flight hour prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import pygaze\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "from sklearn.cluster import DBSCAN\n",
    "import detectors\n",
    "import gazeplotter\n",
    "from collections import defaultdict\n",
    "# import local lib\n",
    "import eye_metrics_utils\n",
    "import data_utils\n",
    "import gaze_entropy\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import explained_variance_score, max_error, mean_absolute_error, mean_squared_error, mean_squared_log_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "# warnings.filterwarnings(action='once')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_all(df_data):\n",
    "    df_x = df_data.copy()\n",
    "    if (data_utils.check_percentage_null(df_x) < 0.3): # if missing value > 50%, remove\n",
    "        return None\n",
    "    \n",
    "    time = np.array(df_data['Start Time (secs)'].tolist())\n",
    "\n",
    "    Efix = eye_metrics_utils.detect_fixations(df_x)\n",
    "    Eblk = eye_metrics_utils.detect_blinks(df_x)\n",
    "    Esac = eye_metrics_utils.detect_saccades(df_x)\n",
    "    Emsac = eye_metrics_utils.detect_microsaccades(df_x)\n",
    "#     print(Efix)\n",
    "    X = np.array(Efix).T[3:].T\n",
    "    Hs, Ht = gaze_entropy.entropy(X)\n",
    "    total_time = time[-1] - time[0]\n",
    "    \n",
    "    return Efix, Hs, Ht, total_time, Eblk, Esac, Emsac\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = glob.glob(\"data/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files_one = [v for v in csv_files if \"One Gaze-Vergence\" in v]\n",
    "csv_files_two = [v for v in csv_files if \"Two Gaze-Vergence\" in v]\n",
    "csv_files_three = [v for v in csv_files if \"Three Go-Around Gaze-Vergence\" in v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "032 10\n",
      "027 18\n",
      "031 26\n",
      "028 28\n",
      "004 35\n",
      "008 35\n",
      "010 35\n",
      "029 40\n",
      "003 42\n",
      "007 49\n",
      "023 50\n",
      "021 63\n",
      "006 90\n",
      "019 98\n",
      "022 100\n",
      "015 133\n",
      "016 200\n",
      "014 155\n",
      "005 180\n",
      "025 200\n",
      "002 220\n",
      "001 230\n",
      "020 230\n",
      "011 300\n",
      "017 420\n",
      "013 23\n",
      "024 28\n",
      "018 116\n",
      "026 150\n",
      "012 175\n",
      "009 220\n",
      "033 1300\n"
     ]
    }
   ],
   "source": [
    "df_par = pd.read_csv(\"participant.csv\")\n",
    "par_id_arr = [v[-3:] for v in df_par['ID'].tolist()]\n",
    "flight_exp_arr = df_par['Flight_hour'].tolist()\n",
    "\n",
    "for i,e in zip(par_id_arr, flight_exp_arr):\n",
    "    print(i, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\PISSS_ID_001_Approach Two Gaze-Vergence.csv 9554\n",
      "data\\PISSS_ID_002_Approach Two Gaze-Vergence.csv 9430\n",
      "data\\PISSS_ID_003_Approach Two Gaze-Vergence.csv 9368\n",
      "data\\PISSS_ID_004_Approach Two Gaze-Vergence.csv 9862\n",
      "data\\PISSS_ID_005_Approach Two Gaze-Vergence.csv 9245\n",
      "data\\PISSS_ID_006_Approach Two Gaze-Vergence.csv 9739\n",
      "data\\PISSS_ID_007_Approach Two Gaze-Vergence.csv 9677\n",
      "data\\PISSS_ID_008_Approach Two Gaze-Vergence.csv 9923\n",
      "data\\PISSS_ID_009_Approach Two Gaze-Vergence.csv 9243\n",
      "data\\PISSS_ID_010_Approach Two Gaze-Vergence.csv 9923\n",
      "data\\PISSS_ID_011_Approach Two Gaze-Vergence.csv 9492\n",
      "data\\PISSS_ID_012_Approach Two Gaze-Vergence.csv 9431\n",
      "data\\PISSS_ID_013_Approach Two Gaze-Vergence.csv 8691\n",
      "data\\PISSS_ID_014_Approach Two Gaze-Vergence.csv 9307\n",
      "data\\PISSS_ID_015_Approach Two Gaze-Vergence.csv 8812\n",
      "data\\PISSS_ID_016_Approach Two Gaze-Vergence.csv 8259\n",
      "data\\PISSS_ID_017_Approach Two Gaze-Vergence.csv 9184\n",
      "data\\PISSS_ID_018_Approach Two Gaze-Vergence.csv 8937\n",
      "data\\PISSS_ID_019_Approach Two Gaze-Vergence.csv 9615\n",
      "data\\PISSS_ID_020_Approach Two Gaze-Vergence.csv 9923\n",
      "data\\PISSS_ID_021_Approach Two Gaze-Vergence.csv 9369\n",
      "data\\PISSS_ID_022_Approach Two Gaze-Vergence.csv 9307\n",
      "data\\PISSS_ID_023_Approach Two Gaze-Vergence.csv 9677\n",
      "data\\PISSS_ID_024_Approach Two Gaze-Vergence.csv 9307\n",
      "data\\PISSS_ID_025_Approach Two Gaze-Vergence.csv 8937\n",
      "data\\PISSS_ID_026_Approach Two Gaze-Vergence.csv 8259\n",
      "data\\PISSS_ID_027_Approach Two Gaze-Vergence.csv 8568\n",
      "data\\PISSS_ID_028_Approach Two Gaze-Vergence.csv 9554\n",
      "data\\PISSS_ID_029_Approach Two Gaze-Vergence.csv 10232\n",
      "data\\PISSS_ID_031_Approach Two Gaze-Vergence.csv 9923\n",
      "data\\PISSS_ID_032_Approach Two Gaze-Vergence.csv 8011\n",
      "data\\PISSS_ID_033_Approach Two Gaze-Vergence.csv 9245\n"
     ]
    }
   ],
   "source": [
    "trials = []\n",
    "# for csv_files in [csv_files_one, csv_files_two, csv_files_three]:\n",
    "for csv_files in [csv_files_two]:\n",
    "\n",
    "    part = defaultdict(dict)\n",
    "    for csv in csv_files:\n",
    "        par_id = csv[14:17]\n",
    "        if par_id not in par_id_arr:\n",
    "            continue\n",
    "        ret = defaultdict(list)\n",
    "        df_data = pd.read_csv(csv)\n",
    "        print(csv, len(df_data))\n",
    "        for v in data_utils.data_slicing(df_data, window_length = 1800, stride = 1800, min_length=1200):\n",
    "            r = run_all(v)\n",
    "            if r != None:\n",
    "                Efix, Hs, Ht, total_time, Eblk, Esac, Emsac = r\n",
    "                ret[\"Eblk\"].append(Eblk)\n",
    "                ret[\"Efix\"].append(Efix)\n",
    "                ret[\"Esac\"].append(Esac)\n",
    "                ret[\"Emsac\"].append(Emsac)\n",
    "                ret[\"Hs\"].append(Hs)\n",
    "                ret[\"Ht\"].append(Ht)\n",
    "                ret[\"total_time\"].append(total_time)\n",
    "        part[par_id] = ret\n",
    "    trials.append(part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>fix_dur</th>\n",
       "      <th>Hs</th>\n",
       "      <th>Ht</th>\n",
       "      <th>fix_rate</th>\n",
       "      <th>blk_rate</th>\n",
       "      <th>sac_rate</th>\n",
       "      <th>msac_rate</th>\n",
       "      <th>exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.775423</td>\n",
       "      <td>1.354584</td>\n",
       "      <td>1.297595</td>\n",
       "      <td>1.060869</td>\n",
       "      <td>0.068443</td>\n",
       "      <td>0.855540</td>\n",
       "      <td>1.368863</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.614045</td>\n",
       "      <td>1.897849</td>\n",
       "      <td>1.517279</td>\n",
       "      <td>1.301890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.890767</td>\n",
       "      <td>1.610233</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.612309</td>\n",
       "      <td>1.660072</td>\n",
       "      <td>1.397686</td>\n",
       "      <td>1.233369</td>\n",
       "      <td>0.068521</td>\n",
       "      <td>0.993547</td>\n",
       "      <td>1.747273</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.499124</td>\n",
       "      <td>2.033565</td>\n",
       "      <td>1.466746</td>\n",
       "      <td>1.439080</td>\n",
       "      <td>0.068528</td>\n",
       "      <td>1.096442</td>\n",
       "      <td>1.850245</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370196</td>\n",
       "      <td>1.924764</td>\n",
       "      <td>1.289418</td>\n",
       "      <td>1.918695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.959348</td>\n",
       "      <td>2.980830</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2</td>\n",
