{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  internetActivity\n",
      "0           2         50.432001\n",
      "1           2         46.158441\n",
      "2           2         35.017853\n",
      "3           2         30.943659\n",
      "4           2         32.049897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14939fcd0>"
      ]
     },
     "execution_count": 1,
     "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('g2.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": 2,
   "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": [
       "<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>0</th>\n",
       "      <td>50432.000754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46158.440804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35017.852508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30943.659343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32049.896849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internetActivity\n",
       "0      50432.000754\n",
       "1      46158.440804\n",
       "2      35017.852508\n",
       "3      30943.659343\n",
       "4      32049.896849"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "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>62017.298694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20996.445589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28871.988261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46480.756454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58884.820675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76683.349611</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       62017.298694\n",
       "std        20996.445589\n",
       "min        28871.988261\n",
       "25%        46480.756454\n",
       "50%        58884.820675\n",
       "75%        76683.349611\n",
       "max       137260.205806"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b60e510>"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "internetActivity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null값\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min internetActivity    28871.988261\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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.19891473]\n",
      " [0.15948645]\n",
      " [0.05670233]\n",
      " [0.01911343]\n",
      " [0.02931969]\n",
      " [0.01740058]\n",
      " [0.10112466]\n",
      " [0.32882381]\n",
      " [0.23963485]\n",
      " [0.19864216]\n",
      " [0.20776207]\n",
      " [0.27312013]\n",
      " [0.24562279]\n",
      " [0.2107183 ]\n",
      " [0.23933122]\n",
      " [0.37494352]\n",
      " [0.38074023]\n",
      " [0.44719646]\n",
      " [0.47908806]\n",
      " [0.47430953]\n",
      " [0.57145109]\n",
      " [0.57540974]\n",
      " [0.44117377]\n",
      " [0.35526852]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.19891473]\n",
      "Y 1\n",
      "0.15948645465844052\n",
      "1\n",
      "X 1 to 2\n",
      "[0.15948645]\n",
      "Y 2\n",
      "0.05670232785440976\n",
      "2\n",
      "X 2 to 3\n",
      "[0.05670233]\n",
      "Y 3\n",
      "0.019113434358144776\n",
      "3\n",
      "X 3 to 4\n",
      "[0.01911343]\n",
      "Y 4\n",
      "0.029319686768286113\n",
      "4\n",
      "X 4 to 5\n",
      "[0.02931969]\n",
      "Y 5\n",
      "0.017400576158421388\n",
      "5\n",
      "X 5 to 6\n",
      "[0.01740058]\n",
      "Y 6\n",
      "0.10112466078073379\n",
      "6\n",
      "X 6 to 7\n",
      "[0.10112466]\n",
      "Y 7\n",
      "0.3288238052866045\n",
      "7\n",
      "X 7 to 8\n",
      "[0.32882381]\n",
      "Y 8\n",
      "0.23963485459260192\n",
      "8\n",
      "X 8 to 9\n",
      "[0.23963485]\n",
      "Y 9\n",
      "0.19864215905662558\n",
      "9\n",
      "X 9 to 10\n",
      "[0.19864216]\n",
      "Y 10\n",
      "0.20776206547621795\n",
      "10\n",
      "X 10 to 11\n",
      "[0.20776207]\n",
      "Y 11\n",
      "0.2731201301391789\n",
      "11\n",
      "X 11 to 12\n",
      "[0.27312013]\n",
      "Y 12\n",
      "0.24562279204990334\n",
      "12\n",
      "X 12 to 13\n",
      "[0.24562279]\n",
      "Y 13\n",
      "0.2107183024592938\n",
      "13\n",
      "X 13 to 14\n",
