{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  internetActivity\n",
      "0           3         50.527840\n",
      "1           3         46.167273\n",
      "2           3         34.979989\n",
      "3           3         30.975184\n",
      "4           3         32.125861\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x153deeed0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://github.com/ivanarielcaceres/timeseries-lstm-keras/blob/master/timeseries-prediction.ipynb\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.layers import LSTM\n",
    "from keras  import callbacks\n",
    "from keras import optimizers\n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv('g3.csv')\n",
    "df=df.drop(['gridID','smsIn','smsOut','callIn','callOut'],axis=1)\n",
    "print(df.head())\n",
    "df.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows: 168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>internetActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50527.840166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46167.272850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34979.988709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30975.184098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32125.861496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internetActivity\n",
       "0      50527.840166\n",
       "1      46167.272850\n",
       "2      34979.988709\n",
       "3      30975.184098\n",
       "4      32125.861496"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "columns_to_keep = ['internetActivity']\n",
    "df = df[columns_to_keep]\n",
    "df['internetActivity'] = df['internetActivity'].apply(lambda x: x*1000)\n",
    "print('Total rows: {}'.format(len(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "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>internetActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59329.403782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19340.680578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28835.128844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45871.092892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55938.847157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73833.796221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>137260.205806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internetActivity\n",
       "count        168.000000\n",
       "mean       59329.403782\n",
       "std        19340.680578\n",
       "min        28835.128844\n",
       "25%        45871.092892\n",
       "50%        55938.847157\n",
       "75%        73833.796221\n",
       "max       137260.205806"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x153eaec90>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "internetActivity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null값\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min internetActivity    28835.128844\n",
      "dtype: float64\n",
      "Max internetActivity    137260.205806\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Min', np.min(df))\n",
    "print('Max', np.max(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min 0.0\n",
      "Max 1.0\n"
     ]
    }
   ],
   "source": [
    "dataset = df.astype('float64')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(dataset)\n",
    "print('Min', np.min(scaled))\n",
    "print('Max', np.max(scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.20007098]\n",
      " [0.15985365]\n",
      " [0.05667379]\n",
      " [0.01973764]\n",
      " [0.03035029]\n",
      " [0.01870095]\n",
      " [0.10339111]\n",
      " [0.33236317]\n",
      " [0.24358072]\n",
      " [0.20221443]\n",
      " [0.21110425]\n",
      " [0.27576923]\n",
      " [0.24869368]\n",
      " [0.21314124]\n",
      " [0.24178533]\n",
      " [0.3789206 ]\n",
      " [0.38566448]\n",
      " [0.45127529]\n",
      " [0.48445495]\n",
      " [0.47844148]\n",
      " [0.57440569]\n",
      " [0.5788427 ]\n",
      " [0.44481729]\n",
      " [0.35800053]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 117\n",
      "test: 51\n"
     ]
    }
   ],
   "source": [
    "#Create RNN\n",
    "train_size = int(len(scaled) * 0.70)\n",
    "test_size = len(scaled - train_size)\n",
    "train, test = scaled[0:train_size, :], scaled[train_size: len(scaled), :]\n",
    "print('train: {}\\ntest: {}'.format(len(train), len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    print(len(dataset), look_back)\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        print(i)\n",
    "        print('X {} to {}'.format(i, i+look_back))\n",
    "        print(a)\n",
    "        print('Y {}'.format(i + look_back))\n",
    "        print(dataset[i + look_back, 0])\n",
    "        dataset[i + look_back, 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return np.array(dataX), np.array(dataY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.20007098]\n",
      "Y 1\n",
      "0.15985364725415807\n",
      "1\n",
      "X 1 to 2\n",
      "[0.15985365]\n",
      "Y 2\n",
      "0.05667378836773396\n",
      "2\n",
      "X 2 to 3\n",
