Time-series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "from shogun import *\n",
    "from scripts import utils\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import time\n",
    "\n",
    "# below imports are used to print out pretty pandas dataframes\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "········\n"
     ]
    }
   ],
   "source": [
    "# SQL database config\n",
    "sqluser = 'o4nguyen'\n",
    "dbname = 'MIMIC3'\n",
    "schema_name = 'mimiciii'\n",
    "hostname = 'hdsldb.uwaterloo.ca'\n",
    "port = 5432\n",
    "pwd = getpass.getpass()\n",
    "\n",
    "# Connect to local postgres version of mimic\n",
    "con = psycopg2.connect(dbname=dbname, user=sqluser, host=hostname, port=5432, password=pwd)\n",
    "cur = con.cursor()\n",
    "cur.execute('SET search_path to ' + schema_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "DatabaseError",
     "evalue": "Execution failed on sql '-- This query pivots the time series vital signs for the first 24 hours of a patient's stay\n-- Vital signs include heart rate, blood pressure, respiration rate, and temperature\n\nset search_path to mimiciii;\nWITH stg AS \n(\n    SELECT pvt.subject_id,\n    pvt.hadm_id,\n    pvt.icustay_id,\n    pvt.\"timestamp\",\n        CASE\n            WHEN pvt.vitalid = 1 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS heartrate,\n        CASE\n            WHEN pvt.vitalid = 2 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS sysbp,\n        CASE\n            WHEN pvt.vitalid = 3 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS diasbp,\n        CASE\n            WHEN pvt.vitalid = 4 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS meanbp,\n        CASE\n            WHEN pvt.vitalid = 5 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS resprate,\n        CASE\n            WHEN pvt.vitalid = 6 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS tempc,\n        CASE\n            WHEN pvt.vitalid = 7 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS spo2\n    FROM ( SELECT ie.subject_id,\n            ie.hadm_id,\n            ie.icustay_id,\n            date_trunc('hour'::text, ce.charttime) AS \"timestamp\",\n                CASE\n                    WHEN (ce.itemid = ANY (ARRAY[211, 220045])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 1\n                    WHEN (ce.itemid = ANY (ARRAY[51, 442, 455, 6701, 220179, 220050])) AND ce.valuenum > 0::double precision AND ce.valuenum < 400::double precision THEN 2\n                    WHEN (ce.itemid = ANY (ARRAY[8368, 8440, 8441, 8555, 220180, 220051])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 3\n                    WHEN (ce.itemid = ANY (ARRAY[456, 52, 6702, 443, 220052, 220181, 225312])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 4\n                    WHEN (ce.itemid = ANY (ARRAY[615, 618, 220210, 224690])) AND ce.valuenum > 0::double precision AND ce.valuenum < 70::double precision THEN 5\n                    WHEN (ce.itemid = ANY (ARRAY[223761, 678])) AND ce.valuenum > 70::double precision AND ce.valuenum < 120::double precision THEN 6\n                    WHEN (ce.itemid = ANY (ARRAY[223762, 676])) AND ce.valuenum > 10::double precision AND ce.valuenum < 50::double precision THEN 6\n                    WHEN (ce.itemid = ANY (ARRAY[646, 220277])) AND ce.valuenum > 0::double precision AND ce.valuenum <= 100::double precision THEN 7\n                    ELSE NULL::integer\n                END AS vitalid,\n                CASE\n                    WHEN ce.itemid = ANY (ARRAY[223761, 678]) THEN (ce.valuenum - 32::double precision) / 1.8::double precision\n                    ELSE ce.valuenum\n                END AS valuenum\n            FROM mimiciii.icustays ie\n                LEFT JOIN mimiciii.chartevents ce ON ie.subject_id = ce.subject_id AND ie.hadm_id = ce.hadm_id AND ie.icustay_id = ce.icustay_id AND ce.charttime >= ie.intime AND ce.charttime <= (ie.intime + '1 day'::interval day) AND ce.error IS DISTINCT FROM 1\n            WHERE ce.itemid = ANY (ARRAY[211, 220045, 51, 442, 455, 6701, 220179, 220050, 8368, 8440, 8441, 8555, 220180, 220051, 456, 52, 6702, 443, 220052, 220181, 225312, 618, 615, 220210, 224690, 223761, 678, 223762, 676, 646, 220277])) pvt\n    GROUP BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id, pvt.\"timestamp\", pvt.vitalid, pvt.valuenum\n    ORDER BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id\n), agg AS \n(\n    SELECT stg.\"timestamp\",\n    stg.subject_id,\n    stg.hadm_id,\n    stg.icustay_id,\n    avg(stg.heartrate) AS heartrate,\n    avg(stg.sysbp) AS sysbp,\n    avg(stg.diasbp) AS diasbp,\n    avg(stg.meanbp) AS meanbp,\n    avg(stg.resprate) AS resprate,\n    avg(stg.tempc) AS tempc,\n    avg(stg.spo2) AS spo2\n    FROM stg\n    GROUP BY stg.icustay_id, stg.hadm_id, stg.subject_id, stg.\"timestamp\"\n    ORDER BY stg.icustay_id, stg.hadm_id, stg.subject_id, stg.\"timestamp\"\n)\n, vitalsfirstday_timeseries AS \n(\n    SELECT agg.\"timestamp\",\n    agg.subject_id,\n    agg.hadm_id,\n    agg.icustay_id,\n    agg.heartrate AS hr,\n    lag(agg.heartrate, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_1h,\n    lag(agg.heartrate, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_2h,\n    lag(agg.heartrate, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_3h,\n    avg(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_mean_6h,\n    min(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_min_6h,\n    max(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_max_6h,\n    median(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_median_6h,\n    agg.sysbp,\n    lag(agg.sysbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_1h,\n    lag(agg.sysbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_2h,\n    lag(agg.sysbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_3h,\n    avg(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_mean_6h,\n    min(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_min_6h,\n    max(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_max_6h,\n    median(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_median_6h,\n    agg.diasbp,\n    lag(agg.diasbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_1h,\n    lag(agg.diasbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_2h,\n    lag(agg.diasbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_3h,\n    avg(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_mean_6h,\n    min(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_min_6h,\n    