{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pickle file checking for AUPRC random lead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> 50\n",
      "<class 'dict'>\n",
      "----------------------\n",
      "epoch: \n",
      "model: \n",
      "train_auprc: \n",
      "valid_auprc: \n",
      "valid_targets: \n",
      "valid_outputs: \n",
      "-----------------------\n",
      "-----------------------\n",
      "[0.20795198881124255, 0.2924131615408049, 0.31194815399388126, 0.357671229080611, 0.3907590012977773, 0.39197022751675975, 0.39688932315376796, 0.41098642756821824, 0.4280303875603716, 0.4251116328825386, 0.41492397254078656, 0.44119503399957305, 0.42866565608661766, 0.42155615910506705, 0.4352771610735857, 0.4355309812927433, 0.4575302940022513, 0.4621060999031488, 0.4615244295921646, 0.4347042141353311, 0.4843673460502776, 0.49216570578173724, 0.49284316077316226, 0.4976730562122618, 0.4981241668777771, 0.4985906269863735, 0.5023674118168958, 0.5039947051779108, 0.5025596400291938, 0.501332454384853, 0.5017141509761979, 0.5033696471830942, 0.5035807094153067, 0.5044712423289812, 0.49912591150498187, 0.5036493639939076, 0.5073756144905568, 0.5066738446153692, 0.5041024684427422, 0.5061074251973712, 0.5079663458037375, 0.5080434717076571, 0.5071731389137064, 0.5066158069067092, 0.5059333249321385, 0.5078252460128987, 0.5081895157894929, 0.5079278975582764, 0.5073543066159428, 0.5078677916025073]\n",
      "0.5081895157894929 46\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import torch\n",
    "\n",
    "address = \"./model_output/model_group5/PROGRESS.pickle\"\n",
    "\n",
    "with open(address, 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "print(type(data), len(data))\n",
    "# print(data[0])\n",
    "print(type(data[1]))\n",
    "print(\"----------------------\")\n",
    "for key, _ in data[1].items():\n",
    "    print(f\"{key}: \")\n",
    "\n",
    "print(\"-----------------------\")\n",
    "AUPRC_list = []\n",
    "for i in range(len(data)):\n",
    "    AUPRC_list.append(data[i][\"valid_auprc\"])\n",
    "\n",
    "print(\"-----------------------\")    \n",
    "print(AUPRC_list)\n",
    "(\"-----------------------\")\n",
    "largest_number  = max(AUPRC_list)\n",
    "index = AUPRC_list.index(largest_number)\n",
    "print(largest_number, index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "group#1\n",
    "\n",
    "[0.24092945522182005, 0.3139675367502194, 0.3062163369752217, 0.32297163568130305, 0.3672050308180419, 0.3801609216698969, 0.3915211363523951, 0.4034875773118736, 0.41721359538446234, 0.41755420607909477, 0.4101699028342543, 0.42683222688245664, 0.4338339272938271, 0.4432706404963518, 0.4451886249025738, 0.4436839678451211, 0.46470292201596697, 0.4619959382638624, 0.4389299870874322, 0.4537386141609928, 0.4880276013143086, 0.48964141469390005, 0.49214694908533474, 0.49336784163926267, 0.4978899412041259, 0.4960868620495151, 0.4949812567178974, 0.49875221067947606, 0.4959535547710648, 0.49723019893878023, 0.49849758106937503, 0.5005045769636993, 0.4968324354226746, 0.4985954057932132, 0.4985684464062525, 0.4948398218890804, 0.5003443438290083, 0.49804674478254773, 0.5015115944170082, 0.5043099513157541, 0.5022930844045073, 0.502102123403741, 0.5025587387783707, 0.5026322695878688, 0.5028108420912678, 0.501853319716798, 0.5044486284061104, 0.5043333679462079, 0.503047975296802, 0.5021477867974229]\n",
    "\n",
    "0.5044486284061104, index: 46\n",
    "\n",
    "group #2\n",
    "\n",