      "[0.2107183]\n",
      "Y 14\n",
      "0.2393312205371415\n",
      "14\n",
      "X 14 to 15\n",
      "[0.23933122]\n",
      "Y 15\n",
      "0.37494352233155626\n",
      "15\n",
      "X 15 to 16\n",
      "[0.37494352]\n",
      "Y 16\n",
      "0.3807402334387191\n",
      "16\n",
      "X 16 to 17\n",
      "[0.38074023]\n",
      "Y 17\n",
      "0.44719645845555955\n",
      "17\n",
      "X 17 to 18\n",
      "[0.44719646]\n",
      "Y 18\n",
      "0.4790880579163985\n",
      "18\n",
      "X 18 to 19\n",
      "[0.47908806]\n",
      "Y 19\n",
      "0.4743095263989243\n",
      "19\n",
      "X 19 to 20\n",
      "[0.47430953]\n",
      "Y 20\n",
      "0.5714510915871255\n",
      "20\n",
      "X 20 to 21\n",
      "[0.57145109]\n",
      "Y 21\n",
      "0.5754097434424896\n",
      "21\n",
      "X 21 to 22\n",
      "[0.57540974]\n",
      "Y 22\n",
      "0.44117377293520055\n",
      "22\n",
      "X 22 to 23\n",
      "[0.44117377]\n",
      "Y 23\n",
      "0.35526851963133843\n",
      "23\n",
      "X 23 to 24\n",
      "[0.35526852]\n",
      "Y 24\n",
      "0.2547246441601163\n",
      "24\n",
      "X 24 to 25\n",
      "[0.25472464]\n",
      "Y 25\n",
      "0.19353350969498462\n",
      "25\n",
      "X 25 to 26\n",
      "[0.19353351]\n",
      "Y 26\n",
      "0.16166784110143312\n",
      "26\n",
      "X 26 to 27\n",
      "[0.16166784]\n",
      "Y 27\n",
      "0.09120462278106473\n",
      "27\n",
      "X 27 to 28\n",
      "[0.09120462]\n",
      "Y 28\n",
      "0.04823352162607858\n",
      "28\n",
      "X 28 to 29\n",
      "[0.04823352]\n",
      "Y 29\n",
      "0.05240980815836532\n",
      "29\n",
      "X 29 to 30\n",
      "[0.05240981]\n",
      "Y 30\n",
      "0.15415562663744303\n",
      "30\n",
      "X 30 to 31\n",
      "[0.15415563]\n",
      "Y 31\n",
      "0.2881991394571133\n",
      "31\n",
      "X 31 to 32\n",
      "[0.28819914]\n",
      "Y 32\n",
      "0.3002240039235514\n",
      "32\n",
      "X 32 to 33\n",
      "[0.300224]\n",
      "Y 33\n",
      "0.25045687661683236\n",
      "33\n",
      "X 33 to 34\n",
      "[0.25045688]\n",
      "Y 34\n",
      "0.2822022869521841\n",
      "34\n",
      "X 34 to 35\n",
      "[0.28220229]\n",
      "Y 35\n",
      "0.24119197583246743\n",
      "35\n",
      "X 35 to 36\n",
      "[0.24119198]\n",
      "Y 36\n",
      "0.2696847173824547\n",
      "36\n",
      "X 36 to 37\n",
      "[0.26968472]\n",
      "Y 37\n",
      "0.2769012453016392\n",
      "37\n",
      "X 37 to 38\n",
      "[0.27690125]\n",
      "Y 38\n",
      "0.22657803649543412\n",
      "38\n",
      "X 38 to 39\n",
      "[0.22657804]\n",
      "Y 39\n",
      "0.25162091840962597\n",
      "39\n",
      "X 39 to 40\n",
      "[0.25162092]\n",
      "Y 40\n",
      "0.3028067473998305\n",
      "40\n",
      "X 40 to 41\n",
      "[0.30280675]\n",
      "Y 41\n",
      "0.34925252870814627\n",
      "41\n",
      "X 41 to 42\n",
      "[0.34925253]\n",
      "Y 42\n",
      "0.4565543419010857\n",
      "42\n",
      "X 42 to 43\n",
      "[0.45655434]\n",
      "Y 43\n",
      "0.5051955405660399\n",
      "43\n",
      "X 43 to 44\n",
      "[0.50519554]\n",
      "Y 44\n",
      "0.5142115153237719\n",
      "44\n",
      "X 44 to 45\n",
      "[0.51421152]\n",
      "Y 45\n",
      "0.7469253300781242\n",
      "45\n",
      "X 45 to 46\n",
      "[0.74692533]\n",
      "Y 46\n",
      "1.0\n",
      "46\n",
      "X 46 to 47\n",
      "[1.]\n",
      "Y 47\n",
      "0.3950919108018165\n",
      "47\n",
      "X 47 to 48\n",
      "[0.39509191]\n",
      "Y 48\n",
      "0.2547246441601163\n",
      "48\n",
      "X 48 to 49\n",
      "[0.25472464]\n",
      "Y 49\n",
      "0.19353350969498462\n",
      "49\n",
      "X 49 to 50\n",
      "[0.19353351]\n",
      "Y 50\n",
      "0.16166784110143312\n",
      "50\n",
      "X 50 to 51\n",
      "[0.16166784]\n",
      "Y 51\n",
      "0.09120462278106473\n",
      "51\n",
      "X 51 to 52\n",
      "[0.09120462]\n",
      "Y 52\n",
      "0.04823352162607858\n",
      "52\n",
      "X 52 to 53\n",
      "[0.04823352]\n",
      "Y 53\n",
      "0.05240980815836532\n",
      "53\n",
      "X 53 to 54\n",
      "[0.05240981]\n",
      "Y 54\n",
      "0.15415562663744303\n",
