Yangzijiang EPOCH100 GRU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.08</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close    Volume\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10       1.10  27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07       1.07  32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09       1.09  38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08       1.08  20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07       1.07  33596100"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "\n",
    "df_Yzj = pd.read_csv('BS6.SI.CSV')\n",
    "df_Yzj.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4/18/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.842029</td>\n",
       "      <td>413736000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4/19/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>95804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4/20/2007</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.816894</td>\n",
       "      <td>70064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4/23/2007</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>36099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4/24/2007</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>14422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3179 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close     Volume\n",
       "0      4/18/2007  1.35  1.39  1.33   1.34   0.842029  413736000\n",
       "1      4/19/2007  1.35  1.35  1.26   1.27   0.798042      95804\n",
       "2      4/20/2007  1.28  1.31  1.28   1.30   0.816894      70064\n",
       "3      4/23/2007  1.31  1.31  1.26   1.27   0.798042      36099\n",
       "4      4/24/2007  1.26  1.27  1.26   1.27   0.798042      14422\n",
       "...          ...   ...   ...   ...    ...        ...        ...\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10   1.100000   27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07   1.070000   32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09   1.090000   38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08   1.080000   20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07   1.070000   33596100\n",
       "\n",
       "[3179 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Yzj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(df_Yzj[\"Open\"])\n",
    "plt.plot(df_Yzj[\"High\"])\n",
    "plt.plot(df_Yzj[\"Low\"])\n",
    "plt.plot(df_Yzj[\"Close\"])\n",
    "plt.title('Yangzijiang Stock price history')\n",
    "plt.ylabel('Price (SGD)')\n",
    "plt.xlabel('Days')\n",
    "plt.legend(['Open','High','Low','Close'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure()\n",
    "plt.plot(df_Yzj[\"Volume\"])\n",
    "plt.title('Yangzijiang stock volume history')\n",
    "plt.ylabel('Volume')\n",
    "plt.xlabel('Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if any null values are present\n",
      " Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"checking if any null values are present\\n\", df_Yzj.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Yzj = df_Yzj.dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if any null values are present\n",
      " Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"checking if any null values are present\\n\", df_Yzj.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4/18/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.842029</td>\n",
       "      <td>413736000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4/19/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>95804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4/20/2007</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.816894</td>\n",
       "      <td>70064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4/23/2007</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>36099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4/24/2007</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>14422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3179 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close     Volume\n",
       "0      4/18/2007  1.35  1.39  1.33   1.34   0.842029  413736000\n",
       "1      4/19/2007  1.35  1.35  1.26   1.27   0.798042      95804\n",
       "2      4/20/2007  1.28  1.31  1.28   1.30   0.816894      70064\n",
       "3      4/23/2007  1.31  1.31  1.26   1.27   0.798042      36099\n",
