fixed-time-horizon-prediction/Exp_1_RNN_Penn_State.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import glob\n",
    "import os.path\n",
    "from pandas.compat import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NREL Bird Model implementation: for obtaining clear sky GHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import clearsky, atmosphere\n",
    "from pvlib.location import Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pns = Location(40.798,-77.859, 'US/Eastern', 351.74, 'Penn State')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=pns.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=pns.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = pns.get_clearsky(times2009) \n",
    "cs_2010and2011 = pns.get_clearsky(times2010and2011) # ineichen with climatology table by default\n",
    "#cs_2011 = bvl.get_clearsky(times2011) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "cs_2010and2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "#cs_2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.reset_index(inplace=True)\n",
    "cs_2010and2011.reset_index(inplace=True)\n",
    "#cs_2011.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009['index']=cs_2009['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2009['year'] = cs_2009['index'].apply(lambda x:x.year)\n",
    "cs_2009['month'] = cs_2009['index'].apply(lambda x:x.month)\n",
    "cs_2009['day'] = cs_2009['index'].apply(lambda x:x.day)\n",
    "cs_2009['hour'] = cs_2009['index'].apply(lambda x:x.hour)\n",
    "cs_2009['min'] = cs_2009['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2010and2011['index']=cs_2010and2011['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2010and2011['year'] = cs_2010and2011['index'].apply(lambda x:x.year)\n",
    "cs_2010and2011['month'] = cs_2010and2011['index'].apply(lambda x:x.month)\n",
    "cs_2010and2011['day'] = cs_2010and2011['index'].apply(lambda x:x.day)\n",
    "cs_2010and2011['hour'] = cs_2010and2011['index'].apply(lambda x:x.hour)\n",
    "cs_2010and2011['min'] = cs_2010and2011['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525601, 7)\n",
      "(1051201, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(cs_2009.index[-1], inplace=True)\n",
    "cs_2010and2011.drop(cs_2010and2011.index[-1], inplace=True)\n",
    "#cs_2011.drop(cs_2011.index[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525600, 7)\n",
      "(1051200, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>index</th>\n",
       "      <th>ghi</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 00:01:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 00:02:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01 00:03:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01 00:04:00-05:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index  ghi  year  month  day  hour  min\n",
       "0 2010-01-01 00:00:00-05:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-05:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-05:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-05:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-05:00  0.0  2010      1    1     0    4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2010and2011.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import files from each year in a separate dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "- year            integer\t year, i.e., 1995\n",
    "- jday            integer\t Julian day (1 through 365 [or 366])\n",
    "- month           integer\t number of the month (1-12)\n",
    "- day             integer\t day of the month(1-31)\n",
    "- hour            integer\t hour of the day (0-23)\n",
    "- min             integer\t minute of the hour (0-59)\n",
    "- dt              real\t decimal time (hour.decimalminutes, e.g., 23.5 = 2330)\n",
    "- zen             real\t solar zenith angle (degrees)\n",
    "- dw_solar        real\t downwelling global solar (Watts m^-2)\n",
    "- uw_solar        real\t upwelling global solar (Watts m^-2)\n",
    "- direct_n        real\t direct-normal solar (Watts m^-2)\n",
    "- diffuse         real\t downwelling diffuse solar (Watts m^-2)\n",
    "- dw_ir           real\t downwelling thermal infrared (Watts m^-2)\n",
    "- dw_casetemp     real\t downwelling IR case temp. (K)\n",
    "- dw_dometemp     real\t downwelling IR dome temp. (K)\n",
    "- uw_ir           real\t upwelling thermal infrared (Watts m^-2)\n",
    "- uw_casetemp     real\t upwelling IR case temp. (K)\n",
    "- uw_dometemp     real\t upwelling IR dome temp. (K)\n",
    "- uvb             real\t global UVB (milliWatts m^-2)\n",
    "- par             real\t photosynthetically active radiation (Watts m^-2)\n",
    "- netsolar        real\t net solar (dw_solar - uw_solar) (Watts m^-2)\n",
    "- netir           real\t net infrared (dw_ir - uw_ir) (Watts m^-2)\n",
    "- totalnet        real\t net radiation (netsolar+netir) (Watts m^-2)\n",
    "- temp            real\t 10-meter air temperature (?C)\n",
    "- rh              real\t relative humidity (%)\n",
    "- windspd         real\t wind speed (ms^-1)\n",
    "- winddir         real\t wind direction (degrees, clockwise from north)\n",
    "- pressure        real\t station pressure (mb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['year', 'jday', 'month', 'day','hour','min','dt','zen','dw_solar','dw_solar_QC','uw_solar',\n",
    "       'uw_solar_QC', 'direct_n','direct_n_QC','diffuse', 'diffuse_QC', 'dw_ir', 'dw_ir_QC', 'dw_casetemp',\n",
    "       'dw_casetemp_QC', 'dw_dometemp','dw_dometemp_QC','uw_ir', 'uw_ir_QC', 'uw_casetemp','uw_casetemp_QC',\n",
    "       'uw_dometemp','uw_dometemp_QC','uvb','uvb_QC','par','par_QC','netsolar','netsolar_QC','netir','netir_QC',\n",
    "       'totalnet','totalnet_QC','temp','temp_QC','rh','rh_QC','windspd','windspd_QC','winddir','winddir_QC',\n",
    "       'pressure','pressure_QC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049577, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Penn_State\\\\Exp_1_train'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_train = pd.concat([pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files],ignore_index=True)\n",
    "df_big_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524846, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Penn_State\\\\Exp_1_test'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_test = pd.concat((pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files),ignore_index=True)\n",
    "df_big_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 48)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_big_test[df_big_test['dw_solar']==-9999.9].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Clear Sky GHI And the big dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049577, 50)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.merge(df_big_train, cs_2010and2011, on=['year','month','day','hour','min'])\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524846, 50)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.merge(df_big_test, cs_2009, on=['year','month','day','hour','min'])\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['index'],axis=1, inplace=True) #updating the same dataframe by dropping the index columns from clear sky model\n",
