fixed-time-horizon-prediction/Exp_1.2_Desert_Rock_2hour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE\n",
    "** the test['Kt'].max() values after grouping into hourly data is 7279.907547 = this is a outlier. The reason might be having a dw_solar reading with QC=2 value. Still haven't checked that. But the second highest value of 'Kt' parameter in the testset is 672 (which is way lower) **"
   ]
  },
  {
   "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": [
    "drk = Location(36.621,-116.043, 'US/Pacific', 1010.1072, 'Desert Rock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=drk.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=drk.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = drk.get_clearsky(times2009) \n",
    "cs_2010and2011 = drk.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 00:00:00-08:00</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 00:01:00-08:00</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 00:02:00-08:00</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 00:03:00-08:00</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 00:04:00-08:00</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 00:00:00-08:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-08:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-08:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-08:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-08:00  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": [
       "(1047761, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Desert_Rock\\\\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": [
       "(525542, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Desert_Rock\\\\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": [
       "(11338, 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": [
       "(1047761, 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": [
       "(525542, 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": [
       "(1047761, 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": [
       "(988587, 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": [
       "(524158, 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": [
       "16067"
      ]
     },
     "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": [
       "(988587, 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": [
       "10590"
      ]
     },
     "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": [
       "(988587, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524158, 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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icOgQNtGEIXc4+UpAUhWl6l0vCZN/MGLatx8aNSp8XkYGDBrEyU3rqNs7BbvG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\nU1ggougUlcGDsU2fpinsFhR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c6ilVekGcSUmk7TrgM/wyYDmDiPHIYaolJXn8hvuSPbZR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\niqIoGaGKQ1EURckIVRyKoihKRvx/n8GpAuLq6boAAAAASUVORK5CYII=\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('Figure2a.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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Mma/K9xDgUAtmVQKw72gvqxaf2MubiiRgCSFESqA/llmMm0ha81Wv6wPzVWtO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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('Figure3a.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: 934060.7967236277\n",
      "test Kt min: -1212.4523861041143\n",
      "test Kt mean: 6.087694158417696\n",
      "\n",
      "\n",
      "train Kt max: 36737.73884840597\n",
      "train Kt min: -404.8538655568123\n",
      "train Kt mean: 2.4557778502312537\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 0x6f3ecc0>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REYWBiIgoDEREBIWBiIigMBARERQGkgfzlnyI1zaEPQwRyYHCQHKyZ187Ty3YyC2zqsMe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\nYSAiIigMREQEhYGIiKAwEBER4P8DLgaOgNe8NIcAAAAASUVORK5CYII=\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 0x6edac88>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Co08e4PorL6Hr3M48l4nPc+LUKD986pf8nQ+u45xlxbytUChw4PUBDhwc4IYr11AoFPK2\nl18f4Gf73mT92pVc/WsX5fE3+4b48TOvcfEF5/LRK9fk8Uqlyl8+cYDrPrCGlectO+05ntn/Fvt7\njnDF+guJy7pP2zY4NMyPd7/Gxz60jmVLl+TxArDvwGGefeEQG9adz1W/WqZQqG378e5XOdg3xLXv\nX826VaXc87bHD/Ch963igpXLqVSqPPrkK1z3gTX148Jpj00BChQ4OTzKD5/qYfPVa1l+xrGpl+Xa\nQgFODY/xg1093HDVGorFDn7w0x62bFxL14pOxipV/u/OlzlxapSbrr2M0orO087Dmc9dKNQuC31/\n58sMDo3wiQ9fyvmlZXz/8QMcP1G73921nI5CgbFKhe0/fYUPv281T+49yPVXrWHZ0iX81a4eNr1/\nNT/de5At16zlnOXF055r/Jy+1nuMH+9+bcbX06bfWM3la7o4NTzGzmdfZ8Pa83n2hUN87IPrWL6s\nyIHXB/jlW4Ncd8XFp71exo9X/8BJntjzBp/cdBlLlhTy+Vq3qsSlq0p/6/mm+to5m6+TiY9RrVb5\n/uMH+PD7VvGeruV5jq+8MchLrx/l2vev5odP9bBl4zqe3vcml67u4rLVpdN6+cnPX2fVe1aw/pKV\ns59Em7j2iospl//2cW5UO64Unkwp/c/6/VdTSmunqn+nK4Vx5XKJ3t6FfQ3bHheOxdDnYugRWt/n\ndCuFtvqgGdgJfAqg/pnCntZOR5IWl3a7fPQI8ImIeJzaav5zLZ6PJC0qbRUKKaUK8HutnockLVbt\ndvlIktRChoIkKTMUJEmZoSBJygwFSVLWVr+8JklqLVcKkqTMUJAkZYaCJCkzFCRJmaEgScoMBUlS\nZihIkrK2+l9S50JEdADfAK4ETgH/OKX0QmtnNXsRsZu3/zrdy8CfAf8RGAW2p5T+7VQ91v9Gxaxq\n57SpuojYBPxJSmlLRGwAHqT2F3X3AltTSpWIuBu4pd7D7SmlXc2obWGf1wDfBX5R3/ynKaXvzNc+\nI2Ip8ABwObAM+CPguUbn3U49TtPnqyyAc7kYVwq/BSxPKX0E+EPgKy2ez6xFxHKAlNKW+r/PAd8E\nfge4HthUf5OZqsezqZ1TEfEHwH8ClteH7gXuSindQO1va9xan+9mYBNwG3BfM2rf7d4mmqTPa4B7\nJ5zT78zzPj8D9NWf92bg643Ouw17hMn7XBDnctGtFKi9If4lQErpyYj4YIvnczauBFZExHZq5+4e\nYFlK6UWAiHgU+BhwMWf0GBFds62d25ayF4G/B3yrfn8j8Fj99jbgk0CitsKpAj0RUYyIchNqH3lX\nOzvdZH1GRNxK7TvM26m9Rudrnw8BD0+4P9qEebdbjzB1n/P+XC7GlUIXb19+ARiLiPkSjkPAl4Gb\nqP0xov9SHxs3CKxkkh7rYwOzqW3F8Ugp/S9gZMJQof7FAVPPdXy80do5M0mfu4B/k1K6EXgJuJt5\n3GdK6VhKaTAiStTeNO9qwrzbqkeYss8FcS4XYygMAKUJ9ztSSqOtmsxZ2g/815RSNaW0n9oL6D0T\ntpeAI0zS4yRjU9a2yfGYeM10qrmOjzda20qPpJSeHr8NXM087zMi1gE/Ar6VUvr2FHOZ1z3CpH0u\niHO5GENhJ/ApgPoHr3taO52z8nnq1/wjYg2wAjgeEesjokBtBbGDSXpMKQ0Aw7OpnduWprQ7IrbU\nb9/M23O9KSI6IuJSagF2qAm1rfRoRHy4fvtjwNPM4z4jYhWwHfhCSumB+vCCO5dT9LkgzuV8uWzS\nTI8An4iIx6l9aPO5Fs/nbPxn4MGI+Am1n0T4PLXvIv4bsITa9cifRsRTTN7j751FbavdAdwfEZ3A\nPuDhlNJYROwAnqD2Dc3WZtTOWUeT++fA1yNiGDgI/NOU0sA87vNOoBv4UkR8qT72+8DXFti5nKzP\nfw18db6fS//rbElSthgvH0mSpmAoSJIyQ0GSlBkKkqTMUJAkZYaCJCkzFCRJ2f8HcylNWvZenSkA\nAAAASUVORK5CYII=\n",
