COVID19_Training.ipynb

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  "metadata": {
    "colab": {
      "name": "COVID19-Training.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-d8h3Re44QOc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0488dd33-1cca-4a4f-ec6e-c1697b111fd5"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=RuntimeWarning)\n",
        "warnings.simplefilter(action='ignore', category=UserWarning)\n",
        "\n",
        "import pandas as pd\n",
        "import glob\n",
        "import numpy as np\n",
        "import datetime\n",
        "from os import path\n",
        "from google.colab import drive\n",
        "import seaborn as sns\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKY3jQzNSdHB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d52bb98-9ad1-47e9-829a-d964ecee993a"
      },
      "source": [
        "doubt = ['A01369_2020-03-23',\n",
        " 'A00685_2020-03-05',\n",
        " 'A00723_2020-04-01',\n",
        " 'A01240_2020-03-27',\n",
        " 'A00691_2020-03-18',\n",
        " 'A01296_2020-03-22',\n",
        " 'A00872_2020-03-13',\n",
        " 'A01297_2020-03-10',\n",
        " 'A01181_2020-03-22',\n",
        " 'A00193_2020-04-09',\n",
        " 'A01358_2020-03-16',\n",
        " 'A00121_2020-03-21',\n",
        " 'A01422_2020-03-23',\n",
        " 'A00829_2020-03-18',\n",
        " 'A01455_2020-03-17',\n",
        " 'A01253_2020-03-31',\n",
        " 'A01636_2020-04-01',\n",
        " 'A01324_2020-04-21',\n",
        " 'A00297_2020-04-01',\n",
        " 'A00132_2020-04-04',\n",
        " 'A01090_2020-03-24',\n",
        " 'A01050_2020-04-08',\n",
        " 'A01456_2020-03-30',\n",
        " 'A00022_2020-03-19',\n",
        " 'A00147_2020-03-09',\n",
        " 'A01482_2020-03-17',\n",
        " 'A01360_2020-03-31',\n",
        " 'A01349_2020-04-07',\n",
        " 'A00511_2020-03-23',\n",
        " 'A00521_2020-04-05',\n",
        " 'A01628_2020-03-19',\n",
        " 'A01089_2020-03-14',\n",
        " 'A01378_2020-03-17',\n",
        " 'A01686_2020-03-21',\n",
        " 'A00359_2020-03-21',\n",
        " 'A00288_2020-03-28',\n",
        " 'A01368_2020-04-11',\n",
        " 'A00690_2020-03-22',\n",
        " 'A00844_2020-04-12',\n",
        " 'A01154_2020-03-18',\n",
        " 'A00240_2020-03-22',\n",
        " 'A01364_2020-03-17',\n",
        " 'A00095_2020-04-05',\n",
        " 'A00561_2020-04-10',\n",
        " 'A00432_2020-03-10',\n",
        " 'A00165_2020-03-16']\n",
        "\n",
        "doubt1 = [\n",
        "'A00723_2020-04-01',\n",
        "'A00779_2020-03-23',\n",
        "'A01456_2020-03-30',\n",
        "'A01174_2020-03-09',\n",
        "'A01686_2020-03-21',\n",
        "'A01240_2020-03-27',\n",
        "'A00872_2020-03-13',\n",
        "'A01628_2020-03-19',\n",
        "'A01349_2020-04-07',\n",
        "'A01154_2020-03-18',\n",
        "'A01482_2020-03-17',\n",
        "'A01146_2020-03-10',\n",
        "'A01559_2020-03-06',\n",
        "'A01378_2020-03-17',\n",
        "'A01089_2020-03-14',\n",
        "'A01296_2020-03-22',\n",
        "'A00432_2020-03-10',\n",
        "'A00144_2020-03-27',\n",
        "'A01636_2020-04-01',\n",
        "'A00186_2020-03-02',\n",
        "'A00297_2020-04-01',\n",
        "'A01625_2020-03-03'\n",
        " ]\n",
        "\n",
        "\n",
        "len(doubt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbo6Oe7Nuz-t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "b4578216-1672-43ab-a2f5-be6cd69a972a"
      },
      "source": [
        "ct = pd.read_excel('gdrive/My Drive/Colab Notebooks/COVID/RXPSNET.xlsx')\n",
        "cols = list(ct.columns)\n",
        "cols[1] = 'id'\n",
        "ct.columns = cols\n",
        "ct.set_index('id', inplace=True)\n",
        "ct"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>COVID/INTERSTIZIOPATIA?</th>\n",
              "      <th>ALTRO TIPO DI POLMONITE/ADDENSAMENTO</th>\n",
              "      <th>VERSAM. PLEURICO-INTERESSAM. PLEURICO</th>\n",
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              "    <tr>\n",
              "      <th>PSMAY0200</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
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              "      <th>A00690</th>\n",
              "      <td>161</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>A01369</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>si</td>\n",
              "      <td>no</td>\n",
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              "<p>165 rows × 8 columns</p>\n",
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            ],
            "text/plain": [
              "           Unnamed: 0  ...  VERSAM. PLEURICO-INTERESSAM. PLEURICO\n",
              "id                     ...                                       \n",
              "A01459              0  ...                                     no\n",
              "A00382              1  ...                                     no\n",
              "A00872              2  ...                                     no\n",
              "A00440              3  ...                           non eseguita\n",
              "PSMAY0200           4  ...                       non disponibile \n",
              "...               ...  ...                                    ...\n",
              "A00355            160  ...                                     no\n",
              "A00690            161  ...                                     no\n",
              "A01369            162  ...                                     no\n",
              "A00540            163  ...                                     no\n",
              "A00373            164  ...                                     no\n",
              "\n",
              "[165 rows x 8 columns]"
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          "metadata": {
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          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu2Fb--SiTUU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1e802ab-f2e7-41dc-86a5-1c6cd5cef8f4"
      },
      "source": [
        "may_data = pd.read_csv('gdrive/My Drive/Colab Notebooks/COVID/may_dataset_avg.csv')\n",
        "\n",
        "may_data.set_index('id', inplace=True)\n",
        "may_data = may_data[['HCTPOC', 'KPOC', 'MCV', 'NE', 'CO2POC', 'CLPOC', 'THBPOC', 'BEPOC',\n",
        "       'PLT1', 'RBC', 'COROQL', 'FIOPOC', 'CASPOC', 'MPV', 'XDP', 'METPOC',\n",
        "       'PO2POC', 'CK', 'BO2POC', 'CAPOC', 'MCH', 'BISPOC', 'PCR', 'MOT', 'FG',\n",
        "       'BAT', 'NAT', 'GGT', 'BILT', 'NAPOC', 'RDW', 'PTTR', 'NET', 'BILD',\n",
        "       'GLUEMO', 'GLU', 'EO', 'HGB', 'LY', 'FO2POC', 'EOT', 'ALP', 'BEEPOC',\n",
        "       'WBC', 'KAL', 'TROPOT', 'BA', 'LATPOC', 'ALT', 'AST', 'OFIPOC', 'BILIN',\n",
        "       'FCOPOC', 'MCHC', 'HCT', 'PHPOC', 'LDH', 'CTOPOC', 'SO2POC', 'BICPOC',\n",
        "       'LYT', 'UREA', 'PTINR', 'MO', 'CA', 'HHBPOC', 'ANGPOC', 'CREA', 'IL6',\n",
        "       'PROBNP', 'Severity', 'Suspect', 'Sex', 'Age']]\n",
        "#index = data['id']\n",
        "\n",
        "#may_data['Suspect'] = may_data['Suspect'].apply(lambda x: 1 if x > 0 else 0)\n",
        "print(may_data['Suspect'].value_counts())\n",
        "#may_data = may_data[(may_data['Suspect'] == 1) | (may_data['COROQL'] == 1)]\n",
        "print(may_data['Suspect'].value_counts())\n",
        "#may_data = may_data[(may_data['COROQL'] == 'S1') | (may_data['COROQL'] == 'S2') | (may_data['COROQL'] == 'S0')]\n",
        "\n",
        "X_may = may_data.drop(columns=['COROQL'])\n",
        "y_may = may_data['COROQL']\n",
        "\n",
        "X_may = X_may.apply(pd.to_numeric,errors='coerce')\n",
        "may_data['COROQL'] = may_data['COROQL'].apply(lambda x: 'S1' if x == 1 else 'S0')\n",
        "print(may_data['COROQL'].value_counts())\n",
        "may_data\n",
        "np.mean(may_data[may_data['COROQL'] == 'S0']['AST'])\n",
        "X_may.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0    180\n",
            "1.0     38\n",
            "0.5     17\n",
            "Name: Suspect, dtype: int64\n",
            "0.0    180\n",
            "1.0     38\n",
            "0.5     17\n",
            "Name: Suspect, dtype: int64\n",
            "S0    203\n",
            "S1     32\n",
            "Name: COROQL, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(235, 73)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeVs9b-UCQ42"
      },
      "source": [
        "galeazzi = ['COROQL','Sex','Age','CA','CK','CREA','ALP','GGT','GLU','AST','ALT','LDH','PCR',\n",
        "            'KAL','NAT','UREA','WBC','RBC','HGB','HCT','MCV','MCH','MCHC','PLT1',\n",
        "            'NE','LY','MO','EO','BA','NET','LYT','MOT','EOT','BAT', 'Suspect']\n",
        "may_data[galeazzi].to_excel('may.xls')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUCrUu3s4Sf2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c4f8fe9-f11d-4abf-85c0-79d8ebaf8ff2"
      },
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "data = pd.read_csv('gdrive/My Drive/Colab Notebooks/COVID/data_final_avg.csv')\n",
        "\n",
        "data.set_index('id', inplace=True)\n",
        "#data['Suspect'] = data['Suspect'].apply(lambda x: 1 if x > 0 else 0)\n",
        "print(data['Suspect'].value_counts())\n",
        "print(data.columns)\n",
        "print(data.shape)\n",
        "\n",
        "data = pd.concat([data, may_data])\n",
        "\n",
        "print(data['COROQL'].value_counts())\n",
        "\n",
        "#index = data['id']\n",
        "#data.loc[doubt, 'COROQL'] = 'S1'\n",
        "\n",
        "print(data['COROQL'].value_counts())\n",
        "\n",
        "code_changes = []\n",
        "counter_fn = 0\n",
        "counter_strange = 0\n",
        "for i in data.index:\n",
        "  code, date = i.split('_')\n",
        "  if code in ct.index:\n",
        "    if ct.loc[code, 'COVID/INTERSTIZIOPATIA?'] == 'si':\n",
        "      data.loc[i, 'COROQL'] = 'S1'\n",
        "      code_changes.append(i)\n",
        "      counter_fn += 1\n",
        "    if ct.loc[code, 'COVID/INTERSTIZIOPATIA?'] != 'si' and i in doubt:\n",
        "      counter_strange += 1\n",
        "print(counter_fn)\n",
        "print(counter_strange)\n",
        "\n",
        "print(data['COROQL'].value_counts())\n",
        "\n",
        "data = data[(data['COROQL'] == 'S1') | (data['COROQL'] == 'S2') | (data['COROQL'] == 'S0')]\n",
        "\n",
        "print(data['COROQL'].value_counts())\n",
        "\n",
        "X = data.drop(columns=['COROQL'])\n",
        "y = data['COROQL']\n",
        "data_swab = data.copy()\n",
        "data_swab[code, 'COROQL'] = \"S0\"\n",
        "y_swab = data_swab['COROQL']\n",
        "\n",
        "y = np.array([1 if t == 'S1' else 1 if t == 'S2' else 0 if t == 'S0' else t for t in y])\n",
        "y_swab = np.array([1 if t == 'S1' else 1 if t == 'S2' else 0 if t == 'S0' else t for t in y_swab])\n",
        "\n",
        "missing = X.isna().sum()/X.shape[0]\n",
        "missing = missing[missing > 0.75]\n",
        "X = X.drop(columns=missing.index)\n",
        "X = X.apply(pd.to_numeric,errors='coerce')\n",
        "cols = X.columns\n",
        "print(X.loc[:,'Severity'].value_counts())\n",
        "print(np.unique(y, return_counts=True))\n",
        "\n",
        "for m in missing.index:\n",
        "  print(m + \" \" + str(missing[m]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0    1121\n",
            "0.5     263\n",
            "0.0      22\n",
            "Name: Suspect, dtype: int64\n",
            "Index(['HCTPOC', 'KPOC', 'MCV', 'NE', 'CO2POC', 'CLPOC', 'THBPOC', 'BEPOC',\n",
            "       'PLT1', 'RBC', 'FIOPOC', 'CASPOC', 'MPV', 'XDP', 'METPOC', 'PO2POC',\n",
            "       'CK', 'BO2POC', 'CAPOC', 'MCH', 'BISPOC', 'PCR', 'MOT', 'FG', 'BAT',\n",
            "       'NAT', 'GGT', 'BILT', 'NAPOC', 'RDW', 'PTTR', 'NET', 'BILD', 'GLUEMO',\n",
            "       'GLU', 'EO', 'HGB', 'LY', 'FO2POC', 'EOT', 'ALP', 'BEEPOC', 'WBC',\n",
            "       'KAL', 'TROPOT', 'BA', 'LATPOC', 'ALT', 'AST', 'OFIPOC', 'BILIN',\n",
            "       'FCOPOC', 'MCHC', 'HCT', 'PHPOC', 'LDH', 'CTOPOC', 'SO2POC', 'BICPOC',\n",
            "       'LYT', 'UREA', 'PTINR', 'MO', 'CA', 'HHBPOC', 'ANGPOC', 'CREA', 'IL6',\n",
            "       'PROBNP', 'Severity', 'COROQL', 'Suspect', 'Sex', 'Age'],\n",
            "      dtype='object')\n",
            "(1406, 74)\n",
            "S0     872\n",
            "S1     648\n",
            "S2      99\n",
            "COM     22\n",
            "Name: COROQL, dtype: int64\n",
            "S0     872\n",
            "S1     648\n",
            "S2      99\n",
            "COM     22\n",
            "Name: COROQL, dtype: int64\n",
            "52\n",
            "24\n",
            "S0     838\n",
            "S1     688\n",
            "S2      98\n",
            "COM     17\n",
            "Name: COROQL, dtype: int64\n",
            "S0    838\n",
            "S1    688\n",
            "S2     98\n",
            "Name: COROQL, dtype: int64\n",
            "0.0    1580\n",
            "1.0      44\n",
            "Name: Severity, dtype: int64\n",
            "(array([0, 1]), array([838, 786]))\n",
            "IL6 0.9211822660098522\n",
            "PROBNP 0.9100985221674877\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fh927lNvUaOi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c436215-1953-4d24-e101-b4fa0ec3dd7c"
      },
      "source": [
        "print(np.unique(X.loc[:,'Sex'], return_counts=True))\n",
        "print(np.unique(X.loc[:,'Suspect'], return_counts=True))\n",
        "print(np.unique(y, return_counts=True))\n",
        "print(np.mean(X.loc[:,'Age']))\n",
        "print(np.std(X.loc[:,'Age']))\n",
        "X.shape[0]\n",
        "data.to_excel('all_data.xls')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([0., 1.]), array([677, 947]))\n",
            "(array([0. , 0.5, 1. ]), array([ 201,  279, 1144]))\n",
            "(array([0, 1]), array([838, 786]))\n",
            "60.54556650246305\n",
            "19.60795913971893\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sx1ZVI7W6jlE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "249f7e62-d087-4528-8c05-1653a324b648"
      },
      "source": [
        "hemo_plus = ['Sex','Age','HCT','HGB','MCH','MCHC','MCV','RBC','WBC','PLT1',\n",
        "             'NE','LY','MO','EO','BA','NET','LYT','MOT','EOT','BAT', 'Suspect']\n",
        "\n",
        "pred = ['Sex','Age','CA','CK','CREA','ALP','GGT','GLU','AST','ALT','LDH','PCR',\n",
        "            'KAL','NAT','UREA','WBC','RBC','HGB','HCT','MCV','MCH','MCHC','PLT1',\n",
        "            'NE','LY','MO','EO','BA','NET','LYT','MOT','EOT','BAT', 'Suspect']\n",
        "\n",
        "print(set(pred) - set(X.columns))\n",
        "\n",
        "X_hemo = X.loc[:,hemo_plus]\n",
        "X_pred = X.loc[:,pred]\n",
        "\n",
        "X_hemo.to_excel('cbc.xls')\n",
        "X_pred.to_excel('covid-specific.xls')\n",
        "X.to_excel('osr.xls')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-77e5ed42c13f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m             'NE','LY','MO','EO','BA','NET','LYT','MOT','EOT','BAT', 'Suspect']\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgaleazzi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mX_hemo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhemo_plus\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p1p-9p57VsR"
      },
      "source": [
        "X_hemo['target'] = y\n",
        "X_hemo['target'].value_counts()\n",
        "X_hemo.to_excel('cbc_with_target.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vd7fYT6t6mDv"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV, cross_validate, StratifiedKFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import RFE, SelectKBest, mutual_info_classif\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "from scipy.stats import randint, uniform"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BujengPV6q54"
      },
      "source": [
        "from flexible import FlexibleScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe4rwAYo6vgf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "6a9fc769-a550-48be-ea01-4dad168c6588"
      },
      "source": [