    "df_test.drop(['index'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049577, 49)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with two or more -9999.9 values in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1039363, 49)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step1: Get indices of all rows with 2 or more -999\n",
    "missing_data_indices = np.where((df_train <=-9999.9).apply(sum, axis=1)>=2)[0]\n",
    "# Step2: Drop those indices\n",
    "df_train.drop(missing_data_indices, axis=0, inplace=True)\n",
    "# Checking that the rows are dropped\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(510853, 49)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_indices_test = np.where((df_test <= -9999.9).apply(sum, axis=1)>=2)[0]\n",
    "df_test.drop(missing_data_indices_test, axis=0, inplace=True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the rows with only one cell as -9999.9, replacing this cell with the mean of the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First resetting index after dropping rows in the previous part of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd time - Reseting Index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_miss_train_idx = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13173"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_miss_train_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1039363, 49)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names = df_train.columns\n",
    "from collections import defaultdict\n",
    "stats = defaultdict(int)\n",
    "total_single_missing_values = 0\n",
    "for name in col_names:\n",
    "    col_mean = df_train[~(df_train[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_train[name] == -9999.9))\n",
    "    stats[name] = len(missing_indices[0])\n",
    "    df_train[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values += sum(df_train[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_col_min = df_train.apply(min, axis=0)\n",
    "df_col_max = df_train.apply(max, axis =0)\n",
    "#print(df_col_min, df_col_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doing the same thing on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6448"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_miss_test_idx = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "len(one_miss_test_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names_test = df_test.columns\n",
    "from collections import defaultdict\n",
    "stats_test = defaultdict(int)\n",
    "total_single_missing_values_test = 0\n",
    "for name in col_names_test:\n",
    "    col_mean = df_test[~(df_test[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_test[name] == -9999.9))\n",
    "    stats_test[name] = len(missing_indices[0])\n",
    "    df_test[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values_test += sum(df_test[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1039363, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(510853, 49)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dw_solar_everyday = df_test.groupby(['jday'])['dw_solar'].mean()\n",
    "ghi_everyday = df_test.groupby(['jday'])['ghi'].mean()\n",
    "j_day = df_test['jday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLVrSqlUbXn75dV55ZS6DB19OmzbtaNGiFZs3bwRg+/aqiq1ly1Zs2aLt//vvHHJycgDo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\nq+Zoc5wk4TA1s7+vDyyZK6gtrWgm2fq+UJDOaTN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No+2FdURHC13C5KEH0/ZA2gfAlVNg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "#axes2 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "\n",
    "axes1.scatter(j_day,dw_solar_everyday,label='Observed dw_solar',color='red')\n",
    "axes1.scatter(j_day, ghi_everyday, label='Clear Sky GHI',color='green')\n",
    "\n",
    "axes1.set_xlabel('Days')\n",
    "axes1.set_ylabel('Solar Irradiance (Watts /m^2)')\n",
    "axes1.set_title('Solar Irradiance - Test Year 2009')\n",
    "axes1.legend(loc='best')\n",
    "\n",
    "fig.savefig('Figure2.png', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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eIpUkYQkhpqRIVKfdGyQyilJAwzB440AjT2+rSZS/lxZksmH9fAqyM1IVqkiS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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dw_solar_everyday,y=ghi_everyday,kind='reg')\n",
    "#plt.title('observed dw_solar vs clear sky ghi')\n",
    "plt.xlabel('Observed global downwelling solar (Watts/m^2)')\n",
    "plt.ylabel('Clear Sky GHI (Watts/m^2)')\n",
    "plt.savefig('Figure3.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### making the Kt (clear sky index at time t) column by first removing rows with ghi==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['ghi']!=0]\n",
    "df_test = df_test[df_test['ghi']!=0]\n",
    "df_train['Kt'] = df_train['dw_solar']/df_train['ghi']\n",
    "df_test['Kt'] = df_test['dw_solar']/df_test['ghi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 15152.440011618102\n",
      "test Kt min: -34.79740973618779\n",
      "test Kt mean: 1.9145243277345676\n",
      "\n",
      "\n",
      "train Kt max: 110113.08976621665\n",
      "train Kt min: -167.27791726909712\n",
      "train Kt mean: 2.453877360947302\n"
     ]
    }
   ],
   "source": [
    "print(\"test Kt max: \"+str(df_test['Kt'].max()))\n",
    "print(\"test Kt min: \"+str(df_test['Kt'].min()))\n",
    "print(\"test Kt mean: \"+str(df_test['Kt'].mean()))\n",
    "print(\"\\n\")\n",
    "print(\"train Kt max: \"+str(df_train['Kt'].max()))\n",
    "print(\"train Kt min: \"+str(df_train['Kt'].min()))\n",
    "print(\"train Kt mean: \"+str(df_train['Kt'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xd154d30>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pf2UlT72wl7++5RH+8t8eOum67Ye72N3YxjMn2UJcs3E3S/71Qe597GU+9ZWVfPJLDwDQ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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc93dda0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_test['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train= df_train[df_train['Kt']< 5000]\n",
    "df_train= df_train[df_train['Kt']> -1000]\n",
    "df_test= df_test[df_test['Kt']< 5000]\n",
    "df_test= df_test[df_test['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data (train dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "zen = df_train.groupby(['year','month','day','hour'])['zen'].mean()\n",
    "dw_solar = df_train.groupby(['year','month','day','hour'])['dw_solar'].mean()\n",
    "uw_solar = df_train.groupby(['year','month','day','hour'])['uw_solar'].mean()\n",
    "direct_n = df_train.groupby(['year','month','day','hour'])['direct_n'].mean()\n",
    "diffuse = df_train.groupby(['year','month','day','hour'])['diffuse'].mean()\n",
    "dw_ir = df_train.groupby(['year','month','day','hour'])['dw_ir'].mean()\n",
    "dw_casetemp = df_train.groupby(['year','month','day','hour'])['dw_casetemp'].mean()\n",
    "dw_dometemp = df_train.groupby(['year','month','day','hour'])['dw_dometemp'].mean()\n",
    "uw_ir = df_train.groupby(['year','month','day','hour'])['uw_ir'].mean()\n",
    "uw_casetemp = df_train.groupby(['year','month','day','hour'])['uw_casetemp'].mean()\n",
    "uw_dometemp = df_train.groupby(['year','month','day','hour'])['uw_dometemp'].mean()\n",
    "uvb = df_train.groupby(['year','month','day','hour'])['uvb'].mean()\n",
    "par = df_train.groupby(['year','month','day','hour'])['par'].mean()\n",