max(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_max_6h,\n    median(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_median_6h,\n    agg.meanbp,\n    lag(agg.meanbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_1h,\n    lag(agg.meanbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_2h,\n    lag(agg.meanbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_3h,\n    avg(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_mean_6h,\n    min(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_min_6h,\n    max(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_max_6h,\n    median(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_median_6h,\n    agg.resprate,\n    lag(agg.resprate, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_1h,\n    lag(agg.resprate, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_2h,\n    lag(agg.resprate, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_3h,\n    avg(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_mean_6h,\n    min(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_min_6h,\n    max(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_max_6h,\n    median(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_median_6h,\n    agg.tempc,\n    lag(agg.tempc, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_1h,\n    lag(agg.tempc, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_2h,\n    lag(agg.tempc, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_3h,\n    avg(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_mean_6h,\n    min(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_min_6h,\n    max(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_max_6h,\n    median(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_median_6h,\n    agg.spo2,\n    lag(agg.spo2, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_1h,\n    lag(agg.spo2, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_2h,\n    lag(agg.spo2, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_3h,\n    avg(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_mean_6h,\n    min(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_min_6h,\n    max(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_max_6h,\n    median(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_median_6h\n    FROM agg\n    ORDER BY agg.subject_id, agg.hadm_id, agg.icustay_id, agg.\"timestamp\";\n)\n\n-- Table : Services\n, serv AS\n(\nSELECT icu.hadm_id, icu.icustay_id, se.curr_service\n, CASE\n    WHEN curr_service like '%SURG' then 1\n    WHEN curr_service = 'ORTHO' then 1\n    ELSE 0 END\n  as surgical\n, RANK() OVER (PARTITION BY icu.hadm_id ORDER BY se.transfertime DESC) as rank\nFROM icustays icu\nLEFT JOIN services se\n ON icu.hadm_id = se.hadm_id\nAND se.transfertime < icu.intime + interval '12' hour\n)\n\n-- Table : Clinical data + demographics\n, co AS\n(\nSELECT icu.subject_id, icu.hadm_id, icu.icustay_id, first_careunit, admission_type\n, icu.los as icu_los\n, round((EXTRACT(EPOCH FROM (adm.dischtime-adm.admittime))/60/60/24) :: NUMERIC, 4) as hosp_los\n, EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age_icu_in\n, icu.intime as icu_intime\n, pat.gender\n, pat.dod_hosp\n, RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n, hospital_expire_flag\n, CASE WHEN pat.dod IS NOT NULL \n       AND pat.dod >= icu.intime - interval '6 hour'\n       AND pat.dod <= icu.outtime + interval '6 hour' THEN 1 \n       ELSE 0 END AS icu_expire_flag\n, CASE WHEN pat.dod IS NOT NULL\n    AND pat.dod < adm.admittime + interval '30' day THEN 1 \n    ELSE 0 END as hospital30day_expire_flag\n, CASE WHEN pat.dod IS NOT NULL\n    AND pat.dod < adm.admittime + interval '1' year THEN 1 \n    ELSE 0 END as hospital1year_expire_flag      \nFROM icustays icu\nINNER JOIN patients pat\n  ON icu.subject_id = pat.subject_id\nINNER JOIN admissions adm\nON adm.hadm_id = icu.hadm_id    \n)\n\n-- Table : Exclusions\n, excl AS\n(\nSELECT\n  co.subject_id, co.hadm_id, co.icustay_id, co.icu_los, co.hosp_los\n  , co.age_icu_in\n  , co.gender\n  , co.icustay_id_order\n  , serv.curr_service\n  , co.first_careunit\n  , co.hospital_expire_flag\n  , co.icu_expire_flag\n  , CASE\n        WHEN co.icu_los < 1 then 1\n    ELSE 0 END\n    AS exclusion_los\n  , CASE\n        WHEN co.age_icu_in < 16 then 1\n    ELSE 0 END\n    AS exclusion_age\n  , CASE \n        WHEN co.icustay_id_order != 1 THEN 1\n    ELSE 0 END \n    AS exclusion_first_stay\n  , CASE\n        WHEN serv.surgical = 1 THEN 1\n    ELSE 0 END\n    as exclusion_surgical\nFROM co\nLEFT JOIN serv\n  ON  co.icustay_id = serv.icustay_id\n  AND serv.rank = 1\n)\n\nSELECT \n\n-- vital signs for the first 24 hours of the icu stay\nvital.*\n\n-- vital.icustay_id, vital.subject_id, vital.hadm_id\n-- , HeartRate\n-- , DiasBP\n-- , SysBP\n-- , MeanBP\n-- , RespRate\n-- , TempC\n-- , SpO2\n\n-- demographic data\n, co.age_icu_in, co.first_careunit, co.gender, co.admission_type\n, hw.height_first, hw.weight_first\n, EXTRACT('epoch' from vital.timestamp - co.icu_intime) / 60.0 / 60.0 / 24.0 / 365.242 as icu_los\n-- outcomes\n, co.hospital_expire_flag, co.icu_expire_flag\n, co.hosp_los, co.icu_los, co.icustay_id_order\n, co.dod_hosp\n, CASE \n    WHEN vital.timestamp >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead\n, CASE \n    WHEN vital.timestamp + interval '1' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_1d\n, CASE \n    WHEN vital.timestamp + interval '7' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_7d\n, CASE \n    WHEN vital.timestamp + interval '30' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_30d\n\n-- exclusions\n, excl.exclusion_los, excl.exclusion_age\n, excl.exclusion_first_stay, excl.exclusion_surgical\n\nFROM vitalsfirstday_timeseries vital\nleft join co\n  ON vital.icustay_id = co.icustay_id\nleft join excl\n  on vital.icustay_id = excl.icustay_id;\n': syntax error at or near \";\"\nLINE 143: ...gg.subject_id, agg.hadm_id, agg.icustay_id, agg.