.5     -7.0625  -7.09375 ...  1.4375   0.96875 -0.9375 ]\n",
            "[ 2.96875  4.4375   4.625   ... -4.34375 -5.3125  -5.5625 ]\n",
            "[-4.53125 -2.875   -1.46875 ... -7.65625 -8.09375 -7.46875]\n",
            "[ 3.96875  5.125    3.5625  ... -3.0625   0.125    1.46875]\n",
            "[-15.03125 -13.9375  -14.21875 ...   4.125     3.53125   2.53125]\n",
            "[ 5.28125  6.21875  5.28125 ... -8.21875 -8.59375 -9.125  ]\n",
            "[10.65625 10.71875 12.375   ... -8.375   -8.21875 -6.84375]\n",
            "[-1.84375 -0.90625 -2.28125 ... -5.09375 -1.8125  -1.40625]\n",
            "[  1.375     1.84375   2.15625 ... -10.28125 -11.96875 -12.75   ]\n",
            "[ 3.6875   5.875    6.1875  ... -8.15625 -6.5625  -6.5625 ]\n",
            "[16.96875 17.84375 19.03125 ... -0.4375   0.71875  1.6875 ]\n",
            "[ -7.09375  -4.       -1.625   ... -12.09375 -10.28125  -8.84375]\n",
            "[ 6.46875  6.21875  5.875   ... -8.90625 -7.0625  -4.9375 ]\n",
            "[ -0.59375   4.84375   9.40625 ... -13.65625 -14.09375 -12.25   ]\n",
            "[16.03125 17.71875 20.34375 ...  0.53125  2.78125  5.875  ]\n",
            "[10.9375   9.96875  9.5     ... -5.875   -5.03125 -4.84375]\n",
            "[-11.40625 -11.03125  -9.84375 ... -43.3125  -39.6875  -33.875  ]\n",
            "[-4.71875 -4.78125 -3.71875 ... 20.84375 17.25    13.34375]\n",
            "[25.1875  25.9375  25.8125  ... -1.09375  0.21875  0.375  ]\n",
            "[ 10.90625  13.96875  15.875   ... -14.34375 -12.0625  -10.75   ]\n",
            "[ -4.40625  -3.1875   -1.84375 ... -12.6875  -11.9375   -9.96875]\n",
            "[-15.9375  -11.78125  -7.09375 ...  -6.9375   -7.       -3.96875]\n",
            "[21.90625 24.5     26.25    ...  0.53125  3.       4.96875]\n",
            "[ 11.28125  10.75     11.21875 ... -15.25    -14.3125  -13.28125]\n",
            "[ -7.96875  -8.34375  -8.78125 ... -13.625   -12.       -9.75   ]\n",
            "[-14.375   -15.09375 -12.375   ...   0.59375  -1.375    -1.375  ]\n",
            "[  9.53125  11.96875  14.1875  ... -20.96875 -18.65625 -16.125  ]\n",
            "[ -7.21875  -7.53125  -6.375   ... -11.9375  -10.875    -8.96875]\n",
            "[  0.5       1.6875    2.875   ... -10.15625  -8.78125  -5.8125 ]\n",
            "[  7.28125   7.09375   8.15625 ... -21.65625 -21.90625 -22.90625]\n",
            "[-4.15625 -4.5625  -4.90625 ... -2.96875 -3.03125 -2.78125]\n",
            "[-1.96875 -2.1875  -4.28125 ...  7.75     8.25     7.78125]\n",
            "[  2.125     3.65625   4.375   ...  -7.3125   -9.1875  -11.53125]\n",
            "[ 3.5      3.3125   3.40625 ... -6.75    -4.46875 -1.375  ]\n",
            "[-0.34375  0.40625  1.5625  ... -2.5625  -2.34375 -0.6875 ]\n",
            "[ 4.4375  4.25    3.0625 ... -5.1875 -4.375  -4.6875]\n",
            "[-6.96875 -6.625   -4.875   ...  2.75     2.625    3.     ]\n",
            "[-8.28125 -8.6875  -7.71875 ... -7.28125 -5.625   -4.625  ]\n",
            "[  4.375     3.1875    2.78125 ... -14.3125  -13.34375 -11.34375]\n",
            "[ 12.5625   14.125    11.65625 ...  -8.21875  -9.1875  -10.78125]\n",
            "[-8.03125 -8.15625 -7.03125 ... -3.25    -4.59375 -5.71875]\n",
            "[-3.      -2.53125 -2.34375 ... -0.40625  2.25     3.46875]\n",
            "[  6.75     7.8125   9.3125 ... -11.875  -11.625   -9.3125]\n",
            "[13.96875 14.5625  12.5625  ... -6.28125 -6.4375  -6.78125]\n",
            "[-10.46875 -10.71875  -8.90625 ...  -1.21875   0.1875    0.78125]\n",
            "[ 13.40625  15.       14.34375 ... -18.46875 -17.3125  -16.21875]\n",
            "[ 0.15625 -1.75    -4.375   ... -9.0625  -8.5     -6.875  ]\n",
            "[ 0.      -2.09375 -5.78125 ... -0.375    0.28125  0.625  ]\n",
            "[-3.34375 -3.      -2.625   ...  1.8125   4.5625   3.875  ]\n",
            "[-13.71875 -13.96875 -14.25    ...  -7.53125  -9.34375  -9.4375 ]\n",
            "[ 2.875    3.5625   3.34375 ...  2.28125 -0.53125 -2.90625]\n",
            "[-4.0625  -4.      -4.28125 ... -5.21875 -5.3125  -5.78125]\n",
            "[-11.96875 -14.125   -17.375   ...  -6.75    -10.09375 -12.21875]\n",
            "[-1.40625 -2.0625  -3.53125 ...  4.90625  3.96875  6.5625 ]\n",
            "[ -5.53125  -6.5625   -7.53125 ... -11.125    -9.21875  -7.625  ]\n",
            "[ -3.59375  -5.375    -7.      ... -14.125   -12.1875   -9.1875 ]\n",
            "[-6.03125 -6.9375  -8.65625 ... -0.84375  0.375   -0.34375]\n",
            "[-1.6875  -1.96875 -3.84375 ... 10.9375   8.03125  5.375  ]\n",
            "[ -7.0625   -7.78125  -8.65625 ... -10.4375  -10.71875 -10.6875 ]\n",