    "netsolar = df_train.groupby(['year','month','day','hour'])['netsolar'].mean()\n",
    "netir = df_train.groupby(['year','month','day','hour'])['netir'].mean()\n",
    "totalnet = df_train.groupby(['year','month','day','hour'])['totalnet'].mean()\n",
    "temp = df_train.groupby(['year','month','day','hour'])['temp'].mean()\n",
    "rh = df_train.groupby(['year','month','day','hour'])['rh'].mean()\n",
    "windspd = df_train.groupby(['year','month','day','hour'])['windspd'].mean()\n",
    "winddir = df_train.groupby(['year','month','day','hour'])['winddir'].mean()\n",
    "pressure = df_train.groupby(['year','month','day','hour'])['pressure'].mean()\n",
    "ghi = df_train.groupby(['year','month','day','hour'])['ghi'].mean()\n",
    "Kt = df_train.groupby(['year','month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = pd.concat([zen,dw_solar,uw_solar,direct_n,diffuse,dw_ir,dw_casetemp,dw_dometemp,uw_ir,uw_casetemp,uw_dometemp,\n",
    "                    uvb,par,netsolar,netir,totalnet,temp,rh,windspd,winddir,pressure,ghi,Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>7.0</th>\n",
       "      <td>143.425455</td>\n",
       "      <td>-1.595455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.354545</td>\n",
       "      <td>-0.481818</td>\n",
       "      <td>294.027273</td>\n",
       "      <td>272.977273</td>\n",
       "      <td>272.813636</td>\n",
       "      <td>303.809091</td>\n",
       "      <td>270.872727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.786364</td>\n",
       "      <td>-9.786364</td>\n",
       "      <td>-1.918182</td>\n",
       "      <td>94.595455</td>\n",
       "      <td>1.645455</td>\n",
       "      <td>251.290909</td>\n",
       "      <td>969.427273</td>\n",
       "      <td>46.729148</td>\n",
       "      <td>-0.035871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>135.844000</td>\n",
       "      <td>-1.611667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.188333</td>\n",
       "      <td>-0.465000</td>\n",
       "      <td>293.376667</td>\n",
       "      <td>272.786667</td>\n",
       "      <td>272.631667</td>\n",
       "      <td>303.080000</td>\n",
       "      <td>270.731667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.711667</td>\n",
       "      <td>-9.711667</td>\n",
       "      <td>-1.998333</td>\n",
       "      <td>95.076667</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>244.951667</td>\n",
       "      <td>969.338333</td>\n",
       "      <td>116.574405</td>\n",
       "      <td>-0.015675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>124.525667</td>\n",
       "      <td>-1.760000</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>-0.393333</td>\n",
       "      <td>299.085000</td>\n",
       "      <td>272.953333</td>\n",
       "      <td>272.795000</td>\n",
       "      <td>305.198333</td>\n",
       "      <td>270.860000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.116667</td>\n",
       "      <td>-6.116667</td>\n",
       "      <td>-1.755000</td>\n",
       "      <td>95.043333</td>\n",
       "      <td>2.110000</td>\n",
       "      <td>239.411667</td>\n",
       "      <td>968.896667</td>\n",
       "      <td>258.670822</td>\n",
       "      <td>-0.006941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>113.255500</td>\n",
       "      <td>-1.645000</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>3.075000</td>\n",
       "      <td>-0.441667</td>\n",
       "      <td>298.563333</td>\n",
       "      <td>273.190000</td>\n",
       "      <td>273.025000</td>\n",
       "      <td>305.490000</td>\n",
       "      <td>271.108333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.930000</td>\n",
       "      <td>-6.930000</td>\n",
       "      <td>-1.506667</td>\n",
       "      <td>94.693333</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>262.800000</td>\n",
       "      <td>969.090000</td>\n",
       "      <td>375.577358</td>\n",
       "      <td>-0.004403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>102.321167</td>\n",
       "      <td>-1.561667</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>3.716667</td>\n",
       "      <td>-0.470000</td>\n",
       "      <td>306.001667</td>\n",
       "      <td>273.680000</td>\n",
       "      <td>273.568333</td>\n",
       "      <td>308.616667</td>\n",
       "      <td>271.521667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.611667</td>\n",
       "      <td>-2.611667</td>\n",
       "      <td>-0.810000</td>\n",
       "      <td>93.096667</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>255.361667</td>\n",
       "      <td>969.346667</td>\n",
       "      <td>445.293463</td>\n",
       "      <td>-0.003517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              zen  dw_solar  uw_solar  direct_n   diffuse  \\\n",
       "year   month day hour                                                       \n",
       "2010.0 1.0   1.0 7.0   143.425455 -1.595455  0.000000  2.354545 -0.481818   \n",
       "                 8.0   135.844000 -1.611667  0.000000  2.188333 -0.465000   \n",
       "                 9.0   124.525667 -1.760000 -0.013333  2.090000 -0.393333   \n",
       "                 10.0  113.255500 -1.645000 -0.045000  3.075000 -0.441667   \n",
       "                 11.0  102.321167 -1.561667 -0.045000  3.716667 -0.470000   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 7.0   294.027273   272.977273   272.813636  303.809091   \n",
       "                 8.0   293.376667   272.786667   272.631667  303.080000   \n",
       "                 9.0   299.085000   272.953333   272.795000  305.198333   \n",
       "                 10.0  298.563333   273.190000   273.025000  305.490000   \n",
       "                 11.0  306.001667   273.680000   273.568333  308.616667   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar     netir  totalnet  \\\n",
       "year   month day hour                 ...                                    \n",
       "2010.0 1.0   1.0 7.0    270.872727    ...          0.0 -9.786364 -9.786364   \n",
       "                 8.0    270.731667    ...          0.0 -9.711667 -9.711667   \n",
       "                 9.0    270.860000    ...          0.0 -6.116667 -6.116667   \n",
       "                 10.0   271.108333    ...          0.0 -6.930000 -6.930000   \n",
       "                 11.0   271.521667    ...          0.0 -2.611667 -2.611667   \n",
       "\n",
       "                           temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                          \n",
       "2010.0 1.0   1.0 7.0  -1.918182  94.595455  1.645455  251.290909  969.427273   \n",
       "                 8.0  -1.998333  95.076667  1.708333  244.951667  969.338333   \n",
       "                 9.0  -1.755000  95.043333  2.110000  239.411667  968.896667   \n",
       "                 10.0 -1.506667  94.693333  1.555000  262.800000  969.090000   \n",
       "                 11.0 -0.810000  93.096667  0.730000  255.361667  969.346667   \n",
       "\n",
       "                              ghi        Kt  \n",
       "year   month day hour                        \n",
       "2010.0 1.0   1.0 7.0    46.729148 -0.035871  \n",
       "                 8.0   116.574405 -0.015675  \n",
       "                 9.0   258.670822 -0.006941  \n",