\"timestamp\";\n                                                                        ^\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mDatabaseError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-b15812a0fca9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu\"query_output = utils.get_query_from_file('sql/vitals-lagged-full.sql', con)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2113\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2114\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2115\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2116\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1183\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             \u001b[0;32mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/home/gsoc2017-shogun-dataproject/scripts/utils.pyc\u001b[0m in \u001b[0;36mget_query_from_file\u001b[0;34m(filename, connection)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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[0mquery_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mquery_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m    330\u001b[0m     return pandas_sql.read_query(\n\u001b[1;32m    331\u001b[0m         \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         parse_dates=parse_dates, chunksize=chunksize)\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m         \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\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[0m\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol_desc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol_desc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1414\u001b[0m             ex = DatabaseError(\n\u001b[1;32m   1415\u001b[0m                 \"Execution failed on sql '%s': %s\" % (args[0], exc))\n\u001b[0;32m-> 1416\u001b[0;31m             \u001b[0mraise_with_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1402\u001b[0m                 \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\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[0m\n\u001b[1;32m   1403\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1404\u001b[0;31m                 \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\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[0m\n\u001b[0m\u001b[1;32m   1405\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDatabaseError\u001b[0m: Execution failed on sql '-- This query pivots the time series vital signs for the first 24 hours of a patient's stay\n-- Vital signs include heart rate, blood pressure, respiration rate, and temperature\n\nset search_path to mimiciii;\nWITH stg AS \n(\n    SELECT pvt.subject_id,\n    pvt.hadm_id,\n    pvt.icustay_id,\n    pvt.\"timestamp\",\n        CASE\n            WHEN pvt.vitalid = 1 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS heartrate,\n        CASE\n            WHEN pvt.vitalid = 2 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS sysbp,\n        CASE\n            WHEN pvt.vitalid = 3 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS diasbp,\n        CASE\n            WHEN pvt.vitalid = 4 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS meanbp,\n        CASE\n            WHEN pvt.vitalid = 5 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS resprate,\n        CASE\n            WHEN pvt.vitalid = 6 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS tempc,\n        CASE\n            WHEN pvt.vitalid = 7 THEN pvt.valuenum\n            ELSE NULL::double precision\n        END AS spo2\n    FROM ( SELECT ie.subject_id,\n            ie.hadm_id,\n            ie.icustay_id,\n            date_trunc('hour'::text, ce.charttime) AS \"timestamp\",\n                CASE\n                    WHEN (ce.itemid = ANY (ARRAY[211, 220045])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 1\n                    WHEN (ce.itemid = ANY (ARRAY[51, 442, 455, 6701, 220179, 220050])) AND ce.valuenum > 0::double precision AND ce.valuenum < 400::double precision THEN 2\n                    WHEN (ce.itemid = ANY (ARRAY[8368, 8440, 8441, 8555, 220180, 220051])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 3\n                    WHEN (ce.itemid = ANY (ARRAY[456, 52, 6702, 443, 220052, 220181, 225312])) AND ce.valuenum > 0::double precision AND ce.valuenum < 300::double precision THEN 4\n                    WHEN (ce.itemid = ANY (ARRAY[615, 618, 220210, 224690])) AND ce.valuenum > 0::double precision AND ce.valuenum < 70::double precision THEN 5\n                    WHEN (ce.itemid = ANY (ARRAY[223761, 678])) AND ce.valuenum > 70::double precision AND ce.valuenum < 120::double precision THEN 6\n                    WHEN (ce.itemid = ANY (ARRAY[223762, 676])) AND ce.valuenum > 10::double precision AND ce.valuenum < 50::double precision THEN 6\n                    WHEN (ce.itemid = ANY (ARRAY[646, 220277])) AND ce.valuenum > 0::double precision AND ce.valuenum <= 100::double precision THEN 7\n                    ELSE NULL::integer\n                END AS vitalid,\n                CASE\n                    WHEN ce.itemid = ANY (ARRAY[223761, 678]) THEN (ce.valuenum - 32::double precision) / 1.8::double precision\n                    ELSE ce.valuenum\n                END AS valuenum\n            FROM mimiciii.icustays ie\n                LEFT JOIN mimiciii.chartevents ce ON ie.subject_id = ce.subject_id AND ie.hadm_id = ce.hadm_id AND ie.icustay_id = ce.icustay_id AND ce.charttime >= ie.intime AND ce.charttime <= (ie.intime + '1 day'::interval day) AND ce.error IS DISTINCT FROM 1\n            WHERE ce.itemid = ANY (ARRAY[211, 220045, 51, 442, 455, 6701, 220179, 220050, 8368, 8440, 8441, 8555, 220180, 220051, 456, 52, 6702, 443, 220052, 220181, 225312, 618, 615, 220210, 224690, 223761, 678, 223762, 676, 646, 220277])) pvt\n    GROUP BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id, pvt.\"timestamp\", pvt.vitalid, pvt.valuenum\n    ORDER BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id\n), agg AS \n(\n    SELECT stg.\"timestamp\",\n    stg.subject_id,\n    stg.hadm_id,\n    stg.icustay_id,\n    avg(stg.heartrate) AS heartrate,\n    avg(stg.sysbp) AS sysbp,\n    avg(stg.diasbp) AS diasbp,\n    avg(stg.meanbp) AS meanbp,\n    avg(stg.resprate) AS resprate,\n    avg(stg.tempc) AS tempc,\n    avg(stg.spo2) AS spo2\n    FROM stg\n    GROUP BY stg.icustay_id, stg.hadm_id, stg.subject_id, stg.\"timestamp\"\n    ORDER BY stg.icustay_id, stg.hadm_id, stg.subject_id, stg.\"timestamp\"\n)\n, vitalsfirstday_timeseries AS \n(\n    SELECT agg.