lsvr_sgd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import dump, load\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from tensorflow import keras as keras\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import ReLU\n",
    "warnings.filterwarnings('error','warnings ignored')\n",
    "random_seed = 1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pretrained models\n",
    "lsvr_30 = load('LinearSVR\\lsvr_30_finetuned.joblib')\n",
    "lsvr_60 = load('LinearSVR\\lsvr_60_finetuned.joblib')\n",
    "lsvr_90 = load('LinearSVR\\lsvr_90_finetuned.joblib')\n",
    "lsvr_120 = load('LinearSVR\\lsvr_120_finetuned.joblib')\n",
    "\n",
    "sgdr_30 = load('SGDregressor\\SGDr_30_finetuned.joblib')\n",
    "sgdr_60 = load('SGDregressor\\SGDr_60_finetuned.joblib')\n",
    "sgdr_90 = load('SGDregressor\\SGDr_90_finetuned.joblib')\n",
    "sgdr_120 = load('SGDregressor\\SGDr_120_finetuned.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# preprocessing\n",
    "def preprocess(df,mode = 'train'):\n",
    "  frac = 0.2\n",
    "  if mode=='train':\n",
    "    # fill missing data\n",
    "    df['Total Cloud Cover [%]'].replace(-7999,np.nan,inplace = True)\n",
    "    df['Total Cloud Cover [%]'].replace(-6999,np.nan,inplace = True)\n",
    "    df['Total Cloud Cover [%]'].interpolate(limit = 10,limit_direction = 'both',inplace = True)  \n",
    "\n",
    "    #  create targets\n",
    "    df['t_30'] = df.groupby('DATE (MM/DD)')['Total Cloud Cover [%]'].shift(periods = -30,fill_value = -1)\n",
    "    df['t_60'] = df.groupby('DATE (MM/DD)')['Total Cloud Cover [%]'].shift(periods = -60,fill_value = -1)\n",
    "    df['t_90'] = df.groupby('DATE (MM/DD)')['Total Cloud Cover [%]'].shift(periods = -90,fill_value = -1)\n",
    "    df['t_120'] = df.groupby('DATE (MM/DD)')['Total Cloud Cover [%]'].shift(periods = -120,fill_value = -1)\n",
    "\n",
    "    cond = (df['Total Cloud Cover [%]'] == -1)\n",
    "    req_samples = df[cond].sample(frac = frac,random_state = random_seed)\n",
    "    not_req_samples = df[cond].drop(req_samples.index)\n",
    "    df.drop(not_req_samples.index,inplace=True)\n",
    "    \n",
    "    # drop unwanted features\n",
    "    df.drop([\n",
    "            'DATE (MM/DD)',\n",
    "            'MST',\n",
    "            'Direct sNIP [W/m^2]',                # this feature is highly correlated with cmp22\n",
    "            'Tower Wet Bulb Temp [deg C]',        # highly correlated with other temperature readings\n",
    "            'Tower Dew Point Temp [deg C]',\n",
    "            'Snow Depth [cm]',\n",
    "            'Moisture',\n",
    "            'Albedo (CMP11)',\n",
    "            'Precipitation (Accumulated) [mm]',\n",
    "            'Azimuth Angle [degrees]'\n",
    "    ],axis =1,inplace = True)\n",
    "\n",
    "    return df\n",
    "  if mode == 'test':\n",
    "    df['Total Cloud Cover [%]'].replace(-7999,np.nan,inplace = True)\n",
    "    df['Total Cloud Cover [%]'].replace(-6999,np.nan,inplace = True)\n",
    "    df['Total Cloud Cover [%]'].interpolate(limit = 10,limit_direction = 'both',inplace = True)  \n",
    "\n",
    "    df.drop(columns={\n",
    "      'Time [Mins]',\n",
    "      'Direct sNIP [W/m^2]',                # this feature is highly correlated with cmp22\n",
    "      'Tower Wet Bulb Temp [deg C]',        # highly correlated with other temperature readings\n",
    "      'Tower Dew Point Temp [deg C]',\n",
    "      'Snow Depth [cm]',\n",
    "      'Moisture',\n",
    "      'Albedo (CMP11)',\n",
    "      'Precipitation (Accumulated) [mm]',\n",
    "      'Azimuth Angle [degrees]',\n",
    "      'scenario_set' \n",
    "    },inplace = True)\n",
    "\n",
    "    return df.iloc[-1,]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE (MM/DD)</th>\n",
       "      <th>MST</th>\n",
       "      <th>Global CMP22 (vent/cor) [W/m^2]</th>\n",
       "      <th>Direct sNIP [W/m^2]</th>\n",
       "      <th>Azimuth Angle [degrees]</th>\n",
       "      <th>Tower Dry Bulb Temp [deg C]</th>\n",
       "      <th>Tower Wet Bulb Temp [deg C]</th>\n",
       "      <th>Tower Dew Point Temp [deg C]</th>\n",
       "      <th>Tower RH [%]</th>\n",
       "      <th>Total Cloud Cover [%]</th>\n",