       "                 10.0  375.577358 -0.004403  \n",
       "                 11.0  445.293463 -0.003517  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_zen = df_test.groupby(['month','day','hour'])['zen'].mean()\n",
    "test_dw_solar = df_test.groupby(['month','day','hour'])['dw_solar'].mean()\n",
    "test_uw_solar = df_test.groupby(['month','day','hour'])['uw_solar'].mean()\n",
    "test_direct_n = df_test.groupby(['month','day','hour'])['direct_n'].mean()\n",
    "test_diffuse = df_test.groupby(['month','day','hour'])['diffuse'].mean()\n",
    "test_dw_ir = df_test.groupby(['month','day','hour'])['dw_ir'].mean()\n",
    "test_dw_casetemp = df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()\n",
    "test_dw_dometemp = df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()\n",
    "test_uw_ir = df_test.groupby(['month','day','hour'])['uw_ir'].mean()\n",
    "test_uw_casetemp = df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()\n",
    "test_uw_dometemp = df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()\n",
    "test_uvb = df_test.groupby(['month','day','hour'])['uvb'].mean()\n",
    "test_par = df_test.groupby(['month','day','hour'])['par'].mean()\n",
    "test_netsolar = df_test.groupby(['month','day','hour'])['netsolar'].mean()\n",
    "test_netir = df_test.groupby(['month','day','hour'])['netir'].mean()\n",
    "test_totalnet = df_test.groupby(['month','day','hour'])['totalnet'].mean()\n",
    "test_temp = df_test.groupby(['month','day','hour'])['temp'].mean()\n",
    "test_rh = df_test.groupby(['month','day','hour'])['rh'].mean()\n",
    "test_windspd = df_test.groupby(['month','day','hour'])['windspd'].mean()\n",
    "test_winddir = df_test.groupby(['month','day','hour'])['winddir'].mean()\n",
    "test_pressure = df_test.groupby(['month','day','hour'])['pressure'].mean()\n",
    "test_ghi = df_test.groupby(['month','day','hour'])['ghi'].mean()\n",
    "test_Kt = df_test.groupby(['month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,\n",
    "                         test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,\n",
    "                    test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,\n",
    "                         test_windspd,test_winddir,test_pressure,test_ghi,test_Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>140.031091</td>\n",
       "      <td>-2.469091</td>\n",
       "      <td>-0.358182</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>195.056364</td>\n",
       "      <td>266.727273</td>\n",
       "      <td>266.285455</td>\n",
       "      <td>269.809091</td>\n",
       "      <td>265.814545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.750909</td>\n",
       "      <td>-74.750909</td>\n",
       "      <td>-6.736364</td>\n",
       "      <td>72.103636</td>\n",
       "      <td>0.458182</td>\n",
       "      <td>193.252727</td>\n",
       "      <td>980.114545</td>\n",
       "      <td>75.495960</td>\n",
       "      <td>-0.036719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>130.593167</td>\n",
       "      <td>-2.996667</td>\n",
       "      <td>-0.261667</td>\n",
       "      <td>5.101667</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>194.861667</td>\n",
       "      <td>266.001667</td>\n",
       "      <td>265.620000</td>\n",
       "      <td>269.271667</td>\n",
       "      <td>264.505000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.410000</td>\n",
       "      <td>-74.410000</td>\n",
       "      <td>-7.518333</td>\n",
       "      <td>77.925000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>205.293333</td>\n",
       "      <td>980.090000</td>\n",
       "      <td>232.239353</td>\n",
       "      <td>-0.013559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>119.707500</td>\n",
       "      <td>-4.038333</td>\n",
       "      <td>-0.643333</td>\n",
       "      <td>4.806667</td>\n",
       "      <td>0.148333</td>\n",
       "      <td>195.403333</td>\n",
       "      <td>266.540000</td>\n",
       "      <td>266.166667</td>\n",
       "      <td>273.628333</td>\n",
       "      <td>264.931667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-78.231667</td>\n",
       "      <td>-78.231667</td>\n",
       "      <td>-6.785000</td>\n",
       "      <td>75.850000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>260.378333</td>\n",
       "      <td>980.315000</td>\n",
       "      <td>404.694221</td>\n",
       "      <td>-0.010033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>108.404667</td>\n",
       "      <td>-3.563333</td>\n",
       "      <td>-0.198333</td>\n",
       "      <td>2.993333</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>197.165000</td>\n",
       "      <td>267.168333</td>\n",
       "      <td>266.723333</td>\n",
       "      <td>277.640000</td>\n",
       "      <td>265.658333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-80.465000</td>\n",
       "      <td>-80.465000</td>\n",
       "      <td>-6.968333</td>\n",
       "      <td>77.696667</td>\n",
       "      <td>2.896667</td>\n",
       "      <td>294.021667</td>\n",
       "      <td>980.585000</td>\n",
       "      <td>540.919433</td>\n",
       "      <td>-0.006609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>97.092500</td>\n",
       "      <td>-2.285000</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>1.671667</td>\n",
       "      <td>1.081667</td>\n",
       "      <td>197.653333</td>\n",
       "      <td>266.230000</td>\n",
       "      <td>265.790000</td>\n",
       "      <td>273.118333</td>\n",
       "      <td>264.701667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.703333</td>\n",
       "      <td>-75.468333</td>\n",
       "      <td>-74.763333</td>\n",
       "      <td>-8.111667</td>\n",
       "      <td>84.283333</td>\n",
       "      <td>1.836667</td>\n",
       "      <td>234.206667</td>\n",
       "      <td>980.305000</td>\n",
       "      <td>625.970675</td>\n",
       "      <td>-0.003699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>85.866500</td>\n",
       "      <td>32.508333</td>\n",
       "      <td>9.730000</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>29.548333</td>\n",
       "      <td>196.690000</td>\n",
       "      <td>265.670000</td>\n",
       "      <td>265.330000</td>\n",
       "      <td>273.705000</td>\n",
       "      <td>263.960000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.581667</td>\n",
       "      <td>-77.023333</td>\n",
       "      <td>-47.456667</td>\n",
       "      <td>-8.293333</td>\n",
       "      <td>85.805000</td>\n",
       "      <td>0.763333</td>\n",
       "      <td>140.258333</td>\n",
       "      <td>980.138333</td>\n",
       "      <td>652.453536</td>\n",
       "      <td>0.049939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>75.707667</td>\n",
       "      <td>210.618333</td>\n",
       "      <td>64.191667</td>\n",
       "      <td>636.253333</td>\n",
       "      <td>73.140000</td>\n",
       "      <td>198.428333</td>\n",
       "      <td>268.711667</td>\n",
       "      <td>268.348333</td>\n",
       "      <td>295.406667</td>\n",
       "      <td>265.903333</td>\n",
       "      <td>...</td>\n",
       "      <td>169.485000</td>\n",
       "      <td>-96.983333</td>\n",
       "      <td>72.503333</td>\n",