       "4      4/24/2007  1.26  1.27  1.26   1.27   0.798042      14422\n",
       "...          ...   ...   ...   ...    ...        ...        ...\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10   1.100000   27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07   1.070000   32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09   1.090000   38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08   1.080000   20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07   1.070000   33596100\n",
       "\n",
       "[3179 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Yzj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train and Test size 2543 636\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_cols = [\"Open\",\"High\",\"Low\",\"Close\",\"Volume\"]\n",
    "df_train, df_test = train_test_split(df_Yzj, train_size=0.8, test_size=0.2, shuffle=False)\n",
    "print(\"Train and Test size\", len(df_train), len(df_test))\n",
    "# scale the feature MinMax, build array\n",
    "x = df_train.loc[:,train_cols].values\n",
    "min_max_scaler = MinMaxScaler()\n",
    "x_train = min_max_scaler.fit_transform(x)\n",
    "x_test = min_max_scaler.transform(df_test.loc[:,train_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_timeseries(mat, y_col_index):\n",
    "    # y_col_index is the index of column that would act as output column\n",
    "    # total number of time-series samples would be len(mat) - TIME_STEPS\n",
    "    dim_0 = mat.shape[0] - TIME_STEPS\n",
    "    dim_1 = mat.shape[1]\n",
    "    x = np.zeros((dim_0, TIME_STEPS, dim_1))\n",
    "    y = np.zeros((dim_0,))\n",
    "    \n",
    "    for i in tqdm_notebook(range(dim_0)):\n",
    "        x[i] = mat[i:TIME_STEPS+i]\n",
    "        y[i] = mat[TIME_STEPS+i, y_col_index]\n",
    "    print(\"length of time-series i/o\",x.shape,y.shape)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trim_dataset(mat, batch_size):\n",
    "    \"\"\"\n",
    "    trims dataset to a size that's divisible by BATCH_SIZE\n",
    "    \"\"\"\n",
    "    no_of_rows_drop = mat.shape[0]%batch_size\n",
    "    if(no_of_rows_drop > 0):\n",
    "        return mat[:-no_of_rows_drop]\n",
    "    else:\n",
    "        return mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from tqdm._tqdm_notebook import tqdm_notebook\n",
    "params = {\n",
    "    \"batch_size\": 20,  # 20<16<10, 25 was a bust\n",
    "    ##\"epochs\": 300,\n",
    "    \"epochs\":100,\n",
    "    \"lr\": 0.00010000,\n",
    "    \"time_steps\": 60\n",
    "}\n",
    "\n",
    "TIME_STEPS = params[\"time_steps\"]\n",
    "BATCH_SIZE = params[\"batch_size\"]\n",
    "stime = time.time()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96d9cc9e49c141d490735ba53be55b77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=2483), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (2483, 60, 5) (2483,)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbbb31f14cc949608a45c99e7d4487c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=576), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (576, 60, 5) (576,)\n"
     ]
    }
   ],
   "source": [
    "x_t, y_t = build_timeseries(x_train, 3)\n",
    "x_t = trim_dataset(x_t, BATCH_SIZE)\n",
    "y_t = trim_dataset(y_t, BATCH_SIZE)\n",
    "x_temp, y_temp = build_timeseries(x_test, 3)\n",
    "x_val, x_test_t = np.split(trim_dataset(x_temp, BATCH_SIZE),2)\n",
    "y_val, y_test_t = np.split(trim_dataset(y_temp, BATCH_SIZE),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import GRU\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, CSVLogger\n",
    "from keras import optimizers\n",
    "\n",
    "\n",
    "def create_model():\n",
    "    gru_model = Sequential()\n",
    "    gru_model.add(GRU(100, batch_input_shape=(BATCH_SIZE, TIME_STEPS, x_t.shape[2]), dropout=0.0, recurrent_dropout=0.0, stateful=True, kernel_initializer='random_uniform'))\n",
    "    gru_model.add(Dropout(0.5))\n",
    "    gru_model.add(Dense(20,activation='relu'))\n",
    "    gru_model.add(Dense(1,activation='sigmoid'))\n",
    "    optimizer = optimizers.RMSprop(lr=params[\"lr\"])\n",
    "    gru_model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "    gru_model.summary()\n",
    "    return gru_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction\\\\outputyzjGRU'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#OUTPUT_PATH ='/home/victor/Stock-Price-Prediction/outputs/'\n",
    "OUTPUT_PATH ='C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction\\\\outputyzjGRU'\n",
