SFINN_repo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10fdc9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "NVIDIA GeForce RTX 3090\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker\n",
    "\n",
    "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
    "from sklearn import linear_model\n",
    "from tqdm import tqdm\n",
    "import pandas as pd  \n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(torch.cuda.is_available())\n",
    "print(torch.cuda.get_device_name(0))\n",
    "time_START = time.time()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e6d281",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c0c31d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_days = 90\n",
    "BatchSize = 2048\n",
    "LearningRate = 1e-3\n",
    "NumEpochs = 500\n",
    "WeightDecay = 0.01\n",
    "StartEpoch = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173ffedb",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef9060a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"./data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "860545f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12609, 6)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(path + '/GSPC.csv', \n",
    "                 usecols = ['Date','Open','High','Low','Close','Volume'])\n",
    "\n",
    "data = df.to_numpy()\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19c7f8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[108.970001 108.93 108.279999 ... 3984.600098 4077.429932 4104.879883]\n"
     ]
    }
   ],
   "source": [
    "print(data[:, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9a7c9d",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32b46f9",
   "metadata": {},
   "source": [
    "## Convert to Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5d64f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stock_return(x):\n",
    "    rt = np.zeros((len(x)-1, 1))\n",
    "    for i in range(len(rt)):\n",
    "        rt[i] = np.log( (x[i+1]+1e-8) / (x[i]+1e-8) )\n",
    "    \n",
    "    return rt\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66ef9675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12609,)\n",
      "[108.970001 108.93 108.279999 ... 3984.600098 4077.429932 4104.879883]\n",
      "(12608, 1)\n",
      "(12608,)\n",
      "[[-0.00036715]\n",
      " [-0.00598502]\n",
      " [-0.00527803]\n",
      " ...\n",
      " [ 0.01506295]\n",
      " [ 0.02302992]\n",
      " [ 0.00670961]]\n"
     ]
    }
   ],
   "source": [
    "P_Close = data[:, 3]\n",
    "\n",
    "print(P_Close.shape)\n",
    "print(P_Close)\n",
    "\n",
    "Return_Close = stock_return(P_Close)\n",
    "\n",
    "print(Return_Close.shape)\n",
    "print(Return_Close.flatten().shape)\n",
    "print(Return_Close)\n",
    "\n",
    "del P_Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb407949",
   "metadata": {},
   "outputs": [],
   "source": [
    "def DW_test_np(x): # Durbin Watson test\n",
    "    indexing = np.arange(0, len(x), 1).reshape(-1,1)\n",
    "    \n",
    "    reg = linear_model.LinearRegression()\n",
    "    reg.fit(indexing, x.reshape(-1,1))\n",
    "    \n",
    "    pred = reg.coef_ * indexing + reg.intercept_\n",
    "    \n",
    "    den = 0\n",
    "    num = 0\n",
    "\n",
    "    for i in range(len(pred) - 1):\n",
    "        den += ((pred[i+1] - x[i+1]) - (pred[i] - x[i]))**2\n",
    "        num += (pred[i+1] - x[i+1])**2\n",
    "\n",
    "    DW = den / num\n",
    "    \n",
    "    return DW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6da240aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(DW_test_np(Return_Close))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f299e31c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# indexing = np.arange(0, 12608, 1).reshape(-1,1)\n",
    "# gfg = np.concatenate((indexing, Return_Close), axis = 1)\n",
    "\n",
    "# print(gfg.shape)\n",
    "# print(gfg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f56e1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import linear_model\n",
    "\n",
    "# reg = linear_model.LinearRegression()\n",
    "\n",
    "# reg.fit(indexing, Return_Close)\n",
    "\n",
    "# print(indexing.shape)\n",
    "# print(Return_Close.shape)\n",
    "\n",
    "# print(reg.coef_)\n",
    "# print(reg.intercept_)\n",
    "\n",
    "# pred = reg.coef_ * indexing + reg.intercept_\n",
    "\n",
    "# print(pred)\n",
    "# print(pred.shape)\n",
    "\n",
    "# den = 0\n",
    "# num = 0\n",
    "\n",
    "# for i in range(len(pred) - 1):\n",
    "#     den += ((pred[i+1] - Return_Close[i+1]) - (pred[i] - Return_Close[i]))**2\n",
    "\n",
    "# for i in range(len(pred) - 1):\n",
    "#     num += (pred[i+1] - Return_Close[i+1])**2\n",