       "      <td>0.201198</td>\n",
       "      <td>2.227963</td>\n",
       "      <td>1.607653</td>\n",
       "      <td>1.747388</td>\n",
       "      <td>0.205575</td>\n",
       "      <td>1.027875</td>\n",
       "      <td>4.282813</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>3</td>\n",
       "      <td>0.146083</td>\n",
       "      <td>1.543022</td>\n",
       "      <td>1.426363</td>\n",
       "      <td>0.890823</td>\n",
       "      <td>0.616723</td>\n",
       "      <td>0.925085</td>\n",
       "      <td>4.419851</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>4</td>\n",
       "      <td>0.193786</td>\n",
       "      <td>0.876716</td>\n",
       "      <td>0.820569</td>\n",
       "      <td>0.925022</td>\n",
       "      <td>0.171300</td>\n",
       "      <td>0.479641</td>\n",
       "      <td>2.295425</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
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       "      <td>0.794477</td>\n",
       "      <td>0.445506</td>\n",
       "      <td>0.822472</td>\n",
       "      <td>0.445506</td>\n",
       "      <td>0.822472</td>\n",
       "      <td>1300.0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.376889</td>\n",
       "      <td>0.479677</td>\n",
       "      <td>0.205576</td>\n",
       "      <td>0.548202</td>\n",
       "      <td>1300.0</td>\n",
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       "  </tbody>\n",
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       "<p>145 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index   fix_dur        Hs        Ht  fix_rate  blk_rate  sac_rate  \\\n",
       "0        0  0.775423  1.354584  1.297595  1.060869  0.068443  0.855540   \n",
       "1        1  0.614045  1.897849  1.517279  1.301890  0.000000  0.890767   \n",
       "2        2  0.612309  1.660072  1.397686  1.233369  0.068521  0.993547   \n",
       "3        3  0.499124  2.033565  1.466746  1.439080  0.068528  1.096442   \n",
       "4        0  0.370196  1.924764  1.289418  1.918695  0.000000  0.959348   \n",
       "..     ...       ...       ...       ...       ...       ...       ...   \n",
       "140      2  0.201198  2.227963  1.607653  1.747388  0.205575  1.027875   \n",
       "141      3  0.146083  1.543022  1.426363  0.890823  0.616723  0.925085   \n",
       "142      4  0.193786  0.876716  0.820569  0.925022  0.171300  0.479641   \n",
       "143      0  0.746337  0.995727  0.794477  0.445506  0.822472  0.445506   \n",
       "144      1  0.643760  0.000000  0.000000  0.376889  0.479677  0.205576   \n",
       "\n",
       "     msac_rate     exp  \n",
       "0     1.368863    10.0  \n",
       "1     1.610233    10.0  \n",
       "2     1.747273    10.0  \n",
       "3     1.850245    10.0  \n",
       "4     2.980830    18.0  \n",
       "..         ...     ...  \n",
       "140   4.282813   220.0  \n",
       "141   4.419851   220.0  \n",
       "142   2.295425   220.0  \n",
       "143   0.822472  1300.0  \n",
       "144   0.548202  1300.0  \n",
       "\n",
       "[145 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x = pd.DataFrame()\n",
    "for trial in trials:\n",
    "    for i, par_id in enumerate(par_id_arr):\n",
    "#         print(par_id)\n",
    "        if len(trial[par_id]) == 0:\n",
    "            continue\n",
    "        fix_dur = []\n",
    "        for p in trial[par_id]['Efix']:\n",
    "            fix_dur = np.append(fix_dur,np.mean(np.array(p).T[2]))\n",
    "\n",
    "        Hs = trial[par_id]['Hs']\n",
    "        Ht = trial[par_id]['Ht']\n",
    "\n",
    "        fix_rate = np.array([len(v) for v in trial[par_id]['Efix']])/np.array(trial[par_id]['total_time'])\n",
    "        blk_rate = np.array([len(v) for v in trial[par_id]['Eblk']])/np.array(trial[par_id]['total_time'])\n",
    "        sac_rate = np.array([len(v) for v in trial[par_id]['Esac']])/np.array(trial[par_id]['total_time'])\n",
    "        msac_rate = np.array([len(v) for v in trial[par_id]['Emsac']])/np.array(trial[par_id]['total_time'])\n",
    "        \n",
    "        exp = flight_exp_arr[i] * np.ones_like(Hs)\n",
    "        df = pd.DataFrame(zip(fix_dur, Hs, Ht, fix_rate, blk_rate, sac_rate, msac_rate, exp), \n",
    "                        columns=[\"fix_dur\", \"Hs\", \"Ht\", \"fix_rate\", \"blk_rate\", \"sac_rate\", \"msac_rate\", \"exp\"])\n",
    "        df_x = pd.concat([df_x, df])\n",
    "    \n",
    "df_x.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(df_x))\n",
    "df_x = df_x[~(df_x.iloc[:,:7] == 0).any(axis=1)]\n",
    "len(df_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_x[['fix_dur', 'Hs', 'Ht', \"fix_rate\", \"blk_rate\", \"sac_rate\", \"msac_rate\"]].values\n",
    "# X = df_x[['fix_dur', 'Hs', \"Ht\"]].values\n",
    "\n",
    "y = df_x[['exp']].values\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "# X = X[:,1:]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Ubuntu'] not found. Falling back to DejaVu Sans.\n"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train = df_x[['fix_dur', 'Hs', 'Ht', \"fix_rate\", \"blk_rate\", \"sac_rate\", \"msac_rate\"]]\n",
    "corrmat = df_train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, annot=True, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y, bins = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skew: 1.1224001692449155\n",
      "kurtosis: 0.9541187399080888\n"
     ]
    }
   ],
   "source": [
    "print(\"skew:\", df_x['exp'].skew())\n",
    "print(\"kurtosis:\", df_x['exp'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uz0M67hX7VXyIwgi3vy2rfviQu99uZmuBG4DbgEcptMQq+SpwCvB48f3NxWkiIhKj4DuMrNEdhohIdK1qVisJa+bRrXH2XBaR6LLwPYvS01sS1MyjW+PsuSwi0WXle6Y7jAwYHhljwz3bGB3Ps29sgtHxPFfcs23a2E7VlnnqhX0zrtvMfkWkOVn6nilhZEAzj27dsuOl2Houi0h0WfqeKWFkQDOPbl114jGx9VwWkeiy9D1TwsiAZh7detrxC2LruSwi0WXpe6ZmtRnSzKNb4+y5LCLRpeV71pKOe5K8/r7eGf+Qai0Tsm4z+xWR5mThe6YiKRERCaKEISIiQZQwREQkiBKGiIgEUaV3B2hV64q0tNIoxTF/Thf7D00mHk8rpOXcijRDCSPjWjUGTVrGsinF4XlnbNKZ21O4CU7r2Doh0nJuRZqlIqkMizoGTb2RbCu3c/ndW/nhk/8c63g25XGMTRb6B42O59s2tk4co4NmaZwgkZnoDiPDSmPQjHJkWIHSGDSVxR71fuVW287YhPPh235J3j22X8TV4iipdVyNiutXf5TPSCTtdIeRYaFj0Mz0K7fadgAOHJqM9RdxrTigtWPrxPmrP0vjBInMRAkjw0LHoJlpNMzy7RzV0zVtP3GNnFkeR29X4TG+c3tyLR9bJ87RQbM0TpDITFQklXFrVi1l9WmL67bACfmVW9rO9ude5oO3DjE2ka+5bDuVH0+7WknF/as/5DMSyQLdYXSA/r5eBk48puaFKPRXbn9fL2edvoRN65L9RVw6ntOOX1D3uJrZfty/+mf6jESyQKPVziJR+gLMhn4Ds+EYRaLSaLUCRBsNMwsjZzZrNhyjSCupSEpERILoDiOFWlFU0u7iluGRMbY/txcwVpywUL/URWYBJYyUaUWHsnZ3Stu8ZRefuGsr48Xe2N05+MKFqzTchUiHU5FUirSiQ1m7O6UNj4xxxd3bDicLgIk8XH73Vg13IdLhlDBSpBUdytrdKW3nnoN05Wza9C6Lp3OfiCRHCSNFWtGhrN2d0pYtmsdkfnpT7EnXcBcinU4JI0Va0aGs3Z3S+vt62bRuJT1dR+4yunOwad2AKr5FOpw67qWQWkmJSFLUcS9jWtGhrN2d0grDiBzXtu2LSPqoSEoiC3nwUL1l4nhwkYi0nu4wJJKQPh71ltHjSkWyS3cYEiykj0e9ZfS4UpFsU8KQYCF9POotE+eDi0Sk9VQkJcFC+njMtIweVyqSXbrDkGAhfTzqLaPHlYpkm/phSGQhfTzqLaMHF4mkV71+GLHdYZjZZWY2ZGZjZnZLxbxzzOwJMztgZg+Z2cll88zMrjOz4eJro5lNH8xIYhPyuNF6y+hxpSLZFGeR1HPA54Gvl080s8XAvcCVwLHAEHBH2SLrgbcDA8BK4HzgQzHEO2uF9pPISn+KZuNspk9JI/tu9XnNyufUiTrt3MdW6e3u9wKY2SCwrGzWO4Dt7n5Xcf5VwItm9mp3fwK4FLje3XcW518PfBC4Ma7YZ5PQfhJZ6U/RbJzN9ClpZN+tPq9Z+Zw6USee+zRUeq8AtpbeuPt+4DfF6dPmF/+/Amm50H4SWelP0WyczfQpaWTfrT6vWfmcOlGnnvs0JIw+YG/FtL3Aghrz9wJ91eoxzGx9sZ5kaPfu3W0JtpOF9pPISn+KZuNspk9JI/tu9XnNyufUiTr13KchYYwACyumLQT21Zi/EBjxKs273P0mdx9098ElS5a0JdhOFvosjXY/c6NVosRZray53vrV5o1N5pk/pyvyvkPjjVoeHufnlJay+rTEkZXvSFRpSBjbKVRoA2Bm84FTi9OnzS/+fzvScqH9JLLSnyI0zs1bdrH6uge5+OZHWX3dg9y3ZdeM65fPm9tT+BqZO+ff8Aj3bdnV0Dmqt06tGFtx/M1qJLZ2SEsckJ3vSFSx9cMws24KleyfpVDp/UFgAlgEPAW8D3gAuBp4s7ufWVzvw8BHgXMBB74PfMnd61Z6qx9G40L7SWSlP8VMfUJWX/cgo+NHfg3O7cnx4w1nH1623vpPvbCP8774Iw6VPeO8fP1GzlHlOiExNnr8zWo2tk6Lo1pcWfiOlEvL8zA+TSFZlFwMXO3uV5nZWuAG4DbgUeBdZct9FTgFeLz4/ubiNGmT0GdptPuZG61SL85SWfMoRy40pbLm0jr11t9/aJLe7i4OTU5UXb+Rc1S5