stackedLSTM_bejing_pm_2.5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:40:53.448798Z",
     "start_time": "2021-04-28T05:40:48.633538Z"
    }
   },
   "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": "2021-04-28T05:40:53.704738Z",
     "start_time": "2021-04-28T05:40:53.498043Z"
    }
   },
   "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 Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:40:54.220749Z",
     "start_time": "2021-04-28T05:40:53.753291Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 43824 entries, 1 to 43824\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   year    43824 non-null  int64  \n",
      " 1   month   43824 non-null  int64  \n",
      " 2   day     43824 non-null  int64  \n",
      " 3   hour    43824 non-null  int64  \n",
      " 4   pm2.5   43824 non-null  float64\n",
      " 5   DEWP    43824 non-null  int64  \n",
      " 6   TEMP    43824 non-null  float64\n",
      " 7   PRES    43824 non-null  float64\n",
      " 8   cbwd    43824 non-null  object \n",
      " 9   Iws     43824 non-null  float64\n",
      " 10  Is      43824 non-null  int64  \n",
      " 11  Ir      43824 non-null  int64  \n",
      "dtypes: float64(4), int64(7), object(1)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('PRSA_data_2010.1.1-2014.12.31.csv', index_col=0)\n",
    "data = data.fillna(0)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:40:54.556739Z",
     "start_time": "2021-04-28T05:40:54.402814Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
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       "      <td>0.443328</td>\n",
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       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.004228</td>\n",
       "      <td>0.345943</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-1.922250</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.343514</td>\n",
       "      <td>-0.069353</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.168183</td>\n",
       "      <td>0.248559</td>\n",
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       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.511594</td>\n",
       "      <td>-2.004228</td>\n",
       "      <td>0.151174</td>\n",
       "      <td>-0.218339</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pm2.5      DEWP      TEMP      PRES       Iws        Is        Ir\n",
       "0 -1.018534 -1.580878 -1.922250  0.443328 -0.441894 -0.069353 -0.137667\n",
       "1 -1.018534 -1.580878 -2.004228  0.345943 -0.379306 -0.069353 -0.137667\n",
       "2 -1.018534 -1.580878 -1.922250  0.248559 -0.343514 -0.069353 -0.137667\n",
       "3 -1.018534 -1.580878 -2.168183  0.248559 -0.280926 -0.069353 -0.137667\n",
       "4 -1.018534 -1.511594 -2.004228  0.151174 -0.218339 -0.069353 -0.137667"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['pm2.5', 'DEWP', 'TEMP', 'PRES', 'Iws', 'Is', 'Ir']\n",
    "scaler = StandardScaler()\n",
    "transformed_data = data[['pm2.5', 'DEWP', 'TEMP', 'PRES', 'Iws', 'Is', 'Ir']]\n",
    "transformed_data = scaler.fit_transform(transformed_data)\n",
    "transformed_data = pd.DataFrame(transformed_data, columns=columns)\n",
    "transformed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:40:54.965313Z",
     "start_time": "2021-04-28T05:40:54.806347Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>cbwd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-1.922250</td>\n",
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       "      <td>-0.441894</td>\n",
       "      <td>-0.069353</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.004228</td>\n",
       "      <td>0.345943</td>\n",
       "      <td>-0.379306</td>\n",
       "      <td>-0.069353</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-1.922250</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.343514</td>\n",
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       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.168183</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.280926</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.511594</td>\n",