        "models = {}\n",
        "models['LR'] =  Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=ExtraTreesClassifier())),\n",
        "    ('clf', LogisticRegression())\n",
        "])\n",
        "\n",
        "\n",
        "models['KNN'] = Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=ExtraTreesClassifier())),\n",
        "    ('clf', KNeighborsClassifier())\n",
        "])\n",
        "                             \n",
        "models['DT'] = Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=LogisticRegression(random_state=0, max_iter=10000))),\n",
        "    ('clf', DecisionTreeClassifier())\n",
        "])\n",
        "\n",
        "models['NB'] = Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=ExtraTreesClassifier())),\n",
        "    ('clf', GaussianNB())\n",
        "])\n",
        "\n",
        "models['SVM'] = Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=ExtraTreesClassifier())),\n",
        "    ('clf', SVC())\n",
        "])\n",
        "\n",
        "models['RF'] = Pipeline([\n",
        "    ('imp',KNNImputer()),\n",
        "    ('scl', FlexibleScaler()),\n",
        "    ('sel', RFE(estimator=ExtraTreesClassifier())),\n",
        "    ('clf', RandomForestClassifier())\n",
        "])\n",
        "\n",
        "methods = ['min-max','standard','yeo-johnson','max-abs','normalize','robust']\n",
        "\n",
        "class_weight = [{0: i*0.1, 1: 1} for i in range(10)]\n",
        "class_weight.append('balanced')\n",
        "class_weight.append(None)\n",
        "\n",
        "params = {}\n",
        "\n",
        "np.random.seed(0)\n",
        "\n",
        "params['LR'] = {\n",
        "    'clf__penalty': ['l2','l1','elasticnet'],\n",
        "    'clf__max_iter': [10000],\n",
        "    'clf__multi_class':['auto'],\n",
        "    'clf__C': uniform(0, 1),\n",
        "    'clf__solver': ['saga'],\n",
        "    'clf__l1_ratio': uniform(0, 1),\n",
        "    'clf__random_state': randint(0,1000000),\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'clf__class_weight': [{0: 0.25,1: 1},{0: 0.5,1: 1},{0: 0.75,1: 1},{0: 0.9,1: 1}, 'balanced', None]\n",
        "}\n",
        "\n",
        "params['KNN'] = {\n",
        "    'clf__n_neighbors': randint(1,10),\n",
        "    'clf__weights': ['uniform','distance'],\n",
        "    'clf__algorithm': ['kd_tree', 'auto', 'ball_tree'],\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'scl__scaler': methods\n",
        "}\n",
        "\n",
        "params['DT'] = {\n",
        "    'clf__criterion': ['gini','entropy'],\n",
        "    'clf__splitter': ['best','random'],\n",
        "    'clf__max_depth': randint(1,100),\n",
        "    'clf__random_state': [0],\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'scl__scaler': methods\n",
        "}\n",
        "\n",
        "params['NB'] = {\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'scl__scaler': methods\n",
        "}\n",
        "\n",
        "params['SVM'] = {\n",
        "    'clf__kernel':['linear','rbf','sigmoid', 'poly'],\n",
        "    'clf__C': uniform(0,1),\n",
        "    'clf__degree': randint(2,10),\n",
        "    'clf__gamma': ['auto','scale'],\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'scl__scaler': methods,\n",
        "    'clf__random_state': randint(0,1000000),\n",
        "    'clf__probability': [True],\n",
        "    'clf__max_iter': [-1],\n",
        "    'clf__class_weight': [{0: 0.25,1: 1},{0: 0.5,1: 1},{0: 0.75,1: 1},{0: 0.9,1: 1}, 'balanced', None]\n",
        "    }\n",
        "\n",
        "params['RF'] = {\n",
        "    'clf__n_estimators':randint(10,1000),\n",
        "    'clf__criterion': ['gini','entropy'],\n",
        "    'clf__max_depth': randint(1,100),\n",
        "    'clf__max_features': ['sqrt','log2'],\n",
        "    'sel__n_features_to_select': randint(5,X.shape[1]),\n",
        "    'scl__scaler': methods,\n",
        "    'clf__random_state': randint(0,1000000),\n",
        "    'clf__class_weight': [{0: 0.25,1: 1},{0: 0.5,1: 1},{0: 0.75,1: 1},{0: 0.9,1: 1},'balanced', None]\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-12b37f1bb15b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m models['LR'] =  Pipeline([\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'imp'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mKNNImputer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m'scl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFlexibleScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'sel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRFE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mExtraTreesClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'clf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'FlexibleScaler' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "auDBitT5HnDw",
        "outputId": "dbf5eb64-e297-46f7-f02b-7d2fe75fb20e"
      },
      "source": [
        "dati_galeazzi = pd.read_excel('gdrive/My Drive/Colab Notebooks/COVID/dati_galeazzi.xlsx')\n",
        "dati_galeazzi\n",
        "ids = [str(r[1].loc[\"numero randomizzazione\"]) + \"_\" + str(r[1].loc[\"Data prelievo\"]) for r in dati_galeazzi.iterrows()]\n",
        "dati_galeazzi[\"id\"] = ids\n",
        "dati_galeazzi.set_index(\"id\", inplace=True)\n",
        "dati_galeazzi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>numero randomizzazione</th>\n",
              "      <th>Sesso</th>\n",
              "      <th>Età</th>\n",
              "      <th>Data prelievo</th>\n",
              "      <th>Calcio</th>\n",
              "      <th>CPK</th>\n",
              "      <th>Creatinina</th>\n",
              "      <th>Fosfatasi Alcalina</th>\n",
              "      <th>Gamma GT</th>\n",
              "      <th>Glucosio</th>\n",
              "      <th>GOT/AST</th>\n",
              "      <th>GPT/ALT</th>\n",
              "      <th>LDH</th>\n",
              "      <th>PCR</th>\n",
              "      <th>Potassio sierico</th>\n",
              "      <th>Sodio sierico</th>\n",
              "      <th>Urea</th>\n",
              "      <th>1 - Globuli bianchi</th>\n",
              "      <th>2 - Globuli rossi</th>\n",
              "      <th>3 - Emoglobina</th>\n",
              "      <th>4 - Ematocrito</th>\n",
              "      <th>5 - MCV (Volume Globulare Medio)</th>\n",
              "      <th>6 - MCH (Emoglobina Corpuscolare Media)</th>\n",
              "      <th>7 - MCHC (Conc. Emogl. Corpusc. Media)</th>\n",
              "      <th>8 - Piastrine</th>\n",
              "      <th>9 - Eritroblasti</th>\n",
              "      <th>1 - Neutrofili</th>\n",
              "      <th>2 - Linfociti</th>\n",
              "      <th>3 - Monociti</th>\n",
              "      <th>4 - Eosinofili</th>\n",
              "      <th>5 - Basofili</th>\n",
              "      <th>6 - Granulociti immaturi</th>\n",
              "      <th>7 - Neutrofili</th>\n",
              "      <th>8 - Linfociti</th>\n",
              "      <th>9 - Monociti</th>\n",
              "      <th>10 - Eosinofili</th>\n",
              "      <th>11 - Basofili</th>\n",
              "      <th>12 - IG</th>\n",
              "      <th>13 - Nota</th>\n",
              "      <th>data tampone</th>\n",
              "      <th>esito tampone</th>\n",
              "      <th>diagnosi di accettazione/ricovero</th>\n",
              "      <th>Pressione Max</th>\n",
              "      <th>Pressione Min</th>\n",
              "      <th>Frequenza cardiaca (bpm)</th>\n",
              "      <th>Frequenza Respiratoria (atti/min)</th>\n",
              "      <th>Saturazione (%)</th>\n",
              "      <th>Temperatura</th>\n",
              "      <th>Unnamed: 48</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35_07/03/2020</th>\n",
              "      <td>35</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>82</td>\n",
              "      <td>07/03/2020</td>\n",
              "      <td>8.9</td>\n",
              "      <td>555.0</td>\n",
              "      <td>0.64</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>5.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>141.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>12.44</td>\n",
              "      <td>4.46</td>\n",
              "      <td>13.1</td>\n",
              "      <td>41.4</td>\n",
              "      <td>92.8</td>\n",
              "      <td>29.4</td>\n",
              "      <td>31.6</td>\n",
              "      <td>175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.6</td>\n",
              "      <td>8.8</td>\n",
              "      <td>10.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.04</td>\n",
              "      <td>1.09</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-06 00:00:00</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>frattura femore destro</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>80.0</td>\n",
              "      <td>12 o 20</td>\n",
              "      <td>97</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10_09/03/2020</th>\n",
              "      <td>10</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>39</td>\n",
              "      <td>09/03/2020</td>\n",
              "      <td>8.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.88</td>\n",
              "      <td>4.32</td>\n",
              "      <td>140.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7.82</td>\n",
              "      <td>4.28</td>\n",
              "      <td>12.8</td>\n",
              "      <td>37.9</td>\n",
              "      <td>88.6</td>\n",
              "      <td>29.9</td>\n",
              "      <td>33.8</td>\n",
              "      <td>349.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.6</td>\n",
              "      <td>15.5</td>\n",
              "      <td>7.7</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-09 00:00:00</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>polmonite multifocale</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>115.0</td>\n",
              "      <td>12 o 20</td>\n",
              "      <td>97</td>\n",
              "      <td>37.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48_10/03/2020</th>\n",
              "      <td>48</td>\n",
              "      <td>Maschio</td>\n",
              "      <td>70</td>\n",
              "      <td>10/03/2020</td>\n",
              "      <td>7.7</td>\n",
              "      <td>342.0</td>\n",
              "      <td>0.64</td>\n",
              "      <td>NaN</td>\n",
              "      <td>228.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>717.0</td>\n",
              "      <td>6.60</td>\n",
              "      <td>4.29</td>\n",
              "      <td>146.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>9.31</td>\n",
              "      <td>3.74</td>\n",
              "      <td>12.5</td>\n",
              "      <td>37.3</td>\n",
              "      <td>99.7</td>\n",
              "      <td>33.4</td>\n",
              "      <td>33.5</td>\n",
              "      <td>233.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86.3</td>\n",
              "      <td>10.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.03</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10/03/2020</td>\n",
              "      <td>Positivo</td>\n",
              "      <td>polmonite (deceduto)</td>\n",
              "      <td>129</td>\n",
              "      <td>70</td>\n",
              "      <td>78.0</td>\n",
              "      <td>14</td>\n",
              "      <td>87</td>\n",
              "      <td>36.7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48_11/03/2020</th>\n",
              "      <td>48</td>\n",
              "      <td>Maschio</td>\n",
              "      <td>70</td>\n",
              "      <td>11/03/2020</td>\n",
              "      <td>7.8</td>\n",
              "      <td>189.0</td>\n",
              "      <td>0.61</td>\n",
              "      <td>NaN</td>\n",
              "      <td>228.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>726.0</td>\n",
              "      <td>8.85</td>\n",
              "      <td>5.21</td>\n",
              "      <td>146.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>12.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>12.8</td>\n",
              "      <td>40.1</td>\n",
              "      <td>102.3</td>\n",
              "      <td>32.7</td>\n",
              "      <td>31.9</td>\n",
              "      <td>230.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.6</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.79</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Positivo</td>\n",
              "      <td>polmonite (deceduto)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48_12/03/2020</th>\n",
              "      <td>48</td>\n",
              "      <td>Maschio</td>\n",
              "      <td>70</td>\n",
              "      <td>12/03/2020</td>\n",
              "      <td>9.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.94</td>\n",
              "      <td>NaN</td>\n",
              "      <td>201.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>10.33</td>\n",
              "      <td>5.02</td>\n",
              "      <td>148.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>8.57</td>\n",
              "      <td>3.62</td>\n",
              "      <td>12.2</td>\n",
              "      <td>38.8</td>\n",
              "      <td>107.2</td>\n",
              "      <td>33.7</td>\n",
              "      <td>31.4</td>\n",
              "      <td>122.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>87.2</td>\n",
              "      <td>8.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>4.1</td>\n",
              "      <td>7.49</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.35</td>\n",
              "      <td>Presenza di granulociti immaturi e di eritrobl...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Positivo</td>\n",
              "      <td>polmonite (deceduto)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <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",
              "      <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>50_15/05/2020</th>\n",
              "      <td>50</td>\n",
              "      <td>Maschio</td>\n",
              "      <td>54</td>\n",
              "      <td>15/05/2020</td>\n",
              "      <td>9.7</td>\n",
              "      <td>101.0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.99</td>\n",
              "      <td>4.30</td>\n",
              "      <td>144.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7.08</td>\n",
              "      <td>5.24</td>\n",
              "      <td>14.3</td>\n",
              "      <td>45.0</td>\n",
              "      <td>85.9</td>\n",
              "      <td>27.3</td>\n",
              "      <td>31.8</td>\n",
              "      <td>241.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.7</td>\n",
              "      <td>16.8</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.29</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/05/2020</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>frattura glena</td>\n",
              "      <td>140</td>\n",
              "      <td>90</td>\n",
              "      <td>102.0</td>\n",
              "      <td>12 o 20</td>\n",
              "      <td>97</td>\n",
              "      <td>36.4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_11/05/2020</th>\n",
              "      <td>11</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>50</td>\n",
              "      <td>11/05/2020</td>\n",
              "      <td>8.8</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>3.80</td>\n",
              "      <td>145.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>8.05</td>\n",
              "      <td>3.79</td>\n",
              "      <td>11.6</td>\n",
              "      <td>34.9</td>\n",
              "      <td>92.1</td>\n",
              "      <td>30.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.33</td>\n",
              "      <td>2.09</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/05/2020</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>frattura scomposta omero</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>80.0</td>\n",
