grouping_features_train_xgbmodel.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bsenst/LoopOfHenle/blob/main/grouping_features_train_xgbmodel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSM1P7pDf4DP",
        "outputId": "4e50dfc0-f747-4ec3-97a7-ddfa9f781146"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-11-26 12:20:09--  https://owncloud.ikem.cz/index.php/s/mFPJKwbgsOOFBsW/download\n",
            "Resolving owncloud.ikem.cz (owncloud.ikem.cz)... 212.67.92.39\n",
            "Connecting to owncloud.ikem.cz (owncloud.ikem.cz)|212.67.92.39|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 76420372 (73M) [application/x-7z-compressed]\n",
            "Saving to: ‘download’\n",
            "\n",
            "download            100%[===================>]  72.88M  11.1MB/s    in 7.4s    \n",
            "\n",
            "2022-11-26 12:20:18 (9.82 MB/s) - ‘download’ saved [76420372/76420372]\n",
            "\n",
            "\n",
            "7-Zip (a) [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21\n",
            "p7zip Version 16.02 (locale=en_US.UTF-8,Utf16=on,HugeFiles=on,64 bits,2 CPUs Intel(R) Xeon(R) CPU @ 2.20GHz (406F0),ASM,AES-NI)\n",
            "\n",
            "Scanning the drive for archives:\n",
            "  0M Scan\b\b\b\b\b\b\b\b\b         \b\b\b\b\b\b\b\b\b1 file, 76420372 bytes (73 MiB)\n",
            "\n",
            "Extracting archive: download\n",
            "--\n",
            "Path = download\n",
            "Type = 7z\n",
            "Physical Size = 76420372\n",
            "Headers Size = 221\n",
            "Method = LZMA2:24\n",
            "Solid = +\n",
            "Blocks = 1\n",
            "\n",
            "  0%\b\b\b\b    \b\b\b\b  0% - Dg from report.xlsx\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                          \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  1% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  2% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  4% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  6% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  7% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  8% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b  9% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 10% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 11% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 13% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 14% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 15% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 17% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 18% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 21% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 23% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 25% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 26% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 27% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 29% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 30% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 32% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 33% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 35% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 37% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 39% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 41% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 43% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 45% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 47% 1 - 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LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 65% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 66% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 68% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 69% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 71% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 72% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 74% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 76% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 78% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 80% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 82% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 84% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 86% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 87% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 88% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 90% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 92% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 93% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 95% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 97% 1 - LabsALL 2015-2022.csv\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                              \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b100% 2\b\b\b\b\b\b      \b\b\b\b\b\bEverything is Ok\n",
            "\n",
            "Files: 2\n",
            "Size:       839390228\n",
            "Compressed: 76420372\n"
          ]
        }
      ],
      "source": [
        "!wget https://owncloud.ikem.cz/index.php/s/mFPJKwbgsOOFBsW/download\n",
        "!7za x download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Ut5hsgaaf9u0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"LabsALL 2015-2022.csv\", sep=\";\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiMSNvbwAUit",
        "outputId": "d063e97a-a061-4395-9d13-b9cce7ebfacc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (10,11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_date(row):\n",
        "    day, month, year = row.split(\".\")\n",
        "    return f\"{year}-{month}-{day}\"\n",
        "df.EntryDate = pd.to_datetime(df.EntryDate.apply(format_date))\n",
        "df[\"pr_id\"]= df.Patient.astype(str) + \"_\"+df.Report.astype(str)"
      ],
      "metadata": {
        "id": "H4fyO4p6AYk0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~df.ValueNumber.isna()]"
      ],
      "metadata": {
        "id": "EOcoHQbvezy1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "5oDVWp1ATNrJ",
        "outputId": "4cf977e4-43fa-4a5f-83b8-273b0a6d87ca"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Patient    Report         ID  EntryDate           EntryTime Code  \\\n",
              "2         324729   1453155   18244355 2015-04-16  30.12.1899 7:31:00  NaN   \n",
              "3         324729   1453155   18244356 2015-04-16  30.12.1899 7:31:00  NaN   \n",
              "4         324729   1453155   18244357 2015-04-16  30.12.1899 7:31:00  NaN   \n",
              "5         324729   1453155   18244358 2015-04-16  30.12.1899 7:31:00  NaN   \n",
              "6         324729   1453155   18244359 2015-04-16  30.12.1899 7:31:00  NaN   \n",
              "...          ...       ...        ...        ...                 ...  ...   \n",
