modelling_pyhealth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling with PyHealth (using NOTEEVENTS_ICD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install torch==2.1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0+cpu\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.__version__)\n",
    "\n",
    "# Install required packages.\n",
    "import os\n",
    "os.environ['TORCH'] = torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: https://data.pyg.org/whl/torch-${TORCH}.html\n",
      "Requirement already satisfied: torch-scatter in c:\\users\\giuse\\desktop\\msc-thesis-project\\proj_venv\\lib\\site-packages (2.1.2+pt21cpu)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Looking in links: https://data.pyg.org/whl/torch-${TORCH}.html\n",
      "Requirement already satisfied: torch-sparse in c:\\users\\giuse\\desktop\\msc-thesis-project\\proj_venv\\lib\\site-packages (0.6.18+pt21cpu)\n",
      "Requirement already satisfied: scipy in c:\\users\\giuse\\desktop\\msc-thesis-project\\proj_venv\\lib\\site-packages (from torch-sparse) (1.9.3)\n",
      "Requirement already satisfied: numpy<1.26.0,>=1.18.5 in c:\\users\\giuse\\desktop\\msc-thesis-project\\proj_venv\\lib\\site-packages (from scipy->torch-sparse) (1.25.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "%pip install torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install torch-geometric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carica i dati dai file csv\n",
    "noteevents = pd.read_csv('data/NOTEEVENTS_ICD.csv')\n",
    "patients = pd.read_csv('data/PATIENTS.csv')\n",
    "\n",
    "# seleziona i pazienti presenti in NOTEEVENTS_ICD\n",
    "selected_patients = patients[patients['SUBJECT_ID'].isin(noteevents['SUBJECT_ID'].unique())]\n",
    "\n",
    "# salva il risultato in un file csv\n",
    "selected_patients.to_csv('data/PATIENTS_SEL.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MIMIC-III into PyHealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.datasets import MIMIC3Dataset\n",
    "\n",
    "dataset = MIMIC3Dataset(\n",
    "    root='data/',\n",
    "    tables=[\"DIAGNOSES_ICD\",\"PROCEDURES_ICD\",\"PRESCRIPTIONS\",\"NOTEEVENTS_ICD\"],\n",
    "    code_mapping={\"NDC\": (\"ATC\", {\"target_kwargs\": {\"level\": 3}})},\n",
    "    # refresh_cache=True,\n",
    "    # dev=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Statistics of base dataset (dev=False):\n",
      "\t- Dataset: MIMIC3Dataset\n",
      "\t- Number of patients: 1560\n",
      "\t- Number of visits: 5014\n",
      "\t- Number of visits per patient: 3.2141\n",
      "\t- Number of events per visit in DIAGNOSES_ICD: 11.5987\n",
      "\t- Number of events per visit in PROCEDURES_ICD: 3.8771\n",
      "\t- Number of events per visit in PRESCRIPTIONS: 44.3879\n",
      "\t- Number of events per visit in NOTEEVENTS_ICD: 11.0782\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nStatistics of base dataset (dev=False):\\n\\t- Dataset: MIMIC3Dataset\\n\\t- Number of patients: 1560\\n\\t- Number of visits: 5014\\n\\t- Number of visits per patient: 3.2141\\n\\t- Number of events per visit in DIAGNOSES_ICD: 11.5987\\n\\t- Number of events per visit in PROCEDURES_ICD: 3.8771\\n\\t- Number of events per visit in PRESCRIPTIONS: 44.3879\\n\\t- Number of events per visit in NOTEEVENTS_ICD: 11.0782\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.stat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading retrieved dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'67017', '67070', '6710', '6711', '6718', '67426', '6749', '67683', '679', '67910', '68184', '68218', '6824', '6828', '68301', '6843', '68453', '6871', '6894', '6917', '69251', '69359', '69398', '6953', '6954', '6973', '69855', '70004', '7009', '7029', '703', '70386', '70494', '7066', '70807', '71192', '7138', '7142', '7169', '7170', '71878', '7188', '7192', '71921', '72083', '72147', '7223', '7232', '7234', '72447', '725', '72707', '7275', '72847', '72940', '73206', '7327', '73370', '7344', '73454', '73509', '7357', '7359', '73770', '7391', '74046', '7413', '74319', '74444', '747', '7477', '7482', '7504', '7514', '75249', '7529', '7552', '7562', '75668', '7589', '7614', '76203', '7621', '7629', '76459', '76646', '7666', '7668', '7681', '7702', '77037', '77067', '7718', '77220', '7726', '773', '77341', '77413', '7752', '7784', '77850', '7798', '7799', '78007', '7809', '7815', '78342', '78419', '78536', '7859', '78641', '787', '78814', '7908', '79100', '7955', '7958', '79673', '79977', '80144', '80163', '80369', '8060', '80791', '808', '8081', '80956', '8109', '81114', '8120', '81209', '81464', '81529', '81543', '8163', '81662', '81778', '82104', '8222', '8231', '8238', '8251', '8258', '8273', '82960', '8309', '83151', '8321', '83210', '8369', '8432', '84350', '8445', '84465', '8447', '8450', '8452', '84595', '8466', '84669', '8467', '8471', '8472', '84745', '849', '8493', '8501', '8505', '8507', '85079', '852', '85350', '8543', '8556', '8564', '8565', '85723', '85740', '85870', '8597', '85999', '86018', '86146', '862', '863', '8665', '86717', '87160', '8734', '8789', '87926', '87978', '8799', '8801', '88206', '8851', '8869', '88711', '88782', '88857', '88882', '8896', '8917', '89180', '89290', '89446', '8947', '89840', '8989', '8992', '9012', '90369', '904', '9061', '90889', '9090', '90902', '9102', '91103', '91295', '9177', '91827', '92066', '9216', '9233', '9248', '9253', '9256', '92895', '9304', '93229', '9324', '93277', '93336', '9362', '9363', '93632', '9395', '9397', '93996', '9402', '94049', '9412', '94264', '94312', '9453', '948', '9484', '9486', '949', '94962', '9505', '95122', '9517', '9518', '952', '95238', '9540', '9544', '95447', '9555', '95637', '9571', '95725', '959', '96010', '96212', '96442', '9656', '96759', '96791', '96950', '9714', '9725', '97307', '97353', '9736', '97443', '97488', '97529', '9753', '97543', '97917', '9795', '97984', '98024', '9818', '98518', '9862', '98744', '98813', '9882', '9887', '9889', '9900', '99031', '9923', '99299', '9930', '99322', '9933', '99384', '99408', '99503', '9962', '9970', '9973', '9976', '9982']\n",
      "1560\n"
     ]
    }
   ],
   "source": [
    "# get patient dictionary\n",
    "patient_dict = dataset.patients\n",
    "print(list(patient_dict.keys()))\n",
    "print(len(patient_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('M',\n",
       " datetime.datetime(2091, 9, 11, 0, 0),\n",
       " 'BLACK/AFRICAN AMERICAN',\n",
       " None,\n",
       " OrderedDict([('124383',\n",
       "               Visit 124383 from patient 4096 with 86 events from tables ['DIAGNOSES_ICD', 'PROCEDURES_ICD', 'PRESCRIPTIONS', 'NOTEEVENTS_ICD']),\n",
       "              ('161677',\n",
       "               Visit 161677 from patient 4096 with 109 events from tables ['DIAGNOSES_ICD', 'PROCEDURES_ICD', 'PRESCRIPTIONS', 'NOTEEVENTS_ICD'])]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the \"10\" patient\n",
    "patient = patient_dict[\"4096\"]\n",
    "patient.gender, patient.birth_datetime, patient.ethnicity, patient.death_datetime, patient.visits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['124383', '161677']\n"
     ]
    }
   ],
   "source": [
    "# get the visit list of this patient\n",
    "visit_dict = patient.visits\n",
    "print (list(visit_dict.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2170, 7, 20, 18, 9),\n",
       " ['DIAGNOSES_ICD', 'PROCEDURES_ICD', 'PRESCRIPTIONS', 'NOTEEVENTS_ICD'],\n",
       " 86,\n",
       " {'DIAGNOSES_ICD': [Event with ICD9CM code 0389 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 5849 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 496 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 4280 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 42731 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 566 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 99592 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 4589 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 25080 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 4019 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 2859 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 78009 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 4439 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 5939 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code 71590 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code V1259 from table DIAGNOSES_ICD,\n",
       "   Event with ICD9CM code V1251 from table DIAGNOSES_ICD],\n",
       "  'PROCEDURES_ICD': [Event with ICD9PROC code 3893 from table PROCEDURES_ICD,\n",
       "   Event with ICD9PROC code 9609 from table PROCEDURES_ICD,\n",
       "   Event with ICD9PROC code 9904 from table PROCEDURES_ICD,\n",
       "   Event with ICD9PROC code 9390 from table PROCEDURES_ICD,\n",
       "   Event with ICD9PROC code 3893 from table PROCEDURES_ICD],\n",
       "  'PRESCRIPTIONS': [Event with ATC code N03AX from table PRESCRIPTIONS,\n",
       "   Event with ATC code A02BC from table PRESCRIPTIONS,\n",
       "   Event with ATC code C09AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code H01AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code J01MA from table PRESCRIPTIONS,\n",
       "   Event with ATC code N02BA from table PRESCRIPTIONS,\n",
       "   Event with ATC code R03AC from table PRESCRIPTIONS,\n",
       "   Event with ATC code C10AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code B05XA from table PRESCRIPTIONS,\n",
       "   Event with ATC code N03AX from table PRESCRIPTIONS,\n",
       "   Event with ATC code A12AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code C01CA from table PRESCRIPTIONS,\n",
       "   Event with ATC code V06DC from table PRESCRIPTIONS,\n",
       "   Event with ATC code J01XD from table PRESCRIPTIONS,\n",
       "   Event with ATC code N02AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code N02BA from table PRESCRIPTIONS,\n",
       "   Event with ATC code B01AB from table PRESCRIPTIONS,\n",
       "   Event with ATC code R03BB from table PRESCRIPTIONS,\n",
       "   Event with ATC code J01XA from table PRESCRIPTIONS,\n",
       "   Event with ATC code C05AD from table PRESCRIPTIONS,\n",
       "   Event with ATC code D04AB from table PRESCRIPTIONS,\n",
       "   Event with ATC code B05XA from table PRESCRIPTIONS,\n",
       "   Event with ATC code N06AX from table PRESCRIPTIONS,\n",
       "   Event with ATC code N05AH from table PRESCRIPTIONS,\n",
       "   Event with ATC code N05AH from table PRESCRIPTIONS,\n",
       "   Event with ATC code C07AB from table PRESCRIPTIONS,\n",
       "   Event with ATC code N02BE from table PRESCRIPTIONS,\n",
       "   Event with ATC code B01AB from table PRESCRIPTIONS,\n",
       "   Event with ATC code C03CA from table PRESCRIPTIONS,\n",
       "   Event with ATC code J01XA from table PRESCRIPTIONS,\n",
       "   Event with ATC code P01AB from table PRESCRIPTIONS,\n",
       "   Event with ATC code C01DA from table PRESCRIPTIONS,\n",
       "   Event with ATC code C09AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code J01MA from table PRESCRIPTIONS,\n",
       "   Event with ATC code N05AD from table PRESCRIPTIONS,\n",
       "   Event with ATC code A10AD from table PRESCRIPTIONS,\n",
       "   Event with ATC code N02AJ from table PRESCRIPTIONS,\n",
       "   Event with ATC code A02AA from table PRESCRIPTIONS,\n",
       "   Event with ATC code A06AD from table PRESCRIPTIONS,\n",
       "   Event with ATC code A12CC from table PRESCRIPTIONS,\n",
       "   Event with ATC code A12BA from table PRESCRIPTIONS],\n",
       "  'NOTEEVENTS_ICD': [Event with ICD9CM code V419 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 4019 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 4439 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 43491 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78060 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 51189 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 7840 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78650 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78609 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78605 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78096 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 2724 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 53081 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 71590 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 5770 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 496 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 27800 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 7823 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 42761 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 2512 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 5849 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 78791 from table NOTEEVENTS_ICD,\n",
