GRUD_mean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import math\n",
    "import warnings\n",
    "import itertools\n",
    "import numbers\n",
    "import torch.utils.data as utils\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputpath = './input/set-a/'\n",
    "inputdict = {\n",
    "    \"ALP\" : 0,             # o\n",
    "    \"ALT\" : 1,             # o\n",
    "    \"AST\" : 2,             # o\n",
    "    \"Albumin\" : 3,         # o\n",
    "    \"BUN\" : 4,             # o\n",
    "    \"Bilirubin\" : 5,       # o\n",
    "    \"Cholesterol\" : 6,     # o\n",
    "    \"Creatinine\" : 7,      # o\n",
    "    \"DiasABP\" : 8,         # o\n",
    "    \"FiO2\" : 9,            # o\n",
    "    \"GCS\" : 10,            # o\n",
    "    \"Glucose\" : 11,        # o\n",
    "    \"HCO3\" : 12,           # o\n",
    "    \"HCT\" : 13,            # o\n",
    "    \"HR\" : 14,             # o\n",
    "    \"K\" : 15,              # o\n",
    "    \"Lactate\" : 16,        # o\n",
    "    \"MAP\" : 17,            # o\n",
    "    \"Mg\" : 18,             # o\n",
    "    \"Na\" : 19,             # o\n",
    "    \"PaCO2\" : 20,          # o\n",
    "    \"PaO2\" : 21,           # o\n",
    "    \"Platelets\" : 22,      # o\n",
    "    \"RespRate\" : 23,       # o\n",
    "    \"SaO2\" : 24,           # o\n",
    "    \"SysABP\" : 25,         # o\n",
    "    \"Temp\" : 26,           # o\n",
    "    \"Tropl\" : 27,          # o\n",
    "    \"TroponinI\" : 27,      # temp: regarded same as Tropl\n",
    "    \"TropT\" : 28,          # o\n",
    "    \"TroponinT\" : 28,      # temp: regarded same as TropT\n",
    "    \"Urine\" : 29,          # o\n",
    "    \"WBC\" : 30,            # o\n",
    "    \"Weight\" : 31,         # o\n",
    "    \"pH\" : 32,             # o\n",
    "    \"NIDiasABP\" : 33,      # unused variable\n",
    "    \"NIMAP\" : 34,          # unused variable\n",
    "    \"NISysABP\" : 35,       # unused variable\n",
    "    \"MechVent\" : 36,       # unused variable\n",
    "    \"RecordID\" : 37,       # unused variable\n",
    "    \"Age\" : 38,            # unused variable\n",
    "    \"Gender\" :39,          # unused variable\n",
    "    \"ICUType\" : 40,        # unused variable\n",
    "    \"Height\": 41           # unused variable\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions to process the time in the data\n",
    "def timeparser(time):\n",
    "    return pd.to_timedelta(time + ':00')\n",
    "\n",
    "def timedelta_to_day_figure(timedelta):\n",
    "    return timedelta.days + (timedelta.seconds/86400) #(24*60*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group the data by time\n",
    "def df_to_inputs(df, inputdict, inputs):\n",
    "    grouped_data = df.groupby('Time')\n",
    "            \n",
    "    for row_index, value in df.iterrows():\n",
    "        '''\n",
    "        t = colum ~ time frame\n",
    "        agg_no = row ~ variable\n",
    "        '''\n",
    "        \n",
    "        agg_no = inputdict[value.Parameter]\n",
    "\n",
    "        #print('agg_no : {}\\t  value : {}'.format(agg_no, value.Value))\n",
    "        inputs[agg_no].append(value.Value)    \n",
    "        \n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127.0\n"
     ]
    }
   ],
   "source": [
    "inputs = []\n",
    "\n",
    "# prepare empty list to put data\n",
    "# len(inputdict)-2: two items has same agg_no\n",
    "for i in range(len(inputdict)-2):\n",
    "    t = []\n",
    "    inputs.append(t)\n",
    "\n",
    "# read all the files in the input folder\n",
    "for filename in os.listdir(inputpath):\n",
    "    df = pd.read_csv(inputpath + filename, header=0,\\\n",
    "                     parse_dates=['Time'], date_parser=timeparser)\n",
    "    \n",
    "    inputs = df_to_inputs(df=df, inputdict=inputdict, inputs=inputs)\n",
    "\n",
    "print(inputs[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127.0\n"
     ]
    }
   ],
   "source": [
    "# save inputs just in case\n",
    "#np.save('./input/inputs', inputs)\n",
    "loaded_inputs = np.load('./input/inputs.npy')\n",
    "print(loaded_inputs[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ALP', 'ALT', 'AST', 'Albumin', 'BUN', 'Bilirubin', 'Cholesterol', 'Creatinine', 'DiasABP', 'FiO2', 'GCS', 'Glucose', 'HCO3', 'HCT', 'HR', 'K', 'Lactate', 'MAP', 'Mg', 'Na', 'PaCO2', 'PaO2', 'Platelets', 'RespRate', 'SaO2', 'SysABP', 'Temp', 'Tropl', 'TropT', 'Urine', 'WBC', 'Weight', 'pH', 'NIDiasABP', 'NIMAP', 'NISysABP', 'MechVent', 'RecordID', 'Age', 'Gender', 'ICUType', 'Height']\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "# make input items list\n",
    "input_columns = list(inputdict.keys())\n",
    "\n",
    "'''\n",
    "remove two overlaped items\n",
    "\"TroponinI\" : 27, #temp\n",
    "\"TroponinT\" : 28, #temp\n",
    "\n",
    "'''\n",
    "input_columns.remove(\"TroponinI\")\n",
    "input_columns.remove(\"TroponinT\")\n",
    "print(input_columns)\n",
    "print(len(input_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# describe the data\n",
    "# print count, min, max, mean, median, std, var and histogram if hist == True\n",
    "# return values as a list\n",
    "def describe(inputs, input_columns, inputdict, hist = False):\n",
    "    \n",
    "    desc = [] \n",
    "    \n",
    "    for i in range(len(inputdict)-2):\n",
    "        input_arr = np.asarray(inputs[i])\n",
    "        \n",
    "        des = []\n",
    "        \n",
    "        des.append(input_arr.size)\n",
    "        des.append(np.amin(input_arr))\n",
    "        des.append(np.amax(input_arr))\n",
    "        des.append(np.mean(input_arr))\n",
    "        des.append(np.median(input_arr))\n",
    "        des.append(np.std(input_arr))\n",
    "        des.append(np.var(input_arr))\n",
    "        \n",
    "        desc.append(des)\n",
    "        \n",
    "        # print histgram\n",
    "        if hist:\n",
    "            a = np.hstack(input_arr)\n",
    "            plt.hist(a, bins='auto')\n",
    "            plt.title(\"Histogram about {}\".format(input_columns[i]))\n",
    "            plt.show()\n",
    "        \n",
    "        print('count: {}, min: {}, max: {}'.format(des[0], des[1], des[2]))\n",
    "        print('mean: {}, median: {}, std: {}, var: {}'.format(des[3], des[4], des[5], des[6]))\n",
    "    \n",
    "    return desc        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 3092, min: 12.0, max: 2205.0\n",
      "mean: 116.77328589909443, median: 82.0, std: 133.88381709195593, var: 17924.87647911231\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 3177, min: 1.0, max: 11470.0\n",
      "mean: 394.4749763928234, median: 43.0, std: 1199.9801908265079, var: 1439952.4583760223\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 3182, min: 4.0, max: 18430.0\n",
      "mean: 506.2840980515399, median: 64.0, std: 1516.1916042785547, var: 2298836.9808847774\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 2356, min: 1.0, max: 5.3\n",
      "mean: 2.9223259762309, median: 2.9, std: 0.6514075310421351, var: 0.4243317714984103\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13916, min: 0.0, max: 197.0\n",
      "mean: 27.416527737855706, median: 20.0, std: 23.393032927627267, var: 547.2339895530536\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 3191, min: 0.1, max: 47.7\n",
      "mean: 2.9085553118144785, median: 0.9, std: 5.903983019417633, var: 34.85701549357175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 315, min: 28.0, max: 330.0\n",
      "mean: 156.51746031746032, median: 152.0, std: 45.99491531484598, var: 2115.532234819854\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13983, min: 0.1, max: 22.1\n",
      "mean: 1.5049774726453549, median: 1.0, std: 1.635940329877449, var: 2.6763007629195372\n"
     ]
    },
    {
     "data": {
      "image/png": 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AJKmnDABJ6ikDQBPyP4eRRp8BIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGwALn8/iS9hUDQJJ6ygCQpJ4yACSppwwASeopA0CSesoAkKSeMgA0JR9DlUaXASBJPWUASFJPGQCS1FMGgCT1lAEgST01bQAkOSLJF5PclGRTkte18oOTbEiyub0e1MqT5D1JtiS5IckxA++1ttXfnGTtvuuWJGk6M7kCeBD446p6FnAccHaSo4BzgCuqaiVwRZsHOBlY2X7OAi6ALjCAc4HnAccC546FhiRp/k0bAFW1raqua9P3ATcBy4DVwPpWbT1wapteDVxcnauAJUkOB04ENlTVzqraBWwATprT3kiSZmxW9wCSrACeC1wNHFZV26ALCeDQVm0ZcPvAaltb2WTl47dxVpKNSTbu2LFjNs0bOf4RlqR9acYBkORA4OPAH1bVvVNVnaCspij/+YKqC6tqVVWtWrp06UybJ0mapRkFQJJH0x38P1RVn2jFd7ahHdrr9la+FThiYPXlwB1TlEuShmAmTwEFuAi4qareObDocmDsSZ61wGUD5We0p4GOA+5pQ0SfB05IclC7+XtCK9MC51CUNJr2n0GdFwCvBL6Z5PpW9kbg7cClSc4Evgec1pZ9FjgF2AL8GHg1QFXtTPIW4JpW781VtXNOeiFJmrVpA6CqvsLE4/cAx09Qv4CzJ3mvdcC62TRQkrRv+JfAktRTBoAk9ZQBIEk9ZQBoRnwSSBo9BoAk9ZQBIEk9ZQBIUk8ZAJox7wNIo8UAWKA82Era1wwASeopA0CSesoAkKSeMgAkqacMAM2aN6il0WAASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9ZQBoD3m3wNIi5sBIEk9ZQAsIJ5RS5pPBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUAaK/5+Kq0OE0bAEnWJdme5MaBsoOTbEiyub0e1MqT5D1JtiS5IckxA+usbfU3J1m7b7ojSZqpmVwB/E/gpHFl5wBXVNVK4Io2D3AysLL9nAVcAF1gAOcCzwOOBc4dCw1J0nBMGwBV9WVg57ji1cD6Nr0eOHWg/OLqXAUsSXI4cCKwoap2VtUuYAO7h4okaR7t6T2Aw6pqG0B7PbSVLwNuH6i3tZVNVr6bJGcl2Zhk444dO/aweZKk6cz1TeBMUFZTlO9eWHVhVa2qqlVLly6d08ZJkh6xpwFwZxvaob1ub+VbgSMG6i0H7piiXJI0JHsaAJcDY0/yrAUuGyg/oz0NdBxwTxsi+jxwQpKD2s3fE1qZJGlI9p+uQpKPAL8OHJJkK93TPG8HLk1yJvA94LRW/bPAKcAW4MfAqwGqameStwDXtHpvrqrxN5YlSfNo2gCoqpdPsuj4CeoWcPYk77MOWDer1mnRWHHOZ7j17S8edjMkzYJ/Caw5418ES4uLASBJPWUAaE55FSAtHgaAJPWUAbAAeNYsaRgMAEnqKQNAknrKAJCknjIAJKmnDABJ6ikDQPuETzZJC58BIEk9ZQAM2aifKY96/6TFzACQpJ4yACSppwwA7XMOA0kLkwGgeWEISAuPATBEHhQlDZMBIEk9ZQBo3njFIy0sBsCQeDCUNGwGgCT1lAGgeefVj7QwGACS1FMGgCT1lAGgoVpxzmccEpKGxACYZx7sJubnIs0/A0ALikEgzR8DYJ54YJuen5E0vwwALUiGgbTvGQD7mAeyvednKO0b8x4ASU5KcnOSLUnOme/tzycPXHNr7PP0c5XmxrwGQJL9gL8CTgaOAl6e5Kj5bMN88NHG+efnLc3efF8BHAtsqapbquofgEuA1fPchjkzdqD3zHR4Bj/zyfbFYNn4cJ6qvjTqUlXzt7HkZcBJVfWaNv9K4HlV9dqBOmcBZ7XZfwzcvBebPAS4ay/WX+hGvX9gH0fFqPdxofXvF6tq6XSV9p+PlgzIBGU/l0BVdSFw4ZxsLNlYVavm4r0WolHvH9jHUTHqfVys/ZvvIaCtwBED88uBO+a5DZIk5j8ArgFWJjkyyQHAGuDyeW6DJIl5HgKqqgeTvBb4PLAfsK6qNu3DTc7JUNICNur9A/s4Kka9j4uyf/N6E1iStHD4l8CS1FMGgCT11EgGwKh+3USSW5N8M8n1STa2soOTbEiyub0eNOx2zkaSdUm2J7lxoGzCPqXznrZfb0hyzPBaPnOT9PG8JN9v+/L6JKcMLHtD6+PNSU4cTqtnLskRSb6Y5KYkm5K8rpWPzH6coo+Lez9W1Uj90N1c/g7wNOAA4BvAUcNu1xz17VbgkHFl/xk4p02fA7xj2O2cZZ9+FTgGuHG6PgGnAJ+j+3uS44Crh93+vejjecDrJ6h7VPudfQxwZPtd3m/YfZimf4cDx7TpJwB/1/oxMvtxij4u6v04ilcAI/V1EzOwGljfptcDpw6xLbNWVV8Gdo4rnqxPq4GLq3MVsCTJ4fPT0j03SR8nsxq4pKoeqKrvAlvofqcXrKraVlXXten7gJuAZYzQfpyij5NZFPtxFANgGXD7wPxWpt5Ri0kBX0hybfvKDIDDqmobdL+kwKFDa93cmaxPo7ZvX9uGQNYNDN0t6j4mWQE8F7iaEd2P4/oIi3g/jmIATPt1E4vYC6rqGLpvUz07ya8Ou0HzbJT27QXA04GjgW3Af23li7aPSQ4EPg78YVXdO1XVCcoWax8X9X4cxQAY2a+bqKo72ut24JN0l5R3jl0+t9ftw2vhnJmsTyOzb6vqzqp6qKoeBv6aR4YHFmUfkzya7sD4oar6RCseqf04UR8X+34cxQAYya+bSPL4JE8YmwZOAG6k69vaVm0tcNlwWjinJuvT5cAZ7SmS44B7xoYYFptxY94vpduX0PVxTZLHJDkSWAl8bb7bNxtJAlwE3FRV7xxYNDL7cbI+Lvr9OOy70Pvih+4pg7+ju/P+p8Nuzxz16Wl0TxV8A9g01i/gycAVwOb2evCw2zrLfn2E7tL5p3RnTWdO1ie6y+q/avv1m8CqYbd/L/r4gdaHG+gOFocP1P/T1sebgZOH3f4Z9O9f0g1v3ABc335OGaX9OEUfF/V+9KsgJKmnRnEISJI0AwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST31/wGmB8BmTVC9FgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 145567, min: 0.0, max: 268.0\n",
      "mean: 59.29051227269917, median: 58.0, std: 13.318150168032814, var: 177.37312389827247\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 32390, min: 0.21, max: 1.0\n",
      "mean: 0.5452014510651436, median: 0.5, std: 0.18984649512536758, var: 0.03604169171138622\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 61563, min: 3.0, max: 15.0\n",
