notebook/tennis/tennis pre-match.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tennis pre-match prediction\n",
    "\n",
    "## Test with a first dataset generate by a postegresql database and 3 tennis statistiques sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get data\n",
    "\n",
    "##### CSV version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"dataset\"\n",
    "NAME = \"dataset_train_final.csv\"\n",
    "PRED = \"dataset_pred_final.csv\"\n",
    "\n",
    "def load_dataset_csv(path=PATH, name=NAME):\n",
    "    csv_path = os.path.join(path, name)\n",
    "    return pd.read_csv(csv_path)\n",
    "\n",
    "def load_predictset_csv(path=PATH, name=PRED):\n",
    "    csv_path = os.path.join(path, name)\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SQL version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import psycopg2 as pg\n",
    "import requests as req\n",
    "\n",
    "URL = 'postgresql://dataset:dataset1747@35.204.62.91:5432/dataset-dump1'\n",
    "engine = create_engine(URL)\n",
    "\n",
    "def load_dataset_sql():\n",
    "    URL = \"https://raw.githubusercontent.com/panaC/tennis-dataset/master/app/dataset/extract_dataset.sql\"\n",
    "    with engine.connect() as conn, conn.begin():\n",
    "        return pd.read_sql(req.get(URL).text, conn)\n",
    "\n",
    "def load_predictset_sql():\n",
    "    URL = \"https://raw.githubusercontent.com/panaC/tennis-dataset/master/app/dataset/extract_predset.sql\"\n",
    "    with engine.connect() as conn, conn.begin():\n",
    "        return pd.read_sql(req.get(URL).text, conn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url_match_flash</th>\n",
       "      <th>url_match_atp</th>\n",
       "      <th>url_tour</th>\n",
       "      <th>name_tour_flash</th>\n",
       "      <th>name_tour_atp</th>\n",
       "      <th>name_tour_csv</th>\n",
       "      <th>round_flash</th>\n",
       "      <th>round_csv</th>\n",
       "      <th>qualif</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30648</td>\n",
       "      <td>https://www.flashscore.com/match/zFScDpJb</td>\n",
       "      <td>/en/scores/archive/madrid/1536/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>ATP World Tour Masters 1000 Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1.631293</td>\n",
       "      <td>2.059026</td>\n",
       "      <td>-2.284791</td>\n",
       "      <td>-0.099016</td>\n",
       "      <td>-5.118173</td>\n",
       "      <td>1.148676</td>\n",
       "      <td>-1.866266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.051257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42244</td>\n",
       "      <td>https://www.flashscore.com/match/jcZcIiNh</td>\n",
       "      <td>/en/scores/archive/rome/416/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Rome</td>\n",
       "      <td>ATP World Tour Masters 1000 Rome</td>\n",
       "      <td>Rome</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.924819</td>\n",
       "      <td>-4.775000</td>\n",
       "      <td>-13.214855</td>\n",
       "      <td>-0.978261</td>\n",
       "      <td>-15.291486</td>\n",
       "      <td>-4.848007</td>\n",
       "      <td>-8.636051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.487892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40048</td>\n",
       "      <td>https://www.flashscore.com/match/G00yITY5</td>\n",
       "      <td>/en/scores/archive/nottingham/741/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.771137</td>\n",
       "      <td>-2.217613</td>\n",
       "      <td>-4.413200</td>\n",
       "      <td>-0.862751</td>\n",
       "      <td>-2.844315</td>\n",
       "      <td>-2.289802</td>\n",
       "      <td>-2.225344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40071</td>\n",
       "      <td>https://www.flashscore.com/match/MPsuaWHS</td>\n",
       "      <td>/en/scores/archive/nottingham/741/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.714286</td>\n",
       "      <td>-2.142857</td>\n",
       "      <td>-17.285714</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-2.571429</td>\n",
       "      <td>-1.857143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.654459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>905</td>\n",
       "      <td>https://www.flashscore.com/match/8xr1BrDj</td>\n",
       "      <td>/en/scores/archive/atlanta/6116/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.217407</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>-1.797011</td>\n",
       "      <td>0.557619</td>\n",
       "      <td>5.830450</td>\n",
       "      <td>-2.368942</td>\n",
       "      <td>1.484709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.461002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                            url_match_flash  \\\n",
       "0  30648  https://www.flashscore.com/match/zFScDpJb   \n",
       "1  42244  https://www.flashscore.com/match/jcZcIiNh   \n",
       "2  40048  https://www.flashscore.com/match/G00yITY5   \n",
       "3  40071  https://www.flashscore.com/match/MPsuaWHS   \n",
       "4    905  https://www.flashscore.com/match/8xr1BrDj   \n",
       "\n",
       "                                    url_match_atp  \\\n",
       "0     /en/scores/archive/madrid/1536/2016/results   \n",
       "1        /en/scores/archive/rome/416/2016/results   \n",
       "2  /en/scores/archive/nottingham/741/2016/results   \n",
       "3  /en/scores/archive/nottingham/741/2016/results   \n",
       "4    /en/scores/archive/atlanta/6116/2016/results   \n",
       "\n",
       "                                            url_tour name_tour_flash  \\\n",
       "0  https://www.flashscore.com/tennis/atp-singles/...          Madrid   \n",
       "1  https://www.flashscore.com/tennis/atp-singles/...            Rome   \n",
       "2  https://www.flashscore.com/tennis/atp-singles/...      Nottingham   \n",
       "3  https://www.flashscore.com/tennis/atp-singles/...      Nottingham   \n",
       "4  https://www.flashscore.com/tennis/atp-singles/...         Atlanta   \n",
       "\n",
       "                        name_tour_atp name_tour_csv    round_flash round_csv  \\\n",
       "0  ATP World Tour Masters 1000 Madrid        Madrid  1/32-finals\\n       R64   \n",
       "1    ATP World Tour Masters 1000 Rome          Rome  1/32-finals\\n       R64   \n",
       "2                          Nottingham    Nottingham  1/16-finals\\n       R32   \n",
       "3                          Nottingham    Nottingham  1/32-finals\\n       R64   \n",
       "4                             Atlanta       Atlanta  1/16-finals\\n       R32   \n",
       "\n",
       "   qualif         ...          return_rating_20years  \\\n",
       "0   False         ...                       1.631293   \n",
       "1   False         ...                     -27.924819   \n",
       "2   False         ...                     -12.771137   \n",
       "3   False         ...                     -20.714286   \n",
       "4   False         ...                     -11.217407   \n",
       "\n",
       "  first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                             2.059026             -2.284791   \n",
       "1                            -4.775000            -13.214855   \n",
       "2                            -2.217613             -4.413200   \n",
       "3                            -2.142857            -17.285714   \n",
       "4                             0.002116             -1.797011   \n",
       "\n",
       "  return_games_played_20years  service_point_won_20years  \\\n",
       "0                   -0.099016                  -5.118173   \n",
       "1                   -0.978261                 -15.291486   \n",
       "2                   -0.862751                  -2.844315   \n",
       "3                   -3.000000                  -1.000000   \n",
       "4                    0.557619                   5.830450   \n",
       "\n",
       "  return_point_won_20years total_point_won_20years winner_loser_match_win  \\\n",
       "0                 1.148676               -1.866266                    0.0   \n",
       "1                -4.848007               -8.636051                    0.0   \n",
       "2                -2.289802               -2.225344                    0.0   \n",
       "3                -2.571429               -1.857143                    0.0   \n",
       "4                -2.368942                1.484709                    0.0   \n",
       "\n",
       "   h2h minutes_loser_winner  \n",
       "0  1.0            -0.051257  \n",
       "1  1.0             0.487892  \n",
       "2  1.0            -0.000953  \n",
       "3  1.0             0.654459  \n",
       "4  1.0            -0.461002  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_dataset_sql()\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url_match_flash</th>\n",
