Jena_climate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "lMWLFva9WIaG"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras import Input\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense, LSTM, GRU\n",
    "from keras.layers import Conv1D\n",
    "from keras.layers import MaxPooling1D \n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "b0wRRC1MWI_O"
   },
   "outputs": [],
   "source": [
    "zip_path = tf.keras.utils.get_file(\n",
    "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
    "    fname='jena_climate_2009_2016.csv.zip',\n",
    "    extract=True)\n",
    "csv_path, _ = os.path.splitext(zip_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "oS_-CU_-WJBi"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path)\n",
    "# slice [start:stop:step], starting from index 5 take every 6th record.\n",
    "df = df[5::6]\n",
    "\n",
    "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "ydWH0hKfWJD0",
    "outputId": "d3c3e6b8-a531-4ee0-8cb8-13f6d11685cc"
   },
   "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>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.40</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>264.54</td>\n",
       "      <td>-9.77</td>\n",
       "      <td>93.20</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1312.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>190.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>264.59</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>93.50</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1312.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>264.34</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>92.60</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1313.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>263.72</td>\n",
       "      <td>-10.65</td>\n",
       "      <td>92.20</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1317.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>157.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420521</th>\n",
       "      <td>1002.18</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>272.01</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>72.00</td>\n",
       "      <td>5.69</td>\n",
       "      <td>4.09</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.54</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1280.70</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.36</td>\n",
       "      <td>190.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420527</th>\n",
       "      <td>1001.40</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>271.66</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>66.29</td>\n",
       "      <td>5.51</td>\n",
       "      <td>3.65</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.65</td>\n",
       "      <td>1281.87</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.92</td>\n",
       "      <td>225.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420533</th>\n",
       "      <td>1001.19</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>270.32</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>72.90</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.26</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1288.02</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.56</td>\n",
       "      <td>158.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420539</th>\n",
       "      <td>1000.65</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>270.22</td>\n",
       "      <td>-7.15</td>\n",
       "      <td>72.30</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.22</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1288.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>216.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420545</th>\n",
       "      <td>1000.11</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>269.23</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>72.60</td>\n",
       "      <td>4.56</td>\n",
       "      <td>3.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.31</td>\n",
       "      <td>1292.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>202.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70091 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        p (mbar)  T (degC)  Tpot (K)  ...  wv (m/s)  max. wv (m/s)  wd (deg)\n",
       "5         996.50     -8.05    265.38  ...      0.21           0.63     192.7\n",
       "11        996.62     -8.88    264.54  ...      0.25           0.63     190.3\n",
       "17        996.84     -8.81    264.59  ...      0.18           0.63     167.2\n",
       "23        996.99     -9.05    264.34  ...      0.10           0.38     240.0\n",
       "29        997.46     -9.63    263.72  ...      0.40           0.88     157.0\n",
