RNN & LSTM - CALCE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "%matplotlib inline\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_outlier(array,count,bins):\n",
    "    index = []\n",
    "    range_ = np.arange(1,count,bins)\n",
    "    for i in range_[:-1]:\n",
    "        array_lim = array[i:i+bins]\n",
    "        sigma = np.std(array_lim)\n",
    "        mean = np.mean(array_lim)\n",
    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
    "        idx = idx[0] + i\n",
    "        index.extend(list(idx))\n",
    "    return np.array(index)\n",
    "\n",
    "\n",
    "def build_instances(sequence, window_size):\n",
    "    #sequence: list of capacity\n",
    "    x, y = [],[]\n",
    "    for i in range(len(sequence) - window_size):\n",
    "        features = sequence[i:i+window_size]\n",
    "        target = sequence[i+window_size]\n",
    "\n",
    "        x.append(features)\n",
    "        y.append(target)\n",
    "        \n",
    "    return np.array(x).astype(np.float32), np.array(y).astype(np.float32)\n",
    "\n",
    "\n",
    "# 留一评估：一组数据为测试集，其他所有数据全部拿来训练\n",
    "def get_train_test(data_dict, name, window_size=8):\n",
    "    data_sequence=data_dict[name]['capacity']\n",
    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
    "    train_x, train_y = build_instances(train_data, window_size)\n",
    "    for k, v in data_dict.items():\n",
    "        if k != name:\n",
    "            data_x, data_y = build_instances(v['capacity'], window_size)\n",
    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
    "            \n",
    "    return train_x, train_y, list(train_data), list(test_data)\n",
    "\n",
    "\n",
    "def relative_error(y_test, y_predict, threshold):\n",
    "    true_re, pred_re = len(y_test), 0\n",
    "    for i in range(len(y_test)-1):\n",
    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
    "            true_re = i - 1\n",
    "            break\n",
    "    for i in range(len(y_predict)-1):\n",
    "        if y_predict[i] <= threshold:\n",
    "            pred_re = i - 1\n",
    "            break\n",
    "    return abs(true_re - pred_re)/true_re if abs(true_re - pred_re)/true_re<=1 else 1\n",
    "\n",
    "\n",
    "def evaluation(y_test, y_predict):\n",
    "    mse = mean_squared_error(y_test, y_predict)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
    "    return rmse\n",
    "    \n",
    "    \n",
    "def setup_seed(seed):\n",
    "    np.random.seed(seed)  # Numpy module.\n",
    "    random.seed(seed)  # Python random module.\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。\n",
    "    torch.manual_seed(seed) # 为CPU设置随机种子\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子\n",
    "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "        torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 提取所需特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load Dataset CS2_35 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_8_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_10_29_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_35\\CS2_35_2_4_11.xlsx ...\n",
      "Load Dataset CS2_36 ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_9_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_36\\CS2_36_2_3_11.xlsx ...\n",
      "Load Dataset CS2_37 ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_37\\CS2_37_2_3_11.xlsx ...\n",
      "Load Dataset CS2_38 ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n"
     ]
    }
   ],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "\n",
    "dir_path = 'dataset/'\n",
    "Battery = {}\n",
    "for name in Battery_list:\n",
    "    print('Load Dataset ' + name + ' ...')\n",
    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
    "    dates = []\n",
    "    for p in path:\n",
    "        df = pd.read_excel(p, sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        dates.append(df['Date_Time'][0])\n",
    "    idx = np.argsort(dates)\n",
    "    path_sorted = np.array(path)[idx]\n",
    "    \n",
    "    count = 0\n",
    "    discharge_capacities = []\n",
    "    health_indicator = []\n",
    "    internal_resistance = []\n",
    "    CCCT = []\n",
    "    CVCT = []\n",
    "    for p in path_sorted:\n",
    "        df = pd.read_excel(p,sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        cycles = list(set(df['Cycle_Index']))\n",
    "        for c in cycles:\n",
    "            df_lim = df[df['Cycle_Index'] == c]\n",
    "            #Charging\n",
    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
    "            c_v = df_c['Voltage(V)']\n",
    "            c_c = df_c['Current(A)']\n",
    "            c_t = df_c['Test_Time(s)']\n",
    "            #CC or CV\n",
    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
    "\n",
    "            #Discharging\n",
    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
    "            d_v = df_d['Voltage(V)']\n",
    "            d_c = df_d['Current(A)']\n",