              "      <td>12 o 20</td>\n",
              "      <td>≥ 96</td>\n",
              "      <td>36.6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_13/05/2020</th>\n",
              "      <td>11</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>50</td>\n",
              "      <td>13/05/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.49</td>\n",
              "      <td>3.48</td>\n",
              "      <td>10.7</td>\n",
              "      <td>32.2</td>\n",
              "      <td>92.5</td>\n",
              "      <td>30.7</td>\n",
              "      <td>33.2</td>\n",
              "      <td>223.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>82.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.98</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_14/05/2022</th>\n",
              "      <td>11</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>50</td>\n",
              "      <td>14/05/2022</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.22</td>\n",
              "      <td>3.03</td>\n",
              "      <td>9.4</td>\n",
              "      <td>28.9</td>\n",
              "      <td>95.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>32.5</td>\n",
              "      <td>231.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.9</td>\n",
              "      <td>18.4</td>\n",
              "      <td>6.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.16</td>\n",
              "      <td>1.51</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36_05/05/2020</th>\n",
              "      <td>36</td>\n",
              "      <td>Femmina</td>\n",
              "      <td>75</td>\n",
              "      <td>05/05/2020</td>\n",
              "      <td>9.7</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.05</td>\n",
              "      <td>3.81</td>\n",
              "      <td>142.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>8.53</td>\n",
              "      <td>4.65</td>\n",
              "      <td>13.5</td>\n",
              "      <td>40.6</td>\n",
              "      <td>87.3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.3</td>\n",
              "      <td>366.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.5</td>\n",
              "      <td>13.1</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.78</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>08/05/2020</td>\n",
              "      <td>Negativo</td>\n",
              "      <td>riduzione e sintesi di frattura radio</td>\n",
              "      <td>130</td>\n",
              "      <td>70</td>\n",
              "      <td>76.0</td>\n",
              "      <td>/</td>\n",
              "      <td>97</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>292 rows × 49 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               numero randomizzazione    Sesso  ...  Temperatura Unnamed: 48\n",
              "id                                              ...                         \n",
              "35_07/03/2020                      35  Femmina  ...           36         NaN\n",
              "10_09/03/2020                      10  Femmina  ...         37.3         NaN\n",
              "48_10/03/2020                      48  Maschio  ...         36.7         NaN\n",
              "48_11/03/2020                      48  Maschio  ...          NaN         NaN\n",
              "48_12/03/2020                      48  Maschio  ...          NaN         NaN\n",
              "...                               ...      ...  ...          ...         ...\n",
              "50_15/05/2020                      50  Maschio  ...         36.4         NaN\n",
              "11_11/05/2020                      11  Femmina  ...         36.6         NaN\n",
              "11_13/05/2020                      11  Femmina  ...          NaN         NaN\n",
              "11_14/05/2022                      11  Femmina  ...          NaN         NaN\n",
              "36_05/05/2020                      36  Femmina  ...           36         NaN\n",
              "\n",
              "[292 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vzL_OqQvoLl"
      },
      "source": [
        "y_ext = dati_galeazzi['esito tampone']\n",
        "dati_galeazzi = dati_galeazzi.drop(columns=['numero randomizzazione','Data prelievo', 'data tampone',\n",
        "                            'Pressione Max','Pressione Min',\n",
        "                            'Frequenza Respiratoria (atti/min)','Saturazione (%)','Temperatura',\n",
        "                            'Unnamed: 48','13 - Nota','6 - Granulociti immaturi', 'Frequenza cardiaca (bpm)',\n",
        "                             '12 - IG','esito tampone','9 - Eritroblasti'])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dati_galeazzi.columns = galeazzi\n",
        "\n",
        "dati_galeazzi['Sex'] = dati_galeazzi['Sex'].apply(lambda x: 1 if x == 'Maschio' else 0)\n",
        "dati_galeazzi['PCR'] = dati_galeazzi['PCR'].apply(lambda x: x*10)\n",
        "\n",
        "dati_galeazzi['Suspect'] = dati_galeazzi['Suspect'].apply(lambda x: \"None\" if type(x) != str else x)\n",
        "dati_galeazzi['Suspect'] = dati_galeazzi['Suspect'].apply(lambda x: 1 if (\n",
        "                                                            ('CoV' in x) or\n",
        "                                                            ('COVID' in x)) else 0)\n",
        "dati_galeazzi['CA'] = dati_galeazzi['CA'].apply(lambda x: x*0.2495)\n",
        "\n",
        "dati_galeazzi_temp = pd.DataFrame(index=np.unique(dati_galeazzi.index), columns=dati_galeazzi.columns)\n",
        "for r in dati_galeazzi_temp.index:\n",
        "  for c in dati_galeazzi_temp.columns:\n",
        "      dati_galeazzi_temp.loc[r,c] = np.mean(dati_galeazzi.loc[r,c])\n",
        "dati_galeazzi_temp\n",
        "\n",
        "y_ext_temp = []\n",
        "for r in dati_galeazzi_temp.index:\n",
        "  check = False\n",
        "  for v in y_ext[r]:\n",
        "    if \"ositivo\" in v:\n",
        "      y_ext_temp.append(1)\n",
        "      check = True\n",
        "      break\n",
        "  if not check:\n",
        "    y_ext_temp.append(0)\n",
        "np.unique(y_ext_temp, return_counts=True)\n",
        "\n",
        "dati_galeazzi = dati_galeazzi_temp\n",
        "y_ext = y_ext_temp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwJTE0RoryzB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3fc15642-fca9-4026-a696-23f786b7b849"
      },
      "source": [
        "\n",
        "\n",
        "dati_galeazzi['COROQL'] = 0\n",
        "dati_galeazzi['COROQL'] = y_ext\n",
        "\n",
        "dati_galeazzi.to_excel('galeazzi.xlsx')\n",
        "X_ext = dati_galeazzi\n",
        "\n",
        "X_temp_ext = X_ext.copy()\n",
        "#X_ext = X_ext.loc[:,pred]\n",
        "X_ext.index = [t+2000 for t in X_ext.index]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-663a8f8ba3b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mX_temp_ext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m#X_ext = X_ext.loc[:,galeazzi]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m2000\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-30-663a8f8ba3b2>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mX_temp_ext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m#X_ext = X_ext.loc[:,galeazzi]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m2000\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX_ext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a72tLUmCu_7T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "c573cc1a-5978-459b-dd5e-87b5b9a56a6a"
      },
      "source": [
        "X_galeazzi = pd.DataFrame(np.nan, index=dati_galeazzi.index, columns=dati_galeazzi.columns)\n",
        "for i in dati_galeazzi.index:\n",
        "  for j in dati_galeazzi.columns:\n",
        "    X_galeazzi.loc[i,j] = dati_galeazzi.loc[i,j]\n",
        "\n",
        "X_galeazzi = X_galeazzi.drop(columns=[\"COROQL\"])\n",
        "X_galeazzi = X_galeazzi.loc[:, hemo_plus]\n",
        "X_galeazzi[\"target\"] = y_ext\n",
        "X_galeazzi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>HCT</th>\n",
              "      <th>HGB</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>RBC</th>\n",
              "      <th>WBC</th>\n",
              "      <th>PLT1</th>\n",
              "      <th>NE</th>\n",
              "      <th>LY</th>\n",
              "      <th>MO</th>\n",
              "      <th>EO</th>\n",
              "      <th>BA</th>\n",
              "      <th>NET</th>\n",
              "      <th>LYT</th>\n",
              "      <th>MOT</th>\n",
              "      <th>EOT</th>\n",
              "      <th>BAT</th>\n",
              "      <th>Suspect</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10_09/03/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>37.9</td>\n",
              "      <td>12.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>33.8</td>\n",
              "      <td>88.6</td>\n",
              "      <td>4.28</td>\n",
              "      <td>7.82</td>\n",
              "      <td>349.0</td>\n",
              "      <td>72.6</td>\n",
              "      <td>15.5</td>\n",
              "      <td>7.7</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_11/05/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>34.9</td>\n",
              "      <td>11.6</td>\n",
              "      <td>30.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>92.1</td>\n",
              "      <td>3.79</td>\n",
              "      <td>8.05</td>\n",
              "      <td>250.0</td>\n",
              "      <td>66.3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.33</td>\n",
              "      <td>2.09</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_13/05/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>32.2</td>\n",
              "      <td>10.7</td>\n",
              "      <td>30.7</td>\n",
              "      <td>33.2</td>\n",
              "      <td>92.5</td>\n",
              "      <td>3.48</td>\n",
              "      <td>8.49</td>\n",
              "      <td>223.0</td>\n",
              "      <td>82.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>6.98</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11_14/05/2022</th>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>28.9</td>\n",
              "      <td>9.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>32.5</td>\n",
              "      <td>95.4</td>\n",
              "      <td>3.03</td>\n",
              "      <td>8.22</td>\n",
              "      <td>231.0</td>\n",
              "      <td>74.9</td>\n",
              "      <td>18.4</td>\n",
              "      <td>6.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>6.16</td>\n",
              "      <td>1.51</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12_03/04/2020</th>\n",
              "      <td>1.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>34.1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>29.2</td>\n",
              "      <td>32.3</td>\n",
              "      <td>90.5</td>\n",
              "      <td>3.77</td>\n",
              "      <td>6.64</td>\n",
              "      <td>250.0</td>\n",
              "      <td>58.5</td>\n",
              "      <td>21.5</td>\n",
              "      <td>14.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>3.89</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</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",
              "      <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>7_11/05/2020</th>\n",
              "      <td>1.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>8.7</td>\n",
              "      <td>31.8</td>\n",
              "      <td>32.7</td>\n",
              "      <td>97.1</td>\n",
              "      <td>2.74</td>\n",
              "      <td>14.82</td>\n",
              "      <td>146.0</td>\n",
              "      <td>58.1</td>\n",
              "      <td>12.2</td>\n",
              "      <td>29.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>8.60</td>\n",
              "      <td>1.81</td>\n",
              "      <td>4.36</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8_01/04/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>34.9</td>\n",
              "      <td>11.5</td>\n",
              "      <td>29.4</td>\n",
              "      <td>33.0</td>\n",
              "      <td>89.3</td>\n",
              "      <td>3.91</td>\n",
              "      <td>5.53</td>\n",
              "      <td>257.0</td>\n",
              "      <td>67.4</td>\n",
              "      <td>19.9</td>\n",
              "      <td>11.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>3.73</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8_19/03/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>32.7</td>\n",
              "      <td>11.3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>34.6</td>\n",
              "      <td>86.7</td>\n",
              "      <td>3.77</td>\n",
              "      <td>5.32</td>\n",
              "      <td>308.0</td>\n",
              "      <td>83.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.44</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8_22/03/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.8</td>\n",
              "      <td>11.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>33.7</td>\n",
              "      <td>87.1</td>\n",
              "      <td>3.88</td>\n",
              "      <td>5.08</td>\n",
              "      <td>391.0</td>\n",
              "      <td>73.6</td>\n",
              "      <td>12.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3.74</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9_22/05/2020</th>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>12.8</td>\n",
              "      <td>27.8</td>\n",
              "      <td>33.1</td>\n",
              "      <td>84.1</td>\n",
              "      <td>4.60</td>\n",
              "      <td>5.98</td>\n",
              "      <td>360.0</td>\n",
              "      <td>44.3</td>\n",
              "      <td>40.5</td>\n",
              "      <td>10.2</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.42</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>275 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Sex   Age   HCT   HGB   MCH  ...   MOT   EOT   BAT  Suspect  target\n",
              "10_09/03/2020  0.0  39.0  37.9  12.8  29.9  ...  0.60  0.28  0.05      0.0       0\n",
              "11_11/05/2020  0.0  50.0  34.9  11.6  30.6  ...  0.59  0.02  0.02      0.0       0\n",
              "11_13/05/2020  0.0  50.0  32.2  10.7  30.7  ...  0.20  0.02  0.02      0.0       0\n",
              "11_14/05/2022  0.0  50.0  28.9   9.4  31.0  ...  0.54  0.00  0.01      0.0       0\n",
              "12_03/04/2020  1.0  61.0  34.1  11.0  29.2  ...  0.94  0.27  0.11      1.0       0\n",
              "...            ...   ...   ...   ...   ...  ...   ...   ...   ...      ...     ...\n",
              "7_11/05/2020   1.0  91.0  26.6   8.7  31.8  ...  4.36  0.03  0.02      0.0       0\n",
              "8_01/04/2020   0.0  77.0  34.9  11.5  29.4  ...  0.64  0.04  0.02      1.0       0\n",
              "8_19/03/2020   0.0  77.0  32.7  11.3  30.0  ...  0.40  0.01  0.00      1.0       1\n",
              "8_22/03/2020   0.0  77.0  33.8  11.4  29.4  ...  0.67  0.05  0.01      1.0       0\n",
              "9_22/05/2020   0.0  66.0  38.7  12.8  27.8  ...  0.61  0.23  0.07      0.0       0\n",
              "\n",
              "[275 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0N1ZaQ9QL7T"
      },
      "source": [
        "X_galeazzi.to_excel('galeazzi_cbc.xls')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QV2P-quf6Ygf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "e42d0a7b-f438-4fa7-c8ba-afc5b78acdc3"
      },
      "source": [
        "from scipy.stats import ks_2samp\n",
        "\n",
        "imp = KNNImputer()\n",
        "\n",
        "X_temp = pd.DataFrame(imp.fit_transform(X), columns=X.columns)\n",
        "X_galeazzi_temp = pd.DataFrame(imp.fit_transform(X_galeazzi), columns=X_galeazzi.columns)\n",