      "[0.05667379]\n",
      "Y 3\n",
      "0.019737641095125213\n",
      "3\n",
      "X 3 to 4\n",
      "[0.01973764]\n",
      "Y 4\n",
      "0.030350291135590735\n",
      "4\n",
      "X 4 to 5\n",
      "[0.03035029]\n",
      "Y 5\n",
      "0.018700952229049883\n",
      "5\n",
      "X 5 to 6\n",
      "[0.01870095]\n",
      "Y 6\n",
      "0.10339111380444765\n",
      "6\n",
      "X 6 to 7\n",
      "[0.10339111]\n",
      "Y 7\n",
      "0.3323631656384561\n",
      "7\n",
      "X 7 to 8\n",
      "[0.33236317]\n",
      "Y 8\n",
      "0.24358072362542177\n",
      "8\n",
      "X 8 to 9\n",
      "[0.24358072]\n",
      "Y 9\n",
      "0.20221442599796652\n",
      "9\n",
      "X 9 to 10\n",
      "[0.20221443]\n",
      "Y 10\n",
      "0.21110425134043626\n",
      "10\n",
      "X 10 to 11\n",
      "[0.21110425]\n",
      "Y 11\n",
      "0.2757692267809732\n",
      "11\n",
      "X 11 to 12\n",
      "[0.27576923]\n",
      "Y 12\n",
      "0.24869368309302237\n",
      "12\n",
      "X 12 to 13\n",
      "[0.24869368]\n",
      "Y 13\n",
      "0.21314124308147664\n",
      "13\n",
      "X 13 to 14\n",
      "[0.21314124]\n",
      "Y 14\n",
      "0.24178533486520642\n",
      "14\n",
      "X 14 to 15\n",
      "[0.24178533]\n",
      "Y 15\n",
      "0.3789206006343419\n",
      "15\n",
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      "[0.3789206]\n",
      "Y 16\n",
      "0.3856644841824414\n",
      "16\n",
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      "[0.38566448]\n",
      "Y 17\n",
      "0.4512752874712785\n",
      "17\n",
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      "[0.45127529]\n",
      "Y 18\n",
      "0.48445494502401726\n",
      "18\n",
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      "[0.48445495]\n",
      "Y 19\n",
      "0.47844148425700345\n",
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      "0.5744056892309004\n",
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      "0.57884270094408\n",
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      "[0.5788427]\n",
      "Y 22\n",
      "0.44481729385316954\n",
      "22\n",
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      "[0.44481729]\n",
      "Y 23\n",
      "0.35800052703867874\n",
      "23\n",
      "X 23 to 24\n",
      "[0.35800053]\n",
      "Y 24\n",
      "0.1991870563082614\n",
      "24\n",
      "X 24 to 25\n",
      "[0.19918706]\n",
      "Y 25\n",
      "0.1597721896644166\n",
      "25\n",
      "X 25 to 26\n",
      "[0.15977219]\n",
      "Y 26\n",
      "0.05702300462099075\n",
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      "[0.057023]\n",
      "Y 27\n",
      "0.0194468895775744\n",
      "27\n",
      "X 27 to 28\n",
      "[0.01944689]\n",
      "Y 28\n",
      "0.029649672342747457\n",
      "28\n",
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      "[0.02964967]\n",
      "Y 29\n",
      "0.017734613668940413\n",
      "29\n",
      "X 29 to 30\n",
      "[0.01773461]\n",
      "Y 30\n",
      "0.1014302360470064\n",
      "30\n",
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      "[0.10143024]\n",
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      "0.3290519735714232\n",
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      "[0.32905197]\n",
      "Y 32\n",
      "0.23989334291866288\n",
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      "[0.23989334]\n",
      "Y 33\n",
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      "Y 34\n",
      "0.20803138904894347\n",
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      "0.2109866210507234\n",
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      "[0.21098662]\n",
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      "0.2395898120844761\n",
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      "[0.23958981]\n",
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      "[0.38095075]\n",
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      "[0.44738439]\n",
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      "X 44 to 45\n",
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      "0.575554084125834\n",
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      "X 45 to 46\n",
      "[0.57555408]\n",
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      "[0.44136375]\n",
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      "[0.3554877]\n",
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      "0.2492412598524854\n",
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      "[0.24924126]\n",
      "Y 49\n",
      "0.13112982100564435\n",
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      "X 49 to 50\n",
      "[0.13112982]\n",
      "Y 50\n",
      "0.07278490107166369\n",
      "50\n",
      "X 50 to 51\n",
      "[0.0727849]\n",
      "Y 51\n",
      "0.023647394064408633\n",
      "51\n",
      "X 51 to 52\n",
      "[0.02364739]\n",
      "Y 52\n",
      "0.0\n",
      "52\n",
      "X 52 to 53\n",
      "[0.]\n",
      "Y 53\n",
      "0.009079397671684475\n",
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      "X 53 to 54\n",