      "mean: 11.398713512986697, median: 13.0, std: 3.973384107597854, var: 15.787781266511196\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13021, min: 10.0, max: 1143.0\n",
      "mean: 141.5095614776131, median: 127.0, std: 67.69497269352857, var: 4582.609327977579\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13613, min: 5.0, max: 50.0\n",
      "mean: 23.11574230514949, median: 23.0, std: 4.710165655553443, var: 22.185660502755194\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 18271, min: 9.0, max: 61.8\n",
      "mean: 30.679043292649556, median: 30.3, std: 5.013220252647734, var: 25.132377301557415\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 228538, min: 0.0, max: 300.0\n",
      "mean: 87.51744217591822, median: 86.0, std: 18.4049963443074, var: 338.7438904339688\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 14440, min: 1.8, max: 22.9\n",
      "mean: 4.136218836565097, median: 4.1, std: 0.7070606291358562, var: 0.49993473327399274\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 8024, min: 0.3, max: 29.3\n",
      "mean: 2.924779411764706, median: 2.1, std: 2.579141072175832, var: 6.6519686701843\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 145760, min: 0.0, max: 300.0\n",
      "mean: 79.74292672886938, median: 77.0, std: 16.926822834796283, var: 286.51733128058083\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13590, min: 0.6, max: 9.9\n",
      "mean: 2.0279102281089036, median: 2.0, std: 0.4221239657011912, var: 0.17818864241930044\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 13570, min: 98.0, max: 177.0\n",
      "mean: 139.07582903463523, median: 139.0, std: 5.19109386912776, var: 26.947455558095825\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 23293, min: 0.3, max: 100.0\n",
      "mean: 40.47474777830249, median: 39.0, std: 9.126020846980067, var: 83.28425649951478\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 23268, min: 0.0, max: 500.0\n",
      "mean: 150.41526860924876, median: 121.0, std: 89.29887429104858, var: 7974.288949648496\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 14104, min: 6.0, max: 1047.0\n",
      "mean: 190.81571894498015, median: 172.0, std: 106.3917362142324, var: 11319.201534678808\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 55051, min: 0.0, max: 98.0\n",
      "mean: 19.723728905923597, median: 19.0, std: 5.552301672088858, var: 30.82805385788073\n"
     ]
    },
    {
     "data": {
      "image/png": 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cuLYVwCVVtRS4pE0DHA8sbY/lwFkwBAZwBvAU4HDgjLHQkKTeLVlx4Zysd9IAqKqvAXeMaz4ROLe9Phd49kj7R2pwGbBXkv2AY4HVVXVHVf0YWM3moSJJmkXbeg1g36raANCe92ntBwA3j/Rb39q21L6ZJMuTrEmyZtOmTdtYniRpMjN9ETgTtNVW2jdvrDq7qpZV1bJFixbNaHGSpAdtawDc1oZ2aM8bW/t64MCRfouBW7fSLkmaI9saAKuAsTt5TgE+P9L+wnY30BHAXW2I6GLgmCQL2sXfY1qbJGmO7DpZhySfAJ4GLEyynuFunrcA5yc5FfgB8NzW/SLgBGAdcC/wYoCquiPJm4DLW7+/r6rxF5YlSbNo0gCoqpO38NbRE/Qt4LQtLGclsHJa1UnSTmjJigu56S1/MtdlTMq/BJakThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOrVdAZDkpiRXJ7kyyZrWtneS1Umub88LWnuSvDvJuiRXJTlsJjZAkubakhUXznUJ22QmzgCeXlWHVtWyNr0CuKSqlgKXtGmA44Gl7bEcOGsG1i1J2kY7YgjoRODc9vpc4Nkj7R+pwWXAXkn22wHrlyRNwfYGQAFfSnJFkuWtbd+q2gDQnvdp7QcAN4/Mu761/Zoky5OsSbJm06ZN21meJGlLdt3O+Y+qqluT7AOsTvK9rfTNBG21WUPV2cDZAMuWLdvsfUnSzNiuM4CqurU9bwQ+CxwO3DY2tNOeN7bu64EDR2ZfDNy6PeuXJG27bQ6AJLsn2XPsNXAMcA2wCjildTsF+Hx7vQp4Ybsb6AjgrrGhIknS7NueIaB9gc8mGVvOx6vqi0kuB85PcirwA+C5rf9FwAnAOuBe4MXbsW5J0nba5gCoqhuAJ03Qfjtw9ATtBZy2reuTJM0s/xJYkjplAEhSpwwASeqUASBJnTIAJKlTBoAkTdPO+uuf4xkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJLmmdn6P44ZAJLUKQNAUtd+U/7/vtvCAJCkThkAkrrS8xH/eLMeAEmOS3JdknVJVsz2+iX1xS/8LZvVAEiyC/Be4HjgEODkJIfMZg2SfrOM/4L3C3/qZvsM4HBgXVXdUFU/B84DTpzlGiTNosm+oLd3WtsuVTV7K0ueAxxXVS9t0y8AnlJVp4/0WQ4sb5OPA66btQI3txD40RyufyqscebsDHVa48z4Ta/xd6tq0WSddt3GhW+rTND2awlUVWcDZ89OOVuXZE1VLZvrOrbGGmfOzlCnNc4MaxzM9hDQeuDAkenFwK2zXIMkidkPgMuBpUkOSvJQ4CRg1SzXIEliloeAqur+JKcDFwO7ACurau1s1jBN82IoahLWOHN2hjqtcWZYI7N8EViSNH/4l8CS1CkDQJI6ZQA0SX47ybeSfCfJ2iRvbO0HJflmkuuTfLJdvJ7LOndJ8q9JLpiP9bWabkpydZIrk6xpbXsnWd3qXJ1kwRzXuFeSTyX5XpJrkxw5n2pM8ri2/8Yedyd55XyqsdX5qvbv5Zokn2j/jubVZzLJK1p9a5O8srXN+X5MsjLJxiTXjLRNWFcG724/oXNVksNmogYD4EH3Ac+oqicBhwLHJTkCeCvwzqpaCvwYOHUOawR4BXDtyPR8q2/M06vq0JH7mFcAl7Q6L2nTc+ldwBer6veBJzHs03lTY1Vd1/bfocAfAvcCn51PNSY5APhrYFlVPZHhxo6TmEefySRPBF7G8CsETwKelWQp82M/fhg4blzbluo6HljaHsuBs2akgqryMe4B7AZ8G3gKw1/i7drajwQunsO6FrcPxTOACxj+sG7e1DdS503AwnFt1wH7tdf7AdfNYX2PAG6k3QQxH2scV9cxwNfnW43AAcDNwN4MdxReABw7nz6TwHOBD4xMvx742/myH4ElwDUj0xPWBbwfOHmiftvz8AxgRBteuRLYCKwG/h9wZ1Xd37qsZ/jQz5V/ZPjw/rJNP4r5Vd+YAr6U5Ir20x4A+1bVBoD2vM+cVQePATYBH2rDaR9Isvs8q3HUScAn2ut5U2NV3QK8DfgBsAG4C7iC+fWZvAZ4apJHJdkNOIHhj1HnzX4cZ0t1jYXtmBnZrwbAiKp6oIZT7sUMp4yPn6jb7FY1SPIsYGNVXTHaPEHX+XBf71FVdRjDaetpSZ461wWNsytwGHBWVT0Z+ClzPyQ1oTZ+/mfAP891LeO18ekTgYOA/YHdGf6bjzdnn8mqupZhSGo18EXgO8D9W51pftoh/9YNgAlU1Z3AV4EjgL2SjP3B3Fz+dMVRwJ8luYnhV1SfwXBGMF/q+5WqurU9b2QYtz4cuC3JfgDteePcVch6YH1VfbNNf4ohEOZTjWOOB75dVbe16flU4x8DN1bVpqr6BfAZ4I+YZ5/JqvpgVR1WVU8F7gCuZ37tx1FbqmuH/IyOAdAkWZRkr/b64Qwf7muBrwDPad1OAT4/F/VV1WuqanFVLWEYEvhyVT1vvtQ3JsnuSfYce80wfn0Nw09+nNK6zWmdVfVD4OYkj2tNRwPfZR7VOOJkHhz+gflV4w+AI5LsliQ8uB/n22dyn/b8aOA/MOzP+bQfR22prlXAC9vdQEcAd40NFW2Xubo4M98ewB8A/wpcxfCF9Xet/THAt4B1DKfhD5sHtT4NuGA+1tfq+U57rAX+W2t/FMMF7Ovb895zXOehwJr23/tzwIJ5WONuwO3AI0fa5luNbwS+1/7N/BPwsHn4mfzfDMH0HeDo+bIfGYJoA/ALhiP8U7dUF8MQ0HsZrktezXDn1XbX4E9BSFKnHAKSpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlT/x8MLVA/AuJdVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 8185, min: 26.0, max: 100.0\n",
      "mean: 96.63706780696396, median: 97.0, std: 3.4000868440410503, var: 11.56059054702103\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 145650, min: 0.0, max: 295.0\n",
      "mean: 118.6978990731205, median: 116.0, std: 25.01615547443329, var: 625.8080347210188\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 86405, min: -17.8, max: 42.1\n",
      "mean: 37.00765117759389, median: 37.1, std: 1.7751633850901565, var: 3.151205043764743\n"
     ]
    },
    {
     "data": {
      "image/png": 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TN5oIr68jLR6O6CWpcwa9JHXOoJekzjlHr0VlIT7S6cdI1TuDXt3wUs7SaAa9tAD8y2Vy9pd+7ouJBX2SE4G3AQcA766qsyd1LGmhLMRfEf7lsvgs9jeXiQR9kgOAvwZ+FdgKfCnJpVX19UkcT9Jk+eaytE1qRH88sLmqvgWQ5IPAWsCgl7Ro9fqGNqmgXw7cMLS+Ffil4QpJ1gPr2+ptSb45h+McBnx3Ti1c2uz3/mV/7TfsB33Pm0cWz7bfD53NMSYV9BlRVv9vpeo84Lx5HSTZWFWr57OPpch+71/2137D/tv3cfd7Ul+Y2gocNbS+Atg2oWNJkvZiUkH/JWBVkocluRdwEnDphI4lSdqLiUzdVNXuJC8H/pnBxyvPr6pNEzjUvKZ+ljD7vX/ZX/sN+2/fx9rvVNXMtSRJS5YXNZOkzhn0ktS5JRn0SU5M8s0km5OcsdDtmaQk5yfZmeTqobJDk1ye5Np2f8hCtnHckhyV5DNJrkmyKckrWnnX/QZIcp8kX0zyH63vb2jlD0vyhdb3D7UPOXQnyQFJ/j3JJ9p69/1OsiXJ15J8JcnGVjbW1/qSC/qhyys8AzgWODnJsQvbqom6ADhxWtkZwIaqWgVsaOs92Q38YVUdA5wAnN5+xr33G+B24MlV9WjgOODEJCcAbwbe2vr+PeC0BWzjJL0CuGZofX/p95Oq6rihz86P9bW+5IKeocsrVNWPganLK3Spqq4EbppWvBa4sC1fCDz3bm3UhFXV9qr6clu+lcEv/nI67zdADdzWVu/ZbgU8GbiklXfZ9yQrgGcB727rYT/o9x6M9bW+FIN+1OUVli9QWxbKEVW1HQahCBy+wO2ZmCQrgccAX2A/6XebvvgKsBO4HPhP4Oaq2t2q9Pqa/yvgT4A72/qD2D/6XcBlSa5ql4aBMb/Wl+L16Ge8vIL6kOQg4MPA71fVLYMBXv+q6g7guCQHAx8FjhlV7e5t1WQleTaws6quSrJmqnhE1a763TyhqrYlORy4PMk3xn2ApTii9/IKsCPJkQDtfucCt2fsktyTQci/v6o+0oq77/ewqroZuILBeYqDk0wNzHp8zT8B+LUkWxhMxz6ZwQi/935TVdva/U4Gb+zHM+bX+lIMei+vMOjvura8Dvj4ArZl7Nrc7HuAa6rqnKGHuu43QJJlbSRPkvsCT2VwjuIzwPNate76XlWvqqoVVbWSwe/0p6vqFDrvd5L7Jbn/1DLwNOBqxvxaX5LfjE3yTAbv9lOXVzhrgZs0MUkuAtYwuGzpDuB1wMeAi4GHANcDz6+q6Sdsl6wkvwz8C/A1fjJfeyaDefpu+w2Q5OcZnHw7gMFA7OKq+rMkD2cw0j0U+Hfg1Kq6feFaOjlt6uaPqurZvfe79e+jbfVA4ANVdVaSBzHG1/qSDHpJ0uwtxakbSdI+MOglqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5/4XjXk9g4AyQqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 435, min: 0.3, max: 49.2\n",
      "mean: 7.1512643678160925, median: 2.6, std: 9.756165961249481, var: 95.18277426344301\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 2126, min: 0.01, max: 24.91\n",
      "mean: 1.1984510818438383, median: 0.2, std: 2.7137681474380155, var: 7.364537558049158\n"
     ]
    },
    {
     "data": {
      "image/png": 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uI0maAzM+cqiqM6tqWVUtZ7igfHVVnQR8DjihNVsLXNamL2/ztOVXV1W1+pp2N9NBwArgyzPtlyRpfOMcOUznfwIXJXkL8DXgvFY/D/hQko0MRwxrAKrq+iSXAN8CtgGnV9WvHoZ+SZJ20KyEQ1V9Hvh8m76ZKe42qqqfASdOs/7ZwNmz0RdJ0vj8hrQkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6hoMkqWM4SJI6Mw6HJAcm+VySG5Jcn+RVrb5fknVJNrTnfVs9Sc5JsjHJN5IcNrKtta39hiRrxx+WJGkc4xw5bAP+e1U9DVgFnJ7kUOAM4KqqWgFc1eYBjgVWtMdpwHthCBPgLODZwOHAWROBIkmaGzMOh6q6vaq+2qbvA24AlgKrgQtaswuA49v0auDCGnwJWJzkAOBoYF1Vba2qu4B1wDEz7ZckaXyzcs0hyXLgmcA1wBOq6nYYAgTYvzVbCmwaWW1zq01Xn+p1TkuyPsn6LVu2zEbXJUlTGDsckuwNfBx4dVXdu72mU9RqO/W+WHVuVa2sqpVLlix56J2VJO2QscIhyaMYguEjVfWJVr6jnS6iPd/Z6puBA0dWXwbctp26JGmOjHO3UoDzgBuq6h0jiy4HJu44WgtcNlI/ud21tAq4p512uhI4Ksm+7UL0Ua0mSZoji8ZY97nAnwDfTPL1Vnsd8FbgkiSnArcCJ7ZlnwaOAzYCPwFOAaiqrUneDHyltXtTVW0do1+SpDHNOByq6gtMfb0A4Mgp2hdw+jTbOh84f6Z9kSTNLr8hLUnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpM4uGw7Lz7jiIdUlaVeyy4aDJGl6hoMkqWM4SJI6hoMkqWM4SJI6hgPeoSRJkxkOkqSO4SBJ6hgOkqSO4SBJ6hgOkqSO4SBJ6hgOkqSO4SBJ6sybcEhyTJKbkmxMcsZc92fCuF+Q8wt2khaieREOSXYD/i9wLHAo8OIkh85tryRp1zUvwgE4HNhYVTdX1S+Ai4DVj2QHRj/hT/60P9Wy5WdcMetHBR5lSJovUlVz3QeSnAAcU1Uvb/N/Ajy7qv58UrvTgNPa7FOBm2b4ko8HfjjDdec7x7Zw7czjc2zzw+9V1ZIdabjo4e7JDsoUtS61qupc4NyxXyxZX1Urx93OfOTYFq6deXyObeGZL6eVNgMHjswvA26bo75I0i5vvoTDV4AVSQ5KsjuwBrh8jvskSbuseXFaqaq2Jflz4EpgN+D8qrr+YXzJsU9NzWOObeHamcfn2BaYeXFBWpI0v8yX00qSpHnEcJAkdXapcJivP9GxPUkOTPK5JDckuT7Jq1p9vyTrkmxoz/u2epKc08b4jSSHjWxrbWu/IcnauRrTZEl2S/K1JJ9q8wcluab18+J2kwJJ9mjzG9vy5SPbOLPVb0py9NyMpJdkcZJLk9zY9uERO8u+S/Ka9m/yuiQfS7LnQt53Sc5PcmeS60Zqs7avkjwryTfbOuckmeoW/vmjqnaJB8OF7u8ABwO7A/8CHDrX/dqBfh8AHNamfxf4NsNPjPwf4IxWPwN4W5s+DvgMw3dHVgHXtPp+wM3ted82ve9cj6/17b8BHwU+1eYvAda06fcBf9amXwG8r02vAS5u04e2/bkHcFDbz7vN9bha3y4AXt6mdwcW7wz7DlgK3AI8emSfvXQh7zvgecBhwHUjtVnbV8CXgSPaOp8Bjp3rf5/bfT/mugOP4I4/ArhyZP5M4My57tcMxnEZ8B8Yvh1+QKsdANzUpt8PvHik/U1t+YuB94/UH9BuDsezDLgKeAHwqfYfzg+BRZP3G8PdbEe06UWtXSbvy9F2czy2x7Y/oJlUX/D7roXDpvZHcFHbd0cv9H0HLJ8UDrOyr9qyG0fqD2g3Hx+70mmliX/MEza32oLRDsWfCVwDPKGqbgdoz/u3ZtONc76O/2+A1wK/bvOPA+6uqm1tfrSfvxlDW35Paz9fx3YwsAX4YDtt9oEke7ET7Luq+j7wduBW4HaGfXEtO8++mzBb+2ppm55cn7d2pXDYoZ/omK+S7A18HHh1Vd27vaZT1Go79TmT5EXAnVV17Wh5iqb1IMvm3diaRQynKd5bVc8EfsxwamI6C2Z87dz7aoZTQU8E9mL4VeXJFuq+ezAPdTwLbpy7Ujgs2J/oSPIohmD4SFV9opXvSHJAW34AcGerTzfO+Tj+5wJ/nOS7DL/E+wKGI4nFSSa+oDnaz9+MoS3fB9jK/BwbDP3aXFXXtPlLGcJiZ9h3LwRuqaotVfVL4BPAc9h59t2E2dpXm9v05Pq8tSuFw4L8iY52R8N5wA1V9Y6RRZcDE3dCrGW4FjFRP7ndTbEKuKcdDl8JHJVk3/ap76hWmzNVdWZVLauq5Qz74+qqOgn4HHBCazZ5bBNjPqG1r1Zf0+6IOQhYwXDxb05V1Q+ATUme2kpHAt9iJ9h3DKeTViV5TPs3OjG2nWLfjZiVfdWW3ZdkVXu/Th7Z1vw01xc9HskHwx0G32a4I+L1c92fHezzHzIcfn4D+Hp7HMdwvvYqYEN73q+1D8P/OOk7wDeBlSPbehmwsT1OmeuxTRrn8/nt3UoHM/yB2Aj8P2CPVt+zzW9syw8eWf/1bcw3MY/uAgGeAaxv+++TDHew7BT7DngjcCNwHfAhhjuOFuy+Az7GcP3klwyf9E+dzX0FrGzv1XeAdzPpRoX59vDnMyRJnV3ptJIkaQcZDpKkjuEgSeoYDpKkjuEgSeoYDpKkjuEgSer8f8rJV9Zn0+uOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 136918, min: 0.0, max: 11000.0\n",
      "mean: 120.7987284359982, median: 70.0, std: 184.725839486602, var: 34123.635774029855\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 12910, min: 0.1, max: 187.5\n",
      "mean: 12.670084430673894, median: 11.4, std: 7.6412549541321315, var: 58.38877727404884\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 129165, min: -1.0, max: 300.0\n",
      "mean: 83.38661525955173, median: 80.6, std: 25.047736295633516, var: 627.3890935355965\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 24355, min: 1.0, max: 735.0\n",