       "   Event with ICD9CM code 3301 from table NOTEEVENTS_ICD]})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the first visit\n",
    "visit = visit_dict['124383']\n",
    "visit.encounter_time, visit.available_tables, visit.num_events, visit.event_list_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['V419',\n",
       " '4019',\n",
       " '4439',\n",
       " '43491',\n",
       " '78060',\n",
       " '51189',\n",
       " '7840',\n",
       " '78650',\n",
       " '78609',\n",
       " '78605',\n",
       " '78096',\n",
       " '2724',\n",
       " '53081',\n",
       " '71590',\n",
       " '5770',\n",
       " '496',\n",
       " '27800',\n",
       " '7823',\n",
       " '42761',\n",
       " '2512',\n",
       " '5849',\n",
       " '78791',\n",
       " '3301']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visit.get_code_list(table='NOTEEVENTS_ICD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling data for task of Drug Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating samples for medication_recommendation_mimic3_fn: 100%|██████████| 1560/1560 [00:00<00:00, 13447.81it/s]\n"
     ]
    }
   ],
   "source": [
    "from pyhealth.tasks import medication_recommendation_mimic3_fn\n",
    "from pyhealth.datasets import split_by_patient, get_dataloader\n",
    "\n",
    "mimic3sample = dataset.set_task(task_fn=medication_recommendation_mimic3_fn) # diagnosis prediction or \n",
    "                                                                                # medication recommendation\n",
    "train_ds, val_ds, test_ds = split_by_patient(mimic3sample, [0.8, 0.1, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics of sample dataset:\n",
      "\t- Dataset: MIMIC3Dataset\n",
      "\t- Task: medication_recommendation_mimic3_fn\n",
      "\t- Number of samples: 2012\n",
      "\t- Number of patients: 906\n",
      "\t- Number of visits: 2012\n",
      "\t- Number of visits per patient: 2.2208\n",
      "\t- diagnosis:\n",
      "\t\t- Number of diagnosis per sample: 18.8827\n",
      "\t\t- Number of unique diagnosis: 2099\n",
      "\t\t- Distribution of diagnosis (Top-10): [('4019', 1225), ('4280', 1021), ('42731', 783), ('41401', 767), ('5849', 594), ('25000', 537), ('53081', 458), ('5990', 393), ('40391', 389), ('2724', 386)]\n",
      "\t- procedures:\n",
      "\t\t- Number of procedures per sample: 7.0055\n",
      "\t\t- Number of unique procedures: 733\n",
      "\t\t- Distribution of procedures (Top-10): [('3893', 1047), ('9904', 776), ('9604', 529), ('3995', 505), ('966', 484), ('9671', 477), ('3891', 295), ('9672', 292), ('3961', 267), ('9915', 257)]\n",
      "\t- medications:\n",
      "\t\t- Number of medications per sample: 24.4841\n",
      "\t\t- Number of unique medications: 178\n",
      "\t\t- Distribution of medications (Top-10): [('A02B', 1797), ('B05X', 1792), ('B01A', 1686), ('N02B', 1681), ('A06A', 1564), ('N02A', 1453), ('V06D', 1383), ('C07A', 1334), ('V04C', 1199), ('A12C', 1190)]\n",
      "\t- drugs_hist:\n",
      "\t\t- Number of drugs_hist per sample: 17.2957\n",
      "\t\t- Number of unique drugs_hist: 174\n",
      "\t\t- Distribution of drugs_hist (Top-10): [('A02B', 1320), ('B05X', 1287), ('B01A', 1236), ('N02B', 1189), ('A06A', 1094), ('N02A', 1024), ('V06D', 999), ('C07A', 976), ('V04C', 840), ('A12C', 834)]\n",
      "\t- symptoms:\n",
      "\t\t- Number of symptoms per sample: 27.8588\n",
      "\t\t- Number of unique symptoms: 714\n",
      "\t\t- Distribution of symptoms (Top-10): [('V419', 3386), ('4019', 2372), ('7823', 2242), ('78060', 2124), ('78096', 2113), ('78650', 1390), ('78609', 1027), ('78605', 1020), ('4289', 1005), ('78900', 901)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Statistics of sample dataset:\\n\\t- Dataset: MIMIC3Dataset\\n\\t- Task: medication_recommendation_mimic3_fn\\n\\t- Number of samples: 2012\\n\\t- Number of patients: 906\\n\\t- Number of visits: 2012\\n\\t- Number of visits per patient: 2.2208\\n\\t- diagnosis:\\n\\t\\t- Number of diagnosis per sample: 18.8827\\n\\t\\t- Number of unique diagnosis: 2099\\n\\t\\t- Distribution of diagnosis (Top-10): [('4019', 1225), ('4280', 1021), ('42731', 783), ('41401', 767), ('5849', 594), ('25000', 537), ('53081', 458), ('5990', 393), ('40391', 389), ('2724', 386)]\\n\\t- procedures:\\n\\t\\t- Number of procedures per sample: 7.0055\\n\\t\\t- Number of unique procedures: 733\\n\\t\\t- Distribution of procedures (Top-10): [('3893', 1047), ('9904', 776), ('9604', 529), ('3995', 505), ('966', 484), ('9671', 477), ('3891', 295), ('9672', 292), ('3961', 267), ('9915', 257)]\\n\\t- medications:\\n\\t\\t- Number of medications per sample: 24.4841\\n\\t\\t- Number of unique medications: 178\\n\\t\\t- Distribution of medications (Top-10): [('A02B', 1797), ('B05X', 1792), ('B01A', 1686), ('N02B', 1681), ('A06A', 1564), ('N02A', 1453), ('V06D', 1383), ('C07A', 1334), ('V04C', 1199), ('A12C', 1190)]\\n\\t- drugs_hist:\\n\\t\\t- Number of drugs_hist per sample: 17.2957\\n\\t\\t- Number of unique drugs_hist: 174\\n\\t\\t- Distribution of drugs_hist (Top-10): [('A02B', 1320), ('B05X', 1287), ('B01A', 1236), ('N02B', 1189), ('A06A', 1094), ('N02A', 1024), ('V06D', 999), ('C07A', 976), ('V04C', 840), ('A12C', 834)]\\n\\t- symptoms:\\n\\t\\t- Number of symptoms per sample: 27.8588\\n\\t\\t- Number of unique symptoms: 714\\n\\t\\t- Distribution of symptoms (Top-10): [('V419', 3386), ('4019', 2372), ('7823', 2242), ('78060', 2124), ('78096', 2113), ('78650', 1390), ('78609', 1027), ('78605', 1020), ('4289', 1005), ('78900', 901)]\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mimic3sample.stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataloaders (torch.data.DataLoader)\n",
    "train_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)\n",
    "val_loader = get_dataloader(val_ds, batch_size=32, shuffle=False)\n",
    "test_loader = get_dataloader(test_ds, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1593, 217, 202)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds), len(val_ds), len(test_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pyhealth.models' from 'c:\\\\users\\\\giuse\\\\desktop\\\\msc-thesis-project\\\\proj_venv\\\\src\\\\pyhealth\\\\pyhealth\\\\models\\\\__init__.py'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import importlib\n",
    "import pyhealth.models\n",
    "\n",
    "importlib.reload(pyhealth.models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import GNN\n",
    "\n",
    "model = GNN(\n",
    "    dataset=mimic3sample,\n",
    "    convlayer=\"GraphConv\",\n",
    "    feature_keys=[\"procedures\", \"diagnosis\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    "    k=0,\n",
    "    embedding_dim=128,\n",
    "    hidden_channels=128\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer = Trainer(model=model)\n",
    "trainer.train(\n",
    "    model_name=\"gnn\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=50,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    "    monitor_criterion=\"max\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.0714\n",
    "New best pr_auc_samples score (0.7785) at epoch-49, step-2550\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true, y_prob, loss = trainer.inference(test_loader)\n",
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_true.shape)\n",
    "\n",
    "print(y_true[y_true == 0].shape)\n",
    "print(y_true[y_true == 1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_prob.shape)\n",
    "print(y_prob.max())\n",
    "print(y_prob.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_true)\n",
    "print(\"------------------\")\n",
    "threshold = 0.5\n",
    "y_prob_threshold = (y_prob >= threshold).astype(float)\n",
    "print(y_prob_threshold)\n",
    "print(y_prob_threshold[y_prob_threshold == 0].shape)\n",
    "print(y_prob_threshold[y_prob_threshold == 1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# risultati\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\", \"precision_samples\", \"recall_samples\"]\n",
    "multilabel_metrics_fn(y_true, y_prob, metrics=metrics, threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "conf_matrix = confusion_matrix(y_true.flatten(), y_prob_threshold.flatten())\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap=\"Blues\", fmt=\".0f\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpret with HeteroExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.datasets import SampleEHRDataset\n",
    "\n",
    "# Dizionario per tenere traccia dell'ultima visita per ciascun paziente\n",
    "last_visit_per_patient = {}\n",
    "\n",
    "# Iterare attraverso le visite\n",
    "for visit in test_loader.dataset:\n",
    "    patient_id = visit['patient_id']\n",
    "    visit_id = visit['visit_id']\n",
    "\n",
    "    # Se il paziente è già nel dizionario e la visita corrente è più recente, aggiorna\n",
    "    if patient_id in last_visit_per_patient:\n",
    "        if visit_id > last_visit_per_patient[patient_id]['visit_id']:\n",
    "            last_visit_per_patient[patient_id] = visit\n",
    "    else:\n",
    "        # Altrimenti, aggiungi il paziente con la visita corrente\n",
    "        last_visit_per_patient[patient_id] = visit\n",
    "\n",
    "explain_dataset = SampleEHRDataset(list(last_visit_per_patient.values()), code_vocs=\"ATC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test originale: 191; Test ridotto: 91; Riduzione del 47.64397905759162%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test originale: {len(test_ds)}; Test ridotto: {len(explain_dataset)}; Riduzione del {(100 * len(explain_dataset)) / len(test_ds)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'pyhealth.explainer' from 'c:\\\\users\\\\giuse\\\\desktop\\\\msc-thesis-project\\\\proj_venv\\\\src\\\\pyhealth\\\\pyhealth\\\\explainer.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import importlib\n",
    "import pyhealth.explainer\n",
    "\n",
    "importlib.reload(pyhealth.explainer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.explainer import HeteroGraphExplainer\n",
    "\n",
    "explainer = HeteroGraphExplainer(\n",
    "    algorithm=\"DummyExplainer\",\n",
    "    dataset=test_loader.dataset,\n",
    "    model=model,\n",
    "    label_key=\"medications\",\n",
    "    threshold_value=20,\n",
    "    top_k=20,\n",
    "    feat_size=128,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index range for patient: 49 - visit: 49 - n=[0 to 23]\n",
      "Index range for patient: 4 - visit: 4 - n=[24 to 42]\n",
      "Index range for patient: 84 - visit: 84 - n=[43 to 56]\n",
      "Index range for patient: 35 - visit: 35 - n=[57 to 79]\n",
      "Index range for patient: 82 - visit: 82 - n=[80 to 123]\n",
      "Index range for patient: 83 - visit: 83 - n=[124 to 160]\n",
      "Index range for patient: 80 - visit: 80 - n=[161 to 201]\n",
      "Index range for patient: 39 - visit: 39 - n=[202 to 215]\n",
      "Index range for patient: 38 - visit: 38 - n=[216 to 307]\n",
      "Index range for patient: 24 - visit: 24 - n=[308 to 345]\n",
      "Index range for patient: 88 - visit: 88 - n=[346 to 383]\n",
      "Index range for patient: 30 - visit: 30 - n=[384 to 403]\n",
      "Index range for patient: 62 - visit: 62 - n=[404 to 471]\n",
      "Index range for patient: 6 - visit: 6 - n=[472 to 511]\n",
      "Index range for patient: 18 - visit: 18 - n=[512 to 561]\n",
      "Index range for patient: 20 - visit: 20 - n=[562 to 591]\n",
      "Index range for patient: 67 - visit: 67 - n=[592 to 628]\n",
      "Index range for patient: 12 - visit: 12 - n=[629 to 661]\n",
      "Index range for patient: 40 - visit: 40 - n=[662 to 697]\n",
      "Index range for patient: 52 - visit: 52 - n=[698 to 712]\n",
      "Index range for patient: 58 - visit: 58 - n=[713 to 750]\n",
      "Index range for patient: 28 - visit: 28 - n=[751 to 773]\n",
      "Index range for patient: 55 - visit: 55 - n=[774 to 780]\n",
      "Index range for patient: 10 - visit: 10 - n=[781 to 793]\n",
      "Index range for patient: 47 - visit: 47 - n=[794 to 824]\n",
      "Index range for patient: 31 - visit: 31 - n=[825 to 857]\n",
      "Index range for patient: 15 - visit: 15 - n=[858 to 877]\n",
      "Index range for patient: 11 - visit: 11 - n=[878 to 913]\n",
      "Index range for patient: 64 - visit: 64 - n=[914 to 943]\n",
      "Index range for patient: 78 - visit: 78 - n=[944 to 958]\n",
      "Index range for patient: 0 - visit: 0 - n=[959 to 1043]\n",
      "Index range for patient: 89 - visit: 89 - n=[1044 to 1072]\n",
      "Index range for patient: 90 - visit: 90 - n=[1073 to 1095]\n",
      "Index range for patient: 19 - visit: 19 - n=[1096 to 1131]\n",
      "Index range for patient: 17 - visit: 17 - n=[1132 to 1154]\n",
      "Index range for patient: 50 - visit: 50 - n=[1155 to 1168]\n",
      "Index range for patient: 77 - visit: 77 - n=[1169 to 1191]\n",
      "Index range for patient: 70 - visit: 70 - n=[1192 to 1213]\n",
