MIMIC_sepsis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "906cf03b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3ee16d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import and Extract CXR files and labels\n",
    "import boto3\n",
    "import zipfile\n",
    "import re\n",
    "import glob\n",
    "import shutil\n",
    "from sagemaker import get_execution_role\n",
    "role = get_execution_role()\n",
    "\n",
    "# Declare bucket name, remote file, and destination\n",
    "my_bucket = 'med-277'\n",
    "files = {\n",
    "    'sepsis_reports/CXR_Sep.zip': 'mimic-sepsis-reports.zip',\n",
    "    'sepsis_reports/Data_Description.csv': 'Data_Description.csv'\n",
    "}\n",
    "\n",
    "# Connect to S3 bucket and download file\n",
    "s3 = boto3.resource('s3')\n",
    "for orig_file, dest_file in files.items():\n",
    "    if not os.path.exists(dest_file):\n",
    "        s3.Bucket(my_bucket).download_file(orig_file, dest_file)\n",
    "        if dest_file == 'mimic-sepsis-reports.zip':\n",
    "            with zipfile.ZipFile(\"mimic-sepsis-reports.zip\",\"r\") as zip_ref:\n",
    "                zip_ref.extractall(\"mimic-sepsis-reports\")\n",
    "        if dest_file == 'Data_Description.csv':\n",
    "            shutil.move('Data_Description.csv', \"mimic-sepsis-reports/Data_Description.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4a303a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(\"CXR_Sep_Validation.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall(\"mimic-sepsis-reports\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53b7b09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "109ef3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import re\n",
    "import collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ba03760",
   "metadata": {},
   "outputs": [],
   "source": [
    "def section_text(text):\n",
    "    \"\"\"Splits text into sections.\n",
    "    Assumes text is in a radiology report format, e.g.:\n",
    "        COMPARISON:  Chest radiograph dated XYZ.\n",
    "        IMPRESSION:  ABC...\n",
    "    Given text like this, it will output text from each section, \n",
    "    where the section type is determined by the all caps header.\n",
    "    Returns a three element tuple:\n",
    "        sections - list containing the text of each section\n",
    "        section_names - a normalized version of the section name\n",
    "        section_idx - list of start indices of the text in the section\n",
    "    \"\"\"\n",
    "    p_section = re.compile(\n",
    "        r'\\n ([A-Z ()/,-]+):\\s', re.DOTALL)\n",
    "\n",
    "    sections = list()\n",
    "    section_names = list()\n",
    "    section_idx = list()\n",
    "\n",
    "    idx = 0\n",
    "    s = p_section.search(text, idx)\n",
    "\n",
    "    if s:\n",
    "        sections.append(text[0:s.start(1)])\n",
    "        section_names.append('preamble')\n",
    "        section_idx.append(0)\n",
    "\n",
    "        while s:\n",
    "            current_section = s.group(1).lower()\n",
    "            # get the start of the text for this section\n",
    "            idx_start = s.end()\n",
    "            # skip past the first newline to avoid some bad parses\n",
    "            idx_skip = text[idx_start:].find('\\n')\n",
    "            if idx_skip == -1:\n",
    "                idx_skip = 0\n",
    "\n",
    "            s = p_section.search(text, idx_start + idx_skip)\n",
    "\n",
    "            if s is None:\n",
    "                idx_end = len(text)\n",
    "            else:\n",
    "                idx_end = s.start()\n",
    "\n",
    "            sections.append(text[idx_start:idx_end])\n",
    "            section_names.append(current_section)\n",
    "            section_idx.append(idx_start)\n",
    "\n",
    "    else:\n",
    "        sections.append(text)\n",
    "        section_names.append('full report')\n",
    "        section_idx.append(0)\n",
    "\n",
    "    section_names = normalize_section_names(section_names)\n",
    "\n",
    "    # remove empty sections\n",
    "    # this handles when the report starts with a finding-like statement\n",
    "    #  .. but this statement is not a section, more like a report title\n",
    "    #  e.g. p10/p10103318/s57408307\n",
    "    #    CHEST, PA LATERAL:\n",
    "    #\n",
    "    #    INDICATION:   This is the actual section ....\n",
    "    # it also helps when there are multiple findings sections\n",
    "    # usually one is empty\n",
    "    for i in reversed(range(len(section_names))):\n",
    "        if section_names[i] in ('impression', 'findings'):\n",
    "            if sections[i].strip() == '':\n",
    "                sections.pop(i)\n",
    "                section_names.pop(i)\n",
    "                section_idx.pop(i)\n",
    "\n",
    "    if ('impression' not in section_names) & ('findings' not in section_names):\n",
    "        # create a new section for the final paragraph\n",
    "        if '\\n \\n' in sections[-1]:\n",
    "            sections.append('\\n \\n'.join(sections[-1].split('\\n \\n')[1:]))\n",
    "            sections[-2] = sections[-2].split('\\n \\n')[0]\n",
    "            section_names.append('last_paragraph')\n",
    "            section_idx.append(section_idx[-1] + len(sections[-2]))\n",
    "\n",
    "    return sections, section_names, section_idx\n",
    "\n",
    "\n",
    "def normalize_section_names(section_names):\n",
    "    # first, lower case all\n",
    "    section_names = [s.lower().strip() for s in section_names]\n",
    "\n",
    "    frequent_sections = {\n",
    "        \"preamble\": \"preamble\",  # 227885\n",
    "        \"impression\": \"impression\",  # 187759\n",
    "        \"comparison\": \"comparison\",  # 154647\n",
    "        \"indication\": \"indication\",  # 153730\n",
    "        \"findings\": \"findings\",  # 149842\n",
    "        \"examination\": \"examination\",  # 94094\n",
    "        \"technique\": \"technique\",  # 81402\n",
    "        \"history\": \"history\",  # 45624\n",
    "        \"comparisons\": \"comparison\",  # 8686\n",
    "        \"clinical history\": \"history\",  # 7121\n",
    "        \"reason for examination\": \"indication\",  # 5845\n",
    "        \"notification\": \"notification\",  # 5749\n",
    "        \"reason for exam\": \"indication\",  # 4430\n",
    "        \"clinical information\": \"history\",  # 4024\n",
    "        \"exam\": \"examination\",  # 3907\n",
    "        \"clinical indication\": \"indication\",  # 1945\n",
    "        \"conclusion\": \"impression\",  # 1802\n",
    "        \"chest, two views\": \"findings\",  # 1735\n",
    "        \"recommendation(s)\": \"recommendations\",  # 1700\n",
    "        \"type of examination\": \"examination\",  # 1678\n",
    "        \"reference exam\": \"comparison\",  # 347\n",
    "        \"patient history\": \"history\",  # 251\n",
    "        \"addendum\": \"addendum\",  # 183\n",
    "        \"comparison exam\": \"comparison\",  # 163\n",
    "        \"date\": \"date\",  # 108\n",
    "        \"comment\": \"comment\",  # 88\n",
    "        \"findings and impression\": \"impression\",  # 87\n",
    "        \"wet read\": \"wet read\",  # 83\n",
    "        \"comparison film\": \"comparison\",  # 79\n",
    "        \"recommendations\": \"recommendations\",  # 72\n",
    "        \"findings/impression\": \"impression\",  # 47\n",
    "        \"pfi\": \"history\",\n",
    "        'recommendation': 'recommendations',\n",
    "        'wetread': 'wet read',\n",
    "        'ndication': 'impression',  # 1\n",
    "        'impresson': 'impression',  # 2\n",
    "        'imprression': 'impression',  # 1\n",
    "        'imoression': 'impression',  # 1\n",
    "        'impressoin': 'impression',  # 1\n",
    "        'imprssion': 'impression',  # 1\n",
    "        'impresion': 'impression',  # 1\n",
    "        'imperssion': 'impression',  # 1\n",
    "        'mpression': 'impression',  # 1\n",
    "        'impession': 'impression',  # 3\n",
    "        'findings/ impression': 'impression',  # ,1\n",
    "        'finding': 'findings',  # ,8\n",
    "        'findins': 'findings',\n",
    "        'findindgs': 'findings',  # ,1\n",
    "        'findgings': 'findings',  # ,1\n",
    "        'findngs': 'findings',  # ,1\n",
    "        'findnings': 'findings',  # ,1\n",
    "        'finidngs': 'findings',  # ,2\n",
    "        'idication': 'indication',  # ,1\n",
    "        'reference findings': 'findings',  # ,1\n",
    "        'comparision': 'comparison',  # ,2\n",
    "        'comparsion': 'comparison',  # ,1\n",
    "        'comparrison': 'comparison',  # ,1\n",
    "        'comparisions': 'comparison'  # ,1\n",
    "    }\n",
    "\n",
    "    p_findings = [\n",
    "        'chest',\n",
    "        'portable',\n",
    "        'pa and lateral',\n",
    "        'lateral and pa',\n",
    "        'ap and lateral',\n",
    "        'lateral and ap',\n",
    "        'frontal and',\n",
    "        'two views',\n",
    "        'frontal view',\n",
    "        'pa view',\n",
    "        'ap view',\n",
    "        'one view',\n",
    "        'lateral view',\n",
    "        'bone window',\n",
    "        'frontal upright',\n",
    "        'frontal semi-upright',\n",
    "        'ribs',\n",
    "        'pa and lat'\n",
    "    ]\n",
    "    p_findings = re.compile('({})'.format('|'.join(p_findings)))\n",
    "\n",
    "    main_sections = [\n",
    "        'impression', 'findings', 'history', 'comparison',\n",
    "        'addendum'\n",
    "    ]\n",
    "    for i, s in enumerate(section_names):\n",
    "        if s in frequent_sections:\n",
    "            section_names[i] = frequent_sections[s]\n",
    "            continue\n",
    "\n",
    "        main_flag = False\n",
    "        for m in main_sections:\n",
    "            if m in s:\n",
    "                section_names[i] = m\n",
    "                main_flag = True\n",
    "                break\n",
    "        if main_flag:\n",
    "            continue\n",
    "\n",
    "        m = p_findings.search(s)\n",
    "        if m is not None:\n",
    "            section_names[i] = 'findings'\n",
    "\n",
    "        # if it looks like it is describing the entire study\n",
    "        # it's equivalent to findings\n",
    "        # group similar phrasings for impression\n",
    "\n",
    "    return section_names\n",
    "\n",
    "\n",
    "def custom_mimic_cxr_rules():\n",
    "    custom_section_names = {\n",
    "        's50913680': 'recommendations',  # files/p11/p11851243/s50913680.txt\n",
    "        's59363654': 'examination',  # files/p12/p12128253/s59363654.txt\n",
    "        's59279892': 'technique',  # files/p13/p13150370/s59279892.txt\n",
    "        's59768032': 'recommendations',  # files/p13/p13249077/s59768032.txt\n",
    "        's57936451': 'indication',  # files/p14/p14325424/s57936451.txt\n",
    "        's50058765': 'indication',  # files/p14/p14731346/s50058765.txt\n",
    "        's53356173': 'examination',  # files/p15/p15898350/s53356173.txt\n",
    "        's53202765': 'technique',  # files/p16/p16076182/s53202765.txt\n",
    "        's50808053': 'technique',  # files/p16/p16631485/s50808053.txt\n",
    "        's51966317': 'indication',  # files/p10/p10817099/s51966317.txt\n",
    "        's50743547': 'examination',  # files/p11/p11388341/s50743547.txt\n",
    "        's56451190': 'note',  # files/p11/p11842879/s56451190.txt\n",
    "        's59067458': 'recommendations',  # files/p11/p11984647/s59067458.txt\n",
    "        's59215320': 'examination',  # files/p12/p12408912/s59215320.txt\n",
    "        's55124749': 'indication',  # files/p12/p12428492/s55124749.txt\n",
    "        's54365831': 'indication',  # files/p13/p13876470/s54365831.txt\n",
    "        's59087630': 'recommendations',  # files/p14/p14267880/s59087630.txt\n",
    "        's58157373': 'recommendations',  # files/p15/p15032392/s58157373.txt\n",
    "        's56482935': 'recommendations',  # files/p15/p15388421/s56482935.txt\n",
    "        's58375018': 'recommendations',  # files/p15/p15505556/s58375018.txt\n",
    "        's54654948': 'indication',  # files/p17/p17090359/s54654948.txt\n",
    "        's55157853': 'examination',  # files/p18/p18975498/s55157853.txt\n",
    "        's51491012': 'history',  # files/p19/p19314266/s51491012.txt\n",
    "\n",
    "    }\n",
    "\n",
    "    custom_indices = {\n",
    "        's50525523': [201, 349],  # files/p10/p10602608/s50525523.txt\n",
    "        's57564132': [233, 554],  # files/p10/p10637168/s57564132.txt\n",
    "        's59982525': [313, 717],  # files/p11/p11989982/s59982525.txt\n",
    "        's53488209': [149, 475],  # files/p12/p12458657/s53488209.txt\n",
    "        's54875119': [234, 988],  # files/p13/p13687044/s54875119.txt\n",
    "        's50196495': [59, 399],  # files/p13/p13894879/s50196495.txt\n",
    "        's56579911': [59, 218],  # files/p15/p15394326/s56579911.txt\n",
    "        's52648681': [292, 631],  # files/p15/p15666238/s52648681.txt\n",
    "        's59889364': [172, 453],  # files/p15/p15835529/s59889364.txt\n",
    "        's53514462': [73, 377],  # files/p16/p16297706/s53514462.txt\n",
    "        's59505494': [59, 450],  # files/p16/p16730991/s59505494.txt\n",
    "        's53182247': [59, 412],  # files/p16/p16770442/s53182247.txt\n",
    "        's51410602': [47, 320],  # files/p17/p17069955/s51410602.txt\n",
    "        's56412866': [522, 822],  # files/p17/p17612000/s56412866.txt\n",
    "        's54986978': [59, 306],  # files/p17/p17912487/s54986978.txt\n",
    "        's59003148': [262, 505],  # files/p17/p17916384/s59003148.txt\n",
    "        's57150433': [61, 394],  # files/p18/p18335791/s57150433.txt\n",
    "        's56760320': [219, 457],  # files/p18/p18418794/s56760320.txt\n",
    "        's59562049': [158, 348],  # files/p18/p18502016/s59562049.txt\n",
    "        's52674888': [145, 296],  # files/p19/p19381919/s52674888.txt\n",
    "        's55258338': [192, 568],  # files/p13/p13719117/s55258338.txt\n",
    "        's59330497': [140, 655],  # files/p15/p15479218/s59330497.txt\n",
    "        's52119491': [179, 454],  # files/p17/p17959278/s52119491.txt\n",
    "        # below have no findings at all in the entire report\n",
    "        's58235663': [0, 0],  # files/p11/p11573679/s58235663.txt\n",
    "        's50798377': [0, 0],  # files/p12/p12632853/s50798377.txt\n",
    "        's54168089': [0, 0],  # files/p14/p14463099/s54168089.txt\n",
    "        's53071062': [0, 0],  # files/p15/p15774521/s53071062.txt\n",
    "        's56724958': [0, 0],  # files/p16/p16175671/s56724958.txt\n",
    "        's54231141': [0, 0],  # files/p16/p16312859/s54231141.txt\n",
    "        's53607029': [0, 0],  # files/p17/p17603668/s53607029.txt\n",
    "        's52035334': [0, 0],  # files/p19/p19349312/s52035334.txt\n",
    "    }\n",
    "\n",
    "    return custom_section_names, custom_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e05ed46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chest report parser function\n",
    "def cxr_report_parser(text):\n",
    "    try:\n",
    "        sections, section_names, section_start_idx = section_text(text.strip())\n",
    "        impression = ''\n",
    "        for i in range(len(section_names)):\n",
    "            if section_names[i] == 'impression':\n",
    "                impression = sections[i]\n",
    "            # Can add other sections if you want\n",
    "            if section_names[i] == 'findings':\n",
    "                findings = sections[i]\n",
    "        return impression.strip()\n",
    "    except:\n",
    "        return np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4c562efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read and parse data from input CSV files\n",
    "train_dir = 'mimic-sepsis-reports/CXR_Sep'\n",
    "valid_dir = 'mimic-sepsis-reports/CXR_Sep_Valid'\n",
    "data_columns = pd.read_csv('mimic-sepsis-reports/Data_Description.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e8b30a62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Len:  1973\n",
      "Valid Set Len:  1469\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Set Len: \", len(os.listdir(train_dir)))\n",
    "print(\"Valid Set Len: \", len(os.listdir(valid_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d9b8385e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df = None\n",
    "valid_data_df = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "3e0343dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23384/509680547.py:192: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].apply(cxr_report_parser)\n",
      "/tmp/ipykernel_23384/509680547.py:193: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].fillna(method='ffill')\n",
      "/tmp/ipykernel_23384/509680547.py:194: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].fillna('')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed train files:  100\n",
      "Processed train files:  200\n",
      "Processed train files:  300\n",
      "Processed train files:  400\n",
      "Processed train files:  500\n",
      "Processed train files:  600\n",
      "Processed train files:  700\n",
      "Processed train files:  800\n",
      "Processed train files:  900\n",
      "Processed train files:  1000\n",
      "Processed train files:  1100\n",
      "Processed train files:  1200\n",
      "Processed train files:  1300\n",
      "Processed train files:  1400\n",
      "Processed train files:  1500\n",
      "Processed train files:  1600\n",
      "Processed train files:  1700\n",
      "Processed train files:  1800\n",
      "Processed train files:  1900\n",
      "Processed train files:  1972\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "for i in os.listdir(train_dir):\n",
    "    if i == '.ipynb_checkpoints':\n",
    "        continue\n",
    "        \n",
    "    data = pd.read_csv(os.path.join(train_dir, i), header=None)\n",
    "    data.columns = data_columns['name'][:300]\n",
    "    \n",
    "    # Sepsis label\n",
    "    # sepsis_label = data['Sepsis']\n",
    "    \n",
    "    # Structured data (e.g. labs). There are a significant number of missing values, so a simple solution\n",