    "OUTPUT_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model not found\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7144d1b8d5a8496c8da307e5f61d1ab4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=576), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (576, 60, 5) (576,)\n",
      "Test size (280, 60, 5) (280,) (280, 60, 5) (280,)\n",
      "Building model...\n",
      "checking if GPU available []\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (20, 100)                 31800     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (20, 100)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (20, 20)                  2020      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (20, 1)                   21        \n",
      "=================================================================\n",
      "Total params: 33,841\n",
      "Trainable params: 33,841\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 2480 samples, validate on 280 samples\n",
      "Epoch 1/100\n",
      " - 7s - loss: 0.0418 - val_loss: 0.0141\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.01406, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 2/100\n",
      " - 6s - loss: 0.0361 - val_loss: 0.0217\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.01406\n",
      "Epoch 3/100\n",
      " - 6s - loss: 0.0358 - val_loss: 0.0236\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.01406\n",
      "Epoch 4/100\n",
      " - 6s - loss: 0.0338 - val_loss: 0.0233\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.01406\n",
      "Epoch 5/100\n",
      " - 6s - loss: 0.0312 - val_loss: 0.0226\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.01406\n",
      "Epoch 6/100\n",
      " - 6s - loss: 0.0272 - val_loss: 0.0213\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.01406\n",
      "Epoch 7/100\n",
      " - 6s - loss: 0.0213 - val_loss: 0.0161\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.01406\n",
      "Epoch 8/100\n",
      " - 6s - loss: 0.0110 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.01406 to 0.00223, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 9/100\n",
      " - 6s - loss: 0.0104 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.00223 to 0.00123, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 10/100\n",
      " - 6s - loss: 0.0066 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.00123 to 0.00111, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 11/100\n",
      " - 6s - loss: 0.0033 - val_loss: 9.6696e-04\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.00111 to 0.00097, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 12/100\n",
      " - 6s - loss: 0.0030 - val_loss: 9.4962e-04\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.00097 to 0.00095, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 13/100\n",
      " - 6s - loss: 0.0028 - val_loss: 9.3980e-04\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.00095 to 0.00094, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 14/100\n",
      " - 6s - loss: 0.0026 - val_loss: 8.2635e-04\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.00094 to 0.00083, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 15/100\n",
      " - 6s - loss: 0.0025 - val_loss: 8.4030e-04\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.00083\n",
      "Epoch 16/100\n",
      " - 6s - loss: 0.0023 - val_loss: 8.3565e-04\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.00083\n",
      "Epoch 17/100\n",
      " - 6s - loss: 0.0023 - val_loss: 8.5633e-04\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.00083\n",
      "Epoch 18/100\n",
      " - 6s - loss: 0.0022 - val_loss: 7.1948e-04\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.00083 to 0.00072, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 19/100\n",
      " - 6s - loss: 0.0021 - val_loss: 7.0315e-04\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.00072 to 0.00070, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 20/100\n",
      " - 6s - loss: 0.0020 - val_loss: 6.7235e-04\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.00070 to 0.00067, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 21/100\n",
      " - 6s - loss: 0.0020 - val_loss: 6.3270e-04\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.00067 to 0.00063, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 22/100\n",