       "...          ...       ...       ...  ...       ...            ...       ...\n",
       "420521   1002.18     -0.98    272.01  ...      0.87           1.36     190.6\n",
       "420527   1001.40     -1.40    271.66  ...      1.02           1.92     225.4\n",
       "420533   1001.19     -2.75    270.32  ...      0.71           1.56     158.7\n",
       "420539   1000.65     -2.89    270.22  ...      0.35           0.68     216.7\n",
       "420545   1000.11     -3.93    269.23  ...      0.56           1.00     202.6\n",
       "\n",
       "[70091 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "3qVk45uXWJIX"
   },
   "outputs": [],
   "source": [
    "# take three day of datas to predict one day\n",
    "timestep = 72\n",
    "# size of training data - 500 days (12000 hours)\n",
    "training_num = 12000\n",
    "\n",
    "epoch = 10\n",
    "batch_size = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WVcWIHxFWJM5",
    "outputId": "e7c15458-bbad-4571-bf16-d0a7bef1b8b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70091, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = df['T (degC)']\n",
    "dt = np.array(dt).reshape(-1,1)\n",
    "dt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "78Tb9hAC6zEC"
   },
   "source": [
    "# <font color = purple>Normalize the data to (0, 1)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dD0c9Lj1hNxV",
    "outputId": "b44dd060-5552-4ad8-8b82-b34121ed8d2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24500333],\n",
       "       [0.23117921],\n",
       "       [0.2323451 ],\n",
       "       ...,\n",
       "       [0.33327781],\n",
       "       [0.33094604],\n",
       "       [0.31362425]])"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "dt_nor = sc.fit_transform(dt)\n",
    "dt_nor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_gTGzxOL9M6C"
   },
   "source": [
    "# <font color = purple>Find the best width</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "zdLlPJOk9RX6"
   },
   "outputs": [],
   "source": [
    "# take three day of datas to predict one day\n",
    "timestep\n",
    "# size of training data - 500 days\n",
    "training_num = 12000\n",
    "\n",
    "epoch = 10\n",
    "batch_size = 200\n",
    "\n",
    "def width(timestep,model_kind):\n",
    "  xTrainSet = dt_nor[:training_num]\n",
    "  yTrainSet = dt_nor[1:training_num+1]\n",
    "\n",
    "  xTrain = []\n",
    "  for i in range(timestep, training_num):\n",
    "    xTrain.append(xTrainSet[i-timestep : i])\n",
    "  xTrain = np.array(xTrain)\n",
    "  xTrain = np.reshape(xTrain, (xTrain.shape[0], xTrain.shape[1], 1))\n",
    "\n",
    "  yTrain = []\n",
    "  for i in range(timestep, training_num):\n",
    "    yTrain.append(yTrainSet[i])\n",
    "  yTrain = np.array(yTrain)\n",
    "\n",
    "  if model_kind == 'model_rnn':\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(128, return_sequences = True, input_shape = (xTrain.shape[1],1)))\n",
    "    model.add(GRU(64))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "  if model_kind == 'model_dense':\n",
    "    model = Sequential()\n",
    "    model.add(Input(shape = (xTrain.shape[1])))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "  if model_kind == 'model_cnn':\n",
    "    conv_width = 3\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(64, kernel_size=(conv_width), input_shape = (xTrain.shape[1],1), activation='relu'))\n",
    "    # model.add(MaxPooling1D(pool_size=(8)))\n",
    "    model.add(Conv1D(32, kernel_size=(conv_width), activation='relu'))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "  \n",
    "  model.compile(optimizer = 'adam',\n",
    "         loss = 'mean_squared_error',\n",
    "         metrics = [tf.metrics.MeanAbsoluteError()])\n",
    "  model.fit(x = xTrain, y = yTrain, epochs = epoch, batch_size = batch_size, verbose=0)\n",
    "\n",
    "  xTestSet = dt_nor[training_num : 40800-2]\n",
    "  xTestSet = np.array(xTestSet)\n",
    "\n",
    "  yTestSet = dt_nor[training_num+1 : 40800-1]\n",
    "  yTestSet = np.array(yTestSet)\n",
    "\n",
    "  xTest = []\n",
    "  for i in range(timestep, len(xTestSet)):\n",
    "      xTest.append(xTestSet[i-timestep : i])\n",
    "  xTest = np.array(xTest)\n",
    "\n",
    "  yTest = []\n",
    "  for i in range(timestep, len(xTestSet)):\n",
    "      yTest.append(yTestSet[i])\n",
    "  yTest = np.array(yTest)\n",
    "  yTest = sc.inverse_transform(yTest)\n",
    "\n",
    "  yPredictes = model.predict(x=xTest)\n",
    "  yPredictes = sc.inverse_transform(yPredictes)\n",
    "\n",
    "  r2 = r2_score(yTest, yPredictes)\n",
    "  return r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "32QZ0loO-unc",