       "      <th>Peak Wind Speed @ 6ft [m/s]</th>\n",
       "      <th>Avg Wind Direction @ 6ft [deg from N]</th>\n",
       "      <th>Station Pressure [mBar]</th>\n",
       "      <th>Precipitation (Accumulated) [mm]</th>\n",
       "      <th>Snow Depth [cm]</th>\n",
       "      <th>Moisture</th>\n",
       "      <th>Albedo (CMP11)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1</td>\n",
       "      <td>00:00</td>\n",
       "      <td>-0.962276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>356.85640</td>\n",
       "      <td>7.216</td>\n",
       "      <td>0.988</td>\n",
       "      <td>-7.312</td>\n",
       "      <td>32.33</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.95</td>\n",
       "      <td>271.3</td>\n",
       "      <td>806.779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1</td>\n",
       "      <td>00:01</td>\n",
       "      <td>-0.937921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>357.65505</td>\n",
       "      <td>7.251</td>\n",
       "      <td>1.040</td>\n",
       "      <td>-7.260</td>\n",
       "      <td>32.40</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.20</td>\n",
       "      <td>272.9</td>\n",
       "      <td>806.840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1</td>\n",
       "      <td>00:02</td>\n",
       "      <td>-0.944395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>358.45438</td>\n",
       "      <td>7.256</td>\n",
       "      <td>1.093</td>\n",
       "      <td>-7.207</td>\n",
       "      <td>32.54</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.70</td>\n",
       "      <td>288.8</td>\n",
       "      <td>806.876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1</td>\n",
       "      <td>00:03</td>\n",
       "      <td>-0.951350</td>\n",
       "      <td>-0.029673</td>\n",
       "      <td>359.25416</td>\n",
       "      <td>7.254</td>\n",
       "      <td>1.060</td>\n",
       "      <td>-7.440</td>\n",
       "      <td>31.89</td>\n",
       "      <td>-1</td>\n",
       "      <td>7.70</td>\n",
       "      <td>294.0</td>\n",
       "      <td>806.823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1</td>\n",
       "      <td>00:04</td>\n",
       "      <td>-0.934976</td>\n",
       "      <td>-0.054401</td>\n",
       "      <td>0.05415</td>\n",
       "      <td>7.331</td>\n",
       "      <td>1.081</td>\n",
       "      <td>-7.419</td>\n",
       "      <td>31.78</td>\n",
       "      <td>-1</td>\n",
       "      <td>7.20</td>\n",
       "      <td>285.5</td>\n",
       "      <td>806.762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DATE (MM/DD)    MST  Global CMP22 (vent/cor) [W/m^2]  Direct sNIP [W/m^2]  \\\n",
       "0          1/1  00:00                        -0.962276             0.000000   \n",
       "1          1/1  00:01                        -0.937921             0.000000   \n",
       "2          1/1  00:02                        -0.944395             0.000000   \n",
       "3          1/1  00:03                        -0.951350            -0.029673   \n",
       "4          1/1  00:04                        -0.934976            -0.054401   \n",
       "\n",
       "   Azimuth Angle [degrees]  Tower Dry Bulb Temp [deg C]  \\\n",
       "0                356.85640                        7.216   \n",
       "1                357.65505                        7.251   \n",
       "2                358.45438                        7.256   \n",
       "3                359.25416                        7.254   \n",
       "4                  0.05415                        7.331   \n",
       "\n",
       "   Tower Wet Bulb Temp [deg C]  Tower Dew Point Temp [deg C]  Tower RH [%]  \\\n",
       "0                        0.988                        -7.312         32.33   \n",
       "1                        1.040                        -7.260         32.40   \n",
       "2                        1.093                        -7.207         32.54   \n",
       "3                        1.060                        -7.440         31.89   \n",
       "4                        1.081                        -7.419         31.78   \n",
       "\n",
       "   Total Cloud Cover [%]  Peak Wind Speed @ 6ft [m/s]  \\\n",
