singleLSTM_carriage_service.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:01:46.328787Z",
     "start_time": "2021-04-28T05:01:42.053110Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import datetime as dt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torch.utils.data import DataLoader, random_split, TensorDataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:01:46.548739Z",
     "start_time": "2021-04-28T05:01:46.366317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "device = 'cuda:2' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:01:46.749678Z",
     "start_time": "2021-04-28T05:01:46.579854Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-08-09</th>\n",
       "      <td>14.250000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>14.892752</td>\n",
       "      <td>1601500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-12</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>14.892752</td>\n",
       "      <td>260900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-13</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>14.779927</td>\n",
       "      <td>162800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-14</th>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>14.779927</td>\n",
       "      <td>59500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-15</th>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16.625000</td>\n",
       "      <td>15.005575</td>\n",
       "      <td>62300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.270000</td>\n",
       "      <td>36.340000</td>\n",
       "      <td>35.099998</td>\n",
       "      <td>36.139999</td>\n",
       "      <td>36.139999</td>\n",
       "      <td>111400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>36.580002</td>\n",
       "      <td>36.849998</td>\n",
       "      <td>36.200001</td>\n",
       "      <td>36.529999</td>\n",
       "      <td>36.529999</td>\n",
       "      <td>114000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>36.650002</td>\n",
       "      <td>36.930000</td>\n",
       "      <td>36.349998</td>\n",
       "      <td>36.660000</td>\n",
       "      <td>36.660000</td>\n",
       "      <td>66100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>36.549999</td>\n",
       "      <td>36.680000</td>\n",
       "      <td>35.570000</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>83300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>35.750000</td>\n",
       "      <td>35.790001</td>\n",
       "      <td>34.910000</td>\n",
       "      <td>35.320000</td>\n",
       "      <td>35.320000</td>\n",
       "      <td>116200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6207 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close   Volume\n",
       "Date                                                                      \n",
       "1996-08-09  14.250000  16.750000  14.250000  16.500000  14.892752  1601500\n",
       "1996-08-12  16.500000  16.750000  16.375000  16.500000  14.892752   260900\n",
       "1996-08-13  16.500000  16.750000  16.375000  16.375000  14.779927   162800\n",
       "1996-08-14  16.375000  16.625000  16.375000  16.375000  14.779927    59500\n",
       "1996-08-15  16.625000  16.625000  16.375000  16.625000  15.005575    62300\n",
       "...               ...        ...        ...        ...        ...      ...\n",
       "2021-04-01  35.270000  36.340000  35.099998  36.139999  36.139999   111400\n",
       "2021-04-05  36.580002  36.849998  36.200001  36.529999  36.529999   114000\n",
       "2021-04-06  36.650002  36.930000  36.349998  36.660000  36.660000    66100\n",
       "2021-04-07  36.549999  36.680000  35.570000  35.759998  35.759998    83300\n",
       "2021-04-08  35.750000  35.790001  34.910000  35.320000  35.320000   116200\n",
       "\n",
       "[6207 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv',parse_dates = True, index_col=0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:07.844744Z",
     "start_time": "2021-04-28T05:02:06.163203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f142dff1a00>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(data['Open'])\n",