    "    \n",
    "# dd = den / num\n",
    "\n",
    "# print(dd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "093bd3f0",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df8f0534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12608, 1)\n",
      "[[-0.06391003]\n",
      " [-0.61208326]\n",
      " [-0.54309806]\n",
      " ...\n",
      " [ 1.441709  ]\n",
      " [ 2.21909913]\n",
      " [ 0.62661707]]\n"
     ]
    }
   ],
   "source": [
    "Return_Close_Nor = (Return_Close - Return_Close.mean(axis=0)) / Return_Close.std(axis=0) # Normalize\n",
    "\n",
    "print(Return_Close_Nor.shape)\n",
    "print(Return_Close_Nor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "294cdec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'torch.Tensor'>\n"
     ]
    }
   ],
   "source": [
    "Return_Mean = Return_Close.mean(axis=0)\n",
    "Return_Std = Return_Close.std(axis=0)\n",
    "\n",
    "R_Mean = torch.tensor(Return_Mean).cuda()\n",
    "R_Std = torch.tensor(Return_Std).cuda()\n",
    "print(type(Return_Mean))\n",
    "print(type(R_Mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885bc9f9",
   "metadata": {},
   "source": [
    "## Set Time Series Sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3989e9fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12518\n",
      "8763\n",
      "3755\n",
      "(8763, 90, 1)\n",
      "(8763, 1)\n",
      "(3755, 90, 1)\n",
      "(3755, 1)\n",
      "[-0.52024491]\n",
      "[0.69115171]\n",
      "[-0.11805309]\n",
      "[0.62661707]\n"
     ]
    }
   ],
   "source": [
    "time_span_total = Return_Close.shape[0] - n_days\n",
    "time_span_train = round(time_span_total * 0.7)\n",
    "time_span_test = time_span_total - time_span_train\n",
    "\n",
    "print(time_span_total)\n",
    "print(time_span_train)\n",
    "print(time_span_test)\n",
    "\n",
    "Return_Close_Data_Train_Nor = np.zeros((time_span_train, n_days, 1))\n",
    "Return_Close_Label_Train_Nor = np.zeros((time_span_train, 1))\n",
    "Return_Close_Label_Train = np.zeros((time_span_train, 1))\n",
    "\n",
    "Return_Close_Data_Test_Nor = np.zeros((time_span_test, n_days, 1))\n",
    "Return_Close_Label_Test_Nor = np.zeros((time_span_test, 1))\n",
    "Return_Close_Label_Test = np.zeros((time_span_test, 1))\n",
    "\n",
    "for i in range(time_span_train):\n",
    "    for j in range(n_days):\n",
    "        Return_Close_Data_Train_Nor[i, j, 0] = Return_Close_Nor[i + j]\n",
    "    Return_Close_Label_Train_Nor[i, 0] = Return_Close_Nor[i + n_days]\n",
    "    Return_Close_Label_Train[i, 0] = Return_Close[i + n_days]\n",
    "\n",
    "for i in range(time_span_test):\n",
    "    for j in range(n_days):\n",
    "        Return_Close_Data_Test_Nor[i, j, 0] = Return_Close_Nor[time_span_train + i + j]\n",
    "    Return_Close_Label_Test_Nor[i, 0] = Return_Close_Nor[time_span_train + i + n_days]\n",
    "    Return_Close_Label_Test[i, 0] = Return_Close[time_span_train + i + n_days]\n",
    "    \n",
    "print(Return_Close_Data_Train_Nor.shape)\n",
    "print(Return_Close_Label_Train_Nor.shape)\n",
    "print(Return_Close_Data_Test_Nor.shape)\n",
    "print(Return_Close_Label_Test_Nor.shape)\n",
    "\n",
    "print(Return_Close_Label_Train_Nor[1])\n",
    "print(Return_Close_Label_Train_Nor[-1])\n",
    "print(Return_Close_Label_Test_Nor[1])\n",
    "print(Return_Close_Label_Test_Nor[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0875ad1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_x_train = torch.Tensor(Return_Close_Data_Train_Nor).cuda()\n",
    "tensor_y_train = torch.Tensor(Return_Close_Label_Train_Nor).cuda()\n",
    "\n",
    "train_dataset = torch.utils.data.TensorDataset(tensor_x_train, tensor_y_train)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = BatchSize, shuffle=False)\n",
    "\n",
    "tensor_x_val = torch.Tensor(Return_Close_Data_Test_Nor).cuda()\n",
    "tensor_y_val = torch.Tensor(Return_Close_Label_Test_Nor).cuda()\n",
    "\n",
    "val_dataset = torch.utils.data.TensorDataset(tensor_x_val, tensor_y_val)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size = BatchSize, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c9c310",
   "metadata": {},
   "source": [
    "# Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce447558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network(\n",
      "  (lstm): LSTM(1, 512, num_layers=4, batch_first=True)\n",
      "  (classification): Sequential(\n",
      "    (0): Linear(in_features=512, out_features=256, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Dropout(p=0.1, inplace=False)\n",
      "    (3): Linear(in_features=256, out_features=128, bias=True)\n",
      "    (4): ReLU()\n",
      "    (5): Dropout(p=0.1, inplace=False)\n",