    "    # is to use mean imputation. You are welcome to use other imputation methods. You will also want to\n",
    "    # reduce the number of columns based on clinically relevant variables/missingness.\n",
    "    \n",
    "#     structured_columns = [\n",
    "#         'HR', \n",
    "#         'SBP', \n",
    "#         'MAP', \n",
    "#         'DBP', \n",
    "#         'Resp', \n",
    "#         'BUN', \n",
    "#         'Calcium', \n",
    "#         'Chloride', \n",
    "#         'Glucose', \n",
    "#         'Lactate', \n",
    "#         'Phosphate', \n",
    "#         'Sodium', \n",
    "#         'TEXT'\n",
    "#     ] # You'll probably want to include more columns\n",
    "    \n",
    "    vital_features = [\n",
    "        'HR',\n",
    "        'O2Sat',\n",
    "        'Temp',\n",
    "        'SBP',\n",
    "        'MAP',\n",
    "        'DBP',\n",
    "        'Resp',\n",
    "        'EtCO2'\n",
    "    ]\n",
    "    \n",
    "    diagnosis_features = [\n",
    "        'BaseExcess',\n",
    "        'HCO3',\n",
    "        'FiO2',\n",
    "        'pH',\n",
    "        'PaCO2',\n",
    "        'SaO2',\n",
    "        'AST',\n",
    "        'BUN',\n",
    "        'Alkalinephos',\n",
    "        'Calcium',\n",
    "        'Chloride',\n",
    "        'Creatinine',\n",
    "        'BilirubinDirect',\n",
    "        'Glucose',\n",
    "        'Lactate',\n",
    "        'Magnesium',\n",
    "        'Phosphate',\n",
    "        'Potassium',\n",
    "        'BilirubinTotal',\n",
    "        'TroponinI',\n",
    "        'Hct',\n",
    "        'Hgb',\n",
    "        'PTT',\n",
    "        'WBC',\n",
    "        'Fibrinogen',\n",
    "        'Platelets',\n",
    "        'PaO2',\n",
    "        'BNP',\n",
    "        'Sodium',\n",
    "        'Procalcitonin',\n",
    "        'CRP',\n",
    "        'Ammonia',\n",
    "        'Hgb_A1C',\n",
    "        'lymphocytes',\n",
    "        'lymphocytes_differential',\n",
    "        'albumin',\n",
    "        'ALT',\n",
    "        'D_Dimer',\n",
    "        'Ferritin',\n",
    "        'Red_cell_width',\n",
    "        'Sedimentation_Rate',\n",
    "        'LDH'\n",
    "    ]\n",
    "\n",
    "    other_features = [\n",
    "        'age',\n",
    "        'gender',\n",
    "        'ICULOS',\n",
    "        'MICU',\n",
    "        'SICU',\n",
    "        'preLOS',\n",
    "        'ICULOS',\n",
    "        'TempSIRS',\n",
    "        'RESPSIRS',\n",
    "        'HRSIRS',\n",
    "        'WBCSIRS'\n",
    "    ]\n",
    "    \n",
    "    sofa_features = [\n",
    "        'RESPSOFA',\n",
    "        'coaSOFA',\n",
    "        'liverSOFA',\n",
    "        'CVSSOFA',\n",
    "        'perfusionSOFA',\n",
    "        'renalSOFA',\n",
    "        'CMSsofa'\n",
    "    ]\n",
    "    \n",
    "    med_features = [\n",
    "        'on_vent',\n",
    "        'on_anesthesia',\n",
    "        'on_anticoagulants',\n",
    "        'on_anticonvulsants',\n",
    "        'on_antipsychotics',\n",
    "        'on_bleed_reversal',\n",
    "        'on_vasodilator',\n",
    "        'on_antihypertensive',\n",
    "        'on_antiarrhythmic',\n",
    "        'on_prostacyclin',\n",
    "        'on_neuro_block',\n",
    "        'on_pain_med'\n",
    "    ]\n",
    "    \n",
    "    comorbidities = [\n",
    "        'has_intravascular_coagulation',\n",
    "        'has_necrotizing_fasciitis',\n",
    "        'has_tumor_lysis_syndrome',\n",
    "        'has_shock',\n",
    "        'has_metastatic_cancer',\n",
    "        'has_stemi',\n",
    "        'has_myeloblastic_leukemia',\n",
    "        'has_liver_failure',\n",
    "        'has_malignant_pleural_effusion',\n",
    "        'has_pneumothorax',\n",
    "        'has_cachexia',\n",
    "        'has_coma',\n",
    "        'has_peritonitis',\n",
    "        'has_lymphoma',\n",
    "        'has_carditis',\n",
    "        'has_septic_empolism',\n",
    "        'has_coagulation_defect',\n",
    "        'has_primary_lung_cancer',\n",
    "        'has_lymphoid_leukemia',\n",
    "        'has_pulmonary_embolism',\n",
    "        'has_pericardial_effusion',\n",
    "        'has_cerebral_infarction',\n",
    "        'has_meningitis',\n",
    "        'has_malignant_liver_cancer',\n",
    "        'has_gi_hemorrhage',\n",
    "        'has_acute_renal_failure',\n",
    "        'has_chronic_liver_disease',\n",
    "        'has_neutropenia',\n",
    "        'has_cardiac_arrythmia',\n",
    "        'has_malnutrition',\n",
    "        'has_pulmonary_heart_disease',\n",
    "        'has_ckd',\n",
    "        'has_plasma_protein_disorder',\n",
    "        'has_aplasia',\n",
    "        'has_aspiration_pneumonitis',\n",
    "        'has_ami',\n",
    "        'has_chf',\n",
    "        'has_encephalopathy',\n",
    "        'has_thrombocytopenia',\n",
    "        'has_coronary_artery_disease',\n",
    "        'has_copd',\n",
    "        'has_diabetes',\n",
    "        'has_hiv',\n",
    "        'has_hypertension',\n",
    "        'has_obesity',\n",
    "        'has_brain_condition',\n",
    "        'has_pulmonary_condition',\n",
    "        'has_valve_disease',\n",
    "        'has_uti',\n",
    "        'has_anemia',\n",
    "        'has_pneumonia',\n",
    "        'has_hyperlipidemia',\n",
    "        'has_depression',\n",
    "        'has_bacteremia',\n",
    "        'has_reflux_disease',\n",
    "        'has_hypothyroidism',\n",
    "        'has_insulin'\n",
    "    ]\n",
    "    \n",
    "    structured_columns = vital_features + diagnosis_features + other_features + sofa_features + med_features + comorbidities + ['Sepsis', 'TEXT']\n",
    "        \n",
    "    structured_data = data[structured_columns]\n",
    "    \n",
    "    # Reports. You can use the parser function to extract whatever section you want from the report.\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].apply(cxr_report_parser)\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].fillna(method='ffill')\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].fillna('')\n",
    "    \n",
    "    # Patient ID\n",
    "    patient_id = i[38:46]\n",
    "    structured_data.insert(loc=0, column='patient_id', value=patient_id)\n",
    "    \n",
    "    # Time\n",
    "    time_seq = [i for i in range(1, structured_data.shape[0] + 1)]\n",
    "    structured_data.insert(loc=1, column='time', value=time_seq)\n",
    "        \n",
    "    if train_data_df is None:\n",
    "        train_data_df = structured_data\n",
    "    else:\n",
    "        train_data_df = pd.concat([train_data_df, structured_data], ignore_index=True)        \n",
    "        \n",
    "    cnt += 1\n",
    "    if cnt % 100 == 0:\n",
    "        print(f\"Processed train files: \", cnt)\n",
    "        \n",
    "print(f\"Processed train files: \", cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4c7824c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                         HR\n",
       "1                                    HR_base\n",
       "2                                   HR_delta\n",
       "3                                    HR_tslm\n",
       "4                                      O2Sat\n",
       "                       ...                  \n",
       "295            AKI-T0 (has not been applied)\n",
       "296        time_to_AKI(has not been applied)\n",
       "297    baseline_T0_AKI(has not been applied)\n",
       "298                                      AKI\n",
       "299                                     TEXT\n",
       "Name: name, Length: 300, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_columns.name[:300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b5a09039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 4)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data_columns = data_columns[data_columns.name != \"AKI\"]\n",
    "valid_data_columns.reset_index(drop=True)\n",
    "valid_data_columns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "91d5e48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23384/3567179575.py:192: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].apply(cxr_report_parser)\n",
      "/tmp/ipykernel_23384/3567179575.py:193: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].fillna(method='ffill')\n",
      "/tmp/ipykernel_23384/3567179575.py:194: 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",
      "  structured_data['TEXT'] = structured_data['TEXT'].fillna('')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed valid files:  100\n",
      "Processed valid files:  200\n",
      "Processed valid files:  300\n",
      "Processed valid files:  400\n",
      "Processed valid files:  500\n",
      "Processed valid files:  600\n",
      "Processed valid files:  700\n",
      "Processed valid files:  800\n",
      "Processed valid files:  900\n",
      "Processed valid files:  1000\n",
      "Processed valid files:  1100\n",
      "Processed valid files:  1200\n",
      "Processed valid files:  1300\n",
      "Processed valid files:  1400\n",
      "Processed valid files:  1468\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "for i in os.listdir(valid_dir):\n",
    "    if i == '.ipynb_checkpoints':\n",
    "        continue\n",
    "        \n",
    "    data = pd.read_csv(os.path.join(valid_dir, i), header=None)\n",
    "    data.columns = valid_data_columns['name'][:299]\n",
    "    \n",
    "    # Sepsis label\n",
    "    # sepsis_label = data['Sepsis']\n",
    "    \n",
    "    # Structured data (e.g. labs). There are a significant number of missing values, so a simple solution\n",
    "    # is to use mean imputation. You are welcome to use other imputation methods. You will also want to\n",
    "    # reduce the number of columns based on clinically relevant variables/missingness.\n",
    "    \n",
    "#     structured_columns = [\n",
    "#         'HR', \n",
    "#         'SBP', \n",
    "#         'MAP', \n",
    "#         'DBP', \n",
    "#         'Resp', \n",
    "#         'BUN', \n",
    "#         'Calcium', \n",
    "#         'Chloride', \n",
    "#         'Glucose', \n",
    "#         'Lactate', \n",
    "#         'Phosphate', \n",
    "#         'Sodium', \n",
    "#         'TEXT'\n",
    "#     ] # You'll probably want to include more columns\n",
    "    \n",
    "    vital_features = [\n",
    "        'HR',\n",
    "        'O2Sat',\n",
    "        'Temp',\n",
    "        'SBP',\n",
    "        'MAP',\n",
    "        'DBP',\n",
    "        'Resp',\n",
    "        'EtCO2'\n",
    "    ]\n",
    "    \n",
    "    diagnosis_features = [\n",
    "        'BaseExcess',\n",
    "        'HCO3',\n",
    "        'FiO2',\n",
    "        'pH',\n",
    "        'PaCO2',\n",
    "        'SaO2',\n",
    "        'AST',\n",
    "        'BUN',\n",
    "        'Alkalinephos',\n",
    "        'Calcium',\n",
    "        'Chloride',\n",
    "        'Creatinine',\n",
    "        'BilirubinDirect',\n",
    "        'Glucose',\n",
    "        'Lactate',\n",
    "        'Magnesium',\n",
    "        'Phosphate',\n",
    "        'Potassium',\n",
    "        'BilirubinTotal',\n",
    "        'TroponinI',\n",
    "        'Hct',\n",
    "        'Hgb',\n",
    "        'PTT',\n",
    "        'WBC',\n",
    "        'Fibrinogen',\n",
    "        'Platelets',\n",
    "        'PaO2',\n",
    "        'BNP',\n",
    "        'Sodium',\n",
    "        'Procalcitonin',\n",
    "        'CRP',\n",
    "        'Ammonia',\n",
    "        'Hgb_A1C',\n",
    "        'lymphocytes',\n",
    "        'lymphocytes_differential',\n",
    "        'albumin',\n",
    "        'ALT',\n",
    "        'D_Dimer',\n",
    "        'Ferritin',\n",
    "        'Red_cell_width',\n",
    "        'Sedimentation_Rate',\n",
    "        'LDH'\n",
    "    ]\n",
    "\n",
    "    other_features = [\n",
    "        'age',\n",
    "        'gender',\n",
    "        'ICULOS',\n",
    "        'MICU',\n",
    "        'SICU',\n",
    "        'preLOS',\n",
    "        'ICULOS',\n",
    "        'TempSIRS',\n",
    "        'RESPSIRS',\n",
    "        'HRSIRS',\n",
    "        'WBCSIRS'\n",
    "    ]\n",
    "    \n",
    "    sofa_features = [\n",
    "        'RESPSOFA',\n",
    "        'coaSOFA',\n",
    "        'liverSOFA',\n",
    "        'CVSSOFA',\n",
    "        'perfusionSOFA',\n",
    "        'renalSOFA',\n",
    "        'CMSsofa'\n",
    "    ]\n",
    "    \n",
    "    med_features = [\n",
    "        'on_vent',\n",
    "        'on_anesthesia',\n",
    "        'on_anticoagulants',\n",
    "        'on_anticonvulsants',\n",
    "        'on_antipsychotics',\n",
    "        'on_bleed_reversal',\n",
    "        'on_vasodilator',\n",
    "        'on_antihypertensive',\n",
    "        'on_antiarrhythmic',\n",
    "        'on_prostacyclin',\n",
    "        'on_neuro_block',\n",
    "        'on_pain_med'\n",
    "    ]\n",
    "    \n",
    "    comorbidities = [\n",
    "        'has_intravascular_coagulation',\n",
    "        'has_necrotizing_fasciitis',\n",
    "        'has_tumor_lysis_syndrome',\n",
    "        'has_shock',\n",
    "        'has_metastatic_cancer',\n",
    "        'has_stemi',\n",
    "        'has_myeloblastic_leukemia',\n",
    "        'has_liver_failure',\n",
    "        'has_malignant_pleural_effusion',\n",
    "        'has_pneumothorax',\n",
    "        'has_cachexia',\n",
    "        'has_coma',\n",
    "        'has_peritonitis',\n",
    "        'has_lymphoma',\n",
    "        'has_carditis',\n",
    "        'has_septic_empolism',\n",
    "        'has_coagulation_defect',\n",
    "        'has_primary_lung_cancer',\n",
    "        'has_lymphoid_leukemia',\n",
    "        'has_pulmonary_embolism',\n",
    "        'has_pericardial_effusion',\n",
    "        'has_cerebral_infarction',\n",
    "        'has_meningitis',\n",
    "        'has_malignant_liver_cancer',\n",
    "        'has_gi_hemorrhage',\n",
    "        'has_acute_renal_failure',\n",
    "        'has_chronic_liver_disease',\n",
    "        'has_neutropenia',\n",
    "        'has_cardiac_arrythmia',\n",
    "        'has_malnutrition',\n",
    "        'has_pulmonary_heart_disease',\n",
    "        'has_ckd',\n",
    "        'has_plasma_protein_disorder',\n",
    "        'has_aplasia',\n",
    "        'has_aspiration_pneumonitis',\n",
    "        'has_ami',\n",
    "        'has_chf',\n",
    "        'has_encephalopathy',\n",
    "        'has_thrombocytopenia',\n",
    "        'has_coronary_artery_disease',\n",
    "        'has_copd',\n",
    "        'has_diabetes',\n",
    "        'has_hiv',\n",
    "        'has_hypertension',\n",
    "        'has_obesity',\n",
    "        'has_brain_condition',\n",
    "        'has_pulmonary_condition',\n",
    "        'has_valve_disease',\n",
    "        'has_uti',\n",
    "        'has_anemia',\n",
    "        'has_pneumonia',\n",
    "        'has_hyperlipidemia',\n",
    "        'has_depression',\n",
    "        'has_bacteremia',\n",
    "        'has_reflux_disease',\n",
    "        'has_hypothyroidism',\n",
    "        'has_insulin'\n",
    "    ]\n",
    "    \n",
    "    structured_columns = vital_features + diagnosis_features + other_features + sofa_features + med_features + comorbidities + ['Sepsis', 'TEXT']\n",
    "        \n",
    "    structured_data = data[structured_columns]\n",
    "    \n",
    "    # Reports. You can use the parser function to extract whatever section you want from the report.\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].apply(cxr_report_parser)\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].fillna(method='ffill')\n",
    "    structured_data['TEXT'] = structured_data['TEXT'].fillna('')\n",
    "    \n",
    "    # Patient ID\n",
    "    patient_id = i[38:46]\n",
    "    structured_data.insert(loc=0, column='patient_id', value=patient_id)\n",
    "    \n",
    "    # Time\n",
    "    time_seq = [i for i in range(1, structured_data.shape[0] + 1)]\n",
    "    structured_data.insert(loc=1, column='time', value=time_seq)\n",
    "        \n",
    "    if valid_data_df is None:\n",
    "        valid_data_df = structured_data\n",
    "    else:\n",
    "        valid_data_df = pd.concat([valid_data_df, structured_data], ignore_index=True)        \n",
    "        \n",
    "    cnt += 1\n",
    "    if cnt % 100 == 0:\n",
    "        print(f\"Processed valid files: \", cnt)\n",
    "        \n",
    "print(f\"Processed valid files: \", cnt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e1ccdc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97512, 141)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "97a9d895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110182, 141)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8174c3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97512 entries, 0 to 97511\n",
      "Columns: 141 entries, patient_id to TEXT\n",
      "dtypes: float64(138), int64(1), object(2)\n",
      "memory usage: 104.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "43351eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110182 entries, 0 to 110181\n",
      "Columns: 141 entries, patient_id to TEXT\n",
      "dtypes: float64(138), int64(1), object(2)\n",
      "memory usage: 118.5+ MB\n"
     ]
    }
   ],
   "source": [
    "valid_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "08d979d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = train_data_df.shape[0] \n",
    "cols = train_data_df.shape[1]\n",
    "column_nan_count = collections.Counter()\n",
    "\n",
    "for col in range(cols):\n",
    "    column_nan_count[train_data_df.columns[col]] = train_data_df.isnull().sum()[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "c7e0e439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O2Sat 60591 62.13696775781442\n",
      "EtCO2 90924 93.2439084420379\n",
      "BaseExcess 34019 34.88698826811059\n",
      "HCO3 96269 98.72528509311674\n",
      "FiO2 97512 100.0\n",
      "pH 26589 27.26741324144721\n",
      "PaCO2 32816 33.65329395356469\n",
      "SaO2 69727 71.50607104766593\n",
      "AST 57755 59.22860776109607\n",
      "Alkalinephos 61563 63.13376815161212\n",