       "      <td>-2.004228</td>\n",
       "      <td>0.151174</td>\n",
       "      <td>-0.218339</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pm2.5      DEWP      TEMP      PRES       Iws        Is        Ir  cbwd\n",
       "0 -1.018534 -1.580878 -1.922250  0.443328 -0.441894 -0.069353 -0.137667     1\n",
       "1 -1.018534 -1.580878 -2.004228  0.345943 -0.379306 -0.069353 -0.137667     1\n",
       "2 -1.018534 -1.580878 -1.922250  0.248559 -0.343514 -0.069353 -0.137667     1\n",
       "3 -1.018534 -1.580878 -2.168183  0.248559 -0.280926 -0.069353 -0.137667     1\n",
       "4 -1.018534 -1.511594 -2.004228  0.151174 -0.218339 -0.069353 -0.137667     1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "cbwd = data['cbwd']\n",
    "transformed_cbwd = le.fit_transform(cbwd)\n",
    "transformed_data['cbwd'] = transformed_cbwd\n",
    "transformed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:44:47.948742Z",
     "start_time": "2021-04-28T05:40:55.840359Z"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>cbwd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00:00</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-1.922250</td>\n",
       "      <td>0.443328</td>\n",
       "      <td>-0.441894</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00:00</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.004228</td>\n",
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       "      <td>-0.379306</td>\n",
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       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-1.922250</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.343514</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00:00</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.580878</td>\n",
       "      <td>-2.168183</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.280926</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00:00</th>\n",
       "      <td>-1.018534</td>\n",
       "      <td>-1.511594</td>\n",
       "      <td>-2.004228</td>\n",
       "      <td>0.151174</td>\n",
       "      <td>-0.218339</td>\n",
       "      <td>-0.069353</td>\n",
       "      <td>-0.137667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        pm2.5      DEWP      TEMP      PRES       Iws  \\\n",
       "2010-01-01 00:00:00 -1.018534 -1.580878 -1.922250  0.443328 -0.441894   \n",
       "2010-01-01 01:00:00 -1.018534 -1.580878 -2.004228  0.345943 -0.379306   \n",
       "2010-01-01 02:00:00 -1.018534 -1.580878 -1.922250  0.248559 -0.343514   \n",
       "2010-01-01 03:00:00 -1.018534 -1.580878 -2.168183  0.248559 -0.280926   \n",
       "2010-01-01 04:00:00 -1.018534 -1.511594 -2.004228  0.151174 -0.218339   \n",
       "\n",
       "                           Is        Ir  cbwd  \n",
       "2010-01-01 00:00:00 -0.069353 -0.137667     1  \n",
       "2010-01-01 01:00:00 -0.069353 -0.137667     1  \n",
       "2010-01-01 02:00:00 -0.069353 -0.137667     1  \n",
       "2010-01-01 03:00:00 -0.069353 -0.137667     1  \n",
       "2010-01-01 04:00:00 -0.069353 -0.137667     1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = list()\n",
    "for i in range(len(data)):\n",
    "    year = int(data.iloc[i]['year'])\n",
    "    month = int(data.iloc[i]['month'])\n",
    "    day = int(data.iloc[i]['day'])\n",
    "    hour = int(data.iloc[i]['hour'])\n",
    "    date_list.append(dt.datetime(year, month, day, hour))\n",
    "transformed_data.index = date_list\n",
    "transformed_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:44:48.977726Z",
     "start_time": "2021-04-28T05:44:48.930608Z"
    }
   },
   "outputs": [],
   "source": [
    "train_start = dt.datetime(2010, 1, 1, 0)\n",
    "train_end = dt.datetime(2012, 12, 31, 23)\n",
    "train = transformed_data.loc[train_start: train_end]\n",
    "\n",
    "valid_start = dt.datetime(2013, 1, 1, 0)\n",
    "valid_end = dt.datetime(2013, 12, 31, 23)\n",
    "valid = transformed_data[valid_start: valid_end]\n",
    "\n",