    "plt.plot(data['High'])\n",
    "plt.plot(data['Low'])\n",
    "plt.plot(data['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:13.976741Z",
     "start_time": "2021-04-28T05:02:12.544469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f13b1afc130>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(data['Volume'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:16.984741Z",
     "start_time": "2021-04-28T05:02:16.946028Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Close       Open       High        Low   Volume\n",
      "Date                                                           \n",
      "1996-08-09  16.500000  14.250000  16.750000  14.250000  1601500\n",
      "1996-08-12  16.500000  16.500000  16.750000  16.375000   260900\n",
      "1996-08-13  16.375000  16.500000  16.750000  16.375000   162800\n",
      "1996-08-14  16.375000  16.375000  16.625000  16.375000    59500\n",
      "1996-08-15  16.625000  16.625000  16.625000  16.375000    62300\n",
      "...               ...        ...        ...        ...      ...\n",
      "2021-04-01  36.139999  35.270000  36.340000  35.099998   111400\n",
      "2021-04-05  36.529999  36.580002  36.849998  36.200001   114000\n",
      "2021-04-06  36.660000  36.650002  36.930000  36.349998    66100\n",
      "2021-04-07  35.759998  36.549999  36.680000  35.570000    83300\n",
      "2021-04-08  35.320000  35.750000  35.790001  34.910000   116200\n",
      "\n",
      "[6207 rows x 5 columns]\n",
      "(3876, 5) (1258, 5) (1073, 5)\n"
     ]
    }
   ],
   "source": [
    "data = data[['Close', 'Open', 'High', 'Low', 'Volume']]\n",
    "print(data)\n",
    "train_start = dt.date(1996,8,9)\n",
    "train_end = dt.date(2011,12,31)\n",
    "train_data = data.loc[train_start:train_end]\n",
    "\n",
    "val_start = dt.date(2012,1,1)\n",
    "val_end = dt.date(2016,12,31)\n",
    "val_data = data.loc[val_start:val_end]\n",
    "\n",
    "test_start = dt.date(2016,1,1)\n",
    "test_end = dt.date(2020,4,7)\n",
    "test_data = data.loc[test_start:test_end]\n",
    "\n",
    "print(train_data.shape,val_data.shape,test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:20.896747Z",
     "start_time": "2021-04-28T05:02:20.843182Z"
    }
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train = sc.fit_transform(train_data)\n",
    "val = sc.fit_transform(val_data)\n",
    "test = sc.fit_transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## torch dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:26.511988Z",
     "start_time": "2021-04-28T05:02:26.507177Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert2torch(data, time_step):\n",
    "    X_data, y_data = list(), list()\n",
    "    for i in range(time_step, len(data)):\n",
    "        X_data.append(data[i-time_step:i])\n",
    "        y_data.append(data[i][0])\n",
    "    X_data, y_data = torch.Tensor(X_data), torch.Tensor(y_data)\n",
    "    return X_data, y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:02:32.185328Z",
     "start_time": "2021-04-28T05:02:27.991541Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "X_train, y_train = convert2torch(train, 50)\n",
    "train_data = TensorDataset(X_train, y_train)\n",
    "train_loader = DataLoader(train_data, shuffle=False, batch_size=batch_size)\n",
    "\n",
    "X_val, y_val = convert2torch(val, 50)\n",
    "val_data = TensorDataset(X_val, y_val)\n",
    "val_loader = DataLoader(val_data, shuffle=False, batch_size=batch_size)\n",
    "\n",
    "X_test, y_test = convert2torch(test, 50)\n",
    "test_data = TensorDataset(X_test, y_test)\n",
    "test_loader = DataLoader(test_data, shuffle=False, batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:04:21.081317Z",
     "start_time": "2021-04-28T05:04:21.055604Z"
    }
   },
   "outputs": [],
   "source": [
    "class MultivariateLSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim):\n",
    "        super(MultivariateLSTM, self).__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_dim, self.hidden_dim, batch_first=True)\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.fc1 = nn.Linear(2000, 1024)\n",