              "8679291   335472  26490462  140942845 2022-05-13  30.12.1899 6:47:00  NaN   \n",
              "8679293   335472  26490462  140942847 2022-05-13  30.12.1899 6:47:00  NaN   \n",
              "8679295   335472  26490462  140942849 2022-05-13  30.12.1899 6:47:00  NaN   \n",
              "8679296   335472  26490462  140942850 2022-05-13  30.12.1899 6:47:00  NaN   \n",
              "8679297   335472  27409261  158447953 2022-09-29  30.12.1899 6:44:00  NaN   \n",
              "\n",
              "            NCLP                Analyte ValueNumber ValueText RefHigh RefLow  \\\n",
              "2         3482.0         s_vápník celk.        2.37       NaN    2.55   2.15   \n",
              "3         2618.0               s_fosfor        1.32       NaN    1.23   0.71   \n",
              "4         3940.0               s_hořčík        0.75       NaN    0.94   0.71   \n",
              "5         8574.0            s_kreatinin       138.9       NaN   104.0   64.0   \n",
              "6         1896.0        p_glukóza (NaF)       10.36       NaN    5.59   3.60   \n",
              "...          ...                    ...         ...       ...     ...    ...   \n",
              "8679291  14011.0     U_epitelie dlaž.UF           1       NaN    20.0      0   \n",
              "8679293   3386.0  U_shluky leukocytů UF           0       NaN     NaN    NaN   \n",
              "8679295   9322.0         U_leukocyty UF           5       NaN    10.0      0   \n",
              "8679296   3078.0           s_kys.močová         327       NaN   420.0    210   \n",
              "8679297  15194.0                B_HbA1c          53       NaN    42.0   20.0   \n",
              "\n",
              "             Unit            pr_id  \n",
              "2          mmol/l   324729_1453155  \n",
              "3          mmol/l   324729_1453155  \n",
              "4          mmol/l   324729_1453155  \n",
              "5          umol/l   324729_1453155  \n",
              "6          mmol/l   324729_1453155  \n",
              "...           ...              ...  \n",
              "8679291       /µL  335472_26490462  \n",
              "8679293    arb.j.  335472_26490462  \n",
              "8679295       /µL  335472_26490462  \n",
              "8679296    µmol/l  335472_26490462  \n",
              "8679297  mmol/mol  335472_27409261  \n",
              "\n",
              "[7340417 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0dae83c-7222-4020-a4fa-e1c1f6ad0d52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Patient</th>\n",
              "      <th>Report</th>\n",
              "      <th>ID</th>\n",
              "      <th>EntryDate</th>\n",
              "      <th>EntryTime</th>\n",
              "      <th>Code</th>\n",
              "      <th>NCLP</th>\n",
              "      <th>Analyte</th>\n",
              "      <th>ValueNumber</th>\n",
              "      <th>ValueText</th>\n",
              "      <th>RefHigh</th>\n",
              "      <th>RefLow</th>\n",
              "      <th>Unit</th>\n",
              "      <th>pr_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>324729</td>\n",
              "      <td>1453155</td>\n",
              "      <td>18244355</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>30.12.1899 7:31:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3482.0</td>\n",
              "      <td>s_vápník celk.</td>\n",
              "      <td>2.37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.55</td>\n",
              "      <td>2.15</td>\n",
              "      <td>mmol/l</td>\n",
              "      <td>324729_1453155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>324729</td>\n",
              "      <td>1453155</td>\n",
              "      <td>18244356</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>30.12.1899 7:31:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2618.0</td>\n",
              "      <td>s_fosfor</td>\n",
              "      <td>1.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.71</td>\n",
              "      <td>mmol/l</td>\n",
              "      <td>324729_1453155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>324729</td>\n",
              "      <td>1453155</td>\n",
              "      <td>18244357</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>30.12.1899 7:31:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3940.0</td>\n",
              "      <td>s_hořčík</td>\n",
              "      <td>0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.71</td>\n",
              "      <td>mmol/l</td>\n",
              "      <td>324729_1453155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>324729</td>\n",
              "      <td>1453155</td>\n",
              "      <td>18244358</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>30.12.1899 7:31:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8574.0</td>\n",
              "      <td>s_kreatinin</td>\n",
              "      <td>138.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>umol/l</td>\n",
              "      <td>324729_1453155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>324729</td>\n",
              "      <td>1453155</td>\n",
              "      <td>18244359</td>\n",
              "      <td>2015-04-16</td>\n",
              "      <td>30.12.1899 7:31:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1896.0</td>\n",
              "      <td>p_glukóza (NaF)</td>\n",
              "      <td>10.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.59</td>\n",
              "      <td>3.60</td>\n",
              "      <td>mmol/l</td>\n",
              "      <td>324729_1453155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8679291</th>\n",
              "      <td>335472</td>\n",
              "      <td>26490462</td>\n",
              "      <td>140942845</td>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>30.12.1899 6:47:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14011.0</td>\n",
              "      <td>U_epitelie dlaž.UF</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>/µL</td>\n",
              "      <td>335472_26490462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8679293</th>\n",
              "      <td>335472</td>\n",
              "      <td>26490462</td>\n",
              "      <td>140942847</td>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>30.12.1899 6:47:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3386.0</td>\n",
              "      <td>U_shluky leukocytů UF</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>arb.j.</td>\n",
              "      <td>335472_26490462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8679295</th>\n",
              "      <td>335472</td>\n",
              "      <td>26490462</td>\n",