      "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": [
       "<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></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>6.0</th>\n",
       "      <td>162.750000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.300000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>243.300000</td>\n",
       "      <td>276.200000</td>\n",
       "      <td>275.700000</td>\n",
       "      <td>308.800000</td>\n",
       "      <td>274.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-65.500000</td>\n",
       "      <td>-65.500000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.400000</td>\n",
       "      <td>915.400000</td>\n",
       "      <td>8.485002</td>\n",
       "      <td>27.021223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>165.356667</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.366667</td>\n",
       "      <td>1.018333</td>\n",
       "      <td>-0.885000</td>\n",
       "      <td>241.946667</td>\n",
       "      <td>275.456667</td>\n",
       "      <td>274.983333</td>\n",
       "      <td>307.460000</td>\n",
       "      <td>274.423333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-65.521667</td>\n",
       "      <td>-65.521667</td>\n",
       "      <td>2.268333</td>\n",
       "      <td>41.808333</td>\n",
       "      <td>2.255000</td>\n",
       "      <td>115.338333</td>\n",
       "      <td>915.410000</td>\n",
       "      <td>72.956955</td>\n",
       "      <td>4.371045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>163.399000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.450000</td>\n",
       "      <td>0.781667</td>\n",
       "      <td>-0.781667</td>\n",
       "      <td>244.203333</td>\n",
       "      <td>275.143333</td>\n",
       "      <td>274.721667</td>\n",
       "      <td>304.498333</td>\n",
       "      <td>273.966667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-60.288333</td>\n",
       "      <td>-60.288333</td>\n",
       "      <td>1.918333</td>\n",
       "      <td>43.545000</td>\n",
       "      <td>0.883333</td>\n",
       "      <td>167.170000</td>\n",
       "      <td>915.458333</td>\n",
       "      <td>234.169751</td>\n",
       "      <td>1.026259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>154.172667</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.561667</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>-0.815000</td>\n",
       "      <td>245.158333</td>\n",
       "      <td>274.786667</td>\n",
       "      <td>274.335000</td>\n",
       "      <td>303.886667</td>\n",
       "      <td>273.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-58.726667</td>\n",
       "      <td>-58.726667</td>\n",
       "      <td>2.043333</td>\n",
       "      <td>43.063333</td>\n",
       "      <td>1.690000</td>\n",
       "      <td>130.491667</td>\n",
       "      <td>915.133333</td>\n",
       "      <td>388.224711</td>\n",
       "      <td>0.596671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>142.737000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.428333</td>\n",
       "      <td>1.516667</td>\n",
       "      <td>-0.661667</td>\n",
       "      <td>256.343333</td>\n",
       "      <td>274.830000</td>\n",
       "      <td>274.453333</td>\n",
       "      <td>305.071667</td>\n",
       "      <td>273.793333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.730000</td>\n",
       "      <td>-48.730000</td>\n",
       "      <td>1.671667</td>\n",
       "      <td>44.681667</td>\n",
       "      <td>1.256667</td>\n",
       "      <td>104.535000</td>\n",
       "      <td>915.161667</td>\n",
       "      <td>496.624772</td>\n",
       "      <td>0.462711</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 6.0   162.750000  229.275124 -1.300000  1.800000 -0.700000   \n",
       "                 7.0   165.356667  229.275124 -1.366667  1.018333 -0.885000   \n",
       "                 8.0   163.399000  229.275124 -1.450000  0.781667 -0.781667   \n",
       "                 9.0   154.172667  229.275124 -1.561667  0.983333 -0.815000   \n",
       "                 10.0  142.737000  229.275124 -1.428333  1.516667 -0.661667   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 6.0   243.300000   276.200000   275.700000  308.800000   \n",
       "                 7.0   241.946667   275.456667   274.983333  307.460000   \n",
       "                 8.0   244.203333   275.143333   274.721667  304.498333   \n",
       "                 9.0   245.158333   274.786667   274.335000  303.886667   \n",
       "                 10.0  256.343333   274.830000   274.453333  305.071667   \n",
       "\n",
       "                       uw_casetemp    ...      netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                       \n",
       "2010.0 1.0   1.0 6.0    274.700000    ...           0.0 -65.500000 -65.500000   \n",
       "                 7.0    274.423333    ...           0.0 -65.521667 -65.521667   \n",
       "                 8.0    273.966667    ...           0.0 -60.288333 -60.288333   \n",
       "                 9.0    273.730000    ...           0.0 -58.726667 -58.726667   \n",
       "                 10.0   273.793333    ...           0.0 -48.730000 -48.730000   \n",
       "\n",
       "                           temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                          \n",
       "2010.0 1.0   1.0 6.0   3.600000  39.000000  1.000000   56.400000  915.400000   \n",
       "                 7.0   2.268333  41.808333  2.255000  115.338333  915.410000   \n",