       "      <th>url_match_atp</th>\n",
       "      <th>url_tour</th>\n",
       "      <th>name_tour_flash</th>\n",
       "      <th>name_tour_atp</th>\n",
       "      <th>name_tour_csv</th>\n",
       "      <th>round_flash</th>\n",
       "      <th>round_csv</th>\n",
       "      <th>qualif</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32463</td>\n",
       "      <td>https://www.flashscore.com/match/rNY1JN1J</td>\n",
       "      <td>/en/scores/archive/memphis/424/2017/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>6.002359</td>\n",
       "      <td>-0.957381</td>\n",
       "      <td>5.242706</td>\n",
       "      <td>-0.004762</td>\n",
       "      <td>1.178377</td>\n",
       "      <td>-0.043485</td>\n",
       "      <td>0.377143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.276590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31626</td>\n",
       "      <td>https://www.flashscore.com/match/6ooVSMmN</td>\n",
       "      <td>/en/scores/archive/marrakech/360/2017/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Marrakech</td>\n",
       "      <td>Marrakech</td>\n",
       "      <td>Marrakech</td>\n",
       "      <td>Semi-finals\\n</td>\n",
       "      <td>SF</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.844612</td>\n",
       "      <td>-1.403509</td>\n",
       "      <td>1.253968</td>\n",
       "      <td>0.340852</td>\n",
       "      <td>-0.273183</td>\n",
       "      <td>-2.252924</td>\n",
       "      <td>-1.174603</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.069335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31714</td>\n",
       "      <td>https://www.flashscore.com/match/pEJHSi7R</td>\n",
       "      <td>/en/scores/archive/marseille/496/2017/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>1/8-finals\\n</td>\n",
       "      <td>R16</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.197581</td>\n",
       "      <td>-2.246334</td>\n",
       "      <td>-9.619048</td>\n",
       "      <td>-0.412547</td>\n",
       "      <td>-10.933107</td>\n",
       "      <td>-2.142555</td>\n",
       "      <td>-6.141421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.069351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31702</td>\n",
       "      <td>https://www.flashscore.com/match/hOjYk3zo</td>\n",
       "      <td>/en/scores/archive/marseille/496/2017/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Final\\n</td>\n",
       "      <td>F</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.615572</td>\n",
       "      <td>-3.607057</td>\n",
       "      <td>-3.547220</td>\n",
       "      <td>0.161451</td>\n",
       "      <td>-5.439763</td>\n",
       "      <td>-1.780724</td>\n",
       "      <td>-3.190885</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31703</td>\n",
       "      <td>https://www.flashscore.com/match/pjFsndOk</td>\n",
       "      <td>/en/scores/archive/marseille/496/2017/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Marseille</td>\n",
       "      <td>Semi-finals\\n</td>\n",
       "      <td>SF</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>20.109863</td>\n",
       "      <td>4.511465</td>\n",
       "      <td>5.104797</td>\n",
       "      <td>-1.269387</td>\n",
       "      <td>3.497913</td>\n",
       "      <td>4.027860</td>\n",
       "      <td>3.203217</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.074847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                            url_match_flash  \\\n",
       "0  32463  https://www.flashscore.com/match/rNY1JN1J   \n",
       "1  31626  https://www.flashscore.com/match/6ooVSMmN   \n",
       "2  31714  https://www.flashscore.com/match/pEJHSi7R   \n",
       "3  31702  https://www.flashscore.com/match/hOjYk3zo   \n",
       "4  31703  https://www.flashscore.com/match/pjFsndOk   \n",
       "\n",
       "                                   url_match_atp  \\\n",
       "0    /en/scores/archive/memphis/424/2017/results   \n",
       "1  /en/scores/archive/marrakech/360/2017/results   \n",
       "2  /en/scores/archive/marseille/496/2017/results   \n",
       "3  /en/scores/archive/marseille/496/2017/results   \n",
       "4  /en/scores/archive/marseille/496/2017/results   \n",
       "\n",
       "                                            url_tour name_tour_flash  \\\n",
       "0  https://www.flashscore.com/tennis/atp-singles/...         Memphis   \n",
       "1  https://www.flashscore.com/tennis/atp-singles/...       Marrakech   \n",
       "2  https://www.flashscore.com/tennis/atp-singles/...       Marseille   \n",
       "3  https://www.flashscore.com/tennis/atp-singles/...       Marseille   \n",
       "4  https://www.flashscore.com/tennis/atp-singles/...       Marseille   \n",
       "\n",
       "  name_tour_atp name_tour_csv    round_flash round_csv  qualif  \\\n",
       "0       Memphis       Memphis  1/16-finals\\n       R32   False   \n",
       "1     Marrakech     Marrakech  Semi-finals\\n        SF   False   \n",
       "2     Marseille     Marseille   1/8-finals\\n       R16   False   \n",
       "3     Marseille     Marseille        Final\\n         F   False   \n",
       "4     Marseille     Marseille  Semi-finals\\n        SF   False   \n",
       "\n",
       "          ...          return_rating_20years  \\\n",
       "0         ...                       6.002359   \n",
       "1         ...                      -7.844612   \n",
       "2         ...                     -20.197581   \n",
       "3         ...                      -9.615572   \n",
       "4         ...                      20.109863   \n",
       "\n",
       "  first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                            -0.957381              5.242706   \n",
       "1                            -1.403509              1.253968   \n",
       "2                            -2.246334             -9.619048   \n",
       "3                            -3.607057             -3.547220   \n",
       "4                             4.511465              5.104797   \n",
       "\n",
       "  return_games_played_20years  service_point_won_20years  \\\n",
       "0                   -0.004762                   1.178377   \n",
       "1                    0.340852                  -0.273183   \n",
       "2                   -0.412547                 -10.933107   \n",
       "3                    0.161451                  -5.439763   \n",
       "4                   -1.269387                   3.497913   \n",
       "\n",
       "  return_point_won_20years total_point_won_20years winner_loser_match_win  \\\n",
       "0                -0.043485                0.377143                    1.0   \n",
       "1                -2.252924               -1.174603                    1.0   \n",
       "2                -2.142555               -6.141421                    0.0   \n",
       "3                -1.780724               -3.190885                    2.0   \n",
       "4                 4.027860                3.203217                    1.0   \n",
       "\n",
       "   h2h minutes_loser_winner  \n",
       "0  0.0            -0.276590  \n",
       "1  0.5             0.069335  \n",
       "2  1.0             0.069351  \n",
       "3  0.0                  NaN  \n",
       "4  0.6             0.074847  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predset = load_predictset_sql()\n",
    "predset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3761, 91)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34148, 91)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34148 entries, 0 to 34147\n",
      "Data columns (total 91 columns):\n",
      "id                                      34148 non-null int64\n",
      "url_match_flash                         34148 non-null object\n",
      "url_match_atp                           34148 non-null object\n",
      "url_tour                                34148 non-null object\n",
      "name_tour_flash                         34148 non-null object\n",
      "name_tour_atp                           34148 non-null object\n",
      "name_tour_csv                           34148 non-null object\n",
      "round_flash                             34148 non-null object\n",
      "round_csv                               34148 non-null object\n",
      "qualif                                  34148 non-null bool\n",
      "door                                    34148 non-null object\n",
      "surface                                 34148 non-null object\n",
      "prize                                   34148 non-null int64\n",
      "year_atp                                34148 non-null object\n",
      "year_flash                              34148 non-null int64\n",