              "      <td>140942849</td>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>30.12.1899 6:47:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9322.0</td>\n",
              "      <td>U_leukocyty UF</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>/µL</td>\n",
              "      <td>335472_26490462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8679296</th>\n",
              "      <td>335472</td>\n",
              "      <td>26490462</td>\n",
              "      <td>140942850</td>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>30.12.1899 6:47:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3078.0</td>\n",
              "      <td>s_kys.močová</td>\n",
              "      <td>327</td>\n",
              "      <td>NaN</td>\n",
              "      <td>420.0</td>\n",
              "      <td>210</td>\n",
              "      <td>µmol/l</td>\n",
              "      <td>335472_26490462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8679297</th>\n",
              "      <td>335472</td>\n",
              "      <td>27409261</td>\n",
              "      <td>158447953</td>\n",
              "      <td>2022-09-29</td>\n",
              "      <td>30.12.1899 6:44:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15194.0</td>\n",
              "      <td>B_HbA1c</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>mmol/mol</td>\n",
              "      <td>335472_27409261</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7340417 rows × 14 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nclp_analytes = df[[\"NCLP\",\"Analyte\"]].groupby(\"NCLP\").Analyte.unique().explode().drop_duplicates().reset_index().values"
      ],
      "metadata": {
        "id": "NTnyYiqVel_m"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "analytes_nclp_mapping = dict(zip(nclp_analytes[:,1], nclp_analytes[:,0]))\n",
        "nclp_analytes_mapping = dict(zip(nclp_analytes[:,0], nclp_analytes[:,1]))"
      ],
      "metadata": {
        "id": "Z5By3uM33Z2M"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nan_nclp = df[df.NCLP.isna()].copy()"
      ],
      "metadata": {
        "id": "VKd3sJSWUALL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nan_nclp.NCLP = df_nan_nclp.Analyte.apply(lambda x: analytes_nclp_mapping[x] if x in analytes_nclp_mapping else -999)"
      ],
      "metadata": {
        "id": "C_V2FRaXULVf"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = pd.concat([df[~df.NCLP.isna()],df_nan_nclp[~(df_nan_nclp.NCLP == -999)]])"
      ],
      "metadata": {
        "id": "Qq-wXPN_UgFP"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#units = df[[\"NCLP\",\"Unit\"]].groupby(\"NCLP\").Unit.unique()\n",
        "#units[units.apply(lambda x: len(x) > 1)].tail(60)"
      ],
      "metadata": {
        "id": "er4QOEyAUphl"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_new.to_csv(\"cleaned_labs.csv.gzip\", index=False, compression=\"gzip\")"
      ],
      "metadata": {
        "id": "yMy9G-TNU73D"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "used_columns = ['Patient', 'Report','EntryDate', 'NCLP',\n",
        "       'Analyte', 'ValueNumber', 'Unit',\n",
        "       'pr_id']\n",
        "df_new = df_new[used_columns]"
      ],
      "metadata": {
        "id": "xWgfDzo2Wgqi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download dgs"
      ],
      "metadata": {
        "id": "WddUGCQxX3J3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dgs = pd.read_excel(\"Dg from report.xlsx\")\n",
        "dgs[\"EntryDate\"] = dgs.Date\n",
        "dgs[\"parsed_dgs\"] = dgs.mainDgCode.apply(lambda x: x.split(\":\")[0] if str(x).find(\":\")>0 else None)"
      ],
      "metadata": {
        "id": "nXyLP0q3XiIQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n18_patients = dgs[dgs.parsed_dgs.apply(lambda x: x[:3]==\"N18\" if x else None).fillna(False)].Patient.unique()\n",
        "dgs = dgs[dgs.Patient.isin(n18_patients)]\n",
        "labs_n18 = df[df.Patient.isin(dgs.Patient.unique())]"
      ],
      "metadata": {
        "id": "3a4vWhaoXzjS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# look at CKD time series"
      ],
      "metadata": {
        "id": "sFxdBE8gXz25"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ckd = df_new[df_new.Patient.isin(labs_n18.Patient.unique())].copy()"
      ],
      "metadata": {
        "id": "iuxpI8R1YJRL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ckd_data =df_ckd.pivot_table(index=\"pr_id\",columns=\"NCLP\", values=\"ValueNumber\", aggfunc=\"first\")\n",
        "ckd_data = ckd_data.merge(df_ckd[[\"pr_id\",\"EntryDate\"]].drop_duplicates(), on=\"pr_id\")\n",
        "\n",
        "ckd_data[\"p_id\"] = ckd_data.pr_id.apply(lambda x: int(x.split(\"_\")[0]))\n",
        "for col in ckd_data.columns[1:-2]:\n",
        "    ckd_data[col] = ckd_data[col].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
        "\n",
        "ckd_data_grouped = ckd_data.groupby([\"p_id\",pd.Grouper(key=\"EntryDate\",freq=\"1M\")]).mean()"
      ],
      "metadata": {
        "id": "SyLNUdczaRU9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "L7IMsoqziiZr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nclp_nan_ratio = ckd_data_grouped.isna().mean().sort_values()"
      ],
      "metadata": {
        "id": "aUK-12XhcUr9"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_columns = nclp_nan_ratio[nclp_nan_ratio <=0.5].index"
      ],
      "metadata": {
        "id": "r3m4Gh6ibNkg"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ckd_data_grouped[feature_columns].to_csv(\"ckd_grouped.csv.gzip\", index=True, compression=\"gzip\")"
      ],
      "metadata": {
        "id": "xrrlpkRvesA2"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ckd_data_grouped[feature_columns]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "tRrAmUKBe4T0",
        "outputId": "0fac58f0-188a-4852-961d-1f6f28261968"
      },
      "execution_count": 22,
      "outputs": [
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              "p_id    EntryDate                                                          \n",
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              "        2022-08-31     4.05     89.9   0.3640    123.0  ...     3.36     1.71   \n",
              "        2022-11-30     4.08     91.7   0.3740    125.0  ...    12.56     0.64   \n",
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              "p_id    EntryDate                                                          \n",
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              "...                     ...      ...      ...      ...      ...      ...   \n",
              "1254969 2021-11-30      0.3     0.03      0.7      2.8     62.9      7.3   \n",
              "        2021-12-31      0.2     0.01      1.2      4.4     61.8      7.4   \n",
              "        2022-06-30      0.4     0.05      0.2      0.0     93.7      0.8   \n",