      "54\n",
      "X 54 to 55\n",
      "[0.15415563]\n",
      "Y 55\n",
      "0.2881991394571133\n",
      "55\n",
      "X 55 to 56\n",
      "[0.28819914]\n",
      "Y 56\n",
      "0.3002240039235514\n",
      "56\n",
      "X 56 to 57\n",
      "[0.300224]\n",
      "Y 57\n",
      "0.25045687661683236\n",
      "57\n",
      "X 57 to 58\n",
      "[0.25045688]\n",
      "Y 58\n",
      "0.2822022869521841\n",
      "58\n",
      "X 58 to 59\n",
      "[0.28220229]\n",
      "Y 59\n",
      "0.24119197583246743\n",
      "59\n",
      "X 59 to 60\n",
      "[0.24119198]\n",
      "Y 60\n",
      "0.2696847173824547\n",
      "60\n",
      "X 60 to 61\n",
      "[0.26968472]\n",
      "Y 61\n",
      "0.2769012453016392\n",
      "61\n",
      "X 61 to 62\n",
      "[0.27690125]\n",
      "Y 62\n",
      "0.22657803649543412\n",
      "62\n",
      "X 62 to 63\n",
      "[0.22657804]\n",
      "Y 63\n",
      "0.25162091840962597\n",
      "63\n",
      "X 63 to 64\n",
      "[0.25162092]\n",
      "Y 64\n",
      "0.3028067473998305\n",
      "64\n",
      "X 64 to 65\n",
      "[0.30280675]\n",
      "Y 65\n",
      "0.34925252870814627\n",
      "65\n",
      "X 65 to 66\n",
      "[0.34925253]\n",
      "Y 66\n",
      "0.4565543419010857\n",
      "66\n",
      "X 66 to 67\n",
      "[0.45655434]\n",
      "Y 67\n",
      "0.5051955405660399\n",
      "67\n",
      "X 67 to 68\n",
      "[0.50519554]\n",
      "Y 68\n",
      "0.5142115153237719\n",
      "68\n",
      "X 68 to 69\n",
      "[0.51421152]\n",
      "Y 69\n",
      "0.7469253300781242\n",
      "69\n",
      "X 69 to 70\n",
      "[0.74692533]\n",
      "Y 70\n",
      "1.0\n",
      "70\n",
      "X 70 to 71\n",
      "[1.]\n",
      "Y 71\n",
      "0.3950919108018165\n",
      "71\n",
      "X 71 to 72\n",
      "[0.39509191]\n",
      "Y 72\n",
      "0.2547246441601163\n",
      "72\n",
      "X 72 to 73\n",
      "[0.25472464]\n",
      "Y 73\n",
      "0.19353350969498462\n",
      "73\n",
      "X 73 to 74\n",
      "[0.19353351]\n",
      "Y 74\n",
      "0.16166784110143312\n",
      "74\n",
      "X 74 to 75\n",
      "[0.16166784]\n",
      "Y 75\n",
      "0.09120462278106473\n",
      "75\n",
      "X 75 to 76\n",
      "[0.09120462]\n",
      "Y 76\n",
      "0.04823352162607858\n",
      "76\n",
      "X 76 to 77\n",
      "[0.04823352]\n",
      "Y 77\n",
      "0.05240980815836532\n",
      "77\n",
      "X 77 to 78\n",
      "[0.05240981]\n",
      "Y 78\n",
      "0.15415562663744303\n",
      "78\n",
      "X 78 to 79\n",
      "[0.15415563]\n",
      "Y 79\n",
      "0.2881991394571133\n",
      "79\n",
      "X 79 to 80\n",
      "[0.28819914]\n",
      "Y 80\n",
      "0.3002240039235514\n",
      "80\n",
      "X 80 to 81\n",
      "[0.300224]\n",
      "Y 81\n",
      "0.25045687661683236\n",
      "81\n",
      "X 81 to 82\n",
      "[0.25045688]\n",
      "Y 82\n",
      "0.2822022869521841\n",
      "82\n",
      "X 82 to 83\n",
      "[0.28220229]\n",
      "Y 83\n",
      "0.24119197583246743\n",
      "83\n",
      "X 83 to 84\n",
      "[0.24119198]\n",
      "Y 84\n",
      "0.2696847173824547\n",
      "84\n",
      "X 84 to 85\n",
      "[0.26968472]\n",
      "Y 85\n",
      "0.2769012453016392\n",
      "85\n",
      "X 85 to 86\n",
      "[0.27690125]\n",
      "Y 86\n",
      "0.22657803649543412\n",
      "86\n",
      "X 86 to 87\n",
      "[0.22657804]\n",
      "Y 87\n",
      "0.25162091840962597\n",
      "87\n",
      "X 87 to 88\n",
      "[0.25162092]\n",
      "Y 88\n",
      "0.3028067473998305\n",
      "88\n",
      "X 88 to 89\n",
      "[0.30280675]\n",
      "Y 89\n",
      "0.34925252870814627\n",
      "89\n",
      "X 89 to 90\n",
      "[0.34925253]\n",
      "Y 90\n",
      "0.4565543419010857\n",
      "90\n",
      "X 90 to 91\n",
      "[0.45655434]\n",
      "Y 91\n",
      "0.5051955405660399\n",
      "91\n",
      "X 91 to 92\n",
      "[0.50519554]\n",
      "Y 92\n",
      "0.5142115153237719\n",
      "92\n",
      "X 92 to 93\n",
      "[0.51421152]\n",
      "Y 93\n",
      "0.7469253300781242\n",
      "93\n",
      "X 93 to 94\n",
      "[0.74692533]\n",
      "Y 94\n",
      "1.0\n",
      "94\n",
      "X 94 to 95\n",
      "[1.]\n",