      "date_flash                              34148 non-null datetime64[ns, UTC]\n",
      "date_csv                                34148 non-null object\n",
      "date_atp                                34148 non-null datetime64[ns, UTC]\n",
      "loc_flash                               34148 non-null object\n",
      "loc_atp                                 34148 non-null object\n",
      "name_win_atp                            34148 non-null object\n",
      "name_win_csv                            34148 non-null object\n",
      "name_los_atp                            34148 non-null object\n",
      "name_los_csv                            34148 non-null object\n",
      "url_win_atp                             34148 non-null object\n",
      "url_los_atp                             34148 non-null object\n",
      "hand_win                                34148 non-null object\n",
      "hand_los                                34148 non-null object\n",
      "draw_size                               34148 non-null object\n",
      "level_tour                              34148 non-null object\n",
      "seed_win                                34148 non-null object\n",
      "entry_win                               34148 non-null object\n",
      "seed_los                                34148 non-null object\n",
      "entry_los                               34148 non-null object\n",
      "ht_win                                  34148 non-null object\n",
      "ht_win                                  34148 non-null object\n",
      "points_win                              34148 non-null int64\n",
      "points_los                              34148 non-null int64\n",
      "age_win                                 34148 non-null float64\n",
      "age_los                                 34148 non-null float64\n",
      "score                                   34148 non-null object\n",
      "url_home                                34148 non-null object\n",
      "name_home                               34148 non-null object\n",
      "birthday_home                           31715 non-null datetime64[ns, UTC]\n",
      "country_home                            33409 non-null object\n",
      "url_away                                34148 non-null object\n",
      "name_away                               34148 non-null object\n",
      "birthday_away                           31184 non-null datetime64[ns, UTC]\n",
      "country_away                            33392 non-null object\n",
      "url_weather                             34148 non-null object\n",
      "loc_weather                             34148 non-null object\n",
      "temp_weather                            26611 non-null float64\n",
      "press_weather                           25347 non-null float64\n",
      "humid_weather                           26600 non-null float64\n",
      "uncertainty_1year                       33667 non-null float64\n",
      "count_match_1year                       33667 non-null float64\n",
      "serve_rating_1year                      33667 non-null float64\n",
      "aces_1year                              33667 non-null float64\n",
      "double_faults_1year                     33667 non-null float64\n",
      "first_serve_1year                       33667 non-null float64\n",
      "first_serve_point_won_1year             33667 non-null float64\n",
      "second_serve_point_won_1year            33667 non-null float64\n",
      "bp_saved_1year                          33667 non-null float64\n",
      "service_game_played_1year               33667 non-null float64\n",
      "return_rating_1year                     33667 non-null float64\n",
      "first_serve_return_point_won_1year      33667 non-null float64\n",
      "bp_converted_1year                      33667 non-null float64\n",
      "return_games_played_1year               33667 non-null float64\n",
      "service_point_won_1year                 33667 non-null float64\n",
      "return_point_won_1year                  33667 non-null float64\n",
      "total_point_won_1year                   33667 non-null float64\n",
      "uncertainty_20years                     33865 non-null float64\n",
      "count_match_20years                     33865 non-null float64\n",
      "serve_rating_20years                    33865 non-null float64\n",
      "aces_20years                            33865 non-null float64\n",
      "double_faults_20years                   33865 non-null float64\n",
      "first_serve_20years                     33865 non-null float64\n",
      "first_serve_point_won_20years           33865 non-null float64\n",
      "second_serve_point_won_20years          33865 non-null float64\n",
      "bp_saved_20years                        33865 non-null float64\n",
      "service_game_played_20years             33865 non-null float64\n",
      "return_rating_20years                   33865 non-null float64\n",
      "first_serve_return_point_won_20years    33865 non-null float64\n",
      "bp_converted_20years                    33865 non-null float64\n",
      "return_games_played_20years             33865 non-null float64\n",
      "service_point_won_20years               33865 non-null float64\n",
      "return_point_won_20years                33865 non-null float64\n",
      "total_point_won_20years                 33865 non-null float64\n",
      "winner_loser_match_win                  34148 non-null float64\n",
      "h2h                                     34148 non-null float64\n",
      "minutes_loser_winner                    30297 non-null float64\n",
      "dtypes: bool(1), datetime64[ns, UTC](4), float64(42), int64(5), object(39)\n",
      "memory usage: 23.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make this notebook's output identical at every run\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split the training set and the test set into 2 batchs with the hash id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash=hashlib.md5):\n",
    "    return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(dataset, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27148, 91)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url_match_flash</th>\n",
       "      <th>url_match_atp</th>\n",
       "      <th>url_tour</th>\n",
       "      <th>name_tour_flash</th>\n",
       "      <th>name_tour_atp</th>\n",
       "      <th>name_tour_csv</th>\n",
       "      <th>round_flash</th>\n",
       "      <th>round_csv</th>\n",
       "      <th>qualif</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30648</td>\n",
       "      <td>https://www.flashscore.com/match/zFScDpJb</td>\n",
       "      <td>/en/scores/archive/madrid/1536/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>ATP World Tour Masters 1000 Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1.631293</td>\n",
       "      <td>2.059026</td>\n",
       "      <td>-2.284791</td>\n",
       "      <td>-0.099016</td>\n",
       "      <td>-5.118173</td>\n",
       "      <td>1.148676</td>\n",
       "      <td>-1.866266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.051257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40048</td>\n",
       "      <td>https://www.flashscore.com/match/G00yITY5</td>\n",
       "      <td>/en/scores/archive/nottingham/741/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.771137</td>\n",
       "      <td>-2.217613</td>\n",
       "      <td>-4.413200</td>\n",
       "      <td>-0.862751</td>\n",
       "      <td>-2.844315</td>\n",
       "      <td>-2.289802</td>\n",
       "      <td>-2.225344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40071</td>\n",
       "      <td>https://www.flashscore.com/match/MPsuaWHS</td>\n",
       "      <td>/en/scores/archive/nottingham/741/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>Nottingham</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.714286</td>\n",
       "      <td>-2.142857</td>\n",
       "      <td>-17.285714</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-2.571429</td>\n",
       "      <td>-1.857143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.654459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>905</td>\n",
       "      <td>https://www.flashscore.com/match/8xr1BrDj</td>\n",
       "      <td>/en/scores/archive/atlanta/6116/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.217407</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>-1.797011</td>\n",
       "      <td>0.557619</td>\n",
       "      <td>5.830450</td>\n",
       "      <td>-2.368942</td>\n",
       "      <td>1.484709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.461002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57660</td>\n",
       "      <td>https://www.flashscore.com/match/drhJu0R2</td>\n",