              "        2022-08-31      0.2     0.01      0.5      2.1     59.5      7.6   \n",
              "        2022-11-30      0.4     0.05      0.2      0.0     94.0      1.0   \n",
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              "                    12471.0  543.0    \n",
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              "        2015-05-31     22.9     1.96  \n",
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              "        2015-11-30     20.4     2.22  \n",
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              "1254969 2021-11-30     26.3     1.13  \n",
              "        2021-12-31     25.2     0.97  \n",
              "        2022-06-30      5.3     1.08  \n",
              "        2022-08-31     30.3      NaN  \n",
              "        2022-11-30      4.8     0.97  \n",
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              "      <th>2015-02-28</th>\n",
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              "      <td>128.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.15</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.6</td>\n",
              "      <td>3.9</td>\n",
              "      <td>61.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>25.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-30</th>\n",
              "      <td>115.7</td>\n",
              "      <td>8.9</td>\n",
              "      <td>3.76</td>\n",
              "      <td>235.0</td>\n",
              "      <td>14.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.22</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.3505</td>\n",
              "      <td>119.5</td>\n",
              "      <td>...</td>\n",
              "      <td>3.68</td>\n",
              "      <td>1.15</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.7</td>\n",
              "      <td>65.1</td>\n",
              "      <td>10.3</td>\n",
              "      <td>20.4</td>\n",
              "      <td>2.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>246.0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.18</td>\n",
              "      <td>83.7</td>\n",
              "      <td>0.3500</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.83</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>62.7</td>\n",
              "      <td>7.7</td>\n",
              "      <td>24.8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">1254969</th>\n",
              "      <th>2021-11-30</th>\n",
              "      <td>83.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>4.30</td>\n",
              "      <td>332.0</td>\n",
              "      <td>12.8</td>\n",
              "      <td>8.9</td>\n",
              "      <td>4.36</td>\n",
              "      <td>89.9</td>\n",
              "      <td>0.3920</td>\n",
              "      <td>129.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.58</td>\n",
              "      <td>2.33</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>62.9</td>\n",
              "      <td>7.3</td>\n",
              "      <td>26.3</td>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>80.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>4.17</td>\n",
              "      <td>293.0</td>\n",
              "      <td>12.2</td>\n",
              "      <td>6.1</td>\n",
              "      <td>4.11</td>\n",
              "      <td>89.3</td>\n",
              "      <td>0.3670</td>\n",
              "      <td>120.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.76</td>\n",
              "      <td>1.53</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.2</td>\n",
              "      <td>4.4</td>\n",
              "      <td>61.8</td>\n",
              "      <td>7.4</td>\n",
              "      <td>25.2</td>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-30</th>\n",
              "      <td>86.4</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.24</td>\n",
              "      <td>319.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>11.5</td>\n",
              "      <td>4.05</td>\n",
              "      <td>89.6</td>\n",
              "      <td>0.3630</td>\n",
              "      <td>123.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10.74</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>5.3</td>\n",
              "      <td>1.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-31</th>\n",
              "      <td>89.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>243.0</td>\n",
              "      <td>12.5</td>\n",
              "      <td>5.7</td>\n",
              "      <td>4.05</td>\n",
              "      <td>89.9</td>\n",
              "      <td>0.3640</td>\n",
              "      <td>123.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.36</td>\n",
              "      <td>1.71</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.1</td>\n",
              "      <td>59.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>30.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-30</th>\n",
              "      <td>81.4</td>\n",
              "      <td>6.5</td>\n",
              "      <td>4.85</td>\n",
              "      <td>297.0</td>\n",
              "      <td>12.4</td>\n",
              "      <td>13.4</td>\n",
              "      <td>4.08</td>\n",
              "      <td>91.7</td>\n",
              "      <td>0.3740</td>\n",
              "      <td>125.0</td>\n",
              "      <td>...</td>\n",
              "      <td>12.56</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2005 rows × 34 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd61fcf7-c66b-43e1-9c2f-8762c0851f73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-fd61fcf7-c66b-43e1-9c2f-8762c0851f73 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd61fcf7-c66b-43e1-9c2f-8762c0851f73');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# get other patients"
      ],
      "metadata": {
        "id": "dZ2Lxxt2ff7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dgs[\"high_level_dgs\"] = dgs.parsed_dgs.apply(lambda x: x.split(\".\")[0] if x else None)\n",
        "other_N18_dgs=dgs[dgs.Patient.isin(n18_patients)].high_level_dgs.value_counts()"
      ],
      "metadata": {
        "id": "u7RHWZ86f4Wh"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "common_N18_dgs = other_N18_dgs.head(20).index"
      ],
      "metadata": {
        "id": "_d1HHjHnf_Pi"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "symptotic_patients = dgs[dgs.high_level_dgs.isin(common_N18_dgs)].Patient"
      ],
      "metadata": {
        "id": "YCUK7eyrg7pg"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df_non_ckd"
      ],
      "metadata": {
        "id": "ALaaIuwviSgL"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_non_ckd = df[(~df.Patient.isin(symptotic_patients))&(df.NCLP.isin(feature_columns))][used_columns].copy()"