    "outputId": "c0763375-4400-4c52-a84e-545b26e891fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-6-7-8-9-10-\n",
      "11-12-13-14-15-16-17-18-19-20-\n",
      "21-22-23-24-25-26-27-28-29-30-\n",
      "31-32-33-34-35-36-37-38-39-40-\n",
      "41-42-43-44-45-46-47-48-49-50-\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_width_dict = {}\n",
    "for step in range(5,51):\n",
    "  rnn_width_dict[step] = width(step,'model_rnn')\n",
    "  print(step,end=\"-\")\n",
    "  if step%10 == 0:\n",
    "    print()\n",
    "rnn_width = max(rnn_width_dict,key=rnn_width_dict.get)\n",
    "rnn_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1DQuIqJoHrvP",
    "outputId": "aa229652-5cce-46c6-880f-55bb73d696f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-6-7-8-9-10-\n",
      "11-12-13-14-15-16-17-18-19-20-\n",
      "21-22-23-24-25-26-27-28-29-30-\n",
      "31-32-33-34-35-36-37-38-39-40-\n",
      "41-42-43-44-45-46-47-48-49-50-\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_width_dict = {}\n",
    "for step in range(5,51):\n",
    "  dense_width_dict[step] = width(step,'model_dense')\n",
    "  print(step,end=\"-\")\n",
    "  if step%10 == 0:\n",
    "    print()\n",
    "dense_width = max(dense_width_dict,key=dense_width_dict.get)\n",
    "dense_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X_ys9ObeHr5O",
    "outputId": "7d244c23-a772-421d-f486-eb3302fc8b00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-6-7-8-9-10-\n",
      "11-12-13-14-15-16-17-18-19-20-\n",
      "21-22-23-24-25-26-27-28-29-30-\n",
      "31-32-33-34-35-36-37-38-39-40-\n",
      "41-42-43-44-45-46-47-48-49-50-\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_width_dict = {}\n",
    "for step in range(5,51):\n",
    "  cnn_width_dict[step] = width(step,'model_cnn')\n",
    "  print(step,end=\"-\")\n",
    "  if step%10 == 0:\n",
    "    print()\n",
    "cnn_width = max(cnn_width_dict,key=cnn_width_dict.get)\n",
    "cnn_width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hyjbp5_Oau0G"
   },
   "source": [
    "# <font color = purple>Rnn</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "MtrN2VVVFZuR"
   },
   "outputs": [],
   "source": [
    "timestep = rnn_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Xd2y306oaB8U"
   },
   "outputs": [],
   "source": [
    "xTrainSet = dt_nor[:training_num]\n",
    "yTrainSet = dt_nor[1:training_num+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iKn3ZKbKWJRZ",
    "outputId": "cfb53cf7-9e80-4443-fb03-cee1f45e3c42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11958, 42, 1)\n",
      "(11958, 1)\n"
     ]
    }
   ],
   "source": [
    "xTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  xTrain.append(xTrainSet[i-timestep : i])\n",
    "xTrain = np.array(xTrain)\n",
    "xTrain = np.reshape(xTrain, (xTrain.shape[0], xTrain.shape[1], 1))\n",
    "print(xTrain.shape)\n",
    "\n",
    "yTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  yTrain.append(yTrainSet[i])\n",
    "yTrain = np.array(yTrain)\n",
    "print(yTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iLCdrDaSKFoQ",
    "outputId": "f104564f-e608-4b55-904c-d252c4952dca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_138\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_46 (LSTM)               (None, 42, 128)           66560     \n",
      "_________________________________________________________________\n",
      "gru_46 (GRU)                 (None, 64)                37248     \n",
      "_________________________________________________________________\n",
      "dense_276 (Dense)            (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 103,873\n",
      "Trainable params: 103,873\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_rnn = Sequential()\n",
    "model_rnn.add(LSTM(128, return_sequences = True, input_shape = (xTrain.shape[1],1)))\n",
    "model_rnn.add(GRU(64))\n",
    "model_rnn.add(Dense(1))\n",
    "\n",
    "model_rnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nfTQXBO8dTke",
    "outputId": "666dae66-1f50-4d3b-8464-3e7e0921fc1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60/60 [==============================] - 3s 10ms/step - loss: 0.0232 - mean_absolute_error: 0.1070\n",
      "Epoch 2/10\n",
      "60/60 [==============================] - 0s 8ms/step - loss: 0.0031 - mean_absolute_error: 0.0433\n",
      "Epoch 3/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 0.0027 - mean_absolute_error: 0.0398\n",
      "Epoch 4/10\n",
      "60/60 [==============================] - 0s 8ms/step - loss: 0.0022 - mean_absolute_error: 0.0360\n",
      "Epoch 5/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 0.0011 - mean_absolute_error: 0.0257\n",
      "Epoch 6/10\n",
      "60/60 [==============================] - 0s 8ms/step - loss: 8.8987e-04 - mean_absolute_error: 0.0224\n",