       "      <td>/en/scores/archive/winston-salem/6242/2016/res...</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.155651</td>\n",
       "      <td>-3.991762</td>\n",
       "      <td>-6.441762</td>\n",
       "      <td>0.786303</td>\n",
       "      <td>-0.790709</td>\n",
       "      <td>-4.116092</td>\n",
       "      <td>-2.654502</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.270004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                            url_match_flash  \\\n",
       "0  30648  https://www.flashscore.com/match/zFScDpJb   \n",
       "2  40048  https://www.flashscore.com/match/G00yITY5   \n",
       "3  40071  https://www.flashscore.com/match/MPsuaWHS   \n",
       "4    905  https://www.flashscore.com/match/8xr1BrDj   \n",
       "6  57660  https://www.flashscore.com/match/drhJu0R2   \n",
       "\n",
       "                                       url_match_atp  \\\n",
       "0        /en/scores/archive/madrid/1536/2016/results   \n",
       "2     /en/scores/archive/nottingham/741/2016/results   \n",
       "3     /en/scores/archive/nottingham/741/2016/results   \n",
       "4       /en/scores/archive/atlanta/6116/2016/results   \n",
       "6  /en/scores/archive/winston-salem/6242/2016/res...   \n",
       "\n",
       "                                            url_tour name_tour_flash  \\\n",
       "0  https://www.flashscore.com/tennis/atp-singles/...          Madrid   \n",
       "2  https://www.flashscore.com/tennis/atp-singles/...      Nottingham   \n",
       "3  https://www.flashscore.com/tennis/atp-singles/...      Nottingham   \n",
       "4  https://www.flashscore.com/tennis/atp-singles/...         Atlanta   \n",
       "6  https://www.flashscore.com/tennis/atp-singles/...   Winston-Salem   \n",
       "\n",
       "                        name_tour_atp  name_tour_csv    round_flash round_csv  \\\n",
       "0  ATP World Tour Masters 1000 Madrid         Madrid  1/32-finals\\n       R64   \n",
       "2                          Nottingham     Nottingham  1/16-finals\\n       R32   \n",
       "3                          Nottingham     Nottingham  1/32-finals\\n       R64   \n",
       "4                             Atlanta        Atlanta  1/16-finals\\n       R32   \n",
       "6                       Winston-Salem  Winston-Salem  1/16-finals\\n       R32   \n",
       "\n",
       "   qualif         ...          return_rating_20years  \\\n",
       "0   False         ...                       1.631293   \n",
       "2   False         ...                     -12.771137   \n",
       "3   False         ...                     -20.714286   \n",
       "4   False         ...                     -11.217407   \n",
       "6   False         ...                     -24.155651   \n",
       "\n",
       "  first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                             2.059026             -2.284791   \n",
       "2                            -2.217613             -4.413200   \n",
       "3                            -2.142857            -17.285714   \n",
       "4                             0.002116             -1.797011   \n",
       "6                            -3.991762             -6.441762   \n",
       "\n",
       "  return_games_played_20years  service_point_won_20years  \\\n",
       "0                   -0.099016                  -5.118173   \n",
       "2                   -0.862751                  -2.844315   \n",
       "3                   -3.000000                  -1.000000   \n",
       "4                    0.557619                   5.830450   \n",
       "6                    0.786303                  -0.790709   \n",
       "\n",
       "  return_point_won_20years total_point_won_20years winner_loser_match_win  \\\n",
       "0                 1.148676               -1.866266                    0.0   \n",
       "2                -2.289802               -2.225344                    0.0   \n",
       "3                -2.571429               -1.857143                    0.0   \n",
       "4                -2.368942                1.484709                    0.0   \n",
       "6                -4.116092               -2.654502                    2.0   \n",
       "\n",
       "   h2h minutes_loser_winner  \n",
       "0  1.0            -0.051257  \n",
       "2  1.0            -0.000953  \n",
       "3  1.0             0.654459  \n",
       "4  1.0            -0.461002  \n",
       "6  0.2             0.270004  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_set.shape)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7000, 91)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url_match_flash</th>\n",
       "      <th>url_match_atp</th>\n",
       "      <th>url_tour</th>\n",
       "      <th>name_tour_flash</th>\n",
       "      <th>name_tour_atp</th>\n",
       "      <th>name_tour_csv</th>\n",
       "      <th>round_flash</th>\n",
       "      <th>round_csv</th>\n",
       "      <th>qualif</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42244</td>\n",
       "      <td>https://www.flashscore.com/match/jcZcIiNh</td>\n",
       "      <td>/en/scores/archive/rome/416/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Rome</td>\n",
       "      <td>ATP World Tour Masters 1000 Rome</td>\n",
       "      <td>Rome</td>\n",
       "      <td>1/32-finals\\n</td>\n",
       "      <td>R64</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.924819</td>\n",
       "      <td>-4.775000</td>\n",
       "      <td>-13.214855</td>\n",
       "      <td>-0.978261</td>\n",
       "      <td>-15.291486</td>\n",
       "      <td>-4.848007</td>\n",
       "      <td>-8.636051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.487892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11572</td>\n",
       "      <td>https://www.flashscore.com/match/tjWesBmN</td>\n",
       "      <td>/en/scores/archive/cincinnati/422/2016/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>ATP World Tour Masters 1000 Cincinnati</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Semi-finals\\n</td>\n",
       "      <td>SF</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.138750</td>\n",
       "      <td>-7.388578</td>\n",
       "      <td>-5.345927</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>6.118737</td>\n",
       "      <td>-8.897202</td>\n",
       "      <td>-1.424943</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>-0.064994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16168</td>\n",
       "      <td>https://www.flashscore.com/match/SCeQru63</td>\n",
       "      <td>/en/scores/archive/doha/451/2008/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Semi-finals\\n</td>\n",
       "      <td>SF</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.518324</td>\n",
       "      <td>-3.023477</td>\n",
       "      <td>-3.507222</td>\n",
       "      <td>-0.570255</td>\n",
       "      <td>-2.045622</td>\n",
       "      <td>-3.413699</td>\n",
       "      <td>-2.469572</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.316686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6713</td>\n",
       "      <td>https://www.flashscore.com/match/SfK5pURK</td>\n",
       "      <td>/en/scores/archive/bastad/316/2013/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Bastad</td>\n",
       "      <td>Bastad</td>\n",
       "      <td>Bastad</td>\n",
       "      <td>1/16-finals\\n</td>\n",
       "      <td>R32</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>40.425170</td>\n",
       "      <td>7.717347</td>\n",
       "      <td>6.785374</td>\n",
       "      <td>-1.143197</td>\n",
       "      <td>-2.169388</td>\n",
       "      <td>8.471769</td>\n",
       "      <td>2.895238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15056</td>\n",
       "      <td>https://www.flashscore.com/match/vg2Z57xB</td>\n",
       "      <td>/en/scores/archive/delray-beach/499/2015/results</td>\n",
       "      <td>https://www.flashscore.com/tennis/atp-singles/...</td>\n",
       "      <td>Delray Beach</td>\n",
       "      <td>Delray Beach</td>\n",
       "      <td>Delray Beach</td>\n",
       "      <td>Quarter-finals\\n</td>\n",
       "      <td>QF</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>8.973000</td>\n",
       "      <td>2.178111</td>\n",
       "      <td>-1.823889</td>\n",
       "      <td>-1.868222</td>\n",
       "      <td>-6.641889</td>\n",
       "      <td>3.478333</td>\n",
       "      <td>-2.047778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.338504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                            url_match_flash  \\\n",
       "1   42244  https://www.flashscore.com/match/jcZcIiNh   \n",
       "5   11572  https://www.flashscore.com/match/tjWesBmN   \n",
       "15  16168  https://www.flashscore.com/match/SCeQru63   \n",