    "[0.24668762844932296, 0.31123092790061574, 0.35728718371921886, 0.37858993755415526, 0.38325613445804607, 0.38183540019756823, 0.40688905625255206, 0.4050292403852287, 0.4103841963804383, 0.4288343036036706, 0.4293594683280219, 0.44373329349811874, 0.44694196761428867, 0.44516332505161516, 0.4570591656299683, 0.44925142278910385, 0.45783436251651694, 0.4512008966459152, 0.4628860929136446, 0.46190128250293605, 0.4891415053038087, 0.4933325648723347, 0.49795793473520533, 0.4989478549566136, 0.507199717375493, 0.5031777644234027, 0.5048360591023886, 0.5026344145441939, 0.5070084702134143, 0.50851780828997, 0.5013767142024679, 0.5077028354409389, 0.5073222030725629, 0.5103865617070087, 0.5070321372047399, 0.5069057373554984, 0.5054984338086199, 0.5052088211513525, 0.5085875776438461, 0.5015018579996042, 0.507983738986951, 0.506001318616706, 0.5078548999343991, 0.5084694227173217, 0.5081644743764611, 0.5070537320211395, 0.5072728550164887, 0.5084469401746737, 0.5081580384861908, 0.5092361778552277]\n",
    "\n",
    "0.5103865617070087, index:  33\n",
    "\n",
    "group #3\n",
    "[0.20546938178065813, 0.31056285598824596, 0.3521164077944065, 0.36566363279169545, 0.3649970330628938, 0.3816742095036071, 0.408841252427171, 0.4192963362391232, 0.419725128897165, 0.4009845215509139, 0.4221866024862177, 0.4383579336817017, 0.41634488480301257, 0.4394011015343916, 0.42674958918677536, 0.4484833626141604, 0.43733868299572076, 0.42813204282903494, 0.44362467579095183, 0.4525213211300688, 0.47993303563958817, 0.48221178536835363, 0.4832912567732829, 0.485964752652683, 0.4894140885779246, 0.49081305081555826, 0.4835906970652839, 0.4881328848995447, 0.49108874994886303, 0.49205732309554323, 0.4918174541861535, 0.49104602501641953, 0.49033495002806987, 0.49255438103140303, 0.4982302563540638, 0.4919847023325378, 0.49138268849817107, 0.49216471663752714, 0.49367968532436873, 0.49558690171904884, 0.4952242601993453, 0.49709259551176815, 0.4969043181087201, 0.49722348299821856, 0.49599951407363857, 0.49572421827303714, 0.49551046935516674, 0.4969339282495756, 0.49522481850002315, 0.4956301125397299]\n",
    "\n",
    "0.4982302563540638, index: 34\n",
    "\n",
    "group #4\n",
    "\n",
    "[0.16705442847351432, 0.2811237847091236, 0.3227277423619332, 0.3459164670019608, 0.3433205542817934, 0.38953865811323535, 0.40093825754134493, 0.4042482476980622, 0.4179255247142833, 0.42026119275049384, 0.415263850960453, 0.4326573070148512, 0.4284856196846552, 0.455811861263988, 0.44742754829379755, 0.4428520431746461, 0.4288860834282809, 0.43801462440444205, 0.441347802107846, 0.4560878428908129, 0.47952984096244766, 0.4859939647185739, 0.48291741623601653, 0.4863560035613435, 0.4879069301596515, 0.49283878286572264, 0.4925634321692941, 0.49296767067476266, 0.4925321693215088, 0.4930295366233496, 0.4927986378984127, 0.49612537918838245, 0.4992350455119594, 0.4951830005033058, 0.49014993853897326, 0.4924448141210762, 0.4945801109607605, 0.4971188401719394, 0.49753234729288465, 0.49315691206981155, 0.4963229926370793, 0.49660539254449804, 0.49752930191373473, 0.4983978705842285, 0.498218560630721, 0.49778016282127696, 0.4980937334749714, 0.4982398417549309, 0.49825272820647715, 0.4978916971990578]\n",
    "\n",
    "0.4992350455119594, index: 32\n",
    "\n",
    "group #5\n",
    "[0.20795198881124255, 0.2924131615408049, 0.31194815399388126, 0.357671229080611, 0.3907590012977773, 0.39197022751675975, 0.39688932315376796, 0.41098642756821824, 0.4280303875603716, 0.4251116328825386, 0.41492397254078656, 0.44119503399957305, 0.42866565608661766, 0.42155615910506705, 0.4352771610735857, 0.4355309812927433, 0.4575302940022513, 0.4621060999031488, 0.4615244295921646, 0.4347042141353311, 0.4843673460502776, 0.49216570578173724, 0.49284316077316226, 0.4976730562122618, 0.4981241668777771, 0.4985906269863735, 0.5023674118168958, 0.5039947051779108, 0.5025596400291938, 0.501332454384853, 0.5017141509761979, 0.5033696471830942, 0.5035807094153067, 0.5044712423289812, 0.49912591150498187, 0.5036493639939076, 0.5073756144905568, 0.5066738446153692, 0.5041024684427422, 0.5061074251973712, 0.5079663458037375, 0.5080434717076571, 0.5071731389137064, 0.5066158069067092, 0.5059333249321385, 0.5078252460128987, 0.5081895157894929, 0.5079278975582764, 0.5073543066159428, 0.5078677916025073]\n",