      "BilirubinDirect 93850 96.2445647715153\n",
      "Lactate 45282 46.437361555500864\n",
      "BilirubinTotal 60881 62.43436705226023\n",
      "TroponinI 80710 82.76930018869473\n",
      "PTT 22861 23.444294035605875\n",
      "Fibrinogen 76313 78.26011157601116\n",
      "PaO2 32707 33.5415128394454\n",
      "BNP 95121 97.5479940930347\n",
      "Procalcitonin 97512 100.0\n",
      "CRP 95861 98.30687505127574\n",
      "Ammonia 97017 99.49237016982525\n",
      "Hgb_A1C 97017 99.49237016982525\n",
      "lymphocytes 97512 100.0\n",
      "lymphocytes_differential 67301 69.01817212240545\n",
      "albumin 71984 73.8206579703011\n",
      "ALT 60827 62.37898925260481\n",
      "D_Dimer 96736 99.20420050865535\n",
      "Ferritin 94424 96.83321027155633\n",
      "Red_cell_width 97512 100.0\n",
      "Sedimentation_Rate 96576 99.04011813930593\n",
      "LDH 73710 75.59069652965789\n",
      "on_vent 97475 99.96205595208795\n",
      "Total number of features:  141\n",
      "Number of features with more than 20% NaN values:  32\n"
     ]
    }
   ],
   "source": [
    "num = 0\n",
    "frequent_nan_features = []\n",
    "for feature, count in column_nan_count.items():\n",
    "    nan_percent = (count / rows) * 100\n",
    "    \n",
    "    if nan_percent > 20:\n",
    "        frequent_nan_features.append(feature)\n",
    "        print(feature, count, nan_percent)\n",
    "        num += 1\n",
    "\n",
    "print(\"Total number of features: \", cols)\n",
    "print(\"Number of features with more than 20% NaN values: \", num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "85ef31e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97512, 109)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_df_clean = train_data_df.drop(columns = frequent_nan_features)\n",
    "train_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ba333ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110182, 109)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data_df_clean = valid_data_df.drop(columns = frequent_nan_features)\n",
    "valid_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "73a7d3f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23384/2032673233.py:4: 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",
      "  train_data_df_clean[col] = train_data_df_clean[col].fillna(train_data_df_clean[col].mean())\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>time</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>SBP</th>\n",
       "      <th>MAP</th>\n",
       "      <th>DBP</th>\n",
       "      <th>Resp</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Calcium</th>\n",
       "      <th>...</th>\n",
       "      <th>has_anemia</th>\n",
       "      <th>has_pneumonia</th>\n",
       "      <th>has_hyperlipidemia</th>\n",
       "      <th>has_depression</th>\n",
       "      <th>has_bacteremia</th>\n",
       "      <th>has_reflux_disease</th>\n",
       "      <th>has_hypothyroidism</th>\n",
       "      <th>has_insulin</th>\n",
       "      <th>Sepsis</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>97508</th>\n",
       "      <td>20644814</td>\n",
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       "    <tr>\n",
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       "      <td>1.  Interval placement of a feeding tube, whic...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97512 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  patient_id  time          HR       Temp         SBP        MAP  \\\n",
       "0       25142978     1   85.366311  36.938694  121.789967  79.274046   \n",
       "1       25142978     2   85.366311  36.938694  121.789967  79.274046   \n",
       "2       25142978     3   85.366311  36.938694  121.789967  79.274046   \n",
       "3       25142978     4  109.000000  36.938694   75.000000  42.000000   \n",
       "4       25142978     5   92.800000  36.938694  100.000000  63.000000   \n",
       "...          ...   ...         ...        ...         ...        ...   \n",
       "97507   20644814    65  106.000000  36.440000  138.000000  88.000000   \n",
       "97508   20644814    66  109.000000  36.440000  134.000000  93.000000   \n",
       "97509   20644814    67  116.000000  36.440000  145.000000  94.000000   \n",
       "97510   20644814    68  113.000000  36.440000  133.000000  83.000000   \n",
       "97511   20644814    69  113.000000  36.440000  133.000000  83.000000   \n",
       "\n",
       "name         DBP       Resp        BUN   Calcium  ...  has_anemia  \\\n",
       "0      63.924287  19.311585  24.373042  7.715339  ...         0.0   \n",
       "1      63.924287  19.311585  24.373042  7.715339  ...         0.0   \n",
       "2      63.924287  19.311585  24.373042  7.715339  ...         0.0   \n",
       "3      31.000000  19.311585  24.373042  4.330800  ...         0.0   \n",
       "4      48.800000  15.200000  13.000000  4.290700  ...         0.0   \n",
       "...          ...        ...        ...       ...  ...         ...   \n",
       "97507  73.000000  33.000000   7.000000  9.300000  ...         1.0   \n",
       "97508  83.000000  36.000000   7.000000  9.300000  ...         1.0   \n",
       "97509  76.000000  36.000000   7.000000  9.300000  ...         1.0   \n",
       "97510  65.000000  36.000000   7.000000  9.300000  ...         1.0   \n",
       "97511  65.000000  36.000000   7.000000  9.300000  ...         1.0   \n",
       "\n",
       "name   has_pneumonia  has_hyperlipidemia  has_depression  has_bacteremia  \\\n",
       "0                0.0                 0.0             0.0             0.0   \n",
       "1                0.0                 0.0             0.0             0.0   \n",
       "2                0.0                 0.0             0.0             0.0   \n",
       "3                0.0                 0.0             0.0             0.0   \n",
       "4                0.0                 0.0             0.0             0.0   \n",
       "...              ...                 ...             ...             ...   \n",
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       "97510            0.0                 0.0             0.0             0.0   \n",
       "97511            0.0                 0.0             0.0             0.0   \n",
       "\n",
       "name   has_reflux_disease  has_hypothyroidism  has_insulin  Sepsis  \\\n",
       "0                     0.0                 1.0          0.0     0.0   \n",
       "1                     0.0                 1.0          0.0     0.0   \n",
       "2                     0.0                 1.0          0.0     0.0   \n",
       "3                     0.0                 1.0          0.0     0.0   \n",
       "4                     0.0                 1.0          0.0     0.0   \n",
       "...                   ...                 ...          ...     ...   \n",
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       "97511                 0.0                 0.0          0.0     0.0   \n",
       "\n",
       "name                                                TEXT  \n",
       "0                                                         \n",
       "1                                                         \n",
       "2                                                         \n",
       "3                                                         \n",
       "4                                                         \n",
       "...                                                  ...  \n",
       "97507  1.  Interval placement of a feeding tube, whic...  \n",
       "97508  1.  Interval placement of a feeding tube, whic...  \n",
       "97509  1.  Interval placement of a feeding tube, whic...  \n",
       "97510  1.  Interval placement of a feeding tube, whic...  \n",
       "97511  1.  Interval placement of a feeding tube, whic...  \n",
       "\n",
       "[97512 rows x 109 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = list(train_data_df_clean.select_dtypes(include=['number']).columns)\n",
    "\n",
    "for col in numeric_columns:\n",
    "    train_data_df_clean[col] = train_data_df_clean[col].fillna(train_data_df_clean[col].mean())\n",
    "train_data_df_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "14eef408",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(97509, 109)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_df_clean.dropna(inplace=True)\n",
    "train_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "91bb15e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23384/718714252.py:4: 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",
      "  valid_data_df_clean[col] = valid_data_df_clean[col].fillna(valid_data_df_clean[col].mean())\n"
     ]
    },
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       "name   patient_id  time     HR   Temp         SBP   MAP        DBP  Resp  \\\n",
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       "\n",
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       "\n",
       "name                                                 TEXT  \n",
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       "2                                                          \n",
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       "110178  Left IJ central venous catheter tip in the dis...  \n",
       "110179  Left IJ central venous catheter tip in the dis...  \n",
       "110180  Left IJ central venous catheter tip in the dis...  \n",
       "110181  Left IJ central venous catheter tip in the dis...  \n",
       "\n",
       "[110182 rows x 109 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = list(valid_data_df_clean.select_dtypes(include=['number']).columns)\n",
    "\n",
    "for col in numeric_columns:\n",
    "    valid_data_df_clean[col] = valid_data_df_clean[col].fillna(valid_data_df_clean[col].mean())\n",
    "valid_data_df_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "652c61cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(110157, 109)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data_df_clean.dropna(inplace=True)\n",
    "valid_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6d039442",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/pandas/core/frame.py:3641: 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",
      "  self[k1] = value[k2]\n"
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       "      <th>has_reflux_disease</th>\n",
       "      <th>has_hypothyroidism</th>\n",
       "      <th>has_insulin</th>\n",
       "      <th>Sepsis</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25142978</td>\n",
       "      <td>1</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25142978</td>\n",
       "      <td>2</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25142978</td>\n",
       "      <td>3</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25142978</td>\n",
       "      <td>4</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>4.330800</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25142978</td>\n",
       "      <td>5</td>\n",
       "      <td>92.800000</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>48.800000</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.290700</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name patient_id  time          HR       Temp         SBP        MAP  \\\n",
       "0      25142978     1   85.366311  36.938694  121.789967  79.274046   \n",
       "1      25142978     2   85.366311  36.938694  121.789967  79.274046   \n",
       "2      25142978     3   85.366311  36.938694  121.789967  79.274046   \n",
       "3      25142978     4  109.000000  36.938694   75.000000  42.000000   \n",
       "4      25142978     5   92.800000  36.938694  100.000000  63.000000   \n",
       "\n",
       "name        DBP       Resp        BUN   Calcium  ...  has_anemia  \\\n",
       "0     63.924287  19.311585  24.373042  7.715339  ...           0   \n",
       "1     63.924287  19.311585  24.373042  7.715339  ...           0   \n",
       "2     63.924287  19.311585  24.373042  7.715339  ...           0   \n",
       "3     31.000000  19.311585  24.373042  4.330800  ...           0   \n",
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       "\n",
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       "\n",
       "name  has_reflux_disease  has_hypothyroidism  has_insulin  Sepsis  TEXT  \n",
       "0                      0                   1            0       0        \n",
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       "3                      0                   1            0       0        \n",
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       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_features = [i for i in med_features if i not in frequent_nan_features]\n",
    "comorbidities = [i for i in comorbidities if i not in frequent_nan_features]\n",
    "\n",
    "train_data_df_clean[['Sepsis']] = train_data_df_clean[['Sepsis']].astype(int)\n",
    "train_data_df_clean[med_features] = train_data_df_clean[med_features].astype(int)\n",
    "train_data_df_clean[comorbidities] = train_data_df_clean[comorbidities].astype(int)\n",
    "train_data_df_clean.reset_index(drop=True, inplace=True)\n",
    "train_data_df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "4a8b81b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/pandas/core/frame.py:3641: 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",
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       "<p>5 rows × 109 columns</p>\n",
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      ],
      "text/plain": [
       "name patient_id  time     HR   Temp         SBP   MAP        DBP  Resp  \\\n",
       "0      28748915     1   93.0  33.71  121.919617  71.5  62.733473  21.0   \n",
       "1      28748915     2   94.0  34.00  121.919617  63.0  62.733473  27.0   \n",
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       "4      28748915     5  113.0  37.00  121.919617  63.0  62.733473  32.0   \n",
       "\n",
       "name        BUN  Calcium  ...  has_anemia  has_pneumonia  has_hyperlipidemia  \\\n",
       "0     30.359004   5.0125  ...           0              0                   0   \n",
       "1     30.359004   5.0125  ...           0              0                   0   \n",
       "2     30.359004   5.1729  ...           0              0                   0   \n",
       "3     30.359004   5.1729  ...           0              0                   0   \n",
       "4     32.000000   6.3000  ...           0              0                   0   \n",
       "\n",
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       "0                  0               1                   0                   0   \n",
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       "3                  0               1                   0                   0   \n",
       "4                  0               1                   0                   0   \n",
       "\n",
       "name  has_insulin  Sepsis  TEXT  \n",
       "0               1     1.0        \n",
       "1               1     1.0        \n",
       "2               1     1.0        \n",
       "3               1     1.0        \n",
       "4               1     1.0        \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_features = [i for i in med_features if i not in frequent_nan_features]\n",
    "comorbidities = [i for i in comorbidities if i not in frequent_nan_features]\n",
    "\n",
    "valid_data_df_clean[med_features] = valid_data_df_clean[med_features].astype(int)\n",
    "valid_data_df_clean[comorbidities] = valid_data_df_clean[comorbidities].astype(int)\n",
    "valid_data_df_clean.reset_index(drop=True, inplace=True)\n",
    "valid_data_df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "47cc75e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df_clean.to_pickle('data/processed_train_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fd463bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_data_df_clean.to_pickle('data/processed_valid_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1cf3729",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79774150",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df_clean = pd.read_pickle('data/processed_train_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2cd6d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_data_df_clean = pd.read_pickle('data/processed_valid_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f0997a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97509, 109)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_df_clean.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8455cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110157, 109)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dae1a4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "661367b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_text_data_df_clean = train_data_df_clean['TEXT']\n",
    "train_structured_data_df_clean = train_data_df_clean.drop(columns = ['TEXT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "54a9ef9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_text_data_df_clean = valid_data_df_clean['TEXT']\n",
    "valid_structured_data_df_clean = valid_data_df_clean.drop(columns = ['TEXT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffa0612",
   "metadata": {},
   "source": [
    "Baseline: Logistic Regression, Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "550c3db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97509, 108)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_structured_data_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "2a058739",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Temp</th>\n",
       "      <th>SBP</th>\n",
       "      <th>MAP</th>\n",
       "      <th>DBP</th>\n",
       "      <th>Resp</th>\n",
       "      <th>BUN</th>\n",
       "      <th>Calcium</th>\n",
       "      <th>...</th>\n",
       "      <th>has_uti</th>\n",
       "      <th>has_anemia</th>\n",
       "      <th>has_pneumonia</th>\n",
       "      <th>has_hyperlipidemia</th>\n",
       "      <th>has_depression</th>\n",
       "      <th>has_bacteremia</th>\n",
       "      <th>has_reflux_disease</th>\n",
       "      <th>has_hypothyroidism</th>\n",