      "mean: 7.489199753644017, median: 7.38, std: 8.24393650029319, var: 67.96248902086631\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 98210, min: 0.0, max: 201.0\n",
      "mean: 57.95386416861827, median: 56.0, std: 15.54770421605256, var: 241.73110638985855\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 96871, min: 0.0, max: 209.0\n",
      "mean: 76.78045028956033, median: 75.0, std: 15.636321918012724, var: 244.4945631237251\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 98331, min: 0.0, max: 296.0\n",
      "mean: 118.59402426498256, median: 116.0, std: 23.260108542419857, var: 541.0326494051532\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 31144, min: 1.0, max: 1.0\n",
      "mean: 1.0, median: 1.0, std: 0.0, var: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 4000, min: 132539.0, max: 142673.0\n",
      "mean: 137605.122, median: 137592.5, std: 2923.2434123958956, var: 8545352.048116\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 4000, min: 15.0, max: 90.0\n",
      "mean: 64.2475, median: 67.0, std: 17.558751201324085, var: 308.30974375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 4000, min: -1.0, max: 1.0\n",
      "mean: 0.56075, median: 1.0, std: 0.4978046177969827, var: 0.2478094375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 4000, min: 1.0, max: 4.0\n",
      "mean: 2.76, median: 3.0, std: 1.002447006080621, var: 1.0049000000000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 4000, min: -1.0, max: 431.8\n",
      "mean: 88.919475, median: 152.4, std: 86.52038637352688, var: 7485.777258224375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(42, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = describe(loaded_inputs, input_columns, inputdict, hist=True)\n",
    "desc = np.asarray(desc)\n",
    "desc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save desc\n",
    "# 0: count, 1: min, 2: max, 3: mean, 4: median, 5: std, 6: var\n",
    "np.save('./input/desc', desc)\n",
    "loaded_desc = np.load('./input/desc.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalization(desc, inputs):\n",
    "    # for each catagory\n",
    "    for i in range(desc.shape[0]):\n",
    "        # for each value\n",
    "        for j in range(len(inputs[i])):\n",
    "            inputs[i][j] = (inputs[i][j] - desc[i][3])/desc[i][5]\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "dataframe to dataset\n",
    "'''\n",
    "\n",
    "def df_to_x_m_d(df, inputdict, size, id_posistion, split):\n",
    "    grouped_data = df.groupby('Time')\n",
    "    \n",
    "    #generate input vectors\n",
    "    x = np.zeros((len(inputdict)-2, grouped_data.ngroups))\n",
    "    masking = np.zeros((len(inputdict)-2, grouped_data.ngroups))\n",
    "    delta = np.zeros((split, size))\n",
    "    timetable = np.zeros(grouped_data.ngroups)\n",
    "    id = 0\n",
    "    \n",
    "    all_x = np.zeros((split,1))\n",
    "    \n",
    "    s_dataset = np.zeros((3, split, size))\n",
    "   \n",
    "    if grouped_data.ngroups > size:\n",
    "        \n",
    "        # fill the x and masking vectors\n",
    "        pre_time = pd.to_timedelta(0)\n",
    "        t = 0\n",
    "        for row_index, value in df.iterrows():\n",
    "            '''\n",
    "            t = colum, time frame\n",
    "            agg_no = row, variable\n",
    "            '''\n",
    "            #print(value)\n",
    "            agg_no = inputdict[value.Parameter]\n",
    "\n",
    "            # same timeline check.        \n",
    "            if pre_time != value.Time:\n",
    "                pre_time = value.Time\n",
    "                t += 1\n",
    "                timetable[t] = timedelta_to_day_figure(value.Time)\n",
    "\n",
    "            #print('agg_no : {}\\t t : {}\\t value : {}'.format(agg_no, t, value.Value))\n",
    "            x[agg_no, t] = value.Value    \n",
    "            masking[agg_no, t] = 1\n",
    "        \n",
    "        '''\n",
    "        # generate random index array \n",
    "        ran_index = np.random.choice(grouped_data.ngroups, size=size, replace=False)\n",
    "        ran_index.sort()\n",
    "        ran_index[0] = 0\n",
    "        ran_index[size-1] = grouped_data.ngroups-1\n",
    "        '''\n",
    "        \n",
    "        # generate index that has most parameters and first/last one.\n",
    "        ran_index = grouped_data.count()\n",
    "        ran_index = ran_index.reset_index()\n",
    "        ran_index = ran_index.sort_values('Value', ascending=False)\n",
    "        ran_index = ran_index[:size]\n",
    "        ran_index = ran_index.sort_index()\n",
    "        ran_index = np.asarray(ran_index.index.values)\n",
    "        ran_index[0] = 0\n",
    "        ran_index[size-1] = grouped_data.ngroups-1\n",
    "        \n",
    "        #print(ran_index)\n",
    "        \n",
    "        # take id for outcome comparing\n",
    "        id = x[id_posistion, 0]\n",
    "        \n",
    "        # remove unnesserly parts(rows)\n",
    "        x = x[:split, :]\n",
    "        masking = masking[:split, :]\n",
    "        \n",
    "        # coulme(time) sampling\n",
    "        x_sample = np.zeros((split, size))\n",
    "        m_sample = np.zeros((split, size))\n",
    "        time_sample = np.zeros(size)\n",
    "\n",
    "        t_x_sample = x_sample.T\n",
    "        t_marsking = m_sample.T\n",
    "        #t_time = t_sample.T\n",
    "        \n",
    "        t_x = x.T\n",
    "        t_m = masking.T\n",
    "        #t_t = t.T\n",
    "\n",
    "        it = np.nditer(ran_index, flags=['f_index'])\n",
    "        while not it.finished:\n",
    "            #print('it.index = {}, it[0] = {}, ran_index = {}'.format(it.index, it[0], ran_index[it.index]))\n",
    "            t_x_sample[it.index] = t_x[it[0]]\n",
    "            t_marsking[it.index] = t_m[it[0]]\n",
    "            time_sample[it.index] = timetable[it[0]]\n",
    "            it.iternext()\n",
    "        \n",
    "        x = x_sample\n",
    "        masking = m_sample\n",
    "        timetable = time_sample\n",
    "        '''\n",
    "        # normalize the X\n",
    "        nor_x = x/max_input[:, np.newaxis]\n",
    "        '''\n",
    "        # fill the delta vectors\n",
    "        for index, value in np.ndenumerate(masking):\n",
    "            '''\n",
    "            index[0] = row, agg\n",
    "            index[1] = col, time\n",
    "            '''\n",
    "            if index[1] == 0:\n",
    "                delta[index[0], index[1]] = 0\n",
    "            elif masking[index[0], index[1]-1] == 0:\n",
    "                delta[index[0], index[1]] = timetable[index[1]] - timetable[index[1]-1] + delta[index[0], index[1]-1]\n",
    "            else:\n",
    "                delta[index[0], index[1]] = timetable[index[1]] - timetable[index[1]-1]\n",
    "    \n",
    "    else:\n",
    "                \n",
    "        # fill the x and masking vectors\n",
    "        pre_time = pd.to_timedelta(0)\n",
    "        t = 0\n",
    "        for row_index, value in df.iterrows():\n",
    "            '''\n",
    "            t = colum, time frame\n",
    "            agg_no = row, variable\n",
    "            '''\n",
    "            #print(value)\n",
    "            agg_no = inputdict[value.Parameter]\n",
    "\n",
    "            # same timeline check.        \n",
    "            if pre_time != value.Time:\n",
    "                pre_time = value.Time\n",
    "                t += 1\n",
    "                timetable[t] = timedelta_to_day_figure(value.Time)\n",
    "\n",
    "            #print('agg_no : {}\\t t : {}\\t value : {}'.format(agg_no, t, value.Value))\n",
    "            x[agg_no, t] = value.Value    \n",
    "            masking[agg_no, t] = 1\n",
    "        \n",
    "        # take id for outcome comparing\n",
    "        id = x[id_posistion, 0]\n",
    "        \n",
    "        # remove unnesserly parts(rows)\n",
    "        x = x[:split, :]\n",
    "        masking = masking[:split, :]\n",
    "        \n",
    "        x = np.pad(x, ((0,0), (size-grouped_data.ngroups, 0)), 'constant')\n",
    "        masking = np.pad(masking, ((0,0), (size-grouped_data.ngroups, 0)), 'constant')\n",
    "        timetable = np.pad(timetable, (size-grouped_data.ngroups, 0), 'constant')\n",
    "        '''\n",
    "        # normalize the X\n",
    "        nor_x = x/max_input[:, np.newaxis]\n",
    "        '''\n",
    "        # fill the delta vectors\n",
    "        for index, value in np.ndenumerate(masking):\n",
    "            '''\n",
    "            index[0] = row, agg\n",
    "            index[1] = col, time\n",
    "            '''\n",
    "            if index[1] == 0:\n",
    "                delta[index[0], index[1]] = 0\n",
    "            elif masking[index[0], index[1]-1] == 0:\n",
    "                delta[index[0], index[1]] = timetable[index[1]] - timetable[index[1]-1] + delta[index[0], index[1]-1]\n",
    "            else:\n",
    "                delta[index[0], index[1]] = timetable[index[1]] - timetable[index[1]-1]\n",
    "    \n",
    "\n",
    "    all_x = np.concatenate((all_x, x), axis=1)\n",
    "    all_x = all_x[:,1:]\n",
    "    \n",
    "    s_dataset[0] = x\n",
    "    s_dataset[1] = masking\n",
    "    s_dataset[2] = delta\n",
    "    \n",
    "    return s_dataset, all_x, id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 3, 33, 49)\n",
      "(3, 33, 49)\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "(33, 196001)\n",
      "(33, 196000)\n"
     ]
    }
   ],
   "source": [
    "# def df_to_x_m_d(df, inputdict, mean, std, size, id_posistion, split):\n",
    "size = 49 # steps ~ from the paper\n",
    "id_posistion = 37\n",
    "input_length = 33 # input variables ~ from the paper\n",
    "dataset = np.zeros((1,3, input_length, size))\n",
    "\n",
    "all_x_add = np.zeros((input_length,1))\n",
    "\n",
    "for filename in os.listdir(inputpath):\n",
    "    df = pd.read_csv(inputpath + filename,\\\n",
    "                     header=0,\\\n",
    "                     parse_dates=['Time'],\\\n",
    "                     date_parser=timeparser)\n",
    "    s_dataset, all_x, id = df_to_x_m_d(df=df, inputdict=inputdict, size=size, id_posistion=id_posistion, split=input_length)\n",
    "    \n",
    "    dataset = np.concatenate((dataset, s_dataset[np.newaxis, :,:,:]))\n",
    "    all_x_add = np.concatenate((all_x_add, all_x), axis=1)\n",
    "    \n",
    "\n",
    "dataset = dataset[1:, :,:,:]    \n",
    "# (total datasets, kind of data(x, masking, and delta), input length, num of varience)\n",
    "# (4000, 3, 33, 49)\n",
    "print(dataset.shape)\n",
    "print(dataset[0].shape)\n",
    "print(dataset[0][0][0])\n",
    "\n",
    "print(all_x_add.shape)\n",
    "all_x_add = all_x_add[:, 1:]\n",
    "print(all_x_add.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 156800)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_proportion = 0.8\n",
    "train_index = int(all_x_add.shape[1] * train_proportion)\n",
    "train_x = all_x_add[:, :train_index]\n",
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean(x):\n",
    "    x_mean = []\n",
    "    for i in range(x.shape[0]):\n",
    "        mean = np.mean(x[i])\n",
    "        x_mean.append(mean)\n",
    "    return x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_median(x):\n",
    "    x_median = []\n",