            "[-5.09375 -5.09375 -6.71875 ... -6.6875  -7.46875 -6.59375]\n",
            "[ -9.78125 -11.25    -12.0625  ...   4.15625   3.21875   1.75   ]\n",
            "[7.3125  7.5     5.59375 ... 8.15625 2.96875 1.15625]\n",
            "[ -5.       -7.65625 -10.21875 ... -13.03125 -11.96875 -10.03125]\n",
            "[ -8.40625  -8.84375 -10.25    ...   3.125     1.71875   0.     ]\n",
            "[20.78125 22.5625  23.78125 ...  4.78125  5.28125  7.1875 ]\n",
            "[40.5625  41.71875 41.53125 ... -3.8125  -3.125   -2.4375 ]\n",
            "[16.59375 17.28125 16.375   ...  3.8125   4.84375  5.90625]\n",
            "[ 3.8125   5.21875  6.09375 ... -9.71875 -8.34375 -8.84375]\n",
            "[ 18.53125  20.1875   21.21875 ...  -8.6875  -10.       -9.1875 ]\n",
            "[25.28125 27.59375 30.46875 ... 13.5     14.75    14.8125 ]\n",
            "[29.40625 28.9375  28.375   ...  9.0625   9.9375  10.4375 ]\n",
            "[ 7.4375   7.75     8.25    ... -3.90625 -3.09375 -2.9375 ]\n",
            "[17.125   18.4375  19.5     ... -0.28125  0.125    2.1875 ]\n",
            "[ 25.78125  26.34375  26.25    ... -10.46875  -8.40625  -3.40625]\n",
            "[19.65625 20.8125  20.5     ...  1.625    2.59375  2.59375]\n",
            "[19.1875  21.15625 21.5625  ...  4.1875   4.25     0.40625]\n",
            "[16.84375 18.71875 20.96875 ... -8.9375  -8.90625 -4.96875]\n",
            "[20.65625 23.125   24.59375 ...  4.53125  7.71875 11.75   ]\n",
            "[30.5625  30.9375  32.125   ... 14.71875 14.5     11.6875 ]\n",
            "[12.625   11.65625 10.65625 ... -0.375   -1.71875 -5.3125 ]\n",
            "[18.25    19.375   20.46875 ... -6.15625 -4.40625  0.09375]\n",
            "[20.4375  21.5     22.40625 ...  5.8125   5.1875   3.03125]\n",
            "[-19.5625  -20.03125 -18.125   ...  20.71875  17.59375  13.09375]\n",
            "[-10.78125 -10.90625 -10.03125 ...  21.4375   19.53125  16.1875 ]\n",
            "[-0.78125 -1.21875 -1.4375  ...  2.375    0.71875 -1.09375]\n",
            "[ 3.1875   1.40625 -0.46875 ... -8.21875 -8.21875 -8.3125 ]\n",
            "[ 3.46875  2.125    1.40625 ... 13.78125  9.3125   4.1875 ]\n",
            "[ 0.75     0.21875  0.34375 ... 12.1875   9.84375  6.90625]\n",
            "[-13.25    -13.78125 -13.875   ...  15.59375  16.5      16.625  ]\n",
            "[-17.84375 -17.5625  -16.25    ...   2.9375    3.96875   4.3125 ]\n",
            "[-7.      -7.25    -5.875   ...  1.5625  -2.65625 -6.84375]\n",
            "[-7.375   -7.9375  -7.71875 ... 19.90625 14.8125   9.4375 ]\n",
            "[-6.      -7.375   -7.625   ...  7.1875   6.28125  4.40625]\n",
            "[-7.5     -8.1875  -8.8125  ...  7.59375 11.1875  12.875  ]\n",
            "[ 2.0625   1.40625  1.84375 ...  5.21875 -0.625   -6.1875 ]\n",
            "[-3.1875  -4.21875 -3.75    ... 20.59375 18.25    15.25   ]\n",
            "[-12.90625 -12.6875  -12.59375 ...  14.21875  13.875    11.     ]\n",
            "[-12.8125  -13.5     -13.8125  ...   4.4375    8.15625  12.03125]\n",
            "[-10.      -11.28125 -11.84375 ...  23.78125  19.90625  14.625  ]\n",
            "[-7.375   -8.5     -8.09375 ... 16.96875 17.65625 15.875  ]\n"
          ]
        }
      ],
      "source": [
        "for rec in recording_data:\n",
        "    if rec[1] != None:\n",
        "        for lead in rec[0]:\n",
        "            print(lead)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5HrNIUQGJFp",
        "outputId": "2ff62c4f-2b61-48d2-bc8c-41b3898356e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finding the Challenge data...\n",
            "Extracting features and labels from the Challenge data...\n",
            "Split the data into 2 cross-validation folds\n",
            "Fold 0:\n",
            "Inception model built.\n",
            "Inception model built.\n",
            "Training the Challenge models on the Challenge data...\n",
            "Epoch 1/15\n",
            " 3/15 [=====>........................] - ETA: 54s - loss: 0.6459 - ROC: 0.0000e+00 - PRC: 0.0000e+00 "