      "    (6): Linear(in_features=128, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class Network(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Network, self).__init__()\n",
    "        \n",
    "#         self.embedding = torch.nn.Sequential(            \n",
    "#             nn.Conv1d(in_channels = 1, \n",
    "#                       out_channels = 128, \n",
    "#                       kernel_size = 5, \n",
    "#                       stride = 2,\n",
    "#                       padding = 2, \n",
    "#                       bias = False),\n",
    "#             nn.BatchNorm1d(128),\n",
    "#             nn.ReLU(),\n",
    "#             nn.Dropout(0.3),\n",
    "            \n",
    "#             nn.Conv1d(in_channels = 128, \n",
    "#                       out_channels = 256, \n",
    "#                       kernel_size = 3, \n",
    "#                       stride = 2,\n",
    "#                       padding = 1, \n",
    "#                       bias = False),\n",
    "#             nn.BatchNorm1d(256),\n",
    "#             nn.ReLU()\n",
    "#         )\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_size = 1, \n",
    "                            hidden_size = 512, \n",
    "                            num_layers = 4,\n",
    "                            bias = True,\n",
    "                            batch_first = True,\n",
    "                            dropout = 0, \n",
    "                            bidirectional = False) \n",
    "\n",
    "        self.classification = torch.nn.Sequential(\n",
    "            #nn.Linear(512, 1) \n",
    "            \n",
    "            nn.Linear(512, 256), \n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.1),\n",
    "            \n",
    "            nn.Linear(256, 128), \n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.1),\n",
    "            \n",
    "            nn.Linear(128, 1)\n",
    "        ) \n",
    "        \n",
    "    def forward(self, x):\n",
    "        out1, (out2, out3) = self.lstm(x)\n",
    "        out = self.classification(out1[:, -1, :])\n",
    "        return out\n",
    "    \n",
    "model = Network().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d160b77e",
   "metadata": {},
   "source": [
    "# Loss Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4cabe2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Custom_MSE(y, y_t, reduction = 'mean'):\n",
    "    assert len(y) == len(y_t)\n",
    "    \n",
    "    if reduction == 'mean':\n",
    "        loss = torch.mean( torch.pow( (y - y_t) , 2) )\n",
    "    elif reduction == 'sum':\n",
    "        loss = torch.sum( torch.pow( (y - y_t) , 2) )\n",
    "    else:\n",
    "        print(\"ERROR. \")\n",
    "        exit()\n",
    "    return loss\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fff7b0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Skewness(y):\n",
    "    den = torch.mean( torch.pow( (y - torch.mean(y)), 3) )\n",
    "    num = torch.pow( torch.mean( torch.pow( (y - torch.mean(y)), 2) ), 3/2 )\n",
    "    return den / num \n",
    "\n",
    "def Kurtosis(y):\n",
    "    den = torch.mean( torch.pow( (y - torch.mean(y)), 4) )\n",
    "    num = torch.pow( torch.mean( torch.pow( (y - torch.mean(y)), 2) ), 2 )\n",
    "    return den / num \n",
    "\n",
    "def DW_test(x): # Durbin Watson test\n",
    "    indexing = np.arange(0, len(x), 1).reshape(-1,1)\n",
    "    x_np = x.cpu().detach().numpy().reshape(-1,1)\n",
    "    \n",
    "    reg = linear_model.LinearRegression()\n",
    "    reg.fit(indexing, x_np)\n",
    "    \n",
    "    pred = reg.coef_ * indexing + reg.intercept_\n",
    "    \n",
    "    den = 0\n",
    "    num = 0\n",
    "\n",
    "    for i in range(len(pred) - 1):\n",
    "        den += ((pred[i+1] - x_np[i+1]) - (pred[i] - x_np[i]))**2\n",
    "        num += (pred[i+1] - x_np[i+1])**2\n",
    "\n",
    "    DW = torch.tensor(den / num).cuda()\n",
    "    \n",
    "    return DW\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1fbc8d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Heavy_Tail_S_Loss(y, data_mean, data_std):  \n",
    "    y_un_nor = data_std * y + data_mean\n",
    "    S = Skewness(y_un_nor)\n",
    "\n",
    "    loss = 1e-3 * (1 / torch.abs(S)).cuda()\n",
    "    return loss\n",
    "\n",
    "\n",
    "def Heavy_Tail_K_Loss(y, data_mean, data_std):\n",
    "    y_un_nor = data_std * y + data_mean\n",
    "    K = Kurtosis(y_un_nor)\n",
    "    \n",
    "    loss = torch.pow(2, -(K) ).cuda()\n",
    "    return loss\n",
    "\n",
    "\n",
    "def Auto_Correlation_Loss(y):\n",
    "    DW = DW_test(y)\n",
    "    loss = 5 ** (torch.abs(DW - 2) / 0.5)\n",
    "    \n",