      "Epoch 7/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 8.7927e-04 - mean_absolute_error: 0.0224\n",
      "Epoch 8/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 8.2940e-04 - mean_absolute_error: 0.0215\n",
      "Epoch 9/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 8.3635e-04 - mean_absolute_error: 0.0217\n",
      "Epoch 10/10\n",
      "60/60 [==============================] - 0s 7ms/step - loss: 8.0309e-04 - mean_absolute_error: 0.0211\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f23fd1f3438>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rnn.compile(optimizer = 'adam',\n",
    "         loss = 'mean_squared_error',\n",
    "         metrics = [tf.metrics.MeanAbsoluteError()])\n",
    "model_rnn.fit(x = xTrain, y = yTrain, epochs = epoch, batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cP8Pn4zJ7vw7"
   },
   "source": [
    "# <font color = purple>Test model's accuracy by r2_score (1200 days)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "EzxEdlTPiTE5"
   },
   "outputs": [],
   "source": [
    "xTestSet = dt_nor[training_num : 40800-2]\n",
    "xTestSet = np.array(xTestSet)\n",
    "\n",
    "yTestSet = dt_nor[training_num+1 : 40800-1]\n",
    "yTestSet = np.array(yTestSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KQbW3X54iTIF",
    "outputId": "8a476ac7-e0a3-47ac-80cd-4ff47945eb3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28756\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "28756"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    xTest.append(xTestSet[i-timestep : i])\n",
    "xTest = np.array(xTest)\n",
    "print(len(xTest))\n",
    "\n",
    "yTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    yTest.append(yTestSet[i])\n",
    "yTest = np.array(yTest)\n",
    "yTest = sc.inverse_transform(yTest)\n",
    "len(yTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_tTE75kxiTNf",
    "outputId": "657f6094-59a2-4af2-c990-7e828d9aeba9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.442964],\n",
       "       [ 9.880576],\n",
       "       [ 9.022699],\n",
       "       ...,\n",
       "       [16.375065],\n",
       "       [15.539744],\n",
       "       [14.877621]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yPredictes = model_rnn.predict(x=xTest)\n",
    "yPredictes = sc.inverse_transform(yPredictes)\n",
    "yPredictes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "UcSj9Hg_HMpC"
   },
   "outputs": [],
   "source": [
    "r2_value = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "o8VQ7teLp0lB",
    "outputId": "640a90e4-a316-460e-d979-b2496f470f25"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.966580459961591\n"
     ]
    }
   ],
   "source": [
    "plt.plot(yTest, 'c-', label='Real')\n",
    "plt.plot(yPredictes, 'm-', label='Predict')\n",
    "# plt.plot(data_original, color='red', label='Real')\n",
    "# plt.plot(range(len(y_train)),yPredicts, color='blue', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2 = r2_score(yTest, yPredictes)\n",
    "r2_value['RNN'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VZfrf8pP3nit"
   },
   "source": [
    "# <font color=purple>Dense</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "zYI4xYFK3vOg"
   },
   "outputs": [],
   "source": [
    "timestep = dense_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Z1azZOyM3vQ3"
   },
   "outputs": [],
   "source": [
    "xTrainSet = dt_nor[:training_num]\n",
    "yTrainSet = dt_nor[1:training_num+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E-VUIv-93vTS",
    "outputId": "c46ba3c1-9fd0-4208-c67d-631dce50a2e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11969, 31, 1)\n",
      "(11969, 1)\n"
     ]
    }
   ],
   "source": [
    "xTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  xTrain.append(xTrainSet[i-timestep : i])\n",
    "xTrain = np.array(xTrain)\n",
    "#xTrain = np.squeeze(xTrain)\n",
    "xTrain = np.reshape(xTrain, (xTrain.shape[0], xTrain.shape[1], 1))\n",
    "print(xTrain.shape)\n",
    "\n",
    "yTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  yTrain.append(yTrainSet[i])\n",
    "yTrain = np.array(yTrain)\n",
    "#yTrain = np.reshape(yTrain, (yTrain.shape[0], 1))\n",
    "print(yTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GUGkjUTkKN9I",
    "outputId": "319aed0f-9e21-4ce0-9461-e01ac9a3c65e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_139\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_92 (Flatten)         (None, 31)                0         \n",
      "_________________________________________________________________\n",
      "dense_277 (Dense)            (None, 128)               4096      \n",
      "_________________________________________________________________\n",