       "      <td>-5.795000</td>\n",
       "      <td>77.586667</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>235.083333</td>\n",
       "      <td>980.205000</td>\n",
       "      <td>618.211997</td>\n",
       "      <td>0.343985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>66.538667</td>\n",
       "      <td>390.026667</td>\n",
       "      <td>101.055000</td>\n",
       "      <td>775.546667</td>\n",
       "      <td>97.310000</td>\n",
       "      <td>202.463333</td>\n",
       "      <td>271.936667</td>\n",
       "      <td>271.526667</td>\n",
       "      <td>313.186667</td>\n",
       "      <td>269.253333</td>\n",
       "      <td>...</td>\n",
       "      <td>306.830000</td>\n",
       "      <td>-110.721667</td>\n",
       "      <td>196.103333</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>69.966667</td>\n",
       "      <td>0.835000</td>\n",
       "      <td>207.591667</td>\n",
       "      <td>980.203333</td>\n",
       "      <td>526.060053</td>\n",
       "      <td>0.751055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>59.110333</td>\n",
       "      <td>552.710000</td>\n",
       "      <td>133.133333</td>\n",
       "      <td>867.756667</td>\n",
       "      <td>115.838333</td>\n",
       "      <td>209.016667</td>\n",
       "      <td>274.416667</td>\n",
       "      <td>273.986667</td>\n",
       "      <td>331.186667</td>\n",
       "      <td>271.878333</td>\n",
       "      <td>...</td>\n",
       "      <td>428.315000</td>\n",
       "      <td>-122.181667</td>\n",
       "      <td>306.143333</td>\n",
       "      <td>-2.396667</td>\n",
       "      <td>63.925000</td>\n",
       "      <td>1.126667</td>\n",
       "      <td>202.361667</td>\n",
       "      <td>979.830000</td>\n",
       "      <td>384.153979</td>\n",
       "      <td>1.473137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>54.222000</td>\n",
       "      <td>620.096667</td>\n",
       "      <td>147.301667</td>\n",
       "      <td>827.500000</td>\n",
       "      <td>139.430000</td>\n",
       "      <td>213.890000</td>\n",
       "      <td>275.918333</td>\n",
       "      <td>275.506667</td>\n",
       "      <td>343.101667</td>\n",
       "      <td>273.151667</td>\n",
       "      <td>...</td>\n",
       "      <td>476.185000</td>\n",
       "      <td>-129.215000</td>\n",
       "      <td>346.971667</td>\n",
       "      <td>-1.636667</td>\n",
       "      <td>60.098333</td>\n",
       "      <td>1.731667</td>\n",
       "      <td>214.810000</td>\n",
       "      <td>979.293333</td>\n",
       "      <td>209.013003</td>\n",
       "      <td>3.211572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>52.557917</td>\n",
       "      <td>641.820833</td>\n",
       "      <td>151.570833</td>\n",
       "      <td>843.958333</td>\n",
       "      <td>132.360417</td>\n",
       "      <td>215.918750</td>\n",
       "      <td>277.354167</td>\n",
       "      <td>276.941667</td>\n",
       "      <td>351.164583</td>\n",
       "      <td>274.708333</td>\n",
       "      <td>...</td>\n",
       "      <td>493.879167</td>\n",
       "      <td>-135.239583</td>\n",
       "      <td>358.635417</td>\n",
       "      <td>-0.281250</td>\n",
       "      <td>51.289583</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>266.472917</td>\n",
       "      <td>978.422917</td>\n",
       "      <td>65.876009</td>\n",
       "      <td>11.914275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar    uw_solar    direct_n     diffuse       dw_ir  \\\n",
       "hour                                                                           \n",
       "7.0   140.031091   -2.469091   -0.358182    2.360000    0.745455  195.056364   \n",
       "8.0   130.593167   -2.996667   -0.261667    5.101667    0.508333  194.861667   \n",
       "9.0   119.707500   -4.038333   -0.643333    4.806667    0.148333  195.403333   \n",
       "10.0  108.404667   -3.563333   -0.198333    2.993333    0.030000  197.165000   \n",
       "11.0   97.092500   -2.285000    0.116667    1.671667    1.081667  197.653333   \n",
       "12.0   85.866500   32.508333    9.730000   70.833333   29.548333  196.690000   \n",
       "13.0   75.707667  210.618333   64.191667  636.253333   73.140000  198.428333   \n",
       "14.0   66.538667  390.026667  101.055000  775.546667   97.310000  202.463333   \n",
       "15.0   59.110333  552.710000  133.133333  867.756667  115.838333  209.016667   \n",
       "16.0   54.222000  620.096667  147.301667  827.500000  139.430000  213.890000   \n",
       "17.0   52.557917  641.820833  151.570833  843.958333  132.360417  215.918750   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "7.0    266.727273   266.285455  269.809091   265.814545    ...       \n",
       "8.0    266.001667   265.620000  269.271667   264.505000    ...       \n",
       "9.0    266.540000   266.166667  273.628333   264.931667    ...       \n",
       "10.0   267.168333   266.723333  277.640000   265.658333    ...       \n",
       "11.0   266.230000   265.790000  273.118333   264.701667    ...       \n",
       "12.0   265.670000   265.330000  273.705000   263.960000    ...       \n",
       "13.0   268.711667   268.348333  295.406667   265.903333    ...       \n",
       "14.0   271.936667   271.526667  313.186667   269.253333    ...       \n",
       "15.0   274.416667   273.986667  331.186667   271.878333    ...       \n",
       "16.0   275.918333   275.506667  343.101667   273.151667    ...       \n",
       "17.0   277.354167   276.941667  351.164583   274.708333    ...       \n",
       "\n",
       "        netsolar       netir    totalnet      temp         rh   windspd  \\\n",
       "hour                                                                      \n",
       "7.0     0.000000  -74.750909  -74.750909 -6.736364  72.103636  0.458182   \n",
       "8.0     0.000000  -74.410000  -74.410000 -7.518333  77.925000  0.680000   \n",
       "9.0     0.000000  -78.231667  -78.231667 -6.785000  75.850000  1.795000   \n",
       "10.0    0.000000  -80.465000  -80.465000 -6.968333  77.696667  2.896667   \n",
       "11.0    0.703333  -75.468333  -74.763333 -8.111667  84.283333  1.836667   \n",
       "12.0   29.581667  -77.023333  -47.456667 -8.293333  85.805000  0.763333   \n",
       "13.0  169.485000  -96.983333   72.503333 -5.795000  77.586667  1.020000   \n",
       "14.0  306.830000 -110.721667  196.103333 -3.860000  69.966667  0.835000   \n",
       "15.0  428.315000 -122.181667  306.143333 -2.396667  63.925000  1.126667   \n",
       "16.0  476.185000 -129.215000  346.971667 -1.636667  60.098333  1.731667   \n",
       "17.0  493.879167 -135.239583  358.635417 -0.281250  51.289583  1.729167   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "7.0   193.252727  980.114545   75.495960  -0.036719  \n",
       "8.0   205.293333  980.090000  232.239353  -0.013559  \n",
       "9.0   260.378333  980.315000  404.694221  -0.010033  \n",
       "10.0  294.021667  980.585000  540.919433  -0.006609  \n",
       "11.0  234.206667  980.305000  625.970675  -0.003699  \n",
       "12.0  140.258333  980.138333  652.453536   0.049939  \n",
       "13.0  235.083333  980.205000  618.211997   0.343985  \n",
       "14.0  207.591667  980.203333  526.060053   0.751055  \n",
       "15.0  202.361667  979.830000  384.153979   1.473137  \n",