    "    for i in range(x.shape[0]):\n",
    "        median = np.median(x[i])\n",
    "        x_median.append(median)\n",
    "    return x_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_std(x):\n",
    "    x_std = []\n",
    "    for i in range(x.shape[0]):\n",
    "        std = np.std(x[i])\n",
    "        x_std.append(std)\n",
    "    return x_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_var(x):\n",
    "    x_var = []\n",
    "    for i in range(x.shape[0]):\n",
    "        var = np.var(x[i])\n",
    "        x_var.append(var)\n",
    "    return x_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.7406377551020409, 5.207219387755102, 6.80234693877551, 0.0329311224489796, 1.8722729591836735, 0.045179846938775504, 0.24171556122448978, 0.10156377551020408, 31.736887755102043, 0.06143579081632654, 3.397672193877551, 9.100899234693877, 1.5541938775510205, 2.0344438775510203, 76.70211734693878, 0.2759260204081632, 0.009047895408163266, 42.51483418367347, 0.12520089285714286, 9.22252551020408, 0.4632161989795919, 1.6417920918367348, 11.77452806122449, 4.587206632653062, 0.6640497448979592, 63.596033163265304, 13.733461096938774, 0.010299107142857141, 0.010318858418367346, 73.55958099489797, 0.7335130739795919, 44.1956806122449, 0.09535822704081633]\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "x_mean = get_mean(train_x)\n",
    "print(x_mean)\n",
    "print(len(x_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21.735116338458592, 132.86302850412753, 174.8418237106656, 0.3158687491418776, 9.227731915982842, 0.8156475803361032, 6.431086679821715, 0.5568008139746999, 31.238178890837617, 0.18250875051606658, 5.651938979162444, 38.59730751167185, 5.919251518041793, 7.81477208410086, 32.51014936667275, 1.0477223399688855, 0.1970469124757705, 41.94215506842406, 0.4982555581463117, 34.642896137671165, 4.4669418041972, 17.668724085768588, 53.69650794955156, 8.6993539874602, 7.997510832623713, 62.13194536447744, 17.921698944193828, 0.4550922056429421, 0.2742431557272444, 149.20206357159026, 3.4554483303980987, 45.16797230138469, 2.750526841554571]\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "x_std = get_std(train_x)\n",
    "print(x_std)\n",
    "print(len(x_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset shape : (4000, 3, 33, 49)\n",
    "def dataset_normalize(dataset, mean, std):\n",
    "    for i in range(dataset.shape[0]):        \n",
    "        dataset[i][0] = (dataset[i][0] - mean[:, None])\n",
    "        dataset[i][0] = dataset[i][0]/std[:, None]\n",
    "        \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_mean = np.asarray(x_mean)\n",
    "x_std = np.asarray(x_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412 -0.08008412\n",
      " -0.08008412]\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset_normalize(dataset=dataset, mean=x_mean, std=x_std)\n",
    "print(dataset[0][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_chk(dataset):\n",
    "    all_x_add = np.zeros((dataset[0][0].shape[0],1))\n",
    "    for i in range(dataset.shape[0]):\n",
    "        all_x_add = np.concatenate((all_x_add, dataset[i][0]), axis=1)\n",
    "    \n",
    "    mean = get_mean(all_x_add)\n",
    "    median = get_median(all_x_add)\n",
    "    std = get_std(all_x_add)\n",
    "    var = get_var(all_x_add)\n",
    "    \n",
    "    print('mean')\n",
    "    print(mean)\n",
    "    print('median')\n",
    "    print(median)\n",
    "    print('std')\n",
    "    print(std)\n",
    "    print('var')\n",
    "    print(var)\n",
    "    \n",
    "    return mean, median, std, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "[0.0016128712193776333, 0.005803899138820891, 0.005263681260760995, 0.0016830722954007168, -0.0006508188207953421, 0.001121864851762637, -0.0010680290512515079, 0.001363467475350284, 0.0018310506499651141, 0.0035421101050264647, 0.0034350055502420738, -0.0012575162696206103, -0.00023910099059353635, -0.000890773127944157, 0.0022476225727017496, -0.0006963563594225052, -0.0008978832274306482, 0.0029197619839160293, -0.00021068295676432436, 9.16047314305429e-05, -0.0007607720555372361, -0.0013607077147688368, -3.748371932907335e-05, 0.007391139782183307, 0.0006897838092908442, 0.0030375047299914977, -0.0031755251974007123, 0.002661198642218062, 7.083466219904147e-06, -0.00012230218600966342, -0.0004426032632945868, 0.00035413661185874234, 0.0006889322175915184]\n",
      "median\n",
      "[-0.08008412414255718, -0.039192388178878025, -0.03890571943491205, -0.10425571550982413, -0.20289633208143093, -0.05539138229302205, -0.037585492663767164, -0.18240593936132243, 0.3605559813283965, -0.33661833003956837, -0.6011516059186204, -0.23579103884232752, -0.26256594652446513, -0.2603331044919522, 0.19372050807977506, -0.26335796220242613, -0.04591746855853843, 0.41688763459582334, -0.25127846706404006, -0.2662169315623522, -0.10369873154477803, -0.09292080649780077, -0.21927921406521975, -0.5273042848084297, -0.08303205319699541, 0.4088712607936279, -0.7663035262283582, -0.02263081418480204, -0.037626676191803425, -0.2919502582750618, -0.2122772514138808, 0.1683564481711741, -0.03466907706558529]\n",
      "std\n",
      "[1.0063100296946206, 1.1407618832615898, 1.1109364368547596, 1.0081688035841403, 1.0020721562013433, 1.0211985367586134, 0.9829157387889461, 1.0255935653361943, 0.9996339853894847, 1.0042007760968106, 1.0015153052583303, 0.998631124081189, 0.999792621531411, 0.9989700240288355, 1.0011139079412263, 0.9978007378625503, 0.9777260476057128, 0.9991678345496462, 0.9989421227773981, 0.9997829328123851, 0.9947871027895271, 0.984513474010669, 1.000661811004203, 1.008235574558683, 1.0036970347366374, 1.0004600454356674, 0.9992532757874064, 1.0713661368262835, 1.0114487421918326, 0.9730576582821981, 1.0075685838217445, 1.0080901696346305, 1.0882729036758458]\n",
      "var\n",
      "[1.012659875863988, 1.301337674302529, 1.2341797667315493, 1.016404336520277, 1.0041486062340093, 1.042846451477933, 0.9661233495590198, 1.0518421612590068, 0.9992681047456645, 1.0084191987134368, 1.0030329066666865, 0.9972641219836592, 0.9995852860686514, 0.9979411089081722, 1.002229056673354, 0.9956063124790497, 0.9559482241666887, 0.998336361598629, 0.9978853646590143, 0.999565912742934, 0.9896013798763811, 0.9692667805085563, 1.0013240600022113, 1.0165389738056778, 1.0074077375391186, 1.0009203025131375, 0.9985071091718626, 1.147825399138075, 1.02302855808144, 0.946841206341635, 1.0151944511045556, 1.016245790113978, 1.1843379128750566]\n"
     ]
    }
   ],
   "source": [
    "nor_mean, nor_median, nor_std, nor_var = normalize_chk(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 3, 33, 49)\n"
     ]
    }
   ],
   "source": [
    "np.save('./input/x_mean_aft_nor', nor_mean)\n",
    "np.save('./input/x_median_aft_nor', nor_median)\n",
    "np.save('./input/dataset', dataset)\n",
    "\n",
    "t_dataset = np.load('./input/dataset.npy')\n",
    "\n",
    "print(t_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Y values\n",
    "'''\n",
    "def df_to_y3(df):\n",
    "    '''\n",
    "    RecordID  SAPS-I  SOFA  Length_of_stay  Survival  In-hospital_death\n",
    "    '''\n",
    "    output = np.zeros((4000,3))\n",
    "    \n",
    "    for row_index, value in df.iterrows():\n",
    "        los = value[3] # Length_of_stay\n",
    "        sur = value[4] # Survival\n",
    "        ihd = value[5] # In-hospital_death\n",
    "\n",
    "        output[row_index][0] = ihd\n",
    "        output[row_index][1] = ihd\n",
    "\n",
    "        # length-of-stay less than 3 yes/no 1/0\n",
    "        if los < 3:\n",
    "            output[row_index][2] = 0\n",
    "        else:\n",
    "            output[row_index][2] = 1\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only check In-hospital_death\n",
    "def df_to_y1(df):\n",
    "    output = df.values\n",
    "    output = output[:,5:]\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 1)\n"
     ]
    }
   ],
   "source": [
    "A_outcomes = pd.read_csv('./input/Outcomes-a.txt')\n",
    "y1_outcomes = df_to_y1(A_outcomes)\n",
    "print(y1_outcomes.shape)\n",
    "np.save('./input/y1_out', y1_outcomes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_y2(df):\n",
    "    '''\n",
    "    RecordID  SAPS-I  SOFA  Length_of_stay  Survival  In-hospital_death\n",
    "    '''\n",
    "    output = np.zeros((4000,2))\n",
    "    \n",
    "    for row_index, value in df.iterrows():\n",
    "        ihd = value[5] # In-hospital_death\n",
    "\n",
    "        output[row_index][0] = ihd\n",
    "        output[row_index][1] = ihd\n",
    "        \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 2)\n"
     ]
    }
   ],
   "source": [
    "A_outcomes = pd.read_csv('./input/Outcomes-a.txt')\n",
    "y2_outcomes = df_to_y2(A_outcomes)\n",
    "print(y2_outcomes.shape)\n",
    "np.save('./input/y2_out', y2_outcomes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "class GRUD(torch.nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size, num_layers=1, x_mean=0,\\\n",
    "                 bias=True, batch_first=False, bidirectional=False, dropout_type='mloss', dropout=0):\n",
    "        super(GRUD, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        self.num_layers = num_layers\n",
    "        self.zeros = torch.autograd.Variable(torch.zeros(input_size))\n",
    "        self.x_mean = torch.autograd.Variable(torch.tensor(x_mean))\n",
    "        self.bias = bias\n",
    "        self.batch_first = batch_first\n",
    "        self.dropout_type = dropout_type\n",
    "        self.dropout = dropout\n",
    "        self.bidirectional = bidirectional\n",
    "        num_directions = 2 if bidirectional else 1\n",
    "        \n",
    "        if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \\\n",
    "                isinstance(dropout, bool):\n",
    "            raise ValueError(\"dropout should be a number in range [0, 1] \"\n",
    "                             \"representing the probability of an element being \"\n",
    "                             \"zeroed\")\n",
    "        if dropout > 0 and num_layers == 1:\n",
    "            warnings.warn(\"dropout option adds dropout after all but last \"\n",
    "                          \"recurrent layer, so non-zero dropout expects \"\n",
    "                          \"num_layers greater than 1, but got dropout={} and \"\n",
    "                          \"num_layers={}\".format(dropout, num_layers))\n",
    "        \n",
    "        ################################\n",
    "        gate_size = 1 # not used\n",
    "        ################################\n",
    "        \n",
    "        self._all_weights = []\n",
    "\n",
    "        '''\n",
    "        w_ih = Parameter(torch.Tensor(gate_size, layer_input_size))\n",
    "        w_hh = Parameter(torch.Tensor(gate_size, hidden_size))\n",
    "        b_ih = Parameter(torch.Tensor(gate_size))\n",
    "        b_hh = Parameter(torch.Tensor(gate_size))\n",
    "        layer_params = (w_ih, w_hh, b_ih, b_hh)\n",
    "        '''\n",
    "        # decay rates gamma\n",
    "        w_dg_x = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "        w_dg_h = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "\n",
    "        # z\n",
    "        w_xz = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "        w_hz = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        w_mz = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "\n",
    "        # r\n",