    "            d_t = df_d['Test_Time(s)']\n",
    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
    "\n",
    "            if(len(list(d_c)) != 0):\n",
    "                time_diff = np.diff(list(d_t))\n",
    "                d_c = np.array(list(d_c))[1:]\n",
    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
    "\n",
    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                health_indicator.append(-1 * (end - start))\n",
    "\n",
    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
    "                count += 1\n",
    "\n",
    "    discharge_capacities = np.array(discharge_capacities)\n",
    "    health_indicator = np.array(health_indicator)\n",
    "    internal_resistance = np.array(internal_resistance)\n",
    "    CCCT = np.array(CCCT)\n",
    "    CVCT = np.array(CVCT)\n",
    "    \n",
    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
    "                              'capacity':discharge_capacities[idx],\n",
    "                              'SoH':health_indicator[idx],\n",
    "                              'resistance':internal_resistance[idx],\n",
    "                              'CCCT':CCCT[idx],\n",
    "                              'CVCT':CVCT[idx]})\n",
    "    Battery[name] = df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**或者**\n",
    "\n",
    "## 1.1 加载已经提取完成的数据\n",
    "\n",
    "**如果上面的读取数据集失败，可以通过下面的方式加载已提取出来的数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "Battery = np.load('dataset/CALCE.npy', allow_pickle=True)\n",
    "Battery = Battery.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 画容量衰减曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24cbb8bf808>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rated_Capacity = 1.1\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
    "for name,color in zip(Battery_list, color_list):\n",
    "    df_result = Battery[name]\n",
    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 建立模型及其训练\n",
    "\n",
    "## 2.1 建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, hidden_dim=8, num_layers=1, mode='LSTM'):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.cell = nn.LSTM(input_size=1, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        if mode == 'GRU':\n",
    "            self.cell = nn.GRU(input_size=1, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        elif mode == 'RNN':\n",
    "            self.cell = nn.RNN(input_size=1, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        self.linear = nn.Linear(hidden_dim, 1)\n",
    " \n",
    "    def forward(self, x):           # x shape: (batch_size, feature_size, 1)\n",
    "        out, _ = self.cell(x)       # out shape: (batch_size, feature_size, hidden_dim)\n",
    "        out = out[:, -1, :]         # 取序列最后一个时间步的输出作为预测  \n",
    "        out = self.linear(out)      # out shape: (batch_size, 1)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr, feature_size, hidden_dim=64, num_layers=2, weight_decay=0.0, mode='RNN', epochs=100, seed=0, device='cpu', metric='rmse'):\n",
    "    score_list, result_list = [], []\n",
    "    setup_seed(seed)\n",
    "    for i in range(4):\n",
    "        name = Battery_list[i]\n",
    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, feature_size)\n",
    "        test_sequence = train_data + test_data\n",
    "        test_x, test_y = build_instances(test_sequence, feature_size)\n",
    "        # print('sample size: {}'.format(len(train_x)))\n",
    "        \n",
    "        model = Net(hidden_dim=hidden_dim, num_layers=num_layers, mode=mode)\n",
    "        model = model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "        criterion = nn.MSELoss()\n",
    "\n",
    "        loss_list, y_ = [0], []\n",
    "        mae, rmse, re = 1, 1, 1\n",
    "        score_, score = 1,1\n",
    "        for epoch in range(epochs):\n",
    "            X = np.reshape(train_x/Rated_Capacity,(-1, feature_size, 1))   # (batch_size, feature_size, 1)\n",
    "            y = np.reshape(train_y/Rated_Capacity,(-1,1))          # shape 为 (batch_size, 1)\n",
    "\n",
    "            X, y = torch.from_numpy(X).to(device), torch.from_numpy(y).to(device)\n",
    "            output = model(X)\n",
    "            output = output.reshape(-1, 1)\n",
    "            loss = criterion(output, y)\n",
    "            optimizer.zero_grad()            \n",
    "            loss.backward()                    \n",
    "            optimizer.step()                   \n",
    "\n",
    "            if (epoch + 1) % 10 == 0:\n",
    "                x = np.reshape(test_x/Rated_Capacity,(-1, feature_size, 1))\n",
    "                x = torch.from_numpy(x).to(device) \n",
    "                pred = model(x) \n",
    "                point_list = pred.data.cpu().numpy() * Rated_Capacity\n",
    "                point_list = point_list.reshape(-1)\n",
    "                \n",
    "                y_.append(point_list)                                 \n",
    "                loss_list.append(loss)\n",