       "      <th>has_insulin</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25142978</td>\n",
       "      <td>1</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25142978</td>\n",
       "      <td>2</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25142978</td>\n",
       "      <td>3</td>\n",
       "      <td>85.366311</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>121.789967</td>\n",
       "      <td>79.274046</td>\n",
       "      <td>63.924287</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>7.715339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25142978</td>\n",
       "      <td>4</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.311585</td>\n",
       "      <td>24.373042</td>\n",
       "      <td>4.330800</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25142978</td>\n",
       "      <td>5</td>\n",
       "      <td>92.800000</td>\n",
       "      <td>36.938694</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>48.800000</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.290700</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name patient_id  time          HR       Temp         SBP        MAP  \\\n",
       "0      25142978     1   85.366311  36.938694  121.789967  79.274046   \n",
       "1      25142978     2   85.366311  36.938694  121.789967  79.274046   \n",
       "2      25142978     3   85.366311  36.938694  121.789967  79.274046   \n",
       "3      25142978     4  109.000000  36.938694   75.000000  42.000000   \n",
       "4      25142978     5   92.800000  36.938694  100.000000  63.000000   \n",
       "\n",
       "name        DBP       Resp        BUN   Calcium  ...  has_uti  has_anemia  \\\n",
       "0     63.924287  19.311585  24.373042  7.715339  ...        0           0   \n",
       "1     63.924287  19.311585  24.373042  7.715339  ...        0           0   \n",
       "2     63.924287  19.311585  24.373042  7.715339  ...        0           0   \n",
       "3     31.000000  19.311585  24.373042  4.330800  ...        0           0   \n",
       "4     48.800000  15.200000  13.000000  4.290700  ...        0           0   \n",
       "\n",
       "name  has_pneumonia  has_hyperlipidemia  has_depression  has_bacteremia  \\\n",
       "0                 0                   0               0               0   \n",
       "1                 0                   0               0               0   \n",
       "2                 0                   0               0               0   \n",
       "3                 0                   0               0               0   \n",
       "4                 0                   0               0               0   \n",
       "\n",
       "name  has_reflux_disease  has_hypothyroidism  has_insulin  Sepsis  \n",
       "0                      0                   1            0       0  \n",
       "1                      0                   1            0       0  \n",
       "2                      0                   1            0       0  \n",
       "3                      0                   1            0       0  \n",
       "4                      0                   1            0       0  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_structured_data_df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "3b77813e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, train_labels = train_structured_data_df_clean.drop(columns = ['Sepsis', 'patient_id', 'time']), train_structured_data_df_clean['Sepsis']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b3c6e722",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_set, valid_labels = valid_structured_data_df_clean.drop(columns = ['Sepsis', 'patient_id', 'time']), valid_structured_data_df_clean['Sepsis']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7567b8e9",
   "metadata": {},
   "source": [
    "### Baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7dfcd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e79e0866",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ce462218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier()\n",
    "rf_clf.fit(train_set, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "8eac7098",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_clf.predict(valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "58687212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set score:  1.0\n",
      "Test set score:  0.9334767649808909\n",
      "Confusion matrix: \n",
      "[[    26   3384]\n",
      " [  3944 102803]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.00654911838790932\n",
      "Specificity or true negative rate:  0.968131692203377\n",
      "Precision or positive predictive value:  0.007624633431085044\n",
      "Negative predictive value:  0.9630528258405389\n",
      "Fall out or false positive rate:  0.03186830779662294\n",
      "False negative rate:  0.9934508816120907\n",
      "False discovery rate:  0.992375366568915\n",
      "Overall accuracy:  0.9334767649808909\n"
     ]
    }
   ],
   "source": [
    "print(\"Train set score: \", rf_clf.score(train_set, train_labels))\n",
    "print(\"Test set score: \", rf_clf.score(valid_set, valid_labels))\n",
    "\n",
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(valid_labels, predictions,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd6c3753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd \n",
    "import seaborn as sn\n",
    "\n",
    "df_cm = pd.DataFrame([[26, 3384],[3944, 102803]], index = [i for i in \"10\"], columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (8,6))\n",
    "sn.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddbb9ea",
   "metadata": {},
   "source": [
    "Since the mean accuracy is 100% on the train set and 95% on the test set, let us safely assume that the model is overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6138c37",
   "metadata": {},
   "source": [
    "A lot of values that should be marked as 1 are marked as 0 which is real bad for this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "3604a15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICULOS</th>\n",
       "      <td>0.052089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICULOS</th>\n",
       "      <td>0.051897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp</th>\n",
       "      <td>0.044193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preLOS</th>\n",
       "      <td>0.040521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBC</th>\n",
       "      <td>0.037242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_depression</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_liver_failure</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_myeloblastic_leukemia</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESPSOFA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_metastatic_cancer</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0\n",
       "name                               \n",
       "ICULOS                     0.052089\n",
       "ICULOS                     0.051897\n",
       "Temp                       0.044193\n",
       "preLOS                     0.040521\n",
       "WBC                        0.037242\n",
       "...                             ...\n",
       "has_depression             0.000000\n",
       "has_liver_failure          0.000000\n",
       "has_myeloblastic_leukemia  0.000000\n",
       "RESPSOFA                   0.000000\n",
       "has_metastatic_cancer      0.000000\n",
       "\n",
       "[105 rows x 1 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_impt = pd.DataFrame(rf_clf.feature_importances_, index=train_set.columns).sort_values(by=0, ascending=False)\n",
    "feature_impt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "422c3aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICULOS</th>\n",
       "      <td>0.052089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICULOS</th>\n",
       "      <td>0.051897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp</th>\n",
       "      <td>0.044193</td>\n",
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       "    <tr>\n",
       "      <th>preLOS</th>\n",
       "      <td>0.040521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBC</th>\n",
       "      <td>0.037242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>0.035675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.035318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>0.033959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hct</th>\n",
       "      <td>0.033346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.032593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calcium</th>\n",
       "      <td>0.030375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phosphate</th>\n",
       "      <td>0.030182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hgb</th>\n",
       "      <td>0.030158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUN</th>\n",
       "      <td>0.030021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potassium</th>\n",
       "      <td>0.028478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sodium</th>\n",
       "      <td>0.028059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBP</th>\n",
       "      <td>0.027470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBP</th>\n",
       "      <td>0.026778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chloride</th>\n",
       "      <td>0.026605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resp</th>\n",
       "      <td>0.026332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.025854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAP</th>\n",
       "      <td>0.025419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnesium</th>\n",
       "      <td>0.025107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_pneumonia</th>\n",
       "      <td>0.011629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_shock</th>\n",
       "      <td>0.010837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td>0.008519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_hypertension</th>\n",
       "      <td>0.006672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td>0.006467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_anemia</th>\n",
       "      <td>0.006440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_uti</th>\n",
       "      <td>0.006422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.006355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_hyperlipidemia</th>\n",
       "      <td>0.006185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_encephalopathy</th>\n",
       "      <td>0.005793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_pulmonary_condition</th>\n",
       "      <td>0.005510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_diabetes</th>\n",
       "      <td>0.005435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TempSIRS</th>\n",
       "      <td>0.005296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_reflux_disease</th>\n",
       "      <td>0.005232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_bacteremia</th>\n",
       "      <td>0.005194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_ami</th>\n",
       "      <td>0.005127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_coronary_artery_disease</th>\n",
       "      <td>0.005029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRSIRS</th>\n",
       "      <td>0.005014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0\n",
       "name                                 \n",
       "ICULOS                       0.052089\n",
       "ICULOS                       0.051897\n",
       "Temp                         0.044193\n",
       "preLOS                       0.040521\n",
       "WBC                          0.037242\n",
       "Platelets                    0.035675\n",
       "Glucose                      0.035318\n",
       "HR                           0.033959\n",
       "Hct                          0.033346\n",
       "age                          0.032593\n",
       "Calcium                      0.030375\n",
       "Phosphate                    0.030182\n",
       "Hgb                          0.030158\n",
       "BUN                          0.030021\n",
       "Potassium                    0.028478\n",
       "Sodium                       0.028059\n",
       "SBP                          0.027470\n",
       "DBP                          0.026778\n",
       "Chloride                     0.026605\n",
       "Resp                         0.026332\n",
       "Creatinine                   0.025854\n",
       "MAP                          0.025419\n",
       "Magnesium                    0.025107\n",
       "has_pneumonia                0.011629\n",
       "has_shock                    0.010837\n",
       "MICU                         0.008519\n",
       "has_hypertension             0.006672\n",
       "SICU                         0.006467\n",
       "has_anemia                   0.006440\n",
       "has_uti                      0.006422\n",
       "gender                       0.006355\n",
       "has_hyperlipidemia           0.006185\n",
       "has_encephalopathy           0.005793\n",
       "has_pulmonary_condition      0.005510\n",
       "has_diabetes                 0.005435\n",
       "TempSIRS                     0.005296\n",
       "has_reflux_disease           0.005232\n",
       "has_bacteremia               0.005194\n",
       "has_ami                      0.005127\n",
       "has_coronary_artery_disease  0.005029\n",
       "HRSIRS                       0.005014"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impt_features = feature_impt.loc[feature_impt[0] > 0.005]\n",
    "impt_features_list = list(impt_features.index.values)\n",
    "impt_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b1db08a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>has_hypothyroidism</th>\n",
       "      <td>0.004958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_anticoagulants</th>\n",
       "      <td>0.004938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBCSIRS</th>\n",
       "      <td>0.004850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_coma</th>\n",
       "      <td>0.004837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_cerebral_infarction</th>\n",
       "      <td>0.004746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_depression</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_liver_failure</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_myeloblastic_leukemia</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESPSOFA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_metastatic_cancer</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0\n",
       "name                               \n",
       "has_hypothyroidism         0.004958\n",
       "on_anticoagulants          0.004938\n",
       "WBCSIRS                    0.004850\n",
       "has_coma                   0.004837\n",
       "has_cerebral_infarction    0.004746\n",
       "...                             ...\n",
       "has_depression             0.000000\n",
       "has_liver_failure          0.000000\n",
       "has_myeloblastic_leukemia  0.000000\n",
       "RESPSOFA                   0.000000\n",
       "has_metastatic_cancer      0.000000\n",
       "\n",
       "[64 rows x 1 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_impt_features = feature_impt.loc[feature_impt[0] <= 0.005]\n",
    "non_impt_features_list = list(non_impt_features.index.values)\n",
    "non_impt_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "3f0bb95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "c2d4e304",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(importance,names,model_type):\n",
    "    #Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "\n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "\n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "    \n",
    "    # important features\n",
    "    fi_df = fi_df.loc[fi_df['feature_importance'] > 0.005]\n",
    "\n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(15,10))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + ' FEATURE IMPORTANCE')\n",
    "    plt.xlabel('FEATURE IMPORTANCE')\n",
    "    plt.ylabel('FEATURE NAMES')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "88fce9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(rf_clf.feature_importances_,train_set.columns,'RANDOM FOREST')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5d70746d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, train_labels = train_structured_data_df_clean.drop(columns = ['Sepsis', 'patient_id', 'time'] + non_impt_features_list), train_structured_data_df_clean['Sepsis']\n",
    "valid_set, valid_labels = valid_structured_data_df_clean.drop(columns = ['Sepsis', 'patient_id', 'time'] + non_impt_features_list), valid_structured_data_df_clean['Sepsis']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "411550fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "e87b6f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "del feature_impt\n",
    "del impt_features\n",
    "del non_impt_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "8be11868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier()\n",
    "rf_clf.fit(train_set, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "8d94d124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set score:  0.9999897445364018\n",
      "Test set score:  0.8978821137104315\n",
      "Confusion matrix: \n",
      "[[   64  3346]\n",
      " [ 7903 98844]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.008033136688841471\n",
      "Specificity or true negative rate:  0.9672570701634211\n",
      "Precision or positive predictive value:  0.0187683284457478\n",
      "Negative predictive value:  0.9259651325095787\n",
      "Fall out or false positive rate:  0.03274292983657892\n",
      "False negative rate:  0.9919668633111586\n",
      "False discovery rate:  0.9812316715542522\n",
      "Overall accuracy:  0.8978821137104315\n"
     ]
    }
   ],
   "source": [
    "predictions = rf_clf.predict(valid_set)\n",
    "\n",
    "print(\"Train set score: \", rf_clf.score(train_set, train_labels))\n",
    "print(\"Test set score: \", rf_clf.score(valid_set, valid_labels))\n",