       "16.0  214.810000  979.293333  209.013003   3.211572  \n",
       "17.0  266.472917  978.422917   65.876009  11.914275  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test.loc[2].xs(17,level='day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting Kt values to make 1 hour ahead forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in levels_index[0]:\n",
    "    for j in levels_index[1]:\n",
    "        df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = df_new_train[~(df_new_train['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in levels_index2[0]:\n",
    "    for j in levels_index2[1]:\n",
    "        df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <th>25.0</th>\n",
       "      <th>19.0</th>\n",
       "      <td>40.905667</td>\n",
       "      <td>716.2</td>\n",
       "      <td>155.285</td>\n",
       "      <td>619.01</td>\n",
       "      <td>237.966667</td>\n",
       "      <td>367.16</td>\n",
       "      <td>305.678333</td>\n",
       "      <td>305.183333</td>\n",
       "      <td>467.875</td>\n",
       "      <td>302.241667</td>\n",
       "      <td>...</td>\n",
       "      <td>545.25</td>\n",
       "      <td>-100.713333</td>\n",
       "      <td>444.538333</td>\n",
       "      <td>28.386667</td>\n",
       "      <td>26.458333</td>\n",
       "      <td>8.356667</td>\n",
       "      <td>239.461667</td>\n",
       "      <td>977.11</td>\n",
       "      <td>56.981915</td>\n",
       "      <td>653.28121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       zen  dw_solar  uw_solar  direct_n     diffuse   dw_ir  \\\n",
       "month day  hour                                                                \n",
       "4.0   25.0 19.0  40.905667     716.2   155.285    619.01  237.966667  367.16   \n",
       "\n",
       "                 dw_casetemp  dw_dometemp    uw_ir  uw_casetemp    ...      \\\n",
       "month day  hour                                                    ...       \n",
       "4.0   25.0 19.0   305.678333   305.183333  467.875   302.241667    ...       \n",
       "\n",
       "                 netsolar       netir    totalnet       temp         rh  \\\n",
       "month day  hour                                                           \n",
       "4.0   25.0 19.0    545.25 -100.713333  444.538333  28.386667  26.458333   \n",
       "\n",
       "                  windspd     winddir  pressure        ghi         Kt  \n",
       "month day  hour                                                        \n",
       "4.0   25.0 19.0  8.356667  239.461667    977.11  56.981915  653.28121  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test[df_new_test['Kt']==df_new_test['Kt'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize train and test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm = (df_new_train - df_new_train.mean()) / (df_new_train.max() - df_new_train.min())\n",
    "test_norm =  (df_new_test - df_new_test.mean()) / (df_new_test.max() - df_new_test.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm.reset_index(inplace=True,drop=True)\n",
    "test_norm.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Making train and test sets with train_norm and test_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### finding the gcf (greatest common factor) of train and test dataset's length and chop off the extra rows to make it divisible with the batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: fractions.gcd() is deprecated. Use math.gcd() instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fractions import gcd\n",
    "gcd(train_norm.shape[0],test_norm.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_lim = roundup(train_norm.shape[0])\n",
    "test_lim = roundup(test_norm.shape[0])\n",
    "\n",
    "train_random = train_norm.sample(train_lim-train_norm.shape[0])\n",
    "test_random = test_norm.sample(test_lim-test_norm.shape[0])\n",
    "\n",
    "train_norm = train_norm.append(train_random)\n",
    "test_norm = test_norm.append(test_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = train_norm.drop('Kt',axis=1)\n",
    "y1 = train_norm['Kt']\n",
    "\n",
    "X2 = test_norm.drop('Kt',axis=1)\n",
    "y2 = test_norm['Kt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9500, 22)\n",
      "y1_train shape is (9500,)\n",
      "X2_test shape is (4300, 22)\n",
      "y2_test shape is (4300,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X1_train shape is {}\".format(X1.shape))\n",
    "print(\"y1_train shape is {}\".format(y1.shape))\n",
    "print(\"X2_test shape is {}\".format(X2.shape))\n",
    "print(\"y2_test shape is {}\".format(y2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X1)\n",
    "y_train  = np.array(y1)\n",
    "X_test = np.array(X2)\n",
    "y_test = np.array(y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### start of RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):\n",
    "        super(RNNModel, self).__init__()\n",
    "        #Hidden Dimension\n",
    "        self.hidden_dim = hidden_dim\n",
    "        \n",
    "        # Number of hidden layers\n",
    "        self.layer_dim = layer_dim\n",
    "        \n",
    "        #Building the RNN\n",
    "        self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')\n",
    "        \n",
    "        # Readout layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Initializing the hidden state with zeros\n",
    "        # (layer_dim, batch_size, hidden_dim)\n",
    "        h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))\n",
    "        \n",
    "        #One time step (the last one perhaps?)\n",
    "        out, hn = self.rnn(x, h0)\n",
    "        \n",
    "        # Indexing hidden state of the last time step\n",
    "        # out.size() --> ??\n",
    "        #out[:,-1,:] --> is it going to be 100,100\n",
    "        out = self.fc(out[:,-1,:])\n",
    "        # out.size() --> 100,1\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating Model Class\n",
    "input_dim = 22\n",
    "hidden_dim = 15\n",
    "layer_dim = 1\n",
    "output_dim = 1\n",
    "batch_size = 100\n",
    "\n",
    "model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)\n",
    "\n",
    "# Instantiating Loss Class\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Instantiate Optimizer Class\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# converting numpy array to torch tensor\n",
    "X_train = torch.from_numpy(X_train)\n",
    "y_train = torch.from_numpy(y_train)\n",
    "X_test = torch.from_numpy(X_test)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "# initializing lists to store losses over epochs:\n",
    "train_loss = []\n",
    "test_loss = []\n",
    "train_iter = []\n",
    "test_iter = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Iteration: 100. Train_MSE: 0.028208661824464798. Test_MSE: 0.0016418469570261855\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.009980670176446438. Test_MSE: 0.0012878175896386518\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 94 Iteration: 8900. Train_MSE: 0.0009068276849575341. Test_MSE: 0.00018071607469116076\n",
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      "Epoch: 885 Iteration: 83200. Train_MSE: 0.00020978528482373804. Test_MSE: 9.440454765638507e-05\n",
