George_Moody_Challenge_2023_train_and_run.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7rpHQfvpZyg",
        "outputId": "bd63b041-ed75-4778-87a7-bda36301b3c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.8/dist-packages (3.2)\n",
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            "Requirement already satisfied: neurokit2 in /usr/local/lib/python3.8/dist-packages (0.2.3)\n",
            "Collecting mne\n",
            "  Downloading mne-1.3.1-py3-none-any.whl (7.6 MB)\n",
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            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.8/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb) (1.15.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/dist-packages (from jinja2->mne) (2.1.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb) (2.21)\n",
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            "Installing collected packages: mne\n",
            "Successfully installed mne-1.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install wget wfdb neurokit2 mne"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!ls ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZJuKV5Hpb0U",
        "outputId": "f0a979b6-6292-45fc-c16a-d97ab37274e3"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kaggle.json\n",
            "/bin/bash: -c: line 0: unexpected EOF while looking for matching `''\n",
            "/bin/bash: -c: line 1: syntax error: unexpected end of file\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d bjoernjostein/george-moody-2023-complete-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYqWe-76pgJt",
        "outputId": "4ac181ca-2f85-4bea-dd21-72a68acf3859"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading george-moody-2023-complete-dataset.zip to /content\n",
            "  4% 658M/17.4G [00:08<03:33, 84.2MB/s]\n",
            "User cancelled operation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install GitPython\n",
        "from git import Repo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9BA35CKz8AY",
        "outputId": "bf0c46d5-41ea-4ff3-b702-cd06504b6fdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting GitPython\n",
            "  Downloading GitPython-3.1.31-py3-none-any.whl (184 kB)\n",
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            "\u001b[?25hCollecting smmap<6,>=3.0.1\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, gitdb, GitPython\n",
            "Successfully installed GitPython-3.1.31 gitdb-4.0.10 smmap-5.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HTTPS_REMOTE_URL = 'https://github.com/Bsingstad/python-example-2023.git'\n",
        "DEST_NAME = 'repo'\n",
        "cloned_repo = Repo.clone_from(HTTPS_REMOTE_URL, DEST_NAME)"
      ],
      "metadata": {
        "id": "wLv8L-uh0Awu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from repo.helper_code import *"
      ],
      "metadata": {
        "id": "0wkCjnFB0QOW"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_line(file_name, line_num, text):\n",
        "    lines = open(file_name, 'r').readlines()\n",
        "    lines[line_num] = text\n",
        "    out = open(file_name, 'w')\n",
        "    out.writelines(lines)\n",
        "    out.close()"
      ],
      "metadata": {
        "id": "4ejmx0YoSkKA"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "replace_line('/content/repo/team_code.py',11,\"from repo.helper_code import * \\n\")"
      ],
      "metadata": {
        "id": "xNoId8s2ScOl"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from repo.team_code import *"
      ],
      "metadata": {
        "id": "HF93NC9bSDej"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "replace_line('/content/repo/run_model.py',12,\"from repo.helper_code import * \\n\")\n",
        "replace_line('/content/repo/run_model.py',13,\"from repo.team_code import load_challenge_models, run_challenge_models \\n\")"
      ],
      "metadata": {
        "id": "aIrxYUSSXocd"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from repo.run_model import *"
      ],
      "metadata": {
        "id": "hPD9Nlw9Xjl8"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "replace_line('/content/repo/evaluate_model.py',15,\"from repo.helper_code import * \\n\")"
      ],
      "metadata": {
        "id": "TPAGQAA7j47F"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from repo.evaluate_model import *"
      ],
      "metadata": {
        "id": "q0sWcdDJkCO5"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "quSp-rnhvG4T"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(\"./models/\", exist_ok=True)"
      ],
      "metadata": {
        "id": "XMICZZD1T12s"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_challenge_model(\"/content/data/training/\",\"/content/models/\",1)"
      ],
      "metadata": {
        "id": "9jVvxq38Tk-l",
        "outputId": "3e98ed47-a84d-4797-91de-cfb9264ee008",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finding the Challenge data...\n",
            "Extracting features and labels from the Challenge data...\n",
            "Training the Challenge models on the Challenge data...\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "un_challenge_models(\"/content/models/\", \"/content/data/training/\", patient_id, verbose)"
      ],
      "metadata": {
        "id": "s4GN3v3fWcyq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(\"./output/\", exist_ok=True)"
      ],
      "metadata": {
        "id": "WzsQrS-qYOro"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run_model(\"/content/models/\", \"/content/data/training/\", \"/content/output/\", allow_failures=True, verbose=1)"
      ],
      "metadata": {
        "id": "v3RANcVmX-GT",
        "outputId": "6d4640db-91e0-4f96-f980-c59df6b2cc95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading the Challenge models...\n",
            "Finding the Challenge data...\n",
            "Running the Challenge models on the Challenge data...\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "challenge_score, auroc_outcomes, auprc_outcomes, accuracy_outcomes, f_measure_outcomes, mse_cpcs, mae_cpcs = evaluate_model(\"/content/data/training/\", \"/content/output/\")"
      ],
      "metadata": {
        "id": "cRqavbQQkfxW",
        "outputId": "b4375889-1440-4b29-95ac-ec26b21c3bad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 1.0, 1.0, 1.0, 1.0, 0.36151132125205926, 0.5052257001647447)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    }
  ]
}