    "\n",
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(valid_labels, predictions,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a7ff17e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6be1ece4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_structured_data_df_clean' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_28214/4189744835.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeature_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_structured_data_df_clean\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mfeature_columns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'patient_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfeature_columns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfeature_columns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sepsis'\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 'train_structured_data_df_clean' is not defined"
     ]
    }
   ],
   "source": [
    "feature_columns = list(train_structured_data_df_clean.columns)\n",
    "feature_columns.remove('patient_id')\n",
    "feature_columns.remove('time')\n",
    "feature_columns.remove('Sepsis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3ba48cd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'non_impt_features_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_13780/1905269602.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# remove this to get previous results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfeature_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeature_columns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnon_impt_features_list\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/tmp/ipykernel_13780/1905269602.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# remove this to get previous results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfeature_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeature_columns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnon_impt_features_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'non_impt_features_list' is not defined"
     ]
    }
   ],
   "source": [
    "# remove this to get previous results\n",
    "feature_columns = [col for col in feature_columns if col not in non_impt_features_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d1ef55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bf9f409",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current sequence generation code\n",
    "def create_sequence_data(df, feature_columns, y_label='y'):\n",
    "    \n",
    "    id_list = df['patient_id'].unique()\n",
    "    \n",
    "    x_sequence = []\n",
    "    y_local = []\n",
    "    ids = []\n",
    "\n",
    "    for idx in id_list:\n",
    "        x_sequence.append(df[df['patient_id'] == idx][feature_columns].values.tolist())\n",
    "        y_local.append(df[df['patient_id'] == idx][y_label].max())\n",
    "        ids.append(idx)\n",
    "\n",
    "    return x_sequence, y_local, ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e94c5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, id_ls_train = create_sequence_data(train_structured_data_df_clean, feature_columns, 'Sepsis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "349a9e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid, y_valid, id_ls_valid = create_sequence_data(valid_structured_data_df_clean, feature_columns, 'Sepsis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "628c44db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad and truncate the data\n",
    "def pad_trunc(data, max_len):\n",
    "    new_data = []\n",
    "    zero_vec = []\n",
    "    for _ in range(len(data[0][0])):\n",
    "        zero_vec.append(0.0)\n",
    "    \n",
    "    for sample in data:\n",
    "        if len(sample) > max_len: # truncate \n",
    "            temp = sample[:max_len]\n",
    "        elif len(sample) < max_len:\n",
    "            temp = sample\n",
    "            number_additonal_elements = max_len - len(sample)\n",
    "            for _ in range(number_additonal_elements):\n",
    "                temp.append(zero_vec)\n",
    "        else:\n",
    "            temp = sample\n",
    "    \n",
    "        new_data.append(temp)\n",
    "    \n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e2fdfc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1972 83 107\n",
      "402\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), len(X_train[0]), len(X_train[0][0]))\n",
    "maxLen = 0\n",
    "for i in X_train:\n",
    "    maxLen = max(maxLen, len(i))\n",
    "print(maxLen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "705deb1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1972, 402, 107) (1972,) (1468, 402, 107) (1468,)\n"
     ]
    }
   ],
   "source": [
    "max_len = 402 # 500\n",
    "batch_size = 32\n",
    "embedding_dim = 107 # number of features # 107\n",
    "\n",
    "X_train = pad_trunc(X_train, max_len)\n",
    "X_valid = pad_trunc(X_valid, max_len)\n",
    "\n",
    "# Reshape x_train and x_test\n",
    "X_train = np.reshape(X_train, (len(X_train), max_len, embedding_dim))\n",
    "X_valid = np.reshape(X_valid, (len(X_valid), max_len, embedding_dim))\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "y_valid = np.array(y_valid)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3a2cace",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import sequence \n",
    "\n",
    "from keras.models import Sequential \n",
    "\n",
    "from keras.layers import Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation\n",
    "from keras.layers import Flatten, LSTM\n",
    "\n",
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f274ff74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 400, 16)           5152      \n",
      "                                                                 \n",
      " global_max_pooling1d (Globa  (None, 16)               0         \n",
      " lMaxPooling1D)                                                  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 280)               4760      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 280)               0         \n",
      "                                                                 \n",
      " activation (Activation)     (None, 280)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 281       \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,193\n",
      "Trainable params: 10,193\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 00:32:03.296385: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-12-06 00:32:03.296454: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ip-172-16-70-211.us-west-2.compute.internal): /proc/driver/nvidia/version does not exist\n",
      "2022-12-06 00:32:03.299290: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "filters = 16\n",
    "kernel_size = 3\n",
    "strides = 1\n",
    "hidden_dims = 280\n",
    "epochs = 50\n",
    "\n",
    "# Build CNN layers using Tensorflow\n",
    "model=Sequential()\n",
    "model.add(Conv1D(filters=filters, kernel_size=kernel_size, padding='valid', activation='relu', strides=strides, input_shape=[max_len,embedding_dim]))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(hidden_dims)) # in this layer after applying the pooling we have 1D vector for each training sample \n",
    "model.add(Dropout(0.2)) # adding drop out on feed forward layer and not a convolution \n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1)) # output layer\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "# Compile the CNN\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90764c29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "62/62 [==============================] - 3s 35ms/step - loss: 1.6320 - accuracy: 0.6268 - val_loss: 2.7449 - val_accuracy: 0.7834\n",
      "Epoch 2/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.6880 - accuracy: 0.7059 - val_loss: 0.4956 - val_accuracy: 0.7984\n",
      "Epoch 3/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5450 - accuracy: 0.7500 - val_loss: 1.6549 - val_accuracy: 0.7807\n",
      "Epoch 4/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5332 - accuracy: 0.7576 - val_loss: 8.2476 - val_accuracy: 0.8481\n",
      "Epoch 5/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5141 - accuracy: 0.7759 - val_loss: 3.5206 - val_accuracy: 0.8440\n",
      "Epoch 6/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4914 - accuracy: 0.7845 - val_loss: 3.1974 - val_accuracy: 0.8638\n",
      "Epoch 7/50\n",
      "62/62 [==============================] - 2s 28ms/step - loss: 0.4950 - accuracy: 0.7804 - val_loss: 5.8777 - val_accuracy: 0.8624\n",
      "Epoch 8/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4747 - accuracy: 0.7870 - val_loss: 13.9370 - val_accuracy: 0.8440\n",
      "Epoch 9/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4673 - accuracy: 0.7941 - val_loss: 12.9570 - val_accuracy: 0.8413\n",
      "Epoch 10/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4494 - accuracy: 0.8012 - val_loss: 9.6328 - val_accuracy: 0.8569\n",
      "Epoch 11/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4541 - accuracy: 0.7987 - val_loss: 9.7194 - val_accuracy: 0.8420\n",
      "Epoch 12/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4473 - accuracy: 0.7956 - val_loss: 11.9588 - val_accuracy: 0.8277\n",
      "Epoch 13/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4397 - accuracy: 0.8038 - val_loss: 7.6673 - val_accuracy: 0.8488\n",
      "Epoch 14/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4408 - accuracy: 0.8124 - val_loss: 10.0547 - val_accuracy: 0.8420\n",
      "Epoch 15/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4395 - accuracy: 0.8088 - val_loss: 12.3506 - val_accuracy: 0.8243\n",
      "Epoch 16/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4336 - accuracy: 0.8093 - val_loss: 13.0793 - val_accuracy: 0.8099\n",
      "Epoch 17/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4132 - accuracy: 0.8200 - val_loss: 12.0839 - val_accuracy: 0.8488\n",
      "Epoch 18/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4048 - accuracy: 0.8124 - val_loss: 19.4071 - val_accuracy: 0.7520\n",
      "Epoch 19/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4173 - accuracy: 0.8124 - val_loss: 20.6831 - val_accuracy: 0.8086\n",
      "Epoch 20/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4095 - accuracy: 0.8164 - val_loss: 16.9792 - val_accuracy: 0.8215\n",
      "Epoch 21/50\n",
      "62/62 [==============================] - 2s 28ms/step - loss: 0.4108 - accuracy: 0.8215 - val_loss: 17.3652 - val_accuracy: 0.8195\n",
      "Epoch 22/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.4119 - accuracy: 0.8190 - val_loss: 19.8825 - val_accuracy: 0.8345\n",
      "Epoch 23/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3986 - accuracy: 0.8266 - val_loss: 22.5334 - val_accuracy: 0.7800\n",
      "Epoch 24/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3893 - accuracy: 0.8337 - val_loss: 16.9754 - val_accuracy: 0.8065\n",
      "Epoch 25/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3838 - accuracy: 0.8357 - val_loss: 18.5018 - val_accuracy: 0.8099\n",
      "Epoch 26/50\n",
      "62/62 [==============================] - 2s 28ms/step - loss: 0.3869 - accuracy: 0.8261 - val_loss: 18.9071 - val_accuracy: 0.8004\n",
      "Epoch 27/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3923 - accuracy: 0.8256 - val_loss: 19.7357 - val_accuracy: 0.8168\n",
      "Epoch 28/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3674 - accuracy: 0.8463 - val_loss: 25.2690 - val_accuracy: 0.8031\n",
      "Epoch 29/50\n",
      "62/62 [==============================] - 3s 45ms/step - loss: 0.3642 - accuracy: 0.8458 - val_loss: 25.2296 - val_accuracy: 0.7936\n",
      "Epoch 30/50\n",
      "62/62 [==============================] - 3s 42ms/step - loss: 0.3657 - accuracy: 0.8504 - val_loss: 26.4116 - val_accuracy: 0.7793\n",
      "Epoch 31/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3761 - accuracy: 0.8367 - val_loss: 26.1322 - val_accuracy: 0.7868\n",
      "Epoch 32/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3678 - accuracy: 0.8463 - val_loss: 36.9021 - val_accuracy: 0.7691\n",
      "Epoch 33/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3549 - accuracy: 0.8494 - val_loss: 31.9063 - val_accuracy: 0.8031\n",
      "Epoch 34/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3551 - accuracy: 0.8529 - val_loss: 32.6240 - val_accuracy: 0.8031\n",
      "Epoch 35/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3517 - accuracy: 0.8489 - val_loss: 32.6725 - val_accuracy: 0.7847\n",
      "Epoch 36/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3670 - accuracy: 0.8428 - val_loss: 37.0660 - val_accuracy: 0.7943\n",
      "Epoch 37/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3585 - accuracy: 0.8433 - val_loss: 33.0103 - val_accuracy: 0.7854\n",
      "Epoch 38/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3478 - accuracy: 0.8529 - val_loss: 38.5727 - val_accuracy: 0.7616\n",
      "Epoch 39/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3560 - accuracy: 0.8479 - val_loss: 43.8119 - val_accuracy: 0.7670\n",
      "Epoch 40/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3711 - accuracy: 0.8443 - val_loss: 40.6816 - val_accuracy: 0.7752\n",
      "Epoch 41/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3327 - accuracy: 0.8509 - val_loss: 37.6222 - val_accuracy: 0.7997\n",
      "Epoch 42/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3309 - accuracy: 0.8570 - val_loss: 42.6773 - val_accuracy: 0.8018\n",
      "Epoch 43/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3290 - accuracy: 0.8605 - val_loss: 44.6610 - val_accuracy: 0.8059\n",
      "Epoch 44/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3316 - accuracy: 0.8621 - val_loss: 44.4705 - val_accuracy: 0.7970\n",
      "Epoch 45/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3404 - accuracy: 0.8540 - val_loss: 49.3804 - val_accuracy: 0.7480\n",
      "Epoch 46/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3663 - accuracy: 0.8372 - val_loss: 47.2908 - val_accuracy: 0.7595\n",
      "Epoch 47/50\n",
      "62/62 [==============================] - 2s 26ms/step - loss: 0.3363 - accuracy: 0.8534 - val_loss: 48.1633 - val_accuracy: 0.7704\n",
      "Epoch 48/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3426 - accuracy: 0.8575 - val_loss: 38.2659 - val_accuracy: 0.8106\n",
      "Epoch 49/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3282 - accuracy: 0.8636 - val_loss: 45.6517 - val_accuracy: 0.8038\n",
      "Epoch 50/50\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.3432 - accuracy: 0.8509 - val_loss: 52.5558 - val_accuracy: 0.8011\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c23965b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f90c55e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a5a047c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 01:49:33.390811: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-12-06 01:49:33.390885: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ip-172-16-79-154.us-west-2.compute.internal): /proc/driver/nvidia/version does not exist\n",
      "2022-12-06 01:49:33.408225: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'max_len' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_23479/322826552.py\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Build CNN layers using Tensorflow\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[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSequential\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---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConv1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkernel_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'valid'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax_len\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0membedding_dim\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     11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGlobalMaxPooling1D\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     12\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_dims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# in this layer after applying the pooling we have 1D vector for each training sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'max_len' is not defined"
     ]
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "filters = 16\n",
    "kernel_size = 3\n",
    "strides = 1\n",
    "hidden_dims = 280\n",
    "epochs = 6\n",
    "\n",
    "# Build CNN layers using Tensorflow\n",
    "model=Sequential()\n",
    "model.add(Conv1D(filters=filters, kernel_size=kernel_size, padding='valid', activation='relu', strides=strides, input_shape=[max_len,embedding_dim]))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(hidden_dims)) # in this layer after applying the pooling we have 1D vector for each training sample \n",
    "model.add(Dropout(0.2)) # adding drop out on feed forward layer and not a convolution \n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1)) # output layer\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "# Compile the CNN\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4930e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "62/62 [==============================] - 2s 34ms/step - loss: 1.6788 - accuracy: 0.6197 - val_loss: 0.5217 - val_accuracy: 0.7841\n",
      "Epoch 2/6\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.6596 - accuracy: 0.7033 - val_loss: 0.5681 - val_accuracy: 0.7663\n",
      "Epoch 3/6\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.6122 - accuracy: 0.7282 - val_loss: 2.4975 - val_accuracy: 0.7248\n",
      "Epoch 4/6\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5639 - accuracy: 0.7419 - val_loss: 1.2805 - val_accuracy: 0.6757\n",
      "Epoch 5/6\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5538 - accuracy: 0.7414 - val_loss: 2.7206 - val_accuracy: 0.8283\n",
      "Epoch 6/6\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 0.5502 - accuracy: 0.7596 - val_loss: 1.1301 - val_accuracy: 0.8358\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_valid, y_valid))\n",
    "\n",