      "Epoch: 886 Iteration: 83300. Train_MSE: 0.0005818920326419175. Test_MSE: 9.416070108939366e-05\n",
      "Epoch: 887 Iteration: 83400. Train_MSE: 0.0003187093825545162. Test_MSE: 9.407839108626011e-05\n",
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      "Epoch: 891 Iteration: 83800. Train_MSE: 8.82917083799839e-05. Test_MSE: 9.405877742153309e-05\n",
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      "Epoch: 894 Iteration: 84100. Train_MSE: 0.0006082984618842602. Test_MSE: 9.381006844691975e-05\n",
      "Epoch: 895 Iteration: 84200. Train_MSE: 0.00027919281274080276. Test_MSE: 9.374538516672145e-05\n",
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      "Epoch: 897 Iteration: 84400. Train_MSE: 0.00019941598293371499. Test_MSE: 9.386104451497973e-05\n",
      "Epoch: 898 Iteration: 84500. Train_MSE: 9.732002217788249e-05. Test_MSE: 9.381552201753931e-05\n",
      "Epoch: 899 Iteration: 84600. Train_MSE: 4.118493234273046e-05. Test_MSE: 9.374589169905572e-05\n",
      "Epoch: 901 Iteration: 84700. Train_MSE: 0.00011721195915015414. Test_MSE: 9.374323645880921e-05\n",
      "Epoch: 902 Iteration: 84800. Train_MSE: 0.00037325909943319857. Test_MSE: 9.353456899121081e-05\n",
      "Epoch: 903 Iteration: 84900. Train_MSE: 0.0007008123211562634. Test_MSE: 9.337133584382249e-05\n",
      "Epoch: 904 Iteration: 85000. Train_MSE: 0.0003656977496575564. Test_MSE: 9.331985016225871e-05\n",
      "Epoch: 905 Iteration: 85100. Train_MSE: 0.0003276954230386764. Test_MSE: 9.3313965448114e-05\n",
      "Epoch: 906 Iteration: 85200. Train_MSE: 0.00020681765454355627. Test_MSE: 9.336394498404629e-05\n",
      "Epoch: 907 Iteration: 85300. Train_MSE: 0.002041230909526348. Test_MSE: 9.333703025535248e-05\n",
      "Epoch: 908 Iteration: 85400. Train_MSE: 6.53546885587275e-05. Test_MSE: 9.331335397466331e-05\n",
      "Epoch: 909 Iteration: 85500. Train_MSE: 0.00037487660301849246. Test_MSE: 9.333444133933061e-05\n",
      "Epoch: 910 Iteration: 85600. Train_MSE: 0.00016992002201732248. Test_MSE: 9.316532105248498e-05\n",
      "Epoch: 911 Iteration: 85700. Train_MSE: 0.0002491703489795327. Test_MSE: 9.304032677952201e-05\n",
      "Epoch: 912 Iteration: 85800. Train_MSE: 0.00027410133043304086. Test_MSE: 9.301405536532669e-05\n",
      "Epoch: 913 Iteration: 85900. Train_MSE: 0.0009735184139572084. Test_MSE: 9.303918255092174e-05\n",
      "Epoch: 914 Iteration: 86000. Train_MSE: 0.000316409656079486. Test_MSE: 9.314117764569269e-05\n",
      "Epoch: 915 Iteration: 86100. Train_MSE: 0.00013192827464081347. Test_MSE: 9.304053257453978e-05\n",
      "Epoch: 917 Iteration: 86200. Train_MSE: 6.8441811890807e-05. Test_MSE: 9.304488297469594e-05\n",
      "Epoch: 918 Iteration: 86300. Train_MSE: 0.0002871684555429965. Test_MSE: 9.301544692370761e-05\n",
      "Epoch: 919 Iteration: 86400. Train_MSE: 0.0002026886068051681. Test_MSE: 9.270296002127327e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 920 Iteration: 86500. Train_MSE: 0.0001925397664308548. Test_MSE: 9.26096004811586e-05\n",
      "Epoch: 921 Iteration: 86600. Train_MSE: 0.0004534038598649204. Test_MSE: 9.255178273276742e-05\n",
      "Epoch: 922 Iteration: 86700. Train_MSE: 0.004300435073673725. Test_MSE: 9.264084790188184e-05\n",
      "Epoch: 923 Iteration: 86800. Train_MSE: 0.0035048809368163347. Test_MSE: 9.26582241503497e-05\n",
      "Epoch: 924 Iteration: 86900. Train_MSE: 0.00010594358900561929. Test_MSE: 9.258309930217544e-05\n",
      "Epoch: 925 Iteration: 87000. Train_MSE: 9.210929420078173e-05. Test_MSE: 9.263188984228539e-05\n",
      "Epoch: 926 Iteration: 87100. Train_MSE: 0.00014607649063691497. Test_MSE: 9.256273025727744e-05\n",
      "Epoch: 927 Iteration: 87200. Train_MSE: 0.00028944446239620447. Test_MSE: 9.237138412028637e-05\n",
      "Epoch: 928 Iteration: 87300. Train_MSE: 0.0002576907572802156. Test_MSE: 9.22839801836087e-05\n",
      "Epoch: 929 Iteration: 87400. Train_MSE: 0.0005886803846806288. Test_MSE: 9.226733863358812e-05\n",
      "Epoch: 930 Iteration: 87500. Train_MSE: 0.00866451021283865. Test_MSE: 9.240153510715681e-05\n",
      "Epoch: 931 Iteration: 87600. Train_MSE: 0.00017706856306176633. Test_MSE: 9.24077431484578e-05\n",
      "Epoch: 932 Iteration: 87700. Train_MSE: 6.940207094885409e-05. Test_MSE: 9.230308314111506e-05\n",
      "Epoch: 934 Iteration: 87800. Train_MSE: 7.1699294494465e-05. Test_MSE: 9.227092034781435e-05\n",
      "Epoch: 935 Iteration: 87900. Train_MSE: 0.0002046861918643117. Test_MSE: 9.220676045453562e-05\n",
      "Epoch: 936 Iteration: 88000. Train_MSE: 0.0005768726696260273. Test_MSE: 9.196516466626599e-05\n",
      "Epoch: 937 Iteration: 88100. Train_MSE: 0.00031359365675598383. Test_MSE: 9.188606416398709e-05\n",
      "Epoch: 938 Iteration: 88200. Train_MSE: 0.0005045704310759902. Test_MSE: 9.185544381029848e-05\n",
      "Epoch: 939 Iteration: 88300. Train_MSE: 0.0002598666469566524. Test_MSE: 9.194141417149337e-05\n",
      "Epoch: 940 Iteration: 88400. Train_MSE: 0.00010762971214717254. Test_MSE: 9.193390419039071e-05\n",
      "Epoch: 941 Iteration: 88500. Train_MSE: 8.459425589535385e-05. Test_MSE: 9.187902480367094e-05\n",
      "Epoch: 942 Iteration: 88600. Train_MSE: 0.00010234966612188146. Test_MSE: 9.194750751013683e-05\n",
      "Epoch: 943 Iteration: 88700. Train_MSE: 0.00010348824434913695. Test_MSE: 9.180133512790802e-05\n",
      "Epoch: 944 Iteration: 88800. Train_MSE: 0.0006028278730809689. Test_MSE: 9.164085664570957e-05\n",
      "Epoch: 945 Iteration: 88900. Train_MSE: 0.00027500474243424833. Test_MSE: 9.157858032007213e-05\n",
      "Epoch: 946 Iteration: 89000. Train_MSE: 0.0003433499368838966. Test_MSE: 9.15731775314386e-05\n",
      "Epoch: 947 Iteration: 89100. Train_MSE: 0.00019218245870433748. Test_MSE: 9.170356673309088e-05\n",
      "Epoch: 948 Iteration: 89200. Train_MSE: 9.439625137019902e-05. Test_MSE: 9.166058662608962e-05\n",
      "Epoch: 949 Iteration: 89300. Train_MSE: 3.898573049809784e-05. Test_MSE: 9.159390042687423e-05\n",
      "Epoch: 951 Iteration: 89400. Train_MSE: 0.00011127660400234163. Test_MSE: 9.159370810517222e-05\n",
      "Epoch: 952 Iteration: 89500. Train_MSE: 0.00036263137008063495. Test_MSE: 9.13873902735572e-05\n",
      "Epoch: 953 Iteration: 89600. Train_MSE: 0.0006952789262868464. Test_MSE: 9.122754787258016e-05\n",
      "Epoch: 954 Iteration: 89700. Train_MSE: 0.0003620758361648768. Test_MSE: 9.117843005012141e-05\n",
      "Epoch: 955 Iteration: 89800. Train_MSE: 0.0003230154106859118. Test_MSE: 9.11753892333104e-05\n",
      "Epoch: 956 Iteration: 89900. Train_MSE: 0.0002017150109168142. Test_MSE: 9.122991953766802e-05\n",
      "Epoch: 957 Iteration: 90000. Train_MSE: 0.002036822959780693. Test_MSE: 9.120604476050065e-05\n",
      "Epoch: 958 Iteration: 90100. Train_MSE: 6.234132160898298e-05. Test_MSE: 9.118458277394275e-05\n",
      "Epoch: 959 Iteration: 90200. Train_MSE: 0.00037239064113236964. Test_MSE: 9.121054983454332e-05\n",
      "Epoch: 960 Iteration: 90300. Train_MSE: 0.00016576888447161764. Test_MSE: 9.104348228917746e-05\n",
      "Epoch: 961 Iteration: 90400. Train_MSE: 0.0002454078639857471. Test_MSE: 9.09210295892212e-05\n",
      "Epoch: 962 Iteration: 90500. Train_MSE: 0.00027051850338466465. Test_MSE: 9.089745705785252e-05\n",
      "Epoch: 963 Iteration: 90600. Train_MSE: 0.0009703810792416334. Test_MSE: 9.092660168728454e-05\n",