    "        self.fc2 = nn.Linear(1024, 512)\n",
    "        self.fc3 = nn.Linear(512, 256)\n",
    "        self.fc4 = nn.Linear(256, 128)\n",
    "        self.fc5 = nn.Linear(128, 32)\n",
    "        self.fc6 = nn.Linear(32, 1)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm1d(1024)\n",
    "        self.bn2 = nn.BatchNorm1d(512)\n",
    "        self.bn3 = nn.BatchNorm1d(256)\n",
    "        self.bn4 = nn.BatchNorm1d(128)\n",
    "        self.bn5 = nn.BatchNorm1d(32)\n",
    "        self.act = nn.ReLU()\n",
    "    def forward(self, inp):\n",
    "        h0, c0 = self.init_hidden(inp)\n",
    "        output, (h_t, c_t) = self.lstm(inp, (h0, c0))\n",
    "        output = self.flatten(output)\n",
    "        output = self.act(self.bn1(self.fc1(output)))\n",
    "        output = self.act(self.bn2(self.fc2(output)))\n",
    "        output = self.act(self.bn3(self.fc3(output)))\n",
    "        output = self.act(self.bn4(self.fc4(output)))\n",
    "        output = self.act(self.bn5(self.fc5(output)))\n",
    "        output = self.fc6(output)\n",
    "        return output\n",
    "        \n",
    "    def init_hidden(self, inp):\n",
    "        b, seq_len, c = inp.size()\n",
    "        h0 = torch.zeros(1, b, self.hidden_dim)\n",
    "        c0 = torch.zeros(1, b, self.hidden_dim)\n",
    "        return [t.cuda() for t in (h0, c0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:04:42.771362Z",
     "start_time": "2021-04-28T05:04:25.231852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 50, 5])\n",
      "torch.Size([128])\n",
      "torch.Size([128, 1])\n",
      "tensor([[-0.9329],\n",
      "        [-0.5199],\n",
      "        [ 0.1613],\n",
      "        [-0.1401],\n",
      "        [-0.6487],\n",
      "        [-0.2604],\n",
      "        [ 0.5294],\n",
      "        [ 0.2653],\n",
      "        [-0.1701],\n",
      "        [ 0.0488],\n",
      "        [ 0.1952],\n",
      "        [-0.1957],\n",
      "        [-0.3060],\n",
      "        [-0.2737],\n",
      "        [-0.2260],\n",
      "        [-0.4145],\n",
      "        [-0.1314],\n",
      "        [-0.3874],\n",
      "        [-0.4576],\n",
      "        [-0.7434],\n",
      "        [-0.6429],\n",
      "        [-0.3689],\n",
      "        [-0.0923],\n",
      "        [-0.7248],\n",
      "        [-0.5087],\n",
      "        [-0.2656],\n",
      "        [-0.5222],\n",
      "        [-0.4203],\n",
      "        [-0.4549],\n",
      "        [-0.4882],\n",
      "        [-0.3088],\n",
      "        [-0.5727],\n",
      "        [-0.1519],\n",
      "        [-0.5552],\n",
      "        [-0.5395],\n",
      "        [-0.5475],\n",
      "        [-0.2000],\n",
      "        [-0.2664],\n",
      "        [-0.4725],\n",
      "        [-0.4083],\n",
      "        [-0.8218],\n",
      "        [-0.2944],\n",
      "        [-0.6630],\n",
      "        [-0.4697],\n",
      "        [-0.3180],\n",
      "        [-0.3744],\n",
      "        [-0.3462],\n",
      "        [-0.5113],\n",
      "        [-0.7268],\n",
      "        [-0.7306],\n",
      "        [-0.3552],\n",
      "        [-0.3987],\n",
      "        [-0.3104],\n",
      "        [-0.3803],\n",
      "        [-0.1052],\n",
      "        [-0.4599],\n",
      "        [-0.5339],\n",
      "        [-0.7737],\n",
      "        [-0.1935],\n",
      "        [-0.4732],\n",
      "        [-0.0985],\n",
      "        [-0.2065],\n",
      "        [-0.1742],\n",
      "        [-0.2439],\n",
      "        [-0.1640],\n",
      "        [-0.0079],\n",
      "        [ 0.0572],\n",
      "        [-0.0348],\n",
      "        [-0.3135],\n",
      "        [ 0.1004],\n",
      "        [-0.4279],\n",
      "        [ 0.0539],\n",
      "        [ 0.2395],\n",
      "        [-0.0964],\n",
      "        [-0.0456],\n",
      "        [ 0.1343],\n",
      "        [ 0.0177],\n",
      "        [-0.2163],\n",
      "        [-0.1351],\n",
      "        [ 0.2164],\n",
      "        [-0.4149],\n",
      "        [-0.5198],\n",
      "        [-0.9615],\n",
      "        [ 0.0352],\n",
      "        [-0.8390],\n",
      "        [-0.3704],\n",