    "# Save the model \n",
    "model_str = model.to_json()\n",
    "with open('cnn_model.json','w') as json_file:\n",
    "    json_file.write(model_str)\n",
    "model.save_weights('cnn_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1fc5bd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the saved model\n",
    "with open(\"cnn_model.json\",'r') as json_file:\n",
    "    json_string = json_file.read()\n",
    "\n",
    "model = model_from_json(json_string)\n",
    "model.load_weights('cnn_weights.h5')\n",
    "\n",
    "predictions = model.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7dd04ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_label = [1 if i > 0.5 else 0 for i in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c88f12ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix: \n",
      "[[363 146]\n",
      " [ 95 864]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.7925764192139738\n",
      "Specificity or true negative rate:  0.8554455445544554\n",
      "Precision or positive predictive value:  0.7131630648330058\n",
      "Negative predictive value:  0.9009384775808134\n",
      "Fall out or false positive rate:  0.14455445544554454\n",
      "False negative rate:  0.2074235807860262\n",
      "False discovery rate:  0.2868369351669941\n",
      "Overall accuracy:  0.8358310626702997\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(y_valid, pred_label,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "caaf8d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame([[363, 146],[95, 864]], index = [i for i in \"10\"], columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (8,6))\n",
    "sn.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c38041",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "47377853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 402, 50)           31600     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 402, 50)           0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 20100)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 20101     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 51,701\n",
      "Trainable params: 51,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters. Structured data will have different input dimensions based on number of features x time points\n",
    "num_neurons = 50\n",
    "epochs = 50\n",
    "\n",
    "# This line may not be necessary for kernels other than conda_tensorflow2_p38\n",
    "tf.keras.backend.set_image_data_format(\"channels_last\")\n",
    "\n",
    "# Initialize network with Sequential keras model\n",
    "model = Sequential()\n",
    "model.add(LSTM(num_neurons, input_shape=(max_len, embedding_dim), return_sequences=True)) # output 100 *50\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten()) # for output with rank > 2\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile('RMSprop', 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a41eb0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "62/62 [==============================] - 15s 221ms/step - loss: 0.5434 - accuracy: 0.7657 - val_loss: 0.5862 - val_accuracy: 0.7330\n",
      "Epoch 2/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.5103 - accuracy: 0.7789 - val_loss: 0.3729 - val_accuracy: 0.8576\n",
      "Epoch 3/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.4934 - accuracy: 0.7906 - val_loss: 0.4681 - val_accuracy: 0.8031\n",
      "Epoch 4/50\n",
      "62/62 [==============================] - 13s 211ms/step - loss: 0.4787 - accuracy: 0.7896 - val_loss: 0.3709 - val_accuracy: 0.8597\n",
      "Epoch 5/50\n",
      "62/62 [==============================] - 13s 212ms/step - loss: 0.4694 - accuracy: 0.7961 - val_loss: 0.3722 - val_accuracy: 0.8576\n",
      "Epoch 6/50\n",
      "62/62 [==============================] - 13s 212ms/step - loss: 0.4542 - accuracy: 0.8002 - val_loss: 0.4156 - val_accuracy: 0.8202\n",
      "Epoch 7/50\n",
      "62/62 [==============================] - 13s 211ms/step - loss: 0.4463 - accuracy: 0.8017 - val_loss: 0.4312 - val_accuracy: 0.8065\n",
      "Epoch 8/50\n",
      "62/62 [==============================] - 13s 213ms/step - loss: 0.4397 - accuracy: 0.8103 - val_loss: 0.3938 - val_accuracy: 0.8454\n",
      "Epoch 9/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.4360 - accuracy: 0.8048 - val_loss: 0.4311 - val_accuracy: 0.8065\n",
      "Epoch 10/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.4152 - accuracy: 0.8139 - val_loss: 0.5007 - val_accuracy: 0.7772\n",
      "Epoch 11/50\n",
      "62/62 [==============================] - 15s 242ms/step - loss: 0.4160 - accuracy: 0.8261 - val_loss: 0.4428 - val_accuracy: 0.8358\n",
      "Epoch 12/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.4140 - accuracy: 0.8240 - val_loss: 0.5830 - val_accuracy: 0.7452\n",
      "Epoch 13/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.4068 - accuracy: 0.8205 - val_loss: 0.4554 - val_accuracy: 0.8065\n",
      "Epoch 14/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.4031 - accuracy: 0.8190 - val_loss: 0.4602 - val_accuracy: 0.8243\n",
      "Epoch 15/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3935 - accuracy: 0.8261 - val_loss: 0.4262 - val_accuracy: 0.8283\n",
      "Epoch 16/50\n",
      "62/62 [==============================] - 13s 213ms/step - loss: 0.3762 - accuracy: 0.8428 - val_loss: 0.6449 - val_accuracy: 0.7159\n",
      "Epoch 17/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.3665 - accuracy: 0.8413 - val_loss: 0.5002 - val_accuracy: 0.7752\n",
      "Epoch 18/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3604 - accuracy: 0.8479 - val_loss: 0.4746 - val_accuracy: 0.7936\n",
      "Epoch 19/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3422 - accuracy: 0.8580 - val_loss: 0.4997 - val_accuracy: 0.8065\n",
      "Epoch 20/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3500 - accuracy: 0.8494 - val_loss: 0.5404 - val_accuracy: 0.8093\n",
      "Epoch 21/50\n",
      "62/62 [==============================] - 13s 213ms/step - loss: 0.3450 - accuracy: 0.8524 - val_loss: 0.5801 - val_accuracy: 0.7827\n",
      "Epoch 22/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3297 - accuracy: 0.8580 - val_loss: 0.6460 - val_accuracy: 0.7289\n",
      "Epoch 23/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.3292 - accuracy: 0.8560 - val_loss: 0.5593 - val_accuracy: 0.7902\n",
      "Epoch 24/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3398 - accuracy: 0.8534 - val_loss: 0.6124 - val_accuracy: 0.7241\n",
      "Epoch 25/50\n",
      "62/62 [==============================] - 15s 243ms/step - loss: 0.3171 - accuracy: 0.8656 - val_loss: 0.6148 - val_accuracy: 0.7752\n",
      "Epoch 26/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.3165 - accuracy: 0.8661 - val_loss: 0.5271 - val_accuracy: 0.7813\n",
      "Epoch 27/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.3197 - accuracy: 0.8611 - val_loss: 0.6719 - val_accuracy: 0.7643\n",
      "Epoch 28/50\n",
      "62/62 [==============================] - 13s 215ms/step - loss: 0.2971 - accuracy: 0.8732 - val_loss: 0.5873 - val_accuracy: 0.7759\n",
      "Epoch 29/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.2989 - accuracy: 0.8742 - val_loss: 0.6031 - val_accuracy: 0.7554\n",
      "Epoch 30/50\n",
      "62/62 [==============================] - 13s 214ms/step - loss: 0.2956 - accuracy: 0.8793 - val_loss: 0.5738 - val_accuracy: 0.7691\n",
      "Epoch 31/50\n",
      "62/62 [==============================] - 13s 213ms/step - loss: 0.2871 - accuracy: 0.8768 - val_loss: 0.5679 - val_accuracy: 0.7977\n",
      "Epoch 32/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2776 - accuracy: 0.8874 - val_loss: 0.5762 - val_accuracy: 0.7704\n",
      "Epoch 33/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2661 - accuracy: 0.8915 - val_loss: 0.8185 - val_accuracy: 0.7466\n",
      "Epoch 34/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2689 - accuracy: 0.8905 - val_loss: 0.6320 - val_accuracy: 0.7589\n",
      "Epoch 35/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2675 - accuracy: 0.8874 - val_loss: 0.8229 - val_accuracy: 0.7323\n",
      "Epoch 36/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2493 - accuracy: 0.8996 - val_loss: 0.7062 - val_accuracy: 0.7834\n",
      "Epoch 37/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2459 - accuracy: 0.8991 - val_loss: 0.8773 - val_accuracy: 0.7289\n",
      "Epoch 38/50\n",
      "62/62 [==============================] - 15s 237ms/step - loss: 0.2370 - accuracy: 0.9077 - val_loss: 0.7042 - val_accuracy: 0.7772\n",
      "Epoch 39/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.2400 - accuracy: 0.9011 - val_loss: 0.7530 - val_accuracy: 0.7486\n",
      "Epoch 40/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2336 - accuracy: 0.8981 - val_loss: 0.6356 - val_accuracy: 0.7922\n",
      "Epoch 41/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.2394 - accuracy: 0.8981 - val_loss: 1.0282 - val_accuracy: 0.6710\n",
      "Epoch 42/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.2263 - accuracy: 0.9006 - val_loss: 0.7193 - val_accuracy: 0.7963\n",
      "Epoch 43/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.2209 - accuracy: 0.9062 - val_loss: 0.7870 - val_accuracy: 0.7520\n",
      "Epoch 44/50\n",
      "62/62 [==============================] - 13s 211ms/step - loss: 0.2169 - accuracy: 0.9072 - val_loss: 0.8614 - val_accuracy: 0.7636\n",
      "Epoch 45/50\n",
      "62/62 [==============================] - 13s 212ms/step - loss: 0.2082 - accuracy: 0.9123 - val_loss: 1.0426 - val_accuracy: 0.7275\n",
      "Epoch 46/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2229 - accuracy: 0.9042 - val_loss: 0.9743 - val_accuracy: 0.7643\n",
      "Epoch 47/50\n",
      "62/62 [==============================] - 13s 211ms/step - loss: 0.2071 - accuracy: 0.9194 - val_loss: 0.8592 - val_accuracy: 0.7289\n",
      "Epoch 48/50\n",
      "62/62 [==============================] - 13s 209ms/step - loss: 0.2036 - accuracy: 0.9108 - val_loss: 0.7837 - val_accuracy: 0.7548\n",
      "Epoch 49/50\n",
      "62/62 [==============================] - 13s 211ms/step - loss: 0.2078 - accuracy: 0.9057 - val_loss: 0.8311 - val_accuracy: 0.7255\n",
      "Epoch 50/50\n",
      "62/62 [==============================] - 13s 210ms/step - loss: 0.2025 - accuracy: 0.9138 - val_loss: 0.9688 - val_accuracy: 0.6771\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb0baa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aacaedf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f1f1bcce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_1 (LSTM)               (None, 402, 50)           31600     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 402, 50)           0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 20100)             0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 20101     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 51,701\n",
      "Trainable params: 51,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters. Structured data will have different input dimensions based on number of features x time points\n",
    "num_neurons = 50\n",
    "epochs = 4\n",
    "\n",
    "# This line may not be necessary for kernels other than conda_tensorflow2_p38\n",
    "tf.keras.backend.set_image_data_format(\"channels_last\")\n",
    "\n",
    "# Initialize network with Sequential keras model\n",
    "model = Sequential()\n",
    "model.add(LSTM(num_neurons, input_shape=(max_len, embedding_dim), return_sequences=True)) # output 100 *50\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten()) # for output with rank > 2\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile('RMSprop', 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b53f9fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "62/62 [==============================] - 16s 225ms/step - loss: 0.5483 - accuracy: 0.7566 - val_loss: 0.3990 - val_accuracy: 0.8535\n",
      "Epoch 2/4\n",
      "62/62 [==============================] - 13s 212ms/step - loss: 0.5094 - accuracy: 0.7743 - val_loss: 0.4370 - val_accuracy: 0.8249\n",
      "Epoch 3/4\n",
      "62/62 [==============================] - 13s 212ms/step - loss: 0.4902 - accuracy: 0.7845 - val_loss: 0.3908 - val_accuracy: 0.8467\n",
      "Epoch 4/4\n",
      "62/62 [==============================] - 13s 213ms/step - loss: 0.4828 - accuracy: 0.7890 - val_loss: 0.3934 - val_accuracy: 0.8495\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_valid, y_valid))\n",
    "\n",
    "# Save the model \n",
    "model_str = model.to_json()\n",
    "with open('lstm_model.json','w') as json_file:\n",
    "    json_file.write(model_str)\n",
    "model.save_weights('lstm_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4bb757e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the saved model\n",
    "with open(\"lstm_model.json\",'r') as json_file:\n",
    "    json_string = json_file.read()\n",
    "\n",
    "model = model_from_json(json_string)\n",
    "model.load_weights('lstm_weights.h5')\n",
    "\n",
    "predictions = model.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8dee4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_label = [1 if i > 0.5 else 0 for i in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b763fc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix: \n",
      "[[365 144]\n",
      " [ 77 882]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.8257918552036199\n",
      "Specificity or true negative rate:  0.8596491228070176\n",
      "Precision or positive predictive value:  0.7170923379174853\n",
      "Negative predictive value:  0.9197080291970803\n",
      "Fall out or false positive rate:  0.14035087719298245\n",
      "False negative rate:  0.17420814479638008\n",
      "False discovery rate:  0.2829076620825147\n",
      "Overall accuracy:  0.8494550408719346\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(y_valid, pred_label,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "46b78a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame([[365, 144],[77, 882]], index = [i for i in \"10\"], columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (8,6))\n",
    "sn.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a18f1b3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d947f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: gensim in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (4.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from gensim) (1.20.3)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from gensim) (6.2.0)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from gensim) (1.7.2)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/tensorflow2_p38/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# How to get representations? See the Chapter 6 exercise on how to use Word2Vec to process biomedical text\n",
    "!pip install -U gensim\n",
    "\n",
    "import nltk\n",
    "# nltk.download('stopwords')\n",
    "\n",
    "from gensim.models.keyedvectors import KeyedVectors\n",
    "\n",
    "from nltk.tokenize import TreebankWordTokenizer\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "word_vectors_pubmed = KeyedVectors.load_word2vec_format('s3://med-277/Labs/pubmed2018_w2v_200D.bin', binary=True, limit=200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ad19272",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize and vectorize the data\n",
    "def tokenizer_and_vectroizer(text):\n",
    "    tokenizer = TreebankWordTokenizer()\n",
    "    vectorized_data = []\n",
    "    expected = []\n",
    "    \n",
    "    # Remove numbers and punctuation\n",
    "    text = ''.join([ch.lower() for ch in text if ch.isalpha() or ch == \" \"]) \n",
    "\n",
    "    # Tokenize the note\n",
    "    tokens = tokenizer.tokenize(re.sub(r'[^\\w\\s]','',text))\n",
    "\n",
    "    # Remove stopwords\n",
    "    tokens_clean = [w for w in tokens if not w in stopwords]\n",
    "\n",
    "    # Remove words with length 1\n",
    "    tokens_clean = [w for w in tokens_clean if len(w) > 1]\n",
    "\n",
    "    vectorized_data = []\n",
    "    for token in tokens:\n",
    "        try:    \n",
    "            vectorized_data.append(word_vectors_pubmed[token])\n",
    "        except:\n",
    "            pass\n",
    "            \n",
    "    return vectorized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05ccc6e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "599d4c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_data_df_clean\n",
    "del valid_data_df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b9ce18e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_structured_data_df_clean\n",
    "del valid_structured_data_df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850d8493",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "588cd961",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_seq_wtext = X_train[:]\n",
    "x_valid_seq_wtext = X_valid[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "95b8e64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "del X_train\n",
    "del X_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dedf85ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt = 0\n",
    "for patient in x_train_seq_wtext:\n",
    "    for row in patient:\n",
    "        vectorized_tokens = tokenizer_and_vectroizer(train_text_data_df_clean[cnt])\n",
    "        if not vectorized_tokens:\n",
    "            row.extend([0] * 200)\n",
    "            continue\n",
    "        vectorized_tokens = np.array(vectorized_tokens)\n",
    "        vectorized_tokens = vectorized_tokens.mean(axis=0)\n",
    "        row.extend(vectorized_tokens)\n",
    "        cnt += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ecb6195",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt = 0\n",
    "for patient in x_valid_seq_wtext:\n",
    "    for row in patient:\n",
    "        vectorized_tokens = tokenizer_and_vectroizer(valid_text_data_df_clean[cnt])\n",
    "        if not vectorized_tokens:\n",
    "            row.extend([0] * 200)\n",
    "            continue\n",
    "        vectorized_tokens = np.array(vectorized_tokens)\n",
    "        vectorized_tokens = vectorized_tokens.mean(axis=0)\n",