    "    return loss\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e1d9e331",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stock_return_np(x):\n",
    "    rt = np.zeros((len(x)-1, 1))\n",
    "    for i in range(len(rt)):\n",
    "        rt[i] = np.log( x[i+1] / x[i] )\n",
    "       \n",
    "    return rt\n",
    "\n",
    "def Skewness_np(y):\n",
    "    den = np.mean( np.power( (y - np.mean(y)), 3) )\n",
    "    num = np.power( np.mean( np.power( (y - np.mean(y)), 2) ), 3/2 )\n",
    "    return den / num \n",
    "\n",
    "def Kurtosis_np(y):\n",
    "    den = np.mean( np.power( (y - np.mean(y)), 4) )\n",
    "    num = np.power( np.mean( np.power( (y - np.mean(y)), 2) ), 2 )\n",
    "    return den / num \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef67c524",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Custom_Loss(y, y_t, data_mean, data_std):\n",
    "    \n",
    "    loss = Custom_MSE(y, y_t) \n",
    "    + 12e-1 * Heavy_Tail_S_Loss(y, data_mean, data_std) \n",
    "    + 12e-1 * Heavy_Tail_K_Loss(y, data_mean, data_std)\n",
    "    + 100e-1 * Auto_Correlation_Loss(y)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "893a2af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def R2_Loss(output, target):\n",
    "    ss_tot = torch.sum((target - torch.mean(target)) ** 2)\n",
    "    ss_res = torch.sum((target - output) ** 2)\n",
    "    r2 = 1 - ss_res / ss_tot\n",
    "    return r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d63f643",
   "metadata": {},
   "source": [
    "# Training Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "557eced2",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion_train = Custom_Loss\n",
    "#criterion_val = torch.nn.MSELoss(reduction='mean')\n",
    "criterion_val = R2_Loss\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr = LearningRate, weight_decay = WeightDecay)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor = 0.5, patience = 5, verbose = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7172180",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_folder(folder_path):\n",
    "#     if not os.path.exists(folder_path):\n",
    "#         os.mkdir(folder_path)\n",
    "        \n",
    "# save_path = \"./model/model_SOTA\"\n",
    "# create_folder(save_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "188846d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_data, optimizer, criterion, data_mean, data_std):\n",
    "    model.train()\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(train_data, batch_size = BatchSize, shuffle=False)\n",
    "        \n",
    "    batch_bar = tqdm(total=len(train_loader), dynamic_ncols=True, leave=False, position=0, desc='Train')\n",
    "    \n",
    "    otpt = []\n",
    "    \n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        \n",
    "        data = data.cuda()\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        \n",
    "        #print(output.shape)\n",
    "        \n",
    "        for i in range(len(output)):\n",
    "            otpt.append(output[i].detach().cpu().numpy())\n",
    "            \n",
    "        loss = criterion(output, target, data_mean, data_std)\n",
    "        print(\"Train Loss: \", loss.item(), \". \")\n",
    "        \n",
    "        batch_bar.set_postfix(\n",
    "            loss=\"{:.05f}\".format(float(loss)),\n",
    "            lr=\"{:.05f}\".format(float(optimizer.param_groups[0]['lr'])) )  \n",
    "            \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "            \n",
    "        batch_bar.update()\n",
    "        \n",
    "    return otpt\n",
    "\n",
    "\n",
    "def val(model, val_data, criterion, data_mean, data_std):\n",
    "\n",
    "    with torch.no_grad():\n",
    "        \n",
    "        model.eval()\n",
    "        true_y_list = []\n",
    "        pred_y_list = []\n",
    "        \n",
    "        otpt = []\n",
    "\n",
    "        val_loader = torch.utils.data.DataLoader(val_data, batch_size = BatchSize, shuffle=False)\n",
    "        \n",
    "        for data, true_y in val_loader:\n",
    "            data = data.cuda()\n",
    "            true_y = true_y.long().cuda()                \n",
    "                \n",
    "            output = model(data)\n",
    "            pred_y = output\n",
    "\n",
    "            pred_y_list.extend(pred_y.tolist())\n",
    "            true_y_list.extend(true_y.tolist())\n",
    "            \n",
    "            for i in range(len(output)):\n",
    "                otpt.append(output[i].detach().cpu().numpy())\n",
    "\n",
    "            loss = criterion(output, true_y)\n",
    "            print(\"Validation Loss: \", loss.item(), \". \")    \n",
    "            \n",
    "    #val_accuracy = accuracy_score(true_y_list, pred_y_list)\n",
