main2.ipynb

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    {
     "ename": "ModuleNotFoundError",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-63936d831ebd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas_datareader\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mweb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackend\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\abolfazl81\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \"\"\"\n\u001b[0;32m     20\u001b[0m \u001b[1;31m# pylint: disable=unused-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sc\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pandas_datareader as web\n",
    "import datetime as dt\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential\n",
    "import keras.backend as k\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from sklearn.model_selection import TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  Open        High         Low       Close   Adj Close  \\\n",
      "Date                                                                     \n",
      "2020-11-19  211.380005  213.029999  209.929993  212.419998  210.654221   \n",
      "2020-11-20  212.199997  213.289993  210.000000  210.389999  208.641113   \n",
      "2020-11-23  210.949997  212.289993  208.160004  210.110001  208.363434   \n",
      "2020-11-24  209.589996  214.250000  208.860001  213.860001  212.082260   \n",
      "2020-11-25  215.110001  215.289993  212.460007  213.869995  212.092178   \n",
      "\n",
      "              Volume  \n",
      "Date                  \n",
      "2020-11-19  24792700  \n",
      "2020-11-20  22843100  \n",
      "2020-11-23  25683500  \n",
      "2020-11-24  33979700  \n",
      "2020-11-25  21012900  \n",
      "contains null: False\n"
     ]
    }
   ],
   "source": [
    "#load data\n",
    "df = pd.read_csv('MSFT.csv', index_col='Date', na_values=['null'], parse_dates=True, infer_datetime_format=True)\n",
    "print()\n",
    "print(df.head())\n",
    "print('contains null:',df.isnull().values.any())\n",
    "#set a target var\n",
    "\n",
    "target_var = df['Adj Close']\n",
    "features = ['Open', 'High', 'Low','Volume']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Open      High       Low    Volume\n",
      "Date                                              \n",
      "2020-11-19  0.013838  0.005685  0.013632  0.240089\n",
      "2020-11-20  0.020178  0.007683  0.014171  0.207224\n",
      "2020-11-23  0.010514  0.000000  0.000000  0.255106\n",
      "2020-11-24  0.000000  0.015058  0.005391  0.394961\n",
      "2020-11-25  0.042675  0.023049  0.033118  0.176371\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'TimeSeriesSplit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-35a399d0d1f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m#training test\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mt_split\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTimeSeriesSplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mt_split\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_transform\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mx_train\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeatures_transform\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mfeatures_transform\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'TimeSeriesSplit' is not defined"
     ]
    }
   ],
   "source": [
    "#scaling data\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "features_transform = scaler.fit_transform(df[features])\n",
    "features_transform = pd.DataFrame(columns=features, data=features_transform, index = df.index)\n",
    "print(features_transform.head())\n",
    "\n",
    "#training test\n",
    "\n",
    "t_split = TimeSeriesSplit(n_splits=10)\n",
    "for train_index , test_index in t_split.split(features_transform):\n",
    "    x_train , x_test = features_transform[:len(train_index)] , features_transform[len(train_index):(len(train_index)+len(test_index))]\n",
    "    y_train , y_test = target_var[:len(train_index)].values.ravel() , target_var[len(train_index):(len(train_index)+len(test_index))].values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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