    "        row.extend(vectorized_tokens)\n",
    "        cnt += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "410e1170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[85.36631097499881, 36.9386943426975, 121.78996661337457, 79.27404637452362, 63.92428738214118, 19.311585458996085, 24.373042170377825, 7.715338702004022, 104.48631425691566, 1.580396475771007, 123.0, 2.0807061968065828, 3.3982078208539073, 5.0, 20.0, 6.7, 11.524149704229972, 195.07442870201098, 138.02054250114747, 24.0, 0.0, 1.0, 1.0, -1.0, -1.0, -9.069722222222222, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_seq_wtext[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6abfb01d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[93.0, 33.71, 121.91961734384762, 71.5, 62.733472951131596, 21.0, 30.35900401978079, 5.012499999999998, 103.48590476737837, 1.7256725077655233, 142.60530052058684, 2.1216519938362417, 3.519760757502272, 3.0, 30.096346821254173, 38.666609213108266, 12.56296357295155, 219.70787463328202, 147.61621875119636, 41.0, 0.0, 1.0, 1.0, 1.0, 0.0, -3.1666666666666665, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "print(x_valid_seq_wtext[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5961c57b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e7a7283",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_text_data_df_clean' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_17444/408551897.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mtrain_text_data_df_clean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mvalid_text_data_df_clean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mword_vectors_pubmed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mstopwords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_text_data_df_clean' is not defined"
     ]
    }
   ],
   "source": [
    "del train_text_data_df_clean\n",
    "del valid_text_data_df_clean\n",
    "del word_vectors_pubmed\n",
    "del stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7a8c56b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0324387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1972\n",
      "83\n",
      "307\n",
      "83\n",
      "105\n",
      "111\n",
      "117\n",
      "143\n",
      "223\n",
      "307\n",
      "402\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train_seq_wtext))\n",
    "print(len(x_train_seq_wtext[0]))\n",
    "print(len(x_train_seq_wtext[0][0]))\n",
    "\n",
    "maxLen = 0\n",
    "for i in x_train_seq_wtext:\n",
    "    temp = len(i)\n",
    "    if temp > maxLen:\n",
    "        print(temp)\n",
    "    maxLen = max(maxLen, temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63f28384",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 402\n",
    "batch_size = 8 #32\n",
    "embedding_dim = 307 # number of features # 307"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7d885c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_seq_wtext = pad_trunc(x_train_seq_wtext, max_len)\n",
    "x_valid_seq_wtext = pad_trunc(x_valid_seq_wtext, max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "029123dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape x_train and x_test\n",
    "x_train_seq_wtext = np.reshape(x_train_seq_wtext, (len(x_train_seq_wtext), max_len, embedding_dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c04c4c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_valid_seq_wtext = np.reshape(x_valid_seq_wtext, (len(x_valid_seq_wtext), max_len, embedding_dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29047655",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_valid = np.array(y_train)\n",
    "y_valid = np.array(y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b33881fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1972, 402, 307) (1972,) (1468, 402, 307) (1468,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_seq_wtext.shape, y_train.shape, x_valid_seq_wtext.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9a9e31d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('x_train_seq_wtext.npy', x_train_seq_wtext)\n",
    "np.save('x_valid_seq_wtext.npy', x_valid_seq_wtext)\n",
    "np.save('y_train_wtext.npy', y_train)\n",
    "np.save('y_valid_wtext.npy', y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5d61173",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_seq_wtext = np.load('x_train_seq_wtext.npy')\n",
    "x_valid_seq_wtext = np.load('x_valid_seq_wtext.npy')\n",
    "y_train = np.load('y_train_wtext.npy')\n",
    "y_valid = np.load('y_valid_wtext.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01340cb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2b4b012d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 400, 16)           14752     \n",
      "                                                                 \n",
      " global_max_pooling1d (Globa  (None, 16)               0         \n",
      " lMaxPooling1D)                                                  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 280)               4760      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 280)               0         \n",
      "                                                                 \n",
      " activation (Activation)     (None, 280)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 281       \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19,793\n",
      "Trainable params: 19,793\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 01:58:28.829474: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-12-06 01:58:28.829552: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ip-172-16-79-154.us-west-2.compute.internal): /proc/driver/nvidia/version does not exist\n",
      "2022-12-06 01:58:28.832524: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "filters = 16\n",
    "kernel_size = 3\n",
    "strides = 1\n",
    "hidden_dims = 280\n",
    "epochs = 50\n",
    "\n",
    "# Build CNN layers using Tensorflow\n",
    "model=Sequential()\n",
    "model.add(Conv1D(filters=filters, kernel_size=kernel_size, padding='valid', activation='relu', strides=strides, input_shape=[max_len,embedding_dim]))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(hidden_dims)) # in this layer after applying the pooling we have 1D vector for each training sample \n",
    "model.add(Dropout(0.2)) # adding drop out on feed forward layer and not a convolution \n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1)) # output layer\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "# Compile the CNN\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2d8e49a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 01:58:29.382413: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 973489632 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "124/124 [==============================] - ETA: 0s - loss: 0.7645 - accuracy: 0.6526"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 01:58:35.927147: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 724687008 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124/124 [==============================] - 7s 53ms/step - loss: 0.7645 - accuracy: 0.6526 - val_loss: 8.6121 - val_accuracy: 0.7738\n",
      "Epoch 2/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.6028 - accuracy: 0.7282 - val_loss: 13.0360 - val_accuracy: 0.8222\n",
      "Epoch 3/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.5582 - accuracy: 0.7535 - val_loss: 17.3196 - val_accuracy: 0.8454\n",
      "Epoch 4/50\n",
      "124/124 [==============================] - 5s 42ms/step - loss: 0.5296 - accuracy: 0.7591 - val_loss: 16.8681 - val_accuracy: 0.8447\n",
      "Epoch 5/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.5164 - accuracy: 0.7698 - val_loss: 11.9360 - val_accuracy: 0.8495\n",
      "Epoch 6/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.4994 - accuracy: 0.7754 - val_loss: 15.3817 - val_accuracy: 0.8583\n",
      "Epoch 7/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.4965 - accuracy: 0.7830 - val_loss: 24.0809 - val_accuracy: 0.8542\n",
      "Epoch 8/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.4812 - accuracy: 0.7860 - val_loss: 16.5947 - val_accuracy: 0.8590\n",
      "Epoch 9/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.4923 - accuracy: 0.7809 - val_loss: 28.5931 - val_accuracy: 0.8583\n",
      "Epoch 10/50\n",
      "124/124 [==============================] - 5s 43ms/step - loss: 0.4761 - accuracy: 0.7870 - val_loss: 11.9897 - val_accuracy: 0.8576\n",
      "Epoch 11/50\n",
      "124/124 [==============================] - 5s 42ms/step - loss: 0.4633 - accuracy: 0.7885 - val_loss: 8.9711 - val_accuracy: 0.8604\n",
      "Epoch 12/50\n",
      "124/124 [==============================] - 5s 42ms/step - loss: 0.4583 - accuracy: 0.7865 - val_loss: 19.4013 - val_accuracy: 0.8576\n",
      "Epoch 13/50\n",
      " 75/124 [=================>............] - ETA: 1s - loss: 0.4409 - accuracy: 0.7892"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_28884/1579110598.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train_seq_wtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_valid_seq_wtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\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[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\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---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1214\u001b[0m                 _r=1):\n\u001b[1;32m   1215\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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   1217\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\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--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3128\u001b[0m       (graph_function,\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3131\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1960\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    599\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\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[1;32m    600\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     56\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[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 58\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "history = model.fit(x_train_seq_wtext, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_valid_seq_wtext, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8ea507a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9e07872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86a3e2d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 02:29:35.778589: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-12-06 02:29:35.778663: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ip-172-16-79-154.us-west-2.compute.internal): /proc/driver/nvidia/version does not exist\n",
      "2022-12-06 02:29:35.785506: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 400, 16)           14752     \n",
      "                                                                 \n",
      " global_max_pooling1d (Globa  (None, 16)               0         \n",
      " lMaxPooling1D)                                                  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 280)               4760      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 280)               0         \n",
      "                                                                 \n",
      " activation (Activation)     (None, 280)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 281       \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 1)                 0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19,793\n",
      "Trainable params: 19,793\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "filters = 16\n",
    "kernel_size = 3\n",
    "strides = 1\n",
    "hidden_dims = 280\n",
    "epochs = 10\n",
    "\n",
    "# Build CNN layers using Tensorflow\n",
    "model=Sequential()\n",
    "model.add(Conv1D(filters=filters, kernel_size=kernel_size, padding='valid', activation='relu', strides=strides, input_shape=[max_len,embedding_dim]))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(hidden_dims)) # in this layer after applying the pooling we have 1D vector for each training sample \n",
    "model.add(Dropout(0.2)) # adding drop out on feed forward layer and not a convolution \n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1)) # output layer\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "# Compile the CNN\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9763c33c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 02:29:43.801940: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 973489632 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "  6/247 [..............................] - ETA: 5s - loss: 3.0359 - accuracy: 0.6042"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 02:29:45.453575: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 30945600 exceeds 10% of free system memory.\n",
      "2022-12-06 02:29:45.490243: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 30945600 exceeds 10% of free system memory.\n",
      "2022-12-06 02:29:45.510096: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 30945600 exceeds 10% of free system memory.\n",
      "2022-12-06 02:29:45.529828: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 30945600 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247/247 [==============================] - 8s 29ms/step - loss: 0.8226 - accuracy: 0.6744 - val_loss: 11.5840 - val_accuracy: 0.8447\n",
      "Epoch 2/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.5625 - accuracy: 0.7495 - val_loss: 17.1718 - val_accuracy: 0.8440\n",
      "Epoch 3/10\n",
      "247/247 [==============================] - 6s 26ms/step - loss: 0.5232 - accuracy: 0.7743 - val_loss: 6.4065 - val_accuracy: 0.8481\n",
      "Epoch 4/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.5118 - accuracy: 0.7779 - val_loss: 6.3219 - val_accuracy: 0.8631\n",
      "Epoch 5/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4816 - accuracy: 0.7901 - val_loss: 11.3732 - val_accuracy: 0.8583\n",
      "Epoch 6/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4853 - accuracy: 0.7885 - val_loss: 4.9723 - val_accuracy: 0.8501\n",
      "Epoch 7/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4731 - accuracy: 0.7916 - val_loss: 12.2630 - val_accuracy: 0.8529\n",
      "Epoch 8/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4688 - accuracy: 0.7885 - val_loss: 4.1745 - val_accuracy: 0.8631\n",
      "Epoch 9/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4498 - accuracy: 0.7921 - val_loss: 21.7729 - val_accuracy: 0.8454\n",
      "Epoch 10/10\n",
      "247/247 [==============================] - 6s 25ms/step - loss: 0.4400 - accuracy: 0.7997 - val_loss: 9.4741 - val_accuracy: 0.8597\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "model.fit(x_train_seq_wtext, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_valid_seq_wtext, y_valid))\n",
    "\n",
    "# Save the model \n",
    "model_str = model.to_json()\n",
    "with open('cnn_model_wtext.json','w') as json_file:\n",
    "    json_file.write(model_str)\n",
    "model.save_weights('cnn_weights_wtext.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "304c4b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the saved model\n",
    "with open(\"cnn_model_wtext.json\",'r') as json_file:\n",
    "    json_string = json_file.read()\n",
    "\n",
    "model = model_from_json(json_string)\n",
    "model.load_weights('cnn_weights_wtext.h5')\n",
    "\n",
    "predictions = model.predict(x_valid_seq_wtext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "59b915ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_label = [1 if i > 0.5 else 0 for i in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "803f47e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix: \n",
      "[[373 136]\n",
      " [ 70 889]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.8419864559819413\n",
      "Specificity or true negative rate:  0.8673170731707317\n",
      "Precision or positive predictive value:  0.7328094302554028\n",
      "Negative predictive value:  0.927007299270073\n",
      "Fall out or false positive rate:  0.1326829268292683\n",
      "False negative rate:  0.1580135440180587\n",
      "False discovery rate:  0.26719056974459726\n",
      "Overall accuracy:  0.8596730245231607\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(y_valid, pred_label,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1aaecd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame([[373, 136],[70, 889]], index = [i for i in \"10\"], columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (8,6))\n",
    "sn.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "224dad07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbebbd08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 402, 50)           71600     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 402, 50)           0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 20100)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 20101     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 91,701\n",
      "Trainable params: 91,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters. Structured data will have different input dimensions based on number of features x time points\n",
    "num_neurons = 50\n",
    "epochs = 50\n",
    "\n",
    "# This line may not be necessary for kernels other than conda_tensorflow2_p38\n",
    "tf.keras.backend.set_image_data_format(\"channels_last\")\n",
    "\n",
    "# Initialize network with Sequential keras model\n",
    "model = Sequential()\n",
    "model.add(LSTM(num_neurons, input_shape=(max_len, embedding_dim), return_sequences=True)) # output 100 *50\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten()) # for output with rank > 2\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile('RMSprop', 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e8a4f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 02:39:20.800968: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 973489632 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "247/247 [==============================] - ETA: 0s - loss: 0.5643 - accuracy: 0.7596"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 02:40:10.209103: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 724687008 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247/247 [==============================] - 55s 214ms/step - loss: 0.5643 - accuracy: 0.7596 - val_loss: 0.4478 - val_accuracy: 0.8188\n",