        "\n",
        "nondiff = []\n",
        "for c in X.columns:\n",
        "  p = ks_2samp(X_temp.loc[:,c], X_galeazzi_temp.loc[:,c]).pvalue\n",
        "  if p > 0.05:\n",
        "    nondiff.append(c)\n",
        "  print(\"%s: p-value = %.3f, mean(OSR) : %.2f, mean(IOG): %.2f\" % (c, p, np.mean(X_temp.loc[:,c]), np.mean(X_galeazzi_temp.loc[:,c])))\n",
        "print(nondiff)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HCTPOC'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-9960b47e1a57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mnondiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m   \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mks_2samp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_temp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_galeazzi_temp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mnondiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1760\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1272\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1273\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1274\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1387\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m                 \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m                 \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1964\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1965\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3528\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3529\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3531\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'HCTPOC'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wq-yTq8CALxw"
      },
      "source": [
        "def tw_accuracy(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return accuracy_score(y_test[check], y_pred)\n",
        "\n",
        "def tw_weighted_accuracy(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  acc = 0\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    acc = 1\n",
        "  acc = accuracy_score(y_test[check], y_pred)\n",
        "  cov = len(y_pred)/len(y_test)\n",
        "  return 0.75*acc + 0.25*cov\n",
        "\n",
        "def tw_auc(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(np.unique(y_test[check])) == 1:\n",
        "    return 0\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return roc_auc_score(y_test[check], y_pred)\n",
        "\n",
        "def tw_sens(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return recall_score(y_test[check], y_pred)\n",
        "\n",
        "def tw_spec(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return recall_score(y_test[check], y_pred, pos_label=0)\n",
        "\n",
        "def tw_ppv(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return precision_score(y_test[check], y_pred)\n",
        "\n",
        "def tw_npv(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_pred = y_pred[check]\n",
        "  if len(y_pred) == 0 or len(y_test[check]) == 0:\n",
        "    return 1\n",
        "  return precision_score(y_test[check], y_pred, pos_label=0)\n",
        "\n",
        "def coverage(clf, X_test, y_test):\n",
        "  y_pred =  clf.predict_proba(X_test)[:,1]\n",
        "  check = (y_pred > 0.7) | (y_pred < 0.3)\n",
        "  y_pred = y_pred[check]\n",
        "  return len(y_pred)/len(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy8DqD8UJQKC"
      },
      "source": [
        "def report(scores, scoring, rank='auc'):\n",
        "  for score in scoring:\n",
        "    i = np.where(scores['rank_test_'+ rank] == 1)\n",
        "    print( score + \" \" + str(np.mean(scores['mean_test_' + str(score)][i])) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-v6BUKpNOPU"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, make_scorer\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from joblib import dump, load\n",
        "\n",
        "\n",
        "scoring = {\n",
        "    'accuracy': make_scorer(accuracy_score),\n",
        "    'sensitivity': make_scorer(recall_score),\n",
        "    'specificity': make_scorer(recall_score, pos_label=0),\n",
        "    'ppv': make_scorer(precision_score),\n",
        "    'npv': make_scorer(precision_score, pos_label=0),\n",
        "    'auc': make_scorer(roc_auc_score),\n",
        "    'tw_accuracy': tw_accuracy,\n",
        "    'tw_sensitivity': tw_sens,\n",
        "    'tw_specificity': tw_spec,\n",
        "    'tw_ppv': tw_ppv,\n",
        "    'tw_npv': tw_npv,\n",
        "    'tw_auc': tw_auc,\n",
        "    'tw_coverage': coverage,\n",
        "    'wa': tw_weighted_accuracy\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDak8aX8Bkwm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "3f9a55a0-ac2f-4932-ecb1-6828133f22c2"
      },
      "source": [
        "\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "names = ['KNN','LR','SVM','RF','NB']\n",
        "\n",
        "all_features = True\n",
        "bootstrap = True\n",
        "\n",
        "\n",
        "#name = 'LR'\n",
        "\n",
        "cv_results = {}\n",
        "best_params = {}\n",
        "selected_feats = {}\n",
        "scores = {}\n",
        "precisions = {}\n",
        "recalls = {}\n",
        "pr_thresholds = {}\n",
        "fprs = {}\n",
        "tprs = {}\n",
        "roc_thresholds = {}\n",
        "best_models = {}\n",
        "seed = {'SVM': 244366, 'RF':389296, 'NB':122579, 'KNN': 725660, 'LR':644330}\n",
        "\n",
        "for name in names:\n",
        "    print(name)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state=seed[name])\n",
        "    clf = RandomizedSearchCV(models[name], param_distributions=params[name], n_iter=100,\n",
        "                         n_jobs=-1, cv=cv, random_state=10, scoring=scoring, verbose=0, refit='auc')\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "    best_params[name] = clf.best_params_\n",
        "    print(best_params[name])\n",
        "    cv_results[name] = clf.cv_results_\n",
        "    report(cv_results[name], scoring)\n",
        "\n",
        "    model = models[name].set_params(**best_params[name])\n",
        "    best_models[name] = model\n",
        "    dump(best_models[name], name + '.joblib')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-7c7c25ab1b0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     clf = RandomizedSearchCV(models[name], param_distributions=params[name], n_iter=100,\n\u001b[1;32m     29\u001b[0m                          n_jobs=-1, cv=cv, random_state=10, scoring=scoring, verbose=0, refit='auc')\n",
            "\u001b[0;31mNameError\u001b[0m: name 'seed' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "LDM9J6HoK_0Z",
        "outputId": "dfa73305-e3aa-4db6-af2e-0bf227e6bf73"
      },
      "source": [
        "dati_nuovi = pd.read_excel('gdrive/My Drive/Colab Notebooks/COVID/pazienti_2018.xlsx',\n",
        "                             sheet_name=\"Sheet\" + str(0), header=None)\n",
        "dati_nuovi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>4</th>\n",
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              "</table>\n",
              "<p>3010 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            0     1   2   3   4   5            6\n",
              "0         Sex     0 NaN NaN NaT NaN          NaN\n",
              "1     Suspect     0 NaN NaN NaT NaN          NaN\n",
              "2         HCT  32.9 NaN NaN NaT NaN          NaN\n",
              "3         HGB  10.9 NaN NaN NaT NaN          NaN\n",
              "4         MCH  22.3 NaN NaN NaT NaN          NaN\n",
              "...       ...   ...  ..  ..  ..  ..          ...\n",
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              "\n",
              "[3010 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2ov4CdEVpTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a236de53-ee15-4cc7-b938-867d7969981e"
      },
      "source": [
        "new_samples = pd.DataFrame(np.nan, index=range(54), columns=X.columns)\n",
        "new_y = []\n",
        "\n",
        "for j in range(54):\n",
        "  new_y.append(0)\n",
        "  dati_nuovi = pd.read_excel('gdrive/My Drive/Colab Notebooks/COVID/pazienti_2018.xlsx',\n",
        "                             sheet_name=\"Sheet\" + str(j), header=None)\n",
        "  dati_nuovi = dati_nuovi[dati_nuovi[0].notna()]\n",
        "  dati_nuovi = dati_nuovi.iloc[:, :2]\n",
        "  dati_nuovi.columns = [\"par\", \"val\"]\n",
        "  for i in new_samples.columns:\n",
        "    if i in np.unique(dati_nuovi[\"par\"]):\n",
        "      temp = dati_nuovi[dati_nuovi[\"par\"] == i]\n",
        "      new_samples.loc[j,i] = np.mean(temp[\"val\"].apply(pd.to_numeric,errors='coerce'))\n",
        "new_samples = new_samples.loc[:, hemo_plus]\n",
        "new_samples"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>5.590</td>\n",
              "      <td>7.10</td>\n",
              "      <td>241.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.2</td>\n",
              "      <td>14.30</td>\n",
              "      <td>34.30</td>\n",
              "      <td>35.60</td>\n",
              "      <td>96.4</td>\n",
              "      <td>4.170</td>\n",
              "      <td>12.10</td>\n",
              "      <td>239.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.7</td>\n",
              "      <td>10.40</td>\n",
              "      <td>30.40</td>\n",
              "      <td>33.90</td>\n",
              "      <td>89.8</td>\n",
              "      <td>3.420</td>\n",
              "      <td>8.70</td>\n",
              "      <td>196.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.7</td>\n",
              "      <td>15.80</td>\n",
              "      <td>30.20</td>\n",
              "      <td>34.60</td>\n",
              "      <td>87.2</td>\n",
              "      <td>5.240</td>\n",
              "      <td>9.10</td>\n",
              "      <td>173.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.2</td>\n",
              "      <td>14.00</td>\n",
              "      <td>31.70</td>\n",
              "      <td>34.00</td>\n",
              "      <td>93.4</td>\n",
              "      <td>4.410</td>\n",
              "      <td>7.80</td>\n",
              "      <td>255.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.9</td>\n",
              "      <td>12.10</td>\n",
              "      <td>31.80</td>\n",
              "      <td>34.70</td>\n",
              "      <td>91.8</td>\n",
              "      <td>3.800</td>\n",
              "      <td>14.10</td>\n",
              "      <td>345.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.2</td>\n",
              "      <td>11.70</td>\n",
              "      <td>31.90</td>\n",
              "      <td>35.20</td>\n",
              "      <td>90.5</td>\n",
              "      <td>3.670</td>\n",
              "      <td>5.90</td>\n",
              "      <td>237.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.9</td>\n",
              "      <td>14.90</td>\n",
              "      <td>28.70</td>\n",
              "      <td>32.50</td>\n",
              "      <td>88.4</td>\n",
              "      <td>5.190</td>\n",
              "      <td>8.10</td>\n",
              "      <td>267.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.7</td>\n",
              "      <td>12.60</td>\n",
              "      <td>29.60</td>\n",
              "      <td>33.40</td>\n",
              "      <td>88.5</td>\n",
              "      <td>4.260</td>\n",
              "      <td>15.00</td>\n",
              "      <td>261.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.9</td>\n",
              "      <td>15.10</td>\n",
              "      <td>29.80</td>\n",
              "      <td>33.60</td>\n",
              "      <td>88.7</td>\n",
              "      <td>5.060</td>\n",
              "      <td>11.00</td>\n",
              "      <td>223.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.6</td>\n",
              "      <td>8.60</td>\n",
              "      <td>38.90</td>\n",
              "      <td>35.00</td>\n",
              "      <td>111.3</td>\n",
              "      <td>2.210</td>\n",
              "      <td>7.70</td>\n",
              "      <td>258.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.8</td>\n",
              "      <td>11.50</td>\n",
              "      <td>28.60</td>\n",
              "      <td>33.00</td>\n",
              "      <td>86.6</td>\n",
              "      <td>4.020</td>\n",
              "      <td>6.00</td>\n",
              "      <td>291.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.1</td>\n",
              "      <td>8.90</td>\n",
              "      <td>19.30</td>\n",
              "      <td>30.60</td>\n",
              "      <td>63.1</td>\n",
              "      <td>4.610</td>\n",
              "      <td>12.60</td>\n",
              "      <td>196.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.4</td>\n",
              "      <td>15.10</td>\n",
              "      <td>29.80</td>\n",
              "      <td>34.80</td>\n",
              "      <td>85.8</td>\n",
              "      <td>5.060</td>\n",
              "      <td>9.90</td>\n",
              "      <td>219.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.7</td>\n",
              "      <td>11.40</td>\n",
              "      <td>31.10</td>\n",
              "      <td>33.80</td>\n",
              "      <td>92.1</td>\n",
              "      <td>3.660</td>\n",
              "      <td>10.20</td>\n",
              "      <td>136.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.8</td>\n",
              "      <td>12.40</td>\n",
              "      <td>28.20</td>\n",
              "      <td>33.70</td>\n",
              "      <td>83.8</td>\n",
              "      <td>4.390</td>\n",
              "      <td>8.10</td>\n",
              "      <td>274.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.8</td>\n",
              "      <td>13.00</td>\n",
              "      <td>27.80</td>\n",
              "      <td>32.70</td>\n",
              "      <td>85.0</td>\n",
              "      <td>4.680</td>\n",
              "      <td>6.00</td>\n",
              "      <td>277.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.7</td>\n",
              "      <td>12.60</td>\n",
              "      <td>30.80</td>\n",
              "      <td>35.30</td>\n",
              "      <td>87.3</td>\n",
              "      <td>4.090</td>\n",
              "      <td>19.00</td>\n",
              "      <td>215.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.7</td>\n",
              "      <td>13.60</td>\n",
              "      <td>30.00</td>\n",
              "      <td>34.30</td>\n",
              "      <td>87.4</td>\n",
              "      <td>4.540</td>\n",
              "      <td>8.30</td>\n",
              "      <td>252.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.7</td>\n",
              "      <td>13.40</td>\n",
              "      <td>31.50</td>\n",
              "      <td>36.50</td>\n",
              "      <td>86.2</td>\n",
              "      <td>4.260</td>\n",
              "      <td>7.90</td>\n",
              "      <td>321.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46.3</td>\n",
              "      <td>15.60</td>\n",
              "      <td>29.30</td>\n",
              "      <td>33.70</td>\n",
              "      <td>87.0</td>\n",
              "      <td>5.320</td>\n",
              "      <td>8.80</td>\n",
              "      <td>293.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.9</td>\n",
              "      <td>12.90</td>\n",
              "      <td>27.60</td>\n",
              "      <td>33.20</td>\n",
              "      <td>83.1</td>\n",
              "      <td>4.680</td>\n",
              "      <td>6.10</td>\n",
              "      <td>334.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.6</td>\n",
              "      <td>12.10</td>\n",
              "      <td>27.60</td>\n",
              "      <td>33.10</td>\n",
              "      <td>83.4</td>\n",
              "      <td>4.390</td>\n",
              "      <td>7.80</td>\n",