      " - 6s - loss: 0.0019 - val_loss: 6.1963e-04\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.00063 to 0.00062, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 23/100\n",
      " - 6s - loss: 0.0018 - val_loss: 5.8957e-04\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.00062 to 0.00059, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 24/100\n",
      " - 6s - loss: 0.0018 - val_loss: 6.0562e-04\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.00059\n",
      "Epoch 25/100\n",
      " - 6s - loss: 0.0018 - val_loss: 6.0464e-04\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.00059\n",
      "Epoch 26/100\n",
      " - 6s - loss: 0.0018 - val_loss: 6.0100e-04\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.00059\n",
      "Epoch 27/100\n",
      " - 6s - loss: 0.0017 - val_loss: 5.3125e-04\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.00059 to 0.00053, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 28/100\n",
      " - 6s - loss: 0.0017 - val_loss: 5.3933e-04\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.00053\n",
      "Epoch 29/100\n",
      " - 6s - loss: 0.0017 - val_loss: 5.3966e-04\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.00053\n",
      "Epoch 30/100\n",
      " - 6s - loss: 0.0016 - val_loss: 5.6993e-04\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.00053\n",
      "Epoch 31/100\n",
      " - 6s - loss: 0.0016 - val_loss: 4.6727e-04\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.00053 to 0.00047, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 32/100\n",
      " - 6s - loss: 0.0015 - val_loss: 5.4551e-04\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.00047\n",
      "Epoch 33/100\n",
      " - 6s - loss: 0.0015 - val_loss: 5.0779e-04\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.00047\n",
      "Epoch 34/100\n",
      " - 6s - loss: 0.0015 - val_loss: 4.3025e-04\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.00047 to 0.00043, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 35/100\n",
      " - 6s - loss: 0.0014 - val_loss: 4.0418e-04\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.00043 to 0.00040, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 36/100\n",
      " - 6s - loss: 0.0014 - val_loss: 4.3339e-04\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.00040\n",
      "Epoch 37/100\n",
      " - 6s - loss: 0.0014 - val_loss: 3.7696e-04\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.00040 to 0.00038, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 38/100\n",
      " - 6s - loss: 0.0013 - val_loss: 4.2991e-04\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.00038\n",
      "Epoch 39/100\n",
      " - 6s - loss: 0.0013 - val_loss: 3.8632e-04\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.00038\n",
      "Epoch 40/100\n",
      " - 6s - loss: 0.0013 - val_loss: 3.7555e-04\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.00038 to 0.00038, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 41/100\n",
      " - 6s - loss: 0.0013 - val_loss: 3.4595e-04\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.00038 to 0.00035, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 42/100\n",
      " - 6s - loss: 0.0012 - val_loss: 3.3206e-04\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.00035 to 0.00033, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 43/100\n",
      " - 6s - loss: 0.0012 - val_loss: 3.4499e-04\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.00033\n",
      "Epoch 44/100\n",
      " - 6s - loss: 0.0012 - val_loss: 3.2551e-04\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.00033 to 0.00033, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 45/100\n",
      " - 6s - loss: 0.0012 - val_loss: 3.6736e-04\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.00033\n",
      "Epoch 46/100\n",
      " - 6s - loss: 0.0011 - val_loss: 3.4006e-04\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.00033\n",
      "Epoch 47/100\n",
      " - 6s - loss: 0.0012 - val_loss: 3.6781e-04\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.00033\n",
      "Epoch 48/100\n",
      " - 6s - loss: 0.0011 - val_loss: 3.6511e-04\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.00033\n",
      "Epoch 49/100\n",
      " - 6s - loss: 0.0011 - val_loss: 3.5250e-04\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.00033\n",
      "Epoch 50/100\n",
      " - 6s - loss: 0.0011 - val_loss: 3.2396e-04\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.00033 to 0.00032, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 51/100\n",