      "Y 95\n",
      "0.3950919108018165\n",
      "95\n",
      "X 95 to 96\n",
      "[0.39509191]\n",
      "Y 96\n",
      "0.19808404737648883\n",
      "96\n",
      "X 96 to 97\n",
      "[0.19808405]\n",
      "Y 97\n",
      "0.1594099038653644\n",
      "97\n",
      "X 97 to 98\n",
      "[0.1594099]\n",
      "Y 98\n",
      "0.05703050821460376\n",
      "98\n",
      "X 98 to 99\n",
      "[0.05703051]\n",
      "Y 99\n",
      "0.01884019696765432\n",
      "99\n",
      "X 99 to 100\n",
      "[0.0188402]\n",
      "Y 100\n",
      "0.02866127145785974\n",
      "100\n",
      "X 100 to 101\n",
      "[0.02866127]\n",
      "Y 101\n",
      "0.016492447359236106\n",
      "101\n",
      "X 101 to 102\n",
      "[0.01649245]\n",
      "Y 102\n",
      "0.09928190127933029\n",
      "102\n",
      "X 102 to 103\n",
      "[0.0992819]\n",
      "Y 103\n",
      "0.32571207095266086\n",
      "103\n",
      "X 103 to 104\n",
      "[0.32571207]\n",
      "Y 104\n",
      "0.2361695922595033\n",
      "104\n",
      "X 104 to 105\n",
      "[0.23616959]\n",
      "Y 105\n",
      "0.19554109012268245\n",
      "105\n",
      "X 105 to 106\n",
      "[0.19554109]\n",
      "Y 106\n",
      "0.20487430454228706\n",
      "106\n",
      "X 106 to 107\n",
      "[0.2048743]\n",
      "Y 107\n",
      "0.2708628282399166\n",
      "107\n",
      "X 107 to 108\n",
      "[0.27086283]\n",
      "Y 108\n",
      "0.24297788827615996\n",
      "108\n",
      "X 108 to 109\n",
      "[0.24297789]\n",
      "Y 109\n",
      "0.20869346934359517\n",
      "109\n",
      "X 109 to 110\n",
      "[0.20869347]\n",
      "Y 110\n",
      "0.23726795042793392\n",
      "110\n",
      "X 110 to 111\n",
      "[0.23726795]\n",
      "Y 111\n",
      "0.3714057029590603\n",
      "111\n",
      "X 111 to 112\n",
      "[0.3714057]\n",
      "Y 112\n",
      "0.3763104449640067\n",
      "112\n",
      "X 112 to 113\n",
      "[0.37631044]\n",
      "Y 113\n",
      "0.44353993456617424\n",
      "113\n",
      "X 113 to 114\n",
      "[0.44353993]\n",
      "Y 114\n",
      "0.47421087668841444\n",
      "114\n",
      "X 114 to 115\n",
      "[0.47421088]\n",
      "Y 115\n",
      "0.47059441206538\n",
      "51 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.57231922]\n",
      "Y 1\n",
      "0.43792825939562185\n",
      "1\n",
      "X 1 to 2\n",
      "[0.43792826]\n",
      "Y 2\n",
      "0.3529070569424597\n",
      "2\n",
      "X 2 to 3\n",
      "[0.35290706]\n",
      "Y 3\n",
      "0.23716393546112602\n",
      "3\n",
      "X 3 to 4\n",
      "[0.23716394]\n",
      "Y 4\n",
      "0.15340649449414645\n",
      "4\n",
      "X 4 to 5\n",
      "[0.15340649]\n",
      "Y 5\n",
      "0.09424374138413527\n",
      "5\n",
      "X 5 to 6\n",
      "[0.09424374]\n",
      "Y 6\n",
      "0.08198187924354283\n",
      "6\n",
      "X 6 to 7\n",
      "[0.08198188]\n",
      "Y 7\n",
      "0.02659965523476232\n",
      "7\n",
      "X 7 to 8\n",
      "[0.02659966]\n",
      "Y 8\n",
      "0.0\n",
      "8\n",
      "X 8 to 9\n",
      "[0.]\n",
      "Y 9\n",
      "0.050021979419022033\n",
      "9\n",
      "X 9 to 10\n",
      "[0.05002198]\n",
      "Y 10\n",
      "0.0822388227924582\n",
      "10\n",
      "X 10 to 11\n",
      "[0.08223882]\n",
      "Y 11\n",
      "0.15691003051616598\n",
      "11\n",
      "X 11 to 12\n",
      "[0.15691003]\n",
      "Y 12\n",
      "0.40373410284359434\n",
      "12\n",
      "X 12 to 13\n",
      "[0.4037341]\n",
      "Y 13\n",
      "0.36471016996319705\n",
      "13\n",
      "X 13 to 14\n",
      "[0.36471017]\n",
      "Y 14\n",
      "0.46602804394585984\n",
      "14\n",
      "X 14 to 15\n",
      "[0.46602804]\n",
      "Y 15\n",
      "0.4837200999639095\n",
      "15\n",
      "X 15 to 16\n",
      "[0.4837201]\n",
      "Y 16\n",
      "0.4603156435384935\n",
      "16\n",
      "X 16 to 17\n",
      "[0.46031564]\n",
      "Y 17\n",
      "0.5213564830726727\n",
      "17\n",
      "X 17 to 18\n",
      "[0.52135648]\n",
      "Y 18\n",
      "0.5976413831337682\n",
      "18\n",
      "X 18 to 19\n",
      "[0.59764138]\n",
      "Y 19\n",
      "0.477577218191364\n",
      "19\n",
      "X 19 to 20\n",
      "[0.47757722]\n",
      "Y 20\n",