       "                 8.0   1.918333  43.545000  0.883333  167.170000  915.458333   \n",
       "                 9.0   2.043333  43.063333  1.690000  130.491667  915.133333   \n",
       "                 10.0  1.671667  44.681667  1.256667  104.535000  915.161667   \n",
       "\n",
       "                              ghi         Kt  \n",
       "year   month day hour                         \n",
       "2010.0 1.0   1.0 6.0     8.485002  27.021223  \n",
       "                 7.0    72.956955   4.371045  \n",
       "                 8.0   234.169751   1.026259  \n",
       "                 9.0   388.224711   0.596671  \n",
       "                 10.0  496.624772   0.462711  \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>6.0</th>\n",
       "      <td>150.286429</td>\n",
       "      <td>-5.457143</td>\n",
       "      <td>4.758099e-17</td>\n",
       "      <td>-0.175000</td>\n",
       "      <td>-0.053571</td>\n",
       "      <td>238.189286</td>\n",
       "      <td>280.017857</td>\n",
       "      <td>279.328571</td>\n",
       "      <td>327.503571</td>\n",
       "      <td>278.535714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-89.325000</td>\n",
       "      <td>-89.325000</td>\n",
       "      <td>5.610714</td>\n",
       "      <td>61.664286</td>\n",
       "      <td>10.210714</td>\n",
       "      <td>198.364286</td>\n",
       "      <td>895.860714</td>\n",
       "      <td>33.869158</td>\n",
       "      <td>-0.199644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>154.152333</td>\n",
       "      <td>-5.783333</td>\n",
       "      <td>-3.450000e-01</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>-0.021667</td>\n",
       "      <td>238.683333</td>\n",
       "      <td>279.945000</td>\n",
       "      <td>279.300000</td>\n",
       "      <td>327.176667</td>\n",
       "      <td>278.526667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-88.500000</td>\n",
       "      <td>-88.500000</td>\n",
       "      <td>5.720000</td>\n",
       "      <td>59.485000</td>\n",
       "      <td>9.733333</td>\n",
       "      <td>198.575000</td>\n",
       "      <td>896.445000</td>\n",
       "      <td>156.492998</td>\n",
       "      <td>-0.043188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>154.031833</td>\n",
       "      <td>-5.948333</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>234.365000</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>279.126667</td>\n",
       "      <td>325.691667</td>\n",
       "      <td>278.390000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-91.336667</td>\n",
       "      <td>-91.336667</td>\n",
       "      <td>5.523333</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>9.743333</td>\n",
       "      <td>206.088333</td>\n",
       "      <td>897.225000</td>\n",
       "      <td>358.157279</td>\n",
       "      <td>-0.016985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>147.761500</td>\n",
       "      <td>-6.048333</td>\n",
       "      <td>-2.966667e-01</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.048333</td>\n",
       "      <td>234.465000</td>\n",
       "      <td>279.631667</td>\n",
       "      <td>278.981667</td>\n",
       "      <td>324.173333</td>\n",
       "      <td>278.226667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-89.698333</td>\n",
       "      <td>-89.698333</td>\n",
       "      <td>5.428333</td>\n",
       "      <td>54.283333</td>\n",
       "      <td>7.906667</td>\n",
       "      <td>204.571667</td>\n",
       "      <td>898.161667</td>\n",
       "      <td>534.190398</td>\n",
       "      <td>-0.011397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>137.994833</td>\n",
       "      <td>-6.270000</td>\n",
       "      <td>-2.583333e-01</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.168333</td>\n",
       "      <td>233.670000</td>\n",
       "      <td>279.040000</td>\n",
       "      <td>278.473333</td>\n",
       "      <td>321.188333</td>\n",
       "      <td>277.590000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.516667</td>\n",
       "      <td>-87.516667</td>\n",
       "      <td>4.856667</td>\n",
       "      <td>58.343333</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>221.958333</td>\n",
       "      <td>898.420000</td>\n",
       "      <td>660.519935</td>\n",
       "      <td>-0.009504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>126.708167</td>\n",
       "      <td>-6.398333</td>\n",
       "      <td>-4.233333e-01</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.118333</td>\n",
       "      <td>232.613333</td>\n",
       "      <td>278.768333</td>\n",
       "      <td>278.190000</td>\n",
       "      <td>318.916667</td>\n",
       "      <td>277.463333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.303333</td>\n",
       "      <td>-86.303333</td>\n",
       "      <td>4.821667</td>\n",
       "      <td>56.403333</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>242.768333</td>\n",
       "      <td>898.488333</td>\n",
       "      <td>725.681709</td>\n",
       "      <td>-0.008817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>114.824167</td>\n",
       "      <td>-6.381667</td>\n",
       "      <td>-4.466667e-01</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.161667</td>\n",
       "      <td>231.091667</td>\n",
       "      <td>277.991667</td>\n",
       "      <td>277.468333</td>\n",
       "      <td>317.415000</td>\n",
       "      <td>276.856667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.321667</td>\n",
       "      <td>-86.321667</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>57.873333</td>\n",
       "      <td>4.493333</td>\n",
       "      <td>240.988333</td>\n",
       "      <td>898.961667</td>\n",
       "      <td>724.381774</td>\n",
       "      <td>-0.008812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>102.804000</td>\n",