       "0                     -1                         9.95   \n",
       "1                     -1                         8.20   \n",
       "2                     -1                         6.70   \n",
       "3                     -1                         7.70   \n",
       "4                     -1                         7.20   \n",
       "\n",
       "   Avg Wind Direction @ 6ft [deg from N]  Station Pressure [mBar]  \\\n",
       "0                                  271.3                  806.779   \n",
       "1                                  272.9                  806.840   \n",
       "2                                  288.8                  806.876   \n",
       "3                                  294.0                  806.823   \n",
       "4                                  285.5                  806.762   \n",
       "\n",
       "   Precipitation (Accumulated) [mm]  Snow Depth [cm]  Moisture  Albedo (CMP11)  \n",
       "0                               0.0            0.219       0.0             0.0  \n",
       "1                               0.0            0.206       0.0             0.0  \n",
       "2                               0.0            0.148       0.0             0.0  \n",
       "3                               0.0            0.235       0.0             0.0  \n",
       "4                               0.0            0.182       0.0             0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'data\\train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Global CMP22 (vent/cor) [W/m^2]</th>\n",
       "      <th>Tower Dry Bulb Temp [deg C]</th>\n",
       "      <th>Tower RH [%]</th>\n",
       "      <th>Total Cloud Cover [%]</th>\n",
       "      <th>Peak Wind Speed @ 6ft [m/s]</th>\n",
       "      <th>Avg Wind Direction @ 6ft [deg from N]</th>\n",
       "      <th>Station Pressure [mBar]</th>\n",
       "      <th>t_30</th>\n",
       "      <th>t_60</th>\n",
       "      <th>t_90</th>\n",
       "      <th>t_120</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "      <td>307216.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>338.139172</td>\n",
       "      <td>14.156688</td>\n",
       "      <td>37.238903</td>\n",
       "      <td>39.764552</td>\n",
       "      <td>2.993360</td>\n",
       "      <td>146.297582</td>\n",
       "      <td>816.845133</td>\n",
       "      <td>39.047805</td>\n",
       "      <td>37.940785</td>\n",
       "      <td>36.795175</td>\n",
       "      <td>35.622423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>311.606434</td>\n",
       "      <td>10.861875</td>\n",
       "      <td>23.357442</td>\n",
       "      <td>37.528009</td>\n",
       "      <td>2.072404</td>\n",
       "      <td>108.565210</td>\n",
       "      <td>5.057391</td>\n",
       "      <td>37.804043</td>\n",
       "      <td>37.948477</td>\n",
       "      <td>38.023448</td>\n",
       "      <td>38.060778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.764200</td>\n",
       "      <td>-16.440000</td>\n",
       "      <td>4.210000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>795.065000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.990850</td>\n",
       "      <td>5.446000</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>43.020000</td>\n",
       "      <td>813.761000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>261.436500</td>\n",
       "      <td>15.040000</td>\n",
       "      <td>30.430000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>817.252000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>551.931250</td>\n",
       "      <td>23.040000</td>\n",
       "      <td>47.930000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>270.600000</td>\n",
       "      <td>820.268000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1428.650000</td>\n",
       "      <td>36.320000</td>\n",
       "      <td>100.100000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>23.450000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>847.963000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Global CMP22 (vent/cor) [W/m^2]  Tower Dry Bulb Temp [deg C]  \\\n",
       "count                    307216.000000                307216.000000   \n",