      " - 6s - loss: 0.0010 - val_loss: 3.2012e-04\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.00032 to 0.00032, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 52/100\n",
      " - 6s - loss: 0.0011 - val_loss: 3.4446e-04\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.00032\n",
      "Epoch 53/100\n",
      " - 6s - loss: 0.0010 - val_loss: 3.2180e-04\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.00032\n",
      "Epoch 54/100\n",
      " - 6s - loss: 0.0010 - val_loss: 3.3170e-04\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.00032\n",
      "Epoch 55/100\n",
      " - 6s - loss: 9.7076e-04 - val_loss: 3.2324e-04\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.00032\n",
      "Epoch 56/100\n",
      " - 6s - loss: 0.0010 - val_loss: 3.1664e-04\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.00032 to 0.00032, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 57/100\n",
      " - 6s - loss: 9.3831e-04 - val_loss: 3.2577e-04\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.00032\n",
      "Epoch 58/100\n",
      " - 6s - loss: 9.9372e-04 - val_loss: 3.4362e-04\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.00032\n",
      "Epoch 59/100\n",
      " - 6s - loss: 9.4242e-04 - val_loss: 3.3656e-04\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.00032\n",
      "Epoch 60/100\n",
      " - 8s - loss: 9.2217e-04 - val_loss: 3.3525e-04\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.00032\n",
      "Epoch 61/100\n",
      " - 6s - loss: 9.1717e-04 - val_loss: 3.4795e-04\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.00032\n",
      "Epoch 62/100\n",
      " - 6s - loss: 9.0869e-04 - val_loss: 3.4102e-04\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.00032\n",
      "Epoch 63/100\n",
      " - 6s - loss: 9.6250e-04 - val_loss: 3.3871e-04\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.00032\n",
      "Epoch 64/100\n",
      " - 7s - loss: 9.5666e-04 - val_loss: 3.2478e-04\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.00032\n",
      "Epoch 65/100\n",
      " - 6s - loss: 9.1159e-04 - val_loss: 3.1470e-04\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.00032 to 0.00031, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 66/100\n",
      " - 6s - loss: 8.9071e-04 - val_loss: 3.3104e-04\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.00031\n",
      "Epoch 67/100\n",
      " - 7s - loss: 8.9713e-04 - val_loss: 3.1893e-04\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.00031\n",
      "Epoch 68/100\n",
      " - 6s - loss: 9.2275e-04 - val_loss: 3.1684e-04\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.00031\n",
      "Epoch 69/100\n",
      " - 6s - loss: 8.8363e-04 - val_loss: 3.0873e-04\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.00031 to 0.00031, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 70/100\n",
      " - 6s - loss: 8.7824e-04 - val_loss: 3.1812e-04\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.00031\n",
      "Epoch 71/100\n",
      " - 6s - loss: 8.7755e-04 - val_loss: 3.4515e-04\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.00031\n",
      "Epoch 72/100\n",
      " - 6s - loss: 9.0958e-04 - val_loss: 3.3505e-04\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.00031\n",
      "Epoch 73/100\n",
      " - 6s - loss: 8.8402e-04 - val_loss: 2.9343e-04\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.00031 to 0.00029, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 74/100\n",
      " - 6s - loss: 8.5574e-04 - val_loss: 3.1344e-04\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.00029\n",
      "Epoch 75/100\n",
      " - 6s - loss: 8.8309e-04 - val_loss: 2.9932e-04\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.00029\n",
      "Epoch 76/100\n",
      " - 6s - loss: 8.2671e-04 - val_loss: 3.2385e-04\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.00029\n",
      "Epoch 77/100\n",
      " - 6s - loss: 8.7614e-04 - val_loss: 2.9983e-04\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.00029\n",
      "Epoch 78/100\n",
      " - 6s - loss: 7.7551e-04 - val_loss: 3.1071e-04\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.00029\n",
      "Epoch 79/100\n",
      " - 6s - loss: 8.4219e-04 - val_loss: 2.9329e-04\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.00029 to 0.00029, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU\\best_model.h5\n",