\"timestamp\",\n    agg.subject_id,\n    agg.hadm_id,\n    agg.icustay_id,\n    agg.heartrate AS hr,\n    lag(agg.heartrate, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_1h,\n    lag(agg.heartrate, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_2h,\n    lag(agg.heartrate, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS hr_3h,\n    avg(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_mean_6h,\n    min(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_min_6h,\n    max(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_max_6h,\n    median(agg.heartrate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS hr_median_6h,\n    agg.sysbp,\n    lag(agg.sysbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_1h,\n    lag(agg.sysbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_2h,\n    lag(agg.sysbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS sysbp_3h,\n    avg(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_mean_6h,\n    min(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_min_6h,\n    max(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_max_6h,\n    median(agg.sysbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS sysbp_median_6h,\n    agg.diasbp,\n    lag(agg.diasbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_1h,\n    lag(agg.diasbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_2h,\n    lag(agg.diasbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS diasbp_3h,\n    avg(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_mean_6h,\n    min(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_min_6h,\n    max(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_max_6h,\n    median(agg.diasbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS diasbp_median_6h,\n    agg.meanbp,\n    lag(agg.meanbp, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_1h,\n    lag(agg.meanbp, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_2h,\n    lag(agg.meanbp, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS meanbp_3h,\n    avg(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_mean_6h,\n    min(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_min_6h,\n    max(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_max_6h,\n    median(agg.meanbp) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS meanbp_median_6h,\n    agg.resprate,\n    lag(agg.resprate, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_1h,\n    lag(agg.resprate, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_2h,\n    lag(agg.resprate, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS resprate_3h,\n    avg(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_mean_6h,\n    min(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_min_6h,\n    max(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_max_6h,\n    median(agg.resprate) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS resprate_median_6h,\n    agg.tempc,\n    lag(agg.tempc, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_1h,\n    lag(agg.tempc, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_2h,\n    lag(agg.tempc, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS tempc_3h,\n    avg(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_mean_6h,\n    min(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_min_6h,\n    max(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_max_6h,\n    median(agg.tempc) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS tempc_median_6h,\n    agg.spo2,\n    lag(agg.spo2, 1) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_1h,\n    lag(agg.spo2, 2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_2h,\n    lag(agg.spo2, 3) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\") AS spo2_3h,\n    avg(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_mean_6h,\n    min(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_min_6h,\n    max(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_max_6h,\n    median(agg.spo2) OVER (ORDER BY agg.icustay_id, agg.subject_id, agg.\"timestamp\" ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS spo2_median_6h\n    FROM agg\n    ORDER BY agg.subject_id, agg.hadm_id, agg.icustay_id, agg.\"timestamp\";\n)\n\n-- Table : Services\n, serv AS\n(\nSELECT icu.hadm_id, icu.icustay_id, se.curr_service\n, CASE\n    WHEN curr_service like '%SURG' then 1\n    WHEN curr_service = 'ORTHO' then 1\n    ELSE 0 END\n  as surgical\n, RANK() OVER (PARTITION BY icu.hadm_id ORDER BY se.transfertime DESC) as rank\nFROM icustays icu\nLEFT JOIN services se\n ON icu.hadm_id = se.hadm_id\nAND se.transfertime < icu.intime + interval '12' hour\n)\n\n-- Table : Clinical data + demographics\n, co AS\n(\nSELECT icu.subject_id, icu.hadm_id, icu.icustay_id, first_careunit, admission_type\n, icu.los as icu_los\n, round((EXTRACT(EPOCH FROM (adm.dischtime-adm.admittime))/60/60/24) :: NUMERIC, 4) as hosp_los\n, EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age_icu_in\n, icu.intime as icu_intime\n, pat.gender\n, pat.dod_hosp\n, RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n, hospital_expire_flag\n, CASE WHEN pat.dod IS NOT NULL \n       AND pat.dod >= icu.intime - interval '6 hour'\n       AND pat.dod <= icu.outtime + interval '6 hour' THEN 1 \n       ELSE 0 END AS icu_expire_flag\n, CASE WHEN pat.dod IS NOT NULL\n    AND pat.dod < adm.admittime + interval '30' day THEN 1 \n    ELSE 0 END as hospital30day_expire_flag\n, CASE WHEN pat.dod IS NOT NULL\n    AND pat.dod < adm.admittime + interval '1' year THEN 1 \n    ELSE 0 END as hospital1year_expire_flag      \nFROM icustays icu\nINNER JOIN patients pat\n  ON icu.subject_id = pat.subject_id\nINNER JOIN admissions adm\nON adm.hadm_id = icu.hadm_id    \n)\n\n-- Table : Exclusions\n, excl AS\n(\nSELECT\n  co.subject_id, co.hadm_id, co.icustay_id, co.icu_los, co.hosp_los\n  , co.age_icu_in\n  , co.gender\n  , co.icustay_id_order\n  , serv.curr_service\n  , co.first_careunit\n  , co.hospital_expire_flag\n  , co.icu_expire_flag\n  , CASE\n        WHEN co.icu_los < 1 then 1\n    ELSE 0 END\n    AS exclusion_los\n  , CASE\n        WHEN co.age_icu_in < 16 then 1\n    ELSE 0 END\n    AS exclusion_age\n  , CASE \n        WHEN co.icustay_id_order != 1 THEN 1\n    ELSE 0 END \n    AS exclusion_first_stay\n  , CASE\n        WHEN serv.surgical = 1 THEN 1\n    ELSE 0 END\n    as exclusion_surgical\nFROM co\nLEFT JOIN serv\n  ON  co.icustay_id = serv.icustay_id\n  AND serv.rank = 1\n)\n\nSELECT \n\n-- vital signs for the first 24 hours of the icu stay\nvital.*\n\n-- vital.icustay_id, vital.subject_id, vital.hadm_id\n-- , HeartRate\n-- , DiasBP\n-- , SysBP\n-- , MeanBP\n-- , RespRate\n-- , TempC\n-- , SpO2\n\n-- demographic data\n, co.age_icu_in, co.first_careunit, co.gender, co.admission_type\n, hw.height_first, hw.weight_first\n, EXTRACT('epoch' from vital.timestamp - co.icu_intime) / 60.0 / 60.0 / 24.0 / 365.242 as icu_los\n-- outcomes\n, co.hospital_expire_flag, co.icu_expire_flag\n, co.hosp_los, co.icu_los, co.icustay_id_order\n, co.dod_hosp\n, CASE \n    WHEN vital.timestamp >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead\n, CASE \n    WHEN vital.timestamp + interval '1' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_1d\n, CASE \n    WHEN vital.timestamp + interval '7' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_7d\n, CASE \n    WHEN vital.timestamp + interval '30' day >= co.dod_hosp THEN 1 \n    ELSE 0 END \n    AS dead_in_30d\n\n-- exclusions\n, excl.exclusion_los, excl.exclusion_age\n, excl.exclusion_first_stay, excl.exclusion_surgical\n\nFROM vitalsfirstday_timeseries vital\nleft join co\n  ON vital.icustay_id = co.icustay_id\nleft join excl\n  on vital.icustay_id = excl.icustay_id;\n': syntax error at or near \";\"\nLINE 143: ...gg.subject_id, agg.hadm_id, agg.icustay_id, agg.\"timestamp\";\n                                                                        ^\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "query_output = utils.get_query_from_file('sql/vitals-lagged-full.sql', con)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 23s, sys: 10.4 s, total: 1min 33s\n",