      "Index range for patient: 34 - visit: 34 - n=[1214 to 1253]\n",
      "Index range for patient: 57 - visit: 57 - n=[1254 to 1285]\n",
      "Index range for patient: 33 - visit: 33 - n=[1286 to 1352]\n",
      "Index range for patient: 86 - visit: 86 - n=[1353 to 1416]\n",
      "Index range for patient: 26 - visit: 26 - n=[1417 to 1434]\n",
      "Index range for patient: 51 - visit: 51 - n=[1435 to 1465]\n",
      "Index range for patient: 16 - visit: 16 - n=[1466 to 1494]\n",
      "Index range for patient: 81 - visit: 81 - n=[1495 to 1513]\n",
      "Index range for patient: 29 - visit: 29 - n=[1514 to 1562]\n",
      "Index range for patient: 41 - visit: 41 - n=[1563 to 1586]\n",
      "Index range for patient: 79 - visit: 79 - n=[1587 to 1615]\n",
      "Index range for patient: 75 - visit: 75 - n=[1616 to 1643]\n",
      "Index range for patient: 8 - visit: 8 - n=[1644 to 1669]\n",
      "Index range for patient: 14 - visit: 14 - n=[1670 to 1694]\n",
      "Index range for patient: 13 - visit: 13 - n=[1695 to 1707]\n",
      "Index range for patient: 87 - visit: 87 - n=[1708 to 1722]\n",
      "Index range for patient: 3 - visit: 3 - n=[1723 to 1758]\n",
      "Index range for patient: 66 - visit: 66 - n=[1759 to 1779]\n",
      "Index range for patient: 42 - visit: 42 - n=[1780 to 1792]\n",
      "Index range for patient: 61 - visit: 61 - n=[1793 to 1817]\n",
      "Index range for patient: 1 - visit: 1 - n=[1818 to 1845]\n",
      "Index range for patient: 21 - visit: 21 - n=[1846 to 1867]\n",
      "Index range for patient: 56 - visit: 56 - n=[1868 to 1888]\n",
      "Index range for patient: 65 - visit: 65 - n=[1889 to 1911]\n",
      "Index range for patient: 36 - visit: 36 - n=[1912 to 1947]\n",
      "Index range for patient: 45 - visit: 45 - n=[1948 to 1974]\n",
      "Index range for patient: 71 - visit: 71 - n=[1975 to 1987]\n",
      "Index range for patient: 9 - visit: 9 - n=[1988 to 2033]\n",
      "Index range for patient: 53 - visit: 53 - n=[2034 to 2070]\n",
      "Index range for patient: 22 - visit: 22 - n=[2071 to 2088]\n",
      "Index range for patient: 73 - visit: 73 - n=[2089 to 2109]\n",
      "Index range for patient: 44 - visit: 44 - n=[2110 to 2156]\n",
      "Index range for patient: 69 - visit: 69 - n=[2157 to 2192]\n",
      "Index range for patient: 7 - visit: 7 - n=[2193 to 2205]\n",
      "Index range for patient: 2 - visit: 2 - n=[2206 to 2222]\n",
      "Index range for patient: 5 - visit: 5 - n=[2223 to 2262]\n",
      "Index range for patient: 43 - visit: 43 - n=[2263 to 2296]\n",
      "Index range for patient: 37 - visit: 37 - n=[2297 to 2315]\n",
      "Index range for patient: 59 - visit: 59 - n=[2316 to 2329]\n",
      "Index range for patient: 76 - visit: 76 - n=[2330 to 2363]\n",
      "Index range for patient: 72 - visit: 72 - n=[2364 to 2397]\n",
      "Index range for patient: 48 - visit: 48 - n=[2398 to 2418]\n",
      "Index range for patient: 74 - visit: 74 - n=[2419 to 2433]\n",
      "Index range for patient: 85 - visit: 85 - n=[2434 to 2436]\n",
      "Index range for patient: 23 - visit: 23 - n=[2437 to 2470]\n",
      "Index range for patient: 25 - visit: 25 - n=[2471 to 2492]\n",
      "Index range for patient: 60 - visit: 60 - n=[2493 to 2541]\n",
      "Index range for patient: 27 - visit: 27 - n=[2542 to 2561]\n",
      "Index range for patient: 63 - visit: 63 - n=[2562 to 2597]\n",
      "Index range for patient: 46 - visit: 46 - n=[2598 to 2622]\n",
      "Index range for patient: 54 - visit: 54 - n=[2623 to 2638]\n",
      "Index range for patient: 68 - visit: 68 - n=[2639 to 2677]\n",
      "Index range for patient: 32 - visit: 32 - n=[2678 to 2705]\n"
     ]
    }
   ],
   "source": [
    "patient_visit = explainer.subgraph['patient', 'visit'].edge_index\n",
    "visit_drug = explainer.subgraph['visit', 'medication'].edge_index\n",
    "\n",
    "patient_visit = patient_visit.T\n",
    "visit_drug = visit_drug.T\n",
    "\n",
    "# Creare un dizionario per tenere traccia del range di indici\n",
    "index_range_per_patient_visit = {}\n",
    "max_index = 0\n",
    "\n",
    "# Iterare attraverso i dati\n",
    "for pat_vis in patient_visit:\n",
    "    # print(pat_vis)\n",
    "    key = f\"patient: {pat_vis[0]} - visit: {pat_vis[1]}\"\n",
    "    for vis_drug in visit_drug:\n",
    "        # print(vis_drug)\n",
    "        if vis_drug[0] == pat_vis[1]:\n",
    "            if key in index_range_per_patient_visit:\n",
    "                # Aggiornare l'indice massimo\n",
    "                index_range_per_patient_visit[key][1] += 1\n",
    "            else:\n",
    "                # Aggiungere al dizionario con il range di indici\n",
    "                index_range_per_patient_visit[key] = [max_index, max_index]\n",
    "    max_index = index_range_per_patient_visit[key][1] + 1\n",
    "\n",
    "# Stampare il range di indici per ciascun paziente\n",
    "for key, index_range in index_range_per_patient_visit.items():\n",
    "    print(f\"Index range for {key} - n=[{index_range[0]} to {index_range[1]}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated explanations in ['node_mask', 'edge_mask']\n",
      "Feature importance plot has been saved to './explainability_results/feature_importance_20240201_115833.png'\n",
      "Edge to predict: tensor([48, 54])\n",
      "Label to predict: 1\n",
      "Label predicted: 1 because [0.954176]\n"
     ]
    }
   ],
   "source": [
    "n=2283\n",
    "explainer.explain(\n",
    "    n=n\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer.explain_graph(k=0, human_readable=False)\n",
    "explainer.explain_graph(k=0, human_readable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fidelity Positive: 0.17713958024978638\n",
      "Fidelity Negative: 0.07474017143249512\n",
      "Fidelity (weighted): 0.29735164286830906\n",
      "Unfaithfulness Score: 0.08574497699737549\n",
      "Sparsity Score: 0.8938615918159485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Fidelity Positive': 0.1771,\n",
       " 'Fidelity Negative': 0.0747,\n",
       " 'Fidelity F1': 0.2974,\n",
       " 'Unfaithfulness': 0.0857,\n",
       " 'Sparsity': 0.8939}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer.explain_metrics(metrics=[\"Fidelity\", \"Fidelity_F1\", \"Unfaithfulness\", \"Sparsity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer.explain_results(n=n, doctor_type=\"Internist_Doctor\")\n",
    "explainer.explain_results(n=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Models\n",
    "\n",
    "Here we can use the models having the same input built in the previous cells. We tried GRASP, SafeDrug, Transformer, and so on. And evaluate them performance with several metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RNN without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import RNN\n",
    "\n",
    "model_rnn = RNN(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_rnn = Trainer(model=model_rnn)\n",
    "trainer_rnn.train(\n",
    "    model_name=\"rnn\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNN Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.1941\n",
    "New best pr_auc_samples score (0.5606) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RNN Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 116.67it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8965680275892758,\n",
       " 'f1_samples': 0.5191358039326918,\n",
       " 'pr_auc_samples': 0.650142302838104,\n",
       " 'jaccard_samples': 0.360480006940703}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_rnn, y_prob_rnn, loss_rnn = trainer_rnn.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_rnn, y_prob_rnn, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RNN with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import RNN\n",
    "\n",
    "model_rnn_s = RNN(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_rnn_s = Trainer(model=model_rnn_s)\n",
    "trainer_rnn_s.train(\n",
    "    model_name=\"rnn\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNN Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.1742\n",
    "New best pr_auc_samples score (0.5763) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RNN Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 86.42it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9011569696295473,\n",
       " 'f1_samples': 0.5329832596908064,\n",
       " 'pr_auc_samples': 0.6633436553585519,\n",
       " 'jaccard_samples': 0.37531534226610286}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_rnn_s, y_prob_rnn_s, loss_rnn_s = trainer_rnn_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_rnn_s, y_prob_rnn_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Transformer without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import Transformer\n",
    "\n",
    "model_tr = Transformer(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_tr = Trainer(model=model_tr)\n",
    "trainer_tr.train(\n",
    "    model_name=\"transformer\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Transformer Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.1995\n",
    "New best pr_auc_samples score (0.5351) at epoch-9, step-510\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Transformer Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 43.21it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8912003559906553,\n",
       " 'f1_samples': 0.5139069711766077,\n",
       " 'pr_auc_samples': 0.619056922816739,\n",
       " 'jaccard_samples': 0.35616655470043834}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_tr, y_prob_tr, loss_tr = trainer_tr.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_tr, y_prob_tr, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Transformer with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import Transformer\n",
    "\n",
    "model_tr_s = Transformer(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_tr_s = Trainer(model=model_tr_s)\n",
    "trainer_tr_s.train(\n",
    "    model_name=\"transformer\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Transformer Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.1959\n",
    "New best pr_auc_samples score (0.5530) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Transformer Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 38.89it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8944821448436978,\n",
       " 'f1_samples': 0.5228339322620008,\n",
       " 'pr_auc_samples': 0.6387211254397213,\n",
       " 'jaccard_samples': 0.36546559602053724}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_tr_s, y_prob_tr_s, loss_tr_s = trainer_tr_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_tr_s, y_prob_tr_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SafeDrug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- SafeDrug without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import SafeDrug\n",
    "\n",
    "model_safe = SafeDrug(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_safe = Trainer(model=model_safe)\n",
    "trainer_safe.train(\n",
    "    model_name=\"safedrug\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SafeDrug Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2631\n",
    "New best pr_auc_samples score (0.6426) at epoch-9, step-510\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- SafeDrug Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:01<00:00,  3.61it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8935921682055846,\n",
       " 'f1_samples': 0.4799128219377393,\n",
       " 'pr_auc_samples': 0.6317901136528216,\n",
       " 'jaccard_samples': 0.3247374452826242}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_safe, y_prob_safe, loss_safe = trainer_safe.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_safe, y_prob_safe, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- SafeDrug with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import SafeDrug\n",
    "\n",
    "model_safe_s = SafeDrug(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_safe_s = Trainer(model=model_safe_s)\n",
    "trainer_safe_s.train(\n",
    "    model_name=\"safedrug\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SafeDrug Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2640\n",
    "New best pr_auc_samples score (0.6477) at epoch-7, step-408\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SafeDrug Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:02<00:00,  3.49it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8940371565246412,\n",
       " 'f1_samples': 0.47729432851886344,\n",
       " 'pr_auc_samples': 0.6323354697324408,\n",
       " 'jaccard_samples': 0.3223972380135409}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_safe_s, y_prob_safe_s, loss_safe_s = trainer_safe_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_safe_s, y_prob_safe_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAMENet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GAMENet without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import GAMENet\n",