TkiMUbbXSs3G1mlxVMrKdyRUnM1qrwKuqjHv74FX15jnwBXFl0hLNVvWHEdZdZrLw9MSW1ri6HRpqMMQSUyzZc1xlFWnuTw8LbGlJY5Op7GkRGi+rDmOsuo0l4enJba0xJFlaanDEEmtZsua4yirTnN5eFpiS0scnUpFUiIiEkQJQ0REgihhiIhIECUMEREJooQhIiJBlDAyKIkB1ir32aoY2rVdEWk9NavNmCQeylK5zwtft4w7H9vZdAzTtju4jDuHmt+uiLSHOu5lSBIDrFXbZ6VGYmjXdkWkOfU67qlIKkOSeChLtX1WaiSGdm1XRNpHCSNDkhhgrdo+KzUSQ7u2KyLto4SRIUkMsFZtn+95w0lNx9Cu7YpI+6gOI4OSGGCt2kN9WhFDu7YrIo3R4IMdJuoAa9UuwjNdmMvnA9OWrRVD1At+5XYaGTyu1UkmyvZase9GPh+RJChhdLhqzXAd6jbNLV9ndGISd2deT/eMTV3T0OS32X1G2V4r9t3I5yOSFBVJdbBqTVd7u3OAMzZR+xnU9Zq71mrqmpYmv83sM8r2WrHv6p+PAcbYRLqeTS2zh5rVzlLVmq525Ywuq900d6bmrrWaura7yW+1HuCt3ufOPQfpzlnQ9mbad0iP9aqfj+XoCowhSeqRPzupSKqDVWu6Opl3YOpdZXnz1Zmau9Zq6lptvbHJPPPndDUYfcHwyBi3P/osX37oKbpyxmTe2bSuUETTbDPjynqCf9i1l5GxybrbK60zf05XzX2HFlVV/Xw8Dz41YaSteXESRY+SDrrD6GDVmq5uWreSTesGajZfrVynp8vozjFjU9fy9eb2FP6szJ3zb3iE+7bsaij+zVt28cZrf8AXvv8kYxN5DhyaZGwiz8fv2srwyFhTzYw3b9nF6use5OKbH2X1dQ9y+8+e4ZoHfj1tuSvPP+Pw9srXOf+GR7hwcNm0fUOh/mF0PM++sQlGx/Nccc+2qr/Eq38+A2xal95nUw+PjAUfn3Qe3WF0uDWrlrL6tMXTWtxUm1ZrHZjeSqrWvs74nYWc98UfATA26TDpXHHPNlaftjhyy64N92ybUtdSMj7pbH9uL2edflzN4wvZ9uh4nlEKv/Cv/s525nRP/f00f04Xrznh6Jrr3Dm0k/sv+wP2H5o8vO+tO14qNBbgyJ1DqUipWmyNfD5JKhWjhR6fdBYljJi1sv/C9udeBpwVJxxdd1uVTVUrm8xu3fES8+d0Hb7wQfVmtCH2H5qkt7uLQ5MTh6dVXlBCzkG1C9NUR4ptojbFrXrR68pxaHJqcpp0n3I+ql0o9x+aZODEYw5Pa6SYrFr85dPS1Ow2idEGJJp2/m0oYcSoVWW/m7fs4uN3bqHUkKany7j+nQNB26rWZLbLjLFJZ25Pjsm8BzejrWamC0oz5fslOYMVJywMjilk25PufPaCM7jm/l9Pia30hQu9UJaKma6oOMZGv7hpa3bb6uOT1mp3/ZKa1cakVU1Ah0fGeOO1P5hWVNPbneMnn6y/rZARYis1EuN9W3ZNu6CsWbU08jm4b8suLr9725QmpgDdOXj0U+c2dZGqF2OtX2e11qmmVR36ojaLjos6FqZPq64x6umdAlHLfmv1tN6552CxWezU1jyG85WHf8OqE4/hDaf2V93mzMU8081UPl3twlGrXD7qOVizainHHDWHD3/zMQ6MHzneeT3dh9dp9MJVK8Z6xVtR6ksa6bFeqdr56spZsRXVkfORRB1CK45PWiuO+iUljJjUK9KovOiVbiu7c8boeB5356g5hSKiK992RqHpZYXRCefmR/4fULio/LcLpxdRLVs0j0OT4cmiPMZq6t3+VrugNFL+veKEheRrNANu9va7kYtenBfKRppFy+wVR/2SmtXGpFYT0EeeenFa885Sa5yRsUkm8s6kc7gJ4zUP/JrPXLCC7jqf3GTeufzu6U0dH3nqRSbr9LGY25MLbkbbSPPKRprB1loHwpuvZlUjzaJl9opjNGvdYcSoWnPVUpljefPOnq76Pa1fc8LRPPqpc9n+3Mvct2Und//yuarLVrZM2nDPNiZq5Is5XXDTJa9jRbEZaSOtmEJufxtpBlttnajNV7Mqa81uJVmNfL+iUMKIWXmRRtWLXleubrFR6Razv6+XPQcOsXnL81WXy5c1CYWZ6y96u7s5et6c4Ga0zdz+tqIoaDY175yp2a1IuXb+bahIKkG1mnde/u9eVXX53u4jt5ilO4bxfPVWbletWTHjBbZcvYtttXGD4rj9rSfp/YvMRrrDSFCtNu1rVi1lfm/34eKpiXyey97ySi76/ZPqtjgq6bJCHUS9fVUbtrzaxbZexXK7b39nkvT+RWYb9cNIgVpNQ+s1GW1mGPLQIT+SGLJcRJKlfhgpV6vMsV5ZZPkdQ86MA4em9suoVQFcuc16F36NGyQi5VSHkWFrVi3lxxvO5saLf6/44J0jWlEBnJaK5aSfvZD0/puR5dglfXSHkXH9fb2cdfpxbFo30PLxfdIwblDSz15Iev/NyHLskk6qw+gg7RrfJ6lxg5KuQ0l6/83IcuySLNVhzBLtan+dVJv/pOtQkt5/M7Icu6SX6jAktZKuQ0l6/83IcuySXplIGGZ2rJl928z2m9kzZnZR0jFJ+yXdOS/p/Tcjy7FLemWiDsPM/geF5PZ+YBXwAPBGd99ea53ZWIfRqZJ+9kLS+29GlmOXZGS6DsPM5gNrgde4+wjwiJndB1wCfDLR4CQWSY+blPT+m5Hl2CV9slAkdTow6e5Plk3bCqyoXNDM1pvZkJkN7d69O7YARURmgywkjD5gb8W0vcCCygXd/SZ3H3T3wSVLlsQSnIjIbJGFhDECLKyYthDYl0AsIiKzVhYSxpNAt5m9smzaAFCzwltERFov9QnD3fcD9wKfM7P5ZrYa+PfAN5ONTERkdslKs9pjga8D/xYYBj7p7t+aYZ3dwDMxhFfNYuDFhPYdFx1jZ9AxdoZWHuPJ7l61EjgTCSNrzGyoVjvmTqFj7Aw6xs4Q1zGmvkhKRETSQQlDRESCKGG0x01JBxADHWNn0DF2hliOUXUYIiISRHcYIiISRAlDRESCKGGIiEgQJYwGhD7QycwuNbPHzOxlM9tpZhvNLPVDykNjD60yswfNzDvxGM3sFDO738z2mdmLZrYxzlgbFeFv1czs82a2y8z2mtnDZjZtROg0MrPLiqNUj5nZLTMs++dm9tviMX7dzDIx9nvoMbb7mqOE0ZgvA4eA44F3A1+p8eU6CvgzCr0wfx84B/hEXEE2KfQYATCzd5OB56tUCDpGM5sDfB94EHgFsAy4LcY4mxH6Ob4TeB/wJuBY4KdkZ/id54DPUxgNoiYz+yMKz9A5B1gOnAJc3e7gWiToGGnzNUetpCIqPtBpD4UHOj1ZnPZNYJe7132gk5l9DHiLu1/Q/kgbF/UYzexo4BfAeyhcaHrcfSLGkCOLcoxmth64xN3fFH+kjYt4jBuA17n7hcX3K4DH3H1uzGE3zMw+Dyxz9/fWmP8t4Gl3/1Tx/TnA7e7+iviibM5Mx1hl+ZZec3SHEV3wA52qOItsjLIb9Rj/CvgK8Nt2B9ZCUY7xTOBpM/tesTjqYTP73ViibE6UY/yfwGlmdrqZ9QCXAv8rhhjjtILC8ZdsBY43s/6E4olDS685WStCSIPgBzqVM7M/BQaBD7QprlYKPkYzGwRWAx+lUFSTFVE+x2XAW4A1wA8oHOtmM3u1ux9qa5TNiXKMzwM/Av4vMAnsAM5ua3Txqzwfpf8voDCoaUdpxzVHdxjRRX6gk5m9HbgWeKu7Z2HUzKBjNLMc8NfAR9NeBFVFlM/xIPCIu3+vmCD+K9AP/Ov2hti0KMf4WeD1wInAXApl+w+a2VFtjTBeleej9P+Oexhbu645ShjRRXqgk5n9MfA3wNkdmrwAAANHSURBVAXu/ngM8bVC6DEupPAL5g4z+y2FegyAnWaW9vL+KJ/jNiCLlX1RjnEAuMPdd7r7hLvfAiwCzmh/mLHZTuE4SwaAF9y9o+4u2nnNUcKIKMoDnczsbOB2YK27/zzeSBsX4Rj3AicAq4qv84rTXwc8GlO4DYn4YK7bgDPN7Fwz66LQCuVF4P/EFnADIh7jL4B3mtnxZpYzs0uAHuCp+CJujJl1m9lcoAvoMrO5NZqS3gq838zOMLNFwKeBW2IMtWGhx9j2a4676xXxRaHZ4d8B+4FngYuK00+icNt7UvH9Q8BEcVrp9b2k42/lMVass5zCL/HupONv9TEC76Bw8XwZeBhYkXT8rTxGCsVQX6ZQl/Ey8Evgj5OOP/AYryr+3ZW/rqrxOX4MeKF4jN8AepOOv5XH2O5rjprViohIEBVJiYhIECUMEREJooQhIiJBlDBERCSIEoaIiARRwhARkSBKGCIiEkQJQySAmb3KzH5VfIBS3syubPH2nzazc1u5TZFW02i1ImGuAB5299cmHYhIUnSHIRLmZFL4LJOsPA5XOoMShsgMzOxBCs/DuMHMRszsW8Unn2FmG8zsZ6ULt5l9xMy2FweKq7fNS4rP2B42s7+omHdLafvF939oZjvL3j9d3O82YL+ShsRFCUNkBu5+NoWHC13m7n0UnpFdsqn4/tPFYcT/CrjY3Udrbc/MzqDwhMJLKIz220/0h0/9CfA24BjP3rNIJKOUMESa4O55Cs8y/0/AfcBGd//VDKutA+539x+6+xhwJZCPuOsvuvsOdz8YOWiRBilhiDTJ3Z+mMKz0cgpDhM/kBAqPQC2tv5/ojwjdMfMiIq2lhCHSJDM7D3gDhed9bwpY5XkKj0ItrX8UhWKpkv1A+aNRX1FlG3ougcROCUOkCWa2GPga8AHgUuCCYgKp527gfDP7AzObA3yOqd/FLcB5Znasmb2CwhP+RBKnhCHSnJuAze7+XS88G/r9wM1m1l9rBXffDvxH4FsU7jb2ADvLFvkmsBV4GvjfwB3tCV0kGj1xT0REgugOQ0REgihhiLSBmb272Mmv8pW63uIioVQkJSIiQXSHISIiQZQwREQkiBKGiIgEUcIQEZEg/x83rW3Th9+fwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'fix_dur'\n",
    "data = pd.concat([df_x['exp'], df_train[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='exp');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## deep learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "import tensorflow_docs as tfdocs\n",
    "import tensorflow_docs.plots\n",
    "import tensorflow_docs.modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1.795918</td>\n",
       "      <td>1.346696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fix_dur</th>\n",
       "      <td>98.0</td>\n",
       "      <td>0.497479</td>\n",
       "      <td>0.206424</td>\n",
       "      <td>0.146083</td>\n",
       "      <td>0.344831</td>\n",
       "      <td>0.452767</td>\n",
       "      <td>0.611564</td>\n",
       "      <td>1.211964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hs</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1.654972</td>\n",
       "      <td>0.496802</td>\n",
       "      <td>0.791858</td>\n",
       "      <td>1.310436</td>\n",
       "      <td>1.597426</td>\n",
       "      <td>2.016577</td>\n",
       "      <td>2.714576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ht</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1.335623</td>\n",
       "      <td>0.336212</td>\n",
       "      <td>0.637441</td>\n",
       "      <td>1.004953</td>\n",
       "      <td>1.410938</td>\n",
       "      <td>1.592441</td>\n",
       "      <td>1.902647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fix_rate</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1.353009</td>\n",
       "      <td>0.365148</td>\n",
       "      <td>0.685276</td>\n",
       "      <td>1.096511</td>\n",
       "      <td>1.284953</td>\n",
       "      <td>1.593295</td>\n",
       "      <td>2.158386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blk_rate</th>\n",
       "      <td>98.0</td>\n",
       "      <td>0.240190</td>\n",
       "      <td>0.228687</td>\n",
       "      <td>0.034250</td>\n",
       "      <td>0.102784</td>\n",
       "      <td>0.171295</td>\n",
       "      <td>0.342602</td>\n",
       "      <td>1.165005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sac_rate</th>\n",
       "      <td>98.0</td>\n",
       "      <td>0.945705</td>\n",
       "      <td>0.341810</td>\n",
       "      <td>0.308372</td>\n",
       "      <td>0.728178</td>\n",
       "      <td>0.925133</td>\n",
       "      <td>1.130640</td>\n",
       "      <td>2.981036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msac_rate</th>\n",
       "      <td>98.0</td>\n",
       "      <td>2.355710</td>\n",
       "      <td>1.010352</td>\n",
       "      <td>0.788067</td>\n",