       "      <td>-6.466667</td>\n",
       "      <td>-3.716667e-01</td>\n",
       "      <td>0.581667</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>231.006667</td>\n",
       "      <td>277.816667</td>\n",
       "      <td>277.293333</td>\n",
       "      <td>317.900000</td>\n",
       "      <td>276.565000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.886667</td>\n",
       "      <td>-86.886667</td>\n",
       "      <td>3.931667</td>\n",
       "      <td>59.148333</td>\n",
       "      <td>4.121667</td>\n",
       "      <td>251.095000</td>\n",
       "      <td>899.751667</td>\n",
       "      <td>656.728953</td>\n",
       "      <td>-0.009876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>90.783167</td>\n",
       "      <td>4.851667</td>\n",
       "      <td>2.676667e+00</td>\n",
       "      <td>113.013333</td>\n",
       "      <td>7.931667</td>\n",
       "      <td>232.438333</td>\n",
       "      <td>278.385000</td>\n",
       "      <td>277.873333</td>\n",
       "      <td>319.696667</td>\n",
       "      <td>276.965000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.883333</td>\n",
       "      <td>-87.261667</td>\n",
       "      <td>-75.386667</td>\n",
       "      <td>4.480000</td>\n",
       "      <td>54.673333</td>\n",
       "      <td>4.691667</td>\n",
       "      <td>230.323333</td>\n",
       "      <td>900.423333</td>\n",
       "      <td>528.243267</td>\n",
       "      <td>0.011476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>79.467333</td>\n",
       "      <td>134.226667</td>\n",
       "      <td>2.904833e+01</td>\n",
       "      <td>583.338333</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>236.191667</td>\n",
       "      <td>280.461667</td>\n",
       "      <td>279.906667</td>\n",
       "      <td>325.915000</td>\n",
       "      <td>277.828333</td>\n",
       "      <td>...</td>\n",
       "      <td>123.505000</td>\n",
       "      <td>-89.710000</td>\n",
       "      <td>33.791667</td>\n",
       "      <td>5.283333</td>\n",
       "      <td>51.800000</td>\n",
       "      <td>3.570000</td>\n",
       "      <td>233.085000</td>\n",
       "      <td>901.383333</td>\n",
       "      <td>350.685281</td>\n",
       "      <td>0.411660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>68.964167</td>\n",
       "      <td>355.316667</td>\n",
       "      <td>7.125333e+01</td>\n",
       "      <td>835.673333</td>\n",
       "      <td>65.670000</td>\n",
       "      <td>238.995000</td>\n",
       "      <td>283.240000</td>\n",
       "      <td>282.521667</td>\n",
       "      <td>346.746667</td>\n",
       "      <td>279.658333</td>\n",
       "      <td>...</td>\n",
       "      <td>294.840000</td>\n",
       "      <td>-107.756667</td>\n",
       "      <td>187.081667</td>\n",
       "      <td>6.493333</td>\n",
       "      <td>47.625000</td>\n",
       "      <td>5.473333</td>\n",
       "      <td>193.385000</td>\n",
       "      <td>902.025000</td>\n",
       "      <td>149.085976</td>\n",
       "      <td>3.013727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>62.158462</td>\n",
       "      <td>500.811538</td>\n",
       "      <td>9.932308e+01</td>\n",
       "      <td>911.876923</td>\n",
       "      <td>77.473077</td>\n",
       "      <td>241.038462</td>\n",
       "      <td>284.615385</td>\n",
       "      <td>283.865385</td>\n",
       "      <td>363.803846</td>\n",
       "      <td>280.730769</td>\n",
       "      <td>...</td>\n",
       "      <td>404.415385</td>\n",
       "      <td>-122.773077</td>\n",
       "      <td>281.650000</td>\n",
       "      <td>7.173077</td>\n",
       "      <td>46.588462</td>\n",
       "      <td>5.292308</td>\n",
       "      <td>201.319231</td>\n",
       "      <td>902.446154</td>\n",
       "      <td>31.373484</td>\n",
       "      <td>21.307784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar      uw_solar    direct_n    diffuse       dw_ir  \\\n",
       "hour                                                                            \n",
       "6.0   150.286429   -5.457143  4.758099e-17   -0.175000  -0.053571  238.189286   \n",
       "7.0   154.152333   -5.783333 -3.450000e-01    0.123333  -0.021667  238.683333   \n",
       "8.0   154.031833   -5.948333 -2.000000e-01   -0.068333   0.016667  234.365000   \n",
       "9.0   147.761500   -6.048333 -2.966667e-01    0.083333   0.048333  234.465000   \n",
       "10.0  137.994833   -6.270000 -2.583333e-01    0.140000   0.168333  233.670000   \n",
       "11.0  126.708167   -6.398333 -4.233333e-01    0.073333   0.118333  232.613333   \n",
       "12.0  114.824167   -6.381667 -4.466667e-01    0.233333   0.161667  231.091667   \n",
       "13.0  102.804000   -6.466667 -3.716667e-01    0.581667   0.128333  231.006667   \n",
       "14.0   90.783167    4.851667  2.676667e+00  113.013333   7.931667  232.438333   \n",
       "15.0   79.467333  134.226667  2.904833e+01  583.338333  46.980000  236.191667   \n",
       "16.0   68.964167  355.316667  7.125333e+01  835.673333  65.670000  238.995000   \n",
       "17.0   62.158462  500.811538  9.932308e+01  911.876923  77.473077  241.038462   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "6.0    280.017857   279.328571  327.503571   278.535714    ...       \n",
       "7.0    279.945000   279.300000  327.176667   278.526667    ...       \n",
       "8.0    279.816667   279.126667  325.691667   278.390000    ...       \n",
       "9.0    279.631667   278.981667  324.173333   278.226667    ...       \n",
       "10.0   279.040000   278.473333  321.188333   277.590000    ...       \n",
       "11.0   278.768333   278.190000  318.916667   277.463333    ...       \n",
       "12.0   277.991667   277.468333  317.415000   276.856667    ...       \n",
       "13.0   277.816667   277.293333  317.900000   276.565000    ...       \n",