       "mean                        338.139172                    14.156688   \n",
       "std                         311.606434                    10.861875   \n",
       "min                          -4.764200                   -16.440000   \n",
       "25%                          54.990850                     5.446000   \n",
       "50%                         261.436500                    15.040000   \n",
       "75%                         551.931250                    23.040000   \n",
       "max                        1428.650000                    36.320000   \n",
       "\n",
       "        Tower RH [%]  Total Cloud Cover [%]  Peak Wind Speed @ 6ft [m/s]  \\\n",
       "count  307216.000000          307216.000000                307216.000000   \n",
       "mean       37.238903              39.764552                     2.993360   \n",
       "std        23.357442              37.528009                     2.072404   \n",
       "min         4.210000              -1.000000                     0.000000   \n",
       "25%        19.760000               7.000000                     1.700000   \n",
       "50%        30.430000              23.000000                     2.700000   \n",
       "75%        47.930000              81.000000                     3.950000   \n",
       "max       100.100000             100.000000                    23.450000   \n",
       "\n",
       "       Avg Wind Direction @ 6ft [deg from N]  Station Pressure [mBar]  \\\n",
       "count                          307216.000000            307216.000000   \n",
       "mean                              146.297582               816.845133   \n",
       "std                               108.565210                 5.057391   \n",
       "min                                 0.000000               795.065000   \n",
       "25%                                43.020000               813.761000   \n",
       "50%                               122.000000               817.252000   \n",
       "75%                               270.600000               820.268000   \n",
       "max                               360.000000               847.963000   \n",
       "\n",
       "                t_30           t_60           t_90          t_120  \n",
       "count  307216.000000  307216.000000  307216.000000  307216.000000  \n",
       "mean       39.047805      37.940785      36.795175      35.622423  \n",
       "std        37.804043      37.948477      38.023448      38.060778  \n",
       "min        -1.000000      -1.000000      -1.000000      -1.000000  \n",
       "25%         6.000000       4.000000      -1.000000      -1.000000  \n",
       "50%        22.000000      20.000000      19.000000      18.000000  \n",
       "75%        81.000000      79.000000      78.000000      76.000000  \n",
       "max       100.000000     100.000000     100.000000     100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = preprocess(df,mode='train')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.000000     0.178884\n",
       " 99.000000    0.034569\n",
       " 98.000000    0.031209\n",
       " 97.000000    0.029439\n",
       " 96.000000    0.025035\n",
       "                ...   \n",
       " 47.750000    0.000003\n",
       " 65.500000    0.000003\n",
       " 28.250000    0.000003\n",
       " 15.857143    0.000003\n",
       " 80.750000    0.000003\n",
       "Name: Total Cloud Cover [%], Length: 353, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total Cloud Cover [%]'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307216, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_30,Y_30 = df.iloc[:,:-4].values,df['t_30'].values\n",
    "X_60,Y_60 = df.iloc[:,:-3].values,df['t_60'].values\n",
    "X_90,Y_90= df.iloc[:,:-2].values,df['t_90'].values\n",
    "X_120,Y_120 = df.iloc[:,:-1].values,df['t_120'].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(x,y,train_size=0.70):\n",
    "  return train_test_split(x,y,train_size=train_size,random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_and_fit_nn(x,y,nn=None,predict=False):\n",
    "    if predict == False:\n",
    "        nn = tf.keras.Sequential()\n",
    "        nn.add(tf.keras.layers.Dense(4, input_shape=(2,)))\n",