       "      <td>1.695876</td>\n",
       "      <td>2.192690</td>\n",
       "      <td>2.569642</td>\n",
       "      <td>8.223547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      mean       std       min       25%       50%       75%  \\\n",
       "index       98.0  1.795918  1.346696  0.000000  1.000000  2.000000  3.000000   \n",
       "fix_dur     98.0  0.497479  0.206424  0.146083  0.344831  0.452767  0.611564   \n",
       "Hs          98.0  1.654972  0.496802  0.791858  1.310436  1.597426  2.016577   \n",
       "Ht          98.0  1.335623  0.336212  0.637441  1.004953  1.410938  1.592441   \n",
       "fix_rate    98.0  1.353009  0.365148  0.685276  1.096511  1.284953  1.593295   \n",
       "blk_rate    98.0  0.240190  0.228687  0.034250  0.102784  0.171295  0.342602   \n",
       "sac_rate    98.0  0.945705  0.341810  0.308372  0.728178  0.925133  1.130640   \n",
       "msac_rate   98.0  2.355710  1.010352  0.788067  1.695876  2.192690  2.569642   \n",
       "\n",
       "                max  \n",
       "index      4.000000  \n",
       "fix_dur    1.211964  \n",
       "Hs         2.714576  \n",
       "Ht         1.902647  \n",
       "fix_rate   2.158386  \n",
       "blk_rate   1.165005  \n",
       "sac_rate   2.981036  \n",
       "msac_rate  8.223547  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = df_x.copy().reset_index()\n",
    "\n",
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)\n",
    "\n",
    "\n",
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"exp\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a694b37a20>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"exp\", \"fix_dur\", \"Hs\", \"Ht\", \"fix_rate\", \"blk_rate\", \"sac_rate\", \"msac_rate\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('exp')\n",
    "test_labels = test_dataset.pop('exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(128, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = np.asarray(train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.09045378],\n",
       "       [-0.02379203],\n",
       "       [ 0.05395069],\n",
       "       [ 0.6610454 ],\n",
       "       [ 0.00710725],\n",
       "       [ 0.2019491 ],\n",
       "       [ 0.0952118 ],\n",
       "       [ 0.40472344],\n",
       "       [-0.04616739],\n",
       "       [ 0.30139542]], dtype=float32)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch.values)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0, loss:22608.7147,  mae:115.0181,  mse:22608.7148,  val_loss:27328.1289,  val_mae:135.8080,  val_mse:27328.1289,  \n",
      "....................................................................................................\n",
      "Epoch: 100, loss:7202.0930,  mae:66.5372,  mse:7202.0928,  val_loss:8445.2002,  val_mae:73.2961,  val_mse:8445.2002,  \n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "EPOCHS = 200\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data.values, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[tfdocs.modeling.EpochDots()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(normed_test_data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[104.4219] 26.0\n",
      "[49.729507] 28.0\n",
      "[6.6602497] 35.0\n",
      "[73.40454] 49.0\n",
      "[143.07607] 50.0\n",
      "[129.41258] 63.0\n",
      "[65.15071] 63.0\n",
      "[53.02487] 63.0\n",
      "[97.63108] 98.0\n",
      "[147.6645] 133.0\n",
      "[123.97555] 133.0\n",
      "[113.5865] 200.0\n",
      "[78.676796] 200.0\n",
      "[160.30737] 200.0\n",
      "[155.17923] 200.0\n",
      "[82.25648] 220.0\n",
      "[115.53788] 220.0\n",
      "[188.36613] 420.0\n",
      "[175.77591] 420.0\n",
      "[46.06647] 28.0\n",
      "[81.30046] 28.0\n",
      "[43.413513] 28.0\n",
      "[119.15832] 220.0\n",
      "[63.273296] 220.0\n"
     ]
    }
   ],
   "source": [
    "for u,v in zip(pred,test_labels):\n",
    "    print(u,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>6382.175105</td>\n",
       "      <td>63.399338</td>\n",
       "      <td>6382.175293</td>\n",
       "      <td>8941.455078</td>\n",
       "      <td>77.602425</td>\n",
       "      <td>8941.455078</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>6365.939578</td>\n",
       "      <td>63.001904</td>\n",
       "      <td>6365.939941</td>\n",
       "      <td>8979.375000</td>\n",
       "      <td>77.629860</td>\n",
       "      <td>8979.375000</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>6399.090025</td>\n",
       "      <td>62.938709</td>\n",
       "      <td>6399.090332</td>\n",
       "      <td>8985.491211</td>\n",
       "      <td>77.665878</td>\n",
       "      <td>8985.491211</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>6362.593894</td>\n",
       "      <td>62.821301</td>\n",
       "      <td>6362.593750</td>\n",
       "      <td>9004.015625</td>\n",
       "      <td>77.810608</td>\n",
       "      <td>9004.015625</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6414.642228</td>\n",
       "      <td>63.109158</td>\n",
       "      <td>6414.642090</td>\n",
       "      <td>9036.445312</td>\n",
       "      <td>77.782913</td>\n",
       "      <td>9036.445312</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            loss        mae          mse     val_loss    val_mae      val_mse  \\\n",
       "195  6382.175105  63.399338  6382.175293  8941.455078  77.602425  8941.455078   \n",
       "196  6365.939578  63.001904  6365.939941  8979.375000  77.629860  8979.375000   \n",
       "197  6399.090025  62.938709  6399.090332  8985.491211  77.665878  8985.491211   \n",
       "198  6362.593894  62.821301  6362.593750  9004.015625  77.810608  9004.015625   \n",
       "199  6414.642228  63.109158  6414.642090  9036.445312  77.782913  9036.445312   \n",
       "\n",
       "     epoch  \n",
       "195    195  \n",
       "196    196  \n",
       "197    197  \n",
       "198    198  \n",
       "199    199  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MsE [exp]')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.plot({'Basic': history}, metric = \"mse\")\n",
    "# plt.ylim([0, 10])\n",
    "plt.ylabel('MsE [exp]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
    "from sklearn.svm import SVR\n",
    "from mlxtend.regressor import StackingCVRegressor\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Stats\n",
    "from scipy.stats import skew, norm\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "\n",
    "# Misc\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_result(pred, test):\n",
    "    print(\"explained_variance_score:\", explained_variance_score(pred,test))    \n",
    "    print(\"max_error:\\t\\t \", max_error(pred,test))    #max(abs(pred-test))\n",
    "    print(\"mean_absolute_error:\\t \", mean_absolute_error(pred,test))\n",
    "    print(\"mean_squared_error:\\t \", mean_squared_error(pred,test))    \n",
    "#     print(\"mean_squared_log_error:\\t\", mean_squared_log_error(pred,test))\n",
    "    print(\"root mean squared error\", np.sqrt(mean_squared_error(pred,test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13413970919792506"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_train, y_train)\n",
    "reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>pred</th>\n",
       "      <th>ground truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[57.24784928376407]</td>\n",
       "      <td>[98.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[129.73851604162016]</td>\n",
       "      <td>[100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[133.2273115284189]</td>\n",
       "      <td>[230.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[42.697821897880914]</td>\n",