       "14.0   278.385000   277.873333  319.696667   276.965000    ...       \n",
       "15.0   280.461667   279.906667  325.915000   277.828333    ...       \n",
       "16.0   283.240000   282.521667  346.746667   279.658333    ...       \n",
       "17.0   284.615385   283.865385  363.803846   280.730769    ...       \n",
       "\n",
       "        netsolar       netir    totalnet      temp         rh    windspd  \\\n",
       "hour                                                                       \n",
       "6.0     0.000000  -89.325000  -89.325000  5.610714  61.664286  10.210714   \n",
       "7.0     0.000000  -88.500000  -88.500000  5.720000  59.485000   9.733333   \n",
       "8.0     0.000000  -91.336667  -91.336667  5.523333  57.200000   9.743333   \n",
       "9.0     0.000000  -89.698333  -89.698333  5.428333  54.283333   7.906667   \n",
       "10.0    0.000000  -87.516667  -87.516667  4.856667  58.343333   4.830000   \n",
       "11.0    0.000000  -86.303333  -86.303333  4.821667  56.403333   3.730000   \n",
       "12.0    0.000000  -86.321667  -86.321667  4.230000  57.873333   4.493333   \n",
       "13.0    0.000000  -86.886667  -86.886667  3.931667  59.148333   4.121667   \n",
       "14.0   11.883333  -87.261667  -75.386667  4.480000  54.673333   4.691667   \n",
       "15.0  123.505000  -89.710000   33.791667  5.283333  51.800000   3.570000   \n",
       "16.0  294.840000 -107.756667  187.081667  6.493333  47.625000   5.473333   \n",
       "17.0  404.415385 -122.773077  281.650000  7.173077  46.588462   5.292308   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "6.0   198.364286  895.860714   33.869158  -0.199644  \n",
       "7.0   198.575000  896.445000  156.492998  -0.043188  \n",
       "8.0   206.088333  897.225000  358.157279  -0.016985  \n",
       "9.0   204.571667  898.161667  534.190398  -0.011397  \n",
       "10.0  221.958333  898.420000  660.519935  -0.009504  \n",
       "11.0  242.768333  898.488333  725.681709  -0.008817  \n",
       "12.0  240.988333  898.961667  724.381774  -0.008812  \n",
       "13.0  251.095000  899.751667  656.728953  -0.009876  \n",
       "14.0  230.323333  900.423333  528.243267   0.011476  \n",
       "15.0  233.085000  901.383333  350.685281   0.411660  \n",
       "16.0  193.385000  902.025000  149.085976   3.013727  \n",
       "17.0  201.319231  902.446154   31.373484  21.307784  \n",
       "\n",
       "[12 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(-2)"
   ]
  },
  {
   "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(-2)"
   ]
  },
  {
   "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>7.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>18.0</th>\n",
       "      <td>23.523667</td>\n",
       "      <td>978.9</td>\n",
       "      <td>196.528333</td>\n",
       "      <td>928.071667</td>\n",
       "      <td>92.3</td>\n",
       "      <td>389.115</td>\n",
       "      <td>315.621667</td>\n",
       "      <td>314.831667</td>\n",
       "      <td>636.071667</td>\n",
       "      <td>312.423333</td>\n",
       "      <td>...</td>\n",
       "      <td>745.785</td>\n",
       "      <td>-246.948333</td>\n",
       "      <td>498.83</td>\n",
       "      <td>36.74</td>\n",
       "      <td>11.503333</td>\n",
       "      <td>5.58</td>\n",
       "      <td>223.173333</td>\n",
       "      <td>903.661667</td>\n",
       "      <td>236.172256</td>\n",
       "      <td>4900.219009</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  \\\n",
       "month day  hour                                                         \n",
       "7.0   16.0 18.0  23.523667     978.9  196.528333  928.071667     92.3   \n",
       "\n",
       "                   dw_ir  dw_casetemp  dw_dometemp       uw_ir  uw_casetemp  \\\n",
       "month day  hour                                                               \n",
       "7.0   16.0 18.0  389.115   315.621667   314.831667  636.071667   312.423333   \n",
       "\n",
       "                    ...       netsolar       netir  totalnet   temp  \\\n",
       "month day  hour     ...                                               \n",
       "7.0   16.0 18.0     ...        745.785 -246.948333    498.83  36.74   \n",
       "\n",
       "                        rh  windspd     winddir    pressure         ghi  \\\n",
       "month day  hour                                                           \n",
       "7.0   16.0 18.0  11.503333     5.58  223.173333  903.661667  236.172256   \n",
       "\n",
       "                          Kt  \n",
       "month day  hour               \n",
       "7.0   16.0 18.0  4900.219009  \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 (9000, 22)\n",
      "y1_train shape is (9000,)\n",
      "X2_test shape is (4100, 22)\n",
      "y2_test shape is (4100,)\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: 0 Iteration: 0. Train_MSE: 0.003966538701206446. Test_MSE: 0.001130812512768086\n",
      "Epoch: 1 Iteration: 100. Train_MSE: 0.002632440533488989. Test_MSE: 0.0011062896374338103\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.012723102234303951. Test_MSE: 0.0011101709757851384\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.003712792182341218. Test_MSE: 0.0011198357270318295\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.0024000676348805428. Test_MSE: 0.0010893943320306424\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.002514314604923129. Test_MSE: 0.0010719508496736451\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.006163809448480606. Test_MSE: 0.0010716691248545676\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.0017601093277335167. Test_MSE: 0.0010757650940062926\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.003962681163102388. Test_MSE: 0.0010588174284900536\n",
      "Epoch: 10 Iteration: 900. Train_MSE: 0.004066580906510353. Test_MSE: 0.0010260814277412865\n",