    "    #fpr, tpr, thresholds = roc_curve(true_y_list, pred_y_list)\n",
    "    #val_auc = auc(fpr, tpr)\n",
    "    #print(val_accuracy)\n",
    "    #print(val_auc)\n",
    "    \n",
    "    return otpt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f28898",
   "metadata": {},
   "source": [
    "## Reload the model if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd9f1326",
   "metadata": {},
   "outputs": [],
   "source": [
    "# checkpoint = torch.load(\"./model/model_SOTA/epoch_120.txt\")\n",
    "# model.load_state_dict(checkpoint['model_state_dict'])\n",
    "# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "# scheduler = checkpoint['scheduler']\n",
    "# epoch = checkpoint['epoch']\n",
    "\n",
    "# print(epoch)\n",
    "\n",
    "# StartEpoch = epoch + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1050f34f",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18edf588",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 1: \n",
      "train loss : 0.8038285613059998 . \n",
      "validation loss : -0.00019657611846923828 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 2: \n",
      "train loss : 0.8036514401435852 . \n",
      "validation loss : -0.00020176172256469727 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 3: \n",
      "train loss : 0.8035320103168487 . \n",
      "validation loss : -7.361173629760742e-06 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 4: \n",
      "train loss : 0.8031420767307281 . \n",
      "validation loss : -6.660819053649902e-05 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 5: \n",
      "train loss : 0.8016829371452332 . \n",
      "validation loss : -0.001201629638671875 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 6: \n",
      "train loss : 0.802743935585022 . \n",
      "validation loss : 0.00091552734375 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 7: \n",
      "train loss : 0.8009752869606018 . \n",
      "validation loss : -0.004163682460784912 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 8: \n",
      "train loss : 0.8007370054721832 . \n",
      "validation loss : 0.0001831352710723877 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 9: \n",
      "train loss : 0.7973032712936401 . \n",
      "validation loss : -0.00868445634841919 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 10: \n",
      "train loss : 0.7954496264457702 . \n",
      "validation loss : -0.0028884410858154297 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 11: \n",
      "train loss : 0.7892539083957673 . \n",
      "validation loss : -0.004385858774185181 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 12: \n",
      "train loss : 0.7780072152614593 . \n",
      "validation loss : 0.0027419626712799072 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 13: \n",
      "train loss : 0.7699124813079834 . \n",
      "validation loss : -0.0024489164352416992 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 14: \n",
      "train loss : 0.7625648617744446 . \n",
      "validation loss : -0.014579921960830688 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 15: \n",
      "train loss : 0.7601973831653595 . \n",
      "validation loss : 0.006753206253051758 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 16: \n",
      "train loss : 0.7562819421291351 . \n",
      "validation loss : -0.04260975122451782 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 17: \n",
      "train loss : 0.7543992578983307 . \n",
      "validation loss : 0.015151381492614746 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 18: \n",
      "train loss : 0.7632556557655334 . \n",
      "validation loss : 0.013609468936920166 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 19: \n",
      "train loss : 0.7621009528636933 . \n",
      "validation loss : 0.0003247857093811035 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 20: \n",
      "train loss : 0.7544919431209565 . \n",
      "validation loss : 4.500150680541992e-06 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 21: \n",
      "train loss : 0.751718419790268 . \n",
      "validation loss : -0.01238924264907837 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 22: \n",
      "train loss : 0.7499899744987488 . \n",
      "validation loss : -0.0037110447883605957 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 23: \n",
      "train loss : 0.7475286722183228 . \n",
      "validation loss : -0.05481535196304321 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 24: \n",
      "train loss : 0.7494391441345215 . \n",
      "validation loss : -0.011400669813156128 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 25: \n",
      "train loss : 0.7453715145587921 . \n",