      ],
      "metadata": {
        "id": "xLtyIVzLhJgJ"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del df\n",
        "import gc\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHQLHXgPimOv",
        "outputId": "6f17aac5-e9c1-47e7-d40f-87b8dba28b72"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "689"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "non_ckd_data =df_non_ckd.pivot_table(index=\"pr_id\",columns=\"NCLP\", values=\"ValueNumber\", aggfunc=\"first\")\n",
        "non_ckd_data = non_ckd_data.merge(df_non_ckd[[\"pr_id\",\"EntryDate\"]].drop_duplicates(), on=\"pr_id\")\n",
        "\n",
        "non_ckd_data[\"p_id\"] = non_ckd_data.pr_id.apply(lambda x: int(x.split(\"_\")[0]))\n",
        "for col in non_ckd_data.columns[1:-2]:\n",
        "    non_ckd_data[col] = non_ckd_data[col].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
        "\n",
        "non_ckd_data = non_ckd_data.groupby([\"p_id\",pd.Grouper(key=\"EntryDate\",freq=\"1M\")]).mean()"
      ],
      "metadata": {
        "id": "0I9hwQraiU2Q"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "non_ckd_data[feature_columns].to_csv(\"non_ckd_grouped.csv.gzip\", index=True, compression=\"gzip\")"
      ],
      "metadata": {
        "id": "YOr0yyu3iy45"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "non_ckd_data.reset_index().p_id.unique().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuxrXJaRjTFJ",
        "outputId": "09878519-a4e5-4156-e3a8-26f779a6e0c6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13645,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nclp_analytes = dict(zip(nclp_analytes[:,0], nclp_analytes[:,1]))"
      ],
      "metadata": {
        "id": "3GxBXYrhi4SJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "del df_ckd, df_non_ckd"
      ],
      "metadata": {
        "id": "Oz9UrTgOk0h-"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lets train"
      ],
      "metadata": {
        "id": "7nv4bKCPlHiY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ckd_data_grouped = ckd_data_grouped[feature_columns]"
      ],
      "metadata": {
        "id": "fiT5RM3IlBUR"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "non_ckd_data = non_ckd_data[feature_columns]"
      ],
      "metadata": {
        "id": "LFTACk8Bkbtf"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ckd_data_grouped[\"target\"] = 1\n",
        "non_ckd_data[\"target\"] = 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Txwtt-2xkzg5",
        "outputId": "ae8326d4-139b-4d08-d86c-bad517c361f0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.concat([ckd_data_grouped,non_ckd_data])"
      ],
      "metadata": {
        "id": "NO1zSYUblKeW"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.to_csv(\"train_data.csv.gzip\", index=True, compression=\"gzip\")"
      ],
      "metadata": {
        "id": "P8bOhLimlSjS"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for col in train_data.columns"
      ],
      "metadata": {
        "id": "jOWKaZSZy54e"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data.fillna(-999)"
      ],
      "metadata": {
        "id": "cZT03-Mqlf7k"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(train_data, stratify=train_data.target)\n"
      ],
      "metadata": {
        "id": "ia7eU5-wl51I"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratio_CKD = 76/13_000"
      ],
      "metadata": {
        "id": "7DsUeYjsnPzK"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sample_weight = np.ones(train.shape[0])\n",
        "train_sample_weight[train.target==0] = ratio_CKD"
      ],
      "metadata": {
        "id": "xvVkQ_PrnClW"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_sample_weight = np.ones(test.shape[0])\n",
        "test_sample_weight[test.target==0] = ratio_CKD"
      ],
      "metadata": {
        "id": "DUTEJbM6ngW1"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = xgb.XGBClassifier(max_depth=5, learning_rate=0.05, n_estimators=1000, missing=-999)"
      ],
      "metadata": {
        "id": "PZ8jCr_Ir-Kg"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(*list((train.iloc[100:101,:-1]).values[0]), sep=\", \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZL0Wvw9nDe7w",
        "outputId": "84fd6ad1-61cf-4755-a617-8bc1b89f1ec3"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, 5.0, 1.044, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0, -999.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model.fit(train.iloc[:,:-1], train.target, sample_weight=train_sample_weight,\n",
        "          eval_set=[(train.iloc[:,:-1], train.target),(test.iloc[:,:-1], test.target)],\n",
        "          sample_weight_eval_set=[train_sample_weight, test_sample_weight], early_stopping_rounds=100)\n"
      ],
      "metadata": {
        "id": "w7-CKduXl9T9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31f36bb6-0616-4f5b-db42-db6c856148a7"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-error:0.104804\tvalidation_1-error:0.128267\n",
            "Multiple eval metrics have been passed: 'validation_1-error' will be used for early stopping.\n",
            "\n",
            "Will train until validation_1-error hasn't improved in 100 rounds.\n",
            "[1]\tvalidation_0-error:0.102066\tvalidation_1-error:0.128205\n",
            "[2]\tvalidation_0-error:0.100273\tvalidation_1-error:0.123522\n",
            "[3]\tvalidation_0-error:0.099319\tvalidation_1-error:0.126256\n",
            "[4]\tvalidation_0-error:0.099984\tvalidation_1-error:0.122404\n",
            "[5]\tvalidation_0-error:0.099126\tvalidation_1-error:0.117247\n",
            "[6]\tvalidation_0-error:0.099317\tvalidation_1-error:0.119196\n",
            "[7]\tvalidation_0-error:0.101317\tvalidation_1-error:0.119832\n",
            "[8]\tvalidation_0-error:0.101396\tvalidation_1-error:0.120727\n",
            "[9]\tvalidation_0-error:0.102949\tvalidation_1-error:0.122628\n",
            "[10]\tvalidation_0-error:0.103337\tvalidation_1-error:0.123016\n",
            "[11]\tvalidation_0-error:0.101821\tvalidation_1-error:0.119289\n",
            "[12]\tvalidation_0-error:0.102276\tvalidation_1-error:0.119747\n",
            "[13]\tvalidation_0-error:0.101617\tvalidation_1-error:0.117899\n",
            "[14]\tvalidation_0-error:0.106784\tvalidation_1-error:0.126927\n",
            "[15]\tvalidation_0-error:0.101946\tvalidation_1-error:0.120524\n",
            "[16]\tvalidation_0-error:0.107526\tvalidation_1-error:0.124209\n",
            "[17]\tvalidation_0-error:0.101835\tvalidation_1-error:0.117743\n",
            "[18]\tvalidation_0-error:0.107278\tvalidation_1-error:0.125304\n",
            "[19]\tvalidation_0-error:0.106321\tvalidation_1-error:0.125196\n",
            "[20]\tvalidation_0-error:0.10577\tvalidation_1-error:0.125483\n",