      "Epoch: 11 Iteration: 1000. Train_MSE: 0.01847498118877411. Test_MSE: 0.001021231942463986\n",
      "Epoch: 12 Iteration: 1100. Train_MSE: 0.0008779580239206553. Test_MSE: 0.0010257577491199423\n",
      "Epoch: 13 Iteration: 1200. Train_MSE: 0.0038836575113236904. Test_MSE: 0.0009972976129075813\n",
      "Epoch: 14 Iteration: 1300. Train_MSE: 0.0026968952734023333. Test_MSE: 0.000977881811966304\n",
      "Epoch: 15 Iteration: 1400. Train_MSE: 0.0070260693319141865. Test_MSE: 0.0009751956168641863\n",
      "Epoch: 16 Iteration: 1500. Train_MSE: 0.004092523828148842. Test_MSE: 0.0009783915239877424\n",
      "Epoch: 17 Iteration: 1600. Train_MSE: 0.006594212260097265. Test_MSE: 0.000964831946760252\n",
      "Epoch: 19 Iteration: 1700. Train_MSE: 0.000636512238997966. Test_MSE: 0.0009342427093092765\n",
      "Epoch: 20 Iteration: 1800. Train_MSE: 0.0044297524727880955. Test_MSE: 0.0009296148883555528\n",
      "Epoch: 21 Iteration: 1900. Train_MSE: 0.0025106817483901978. Test_MSE: 0.0009332740206051435\n",
      "Epoch: 22 Iteration: 2000. Train_MSE: 0.009780767373740673. Test_MSE: 0.0009098385143458762\n",
      "Epoch: 23 Iteration: 2100. Train_MSE: 0.003580279415473342. Test_MSE: 0.0008914227579690638\n",
      "Epoch: 24 Iteration: 2200. Train_MSE: 0.01117368321865797. Test_MSE: 0.0008872915792293495\n",
      "Epoch: 25 Iteration: 2300. Train_MSE: 0.0037650959566235542. Test_MSE: 0.0008906215896318069\n",
      "Epoch: 26 Iteration: 2400. Train_MSE: 0.0009991575498133898. Test_MSE: 0.0008816965280921379\n",
      "Epoch: 28 Iteration: 2500. Train_MSE: 0.0011064144782721996. Test_MSE: 0.0008523805047713985\n",
      "Epoch: 29 Iteration: 2600. Train_MSE: 0.00243282038718462. Test_MSE: 0.0008473131664291292\n",
      "Epoch: 30 Iteration: 2700. Train_MSE: 0.003163577290251851. Test_MSE: 0.000850715891033588\n",
      "Epoch: 31 Iteration: 2800. Train_MSE: 0.0053825764916837215. Test_MSE: 0.0008343098489496161\n",
      "Epoch: 32 Iteration: 2900. Train_MSE: 0.004107072949409485. Test_MSE: 0.0008154254616268096\n",
      "Epoch: 33 Iteration: 3000. Train_MSE: 0.010047211311757565. Test_MSE: 0.0008111764194197493\n",
      "Epoch: 34 Iteration: 3100. Train_MSE: 0.0025134228635579348. Test_MSE: 0.000813158511953906\n",
      "Epoch: 35 Iteration: 3200. Train_MSE: 0.0029829421546310186. Test_MSE: 0.000807188979919284\n",
      "Epoch: 37 Iteration: 3300. Train_MSE: 0.0033949431963264942. Test_MSE: 0.0007813910847005286\n",
      "Epoch: 38 Iteration: 3400. Train_MSE: 0.001780059770680964. Test_MSE: 0.0007759604931627547\n",
      "Epoch: 39 Iteration: 3500. Train_MSE: 0.0031103312503546476. Test_MSE: 0.0007782083149177053\n",
      "Epoch: 40 Iteration: 3600. Train_MSE: 0.0035525041166692972. Test_MSE: 0.0007671551011930122\n",
      "Epoch: 41 Iteration: 3700. Train_MSE: 0.001798403449356556. Test_MSE: 0.0007490706207189032\n",
      "Epoch: 42 Iteration: 3800. Train_MSE: 0.0016871823463588953. Test_MSE: 0.0007446552751385262\n",
      "Epoch: 43 Iteration: 3900. Train_MSE: 0.002585937734693289. Test_MSE: 0.0007457396555765627\n",
      "Epoch: 44 Iteration: 4000. Train_MSE: 0.0012791657354682684. Test_MSE: 0.0007430723907679359\n",
      "Epoch: 46 Iteration: 4100. Train_MSE: 0.0027531899977475405. Test_MSE: 0.0007200428385540608\n",
      "Epoch: 47 Iteration: 4200. Train_MSE: 0.0014021716779097915. Test_MSE: 0.0007136641476152557\n",
      "Epoch: 48 Iteration: 4300. Train_MSE: 0.0030072287190705538. Test_MSE: 0.0007149493814580609\n",
      "Epoch: 49 Iteration: 4400. Train_MSE: 0.0020594054367393255. Test_MSE: 0.0007078699730469645\n",
      "Epoch: 50 Iteration: 4500. Train_MSE: 0.0003851389919873327. Test_MSE: 0.0006910597210597519\n",
      "Epoch: 51 Iteration: 4600. Train_MSE: 0.0015721111558377743. Test_MSE: 0.0006858170020316086\n",
      "Epoch: 52 Iteration: 4700. Train_MSE: 0.00201384793035686. Test_MSE: 0.0006862959360453046\n",
      "Epoch: 53 Iteration: 4800. Train_MSE: 0.0017666761996224523. Test_MSE: 0.0006858110420028421\n",
      "Epoch: 55 Iteration: 4900. Train_MSE: 0.0014921368565410376. Test_MSE: 0.0006657834753139417\n",
      "Epoch: 56 Iteration: 5000. Train_MSE: 0.001206572400406003. Test_MSE: 0.0006583044536647661\n",
      "Epoch: 57 Iteration: 5100. Train_MSE: 0.002604764187708497. Test_MSE: 0.0006589268171892622\n",
      "Epoch: 58 Iteration: 5200. Train_MSE: 0.0005825249245390296. Test_MSE: 0.0006548667635292508\n",
      "Epoch: 59 Iteration: 5300. Train_MSE: 0.00031009313534013927. Test_MSE: 0.000638603727559202\n",
      "Epoch: 60 Iteration: 5400. Train_MSE: 0.001229041488841176. Test_MSE: 0.0006339188372510712\n",
      "Epoch: 61 Iteration: 5500. Train_MSE: 0.0013950507855042815. Test_MSE: 0.0006336930374798867\n",
      "Epoch: 62 Iteration: 5600. Train_MSE: 0.0008560789283365011. Test_MSE: 0.0006345071429343099\n",
      "Epoch: 64 Iteration: 5700. Train_MSE: 0.004552674479782581. Test_MSE: 0.0006173100852750431\n",
      "Epoch: 65 Iteration: 5800. Train_MSE: 0.0010453606955707073. Test_MSE: 0.0006095464482556229\n",
      "Epoch: 66 Iteration: 5900. Train_MSE: 0.014544585719704628. Test_MSE: 0.0006092862262551527\n",
      "Epoch: 67 Iteration: 6000. Train_MSE: 0.0006693881587125361. Test_MSE: 0.0006069081980489236\n",
      "Epoch: 68 Iteration: 6100. Train_MSE: 0.0015128572704270482. Test_MSE: 0.0005923252799712376\n",
      "Epoch: 69 Iteration: 6200. Train_MSE: 0.0006591220735572278. Test_MSE: 0.0005875658878874197\n",
      "Epoch: 70 Iteration: 6300. Train_MSE: 0.0007953051244840026. Test_MSE: 0.0005874081760468226\n",
      "Epoch: 71 Iteration: 6400. Train_MSE: 0.008258244022727013. Test_MSE: 0.0005880028035722553\n",
      "Epoch: 73 Iteration: 6500. Train_MSE: 0.003295904491096735. Test_MSE: 0.0005751955881137903\n",
      "Epoch: 74 Iteration: 6600. Train_MSE: 0.0007390787359327078. Test_MSE: 0.000566022512737565\n",
      "Epoch: 75 Iteration: 6700. Train_MSE: 0.0069909174926579. Test_MSE: 0.000565715977560032\n",