    "\n",
    "model_game = GAMENet(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_game = Trainer(model=model_game)\n",
    "trainer_game.train(\n",
    "    model_name=\"gamenet\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GAMENet Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2696\n",
    "New best pr_auc_samples score (0.6411) at epoch-8, step-459\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GAMENet Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 70.71it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8936199799755257,\n",
       " 'f1_samples': 0.4769334560619558,\n",
       " 'pr_auc_samples': 0.6309796867729162,\n",
       " 'jaccard_samples': 0.3221964397546095}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_game, y_prob_game, loss_game = trainer_game.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_game, y_prob_game, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GAMENet with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import GAMENet\n",
    "\n",
    "model_game_s = GAMENet(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_game_s = Trainer(model=model_game_s)\n",
    "trainer_game_s.train(\n",
    "    model_name=\"gamenet\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GAMENet Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2682\n",
    "New best pr_auc_samples score (0.6411) at epoch-8, step-459\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GAMENet Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 72.15it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8942318389142285,\n",
       " 'f1_samples': 0.48071149072260616,\n",
       " 'pr_auc_samples': 0.6329302151836249,\n",
       " 'jaccard_samples': 0.3255527183258041}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_game_s, y_prob_game_s, loss_game_s = trainer_game_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_game_s, y_prob_game_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRASP (NOT WORKS FOR MEDICATIONS RECOMMENDATION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GRASP without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import GRASP\n",
    "\n",
    "model_grasp = GRASP(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    "    use_embedding=[True, True],\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_grasp = Trainer(model=model_grasp)\n",
    "trainer_grasp.train(\n",
    "    model_name=\"grasp\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GRASP Results (without symptoms)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.xxxx\n",
    "New best pr_auc_samples score (0.xxxx) at epoch-x, step-xxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GRASP Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_grasp, y_prob_grasp, loss_grasp = trainer_grasp.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_grasp, y_prob_grasp, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- GRASP with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import GRASP\n",
    "\n",
    "model_grasp_s = GRASP(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    "    use_embedding=[True, True, True],\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_grasp_s = Trainer(model=model_grasp_s)\n",
    "trainer_grasp_s.train(\n",
    "    model_name=\"grasp\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GRASP Results (with symptoms)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.xxxx\n",
    "New best pr_auc_samples score (0.xxxx) at epoch-x, step-xxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GRASP Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_grasp_s, y_prob_grasp_s, loss_grasp_s = trainer_grasp_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_grasp_s, y_prob_grasp_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RETAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RETAIN without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import RETAIN\n",
    "\n",
    "model_retain = RETAIN(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_retain = Trainer(model=model_retain)\n",
    "trainer_retain.train(\n",
    "    model_name=\"retain\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RETAIN Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2999\n",
    "New best pr_auc_samples score (0.6474) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RETAIN Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 55.99it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8948436978529314,\n",
       " 'f1_samples': 0.5281752613700492,\n",
       " 'pr_auc_samples': 0.6433994977644366,\n",
       " 'jaccard_samples': 0.37059068196064454}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_retain, y_prob_retain, loss_retain = trainer_retain.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_retain, y_prob_retain, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- RETAIN with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import RETAIN\n",
    "\n",
    "model_retain_s = RETAIN(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    "    mode=\"multilabel\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_retain_s = Trainer(model=model_retain_s)\n",
    "trainer_retain_s.train(\n",
    "    model_name=\"retain\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RETAIN Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2677\n",
    "New best pr_auc_samples score (0.6681) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RETAIN Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 57.38it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9002391812214929,\n",
       " 'f1_samples': 0.5372509488484144,\n",
       " 'pr_auc_samples': 0.6632249734770583,\n",
       " 'jaccard_samples': 0.37993752692087424}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_retain_s, y_prob_retain_s, loss_retain_s = trainer_retain_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_retain_s, y_prob_retain_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MICRON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MICRON without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import MICRON\n",
    "\n",
    "model_micron = MICRON(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_micron = Trainer(model=model_micron)\n",
    "trainer_micron.train(\n",
    "    model_name=\"micron\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MICRON Results (without symptoms)**"
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2727\n",
    "New best pr_auc_samples score (0.6665) at epoch-9, step-500\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MICRON Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 126.88it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8884748025364334,\n",
       " 'f1_samples': 0.5359780216472592,\n",
       " 'pr_auc_samples': 0.6506334390240331,\n",
       " 'jaccard_samples': 0.37877685360082847}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_micron, y_prob_micron, loss_micron = trainer_micron.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_micron, y_prob_micron, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MICRON with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import MICRON\n",
    "\n",
    "model_micron_s = MICRON(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_micron_s = Trainer(model=model_micron_s)\n",
    "trainer_micron_s.train(\n",
    "    model_name=\"micron\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MICRON Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.2676\n",
    "New best pr_auc_samples score (0.6946) at epoch-8, step-459\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MICRON Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00, 134.61it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9050784291912337,\n",
       " 'f1_samples': 0.560547721862238,\n",
       " 'pr_auc_samples': 0.6825147519987713,\n",
       " 'jaccard_samples': 0.4044750531468177}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_micron_s, y_prob_micron_s, loss_micron_s = trainer_micron_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_micron_s, y_prob_micron_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MoleRec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MoleRec without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import MoleRec\n",
    "\n",
    "model_molerec = MoleRec(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training without symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_molerec = Trainer(model=model_molerec)\n",
    "trainer_molerec.train(\n",
    "    model_name=\"molerec\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MoleRec Results (without symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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CaP369XTflI5OO/BII32KWzZ7e3vsI+eGZEZRuhtUikIqqVKskZacnJz69eu3bNlyy5Ytly9fDgoK8vHxYc5mluxLsUbPmG2opEqxRm9IkvT09FSzSiGEEhMTN27c2K9fP0NDQysrq8ePH9M33J07dxYtWtS1a1eCIDp06JCfn09f4nS0a9fO2dkZIbRhwwaCIOjOwapVq/Av9uTJk9euXQsKCurQoYO7uzs2WB2VmjJliq6u7h9//HHmzJnr168HBQXVr18fI0UIKUWl5NQvC4islhQhJF+lEEIFBQWnTp2aNGmSra0t63klPT19586dQ4cONTU1NTY2pl8esFJnnYaGhuro6Li7ux84cCAgICAoKAgPltLB8Lt9+hQhRN+NVVMp5hw/plnJ91L5+fn29vaurq50Lxa/CGG29diNh6kRQqyxXNr+5MmTjY2N8am/vz9zvJcOI8fBqVK4TnGPpH///u3ataOttW3btmPHjp/3gqgz/CoOP7Cyfu90XNpRWZUqKytjqRTzGQ4hpLhKsZo+Zssm/4akM690B6gUhZRTpfC7GbrnhBDCvx/6mxtmXeLmVU4rprhKCYVCY2Nj1Y34OTk54RE/1o0VHx9vamr6008/sfwRQsePHycIYt++fZKXZPlgNXry5Imrq2vr1q3pYC4uLnTPCXs2a9ZMEZXiHPGztLSke04IoeLiYj09PVql8KsOuu5w0sz3UpIDPvjbL/xmkbN+6QJihxyVkhzxw4+6eMSPaae0tHTQoEF6enrM1wk4QFZWVrNmzSQ/M2JGp92zZ882MTFh9oYVVyk8/lzZET/FVQohhCf4nDx5EmfY3d3d2dn5zOdH3759W7VqhQPgwYbFixfTBcSOXr160d/Apqam6unp9e/fnxVGzqn8GwAPAzRp0mTs2LFv377V19dnLnoycODAZs2aMQfNWAlJPgewAuDhHJZaMMPgBkTxEb/MzEzFR/xY6TJbNmYe5NyQzGBKcYNKURg5VerSpUsEQfz3338Yemlpqaurqxr6UgihwYMHK3H2RKdOnegWKiAggH48LywsZDZ/IpHI1tYWDxG8f/+e+ZOLjo4mCAKPfSl4CyYlJREEMXToUIIg6GFPhFCXLl169+5NG/n7778JglBEpSIiIuTPnrCxsWHOFcZ9OFqlAgMDCYJgDc0zVQq/PKfnQ5eVlf3vf/9jzZ6Q8xRClwg75KgUnj0RHh6OQxYUFDg4ONCzJ+h5nvjq/PnzdXV1P3z4IBQKc3Nzmal88cUXzLc1zEss9x9//GFqakpPT3j58qWpqSnzxZJkG0r3pfBM1yrMnmDlgT7FrS3zeymBQGBnZ+fq6ooQSk1N1dHRoWfT0LHwc9Ldu3exj6urK0mS9FRVhNDDhw91dXXp15AIoWnTphEEsW3bNtoIVpqNGzeyBiFxAPk3AA4zc+ZMMzMzPALJfAl0+PBhgiD8/f2ZaRUVFdEz9CQJM0NityJ9KcnZE/g1OZ498euvv9JmFyxYgD84Qwjl5eWZm5t3796d+Xunf+CS6TJVStYNSSekIgeoFAWWU6Wys7PxbOZNmzb5+fl17twZTwenn8eZdcn5rK14Xwr/5AwNDe3t7X18fNauXdu0aVMskLJuCE9PT0NDw1WfHzt37qQfVHv06LFly5aFCxeamprSM9EjIiJsbGymTZu2bdu2Xbt24cU4/vnnH4TQ5s2bW7duvWDBAn9//40bN7Zt29bCwoIeE8dzXunJEbJy1atXLzxWgydr4WDLli0jCGLSpEl79+6dOXOmjY2Ng4ODIiqFEPrxxx8Jgvjpp5927twpORN94sSJenp6s2fP9vf3nzRpkp2dHXPELyMjQ09Pr2fPnocPHz558iT+nIupUngisqGh4bx587Zv344nPrFmoldWpcaOHft5naxKTU3FM9EtLS2XLl26efNmV1dXHR0deiZ6ly5dBg4cuGbNmv3798+bN8/IyAgv2JGTk2NmZubp6enn57d3797Ro0cTBLFp0yZMFYsi8+meWSk3btwgCMLNzW337t0rVqxo1KgRayq5ZBvKVKknT54YGxu3aNFi/fr1q1evrsJMdGZmJFUKIeTr60sQRGBgIO5W0hOU6Ig5OTn6+vozZ87EPvhrX5Ikvb29/f3958yZY2pq2qxZM+ZXeoWFhfiW7t2798aNGw8cOODt7d2+fXtdXV3mJ4N0EvJvABwsLCyMIAhzc/OOHTvSEbH4DRw4UEdHZ+zYsdu3b9+yZcu0adNsbGzo1ZgkCTOjY7fUtSeOHTuGr2JueCb6zp076a8yaDv4Vzl69OidO3diNz0pCSGEX6Q5OzuvXbt29+7d06ZNw1+GSO3DMVs2WTckna6KHKBSFFhOlUII3b59u2fPnvhLtwULFuDZqGpQKYTQjRs3OnfubGho6OjoiBssesxd8rbANyVrKN/R0RGHPH36dOfOnY2MjGxsbJhf9WZnZ0+fPr1du3ZmZmaWlpY9evT4+++/cZTHjx//+OOPLVq0MDIyatSo0eDBg5mzsEaMGGFiYsJ8kpXMEkJo586dBEGw5rOVlJTMmzevSZMmJiYm//vf/+7cucOUCjnvpfAg3qxZs+rXr48XW2J91ZuTkzN58mT8ze+AAQPi4uKYTS1CaN++fQ4ODnjWBq5EZtL4KzFswdDQsGPHjvSit/TsicqqFKtG6Ano+KteKysrY2Pj7t27Mz9d8vf3//rrr+vXr29kZOTo6Dh//vy8vDyEUGlp6fz5811cXMzNzc3MzFxcXJhTNy9fviz5HQWzUg4cONC6dWsjI6N27dodOnSoUs9MCKGQkJCuXbvib9ur+Y25VJXKy8uztLR0d3fv2LFjixYtmDmn3b17927UqBH96uXu3buDBw+2trbW19dv1qzZL7/8Iqk9QqFw//79bm5ulpaWBgYGJElOnjxZzvT0rKwsWTcAzoZYLG7evDlz8h6dPYFA4OPj06FDByMjI2tr665du65YsQLXneLvpSRvGLrLi7nFxMSMHDnS3Nzc2tp6xowZzL5RWVnZihUrWrZsaWBg0Lx5c8mvei9dutSrVy8TExMLC4vu3bszh1jljPjJuiHpgqvIASqlIrAqNDt06FB6XF6FyShmulGjRsyvmxWLBKFUSGD+/Pl2dnb0uK4KUwLTNURAqrrXUF7UkSyolDooVzMN5meY8fHxBgYGzE/2qmm8OtGfPXtmbm7OfK9QHWsQVykEunXrxnopohSzYERzCIBKaU5dQE4qCDRu3NjLy2vv3r2LFy+2sbExMzOLj48HOkAACNRNAqBSdbPeNbrUkyZNIknSyMjIwsJiwIABiq8rqtGlgswBASBQJQKgUlXCBpGAABAAAkAACKiAALyXUgFUMAkEgAAQAAJKIgAqpSSQYAYIAAEgAARUQEArVUokEqWlpeXm5ubBAQSAABAAAtpMIDc3Ny0tjV68UVLmtFKl8CecUr96A08gAASAABDQOgJSl6rCiqWVKpWbm0sQRFpamjY/QEDegQAQAAJAIA/3OlhLUzJ7VFqpUngzIXrFEWZ5wA0EgAAQAAJaRICzPQeV0qLahKwCASAABGobAVCp2lajUB4gAASAQG0iACpVm2oTygIEgAAQqG0EQKVqW41CeYBAnSUgFAqL4dBmAlKnm4NK1dlfNBQcCNQeAmKxOD09PQYOLScQFxdXWlrKui9BpVhA4BQIAAHtI4AlKjs7u6ioSJv7EnU674WFhQkJCcnJyfQG9vhGBJXSvh8k5BgIAAEmAaFQGBMTk52dzfQEtzYSyM3NjYmJEQgEzMyDSjFpgBsIAAHtI1BcXBwTE8PcC1T7ygA5LidQVFQUExNTXFzM5AEqxaQBbiAABLSPAFYpVtOmfcWAHCMktSpBpaTfGkKRODwx+0LEq/DEbKFILD0Q+AIBIKABBKQ2bRqQL8hCpQlIrUpQKSkcA6PSe679l+Tx8V/Ptf8GRqVLCQdeQAAIaAABqU2bBuSr0lkgSXLz5s3yowUHBxMEkZOTIz+Yll6VWpWgUuzaDIxKt/+oT1il7Hl8ex4fhIpNCs6BgGYQkNq0cWZN6eMlslYZ9/b25swMDvDmzZvCwkL5gUtLSzMyMliz4ORHkXNV0zRPalWCSn1Wg0KRmNmLortT9jx+z7X/wtDfZ7DgBAhoBgGpTZv8rKlivCTj47FlyxYLC4uPZxn5+fk4M2KxuKysTH7G1HwVVErNwD8lx6m9n4J+7gpPzKaVSdIRnghTXT/nBWdAQAMIVFalVD1ecujQIUtLSwwGy0BAQECXLl0MDAyCg4MTExOHDBnSqFEjMzOzbt26BQUF0QiZI34EQezbt2/YsGEmJiatWrW6ePEi0yAe8cMJXb16tV27dmZmZgMGDEhPr3g3UVZWNnPmTEtLSxsbmwULFkycOHHo0KF0QrRDlkq9f/9+woQJVlZWJiYm3333XXx8PI6SnJw8ePBgKysrU1PT9u3bX7lyBSH0/v37cePGNWjQwNjYuFWrVgcPHqTtV9YhtSo52/O6tSb6hYhXkuJE+1yIeFVZ6BAeCAABVRNgNW1isbiwtEzW34diQfc1QfSPmnbY8/g91vz7oVggK2JhaZmC42ySKtWpU6fr168nJia+e/cuMjJyz549UVFR8fHxS5YsMTY2TklJwYhYKmVnZ3fixImEhIRZs2bVq1fv3bt3CCGmrhw6dMjAwKBv374PHjx49OiRk5PTuHHjsKnVq1fb2NicO3cuNjZ22rRpFhYWlVKpIUOGODk5hYaGRkZGDhgwoFWrVvgbpkGDBvXr1+/p06cvXry4fPlySEgIQmj69Omurq4PHjx4+fJlUFDQpUuXqlzjrKrEdkClPuMJfanPcMAJENAGAqymrbC0jNYe5ToKSxUar5NUqQsXLsgC2aFDh+3bt+OrLJVasmQJ9i8oKCAIIjAwUFKlCIJITEzEwXbu3Glra4vdtra2vr6+2C0UClu0aKG4SsXHxxMEcfv2bRw9OzvbxMTk77//Rgh17Nhx+fLl2J/+18PDY/LkyfRpdRysqsSmQKU+Q4rfS7FmT5DlsyfgvdRnpOAECGgMAVbTpoEq9erVp2GY/Pz8efPmtWvXztLS0szMTFdXd/78+ZglS6WwMOBLFhYWR44ckVQpU1NTuh7OnTuno6ODEMKbleOODr46fPhwxVXq4sWL+vr6QqGQtuzq6rpixQqE0L59+/T19Xv16rVs2bInT57gAAEBASYmJi4uLvPnz6e1jY5bKQerKnFcUCk2QzxmLSlUMMePTQrOgYBmEGA1bfJH/ILjsuR0sILjslQx4secOD516lQHB4dz5849ffo0ISHBxcVl9uzZGCRLpc6fP08DtrS0PHTokKRK0S/AEELnz58nCOodjepUCiGUmpq6e/fu4cOHGxgYbNu2DefwzZs3hw8f/umnn4yNjefNm0dnu7IOVlXi6KBSUjCy5v/ge/ruC5g6IYUVeAGBGicgtWmTlSs1jJdIjvgxVcrZ2XnlypU4e/n5+ZaWlkpXKYSQra3txo0bcSpCoZAkScX7UlJH/M6cOcNC6uXl1bFjR5bnnj17zM3NWZ6Kn0qtSlAp6QCZ31L8+XckyeN/veFmUemnLrD0aOALBICA2glIbdrk5EJyvES530TKV6nhw4e7urpGRERERkZ6eHiYm5urQqVWr15dv379CxcuxMXFTZ8+3cLCYtiwYZJM8FyM0NDQiI9HZGQkQmjo0KHt27e/detWZGTkd999R8+emD179tWrV5OSkh49etSjR4/Ro0cjhJYuXXrhwoWEhIRnz54NHjy4e/fukgkp6CO1KkGluOnlFQvwR1QrLkVzh4YQQAAIqJeA1KZNfhZY4yXKXV9Gvkq9fPmyT58+JiYmzZs337Fjh7u7uypUqqysbMaMGRYWFtbW1jweb9SoUWPHjpVkglWK+T2ynp4enlw+YcIES0tLExOTAQMG0DPRZ8yY4ejoaGRk1LBhwwkTJuB16FetWuXk5GRiYmJjYzN06NCkpCQ9RIx2AAAgAElEQVTJhBT0kVqVoFIK0btZPpZt78V/8JKaDAoHEAACmkNAatPGmT3meEnt/mBfJBK1adOGnjHISaYGA0ityhpWqZCQkMGDBzdp0oQgCOarQhamkpKSRYsWtWjRwtDQkCTJAwcOsAKwTjlLxQqvyOm88nG/Pr7BxQIY91MEGIQBAmoiILVpU1PamppMcnLy3r17nz9//vTp019//dXAwCAmJkZTM/spX1KrkrM9V+1XvQEBAYsXLz537px8lRoyZEiPHj2CgoJevnwZHh4eFhb2qVjSXJylkhaJwy+3sOJjwDVXtKCyOQoDl4FALSIgtWmrReWrSlFSU1N79eplYWFhbm7+5ZdfMmelV8WcuuJIrUrO9ly1KkWXXY5KBQYGWlpa4u+u6fDyHZylkh9d1tV/YzJJHr+lF/9h8ntZYcAfCAABNROQ2rSpOQ+QnFIISK1Kzva85lXqt99++/bbb3k8XtOmTVu3bj1v3jypm3KWlJTkfTzS0tIIgsjLy1MKOKaRuaciSB7/m40w7sekAm4gUJMEpDZtNZkhSLuqBKRWpRao1IABA4yMjAYNGnTv3r0rV66QJDlp0iRJCN7e3syZKipSqZzC0m6rqUXA1gXESuYBfIAAEFA/AalNm/qzASlWn4DUqtQClerXr5+xsXFubi5GcPbsWR0dHcnulHr6Ugiha88y8LhfRGrt3Iis+rcaWAAC6iQgtWlTZwYgLWURkFqVWqBSEydOdHR0pCnExMQQBEHP36f9mQ7OUjEDV8E96+Rjksfvu+m/kjKY71cFfhAFCCiTgNSmTZkJgC11EZBalZztec2/l/L39zcxMaF3Ertw4YKurq5kX4qJkbNUzMBVcL8vKO266jrJ42+4CuN+VeAHUYCAMglIbdqUmQDYUhcBqVXJ2Z6rVqXy8/PxwhwEQfj5+UVEROCtVry8vCZMmIDJ5Ofn29nZjRw5Mjo6OiQkpHXr1r/88ot8aJylkh9dkauBUekkj++w8MrTtIqhSEViQRggAASUTkBq06b0VMCgGghIrUrO9ly1KiW5PoenpydCyNPT093dnYYSGxvbt29fExMTOzu7P/74Q35HCiHEWSracnUc048/Inn8/n4hpWWi6tiBuEAACFSHgNSmrToG1RlX1gpJrDzI+VaHFZJ5WrVYTAtqdkutSs72XLUqpSIEnKVSSrrZ+SVdVlLjfpuuxSnFIBgBAkCgCgSkNm3cdkRClBSKnp6h/hUp4QXz4MGDBwwYwEo3NDSUIAh6KybWVYQQU6XevHlTWFgoGQYhpIjeeHt7u7i4MKNnZGSUlJQwfarsZi5OWGUjnBGlViVnew4qJQ8s/0nFuF/UKxj3kwcKrgEB1RGQ2rRxJBd9EW1qh7wtKv42tUPRFzmicF0+f/68rq5uWloaM+DkyZO7devG9GG5mSrFusQ8rZpKMS1U0w0qVU2A7Oic2suOUI3z3/56SPL4320JhXG/alCEqECg6gQqrVLRF5G35SeJorTKkvqrnlCVlZXZ2tquWrWKLkl+fn69evV2796dnZ09duzYpk2bmpiYODs7nzhxgg7DVCnmLojx8fFubm5GRkZOTk7Xr19nqtSCBQtat25tYmLSsmXLJUuWCAQChNChQ4eYH4ziLROZsZ4+fdqnTx9jY2MbG5v/+7//o+ejeXp6Dh061NfXt3HjxjY2Nr///js2SOcQO2SpVEpKypAhQ8zMzMzNzUeNGpWZmYnDR0ZG9u7du169eubm5l26dHnw4AFCKDk5efDgwVZWVqampu3bt79y5QorFalVydmeQ1+KhZF9+ja/xHXFNZLH3xz0nH0NzoEAEFA9AXbTJhaj0gKZf8V5aGPbzyUK96gsqd5VcZ7MiKUFSCyWX5r58+c7OjqKPwY7ePCgiYlJbm7uq1evfH19IyIiXrx4sW3bNj09vXv37mFTUlVKJBI5Ozt/++23kZGRISEhnTt3ZurNqlWrbt++/fLly0uXLtna2vr4+CCEioqK5s2b16FDh4zyA7+8p2MVFBQ0adLkhx9+iIqKunHjRsuWLfEMADwJwMLCYtq0abGxsZcvXzY1Nd27d69kMaWqlEgkcnV1/eqrrx4+fHj37t2uXbvS8wk6dOgwfvz42NjY+Pj4v//+G29bNWjQoH79+j19+vTFixeXL1+WXF2QXZXl+QCVkqyOSvtcjHxN8viOC69Ev1b+mkyVzg1EAAJ1jAC7aSstkCZCHwf36FG+KjhKC+SjjY2NJQgiODgYB3Nzcxs/frxklEGDBtHbrktVqWvXrunr679+/RrHDQwMpPWGZc3X17dr167YU/K9FB1r79691tbWBQUV+b9y5Yquri7u93h6epIkKRRWvJkbNWrUmDFjWKngvhpz93oc4Pr163p6eqmpqfg0OjqaIIj79+8jhMzNzQ8fPsyy07Fjx+XLl7M8mafsqiy/BirFRFRFt1gs/vXoA5LHH7g1VCCE+X5VxAjRgEDVCLCbtppTKYRQr1698Fc0CQkJtGIJhcKVK1c6OztbW1ubmZnp6+uPGjUKF1aqSm3ZsqVly5Y0jdzcXFpvEEKnTp3q1auXra2tmZkZ3pAQh5SjUnPnzu3duzfLIO7KeHp6Dhw4kL40a9asPn360Ke0Q2pfauvWrfb29nQYhJCVldWRI0cQQt7e3vr6+t9+++26desSExNxmH379unr6/fq1WvZsmVSZ5Swq7I8GqgUk3DV3Vkfil3Kx/22/RtfdSsQEwgAgcoTYDdt8kf84q/L62nFX6/OiB9C6MCBA6amph8+fFi0aBE9+rdu3br69esfO3YsMjIyISFh0KBBQ4cOxQWtrEqFh4fr6emtXr36wYMH8fHxK1eupLs4VVYpOjMIodmzZ9OjdsyqqKxKIYSeP3/u5+fXr18/Q0PDc+fOYWupqam7d+8ePny4gYHBtm3bmEkghNhVWX4ZVIpFqeqn5x+/Inn8VouuxGV8qLoViAkEgEAlCUht2mTaEAnLZ/exZk+UT6DY5FT9Kel4xsSePXvs7OzWrFmDszF48OCff/4Zu0UiUevWrWlhkKpSeMQvPT0dR7l69Srdl9q4caODgwNduilTptAqtWbNGmdnZ/oSc/66/BE/OjOVVSmpI354ogQzG2PHjvXw8GD6IIS8vLw6duzI8pRalaBSLEpVPxWLxVMOU+N+g7fdKoNxv6qDhJhAoHIEpDZt8kxUzPFjCpUS5vjRKU6ZMsXa2lpPT49+sTR37tzmzZvfvn07Jibml19+sbCwoIVBqkqJRKL27dv369cvMjIyNDS0a9eutEpdvHhRX1//5MmTiYmJW7dutbGxoVXq+PHjZmZmERERb9++xZ9J0bEKCwubNGkyYsSIqKiomzdvOjg4MGdP0JmRr1L16tXDSwXhf2NiYsRisaurq5ub26NHj+7du0fPnigqKpo+fXpwcHBycnJYWJijo+OCBQuw8atXryYlJT169KhHjx6jR4+moWGH1KoElWJRqtZpVl5xR++rJI+/42ZCtQxBZCAABBQmILVp44jN/l7KqZrT0JnJhYeHEwTBfNnz7t27oUOH1qtXr1GjRkuWLJk4cSItDFJVCg+XffXVV4aGhm3atGH2pRBC8+fPr1+/fr169caMGbN582ZapUpKSkaMGGFlZUUQRGVnotP5lzPix5zpThAEXgRc6kz00tLSsWPHNm/e3NDQsGnTpjNmzCguLkYIzZgxw9HREb9LmzBhQnZ2Np0udkitSlApFqXqnv7zMI3k8VsvCojPhHG/6sKE+EBAEQJSmzbuiMpee4I7RQjBRUBqVYJKcWGr5HWxWDzp4D2Sxx+yIwzG/SoJD4IDgaoQkNq0VcUQxKlpAlKrElRK+dWSkVvsXD7ut/u/ivmXyk8DLAIBIPCRgNSm7eNF+F+bCEitSlAplVTh6Qep1Ljf4oCErHyVJABGgQAQ+EhAatP28SL8r00EpFYlqJRKqlAsFk84QI37DdsZJhRxrKqikhyAUSBQZwhIbdrqTOlrVUGlViWolKrq+HVOUYdl1Hy/faEvVJUG2AUCQEDGp6AARhsJgEqpu9ZO3kshefw2iwNevIFxP3XDh/TqDgGpTVvdKX5tKqnUqoS+lAqrWCwWj99/l+TxR+y6DeN+KgQNpus2AalNW91Goq2ll1qVoFKqrc6094XtlwaSPP6BW0mqTQmsA4G6SkBq01ZXYWh3uaVWJaiUyiv1r7vJJI/fdknAy7ccy/6rPCuQABCojQSkNm21saC1v0xSqxJUSuUVLxaLf9x7h+TxR+0JF8F8P5XzhgTqHAGpTVudo1ArCiy1KkGl1FG3qe8KncrH/Q7ffqmO9CANIFCXCEht2moKgKenJ17vzsDAwNHRccWKFWVlZcHBwfQieA0aNPj++++fPn3KymH//v11dXXxFoL0JeYqf9iTtYNGXl7eokWL2rZta2RkZGtr++233549e5beLJi2oy0OqVUJKqWm6jsS/pLk8dstCUzJLlRTkpAMEKgbBKQ2bYoU/dnbZz9f/fnZ22eKBFYwjKen53fffZeRkZGcnLxr1y4dHZ21a9dilXr+/HlGRsajR4+++eab5s2bl5aW0jZTUlLq1as3a9asadOm0Z4IIfkqlZOT06FDBzs7u8OHD0dHRz9//nzv3r2Ojo45OTlMI1rkllqVoFJqqkGRSDx6TzjJ44/xh3E/NTGHZOoIAalNmyJlX3t3rfNh53X31ikSWMEwnp6e9HrnCKF+/fr17NkTqxQtHpcuXSIIgrlZ7fLly8eOHRsbG2tpaVlUVESnJV+lfvvtNzMzM3p/EBwrPz+/rKyMtqBdDqlVCSqlvkpMzi5ot4Sa73f0TrL6UoWUgEBtJ8Bq2sRicaGgUM5fYk7iw8yHjzIfuZ10cz7s7HbS7VHmo4eZDxNzEuXEKhQUKjKSxlKpIUOGdOnShalSubm548aNIwgiNjYW14xYLCZJks/nI4S6du169OhRusbkqJRIJLK2tv7111/pwLXAwapKXCJQKbXW7MGwJJLHd1oamPoOxv3USh4Sq8UEWE1boaDQ+bCzKv4KBdw/W1qlxGJxUFCQkZHRn3/+iVXKrPzAL6iGDBlC18j169cbNmyIO0CbN29mbuguR6WysrIIgvDz86Pt1AIHqypxiUCl1FqzIpF41G5q3G/cvjuKPJepNXOQGBDQTgKspq3GVUpPT8/MzMzQ0FBfX3/ixIkFBQVYpR4/fhwXF3f48OE2bdrQu8UjhMaOHTtjxgzMPjMzU19fPzGxYjsFOSqVmZkJKoWhEdp433Jqbw0WKultQdslASSPf/xuSg1mA5IGArWGAEulOEf8CgWFjzIfsTpbjzIfyR/uU3zEr2/fvgkJCSkpKfT7IeaIH0Jow4YNbm5umP+7d++MjIx0dXX1Ph4EQSxatAhf9fDwmDRpErOmNm/e3KJFC4SQSCSysrKCET+EEKgU8w5Rjntf6AuSx++w7OqrnE+vSZVjGqwAgbpHgKVSigCIzo52Puzc8XBH+t/o7GhFInKGoUf8mCFZKlVYWGhtbX3u3DmE0LZt2xwdHaMYx6ZNm5o2bSoUChFCf/75Z6dOnZimJkyY0LdvX+wzbdo0mD0BKsW8PZTmForEP+y6TfL44/ffhXE/pWEFQ3WVQBVUKqMgw/2U+5jLY07HnR5zeYz7KfeMggyl8FNEpRBCCxYs6Nixo1gsdnFx4fF4zKRzc3MNDQ3xZIoXL14YGxvPnDnzyZMncXFxmzZt0tfXDwwMxOHfvXvXrl07Ozu7I0eOREdHx8fHHzhwoFWrVvRkQqZZrXBLrUrOsTHoS6mkchPf5LdZTI37nboP434qIQxG6w4BqU0bZ/FLhaX4GVEsFpcKP326xBlRfgAFVSo1NVVfX3/9+vUEQbC+5EUIff/998OHD8cJ3b9/v1+/fg0bNrS0tOzRo8f58+eZGcjNzfXy8mrdurWhoaGtrW3fvn3Pnz+vvc++UqsSVIpZ4wy3SIiSQtHTM9S/IqrrrfTDPySR5PGdl11Nz4VxP6XTBYN1iIDUpq0Olb8WFVVqVYJKSavh6ItoUzvkbVHxt6kdir4oLVy1/IQi8bCdYSSP73nwnvY++1QLAUQGAsogILVpU4ZhsKFuAlKrElRKohqiLyJvy08SRWmVJfWnAqFKyPrQunzc7+8HqRL5AA8gAAQUIiC1aVMoJgTSMAJSqxJU6vNaEgk/60XR3SlvS7TJSRVDf7uCy8f9vK9m5BZ/nhU4AwJAQCECUps2hWJCIA0jILUqa1ilQkJCBg8e3KRJE4IgWG8FJemFhYXp6em5uLhIXmL5cJaKFf7TaVLo572oj4N+WK6SQj+FVJKrTCgasv0WyeP/fOg+jPspCSqYqVsEpDZtdQtBbSmt1KrkbM9VO8cvICBg8eLF586d41SpnJwcBweH/v37q1alnp6Rp1JPz6jiZnie+aH1Imq+37nHaaqwDzaBQO0mgJs25iKttbu8tbh0RUVFMTExxcWfDSzVsErRuDlVasyYMUuWLPH29latSqm9L4UJ7LiZQPL4nZZfy8r7rHpoPuAAAkBAFgGhUBgTE5OdnS0rAPhrC4Hc3NyYmBiBQMDMsHao1MGDB7/44ouysjI5KlVSUpL38UhLSyMIIi8vj1lUhdwV76VYsyfKJ1Co5r0UzlWZUDR4GzXu98uRBzDup1BNQSAgwCCQnp6OhaqoqKgYDu0kUFhYmJCQkJyczGoDtUCl4uPjGzVq9Pz5c4SQHJXy9vamd8PEjqqoFELUXD48qe/T1Inyt1MqmOPH+JWh2Iy8VouukDz+hYhXTH9wAwEgwElALBZjoYqBQ5sJxMXFMTeHxPWu6SolFAq7deu2e/dunF05KqWcvhROhvW9lLcF2v0VEos5fyrVDLD133iSx3dZce3Nh5JqmoLoQKAOEhAKhdrZi4BcVxAQiUSS962mq1ROTg5BEB8XC9bT0dHBpzdu3JAsDO3DWSo6pEwHvfZE5Cm03IaaUhEXIDOwki4IhKLvt4SSPP7Uow9ZfV4lpQBmgAAQAAJaRoCzPVftHD+alqzZEyKRiLFYcNRvv/3Wtm3bqKiogoICOq6kg7NUklHk+VxfRqnUlk5IoPKpDc9e5zoupMb9Lj95LS9LcA0IAAEgUDcIcLbnqlWp/Pz8iPIDb+cVERGRkkKtvurl5TVhwgTJKpAz4scMzFkqZmBud0k+2tiWEqoQX+7A1Q7hd/05yeN3Xnk9Ox/G/apNEwwAASCg5QQ423PVqhTedoU568HT0xMh5OnpydxWmYZcMyqFEHpymlKp1Y1RrsqnNpSWiQZsDiF5/N+PP6ILDg4gAASAQN0kUMMqpSLonKWqdLpiMdrfnxKqM5MrHbfyEaJe5TqUj/tdeZpe+dgQAwgAASBQewhwtueq7UupCCRnqaqSbnpkxSq0L8OqEr2ScTZeiyN5/C4rr78rUNrON5XMAgQHAkAACNQ8Ac72HFSKUUmX51DdqV29kLCM4asSZ0mZsJ/ffySPP/PEY5UkAEaBABAAAtpAAFSqMrVU+A6ta0EJ1b29lYlWxbBP0nLwuN/VZ8rZ67qK+YBoQAAIAIGaIwAqVUn29/ZSKrWuBSpQx6JhPoGxJI/fdVVQTiGM+1WypiA4EAACtYIAqFQlq1FYRo34eVugy3MqGbMqwYsFwm83UeN+c05FVCU+xAECQAAIaDkBUKnKV+DLMEqlvC1RemTlI1c6xuOU9y29+CSPHxSdWenIEAEIAAEgoOUEQKWqVIFnJlNCtb+/Ghb3QwitvRJD8vhfrA7KLfxsQfsqZR0iAQEgAAS0iQCoVJVqK/cV9YWvtwX1ta/qj2KBsM/GYJLHn3sqIjwx+0LEq/DEbKFI5avfqr5kkAIQAAJAgIMAqBQHIJmXQ3wplfJtg0o+yAyjvAsPk9+TPGrcj/7rufbfwCj45ld5iMESEAACGkkAVKqq1SIoRltcKKG6vqyqJioRLzAqndYn7LDn8e15fBCqSkCEoEAACGghAVCpalRaXAClUivqo+zEaljhjioUiXuu/ZelUmS5SvVc+y8M/XEThBBAAAhoLQFQqWpUnViMjo2ghOqvkdWwwh01PDFbUqJon/BEdXy5xZ1LCAEEgAAQUAEBUKnqQX2bQPWlvC3Q86vVMyQv9oWIV7QmSTpgB3p57OAaEAACWk4AVKraFXh9KaVSW11Rmaq2g4K+VLUrCQwAASCgrQRApapdcyUfqJl+3hYodFO1bUk3gN9L2TMm+NE9KngvJR0Z+AIBIFBbCIBKKaMmI09RKrW6CcpT1TbwgVHpeFIfrU/YcTFC5bsyKgMQ2AACQAAIVJEAqFQVwX0WjdojsR8lVP9M+cxfqSeBUenMmX542STfq3FKTQSMAQEgAAQ0iwColJLq43VExR6JybeVZFGKGaFITK89ceXJa5LHd1h45dnrXClBwQsIAAEgUCsIgEoprxovzaK6U7v+h0RC5RmVZ+m3vx6SPP7AraFlQpG8cHANCAABIKC1BECllFd1BdloXXNKqO7vU55ReZayPhR3Wn6N5PF3Bav2s2J5mYBrQAAIAAFVEgCVUirdu/6USq0nUeE7pdqVaezMwzSSx2+9OCDxTb7MQHABCAABIKC1BECllFp1wjK080tKqC7PVapdmcbEYvGEA/dIHn/k7tsiWCVdJie4AASAgLYSAJVSds29vEWp1HIrlP5E2aal20t7X+i0NJDk8Y+Gv5QeAnyBABAAAlpLAFRKBVX39yRKqA58p549EhFCh2+/JHn89ksDX+UUqaA8YBIIAAEgUGMEQKVUgD43rWKPxKdnVGBdikmRSDxi122Sx5944J5YDLsjSkEEXkAACGgpAVAp1VRcyAaqO7WxLSpR06SGxDf5rRcHkDz+2UdpqikSWAUCQAAI1AABUCnVQKf2SOxECVXQctUkIMXqjpsJJI/vsuLamw+qWvdWSqrgBQSAABBQJQFQKZXRjb1CqdTKBqreI5EugEAoGrg1lOTxf//rEe0JDiAABICAVhMAlVJZ9YnF6OhwSqiOj1ZZGmzDUa9yHRZeIamd5jPY1+AcCAABIKCFBEClVFlpb+PRChtKqJ5fU2Uyn9n2CYwlefxuq4NyCwWfXYATIAAEgIAWEgCVUnGlXVtMqdTWzqrbI5FVgGKBsM/GYJLHn38mknUJToEAEAACWkcAVErFVVach3xbU0J1y0/FKX0y/+DlO3svPsnj34p/+8kXXEAACAABLSQAKqX6Sos4QakUtUdiuuoTq0hh2YUoksf/3/obhaVlaksUEgICQAAIKJ0AqJTSkUoYFInQvr6UUP3zi8Q1VXnkl5T1WneD5PGXX3qmqjTALhAAAkBA9QRqWKVCQkIGDx7cpEkTgiDOnz8vtbxnz57t27dvgwYNzM3Ne/bsefXqVanBmJ6cpWIGVof71aOKPRJT7qgjufI0/nv+huTx7b34D5Pfqy1RSAgIAAEgoFwCnO05odz0WNYCAgIWL1587tw5OSo1e/ZsHx+f+/fvx8fHL1y40MDA4PHjxyw7rFPOUrHCq+P04gyqO7VbfXskIoT+OB1J8vjfbvqvpExNGzOqgySkAQSAQF0iwNmeq1alaNRyVIoOgx3t27dfsWIFy5N1ylkqVnh1nBa8RWvL90h8cEAdyZWnkVNY2nVVEMnjb7wWp7ZEISEgAASAgBIJcLbnmqVSIpGoefPm27dvl0RQUlKS9/FIS0sjCCIvL08yWE363NlNdafW26ttj0SEUMDTdJLHd1x4Jfq1htGoyZqAtIEAENAaAlqmUj4+PtbW1llZWZKAvb29ic8PjVMpYRna0YMSKv48yfyrzmfq0Yckjz94260yoUh1qYBlIAAEgIAqCGiTSh0/ftzU1DQoKEgqCC3oSyGEkkIolVpuhTKeSi2FKjyz8oo7el8lefzd/yWqwj7YBAJAAAiojoDWqNTJkydNTEz4fL4iLDhLpYgRVYU5PZESqoPfq22PRITQ3w9SSR6/zeKAF2/UtJOIquiBXSAABOoYAc72XCPeS504ccLY2PjChQsK1g5nqRS0o5JgOalolS0lVOraIxEhJBaLx++/S/L4o/aEi0SwTaJKKhaMAgEgoAoCnO25alUqPz8/ovwgCMLPzy8iIiIlJQUh5OXlNWHCBFzg48eP6+vr79y5M+PjkZubK58FZ6nkR1f51f98KJXa2A6VFqg8rY8JpL4rdFoaSPL4x+4kf/SD/4EAEAACmk6Asz1XrUoFBwd/PuOB8PT0RAh5enq6u7tjeO7u7lLDyEHLWSo5cdVxSVCENjtTQvUvx5R65WbmYFgSyeN3WHb1dU6Rci2DNSAABICAighwtueqVamaKpWK0q2E2ZjLlEqtbIDevahErOoFFYrEP+y6TfL4ngfvicUw7lc9mhAbCAABtRAAlVILZslExGJ0ZCglVMfHSF5UnU9C1ofWiwJIHv/841eqSwUsAwEgAASURQBUSlkkK2/nTVzFHonx1ysfueoxtt+IJ3l81xXX3uaXVN0KxAQCQAAIqIUAqJRaMMtK5Ooiqju1rQsqK5UVROn+AqHouy2hJI8//fgjpRsHg0AACAAB5RIAlVIuz0paK85DG1pRQhW2pZIxqxU86lWuw8IrJI9/7VlGtQxBZCAABICAigmASqkYMKf5x39RKrWmqTr3SEQIrQuIJXn8L1YH5RYJOPMIAYAAEAACNUUAVKqmyH9MVyRCe7+hhOrsrx+91PF/sUDYxzeY5PF5/zxRR3qQBhAAAkCgSgRApaqETbmRXj38uEfiXeUalm/tXtI7kscnefywhLfyQ8JVIAAEgEBNEQCVqinyn6d7YTrVndrjhkRq3a5wyfkoksf/yudGYWnZ5xmCMyAABICARhAAldKIakD5bz7ukXhQnRnKLyn7cu2/JI+/8nK0OtOFtIAAEAACChIAlVIQlOqD3dlFdafW26Oi96pP7FMKN5l5NJUAACAASURBVOOySB7f3ov/KEWt6X7KAbiAABAAArIJgErJZqPmK0IB2tGdEqorf6o55bmnIkgev++m/0rK1DreqOZiQnJAAAhoIwFQKU2qtRfBlEott0KZz9SZrfcFpV1XXSd5/E3Xn6szXUgLCAABIMBJAFSKE5F6A5waTwnVwYHq3CMRIcR/kk7y+I4Lr8Sk56m3