    "        nn.add(ReLU(max_value=100,threshold=-1.0)) \n",
    "        nn.add(tf.keras.layers.Dense(1))\n",
    "        # early stopping\n",
    "        callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss',restore_best_weights=True, patience=5)\n",
    "\n",
    "        nn.compile(\n",
    "            optimizer='sgd',\n",
    "            loss=tf.keras.losses.MeanAbsoluteError(),\n",
    "            metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
    "        # print(nn.summary())\n",
    "\n",
    "        history = nn.fit(x, y,validation_split = 0.3,verbose = 1, epochs=50,callbacks = [callback])\n",
    "        print(min(history.history['loss']))\n",
    "        print(min(history.history['val_loss']))\n",
    "        return nn\n",
    "\n",
    "    if predict ==True:\n",
    "        nn_preds = nn.predict(x)\n",
    "        score = mae(y,nn_preds)\n",
    "        nn.evaluate(x, y, verbose=2)\n",
    "        return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(lsvr,sgd,x,y,nn=None,mode = 'train'):\n",
    "    # split data\n",
    "    x_train,x_test,y_train,y_test = split(x,y)\n",
    "    \n",
    "    if mode == 'train':\n",
    "        lsvr_preds = lsvr.predict(x_train)\n",
    "        sgd_preds = sgd.predict(x_train)\n",
    "        inputs = np.column_stack((lsvr_preds,sgd_preds))\n",
    "        nn = create_and_fit_nn(inputs,y_train,predict=False)\n",
    "        print(f'--------training finished--------')\n",
    "        return nn\n",
    "    if mode == 'test':\n",
    "        lsvr_preds = lsvr.predict(x_test)\n",
    "        sgd_preds = sgd.predict(x_test)\n",
    "        inputs = np.column_stack((lsvr_preds,sgd_preds))\n",
    "        mae_score = create_and_fit_nn(inputs,y_test,nn,predict=True)\n",
    "        print('mae score:', mae_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "4705/4705 [==============================] - 11s 2ms/step - loss: 32.3539 - mean_absolute_error: 32.3539 - val_loss: 28.9631 - val_mean_absolute_error: 28.9631\n",
      "Epoch 2/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 28.2602 - mean_absolute_error: 28.2602 - val_loss: 27.8035 - val_mean_absolute_error: 27.8035\n",
      "Epoch 3/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 27.3810 - mean_absolute_error: 27.3810 - val_loss: 27.1120 - val_mean_absolute_error: 27.1120\n",
      "Epoch 4/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 26.2954 - mean_absolute_error: 26.2954 - val_loss: 26.3653 - val_mean_absolute_error: 26.3653\n",
      "Epoch 5/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 23.5662 - mean_absolute_error: 23.5662 - val_loss: 20.6034 - val_mean_absolute_error: 20.6034\n",
      "Epoch 6/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 23.4205 - mean_absolute_error: 23.4205 - val_loss: 18.1654 - val_mean_absolute_error: 18.1654\n",
      "Epoch 7/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 15.9870 - mean_absolute_error: 15.9870 - val_loss: 63.3039 - val_mean_absolute_error: 63.3039\n",
      "Epoch 8/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 14.9102 - mean_absolute_error: 14.9102 - val_loss: 17.3041 - val_mean_absolute_error: 17.3041\n",
      "Epoch 9/50\n",
      "4705/4705 [==============================] - 10s 2ms/step - loss: 14.3559 - mean_absolute_error: 14.3559 - val_loss: 22.6773 - val_mean_absolute_error: 22.6773\n",
      "Epoch 10/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 14.0562 - mean_absolute_error: 14.0562 - val_loss: 12.3566 - val_mean_absolute_error: 12.3566\n",
      "Epoch 11/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 13.7519 - mean_absolute_error: 13.7519 - val_loss: 14.8610 - val_mean_absolute_error: 14.8610\n",
      "Epoch 12/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.6236 - mean_absolute_error: 13.6236 - val_loss: 11.4120 - val_mean_absolute_error: 11.4120\n",
      "Epoch 13/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.4423 - mean_absolute_error: 13.4423 - val_loss: 16.9923 - val_mean_absolute_error: 16.9923\n",
      "Epoch 14/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 13.3728 - mean_absolute_error: 13.3728 - val_loss: 32.5283 - val_mean_absolute_error: 32.5283\n",