      "Epoch: 76 Iteration: 6800. Train_MSE: 0.0008509436738677323. Test_MSE: 0.0005643294203256877\n",
      "Epoch: 77 Iteration: 6900. Train_MSE: 0.000861118664033711. Test_MSE: 0.0005519705805710923\n",
      "Epoch: 78 Iteration: 7000. Train_MSE: 0.0010110000148415565. Test_MSE: 0.0005468279434197962\n",
      "Epoch: 79 Iteration: 7100. Train_MSE: 0.0012621725909411907. Test_MSE: 0.0005461059756616668\n",
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      "Epoch: 82 Iteration: 7300. Train_MSE: 0.003273602109402418. Test_MSE: 0.0005372949481933297\n",
      "Epoch: 83 Iteration: 7400. Train_MSE: 0.0012498400174081326. Test_MSE: 0.0005271994673986706\n",
      "Epoch: 84 Iteration: 7500. Train_MSE: 0.002953065326437354. Test_MSE: 0.0005268651405352569\n",
      "Epoch: 85 Iteration: 7600. Train_MSE: 0.0005044121062383056. Test_MSE: 0.0005265114637036104\n",
      "Epoch: 86 Iteration: 7700. Train_MSE: 0.00034752089413814247. Test_MSE: 0.0005151759539985045\n",
      "Epoch: 87 Iteration: 7800. Train_MSE: 0.0008928135503083467. Test_MSE: 0.0005104079259964176\n",
      "Epoch: 88 Iteration: 7900. Train_MSE: 0.00896431878209114. Test_MSE: 0.0005088764592753559\n",
      "Epoch: 89 Iteration: 8000. Train_MSE: 0.0006489250808954239. Test_MSE: 0.0005103109562084085\n",
      "Epoch: 91 Iteration: 8100. Train_MSE: 0.0009797611273825169. Test_MSE: 0.0005032614762663115\n",
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      "Epoch: 95 Iteration: 8500. Train_MSE: 0.0004954547039233148. Test_MSE: 0.0004820953438246205\n",
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      "Epoch: 98 Iteration: 8800. Train_MSE: 0.0007589826709590852. Test_MSE: 0.0004769846912608929\n",
      "Epoch: 100 Iteration: 8900. Train_MSE: 0.0006042423192411661. Test_MSE: 0.0004718578316815839\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 101 Iteration: 9000. Train_MSE: 0.0007952108862809837. Test_MSE: 0.00046154360469494334\n",
      "Epoch: 102 Iteration: 9100. Train_MSE: 0.007126305717974901. Test_MSE: 0.00046041190280596603\n",
      "Epoch: 103 Iteration: 9200. Train_MSE: 0.0014075912768021226. Test_MSE: 0.0004614066513016144\n",
      "Epoch: 104 Iteration: 9300. Train_MSE: 0.0002789014542941004. Test_MSE: 0.00045244103593228144\n",
      "Epoch: 105 Iteration: 9400. Train_MSE: 0.0003866464539896697. Test_MSE: 0.00044783807936535824\n",
      "Epoch: 106 Iteration: 9500. Train_MSE: 0.0013583694817498326. Test_MSE: 0.0004470002001307655\n",
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      "Epoch: 916 Iteration: 81600. Train_MSE: 0.001892428263090551. Test_MSE: 0.00011832554434776848\n",
      "Epoch: 917 Iteration: 81700. Train_MSE: 0.0001910761056933552. Test_MSE: 0.00011868598178379614\n",
      "Epoch: 919 Iteration: 81800. Train_MSE: 0.00016429599781986326. Test_MSE: 0.00011847235530146388\n",
      "Epoch: 920 Iteration: 81900. Train_MSE: 0.00016905232041608542. Test_MSE: 0.00011875591616478999\n",
      "Epoch: 921 Iteration: 82000. Train_MSE: 0.00015807716408744454. Test_MSE: 0.00011817730597560452\n",
      "Epoch: 922 Iteration: 82100. Train_MSE: 0.00017066490545403212. Test_MSE: 0.00011816101665792325\n",
      "Epoch: 923 Iteration: 82200. Train_MSE: 0.0018442822620272636. Test_MSE: 0.0001184657291401657\n",
      "Epoch: 924 Iteration: 82300. Train_MSE: 0.0076046232134103775. Test_MSE: 0.00011844972519324128\n",
      "Epoch: 925 Iteration: 82400. Train_MSE: 0.0004796972207259387. Test_MSE: 0.0001180874644235021\n",
      "Epoch: 926 Iteration: 82500. Train_MSE: 0.00013001174374949187. Test_MSE: 0.00011830745094185594\n",
      "Epoch: 928 Iteration: 82600. Train_MSE: 0.0001445702655473724. Test_MSE: 0.00011805413285740062\n",
      "Epoch: 929 Iteration: 82700. Train_MSE: 0.00017256276623811573. Test_MSE: 0.00011842187521787315\n",
      "Epoch: 930 Iteration: 82800. Train_MSE: 0.0002675006981007755. Test_MSE: 0.00011780823747994198\n",
      "Epoch: 931 Iteration: 82900. Train_MSE: 0.00011629966320469975. Test_MSE: 0.00011788405562259403\n",
      "Epoch: 932 Iteration: 83000. Train_MSE: 0.0020379929337650537. Test_MSE: 0.00011812452344632537\n",
      "Epoch: 933 Iteration: 83100. Train_MSE: 0.006616469006985426. Test_MSE: 0.00011822368505226223\n",
      "Epoch: 934 Iteration: 83200. Train_MSE: 0.0002770001592580229. Test_MSE: 0.00011772734393698885\n",
      "Epoch: 935 Iteration: 83300. Train_MSE: 9.75426664808765e-05. Test_MSE: 0.00011786251291914721\n",
      "Epoch: 937 Iteration: 83400. Train_MSE: 0.00018146094225812703. Test_MSE: 0.00011768721291506531\n",
      "Epoch: 938 Iteration: 83500. Train_MSE: 0.00011801450455095619. Test_MSE: 0.00011807581176987201\n",
      "Epoch: 939 Iteration: 83600. Train_MSE: 0.0005429633893072605. Test_MSE: 0.00011738740793366836\n",
      "Epoch: 940 Iteration: 83700. Train_MSE: 7.355713023571298e-05. Test_MSE: 0.00011758852705314971\n",
      "Epoch: 941 Iteration: 83800. Train_MSE: 0.00015803238784428686. Test_MSE: 0.00011785425418174127\n",
      "Epoch: 942 Iteration: 83900. Train_MSE: 0.0001270861248485744. Test_MSE: 0.00011800932445879539\n",
      "Epoch: 943 Iteration: 84000. Train_MSE: 0.0003808872716035694. Test_MSE: 0.00011738724264705339\n",
      "Epoch: 944 Iteration: 84100. Train_MSE: 0.00010325564653612673. Test_MSE: 0.00011755190425711133\n",
      "Epoch: 946 Iteration: 84200. Train_MSE: 0.00017533759819343686. Test_MSE: 0.00011738422612544843\n",
      "Epoch: 947 Iteration: 84300. Train_MSE: 0.00011348225234542042. Test_MSE: 0.00011768473990754613\n",
      "Epoch: 948 Iteration: 84400. Train_MSE: 0.000910822011064738. Test_MSE: 0.00011701579976100634\n",
      "Epoch: 949 Iteration: 84500. Train_MSE: 0.00012443029845599085. Test_MSE: 0.00011730504570274794\n",
      "Epoch: 950 Iteration: 84600. Train_MSE: 0.00016961878282018006. Test_MSE: 0.0001175061062709637\n",