wJAaEAACQEAeAVApeXRq4FpOClrViBKqqLPqTF0sFv/fkQckj++x/VaZUKTOpCEtIAAEgIAcAqBScuDU0KXgdZRKbXJS5x6JCKHMvGJn76skj+8fklhDJYdkgQAQAAJsAqBSbCI1fy4oQn7leyTeWKXmzJy+n0ry+G0WB7x8q75NhNVcRkgOCAAB7SIAKqWR9RV9kepOrWyI3iWpM39isXjcvjskjz96T7hIBNskqpM9pAUEgIB0AqBS0rnUsC+1R+IQSqhO/KjmnKS+K2y3JJDk8f+6m6zmpCE5IAAEgIAkAVApSSaa4ZMVW7FHYkKQmjN04FYSyeN3WHY1PbdIzUlDckAACAABFgFQKRYQTToNXEh1p7Z1VeceiQghoUg8bGcYyeNPPnRfLIZxP026JSAvQKDuEQCV0uA6L85FGxwpoQrbquZcxmd+aL0ogOTxL0S8UnPSkBwQAAJAgEkAVIpJQ/Pcj49RKrWmKfqg7k11t/0bT/L4riuuZeeXaB4XyBEQAAJ1hYByVOrGjRtOTk55eZ8tW5Cbm9u+ffvQ0FD1s+QslfqzVMUUqT0S+1BCdW5qFS1UNVppmWjA5hCSx5954nFVbUA8IAAEgEB1CXC254QiKXh4ePj5+UmG3Lp167BhwyT9Ve3DWSpVZ0CZ9tMeUirlbYFS7ynTrAK2nqTltPSitkkMis5UIDgEAQJAAAgonwBne66QSrVo0SImJkYyd7Gxsc2bN5f0V7UPZ6lUnQEl2z//O6VSe75W8x6JCKG1V2JIHr/7mqC8YoGSCwXmgAAQAAIKEOBszxVSKSMjo4SEBMnkEhISjI2NJf1V7cNZKlVnQMn287PQWjtKqB4cREmh6OkZ6l+17OpbLBC6b7hJ8vheZ58quVBgDggAASCgAAHO9lwhlXJwcDh//rxkcmfPnm3ZsqWkv6p9OEul6gwo3374DkqllltR/+K/Te1Q9EXlJyRh8c6LbJJHjfvdTnwrcRE8gAAQAAKqJcDZniukUjNmzHB2di4uLmZmtqioyNnZeebMmUxP9bg5S6WebCgzlahzn/SpQqgskbeleoRq0bmnJI/v5nOzqBS2SVRmrYItIAAEOAlwtucKqVRmZmbTpk2bN2/u4+NzofxYv3598+bNmzZtmplZAy/eOUvFyUWzAoiEaFM7CZWyoFRqk5Mahv4+FAt6rv2X5PFX86M1iwzkBggAgdpOgLM9V0ilEELJycnff/+9rq6uTvmhq6v7/fffJyWpdbFUurI4S0WH1A5HUqg0ifo49Jekjrn+N2IzSR6/pRc/IjVHO6BBLoEAEKgVBDjbc0VVCtN4//79/fv379279/79+xrkw1mqGsxbVZJ+ekaeSp2eiF78h0ryq2K5MnFmn3xM8vj9/P4rLYNtEisDDsICASBQDQKc7XnlVKoaOVFmVM5SKTMxNdiS35fCr6mWW6Fd/0OX56CIEyg7URUb0r8rKO2y8jrJ428Oeq6GQkMSQAAIAAGEEGd7rpBKubq6dv786N2796+//ir1Iyo1cOcslRryoMwkKt5LWUrpUa1rjk5PRH4d2Jd8WqLjo1GIL0oKUWI361Lka5LHb7XoSlzGB2UWEGwBASAABGQQ4GzPFVKp5RLHnDlzvv76a0NDw7CwMBlJq9Cbs1QqTFtFpql9Ecsn9dEz0fEpPRk97zV6dh5dXYT29UUrG3wmWsut0O7/octzUeTJanazxGLxlMMPSB5/yPZbQtgmUUV1DWaBABBgEOBszxVSKYbBz5yLFi365ptvPvP6/CQkJGTw4MFNmjQhCELqF1c4eHBwcOfOnQ0NDR0dHQ8dOvS5DSlnnKWSEkfzvaIvfjbTb5OTzGnoZSUo9T4K30F1szY5faZY3hbIxwEdH4NCN1KfBpdWeuf4zLxiZ++rJI+/L/SF5jODHAIBIKDtBDjb82qp1LNnzxo2bCiHUUBAwOLFi8+dOydHpZKSkkxNTf/444+YmJjt27fr6eldvXpVjk1FxjHlR9fcqyJhVdaeyH2Fnp1DgQvRvm8lulnWaPdXiP8HijyF3r1Q8G3WyXspJI/fdklAcnalRU5z2ULOgAAQ0EgCqlWp2NjY+vXrK1JwOSq1YMGCDh060EbGjBkzYMAA+lSqg7NUUmPVCU+qm3UP3d6OTk9AGyW+wdrgiE6MRaGb0MtbqLRQFhCxWDzW/w7J44/1vwPbJMqiBP5AAAgohQBne16tvtSaNWvc3NwUyagclXJzc5s9ezZt5ODBgxYWFvQp7SgpKcn7eKSlpREEwdpJhA4Jjk8E5Hez9rgh/jz05DR6l8TqZiVnF7RdQm2TeOJeCvVZsXpXF/yUf3ABASBQ2wkoR6W2ShwrV64cOnSovr5+UFCQIgzlqFTr1q3Xrl1LG7ly5QpBEEVFRbQPdnh7exOfH6BSLEQcp4Jirm7Wj+iWH3oZhrtZ+0JfkDz+7GUrhL5tP736UtfqghxlgctAAAjUFgLKUSl7iaNTp06jRo0KDw9XEFT1VQr6UgqiVjRYbhqKOosCvdDeb9CK+p90yNsCrbBBe74W8eddWDdBvMxC/GnaoYWYNfNQ0cTqTDjod9aZqoaCKouAclSq+rmRo1IKjvgx88BZKmZgcHMTEBSjlLvo9jZ0ajzayOg5MfSJljFKqNSyuiB3tjUtBHuWpppWtdc0DJAfIFApApztebXeS8XExMybN0+RDMlRqQULFjg7O9NGfvzxR5g9QdOoAYdYjHJSUdQ/lGJJU6kKT7WsLlgDxa9ykhVfvH1cfZFCp75V7auca4gIBGqcgEpUqqCgYP/+/V9++aWOjg5zep5kafPz8yPKD4Ig/Pz8IiIiUlJSEEJeXl4TJkzA4fFM9Pnz58fGxu7cubNOz0SXJFhzPqInf8tRKeoqHDSBml7Vns4IOICA1hFQskqFhYVNnjzZzMxMV1d33rx5sbGx8okEBwd/PuOB8PT0RAh5enq6u7vTcYODg11dXQ0NDR0cHOruV700Ds1wPAvjy1Ep6iocNAH5KzFCv5MGBQ4gIEFAOSqVlZXl4+PTtm3bxo0bz50798GDB/r6+tHRNbYXEWepJDiAR6UJXHic8nqZvWgZcwirwi1aZnH3yuFKW6zFEeSvav/0TC0uOhQNCFSTAGd7rtB7KWNj4/Hjx1+9elUkqtjTAVSqmhWj+dHDE7N/XbhMtMyCKVSiZRbict2i5lDc3sb6ykrzC6WqHMZfl9PvpL42gwMIAAEZBJSjUm3btrW3t1+0aBE9xAcqJQN47fEWisQ91/47deGy18vs6Sb49bKWvy1cfG7FyAqfS7OQUFB7yly1krxPRjt70YjYDh8HNey2XLWMQywgoAkElKNSCCH8RqpevXpdunTx8/PT19evqW07avM6fppwyzDyEBiVbs/jO/AujVm4YebCRWMWbmjJu0Ty+DOPP6TWuqXmsFmgI0NQUR3e3jcpFPm0pDistSvXJ4ntV1bZoswaGxtnVCY4gYCGElCaSuHy5efn7927F8/u69279969e9+8eaP+onOWSv1Zqq0pBkal91z7L8nj479Oy6+RPL69Fz/k+RsUF4BWN6Ga5u3dqKVs69ohFqO7e9Bya4rAnq9Rbhq1hv0mxtqJm9qhHd2pq34d0IfMuoYHygsEFCTA2Z4r9F5KMjH8pVSjRo309fUlr6rah7NUqs5AnbIvFInDE7MvRLwKT8wWisReZ5+QPL7Limup7wpR+pOKrUN8WqKUO3UIi6AYnf+9vPNkgc7+HxJ8XNCLtfZE4Tu0rQsVzN+9Cruo1CGeUNQ6TICzPa+iSmGkZWVlPj4+6sfLWSr1Z6nupFgsEHpsv0Xy+IO33SoWCFFeOtWT8LagNg15crpOcMhLp5aV8rZAy62o5efFYnmlzk5E68tf7J34EV5QyQMF1+oqAc72vBIqlZ+fz1wENiIiwsPDQ1dXV/1sOUul/izVqRTT3he6rqCG/nj/PKEKXlqATo6r6FjcWM3Rams7qdR7yLc1Vdh1LVDiDYVKk3IHrWxIRbm6SKHwEAgI1CUCnO25QiqVmpras2dPXV1dAwODuXPnFhYWTpgwwdDQcMyYMXfv3lU/T85SqT9LdS3F0Pg39l7Uy6qT96jFRJBIhK4vqxCqM5ORoLh2Anl0pGKfyZ09K/cqjv6g6t7e2kkGSgUEqkqAsz1XSKXGjBnj6uq6ffv2Pn366OrqduvWbfr06WlpaVXNVXXjcZaquglAfAUI7LiZQPL4rRcHPEn7OMfv0VFqPXVvC2rX4PwsBWxoTxChgNqLCy9seOonVJJf6ayH+FLRl1uh59cqHRciAIHaS4CzPVdIpZo0aXLnDvVuPCsrS0dHZ/PmzTVLjLNUNZu9OpK6SCSecvgByeP3WnfjXUFpRamTQqihMGpim3PtmYFd8BYdHFghUf9toDqOVTjEYnShfMLFmqbUrBM4gAAQKCfA2Z4rpFK6urqZmRVTac3MzOLi4moWL2epajZ7dSf13CKB+4abJI8/fv9doejjJIK3CWirK9Wmr2mGEhTaJFOjib2OoKaS4+LEBVQrq0IBOuxBmdrYFuW+qpYpiAwEagsBzvZcUZWiv4syNzdPSkqqWT6cparZ7NWp1GMz8totCSR5fN+rjGeXwnfo4PdUc7zcGmn1m5inZ9AqW6ogWzujN4wCVrmOi3IqPqLa/T9U8qHKZiAiEKg1BDjbc4VUSkdHx8rKyrr80NHRsbS0xG78r/phcZZK/VmqyyleiHiFv/m9Hs34drWsFJ2bRrXv3hYoYIH2TcIWCdG1JRX5PzZCmetrvE9GGxwpy3+NRMKyunznQNmBgCJrCSmkUoflHuoHDSqlfubyU/S++Izk8Z2XXU16W/AppFiMQjdWNPR/jdKmrkPRe3R0WEXOg7yVL7FpDyu6aPw/avnE/U93A7iAgHQCnO25Qiol3XbN+XKWquayVkdTLi0Tjdh1m+TxB2wOKSz9vH/w7Dxa1Yhq8Xf1onYB1vwjKwZtcaEyvLoxijqrqvzGXKpYCDF8h6qSALtAQBsIcLbniqrUqVOnxo0bN3LkyN27d9d4wTlLVeM5rIMZyMwr7roqiOTxZ518LGYtx5D2EG1oRbX7G1qhtIcaDSfmMlrTlMqqnzPKeKrarN7eRiXkbYliLqs2IbBeCwiwFt+qBSX6WATO9lwhldq1a5eOjk6bNm1cXFx0dXX//PPPj/Zr5n/OUtVMtup8qndfZDssvELy+IfCJObX5KSinV9SjfKqRujZeU1EJRKhm2vLZcMCHRqECrJVnkmxGF2eW87EFr16pPLkIAHtJSC5kHH0Re0tDSvnnO25QirVvn375cuXY9PHjh0zNTVlJaPmU85SqTk/kBxNYF/oC5LHd1x45cHLd7RnhaPkAzVfAM+nCN2oWe9jSj58WuQpgKe+TbOEZejYDxSTDa1QTvkqHmxqcF7nCURfrBgcxr8d3P/2tqTW4K8VB2d7rpBKGRsbv3z5EgMRiUSGhobp6ek1yIezVDWYtzqetFgs/v34I5LH/2J1UNYHiXWShGXUfD/8Yzs3DZV9/Ba4ZqllJ1bMDl/ZAD3+S915Kc6j3th5W6AdPVBxrrpTh/Q0nIBIiDYytoNhCtUmJ+XP66kJGpztuUIqpaOjQ38vhRCqV6/eixc1uZ8QZ6lqAjWkWUGgoKSs76b/SB5/1J5wgVDaMg339lZsy3Twe1Qo0eVSM8j4ILSuOSUSG9uitAdqTrwiudxXVOp4S0nY+7hm6kBjUi3Opd7dRhxHQd7oxI8VO+N8EqfyTzvo06RQjcl31TPC2Z4rqlJTp06d+/EwNDT8+eefP57NrXruqhqTs1RVNQzxlEMg8U1+h2VXSR5/1WUZ29TGB1ErU1Bfy7qitwnKSbWyVsRiFLaFWliPWniwL/qQUVkDygyfHlmxpeSF6Zo1FqrMQoKtzwmIxSjvNUq8ie76U6tEHh5c8bBCixCn4+mZzy1q5Rlne66QSrm7u/eWcfTp00f9YDhLpf4sQYosAoFRGfhT38tPXrMuVZxmRlPz6PAWGEkh0sOozre0EJ35mUrd2wJdnIHKSlSXlKKW4wIrJDN0k6JRIJwWERAK0JvnKOYS9RHh2V+Rf++KBzVJKfJtQykW/w9KvcK2VtylksG8LRD0pTT2BgCV0tiqYWZsXUAsyeM7LQ2Mz5SxFFB+FrV6urcFtZL6o6PMuKp156Sg3V9VpHtvrwb1Xe76VzRJqvtOS7Vka4V1pcz5Ls4rH7g7QQ3cnRyHtner2C6AJTbLrandnE/8SG18E3GcGnNmvZsUCdGmdhKzJ8qfrta1gPdSn244Nze3jRs3xsfHf/KqUReoVI3iVzTxMqHox713SB6/j2/wh2KB9GiCIvT3pIqm+frSKi43Lt20DN+Xt5BPSypFHwf0MkxGoJrzDuBReVvZEKXeq7lM1OGUqzDnmxq4S0cvgqklK6mBOw+ZA3drmiJ/d3T2/1CILzVD702cQhOIKub4WVb8TJg6V9OfhAtF4vDE7AsRr8ITsz+tN13J24ezPVdoxO/w4cM//PBDvXr12rVrt2DBgrCwMPZnm5XMVjWDc5aqmvYhurIIvM0v6bn2X5LH//XoA5n3jEiEbqyu+AWeHEft/KuiQyym2hG8A9YeNw1dCEMkRCfGlotoy8pttKgiaHXKrCJzvisG7i5TA3fnpsoduGtNfXh3eS66u4fa1jn3VdV77WztdEInf6z4yfznU3Wz1avcwKh0/OvGY/s91/4bGFWVud+c7blCKoXLUlJScuXKlV9//bVJkyaNGjWaPHny+fPnmXvMV6/IlYjNWapK2IKgKibwOOV9q0XUp767/0uUl1TkqYptcPd8TT2ZKv0oK6nY3snbgnojVVqo9BSUZrC0AO35mmqDtnWt+TmQSiuVxhuqGFv7fBId7risJ6kROe6Bu7FUsMd/odT7ylyeGJNjjUOKxei/DRVCdW2J+oUqMCrdnkft1k3/2fP49jx+FYSKsz2vhEox77K7d+8uWrTI2dnZxMRk0KBBYWFqHTnhLBUzq+CucQLH7iSTPH5LL/7thLfyMpMcXjEWt8lJyfsE5qVXvABbbkW9jmYt4CQvTzV07UMG2tSeaoMODtSImR01hEGtySaFVjT6zCE1qe7VTajHiH9+QSEbUPQFlBVbY3UUvrMiz5fnqGO0/GN9CEViZi+KKVQ91/5b2aE/zva8iir1MbcoMTHRz8/vzBm1TojkLBWdPXBoAgGxWPzH6UiSx++y8vrrnCJ5WXr3gnrPTK302gRVc8tBOpm0B8i3DWVzXXNt2pUx81nFHLCzv2qBrNK0tdfx9Iw8lTo8BN3ZjRL+Rblp6tQDbpwPD1fMrTj7q6o3giksLXvw8t3BsKTx++7SyiTpCE+s3OpinO15JVQqNTU1LS0NU7t3797s2bP9/f25IaogBGepVJAmmKwWgWKB8PstoSSPP3RHWEmZUJ6topyKDW29LVH4juo20I+PVQwk7uiBsuUOOcrLUw1dS/i34vPn4PU1lIO6lGxCkDyV0uQ530/PVNwnp35Sbq+uqFT4MPn9obCkP05H9vP7r6XXp/E9SXGifS5EVG4fas72vBIq9dVXXx09Sk0XzsjIMDc3//LLLxs0aLBixQr138icpVJ/liBFTgIp2YWdll8jefzF57nWGhcK0KVZFU3GpdlVXFVPKEBX5lcYOTlOm3a3YqJ8eKiiCJGnmN7gVjKBjCi0vbsMlbKkFoAQyX20UnJuKm8ull/xNHbsh+q8cy0WCB+nvD8S/vLPvyMHbA7Bi0fT8oNXPpty+P4fpyOYnix3TfalrKys4uKoTbW3bt3aq1cvhNC1a9datmxZeaLVjQEqVV2CNRT/ZlyWffnj2JmHFZ1ymRkRi9Ht7RVDGUeGVvpddMFbanoVfqkQvE6zhmhkllnGhetLqYKsqK+J8+ZlZFmbvMVidGcXNfXf2wKttSu/Z5hzvi2pm1Ar1nVNvEHtiEa9y/weFecpWAXFAmFkas7RO8nzz0R+tyVUUpa6rgqafOi+3/XnQdGZmXkVK3Pi91Ks2RNk+eyJGn4vZWZmhtec9fDwWL+eGoJISUkxNjZWEIcSg4FKKRGmmk1tDnpO8vhtFgdEvVJgZdXYKxU/vO1foHcSu4HIynr6k4pVLdY0RbF8WaG0xl8kQqcnUK3PuhboraZ8s6g19ORn9ENmxZr03hbo+GiU/4YSJOor2o8z/TY5aYdE4WKm3KkQWv/esmaHlpQJn6Tl/HU3mffPk4FbQx3Lt9phdoa6rLzuefDepmtx16MzM3KLZX1Aguf4MYVKI+b4de/encfjhYaGGhsbR0ZGIoTu3LnTrFkz+beBKq6CSqmCqnpsikTiSQfvkTz+Vz43cgoVWBM9PbJiTWiflijlDncmn56p2Kx9qys1+Uq9h1I+cpSSZUER2vsN1XRu6YQK5M6TlBIZvGQQiAukvuzGe54xlyBhzfmWEVtDvV8/RuvtqULt/BLlZyGESstEUa9yT9xL8Tr7dNC2UPxZCFOWOq+8PvHAPd+rcVefZbzOKZIlS5Ll1cTvpYKDg62srHR1dSdPnoxzvHDhwuHDh0vmXtU+oFKqJqxS+zmFpV/53CB5fM+D90QiMXdaea/RHjfqh7eyAXpyWmZ4kZD6WgU/BR/7ARW9lxlSNReU9aOVnrv8N2hzR6p0+/shgcSWKNLjgK8MAoIiapEIfKvs6oWyYmSE0yBvxR+ABOnPBD7U1thv17SfvOVs60UBTE0ieXyXFdfG77+74WpsYFT6q8rI0v+3dyZgUVX9H5+yAP9vKVm5J2qZ9b6oLTZaLpT2ZqWvmuWSpfjqq6ngVhkoBCYC7ivigsu4ICDGIjsim+wg+yL7voPswsDM3P8zXrxNMzBzGWbnex+eOvfcs/0+53q+c5Z7jigO+qUSjUv5SGzP+7B6giAIDofz+PFf//gLCwurq/lyLf6ys7PT09PT1tZmMpmxsT3v+3LixIm3335bR0dn7NixO3fubG8X9+9QolXiy4OnSieQVtb4thn/H8+Je9m0CsNu/euUwmAb/sI/oR+8TxqIG992tzuBFoqf65bhR469Aql51H3IyO116j3T1quFCnlQmdZ9nJjlEMJvj1pIvvgfQF0cbmZFk0tcibl72mK7iElmvnNNL5Va8LuJpRYT55pemrov4AeHmIN+WT6pFSX1bfR7SwqpD0Jie94HlXry5ElbW/cX+0VFRSdOnPD395dohrOzs5aW1pUrVzIyMjZu3KirqysqbI6Ojtra2o6OjoWFhQEBAaNGjdq1S9yBIBKtklgqBFA6AdeEUv50q6l3cJbkHzr80nK5RIB5tw5dWvC3rdIOv0UcmcR/ZDWCUMZZBrL9yFFc1RSE8ZdRWA4hgpSwtlZcwdTiGZdLRJ3tXgt3ZJK6fDzX4w8gPRPv3z3SfvdIW3o2gvzBJ9hh0rf0Nz7nWWfLP3OAc+gtXlUvB+ioRq1JbM/7oFL//ve/z507RxBEQ0PDiBEjxo4dq6OjY29vL95SJpNpZGREhuFyuaNHj7a1tRWKYmRkNG/ePMrz559/njVrFnUr6pBolWgU+KggATP3VD0T7ymW/sV1tPcrSmB1H29BzW9TjkMTiAr+dKnir6i8OsE2Qsi9+mL02ZDcoMyqkvo2WiOc4g1IcuyWakXuIi++SGrxtLmSuP5NNzrHleoyvdfbDyChd0zfwn/VhWhrn8y7yeWFta3dvaWWav7slOUQ/kxVeaLK1pLE9rwPKvXqq6+mp6cTBOHg4DB16lQul3v79u133nlHjPFsNnvQoEHu7u5UmLVr1y5evJi6JR2Ojo5Dhw4lBwPz8/Pfeecda2troTAdHR1Nz67S0lIGg9HURHe1pVBSuFURAh1dnCV2EXom3l+dDH/Cpvc9CpfTvYsSJU6U4+hkxQ/0kSSvPCgQajV6u333d7/FdhG/3k52CM8Pza4Rs4ZKXB2Rm/P+MYx/gB4uOgT4CyWeboRvNZyIc+jvp+J0cuxfGB6PV1TX6plcvvVmQm/vkp6J99abCR5JZQW1rb3++mmrJy5+xhcqm7FEUVT/CiWv2LJUqcGDBxcXFxMEsXz58n379hEEUVJSMnjwYDFlLy8vZzAYUVF/0dm9ezeTyRSNcurUqRdffPGFF15gMBibN28WDWBpacn4+wWVEqWkdj4VjU8+2B+oZ+K9yyWJ1nC5+M3WFL5BQHVzu4VH2sQ94r7J/+1OyrZbiQtOhInOY5NdyW/tI/e4pbIiCyPzautaaJzHyOMRdzZ0Nz3qMPOvzNeS3cY/TpD8KWM/S/HLPmnazuPxyhue+KVVHvLL+vFSDPn9uxh9Ih/R2uWho5m/GyR/17GR/K3ZVe+SpUpNmTLl1KlTJSUlQ4YMIYUnISFhxIgRYqymqVIhISEjRoxwcHBITU11c3N744039u/fL5Qs+lJCQDTmNjK3ltx55UZ0kWSjxG+2psBJqYY29kG/rMnm3QuoelSg8Sbegh85dnG4udUtPqkVxwOzt9xMmHc0RPQjSnK3w1UXoi0902/GFMUX1jc+6elorq4O4vICftNzXJ9orpLMbWCGqEwlznzULVH+e2W7e1D/idY0d9zPqjoemP3fq3EfWt0T0qRJe30X20VsvBYv5C94S3eXB3Zb92dh+19TwS8IZalSrq6uL7744vPPP//555+TNWRjY/Pll1+KqS2aI36zZ8/+9ddfqXRu3LgxePBgLpdL+Qg5JFolFB63Kk7gXGienon3W3t9Eov/WkHac5lV+5krpAAAIABJREFUoC/V2tF1OihH38KfbC+Wno2IzK2V7iPHji5OZkWTR1LZYf+sDaz4OYeCyb05BFsiPRPvGdZBay7HHvDOcIkvSS5paGN38eG01ROn3uc3wRc+Fb8pjkyWC/dcHSrry18oYSewUCJIFUra2NYZnlNjF5y76Xr8x0/PXROs6Il7fL46GW76Z4pjTHFaWSO7i98AymyXh64OwvlH/tuy7xWlrDASw19ie96HeSlyB7/ExERKP2JjY7OyJHw4yWQyjY2NySJyudwxY8aIrp744IMPfvvtN8qMW7duDR48mMPpdaJColVUUnCoBQEej7fpOv8340yboFrxQ169HrCtiM3W2js5lx4UkEOUeibeC06EBWVWUQOV4pcL06yINnZXSmmDa0KptU/m2suxom0Z2a7NPnR/AyvuvHtgh/U4/jquW6t7m5OTSaloFl5VgvEXSizt7kLdWqXEhRItHV0x+XUXw/KNbyXOPRwsqEnkAtfPj4XuckliRRY+LH7c3tlziyfdD6Ae6oLTxT+2kT/4OZRIuNpDACV5SWzP+6ZSpBWlTy+aFjk7O2tra7NYrMzMzE2bNunq6lZV8Qco1qxZY2pqSiZiaWn58ssvOzk5FRQUBAYGvvnmmytWrBCTvkSrxMTFI9Uk0Nze+dmRED0T71UXors4vXaj+YXv4YBtuW+21sXhOsUWU2fqGBwO9kwuF52ylkevpam9M6Go/lZssaVn+vcXo4WGhr41PdphMYywHOJyYO3mGwnHArO9Uypyqpo7nzLscRGzdEfVqeZr00OpsnyeLZQYQcRdUvBCifZOzsNi/ibiu1ySPj8WKtoznns42PhW4sWw/Jj8upaOp33iHmwQ9pLZTw0ul392MDlLp+zT6CkjJbbnfVApLpf7xx9/DBky5Pmn19ChQ/fv30/1q6gsRR1nzpwZN26clpYWk8mMiYkhAxgYGBgaGpLurq6uffv2vfnmmzo6Om+88cbWrVsbGhpE06F8JFpFhYRDjQjkVDW/+7ufnom3ra+EDroiN1vjcnmeyeWfPlVQsrfnFFtMaoCy2Na3sqPz665FFe51S11+Luo3y6fb0VoOMdu7g/q1/tZeny+Oh1LTZpS/1FuCKsvYPuTLbiO8dnY3wfaziBr+1thSXH36qdHJ4e8/5BhTbHIn5auTPWyL97FN0Kbr8XbBueE5NbS2BOulxH0qVS9pPPXm8Qhy/2LLIYTyTqMXLKHE9rwPKmVqavr666/b29unPL3Onj37+uuv7927VzA/xbglWqWYYiAXmRPwSikn21PJ51IL7T0h86IQBI/Hu5dRteBEGFmkD/YHXnpQ0NuwjBzyp5skj8drCbQhLIdwLXUvXzm/xC7in0/FXlCZRN10J97plkLZ4SpSZLJQQmKvhcPlZVc1344v+d2je6MHIbYfWgWSm4jfz6qqaaaxYlPx5JR9Gr2QxRLb8z6o1KhRozw9PQUz8PDwGD16tKCPYtwSrVJMMZCLPAhYeWXomXj/y8I/t7pFHunTTDMyr3bpWf63XHom3vqW/mfu57TSHp+hmYUsg/F4hPsWfjfCejRRmcrl8koft1n78En29reBFRdXWC9hcFWWRZRbWlwu/5CX/a/xzT/ydn8WW/c2QMqKLPRIKtvvlbH8XBTZ3RekOsXS/weHmEN+/G3x+rRbq9yI0EtYSafRixZOYnveB5XS1tbOzv7brmuPHj3CyR2i0OHTHwKdHO7y81F6Jt7zj4UqRRiSShp+eHZg9mRz34N+Wf0ZqOkPir7F7WITrEX8lvroO0RTOUEQ4nfEINvZqfsCtt1KdE8sq2+lsT993wqkkNBNFcS1Jd2jfPyFEn07y1ywiDR3edAz8X73d7/l56OsvDI8BTd6EExLXdwKPI1eDBJZqhSTydy2bZtgZsbGxj1+oisYRh5uiVbJI1OkqTAC1c3tHx3gfz6y1fEhtYJOAblnVTb979nnKW/t9bHwSKt+duybAnKXQRZPGrpHvc7NIjpaxCxinvZHwLZbD6f9wT86mfybYOr9zdmIM/dz0ssbFcm8X1Zn+XSfUmE1goi/3J+FElwuzzmumKIh6ph3NMTCI801oTSnqplDZyP/fhmmwMhyO42evg0S2/M+9KVCQ0P/8Y9/vPvuu+ufXu++++5LL70UHh5OvzSyCinRKlllhHSURSC+sJ48n80hPF8BZSiqa93hlEiuyJpg6v3L7eSSetpbCyqgfPSzeFxIHH6T37e4uZzgdIlfxMzh8uIL6w/5ZVFzb2TrPMM6yPTPlID0SqX0ZWnZKrhQ4pyUCyU6ujjxhfVnQ3L/ezVuimX312+i+kT60NrlgVbRVS+QjE6jl9owie15H1SKIIjy8vK9e/cue3qZmZkVFxdv3LhR6sJJHVGiVVKnjIiqQ+BqBH9zvIl7fGLypR/GkWhOZWP7HrdU6sTSrTcf5lY3S4yl0gFK4wmr4Xyh8v6F4PEkLgcgbSlveHIzpmgDK+4dc/4yS/Jv0l7fHy/FXIkoKKprVSGTK5KJM9O7R/kCzPq0o0Tjk87gR9WH/LKWn4ua9PTsGMpY0Z3FqUd6Jt6atthEqDqlOo1eKA2pbyW2531TKaFyJCcnP//880KeCriVaJUCyoAs5E2Ax+Ntd0rUM/H+0OpelRxG3upb2Qe8M6imyvBKLK1D7uVttkzSz/Dgf7lpOYR/UMXT/Qui8uo8ksqi8uokjla1d3JCHlVbeKSRJ1VSLfVnR0OsvDIic2vJPRFkUsw+J8LlEhGnus8u4S+UoLXZbmVj+93k8t890r48GS70AdOHVoE/XU+49KAgpbSho5Mz0yZI8Ih00nahba76XGZ1iUDjNHo5mSKxPYdKyYk8kpUBgTZ2FzkYtcw+UoaNY1N757HAbGq59nfnImML6mVQXJVKIuLU097GUKn3bePxeLnVLRfD8lddiKb6muTyy803ElziS6qbxR1VKnsY/IUSi7u7ULe+F7NQ4mnJm2/FFu9yThLSWj0Tb4PDwb/cTnaJK8mvaRGagRM/QCp7i1QtRZHT6BVTQKiUYjgjF3kRKKxt1X86Z2DpyT81pp/XEzbnfGgetWpg4enwkEfVQk1VP7NQleg8HnF3B79NPzCSKIkjCsL5u7cVhPe2kZL4Yje1d/qkVvxyO/lDK/4G9tTff848OB6YnVzSILoNh/gE+/w000tgocQV0YUSnRxuYvHjC2F5/7sW/57AqhA9E+8Jpt4LT4dbeqb7pFZIXA5Dc4C0z+VXlwjVmfzV/JZD+FtENpQoptRQKcVwRi5yJBCYUUU2i+6JZVJnw+7iXo8uIpcO6pl4zzsa4ptaoZn6RDHidHXvhL1Pt7sLYjmEOPYOf+cOaS8ul5dc0nA8MPs/Zx5QWkWOyv5yO9kntaKpvacd3KXNjh+P3Urc3d5d/nOziZq/PoZp6egKz6k5FvBo1YVooV023jbzXXkh6mjAo7DsmuY+Fklmuzz0x2olxq3L42+0z99u/19EXZ4CCiIblfqml+uzzz7DvJQCahFZHPF/pGfiPdncN7Oiz0dfcri8Owml1MjPJ7b3XRNKNeFrVjqvRbLzX/rUfVykzPY8rG5ud4kv2Xwj4V/PtofXM/F+c4/PygtRF8LycqubZfAjoCKZOP1htwkB5kRXR01zh29qxb676YtOPxA692TaHwEbWPHnQ/MeFj+W4fgwHcyaFqaxlDj9AR/7kUmE/A8wk41KrRN7Kb6GJFql+CIhR7kS4HB5P16KIScVej5vqafseU9XuH1+LJT81f+h1b1rUYUdXT3vPN1TAmru171//JAehOrYu9IN/fVIhN3FjcyttfLK+Owof79g6m/OoWALj7SQR9W9bSslrtcisFCi6/DbIX63f72dTO2mSGUx6+D9nc5JjjHFOVXNch917NF4TfVU4Gn0Etvzfq2eUFYFSbRKWQVDvvIjUN/K/sT2vp6J9wZWnMT2iMfjhWXXLDrdPSo1dV+AfUhe97FM8iuiqqUs/iwu1n+IoP3Ew2tEfijxuJDg0N2fW7yVRXWtVyIKfrwUI3gs5DvmfhtYcTdjiioan1DRxcwAdT0ubbnwFSmu9/f9e5qJEyVL4035B6aYu6d5JpcLpkYlC4fMCCjqNHqJ7TlUSmZ1ioTkTSCltIFs++yCc8XkFV9Yv+LpHkvkZjbHAh7R736JSVb9Hok/17h7APBZT2vfK8QJfeLqQsJjKxF6mEhxIYpjiOZK0XUKNDm0dnQFZlSZ/pk6wzqI0hjyUK5Dflln7ucIrfkmb40dH544fazBYjRhOaTN4nWTvT/rmXhN2uv73bnIg35ZwVnVjW2ynveiac/ADKaQ0+ihUgPz5dJYq51i+dvYjDf1DsmqFv0GKK2scd2VWLJNnGTmu98rQ8KZihrL6alh4vtSntv551xc/4Y/A0Fu1SqkW+St1XD+J7Q3viW8f+Z/q5ThQZQnEU8kHaksAJbH42WUN9kF5y6zj5xg+td4IH/1ncndlXsOb9uzd+WewxNM7r5jcsfRrPv0wgzLaaYX7tgF58YV1vc2YCiQCZxyIyD/0+ihUnKrPCSsJAK/uaaQQkX9Qp9pE3Q1smCr40PSZ+IeH9M/U8ob/hpcUlJJlZ0t/XONuVz+BrVFUUSyExFykL+9+pWv+Uu8BBcHCmmYzRvEuVmE02rCfy8Rc4HI9ieqs8QfbE8QRH0r2z2x7PuL0Xom3pv2WJRbjKfmzGosxlb+zj93mGc5tNbNhNOpkmdeKLtKlZO/nE+jh0opp1qRq/wIeCaVUfok5Bhv6r3dKbGgVpW285EfCDop9/Nc4y42UZ/P3+Ih4Spxbx/h+l/i4rzufQKFRIu6PfwW4TCfcF1PBP1BJLCI/BCivoDg/G2YziOpbNMeC67FEK7Fs/FGyyG8p+7HFqMiAu/QsQxhFEpAnqfRQ6UUWpXITN4ExByv8LaZr+ZscSRDjhme/G+kKBU59m5/vpfqLhe7lb9A+ZEfEXOe8NvD71HZzyJsxv6VC5Ud6diny++ZXfma30sLOZgTcKHWYqygRJEReRZDKiz0onKqZWg9kpIZAbmdRg+VklkdISFVICD+zCQN3xJU6gqQ/7nG/KLxeERbPVGeSKS7ExEnCa9d/G+KT39I7H+9V/USErOnt5z8MKkNRUT5EuDxiADz7tokT6OXxasFlZJvrSF1BRPw6H24T8/EW5OPV1AwaBlmx5/0qiCKo4lkZyL0EOG+9a+j33tSKf5OTrhUloDgafS3Vv29my7ltiZQKZWtbRRMGgLoS0lDTdXiiF98WKCEI+tUjZCql4c6jf5vvzOk3NYEKqXq1Y3y9YmAmPNnZ9oESTyWok95IbC8CNBffCivEiDd/hHgcghb/oJMkb+hRN+3NYFK9a8yEFv1CAz04xVUr0akKVE/Fx9KkyXiyI6ATHvDUCnZVQxSUhkCYjbXUZkyoiCSCMhj8aGkPPFcNgTEb2vSx5lFqJRsKgWpqBoBcRuVqlpZUZ7eCMhihVhvacNfjgTQl5IIV6L2SkwBAUAABEAABKQkINOZRYntOXablbKaEA0EQAAEBi4B2c0sQqUG7lsEy0EABEBAjgRkNLMIlZJjHSFpEAABEBjQBGQxswiVGtCvEIwHARAAARUnAJVS8QpC8UAABEBgQBOASg3o6ofxIAACIKDiBKBSKl5BKB4IgAAIDGgCUKkBXf0wHgRAAARUnABUSsUrCMUDARAAgQFNQCVUys7OTk9PT1tbm8lkxsbG9lghDQ0NW7duHTlypJaW1qRJk3x8fHoMRnpKtEpMXDwCARAAARBQHQIS23O57z3h7OyspaV15cqVjIyMjRs36urqVlcLnxjNZrOnT5/+9ddfR0REFBYWhoaGJicni4Eo0SoxcfEIBEAABEBAdQhIbM/lrlJMJtPIyIgkwuVyR48ebWtrKwTo3LlzEydO7OzsFPLv7VaiVb1FhD8IgAAIgIBKEZDYnstXpdhs9qBBg9zd3Skoa9euXbx4MXVLOr766qsffvhh48aNw4cP/9e//mVtbc3hcITCdHR0ND27SktLGQxGU1OTUBjcggAIgAAIqBcBJatUeXk5g8GIioqiqO3evZvJZFK3pGPy5Mna2trr169PSEhwdnYeNmzYvn37hMJYWloy/n5BpYQQ4RYEQAAE1I6AeqjUpEmT3njjDar/dOzYsZEjRwqxRl9KCAhuQQAEQEADCChZpWiO+M2dO3f+/PkUbl9fXwaDwWazKR8hh0SrhMLjFgRAAARAQDUJSGzP5TsvRRAEk8k0NjYm6XC53DFjxoiuntizZ4+enh6XyyWDnTx5ctSoUWKASrRKTFw8AgEQAAEQUB0CEttzuauUs7OztrY2i8XKzMzctGmTrq5uVVUVQRBr1qwxNTUlSZWUlLz88svGxsbZ2dne3t7Dhw8/cOCAGIgSrRITF49AAARAAARUh4DE9lzuKkUQxJkzZ8aNG6elpcVkMmNiYkg6BgYGhoaGFKmoqKgZM2Zoa2tPnDixxzV+VEiCICRaJRgYbhAAARAAAZUlILE9V4RKyZyORKtkniMSBAEQAAEQkAcBie05VEoe2JEmCIAACIAALQJQKVqYEAgEQAAEQEApBKBSSsGOTEEABEAABGgRgErRwoRAIAACIAACSiEAlVIKdmQKAiAAAiBAiwBUihYmBAIBEAABEFAKAaiUUrAjUxAAARAAAVoEoFK0MCEQCIAACICAUghApZSCHZmCAAiAAAjQIgCVooUJgUAABEAABJRCACqlFOzIFARAAARAgBYBqBQtTAgEAiAAAiCgFAJQKaVgR6YgAAIgAAK0CEClaGFCIBAAARAAAaUQgEopBTsyBQEQAAEQoEUAKkULEwKBAAiAAAgohQBUSinYkSkIgAAIgAAtAlApWpgQCARAAARAQCkEoFJKwY5MQQAEQAAEaBGAStHChEAgAAIgAAJKIQCVUgp2ZAoCIAACIECLAFSKFiYEAgEQAAEQUAoBqJRSsCNTEAABEAABWgSgUrQwIRAIgAAIgIBSCECllIIdmYIACIAACNAiAJWihQmBQAAEQAAElEIAKqUU7MgUBEAABECAFgGoFC1MCAQCIAACIKAUAlAppWBHpiAAAiAAArQIQKVoYUIgEAABEAABpRCASikFOzIFARAAARCgRQAqRQsTAoEACIAACCiFAFRKKdiRKQiAAAiAAC0CUClamBAIBEAABEBAKQRUQqXs7Oz09PS0tbWZTGZsbKwYEE5OTgwGY8mSJWLCEAQh0Srx0fEUBEAABEBARQhIbM8Z8i6os7OzlpbWlStXMjIyNm7cqKurW11d3WOmhYWFY8aMmTNnDlSqRz7wBAEQAAHNI6B8lWIymUZGRiRZLpc7evRoW1tbUdAcDueTTz65dOmSoaEhVEqUD3xAAARAQCMJKFml2Gz2oEGD3N3dKbhr165dvHgxdUs5LCwsli5dShBEbyrV0dHR9OwqLS1lMBhNTU1UdDhAAARAAATUkYCSVaq8vJzBYERFRVHsdu/ezWQyqVvS8eDBgzFjxtTW1opRKUtLS8bfL6iUEEbcggAIgIDaEVADlWpubh4/fryvry8JF30ptXvJUGAQAAEQkJqAklWKzohfUlISg8EY9Ox67uk1aNCgvLy83syWaFVvEeEPAiAAAiCgUgQktudyX+PHZDKNjY1JKFwud8yYMUKrJ9rb29MEriVLlsybNy8tLY3NZveGUqJVvUWEPwiAAAiAgEoRkNiey12lnJ2dtbW1WSxWZmbmpk2bdHV1q6qqCIJYs2aNqampKKzeRvwEQ0q0SjAw3CAAAiAAAipLQGJ7LneVIgjizJkz48aN09LSYjKZMTExJCwDAwNDQ0NRcFApUSbwAQEQAAFNJaASKiVzuBKtknmOSBAEQAAEQEAeBCS254roS8ncMIlWyTxHJAgCIAACICBKIL02fb3/+vTadNFHNH0ktudQKZokEQwEQAAElEmg/3ogj9LbxNjos/RtY3vYUYhmdlApmqAQDARAAARUmkD/9UCG5pW3lKfXpWfUZcx1nqvP0p/rPDejLiO9Lr28pbyvuUCl+koM4UEABAY6AZXqtYjqQUpNSlJ1Ul5DXjO7uaG9oe5JXXVbdUVLRWlzaVFTUX5jfs7jnEf1j9Lr0lNrUpOqk+Ir42MrYiPLIx+UPQgtCb1ffD+wKNCv0M8739szz9Mtx80129XlkYtjpuONjBusdNbltMsXUy7aJ9vbJdmdenjqeMLxI3FHDsYetI6xtoq2soy0NI8w12fp9/bX17cHKtVXYggPAiCgOAIqpQeU2TLvtXC4nBZ2S01bTUlTyaP6R0nVSdEV0cHFwT75Pn/m/Hkz86ZDqsPpxNOH4g7ti9pnGm66I3jHT4E/rfVdu/zu8t7EQAX9p12b5pXvRWGk6YBK0QSFYCCgyQRUUwwIgpC5HvSnFkV7Lak1qfGV8ak1qcVNxTTVZVPgJlJdFrktmn97/ie3Pnn/+vvyVpSp16a+f/39j25+NMNxxie3PpnjNOdTl0/n357/hesXX/351SK3RUvcl3zj+c3yu8tXea1a7bN6re/adX7rNgRs2BS4acu9Lcb3jXcE7/g55OfdobtNw03NHphZRFr8EfWHVbSVbaztobhDx+KPnXx48nTiafsk+/PJ5x1SHa6kXTkce1jIroy6DCn4Q6WkgIYoIKBpBFRKDAiCENUDibMaPB6vk9vZ1tnW2NFY+6S2oqWiqKko93FuRl1Gck1yfGV8ZHlkWGlYUFGQb4GvZ56na7brraxb19KvOaQ62Cfbn3p46mj8UdtY2/1R+80jzE3CTXaF7DK+b/zTvZ/W+69f47tmldcqoTZX5rdTWFM+uvnRXOe5C+4sWOqx9Hvv79f7r98atPWX0F/MHpgdiD5wLOGYfZL91bSrzlnOHrkeAYUB4aXhcZVxXnleQoVJqExo62xjc9hd3C4ej6eU9zWjLkOfpT+FNYX6L1Tqr4qQqL1/BYULBAYwAUoM5jjN0Wfpz7o1yyvf627e3aDioKTqpMTqxPjK+LjKuOiK6MjyyIiyiLDSMHLeIqgoiJy68C3wJaO457q75bjdyb7j8sjFOcvZMdPxZubN6xnXWemsK2lXHFIdLqZcPJd8zj6pezLj5MOTxxKOHY0/ejju8MHYgzYxNlbRVvuj9ltGWlpEWgi1uYK3K7xWfOP5zSK3RQvuLJjnMm+20+wZjjM+uP4B2RoKhlSkm3mTaeBsQKrLau/V6/3XGwUZ/RL6i3mEuXWMNV9dku1Z6SznLGfPPM/AosDw0vD4yvj0uvT8hvyKlorH7Y/bu9qllhNZ6YFs/ylUtlYaOBus9Frp8shlpddKA2eDytZKKbKQ2J5jJboUVBEFBFSaAJfHLWkuCSoOUmQ7rsi8prCmfHjjw5mOM+c6z513e96COwv+4/6fZZ7LVnqtXOO7Zr3/+p8CfzK+b7wrZJdJuIl5hPkfUX/YxtoejT966uEp+2R7h1QHVjrrVtYt12xXzzxP3wLfe0X3wkrDIssjXbNdhQxJrk6WWl1k9ZbISg9kVR4qHTaHTcLh8XhsTq87r1Lhe3RApXrEAk8Q0CgCjR2N8ZXxjpmO+6L2rfZZzbzJFGpqhW7nOM358s6XC90WkjMWSz2WLvNctvzu8hVeK773/n61z+offX7snrrw3/C/gP/9FPjT5nubtwZtNb5vvP3+9l0hu34O+fnX0F9/C/vNNNx074O95hHmlpGW+6L27Y/afyD6gE2MzcHYg4fjDh+LP3Y84Tg5pWGXZGefzJ/VuJhy8VLqJdtYW6FSOaQ4BBUHhZWGRZVHJVQlpNSkZNVn5TXkFTcVV7ZW1j6pbexofNL1RK5jXKrZayEIQiZ6oJovPVRKNesFpQIB6Ql0cjqzH2d753sfTzi+5d6W+bfnCzX3+iz996+//93d7/Y+2Hsw9qDQU+kmD6Qvbi8xVVMPVLbX0gtFTfCGSmlCLcKGgUyAx+NVtVaFl4ZfTrtsEm7yjec3711/T0h49Fn6X7h+YRxkfOrhKb8Cv7yGvE5uJwlNNcWAIAiV1QMN7rWo5r8jqJRq1gtKBQK9EmjrbEuuSXbNdrWOsV7nt+6TW5+IatJMx5lrfNdYRVs5ZzknVic2s5t7S05lxUCzR7F6qw74ixKASokygQ8IqBABDpdT2FgYUBhgl2S3/f72r/78SlSTpl2btsR9ye7Q3RdTLoaWhJa3lPdpPh+dAxWqbxRFhABUSgQJPEBAngQkfj9b314fUxFzPeP67xG/r/RaOf3GdFFZ+szls02Bm47GH/XM88yqz+rgdMizyEgbBJRJACqlTPrIewASEPp+toPTkVmX6ZnneSTuyMaAjZ+6fCqqSdNvTF/ltcoi0uJGxo3YitjH7Y8HIDeYPGAJQKUGbNXDcMUR4PF4eQ15EWURgUWB5DTSRzc/2hiwccGdBVNZU4VkaQprytd/fr0jeMfZpLOBRYFFTUUcLkdxZUVOIKBiBKBSKlYhKI7sCEgcW5MiKx6P18HpqHtSV9RUlF6XHlMRE1Qc5JnneTPz5oWUC8cSjv0R9cfusN1b7m1Z67t2meeyL1y/+OTWJ9OuTROSIsHbWU6z/uv/X9tY2zvZd1JrUts626QoGKKAgKYSgEppas0q1C556EH/DRAaWxNNsJPb2dDeUNJcklWfFV8ZH1IS4pXv5ZTl5JDqcPLhSatoK9NwU+Mg43V+65bfXf7lnS/nOM3pcZG3oOT0yT312lTHTMc+rXQQtQI+IKDZBKBSml2/CrJOoh70sxwcLqe9q72Z3fy4/XF1W3VZS1lhY2HO4xxyI9GEqoToiujw0vD7xff9C/2vZ1y3S7Q7+fAkucPC9BvTfw39dWvQ1k2Bmzb4b1jptXKh20IDZ4MeVyXQ15gZjjPm356/xH3JDz4//BT40y+hv1hGWh6OO2yfbH8947pbjltgUWBkeWRqTWp+Y35NW00SrzDRAAAR0ElEQVRbZ1t6bbpQ+iry/Ww/awfRQUCuBKBScsWryYmzOeyMuoygoiCvfK+Pb32sz9Kf6TiTPBvtUuolpyyn6xnXL6ddPp98/kzimeMJxw/HHbaOsd4Xtc/sgdlvYb+RG05vvrd5Q8CGtb5rV3uvXn53+VKPpQvdFn7h+sU8l3lznObMdJz54Y0Pp14TnrkRauv7efvRzY8+c/lskduiVV6r/hfwv53BO80jzA/GHjyTeIaVznLNdvUr9HtQ9iCpOin3cW5la2ULu4XL40pRtSr7/awUtiAKCCiMAFRKYahlk5E8xtZ4PF5bZ1tNW01+Y35KTUpkWaR/of+d7DusdJZdkt3B2INmD8x2BO/YENDdEZnrPPeD6x/0Uxukjv7etfem35j+sePH1Eaii9wWfeP5zQqvFat9Vhv6GS71WCq6PfZU1lTzCHPvfO+w0rCEqoRH9Y/KWsoaOxq7uF2yqRgaqajy97M0io8gIKAcAlAp5XCXOleJY2tcHreZ3VzZWpnzOCexOjGsNMwn38flkcvltMunHp46EH2Ammv57u53C+4smOU0671rPWyoI7WKTGFNWX53+a6QXbvDdu99sNcy0vJA9IFDcYeOJxw/k3jmfPL5S6mXrqVfIzec9sj18Mn3CSwKDC4OflD2IKYi5mHVw9SaVGoX0fKW8pq2mob2htbOVjaHTbMTQ/ZaBE1QkbE1fD8r9ZuPiAOWAFRKPaqePAcovTadHFv76OZHex7s2Rm80zjIePO9zWt81yz1WPq56+czHWeKdiMEG2sx7qnXpn586+MvXL9Y5rlsre9aoyAjk3ATq2irEwknHFIdnLOcvfO9Q0tCyY5IeUt5E7uJw+Woph5gbE09XmuUEgRoEIBK0YCkAkHEqEuPj96//v5c57lf/fnV8rvL1/uv335/+94He21jbU8nnr6adpWca4koi0iqTspryKtqrWrrbJNupZlq6gHG1lTgnUURQEA2BKBSsuEov1QaOxodUh0+cexhR9EprCkm4SZuOW73iu5FV0Sn16YXNhbWPqlV5H45KqsHGFuT3zuJlEFAkQSgUoqk3be8ipqKrKKtPrr5Edlbmu00W6jbhLmWvgFFaBAAATUkAJVSuUrj8XhxlXHG942pGaZvPb/1zPNMrk7WZ+mTnuR/VUSlVI4gCgQCIKBBBKBSKlSZnZzOu3l3l99dTvWZjIKMYitiyRkjlR1bUyGCKAoIgIDGEYBKqUSVkpNP81zmkfo0/cb0/VH7CxoLhAqHuRYhILgFARDQeAJQKSVXsdDk06cun15MudjQ3qDkYiF7EAABEFANAlAp5dRDb5NPbA5bOQVCriAAAiCgkgSgUoqulk5Op1e+V2+TT4ouDfIDARAAAdUmAJVSXP10Tz7dljD5pLgCIScQAAEQUHkCKqFSdnZ2enp62traTCYzNjZWFNrFixdnz56t+/SaP39+j2EEY0m0SjCwAtxFTUUHog9QXz596vLphZQLOBdcAeSRBQiAgLoTkNieM+RtobOzs5aW1pUrVzIyMjZu3Kirq1tdXS2U6erVq8+ePZuUlJSVlbVu3bqhQ4eWlZUJhRG8lWiVYGD5uTH5JD+2SBkEQGCAEJDYnstdpZhMppGREYmby+WOHj3a1tZWDH0Oh/Pyyy9fu3ZNTBiJVomJK5NHopNPW4O2xlTESLdXnkyKhERAAARAQB0JSGzP5atSbDZ70KBB7u7uFLu1a9cuXryYuhV1NDc36+joeHl5CT3q6OhoenaVlpYyGIympiahMAq47XHyKb8xXwFZIwsQAAEQ0DwCSlap8vJyBoMRFRVFkd29ezeTyaRuRR1btmyZOHFie3u70CNLS0vG3y8FqxQmn4RqBLcgAAIg0H8CaqZStra2r7zySkpKiqjlyupL8Xi8+Mr4bfe3CW27hy+fROsIPiAAAiDQVwJKVqk+jfgdOXJk6NCh8fHxEo2UaJXEFOgE6OQKf/mEySc63BAGBEAABOgTkNiey3deiiAIJpNpbGxMlpjL5Y4ZM6bH1ROHDh0aMmRIdHQ0HdskWkUnETFhGjsaL6Vemvf3L58w+SSGGB6BAAiAgHQEJLbnclcpZ2dnbW1tFouVmZm5adMmXV3dqqoqgiDWrFljampKWnXw4EEtLa07d+5UPrtaWlrEGCzRKjFxxT/C5JN4PngKAiAAArIlILE9l7tKEQRx5syZcePGaWlpMZnMmJgY0kIDAwNDQ0PSraen9/eFEQxLS0sxICRaJSYu9Si9Nn29//r02nSCIDD5RGGBAwRAAAQUSUBie64IlZK5wRKtopOjTYyNPkvfOsbaO997hdcK6swnTD7RoYcwIAACICATAhLb8wGnUuUt5el16Rl1GXOc5lBn4+qz9D+8/uH+qP2YfJLJa4dEQAAEQIAmAaiUMCiqzyTqEA6KexAAARAAATkTgEoJA/bK95p2bZqQRE27Ns0rX3i3C+GYuAcBEAABEJA1AahUD0Qz6jKEVCqjLqOHcPACARAAARCQMwGoVA+ASZUi95Ig/wuV6gETvEAABEBA/gSgUj0wrmytNHA2WOm10uWRy0qvlQbOBpWtlT2EgxcIgAAIgICcCUClegbM5rDJUzZ4PB525OuZEXxBAARAQP4EoFLyZ4wcQAAEQAAEpCUAlZKWHOKBAAiAAAjInwBUSv6MkQMIgAAIgIC0BKBS0pJDPBAAARAAAfkTgErJnzFyAAEQAAEQkJYAVEpacogHAiAAAiAgfwJQKfkzRg4gAAIgAALSEoBKSUsO8UAABEAABORPAColf8bIAQRAAARAQFoCUClpySEeCIAACICA/AlApeTPGDmAAAiAAAhIS0AzVaqxsZHBYJSWljbhAgEQAAEQUGcCpaWlDAajsbGxN5lTyxPlSasYuEAABEAABDSCQGlpqUapFJfLLS0tbWxsVOcfED2XnRRgdBN7pvN3X7D6Ow9xd2Aljo7IM+ASQdKrR/9ZNTY2lpaWcrlcjVKp3ozRAH+JQ7QaYKOsTAAr+iTBij4rgiCAiz4uBbBSyxE/+gTVLqQCqlztmPRWYLDqjYyoP1iJMhHjA1xi4Ag9UgArqJQQcyXfKqDKlWyh7LIHK/oswYo+K/SlVI0VVKpPNSL3wB0dHZaWlh0dHXLPSf0zACv6dQhW9FkRBAFc9HEpgBVUin51ICQIgAAIgICiCUClFE0c+YEACIAACNAnAJWizwohQQAEQAAEFE0AKqVo4sgPBEAABECAPgGoFH1WCAkCIAACIKBoAlApRRPvLT8bG5vp06e/9NJLr7/++pIlSx49etRbSPgLErC1tWUwGDt27BD0hFuQQFlZ2Q8//DBs2DAdHR19ff34+HjBp3BTBDgcjrm5+fjx43V0dCZOnLh//34ej0c9hYMgiLCwsEWLFo0aNYrBYLi7u1NMeDze77//PnLkSB0dnfnz5+fk5FCP+u+ASvWfoWxSWLBgwdWrV9PT05OTk7/++utx48a1trbKJmnNTSUuLm78+PFTp06FSvVWyY8fP9bT01u3bl1sbGxBQUFAQEBeXl5vgQe4v7W19auvvurt7V1YWOjq6vrSSy+dOnVqgDMRMt/X19fMzMzNzU1IpQ4ePDh06FAPD4+UlJTFixdPmDChvb1dKK7Ut1ApqdHJMWJNTQ2DwQgLC5NjHuqfdEtLy6RJk+7du2dgYACV6q0+TUxMZs+e3dtT+AsSWLhw4fr16ymfZcuW/fDDD9QtHIIEBFWKx+ONHDnyyJEjZIDGxkZtbW0nJyfB8P1xQ6X6Q09ecXNzcxkMRlpamrwy0Ih0165du3PnToIgoFJi6vPdd9/duXPnd9999/rrr7/33nsXL14UE3iAP7K2ttbT08vOziYIIjk5efjw4Tdv3hzgTHozX1Cl8vPzGQxGUlISFXju3Lnbt2+nbvvpgEr1E6Dso3O53IULF86aNUv2SWtQik5OTvr6+uSoAlRKTMVqP7327NmTmJh44cIFHR0dFoslJvxAfsTlck1MTJ577rkXXnjhueees7GxGcg0xNsuqFKRkZEMBqOiooKKsnz58hUrVlC3/XRApfoJUPbRN2/erKenJ+a0FdlnqW4plpSUDB8+PCUlhSw4VEpMBb744osff/wxFWDbtm0zZ86kbuEQJODk5DR27FgnJ6fU1NTr168PGzYMii7IR9ANlRKkMbDcRkZGY8eOLSgoGFhm99Fad3d3BoMx6NnFYDCee+65QYMGcTicPqak+cHHjRu3YcMGyk57e/vRo0dTt3AIEhg7dqydnR3lY2VlNXnyZOoWDkECgiqFET9BMprs5vF4RkZGo0ePlu0iTo1E1tzcnCZwTZ8+/ccff8Q0Xo91/f333wuunti5c6dg16rHKAPWc9iwYfb29pT5NjY2kyZNom7hECQgqFLk6omjR4+SAZqamrB6QpCV5ri3bNkydOjQ0NDQymfXkydPNMc8eVqCET8xdOPi4l544QVra+vc3FxHR8f/+7//w4qA3nAZGhqOGTOGXInu5ub22muv/fbbb70FHpj+LS0tSU8vBoNx/PjxpKSk4uJigiAOHjyoq6vr6emZmpq6ZMkSrETXzNeDIXJdvXpVM02VtVVQKfFEvby89PX1tbW133nnHazxE8Oqubl5x44d48aNI7/qNTMzY7PZYsIPwEchISFCDZWhoSFBEORXvSNGjNDW1p4/fz65TlJWfLB6QlYkkQ4IgAAIgIDsCUClZM8UKYIACIAACMiKAFRKViSRDgiAAAiAgOwJQKVkzxQpggAIgAAIyIoAVEpWJJEOCIAACICA7AlApWTPFCmCAAiAAAjIigBUSlYkkQ4IgAAIgIDsCUClZM8UKYIACIAACMiKAFRKViSRDggok4DgjjXKLAfyBgFZE4BKyZoo0gMBEQKGhoZCX+wvWLBAJFS/PKBS/cKHyCpMACqlwpWDomkKAUNDwy+//PLZBo38/z9+/Fi2xkGlZMsTqakOAaiU6tQFSqKxBAwNDZcsWSJqHoPBsLe3//LLL3V0dCZMmODq6kqFSU1N/eyzz3R0dIYNG7Zx48aWlhbq0eXLl//5z39qaWmNHDnSyMiI9GcwGA4ODkuXLh08ePBbb73l6elJhYcDBNSaAFRKrasPhVcPAmJU6tVXX3VwcMjOzjY3Nx80aFBmZiZBEK2traNGjVq2bFlaWtr9+/cnTJhA7ulJEIS9vb2Ojs7Jkyezs7Pj4uJOnDhBImAwGGPHjr1161Zubu727dtfeuml+vp69aCDUoKAWAJQKbF48BAEZEHA0NBw0KBB/xC4rK2tCYJgMBibN2+mcpgxY8aWLVsIgrh48eIrr7zS2tpKPvLx8Xn++eerqqoIghg9erSZmRkVhXIwGAxzc3PytrW1lcFg+Pn5UU/hAAH1JQCVUt+6Q8nVhoChoeHnn3+eK3CRHR0Gg3Ht2jXKjJ07d3766acEQezatYt0kI8aGxsZDEZYWFh1dTWDwQgODqaiUA4Gg3H79m3qdsiQIYIpU/5wgIDaEYBKqV2VocDqR0DMiJ+glkhUqebmZjEq5e7uTqEZOnQoziejaMCh1gSgUmpdfSi8ehAQo1LkEB9pxsyZMyWO+I0fP763ET+olHq8DShlHwlApfoIDMFBoO8ERFei19bWkvNSr7322uXLl7Ozsy0sLJ5//vmMjAyCINra2kaNGvXtt9+mpaUFBwdPnDiRWj3BYrF0dHROnTqVk5Pz8OHD06dPk8URWomOvlTfawkxVJQAVEpFKwbF0iQCol/1Tp48mVSps2fP/vvf/9bW1h4/fryLiwtltZiV6OfPn588efKLL744atSobdu2kVGgUhQ6ODSMAFRKwyoU5qgTASFpUaeio6wgoCgCUClFkUY+ICBCAColggQeICBMAColTAT3IKAwAlAphaFGRupLACqlvnWHkoMACICA5hOASml+HcNCEAABEFBfAlAp9a07lBwEQAAENJ8AVErz6xgWggAIgID6EoBKqW/doeQgAAIgoPkEoFKaX8ewEARAAATUlwBUSn3rDiUHARAAAc0nAJXS/DqGhSAAAiCgvgSgUupbdyg5CIAACGg+AaiU5tcxLAQBEAAB9SXw/2xGEv0pF8h7AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.6719\n",