    "        w_xr = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "        w_hr = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        w_mr = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "\n",
    "        # h_tilde\n",
    "        w_xh = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "        w_hh = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        w_mh = torch.nn.Parameter(torch.Tensor(input_size))\n",
    "\n",
    "        # y (output)\n",
    "        w_hy = torch.nn.Parameter(torch.Tensor(output_size, hidden_size))\n",
    "\n",
    "        # bias\n",
    "        b_dg_x = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        b_dg_h = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        b_z = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        b_r = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        b_h = torch.nn.Parameter(torch.Tensor(hidden_size))\n",
    "        b_y = torch.nn.Parameter(torch.Tensor(output_size))\n",
    "\n",
    "        layer_params = (w_dg_x, w_dg_h,\\\n",
    "                        w_xz, w_hz, w_mz,\\\n",
    "                        w_xr, w_hr, w_mr,\\\n",
    "                        w_xh, w_hh, w_mh,\\\n",
    "                        w_hy,\\\n",
    "                        b_dg_x, b_dg_h, b_z, b_r, b_h, b_y)\n",
    "\n",
    "        param_names = ['weight_dg_x', 'weight_dg_h',\\\n",
    "                       'weight_xz', 'weight_hz','weight_mz',\\\n",
    "                       'weight_xr', 'weight_hr','weight_mr',\\\n",
    "                       'weight_xh', 'weight_hh','weight_mh',\\\n",
    "                       'weight_hy']\n",
    "        if bias:\n",
    "            param_names += ['bias_dg_x', 'bias_dg_h',\\\n",
    "                            'bias_z',\\\n",
    "                            'bias_r',\\\n",
    "                            'bias_h',\\\n",
    "                            'bias_y']\n",
    "        \n",
    "        for name, param in zip(param_names, layer_params):\n",
    "            setattr(self, name, param)\n",
    "        self._all_weights.append(param_names)\n",
    "\n",
    "        self.flatten_parameters()\n",
    "        self.reset_parameters()\n",
    "        \n",
    "    def flatten_parameters(self):\n",
    "        \"\"\"\n",
    "        Resets parameter data pointer so that they can use faster code paths.\n",
    "        Right now, this works only if the module is on the GPU and cuDNN is enabled.\n",
    "        Otherwise, it's a no-op.\n",
    "        \"\"\"\n",
    "        any_param = next(self.parameters()).data\n",
    "        if not any_param.is_cuda or not torch.backends.cudnn.is_acceptable(any_param):\n",
    "            return\n",
    "\n",
    "        # If any parameters alias, we fall back to the slower, copying code path. This is\n",
    "        # a sufficient check, because overlapping parameter buffers that don't completely\n",
    "        # alias would break the assumptions of the uniqueness check in\n",
    "        # Module.named_parameters().\n",
    "        all_weights = self._flat_weights\n",
    "        unique_data_ptrs = set(p.data_ptr() for p in all_weights)\n",
    "        if len(unique_data_ptrs) != len(all_weights):\n",
    "            return\n",
    "\n",
    "        with torch.cuda.device_of(any_param):\n",
    "            import torch.backends.cudnn.rnn as rnn\n",
    "\n",
    "            # NB: This is a temporary hack while we still don't have Tensor\n",
    "            # bindings for ATen functions\n",
    "            with torch.no_grad():\n",
    "                # NB: this is an INPLACE function on all_weights, that's why the\n",
    "                # no_grad() is necessary.\n",
    "                torch._cudnn_rnn_flatten_weight(\n",
    "                    all_weights, (4 if self.bias else 2),\n",
    "                    self.input_size, rnn.get_cudnn_mode(self.mode), self.hidden_size, self.num_layers,\n",
    "                    self.batch_first, bool(self.bidirectional))\n",
    "\n",
    "    def _apply(self, fn):\n",
    "        ret = super(GRUD, self)._apply(fn)\n",
    "        self.flatten_parameters()\n",
    "        return ret\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        stdv = 1.0 / math.sqrt(self.hidden_size)\n",
    "        for weight in self.parameters():\n",
    "            torch.nn.init.uniform_(weight, -stdv, stdv)\n",
    "\n",
    "    def check_forward_args(self, input, hidden, batch_sizes):\n",
    "        is_input_packed = batch_sizes is not None\n",
    "        expected_input_dim = 2 if is_input_packed else 3\n",
    "        if input.dim() != expected_input_dim:\n",
    "            raise RuntimeError(\n",
    "                'input must have {} dimensions, got {}'.format(\n",
    "                    expected_input_dim, input.dim()))\n",
    "        if self.input_size != input.size(-1):\n",
    "            raise RuntimeError(\n",
    "                'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(\n",
    "                    self.input_size, input.size(-1)))\n",
    "\n",
    "        if is_input_packed:\n",
    "            mini_batch = int(batch_sizes[0])\n",
    "        else:\n",
    "            mini_batch = input.size(0) if self.batch_first else input.size(1)\n",
    "\n",
    "        num_directions = 2 if self.bidirectional else 1\n",
    "        expected_hidden_size = (self.num_layers * num_directions,\n",
    "                                mini_batch, self.hidden_size)\n",
    "        \n",
    "        def check_hidden_size(hx, expected_hidden_size, msg='Expected hidden size {}, got {}'):\n",
    "            if tuple(hx.size()) != expected_hidden_size:\n",
    "                raise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\n",
    "\n",
    "        if self.mode == 'LSTM':\n",
    "            check_hidden_size(hidden[0], expected_hidden_size,\n",
    "                              'Expected hidden[0] size {}, got {}')\n",
    "            check_hidden_size(hidden[1], expected_hidden_size,\n",
    "                              'Expected hidden[1] size {}, got {}')\n",
    "        else:\n",
    "            check_hidden_size(hidden, expected_hidden_size)\n",
    "    \n",
    "    def extra_repr(self):\n",
    "        s = '{input_size}, {hidden_size}'\n",
    "        if self.num_layers != 1:\n",
    "            s += ', num_layers={num_layers}'\n",
    "        if self.bias is not True:\n",
    "            s += ', bias={bias}'\n",
    "        if self.batch_first is not False:\n",
    "            s += ', batch_first={batch_first}'\n",
    "        if self.dropout != 0:\n",
    "            s += ', dropout={dropout}'\n",
    "        if self.bidirectional is not False:\n",
    "            s += ', bidirectional={bidirectional}'\n",
    "        return s.format(**self.__dict__)\n",
    "    \n",
    "    \n",
    "    def __setstate__(self, d):\n",
    "        super(GRUD, self).__setstate__(d)\n",
    "        if 'all_weights' in d:\n",
    "            self._all_weights = d['all_weights']\n",
    "        if isinstance(self._all_weights[0][0], str):\n",
    "            return\n",
    "        num_layers = self.num_layers\n",
    "        num_directions = 2 if self.bidirectional else 1\n",
    "        self._all_weights = []\n",
    "\n",
    "        weights = ['weight_dg_x', 'weight_dg_h',\\\n",
    "                   'weight_xz', 'weight_hz','weight_mz',\\\n",
    "                   'weight_xr', 'weight_hr','weight_mr',\\\n",
    "                   'weight_xh', 'weight_hh','weight_mh',\\\n",
    "                   'weight_hy',\\\n",
    "                   'bias_dg_x', 'bias_dg_h',\\\n",
    "                   'bias_z', 'bias_r', 'bias_h','bias_y']\n",
    "\n",
    "        if self.bias:\n",
    "            self._all_weights += [weights]\n",
    "        else:\n",
    "            self._all_weights += [weights[:2]]\n",
    "\n",
    "    @property\n",
    "    def _flat_weights(self):\n",
    "        return list(self._parameters.values())\n",
    "\n",
    "    @property\n",
    "    def all_weights(self):\n",
    "        return [[getattr(self, weight) for weight in weights] for weights in self._all_weights]\n",
    "    \n",
    "    def forward(self, input):\n",
    "        # input.size = (3, 33,49) : num_input or num_hidden, num_layer or step\n",
    "        X = torch.squeeze(input[0]) # .size = (33,49)\n",
    "        Mask = torch.squeeze(input[1]) # .size = (33,49)\n",
    "        Delta = torch.squeeze(input[2]) # .size = (33,49)\n",
    "        Hidden_State = torch.autograd.Variable(torch.zeros(input_size))\n",
    "        \n",
    "        step_size = X.size(1) # 49\n",
    "        #print('step size : ', step_size)\n",
    "        \n",
    "        output = None\n",
    "        h = Hidden_State\n",
    "\n",
    "        # decay rates gamma\n",
    "        w_dg_x = getattr(self, 'weight_dg_x')\n",
    "        w_dg_h = getattr(self, 'weight_dg_h')\n",
    "\n",
    "        #z\n",
    "        w_xz = getattr(self, 'weight_xz')\n",
    "        w_hz = getattr(self, 'weight_hz')\n",
    "        w_mz = getattr(self, 'weight_mz')\n",
    "\n",
    "        # r\n",
    "        w_xr = getattr(self, 'weight_xr')\n",
    "        w_hr = getattr(self, 'weight_hr')\n",
    "        w_mr = getattr(self, 'weight_mr')\n",
    "\n",
    "        # h_tilde\n",
    "        w_xh = getattr(self, 'weight_xh')\n",
    "        w_hh = getattr(self, 'weight_hh')\n",
    "        w_mh = getattr(self, 'weight_mh')\n",
    "\n",
    "        # bias\n",
    "        b_dg_x = getattr(self, 'bias_dg_x')\n",
    "        b_dg_h = getattr(self, 'bias_dg_h')\n",
    "        b_z = getattr(self, 'bias_z')\n",
    "        b_r = getattr(self, 'bias_r')\n",
    "        b_h = getattr(self, 'bias_h')\n",
    "        \n",
    "        for layer in range(num_layers):\n",
    "            \n",
    "            x = torch.squeeze(X[:,layer:layer+1])\n",
    "            m = torch.squeeze(Mask[:,layer:layer+1])\n",
    "            d = torch.squeeze(Delta[:,layer:layer+1])\n",
    "\n",
    "\n",
    "            #(4)\n",
    "            gamma_x = torch.exp(-torch.max(self.zeros, (w_dg_x * d + b_dg_x)))\n",
    "            gamma_h = torch.exp(-torch.max(self.zeros, (w_dg_h * d + b_dg_h)))\n",
    "\n",
    "            #(5)\n",
    "            x = m * x + (1 - m) * (gamma_x * x + (1 - gamma_x) * self.x_mean)\n",
    "\n",
    "            #(6)\n",
    "            if self.dropout == 0:\n",
    "                h = gamma_h * h\n",
    "\n",
    "                z = torch.sigmoid((w_xz*x + w_hz*h + w_mz*m + b_z))\n",
    "                r = torch.sigmoid((w_xr*x + w_hr*h + w_mr*m + b_r))\n",
    "                h_tilde = torch.tanh((w_xh*x + w_hh*(r * h) + w_mh*m + b_h))\n",
    "\n",
    "                h = (1 - z) * h + z * h_tilde\n",
    "\n",
    "            elif self.dropout_type == 'Moon':\n",
    "                '''\n",
    "                RNNDROP: a novel dropout for rnn in asr(2015)\n",
    "                '''\n",
    "                h = gamma_h * h\n",
    "\n",
    "                z = torch.sigmoid((w_xz*x + w_hz*h + w_mz*m + b_z))\n",
    "                r = torch.sigmoid((w_xr*x + w_hr*h + w_mr*m + b_r))\n",
    "\n",
    "                h_tilde = torch.tanh((w_xh*x + w_hh*(r * h) + w_mh*m + b_h))\n",
    "\n",
    "                h = (1 - z) * h + z * h_tilde\n",