    "\n",
    "0.5081895157894929, index: 46\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A0004.hea\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17651/17651 [00:02<00:00, 6737.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "164889003    1051.0\n",
      "164890007    1675.0\n",
      "6374002       103.0\n",
      "426627000      59.0\n",
      "733534002     299.0\n",
      "713427006     963.0\n",
      "270492004     706.0\n",
      "713426002     372.0\n",
      "39732003     1526.0\n",
      "445118002     437.0\n",
      "164947007      74.0\n",
      "251146004     320.0\n",
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      "698252002     354.0\n",
      "426783006    5794.0\n",
      "284470004     653.0\n",
      "10370003      296.0\n",
      "365413008     120.0\n",
      "427172004     387.0\n",
      "164917005     415.0\n",
      "47665007      256.0\n",
      "427393009     758.0\n",
      "426177001    3784.0\n",
      "427084000    1932.0\n",
      "164934002    2343.0\n",
      "59931005      798.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/138 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import average_precision_score, roc_auc_score, f1_score\n",
    "import pandas as pd\n",
    "from dataset import dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from model import NN\n",
    "import pickle\n",
    "\n",
    "\n",
    "DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "DEVICE = 'cpu'\n",
    "\n",
    "address = \"./model_output/model_group5/PROGRESS.pickle\"\n",
    "\n",
    "with open(address, 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "new_state_dict = data[46]['model']\n",
    "\n",
    "def collate(batch):\n",
    "\n",
    "    ch = batch[0][0].shape[0]\n",
    "    maxL = 8192\n",
    "    X = np.zeros((len(batch), ch, maxL))\n",
    "    \n",
    "    for i in range(len(batch)):\n",
    "        X[i, :, -batch[i][0].shape[-1]:] = batch[i][0]\n",
    "    \n",
    "    t = np.array([b[1] for b in batch])\n",
    "    l = np.concatenate([b[2].reshape(1,12) for b in batch], axis=0)\n",
    "\n",
    "    X = torch.from_numpy(X)\n",
    "    t = torch.from_numpy(t)\n",
    "    l = torch.from_numpy(l)\n",
    "    return X, t, l\n",
    "\n",
    "def valid_part(model, dataset):\n",
    "    targets = []\n",
    "    outputs = []\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for i, (x, t, l) in enumerate(tqdm(dataset)):\n",
    "            x = x.unsqueeze(2).float().to(DEVICE)\n",
    "            t = t.to(DEVICE)\n",
    "            l = l.float().to(DEVICE)\n",
    "\n",
    "            y,p = model(x, l)\n",
    "            #p = torch.sigmoid(y)\n",
    "\n",
    "            targets.append(t.data.cpu().numpy())\n",
    "            outputs.append(p.data.cpu().numpy())\n",
    "            \n",
    "    targets = np.concatenate(targets, axis=0)\n",
    "    outputs = np.concatenate(outputs, axis=0)\n",
    "    auprc = average_precision_score(y_true=targets, y_score=outputs)\n",
    "    auroc = roc_auc_score(targets, outputs)\n",
    "\n",
    "    outputs_f1 = np.array([[(1 if prob > 0.5 else 0) for prob in probs] for probs in np.array(outputs)])\n",
    "    f1 = f1_score(targets, outputs_f1, average='weighted')\n",
    "    print(\"This is the auroc of testing:\", auroc)\n",
    "    print(\"This is the f1 of testing:\", f1)\n",
    "\n",
    "    return auprc, targets, outputs, auroc, f1\n",
    "\n",
    "file_address = \"../collection_of_all_datasets/\"\n",
    "data_directory = \"./csv-file/training_validation_testing/group5\"\n",
    "\n",
    "############ testing area #########################\n",
    "the_testing_address = data_directory + \"/testing_group\"+data_directory[-1]+\".csv\"\n",
    "df = pd.read_csv(the_testing_address)\n",
    "print(df['Name'][0])\n",
    "\n",
    "testing_header_files=[]\n",
    "\n",
    "for i in range(len(df['Name'])):\n",