       "20   6713  https://www.flashscore.com/match/SfK5pURK   \n",
       "21  15056  https://www.flashscore.com/match/vg2Z57xB   \n",
       "\n",
       "                                       url_match_atp  \\\n",
       "1           /en/scores/archive/rome/416/2016/results   \n",
       "5     /en/scores/archive/cincinnati/422/2016/results   \n",
       "15          /en/scores/archive/doha/451/2008/results   \n",
       "20        /en/scores/archive/bastad/316/2013/results   \n",
       "21  /en/scores/archive/delray-beach/499/2015/results   \n",
       "\n",
       "                                             url_tour name_tour_flash  \\\n",
       "1   https://www.flashscore.com/tennis/atp-singles/...            Rome   \n",
       "5   https://www.flashscore.com/tennis/atp-singles/...      Cincinnati   \n",
       "15  https://www.flashscore.com/tennis/atp-singles/...            Doha   \n",
       "20  https://www.flashscore.com/tennis/atp-singles/...          Bastad   \n",
       "21  https://www.flashscore.com/tennis/atp-singles/...    Delray Beach   \n",
       "\n",
       "                             name_tour_atp name_tour_csv       round_flash  \\\n",
       "1         ATP World Tour Masters 1000 Rome          Rome     1/32-finals\\n   \n",
       "5   ATP World Tour Masters 1000 Cincinnati    Cincinnati     Semi-finals\\n   \n",
       "15                                    Doha          Doha     Semi-finals\\n   \n",
       "20                                  Bastad        Bastad     1/16-finals\\n   \n",
       "21                            Delray Beach  Delray Beach  Quarter-finals\\n   \n",
       "\n",
       "   round_csv  qualif         ...          return_rating_20years  \\\n",
       "1        R64   False         ...                     -27.924819   \n",
       "5         SF   False         ...                     -41.138750   \n",
       "15        SF   False         ...                     -17.518324   \n",
       "20       R32   False         ...                      40.425170   \n",
       "21        QF   False         ...                       8.973000   \n",
       "\n",
       "   first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "1                             -4.775000            -13.214855   \n",
       "5                             -7.388578             -5.345927   \n",
       "15                            -3.023477             -3.507222   \n",
       "20                             7.717347              6.785374   \n",
       "21                             2.178111             -1.823889   \n",
       "\n",
       "   return_games_played_20years  service_point_won_20years  \\\n",
       "1                    -0.978261                 -15.291486   \n",
       "5                     0.495614                   6.118737   \n",
       "15                   -0.570255                  -2.045622   \n",
       "20                   -1.143197                  -2.169388   \n",
       "21                   -1.868222                  -6.641889   \n",
       "\n",
       "   return_point_won_20years total_point_won_20years winner_loser_match_win  \\\n",
       "1                 -4.848007               -8.636051                    0.0   \n",
       "5                 -8.897202               -1.424943                    3.0   \n",
       "15                -3.413699               -2.469572                    3.0   \n",
       "20                 8.471769                2.895238                    0.0   \n",
       "21                 3.478333               -2.047778                    0.0   \n",
       "\n",
       "         h2h minutes_loser_winner  \n",
       "1   1.000000             0.487892  \n",
       "5   0.454545            -0.064994  \n",
       "15  0.250000             0.316686  \n",
       "20  1.000000                  NaN  \n",
       "21  1.000000             0.338504  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_set.shape)\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32     9754\n",
       "128    4758\n",
       "28     3155\n",
       "56     3120\n",
       "48     2549\n",
       "96     1990\n",
       "64     1737\n",
       "8        74\n",
       "16       11\n",
       "Name: draw_size, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"draw_size\"] = dataset[\"draw_size\"].apply(lambda x: str(x))\n",
    "dataset[\"draw_size\"] = dataset[\"draw_size\"].astype(\"object\")\n",
    "index_draw_size = list(dataset[\"draw_size\"].value_counts().index)\n",
    "dataset[\"draw_size\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    16240\n",
       "M     6233\n",
       "G     4590\n",
       "F       85\n",
       "Name: level_tour, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_level_tour = list(dataset[\"level_tour\"].value_counts().index)\n",
    "dataset[\"level_tour\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hard      15239\n",
       "Clay       8119\n",
       "Grass      3231\n",
       "Carpet      559\n",
       "Name: surface, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_surface = list(dataset[\"surface\"].value_counts().index)\n",
    "dataset[\"surface\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outdoor    22498\n",
       "Indoor      4650\n",
       "Name: door, dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_door = list(dataset[\"door\"].value_counts().index)\n",
    "dataset[\"door\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R32     9529\n",
       "R16     5100\n",
       "R64     4937\n",
       "R128    3039\n",
       "QF      2552\n",
       "SF      1293\n",
       "F        627\n",
       "RR        71\n",
       "Name: round_csv, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_round_csv = list(dataset[\"round_csv\"].value_counts().index)\n",
    "dataset[\"round_csv\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    1906\n",
       "2009    1826\n",
       "2014    1798\n",
       "2010    1792\n",
       "2006    1785\n",
       "2011    1774\n",
       "2005    1764\n",
       "2013    1745\n",
       "2012    1727\n",
       "2004    1727\n",
       "2008    1719\n",
       "2007    1705\n",
       "2003    1658\n",
       "2001    1590\n",
       "2002    1572\n",
       "2015    1060\n",
       "Name: year_atp, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"year_atp\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### add column result that will be the y axis on the dataset for the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 91)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.insert(dataset.shape[1], \"result\", np.random.randint(2, size=dataset.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    13585\n",
       "0    13563\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"result\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.insert(test_set.shape[1], \"result\", np.random.randint(2, size=test_set.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3535\n",
       "0    3465\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set[\"result\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# A class to select numerical or categorical columns \n",
    "# since Scikit-Learn doesn't handle DataFrames yet\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "class reverseStats(BaseEstimator, TransformerMixin):\n",
    "       \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        ## while on each row : if result = 1 nothing else opponent each value in each collumn\n",
    "        def wh(a):\n",
    "            if (a[\"result\"] == 0):\n",
    "                a = a.apply(lambda x: x * -1)\n",
    "            return a\n",
    "        \n",
    "        X = X.fillna(0)\n",
    "        X = X.apply(wh, axis=1)\n",
    "        X = X.drop(\"result\", axis=1)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_col = [\"serve_rating_1year\", \"aces_1year\", \"double_faults_1year\", \"first_serve_1year\", \"first_serve_point_won_1year\",\n",
    "       \"second_serve_point_won_1year\", \"bp_saved_1year\", \"service_game_played_1year\", \"return_rating_1year\",\n",
    "       \"first_serve_return_point_won_1year\", \"bp_converted_1year\", \"return_games_played_1year\", \"service_point_won_1year\",\n",
    "       \"return_point_won_1year\", \"total_point_won_1year\", \"serve_rating_20years\",\n",
    "       \"aces_20years\", \"double_faults_20years\", \"first_serve_20years\", \"first_serve_point_won_20years\", \"second_serve_point_won_20years\",\n",
    "       \"bp_saved_20years\", \"service_game_played_20years\", \"return_rating_20years\", \"first_serve_return_point_won_20years\",\n",
    "       \"bp_converted_20years\", \"return_games_played_20years\", \"service_point_won_20years\", \"return_point_won_20years\",\n",
    "       \"total_point_won_20years\", \"h2h\", \"minutes_loser_winner\", \"result\"]\n",