    "                dropout = torch.nn.Dropout(p=self.dropout)\n",
    "                h = dropout(h)\n",
    "\n",
    "            elif self.dropout_type == 'Gal':\n",
    "                '''\n",
    "                A Theoretically grounded application of dropout in recurrent neural networks(2015)\n",
    "                '''\n",
    "                dropout = torch.nn.Dropout(p=self.dropout)\n",
    "                h = dropout(h)\n",
    "\n",
    "                h = gamma_h * h\n",
    "\n",
    "                z = torch.sigmoid((w_xz*x + w_hz*h + w_mz*m + b_z))\n",
    "                r = torch.sigmoid((w_xr*x + w_hr*h + w_mr*m + b_r))\n",
    "                h_tilde = torch.tanh((w_xh*x + w_hh*(r * h) + w_mh*m + b_h))\n",
    "\n",
    "                h = (1 - z) * h + z * h_tilde\n",
    "\n",
    "            elif self.dropout_type == 'mloss':\n",
    "                '''\n",
    "                recurrent dropout without memory loss arXiv 1603.05118\n",
    "                g = h_tilde, p = the probability to not drop a neuron\n",
    "                '''\n",
    "\n",
    "                h = gamma_h * h\n",
    "\n",
    "                z = torch.sigmoid((w_xz*x + w_hz*h + w_mz*m + b_z))\n",
    "                r = torch.sigmoid((w_xr*x + w_hr*h + w_mr*m + b_r))\n",
    "                h_tilde = torch.tanh((w_xh*x + w_hh*(r * h) + w_mh*m + b_h))\n",
    "\n",
    "                dropout = torch.nn.Dropout(p=self.dropout)\n",
    "                h_tilde = dropout(h_tilde)\n",
    "\n",
    "                h = (1 - z)* h + z*h_tilde\n",
    "\n",
    "            else:\n",
    "                h = gamma_h * h\n",
    "\n",
    "                z = torch.sigmoid((w_xz*x + w_hz*h + w_mz*m + b_z))\n",
    "                r = torch.sigmoid((w_xr*x + w_hr*h + w_mr*m + b_r))\n",
    "                h_tilde = torch.tanh((w_xh*x + w_hh*(r * h) + w_mh*m + b_h))\n",
    "\n",
    "                h = (1 - z) * h + z * h_tilde\n",
    "\n",
    "            \n",
    "        w_hy = getattr(self, 'weight_hy')\n",
    "        b_y = getattr(self, 'bias_y')\n",
    "\n",
    "        output = torch.matmul(w_hy, h) + b_y\n",
    "        output = torch.sigmoid(output)\n",
    "        \n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_dataloader(dataset, outcomes,\\\n",
    "                    train_proportion = 0.8, dev_proportion = 0.2, test_proportion = 0.2):\n",
    "    \n",
    "    train_index = int(np.floor(dataset.shape[0] * train_proportion))\n",
    "    dev_index = int(np.floor(dataset.shape[0] * (train_proportion - dev_proportion)))\n",
    "    \n",
    "    # split dataset to tarin/dev/test set\n",
    "    train_data, train_label = dataset[:train_index, :,:,:], outcomes[:train_index, :]\n",
    "    dev_data, dev_label = dataset[dev_index:train_index, :,:,:], outcomes[dev_index:train_index, :]\n",
    "    test_data, test_label = dataset[train_index:, :,:,:], outcomes[train_index:, :]   \n",
    "    \n",
    "    # ndarray to tensor\n",
    "    train_data, train_label = torch.Tensor(train_data), torch.Tensor(train_label)\n",
    "    dev_data, dev_label = torch.Tensor(dev_data), torch.Tensor(dev_label)\n",
    "    test_data, test_label = torch.Tensor(test_data), torch.Tensor(test_label)\n",
    "    \n",
    "    # tensor to dataset\n",
    "    train_dataset = utils.TensorDataset(train_data, train_label)\n",
    "    dev_dataset = utils.TensorDataset(dev_data, dev_label)\n",
    "    test_dataset = utils.TensorDataset(test_data, test_label)\n",
    "    \n",
    "    # dataset to dataloader \n",
    "    train_dataloader = utils.DataLoader(train_dataset)\n",
    "    dev_dataloader = utils.DataLoader(dev_dataset)\n",
    "    test_dataloader = utils.DataLoader(test_dataset)\n",
    "    \n",
    "    print(\"train_data.shape : {}\\t train_label.shape : {}\".format(train_data.shape, train_label.shape))\n",
    "    print(\"dev_data.shape : {}\\t dev_label.shape : {}\".format(dev_data.shape, dev_label.shape))\n",
    "    print(\"test_data.shape : {}\\t test_label.shape : {}\".format(test_data.shape, test_label.shape))\n",
    "    \n",
    "    return train_dataloader, dev_dataloader, test_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 3, 33, 49)\n",
      "(4000, 1)\n",
      "train_data.shape : torch.Size([3200, 3, 33, 49])\t train_label.shape : torch.Size([3200, 1])\n",
      "dev_data.shape : torch.Size([800, 3, 33, 49])\t dev_label.shape : torch.Size([800, 1])\n",
      "test_data.shape : torch.Size([800, 3, 33, 49])\t test_label.shape : torch.Size([800, 1])\n"
     ]
    }
   ],
   "source": [
    "t_dataset = np.load('./input/dataset.npy')\n",
    "t_out = np.load('./input/y1_out.npy')\n",
    "\n",
    "print(t_dataset.shape)\n",
    "print(t_out.shape)\n",
    "\n",
    "train_dataloader, dev_dataloader, test_dataloader = data_dataloader(t_dataset, t_out, train_proportion=0.8, dev_proportion=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nin the paper : 49 layers, 33 input, 18838 parameters\\ninput : 10-weights(*input), 6 - biases\\nY: 1 weight(hidden*output), 1 bias(output)\\nInput : hidden : output : layer  = # of parameters : len(para)\\n1:1:1:1 = 18 : 18\\n2:1:1:1 = 25 : 18  // +7 as expected\\n1:1:1:2 = 34 : 18 // 34 = 16*2 + 2\\n33:33:1:1 = 562 : 18 // 16*33(528) + 33*1 +1 = 562\\n33:33:5:1 = 698 : 18 // 16*33(528) + 33*5(165) +5 = 698\\n33:33:5:49 = 26042 : 18 // 16*33*49(25872) + 33*5(165) +5 = 698\\nweights = 10*33*49(16170) + 33*5(165) = 16335 gap : 2503\\n\\n'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "in the paper : 49 layers, 33 input, 18838 parameters\n",
    "input : 10-weights(*input), 6 - biases\n",
    "Y: 1 weight(hidden*output), 1 bias(output)\n",
    "Input : hidden : output : layer  = # of parameters : len(para)\n",
    "1:1:1:1 = 18 : 18\n",
    "2:1:1:1 = 25 : 18  // +7 as expected\n",
    "1:1:1:2 = 34 : 18 // 34 = 16*2 + 2\n",
    "33:33:1:1 = 562 : 18 // 16*33(528) + 33*1 +1 = 562\n",
    "33:33:5:1 = 698 : 18 // 16*33(528) + 33*5(165) +5 = 698\n",
    "33:33:5:49 = 26042 : 18 // 16*33*49(25872) + 33*5(165) +5 = 698\n",
    "weights = 10*33*49(16170) + 33*5(165) = 16335 gap : 2503\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(model, criterion, learning_rate,\\\n",
    "        train_dataloader, dev_dataloader, test_dataloader,\\\n",
    "        learning_rate_decay=0, n_epochs=30):\n",
    "    epoch_losses = []\n",
    "    \n",
    "    # to check the update \n",
    "    old_state_dict = {}\n",
    "    for key in model.state_dict():\n",
    "        old_state_dict[key] = model.state_dict()[key].clone()\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        \n",
    "        if learning_rate_decay != 0:\n",
    "\n",
    "            # every [decay_step] epoch reduce the learning rate by half\n",
    "            if  epoch % learning_rate_decay == 0:\n",
    "                learning_rate = learning_rate/2\n",
    "                optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "                print('at epoch {} learning_rate is updated to {}'.format(epoch, learning_rate))\n",
    "        \n",
    "        # train the model\n",
    "        losses, acc = [], []\n",
    "        label, pred = [], []\n",
    "        y_pred_col= []\n",
    "        model.train()\n",
    "        for train_data, train_label in train_dataloader:\n",
    "            # Zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # Squeeze the data [1, 33, 49], [1,5] to [33, 49], [5]\n",
    "            train_data = torch.squeeze(train_data)\n",
    "            train_label = torch.squeeze(train_label)\n",
    "            \n",
    "            # Forward pass : Compute predicted y by passing train data to the model\n",
    "            y_pred = model(train_data)\n",
    "            \n",
    "            # y_pred = y_pred[:, None]\n",
    "            # train_label = train_label[:, None]\n",
    "            \n",
    "            #print(y_pred.shape)\n",
    "            #print(train_label.shape)\n",
    "            \n",
    "            # Save predict and label\n",
    "            y_pred_col.append(y_pred.item())\n",
    "            pred.append(y_pred.item() > 0.5)\n",
    "            label.append(train_label.item())\n",
    "            \n",
    "            #print('y_pred: {}\\t label: {}'.format(y_pred, train_label))\n",
    "\n",
    "            # Compute loss\n",
    "            loss = criterion(y_pred, train_label)\n",
    "            acc.append(\n",
    "                torch.eq(\n",
    "                    (torch.sigmoid(y_pred).data > 0.5).float(),\n",
    "                    train_label)\n",
    "            )\n",
    "            losses.append(loss.item())\n",
    "\n",
    "            # perform a backward pass, and update the weights.\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        \n",
    "        train_acc = torch.mean(torch.cat(acc).float())\n",
    "        train_loss = np.mean(losses)\n",
    "        \n",
    "        train_pred_out = pred\n",
    "        train_label_out = label\n",
    "        \n",
    "        # save new params\n",
    "        new_state_dict= {}\n",
    "        for key in model.state_dict():\n",
    "            new_state_dict[key] = model.state_dict()[key].clone()\n",
    "            \n",
    "        # compare params\n",
    "        for key in old_state_dict:\n",
    "            if (old_state_dict[key] == new_state_dict[key]).all():\n",
    "                print('Not updated in {}'.format(key))\n",
    "   \n",
    "        \n",
    "        # dev loss\n",
    "        losses, acc = [], []\n",
    "        label, pred = [], []\n",
    "        model.eval()\n",
    "        for dev_data, dev_label in dev_dataloader:\n",
    "            # Squeeze the data [1, 33, 49], [1,5] to [33, 49], [5]\n",
    "            dev_data = torch.squeeze(dev_data)\n",
    "            dev_label = torch.squeeze(dev_label)\n",
    "            \n",
    "            # Forward pass : Compute predicted y by passing train data to the model\n",
    "            y_pred = model(dev_data)\n",
    "            \n",
    "            # Save predict and label\n",
    "            pred.append(y_pred.item())\n",
    "            label.append(dev_label.item())\n",
    "\n",
    "            # Compute loss\n",
    "            loss = criterion(y_pred, dev_label)\n",
    "            acc.append(\n",
    "                torch.eq(\n",
    "                    (torch.sigmoid(y_pred).data > 0.5).float(),\n",
    "                    dev_label)\n",
    "            )\n",
    "            losses.append(loss.item())\n",
    "            \n",
    "        dev_acc = torch.mean(torch.cat(acc).float())\n",
    "        dev_loss = np.mean(losses)\n",
    "        \n",
    "        dev_pred_out = pred\n",
    "        dev_label_out = label\n",
    "        \n",
    "        # test loss\n",
    "        losses, acc = [], []\n",
    "        label, pred = [], []\n",
    "        model.eval()\n",
    "        for test_data, test_label in test_dataloader:\n",
    "            # Squeeze the data [1, 33, 49], [1,5] to [33, 49], [5]\n",
    "            test_data = torch.squeeze(test_data)\n",
    "            test_label = torch.squeeze(test_label)\n",
    "            \n",
    "            # Forward pass : Compute predicted y by passing train data to the model\n",
    "            y_pred = model(test_data)\n",
    "            \n",
    "            # Save predict and label\n",
    "            pred.append(y_pred.item())\n",