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[52], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m challenge_score, auroc_outcomes, auprc_outcomes, accuracy_outcomes, f_measure_outcomes, mse_cpcs, mae_cpcs \u001b[39m=\u001b[39m cross_validate_model(\u001b[39m\"\u001b[39;49m\u001b[39m./training_subset/\u001b[39;49m\u001b[39m\"\u001b[39;49m,\u001b[39m2\u001b[39;49m,\u001b[39m1\u001b[39;49m)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\Documents\\Github\\EEG-predict-cardiac-arrest-recovery\\team_code.py:260\u001b[0m, in \u001b[0;36mcross_validate_model\u001b[1;34m(data_folder, num_folds, verbose)\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[39mif\u001b[39;00m verbose \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m    258\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mTraining the Challenge models on the Challenge data...\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m--> 260\u001b[0m outcome_model\u001b[39m.\u001b[39;49mfit(tf_outcome_train, epochs\u001b[39m=\u001b[39;49mEPOCHS, batch_size\u001b[39m=\u001b[39;49mBATCH_SIZE, validation_data\u001b[39m=\u001b[39;49mtf_outcome_val)\n\u001b[0;32m    263\u001b[0m cpc_model\u001b[39m.\u001b[39mfit(tf_cpcs_train, epochs\u001b[39m=\u001b[39mEPOCHS, batch_size\u001b[39m=\u001b[39mBATCH_SIZE, validation_data\u001b[39m=\u001b[39mtf_cpcs_val)\n\u001b[0;32m    265\u001b[0m \u001b[39m# Apply model on validation fold\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     63\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 64\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:1409\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1402\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[0;32m   1403\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   1404\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m   1405\u001b[0m     step_num\u001b[39m=\u001b[39mstep,\n\u001b[0;32m   1406\u001b[0m     batch_size\u001b[39m=\u001b[39mbatch_size,\n\u001b[0;32m   1407\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[0;32m   1408\u001b[0m   callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1409\u001b[0m   tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[0;32m   1410\u001b[0m   \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[0;32m   1411\u001b[0m     context\u001b[39m.\u001b[39masync_wait()\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m    917\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    918\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[0;32m    945\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_stateless_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateful_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    949\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2453\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2450\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m   2451\u001b[0m   (graph_function,\n\u001b[0;32m   2452\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2453\u001b[0m \u001b[39mreturn\u001b[39;00m graph_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[0;32m   2454\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mgraph_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1860\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1856\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1857\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1858\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1859\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1860\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[0;32m   1861\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[0;32m   1862\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1863\u001b[0m     args,\n\u001b[0;32m   1864\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1865\u001b[0m     executing_eagerly)\n\u001b[0;32m   1866\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:497\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    495\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[0;32m    496\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 497\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[0;32m    498\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[0;32m    499\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[0;32m    500\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[0;32m    501\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[0;32m    502\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[0;32m    503\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    505\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[0;32m    506\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    509\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[0;32m    510\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
            "File \u001b[1;32mc:\\Users\\Bjorn\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[0;32m     55\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "challenge_score, auroc_outcomes, auprc_outcomes, accuracy_outcomes, f_measure_outcomes, mse_cpcs, mae_cpcs = cross_validate_model(\"./training_subset/\",2,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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  "nbformat": 4,
  "nbformat_minor": 0
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