              "      <td>434.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>14.30</td>\n",
              "      <td>29.70</td>\n",
              "      <td>33.30</td>\n",
              "      <td>89.2</td>\n",
              "      <td>4.820</td>\n",
              "      <td>6.10</td>\n",
              "      <td>181.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.7</td>\n",
              "      <td>15.20</td>\n",
              "      <td>31.00</td>\n",
              "      <td>35.60</td>\n",
              "      <td>87.1</td>\n",
              "      <td>4.900</td>\n",
              "      <td>7.90</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.1</td>\n",
              "      <td>11.80</td>\n",
              "      <td>31.10</td>\n",
              "      <td>33.60</td>\n",
              "      <td>92.6</td>\n",
              "      <td>3.790</td>\n",
              "      <td>12.50</td>\n",
              "      <td>168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.9</td>\n",
              "      <td>14.30</td>\n",
              "      <td>29.90</td>\n",
              "      <td>34.10</td>\n",
              "      <td>87.7</td>\n",
              "      <td>4.780</td>\n",
              "      <td>7.90</td>\n",
              "      <td>265.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.20</td>\n",
              "      <td>29.20</td>\n",
              "      <td>33.90</td>\n",
              "      <td>85.9</td>\n",
              "      <td>3.840</td>\n",
              "      <td>7.20</td>\n",
              "      <td>310.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.8</td>\n",
              "      <td>11.20</td>\n",
              "      <td>28.70</td>\n",
              "      <td>33.10</td>\n",
              "      <td>86.7</td>\n",
              "      <td>3.900</td>\n",
              "      <td>9.10</td>\n",
              "      <td>208.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>10.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>15.10</td>\n",
              "      <td>28.30</td>\n",
              "      <td>34.30</td>\n",
              "      <td>82.4</td>\n",
              "      <td>5.340</td>\n",
              "      <td>7.30</td>\n",
              "      <td>204.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.3</td>\n",
              "      <td>14.50</td>\n",
              "      <td>30.50</td>\n",
              "      <td>35.10</td>\n",
              "      <td>86.9</td>\n",
              "      <td>4.750</td>\n",
              "      <td>18.20</td>\n",
              "      <td>268.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.8</td>\n",
              "      <td>10.90</td>\n",
              "      <td>27.30</td>\n",
              "      <td>32.20</td>\n",
              "      <td>84.7</td>\n",
              "      <td>3.990</td>\n",
              "      <td>7.20</td>\n",
              "      <td>199.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.6</td>\n",
              "      <td>8.70</td>\n",
              "      <td>29.60</td>\n",
              "      <td>32.70</td>\n",
              "      <td>90.5</td>\n",
              "      <td>2.940</td>\n",
              "      <td>12.00</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.3</td>\n",
              "      <td>13.30</td>\n",
              "      <td>32.20</td>\n",
              "      <td>33.80</td>\n",
              "      <td>95.2</td>\n",
              "      <td>4.130</td>\n",
              "      <td>13.00</td>\n",
              "      <td>181.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.9</td>\n",
              "      <td>9.50</td>\n",
              "      <td>32.60</td>\n",
              "      <td>31.80</td>\n",
              "      <td>102.7</td>\n",
              "      <td>2.910</td>\n",
              "      <td>6.30</td>\n",
              "      <td>92.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.4</td>\n",
              "      <td>11.35</td>\n",
              "      <td>27.80</td>\n",
              "      <td>33.00</td>\n",
              "      <td>84.2</td>\n",
              "      <td>4.085</td>\n",
              "      <td>14.80</td>\n",
              "      <td>162.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.1</td>\n",
              "      <td>13.80</td>\n",
              "      <td>26.70</td>\n",
              "      <td>33.60</td>\n",
              "      <td>79.5</td>\n",
              "      <td>5.170</td>\n",
              "      <td>13.40</td>\n",
              "      <td>276.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.4</td>\n",
              "      <td>12.90</td>\n",
              "      <td>26.90</td>\n",
              "      <td>31.90</td>\n",
              "      <td>84.3</td>\n",
              "      <td>4.790</td>\n",
              "      <td>8.20</td>\n",
              "      <td>422.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.2</td>\n",
              "      <td>13.70</td>\n",
              "      <td>26.30</td>\n",
              "      <td>32.50</td>\n",
              "      <td>81.0</td>\n",
              "      <td>5.210</td>\n",
              "      <td>9.40</td>\n",
              "      <td>189.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.9</td>\n",
              "      <td>12.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>33.40</td>\n",
              "      <td>107.8</td>\n",
              "      <td>3.330</td>\n",
              "      <td>8.80</td>\n",
              "      <td>223.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.3</td>\n",
              "      <td>11.30</td>\n",
              "      <td>30.00</td>\n",
              "      <td>33.90</td>\n",
              "      <td>88.3</td>\n",
              "      <td>3.770</td>\n",
              "      <td>6.30</td>\n",
              "      <td>257.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.2</td>\n",
              "      <td>13.90</td>\n",
              "      <td>28.80</td>\n",
              "      <td>34.60</td>\n",
              "      <td>83.4</td>\n",
              "      <td>4.820</td>\n",
              "      <td>11.40</td>\n",
              "      <td>268.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.0</td>\n",
              "      <td>15.90</td>\n",
              "      <td>30.10</td>\n",
              "      <td>33.10</td>\n",
              "      <td>90.9</td>\n",
              "      <td>5.280</td>\n",
              "      <td>18.00</td>\n",
              "      <td>233.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Sex  Age   HCT    HGB    MCH   MCHC  ...  NET  LYT  MOT  EOT  BAT  Suspect\n",
              "0   0.0  NaN  32.9  10.90  22.30  33.10  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "1   0.0  NaN  36.6  11.90  27.70  32.50  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "2   0.0  NaN  32.8  11.10  28.55  33.95  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "3   0.0  NaN  37.4  13.00  31.80  34.80  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "4   0.0  NaN  42.2  15.00  29.50  35.50  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "5   0.0  NaN  50.4  17.00  32.60  33.70  ...  8.6  1.5  0.9  0.1  0.0      1.0\n",
              "6   1.0  NaN   NaN    NaN    NaN    NaN  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "7   0.0  NaN  40.3  13.60  29.55  33.75  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "8   1.0  NaN  41.5  14.50  30.90  34.90  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "9   1.0  NaN  44.5  14.90  30.40  33.50  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "10  1.0  NaN  36.2  11.60  20.80  32.00  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "11  1.0  NaN  40.2  14.30  34.30  35.60  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "12  1.0  NaN  30.7  10.40  30.40  33.90  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "13  1.0  NaN  45.7  15.80  30.20  34.60  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "14  1.0  NaN  41.2  14.00  31.70  34.00  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "15  1.0  NaN  34.9  12.10  31.80  34.70  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "16  1.0  NaN  33.2  11.70  31.90  35.20  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "17  0.0  NaN  45.9  14.90  28.70  32.50  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "18  1.0  NaN  37.7  12.60  29.60  33.40  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "19  0.0  NaN  44.9  15.10  29.80  33.60  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "20  1.0  NaN  24.6   8.60  38.90  35.00  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "21  0.0  NaN  34.8  11.50  28.60  33.00  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "22  0.0  NaN  29.1   8.90  19.30  30.60  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "23  1.0  NaN  43.4  15.10  29.80  34.80  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "24  0.0  NaN  33.7  11.40  31.10  33.80  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "25  0.0  NaN  36.8  12.40  28.20  33.70  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "26  0.0  NaN  39.8  13.00  27.80  32.70  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "27  0.0  NaN  35.7  12.60  30.80  35.30  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "28  0.0  NaN  39.7  13.60  30.00  34.30  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "29  0.0  NaN   NaN    NaN    NaN    NaN  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "30  0.0  NaN  36.7  13.40  31.50  36.50  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "31  1.0  NaN  46.3  15.60  29.30  33.70  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "32  1.0  NaN  38.9  12.90  27.60  33.20  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "33  0.0  NaN  36.6  12.10  27.60  33.10  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "34  1.0  NaN  43.0  14.30  29.70  33.30  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "35  1.0  NaN  42.7  15.20  31.00  35.60  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "36  1.0  NaN  35.1  11.80  31.10  33.60  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "37  0.0  NaN  41.9  14.30  29.90  34.10  ...  NaN  NaN  NaN  NaN  NaN      0.0\n",
              "38  1.0  NaN  33.0  11.20  29.20  33.90  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "39  0.0  NaN  33.8  11.20  28.70  33.10  ...  7.5  0.9  0.7  0.0  0.0      1.0\n",
              "40  1.0  NaN  44.0  15.10  28.30  34.30  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "41  1.0  NaN  41.3  14.50  30.50  35.10  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "42  1.0  NaN  33.8  10.90  27.30  32.20  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "43  1.0  NaN  26.6   8.70  29.60  32.70  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "44  0.0  NaN  39.3  13.30  32.20  33.80  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "45  1.0  NaN  29.9   9.50  32.60  31.80  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "46  0.0  NaN  34.4  11.35  27.80  33.00  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "47  0.0  NaN  41.1  13.80  26.70  33.60  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "48  0.0  NaN  40.4  12.90  26.90  31.90  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "49  0.0  NaN  42.2  13.70  26.30  32.50  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "50  0.0  NaN  35.9  12.00  36.00  33.40  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "51  1.0  NaN  33.3  11.30  30.00  33.90  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "52  1.0  NaN  40.2  13.90  28.80  34.60  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "53  1.0  NaN  48.0  15.90  30.10  33.10  ...  NaN  NaN  NaN  NaN  NaN      1.0\n",
              "\n",
              "[54 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zC6-oAeFwtFJ"
      },
      "source": [
        "new_samples['target'] = new_y\n",
        "new_samples.to_excel('dataset_2018.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpKRKvdVjjJn"
      },
      "source": [
        "X_training = pd.concat([X_pred,X_galeazzi,new_samples])\n",
        "y_training = np.array(list(y) + list(y_ext) + list(new_y))\n",
        "X_training[\"target\"] = y_training\n",
        "X_training[\"target\"].value_counts()\n",
        "X_training.to_excel(\"all_training.xlsx\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoG91F0OvbVL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "65e6430d-df32-44b1-c38e-440bd2f537a3"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, make_scorer\n",
        "from sklearn.metrics import brier_score_loss, cohen_kappa_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.calibration import calibration_curve, CalibratedClassifierCV\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from joblib import dump, load\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.utils import resample\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "\n",
        "seed = {'SVM': 244366, 'RF':389296, 'NB':122579, 'KNN': 725660, 'LR':644330}\n",
        "names = ['KNN','RF', 'SVM', 'LR','NB']\n",
        "\n",
        "\n",
        "\n",
        "for name in names:\n",
        "  scores = {'accuracy': 0,\n",
        "    'sensitivity':0,\n",
        "    'specificity':0,\n",
        "    'ppv': 0,\n",
        "    'npv': 0,\n",
        "    'auc': 0,\n",
        "    'tw_accuracy': 0,\n",
        "    'tw_sensitivity': 0,\n",
        "    'tw_specificity': 0,\n",
        "    'tw_ppv': 0,\n",
        "    'tw_npv': 0,\n",
        "    'tw_auc': 0,\n",
        "    'tw_coverage': 0,\n",
        "    'wa': 0\n",
        "  }\n",
        "  model = load(name + \".joblib\")\n",
        "  for i in range(10):\n",
        "    X_ext_train, X_ext_test, y_ext_train, y_ext_test = train_test_split(X_galeazzi, y_ext, train_size=0.5, random_state=i)\n",
        "    \n",
        "    X_new = pd.concat([pd.DataFrame(X), pd.DataFrame(X_ext_train)])\n",
        "    y_new = np.array(list(y) + list(y_ext_train))\n",
        "    y_new = [y_new[i] if i < X.shape[0] else y_new[i]+2 for i in range(X_new.shape[0])]\n",
        "\n",
        "    imp = KNNImputer()\n",
        "    sm = SMOTE()\n",
        "    X_res, y_res = sm.fit_resample(imp.fit_transform(X_new), y_new)\n",
        "    y_res = [0 if yi in [0,2] else 1 for yi in y_res]\n",
        "    \n",
        "    model.fit(X_res, y_res)\n",
        "    for score in scoring:\n",
        "      scores[score] += scoring[score](model, X_ext_test, np.array(y_ext_test))\n",
        "\n",
        "  print(name)\n",
        "  for score in scoring:\n",
        "    print(score + \" \" + str(scores[score]/10))\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN\n",
            "accuracy 0.7379310344827587\n",
            "sensitivity 0.6880204281442672\n",
            "specificity 0.8027354753090046\n",
            "ppv 0.8129958929958929\n",
            "npv 0.6817565694539379\n",
            "auc 0.745377951726636\n",
            "tw_accuracy 0.7646808870001089\n",
            "tw_sensitivity 0.6859829059829059\n",
            "tw_specificity 0.8470313020313022\n",
            "tw_ppv 0.8317340992340991\n",
            "tw_npv 0.7244409512056571\n",
            "tw_auc 0.755379971879972\n",
            "tw_coverage 0.7827586206896551\n",
            "wa 0.7692003204224955\n",
            "\n",
            "RF\n",
            "accuracy 0.6827586206896552\n",
            "sensitivity 0.4738993741896219\n",
            "specificity 0.9452421353156648\n",
            "ppv 0.9079029304029305\n",
            "npv 0.603531853348787\n",
            "auc 0.7095707547526434\n",
            "tw_accuracy 0.7446052631578948\n",
            "tw_sensitivity 0.46586580086580087\n",
            "tw_specificity 1.0\n",
            "tw_ppv 1.0\n",
            "tw_npv 0.6741101953601953\n",
            "tw_auc 0.8086645723104058\n",
            "tw_coverage 0.6310344827586206\n",
            "wa 0.7162125680580762\n",
            "\n",
            "SVM\n",
            "accuracy 0.710344827586207\n",
            "sensitivity 0.5573300852054722\n",
            "specificity 0.8958318642142172\n",
            "ppv 0.8658058608058606\n",
            "npv 0.6334494782935016\n",
            "auc 0.7265809747098447\n",
            "tw_accuracy 0.7474644920297094\n",