    "len(stats_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 32)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "stats_pipeline = Pipeline([\n",
    "        (\"select_stats\", DataFrameSelector(stats_col)),\n",
    "        (\"reverse\", reverseStats()),\n",
    "    ])\n",
    "\n",
    "stats_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "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>serve_rating_1year</th>\n",
       "      <th>aces_1year</th>\n",
       "      <th>double_faults_1year</th>\n",
       "      <th>first_serve_1year</th>\n",
       "      <th>first_serve_point_won_1year</th>\n",
       "      <th>second_serve_point_won_1year</th>\n",
       "      <th>bp_saved_1year</th>\n",
       "      <th>service_game_played_1year</th>\n",
       "      <th>return_rating_1year</th>\n",
       "      <th>first_serve_return_point_won_1year</th>\n",
       "      <th>...</th>\n",
       "      <th>service_game_played_20years</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.680874</td>\n",
       "      <td>-1.353279</td>\n",
       "      <td>-0.859836</td>\n",
       "      <td>8.912022</td>\n",
       "      <td>-0.228962</td>\n",
       "      <td>4.176776</td>\n",
       "      <td>-0.974317</td>\n",
       "      <td>0.684973</td>\n",
       "      <td>-6.111202</td>\n",
       "      <td>-2.769672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037864</td>\n",
       "      <td>-1.631293</td>\n",
       "      <td>-2.059026</td>\n",
       "      <td>2.284791</td>\n",
       "      <td>0.099016</td>\n",
       "      <td>5.118173</td>\n",
       "      <td>-1.148676</td>\n",
       "      <td>1.866266</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.051257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.668889</td>\n",
       "      <td>-1.328148</td>\n",
       "      <td>0.091852</td>\n",
       "      <td>-2.805926</td>\n",
       "      <td>-0.319630</td>\n",
       "      <td>-1.348519</td>\n",
       "      <td>-1.214815</td>\n",
       "      <td>-1.078889</td>\n",
       "      <td>-6.271481</td>\n",
       "      <td>-1.893704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891805</td>\n",
       "      <td>-12.771137</td>\n",
       "      <td>-2.217613</td>\n",
       "      <td>-4.413200</td>\n",
       "      <td>-0.862751</td>\n",
       "      <td>-2.844315</td>\n",
       "      <td>-2.289802</td>\n",
       "      <td>-2.225344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>20.714286</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>17.285714</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.654459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-17.928571</td>\n",
       "      <td>-5.785714</td>\n",
       "      <td>-0.428571</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>-8.142857</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>-4.071429</td>\n",
       "      <td>-2.071429</td>\n",
       "      <td>13.285714</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760106</td>\n",
       "      <td>11.217407</td>\n",
       "      <td>-0.002116</td>\n",
       "      <td>1.797011</td>\n",
       "      <td>-0.557619</td>\n",
       "      <td>-5.830450</td>\n",
       "      <td>2.368942</td>\n",
       "      <td>-1.484709</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.461002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.351282</td>\n",
       "      <td>-5.203846</td>\n",
       "      <td>-0.489744</td>\n",
       "      <td>7.125321</td>\n",
       "      <td>-1.542949</td>\n",
       "      <td>6.682372</td>\n",
       "      <td>8.243269</td>\n",
       "      <td>-0.016987</td>\n",
       "      <td>16.787179</td>\n",
       "      <td>2.906731</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334195</td>\n",
       "      <td>24.155651</td>\n",
       "      <td>3.991762</td>\n",
       "      <td>6.441762</td>\n",
       "      <td>-0.786303</td>\n",
       "      <td>0.790709</td>\n",
       "      <td>4.116092</td>\n",
       "      <td>2.654502</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.270004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-18.353409</td>\n",
       "      <td>-4.653409</td>\n",
       "      <td>-0.875758</td>\n",
       "      <td>2.303788</td>\n",
       "      <td>-7.685985</td>\n",
       "      <td>0.936742</td>\n",
       "      <td>2.662879</td>\n",
       "      <td>-1.681818</td>\n",
       "      <td>6.138258</td>\n",
       "      <td>1.996591</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.380606</td>\n",
       "      <td>8.924113</td>\n",
       "      <td>2.322814</td>\n",
       "      <td>-1.377749</td>\n",
       "      <td>-1.212381</td>\n",
       "      <td>-2.773853</td>\n",
       "      <td>2.779481</td>\n",
       "      <td>-0.078052</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70.226190</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>-1.130952</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>23.380952</td>\n",
       "      <td>13.190476</td>\n",
       "      <td>13.833333</td>\n",
       "      <td>1.797619</td>\n",
       "      <td>68.202381</td>\n",
       "      <td>11.892857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.658095</td>\n",
       "      <td>42.002381</td>\n",
       "      <td>7.269762</td>\n",
       "      <td>14.875952</td>\n",
       "      <td>0.642381</td>\n",
       "      <td>11.011429</td>\n",
       "      <td>6.719048</td>\n",
       "      <td>8.235476</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.988188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.619211</td>\n",
       "      <td>0.216533</td>\n",
       "      <td>0.910997</td>\n",
       "      <td>5.292173</td>\n",
       "      <td>1.937366</td>\n",
       "      <td>2.542619</td>\n",
       "      <td>3.370908</td>\n",
       "      <td>-0.091845</td>\n",
       "      <td>15.568988</td>\n",
       "      <td>4.435193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.268349</td>\n",
       "      <td>14.370963</td>\n",
       "      <td>3.861322</td>\n",
       "      <td>0.921584</td>\n",
       "      <td>0.439661</td>\n",
       "      <td>3.069863</td>\n",
       "      <td>3.633980</td>\n",
       "      <td>2.877679</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.276389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-28.925223</td>\n",
       "      <td>-4.390253</td>\n",
       "      <td>2.524330</td>\n",
       "      <td>0.473437</td>\n",
       "      <td>-8.280506</td>\n",
       "      <td>-3.186012</td>\n",
       "      <td>-7.716369</td>\n",
       "      <td>-1.606324</td>\n",
       "      <td>5.387798</td>\n",
       "      <td>6.242336</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.017434</td>\n",
       "      <td>2.654034</td>\n",
       "      <td>3.187368</td>\n",
       "      <td>-3.777288</td>\n",
       "      <td>-2.050648</td>\n",
       "      <td>-6.983664</td>\n",
       "      <td>1.720767</td>\n",
       "      <td>-2.407857</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.833793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.760000</td>\n",
       "      <td>1.307415</td>\n",
       "      <td>-0.039048</td>\n",
       "      <td>-1.938231</td>\n",
       "      <td>-1.163401</td>\n",
       "      <td>-2.767347</td>\n",
       "      <td>1.696871</td>\n",
       "      <td>0.855238</td>\n",
       "      <td>-24.072517</td>\n",
       "      <td>-2.518912</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695082</td>\n",
       "      <td>-17.435662</td>\n",
       "      <td>-1.833359</td>\n",
       "      <td>-5.655868</td>\n",
       "      <td>0.719789</td>\n",
       "      <td>-1.955712</td>\n",
       "      <td>-2.947333</td>\n",
       "      <td>-2.154163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.866020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.183059</td>\n",
       "      <td>-6.966392</td>\n",
       "      <td>-2.358852</td>\n",
       "      <td>8.609417</td>\n",
       "      <td>-3.659661</td>\n",
       "      <td>8.769414</td>\n",
       "      <td>-7.880327</td>\n",
       "      <td>-1.177961</td>\n",
       "      <td>60.143971</td>\n",
       "      <td>10.854823</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.181322</td>\n",
       "      <td>59.924116</td>\n",
       "      <td>12.098007</td>\n",
       "      <td>12.726353</td>\n",
       "      <td>-1.500216</td>\n",
       "      <td>0.895951</td>\n",
       "      <td>12.855204</td>\n",
       "      <td>6.047258</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.826643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-15.372953</td>\n",
       "      <td>0.937427</td>\n",
       "      <td>-0.627339</td>\n",
       "      <td>-4.621053</td>\n",
       "      <td>-3.463158</td>\n",