       "      <td>[116.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[71.61050746056287]</td>\n",
       "      <td>[35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[58.55375260970439]</td>\n",
       "      <td>[50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[81.48085829226628]</td>\n",
       "      <td>[180.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[122.80905037717966]</td>\n",
       "      <td>[50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[65.2475791326607]</td>\n",
       "      <td>[49.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[66.92849139335596]</td>\n",
       "      <td>[28.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[122.08618808329443]</td>\n",
       "      <td>[300.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[115.7457106362767]</td>\n",
       "      <td>[100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[92.5044414099848]</td>\n",
       "      <td>[40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[102.74492965753433]</td>\n",
       "      <td>[155.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[142.42113530903364]</td>\n",
       "      <td>[200.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[183.94713295444708]</td>\n",
       "      <td>[133.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[151.39410019099614]</td>\n",
       "      <td>[26.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[238.61245507307484]</td>\n",
       "      <td>[420.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[149.1717958075286]</td>\n",
       "      <td>[220.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[72.4991633499233]</td>\n",
       "      <td>[49.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    pred ground truth\n",
       "0    [57.24784928376407]       [98.0]\n",
       "1   [129.73851604162016]      [100.0]\n",
       "2    [133.2273115284189]      [230.0]\n",
       "3   [42.697821897880914]      [116.0]\n",
       "4    [71.61050746056287]       [35.0]\n",
       "5    [58.55375260970439]       [50.0]\n",
       "6    [81.48085829226628]      [180.0]\n",
       "7   [122.80905037717966]       [50.0]\n",
       "8     [65.2475791326607]       [49.0]\n",
       "9    [66.92849139335596]       [28.0]\n",
       "10  [122.08618808329443]      [300.0]\n",
       "11   [115.7457106362767]      [100.0]\n",
       "12    [92.5044414099848]       [40.0]\n",
       "13  [102.74492965753433]      [155.0]\n",
       "14  [142.42113530903364]      [200.0]\n",
       "15  [183.94713295444708]      [133.0]\n",
       "16  [151.39410019099614]       [26.0]\n",
       "17  [238.61245507307484]      [420.0]\n",
       "18   [149.1717958075286]      [220.0]\n",
       "19    [72.4991633499233]       [49.0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(zip(pred, y_test), columns=['pred','ground truth']).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained_variance_score: -0.13044771887577356\n",
      "max_error:\t\t  297.1378659424799\n",
      "mean_absolute_error:\t  78.95383471166213\n",
      "mean_squared_error:\t  9801.651106357253\n",
      "root mean squared error 99.00328836133299\n"
     ]
    }
   ],
   "source": [
    "metric_result(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X = np.append(arr = np.ones((len(X),1)).astype(int), values = X, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>0.000141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:25:49</td>     <th>  Log-Likelihood:    </th> <td> -720.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   122</td>      <th>  AIC:               </th> <td>   1457.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   114</td>      <th>  BIC:               </th> <td>   1479.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5902</td> <td>    8.304</td> <td>   14.883</td> <td> 0.000</td> <td>  107.139</td> <td>  140.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>  -39.9832</td> <td>   13.760</td> <td>   -2.906</td> <td> 0.004</td> <td>  -67.242</td> <td>  -12.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.8400</td> <td>   18.166</td> <td>    0.046</td> <td> 0.963</td> <td>  -35.146</td> <td>   36.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -10.0190</td> <td>   18.868</td> <td>   -0.531</td> <td> 0.596</td> <td>  -47.397</td> <td>   27.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -2.3161</td> <td>   11.598</td> <td>   -0.200</td> <td> 0.842</td> <td>  -25.292</td> <td>   20.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>  -45.4523</td> <td>    9.943</td> <td>   -4.571</td> <td> 0.000</td> <td>  -65.149</td> <td>  -25.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>  -23.9739</td> <td>   11.355</td> <td>   -2.111</td> <td> 0.037</td> <td>  -46.468</td> <td>   -1.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>  -16.1251</td> <td>   13.129</td> <td>   -1.228</td> <td> 0.222</td> <td>  -42.134</td> <td>    9.884</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.768</td> <th>  Durbin-Watson:     </th> <td>   0.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.034</td> <th>  Jarque-Bera (JB):  </th> <td>   6.830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.543</td> <th>  Prob(JB):          </th> <td>  0.0329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.593</td> <th>  Cond. No.          </th> <td>    5.10</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.221\n",
       "Model:                            OLS   Adj. R-squared:                  0.173\n",
       "Method:                 Least Squares   F-statistic:                     4.617\n",
       "Date:                Sat, 13 Jun 2020   Prob (F-statistic):           0.000141\n",
       "Time:                        21:25:49   Log-Likelihood:                -720.27\n",
       "No. Observations:                 122   AIC:                             1457.\n",
       "Df Residuals:                     114   BIC:                             1479.\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5902      8.304     14.883      0.000     107.139     140.041\n",
       "x1           -39.9832     13.760     -2.906      0.004     -67.242     -12.724\n",
       "x2             0.8400     18.166      0.046      0.963     -35.146      36.826\n",
       "x3           -10.0190     18.868     -0.531      0.596     -47.397      27.359\n",
       "x4            -2.3161     11.598     -0.200      0.842     -25.292      20.660\n",
       "x5           -45.4523      9.943     -4.571      0.000     -65.149     -25.756\n",
       "x6           -23.9739     11.355     -2.111      0.037     -46.468      -1.480\n",
       "x7           -16.1251     13.129     -1.228      0.222     -42.134       9.884\n",
       "==============================================================================\n",
       "Omnibus:                        6.768   Durbin-Watson:                   0.387\n",
       "Prob(Omnibus):                  0.034   Jarque-Bera (JB):                6.830\n",
       "Skew:                           0.543   Prob(JB):                       0.0329\n",
       "Kurtosis:                       2.593   Cond. No.                         5.10\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,1,2,3,4,5,6,7]]\n",