      "        [-0.0598],\n",
      "        [-0.1542],\n",
      "        [-0.6311],\n",
      "        [-0.4836],\n",
      "        [ 0.0850],\n",
      "        [-0.8448],\n",
      "        [-1.1873],\n",
      "        [-1.3264],\n",
      "        [-0.5484],\n",
      "        [-0.3923],\n",
      "        [-0.6371],\n",
      "        [-0.9336],\n",
      "        [-0.2810],\n",
      "        [-0.2634],\n",
      "        [-0.4509],\n",
      "        [-0.3018],\n",
      "        [ 0.0503],\n",
      "        [-0.2649],\n",
      "        [-0.3023],\n",
      "        [-1.2496],\n",
      "        [-0.8560],\n",
      "        [-1.1049],\n",
      "        [-0.9545],\n",
      "        [-0.6303],\n",
      "        [-0.6778],\n",
      "        [-0.8276],\n",
      "        [-0.9333],\n",
      "        [-1.1857],\n",
      "        [-1.0773],\n",
      "        [-1.0385],\n",
      "        [-0.2513],\n",
      "        [ 0.0630],\n",
      "        [-0.3384],\n",
      "        [-0.3312],\n",
      "        [-0.6112],\n",
      "        [-0.5298],\n",
      "        [-0.6553],\n",
      "        [-0.9356],\n",
      "        [-0.3854],\n",
      "        [-0.3961],\n",
      "        [-0.3231],\n",
      "        [-0.9315]], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    dataiter = iter(train_loader)\n",
    "    X, y = dataiter.next()\n",
    "    print(X.shape)\n",
    "    print(y.shape)\n",
    "    model = MultivariateLSTM(5, 40).cuda()\n",
    "    output = model(X.cuda())\n",
    "    print(output.shape)\n",
    "    print(output)\n",
    "    criterion = nn.MSELoss()\n",
    "    loss = criterion(output.squeeze(), y.cuda())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:05:36.557323Z",
     "start_time": "2021-04-28T05:05:36.538251Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n",
    "    def __init__(self, patience=7, verbose=False, delta=0, path='carr_ser_singleLSTM.pth', trace_func=print):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            patience (int): How long to wait after last time validation loss improved.\n",
    "                            Default: 7\n",
    "            verbose (bool): If True, prints a message for each validation loss improvement. \n",
    "                            Default: False\n",
    "            delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n",
    "                            Default: 0\n",
    "            path (str): Path for the checkpoint to be saved to.\n",
    "                            Default: 'checkpoint.pt'\n",
    "            trace_func (function): trace print function.\n",
    "                            Default: print            \n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.verbose = verbose\n",
    "        self.counter = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "        self.val_loss_min = np.Inf\n",
    "        self.delta = delta\n",
    "        self.path = path\n",
    "        self.trace_func = trace_func\n",
    "    def __call__(self, val_loss, model):\n",
    "\n",
    "        score = -val_loss\n",
    "\n",
    "        if self.best_score is None:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "        elif score < self.best_score + self.delta:\n",
    "            self.counter += 1\n",
    "            self.trace_func(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "            self.counter = 0\n",
    "\n",
    "    def save_checkpoint(self, val_loss, model):\n",
    "        '''Saves model when validation loss decrease.'''\n",
    "        if self.verbose:\n",
    "            self.trace_func(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')\n",
    "        torch.save(model.state_dict(), self.path)\n",
    "        self.val_loss_min = val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:05:39.832755Z",
     "start_time": "2021-04-28T05:05:39.681427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultivariateLSTM(\n",
      "  (lstm): LSTM(5, 40, batch_first=True)\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (fc1): Linear(in_features=2000, out_features=1024, bias=True)\n",
      "  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n",