      "Epoch 15/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.2411 - mean_absolute_error: 13.2411 - val_loss: 24.6621 - val_mean_absolute_error: 24.6621\n",
      "Epoch 16/50\n",
      "4705/4705 [==============================] - 10s 2ms/step - loss: 13.0687 - mean_absolute_error: 13.0687 - val_loss: 12.2595 - val_mean_absolute_error: 12.2595\n",
      "Epoch 17/50\n",
      "4705/4705 [==============================] - 10s 2ms/step - loss: 13.0831 - mean_absolute_error: 13.0831 - val_loss: 12.4102 - val_mean_absolute_error: 12.4102\n",
      "13.068653106689453\n",
      "11.412003517150879\n",
      "--------training finished--------\n",
      "2881/2881 - 2s - loss: 11.4492 - mean_absolute_error: 11.4492\n",
      "mae score: 11.449235995026786\n",
      "Epoch 1/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.2072 - mean_absolute_error: 13.2072 - val_loss: 26.4815 - val_mean_absolute_error: 26.4815\n",
      "Epoch 2/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 13.1762 - mean_absolute_error: 13.1762 - val_loss: 14.2055 - val_mean_absolute_error: 14.2055\n",
      "Epoch 3/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.0376 - mean_absolute_error: 13.0376 - val_loss: 15.2714 - val_mean_absolute_error: 15.2714\n",
      "Epoch 4/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 13.0154 - mean_absolute_error: 13.0154 - val_loss: 10.2529 - val_mean_absolute_error: 10.2529\n",
      "Epoch 5/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 13.0967 - mean_absolute_error: 13.0967 - val_loss: 11.3146 - val_mean_absolute_error: 11.3146\n",
      "Epoch 6/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.9805 - mean_absolute_error: 12.9805 - val_loss: 11.4904 - val_mean_absolute_error: 11.4904\n",
      "Epoch 7/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.9146 - mean_absolute_error: 12.9146 - val_loss: 9.3607 - val_mean_absolute_error: 9.3607\n",
      "Epoch 8/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.8559 - mean_absolute_error: 12.8559 - val_loss: 17.2717 - val_mean_absolute_error: 17.2717\n",
      "Epoch 9/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.7693 - mean_absolute_error: 12.7693 - val_loss: 22.1599 - val_mean_absolute_error: 22.1599\n",
      "Epoch 10/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.6160 - mean_absolute_error: 12.6160 - val_loss: 17.0531 - val_mean_absolute_error: 17.0531\n",
      "Epoch 11/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.5163 - mean_absolute_error: 12.5163 - val_loss: 34.1986 - val_mean_absolute_error: 34.1986\n",
      "Epoch 12/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.5928 - mean_absolute_error: 12.5928 - val_loss: 12.5272 - val_mean_absolute_error: 12.5272\n",
      "12.516263961791992\n",
      "9.360718727111816\n",
      "--------training finished--------\n",
      "2881/2881 - 2s - loss: 9.4086 - mean_absolute_error: 9.4086\n",
      "mae score: 9.408563126318946\n",
      "Epoch 1/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.8454 - mean_absolute_error: 12.8454 - val_loss: 11.2240 - val_mean_absolute_error: 11.2240\n",
      "Epoch 2/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.8561 - mean_absolute_error: 12.8561 - val_loss: 17.9204 - val_mean_absolute_error: 17.9204\n",
      "Epoch 3/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.7901 - mean_absolute_error: 12.7901 - val_loss: 11.0541 - val_mean_absolute_error: 11.0541\n",
      "Epoch 4/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.7641 - mean_absolute_error: 12.7641 - val_loss: 9.4917 - val_mean_absolute_error: 9.4917\n",
      "Epoch 5/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.6451 - mean_absolute_error: 12.6451 - val_loss: 11.5201 - val_mean_absolute_error: 11.5201\n",
      "Epoch 6/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.6076 - mean_absolute_error: 12.6076 - val_loss: 12.9928 - val_mean_absolute_error: 12.9928\n",
      "Epoch 7/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.6213 - mean_absolute_error: 12.6213 - val_loss: 17.9422 - val_mean_absolute_error: 17.9422\n",
      "Epoch 8/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.4407 - mean_absolute_error: 12.4407 - val_loss: 9.5707 - val_mean_absolute_error: 9.5707\n",