            "[21]\tvalidation_0-error:0.106277\tvalidation_1-error:0.124691\n",
            "[22]\tvalidation_0-error:0.106213\tvalidation_1-error:0.125514\n",
            "[23]\tvalidation_0-error:0.106014\tvalidation_1-error:0.123992\n",
            "[24]\tvalidation_0-error:0.106014\tvalidation_1-error:0.123876\n",
            "[25]\tvalidation_0-error:0.105515\tvalidation_1-error:0.123712\n",
            "[26]\tvalidation_0-error:0.105491\tvalidation_1-error:0.123697\n",
            "[27]\tvalidation_0-error:0.105587\tvalidation_1-error:0.122485\n",
            "[28]\tvalidation_0-error:0.099424\tvalidation_1-error:0.116035\n",
            "[29]\tvalidation_0-error:0.100335\tvalidation_1-error:0.118272\n",
            "[30]\tvalidation_0-error:0.100206\tvalidation_1-error:0.118195\n",
            "[31]\tvalidation_0-error:0.106281\tvalidation_1-error:0.123115\n",
            "[32]\tvalidation_0-error:0.099199\tvalidation_1-error:0.117612\n",
            "[33]\tvalidation_0-error:0.096559\tvalidation_1-error:0.116308\n",
            "[34]\tvalidation_0-error:0.095977\tvalidation_1-error:0.115229\n",
            "[35]\tvalidation_0-error:0.096285\tvalidation_1-error:0.113823\n",
            "[36]\tvalidation_0-error:0.095886\tvalidation_1-error:0.113947\n",
            "[37]\tvalidation_0-error:0.095527\tvalidation_1-error:0.113994\n",
            "[38]\tvalidation_0-error:0.096014\tvalidation_1-error:0.113645\n",
            "[39]\tvalidation_0-error:0.094845\tvalidation_1-error:0.113878\n",
            "[40]\tvalidation_0-error:0.094002\tvalidation_1-error:0.1122\n",
            "[41]\tvalidation_0-error:0.093756\tvalidation_1-error:0.111346\n",
            "[42]\tvalidation_0-error:0.091822\tvalidation_1-error:0.116114\n",
            "[43]\tvalidation_0-error:0.091535\tvalidation_1-error:0.115718\n",
            "[44]\tvalidation_0-error:0.091675\tvalidation_1-error:0.116736\n",
            "[45]\tvalidation_0-error:0.091509\tvalidation_1-error:0.115268\n",
            "[46]\tvalidation_0-error:0.091535\tvalidation_1-error:0.115283\n",
            "[47]\tvalidation_0-error:0.091328\tvalidation_1-error:0.116417\n",
            "[48]\tvalidation_0-error:0.090958\tvalidation_1-error:0.116052\n",
            "[49]\tvalidation_0-error:0.090539\tvalidation_1-error:0.116006\n",
            "[50]\tvalidation_0-error:0.090425\tvalidation_1-error:0.115804\n",
            "[51]\tvalidation_0-error:0.090677\tvalidation_1-error:0.114452\n",
            "[52]\tvalidation_0-error:0.09047\tvalidation_1-error:0.114266\n",
            "[53]\tvalidation_0-error:0.089771\tvalidation_1-error:0.114064\n",
            "[54]\tvalidation_0-error:0.089091\tvalidation_1-error:0.112496\n",
            "[55]\tvalidation_0-error:0.088998\tvalidation_1-error:0.112379\n",
            "[56]\tvalidation_0-error:0.08833\tvalidation_1-error:0.112178\n",
            "[57]\tvalidation_0-error:0.088565\tvalidation_1-error:0.109615\n",
            "[58]\tvalidation_0-error:0.087926\tvalidation_1-error:0.109468\n",
            "[59]\tvalidation_0-error:0.08645\tvalidation_1-error:0.111572\n",
            "[60]\tvalidation_0-error:0.087259\tvalidation_1-error:0.111517\n",
            "[61]\tvalidation_0-error:0.085369\tvalidation_1-error:0.110749\n",
            "[62]\tvalidation_0-error:0.085428\tvalidation_1-error:0.109359\n",
            "[63]\tvalidation_0-error:0.084438\tvalidation_1-error:0.108427\n",
            "[64]\tvalidation_0-error:0.084218\tvalidation_1-error:0.108264\n",
            "[65]\tvalidation_0-error:0.084397\tvalidation_1-error:0.107977\n",
            "[66]\tvalidation_0-error:0.083799\tvalidation_1-error:0.106556\n",
            "[67]\tvalidation_0-error:0.08399\tvalidation_1-error:0.106299\n",
            "[68]\tvalidation_0-error:0.083504\tvalidation_1-error:0.105888\n",
            "[69]\tvalidation_0-error:0.083755\tvalidation_1-error:0.102254\n",
            "[70]\tvalidation_0-error:0.083664\tvalidation_1-error:0.10244\n",
            "[71]\tvalidation_0-error:0.08354\tvalidation_1-error:0.101951\n",
            "[72]\tvalidation_0-error:0.083186\tvalidation_1-error:0.104297\n",
            "[73]\tvalidation_0-error:0.082868\tvalidation_1-error:0.104972\n",
            "[74]\tvalidation_0-error:0.08241\tvalidation_1-error:0.106262\n",
            "[75]\tvalidation_0-error:0.082656\tvalidation_1-error:0.106099\n",
            "[76]\tvalidation_0-error:0.082754\tvalidation_1-error:0.106285\n",
            "[77]\tvalidation_0-error:0.081973\tvalidation_1-error:0.105555\n",
            "[78]\tvalidation_0-error:0.081611\tvalidation_1-error:0.105221\n",
            "[79]\tvalidation_0-error:0.081381\tvalidation_1-error:0.104476\n",
            "[80]\tvalidation_0-error:0.081089\tvalidation_1-error:0.104212\n",
            "[81]\tvalidation_0-error:0.081081\tvalidation_1-error:0.104181\n",
            "[82]\tvalidation_0-error:0.080721\tvalidation_1-error:0.103862\n",
            "[83]\tvalidation_0-error:0.079819\tvalidation_1-error:0.103808\n",
            "[84]\tvalidation_0-error:0.079777\tvalidation_1-error:0.1038\n",
            "[85]\tvalidation_0-error:0.078857\tvalidation_1-error:0.105556\n",
            "[86]\tvalidation_0-error:0.079395\tvalidation_1-error:0.104313\n",
            "[87]\tvalidation_0-error:0.078583\tvalidation_1-error:0.104935\n",
            "[88]\tvalidation_0-error:0.078559\tvalidation_1-error:0.104942\n",
            "[89]\tvalidation_0-error:0.077946\tvalidation_1-error:0.104725\n",
            "[90]\tvalidation_0-error:0.077328\tvalidation_1-error:0.104593\n",
            "[91]\tvalidation_0-error:0.076485\tvalidation_1-error:0.105307\n",
            "[92]\tvalidation_0-error:0.076617\tvalidation_1-error:0.104741\n",
            "[93]\tvalidation_0-error:0.075996\tvalidation_1-error:0.10335\n",
            "[94]\tvalidation_0-error:0.073821\tvalidation_1-error:0.104158\n",
            "[95]\tvalidation_0-error:0.073544\tvalidation_1-error:0.10377\n",
            "[96]\tvalidation_0-error:0.073477\tvalidation_1-error:0.103762\n",
            "[97]\tvalidation_0-error:0.072928\tvalidation_1-error:0.103653\n",
            "[98]\tvalidation_0-error:0.073673\tvalidation_1-error:0.103584\n",
            "[99]\tvalidation_0-error:0.073666\tvalidation_1-error:0.103444\n",
            "[100]\tvalidation_0-error:0.07335\tvalidation_1-error:0.103234\n",
            "[101]\tvalidation_0-error:0.0732\tvalidation_1-error:0.103087\n",
            "[102]\tvalidation_0-error:0.073024\tvalidation_1-error:0.102854\n",
            "[103]\tvalidation_0-error:0.072408\tvalidation_1-error:0.105354\n",
            "[104]\tvalidation_0-error:0.072315\tvalidation_1-error:0.105315\n",
            "[105]\tvalidation_0-error:0.072393\tvalidation_1-error:0.103669\n",
            "[106]\tvalidation_0-error:0.072259\tvalidation_1-error:0.103498\n",
            "[107]\tvalidation_0-error:0.072272\tvalidation_1-error:0.103599\n",