      "Epoch 80/100\n",
      " - 6s - loss: 8.3918e-04 - val_loss: 2.9471e-04\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.00029\n",
      "Epoch 81/100\n",
      " - 6s - loss: 8.5501e-04 - val_loss: 3.0518e-04\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.00029\n",
      "Epoch 00081: early stopping\n",
      "saving model...\n"
     ]
    }
   ],
   "source": [
    "model = None\n",
    "try:\n",
    "    model = pickle.load(open(\"GRU_model\", 'rb'))\n",
    "    print(\"Loaded saved model...\")\n",
    "except FileNotFoundError:\n",
    "    print(\"Model not found\")\n",
    "\n",
    "\n",
    "x_temp, y_temp = build_timeseries(x_test, 3)\n",
    "x_val, x_test_t = np.split(trim_dataset(x_temp, BATCH_SIZE),2)\n",
    "y_val, y_test_t = np.split(trim_dataset(y_temp, BATCH_SIZE),2)\n",
    "\n",
    "print(\"Test size\", x_test_t.shape, y_test_t.shape, x_val.shape, y_val.shape)\n",
    "    \n",
    "is_update_model = True\n",
    "if model is None or is_update_model:\n",
    "    from keras import backend as K\n",
    "    print(\"Building model...\")\n",
    "    print(\"checking if GPU available\", K.tensorflow_backend._get_available_gpus())\n",
    "    model = create_model()\n",
    "    \n",
    "    es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,\n",
    "                       patience=40, min_delta=0.0001)\n",
    "    \n",
    "    mcp = ModelCheckpoint(os.path.join(OUTPUT_PATH,\n",
    "                          \"best_model.h5\"), monitor='val_loss', verbose=1,\n",
    "                          save_best_only=True, save_weights_only=False, mode='min', period=1)\n",
    "\n",
    "    # Not used here. But leaving it here as a reminder for future\n",
    "    r_lr_plat = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=30, \n",
    "                                  verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)\n",
    "    \n",
    "   ## csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_' + time.ctime().replace(\" \",\"_\") + '.log'), append=True)\n",
    "    csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_' + time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime()) + '.log'), append=True)\n",
    "    \n",
    "  #  csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_'  + '.log'), append=True)\n",
    "    \n",
    "    history = model.fit(x_t, y_t, epochs=params[\"epochs\"], verbose=2, batch_size=BATCH_SIZE,\n",
    "                        shuffle=False, validation_data=(trim_dataset(x_val, BATCH_SIZE),\n",
    "                        trim_dataset(y_val, BATCH_SIZE)), callbacks=[es, mcp, csv_logger])\n",
    "    \n",
    "    print(\"saving model...\")\n",
    "    pickle.dump(model, open(\"GRU_model\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error is 0.0002599809022329932 (280,) (280,)\n",
      "[0.38970703 0.38743505 0.3897972  0.40029156 0.41541466 0.41317624\n",
      " 0.38774088 0.37626073 0.3692392  0.3581363  0.35084316 0.35350865\n",
      " 0.3570015  0.3663466  0.38143504]\n",
      "[0.3877551  0.39591837 0.41632653 0.44081633 0.36326531 0.37142857\n",
      " 0.3755102  0.37142857 0.36326531 0.35102041 0.3755102  0.36734694\n",
      " 0.3877551  0.4        0.40816327]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# model.evaluate(x_test_t, y_test_t, batch_size=BATCH_SIZE\n",
    "y_pred = model.predict(trim_dataset(x_test_t, BATCH_SIZE), batch_size=BATCH_SIZE)\n",
    "y_pred = y_pred.flatten()\n",
    "y_test_t = trim_dataset(y_test_t, BATCH_SIZE)\n",
    "error = mean_squared_error(y_test_t, y_pred)\n",
    "print(\"Mean Square Error is\", error, y_pred.shape, y_test_t.shape)\n",
    "print(y_pred[0:15])\n",
    "print(y_test_t[0:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2447822 1.2392159 1.2450032 1.2707143 1.307766  1.3022817 1.2399652\n",
      " 1.2118388 1.1946361 1.167434  1.1495657 1.1560962 1.1646537 1.1875491\n",
      " 1.2245158]\n",
      "[1.24 1.26 1.31 1.37 1.18 1.2  1.21 1.2  1.18 1.15 1.21 1.19 1.24 1.27\n",
      " 1.29]\n"
     ]
    }
   ],
   "source": [
    "# convert the predicted value to range of real data\n",
    "y_pred_org = (y_pred * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3]\n",
    "# min_max_scaler.inverse_transform(y_pred)\n",
    "y_test_t_org = (y_test_t * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3]\n",
    "# min_max_scaler.inverse_transform(y_test_t)\n",
    "print(y_pred_org[0:15])\n",