      "0.5352838443319591\n",
      "20\n",
      "X 20 to 21\n",
      "[0.53528384]\n",
      "Y 21\n",
      "0.5182579780534364\n",
      "21\n",
      "X 21 to 22\n",
      "[0.51825798]\n",
      "Y 22\n",
      "0.46331012147015727\n",
      "22\n",
      "X 22 to 23\n",
      "[0.46331012]\n",
      "Y 23\n",
      "0.443158847634495\n",
      "23\n",
      "X 23 to 24\n",
      "[0.44315885]\n",
      "Y 24\n",
      "0.47415099436458963\n",
      "24\n",
      "X 24 to 25\n",
      "[0.47415099]\n",
      "Y 25\n",
      "0.36387238632194774\n",
      "25\n",
      "X 25 to 26\n",
      "[0.36387239]\n",
      "Y 26\n",
      "0.2962722690041001\n",
      "26\n",
      "X 26 to 27\n",
      "[0.29627227]\n",
      "Y 27\n",
      "0.1627242789441734\n",
      "27\n",
      "X 27 to 28\n",
      "[0.16272428]\n",
      "Y 28\n",
      "0.135689434217437\n",
      "28\n",
      "X 28 to 29\n",
      "[0.13568943]\n",
      "Y 29\n",
      "0.09622253217969345\n",
      "29\n",
      "X 29 to 30\n",
      "[0.09622253]\n",
      "Y 30\n",
      "0.050775501746612994\n",
      "30\n",
      "X 30 to 31\n",
      "[0.0507755]\n",
      "Y 31\n",
      "0.01877441239227723\n",
      "31\n",
      "X 31 to 32\n",
      "[0.01877441]\n",
      "Y 32\n",
      "0.019429049742062\n",
      "32\n",
      "X 32 to 33\n",
      "[0.01942905]\n",
      "Y 33\n",
      "0.03922124825178641\n",
      "33\n",
      "X 33 to 34\n",
      "[0.03922125]\n",
      "Y 34\n",
      "0.15658729000693622\n",
      "34\n",
      "X 34 to 35\n",
      "[0.15658729]\n",
      "Y 35\n",
      "0.20582698250027487\n",
      "35\n",
      "X 35 to 36\n",
      "[0.20582698]\n",
      "Y 36\n",
      "0.32475304047076037\n",
      "36\n",
      "X 36 to 37\n",
      "[0.32475304]\n",
      "Y 37\n",
      "0.3262494635625881\n",
      "37\n",
      "X 37 to 38\n",
      "[0.32624946]\n",
      "Y 38\n",
      "0.35008396372993345\n",
      "38\n",
      "X 38 to 39\n",
      "[0.35008396]\n",
      "Y 39\n",
      "0.44043175524260014\n",
      "39\n",
      "X 39 to 40\n",
      "[0.44043176]\n",
      "Y 40\n",
      "0.4012271757846906\n",
      "40\n",
      "X 40 to 41\n",
      "[0.40122718]\n",
      "Y 41\n",
      "0.37983194001546716\n",
      "41\n",
      "X 41 to 42\n",
      "[0.37983194]\n",
      "Y 42\n",
      "0.36712420369492926\n",
      "42\n",
      "X 42 to 43\n",
      "[0.3671242]\n",
      "Y 43\n",
      "0.44109162961302684\n",
      "43\n",
      "X 43 to 44\n",
      "[0.44109163]\n",
      "Y 44\n",
      "0.4396199605380112\n",
      "44\n",
      "X 44 to 45\n",
      "[0.43961996]\n",
      "Y 45\n",
      "0.54740960055119\n",
      "45\n",
      "X 45 to 46\n",
      "[0.5474096]\n",
      "Y 46\n",
      "0.5412130586818085\n",
      "46\n",
      "X 46 to 47\n",
      "[0.54121306]\n",
      "Y 47\n",
      "0.5521488307426418\n",
      "47\n",
      "X 47 to 48\n",
      "[0.55214883]\n",
      "Y 48\n",
      "0.7696334419151003\n",
      "48\n",
      "X 48 to 49\n",
      "[0.76963344]\n",
      "Y 49\n",
      "0.45620070429788795\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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.0682\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0400\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0381\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0377\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0356\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0360\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0343\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0335\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0328\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0327\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0316\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0291\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0304\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0271\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0270\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0273\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0259\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0258\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0258\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0228\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0241\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0193\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0217\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0221\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0194\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0191\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0181\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0187\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0173\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0181\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0178\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0179\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0176\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0162\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0167\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0168\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0156\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0157\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0143\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0153\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0153\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0149\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0150\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0152\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0148\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0141\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0147\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0158\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0145\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0149\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0152\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0142\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0147\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0134\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0138\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0151\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0147\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0143\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0139\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0150\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0147\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0145\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0141\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0145\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0143\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0139\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0134\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0148\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0138\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0145\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0142\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0140\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0138\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0131\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0130\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0135\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0142\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0139\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0131\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0143\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0126\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0132\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0139\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0142\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0134\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x14939f250>"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 13164.78 RMSE\n",
      "Test Score: 10007.81 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",
    "col=['gridID','Internet Activity Prediction']\n",
    "list1={'gridID':[2],'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": 15,
   "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": 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
}