            "[108]\tvalidation_0-error:0.071734\tvalidation_1-error:0.103514\n",
            "[109]\tvalidation_0-error:0.07237\tvalidation_1-error:0.103149\n",
            "[110]\tvalidation_0-error:0.072285\tvalidation_1-error:0.103071\n",
            "[111]\tvalidation_0-error:0.072202\tvalidation_1-error:0.102978\n",
            "[112]\tvalidation_0-error:0.071136\tvalidation_1-error:0.102776\n",
            "[113]\tvalidation_0-error:0.071058\tvalidation_1-error:0.102691\n",
            "[114]\tvalidation_0-error:0.071431\tvalidation_1-error:0.103903\n",
            "[115]\tvalidation_0-error:0.071791\tvalidation_1-error:0.103957\n",
            "[116]\tvalidation_0-error:0.070774\tvalidation_1-error:0.103786\n",
            "[117]\tvalidation_0-error:0.070482\tvalidation_1-error:0.102062\n",
            "[118]\tvalidation_0-error:0.070427\tvalidation_1-error:0.103328\n",
            "[119]\tvalidation_0-error:0.070345\tvalidation_1-error:0.103258\n",
            "[120]\tvalidation_0-error:0.070174\tvalidation_1-error:0.103149\n",
            "[121]\tvalidation_0-error:0.069985\tvalidation_1-error:0.102901\n",
            "[122]\tvalidation_0-error:0.069478\tvalidation_1-error:0.102831\n",
            "[123]\tvalidation_0-error:0.06895\tvalidation_1-error:0.10273\n",
            "[124]\tvalidation_0-error:0.06885\tvalidation_1-error:0.102676\n",
            "[125]\tvalidation_0-error:0.069137\tvalidation_1-error:0.102365\n",
            "[126]\tvalidation_0-error:0.068203\tvalidation_1-error:0.102357\n",
            "[127]\tvalidation_0-error:0.068156\tvalidation_1-error:0.102357\n",
            "[128]\tvalidation_0-error:0.067973\tvalidation_1-error:0.10207\n",
            "[129]\tvalidation_0-error:0.067895\tvalidation_1-error:0.102031\n",
            "[130]\tvalidation_0-error:0.067683\tvalidation_1-error:0.103096\n",
            "[131]\tvalidation_0-error:0.067572\tvalidation_1-error:0.103065\n",
            "[132]\tvalidation_0-error:0.067841\tvalidation_1-error:0.103336\n",
            "[133]\tvalidation_0-error:0.067577\tvalidation_1-error:0.103018\n",
            "[134]\tvalidation_0-error:0.067427\tvalidation_1-error:0.104268\n",
            "[135]\tvalidation_0-error:0.067251\tvalidation_1-error:0.105394\n",
            "[136]\tvalidation_0-error:0.067192\tvalidation_1-error:0.10541\n",
            "[137]\tvalidation_0-error:0.066636\tvalidation_1-error:0.105184\n",
            "[138]\tvalidation_0-error:0.066501\tvalidation_1-error:0.104998\n",
            "[139]\tvalidation_0-error:0.066434\tvalidation_1-error:0.104959\n",
            "[140]\tvalidation_0-error:0.066315\tvalidation_1-error:0.104804\n",
            "[141]\tvalidation_0-error:0.066201\tvalidation_1-error:0.104649\n",
            "[142]\tvalidation_0-error:0.066051\tvalidation_1-error:0.104486\n",
            "[143]\tvalidation_0-error:0.065608\tvalidation_1-error:0.1044\n",
            "[144]\tvalidation_0-error:0.065456\tvalidation_1-error:0.104268\n",
            "[145]\tvalidation_0-error:0.065433\tvalidation_1-error:0.104229\n",
            "[146]\tvalidation_0-error:0.065301\tvalidation_1-error:0.104035\n",
            "[147]\tvalidation_0-error:0.065182\tvalidation_1-error:0.10388\n",
            "[148]\tvalidation_0-error:0.065146\tvalidation_1-error:0.103849\n",
            "[149]\tvalidation_0-error:0.064595\tvalidation_1-error:0.103772\n",
            "[150]\tvalidation_0-error:0.064476\tvalidation_1-error:0.103632\n",
            "[151]\tvalidation_0-error:0.063953\tvalidation_1-error:0.103562\n",
            "[152]\tvalidation_0-error:0.063919\tvalidation_1-error:0.103523\n",
            "[153]\tvalidation_0-error:0.064264\tvalidation_1-error:0.103461\n",
            "[154]\tvalidation_0-error:0.063273\tvalidation_1-error:0.10329\n",
            "[155]\tvalidation_0-error:0.06319\tvalidation_1-error:0.103228\n",
            "[156]\tvalidation_0-error:0.063092\tvalidation_1-error:0.103197\n",
            "[157]\tvalidation_0-error:0.061661\tvalidation_1-error:0.103958\n",
            "[158]\tvalidation_0-error:0.061586\tvalidation_1-error:0.103888\n",
            "[159]\tvalidation_0-error:0.061535\tvalidation_1-error:0.103849\n",
            "[160]\tvalidation_0-error:0.06147\tvalidation_1-error:0.103725\n",
            "[161]\tvalidation_0-error:0.061392\tvalidation_1-error:0.103616\n",
            "[162]\tvalidation_0-error:0.061343\tvalidation_1-error:0.103616\n",
            "[163]\tvalidation_0-error:0.061305\tvalidation_1-error:0.10357\n",
            "[164]\tvalidation_0-error:0.060741\tvalidation_1-error:0.103562\n",
            "[165]\tvalidation_0-error:0.060707\tvalidation_1-error:0.103516\n",
            "[166]\tvalidation_0-error:0.060585\tvalidation_1-error:0.103422\n",
            "[167]\tvalidation_0-error:0.060557\tvalidation_1-error:0.103368\n",
            "[168]\tvalidation_0-error:0.060397\tvalidation_1-error:0.103197\n",
            "[169]\tvalidation_0-error:0.060322\tvalidation_1-error:0.104432\n",
            "[170]\tvalidation_0-error:0.060285\tvalidation_1-error:0.104347\n",
            "[171]\tvalidation_0-error:0.060226\tvalidation_1-error:0.104285\n",
            "Stopping. Best iteration:\n",
            "[71]\tvalidation_0-error:0.08354\tvalidation_1-error:0.101951\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(learning_rate=0.05, max_depth=5, missing=-999, n_estimators=1000)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pickle\n",
        "\n",
        "with open(\"model_weighted.pkl\", \"wb\") as f:\n",
        "    pickle.dump(model, f)"
      ],
      "metadata": {
        "id": "n3Wu0I46oVTP"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "\n",
        "\n",
        "y_pred = model.predict(test.iloc[:,:-1])\n",
        "\n",
        "precision_recall_fscore_support(test.target, y_pred, sample_weight=test_sample_weight)[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkItt4GsmAdp",
        "outputId": "148dea9c-08fd-4a3e-e84f-4f4221bccd60"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.87220121, 0.90970248]),\n",
              " array([0.8141054 , 0.94011976]),\n",
              " array([0.84215256, 0.92466104]))"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.iloc[:,:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "rOKFoLlx8tmv",
        "outputId": "f5ca117d-fc83-42e0-82f6-8572de3eaefb"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    8574.0   3086.0   5254.0   2688.0   4769.0   13808.0  \\\n",
              "p_id    EntryDate                                                          \n",
              "42198   2018-06-30     87.2      9.6  -999.00    214.0     13.4     12.4   \n",
              "1213024 2016-07-31     69.2      1.7     4.95    185.0     13.5      6.4   \n",
              "41412   2021-02-28     94.0      3.3  -999.00    242.0     22.7      9.0   \n",