      "  (fc3): Linear(in_features=512, out_features=256, bias=True)\n",
      "  (fc4): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (fc5): Linear(in_features=128, out_features=32, bias=True)\n",
      "  (fc6): Linear(in_features=32, out_features=1, bias=True)\n",
      "  (bn1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (act): ReLU()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = MultivariateLSTM(5, 40).cuda()\n",
    "\n",
    "lr = 0.0001\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                                       mode='min',\n",
    "                                                       factor=0.2\n",
    "                                                    )\n",
    "criterion = nn.MSELoss().cuda()\n",
    "patience = 20\n",
    "n_epochs = 300\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:05:42.085623Z",
     "start_time": "2021-04-28T05:05:42.069316Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit(model, batch_size, patience, n_epochs):\n",
    "    \n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "    avg_train_losses = []\n",
    "    avg_valid_losses = [] \n",
    "    \n",
    "    early_stopping = EarlyStopping(patience=patience, verbose=True)\n",
    "    \n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "\n",
    "        ###################\n",
    "        # train the model #\n",
    "        ###################\n",
    "        model.train()\n",
    "        for data, target in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "            output = model(data)\n",
    "            loss = criterion(output.squeeze(), target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_losses.append(loss.item())\n",
    "\n",
    "        ######################    \n",
    "        # validate the model #\n",
    "        ######################\n",
    "        model.eval()\n",
    "        for data, target in val_loader:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "            output = model(data)\n",
    "            loss = criterion(output.squeeze(), target)\n",
    "            valid_losses.append(loss.item())\n",
    "\n",
    "        train_loss = np.average(train_losses)\n",
    "        valid_loss = np.average(valid_losses)\n",
    "        avg_train_losses.append(train_loss)\n",
    "        avg_valid_losses.append(valid_loss)\n",
    "            \n",
    "        epoch_len = len(str(n_epochs))\n",
    "        \n",
    "        print_msg = (f'[{epoch:>{epoch_len}}/{n_epochs:>{epoch_len}}] ' +\n",
    "                     f'train_loss: {train_loss:.5f} ' +\n",
    "                     f'valid_loss: {valid_loss:.5f}')\n",
    "        \n",
    "        print(print_msg)\n",
    "        \n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        early_stopping(valid_loss, model)\n",
    "        \n",
    "        if early_stopping.early_stop:\n",
    "            print(\"Early stopping\")\n",
    "            break\n",
    "\n",
    "    model.load_state_dict(torch.load('carr_ser_singleLSTM.pth'))\n",
    "\n",
    "    return  model, avg_train_losses, avg_valid_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:05:44.549701Z",
     "start_time": "2021-04-28T05:05:44.543769Z"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_model(model, test_loader):\n",
    "    test_loss = 0.0\n",
    "    y_test = []\n",
    "    y_hat = []\n",
    "    model.eval()\n",
    "    for data, target in test_loader:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "        y_test.append(target.cpu().detach().numpy())\n",
    "        output = model(data)\n",
    "        y_hat.append(output.view(1).cpu().detach().numpy())\n",
    "        loss = criterion(output.view(1), target)\n",
    "        test_loss += loss.item()\n",
    "    return test_loss/len(test_loader), y_test, y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:06:30.932756Z",
     "start_time": "2021-04-28T05:05:45.327658Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1/300] train_loss: 1.21520 valid_loss: 1.07272\n",
      "Validation loss decreased (inf --> 1.072722).  Saving model ...\n",