       "      <td>-3.182164</td>\n",
       "      <td>-5.368567</td>\n",
       "      <td>1.063596</td>\n",
       "      <td>-30.667690</td>\n",
       "      <td>-6.365789</td>\n",
       "      <td>...</td>\n",
       "      <td>1.308291</td>\n",
       "      <td>-25.285201</td>\n",
       "      <td>-6.110167</td>\n",
       "      <td>-6.432567</td>\n",
       "      <td>1.131968</td>\n",
       "      <td>-1.232124</td>\n",
       "      <td>-4.869222</td>\n",
       "      <td>-2.768531</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.524411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.458225</td>\n",
       "      <td>-0.296050</td>\n",
       "      <td>0.237635</td>\n",
       "      <td>-3.945671</td>\n",
       "      <td>2.145996</td>\n",
       "      <td>2.669318</td>\n",
       "      <td>-1.399729</td>\n",
       "      <td>-0.385119</td>\n",
       "      <td>30.328084</td>\n",
       "      <td>5.997321</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.599471</td>\n",
       "      <td>27.540083</td>\n",
       "      <td>5.663394</td>\n",
       "      <td>5.387453</td>\n",
       "      <td>-0.629917</td>\n",
       "      <td>1.070907</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>2.989335</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.340478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>48.040292</td>\n",
       "      <td>0.343161</td>\n",
       "      <td>-2.199782</td>\n",
       "      <td>8.098888</td>\n",
       "      <td>8.354838</td>\n",
       "      <td>8.379346</td>\n",
       "      <td>1.881370</td>\n",
       "      <td>-0.666527</td>\n",
       "      <td>43.985568</td>\n",
       "      <td>7.936815</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.357638</td>\n",
       "      <td>42.849211</td>\n",
       "      <td>7.384862</td>\n",
       "      <td>10.514026</td>\n",
       "      <td>-0.264745</td>\n",
       "      <td>10.867629</td>\n",
       "      <td>7.906587</td>\n",
       "      <td>8.417840</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.440061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-8.989354</td>\n",
       "      <td>-1.820860</td>\n",
       "      <td>-0.403192</td>\n",
       "      <td>-0.158523</td>\n",
       "      <td>-2.315164</td>\n",
       "      <td>-0.375871</td>\n",
       "      <td>-0.249014</td>\n",
       "      <td>-0.573862</td>\n",
       "      <td>-1.164510</td>\n",
       "      <td>0.871784</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053983</td>\n",
       "      <td>-5.769948</td>\n",
       "      <td>0.070507</td>\n",
       "      <td>-2.054217</td>\n",
       "      <td>0.250268</td>\n",
       "      <td>-1.848756</td>\n",
       "      <td>-0.579861</td>\n",
       "      <td>-1.283191</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-0.108866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-18.981481</td>\n",
       "      <td>5.064815</td>\n",
       "      <td>0.981481</td>\n",
       "      <td>-3.296296</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>-14.629630</td>\n",
       "      <td>-12.518519</td>\n",
       "      <td>0.953704</td>\n",
       "      <td>-14.055556</td>\n",
       "      <td>-9.685185</td>\n",
       "      <td>...</td>\n",
       "      <td>1.346667</td>\n",
       "      <td>-7.763333</td>\n",
       "      <td>-8.060000</td>\n",
       "      <td>16.538333</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>-4.775000</td>\n",
       "      <td>-7.723333</td>\n",
       "      <td>-5.661667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.901450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-41.194203</td>\n",
       "      <td>-4.696377</td>\n",
       "      <td>-1.065217</td>\n",
       "      <td>-6.203623</td>\n",
       "      <td>-9.830435</td>\n",
       "      <td>-2.342754</td>\n",
       "      <td>-16.426812</td>\n",
       "      <td>-1.119565</td>\n",
       "      <td>-1.856522</td>\n",
       "      <td>-1.342754</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.119565</td>\n",
       "      <td>-1.856522</td>\n",
       "      <td>-1.342754</td>\n",
       "      <td>0.431884</td>\n",
       "      <td>-1.018841</td>\n",
       "      <td>-9.797101</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-4.345652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.917861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-7.166667</td>\n",
       "      <td>-0.273810</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>0.936508</td>\n",
       "      <td>-1.757937</td>\n",
       "      <td>-5.333333</td>\n",
       "      <td>6.837302</td>\n",
       "      <td>-3.035714</td>\n",
       "      <td>-43.638889</td>\n",
       "      <td>-7.261905</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.698718</td>\n",
       "      <td>-22.342735</td>\n",
       "      <td>-4.038034</td>\n",
       "      <td>-7.508547</td>\n",
       "      <td>-1.421795</td>\n",
       "      <td>-2.129915</td>\n",
       "      <td>-5.091453</td>\n",
       "      <td>-3.359402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-11.643411</td>\n",
       "      <td>-0.515504</td>\n",
       "      <td>0.957364</td>\n",
       "      <td>0.290698</td>\n",
       "      <td>-1.531008</td>\n",
       "      <td>-3.980620</td>\n",
       "      <td>-6.360465</td>\n",
       "      <td>0.344961</td>\n",
       "      <td>-7.930233</td>\n",
       "      <td>-1.930233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344961</td>\n",
       "      <td>-7.930233</td>\n",
       "      <td>-1.930233</td>\n",
       "      <td>-1.589147</td>\n",
       "      <td>0.472868</td>\n",
       "      <td>-3.003876</td>\n",
       "      <td>-2.310078</td>\n",
       "      <td>-2.193798</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.135211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-17.962963</td>\n",
       "      <td>0.169753</td>\n",
       "      <td>-0.922840</td>\n",
       "      <td>-7.986111</td>\n",
       "      <td>-2.825617</td>\n",
       "      <td>-2.270062</td>\n",
       "      <td>6.654321</td>\n",
       "      <td>-0.507716</td>\n",
       "      <td>-10.188272</td>\n",
       "      <td>-0.179012</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.507716</td>\n",
       "      <td>-10.188272</td>\n",
       "      <td>-0.179012</td>\n",
       "      <td>-6.567901</td>\n",
       "      <td>-0.632716</td>\n",
       "      <td>-3.925926</td>\n",
       "      <td>-0.391975</td>\n",
       "      <td>-1.966049</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.186507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-5.536765</td>\n",
       "      <td>-1.281863</td>\n",
       "      <td>1.568627</td>\n",
       "      <td>0.377451</td>\n",
       "      <td>-2.544118</td>\n",
       "      <td>1.301471</td>\n",
       "      <td>2.990196</td>\n",
       "      <td>-0.291667</td>\n",
       "      <td>5.004902</td>\n",
       "      <td>6.514706</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.291667</td>\n",
       "      <td>5.004902</td>\n",
       "      <td>6.514706</td>\n",
       "      <td>-2.110294</td>\n",
       "      <td>-0.323529</td>\n",
       "      <td>-1.372549</td>\n",
       "      <td>2.769608</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.094218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.942308</td>\n",
       "      <td>3.147436</td>\n",
       "      <td>0.262821</td>\n",
       "      <td>3.839744</td>\n",
       "      <td>1.570513</td>\n",
       "      <td>-8.096154</td>\n",
       "      <td>-20.608974</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>-38.435897</td>\n",
       "      <td>-7.243590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>-38.435897</td>\n",
       "      <td>-7.243590</td>\n",
       "      <td>-3.128205</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-9.634615</td>\n",
       "      <td>-4.897436</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.886092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-17.757687</td>\n",
       "      <td>-3.441624</td>\n",
       "      <td>0.347050</td>\n",
       "      <td>-0.428061</td>\n",
       "      <td>-3.258813</td>\n",
       "      <td>-0.933733</td>\n",
       "      <td>-4.512689</td>\n",
       "      <td>-1.165750</td>\n",
       "      <td>-4.230334</td>\n",
       "      <td>-0.693537</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.165750</td>\n",
       "      <td>-4.230334</td>\n",
       "      <td>-0.693537</td>\n",
       "      <td>-4.606158</td>\n",
       "      <td>-1.253664</td>\n",
       "      <td>-3.394140</td>\n",
       "      <td>-0.009770</td>\n",
       "      <td>-1.534911</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.001228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-59.000000</td>\n",