      "Wall time: 3min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "query_output = utils.get_query_from_file('sql/firstday-lagged-materialized.sql', con)\n",
    "query_output.to_csv('./vitals-lagged.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_type</th>\n",
       "      <th>age_icu_in</th>\n",
       "      <th>diasbp</th>\n",
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       "      <th>diasbp_median_6h</th>\n",
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       "      <th>...</th>\n",
       "      <th>sysbp_median_6h</th>\n",
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       "      <td>53.0</td>\n",
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       "      <td>107.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                     admission_type  age_icu_in  diasbp  diasbp_1h  diasbp_2h  \\\n",
       "timestamp                                                                       \n",
       "2111-11-07 02:00:00               1   37.446714    54.0  60.571963  60.579264   \n",
       "2111-11-07 03:00:00               1   37.446714    60.0  54.000000  60.579264   \n",
       "2111-11-07 04:00:00               1   37.446714    56.0  60.000000  54.000000   \n",
       "2111-11-07 05:00:00               1   37.446714    50.0  56.000000  60.000000   \n",
       "2111-11-07 06:00:00               1   37.446714    40.5  50.000000  56.000000   \n",
       "\n",
       "                     diasbp_3h  diasbp_max_6h  diasbp_mean_6h  \\\n",
       "timestamp                                                       \n",
       "2111-11-07 02:00:00  60.588039           54.0       53.500000   \n",
       "2111-11-07 03:00:00  60.588039           60.0       55.666667   \n",
       "2111-11-07 04:00:00  60.588039           60.0       55.750000   \n",
       "2111-11-07 05:00:00  54.000000           60.0       55.000000   \n",
       "2111-11-07 06:00:00  60.000000           60.0       52.100000   \n",
       "\n",
       "                     diasbp_median_6h  diasbp_min_6h      ...       \\\n",
       "timestamp                                                 ...        \n",
       "2111-11-07 02:00:00              53.5           53.0      ...        \n",
       "2111-11-07 03:00:00              54.0           53.0      ...        \n",
       "2111-11-07 04:00:00              55.0           53.0      ...        \n",
       "2111-11-07 05:00:00              55.0           50.0      ...        \n",
       "2111-11-07 06:00:00              54.0           40.5      ...        \n",
       "\n",
       "                     sysbp_median_6h  sysbp_min_6h      tempc   tempc_1h  \\\n",
       "timestamp                                                                  \n",
       "2111-11-07 02:00:00            107.5         107.0  37.388891  36.975663   \n",
       "2111-11-07 03:00:00            107.0         107.0  36.978712  37.388891   \n",
       "2111-11-07 04:00:00            107.0         102.0  36.978712  36.975663   \n",
       "2111-11-07 05:00:00            104.5          96.0  36.978712  36.975663   \n",
       "2111-11-07 06:00:00            102.0          94.0  36.777778  36.975663   \n",
       "\n",
       "                      tempc_2h   tempc_3h  tempc_max_6h  tempc_mean_6h  \\\n",
       "timestamp                                                                \n",
       "2111-11-07 02:00:00  36.972327  36.967630     37.388891      37.388891   \n",
       "2111-11-07 03:00:00  36.972327  36.967630     37.388891      37.388891   \n",
       "2111-11-07 04:00:00  37.388891  36.967630     37.388891      37.388891   \n",
       "2111-11-07 05:00:00  36.972327  37.388891     37.388891      37.388891   \n",
       "2111-11-07 06:00:00  36.972327  36.967630     37.388891      37.083334   \n",
       "\n",
       "                     tempc_median_6h  tempc_min_6h  \n",
       "timestamp                                           \n",
       "2111-11-07 02:00:00        37.388891     37.388891  \n",
       "2111-11-07 03:00:00        37.388891     37.388891  \n",
       "2111-11-07 04:00:00        37.388891     37.388891  \n",
       "2111-11-07 05:00:00        37.388891     37.388891  \n",
       "2111-11-07 06:00:00        37.083334     36.777778  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_output = pd.read_csv('./vitals-lagged.csv') #, index_col='timestamp') #.dropna().reset_index(drop=True)\n",
    "\n",
    "query_output = query_output.dropna(subset=['timestamp'], how='any')\n",
    "query_output= query_output.set_index('timestamp')\n",
    "\n",
    "query_output.first_careunit = pd.Categorical(query_output.first_careunit)\n",
    "query_output.gender = pd.Categorical(query_output.gender)\n",
    "query_output.admission_type = pd.Categorical(query_output.admission_type)\n",
    "\n",
    "query_output['gender'] = query_output.gender.cat.codes\n",
    "query_output['first_careunit'] = query_output.first_careunit.cat.codes\n",
    "query_output['admission_type'] = query_output.admission_type.cat.codes\n",
    "\n",
    "query_output = query_output[(query_output.exclusion_los == 0) & (query_output.exclusion_age == 0) \n",
    "                            & (query_output.exclusion_first_stay == 0) ]#& (query_output.age_icu_in < 250)]\n",
    "\n",
    "# Median age of patients > 89 is 91.6\n",
    "query_output.loc[query_output.age_icu_in > 89, 'age_icu_in'] = 91.6\n",
    "\n",
    "mortality_names = ['hospital_expire_flag', 'icu_expire_flag', \n",
    "                   'dod_hosp', 'dead_in_1d', 'dead_in_7d']\n",
    "\n",
    "# Demographic information\n",
    "demographics =  ['subject_id', 'age_icu_in', 'gender', \n",