      "[0.0090794]\n",
      "Y 54\n",
      "0.07747031759430861\n",
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      "[0.07747032]\n",
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      "0.21560516242380584\n",
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      "0.18573554559087663\n",
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      "[0.5489922]\n",
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      "[0.64625797]\n",
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      "0.09151357039289065\n",
      "99\n",
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      "[0.09151357]\n",
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      "[0.04855708]\n",
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      "[0.2824463]\n",
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      "[0.26993299]\n",
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      "[0.27714706]\n",
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      "0.22684096352449729\n",
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      "[0.22684096]\n",
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      "0.2518753320386932\n",
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      "[0.25187533]\n",
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      "0.303043760258579\n",
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      "[0.30304376]\n",
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      "0.3494737521894679\n",
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      "[0.34947375]\n",
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      "[0.45673909]\n",
      "Y 115\n",
      "0.5053637507703335\n",
      "51 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.74701136]\n",
      "Y 1\n",
      "1.0\n",
      "1\n",
      "X 1 to 2\n",
      "[1.]\n",
      "Y 2\n",
      "0.39529755105287495\n",
      "2\n",
      "X 2 to 3\n",
      "[0.39529755]\n",
      "Y 3\n",
      "0.19835666101940402\n",
      "3\n",
      "X 3 to 4\n",
      "[0.19835666]\n",
      "Y 4\n",
      "0.15969566489500353\n",
      "4\n",
      "X 4 to 5\n",
      "[0.15969566]\n",
      "Y 5\n",
      "0.05735107341532397\n",
      "5\n",
      "X 5 to 6\n",
      "[0.05735107]\n",
      "Y 6\n",
      "0.019173745074923587\n",
      "6\n",
      "X 6 to 7\n",
      "[0.01917375]\n",
      "Y 7\n",
      "0.028991480862506414\n",
      "7\n",
      "X 7 to 8\n",
      "[0.02899148]\n",
      "Y 8\n",
      "0.016826793590761835\n",
      "8\n",
      "X 8 to 9\n",
      "[0.01682679]\n",
      "Y 9\n",
      "0.09958810299701476\n",
      "9\n",
      "X 9 to 10\n",
      "[0.0995881]\n",
      "Y 10\n",
      "0.3259412970805487\n",
      "10\n",
      "X 10 to 11\n",
      "[0.3259413]\n",
      "Y 11\n",
      "0.23642925861149738\n",
      "11\n",
      "X 11 to 12\n",
      "[0.23642926]\n",
      "Y 12\n",
      "0.19581456825125665\n",
      "12\n",
      "X 12 to 13\n",
      "[0.19581457]\n",
      "Y 13\n",
      "0.2051446098176779\n",
      "13\n",
      "X 13 to 14\n",
      "[0.20514461]\n",
      "Y 14\n",
      "0.27111070052619307\n",
      "14\n",
      "X 14 to 15\n",
      "[0.2711107]\n",
      "Y 15\n",
      "0.2432352401282702\n",
      "15\n",
      "X 15 to 16\n",
      "[0.24323524]\n",
      "Y 16\n",
      "0.2089624762828874\n",
      "16\n",
      "X 16 to 17\n",
      "[0.20896248]\n",
      "Y 17\n",
      "0.237527243389898\n",
      "17\n",
      "X 17 to 18\n",
      "[0.23752724]\n",
      "Y 18\n",
      "0.37161939540484773\n",
      "18\n",
      "X 18 to 19\n",
      "[0.3716194]\n",
      "Y 19\n",
      "0.37652247002859146\n",
      "19\n",
      "X 19 to 20\n",
      "[0.37652247]\n",
      "Y 20\n",
      "0.4437291047717032\n",
      "20\n",
      "X 20 to 21\n",
      "[0.4437291]\n",
      "Y 21\n",
      "0.4743896202186339\n",
      "21\n",
      "X 21 to 22\n",
      "[0.47438962]\n",
      "Y 22\n",
      "0.4707743850231874\n",
      "22\n",
      "X 22 to 23\n",
      "[0.47077439]\n",
      "Y 23\n",
      "0.5689579658170583\n",
      "23\n",
      "X 23 to 24\n",
      "[0.56895797]\n",
      "Y 24\n",
      "0.5724646157963273\n",
      "24\n",
      "X 24 to 25\n",
      "[0.57246462]\n",
      "Y 25\n",
      "0.43811933730632385\n",
      "25\n",
      "X 25 to 26\n",
      "[0.43811934]\n",
      "Y 26\n",
      "0.353127038056359\n",
      "26\n",
      "X 26 to 27\n",
      "[0.35312704]\n",
      "Y 27\n",
      "0.23742326378327833\n",
      "27\n",
      "X 27 to 28\n",
      "[0.23742326]\n",
      "Y 28\n",
      "0.15369429640013083\n",
      "28\n",
      "X 28 to 29\n",
      "[0.1536943]\n",
      "Y 29\n",
      "0.0945516558388193\n",
      "29\n",
      "X 29 to 30\n",
      "[0.09455166]\n",
      "Y 30\n",
      "0.08229396215365686\n",
      "30\n",
      "X 30 to 31\n",
      "[0.08229396]\n",
      "Y 31\n",
      "0.026930565491795122\n",
      "31\n",
      "X 31 to 32\n",
      "[0.02693057]\n",
      "Y 32\n",
      "0.0003399528866128154\n",
      "32\n",
      "X 32 to 33\n",
      "[0.00033995]\n",
      "Y 33\n",