      "Epoch 2/50\n",
      "247/247 [==============================] - 49s 198ms/step - loss: 0.5362 - accuracy: 0.7733 - val_loss: 0.3936 - val_accuracy: 0.8576\n",
      "Epoch 3/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.5265 - accuracy: 0.7799 - val_loss: 0.6211 - val_accuracy: 0.7623\n",
      "Epoch 4/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.5221 - accuracy: 0.7794 - val_loss: 0.4086 - val_accuracy: 0.8644\n",
      "Epoch 5/50\n",
      "247/247 [==============================] - 51s 206ms/step - loss: 0.5210 - accuracy: 0.7819 - val_loss: 0.6192 - val_accuracy: 0.8065\n",
      "Epoch 6/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.5350 - accuracy: 0.7809 - val_loss: 0.4611 - val_accuracy: 0.8283\n",
      "Epoch 7/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.5057 - accuracy: 0.7865 - val_loss: 0.3824 - val_accuracy: 0.8474\n",
      "Epoch 8/50\n",
      "247/247 [==============================] - 51s 208ms/step - loss: 0.5096 - accuracy: 0.7754 - val_loss: 0.5196 - val_accuracy: 0.8072\n",
      "Epoch 9/50\n",
      "247/247 [==============================] - 50s 201ms/step - loss: 0.5059 - accuracy: 0.7850 - val_loss: 0.8649 - val_accuracy: 0.7595\n",
      "Epoch 10/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.5121 - accuracy: 0.7804 - val_loss: 0.5332 - val_accuracy: 0.8072\n",
      "Epoch 11/50\n",
      "247/247 [==============================] - 50s 201ms/step - loss: 0.5009 - accuracy: 0.7865 - val_loss: 0.4886 - val_accuracy: 0.8134\n",
      "Epoch 12/50\n",
      "247/247 [==============================] - 51s 206ms/step - loss: 0.5114 - accuracy: 0.7941 - val_loss: 0.4673 - val_accuracy: 0.8127\n",
      "Epoch 13/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4945 - accuracy: 0.7926 - val_loss: 0.6300 - val_accuracy: 0.7575\n",
      "Epoch 14/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4924 - accuracy: 0.7804 - val_loss: 0.4891 - val_accuracy: 0.8256\n",
      "Epoch 15/50\n",
      "247/247 [==============================] - 51s 206ms/step - loss: 0.4897 - accuracy: 0.7911 - val_loss: 0.5525 - val_accuracy: 0.8181\n",
      "Epoch 16/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.5237 - accuracy: 0.7870 - val_loss: 1.4512 - val_accuracy: 0.7078\n",
      "Epoch 17/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.4945 - accuracy: 0.7946 - val_loss: 0.8431 - val_accuracy: 0.7779\n",
      "Epoch 18/50\n",
      "247/247 [==============================] - 50s 201ms/step - loss: 0.4667 - accuracy: 0.7850 - val_loss: 0.8537 - val_accuracy: 0.8031\n",
      "Epoch 19/50\n",
      "247/247 [==============================] - 51s 206ms/step - loss: 0.4838 - accuracy: 0.8068 - val_loss: 1.3353 - val_accuracy: 0.7275\n",
      "Epoch 20/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4981 - accuracy: 0.8007 - val_loss: 0.7697 - val_accuracy: 0.8025\n",
      "Epoch 21/50\n",
      "247/247 [==============================] - 49s 198ms/step - loss: 0.4939 - accuracy: 0.8048 - val_loss: 1.5018 - val_accuracy: 0.7827\n",
      "Epoch 22/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4891 - accuracy: 0.7977 - val_loss: 0.9722 - val_accuracy: 0.7800\n",
      "Epoch 23/50\n",
      "247/247 [==============================] - 51s 207ms/step - loss: 0.4599 - accuracy: 0.8154 - val_loss: 0.7760 - val_accuracy: 0.8208\n",
      "Epoch 24/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.4565 - accuracy: 0.8139 - val_loss: 0.8540 - val_accuracy: 0.7970\n",
      "Epoch 25/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4445 - accuracy: 0.8048 - val_loss: 1.1717 - val_accuracy: 0.7684\n",
      "Epoch 26/50\n",
      "247/247 [==============================] - 51s 207ms/step - loss: 0.4338 - accuracy: 0.8240 - val_loss: 0.8586 - val_accuracy: 0.7520\n",
      "Epoch 27/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.4307 - accuracy: 0.8225 - val_loss: 1.0767 - val_accuracy: 0.7548\n",
      "Epoch 28/50\n",
      "247/247 [==============================] - 50s 202ms/step - loss: 0.4231 - accuracy: 0.8306 - val_loss: 0.7461 - val_accuracy: 0.7779\n",
      "Epoch 29/50\n",
      "247/247 [==============================] - 50s 201ms/step - loss: 0.4188 - accuracy: 0.8251 - val_loss: 0.7629 - val_accuracy: 0.7452\n",
      "Epoch 30/50\n",
      "247/247 [==============================] - 51s 207ms/step - loss: 0.4114 - accuracy: 0.8372 - val_loss: 0.6213 - val_accuracy: 0.8004\n",
      "Epoch 31/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4067 - accuracy: 0.8357 - val_loss: 0.8028 - val_accuracy: 0.7514\n",
      "Epoch 32/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4272 - accuracy: 0.8276 - val_loss: 0.7001 - val_accuracy: 0.8161\n",
      "Epoch 33/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4006 - accuracy: 0.8347 - val_loss: 0.6528 - val_accuracy: 0.7950\n",
      "Epoch 34/50\n",
      "247/247 [==============================] - 51s 206ms/step - loss: 0.3952 - accuracy: 0.8433 - val_loss: 0.8055 - val_accuracy: 0.8011\n",
      "Epoch 35/50\n",
      "247/247 [==============================] - 49s 198ms/step - loss: 0.4083 - accuracy: 0.8479 - val_loss: 0.7647 - val_accuracy: 0.8065\n",
      "Epoch 36/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4147 - accuracy: 0.8352 - val_loss: 0.7527 - val_accuracy: 0.8277\n",
      "Epoch 37/50\n",
      "247/247 [==============================] - 51s 207ms/step - loss: 0.4048 - accuracy: 0.8367 - val_loss: 0.7567 - val_accuracy: 0.8106\n",
      "Epoch 38/50\n",
      "247/247 [==============================] - 50s 201ms/step - loss: 0.4135 - accuracy: 0.8387 - val_loss: 0.7352 - val_accuracy: 0.8052\n",
      "Epoch 39/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.3472 - accuracy: 0.8590 - val_loss: 0.9826 - val_accuracy: 0.7943\n",
      "Epoch 40/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.3953 - accuracy: 0.8418 - val_loss: 0.7271 - val_accuracy: 0.7956\n",
      "Epoch 41/50\n",
      "247/247 [==============================] - 51s 205ms/step - loss: 0.3832 - accuracy: 0.8540 - val_loss: 1.1412 - val_accuracy: 0.7650\n",
      "Epoch 42/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.4397 - accuracy: 0.8342 - val_loss: 1.0610 - val_accuracy: 0.8113\n",
      "Epoch 43/50\n",
      "247/247 [==============================] - 51s 207ms/step - loss: 0.4106 - accuracy: 0.8458 - val_loss: 1.0243 - val_accuracy: 0.8174\n",
      "Epoch 45/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.3841 - accuracy: 0.8509 - val_loss: 1.1125 - val_accuracy: 0.6955\n",
      "Epoch 46/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.4054 - accuracy: 0.8534 - val_loss: 1.1662 - val_accuracy: 0.7902\n",
      "Epoch 47/50\n",
      "247/247 [==============================] - 49s 200ms/step - loss: 0.3681 - accuracy: 0.8565 - val_loss: 0.8673 - val_accuracy: 0.8127\n",
      "Epoch 48/50\n",
      "247/247 [==============================] - 51s 205ms/step - loss: 0.3439 - accuracy: 0.8656 - val_loss: 1.3434 - val_accuracy: 0.7950\n",
      "Epoch 49/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.3707 - accuracy: 0.8595 - val_loss: 1.6054 - val_accuracy: 0.7091\n",
      "Epoch 50/50\n",
      "247/247 [==============================] - 49s 199ms/step - loss: 0.3299 - accuracy: 0.8727 - val_loss: 1.0080 - val_accuracy: 0.7452\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "history = model.fit(x_train_seq_wtext, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_valid_seq_wtext, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db99a033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e0e4f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f96fdcce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_2 (LSTM)               (None, 402, 50)           71600     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 402, 50)           0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 20100)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 20101     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 91,701\n",
      "Trainable params: 91,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters. Structured data will have different input dimensions based on number of features x time points\n",
    "num_neurons = 50\n",
    "epochs = 3\n",
    "\n",
    "# This line may not be necessary for kernels other than conda_tensorflow2_p38\n",
    "tf.keras.backend.set_image_data_format(\"channels_last\")\n",
    "\n",
    "# Initialize network with Sequential keras model\n",
    "model = Sequential()\n",
    "model.add(LSTM(num_neurons, input_shape=(max_len, embedding_dim), return_sequences=True)) # output 100 *50\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten()) # for output with rank > 2\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile('RMSprop', 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fdc3f27",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27554/4149140418.py\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train_seq_wtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_valid_seq_wtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Save the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_json\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 'model' is not defined"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "model.fit(x_train_seq_wtext, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_valid_seq_wtext, y_valid))\n",
    "\n",
    "# Save the model \n",
    "model_str = model.to_json()\n",
    "with open('lstm_model_wtext.json','w') as json_file:\n",
    "    json_file.write(model_str)\n",
    "model.save_weights('lstm_weights_wtext.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2dcfe5d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-06 04:25:30.347114: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 724687008 exceeds 10% of free system memory.\n"
     ]
    }
   ],
   "source": [
    "# Import the saved model\n",
    "with open(\"lstm_model_wtext.json\",'r') as json_file:\n",
    "    json_string = json_file.read()\n",
    "\n",
    "model = model_from_json(json_string)\n",
    "model.load_weights('lstm_weights_wtext.h5')\n",
    "\n",
    "predictions = model.predict(x_valid_seq_wtext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31aa664e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_label = [1 if i > 0.5 else 0 for i in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a53b60f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix: \n",
      "[[357 152]\n",
      " [196 763]]\n",
      "Sensitivity, hit rate, recall, or true positive rate:  0.6455696202531646\n",
      "Specificity or true negative rate:  0.833879781420765\n",
      "Precision or positive predictive value:  0.7013752455795678\n",
      "Negative predictive value:  0.7956204379562044\n",
      "Fall out or false positive rate:  0.16612021857923498\n",
      "False negative rate:  0.35443037974683544\n",
      "False discovery rate:  0.29862475442043224\n",
      "Overall accuracy:  0.7629427792915532\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion matrix: \")\n",
    "cm = confusion_matrix(y_valid, pred_label,  labels = [1, 0])\n",
    "print(cm)\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print(\"Sensitivity, hit rate, recall, or true positive rate: \", TPR)\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "print(\"Specificity or true negative rate: \", TNR)\n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print(\"Precision or positive predictive value: \", PPV)\n",
    "\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "print(\"Negative predictive value: \", NPV)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "print(\"Fall out or false positive rate: \", FPR)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "print(\"False negative rate: \", FNR)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "print(\"False discovery rate: \", FDR)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print(\"Overall accuracy: \", ACC)\n",
    "\n",
    "# AUC, PPV, NPV, Sensitivity, Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e628c3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cm = pd.DataFrame([[365, 144],[77, 882]], index = [i for i in \"10\"], columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (8,6))\n",
    "sn.heatmap(df_cm, annot=True, cmap='Blues', fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9aec21a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88c40a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Train set score:  1.0\n",
    "Test set score:  0.9504573573732353\n",
    "Confusion matrix: \n",
    "[[    3   929]\n",
    " [    8 17973]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.2727272727272727\n",
    "Specificity or true negative rate:  0.9508517617183366\n",
    "Precision or positive predictive value:  0.003218884120171674\n",
    "Negative predictive value:  0.999555085924031\n",
    "Fall out or false positive rate:  0.049148238281663315\n",
    "False negative rate:  0.7272727272727273\n",
    "False discovery rate:  0.9967811158798283\n",
    "Overall accuracy:  0.9504573573732353\n",
    "    \n",
    "Train set score:  1.0\n",
    "Test set score:  0.950245862634167\n",
    "Confusion matrix: \n",
    "[[   10   922]\n",
    " [   19 17962]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.3448275862068966\n",
    "Specificity or true negative rate:  0.9511755983901716\n",
    "Precision or positive predictive value:  0.01072961373390558\n",
    "Negative predictive value:  0.9989433290695734\n",
    "Fall out or false positive rate:  0.04882440160982843\n",
    "False negative rate:  0.6551724137931034\n",
    "False discovery rate:  0.9892703862660944\n",
    "Overall accuracy:  0.950245862634167\n",
    "    \n",
    "CNN\n",
    "Confusion matrix: \n",
    "[[ 36  85]\n",
    " [ 14 258]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.72\n",
    "Specificity or true negative rate:  0.7521865889212828\n",
    "Precision or positive predictive value:  0.2975206611570248\n",
    "Negative predictive value:  0.9485294117647058\n",
    "Fall out or false positive rate:  0.2478134110787172\n",
    "False negative rate:  0.28\n",
    "False discovery rate:  0.7024793388429752\n",
    "Overall accuracy:  0.7480916030534351\n",
    "    \n",
    "Confusion matrix: \n",
    "[[ 67  54]\n",
    " [ 34 238]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.6633663366336634\n",
    "Specificity or true negative rate:  0.815068493150685\n",
    "Precision or positive predictive value:  0.5537190082644629\n",
    "Negative predictive value:  0.875\n",
    "Fall out or false positive rate:  0.18493150684931506\n",
    "False negative rate:  0.33663366336633666\n",
    "False discovery rate:  0.4462809917355372\n",
    "Overall accuracy:  0.7760814249363868\n",
    "    \n",
    "LSTM\n",
    "Confusion matrix: \n",
    "[[ 54  67]\n",
    " [ 19 253]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.7397260273972602\n",
    "Specificity or true negative rate:  0.790625\n",
    "Precision or positive predictive value:  0.4462809917355372\n",
    "Negative predictive value:  0.9301470588235294\n",
    "Fall out or false positive rate:  0.209375\n",
    "False negative rate:  0.2602739726027397\n",
    "False discovery rate:  0.5537190082644629\n",
    "Overall accuracy:  0.7811704834605598\n",
    "    \n",
    "Confusion matrix: \n",
    "[[ 76  45]\n",
    " [ 66 206]]\n",
    "Sensitivity, hit rate, recall, or true positive rate:  0.5352112676056338\n",
    "Specificity or true negative rate:  0.8207171314741036\n",
    "Precision or positive predictive value:  0.628099173553719\n",
    "Negative predictive value:  0.7573529411764706\n",
    "Fall out or false positive rate:  0.17928286852589642\n",
    "False negative rate:  0.4647887323943662\n",
    "False discovery rate:  0.371900826446281\n",
    "Overall accuracy:  0.7175572519083969"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caa6310f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e5b9198",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a7544f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0bffff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# How to train a model? See the Chapter 8 exercise on how to compile a RNN using Tensorflow. You also might consider using an LSTM (see below).\n",
    "from keras.models import Sequential \n",
    "from keras.layers import Dense, Dropout, Flatten, LSTM\n",
    "\n",
    "# Hyperparameters. Structured data will have different input dimensions based on number of features x time points\n",
    "maxlen = 100\n",
    "embedding_dim = 300\n",
    "batch_size = 32\n",
    "num_neurons = 50\n",
    "epochs = 2\n",
    "\n",
    "# This line may not be necessary for kernels other than conda_tensorflow2_p38\n",
    "tf.keras.backend.set_image_data_format(\"channels_last\")\n",
    "\n",
    "# Initialize network with Sequential keras model\n",
    "model = Sequential()\n",
    "model.add(LSTM(num_neurons, input_shape=(maxlen, embedding_dim), return_sequences=True)) # output 100 *50\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten()) # for output with rank > 2\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile('RMSprop', 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "### YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9e360a",
   "metadata": {},
   "source": [
    "## Recommendations for the final project using sepsis labels\n",
    "### 1) Train a RNN/LSTM on the structured data separately. Evaluate model using standard metrics (e.g. AUC, PPV, NPV, Sensitivity, Specificity)\n",
    "### 2) Use Word2Vec to generate representations from the reports\n",
    "### 3) Append representations to trained neural network and evaluate model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d7fc28a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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