      "[  2/300] train_loss: 1.08163 valid_loss: 0.22125\n",
      "Validation loss decreased (1.072722 --> 0.221247).  Saving model ...\n",
      "[  3/300] train_loss: 0.98179 valid_loss: 0.60108\n",
      "EarlyStopping counter: 1 out of 20\n",
      "[  4/300] train_loss: 0.91762 valid_loss: 1.46772\n",
      "EarlyStopping counter: 2 out of 20\n",
      "[  5/300] train_loss: 0.88057 valid_loss: 0.50155\n",
      "EarlyStopping counter: 3 out of 20\n",
      "[  6/300] train_loss: 0.84869 valid_loss: 1.90760\n",
      "EarlyStopping counter: 4 out of 20\n",
      "[  7/300] train_loss: 0.81707 valid_loss: 3.36404\n",
      "EarlyStopping counter: 5 out of 20\n",
      "[  8/300] train_loss: 0.79043 valid_loss: 1.07070\n",
      "EarlyStopping counter: 6 out of 20\n",
      "[  9/300] train_loss: 0.77410 valid_loss: 3.32994\n",
      "EarlyStopping counter: 7 out of 20\n",
      "[ 10/300] train_loss: 0.75127 valid_loss: 3.88688\n",
      "EarlyStopping counter: 8 out of 20\n",
      "[ 11/300] train_loss: 0.72465 valid_loss: 4.03455\n",
      "EarlyStopping counter: 9 out of 20\n",
      "[ 12/300] train_loss: 0.71145 valid_loss: 3.19433\n",
      "EarlyStopping counter: 10 out of 20\n",
      "[ 13/300] train_loss: 0.69594 valid_loss: 4.61417\n",
      "EarlyStopping counter: 11 out of 20\n",
      "[ 14/300] train_loss: 0.68078 valid_loss: 1.37191\n",
      "EarlyStopping counter: 12 out of 20\n",
      "[ 15/300] train_loss: 0.67162 valid_loss: 3.10055\n",
      "EarlyStopping counter: 13 out of 20\n",
      "[ 16/300] train_loss: 0.66147 valid_loss: 3.88309\n",
      "EarlyStopping counter: 14 out of 20\n",
      "[ 17/300] train_loss: 0.65149 valid_loss: 5.95906\n",
      "EarlyStopping counter: 15 out of 20\n",
      "[ 18/300] train_loss: 0.63634 valid_loss: 2.08233\n",
      "EarlyStopping counter: 16 out of 20\n",
      "[ 19/300] train_loss: 0.62257 valid_loss: 4.79976\n",
      "EarlyStopping counter: 17 out of 20\n",
      "[ 20/300] train_loss: 0.61105 valid_loss: 3.82504\n",
      "EarlyStopping counter: 18 out of 20\n",
      "[ 21/300] train_loss: 0.60472 valid_loss: 4.49381\n",
      "EarlyStopping counter: 19 out of 20\n",
      "[ 22/300] train_loss: 0.59351 valid_loss: 3.54713\n",
      "EarlyStopping counter: 20 out of 20\n",
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "model, train_loss, valid_loss = fit(model, batch_size, patience, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-14T14:04:01.950563Z",
     "start_time": "2021-04-14T14:04:01.903993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('carr_ser_singleLSTM.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:07:03.876756Z",
     "start_time": "2021-04-28T05:06:47.285058Z"
    }
   },
   "outputs": [],
   "source": [
    "test_loss, y_test, y_hat = evaluate_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:11:07.109115Z",
     "start_time": "2021-04-28T05:11:07.104442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4399351284724508"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T05:48:08.487450Z",
     "start_time": "2021-04-13T05:48:08.473735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.343302], dtype=float32), array([-0.01452462], dtype=float32))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0], y_hat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:07:08.112738Z",
     "start_time": "2021-04-28T05:07:08.085641Z"
    }
   },
   "outputs": [],
   "source": [
    "target, predicted = list(), list()\n",
    "for i in range(len(y_test)):\n",
    "    target.append(y_test[i].item())\n",
    "    predicted.append(y_hat[i][0].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:07:13.624744Z",
     "start_time": "2021-04-28T05:07:12.426307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(target[:200], label='target')\n",
    "plt.plot(predicted[:200], label='predicted')\n",
    "plt.legend(loc='upper left', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:33:50.412744Z",
     "start_time": "2021-04-28T05:33:49.072228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(target, label='target')\n",
    "plt.plot(predicted, label='predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}