      "Epoch: 964 Iteration: 90700. Train_MSE: 0.0003130200784653425. Test_MSE: 9.103394217650726e-05\n",
      "Epoch: 965 Iteration: 90800. Train_MSE: 0.00012754228373523802. Test_MSE: 9.093551717624223e-05\n",
      "Epoch: 967 Iteration: 90900. Train_MSE: 6.50053916615434e-05. Test_MSE: 9.094227164587451e-05\n",
      "Epoch: 968 Iteration: 91000. Train_MSE: 0.0002840194501914084. Test_MSE: 9.091594154003808e-05\n",
      "Epoch: 969 Iteration: 91100. Train_MSE: 0.00019748038903344423. Test_MSE: 9.060510631051275e-05\n",
      "Epoch: 970 Iteration: 91200. Train_MSE: 0.00018895471293944865. Test_MSE: 9.051518877364148e-05\n",
      "Epoch: 971 Iteration: 91300. Train_MSE: 0.00044720005826093256. Test_MSE: 9.045929629321512e-05\n",
      "Epoch: 972 Iteration: 91400. Train_MSE: 0.004300874657928944. Test_MSE: 9.055337995912978e-05\n",
      "Epoch: 973 Iteration: 91500. Train_MSE: 0.003503568470478058. Test_MSE: 9.057396411707996e-05\n",
      "Epoch: 974 Iteration: 91600. Train_MSE: 0.00010420619219075888. Test_MSE: 9.050125168284424e-05\n",
      "Epoch: 975 Iteration: 91700. Train_MSE: 8.758828334975988e-05. Test_MSE: 9.055261451710116e-05\n",
      "Epoch: 976 Iteration: 91800. Train_MSE: 0.00014237475988920778. Test_MSE: 9.048756213492172e-05\n",
      "Epoch: 977 Iteration: 91900. Train_MSE: 0.0002845669805537909. Test_MSE: 9.029803579193025e-05\n",
      "Epoch: 978 Iteration: 92000. Train_MSE: 0.0002543869486544281. Test_MSE: 9.021291893664891e-05\n",
      "Epoch: 979 Iteration: 92100. Train_MSE: 0.0005830285954289138. Test_MSE: 9.019908373516653e-05\n",
      "Epoch: 980 Iteration: 92200. Train_MSE: 0.008662151172757149. Test_MSE: 9.033953620588357e-05\n",
      "Epoch: 981 Iteration: 92300. Train_MSE: 0.00017326651141047478. Test_MSE: 9.0348814799678e-05\n",
      "Epoch: 982 Iteration: 92400. Train_MSE: 6.722465332131833e-05. Test_MSE: 9.02467826660653e-05\n",
      "Epoch: 984 Iteration: 92500. Train_MSE: 6.937582656973973e-05. Test_MSE: 9.021687934470536e-05\n",
      "Epoch: 985 Iteration: 92600. Train_MSE: 0.0001998734805965796. Test_MSE: 9.0155806905985e-05\n",
      "Epoch: 986 Iteration: 92700. Train_MSE: 0.0005720952758565545. Test_MSE: 8.991636221538103e-05\n",
      "Epoch: 987 Iteration: 92800. Train_MSE: 0.0003089515957981348. Test_MSE: 8.984046633102407e-05\n",
      "Epoch: 988 Iteration: 92900. Train_MSE: 0.0005007447325624526. Test_MSE: 8.981209490260521e-05\n",
      "Epoch: 989 Iteration: 93000. Train_MSE: 0.0002551318611949682. Test_MSE: 8.990334606323983e-05\n",
      "Epoch: 990 Iteration: 93100. Train_MSE: 0.00010458505130372941. Test_MSE: 8.989890327367542e-05\n",
      "Epoch: 991 Iteration: 93200. Train_MSE: 8.130961214192212e-05. Test_MSE: 8.984634412854727e-05\n",
      "Epoch: 992 Iteration: 93300. Train_MSE: 9.82778801699169e-05. Test_MSE: 8.991862792366215e-05\n",
      "Epoch: 993 Iteration: 93400. Train_MSE: 0.00010069718700833619. Test_MSE: 8.977537566351527e-05\n",
      "Epoch: 994 Iteration: 93500. Train_MSE: 0.0005977414548397064. Test_MSE: 8.961742259747576e-05\n",
      "Epoch: 995 Iteration: 93600. Train_MSE: 0.00027114979457110167. Test_MSE: 8.955719423383759e-05\n",
      "Epoch: 996 Iteration: 93700. Train_MSE: 0.00033874972723424435. Test_MSE: 8.95546328827408e-05\n",
      "Epoch: 997 Iteration: 93800. Train_MSE: 0.0001855529990280047. Test_MSE: 8.969091986917926e-05\n",
      "Epoch: 998 Iteration: 93900. Train_MSE: 9.176387538900599e-05. Test_MSE: 8.965006321646914e-05\n",
      "Epoch: 999 Iteration: 94000. Train_MSE: 3.7036665162304416e-05. Test_MSE: 8.958613796899753e-05\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "seq_dim = 1\n",
    "\n",
    "n_iter =0\n",
    "num_samples = len(X_train)\n",
    "test_samples = len(X_test)\n",
    "batch_size = 100\n",
    "num_epochs = 1000\n",
    "feat_dim = X_train.shape[1]\n",
    "\n",
    "X_train = X_train.type(torch.FloatTensor)\n",
    "y_train = y_train.type(torch.FloatTensor)\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i in range(0, int(num_samples/batch_size -1)):\n",
    "        \n",
    "        \n",
    "        features = Variable(X_train[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "        Kt_value = Variable(y_train[i*batch_size:(i+1)*batch_size])\n",
    "        \n",
    "        #print(\"Kt_value={}\".format(Kt_value))\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(features)\n",
    "        #print(\"outputs ={}\".format(outputs))\n",
    "        \n",
    "        loss = criterion(outputs, Kt_value)\n",
    "        \n",
    "        train_loss.append(loss.data[0])\n",
    "        train_iter.append(n_iter)\n",
    "\n",
    "        #print(\"loss = {}\".format(loss))\n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        n_iter += 1  \n",
    "            \n",
    "        if n_iter%100 == 0:\n",
    "            for i in range(0,int(test_samples/batch_size -1)):\n",
    "                features = Variable(X_test[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "                Kt_test = Variable(y_test[i*batch_size:(i+1)*batch_size])\n",
    "                \n",
    "                outputs = model(features)\n",
    "                \n",
    "                mse = np.sqrt(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2)/num_samples)\n",
    "                \n",
    "                test_iter.append(n_iter)\n",
    "                test_loss.append(mse)\n",
    "                \n",
    "            print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], mse))\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39480\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13846390>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dqzoqup2Xb1jPp/fcveTtOzF7hfJ6vfG/ol6vMzNTZ3qmzvT0DNMzdaamZzg3NXP+v3P/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\nFchwl6QCGe6SVKD/B+YQV0o4CAE4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(test_loss))\n",
    "#plt.plot(test_loss)\n",
    "plt.plot(train_loss,'-')\n",
    "#plt.ylim([0.000,0.99])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xdc358d0>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2LuDD0XRzgG53X2Bm9wDnAb1R3xXufmDsuywiItUYMSiAK4EWd19oZguAO4ArAMxsDnAj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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_loss,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Demornamization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_denorm = (rmse * (df_new_test['Kt'].max() - df_new_test['Kt'].min()))+ df_new_test['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.598802517989334"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4271.000000\n",
       "mean        3.414005\n",
       "std        17.372489\n",
       "min        -0.157857\n",
       "25%        -0.004881\n",
       "50%         0.188162\n",
       "75%         1.119065\n",
       "max       653.281210\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test['Kt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss, 'iteration': train_iter}, columns=['Train Loss','iteration'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_PennState_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss, 'iteration': test_iter}, columns=['Test Loss','iteration'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_PenState_TestLoss.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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