    "                 'first_careunit', 'admission_type']\n",
    "\n",
    "vital_signs = ['hr', 'diasbp', 'sysbp', 'meanbp', 'resprate', 'tempc', 'spo2']\n",
    "features = [[v, v + '_mean_6h', v + '_median_6h', v + '_max_6h', v + '_min_6h', \n",
    "             v + '_1h', v + '_2h', v + '_3h'] for v in vital_signs]\n",
    "feature_names = []\n",
    "for f in features:\n",
    "    feature_names += f\n",
    "    \n",
    "# feature_names[0] = 'heartrate'\n",
    "\n",
    "query_output = query_output.loc[:, mortality_names + demographics + feature_names]\n",
    "query_output = query_output.fillna(query_output.mean())\n",
    "query_output.index = pd.to_datetime(query_output.index)\n",
    "\n",
    "# Remove points after a patient has already died\n",
    "will_die = (~query_output.dod_hosp.isnull())\n",
    "point_after_dead = (query_output.index > pd.DatetimeIndex(query_output.dod_hosp))\n",
    "query_output = query_output.loc[~(will_die & point_after_dead)]\n",
    "\n",
    "# Undersample negative data points\n",
    "\"\"\"\n",
    "will_survive = (query_output.dod_hosp.isnull())\n",
    "will_die = ~will_survive\n",
    "num_to_remove = len(will_survive)\n",
    "to_remove = np.random.choice(np.where(will_survive == True)[0], num_to_remove)\n",
    "will_survive[to_remove] = False\n",
    "query_output = query_output.loc[will_survive | will_die]\n",
    "\"\"\"\n",
    "\n",
    "df_labels = query_output.loc[:, mortality_names]\n",
    "df_features = query_output.loc[:, query_output.columns.difference(mortality_names)]\n",
    "\n",
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vqOci0rOQ2zch21oRdkdQx1b0HyEEM9PjWFd0Er8uB2RFWzixd3sbTQ3BdRPGxBmYMPXi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\nEDXxsxAa+cywaxFwAQNAaI1YE2bTHJxEWPlygmt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PzycuLo6srCz0ev1N16fXA8bKlSt5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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a4257d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VRaeTFCgqbrcoZltts7M0/G2nyWTy+3lUFedmVVBX7PSVGj+DJ02axKRJk1i2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+AAAC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a4257d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0AkGp6nyn9FRbxFIv+i9aEKJiEQ/uQbRaeqScukT38PWTIVfYWraGD3bgXpJo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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a4257d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dovX1fFqlPmbNmsXs2bNJSUlBlmWio6OZP38+999/P4888giLFi1izJgxDdbD0Ov1zjAX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a34eeac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FXHU7K1UCao2ASiU0adXhihNcXch2Oo7sMC7D2Q6jmoyz++lmXIbVriDV825C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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a3eef13d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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89913vPvuu61tkkIbpm3H8Qr1EhUVxZ133kl+fj7bt2/n22+/pbi4mNGjR5MW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a58a5eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JxMbGdnq0RCB4PB62b9/O7t27MRgMzJgxg5EjRyJ3gwhRX6XR18APxW9QULud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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a40c1eb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_surv_rows = np.random.choice(non_survival.subject_id.values, 1)\n",
    "non_survival = non_survival.loc[non_survival.subject_id.isin(non_surv_rows)]\n",
    "\n",
    "feats = ['hr', 'sysbp', 'diasbp', 'meanbp', 'resprate', 'tempc', 'spo2']\n",
    "for title, group in non_survival.groupby('subject_id'):\n",
    "    for f in feats:\n",
    "        group.filter(regex=f).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jzg0aNHBAQEDNh+KOnp6aSnp3tet7W1jdmYnhQaGjqu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\nyNTmdUNJQUHBU987d+6ct7sXEZlyvKmbQ92syszMpKSkhOrqa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iOukbAHjDlR7fkJ6erg0bNkiSNmzYoIyMDGf7xo0bZYzRnj17FBkZqbi4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\nOH78OC+//DLRaJTdu3fzyiuvAPDGG2/wwgsvkJ6ezo4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H777RQUFBAYGNjuOTQ1NTF16lR+9atf8cIL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8eb40d8a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "will_die = (~query_output.dod_hosp.isnull())\n",
    "died = query_output.loc[will_die]\n",
    "survived = query_output.loc[~will_die]\n",
    "\n",
    "survived = df_features.loc[df_labels.dead_in_7d == 0].sample(frac=0.20)\n",
    "died = df_features.loc[df_labels.dead_in_7d != 0]\n",
    "\n",
    "f, axarr = plt.subplots(7, 6, figsize=(28, 22))\n",
    "\n",
    "i = -1\n",
    "for idx, feature_name in enumerate(feature_names):\n",
    "    x1 = survived.loc[:, feature_name].dropna()\n",
    "    x2 = died.loc[:, feature_name].dropna()\n",
    "    if idx % 6 == 0:\n",
    "        i += 1\n",
    "    p = axarr[i, idx % 6].hist([x1, x2], stacked=True, bins=50, normed=False)\n",
    "    axarr[i, idx % 6].set_ylabel('Count')\n",
    "    axarr[i, idx % 6].set_title(feature_name, fontsize=18)\n",
    "    \n",
    "axarr[0, 5].legend(['Survival', 'Non-survival'], loc=(1, 1.5), fontsize=20)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of training samples:', 50000)\n",
      "('Number of testing samples:', 50000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_features.drop('subject_id', axis=1).reset_index(drop=True)\n",
    "y = df_labels['dead_in_7d'].reset_index(drop=True)\n",
    "\n",
    "dataset_size = 100000\n",
    "subset_idx = np.random.choice(len(df_features), dataset_size)\n",
    "\n",
    "X = X.loc[subset_idx].reset_index(drop=True)\n",
    "y = y.loc[subset_idx].reset_index(drop=True)\n",
    "\n",
    "split = int(len(X) * 0.5)\n",
    "\n",
    "idx = np.random.permutation(np.arange(len(y)))\n",
    "train_idx = idx[:split]\n",
    "test_idx = idx[split:]\n",
    "\n",
    "X_train = RealFeatures(np.array(X.loc[train_idx].T))\n",
    "X_test = RealFeatures(np.array(X.loc[test_idx].T))\n",
    "\n",
    "y_train = MulticlassLabels(np.array(y.loc[train_idx]).astype(np.float))\n",
    "y_test = MulticlassLabels(np.array(y.loc[test_idx]).astype(np.float))\n",
    "\n",
    "print(\"Number of training samples:\", y_train.get_num_labels())\n",
    "print(\"Number of testing samples:\", y_test.get_num_labels())\n",
    "\n",
    "preprocessor = PruneVarSubMean(True)\n",
    "preprocessor.init(X_train)\n",
    "X_train.add_preprocessor(preprocessor)\n",
    "X_train.apply_preprocessor(True)\n",