      "0.05034492718933725\n",
      "33\n",
      "X 33 to 34\n",
      "[0.05034493]\n",
      "Y 34\n",
      "0.08255081835387107\n",
      "34\n",
      "X 34 to 35\n",
      "[0.08255082]\n",
      "Y 35\n",
      "0.1571966413849663\n",
      "35\n",
      "X 35 to 36\n",
      "[0.15719664]\n",
      "Y 36\n",
      "0.4039368051565215\n",
      "36\n",
      "X 36 to 37\n",
      "[0.40393681]\n",
      "Y 37\n",
      "0.3649261385747539\n",
      "37\n",
      "X 37 to 38\n",
      "[0.36492614]\n",
      "Y 38\n",
      "0.4662095692536908\n",
      "38\n",
      "X 38 to 39\n",
      "[0.46620957]\n",
      "Y 39\n",
      "0.48389561080622706\n",
      "39\n",
      "X 39 to 40\n",
      "[0.48389561]\n",
      "Y 40\n",
      "0.46049911079333244\n",
      "40\n",
      "X 40 to 41\n",
      "[0.46049911]\n",
      "Y 41\n",
      "0.5215191993179107\n",
      "41\n",
      "X 41 to 42\n",
      "[0.5215192]\n",
      "Y 42\n",
      "0.5977781661070254\n",
      "42\n",
      "X 42 to 43\n",
      "[0.59777817]\n",
      "Y 43\n",
      "0.47775481732407216\n",
      "43\n",
      "X 43 to 44\n",
      "[0.47775482]\n",
      "Y 44\n",
      "0.535441825930534\n",
      "44\n",
      "X 44 to 45\n",
      "[0.53544183]\n",
      "Y 45\n",
      "0.5184217476444\n",
      "45\n",
      "X 45 to 46\n",
      "[0.51842175]\n",
      "Y 46\n",
      "0.46349257074357936\n",
      "46\n",
      "X 46 to 47\n",
      "[0.46349257]\n",
      "Y 47\n",
      "0.4433481473916265\n",
      "47\n",
      "X 47 to 48\n",
      "[0.44334815]\n",
      "Y 48\n",
      "0.4743297582519778\n",
      "48\n",
      "X 48 to 49\n",
      "[0.47432976]\n",
      "Y 49\n",
      "0.36408863974047173\n"
     ]
    }
   ],
   "source": [
    "look_back = 1\n",
    "X_train, y_train = create_dataset(train, look_back)\n",
    "X_test, y_test = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(115, 1, 1)\n",
      "(49, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.0524\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0266\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0258\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0246\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0239\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0221\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0220\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0217\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0205\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0195\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0190\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0178\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0175\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0159\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0162\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0141\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0130\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0093\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0100\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0091\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0078\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0074\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0078\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0073\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0070\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0069\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0070\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x153fdb110>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 1\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=batch_size, verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 9031.78 RMSE\n",
      "Test Score: 12864.90 RMSE\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "trainPredict = model.predict(X_train, batch_size=batch_size)\n",
    "model.reset_states()\n",
    "\n",
    "testPredict = model.predict(X_test, batch_size=batch_size)\n",
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "y_train = scaler.inverse_transform([y_train])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "y_test = scaler.inverse_transform([y_test])\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(y_train[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(y_test[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))\n",
    "\n",
    "col=['gridID','Internet Activity Prediction']\n",
    "list1={'gridID':[3],'Internet Activity Prediction':[testScore]}\n",
    "record=pd.DataFrame(list1)\n",
    "with open('predictions.csv','r') as infile:\n",
    "          record.to_csv('predictions.csv',mode='a',header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainPredictPlot = np.empty_like(scaled)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(scaled)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(scaled)-1, :] = testPredict\n",
    "# plot baseline and predictions\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(scaler.inverse_transform(scaled))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14048055.327840425\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}