            "tw_sensitivity 0.5672083472083472\n",
            "tw_specificity 0.9423534798534797\n",
            "tw_ppv 0.9047619047619048\n",
            "tw_npv 0.6774353716458978\n",
            "tw_auc 0.848832695082695\n",
            "tw_coverage 0.7827586206896553\n",
            "wa 0.7562880241946959\n",
            "\n",
            "LR\n",
            "accuracy 0.7655172413793103\n",
            "sensitivity 0.6659565769627689\n",
            "specificity 0.8923405516052576\n",
            "ppv 0.8791919191919192\n",
            "npv 0.7008547661488839\n",
            "auc 0.7791485642840132\n",
            "tw_accuracy 0.890392899584076\n",
            "tw_sensitivity 0.799206349206349\n",
            "tw_specificity 0.9833333333333332\n",
            "tw_ppv 0.9833333333333332\n",
            "tw_npv 0.8408347208347207\n",
            "tw_auc 0.9161873697290364\n",
            "tw_coverage 0.5793103448275863\n",
            "wa 0.8126222608949536\n",
            "\n",
            "NB\n",
            "accuracy 0.6517241379310346\n",
            "sensitivity 0.47992782673123224\n",
            "specificity 0.8544786341109871\n",
            "ppv 0.8097113997113997\n",
            "npv 0.5782671765051626\n",
            "auc 0.6672032304211096\n",
            "tw_accuracy 0.6736998001998002\n",
            "tw_sensitivity 0.472075277663513\n",
            "tw_specificity 0.8858275058275058\n",
            "tw_ppv 0.8153535353535354\n",
            "tw_npv 0.6179663448007101\n",
            "tw_auc 0.7730731523378582\n",
            "tw_coverage 0.8379310344827585\n",
            "wa 0.7147576087705397\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXmsMBtxwERM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "b05e4f5a-4056-4247-fa32-cf6934c55245"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, make_scorer\n",
        "from sklearn.metrics import brier_score_loss, cohen_kappa_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.calibration import calibration_curve, CalibratedClassifierCV\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from joblib import dump, load\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "\n",
        "seed = {'SVM': 244366, 'RF':389296, 'NB':122579, 'KNN': 725660, 'LR':644330}\n",
        "names = ['KNN','RF','SVM']\n",
        "precisions = {}\n",
        "recalls = {}\n",
        "pr_thresholds = {}\n",
        "fprs = {}\n",
        "tprs = {}\n",
        "roc_thresholds = {}\n",
        "best_models = {}\n",
        "selected_feats = {}\n",
        "fops = {}\n",
        "mpvs = {}\n",
        "\n",
        "external = False\n",
        "\n",
        "\n",
        "for name in names:\n",
        "    print(name)\n",
        "    model = load(name + \".joblib\")\n",
        "    best_models[name] = model\n",
        "    #print(model)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state=seed[name])\n",
        "\n",
        "    clf = CalibratedClassifierCV(model, cv=2, method='isotonic')\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_proba = clf.predict_proba(X_test)\n",
        "\n",
        "    print(\"brier \" + str(brier_score_loss(y_test, y_proba[:,1])) )\n",
        "\n",
        "    for score in scoring:\n",
        "      print(score + \" \" + str(scoring[score](clf, X_test, np.array(y_test))))\n",
        "\n",
        "    precs, recs, thresholds = precision_recall_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
        "    precisions[name] = precs\n",
        "    recalls[name] = recs\n",
        "    pr_thresholds[name] = thresholds\n",
        "\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
        "    fprs[name] = fpr\n",
        "    tprs[name] = tpr\n",
        "    roc_thresholds[name] = thresholds\n",
        "\n",
        "    fop, mpv = calibration_curve(y_test, clf.predict_proba(X_test)[:,1], n_bins=10)\n",
        "    fops[name] = fop\n",
        "    mpvs[name] = mpv \n",
        "\n",
        "    model.fit(X,y)\n",
        "    selected_feats[name] = model['sel'].get_support()\n",
        "    print()\n",
        "\n",
        "    if external:\n",
        "      print(\"Negative Validation\")\n",
        "      clf.fit(pd.concat([X, X_galeazzi]), np.array(list(y) + list(y_ext)) )\n",
        "      print(accuracy_score(new_y, clf.predict(new_samples)))\n",
        "    \n",
        "    print()\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN\n",
            "brier 0.12207442017302564\n",
            "accuracy 0.8276923076923077\n",
            "sensitivity 0.7763975155279503\n",
            "specificity 0.8780487804878049\n",
            "ppv 0.8620689655172413\n",
            "npv 0.8\n",
            "auc 0.8272231480078776\n",
            "tw_accuracy 0.888030888030888\n",
            "tw_sensitivity 0.8429752066115702\n",
            "tw_specificity 0.927536231884058\n",
            "tw_ppv 0.9107142857142857\n",
            "tw_npv 0.8707482993197279\n",
            "tw_auc 0.9084321475625823\n",
            "tw_coverage 0.796923076923077\n",
            "wa 0.8652539352539352\n",
            "\n",
            "\n",
            "\n",
            "RF\n",
            "brier 0.11697722076780105\n",
            "accuracy 0.8492307692307692\n",
            "sensitivity 0.8280254777070064\n",
            "specificity 0.8690476190476191\n",
            "ppv 0.8552631578947368\n",
            "npv 0.8439306358381503\n",
            "auc 0.8485365483773127\n",
            "tw_accuracy 0.9094488188976378\n",
            "tw_sensitivity 0.9076923076923077\n",
            "tw_specificity 0.9112903225806451\n",
            "tw_ppv 0.9147286821705426\n",
            "tw_npv 0.904\n",
            "tw_auc 0.926923076923077\n",
            "tw_coverage 0.7815384615384615\n",
            "wa 0.8774712295578437\n",
            "\n",
            "\n",
            "\n",
            "SVM\n",
            "brier 0.12913912277124723\n",
            "accuracy 0.8461538461538461\n",
            "sensitivity 0.8395061728395061\n",
            "specificity 0.852760736196319\n",
            "ppv 0.85\n",
            "npv 0.8424242424242424\n",
            "auc 0.8461334545179124\n",
            "tw_accuracy 0.8777292576419214\n",
            "tw_sensitivity 0.8928571428571429\n",
            "tw_specificity 0.8632478632478633\n",
            "tw_ppv 0.8620689655172413\n",
            "tw_npv 0.8938053097345132\n",
            "tw_auc 0.9167811355311355\n",
            "tw_coverage 0.7046153846153846\n",
            "wa 0.8344507893852873\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGRRzxT5W3Ls",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "518b677e-f600-420e-a925-656551095710"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, make_scorer\n",
        "from sklearn.metrics import brier_score_loss, cohen_kappa_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.calibration import calibration_curve, CalibratedClassifierCV\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from joblib import dump, load\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "\n",
        "seed = {'SVM': 244366, 'RF':389296, 'NB':122579, 'KNN': 725660, 'LR':644330}\n",
        "\n",
        "scores = {}\n",
        "scores_susp = {}\n",
        "scores_nosusp = {}\n",
        "for score in scoring:\n",
        "  scores[score] = 0\n",
        "  scores_susp[score] = 0\n",
        "  scores_nosusp[score] = 0\n",
        "\n",
        "X_new = pd.concat([pd.DataFrame(X), pd.DataFrame(X_galeazzi)])\n",
        "y_new = np.array(list(y) + list(y_ext))\n",
        "\n",
        "for name in ['KNN','RF','SVM','LR','NB']:\n",
        "  \n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_new, y_new, train_size=0.80, random_state=seed[name])\n",
        "  X_test_copy = X_test.copy()\n",
        "  X_train = X_train.drop(columns='Suspect')\n",
        "  X_test = X_test.drop(columns='Suspect')\n",
        "\n",
        "  clf = load(name + \".joblib\")\n",
        "  clf.fit(X_train, y_train)\n",
        "\n",
        "  X_susp = X_test[X_test_copy['Suspect'] == 1]\n",
        "  y_susp = y_test[X_test_copy['Suspect'] == 1]\n",
        "  X_nosusp = X_test[X_test_copy['Suspect'] == 0]\n",
        "  y_nosusp = y_test[X_test_copy['Suspect'] == 0]\n",
        "\n",
        "\n",
        "  for score in scoring:\n",
        "    scores[score] = scoring[score](clf, X_test, np.array(y_test))\n",
        "    scores_susp[score] = scoring[score](clf, X_susp, np.array(y_susp))\n",
        "    scores_nosusp[score] = scoring[score](clf, X_nosusp, np.array(y_nosusp))\n",
        "  print(name)\n",
        "  print(\"Total Performance\")\n",
        "  for score in scoring:\n",
        "    print(score + \" \" + str( scores[score] ))\n",
        "  print()\n",
        "  print(\"Suspect Performance\")\n",
        "  for score in scoring:\n",
        "    print(score + \" \" + str( scores_susp[score] ))\n",
        "  print()\n",
        "  print(\"No Suspect Performance\")\n",
        "  for score in scoring:\n",
        "    print(score + \" \" + str( scores_nosusp[score] ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN\n",
            "Total Performance\n",
            "accuracy 0.7734375\n",
            "sensitivity 0.7352941176470589\n",
            "specificity 0.8037383177570093\n",
            "ppv 0.7485029940119761\n",
            "npv 0.7926267281105991\n",
            "auc 0.769516217702034\n",
            "tw_accuracy 0.8114754098360656\n",
            "tw_sensitivity 0.7543859649122807\n",
            "tw_specificity 0.8615384615384616\n",
            "tw_ppv 0.8269230769230769\n",
            "tw_npv 0.8\n",
            "tw_auc 0.8507085020242915\n",
            "tw_coverage 0.6354166666666666\n",
            "wa 0.7674607240437158\n",
            "\n",
            "Suspect Performance\n",
            "accuracy 0.7862903225806451\n",
            "sensitivity 0.7857142857142857\n",
            "specificity 0.7870370370370371\n",
            "ppv 0.8270676691729323\n",
            "npv 0.7391304347826086\n",
            "auc 0.7863756613756613\n",
            "tw_accuracy 0.8292682926829268\n",
            "tw_sensitivity 0.8061224489795918\n",
            "tw_specificity 0.8636363636363636\n",
            "tw_ppv 0.8977272727272727\n",
            "tw_npv 0.75\n",
            "tw_auc 0.8627860235003091\n",
            "tw_coverage 0.6612903225806451\n",
            "wa 0.7872738001573564\n",
            "\n",
            "No Suspect Performance\n",
            "accuracy 0.7464788732394366\n",
            "sensitivity 0.25\n",
            "specificity 0.847457627118644\n",
            "ppv 0.25\n",
            "npv 0.847457627118644\n",
            "auc 0.5487288135593219\n",
            "tw_accuracy 0.825\n",
            "tw_sensitivity 0.25\n",
            "tw_specificity 0.8888888888888888\n",
            "tw_ppv 0.2\n",
            "tw_npv 0.9142857142857143\n",
            "tw_auc 0.6527777777777777\n",
            "tw_coverage 0.5633802816901409\n",
            "wa 0.7595950704225352\n",
            "RF\n",
            "Total Performance\n",
            "accuracy 0.7734375\n",
            "sensitivity 0.7277777777777777\n",
            "specificity 0.8137254901960784\n",
            "ppv 0.7751479289940828\n",
            "npv 0.772093023255814\n",
            "auc 0.7707516339869281\n",
            "tw_accuracy 0.8888888888888888\n",
            "tw_sensitivity 0.8691588785046729\n",
            "tw_specificity 0.9067796610169492\n",
            "tw_ppv 0.8942307692307693\n",
            "tw_npv 0.8842975206611571\n",
            "tw_auc 0.9153334389355299\n",
            "tw_coverage 0.5859375\n",
            "wa 0.8131510416666666\n",
            "\n",
            "Suspect Performance\n",
            "accuracy 0.7558139534883721\n",
            "sensitivity 0.75\n",
            "specificity 0.7647058823529411\n",
            "ppv 0.8297872340425532\n",
            "npv 0.6666666666666666\n",
            "auc 0.7573529411764706\n",
            "tw_accuracy 0.8807947019867549\n",
            "tw_sensitivity 0.8924731182795699\n",
            "tw_specificity 0.8620689655172413\n",
            "tw_ppv 0.9120879120879121\n",
            "tw_npv 0.8333333333333334\n",
            "tw_auc 0.9032258064516129\n",
            "tw_coverage 0.5852713178294574\n",
            "wa 0.8069138559474306\n",
            "\n",
            "No Suspect Performance\n",
            "accuracy 0.8108108108108109\n",
            "sensitivity 0.5294117647058824\n",
            "specificity 0.8947368421052632\n",
            "ppv 0.6\n",
            "npv 0.864406779661017\n",
            "auc 0.7120743034055727\n",
            "tw_accuracy 0.8863636363636364\n",
            "tw_sensitivity 0.6\n",
            "tw_specificity 0.9705882352941176\n",
            "tw_ppv 0.8571428571428571\n",
            "tw_npv 0.8918918918918919\n",
            "tw_auc 0.8764705882352941\n",
            "tw_coverage 0.5945945945945946\n",
            "wa 0.813421375921376\n",
            "SVM\n",
            "Total Performance\n",
            "accuracy 0.7526041666666666\n",
            "sensitivity 0.7948717948717948\n",
            "specificity 0.708994708994709\n",
            "ppv 0.7380952380952381\n",
            "npv 0.7701149425287356\n",
            "auc 0.751933251933252\n",
            "tw_accuracy 0.824\n",
            "tw_sensitivity 0.8461538461538461\n",
            "tw_specificity 0.8\n",
            "tw_ppv 0.8208955223880597\n",
            "tw_npv 0.8275862068965517\n",
            "tw_auc 0.8622435897435898\n",
            "tw_coverage 0.6510416666666666\n",
            "wa 0.7807604166666666\n",
            "\n",
            "Suspect Performance\n",
            "accuracy 0.788235294117647\n",
            "sensitivity 0.8441558441558441\n",
            "specificity 0.7029702970297029\n",
            "ppv 0.8125\n",
            "npv 0.7473684210526316\n",
            "auc 0.7735630705927735\n",
            "tw_accuracy 0.8536585365853658\n",
            "tw_sensitivity 0.9\n",
            "tw_specificity 0.78125\n",
            "tw_ppv 0.8653846153846154\n",
            "tw_npv 0.8333333333333334\n",
            "tw_auc 0.870625\n",
            "tw_coverage 0.6431372549019608\n",
            "wa 0.8010282161645146\n",
            "\n",
            "No Suspect Performance\n",
            "accuracy 0.703125\n",
            "sensitivity 0.5333333333333333\n",
            "specificity 0.7551020408163265\n",
            "ppv 0.4\n",
            "npv 0.8409090909090909\n",
            "auc 0.6442176870748298\n",
            "tw_accuracy 0.7954545454545454\n",
            "tw_sensitivity 0.5454545454545454\n",
            "tw_specificity 0.8787878787878788\n",
            "tw_ppv 0.6\n",
            "tw_npv 0.8529411764705882\n",
            "tw_auc 0.7520661157024794\n",
            "tw_coverage 0.6875\n",
            "wa 0.7684659090909091\n",
            "LR\n",
            "Total Performance\n",
            "accuracy 0.7109375\n",
            "sensitivity 0.7070707070707071\n",
            "specificity 0.7150537634408602\n",
            "ppv 0.7253886010362695\n",
            "npv 0.6963350785340314\n",
            "auc 0.7110622352557837\n",
            "tw_accuracy 0.8170731707317073\n",
            "tw_sensitivity 0.7325581395348837\n",
            "tw_specificity 0.9102564102564102\n",
            "tw_ppv 0.9\n",
            "tw_npv 0.7553191489361702\n",
            "tw_auc 0.8489862850327967\n",
            "tw_coverage 0.4270833333333333\n",
            "wa 0.7195757113821138\n",
            "\n",
            "Suspect Performance\n",
            "accuracy 0.7452471482889734\n",
            "sensitivity 0.7485029940119761\n",
            "specificity 0.7395833333333334\n",
            "ppv 0.8333333333333334\n",
            "npv 0.6283185840707964\n",
            "auc 0.7440431636726547\n",
            "tw_accuracy 0.8407079646017699\n",
            "tw_sensitivity 0.8082191780821918\n",
            "tw_specificity 0.9\n",
            "tw_ppv 0.9365079365079365\n",
            "tw_npv 0.72\n",
            "tw_auc 0.8866438356164383\n",
            "tw_coverage 0.4296577946768061\n",
            "wa 0.7379454221205288\n",
            "\n",
            "No Suspect Performance\n",