    "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>5.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:25:49</td>     <th>  Log-Likelihood:    </th> <td> -720.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   122</td>      <th>  AIC:               </th> <td>   1455.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   115</td>      <th>  BIC:               </th> <td>   1474.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5902</td> <td>    8.268</td> <td>   14.948</td> <td> 0.000</td> <td>  107.212</td> <td>  139.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>  -39.9171</td> <td>   13.626</td> <td>   -2.929</td> <td> 0.004</td> <td>  -66.908</td> <td>  -12.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>   -9.2767</td> <td>    9.873</td> <td>   -0.940</td> <td> 0.349</td> <td>  -28.834</td> <td>   10.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -2.1926</td> <td>   11.237</td> <td>   -0.195</td> <td> 0.846</td> <td>  -24.452</td> <td>   20.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>  -45.3872</td> <td>    9.799</td> <td>   -4.632</td> <td> 0.000</td> <td>  -64.798</td> <td>  -25.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>  -24.0428</td> <td>   11.207</td> <td>   -2.145</td> <td> 0.034</td> <td>  -46.243</td> <td>   -1.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>  -16.0423</td> <td>   12.950</td> <td>   -1.239</td> <td> 0.218</td> <td>  -41.694</td> <td>    9.609</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.795</td> <th>  Durbin-Watson:     </th> <td>   0.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.033</td> <th>  Jarque-Bera (JB):  </th> <td>   6.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.543</td> <th>  Prob(JB):          </th> <td>  0.0326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.588</td> <th>  Cond. No.          </th> <td>    3.39</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.221\n",
       "Model:                            OLS   Adj. R-squared:                  0.180\n",
       "Method:                 Least Squares   F-statistic:                     5.433\n",
       "Date:                Sat, 13 Jun 2020   Prob (F-statistic):           5.61e-05\n",
       "Time:                        21:25:49   Log-Likelihood:                -720.27\n",
       "No. Observations:                 122   AIC:                             1455.\n",
       "Df Residuals:                     115   BIC:                             1474.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5902      8.268     14.948      0.000     107.212     139.968\n",
       "x1           -39.9171     13.626     -2.929      0.004     -66.908     -12.926\n",
       "x2            -9.2767      9.873     -0.940      0.349     -28.834      10.280\n",
       "x3            -2.1926     11.237     -0.195      0.846     -24.452      20.067\n",
       "x4           -45.3872      9.799     -4.632      0.000     -64.798     -25.976\n",
       "x5           -24.0428     11.207     -2.145      0.034     -46.243      -1.843\n",
       "x6           -16.0423     12.950     -1.239      0.218     -41.694       9.609\n",
       "==============================================================================\n",
       "Omnibus:                        6.795   Durbin-Watson:                   0.387\n",
       "Prob(Omnibus):                  0.033   Jarque-Bera (JB):                6.846\n",
       "Skew:                           0.543   Prob(JB):                       0.0326\n",
       "Kurtosis:                       2.588   Cond. No.                         3.39\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,1,3,4,5,6,7]]\n",
    "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>2.05e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:19:43</td>     <th>  Log-Likelihood:    </th> <td> -720.29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   122</td>      <th>  AIC:               </th> <td>   1453.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   116</td>      <th>  BIC:               </th> <td>   1469.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5902</td> <td>    8.234</td> <td>   15.010</td> <td> 0.000</td> <td>  107.282</td> <td>  139.898</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>  -38.4644</td> <td>   11.365</td> <td>   -3.385</td> <td> 0.001</td> <td>  -60.973</td> <td>  -15.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>   -9.7224</td> <td>    9.565</td> <td>   -1.016</td> <td> 0.312</td> <td>  -28.668</td> <td>    9.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -44.6079</td> <td>    8.911</td> <td>   -5.006</td> <td> 0.000</td> <td>  -62.258</td> <td>  -26.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>  -23.8265</td> <td>   11.106</td> <td>   -2.145</td> <td> 0.034</td> <td>  -45.824</td> <td>   -1.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>  -15.9832</td> <td>   12.893</td> <td>   -1.240</td> <td> 0.218</td> <td>  -41.519</td> <td>    9.552</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.694</td> <th>  Durbin-Watson:     </th> <td>   0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.035</td> <th>  Jarque-Bera (JB):  </th> <td>   6.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.544</td> <th>  Prob(JB):          </th> <td>  0.0335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.608</td> <th>  Cond. No.          </th> <td>    2.99</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.221\n",
       "Model:                            OLS   Adj. R-squared:                  0.187\n",
       "Method:                 Least Squares   F-statistic:                     6.567\n",
       "Date:                Mon, 08 Jun 2020   Prob (F-statistic):           2.05e-05\n",
       "Time:                        12:19:43   Log-Likelihood:                -720.29\n",
       "No. Observations:                 122   AIC:                             1453.\n",
       "Df Residuals:                     116   BIC:                             1469.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5902      8.234     15.010      0.000     107.282     139.898\n",
       "x1           -38.4644     11.365     -3.385      0.001     -60.973     -15.955\n",
       "x2            -9.7224      9.565     -1.016      0.312     -28.668       9.223\n",
       "x3           -44.6079      8.911     -5.006      0.000     -62.258     -26.958\n",
       "x4           -23.8265     11.106     -2.145      0.034     -45.824      -1.829\n",
       "x5           -15.9832     12.893     -1.240      0.218     -41.519       9.552\n",
       "==============================================================================\n",
       "Omnibus:                        6.694   Durbin-Watson:                   0.385\n",
       "Prob(Omnibus):                  0.035   Jarque-Bera (JB):                6.792\n",
       "Skew:                           0.544   Prob(JB):                       0.0335\n",
       "Kurtosis:                       2.608   Cond. No.                         2.99\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,1,3,5,6,7]]\n",
    "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>1.06e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:19:44</td>     <th>  Log-Likelihood:    </th> <td> -720.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   122</td>      <th>  AIC:               </th> <td>   1452.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   117</td>      <th>  BIC:               </th> <td>   1466.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5902</td> <td>    8.235</td> <td>   15.008</td> <td> 0.000</td> <td>  107.281</td> <td>  139.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>  -35.2323</td> <td>   10.912</td> <td>   -3.229</td> <td> 0.002</td> <td>  -56.843</td> <td>  -13.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>  -43.9253</td> <td>    8.887</td> <td>   -4.943</td> <td> 0.000</td> <td>  -61.526</td> <td>  -26.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -28.7150</td> <td>   10.012</td> <td>   -2.868</td> <td> 0.005</td> <td>  -48.544</td> <td>   -8.887</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>  -10.4581</td> <td>   11.692</td> <td>   -0.894</td> <td> 0.373</td> <td>  -33.614</td> <td>   12.698</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.063</td> <th>  Durbin-Watson:     </th> <td>   0.367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.029</td> <th>  Jarque-Bera (JB):  </th> <td>   7.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.591</td> <th>  Prob(JB):          </th> <td>  0.0249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.762</td> <th>  Cond. No.          </th> <td>    2.51</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.214\n",