      "validation loss : -0.012970805168151855 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 26: \n",
      "train loss : 0.7493287026882172 . \n",
      "validation loss : -0.0014199316501617432 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 27: \n",
      "train loss : 0.7562991082668304 . \n",
      "validation loss : 0.004062920808792114 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 28: \n",
      "train loss : 0.7539209365844727 . \n",
      "validation loss : -0.024180293083190918 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 29: \n",
      "train loss : 0.7478154361248016 . \n",
      "validation loss : -0.008984655141830444 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 30: \n",
      "train loss : 0.7430643677711487 . \n",
      "validation loss : -0.03336817026138306 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 31: \n",
      "train loss : 0.7413654565811157 . \n",
      "validation loss : -0.018386900424957275 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 32: \n",
      "train loss : 0.740785664319992 . \n",
      "validation loss : -0.027141034603118896 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 33: \n",
      "train loss : 0.7351253449916839 . \n",
      "validation loss : -0.05881744623184204 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 34: \n",
      "train loss : 0.7301907181739807 . \n",
      "validation loss : -0.017828136682510376 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 35: \n",
      "train loss : 0.7388005793094635 . \n",
      "validation loss : -0.045474469661712646 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 36: \n",
      "train loss : 0.7328652977943421 . \n",
      "validation loss : -0.05947691202163696 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 37: \n",
      "train loss : 0.7247248291969299 . \n",
      "validation loss : -0.08832508325576782 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 38: \n",
      "train loss : 0.7241030812263489 . \n",
      "validation loss : -0.05326437950134277 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 39: \n",
      "train loss : 0.7191103398799896 . \n",
      "validation loss : -0.08001285791397095 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
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     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 456: \n",
      "train loss : 0.00636243405751884 . \n",
      "validation loss : -0.32240545749664307 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 457: \n",
      "train loss : 0.005820296378806234 . \n",
      "validation loss : -0.32762300968170166 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 458: \n",
      "train loss : 0.006401140335947275 . \n",
      "validation loss : -0.3148549795150757 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 459: \n",
      "train loss : 0.006568494439125061 . \n",
      "validation loss : -0.3172810673713684 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 460: \n",
      "train loss : 0.006479846453294158 . \n",
      "validation loss : -0.3283281922340393 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 461: \n",
      "train loss : 0.006698923092335463 . \n",
      "validation loss : -0.3298633098602295 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 462: \n",
      "train loss : 0.006003545317798853 . \n",
      "validation loss : -0.3154938817024231 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 463: \n",
      "train loss : 0.006649802904576063 . \n",
      "validation loss : -0.32793253660202026 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 464: \n",
      "train loss : 0.006800495693460107 . \n",
      "validation loss : -0.3264423608779907 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 465: \n",
      "train loss : 0.006257757963612676 . \n",
      "validation loss : -0.31940722465515137 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 466: \n",
      "train loss : 0.006239657429978252 . \n",
      "validation loss : -0.32129770517349243 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 467: \n",
      "train loss : 0.0063288961071521046 . \n",
      "validation loss : -0.3251991271972656 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 468: \n",
      "train loss : 0.006354959029704332 . \n",
      "validation loss : -0.31663626432418823 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 469: \n",
      "train loss : 0.005779778957366944 . \n",
      "validation loss : -0.32339274883270264 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 470: \n",
      "train loss : 0.006692838575690985 . \n",
      "validation loss : -0.32803601026535034 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 471: \n",
      "train loss : 0.006167974974960089 . \n",