      "dense_278 (Dense)            (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_279 (Dense)            (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 12,417\n",
      "Trainable params: 12,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_dense = Sequential()\n",
    "model_dense.add(Input(shape = (xTrain.shape[1])))\n",
    "model_dense.add(Flatten())\n",
    "model_dense.add(Dense(128, activation='relu'))\n",
    "model_dense.add(Dense(64, activation='relu'))\n",
    "model_dense.add(Dense(1))\n",
    "\n",
    "model_dense.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y3gAF-8L3vYC",
    "outputId": "00daeeee-fb43-4590-b421-06b71cdedde8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 0.0367 - mean_absolute_error: 0.1341\n",
      "Epoch 2/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 0.0011 - mean_absolute_error: 0.0255\n",
      "Epoch 3/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 8.4854e-04 - mean_absolute_error: 0.0222\n",
      "Epoch 4/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 7.3950e-04 - mean_absolute_error: 0.0204\n",
      "Epoch 5/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 6.8657e-04 - mean_absolute_error: 0.0195\n",
      "Epoch 6/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 6.4759e-04 - mean_absolute_error: 0.0188\n",
      "Epoch 7/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 5.9562e-04 - mean_absolute_error: 0.0181\n",
      "Epoch 8/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 5.7174e-04 - mean_absolute_error: 0.0177\n",
      "Epoch 9/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 5.9664e-04 - mean_absolute_error: 0.0181\n",
      "Epoch 10/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 5.8784e-04 - mean_absolute_error: 0.0180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f23f3db7668>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dense.compile(optimizer = 'adam',\n",
    "          loss = 'mean_squared_error',\n",
    "          metrics = [tf.metrics.MeanAbsoluteError()])\n",
    "model_dense.fit(x = xTrain, y = yTrain, epochs = epoch, batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AXz4r4Gk7g-y"
   },
   "source": [
    "# <font color=purple>Test model's accuracy by r2_score (1200 days)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "QBPdG6rf3vaq"
   },
   "outputs": [],
   "source": [
    "xTestSet = dt_nor[training_num : 40800-2]\n",
    "xTestSet = np.array(xTestSet)\n",
    "\n",
    "yTestSet = dt_nor[training_num+1 : 40800-1]\n",
    "yTestSet = np.array(yTestSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A6q9mX1i6Eke",
    "outputId": "69490693-930e-49d1-8c25-f9f41ac87b2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28767"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    xTest.append(xTestSet[i-timestep : i])\n",
    "xTest = np.array(xTest)\n",
    "#xTest = np.squeeze(xTest)\n",
    "\n",
    "yTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    yTest.append(yTestSet[i])\n",
    "yTest = np.array(yTest)\n",
    "yTest = sc.inverse_transform(yTest)\n",
    "len(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sP2mCgPv8fm5",
    "outputId": "6acb498f-5b8d-4add-84df-7d1c29abe4ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28767, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yTest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B1R_0pKF6Em0",
    "outputId": "c584f58b-2a4e-4156-b04e-91142a14f6cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.05247 ],\n",
       "       [13.334333],\n",
       "       [14.62565 ],\n",
       "       ...,\n",
       "       [17.424633],\n",
       "       [16.384659],\n",
       "       [15.441449]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yPredictes = model_dense.predict(x=xTest)\n",
    "yPredictes = sc.inverse_transform(yPredictes)\n",
    "yPredictes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "Ropf9xE06EpC",
    "outputId": "776d8949-7a67-4370-894b-4af506663879"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9754810370866053\n"
     ]
    }
   ],
   "source": [
    "plt.plot(yTest, 'c-', label='Real')\n",
    "plt.plot(yPredictes, 'm-', label='Predict')\n",
    "# plt.plot(data_original, color='red', label='Real')\n",
    "# plt.plot(range(len(y_train)),yPredicts, color='blue', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2 = r2_score(yTest, yPredictes)\n",
    "r2_value['Dense'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "58nqhD7H4lcf"
   },
   "source": [
    "# <font color=purple>Cnn</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "u1-tI1mhINw9"
   },
   "outputs": [],
   "source": [
    "timestep = cnn_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "LbDV9Xd23vdA"
   },
   "outputs": [],