    "            label.append(test_label.item())\n",
    "\n",
    "            # Compute loss\n",
    "            loss = criterion(y_pred, test_label)\n",
    "            acc.append(\n",
    "                torch.eq(\n",
    "                    (torch.sigmoid(y_pred).data > 0.5).float(),\n",
    "                    test_label)\n",
    "            )\n",
    "            losses.append(loss.item())\n",
    "            \n",
    "        test_acc = torch.mean(torch.cat(acc).float())\n",
    "        test_loss = np.mean(losses)\n",
    "        \n",
    "        test_pred_out = pred\n",
    "        test_label_out = label\n",
    "                \n",
    "        epoch_losses.append([\n",
    "             train_loss, dev_loss, test_loss,\n",
    "             train_acc, dev_acc, test_acc,\n",
    "             train_pred_out, dev_pred_out, test_pred_out,\n",
    "             train_label_out, dev_label_out, test_label_out,\n",
    "         ])\n",
    "        \n",
    "        pred = np.asarray(pred)\n",
    "        label = np.asarray(label)\n",
    "        \n",
    "        auc_score = roc_auc_score(label, pred)\n",
    "        \n",
    "        # print(\"Epoch: {} Train: {:.4f}/{:.2f}%, Dev: {:.4f}/{:.2f}%, Test: {:.4f}/{:.2f}% AUC: {:.4f}\".format(\n",
    "        #     epoch, train_loss, train_acc*100, dev_loss, dev_acc*100, test_loss, test_acc*100, auc_score))\n",
    "        print(\"Epoch: {} Train loss: {:.4f}, Dev loss: {:.4f}, Test loss: {:.4f}, Test AUC: {:.4f}\".format(\n",
    "            epoch, train_loss, dev_loss, test_loss, auc_score))\n",
    "        \n",
    "        # save the parameters\n",
    "        train_log = []\n",
    "        train_log.append(model.state_dict())\n",
    "        torch.save(model.state_dict(), './save/grud_mean_grud_para.pt')\n",
    "        \n",
    "        #print(train_log)\n",
    "    \n",
    "    return epoch_losses        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_and_auc_score(outputs, labels, title):\n",
    "    false_positive_rate, true_positive_rate, threshold = roc_curve(labels, outputs)\n",
    "    auc_score = roc_auc_score(labels, outputs)\n",
    "    plt.plot(false_positive_rate, true_positive_rate, label = 'ROC curve, AREA = {:.4f}'.format(auc_score))\n",
    "    plt.plot([0,1], [0,1], 'red')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.title(title)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 33 # num of variables base on the paper\n",
    "hidden_size = 33 # same as inputsize\n",
    "output_size = 1\n",
    "num_layers = 49 # num of step or layers base on the paper\n",
    "\n",
    "x_mean = torch.Tensor(np.load('./input/x_mean_aft_nor.npy'))\n",
    "x_median = torch.Tensor(np.load('./input/x_median_aft_nor.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of parameters :  562\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#dropout_type : Moon, Gal, mloss\n",
    "model = GRUD(input_size = input_size, hidden_size= hidden_size, output_size=output_size, dropout=0, dropout_type='mloss', x_mean=x_mean, num_layers=num_layers)\n",
    "\n",
    "# load the parameters\n",
    "# model.load_state_dict(torch.load('./save/grud_para.pt'))\n",
    "# model.eval()\n",
    "\n",
    "count = count_parameters(model)\n",
    "print('number of parameters : ' , count)\n",
    "print(list(model.parameters())[0].grad)\n",
    "\n",
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at epoch 0 learning_rate is updated to 0.005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\woare\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1594: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Train loss: 0.3754, Dev loss: 0.3316, Test loss: 0.3225, Test AUC: 0.8113\n",
      "Epoch: 1 Train loss: 0.3406, Dev loss: 0.3249, Test loss: 0.3129, Test AUC: 0.8189\n",
      "Epoch: 2 Train loss: 0.3365, Dev loss: 0.3220, Test loss: 0.3132, Test AUC: 0.8189\n",
      "Epoch: 3 Train loss: 0.3344, Dev loss: 0.3203, Test loss: 0.3126, Test AUC: 0.8195\n",
      "Epoch: 4 Train loss: 0.3331, Dev loss: 0.3185, Test loss: 0.3105, Test AUC: 0.8226\n",
      "Epoch: 5 Train loss: 0.3317, Dev loss: 0.3170, Test loss: 0.3094, Test AUC: 0.8250\n",
      "Epoch: 6 Train loss: 0.3303, Dev loss: 0.3153, Test loss: 0.3081, Test AUC: 0.8267\n",
      "at epoch 7 learning_rate is updated to 0.0025\n",
      "Epoch: 7 Train loss: 0.3248, Dev loss: 0.3121, Test loss: 0.3048, Test AUC: 0.8295\n",
      "Epoch: 8 Train loss: 0.3229, Dev loss: 0.3102, Test loss: 0.3032, Test AUC: 0.8326\n",
      "Epoch: 9 Train loss: 0.3211, Dev loss: 0.3084, Test loss: 0.3016, Test AUC: 0.8355\n",
      "Epoch: 10 Train loss: 0.3198, Dev loss: 0.3072, Test loss: 0.3007, Test AUC: 0.8376\n",
      "Epoch: 11 Train loss: 0.3188, Dev loss: 0.3060, Test loss: 0.2999, Test AUC: 0.8395\n",
      "Epoch: 12 Train loss: 0.3178, Dev loss: 0.3048, Test loss: 0.2992, Test AUC: 0.8408\n",
      "Epoch: 13 Train loss: 0.3169, Dev loss: 0.3036, Test loss: 0.2987, Test AUC: 0.8419\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "def fit(model, criterion, learning_rate,\\\n",
    "        train_dataloader, dev_dataloader, test_dataloader,\\\n",
    "        learning_rate_decay=0, n_epochs=30):\n",
    "'''\n",
    "learning_rate = 0.01\n",
    "learning_rate_decay =7 \n",
    "n_epochs = 14\n",
    "\n",
    "# learning_rate = 0.1 learning_rate_decay=True\n",
    "epoch_losses = fit(model, criterion, learning_rate,\\\n",
    "                   train_dataloader, dev_dataloader, test_dataloader,\\\n",
    "                   learning_rate_decay, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at epoch 0 learning_rate is updated to 5e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\woare\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1594: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Train loss: 0.3105, Dev loss: 0.3036, Test loss: 0.2986, Test AUC: 0.8420\n",
      "Epoch: 1 Train loss: 0.3104, Dev loss: 0.3035, Test loss: 0.2986, Test AUC: 0.8420\n",
      "Epoch: 2 Train loss: 0.3103, Dev loss: 0.3035, Test loss: 0.2985, Test AUC: 0.8419\n",
      "Epoch: 3 Train loss: 0.3103, Dev loss: 0.3035, Test loss: 0.2985, Test AUC: 0.8419\n",
      "Epoch: 4 Train loss: 0.3102, Dev loss: 0.3035, Test loss: 0.2985, Test AUC: 0.8421\n",
      "Epoch: 5 Train loss: 0.3101, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8420\n",
      "Epoch: 6 Train loss: 0.3100, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8420\n",
      "at epoch 7 learning_rate is updated to 2.5e-05\n",
      "Epoch: 7 Train loss: 0.3099, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8421\n",
      "Epoch: 8 Train loss: 0.3099, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8421\n",
      "Epoch: 9 Train loss: 0.3098, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8421\n",
      "Epoch: 10 Train loss: 0.3098, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8421\n",
      "Epoch: 11 Train loss: 0.3098, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8422\n",
      "Epoch: 12 Train loss: 0.3097, Dev loss: 0.3034, Test loss: 0.2984, Test AUC: 0.8423\n",
      "Epoch: 13 Train loss: 0.3097, Dev loss: 0.3034, Test loss: 0.2983, Test AUC: 0.8422\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.0001\n",
    "learning_rate_decay =7 \n",
    "n_epochs = 14\n",
    "\n",
    "# learning_rate = 0.1 learning_rate_decay=True\n",
    "epoch_losses_s = fit(model, criterion, learning_rate,\\\n",
    "                   train_dataloader, dev_dataloader, test_dataloader,\\\n",
    "                   learning_rate_decay, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at epoch 0 learning_rate is updated to 0.0025\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\woare\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1594: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Train loss: 0.3159, Dev loss: 0.3020, Test loss: 0.2984, Test AUC: 0.8430\n",
      "Epoch: 1 Train loss: 0.3152, Dev loss: 0.3016, Test loss: 0.2987, Test AUC: 0.8431\n",
      "Epoch: 2 Train loss: 0.3150, Dev loss: 0.3012, Test loss: 0.2988, Test AUC: 0.8430\n",
      "Epoch: 3 Train loss: 0.3147, Dev loss: 0.3008, Test loss: 0.2990, Test AUC: 0.8430\n",
      "Epoch: 4 Train loss: 0.3144, Dev loss: 0.3006, Test loss: 0.2991, Test AUC: 0.8430\n",
      "Epoch: 5 Train loss: 0.3140, Dev loss: 0.3002, Test loss: 0.2993, Test AUC: 0.8428\n",
      "Epoch: 6 Train loss: 0.3137, Dev loss: 0.3000, Test loss: 0.2994, Test AUC: 0.8426\n",
      "at epoch 7 learning_rate is updated to 0.00125\n",
      "Epoch: 7 Train loss: 0.3104, Dev loss: 0.3001, Test loss: 0.2991, Test AUC: 0.8421\n",
      "Epoch: 8 Train loss: 0.3098, Dev loss: 0.2998, Test loss: 0.2990, Test AUC: 0.8420\n",
      "Epoch: 9 Train loss: 0.3094, Dev loss: 0.2995, Test loss: 0.2990, Test AUC: 0.8421\n",
      "Epoch: 10 Train loss: 0.3091, Dev loss: 0.2993, Test loss: 0.2990, Test AUC: 0.8419\n",
      "Epoch: 11 Train loss: 0.3089, Dev loss: 0.2991, Test loss: 0.2990, Test AUC: 0.8415\n",
      "Epoch: 12 Train loss: 0.3087, Dev loss: 0.2989, Test loss: 0.2990, Test AUC: 0.8414\n",
      "Epoch: 13 Train loss: 0.3084, Dev loss: 0.2987, Test loss: 0.2991, Test AUC: 0.8413\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.005\n",
    "learning_rate_decay =7 \n",
    "n_epochs = 14\n",
    "\n",
    "# learning_rate = 0.1 learning_rate_decay=True\n",
    "epoch_losses_t = fit(model, criterion, learning_rate,\\\n",
    "                   train_dataloader, dev_dataloader, test_dataloader,\\\n",
    "                   learning_rate_decay, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mClVdBiwTkY9V9UAYY4op1sjPRJ1Jk1z18PPPruNr795QrpzXURkPBTNGcZaIvAAkAmVzn1TV2iGLKkb4VxPWyM9EvJ9/dqupP/nE7RkxeTIkJ3sdlYkAwayjGA78FxCgJfAJMDqEMcUMqyZMVFCFjz6CxESYONHtX52SYknC5AkmUZykqtMAVPV7VX0KsGbyRbBqwkSFTZvg73+He+6BCy6A1FTo2RPKlPE6MhNBgrn1dFBc/47vRaQDsBmoEtqwop9VEyai5eTAu++6MYicHHjjDXj4YevPZAoUTKJ4DCgPdAFeACoB/wxlUNEqf88mqyZMRFqzxrXbmDsXrrnGbU9as6bXUZkIVuStJ1VdrKp7VHWTqt6jqq2AH8IQW9Sxnk0momVnwyuvQFKS69X0/vvw1VeWJEyRAlYUItIYOAuYp6q/iEhdXCuPq4HqYYgvKtgeEibiLV8O//wnfPst3HQTvPUWVKvmdVQmShRaUYjIS8DHwF3AlyLSE5gFLAdsaqwf20PCRKwDB+Cpp9wMps2bYdw4GD/ekoQplkAVRWsgSVX3i0hlYIvv8epgTy4i1wNvAAnAUFXtW8AxtwO9cXtcLFfVtsWIP2JYJWEizoIFronfd9/Bvfe6XecqV/Y6KhOFAiWKA6q6H0BVfxOR74qZJBKAt4AWQCawVEQmq2qG3zG1cC3Lr1DVHSJis6mMOVZ797oprm++CWefDV9+Cddd53VUJooFShTnikhuh1gBavg9RlVvLuLclwDrVHU9gIiMxlUpGX7HPAi8pao7fOfcVsz4Pee/XsIYz331FbRvDz/8AJ07u30jKlTwOioT5QIlilvyPR5UzHOfBfzo9zgTt/e2v9oAIjIfd3uqt6p+mf9EItIeaA9wzjmRNd3U1kuYiLBjB3TtCsOHu4Vzc+fClVd6HZWJEYGaAs48xnMXtOVV/r22jwdqAc1ws6jmikg9Vd2ZL5YhwBCA5OTkiNuv29ZLGE+NH+8Wy23fDk8+Cc88A2XLFv0+Y4IUTAuPksoEzvZ7XB03IJ7/mEmqmqWqG4DVuMRhjCnKTz/BrbfCLbfAmWfC0qXuVpMlCVPKQpkolgK1RKSmiJwA3AFMznfMRHx9o0TkdNytqPUhjMmY6KcKI0a4Jn6ff+6Sw5Il0LCh15GZGBV0ohCRE4tzYlXNBjoD04BVwCequlJE+ohIK99h04BfRSQDt0aju6r+WpzPMSau/PADtGwJ7dq5RJGa6m43WRM/E0JF9noSkUuAYbgeT+eISBLwgKo+UtR7VXUqMDXfc8/4fa9AV9+XMaYwOTkweDD08G0u+eab0KkTHBfKmwLGOMH8LRsI3AD8CqCqy7E244xcvIk27y7M6+1kTMh8953bhvSRR9xMppUr3dRXSxImTIL5m3acquZvAng4FMFEE2vbYUIuK8uNPyQlQUaGG5f44gv485+9jszEmWDajP/ou/2kvtXWjwBrQhtWZPNfZGdtO0xILFvmmvilprqZTYMGQdWqXkdl4lQwFUVH3BjCOcDPwGW+5+KWLbIzIXPggBucbtzYTX/99FMYO9aShPFUMBVFtqreEfJIooRtcWpCZt4818RvzRq47z547TU49VSvozImqIpiqYhMFZF7RSTum8ZYNWFK3Z49bnC6aVM4dMj1a3r/fUsSJmIEs8PdecDzQCNghYhMFJG4rjCsmjCl5ssvoV49N/X10UfdznMtWngdlTFHCGp+naouUNUuwMXAbtyGRsaYkvr1V7dHRMuWcPLJMH8+DBgA5ct7HZkxRykyUYhIeRG5S0Q+A5YA24HLQx6ZMbFI1e0yl5gII0e63eeWLYMmNnvORK5gBrPTgc+AV1R1bojjMSZ2bd3qurxOmACNGrmxiKQkr6MypkjBJIpzVTUn5JEYE6tU