      "validation loss : -0.30672693252563477 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 472: \n",
      "train loss : 0.006065365392714739 . \n",
      "validation loss : -0.3214606046676636 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 473: \n",
      "train loss : 0.006977656669914722 . \n",
      "validation loss : -0.3216334581375122 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 474: \n",
      "train loss : 0.005961922183632851 . \n",
      "validation loss : -0.32760751247406006 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 475: \n",
      "train loss : 0.006603844556957483 . \n",
      "validation loss : -0.31153595447540283 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 476: \n",
      "train loss : 0.006261948496103287 . \n",
      "validation loss : -0.32392066717147827 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 477: \n",
      "train loss : 0.007145981304347515 . \n",
      "validation loss : -0.3262981176376343 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 478: \n",
      "train loss : 0.006864248169586062 . \n",
      "validation loss : -0.32285672426223755 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 479: \n",
      "train loss : 0.0071573843713849785 . \n",
      "validation loss : -0.3399512767791748 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 480: \n",
      "train loss : 0.006743023544549942 . \n",
      "validation loss : -0.32589179277420044 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 481: \n",
      "train loss : 0.006414226070046425 . \n",
      "validation loss : -0.33521026372909546 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 482: \n",
      "train loss : 0.007203037524595857 . \n",
      "validation loss : -0.31446129083633423 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 483: \n",
      "train loss : 0.007048171106725931 . \n",
      "validation loss : -0.3263683319091797 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 484: \n",
      "train loss : 0.006558938883244991 . \n",
      "validation loss : -0.3433435559272766 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 485: \n",
      "train loss : 0.006469393707811832 . \n",
      "validation loss : -0.2957993745803833 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 486: \n",
      "train loss : 0.006414852570742369 . \n",
      "validation loss : -0.3444713354110718 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 487: \n",
      "train loss : 0.006917557492852211 . \n",
      "validation loss : -0.3260326385498047 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 488: \n",
      "train loss : 0.0060655698645859955 . \n",
      "validation loss : -0.30922895669937134 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 489: \n",
      "train loss : 0.0058575735427439215 . \n",
      "validation loss : -0.33458590507507324 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 490: \n",
      "train loss : 0.005982080474495888 . \n",
      "validation loss : -0.3250088691711426 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 491: \n",
      "train loss : 0.005766600184142589 . \n",
      "validation loss : -0.31367945671081543 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 492: \n",
      "train loss : 0.006253855954855681 . \n",
      "validation loss : -0.3220985531806946 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 493: \n",
      "train loss : 0.00703410329297185 . \n",
      "validation loss : -0.30285900831222534 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 494: \n",
      "train loss : 0.00644309283234179 . \n",
      "validation loss : -0.3120189905166626 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 495: \n",
      "train loss : 0.005888180248439312 . \n",
      "validation loss : -0.3140127658843994 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 496: \n",
      "train loss : 0.007230496918782592 . \n",
      "validation loss : -0.301846444606781 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 497: \n",
      "train loss : 0.007711081532761455 . \n",
      "validation loss : -0.3355393409729004 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 498: \n",
      "train loss : 0.006991157308220863 . \n",
      "validation loss : -0.3201636075973511 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 499: \n",
      "train loss : 0.006990525964647531 . \n",
      "validation loss : -0.3041761517524719 . \n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "epoch 500: \n",
      "train loss : 0.00678876405581832 . \n",
      "validation loss : -0.3580934405326843 . \n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(StartEpoch, NumEpochs):\n",
    "    \n",
    "    #start_time = time.time()\n",