       "Model:                            OLS   Adj. R-squared:                  0.187\n",
       "Method:                 Least Squares   F-statistic:                     7.948\n",
       "Date:                Mon, 08 Jun 2020   Prob (F-statistic):           1.06e-05\n",
       "Time:                        12:19:44   Log-Likelihood:                -720.83\n",
       "No. Observations:                 122   AIC:                             1452.\n",
       "Df Residuals:                     117   BIC:                             1466.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5902      8.235     15.008      0.000     107.281     139.899\n",
       "x1           -35.2323     10.912     -3.229      0.002     -56.843     -13.621\n",
       "x2           -43.9253      8.887     -4.943      0.000     -61.526     -26.325\n",
       "x3           -28.7150     10.012     -2.868      0.005     -48.544      -8.887\n",
       "x4           -10.4581     11.692     -0.894      0.373     -33.614      12.698\n",
       "==============================================================================\n",
       "Omnibus:                        7.063   Durbin-Watson:                   0.367\n",
       "Prob(Omnibus):                  0.029   Jarque-Bera (JB):                7.389\n",
       "Skew:                           0.591   Prob(JB):                       0.0249\n",
       "Kurtosis:                       2.762   Cond. No.                         2.51\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,1,5,6,7]]\n",
    "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>4.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:19:45</td>     <th>  Log-Likelihood:    </th> <td> -721.24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   122</td>      <th>  AIC:               </th> <td>   1450.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   118</td>      <th>  BIC:               </th> <td>   1462.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  123.5902</td> <td>    8.228</td> <td>   15.021</td> <td> 0.000</td> <td>  107.296</td> <td>  139.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>  -30.0628</td> <td>    9.248</td> <td>   -3.251</td> <td> 0.002</td> <td>  -48.377</td> <td>  -11.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>  -42.5575</td> <td>    8.747</td> <td>   -4.865</td> <td> 0.000</td> <td>  -59.879</td> <td>  -25.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -32.5744</td> <td>    9.027</td> <td>   -3.609</td> <td> 0.000</td> <td>  -50.450</td> <td>  -14.699</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.824</td> <th>  Durbin-Watson:     </th> <td>   0.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.033</td> <th>  Jarque-Bera (JB):  </th> <td>   7.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.585</td> <th>  Prob(JB):          </th> <td>  0.0295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.872</td> <th>  Cond. No.          </th> <td>    1.65</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.208\n",
       "Model:                            OLS   Adj. R-squared:                  0.188\n",
       "Method:                 Least Squares   F-statistic:                     10.35\n",
       "Date:                Mon, 08 Jun 2020   Prob (F-statistic):           4.25e-06\n",
       "Time:                        12:19:45   Log-Likelihood:                -721.24\n",
       "No. Observations:                 122   AIC:                             1450.\n",
       "Df Residuals:                     118   BIC:                             1462.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        123.5902      8.228     15.021      0.000     107.296     139.884\n",
       "x1           -30.0628      9.248     -3.251      0.002     -48.377     -11.749\n",
       "x2           -42.5575      8.747     -4.865      0.000     -59.879     -25.236\n",
       "x3           -32.5744      9.027     -3.609      0.000     -50.450     -14.699\n",
       "==============================================================================\n",
       "Omnibus:                        6.824   Durbin-Watson:                   0.349\n",
       "Prob(Omnibus):                  0.033   Jarque-Bera (JB):                7.044\n",
       "Skew:                           0.585   Prob(JB):                       0.0295\n",
       "Kurtosis:                       2.872   Cond. No.                         1.65\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,1,5,6]]\n",
    "regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_opt, X_test_opt, y_train_opt, y_test_opt = train_test_split(X_opt, y, test_size=0.5, random_state=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2012100513178291"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_train_opt, y_train_opt)\n",
    "reg.score(X_test_opt, y_test_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = reg.predict(X_test_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained_variance_score: -1.3013085362078174\n",
      "max_error:\t\t  198.85626710254772\n",
      "mean_absolute_error:\t  70.9124975006001\n",
      "mean_squared_error:\t  6903.435547446554\n",
      "root mean squared error 83.08691562121291\n"
     ]
    }
   ],
   "source": [
    "metric_result(pred, y_test_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup cross validation folds\n",
    "kf = KFold(n_splits=5, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Light Gradient Boosting Regressor\n",
    "lightgbm = LGBMRegressor(objective='regression', \n",
    "                       num_leaves=6,\n",
    "                       learning_rate=0.01, \n",
    "                       n_estimators=7000,\n",
    "                       max_bin=200, \n",
    "                       bagging_fraction=0.8,\n",
    "                       bagging_freq=4, \n",
    "                       bagging_seed=8,\n",
    "                       feature_fraction=0.2,\n",
    "                       feature_fraction_seed=8,\n",
    "                       min_sum_hessian_in_leaf = 11,\n",
    "                       verbose=-1,\n",
    "                       random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost Regressor\n",
    "xgboost = XGBRegressor(learning_rate=0.01,\n",
    "                       n_estimators=6000,\n",
    "                       max_depth=4,\n",
    "                       min_child_weight=0,\n",
    "                       gamma=0.6,\n",
    "                       subsample=0.7,\n",
    "                       colsample_bytree=0.7,\n",
    "                       objective='reg:linear',\n",
    "                       nthread=-1,\n",
    "                       scale_pos_weight=1,\n",
    "                       seed=27,\n",
    "                       reg_alpha=0.00006,\n",
    "                       random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ridge Regressor\n",
    "ridge_alphas = [1e-15, 1e-10, 1e-8, 9e-4, 7e-4, 5e-4, 3e-4, 1e-4, 1e-3, 5e-2, 1e-2, 0.1, 0.3, 1, 3, 5, 10, 15, 18, 20, 30, 50, 75, 100]\n",
    "ridge = make_pipeline(RobustScaler(), RidgeCV(alphas=ridge_alphas, cv=kf))\n",
    "\n",
    "# Support Vector Regressor\n",
    "svr = make_pipeline(RobustScaler(), SVR(C= 20, epsilon= 0.008, gamma=0.0003))\n",
    "\n",
    "# Gradient Boosting Regressor\n",
    "gbr = GradientBoostingRegressor(n_estimators=6000,\n",
    "                                learning_rate=0.01,\n",