    "    each_header_file = file_address + df['Name'][i]\n",
    "    testing_header_files.append(each_header_file)\n",
    "    \n",
    "test_dataset = dataset(testing_header_files)\n",
    "print(test_dataset.summary('pandas'))\n",
    "    \n",
    "\n",
    "test_dataset.num_leads = 12\n",
    "test_dataset.sample = True\n",
    "###################################################\n",
    "valid = DataLoader(dataset=test_dataset,\n",
    "                       batch_size=128,\n",
    "                       shuffle=False,\n",
    "                       num_workers=8,\n",
    "                       collate_fn=collate,\n",
    "                       pin_memory=True,\n",
    "                       drop_last=False)\n",
    "\n",
    "model = NN(nOUT=26).to(DEVICE)\n",
    "model.load_state_dict(new_state_dict)\n",
    "\n",
    "auprc, targets, outputs, auroc, f1 = valid_part(model, valid)\n",
    "print(\"============================================\")\n",
    "print(\"This is the auprc:\", auprc)\n",
    "print(\"This is the auroc:\", auroc)\n",
    "print(\"This is the f1: \", f1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUPRC Checking for the 12-lead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> 70\n",
      "<class 'dict'>\n",
      "----------------------\n",
      "epoch: \n",
      "model: \n",
      "train_auprc: \n",
      "valid_auprc: \n",
      "valid_auroc: \n",
      "valid_targets: \n",
      "valid_outputs: \n",
      "-----------------------\n",
      "-----------------------\n",
      "[0.22930097130178695, 0.32832962591991394, 0.35747821563458165, 0.40334068352552854, 0.4143247499932358, 0.4029527044033194, 0.43389495514664134, 0.43227590210447153, 0.42763094099224175, 0.44957633208074327, 0.4597494213037991, 0.4526247117906813, 0.43743989538814887, 0.45792376450331546, 0.451313159856729, 0.4639358454505829, 0.47232763045243853, 0.4606242649328537, 0.4639758325284892, 0.46652311062551555, 0.5042587688515305, 0.5066928376264266, 0.5095729769831645, 0.5080036767983908, 0.5127449760336349, 0.5125109269447715, 0.5122839697295523, 0.5136472386495577, 0.511262098976581, 0.5164922389762391, 0.5140972681257835, 0.5178319592806367, 0.5164327367393361, 0.5148920196758733, 0.5163729160832938, 0.5160267502885856, 0.5145344650062585, 0.5168218047546651, 0.5167044163613791, 0.516398763630853, 0.519743273529755, 0.5180094428789471, 0.5187746854102783, 0.5189446261317721, 0.5191071800725848, 0.5190530337013738, 0.5181188209013248, 0.5198348158610016, 0.5201651447934246, 0.5200725282638343, 0.5189072362353084, 0.5188762698996898, 0.5176415639383447, 0.5190125547904473, 0.5182098122362689, 0.5199563616134137, 0.5200210859305623, 0.5213200600204746, 0.5191351758073407, 0.518985947976628, 0.5195132598638488, 0.519068429782278, 0.5185359984682572, 0.5184963313155649, 0.5190214730287106, 0.5187674089918028, 0.5193985132112929, 0.5195558026974484, 0.5183350858698199, 0.5191250549273707]\n",
      "0.5213200600204746 57\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import torch\n",
    "\n",
    "address = \"./model_output_12_lead/model_group4/PROGRESS.pickle\"\n",
    "\n",
    "with open(address, 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "print(type(data), len(data))\n",
    "\n",
    "print(type(data[1]))\n",
    "print(\"----------------------\")\n",
    "for key, _ in data[1].items():\n",
    "    print(f\"{key}: \")\n",
    "\n",
    "print(\"-----------------------\")\n",
    "AUPRC_list = []\n",
    "for i in range(len(data)):\n",
    "    AUPRC_list.append(data[i][\"valid_auprc\"])\n",
    "\n",
    "print(\"-----------------------\")    \n",
    "print(AUPRC_list)\n",
    "(\"-----------------------\")\n",
    "largest_number  = max(AUPRC_list)\n",
    "index = AUPRC_list.index(largest_number)\n",
    "print(largest_number, index)"
   ]
  }
 ],
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