    "                rmse = evaluation(y_test=test_y, y_predict=y_[-1])\n",
    "                re = relative_error(y_test=test_y, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
    "                #print('epoch:{:<2d} | loss:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, rmse, re))\n",
    "            \n",
    "            if metric == 're':\n",
    "                score = [re]\n",
    "            else:\n",
    "                score = [rmse]\n",
    "                \n",
    "            if (loss < 1e-3) and (score_[0] < score[0]):\n",
    "                break\n",
    "            score_ = score.copy()\n",
    "            \n",
    "        score_list.append(score_)\n",
    "        result_list.append(train_data.copy()[:-1] + list(y_[-1]))\n",
    "        \n",
    "    return score_list, result_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 设置参开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b443cad9f04d4fc19bfa943ae075b0bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed:  0\n",
      "[[0.01837623]\n",
      " [0.13053122]\n",
      " [0.01477865]\n",
      " [0.02336097]]\n",
      "rmse: for this seed: 0.0468\n",
      "------------------------------------------------------------------\n",
      "seed:  1\n",
      "[[0.0362411 ]\n",
      " [0.04033594]\n",
      " [0.03603044]\n",
      " [0.03557413]]\n",
      "rmse: for this seed: 0.0370\n",
      "------------------------------------------------------------------\n",
      "seed:  2\n",
      "[[0.01458093]\n",
      " [0.03992219]\n",
      " [0.03411146]\n",
      " [0.01407477]]\n",
      "rmse: for this seed: 0.0257\n",
      "------------------------------------------------------------------\n",
      "seed:  3\n",
      "[[0.0160389 ]\n",
      " [0.05345531]\n",
      " [0.07402331]\n",
      " [0.0365984 ]]\n",
      "rmse: for this seed: 0.0450\n",
      "------------------------------------------------------------------\n",
      "rmse: mean: 0.0386\n"
     ]
    }
   ],
   "source": [
    "feature_size = 64\n",
    "epochs = 100\n",
    "lr = 0.001           # learning rate\n",
    "hidden_dim = 64\n",
    "num_layers = 1\n",
    "weight_decay = 0.0\n",
    "mode = 'RNN'        # RNN, LSTM, GRU\n",
    "Rated_Capacity = 2.0\n",
    "metric = 'rmse'\n",
    "device = 'cpu'\n",
    "\n",
    "SCORE = []\n",
    "for seed in tqdm(range(4)):\n",
    "    print('seed: ', seed)\n",
    "    score_list, _ = train(lr=lr, feature_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, \n",
    "                         weight_decay=weight_decay, mode=mode, epochs=epochs, seed=seed, device=device, metric=metric)\n",
    "    print(np.array(score_list))\n",
    "    print(metric + ': for this seed: {:<6.4f}'.format(np.mean(np.array(score_list))))\n",
    "    for s in score_list:\n",
    "        SCORE.append(s)\n",
    "    print('------------------------------------------------------------------')\n",
    "print(metric + ': mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 画预测曲线\n",
    "\n",
    "**从上面的种子中选择一个表现最好的种子**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed:  2\n",
      "[[0.01458093]\n",
      " [0.03992219]\n",
      " [0.03411146]\n",
      " [0.01407477]]\n",
      "rmse for this seed: 0.0257\n"
     ]
    }
   ],
   "source": [
    "seed = 2\n",
    "\n",
    "feature_size = 64\n",
    "epochs = 100\n",
    "lr = 0.001           # learning rate\n",
    "hidden_dim = 64\n",
    "num_layers = 1\n",
    "weight_decay = 0.0\n",
    "mode = 'RNN'        # RNN, LSTM, GRU\n",
    "Rated_Capacity = 2.0\n",
    "metric = 'rmse'\n",
    "device = 'cpu'\n",
    "\n",
    "SCORE = []\n",
    "print('seed: ', seed)\n",
    "score_list, prediction_list = train(lr=lr, feature_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, \n",
    "                                    weight_decay=weight_decay, mode=mode, epochs=epochs, seed=seed, device=device, metric=metric)\n",
    "\n",
    "print(np.array(score_list))\n",
    "print(metric + ' for this seed: {:<6.4f}'.format(np.mean(np.array(score_list))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2, 2, figsize=(24, 18))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        t = i + j\n",
    "        battery_name = Battery_list[t]\n",
    "        test_data = Battery[battery_name]['capacity']\n",
    "        predict_data = prediction_list[t]\n",
    "        x = [t for t in range(len(test_data))]\n",
    "        threshold = [Rated_Capacity*0.7] * len(test_data)\n",
    "        ax[i][j].plot(x, test_data, 'c', label='test data')\n",
    "        ax[i][j].plot(x, predict_data, 'b', label='predicted data')\n",
    "        ax[i][j].plot(x, threshold, 'black', ls=':', label='stop line')\n",
    "        ax[i][j].legend()\n",
    "        ax[i][j].set_xlabel('Discharge cycles', fontsize=15)\n",
    "        ax[i][j].set_ylabel('Capacity (Ah)', fontsize=15)\n",
    "        ax[i][j].set_title('test v.s. prediction of battery ' + battery_name, fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}