    "test_start = dt.datetime(2014, 1, 1, 0)\n",
    "test_end = dt.datetime(2014, 12, 31 ,23)\n",
    "test = transformed_data[test_start:test_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:44:50.253656Z",
     "start_time": "2021-04-28T05:44:50.205342Z"
    }
   },
   "outputs": [],
   "source": [
    "def dataprocessing(transformed_df):\n",
    "    X_data, y_data = list(), list()\n",
    "    for i in tqdm(range(0, len(transformed_df) - 36)):\n",
    "        pm = transformed_df.iloc[i : i + 24]['pm2.5']\n",
    "        dewp = transformed_df.iloc[i : i + 24]['DEWP']\n",
    "        temp = transformed_df.iloc[i : i + 24]['TEMP']\n",
    "        pres = transformed_df.iloc[i : i + 24]['PRES']\n",
    "        lws = transformed_df.iloc[i : i + 24]['Iws']\n",
    "        cbwd = transformed_df.iloc[i : i + 24]['cbwd']\n",
    "        Is = transformed_df.iloc[i : i + 24]['Is']\n",
    "        Ir = transformed_df.iloc[i : i + 24]['Ir']\n",
    "        X_data.append([pm, dewp, temp, pres, lws, cbwd, Is, Ir])\n",
    "        \n",
    "        pm = transformed_df.iloc[i + 24]['pm2.5']\n",
    "        y_data.append(pm)\n",
    "#     print(X_data)\n",
    "#     print(y_data)\n",
    "    return torch.Tensor(X_data), torch.Tensor(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:55:38.960756Z",
     "start_time": "2021-04-28T05:44:51.493421Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26268/26268 [06:30<00:00, 67.29it/s]\n",
      "100%|██████████| 8724/8724 [01:56<00:00, 74.64it/s] \n",
      "100%|██████████| 8724/8724 [01:59<00:00, 72.96it/s]\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = dataprocessing(train)\n",
    "X_valid, y_valid = dataprocessing(valid)\n",
    "X_test, y_test = dataprocessing(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T05:55:40.609315Z",
     "start_time": "2021-04-28T05:55:40.593372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train shape  torch.Size([26268, 8, 24])\n",
      "X Valid shape  torch.Size([8724, 8, 24])\n",
      "X Test shape torch.Size([8724, 8, 24])\n"
     ]
    }
   ],
   "source": [
    "print('X Train shape ', X_train.shape)\n",
    "print('X Valid shape ', X_valid.shape)\n",
    "print('X Test shape', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:00:17.403733Z",
     "start_time": "2021-04-28T06:00:17.398853Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "train_data = TensorDataset(X_train, y_train)\n",
    "train_loader = DataLoader(train_data, shuffle=False, batch_size=batch_size)\n",
    "\n",
    "valid_data = TensorDataset(X_valid, y_valid)\n",
    "valid_loader = DataLoader(valid_data, shuffle=False, batch_size=batch_size)\n",
    "\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": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:00:18.821311Z",
     "start_time": "2021-04-28T06:00:18.771529Z"
    }
   },
   "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_dim1 = hidden_dim[0]\n",
    "        self.hidden_dim2 = hidden_dim[1]\n",
    "        \n",
    "        self.lstm1 = nn.LSTM(input_dim, input_dim, batch_first=True)\n",
    "        self.lstm2 = nn.LSTM(input_dim, self.hidden_dim1, batch_first=True)\n",
    "        self.lstm3 = nn.LSTM(self.hidden_dim1, self.hidden_dim2, batch_first=True)\n",
    "        \n",
    "        self.fc1 = nn.Linear(8, 40)\n",
    "        self.fc2 = nn.Linear(40, 35)\n",
    "        self.fc3 = nn.Linear(35, 1)\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        h0, c0 = self.init_hidden(inp, self.input_dim)\n",
    "        output, (h_t, c_t) = self.lstm1(inp, (h0, c0))\n",
    "        h_t, c_t = self.fc1(h_t), self.fc1(c_t)\n",
    "        output, (h_t, c_t) = self.lstm2(output, (h_t, c_t))\n",
    "        h_t, c_t = self.fc2(h_t), self.fc2(c_t)\n",
    "        output, (h_t, c_t) = self.lstm3(output, (h_t, c_t))\n",
    "        output = self.fc3(h_t)\n",
    "        return output\n",
    "        \n",
    "    def init_hidden(self, inp, c_size):\n",
    "        b, seq_len, c = inp.size()\n",
    "        h0 = torch.zeros(1, b, c_size)\n",