    "print(y_test_t_org[0:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mean_absolute_percentage_error(y_test_t, y_pred): \n",
    "    y_test_t, y_pred = np.array(y_test_t), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_test_t - y_pred) / y_test_t)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0365431112457615"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test_t, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#score, acc = model.evaluate(trim_dataset(x_test_t,BATCH_SIZE),trim_dataset(y_test_t,BATCH_SIZE),batch_size=BATCH_SIZE)\n",
    "#print('Test score:', score)\n",
    "#print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the training data\n",
    "from matplotlib import pyplot as plt\n",
    "#plt.figure()\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('GRU Model loss')\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "#plt.show()\n",
    "#plt.savefig(os.path.join(OUTPUT_PATH, 'train_vis_BS_'+str(BATCH_SIZE)+\"_\"+time.ctime()+'.png'))\n",
    "plt.savefig(os.path.join(OUTPUT_PATH, 'train_vis_BS_'+str(BATCH_SIZE)+\"_\"+time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime())+'.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.sequential.Sequential object at 0x000000000527A808>\n",
      "Mean Square Error is 0.00026517919653739453 (280,) (280,)\n",
      "[0.38737398 0.38497597 0.38718492 0.39742237 0.41232377 0.41002688\n",
      " 0.38525134 0.37400436 0.36696097 0.3557148  0.348262   0.35055768\n",
      " 0.3538639  0.36305264 0.3780403 ]\n",
      "[0.3877551  0.39591837 0.41632653 0.44081633 0.36326531 0.37142857\n",
      " 0.3755102  0.37142857 0.36326531 0.35102041 0.3755102  0.36734694\n",
      " 0.3877551  0.4        0.40816327]\n",
      "[1.2390662 1.2331911 1.2386031 1.2636849 1.3001932 1.2945658 1.2338659\n",
      " 1.2063107 1.1890544 1.1615013 1.1432419 1.1488663 1.1569666 1.179479\n",
      " 1.2161988]\n",
      "[1.24 1.26 1.31 1.37 1.18 1.2  1.21 1.2  1.18 1.15 1.21 1.19 1.24 1.27\n",
      " 1.29]\n"
     ]
    }
   ],
   "source": [
    "# load the saved best model from above\n",
    "saved_model = load_model(os.path.join(OUTPUT_PATH, 'best_model.h5')) # , \"lstm_best_7-3-19_12AM\",\n",
    "print(saved_model)\n",
    "\n",
    "y_pred = saved_model.predict(trim_dataset(x_test_t, BATCH_SIZE), batch_size=BATCH_SIZE)\n",
    "y_pred = y_pred.flatten()\n",
    "y_test_t = trim_dataset(y_test_t, BATCH_SIZE)\n",
    "error = mean_squared_error(y_test_t, y_pred)\n",
    "print(\"Mean Square Error is\", error, y_pred.shape, y_test_t.shape)\n",
    "print(y_pred[0:15])\n",
    "print(y_test_t[0:15])\n",
    "y_pred_org = (y_pred * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3] # min_max_scaler.inverse_transform(y_pred)\n",
    "y_test_t_org = (y_test_t * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3] # min_max_scaler.inverse_transform(y_test_t)\n",
    "print(y_pred_org[0:15])\n",
    "print(y_test_t_org[0:15])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mean_absolute_percentage_error(y_test_t, y_pred): \n",
    "    y_test_t, y_pred = np.array(y_test_t), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_test_t - y_pred) / y_test_t)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.082979875576427"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test_t, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_time(text, stime):\n",
    "    seconds = (time.time()-stime)\n",
    "    print(text, seconds//60,\"minutes : \",np.round(seconds%60),\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "program completed  8.0 minutes :  18.0 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the prediction\n",
    "from matplotlib import pyplot as plt\n",
    "#plt.figure()\n",
    "plt.figure(figsize=(10,5))\n",
    "#fig.set_figheight(20)\n",
    "#fig.set_figwidth(15)\n",
    "plt.plot(y_pred_org)\n",
    "plt.plot(y_test_t_org)\n",
    "plt.title('GRU Prediction vs Real Stock Price')\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Days')\n",
    "plt.legend(['Prediction', 'Real'], loc='upper left')\n",
    "#plt.show()\n",
    "##plt.savefig(os.path.join(OUTPUT_PATH, 'pred_vs_real_BS'+str(BATCH_SIZE)+\"_\"+time.ctime()+'.png'))\n",
    "plt.savefig(os.path.join(OUTPUT_PATH, 'pred_vs_real_BS'+str(BATCH_SIZE)+\"_\"+time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime())+'.png'),dpi=300)\n",
    "print_time(\"program completed \", stime)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}