3T4RXbu66a8vv+y+Pz6Yf37GeK/Qv6ki8pqqdgM+FZGj9oAIYoc7Y8yGDW7HuRkz3KymoUOhdm2vozKmWAL9SjPG99/i7mwXk0Yu3nRE2w5jAjp82K2m/s9/ICHBzWp66CHrz2SiUqAd7pb4vq2jqkckCxHpDBzrDnhRxXo7maBlZMADD8DChW5W07vvwtlnF/0+YyJUML/e/LOA5+4v7UAilX+X2MRqFRnzUBNbQ2EKlpUFzz/vNhBaswY++gimTLEkYaJeoDGKNrhd6WqKyHi/lyoAOwt+V+yxSsIE5ZtvXBO/tDRo0wYGDoQqVbyOyphSEWiMYgluD4rqwFt+z+8BloUyqEiTW0kYc5T9+6F3b3j1Vde4b+JEaN3a66iMKVWBxig2ABuAGeELx5goMmcOPPggrF3rxiT69YNTTvE6KmNKXaFjFCIyx/ffHSLym9/XDhH5LXwhGhNhdu+Gjh2hWTM3u2nGDHjvPUsSJmYFuvWUu93p6eEIxJioMHWqm+a6ZYtbNNenj+vVZEwMK7Si8FuNfTaQoKqHgSbAQ0Bc/MvI3XvCGH75Be6+G/7+d6hYERYscPtFWJIwcSCY6bETcdugngd8ANQBRoY0qghhe08YVGHMGNfEb8wY6NULvv0WLr3U68iMCZtgms3kqGqWiNwMDFDVgSIS87OebCc7w+bN0KkTTJ4MyckwcyZcdJHXURkTdsFUFNkichtwD/C577kyoQspMlg1EcdU3eB0YqLr8Prqq26VtSUJE6eCXZl9Fa7N+HoRqQmMCm1YkcGqiTj0/ffQvLlr5HfxxW7HuW7drNOriWvBbIWaDnQBUkTkQuBHVX0h5JEZE06HD0P//q5q+OYb159p5kw4/3yvIzPGc0X+miQiTYEPgc2AAGeKyD2qOj/UwRkTFunpcP/9sGQJ3HADvP02VK/udVTGRIxg6unXgb+pagaAiNTBJY7kUAZmTMgdOgQvvQQvvACVKrmtSe+4A0S8jsyYiBJMojghN0kAqOoqETkhhDEZE3pLlrgqIj0d2raFAQPgjDO8jsqYiBTMYPa3IvKuiFzp+3qbGG8KaAvtYtjvv8Pjj0OTJrBjh5v6+vHHliSMCSCYiqIDbjD7CdwYxdfAm6EMyms2NTZGzZrlmvetX+/acLz8srvlZIwJKGCiEJGLgPOACar6SnhC8pYttItBu3bBE0/AkCFw3nkuYTRr5nVUxkSNQN1j/4Nr33EXMF1ECtrpLuZYNRFjPvvMLZwbOtTdckpLsyRhTDEFqijuAuqr6j4ROQOYCrwfnrC8ZdVEDNi+HR59FEaNcmsjJk6Exo29jsqYqBRoMPugqu4DUNXtRRxrTGRQddNc69SBcePg2WchJcWShDHHIFBFca7fXtkCnOe/d7aq3lzUyUXkeuANIAEYqqp9CznuVmAs0FhVU4IN3pgj/Pij21BoyhTX3XXYMKhb1+uojIl6gRLFLfkeDyrOiUUkAbfXdgsgE1gqIpP912T4jquAm1W1uDjnNyZPTo5r4te9u2vF8frr8MgjkJDgdWTGxIRAe2bPPMZzXwKsU9X1ACIyGmgNZOQ77jngFeDxY/w8E4/WrnX7Vs+Z45r5DRkC557rdVTGxJRQjjucBfzo9zjT91weEWkInK2qnxOAiLQXkRQRSdm+fXvpR2qiT3a2a/9dvz6kprpZTdOnW5IwJgRC2Tu5oIY5mveiyHG4PlLtijqRqg4BhgAkJydrEYcX28jFm/KmxWZs3U1itYql/RGmNKWlufYbKSnQujUMHgx/+pPXURkTs4KuKETkxGKeOxO333au6sAWv8cVgHrAbBHZCFwGTBaRsDcbnJS6mYytuwFIrFbR1lBEqoMH4ZlnoFEj+OEHtzXphAmWJIwJsWDajF8CDAMqAeeISBLwgKo+UsRblwK1fBsdbQbuANrmvqiqu4DT/T5nNvB4uGc9+a/EHvNQk3B+tCmORYtcFZGRAXff7Zr4nXaa11EZExeCqSgGAjcAvwKo6nLcjncBqWo20BmYBqwCPlHVlSLSR0RalTzk0mUrsSPcvn3w2GNw+eWwZ4+b+vrhh5YkjAmjYMYojlPVH+TIHv2Hgzm5qk7Frej2f+6ZQo5tFsw5Q8FWYkeomTPdjKYNG6BTJ7d3REUbPzIm3IKpKH703X5SEUkQkX8Ba0Icl4lnO3e6Lq/XXOP2qp4zB956y5KEMR4JpqLoiLv9dA7wMzDD91zUsllOEWziRFc9bNsG//439OoF5cp5HZUxca3IRKGq23AD0TEjd5ZTYrWKNsspUvz8s1tNPXYsJCW5rq+NGnkdlTGG4GY9vYff+odcqto+JBGFmM1yijCq8NFH8K9/wd698Pzzbu+IMmW8jswY4xPMracZft+XBW7iyBXXUcVmOUWQTZugQwf44gu3NemwYa7rqzEmogRz62mM/2MR+RCYHrKIwsBmOXksJwfeeceNQajCwIFuXMKa+BkTkUrSwqMm8OfSDsTEiTVr3IymuXOhRQvXxK9GDa+jMsYEUOT0WBHZISK/+b524qqJ/4Q+tNKXOz5hPJCdDS+/7Jr4rVgB//0vTJtmScKYKBCwohC3yi4J14IDIEdVS70pX7jY+IRHUlNd+41vv4WbbnJrIqpV8zoqY0yQAlYUvqQwQVUP+76iNknksvGJMDpwAHr2hORk2LzZbU06frwlCWOiTDArs5eIyMUhj8TElgULoGFDePFF18QvIwNuyb9pojEmGhSaKEQk97bUlbhksVpEvhWRZSLybXjCKz02PhEme/dCly5w5ZXw++/w5ZcwfDhUrux1ZMaYEgo0RrEEuBj4vzDFElI2PhEGX30F7du79REPP+yqiQoVvI7KGHOMAiUKAVDV78MUS8jZ+ESI/PYbdOvmKocLLoCvv3YVhTEmJgRKFGeISNfCXlTV/iGIx0SbTz911cMvv8CTT7od6MqW9ToqY0wpCpQoEoDyFLz3tYl3P/0EnTu7RNGggWvD0bCh11EZY0IgUKLYqqp9whaJiQ6qMGIEdO3qBqtfesnddrImfsbErCLHKIzJs3EjPPSQG7S+8koYOtSNSRhjYlqgdRTNwxZFiNnU2GOUkwNvvgn16rn1EYMGuV3nLEkYExcKrShUNWZ+strU2GPw3Xeuid/8+XDddfDuu/Bn6wlpTDwJZmV2TLCpscWUleXWQSQluVXVI0a4AWtLEsbEnZK0GTex7ttvXRO/1FS47TZ326lqVa+jMsZ4JG4qChOE/fvdWohLLnHTX8ePh08+sSRhTJyzisI48+a5KmLNGvjnP+HVV+HUU72OyhgTAWK+orAZT0XYs8ctnGvaFA4dgunT3d7VliSMMT4xnyhsxlMAX3wBdevC4MHw6KNu57lrrvE6KmNMhInJW08jF2/KSxAZW3fbjKf8fv0VHnsMPvwQ6tRxU1+bNPE6KmNMhIrJimJS6mYytu4GILFaRasmcqnC2LGQmAijRsHTT8OyZZYkjDEBxWRFAS5BjHnIfgDm2boVOnWCiROhUSPXhiMpyeuojDFRICYrCuNHFd5/391i+vJLeOUVWLTIkoQxJmgxlyhslpOf9evh2mvdtNekJFi+HLp3h+NjtpA0xoRAzCUKm+UEHD4MAwbARRfB4sXw9tswaxbUru11ZMaYKBSTv1rG9SynjAxXQSxaBC1buiZ+Z5/tdVTGmCgWcxVF3Dp0CJ57zu0yt3YtfPQRTJliScIYc8xCmihE5HoRWS0i60SkRwGvdxWRDBFJE5GZImKtSUsiJQUaN3b7Vd98s6sq7roLxPaeMsYcu5AlChFJAN4CWgKJwJ0ikpjvsGVAsqrWB8YBr4Qqnpi0fz888QRcein88gtMmuTWR1Sp4nVkxpgYEsqK4hJgnaquV9VDwGigtf8BqjpLVX/3PVwEVC/ph41cvIk27y7MW2gX8+bMgfr1oV8/NyaxciW0auV1VMaYGBTKRHEW8KPf40zfc4W5H/iioBdEpL2IpIhIyvbt2wt8c+5q7Jhfib17N3TsCM2auS1KZ86EIUPglFO8jswYE6NCOeupoBvkWuCBIncDycBfC3pdVYcAQwCSk5MLPAfEwWrsKVOgQwfYsgW6doU+feDkk72OyhgT40JZUWQC/lNuqgNb8h8kItcAPYFWqnowhPFEr19+gbvvhhtugEqVYMECeO01SxLGmLAIZaJYCtQSkZoicgJwBzDZ/wARaQi8i0sS20IYS3RShdE+vfCHAAAUsklEQVSjXfuNTz6BXr3cNqWXXup1ZMaYOBKyW0+qmi0inYFpQALwvqquFJE+QIqqTgb6AeWBseKmcm5SVRuRBdi82TXxmzzZTX0dNsyttDbGmDAL6cpsVZ0KTM333DN+39suOfmpwtCh8PjjkJXltiT9178gIcHryIwxcSomW3hEre+/hwcfdH2ZmjWD996D88/3OipjTJyzFh6R4PBh6N/f3Vr65hvXn2nmTEsSxpiIYBWF19LT3YK5JUvcrKa334bqJV53aIwxpc4qCq8cOgTPPgsXX+z2jRg1yg1cW5IwxkQYqyi8sGSJqyLS06FtW3jjDTj9dK+jMsaYAllFEU6//w7dukGTJrBjB3z2GXz8sSUJY0xEi4lEERXbn86a5Qar+/d3M5tWrnRjEsYYE+FiIlFE9Panu3ZB+/Zw9dVuf4hZs+Cdd1wrDmOMiQIxkSggQrc//ewzSEx0q6q7d4e0NLc+whhjokjMJIqIsn073Hmn2x/itNNg8WJ45RU46SSvIzPGmGKzRFGaVGHkSNfE79NPXRvwlBRITvY6MmOMKTGbHltafvzRbSg0ZYrr7jpsGNSt63VUxhhzzKyiOFY5OW5wum5dN1D9+uswf74lCWNMzLCK4lisXeumus6ZA82buy1Jzz3X66iMMaZURX1F4ckaiuxs6NcP6teH1FTXFnz6dEsSxpiYFPUVRdjXUKSlufYbKSnQujUMHgx/+lN4Ptt4Kisri8zMTA4cOOB1KMYUqmzZslSvXp0yZcqU2jmjOlHkVhNhWUNx8CC88AK89BJUruy2Jr31VreIzsSFzMxMKlSoQI0aNRD7/24ikKry66+/kpmZSc2aNUvtvFF96yls1cTChdCwITz3nFsfkZEBt91mSSLOHDhwgNNOO82ShIlYIsJpp51W6lVv1CaKsFQT+/a5bUivuAL27oWpU+GDD9wiOhOXLEmYSBeKv6NRe+sp5NXEjBluRtPGjdCpk7vlVLFiaD7LGGMiWNRWFBCi/k47d7rB6hYtoEwZN/X1rbcsSZiIkJCQQIMGDahXrx433ngjO3fuzHtt5cqVXH311dSuXZtatWrx3HPPoap5r3/xxRckJydTp04dLrzwQh5//HEv/gjF8vrrr1O2bFl27dqV99zs2bOpVKkSDRs2POrPMXz4cM444wwaNGiQ95WRkRHwfMfit99+o0WLFtSqVYsWLVqwY8eOAo974oknqFu3LnXq1KFLly5H/H8BaNWqFfXq1ct7/PTTT1O/fn0aNGjAtddey5YtWwD47rvvaNKkCSeeeCKvvvpqqfwZghHViaLUTZzomviNGAE9esDy5fCXv3gdlTF5ypUrR2pqKunp6VSuXJm33noLgP3799OqVSt69OjBmjVrWL58OQsWLGDw4MEApKen07lzZz766CNWrVpFeno655bydO7s7OxSPR/AqFGjaNy4MRMmTDji+aZNm7Js2TKWLVvG559/zvz58/Nea9OmDampqXlfiYmJRZ6vpPr27Uvz5s1Zu3YtzZs3p2/fvkcds2DBAubPn09aWhrp6eksXbqUOXPm5L0+fvx4ypcvf8R7unfvTlpaGqmpqdxwww306dMHgMqVKzNw4MCwJ/movPXkPz5RKn7+GR55BMaOhaQk1/W1UaPSObeJSc9+tpKMLbtL9ZyJf6pIrxuDX9HfpEkT0tLSABg5ciRXXHEF1157LQAnnXQSgwYNolmzZjz88MO88sor9OzZkwsvvBCA448/nk6dOh11zr179/LII4+QkpKCiNCrVy9uueUWypcvz969ewEYN24cn3/+OcOHD6ddu3ZUrlyZZcuW0aBBAyZMmEBqaiqnnHIKAOeffz7z58/nuOOOo0OHDmzatAmAAQMGcMUVVwT8833//ffs3buXfv368eKLL9KuXbujjilXrhwNGjRg8+bNRV6vYM5XXJMmTWL27NkA3HvvvTRr1oyXX375iGNEhAMHDnDo0CFUlaysLKpWrQq4692/f3+GDBnC7bffnveein53MPbt25c37lClShWqVKnClClTjjn24ojKRFFq4xOq8OGHbsB63z43/bV7d3fLyZgIdvjwYWbOnMn9998PuNtOjfL9cnPeeeexd+9edu/eTXp6Ot26dSvyvM899xyVKlVixYoVAIXeSvG3Zs0aZsyYQUJCAjk5OUyYMIH77ruPxYsXU6NGDapWrUrbtm157LHHuPLKK9m0aRPXXXcdq1atCnjeUaNGceedd9K0aVNWr17Ntm3bqFKlyhHH7Nixg7Vr1/IXv8p/zJgxzJs3L+/xwoULKVeuXFDn27NnD02bNi0wnpEjRx5RnQD8/PPPVKtWDYBq1aqxbdu2o97XpEkTrrrqKqpVq4aq0rlzZ+rUqQO4W0zdunXjpAI6S/fs2ZMPPviASpUqMWvWrECXKuSiMlFAKYxPbNoEDz0EX37ptiYdNsx1fTUmCMX5zb807d+/nwYNGrBx40YaNWpEixYtADd/vrDZLsWZBTNjxgxGjx6d9/jUU08t8j233XYbCQkJgLvt06dPH+677z5Gjx5NmzZt8s7rP1awe/du9uzZQ4UKFQo97+jRo5kwYQLHHXccN998M2PHjuXhhx8GYO7cudSvX5/Vq1fTo0cPzjzzzLz3tWnThkGDBhXrfLkqVKhAampqkX/m4li3bh2rVq0iMzMTgBYtWvD1119TsWJF1q1bx+uvv87GjRuPet8LL7zACy+8wEsvvcSgQYN49tlnSzWu4oi/MYqcHDc4XbcuzJ0LAwe6/1qSMFEgd4zihx9+4NChQ3ljFHXr1iUlJeWIY9evX0/58uWpUKECdevW5Ztvviny/IUlHP/n8s/RP/nkk/O+b9KkCevWrWP79u1MnDiRm2++GYCcnBwWLlyYN26wefPmgEkiLS2NtWvX0qJFC2rUqMHo0aMZNWpU3utNmzYlLS2NFStW8Pbbbxf5w72o8+Xas2fPEQPhhQ2K56patSpbt24FYOvWrUdVKAATJkzgsssuo3z58pQvX56WLVuyaNEiFi5cyDfffEONGjW48sorWbNmDc0K2Nisbdu2fPrppwH/fKEWX4li9Wr461+hc2dXRaSnu7EJ329DxkSLSpUqMXDgQF599VWysrK46667mDdvHjNmzABc5dGlSxeeeOIJwA2Ovvjii6xZswZwP7j79+9/1HmvvfbaI34bz731VLVqVVatWpV3a6kwIsJNN91E165dqVOnDqf51hzlP2/uD/YlS5bwj3/846jzjBo1it69e7Nx40Y2btzIli1b2Lx5Mz/88MMRx9WuXZsnn3zyqHGBkp4vt6Io6Cv/bSdws5VGjBgBwIgRI2jduvVRx5xzzjnMmTOH7OxssrKymDNnDnXq1KFjx45s2bKFjRs3Mm/ePGrXrp033rF27dq890+ePDlvbMkr8ZEosrOhb183UJ2eDv/9L0ybBjVqeB2ZMSXWsGFDkpKSGD16NOXKlWPSpEk8//zzXHDBBVx00UU0btyYzp07A1C/fn0GDBjAnXfeSZ06dahXr17eb8L+nnrqKXbs2EG9evVISkrKuzfet29fbrjhBq6++uq8e/KFadOmDR999FHebSeAgQMHkpKSQv369UlMTOSdd94BYNOmTZQrV+6oc4wePZqbbrrpiOduuummI26L5erQoQNff/01GzZsANwYhX8lsGDBgmKdrzh69OjB9OnTqVWrFtOnT6dHjx4ApKSk8MADDwBw6623ct5553HRRReRlJREUlISN954Y5HnrVevHvXr1+err77ijTfeAOCnn36ievXq9O/fn+eff57q1auze3fpTqooiOSfzxvpkpOT9bwH3wRgzENNin5DaqpbF/Htt3Dzze62k9/9TGOCtWrVqrxBSFM6unfvzj333EP9+vW9DiWmFPR3VUS+UdUSbbcZtYPZRTpwwPVmevllOP10GDcObrnF66iMMX769evndQgmCLGZKObPd1XE6tVw773Qv7/r+GqMMabYom6M4rd9hwrfqGjvXujSBZo2hf373dTX4cMtSZhSE223ak38CcXf0ahLFDt/zwIKWGz31VdQrx4MGuRmNaWnw3XXeRChiVVly5bl119/tWRhIlbufhRly5Yt1fNG3a2nfYeyaeG/2O6336BbN1c5XHCBWxNRRGsAY0qievXqZGZmsn37dq9DMaZQuTvclaaoSxTgV018+ik8/DD88gv85z/w9NNQypnUmFxlypQp1V3DjIkWIb31JCLXi8hqEVknIj0KeP1EERnje32xiNQo6pwnn3A8bc8p42Yw3Xqr2686JcX1abIkYYwxpS5kFYWIJABvAS2ATGCpiExWVf918PcDO1T1fBG5A3gZaHP02f5Q+cBu1wp8/363mVC3btbEzxhjQiiUFcUlwDpVXa+qh4DRQP717a2BEb7vxwHNpYgOZqds2+IGrZcvd3tGWJIwxpiQCuUYxVnAj36PM4FLCztGVbNFZBdwGvCL/0Ei0h5o73t4UObNS8fj3icR4nTyXas4ZtfiD3Yt/mDX4g8XlPSNoUwUBVUG+ecVBnMMqjoEGAIgIiklXYYea+xa/MGuxR/sWvzBrsUfRCSl6KMKFspbT5nA2X6PqwNbCjtGRI4HKgGFrKYzxhjjhVAmiqVALRGpKSInAHcAk/MdMxm41/f9rcD/1FYzGWNMRAnZrSffmENnYBqQALyvqitFpA+QoqqTgWHAhyKyDldJ3BHEqYeEKuYoZNfiD3Yt/mDX4g92Lf5Q4msRdW3GjTHGhFfU9XoyxhgTXpYojDHGBBSxiSIU7T+iVRDXoquIZIhImojMFJE/exFnOBR1LfyOu1VEVERidmpkMNdCRG73/d1YKSIjwx1juATxb+QcEZklIst8/07+5kWcoSYi74vINhFJL+R1EZGBvuuUJiIXB3ViVY24L9zg9/fAucAJwHIgMd8xnYB3fN/fAYzxOm4Pr8VVwEm+7zvG87XwHVcB+BpYBCR7HbeHfy9qAcuAU32Pq3gdt4fXYgjQ0fd9IrDR67hDdC3+AlwMpBfy+t+AL3Br2C4DFgdz3kitKELS/iNKFXktVHWWqv7ue7gIt2YlFgXz9wLgOeAV4EA4gwuzYK7Fg8BbqroDQFW3hTnGcAnmWihQ0fd9JY5e0xUTVPVrAq9Faw18oM4i4BQRqVbUeSM1URTU/uOswo5R1Wwgt/1HrAnmWvi7H/cbQywq8lqISEPgbFX9PJyBeSCYvxe1gdoiMl9EFonI9WGLLryCuRa9gbtFJBOYCjwSntAiTnF/ngCRux9FqbX/iAFB/zlF5G4gGfhrSCPyTsBrISLHAa8D7cIVkIeC+XtxPO72UzNclTlXROqp6s4QxxZuwVyLO4HhqvqaiDTBrd+qp6o5oQ8vopTo52akVhTW/uMPwVwLROQaoCfQSlUPhim2cCvqWlQA6gGzRWQj7h7s5Bgd0A7238gkVc1S1Q3AalziiDXBXIv7gU8AVHUhUBbXMDDeBPXzJL9ITRTW/uMPRV4L3+2Wd3FJIlbvQ0MR10JVd6nq6apaQ1Vr4MZrWqlqiZuhRbBg/o1MxE10QEROx92KWh/WKMMjmGuxCWgOICJ1cIkiHve0nQz8wzf76TJgl6puLepNEXnrSUPX/iPqBHkt+gHlgbG+8fxNqtrKs6BDJMhrEReCvBbTgGtFJAM4DHRX1V+9izo0grwW3YD3ROQx3K2WdrH4i6WIjMLdajzdNx7TCygDoKrv4MZn/gasA34H7gvqvDF4rYwxxpSiSL31ZIwxJkJYojDGGBOQJQpjjDEBWaIwxhgTkCUKY4wxAVmiMBFHRA6LSKrfV40Ax9YorFNmMT9ztq/76HJfy4sLSnCODiLyD9/37UTkT36vDRWRxFKOc6mINAjiPf8SkZOO9bNN/LJEYSLRflVt4Pe1MUyfe5eqJuGaTfYr7ptV9R1V/cD3sB3wJ7/XHlDVjFKJ8o84BxNcnP8CLFGYErNEYaKCr3KYKyLf+r4uL+CYuiKyxFeFpIlILd/zd/s9/66IJBTxcV8D5/ve29y3h8EKX6//E33P95U/9gB51fdcbxF5XERuxfXc+tj3meV8lUCyiHQUkVf8Ym4nIm+WMM6F+DV0E5G3RSRF3N4Tz/qe64JLWLNEZJbvuWtFZKHvOo4VkfJFfI6Jc5YoTCQq53fbaYLvuW1AC1W9GGgDDCzgfR2AN1S1Ae4HdaavXUMb4Arf84eBu4r4/BuBFSJSFhgOtFHVi3CdDDqKSGXgJqCuqtYHnvd/s6qOA1Jwv/k3UNX9fi+PA272e9wGGFPCOK/HtenI1VNVk4H6wF9FpL6qDsT18rlKVa/ytfJ4CrjGdy1TgK5FfI6JcxHZwsPEvf2+H5b+ygCDfPfkD+P6FuW3EOgpItWB8aq6VkSaA42Apb72JuVwSacgH4vIfmAjrg31BcAGVV3je30E8DAwCLfXxVARmQIE3dJcVbeLyHpfn521vs+Y7ztvceI8Gdeuwn+HsttFpD3u33U13AY9afnee5nv+fm+zzkBd92MKZQlChMtHgN+BpJwlfBRmxKp6kgRWQz8HZgmIg/g2iqPUNUng/iMu/wbCIpIgfub+HoLXYJrMncH0Bm4uhh/ljHA7cB3wARVVXE/tYOOE7eLW1/gLeBmEakJPA40VtUdIjIc1/guPwGmq+qdxYjXxDm79WSiRSVgq2//gHtwv00fQUTOBdb7brdMxt2CmQncKiJVfMdUluD3FP8OqCEi5/se3wPM8d3Tr6SqU3EDxQXNPNqDa3tekPHA/+H2SBjje65YcapqFu4W0mW+21YVgX3ALhGpCrQsJJZFwBW5fyYROUlECqrOjMljicJEi8HAvSKyCHfbaV8Bx7QB0kUkFbgQt+VjBu4H6lcikgZMx92WKZKqHsB11xwrIiuAHOAd3A/dz33nm4OrdvIbDryTO5id77w7gAzgz6q6xPdcseP0jX28Bjyuqstx+2OvBN7H3c7KNQT4QkRmqep23IysUb7PWYS7VsYUyrrHGmOMCcgqCmOMMQFZojDGGBOQJQpjjDEBWaIwxhgTkCUKY4wxAVmiMMYYE5AlCmOMMQH9P62Qc2uM/sILAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_preds , test_labels = epoch_losses_t[1][8], epoch_losses_s[1][11]\n",
    "\n",
    "plot_roc_and_auc_score(test_preds, test_labels, 'GRU-D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}