      "Epoch 9/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.4730 - mean_absolute_error: 12.4730 - val_loss: 13.5008 - val_mean_absolute_error: 13.5008\n",
      "12.440703392028809\n",
      "9.491732597351074\n",
      "--------training finished--------\n",
      "2881/2881 - 2s - loss: 9.5866 - mean_absolute_error: 9.5866\n",
      "mae score: 9.58660622503523\n",
      "Epoch 1/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.5072 - mean_absolute_error: 12.5072 - val_loss: 12.7066 - val_mean_absolute_error: 12.7066\n",
      "Epoch 2/50\n",
      "4705/4705 [==============================] - 10s 2ms/step - loss: 12.4574 - mean_absolute_error: 12.4574 - val_loss: 12.5144 - val_mean_absolute_error: 12.5144\n",
      "Epoch 3/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.3443 - mean_absolute_error: 12.3443 - val_loss: 21.6058 - val_mean_absolute_error: 21.6058\n",
      "Epoch 4/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.3918 - mean_absolute_error: 12.3918 - val_loss: 19.3015 - val_mean_absolute_error: 19.3015\n",
      "Epoch 5/50\n",
      "4705/4705 [==============================] - 10s 2ms/step - loss: 12.2349 - mean_absolute_error: 12.2349 - val_loss: 21.1416 - val_mean_absolute_error: 21.1416\n",
      "Epoch 6/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.2447 - mean_absolute_error: 12.2447 - val_loss: 10.4560 - val_mean_absolute_error: 10.4560\n",
      "Epoch 7/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.1959 - mean_absolute_error: 12.1959 - val_loss: 9.0704 - val_mean_absolute_error: 9.0704\n",
      "Epoch 8/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.0744 - mean_absolute_error: 12.0744 - val_loss: 13.3970 - val_mean_absolute_error: 13.3970\n",
      "Epoch 9/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.0547 - mean_absolute_error: 12.0547 - val_loss: 10.1087 - val_mean_absolute_error: 10.1087\n",
      "Epoch 10/50\n",
      "4705/4705 [==============================] - 9s 2ms/step - loss: 12.1313 - mean_absolute_error: 12.1313 - val_loss: 16.7991 - val_mean_absolute_error: 16.7991\n",
      "Epoch 11/50\n",
      "4705/4705 [==============================] - 8s 2ms/step - loss: 12.0540 - mean_absolute_error: 12.0540 - val_loss: 10.4144 - val_mean_absolute_error: 10.4144\n",
      "Epoch 12/50\n",
      "4705/4705 [==============================] - 11s 2ms/step - loss: 11.9752 - mean_absolute_error: 11.9752 - val_loss: 9.7947 - val_mean_absolute_error: 9.7947\n",
      "11.975190162658691\n",
      "9.07041072845459\n",
      "--------training finished--------\n",
      "2881/2881 - 2s - loss: 9.1503 - mean_absolute_error: 9.1503\n",
      "mae score: 9.150336147280774\n"
     ]
    }
   ],
   "source": [
    "nn_30 = run(lsvr_30,sgdr_30,X_30,Y_30,mode='train')\n",
    "run(lsvr_30,sgdr_30,X_30,Y_30,nn = nn_30,mode='test')\n",
    "\n",
    "nn_60 = run(lsvr_60,sgdr_60,X_60,Y_60,mode='train')\n",
    "run(lsvr_60,sgdr_60,X_60,Y_60,nn = nn_60,mode='test')\n",
    "\n",
    "nn_90 = run(lsvr_90,sgdr_90,X_90,Y_90,mode='train')\n",
    "run(lsvr_90,sgdr_90,X_90,Y_90,nn = nn_90,mode='test')\n",
    "\n",
    "nn_120 = run(lsvr_120,sgdr_120,X_120,Y_120,mode='train')\n",
    "run(lsvr_120,sgdr_120,X_120,Y_120,nn = nn_120,mode='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ensemble_preds(lsvr,sgd,nn,x):\n",
    "    lsvr_preds = lsvr.predict(x.values.reshape(1,-1))\n",
    "    sgd_preds = sgd.predict(x.values.reshape(1,-1))\n",
    "    inputs = np.column_stack((lsvr_preds,sgd_preds))\n",
    "    nn_preds = nn.predict(inputs)\n",
    "    return nn_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "wd_test = pd.read_csv(r'data\\shell_test.csv')\n",
    "test = pd.read_csv(r'data\\test.csv')\n",
    "\n",
    "file_count = 0\n",
    "for set in range(1,301):\n",
    "  wd = wd_test[wd_test['scenario_set'] == set]\n",
    "  # preprocessing test data\n",
    "  last_sample = preprocess(wd,mode='test')\n",
    "\n",
    "  # predicting test samples\n",
    "  pred_30 = ensemble_preds(lsvr_30,sgdr_30,nn_30,last_sample)\n",
    "  last_sample['t_30'] = pred_30.item()\n",
    "  pred_60 = ensemble_preds(lsvr_60,sgdr_60,nn_60,last_sample)\n",