   "source": [
    "xTrainSet = dt_nor[:training_num]\n",
    "yTrainSet = dt_nor[1:training_num+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jSaV4zYq3vgW",
    "outputId": "a45a2092-08a2-433b-de49-370a500935ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11975, 25, 1)\n",
      "(11975, 1)\n"
     ]
    }
   ],
   "source": [
    "xTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  xTrain.append(xTrainSet[i-timestep : i])\n",
    "xTrain = np.array(xTrain)\n",
    "# xTrain = np.squeeze(xTrain)\n",
    "xTrain = np.reshape(xTrain, (xTrain.shape[0], xTrain.shape[1], 1))\n",
    "print(xTrain.shape)\n",
    "\n",
    "yTrain = []\n",
    "for i in range(timestep, training_num):\n",
    "  yTrain.append(yTrainSet[i])\n",
    "yTrain = np.array(yTrain)\n",
    "#yTrain = np.reshape(yTrain, (yTrain.shape[0], 1))\n",
    "print(yTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jddy-dawKYbm",
    "outputId": "2ec4e577-527f-4012-c8d0-267cde1e23bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_140\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_92 (Conv1D)           (None, 23, 64)            256       \n",
      "_________________________________________________________________\n",
      "conv1d_93 (Conv1D)           (None, 21, 32)            6176      \n",
      "_________________________________________________________________\n",
      "flatten_93 (Flatten)         (None, 672)               0         \n",
      "_________________________________________________________________\n",
      "dense_280 (Dense)            (None, 32)                21536     \n",
      "_________________________________________________________________\n",
      "dense_281 (Dense)            (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 28,001\n",
      "Trainable params: 28,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_width = 3\n",
    "model_cnn = Sequential()\n",
    "model_cnn.add(Conv1D(64, kernel_size=(conv_width),  input_shape = (xTrain.shape[1],1), activation='relu'))\n",
    "model_cnn.add(Conv1D(32, kernel_size=(conv_width), activation='relu'))\n",
    "model_cnn.add(Flatten())\n",
    "model_cnn.add(Dense(32, activation='relu'))\n",
    "model_cnn.add(Dense(1))\n",
    "\n",
    "model_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YosJ3W-48aE9",
    "outputId": "582b8c95-c080-4a66-c6e8-8c3ffa75d4cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60/60 [==============================] - 1s 3ms/step - loss: 0.0913 - mean_absolute_error: 0.2289\n",
      "Epoch 2/10\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 0.0034 - mean_absolute_error: 0.0453\n",
      "Epoch 3/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 0.0016 - mean_absolute_error: 0.0305\n",
      "Epoch 4/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 0.0012 - mean_absolute_error: 0.0267\n",
      "Epoch 5/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 0.0010 - mean_absolute_error: 0.0244\n",
      "Epoch 6/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 9.7746e-04 - mean_absolute_error: 0.0235\n",
      "Epoch 7/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 8.5505e-04 - mean_absolute_error: 0.0219\n",
      "Epoch 8/10\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 7.4004e-04 - mean_absolute_error: 0.0205\n",
      "Epoch 9/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 6.1827e-04 - mean_absolute_error: 0.0185\n",
      "Epoch 10/10\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 5.8741e-04 - mean_absolute_error: 0.0180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f23f3e73940>"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cnn.compile(optimizer = 'adam',\n",
    "          loss = 'mean_squared_error',\n",
    "          metrics = [tf.metrics.MeanAbsoluteError()])\n",
    "model_cnn.fit(x = xTrain, y = yTrain, epochs = epoch, batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R1kjq6i77qrp"
   },
   "source": [
    "# <font color=purple>Test model's accuracy by r2_score (1200 days)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "2HcWUyDc8aHY"
   },
   "outputs": [],
   "source": [
    "xTestSet = dt_nor[training_num : 40800-2]\n",
    "xTestSet = np.array(xTestSet)\n",
    "\n",
    "yTestSet = dt_nor[training_num+1 : 40800-1]\n",
    "yTestSet = np.array(yTestSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xIII9cjH8aJh",
    "outputId": "0959322e-879b-4580-ceb0-f12d6604da7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28773"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    xTest.append(xTestSet[i-timestep : i])\n",
    "xTest = np.array(xTest)\n",
    "# xTest = np.squeeze(xTest)\n",
    "\n",
    "yTest = []\n",
    "for i in range(timestep, len(xTestSet)):\n",
    "    yTest.append(yTestSet[i])\n",