    "                                max_depth=4,\n",
    "                                max_features='sqrt',\n",
    "                                min_samples_leaf=15,\n",
    "                                min_samples_split=10,\n",
    "                                loss='huber',\n",
    "                                random_state=42)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Regressor\n",
    "rf = RandomForestRegressor(n_estimators=1200,\n",
    "                          max_depth=15,\n",
    "                          min_samples_split=5,\n",
    "                          min_samples_leaf=5,\n",
    "                          max_features=None,\n",
    "                          oob_score=True,\n",
    "                          random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Stack up all the models above, optimized using xgboost\n",
    "stack_gen = StackingCVRegressor(regressors=(xgboost, lightgbm, svr, ridge, gbr, rf),\n",
    "                                meta_regressor=xgboost,\n",
    "                                use_features_in_secondary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define error metrics\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "def cv_rmse(model, X=X):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=kf))\n",
    "    return (rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lightgbm: 152.2799 (62.5830)\n"
     ]
    }
   ],
   "source": [
    "scores = {}\n",
    "\n",
    "score = cv_rmse(lightgbm)\n",
    "print(\"lightgbm: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['lgb'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:49:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:49:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "xgboost: 129.1175 (67.8756)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(xgboost)\n",
    "print(\"xgboost: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['xgb'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR: 127.5856 (76.3071)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(svr)\n",
    "print(\"SVR: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['svr'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ridge: 128.4701 (72.7731)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(ridge)\n",
    "print(\"ridge: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['ridge'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf: 131.9529 (70.9292)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(rf)\n",
    "print(\"rf: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['rf'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbr: 133.8654 (68.6628)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(gbr)\n",
    "print(\"gbr: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['gbr'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:50:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:50:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:51:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:52:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:52:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:52:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:52:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:52:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:53:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:54:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:54:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:55:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "stack: 132.0951 (73.7200)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(stack_gen)\n",
    "print(\"stack: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['stack'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stack_gen\n",
      "[15:56:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:56:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:57:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:57:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:57:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:58:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:58:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:58:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:58:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "print('stack_gen')\n",
    "stack_gen_model = stack_gen.fit(np.array(X), np.array(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lightgbm\n"
     ]
    }
   ],
   "source": [
    "print('lightgbm')\n",
    "lgb_model_full_data = lightgbm.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboost\n",
      "[15:58:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[15:58:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/objective/regression_obj.cu:168: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "print('xgboost')\n",
    "xgb_model_full_data = xgboost.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Svr\n"
     ]
    }
   ],
   "source": [
    "print('Svr')\n",
    "svr_model_full_data = svr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge\n"
     ]
    }
   ],
   "source": [
    "print('Ridge')\n",
    "ridge_model_full_data = ridge.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest')\n",
    "rf_model_full_data = rf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoosting\n"
     ]
    }
   ],
   "source": [
    "print('GradientBoosting')\n",
    "gbr_model_full_data = gbr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Blend models in order to make the final predictions more robust to overfitting\n",
    "def blended_predictions(X):\n",
    "    return ((0.1 * ridge_model_full_data.predict(X).reshape(-1)) + \\\n",
    "            (0.2 * svr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * gbr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * xgb_model_full_data.predict(X)) + \\\n",
    "            (0.1 * lgb_model_full_data.predict(X)) + \\\n",
    "            (0.05 * rf_model_full_data.predict(X)) + \\\n",
    "            (0.35 * stack_gen_model.predict(np.array(X))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lgb': (152.2798554673976, 62.58300859844062),\n",
       " 'xgb': (129.1174601974425, 67.87556119703407),\n",
       " 'svr': (127.58561380622136, 76.3071265344346),\n",
       " 'ridge': (128.47013095500625, 72.7730686985179),\n",
       " 'rf': (131.95293014901335, 70.9292195521907),\n",
       " 'gbr': (133.86538851125448, 68.6627760595128),\n",
       " 'stack': (132.09509799330354, 73.71995266639117)}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on train data:\n",
      "77.9006514624597\n"
     ]
    }
   ],
   "source": [
    "# Get final precitions from the blended model\n",
    "blended_score = rmsle(y, blended_predictions(X))\n",
    "scores['blended'] = (blended_score, 0)\n",
    "print('RMSLE score on train data:')\n",
    "print(blended_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(24, 12))\n",
    "\n",
    "ax = sns.pointplot(x=list(scores.keys()), y=[score for score, _ in scores.values()], markers=['o'], linestyles=['-'])\n",
    "for i, score in enumerate(scores.values()):\n",
    "    ax.text(i, score[0] + 0.002, '{:.6f}'.format(score[0]), horizontalalignment='left', size='large', color='black', weight='semibold')\n",
    "\n",
    "plt.ylabel('Score (RMSE)', size=20, labelpad=12.5)\n",
    "plt.xlabel('Model', size=20, labelpad=12.5)\n",
    "plt.tick_params(axis='x', labelsize=13.5)\n",
    "plt.tick_params(axis='y', labelsize=12.5)\n",
    "\n",
    "plt.title('Scores of Models', size=20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "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": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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