    "        c0 = torch.zeros(1, b, c_size)\n",
    "        return [t.cuda() for t in (h0, c0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:00:34.610251Z",
     "start_time": "2021-04-28T06:00:19.999383Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 8, 24])\n",
      "torch.Size([128])\n",
      "torch.Size([1, 128, 1])\n",
      "tensor([[[-0.0093],\n",
      "         [-0.0099],\n",
      "         [-0.0088],\n",
      "         [-0.0090],\n",
      "         [-0.0099],\n",
      "         [-0.0107],\n",
      "         [-0.0091],\n",
      "         [-0.0074],\n",
      "         [-0.0091],\n",
      "         [-0.0092],\n",
      "         [-0.0108],\n",
      "         [-0.0126],\n",
      "         [-0.0097],\n",
      "         [-0.0097],\n",
      "         [-0.0079],\n",
      "         [-0.0087],\n",
      "         [-0.0092],\n",
      "         [-0.0087],\n",
      "         [-0.0097],\n",
      "         [-0.0117],\n",
      "         [-0.0095],\n",
      "         [-0.0096],\n",
      "         [-0.0087],\n",
      "         [-0.0100],\n",
      "         [-0.0090],\n",
      "         [-0.0091],\n",
      "         [-0.0097],\n",
      "         [-0.0111],\n",
      "         [-0.0092],\n",
      "         [-0.0094],\n",
      "         [-0.0076],\n",
      "         [-0.0083],\n",
      "         [-0.0053],\n",
      "         [-0.0049],\n",
      "         [-0.0045],\n",
      "         [-0.0054],\n",
      "         [-0.0058],\n",
      "         [-0.0061],\n",
      "         [-0.0059],\n",
      "         [-0.0050],\n",
      "         [-0.0028],\n",
      "         [-0.0022],\n",
      "         [-0.0031],\n",
      "         [-0.0038],\n",
      "         [-0.0039],\n",
      "         [-0.0056],\n",
      "         [-0.0065],\n",
      "         [-0.0070],\n",
      "         [-0.0083],\n",
      "         [-0.0093],\n",
      "         [-0.0106],\n",
      "         [-0.0100],\n",
      "         [-0.0106],\n",
      "         [-0.0088],\n",
      "         [-0.0089],\n",
      "         [-0.0089],\n",
      "         [-0.0100],\n",
      "         [-0.0119],\n",
      "         [-0.0127],\n",
      "         [-0.0099],\n",
      "         [-0.0111],\n",
      "         [-0.0091],\n",
      "         [-0.0094],\n",
      "         [-0.0079],\n",
      "         [-0.0086],\n",
      "         [-0.0102],\n",
      "         [-0.0106],\n",
      "         [-0.0082],\n",
      "         [-0.0096],\n",
      "         [-0.0072],\n",
      "         [-0.0089],\n",
      "         [-0.0089],\n",
      "         [-0.0089],\n",
      "         [-0.0089],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0088],\n",
      "         [-0.0085],\n",
      "         [-0.0089],\n",
      "         [-0.0090],\n",
      "         [-0.0088],\n",
      "         [-0.0087],\n",
      "         [-0.0083],\n",
      "         [-0.0091],\n",
      "         [-0.0089],\n",
      "         [-0.0091],\n",
      "         [-0.0093],\n",
      "         [-0.0085],\n",
      "         [-0.0090],\n",
      "         [-0.0095],\n",
      "         [-0.0089],\n",
      "         [-0.0092],\n",
      "         [-0.0086],\n",
      "         [-0.0090],\n",
      "         [-0.0092],\n",
      "         [-0.0086],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0089],\n",
      "         [-0.0091],\n",
      "         [-0.0086],\n",
      "         [-0.0088],\n",
      "         [-0.0088],\n",
      "         [-0.0087],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0091],\n",
      "         [-0.0090],\n",
      "         [-0.0088],\n",
      "         [-0.0091],\n",
      "         [-0.0088],\n",
      "         [-0.0092],\n",
      "         [-0.0087],\n",
      "         [-0.0092],\n",
      "         [-0.0098],\n",
      "         [-0.0093],\n",
      "         [-0.0085],\n",
      "         [-0.0086],\n",
      "         [-0.0092],\n",
      "         [-0.0092],\n",
      "         [-0.0092],\n",
      "         [-0.0090],\n",
      "         [-0.0090],\n",
      "         [-0.0087],\n",