      "Epoch: 951 Iteration: 84700. Train_MSE: 8.078268001554534e-05. Test_MSE: 0.0001176224098266825\n",
      "Epoch: 952 Iteration: 84800. Train_MSE: 0.0002646943321451545. Test_MSE: 0.00011703513591799666\n",
      "Epoch: 953 Iteration: 84900. Train_MSE: 0.00023483793484047055. Test_MSE: 0.00011716165743325692\n",
      "Epoch: 955 Iteration: 85000. Train_MSE: 0.00011583699961192906. Test_MSE: 0.00011709033588194294\n",
      "Epoch: 956 Iteration: 85100. Train_MSE: 0.00019158236682415009. Test_MSE: 0.00011730769211368961\n",
      "Epoch: 957 Iteration: 85200. Train_MSE: 0.00031747540924698114. Test_MSE: 0.00011671251074324528\n",
      "Epoch: 958 Iteration: 85300. Train_MSE: 0.0002489733451511711. Test_MSE: 0.00011699820170283944\n",
      "Epoch: 959 Iteration: 85400. Train_MSE: 0.00013210884935688227. Test_MSE: 0.00011709651516621534\n",
      "Epoch: 960 Iteration: 85500. Train_MSE: 0.00012298377987463027. Test_MSE: 0.0001172676242987899\n",
      "Epoch: 961 Iteration: 85600. Train_MSE: 0.0002406841958872974. Test_MSE: 0.00011669873260120127\n",
      "Epoch: 962 Iteration: 85700. Train_MSE: 0.0002288970135850832. Test_MSE: 0.00011675832228019122\n",
      "Epoch: 964 Iteration: 85800. Train_MSE: 0.0002143194287782535. Test_MSE: 0.00011679128992232371\n",
      "Epoch: 965 Iteration: 85900. Train_MSE: 0.00015780789544805884. Test_MSE: 0.00011693915597603587\n",
      "Epoch: 966 Iteration: 86000. Train_MSE: 0.012918991036713123. Test_MSE: 0.00011640378534184812\n",
      "Epoch: 967 Iteration: 86100. Train_MSE: 6.802539428463206e-05. Test_MSE: 0.00011651625604644638\n",
      "Epoch: 968 Iteration: 86200. Train_MSE: 0.00011964733130298555. Test_MSE: 0.0001167416964673632\n",
      "Epoch: 969 Iteration: 86300. Train_MSE: 8.9150300482288e-05. Test_MSE: 0.00011690909983971454\n",
      "Epoch: 970 Iteration: 86400. Train_MSE: 0.00013694893277715892. Test_MSE: 0.00011640826833867439\n",
      "Epoch: 971 Iteration: 86500. Train_MSE: 0.007947801612317562. Test_MSE: 0.00011633869472242925\n",
      "Epoch: 973 Iteration: 86600. Train_MSE: 0.0005472307675518095. Test_MSE: 0.00011660592155146697\n",
      "Epoch: 974 Iteration: 86700. Train_MSE: 0.00020307116210460663. Test_MSE: 0.00011657588352229467\n",
      "Epoch: 975 Iteration: 86800. Train_MSE: 0.00543093541637063. Test_MSE: 0.00011628154933246063\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 976 Iteration: 86900. Train_MSE: 9.989670797949657e-05. Test_MSE: 0.00011616115776684386\n",
      "Epoch: 977 Iteration: 87000. Train_MSE: 0.00013850376126356423. Test_MSE: 0.0001164313438387062\n",
      "Epoch: 978 Iteration: 87100. Train_MSE: 0.00012078772124368697. Test_MSE: 0.00011658268644084124\n",
      "Epoch: 979 Iteration: 87200. Train_MSE: 0.00022074107255320996. Test_MSE: 0.00011613409901831018\n",
      "Epoch: 980 Iteration: 87300. Train_MSE: 0.0001603810815140605. Test_MSE: 0.00011606555213883996\n",
      "Epoch: 982 Iteration: 87400. Train_MSE: 0.00014690456737298518. Test_MSE: 0.0001163876923569648\n",
      "Epoch: 983 Iteration: 87500. Train_MSE: 0.00023148208856582642. Test_MSE: 0.00011621783715890919\n",
      "Epoch: 984 Iteration: 87600. Train_MSE: 0.0011029931483790278. Test_MSE: 0.00011608990748897914\n",
      "Epoch: 985 Iteration: 87700. Train_MSE: 0.00022949225967749953. Test_MSE: 0.00011585665374444463\n",
      "Epoch: 986 Iteration: 87800. Train_MSE: 0.00010697000834625214. Test_MSE: 0.00011605619030144985\n",
      "Epoch: 987 Iteration: 87900. Train_MSE: 0.000187319063115865. Test_MSE: 0.0001162777880051229\n",
      "Epoch: 988 Iteration: 88000. Train_MSE: 0.007216361351311207. Test_MSE: 0.0001157941006401087\n",
      "Epoch: 989 Iteration: 88100. Train_MSE: 0.000173476233612746. Test_MSE: 0.0001157561942742146\n",
      "Epoch: 991 Iteration: 88200. Train_MSE: 0.00010975250916089863. Test_MSE: 0.0001161811893607888\n",
      "Epoch: 992 Iteration: 88300. Train_MSE: 0.00023610697826370597. Test_MSE: 0.00011589889043977262\n",
      "Epoch: 993 Iteration: 88400. Train_MSE: 0.0007677107932977378. Test_MSE: 0.00011580894976505022\n",
      "Epoch: 994 Iteration: 88500. Train_MSE: 0.00015831802738830447. Test_MSE: 0.0001154912293505494\n",
      "Epoch: 995 Iteration: 88600. Train_MSE: 0.0001841172925196588. Test_MSE: 0.00011570201029905256\n",
      "Epoch: 996 Iteration: 88700. Train_MSE: 0.00016009679529815912. Test_MSE: 0.0001159187023361823\n",
      "Epoch: 997 Iteration: 88800. Train_MSE: 0.0025265139993280172. Test_MSE: 0.00011560064717158793\n",
      "Epoch: 998 Iteration: 88900. Train_MSE: 0.00018885066674556583. Test_MSE: 0.00011541892111372434\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",
    "            \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",
    "         \n",
    "        n_iter += 1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35600\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x164ec320>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KiIGIWAWcAGxpXAd4B/AAcDywISJ6IqKX2iWgbx3arkiSjkQzoX87sCciHgRuBD4YEVdG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\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 0xbb98710>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KuQuALwOvAZqBv3D3HZU8SBERmVo5ZwSXAq3uvgK4Ftg8scDMlgDrgXOBVcAmM2sBbgC2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\npyIQEYm5/w8YHNZohwbyMgAAAABJRU5ErkJggg==\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": [
       "59.72919367089158"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4071.000000\n",
       "mean        7.084186\n",
       "std        81.754144\n",
       "min        -0.036096\n",
       "25%        -0.010877\n",
       "50%        -0.003836\n",
       "75%         1.108582\n",
       "max      4900.219009\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_DesertRock_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_DesertRock_TestLoss.csv')"
   ]
  }
 ],
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