    "\n",
    "preprocessor.init(X_test)\n",
    "X_test.add_preprocessor(preprocessor)\n",
    "X_test.apply_preprocessor(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of positive (death) labels: ', 6473)\n",
      "('Number of negative (alive) labels: ', 93527)\n",
      "('positive/negative ratio:', 14.44878727019929)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of positive (death) labels: \", sum(y == 1))\n",
    "print(\"Number of negative (alive) labels: \", sum(y == 0))\n",
    "\n",
    "print(\"positive/negative ratio:\", sum(y == 0) / float(sum(y == 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Accuracy(%):', 93.396)\n",
      "('Area under ROC(%):', 0.7266923626610214)\n",
      "CPU times: user 3.56 s, sys: 0 ns, total: 3.56 s\n",
      "Wall time: 1.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Parameters to svm\n",
    "C = 0.1\n",
    "epsilon = 0.001\n",
    "\n",
    "svm = LibLinear(C, X_train, y_train.get_binary_for_class(1))\n",
    "svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)\n",
    "svm.set_epsilon(epsilon)\n",
    "\n",
    "svm.train()\n",
    "\n",
    "y_pred = svm.apply_binary(X_test)\n",
    "\n",
    "#use AccuracyMeasure to get accuracy\n",
    "acc = AccuracyMeasure()\n",
    "acc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "\n",
    "accuracy = acc.get_accuracy() * 100\n",
    "print('Accuracy(%):', accuracy)\n",
    "\n",
    "roc = ROCEvaluation()\n",
    "roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "\n",
    "auc = roc.get_auROC()\n",
    "print(\"Area under ROC(%):\", auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Area under ROC(%):', 0.728990361122668)\n",
      "CPU times: user 3.51 s, sys: 0 ns, total: 3.51 s\n",
      "Wall time: 1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Parameters to svm\n",
    "C = 0.1\n",
    "epsilon = 0.001\n",
    "\n",
    "svm = LibLinear(C, X_train, y_train.get_binary_for_class(1))\n",
    "svm.set_liblinear_solver_type(L2R_LR)\n",
    "svm.set_epsilon(epsilon)\n",
    "\n",
    "svm.train()\n",
    "\n",
    "y_pred = svm.apply_binary(X_test)\n",
    "\n",
    "lr_roc = ROCEvaluation()\n",
    "lr_roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "\n",
    "auc = lr_roc.get_auROC()\n",
    "print(\"Area under ROC(%):\", auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.0, 'Area under ROC(%):', 0.5)\n",
      "(0.01, 'Area under ROC(%):', 0.726678192457378)\n",
      "(0.1, 'Area under ROC(%):', 0.7266923626610214)\n",
      "(1.0, 'Area under ROC(%):', 0.7266949372929975)\n",
      "(10.0, 'Area under ROC(%):', 0.7266945222389782)\n",
      "(100.0, 'Area under ROC(%):', 0.7266951318495695)\n",
      "(1000.0, 'Area under ROC(%):', 0.7266977324224156)\n",
      "CPU times: user 19.6 s, sys: 48 ms, total: 19.7 s\n",
      "Wall time: 5.54 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Parameters to svm\n",
    "C = 0.1\n",
    "epsilon = 0.001\n",
    "aucs = []\n",
    "for c in [0.0, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]:\n",
    "    svm = LibLinear(c, X_train, y_train.get_binary_for_class(1))\n",
    "    svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)\n",
    "    svm.set_epsilon(epsilon)\n",
    "\n",
    "    svm.train()\n",
    "\n",
    "    y_pred = svm.apply_binary(X_test)\n",
    "\n",
    "    roc = ROCEvaluation()\n",
    "    roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "\n",
    "    auc = roc.get_auROC()\n",
    "    aucs.append(auc)\n",
    "    print(svm.get_C1(), \"Area under ROC(%):\", auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Area under ROC(%):', 0.8313112879336093)\n",
      "CPU times: user 10min 10s, sys: 3.38 s, total: 10min 14s\n",
      "Wall time: 3min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "n_trees = 100\n",
    "\n",
    "feature_types = np.zeros_like(X.columns, dtype=bool)\n",
    "feature_types[np.where( (X.columns == 'admission_type') | (X.columns == 'first_careunit') |(X.columns == 'gender'))[0]] = True\n",
    "\n",
    "m_vote = MeanRule()\n",
    "\n",
    "rand_forest = RandomForest(X_train, y_train, n_trees)\n",
    "rand_forest.set_combination_rule(m_vote)\n",
    "rand_forest.set_feature_types(feature_types)\n",
    "\n",
    "rand_forest.train()\n",
    "\n",
    "y_pred = rand_forest.apply_binary(X_test)\n",
    "\n",
    "roc = ROCEvaluation()\n",
    "roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "\n",
    "auc = roc.get_auROC()\n",
    "print(\"Area under ROC(%):\", auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling 100,000 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.786942159003\n",
      "0.770087874176\n",
      "0.783132809843\n",
      "0.793954322144\n",
      "0.788214041079\n",
      "0.781461674795\n",
      "0.786556314303\n",
      "0.783661404784\n",
      "0.794555560961\n",
      "0.781883672388\n",
      "CPU times: user 46min 34s, sys: 9.93 s, total: 46min 43s\n",
      "Wall time: 13min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dataset_size = 100000\n",
    "n_trees = 100\n",
    "feature_types = np.zeros_like(X.columns, dtype=bool)\n",
    "feature_types[np.where( (X.columns == 'admission_type') | (X.columns == 'first_careunit') |(X.columns == 'gender'))[0]] = True\n",
    "\n",
    "svm_aucs = []\n",
    "lr_aucs = []\n",
    "rf_aucs = []\n",
    "roc = ROCEvaluation()\n",
    "\n",
    "for i in range(10):\n",
    "    \n",
    "    X = df_features.drop('subject_id', axis=1).reset_index(drop=True)\n",
    "    y = df_labels['dead_in_1d'].reset_index(drop=True)    \n",
    "    # Randomly select 100,000 points\n",
    "    subset_idx = np.random.choice(len(y), dataset_size)\n",
    "    X = X.loc[subset_idx].reset_index(drop=True)\n",
    "    y = y.loc[subset_idx].reset_index(drop=True)\n",
    "\n",
    "    split = int(len(X) * 0.5)\n",
    "\n",
    "    idx = np.random.permutation(np.arange(len(y)))\n",
    "    train_idx = idx[:split]\n",
    "    test_idx = idx[split:]\n",
    "\n",
    "    X_train = RealFeatures(np.array(X.loc[train_idx].T))\n",
    "    X_test = RealFeatures(np.array(X.loc[test_idx].T))\n",
    "\n",
    "    y_train = MulticlassLabels(np.array(y.loc[train_idx]).astype(np.float))\n",
    "    y_test = MulticlassLabels(np.array(y.loc[test_idx]).astype(np.float))\n",
    "\n",
    "    preprocessor = PruneVarSubMean(True)\n",
    "    preprocessor.init(X_train)\n",
    "    X_train.add_preprocessor(preprocessor)\n",
    "    X_train.apply_preprocessor(True)\n",
    "\n",