    "yTest = np.array(yTest)\n",
    "yTest = sc.inverse_transform(yTest)\n",
    "len(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hc-KT-zY8aLm",
    "outputId": "5c66d751-0241-4873-c7a6-4b7b1339c645"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28773, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yPredictes = model_cnn.predict(x=xTest)\n",
    "yPredictes = sc.inverse_transform(yPredictes)\n",
    "yPredictes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "Jf1Jsdpn8aNw",
    "outputId": "a944f65e-6eaf-4111-81c8-50f4b4e0aa0b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.970434208330225\n"
     ]
    }
   ],
   "source": [
    "plt.plot(yTest, 'c-', label='Real')\n",
    "plt.plot(yPredictes, 'm-', label='Predict')\n",
    "# plt.plot(data_original, color='red', label='Real')\n",
    "# plt.plot(range(len(y_train)),yPredicts, color='blue', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2 = r2_score(yTest, yPredictes)\n",
    "r2_value['CNN'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=purple>Compare</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JFDYLKRR8aRU",
    "outputId": "45f3f16d-6246-4142-c3db-7cba14e0db4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CNN': 0.970434208330225,\n",
       " 'Dense': 0.9754810370866053,\n",
       " 'RNN': 0.966580459961591}"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "ICAFX9ZaHzSv",
    "outputId": "1dc646a3-574d-4715-b0e5-c098a453a610"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(3)\n",
    "width = 0.2\n",
    "\n",
    "val_r2 = r2_value.values()\n",
    "\n",
    "plt.figure(figsize=(4,7))\n",
    "plt.ylabel('r2_score [T (degC)]')\n",
    "plt.bar(x , val_r2, 0.4, label='Test')\n",
    "plt.xticks(ticks=x, labels=r2_value.keys(), rotation=45)\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IUfXqKYFg-Z3"
   },
   "source": [
    "# <font color=purple>Use predict data to predict future</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ePrX7u4OVqtK"
   },
   "source": [
    "# <font color=purple>Rnn</fnt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "7eM2Zs3ThDrK"
   },
   "outputs": [],
   "source": [
    "# Take last 24 hours to predict\n",
    "Predict_hours = 24\n",
    "\n",
    "xPred = dt_nor[-Predict_hours-rnn_width:-Predict_hours]\n",
    "xPred = np.array(xPred)\n",
    "xPred_in = sc.inverse_transform(xPred)\n",
    "xPred = np.reshape(xPred, (xPred.shape[1], xPred.shape[0], 1))\n",
    "\n",
    "yFutureTest = dt_nor[-Predict_hours:]\n",
    "yFutureTest = np.array(yFutureTest)\n",
    "yFutureTest = sc.inverse_transform(yFutureTest)\n",
    "\n",
    "real = []\n",
    "real = np.append(xPred_in, yFutureTest, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RQkZ9apafqqm",
    "outputId": "63152061-ed59-460d-c405-65d9ee6c621c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 42, 1)"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xPred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "Le1hsun3i-qm"
   },
   "outputs": [],
   "source": [
    "def PredFuture_rnn(xPred):\n",
    "  yPred = model_rnn.predict(x=xPred)\n",
    "  yPred = np.reshape(yPred, (1, 1, 1))\n",
    "  data = np.append(xPred, yPred, axis = 1)\n",
    "  data = data[:, -(rnn_width):, :]\n",
    "  return data\n",
    "\n",
    "yModelPred = []\n",
    "for i in range (Predict_hours):\n",
    "  xPred = PredFuture_rnn(xPred)\n",
    "  yModelPred.append(xPred[0][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "6D6FFRIuttNG"
   },
   "outputs": [],
   "source": [
    "yModelPred = np.array(yModelPred)\n",
    "yModelPred = sc.inverse_transform(yModelPred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "qwRIuIrywg6f",
    "outputId": "3baff5b5-57e3-4a95-f0b7-a7cbc3fc8e2f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6429095118313762\n"
     ]
    }
   ],
   "source": [
    "plt.plot(real, 'y-', label='train')\n",
    "plt.plot(range(rnn_width, Predict_hours + rnn_width), yFutureTest, 'c-', label='Real')\n",
    "plt.plot(range(rnn_width, Predict_hours + rnn_width), yModelPred, 'm-', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2_future = {}\n",
    "r2 = r2_score(yFutureTest, yModelPred)\n",
    "r2_future['RNN'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtQD4DoNbbHp"
   },
   "source": [
    "# <font color=purple>Cnn</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "l8048wtcbdJG"
   },
   "outputs": [],
   "source": [
    "# Take last 24 hours to predict\n",
    "Predict_hours = 24\n",
    "\n",
    "xPred = dt_nor[-Predict_hours-cnn_width:-Predict_hours]\n",
    "xPred = np.array(xPred)\n",
    "xPred_in = sc.inverse_transform(xPred)\n",
    "# #xPred = np.reshape(xPred, (1,xPred.shape[0],xPred.shape[1]))\n",