              "589476  2022-03-31     93.1      7.7  -999.00    238.0     13.9     14.9   \n",
              "1204096 2015-10-31    115.3      4.9     4.27    149.0     14.1      7.3   \n",
              "...                     ...      ...      ...      ...      ...      ...   \n",
              "157674  2017-12-31   -999.0   -999.0  -999.00   -999.0   -999.0   -999.0   \n",
              "73948   2015-07-31   -999.0   -999.0  -999.00   -999.0   -999.0   -999.0   \n",
              "141211  2022-06-30     98.5      7.7     3.47   -999.0   -999.0   -999.0   \n",
              "28060   2020-07-31     91.0      9.8     4.14    160.0     13.2      7.9   \n",
              "1251984 2019-01-31    153.0     11.1     4.52    253.0     14.1      8.4   \n",
              "\n",
              "                    1675.0   2419.0   2099.0   1991.0   ...  12369.0  12365.0  \\\n",
              "p_id    EntryDate                                       ...                     \n",
              "42198   2018-06-30     4.94     89.7    0.443    151.0  ...  -999.00  -999.00   \n",
              "1213024 2016-07-31     4.92     98.4    0.484    172.0  ...     4.11     1.17   \n",
              "41412   2021-02-28     4.09     79.2    0.324     98.0  ...  -999.00  -999.00   \n",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YH0_xIKg_0r0",
        "outputId": "c75ac306-75cb-4a6f-b6be-a3ca4c458135"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 43520 entries, (42198, Timestamp('2018-06-30 00:00:00')) to (1251984, Timestamp('2019-01-31 00:00:00'))\n",
            "Data columns (total 35 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   8574.0   43520 non-null  float64\n",
            " 1   3086.0   43520 non-null  float64\n",
            " 2   5254.0   43520 non-null  float64\n",
            " 3   2688.0   43520 non-null  float64\n",
            " 4   4769.0   43520 non-null  float64\n",
            " 5   13808.0  43520 non-null  float64\n",
            " 6   1675.0   43520 non-null  float64\n",
            " 7   2419.0   43520 non-null  float64\n",
            " 8   2099.0   43520 non-null  float64\n",
            " 9   1991.0   43520 non-null  float64\n",
            " 10  4726.0   43520 non-null  float64\n",
            " 11  16263.0  43520 non-null  float64\n",
            " 12  18895.0  43520 non-null  float64\n",
            " 13  5272.0   43520 non-null  float64\n",
            " 14  3078.0   43520 non-null  float64\n",
            " 15  582.0    43520 non-null  float64\n",
            " 16  17339.0  43520 non-null  float64\n",
            " 17  921.0    43520 non-null  float64\n",
            " 18  1961.0   43520 non-null  float64\n",
            " 19  3410.0   43520 non-null  float64\n",
            " 20  5143.0   43520 non-null  float64\n",
            " 21  12367.0  43520 non-null  float64\n",
            " 22  12348.0  43520 non-null  float64\n",
            " 23  12347.0  43520 non-null  float64\n",
            " 24  12369.0  43520 non-null  float64\n",
            " 25  12365.0  43520 non-null  float64\n",
            " 26  18029.0  43520 non-null  float64\n",
            " 27  18027.0  43520 non-null  float64\n",
            " 28  12449.0  43520 non-null  float64\n",
            " 29  12460.0  43520 non-null  float64\n",
            " 30  12483.0  43520 non-null  float64\n",
            " 31  12478.0  43520 non-null  float64\n",
            " 32  12471.0  43520 non-null  float64\n",
            " 33  543.0    43520 non-null  float64\n",
            " 34  target   43520 non-null  int64  \n",
            "dtypes: float64(34), int64(1)\n",
            "memory usage: 12.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[nclp_analytes_mapping[i] for i in train_data.columns[:-1]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCN0JIPdmQtK",
        "outputId": "626f967c-e489-44a8-9bb8-abc80be47aa8"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['S-kreatinin',\n",
              " 'S-urea',\n",
              " 'S-draslík (K)',\n",
              " 'trombo',\n",
              " 'anizo ERY-CV',\n",
              " 'Leukocyty WBC',\n",
              " 'Erytrocyty RBC',\n",
              " 'MCV (střední objem ery)',\n",
              " 'Hkt',\n",
              " 'Hemoglobin Hb',\n",
              " 'Objem dest.',\n",
              " 'B_Thr_Aniz_SD',\n",
              " 'B_Thr velké>12fl',\n",
              " 'S-sodík (Na)',\n",
              " 'S-kyselina močová\\xa0',\n",
              " 'S-ALT',\n",
              " 'eGFR-CKD-EPI',\n",
              " 'S-AST',\n",
              " 'GMT',\n",
              " 'pH moč',\n",
              " 'U_spec.hmotnost',\n",
              " 'Monocyty Abs',\n",
              " 'Eozinofily Abs',\n",
              " 'Bazofily Abs',\n",
              " 'Neutrofily Abs',\n",
              " 'Lymfocyty Abs',\n",
              " 'B_Nezr.gran.rel',\n",
              " 'B_Nezr.gran.abs',\n",
              " 'Bazofilní segmenty',\n",
              " 'Eozinofily  % 5P',\n",
              " 'Neutrofilní segmenty % 5P',\n",
              " 'Monocyty % 5P',\n",
              " 'Lymfocyty % 5P',\n",
              " 'Alkalická foshatáza (ALP)']"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp ./*.gzip drive/MyDrive/ikem_hackathon/data"
      ],
      "metadata": {
        "id": "dtDebJv1wjy7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a685c2b2-c6db-42a6-9bc2-6d0e74a10d18"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: target 'drive/MyDrive/ikem_hackathon/data' is not a directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp ./*.pkl drive/MyDrive/ikem_hackathon/data\n"
      ],
      "metadata": {
        "id": "DPr_6yHUwuAr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25ccbf10-0d3c-49a0-faca-fe9b5d41e850"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot create regular file 'drive/MyDrive/ikem_hackathon/data': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "!pip install shap -q\n",
        "import shap\n",
        "explainer = shap.TreeExplainer(model)\n",
        "shap_values = explainer.shap_values(test.iloc[:,:-1])\n",
        "\n",
        "shap.summary_plot(shap_values, test.iloc[:,:-1], max_display=100, feature_names=[nclp_analytes[i] for i in train_data.columns[:-1]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wnD5RasJlkBn",
        "outputId": "40e973bd-15da-4790-c769-0f1ec4be69fb"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x1087.2 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "shap.summary_plot(shap_values, test.iloc[:,:-1], plot_type=\"bar\", feature_names = [nclp_analytes[i] for i in train_data.columns[:-1]])"
      ],
      "metadata": {
        "id": "UKXhJrDErGhJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "c5e9144c-e6e2-4848-9760-73fc362eeff9"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x684 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}