    "    preprocessor.init(X_test)\n",
    "    X_test.add_preprocessor(preprocessor)\n",
    "    X_test.apply_preprocessor(True)    \n",
    "    \n",
    "    # SVM\n",
    "    svm = LibLinear(C, X_train, y_train.get_binary_for_class(1))\n",
    "    svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)\n",
    "    svm.set_epsilon(epsilon)\n",
    "    svm.train()\n",
    "    y_pred = svm.apply_binary(X_test)\n",
    "    roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "    auc = roc.get_auROC()\n",
    "    svm_aucs.append(auc)\n",
    "    \n",
    "    # Logistic regression\n",
    "    svm = LibLinear(C, X_train, y_train.get_binary_for_class(1))\n",
    "    svm.set_liblinear_solver_type(L2R_LR)\n",
    "    svm.set_epsilon(epsilon)\n",
    "    svm.train()\n",
    "    y_pred = svm.apply_binary(X_test)\n",
    "    roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "    auc = roc.get_auROC()\n",
    "    lr_aucs.append(auc)\n",
    "    \n",
    "    # Random Forest\n",
    "    feature_types = np.zeros_like(X.columns, dtype=bool)\n",
    "    feature_types[np.where( (X.columns == 'admission_type') | (X.columns == 'first_careunit') |(X.columns == 'gender'))[0]] = True\n",
    "    \n",
    "    m_vote = MeanRule()\n",
    "    rand_forest = RandomForest(X_train, y_train, n_trees)\n",
    "    rand_forest.set_combination_rule(m_vote)\n",
    "    rand_forest.set_feature_types(feature_types)\n",
    "    rand_forest.train()\n",
    "    y_pred = rand_forest.apply_binary(X_test)\n",
    "    roc.evaluate(y_pred, y_test.get_binary_for_class(1))\n",
    "    auc = roc.get_auROC()\n",
    "    print(auc)\n",
    "    rf_aucs.append(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rr78+5s+fH9ddd11MmTIlzj///Ox9jj/++Nhtt93i05/+dEydOjWef/75ePjhh+Pyyy+P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\nQgghhBBCtOF//9tYTeZbfIgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a582f0b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# styling taken from http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/\n",
    "\n",
    "aucs = [rf_aucs, lr_aucs, svm_aucs]\n",
    "data = np.array(aucs).T\n",
    "xlabels = [0, 1, 2] #[clf for clf in results]\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "bp = plt.boxplot(data, 0, 'gD', patch_artist=True)\n",
    "plt.ylabel('AUROC')\n",
    "plt.xlabel('Classifier')\n",
    "plt.xticks([1, 2, 3], ['Random Forest', 'Logistic Regression', 'Linear SVM'])\n",
    "plt.title('1-week mortality prediction boxplot')\n",
    "plt.grid(b=False)\n",
    "\n",
    "## change outline color, fill color and linewidth of the boxes\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "    box.set( color='#7570b3', linewidth=2)\n",
    "    # change fill color\n",
    "    box.set( facecolor = '#1b9e77' )\n",
    "\n",
    "## change color and linewidth of the whiskers\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the caps\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the medians\n",
    "for median in bp['medians']:\n",
    "    median.set(color='#b2df8a', linewidth=2)\n",
    "\n",
    "## change the style of fliers and their fill\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(marker='o', color='#e7298a', alpha=0.5)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AtBbZaJo6dWqcfPLJm33s4x//eNTU1DTLUAAAAK1FNprq6uqivLx8s4+Vl5dH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\nysTtg+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a38f3e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# styling taken from http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/\n",
    "\n",
    "aucs = [rf_aucs, lr_aucs, svm_aucs]\n",
    "data = np.array(aucs).T\n",
    "xlabels = [0, 1, 2] #[clf for clf in results]\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "bp = plt.boxplot(data, 0, 'gD', patch_artist=True)\n",
    "plt.ylabel('AUROC')\n",
    "plt.xlabel('Classifier')\n",
    "plt.xticks([1, 2, 3], ['Random Forest', 'Logistic Regression', 'Linear SVM'])\n",
    "plt.title('1-day mortality prediction boxplot')\n",
    "plt.grid(b=False)\n",
    "\n",
    "## change outline color, fill color and linewidth of the boxes\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "    box.set( color='#7570b3', linewidth=2)\n",
    "    # change fill color\n",
    "    box.set( facecolor = '#1b9e77' )\n",
    "\n",
    "## change color and linewidth of the whiskers\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the caps\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the medians\n",
    "for median in bp['medians']:\n",
    "    median.set(color='#b2df8a', linewidth=2)\n",
    "\n",
    "## change the style of fliers and their fill\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(marker='o', color='#e7298a', alpha=0.5)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UKULZsmWNlt+/f9+oxbto0aLcv38/n3MnhBAi3dq1a3F3d2f//v15fqxZs2bh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3r1OKxenY06daT1RlAah0PcALp1awRmzcrF6NH+74ENiKeLp0+PwZo1meja1cv6QbsdijNn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\nv0vFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a3765a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival = df_features.loc[df_labels.dead_in_1d == 0]\n",
    "non_survival = df_features.loc[df_labels.dead_in_1d != 0]\n",
    "\n",
    "surv_rows = np.random.choice(survival.subject_id.values, 10)\n",
    "non_surv_rows = np.random.choice(non_survival.subject_id.values, 10)\n",
    "\n",
    "survival = survival.loc[survival.subject_id.isin(surv_rows)]\n",
    "non_survival = non_survival.loc[non_survival.subject_id.isin(non_surv_rows)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "for title, group in survival.groupby('subject_id'):\n",
    "    group.plot(y='heartrate', x=group.index - group.index[0], label='survival', ax=ax, color='blue')\n",
    "\n",
    "for title, group in non_survival.groupby('subject_id'):\n",
    "    death_time = pd.to_datetime(query_output[query_output.subject_id == title].dod_hosp[0])\n",
    "    group.loc[group.index > death_time, 'heartrate'] = 0\n",
    "    group.plot(y='heartrate', x=group.index - group.index[0], label='non-survival', ax=ax, color='red')    \n",
    "\n",
    "legend = plt.legend(['Survival', 'Non-Survival'], fontsize=18)\n",
    "legend.get_lines()[1].set_color('red')\n",
    "plt.xlabel('Time (hours)', fontsize=22)\n",
    "plt.ylabel('Heartrate (raw)', fontsize=16)\n",
    "plt.title('24 hrs heart rate comparison between survival/non-survival groups')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}