{
 "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           1         50.341965\n",
      "1           1         46.150144\n",
      "2           1         35.053423\n",
      "3           1         30.914044\n",
      "4           1         31.978532\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x144be2a50>"
      ]
     },
     "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('g1.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": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>50341.965081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46150.143600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35053.423392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30914.043629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31978.532387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internetActivity\n",
       "0      50341.965081\n",
       "1      46150.143600\n",
       "2      35053.423392\n",
       "3      30914.043629\n",
       "4      31978.532387"
      ]
     },
     "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>60461.824013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19113.807660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30659.575234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46150.143600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59414.298365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76521.612181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112291.185193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internetActivity\n",
       "count        168.000000\n",
       "mean       60461.824013\n",
       "std        19113.807660\n",
       "min        30659.575234\n",
       "25%        46150.143600\n",
       "50%        59414.298365\n",
       "75%        76521.612181\n",
       "max       112291.185193"
      ]
     },
     "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 0x146e5f510>"
      ]
     },
     "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    30659.575234\n",
      "dtype: float64\n",
      "Max internetActivity    112291.185193\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Min', np.min(df))\n",
    "print('Max', np.max(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.24111236]\n",
      " [0.18976189]\n",
      " [0.05382533]\n",
      " [0.00311728]\n",
      " [0.01615743]\n",
      " [0.        ]\n",
      " [0.10992557]\n",
      " [0.41057335]\n",
      " [0.2916813 ]\n",
      " [0.23773589]\n",
      " [0.25012827]\n",
      " [0.33774603]\n",
      " [0.30072117]\n",
      " [0.25519926]\n",
      " [0.29313967]\n",
      " [0.47124411]\n",
      " [0.47775649]\n",
      " [0.56702197]\n",
      " [0.607746  ]\n",
      " [0.60294416]\n",
      " [0.73335397]\n",
      " [0.73801158]\n",
      " [0.55957094]\n",
      " [0.44668211]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "X 26 to 27\n",
      "[0.46694697]\n",
      "Y 27\n",
      "0.3324623175695592\n",
      "27\n",
      "X 27 to 28\n",
      "[0.33246232]\n",
      "Y 28\n",
      "0.16399681181945874\n",
      "28\n",
      "X 28 to 29\n",
      "[0.16399681]\n",
      "Y 29\n",
      "0.12920943497834386\n",
      "29\n",
      "X 29 to 30\n",
      "[0.12920943]\n",
      "Y 30\n",
      "0.028940927125846283\n",
      "30\n",
      "X 30 to 31\n",
      "[0.02894093]\n",
      "Y 31\n",
      "0.008787709599856364\n",
      "31\n",
      "X 31 to 32\n",
      "[0.00878771]\n",
      "Y 32\n",
      "0.0512303609595417\n",
      "32\n",
      "X 32 to 33\n",
      "[0.05123036]\n",
      "Y 33\n",
      "0.05826847426890919\n",
      "33\n",
      "X 33 to 34\n",
      "[0.05826847]\n",
      "Y 34\n",
      "0.1503575507567087\n",
      "34\n",
      "X 34 to 35\n",
      "[0.15035755]\n",
      "Y 35\n",
      "0.357964360884446\n",
      "35\n",
      "X 35 to 36\n",
      "[0.35796436]\n",
      "Y 36\n",
      "0.49583652061068156\n",
      "36\n",
      "X 36 to 37\n",
      "[0.49583652]\n",
      "Y 37\n",
      "0.6257317716270365\n",
      "37\n",
      "X 37 to 38\n",
      "[0.62573177]\n",
      "Y 38\n",
      "0.570891164493458\n",
      "38\n",
      "X 38 to 39\n",
      "[0.57089116]\n",
      "Y 39\n",
      "0.5415014301053864\n",
      "39\n",
      "X 39 to 40\n",
      "[0.54150143]\n",
      "Y 40\n",
      "0.5824810016051338\n",
      "40\n",
      "X 40 to 41\n",
      "[0.582481]\n",
      "Y 41\n",
      "0.725289022389892\n",
      "41\n",
      "X 41 to 42\n",
      "[0.72528902]\n",
      "Y 42\n",
      "0.6972960942919606\n",
      "42\n",
      "X 42 to 43\n",
      "[0.69729609]\n",
      "Y 43\n",
      "0.48451597732341806\n",
      "43\n",
      "X 43 to 44\n",
      "[0.48451598]\n",
      "Y 44\n",
      "0.5590311695620611\n",
      "44\n",
      "X 44 to 45\n",
      "[0.55903117]\n",
      "Y 45\n",
      "0.7050350480088428\n",
      "45\n",
      "X 45 to 46\n",
      "[0.70503505]\n",
      "Y 46\n",
      "0.6031811576722008\n",
      "46\n",
      "X 46 to 47\n",
      "[0.60318116]\n",
      "Y 47\n",
      "0.5075434299310404\n",
      "47\n",
      "X 47 to 48\n",
      "[0.50754343]\n",
      "Y 48\n",
      "0.37654307497093914\n",
      "48\n",
      "X 48 to 49\n",
      "[0.37654307]\n",
      "Y 49\n",
      "0.3465353579340424\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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.1125\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0535\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0503\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0470\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0446\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0428\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0402\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0377\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0369\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0321\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0306\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0252\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0272\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0223\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0198\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0196\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0178\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0176\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0162\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0151\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0144\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0130\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0127\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0123\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0123\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0120\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0124\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0109\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0120\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0120\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x147199d10>"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 8557.80 RMSE\n",
      "Test Score: 9952.92 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':[1],'Internet Activity Prediction':[testScore]}\n",
    "record=pd.DataFrame(list1)\n",
    "record.to_csv('predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": []
  }
 ],
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