    "    batch_bar = tqdm(total=len(train_loader), dynamic_ncols=True, leave=False, position=0, desc='Train')\n",
    "    \n",
    "    model.train()\n",
    "    running_loss_train = 0\n",
    "    otpt_train = []\n",
    "    \n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        #data, target= data.cuda(), target.cuda()\n",
    "        output = model(data)\n",
    "        \n",
    "        for i in range(len(output)):\n",
    "            otpt_train.append(output[i].detach().cpu().numpy())\n",
    "            \n",
    "        loss_train = criterion_train(output, target, R_Mean, R_Std)\n",
    "        running_loss_train += loss_train.item()\n",
    "        \n",
    "        batch_bar.set_postfix(\n",
    "            loss=\"{:.05f}\".format(float(loss_train)),\n",
    "            lr=\"{:.05f}\".format(float(optimizer.param_groups[0]['lr'])) ) \n",
    "    \n",
    "        loss_train.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        #scheduler.step(loss_train)\n",
    "        batch_bar.update()\n",
    "        \n",
    "    loss_train = running_loss_train / len(train_loader)\n",
    "    batch_bar.close()\n",
    "    \n",
    "    \n",
    "    # Validation\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        running_loss_val = 0\n",
    "        otpt_val = []\n",
    "        \n",
    "        for batch_idx, (data, target) in enumerate(val_loader):\n",
    "\n",
    "            #data, target = data.cuda(), target.cuda()\n",
    "            output = model(data)   \n",
    "            \n",
    "            for i in range(len(output)):\n",
    "                otpt_val.append(output[i].detach().cpu().numpy())\n",
    "                \n",
    "            loss_val = criterion_val(output, target)\n",
    "            running_loss_val += loss_val.item()\n",
    "\n",
    "        loss_val = running_loss_val / len(val_loader)\n",
    "\n",
    "      \n",
    "    # Save the Model\n",
    "#     model_path = save_path + f\"/epoch_{epoch + 1}.txt\"\n",
    "#     torch.save({\n",
    "#         'epoch': epoch,\n",
    "#         'model_state_dict': model.state_dict(),\n",
    "#         'optimizer_state_dict': optimizer.state_dict(), \n",
    "#         'scheduler': scheduler,\n",
    "#     }, model_path)    \n",
    "    \n",
    "    print(\"--------------------------------------------------\")\n",
    "    print(\"epoch \" + str(epoch + 1) + \": \")\n",
    "     \n",
    "    print(\"train loss :\", loss_train, \". \")\n",
    "    print(\"validation loss :\", loss_val, \". \")\n",
    "\n",
    "    print(\"--------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "897e608c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8763, 1)\n",
      "(3755, 1)\n"
     ]
    }
   ],
   "source": [
    "Return_Close_Pred_Train_Nor = np.array(otpt_train)\n",
    "Return_Close_Pred_Test_Nor = np.array(otpt_val)\n",
    "\n",
    "print(Return_Close_Pred_Train_Nor.shape)\n",
    "print(Return_Close_Pred_Test_Nor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "41d6cdd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(Return_Close_Pred_Train_Nor, alpha = 1, label = \"Pred_Nor\")\n",
    "plt.plot(Return_Close_Label_Train_Nor, alpha = 0.3, label = \"GT_Nor\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "270466e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P_Close_Pred_N = (P_Close - P_Close.mean(axis=0)) / P_Close.std(axis=0)\n",
    "\n",
    "Return_Close_Pred_Train = Return_Std * Return_Close_Pred_Train_Nor + Return_Mean\n",
    "\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.plot(Return_Close_Pred_Train, alpha = 1, label = \"Pred\")\n",
    "plt.plot(Return_Close_Label_Train, alpha = 0.3, label = \"GT\")\n",
    "plt.title(\"Train Set\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d3edc52c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Return_Close_Pred_Test = Return_Std * Return_Close_Pred_Test_Nor + Return_Mean\n",
    "\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.plot(Return_Close_Pred_Test, alpha = 1, label = \"Pred\")\n",
    "plt.plot(Return_Close_Label_Test, alpha = 0.3, label = \"GT\")\n",
    "plt.title(\"Test Set\")\n",
    "plt.legend()\n",
    "#plt.savefig('./output/Test Set - 12-12-20 - 3.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a214b386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Return_Close_Pred_Test = Return_Std * Return_Close_Pred_Test_Nor + Return_Mean\n",
    "\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.plot(Return_Close_Pred_Test, Return_Close_Label_Test, \"x\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Ground Truth')\n",
    "plt.title(\"Test Set\")\n",
    "#plt.legend()\n",
    "#plt.savefig('./output/Test Set - GT vs Pred - SPINN 3.png')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}