      "         [-0.0092],\n",
      "         [-0.0094]]], device='cuda:0')\n",
      "tensor(0.3084, 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",
    "    X = X.reshape(-1, 24, 8)\n",
    "    model = MultivariateLSTM(8, [40, 35]).cuda()\n",
    "    output = model(X.cuda())\n",
    "    criterion = nn.MSELoss().cuda()\n",
    "    print(output.shape)\n",
    "    print(output)\n",
    "    loss = criterion(output.squeeze(), y.cuda())\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-14T14:07:33.078549Z",
     "start_time": "2021-04-14T14:07:33.066798Z"
    }
   },
   "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='stackedLSTM_pm2.5(v1).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": "2021-04-28T06:00:36.281318Z",
     "start_time": "2021-04-28T06:00:36.229264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultivariateLSTM(\n",
      "  (lstm1): LSTM(8, 8, batch_first=True)\n",
      "  (lstm2): LSTM(8, 40, batch_first=True)\n",
      "  (lstm3): LSTM(40, 35, batch_first=True)\n",
      "  (fc1): Linear(in_features=8, out_features=40, bias=True)\n",
      "  (fc2): Linear(in_features=40, out_features=35, bias=True)\n",
      "  (fc3): Linear(in_features=35, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = MultivariateLSTM(8, [40, 35]).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": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:02:02.900501Z",
     "start_time": "2021-04-13T07:02:02.874926Z"
    }
   },
   "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.reshape(-1, 24, 8).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 valid_loader:\n",
    "            data, target = data.reshape(-1, 24, 8).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('stackedLSTM_pm2.5(v1).pth'))\n",
    "\n",
    "    return  model, avg_train_losses, avg_valid_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:00:37.668735Z",
     "start_time": "2021-04-28T06:00:37.645940Z"
    }
   },
   "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.reshape(-1, 24, 8).cuda(), target.cuda()\n",
    "        y_test.append(target.cpu().numpy())\n",
    "        output = model(data)\n",
    "        y_hat.append(output.detach().cpu().numpy())\n",
    "        loss = criterion(output.reshape(1), target)\n",
    "        test_loss += loss.item()\n",
    "    return test_loss/len(test_loader), y_test, y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T07:26:35.579421Z",
     "start_time": "2021-04-13T07:03:35.281336Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1/300] train_loss: 0.94870 valid_loss: 1.12362\n",
      "Validation loss decreased (inf --> 1.123619).  Saving model ...\n",
      "[  2/300] train_loss: 0.94427 valid_loss: 1.12747\n",
      "EarlyStopping counter: 1 out of 20\n",
      "[  3/300] train_loss: 0.94305 valid_loss: 1.13007\n",
      "EarlyStopping counter: 2 out of 20\n",
      "[  4/300] train_loss: 0.94267 valid_loss: 1.13138\n",
      "EarlyStopping counter: 3 out of 20\n",
      "[  5/300] train_loss: 0.94239 valid_loss: 1.13191\n",
      "EarlyStopping counter: 4 out of 20\n",
      "[  6/300] train_loss: 0.94194 valid_loss: 1.13198\n",
      "EarlyStopping counter: 5 out of 20\n",
      "[  7/300] train_loss: 0.94091 valid_loss: 1.13144\n",
      "EarlyStopping counter: 6 out of 20\n",
      "[  8/300] train_loss: 0.93707 valid_loss: 1.12795\n",
      "EarlyStopping counter: 7 out of 20\n",
      "[  9/300] train_loss: 0.92526 valid_loss: 1.10739\n",
      "Validation loss decreased (1.123619 --> 1.107385).  Saving model ...\n",
      "[ 10/300] train_loss: 0.89425 valid_loss: 1.07377\n",
      "Validation loss decreased (1.107385 --> 1.073765).  Saving model ...\n",
      "[ 11/300] train_loss: 0.83650 valid_loss: 1.03899\n",
      "Validation loss decreased (1.073765 --> 1.038993).  Saving model ...\n",
      "[ 12/300] train_loss: 0.81153 valid_loss: 1.01693\n",
      "Validation loss decreased (1.038993 --> 1.016934).  Saving model ...\n",
      "[ 13/300] train_loss: 0.80030 valid_loss: 1.00760\n",
      "Validation loss decreased (1.016934 --> 1.007602).  Saving model ...\n",