    "  last_sample['t_60'] = pred_60.item()\n",
    "  pred_90 = ensemble_preds(lsvr_90,sgdr_90,nn_90,last_sample)\n",
    "  last_sample['t_90'] = pred_90.item()\n",
    "  pred_120 = ensemble_preds(lsvr_120,sgdr_120,nn_120,last_sample)\n",
    "  \n",
    "  # fill in test data using above predictions\n",
    "  test.iloc[set-1,test.columns.get_indexer(['30_min_horizon'])] = np.round(pred_30.item())\n",
    "  test.iloc[set-1,test.columns.get_indexer(['60_min_horizon'])] = np.round(pred_60.item())\n",
    "  test.iloc[set-1,test.columns.get_indexer(['90_min_horizon'])] = np.round(pred_90.item())\n",
    "  test.iloc[set-1,test.columns.get_indexer(['120_min_horizon'])] = np.round(pred_120.item())\n",
    "\n",
    "  file_count += 1\n",
    "  if file_count%30 == 0 :\n",
    "      print(file_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       scenario_set  30_min_horizon  60_min_horizon  90_min_horizon  \\\n",
      "count    300.000000      300.000000      300.000000      300.000000   \n",
      "mean     150.500000       49.930000       50.823333       58.073333   \n",
      "std       86.746758       28.731925       29.290132       33.776780   \n",
      "min        1.000000        3.000000        1.000000       -1.000000   \n",
      "25%       75.750000       18.000000       18.000000       22.000000   \n",
      "50%      150.500000       55.000000       59.000000       69.000000   \n",
      "75%      225.250000       79.000000       78.250000       88.250000   \n",
      "max      300.000000       82.000000       84.000000       95.000000   \n",
      "\n",
      "       120_min_horizon  \n",
      "count       300.000000  \n",
      "mean         57.126667  \n",
      "std          32.869418  \n",
      "min          -1.000000  \n",
      "25%          24.000000  \n",
      "50%          70.000000  \n",
      "75%          85.000000  \n",
      "max          92.000000  \n"
     ]
    },
    {
     "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>scenario_set</th>\n",
       "      <th>30_min_horizon</th>\n",
       "      <th>60_min_horizon</th>\n",
       "      <th>90_min_horizon</th>\n",
       "      <th>120_min_horizon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>47</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>94</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>296</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>53</td>\n",
       "      <td>57</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     scenario_set  30_min_horizon  60_min_horizon  90_min_horizon  \\\n",
       "0               1              81              81              91   \n",
       "1               2              41              47              60   \n",
       "2               3              82              83              94   \n",
       "3               4              52              56              68   \n",
       "4               5              20              23              29   \n",
       "..            ...             ...             ...             ...   \n",
       "295           296               7               4               1   \n",
       "296           297              53              57              67   \n",
       "297           298               9               6               2   \n",
       "298           299              37              40              48   \n",
       "299           300              27              31              40   \n",
       "\n",
       "     120_min_horizon  \n",
       "0                 88  \n",
       "1                 65  \n",
       "2                 90  \n",
       "3                 69  \n",
       "4                 34  \n",
       "..               ...  \n",
       "295               -1  \n",
       "296               67  \n",
       "297               -1  \n",
       "298               51  \n",
       "299               45  \n",
       "\n",
       "[300 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.applymap(int)\n",
    "test.to_csv('lsvr_sgd_preds.csv',index=False)\n",
    "\n",
    "print(test.describe())\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "518a807dccee1bb2cf00c0cea9388abbe4210a3c385c01718c979ef7759eaf87"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}