    "New best pr_auc_samples score (0.3511) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MoleRec Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00,  9.90it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8940649682945823,\n",
       " 'f1_samples': 0.4910521968570858,\n",
       " 'pr_auc_samples': 0.3663806681805927,\n",
       " 'jaccard_samples': 0.33483568237985833}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_molerec, y_prob_molerec, loss_molerec = trainer_molerec.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_molerec, y_prob_molerec, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- MoleRec with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.models import MoleRec\n",
    "\n",
    "model_molerec_s = MoleRec(\n",
    "    dataset=mimic3sample,\n",
    "    feature_keys=[\"diagnosis\", \"procedures\", \"symptoms\"],\n",
    "    label_key=\"medications\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**- Training with symptoms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyhealth.trainer import Trainer\n",
    "\n",
    "trainer_molerec_s = Trainer(model=model_molerec_s)\n",
    "trainer_molerec_s.train(\n",
    "    model_name=\"molerec\",\n",
    "    train_dataloader=train_loader,\n",
    "    val_dataloader=val_loader,\n",
    "    epochs=10,\n",
    "    optimizer_params = {\"lr\": 2 * 1e-4},\n",
    "    monitor=\"pr_auc_samples\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MoleRec Results (with symptoms)**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss: 0.8862\n",
    "New best pr_auc_samples score (0.3449) at epoch-9, step-510\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MoleRec Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 7/7 [00:00<00:00,  9.20it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8924796974079431,\n",
       " 'f1_samples': 0.5099994539957666,\n",
       " 'pr_auc_samples': 0.3722004441177325,\n",
       " 'jaccard_samples': 0.35187002432019887}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyhealth.metrics import multilabel_metrics_fn\n",
    "\n",
    "y_true_molerec_s, y_prob_molerec_s, loss_molerec_s = trainer_molerec_s.inference(test_loader)\n",
    "metrics = [\"accuracy\", \"f1_samples\", \"pr_auc_samples\", \"jaccard_samples\"]\n",
    "multilabel_metrics_fn(y_true_molerec_s, y_prob_molerec_s, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let'see some recommendation..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyhealth.medcode import InnerMap\n",
    "\n",
    "# initialize an InnerMap\n",
    "atc = InnerMap.load(\"ATC\")\n",
    "\n",
    "sorted_indices = []\n",
    "for i in range(len(y_prob)):\n",
    "    top_indices = np.argsort(y_prob[i, :])[::-1][:10]\n",
    "    sorted_values = y_prob[i, top_indices]\n",
    "    sorted_indices.append(top_indices)\n",
    "\n",
    "patient_ids = []\n",
    "visit_ids = []\n",
    "rec_drug = []\n",
    "\n",
    "# get the list of all drugs in the dataset\n",
    "list_drugs = mimic3sample.get_all_tokens('drugs')\n",
    "\n",
    "# iterate over the top indexes for each sample in test_ds\n",
    "for sample, top in zip(test_ds, sorted_indices):\n",
    "   \n",
    "    # append the patient id and visit id to their respective lists\n",
    "    patient_ids.append(sample['patient_id'])\n",
    "    visit_ids.append(sample['visit_id'])\n",
    "   \n",
    "    # create an empty list to store the recommended drugs for this sample\n",
    "    sample_rec_drug = []\n",
    "   \n",
    "    # iterate over the top indexes for this sample\n",
    "    for i in top:\n",
    "       \n",
    "        # append the drug at the i-th index to the recommended drugs list for this sample\n",
    "        sample_rec_drug.append(list_drugs[i])\n",
    "   \n",
    "    # append the recommended drugs for this sample to the recommended drugs list\n",
    "    rec_drug.append(sample_rec_drug)\n",
    "\n",
    "# create a dataframe with the patient ids, visit ids, and recommended drugs\n",
    "df_rec_drug = pd.DataFrame({'patient_id': patient_ids, 'visit_id': visit_ids, 'rec_drug': rec_drug})\n",
    "df_rec_drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B05XA: Electrolyte solutions\n",
      "N03AX: Other antiepileptics in ATC\n",
      "M04AC: Preparations with no effect on uric acid metabolism\n",
      "M03BA: Carbamic acid esters, centrally acting muscle relaxants\n",
      "G04CA: Alpha-adrenoreceptor antagonists, benign prostatic hypertrophy\n",
      "J01EE: Combinations of sulfonamides and trimethoprim, incl. derivatives\n",
      "N02BE: Anilide analgesics and antipyretics\n",
      "B01AB: Heparin group antithrombotic drugs\n",
      "N05CF: Benzodiazepine related hypnotics and sedatives\n",
      "A02BC: Proton pump inhibitors for peptic ulcer and GORD\n"
     ]
    }
   ],
   "source": [
    "from pyhealth.medcode import InnerMap\n",
    "\n",
    "# initialize an InnerMap\n",
    "atc = InnerMap.load(\"ATC\")\n",
    "\n",
    "# select a patient from df_rec_drug\n",
    "patient_id = 10884\n",
    "visit_id = 150905\n",
    "patient_df = df_rec_drug[df_rec_drug['patient_id'].astype(int)==patient_id]\n",
    "patient_df = patient_df[patient_df['visit_id'].astype(int)==visit_id]\n",
    "\n",
    "# iterate over the recommended drugs for the selected patient\n",
    "for drugs in patient_df['rec_drug']:\n",
    "    # lookup the ATC code for each drug and print it\n",
    "    for drug in drugs:\n",
    "        print(f\"{drug}: {atc.lookup(drug)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition 0: Acute and subacute necrosis of liver\n",
      "Condition 1: Alcoholic cirrhosis of liver\n",
      "Condition 2: Hepatorenal syndrome\n",
      "Condition 3: Acute kidney failure with lesion of tubular necrosis\n",
      "Condition 4: Congestive heart failure, unspecified\n",
      "Condition 5: Hyposmolality and/or hyponatremia\n",
      "Condition 6: Chronic kidney disease, unspecified\n",
      "Condition 7: Toxic encephalopathy\n",
      "Condition 8: Hepatic encephalopathy\n",
      "Condition 9: Calculus of gallbladder with other cholecystitis, without mention of obstruction\n",
      "Condition 10: Ascites\n",
      "Condition 11: Acquired coagulation factor deficiency\n",
      "Condition 12: Thrombocytopenia, unspecified\n",
      "Condition 13: Unspecified essential hypertension\n",
      "Condition 14: Other specified disorders of pancreatic internal secretion\n",
      "Condition 15: Adrenal cortical steroids causing adverse effects in therapeutic use\n",
      "Condition 16: Acute alcoholic intoxication in alcoholism, in remission\n",
      "Condition 17: Esophageal reflux\n",
      "Condition 18: Personal history of urinary calculi\n",
      "Condition 19: Aphasia\n",
      "Condition 20: Spinal stenosis, lumbar region, with neurogenic claudication\n",
      "Condition 21: Liver replaced by transplant\n",
      "Condition 22: Body Mass Index 40.0-44.9, adult\n",
      "Condition 23: Acquired spondylolisthesis\n",
      "Condition 24: Secondary diabetes mellitus without mention of complication, not stated as uncontrolled, or unspecified\n",
      "Condition 25: Adrenal cortical steroids causing adverse effects in therapeutic use\n",
      "Condition 26: Hypertensive chronic kidney disease, unspecified, with chronic kidney disease stage I through stage IV, or unspecified\n",
      "Condition 27: Chronic kidney disease, unspecified\n",
      "Condition 28: Gout, unspecified\n",
      "Condition 29: Obstructive sleep apnea (adult)(pediatric)\n",
      "Condition 30: Depressive disorder, not elsewhere classified\n",
      "Condition 31: Overweight\n",
      "Condition 32: Anemia in chronic kidney disease\n",
      "Condition 33: Postprocedural fever\n",
      "Procedure 0: Other transplant of liver\n",
      "Procedure 1: Cholecystectomy\n",
      "Procedure 2: Transplant from cadaver\n",
      "Procedure 3: Closed (percutaneous) [needle] biopsy of liver\n",
      "Procedure 4: Hemodialysis\n",
      "Procedure 5: Venous catheterization for renal dialysis\n",
      "Procedure 6: Pulmonary artery wedge monitoring\n",
      "Procedure 7: Percutaneous abdominal drainage\n",
      "Procedure 8: Parenteral infusion of concentrated nutritional substances\n",
      "Procedure 9: Enteral infusion of concentrated nutritional substances\n",
      "Procedure 10: Transfusion of packed cells\n",
      "Procedure 11: Transfusion of platelets\n",
      "Procedure 12: Venous catheterization, not elsewhere classified\n",
      "Procedure 13: Lumbar and lumbosacral fusion of the posterior column, posterior technique\n",
      "Procedure 14: Fusion or refusion of 2-3 vertebrae\n",
      "Procedure 15: Insertion of interbody spinal fusion device\n",
      "Symptom 0: Hepatorenal syndrome\n",
      "Symptom 1: Unspecified problem with special functions\n",
      "Symptom 2: Renal failure, unspecified\n",
      "Symptom 3: Jaundice, unspecified, not of newborn\n",
      "Symptom 4: Edema\n",
      "Symptom 5: Cirrhosis of liver without mention of alcohol\n",
      "Symptom 6: Esophageal reflux\n",
      "Symptom 7: Unspecified essential hypertension\n",
      "Symptom 8: Unspecified disorder of metabolism\n",
      "Symptom 9: Obstructive hydrocephalus\n",
      "Symptom 10: Dysphagia, unspecified\n",
      "Symptom 11: Other abnormal glucose\n",
      "Symptom 12: Chronic kidney disease, unspecified\n",
      "Symptom 13: Pain in limb\n",
      "Symptom 14: Unspecified problem with special functions\n",
      "Symptom 15: Gout, unspecified\n",
      "Symptom 16: Unspecified essential hypertension\n",
      "Symptom 17: Anemia, unspecified\n",
      "Symptom 18: Chronic kidney disease, unspecified\n",
      "Symptom 19: Generalized pain\n",
      "Symptom 20: Fever, unspecified\n",
      "Symptom 21: Constipation, unspecified\n",
      "Symptom 22: Acquired spondylolisthesis\n"
     ]
    }
   ],
   "source": [
    "from pyhealth.medcode import InnerMap\n",
    "\n",
    "# select a patient from df_rec_drug\n",
    "patient_id = 10884\n",
    "visit_id = 150905\n",
    "\n",
    "for sample in test_ds:\n",
    "    if (sample['patient_id'] == str(patient_id)) and (sample['visit_id'] == str(visit_id)):\n",
    "        conditions = sample['conditions']\n",
    "        procedures = sample['procedures']\n",
    "        symptoms = sample['symptoms']\n",
    "\n",
    "# initialize an InnerMap\n",
    "icd9cm = InnerMap.load(\"ICD9CM\")\n",
    "i=0\n",
    "for condition in conditions:\n",
    "    for code in condition:\n",
    "        print(f\"Condition {i}: {icd9cm.lookup(code)}\")\n",
    "        i+=1\n",
    "\n",
    "# initialize an InnerMap\n",
    "icd9pr = InnerMap.load(\"ICD9PROC\")\n",
    "i=0\n",
    "for procedure in procedures:\n",
    "    for code in procedure:\n",
    "        print(f\"Procedure {i}: {icd9pr.lookup(code)}\")\n",
    "        i+=1\n",
    "\n",
    "i=0\n",
    "for symptom in symptoms:\n",
    "    for code in symptom:\n",
    "        print(f\"Symptom {i}: {icd9cm.lookup(code)}\")\n",
    "        i+=1"
   ]
  }
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