      "[ 14/300] train_loss: 0.79299 valid_loss: 1.00140\n",
      "Validation loss decreased (1.007602 --> 1.001405).  Saving model ...\n",
      "[ 15/300] train_loss: 0.78700 valid_loss: 0.99647\n",
      "Validation loss decreased (1.001405 --> 0.996469).  Saving model ...\n",
      "[ 16/300] train_loss: 0.78146 valid_loss: 0.99223\n",
      "Validation loss decreased (0.996469 --> 0.992235).  Saving model ...\n",
      "[ 17/300] train_loss: 0.77577 valid_loss: 0.98843\n",
      "Validation loss decreased (0.992235 --> 0.988433).  Saving model ...\n",
      "[ 18/300] train_loss: 0.76924 valid_loss: 0.98477\n",
      "Validation loss decreased (0.988433 --> 0.984768).  Saving model ...\n",
      "[ 19/300] train_loss: 0.76072 valid_loss: 0.98060\n",
      "Validation loss decreased (0.984768 --> 0.980603).  Saving model ...\n",
      "[ 20/300] train_loss: 0.74822 valid_loss: 0.97449\n",
      "Validation loss decreased (0.980603 --> 0.974489).  Saving model ...\n",
      "[ 21/300] train_loss: 0.72987 valid_loss: 0.96432\n",
      "Validation loss decreased (0.974489 --> 0.964325).  Saving model ...\n",
      "[ 22/300] train_loss: 0.70787 valid_loss: 0.94849\n",
      "Validation loss decreased (0.964325 --> 0.948493).  Saving model ...\n",
      "[ 23/300] train_loss: 0.68728 valid_loss: 0.92750\n",
      "Validation loss decreased (0.948493 --> 0.927500).  Saving model ...\n",
      "[ 24/300] train_loss: 0.66908 valid_loss: 0.90351\n",
      "Validation loss decreased (0.927500 --> 0.903507).  Saving model ...\n",
      "[ 25/300] train_loss: 0.65264 valid_loss: 0.87811\n",
      "Validation loss decreased (0.903507 --> 0.878108).  Saving model ...\n",
      "[ 26/300] train_loss: 0.63764 valid_loss: 0.85292\n",
      "Validation loss decreased (0.878108 --> 0.852920).  Saving model ...\n",
      "[ 27/300] train_loss: 0.62370 valid_loss: 0.82890\n",
      "Validation loss decreased (0.852920 --> 0.828899).  Saving model ...\n",
      "[ 28/300] train_loss: 0.61053 valid_loss: 0.80710\n",
      "Validation loss decreased (0.828899 --> 0.807099).  Saving model ...\n",
      "[ 29/300] train_loss: 0.59855 valid_loss: 0.78807\n",
      "Validation loss decreased (0.807099 --> 0.788071).  Saving model ...\n",
      "[ 30/300] train_loss: 0.58756 valid_loss: 0.76919\n",
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      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "model, train_loss, valid_loss = fit(model, batch_size, patience, n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:00:43.451145Z",
     "start_time": "2021-04-28T06:00:43.442984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('stackedLSTM_pm2.5(v1).pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:03:12.105357Z",
     "start_time": "2021-04-28T06:00:45.154948Z"
    }
   },
   "outputs": [],
   "source": [
    "test_loss, y_test, y_hat = evaluate_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:03:40.702766Z",
     "start_time": "2021-04-28T06:03:40.689348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10245632597349454"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:04:09.402201Z",
     "start_time": "2021-04-28T06:04:09.326995Z"
    }
   },
   "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": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T13:14:33.560757Z",
     "start_time": "2021-04-15T13:14:31.994173Z"
    }
   },
   "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[200:400], label='target')\n",
    "plt.plot(predicted[200:400], label='predicted')\n",
    "plt.legend(loc='upper left', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:04:39.172741Z",
     "start_time": "2021-04-28T06:04:38.081334Z"
    }
   },
   "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[200:300], label='target')\n",
    "plt.plot(predicted[200:300], label='predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-28T06:05:09.332742Z",
     "start_time": "2021-04-28T06:05:08.159446Z"
    }
   },
   "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
}