            "accuracy 0.6268656716417911\n",
            "sensitivity 0.4\n",
            "specificity 0.6923076923076923\n",
            "ppv 0.2727272727272727\n",
            "npv 0.8\n",
            "auc 0.5461538461538461\n",
            "tw_accuracy 0.7777777777777778\n",
            "tw_sensitivity 0.0\n",
            "tw_specificity 0.9545454545454546\n",
            "tw_ppv 0.0\n",
            "tw_npv 0.8076923076923077\n",
            "tw_auc 0.39999999999999997\n",
            "tw_coverage 0.40298507462686567\n",
            "wa 0.6840796019900498\n",
            "NB\n",
            "Total Performance\n",
            "accuracy 0.7109375\n",
            "sensitivity 0.7543859649122807\n",
            "specificity 0.676056338028169\n",
            "ppv 0.6515151515151515\n",
            "npv 0.7741935483870968\n",
            "auc 0.7152211514702249\n",
            "tw_accuracy 0.7371601208459214\n",
            "tw_sensitivity 0.7843137254901961\n",
            "tw_specificity 0.6966292134831461\n",
            "tw_ppv 0.6896551724137931\n",
            "tw_npv 0.7898089171974523\n",
            "tw_auc 0.8014246897260776\n",
            "tw_coverage 0.8619791666666666\n",
            "wa 0.7683648823011077\n",
            "\n",
            "Suspect Performance\n",
            "accuracy 0.716\n",
            "sensitivity 0.7876712328767124\n",
            "specificity 0.6153846153846154\n",
            "ppv 0.7419354838709677\n",
            "npv 0.6736842105263158\n",
            "auc 0.7015279241306639\n",
            "tw_accuracy 0.7333333333333333\n",
            "tw_sensitivity 0.8059701492537313\n",
            "tw_specificity 0.6263736263736264\n",
            "tw_ppv 0.7605633802816901\n",
            "tw_npv 0.6867469879518072\n",
            "tw_auc 0.791044776119403\n",
            "tw_coverage 0.9\n",
            "wa 0.7749999999999999\n",
            "\n",
            "No Suspect Performance\n",
            "accuracy 0.6962025316455697\n",
            "sensitivity 0.4\n",
            "specificity 0.7391304347826086\n",
            "ppv 0.18181818181818182\n",
            "npv 0.8947368421052632\n",
            "auc 0.5695652173913043\n",
            "tw_accuracy 0.7049180327868853\n",
            "tw_sensitivity 0.42857142857142855\n",
            "tw_specificity 0.7407407407407407\n",
            "tw_ppv 0.17647058823529413\n",
            "tw_npv 0.9090909090909091\n",
            "tw_auc 0.6613756613756613\n",
            "tw_coverage 0.7721518987341772\n",
            "wa 0.7217264992737082\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwLH8sv3iHyt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "7f9d3606-2e5d-42bd-c84c-71d09aedea74"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sens = 0.84\n",
        "spec = 0.82\n",
        "\n",
        "prevs = np.array(range(101))\n",
        "formula_ppv = lambda x: sens*x/(sens*x + (1-spec)*(1-x))\n",
        "formula_npv = lambda x: spec*(1-x)/(spec*(1-x) + (1-sens)*x)\n",
        "\n",
        "ppvs = np.array([formula_ppv(p/100.0) for p in prevs])\n",
        "npvs = np.array([formula_npv(p/100.0) for p in prevs])\n",
        "\n",
        "plt.plot(prevs/100.0, ppvs, label= \"PPV - RF\")\n",
        "plt.plot([0.48], [0.82], 'bo')\n",
        "plt.plot(prevs/100.0, npvs, label= \"NPV - RF\")\n",
        "plt.plot([0.52], [0.82], 'o', color='orange')\n",
        "\n",
        "sens = 0.82\n",
        "spec = 0.89\n",
        "\n",
        "prevs = np.array(range(101))\n",
        "formula_ppv = lambda x: sens*x/(sens*x + (1-spec)*(1-x))\n",
        "formula_npv = lambda x: spec*(1-x)/(spec*(1-x) + (1-sens)*x)\n",
        "\n",
        "ppvs = np.array([formula_ppv(p/100.0) for p in prevs])\n",
        "npvs = np.array([formula_npv(p/100.0) for p in prevs])\n",
        "\n",
        "plt.plot(prevs/100.0, ppvs, label= \"PPV - KNN\")\n",
        "plt.plot([0.48], [0.87], 'go')\n",
        "plt.plot(prevs/100.0, npvs, label= \"NPV - KNN\")\n",
        "plt.plot([0.52], [0.82], 'ro')\n",
        "\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('Prevalence')\n",
        "#plt.ylabel('PPV')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.title('PPV/NPV - prevalence curve')\n",
        "plt.gca().set_aspect('equal', adjustable='box')\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"ppv-prev-hemo.png\", dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wvShKcjsvQd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "6ffe18c8-181a-4303-d5e4-5b2aea72d98f"
      },
      "source": [
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "importances_mean = {}\n",
        "importances_std = {}\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state=seed[name])\n",
        "\n",
        "for name in names:\n",
        "  print(name)\n",
        "  clf = best_models[name]\n",
        "  clf.fit(X_train, y_train)\n",
        "  result = permutation_importance(clf, X_test, y_test, random_state=seed[name], scoring='roc_auc')\n",
        "  importances_mean[name] = np.abs(result.importances_mean)\n",
        "  importances_std[name] = result.importances_std\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR\n",
            "KNN\n",
            "SVM\n",
            "RF\n",
            "NB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZKIofvBNaHe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1c03ee5f-18eb-4d17-b81f-e517edc1ab36"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "importances_mean_temp = {}\n",
        "\n",
        "for name in names:\n",
        "  importances_mean_temp[name] = importances_mean[name][selected_feats[name]]\n",
        "  sorted_idx = importances_mean_temp[name].argsort()\n",
        "  fig, ax = plt.subplots()\n",
        "  \n",
        "  ax.barh(X.columns[selected_feats[name]][sorted_idx][::-1][:15][::-1],\n",
        "          importances_mean_temp[name][sorted_idx][::-1][:15][::-1])\n",
        "  ax.set_title(\"Permutation Importances (\" + name + \")\")\n",
        "  fig.tight_layout()\n",
        "  plt.savefig(\"importances_\" + name + \".png\", dpi=300)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NgSlp+/nAeEmvTflJGvRVds3MrDvVkaAO4vWjpUuBTYCFwJ3ABcBtwNMR8RJFUjtV0mxgFvDu6sI1M7M6KKfLLUlaPyKek7QRcCvwnoh4ZHXa6uvri/7+/vYGaGZmgyZpZkT0re7+udXiu1LShsA6wDdXNzmZmVn3yypBRcSUumMwM7M8ZJWg2mm4VTN3BXMz6zUuFmtmZllygjIzsyx1PEFJ+r6ko0qP/1PSv5Yeny7pGElLJc2SNFvSTZK2K23zkVTC6E5Jt0s6vdNxm5lZvaoYQd1I+t2SpDcA44DyD23fDdwE3BcRkyJiN+A84MS0z87AmcBnImJHoA+4t4K4zcysRlUkqJtIhV8pEtMdwLOS3ihpJLADRRWJsg2Ap9L944CTI+JugIhYHhH/3PmwzcysTh0/iy8iFkt6RdJbKEZLNwObUSStp4G5wEvANpJmAWOA0cAeqYmdgUFN6UmaCkwFGLHB+HZ2w8zMKlbVSRI3USSnRoK6ufT4xrRNY4pvG+AoUlXyoYiI6RHRFxF9I0aPbU/kZmZWi6oSVOM41C4UU3x/pBhBNY4/NbsC+B/p/jxgcgUxmplZRqocQX0MeDIdQ3oS2JAiSbVKUHsC96X73wNOlPQ2eO3Ku0dUELOZmdWoqkoScynO3vt507L1I+LxdAn4xjEoURyT+jxARMxJp6lfKGk0xbWjrqwobjMzq0klCSoillOcmVdedljp/gJg1Er2vxInJTOzYaVna/HtstlY+l2fzsysa7nUkZmZZckJyszMstSzU3zD4XIbvsSGmfUyj6DMzCxLtScoSc+V7v9M0hdKj/eQNEfSzFTp/CFJj6X7syRNrCNmMzPrvNym+I4Bbpb0S+AJiirmX4yIGwAkHQb0RcSX6gvRzMyqkFWCiohHJZ0GfBf4EzCnkZzMzGx4ySpBJWcBhwJTKK79NGiuZm5m1jtqPwbVLCJeBf4f8JuIeGKI+7qauZlZj8guQSWvppuZmQ1TuSYoMzMb5nI4BjVa0sLS4zN4/SXgzcxsmKk9QUXEQKO4c1tse26r5WZm1ntqT1Cd4mrmZmbdzcegzMwsS05QZmaWpZ6d4uulauauWm5mw5FHUGZmliUnKDMzy1I2CUrS8nQJjdmSbpP07qb1R0l6UZJrGJmZDQPZJChgaURMiojdgBOAU5rWH0RR4Xz/yiMzM7PK5ZSgyjYAnmo8kLQNsD5wEkWiMjOzHpfTWXyjJM0C1gUmAB8orTsQuAj4A7CdpI0j4tHmBny5DTOz3pHTCKoxxbc98GHgfElK6w4CLkqX4rgU+FSrBny5DTOz3pHTCOo1EXGzpHHAeEkbA9sC16R8tQ7wAMXl4M3MrEflNIJ6jaTtgRHAExSjp2kRMTHdNgU2lbRlrUGamVlH5TSCahyDAhBwaEQsl3QgsE/TtpdTHJc6tcoAzcysOoqIumPoiL6+vujv7687DDOzYUvSzIjoW939s5ziMzMzc4IyM7Ms5XQMqq3qrmbuCuRmZmvGIygzM8uSE5SZmWUpmwRVqmbeuB2flq8j6Z8k3Svpz5L+XdLmdcdrZmadldMxqKURManF8m8DY4Dt0u+iDgcuk7RH9Oo58mZmls8IqhVJo4HDgaMjYjlARJwDLGPFYrJmZtZjckpQo5qm+A4A3go8FBHPNG3bD+zU3ICkqZL6JfUvf+HpKmI2M7MOyXqKT9KuQ2kgIqYD0wFGTtjW039mZl0spxFUK/cBb5E0pmn5ZGBeDfGYmVlFsk5QEfE8cB5whqQRAJIOAUYD19YZm5mZdVZOU3zlauYA/xERxwMnAKcB90h6Fbgb+LjP4DMz623ZJKiIGDHA8mXAl9PNzMyGiWwSVLvtstlY+l0Pz8ysa2V9DMrMzIYvJygzM8tSz07x1Xm5DV9qw8xszXkEZWZmWapkBCVpOTA3Pd9dwKER8YKk5yJi/bTNLcBI4E3AKGBR2n0/4O+BQ4A3NrY3M7PeVtUIamlETIqInYGXgCOaN4iIPVKpo38ELk7bT4qIBcCvgd0ritXMzDJQxzGoPwBDrbH3RwBJHQnIzMzyU+kxKElrAR+hmO7rRPuuZm5m1iOqSlCNMkb9wEPATzvxJBExPSL6IqJvxOixnXgKMzOrSFVTfANdLdfMzKwln2ZuZmZZqjtBjZa0sHQ7ptVGkr4raWFp+2nVhmlmZlWrZIpvoN8uRcRACfLcpu2OA45rc1hmZpaxni115GrmZmbdre4pPjMzs5acoMzMLEs9O8VXdTVzVzA3M2svj6DMzCxLTlBmZpalSqf4ypfXSI8PA/oi4kvp8SEUp5MH8ArwM2Ar4D3AOun+/LT7tyLil9VFb2ZmVcrmGJSkjwBHAR+MiMWSRgKHRMSRaf1E4EqXTDIzGx6ySVDACcCxEbEYICKWAf9Sb0hmZlaXqhNUo6p5w5uAK9L9nYGZa9K4pKnAVIARG4xfk6bMzKxmVSeoFaqaN45BtavxiJgOTAcYOWHbaFe7ZmZWvZzO4psHTK47CDMzy0NOCeoU4HuSNgGQtI6kz9cck5mZ1SSbkyQi4mpJGwO/kySKU83PrjksMzOrSaUJqvmyGxFxLqVLa0TEOcA5A+y7gOJECjMzGwayGUG1my+3YWbW3XI6BmVmZvaanh1BuZq5mVl38wjKzMyy5ARlZmZZaluCkhSSLig9XkvSY5KuLC37iKR+SXdKul3S6Wn5NEnHNrW3QNK4dH8TSRdJuk/STElXS3pbu2I3M7P8tHME9Tyws6RR6fHewKLGSkk7A2cCn4mIHSlKHN27qkbTb6IuB2ZExDYRMZmisOzGbYzdzMwy0+4pvquBxtkCBwEXltYdB5wcEXcDRMTyiPjnQbT5fuDliDirsSAiZkfEH9oUs5mZZajdCeoi4EBJ6wK7AreU1q2qWvnRkmY1bsCmg9zvNZKmpinE/uUvPL0a4ZuZWS7amqAiYg4wkWL0dPUQd/9+RExq3IDFq/H80yOiLyL6RoweO9TdzcwsI504i+8K4DRWnN6D1a9W7irnZmbDUCcS1NnA1yNibtPy7wEnNs6+k/QGSUcMor1rgZHpYoSkfXeV9N62RWxmZtlpe4KKiIUR8cMWy+cARwEXSroLuAPYehDtBfBxYK90mvk8iktzPNLeyM3MLCdtK3XUXKk8LZsBzCg9vhK4ssV201osm1i6vxj4dFsCNTOzrtCztfhczdzMrLu51JGZmWXJCcrMzLLUs1N8VV1uw5fZMDPrDI+gzMwsS5UkqEFWOt9P0hxJd0maK2m/tPzHqfzRnZKWlsohfbKK2M3MrB5VTfG9Vuk8Ipby+krnu1FUn9g7Ih6QtBVwjaT7I+LItM1E4MpUBsnMzHpclVN8K6t0fizw7Yh4ACD9ewrwlQrjMzOzjFSZoFZW6XwnXl+xvD8tHzRXMzcz6x2VJag1rHQ+2OdwNXMzsx5R9Vl8A1U6v5PXVyyfTFHJ3MzMhqGqfwd1NrAkIuZKmlJafhpwiaRrI2JBOiHiRMBn6pmZDVOVJqiIWAi0qnQ+S9I/AL+WtDbwMnBcRMyqMj4zM8tHJQlqkJXOLwMuW0kbCygu/25mZsNAz5Y6cjVzM7Pu5lJHZmaWJScoMzPLUs9O8XW6mrmrmJuZdZZHUGZmliUnKDMzy1LlCSpdeuP00uNjJU1L96dJWlS6pMYsSQeU7j8naX66f37VsZuZWXXqOAa1DNhf0ikR8XiL9d+PiNOall0MIGkGcGxE9Hc4RjMzq1kdU3yvANOBo2t4bjMz6xJ1HYP6MXCwpFYlx48uTeldN5RGfbkNM7PeUctp5hHxTDqG9L+BpU2rW03xDbbd6RSjM0ZO2DbWLEozM6tTnWfx/RPwOWC9GmMwM7NM1ZagIuJJ4BcUScrMzGwFdf8O6nRgXNOyo5tOM59YfVhmZla3yo9BlS+9ERGPAqNLj6cB01ay75QOhmZmZhnp2Vp8vtyGmVl3q3uKz8zMrKWeHUF1spq5K5mbmXWeR1BmZpYlJygzM8tSNglqNaqcb1hbsGZm1nHZJCj+WuW8+XdRDd+PiEml25IqgzMzs2rllKBc5dzMzF6TU4KCNaxy7mrmZma9I6vTzNe0yrmrmZuZ9Y7cRlDgKudmZkaGCcpVzs3MDDJMUImrnJuZDXPZHINakyrnZmbWe7JJUO3mauZmZt0t1yk+MzMb5pygzMwsSz07xTfUy234EhpmZnnxCMrMzLJUa4JKFcwvKD1eS9Jjkq4sLdtP0hxJd0maK2m/eqI1M7Mq1T3F9zyws6RREbEU2BtY1FgpaTfgNGDviHhA0lbANZLuj4g59YRsZmZVyGGK72qgcQDoIODC0rpjgW9HxAMA6d9TgK9UGqGZmVUuhwR1EXCgpHWBXYFbSut2AmY2bd+flr+Oq5mbmfWO2hNUmqqbSDF6unoN25oeEX0R0TdidKsrdpiZWbeoPUElV1Aca7qwafmdwOSmZZOBeVUEZWZm9an7JImGs4ElETFX0pTS8tOASyRdGxELUoHYE4FPVh+imZlVKYsEFRELgR+2WD5L0j8Av5a0NvAycFxEzKo6RjMzq1atCapcwby0bAYwo/T4MuCy6qIyM7McZDGC6gRXMzcz6265nCRhZma2AicoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSIqLuGDpC0rPA/LrjaINxwON1B9EG7kc+eqEP4H7kplU/toyI8avbYM/W4gPmR0Rf3UGsKUn97kc+eqEfvdAHcD9y04l+eIrPzMyy5ARlZmZZ6uUENb3uANrE/chLL/SjF/oA7kdu2t6Pnj1JwszMulsvj6DMzKyLOUGZmVmWuiJBSfqwpPmS7pV0fIv1IyVdnNbfImliad0Jafl8SR8abJtd1I8FkuZKmiWpP+d+SNpI0nWSnpN0ZtM+k1M/7pX0Q0nq0n7MSG3OSrc3Z9yPvSXNTK/7TEkfKO3TTe/HyvrRTe/H7qU4Z0v6+GDb7KJ+DO3zKiKyvgEjgPuArYF1gNnAjk3bfBE4K90/ELg43d8xbT8S2Cq1M2IwbXZDP9K6BcC4Lnk/1gP2BI4Azmza51bgbwABvwE+0qX9mAH0dcn78XZg03R/Z2BRl74fK+tHN70fo4G10v0JwH9T/Fa12z6vWvYjPV7AED6vumEEtTtwb0TcHxEvARcB+zZtsy9wXrr/S+B/pm98+wIXRcSyiHgAuDe1N5g2u6EfdVjtfkTE8xFxA/BieWNJE4ANIuKPUfwVnw/s19FedKAfNVmTftweEYvT8nnAqPStuNvej5b96HC8A1mTfrwQEa+k5esCjTPYuurzaiX9GLJuSFCbAQ+XHi9My1puk16Yp4GNVrLvYNpst070A4o3/7dpamNqB+Jutib9WFmbC1fRZrt1oh8N56QpjK9VMDXWrn58ArgtIpbR3e9HuR8NXfN+SNpD0jxgLnBEWt9tn1cD9QOG+HnVy6WOhos9I2JRmlu/RtLdEfH7uoMaxg5O78cY4FLgsxQjkGxJ2gk4Ffhg3bGsiQH60VXvR0TcAuwkaQfgPEm/qTum1dGqHxHxIkP8vOqGEdQiYIvS483TspbbSFoLGAs8sZJ9B9Nmu3WiH0RE49//Bi6n81N/a9KPlbW5+SrabLdO9KP8fjwL/JzM3w9Jm1P83RwSEfeVtu+q92OAfnTd+9EQEXcBz5GOqQ2izXbrRD+G/nnVyQNt7bhRjPLupzg5oHGwbqembY5kxYN1v0j3d2LFkwvupzj4t8o2u6Qf6wFj0jbrATcBH861H6X1h7HqkyT26bZ+pDbHpftrU8zLH5FrP4AN0/b7t2i3a96PgfrRhe/HVvz1ZIItgcUUFcK77fNqoH4M+fOqYx1s84u1D3APxVklX03LvgH8Xbq/LnAJxckDtwJbl/b9atpvPqUzkVq12W39oDjDZna6zeuSfiwAnqT4VrWQdGYQ0Afckdo8k1TlpJv6kf7TzQTmpPfjB6SzLXPsB3AS8Dwwq3R7c7e9HwP1owvfj8+mOGcBtwH7razNbusHq/F55VJHZmaWpW44BmVmZsOQE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEv/HyXkKKSSOrf5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wCFmF6a3Jhl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "4c05a9d1-93ab-42c4-aac8-824de212cebc"
      },
      "source": [
        "importances_mean_temp = {}\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "\n",
        "i = 0\n",
        "for name in ['RF','KNN']:\n",
        "  importances_mean_temp[name] = importances_mean[name][selected_feats[name]]\n",
        "  sorted_idx = importances_mean_temp[name].argsort()\n",
        "  \n",
        "  axes[i].barh(X.columns[selected_feats[name]][sorted_idx][::-1][:15][::-1],\n",
        "          importances_mean_temp[name][sorted_idx][::-1][:15][::-1])\n",
        "  axes[i].set_title(\"Permutation Importances (\" + name + \")\")\n",
        "\n",
        "  i+=1\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.savefig(\"importances_best.png\", dpi=300)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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igZHuaM/tJ9Pv2mhmI+L8sSooVQMWEfsXHYOZmVWDayGamVklleoMrJWqOBzEaOVhMMqnrPnjz4rl+QzMzMwqqfAGrK6i9g8k/VNuerqkxakGYsNK20XEbFY0j+ZgVoIGrM5xwOclbSVpI+Bc4FMRse8AlbbNRiOP5mCjXqkasIh4EJgDnEE2ZMTiiPhNsVGZlYtHczDLlPEmjvOAw8lKSfUOZ0NJRwNHA4zZfKuWB2ZWEi+N5iCpNprDzrw8msPfAH8ELsiN5vChiHhI0iFkozn8r/qdOn+sakrXgEXEi5L+BeiNiEeGua2radto4NEczChhA5a8mB5mluPRHMxeVqprYGY2KI/mYJaU4QxsQqqUXXMWWTKa2fpmAt+om3cF8DpeHs3hPnKjOaSbOb4laTJZzp8D3N65kM3ao/AGbKgVtdO6cxvNNxstPJqD2csKb8DaxdW0bRTyaA42qnRtA2Y22ng0BxttfBOHmZlVUteegZW1mra5ongVdDp//JmwkfAZmJmZVZIbMDMzq6TSNGCS1uaGSVkk6YQ0f6ykcyTdI+luST+RtEPR8ZoVJZcrt0m6RdJ+dcuPlfSspMmStszl1AOSVuSmxxZ1DGatUKZrYKvTkCn1TgMmAbtGxFpJRwJXSpoeEa7XZqPRS7ki6T3A6cDbc8tnAjcBB0fEhUBt3T5gVUTM6Wy4Zu1RmjOwRiRNAI4EZkXEWoCUkGvI6sGZjXabA4/VJiRNBSYCJ+OxwqzLlekMbLykRbnp08mGhPhLRDxZt24/sAfwX/mZHg7CRolarmwKbMu6X+ZmkFWovw7YVdLWaZy9QTl/rGrKdAa2OjfS8j4RcdlwdxAR50dEb0T0jpkwuR0xmpVBLVd2A94LXKw0VgrZWde8NKzKFcDHhrpT549VTZnOwBpZBuwoaVJEPJWbvy8wv6CYzEojIm6UNIWs4vzWwC68PPbXWLJK9ecWGKJZ25TpDGw9EfE0cBFwlqQxAJIOAyYA1xYZm1kZSNqNbEywR8jOvvrSMCs9EbEdsJ2knQoN0qxNynQGVn8N7D8i4gTgRGAOcJekF4E7gQ/7DkQbxfK5IuDwdIfuDOB9deteRXZdrH4IFrPKU7e2A729vdHf3190GFZSkm6OiN6i4ygr548NpCz5U+ouRDMzs2bcgJmZWSWV6RpYS7kafee4knj3cTV6qwKfgZmZWSW5ATMzs0pqewMm6WxJx+amfyHpe7npMyUdJ2l1rsL2DZJ2za1zoKR+SXdIulXSme2O26zKJK3KPf+BpH/KTU+XtFjSJsVEZ9YanTgDux7YD0DSRsAUsjqGNfsBNwDLUnmcvcl+vHxS2mYaWSWBT0TE7kAvcE8H4jbrFscBn5e0VcrBc4FPRcTzBcdltkE60YDdALw5Pd8D+APwlKRXSBoHvA54tG6bfIXt44FTI+JOgIhYGxHfbX/YZt0hFfOdA5wBHAMsjojfFBuV2YZr+12IEbFS0guSdiQ727oR2J6sUXsCWAI8B0xN1QUmkZWKmp52MQ0YUpehq2mbNXUecDiwP1kvxnqcP1Y1nbqJ4wayxqvWgN2Ym74+rVPrQpwKHAucP9wXcTVts8ZSdfp/AX4eEY80Wcf5Y5XSqQasdh1sT7IuxN+SnYHVrn/Vuxp4W3p+O1n1eTPbMC+mh1lX6OQZ2AeAR9M1rEeBLcgasUYN2FvIhlIB+CZwkqTXQnYjiKRjOhCzmZmVWKcqcSwhu/vwh3XzJkbEw5Im8vI1MJFdEzsKICIWp9vwL5U0AQg8FpjZYCZIuj83fRbr3yxlVmkdacAiYi3ZnYX5eUfkni8Hxg+w/XzcaJkNWUQ0612Z28k4zNqpa2sh7rn9ZPpdX81sRJw/VgUuJWVmZpXkBszMzCqpa7sQqzCcioeQsLLycCpWBT4DMzOzSipNAyYp8lXmJc2W1Jee90lakarV1x5bFBasWclIWpsbzeEWSfvVLT9W0rOSXGLDukZpGjBgDXCwpClNlp+dSk3VHo93MjizkludG83hROD0uuUzgZuAgzsemVmblKkBe4Gs/uGsogMxq7j8aA5ImgpMBE4ma8jMukLZbuL4DrBY0hkNls2S9In0/LGIeEf9Cq6mbaPY+FTJZlNgW+CduWUzgHnAdcCukrZOQ6ysw/ljVVOmMzAi4kngYuBzDRbnuxDXa7zS9q6mbaNVrQtxN+C9wMWSlJbNBOalivRXAB9rtAPnj1VN2c7AAM4BbgEuLDoQsyqKiBvTteStJG0N7AJck9qzscC9ZKMym1Vaqc7AAFKl+suBTxYdi1kVSdoNGAM8Qnb21RcRPemxHbCdpJ0KDdKsBUrXgCVnklWvz5tVdxt9T+fDMiut8bXcAC4DDk9FtGcAV9Wte1Wab1ZppelCjIiJuecPAhNy031AX+ejMquGiBjTZP6rG8w7rv0RmbVfaRqwVnM1bbORc/5YFZS1C9HMzGxAbsDMzKySurYLsezV6F1928rM1eitCnwGZmZmleQGzMzMKqkjXYiS1gJL0uv9kew3Ks9IWlW7fV7S74BxwCuB8cCKtPlBwD8ChwGvyN9ubzba5HKpZl5EfF3SWOAM4ANAAHcAnwZWA/+V1t0GWAs8lKbfFBHPdSRwszbo1DWw1RGxD4CkHwDHAGflV4iI6Wn5EUBvRHymtkzST8lK39zdoXjNyuqlXKpzGjAJ2DUi1ko6ErgSmJ7LvT5gVUTM6Vi0Zm1URBfidcBrhrNBRPw2Iv7apnjMKk3SBOBIYFaqvkFEXEg2xt47B9rWrMo62oBJ2hg4kHW7QFq5/6Ml9UvqX/vME+14CbOija8rqXYI2RfCv6TRHPL6gT2GumPnj1VNp7oQa2MVQXYG9v12vEhEnE82KCbjtt0l2vEaZgVbrwtR0l6t2LHzx6qm49fAzKzllgE7SpoUEU/l5u8LzC8oJrO28230ZhUXEU8DFwFnSRoDIOkwsoLY1xYZm1k7Fd2ATZB0f+7RsEq2pDMk3Z9bv6+zYZqVRv01sK+n+ScCzwJ3SbqbbNTlD0eEuwKta6lbP9+9vb3R399fdBhWUpJujojeouMoK+ePDaQs+VP0GZiZmdmIuAEzM7NKcjX6DnClbauaTuWPc8M2hM/AzMysktyAmZlZJXW8AZMUks7MTc+u3RYvqU/SivpSObnnqyQtTc8v7nTsZmUlaW1d3pyQ5o+VdI6keyTdLeknknYoOl6zVijiGtga4GBJp0fEww2Wn92gWvZlAJIWArMjwvf3mq1rWFXqJU33b8Ss6oroQnyBrN7arAJe22zUcJV663ZFXQP7DnCopMkNls3KdYP8ajg7dTVtG8U2uEq988eqppDb6CPiyXQN63NkI8bmNepCHOp+XU3bRqsNrlLv/LGqKfIuxHOATwKbFRiDWTd7qUp93fx9gdsLiMespQprwCLiUeByskbMzFrMVeqt2xX9O7AzgSl182bV9eX3dD4ss8pxlXobdTp+DSwiJuaeP0j2bbA23Qf0DbDt/m0MzayyImJMk/lrgM+mh1lX6dpaiHtuP5l+11kzGxHnj1VB0V2IZmZmI+IGzMzMKqlruxDLMJyKh4qwqmpF/vjzb+3mMzAzM6uk0jRguWrat0m6RdJ+dcuPlfRsk/JTZl1J0tmSjs1N/0LS93LTZ0o6TtLqXP7cIGnX3DoHphJRd0i6NT8ahFmVlaYBI5XCiYi9yX67cnrd8pnATcDBHY/MrDjXA/sBSNqI7HeT+TqG+wE3AMty+XMRcFLaZhpwLvCJiNgd6AXu6Vz4Zu1TpgYsb3PgsdqEpKnAROBksobMbLS4AXhzer4H8AfgKUmvkDQOeB3waN02+fw5Hjg1Iu4EiIi1EfHd9odt1n5luoljvKRFwKbAtqw73MMMYB5wHbCrpK3Tj6DXIelo4GiAMZtv1f6IzdosIlZKekHSjmRnWzcC25M1ak8AS4DngKkpfyaRFQeYnnYxjazizaCcP1Y1ZToDq3Uh7ga8F7hYktKymcC8iHgRuIKsHM56IuL8iOiNiN4xE3ypzLrGDWSNV60BuzE3fX1ap9aFOBU4llRVfjicP1Y1ZWrAXhIRN5L19W8laU9gF+AaScvJzsbcjWijSe062J5kXYi/JTsDq13/qnc18Lb0/Hay6vNmXaeUDZik3YAxwCNkjVVfRPSkx3bAdpJ2KjRIs865AfgA8Gi6hvUosAVZI9aoAXsL2VAqAN8ETpL0WshuBJF0TAdiNmu7Ml4DAxBweESslTQDeF/duleRnYl9o5MBmhVkCVmPxA/r5k2MiIclTeTla2AiuyZ2FEBELE634V8qaQIQwPyORm/WJqVpwAaopv3qBvOOa39EZuUQEWvJ7izMzzsi93w5MH6A7efjRsu6UGkasFZzNW2zkXP+WBWU8hqYmZnZYNyAmZlZJXVtF+JIqmm7erZZxvljVeAzMDMzqyQ3YGZmVkmFNmCSQtIluemNJT0kaX5u3kGSFkv6o6Qlkg4qJlqz4khaVTd9hKRzc9OfSHlyexpS5XuStkjLFkpamoZb+WOqeWhWeUVfA3samCZpfESsBg4AVtQWStobmAMcEBH3StqZrKTUnyJicTEhm5WLpPcCs4ADI2KFpDHA4cDWwONptUMjol/SK4FlkuZGxHMFhWzWEmXoQlwA1K7+zgQuzS2bDZwWEfcCpH9PBz7f0QjNyu2LwOyIWAEvDZlyQUQsbbDuRLIvjms7GaBZO5ShAZsHzJC0KbAX8Lvcsj2Am+vW72fdAf1eIunoNPJs/9pnnmhLsGYFGZ+6ABelklFfyS3bA7hlkO1/IGkxsBT4aqrusQ7nj1VN4Q1Y6grsITv7WrCB+/JwENatasMN7RMR+wBfbrSSpD1TI7dM0iG5RYdGxF7AjsDsRsWwnT9WNYU3YMnVZNe6Lq2bfwfrDwWxL9kQEWaWuR14A0BELEkN3M9pUB8xIh4iO1ubXr/MrGrK0oBdAJwSEUvq5s8BTpTUA5D+PYkhjjBrNkqcDsyRtENuXsPivqki/et5ebgVs8oq+i5EACLifuBbDeYvkvQF4KeSNgGeB46PiEX165qNVhGxQNJWwM/THYiPkw18+Yvcaj+QtBoYB8yNiPpry2aVU2gDFhETG8xbCCzMTV8JXNm5qMzKpz5XImIuMDc3fRFwUZNt929jaGaFKcUZWDt4OAizkXP+WBWU5RqYmZnZsHTtGZiraZuN3GD541yxMvAZmJmZVZIbMDMzq6SON2CS1uZL4kg6Ic0fK+kcSfdIulvSTyTtIGnL3LoPSFqRmx7b6fjNipRGcDgzNz1bUl963leXH4skHZJ7vipXlf7iwg7CrEWKuAa2OlUKqHcaMAnYNSLWSjqS7Pb56bX1U6Kuiog5HYvWrFzWAAdLOj0iHm6w/OwG+XEZZMOqkBX97W9zjGYdUYouxFQd4EhgVq3IaERcSJas7ywyNrOSeQE4n2z4FLNRrYgGbHx9FwfwGuAvEfFk3bpNK8834mraNkp8BzhUUqOKu7NyufWr4ezU+WNVU4ouREl7tWLHEXE+2bdTxm27S7Rin2ZlExFPpmtYnwNW1y1u1IU41P06f6xSStGFSFZYdEdJk+rmu/K8WWPnAJ8ENis6ELOilKIBi4inyeq4nZWKkSLpMGACcG2RsZmVUUQ8ClxO1oiZjUpluAb29TT/ROBZ4C5JdwMfAz4cEe7KMGvsTGBK3bxZdfnV0/mwzDqj49fAImJMk/lrgM+mR7Nt+9oUllkl5KvSR8SDZL0Utek+oG+AbfdvY2hmHde1tRBdTdts5Jw/VgWluAZmZmY2XG7AzMyskrq2C3G4w6l4eAizlw2UP84VKwufgZmZWSV1pAFLFbQvyU1vLOkhSfNz8w6StFjSHyUtkXRQmv+ddDvwHZJW524P/mgnYjcrC0mr6qaPkHRubvowSX9I+XNrqlTv/LGu1akuxKeBaZLGR8Rq4ABgRW2hpL2BOcABEXGvpJ2BayT9KSI+ndbpAeY3qWRvNqpJOhA4Fnh3RKyUNA44zPlj3ayTXYgLgFrn+Uzg0tyy2cBpEXEvQPr3dODzHYzPrMpOJBsqZSVkv6uMiH8tOCaztupkAzYPmCFpU2Av4He5ZXsAN9etP6xK9OBq2tb11qliA3wlt96wcy0AAAprSURBVGwa6+fQsDh/rGo6dhdiRCxO3Rgzyc7G2vEarqZt3WydkRwkHQH0tmrnzh+rmk7fhXg12bWuS+vm30FWeT7PlejNhu521s8hs67W6QbsAuCUiFhSN38OcGKt8Gj69ySyYqVmNrjTgW9K2gZA0lhJRxUck1lbdfSHzBFxP/CtBvMXSfoC8FNJmwDPA8dHxKJOxmdWVRGxQNLWwC8lCQiyL4xmXasjDVi+gnZu3kJgYW76SuDKAfaxnOxCtdmoVJ9HETEXmJubvhC4sMm2y3H+WJfp2lJSrqZtNnLOH6sCl5IyM7NKcgNmZmaV1LVdiINVo3dFbbPmmuWP88bKxGdgZmZWSW7AzMyskjragElam+q4/UHSTyVtkeb35IZ6uE3SDZJ2zW13YKrRdkcaJsI/cLauMsQhhxrmgaQ+SbPr9rdc0pT0fBtJ8yQtk3SzpAWSXtupYzNrl06fga2OiH0iYhrwKPDp3LJladnewEVklTiQNA04F/hEROxOVvvtng7HbdZuLw05lKbrhxwaUR6kHzVfBSyMiKkRsS9Z5fqtWxy/WccV2YV4I7B9k2WbA4+l58cDp0bEnQARsTYivtuB+Mw6baAhh0aaB+8Ano+I82ozIuK2iLiuRTGbFaaQBkzSGOBdZMV9a6amLsRlwHHAWWn+kIeJ8HAQVnEDDTk0WB7MqhtqZbshbvcS549VTacbsPEpuR4g68K4Jres1oU4lWxk2fOHu/OIOD8ieiOid8yEya2J2KxDImIx0MPIhhw6O+XPPmnIlZUjeH3nj1VKIdfAgJ0Ase41sLyrgbel5x4mwkaTZkMOjTQPnD/WtQrpQoyIZ4DPAf9XUqMfU78FWJaefxM4qXbXlKSNJB3TmUjNOq7ZkEMjzYNrgXGSjq7NkLSXpLe2LGKzghRWiSMibpW0mKy75DrSNTCyM7PngKPSeoslHQtcKmkC2TAR85vs1qzSBhhyaER5EBEh6cPAOWnIomeB5WTd9GaV1unxwOqHg/hgbnI8TUTEfNxoWRcb4pBDDfMgIvoazOvJPV8JfLwlgZqVSNfWQvRwEGYj5/yxKnApKTMzq6SuPQMbqBq9K2qbDSyfP84XKyufgZmZWSW5ATMzs0rqdDX6VXXTR0g6Nzd9WKpUvyRV254t6TupPM4duYr1iyR9tJOxm5VZqmZ/Zm56tqS+9LxP0op8qanaSBBmVVaaa2CSDiT7bcq7I2KlpHHAYRHx6bS8B5ifKnmY2brWAAdLOj0iHm6w/OyImNPpoMzaqUxdiCcCs9NvVoiINRHxrwXHZFYVL5DVD51VdCBmndLpM7BaMd+aV/JyRfohV81uJpXLORpgzOZbbciuzKroO8BiSWc0WDZL0ifS88ci4h31Kzh/rGo63YCtzncBSjqCbGC+loiI80lV7Mdtu0u0ar9mVRART0q6mKzO6Oq6xYN2ITp/rGrK1IXoqtlmG+4c4JPAZkUHYtZuZWrATge+KWkbAEljJR1VcExmlRIRjwKXkzViZl2tNA1YRCwAzgV+Kel24BZg82KjMqukM4EpdfNm1d1G39P5sMxaq+hq9HOBubnpC4ELm2y7nOxGDzOrk8+tiHgQmJCb7gP6Oh+VWXuV5ndgreZq2mYj5/yxKihNF6KZmdlwuAEzM7NK6touRA+nYjZytfxxrliZ+QzMzMwqqWUNWKqGfUluemNJD0man5t3oKT+VFn+1lr17FQte3bd/pZLmpKebyNpnqRlkm6WtEDSa1sVu1kVDTHnDpK0WNIf0ygPBxUTrVnrtbIL8WlgmqTxEbEaOABYUVsoaRrZ77zeHxF3ShpDqrs2EEkCrgIuiogZad7ewNbAXS2M36xqBsu5vYE5wAERca+knYFrJP0pIhYXE7JZ67S6C3EBUOs0nwlcmlt2PHBqRNwJEBFrI+K7Q9jnO4DnI+K82oyIuC0irmtRzGZVNlDOzQZOi4h7AdK/pwOf72iEZm3S6gZsHjBD0qbAXsDvcssGqza/TqUAYLshbvcSSUenLsr+tc88MYLwzSpnoJzbg/Vzpz/NX4/zx6qmpQ1Y6pboIfsmuGCYm58dEfvUHsDKEbz++RHRGxG9YyZMHu7mZpWzgTlXvy/nj1VKO+5CvJqs3/3SuvkjrTbvKvVmA2uWc3ewfu7sS5ZTZpXXjgbsAuCUiFhSN/+bwEm1uwclbSTpmCHs71pgXBpsj7TtXpLe2rKIzaqtWc7NAU6sFe5N/55EVuzXrPJa/kPmiLgf+FaD+YslHQtcKmkCEMD8+vUabBeSPgycI+kLwLPAcuDYlgZuVlED5NyilDM/lbQJ8DxwfEQsql/XrIpa1oDVV5pP8xYCC3PT82nQaKVq2fXzenLPVwIfb0mgZl1iiDl3JXBl56Iy65yuLSXlatpmI+f8sSpwKSkzM6skN2BmZlZJbsDMzKyS3ICZmVkluQEzM7NKcgNmZmaV5AbMzMwqyQ2YmZlVkhswMzOrJDdgZmZWSYqIomNoC0lPAUuLjqMAU4CHiw6iAMM97p0iYqt2BVN1XZw/3ZwfnTy2UuRP19ZCBJZGRG/RQXSapH4ft7VAV+ZPN39OuvnYmnEXopmZVZIbMDMzq6RubsDOLzqAgvi4rRW69f3s1uOC7j62hrr2Jg4zM+tu3XwGZmZmXcwNmJmZVVLlGjBJ75W0VNI9kk5osHycpMvS8t9J6sktOzHNXyrpPZ2Me0ON9LglbSnpV5JWSTq303FvqA047gMk3SxpSfr3nZ2OvYy6OX+6+bOyIf9vafmO6W/A7E7F3BERUZkHMAZYBrwaGAvcBuxet86ngPPS8xnAZen57mn9ccDOaT9jij6mDhz3ZsBbgGOAc4s+lg4e9+uB7dLzacCKoo+n6Ec35083f1Y25Nhyy/8d+BEwu+jjaeWjamdgbwLuiYg/RcRzwDzgQ3XrfAi4KD3/d+BdkpTmz4uINRFxL3BP2l8VjPi4I+LpiPgN8Gznwm2ZDTnuWyNiZZp/OzBe0riORF1e3Zw/3fxZ2ZD/NyQdBNxLdmxdpWoN2PbAfbnp+9O8hutExAvAE8CWQ9y2rDbkuKusVcf9EeCWiFjTpjiropvzp5s/KyM+NkkTgS8Ap3Qgzo7r5lJSZkjaA/gG8O6iY7Fy69LPSh9wdkSsSidkXaVqZ2ArgFflpndI8xquI2ljYDLwyBC3LasNOe4q26DjlrQDcBVwWEQsa3u05dfN+dPNn5UNObbpwBmSlgPHAidJ+ky7A+6UqjVgNwG7SNpZ0liyi5VX161zNXB4ev5R4NrIrmJeDcxId+vsDOwC/L5DcW+oDTnuKhvxcUvaAvgZcEJEXN+xiMutm/Onmz8rIz62iHhrRPRERA9wDnBaRFTubuSmir6LZLgP4H3AXWR35XwxzfsK8Hfp+aZkd9vcQ5Zgr85t+8W03VLgwKKPpYPHvRx4FFhF1n++e6fj7/RxAycDTwOLco+/Kfp4in50c/5082dlQ/7fcvvoo8vuQnQpKTMzq6SqdSGamZkBbsDMzKyi3ICZmVkluQEzM7NKcgNmZmaV5AbMzMwqyQ2YmZlV0v8HTAoyQTpvbWUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_gfUXuZ8trx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "f3203b98-ef1d-4971-c792-3937c96b43b6"
      },
      "source": [
        "for name in names:\n",
        "  print(name)\n",
        "  print(X.columns[selected_feats[name]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR\n",
            "Index(['Age', 'HGB', 'RBC', 'WBC', 'PLT1', 'NE', 'MO', 'EO', 'BA', 'NET',\n",
            "       'LYT', 'MOT', 'EOT', 'Suspect'],\n",
            "      dtype='object')\n",
            "KNN\n",
            "Index(['Age', 'HCT', 'HGB', 'MCHC', 'MCV', 'RBC', 'WBC', 'PLT1', 'NE', 'LY',\n",
            "       'MO', 'EO', 'BA', 'NET', 'LYT', 'MOT', 'EOT', 'BAT', 'Suspect'],\n",
            "      dtype='object')\n",
            "SVM\n",
            "Index(['Age', 'HCT', 'HGB', 'RBC', 'WBC', 'PLT1', 'NE', 'LY', 'MO', 'EO', 'BA',\n",
            "       'NET', 'LYT', 'MOT', 'EOT', 'BAT', 'Suspect'],\n",
            "      dtype='object')\n",
            "RF\n",
            "Index(['Age', 'HCT', 'HGB', 'MCHC', 'RBC', 'WBC', 'PLT1', 'NE', 'MO', 'EO',\n",
            "       'BA', 'NET', 'LYT', 'MOT', 'EOT', 'BAT', 'Suspect'],\n",
            "      dtype='object')\n",
            "NB\n",
            "Index(['Age', 'HCT', 'HGB', 'MCHC', 'RBC', 'WBC', 'PLT1', 'NE', 'MO', 'EO',\n",
            "       'BA', 'NET', 'LYT', 'MOT', 'EOT', 'BAT', 'Suspect'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7Elx5OfmFaj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "3e0e09a3-bc2e-4630-cb9e-1350f38452ac"
      },
      "source": [
        "from sklearn.metrics import auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Compute fpr, tpr, thresholds and roc auc\n",
        "for name in names:\n",
        "  roc_auc = auc(fprs[name], tprs[name])\n",
        "\n",
        "  # Plot ROC curve\n",
        "  plt.plot(fprs[name], tprs[name], label= \"%s\" % (name), alpha=1 if name in ['RF', 'KNN'] else 0.4)\n",
        "  print()\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('1 - Specificity')\n",
        "plt.ylabel('Sensitivity')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.savefig('roc_curve_hemo.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPlsqCVG7lu-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "16e5a076-26ae-427d-eaf8-c0d34f929549"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, accuracy_score, make_scorer\n",
        "from sklearn.metrics import brier_score_loss, cohen_kappa_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.calibration import calibration_curve, CalibratedClassifierCV\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from joblib import dump, load\n",
        "from sklearn.model_selection import cross_validate\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "names = ['RF - OSR', 'KNN - COVID-specific', 'RF - CBC', 'KNN - CBC']\n",
        "seed = {'RF - OSR':389296,  'KNN - COVID-specific': 725660,\n",
        "        'RF - CBC':389296,  'KNN - CBC': 725660}\n",
        "fprs = {}\n",
        "tprs = {}\n",
        "roc_thresholds = {}\n",
        "\n",
        "for name in names:\n",
        "  model = load(name + \".joblib\")\n",
        "  X_local = X if 'OSR' in name else X_pred if 'COVID' in name else X_hemo\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_local, y, train_size=0.80, random_state=seed[name])\n",
        "\n",
        "  clf = model#CalibratedClassifierCV(model, cv=2, method='isotonic')\n",
        "\n",
        "  clf.fit(X_train, y_train)\n",
        "  fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
        "  fprs[name] = fpr\n",
        "  tprs[name] = tpr\n",
        "  roc_thresholds[name] = thresholds\n",
        "\n",
        "  plt.plot(fprs[name], tprs[name], label= \"%s\" % (name))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='red')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('1 - Specificity')\n",
        "plt.ylabel('Sensitivity')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.savefig('roc_curve_all.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmdfRsE15LHO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "d9920e5f-775c-49ff-8e1c-bc3831a0e266"
      },
      "source": [
        "# Compute precision-recall curve\n",
        "for name in names:\n",
        "  area = auc(recalls[name], precisions[name])\n",
        "\n",
        "  # Plot ROC curve\n",
        "  plt.plot(recalls[name], precisions[name], label= \"%s\" % (name), alpha=1 if name in ['RF','KNN'] else 0.4)\n",
        "  print()\n",
        "\n",
        "#plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('Sensitivity')\n",
        "plt.ylabel('Positive Predictive Value')\n",
        "plt.title('PPV-Sensitivity Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.tight_layout()\n",
        "plt.savefig('pr_curve.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpMWp-xkurhw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "3501c721-dbff-46bd-c234-7c047fc7d16f"
      },
      "source": [
        "# Compute calibration curve\n",
        "for name in names:\n",
        "\n",
        "  # Plot curve\n",
        "  plt.plot(fops[name], mpvs[name], label= \"%s\" % (name), alpha=1 if name in ['RF','KNN'] else 0.4)\n",
        "  print()\n",
        "\n",
        "plt.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('Predicted probability')\n",
        "plt.ylabel('True probability')\n",
        "plt.title('Calibration Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.tight_layout()\n",
        "plt.savefig('cal_curve.png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNoeYCYc9zKq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "5004e31d-9694-4e63-fdcc-edb1fc9952f9"
      },
      "source": [
        "X_temp = X[['LYT','WBC','CA','AST','PLT1','LDH','PCR','FG']].copy()\n",
        "X_temp.columns = ['LYT','WBC','CA','AST','PLT','LDH','PCR','FG']\n",
        "units = ['10^9/L','10^9/L','mmol/L','U/L','10^9/L','U/L','mg/L','mg/mL']\n",
        "X_temp['COVID-19 Positive'] = y\n",
        "X_temp['COVID-19 Positive'] = y\n",
        "X_temp['COVID-19 Positive'] = X_temp['COVID-19 Positive'].apply(lambda x: 'Yes' if x==1 else 'No')\n",
        "\n",
        "\n",
        "temp = X_temp.melt(id_vars=['COVID-19 Positive'], var_name='variable', value_name='values')\n",
        "temp[' '] = \"\"\n",
        "\n",
        "#temp = X[X.columns[sorted_idx][::-1][:20][::-1]].melt(var_name='variable', value_name='values')\n",
        "g = sns.catplot(x=\" \", y=\"values\",\n",
        "                col=\"variable\", sharey=False, cut = 0,\n",
        "                data=temp, kind=\"violin\", split=True, hue='COVID-19 Positive',\n",
        "                height=3, aspect=1, col_wrap=4)\n",
        "g.set_titles(\"{col_name}\")\n",
        "axes = g.axes.flatten()\n",
        "for a in range(len(axes)):\n",
        "  print(axes[a].set_ylabel(units[a]))\n",
        "g.savefig('violinplots.png',dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text(32.79785879629631, 0.5, '10^9/L')\n",
            "Text(268.169140625, 0.5, '10^9/L')\n",
            "Text(500.16542245370374, 0.5, 'mmol/L')\n",
            "Text(726.4117042824074, 0.5, 'U/L')\n",
            "Text(26.54785879629631, 0.5, '10^9/L')\n",
            "Text(255.669140625, 0.5, 'U/L')\n",
            "Text(497.29042245370374, 0.5, 'mg/L')\n",
            "Text(726.2867042824074, 0.5, 'mg/mL')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 962x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_ifBwbQMO53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "6cdd3a25-1247-4d98-a1f8-ed8fca60dda3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X_temp = X.loc[:,['Sex','Age']].copy()\n",
        "\n",
        "X_temp.columns = ['Gender','Age']\n",
        "X_temp['Gender'] = X_temp['Gender'].apply(lambda x: 'Male' if x > 0 else 'Female')\n",
        "X_temp['Age'] = X_temp['Age'].astype('float64')\n",
        "X_temp['COVID-19 Positive'] = y\n",
        "X_temp['COVID-19 Positive'] = X_temp['COVID-19 Positive'].apply(lambda x: 'Yes' if x == 1 else 'No')\n",
        "\n",
        "temp = X_temp.melt(id_vars=['COVID-19 Positive'], var_name='variable', value_name='values')\n",
        "\n",
        "temp_age = temp[temp['variable'] == 'Age']\n",
        "temp_age['values'] = temp_age['values'].astype('float64')\n",
        "temp_age[' '] = \"\"\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "sns.countplot(x=\"values\", hue=\"COVID-19 Positive\", data=temp[temp['variable'] == 'Gender'], ax=axes[0])\n",
        "sns.violinplot( x= \" \", y=\"values\", cut = 0,\n",
        "                data=temp_age,\n",
        "                split=True, hue='COVID-19 Positive', ax=axes[1])\n",
        "\n",
        "plt.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", title=\"COVID-19 Positive\", frameon=False)\n",
        "\n",
        "#g.axes.flatten()[0].set_xticklabels([\"Female\", \"Male\"])\n",
        "#g.axes.flatten()[1].set_xticklabels([\"No\", \"Yes\"])\n",
        "\n",
        "axes[0].set_ylabel(\"Number of cases\")\n",
        "axes[0].set_xlabel(\"Gender\")\n",
        "axes[0].get_legend().remove()\n",
        "\n",
        "axes[1].set_ylabel(\"Years\")\n",
        "axes[1].set_xlabel(\"Age\")\n",
        "\n",
        "axes[0].set(aspect=1./axes[0].get_data_ratio(), adjustable='box')\n",
        "axes[1].set(aspect=1./axes[1].get_data_ratio(), adjustable='box')\n",
        "\n",
        "fig.tight_layout(pad=0.5)\n",
        "\n",
        "fig.savefig('bars.png',dpi=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: 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",
            "  from ipykernel import kernelapp as app\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}