    "xPred = np.reshape(xPred, (xPred.shape[1], xPred.shape[0], 1))\n",
    "\n",
    "yFutureTest = dt_nor[-Predict_hours:]\n",
    "yFutureTest = np.array(yFutureTest)\n",
    "yFutureTest = sc.inverse_transform(yFutureTest)\n",
    "\n",
    "real = []\n",
    "real = np.append(xPred_in, yFutureTest, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "SvEEMRkYbdP9"
   },
   "outputs": [],
   "source": [
    "def PredFuture_cnn(xPred):\n",
    "  yPred = model_cnn.predict(xPred)\n",
    "  yPred = np.reshape(yPred, (1, 1, 1))\n",
    "  data = np.append(xPred, yPred, axis = 1)\n",
    "  data = data[:, -(cnn_width):, :]\n",
    "  return data\n",
    "\n",
    "yModelPred = []\n",
    "for i in range (Predict_hours):\n",
    "  xPred = PredFuture_cnn(xPred)\n",
    "  yModelPred.append(xPred[0][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "Yyrt7gXBbdSo"
   },
   "outputs": [],
   "source": [
    "yModelPred = np.array(yModelPred)\n",
    "yModelPred = sc.inverse_transform(yModelPred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "spwbdFjTbdV9",
    "outputId": "ea9919b8-01a4-452d-c29e-00a4e4461ecd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7851424365003093\n"
     ]
    }
   ],
   "source": [
    "plt.plot(real, 'y-', label='train')\n",
    "plt.plot(range(cnn_width, Predict_hours + cnn_width), yFutureTest, 'c-', label='Real')\n",
    "plt.plot(range(cnn_width, Predict_hours + cnn_width), yModelPred, 'm-', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2 = r2_score(yFutureTest, yModelPred)\n",
    "r2_future['CNN'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBzs_D3z4ZRP"
   },
   "source": [
    "# <font color=purple>Dense</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "qbSXOc1F4bXW"
   },
   "outputs": [],
   "source": [
    "# Take last 24 hours to predict\n",
    "Predict_hours = 24\n",
    "\n",
    "xPred = dt_nor[-Predict_hours-dense_width:-Predict_hours]\n",
    "xPred = np.array(xPred)\n",
    "xPred_in = sc.inverse_transform(xPred)\n",
    "# #xPred = np.reshape(xPred, (1,xPred.shape[0],xPred.shape[1]))\n",
    "xPred = np.reshape(xPred, (xPred.shape[1], xPred.shape[0], 1))\n",
    "\n",
    "yFutureTest = dt_nor[-Predict_hours:]\n",
    "yFutureTest = np.array(yFutureTest)\n",
    "yFutureTest = sc.inverse_transform(yFutureTest)\n",
    "\n",
    "real = []\n",
    "real = np.append(xPred_in, yFutureTest, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "G_ZCE2YO4bZ_"
   },
   "outputs": [],
   "source": [
    "def PredFuture_dense(xPred):\n",
    "  yPred = model_dense.predict(xPred)\n",
    "  yPred = np.reshape(yPred, (1, 1, 1))\n",
    "  data = np.append(xPred, yPred, axis = 1)\n",
    "  data = data[:, -(dense_width):, :]\n",
    "  return data\n",
    "\n",
    "yModelPred = []\n",
    "for i in range (Predict_hours):\n",
    "  xPred = PredFuture_dense(xPred)\n",
    "  yModelPred.append(xPred[0][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "U5J64VO34bdd"
   },
   "outputs": [],
   "source": [
    "yModelPred = np.array(yModelPred)\n",
    "yModelPred = sc.inverse_transform(yModelPred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "a7GCw57B4bf0",
    "outputId": "3b5f5452-4af5-447a-ba6f-c79fc15eff2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.682133078239662\n"
     ]
    }
   ],
   "source": [
    "plt.plot(real, 'y-', label='train')\n",
    "plt.plot(range(dense_width, Predict_hours + dense_width), yFutureTest, 'c-', label='Real')\n",
    "plt.plot(range(dense_width, Predict_hours + dense_width), yModelPred, 'm-', label='Predict')\n",
    "plt.title(label='Prediction')\n",
    "plt.xlabel(xlabel='Time')\n",
    "plt.ylabel(ylabel='T')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "r2 = r2_score(yFutureTest, yModelPred)\n",
    "r2_future['Dense'] = r2\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=purple>Compare</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2BUqUqJd6W4P",
    "outputId": "47851857-b854-4fbc-d1ba-b6185053b64f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CNN': 0.7851424365003093,\n",
       " 'Dense': 0.682133078239662,\n",
       " 'RNN': 0.6429095118313762}"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "To-Pb2R_6Y0h",
    "outputId": "5ec6b25a-d356-4652-ea82-bf1134716e03"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(3)\n",
    "width = 0.2\n",
    "\n",
    "val_r2 = r2_future.values()\n",
    "\n",
    "plt.figure(figsize=(4,7))\n",
    "plt.ylabel('r2_score [T (degC)]')\n",
    "plt.axhline(0, color= 'r')\n",
    "plt.bar(x , val_r2, 0.4, label='Test')\n",
    "plt.xticks(ticks=x, labels=r2_future.keys(), rotation=45)\n",
    "_ = plt.legend()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "finalCRD_v1",
   "provenance": []
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}