       "      <td>-6.941176</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>-1.264706</td>\n",
       "      <td>-12.000000</td>\n",
       "      <td>-9.235294</td>\n",
       "      <td>-5.411765</td>\n",
       "      <td>-0.970588</td>\n",
       "      <td>-3.852941</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.970588</td>\n",
       "      <td>-3.852941</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>-5.735294</td>\n",
       "      <td>-1.117647</td>\n",
       "      <td>-13.176471</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>-5.941176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.425738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>66.250000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>-7.250000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>-7.750000</td>\n",
       "      <td>-38.250000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-46.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-46.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-22.500000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-4.750000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.796990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-21.912863</td>\n",
       "      <td>-0.929704</td>\n",
       "      <td>0.114954</td>\n",
       "      <td>4.408653</td>\n",
       "      <td>-5.380052</td>\n",
       "      <td>-8.326539</td>\n",
       "      <td>-3.036150</td>\n",
       "      <td>-1.622880</td>\n",
       "      <td>-30.937979</td>\n",
       "      <td>-4.218786</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.622880</td>\n",
       "      <td>-30.937979</td>\n",
       "      <td>-4.218786</td>\n",
       "      <td>-10.944048</td>\n",
       "      <td>-1.366725</td>\n",
       "      <td>-5.497648</td>\n",
       "      <td>-5.081707</td>\n",
       "      <td>-5.383595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.710295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-17.857143</td>\n",
       "      <td>-0.571429</td>\n",
       "      <td>-1.357143</td>\n",
       "      <td>-4.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>-0.785714</td>\n",
       "      <td>-16.214286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>31.357143</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>31.357143</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.428571</td>\n",
       "      <td>8.071429</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13.535380</td>\n",
       "      <td>-0.739181</td>\n",
       "      <td>-2.824561</td>\n",
       "      <td>8.489766</td>\n",
       "      <td>-3.667251</td>\n",
       "      <td>3.020468</td>\n",
       "      <td>0.237719</td>\n",
       "      <td>0.438304</td>\n",
       "      <td>-11.528070</td>\n",
       "      <td>-2.181579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438304</td>\n",
       "      <td>-11.528070</td>\n",
       "      <td>-2.181579</td>\n",
       "      <td>-4.554094</td>\n",
       "      <td>0.411988</td>\n",
       "      <td>0.936550</td>\n",
       "      <td>-1.727485</td>\n",
       "      <td>-0.585088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.233179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34112</th>\n",
       "      <td>19.490643</td>\n",
       "      <td>1.328363</td>\n",
       "      <td>-0.362865</td>\n",
       "      <td>-1.978947</td>\n",
       "      <td>5.754971</td>\n",
       "      <td>1.038889</td>\n",
       "      <td>-4.066082</td>\n",
       "      <td>0.152924</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>-0.122222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>15.980882</td>\n",
       "      <td>0.601961</td>\n",
       "      <td>9.165196</td>\n",
       "      <td>0.368627</td>\n",
       "      <td>3.966912</td>\n",
       "      <td>1.522794</td>\n",
       "      <td>2.501471</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34113</th>\n",
       "      <td>-8.917708</td>\n",
       "      <td>-1.684375</td>\n",
       "      <td>-0.636458</td>\n",
       "      <td>-0.900000</td>\n",
       "      <td>-3.576042</td>\n",
       "      <td>2.856250</td>\n",
       "      <td>-5.985417</td>\n",
       "      <td>2.568750</td>\n",
       "      <td>7.085417</td>\n",
       "      <td>0.193750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.949371</td>\n",
       "      <td>11.403459</td>\n",
       "      <td>1.970755</td>\n",
       "      <td>5.833019</td>\n",
       "      <td>0.732390</td>\n",
       "      <td>-0.650943</td>\n",
       "      <td>1.630189</td>\n",
       "      <td>0.652201</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34114</th>\n",
       "      <td>-29.523770</td>\n",
       "      <td>-5.727643</td>\n",
       "      <td>1.334153</td>\n",
       "      <td>3.622619</td>\n",
       "      <td>-8.289770</td>\n",
       "      <td>-0.242718</td>\n",
       "      <td>-7.529836</td>\n",
       "      <td>-0.468978</td>\n",
       "      <td>5.017827</td>\n",
       "      <td>1.105787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.917059</td>\n",
       "      <td>7.812599</td>\n",
       "      <td>1.261038</td>\n",
       "      <td>-0.280566</td>\n",
       "      <td>-0.974649</td>\n",
       "      <td>-6.352350</td>\n",
       "      <td>1.773932</td>\n",
       "      <td>-2.037261</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.455270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34115</th>\n",
       "      <td>-27.282813</td>\n",
       "      <td>-2.324890</td>\n",
       "      <td>2.789886</td>\n",
       "      <td>-8.875360</td>\n",
       "      <td>-2.175160</td>\n",
       "      <td>-0.517317</td>\n",
       "      <td>2.905442</td>\n",
       "      <td>0.626901</td>\n",
       "      <td>14.551190</td>\n",
       "      <td>1.781212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583730</td>\n",
       "      <td>11.592345</td>\n",
       "      <td>1.918882</td>\n",
       "      <td>3.961730</td>\n",
       "      <td>0.771866</td>\n",
       "      <td>-4.823175</td>\n",
       "      <td>1.970888</td>\n",
       "      <td>-1.208108</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34116</th>\n",
       "      <td>45.023611</td>\n",
       "      <td>6.227778</td>\n",
       "      <td>-1.484722</td>\n",
       "      <td>-7.474653</td>\n",
       "      <td>16.486111</td>\n",
       "      <td>4.448264</td>\n",
       "      <td>13.433333</td>\n",
       "      <td>-0.184375</td>\n",
       "      <td>26.193056</td>\n",
       "      <td>4.938194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239747</td>\n",
       "      <td>10.739969</td>\n",
       "      <td>2.270792</td>\n",
       "      <td>2.006561</td>\n",
       "      <td>0.103276</td>\n",
       "      <td>8.889290</td>\n",
       "      <td>2.454088</td>\n",
       "      <td>5.330413</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.147449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34117</th>\n",
       "      <td>17.829545</td>\n",
       "      <td>-1.429924</td>\n",
       "      <td>-0.056818</td>\n",
       "      <td>5.017045</td>\n",
       "      <td>2.914773</td>\n",
       "      <td>2.784091</td>\n",
       "      <td>5.378788</td>\n",
       "      <td>-0.630682</td>\n",
       "      <td>27.994318</td>\n",
       "      <td>6.143939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241987</td>\n",
       "      <td>31.984295</td>\n",
       "      <td>5.411538</td>\n",
       "      <td>10.759615</td>\n",
       "      <td>0.345833</td>\n",
       "      <td>5.558974</td>\n",
       "      <td>6.017628</td>\n",
       "      <td>4.774038</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.492202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34118</th>\n",
       "      <td>-36.429762</td>\n",
       "      <td>-3.016270</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-7.359524</td>\n",
       "      <td>-5.317857</td>\n",
       "      <td>-3.999603</td>\n",
       "      <td>-2.807540</td>\n",
       "      <td>-1.080159</td>\n",
       "      <td>-19.117460</td>\n",
       "      <td>-2.252778</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.617334</td>\n",
       "      <td>-18.293965</td>\n",
       "      <td>-3.154905</td>\n",
       "      <td>-5.488134</td>\n",
       "      <td>-0.235731</td>\n",
       "      <td>-7.218753</td>\n",
       "      <td>-3.449184</td>\n",
       "      <td>-4.952832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34119</th>\n",
       "      <td>3.106831</td>\n",
       "      <td>-4.228223</td>\n",
       "      <td>0.270624</td>\n",
       "      <td>1.608743</td>\n",
       "      <td>-1.445853</td>\n",
       "      <td>11.275585</td>\n",
       "      <td>4.949383</td>\n",
       "      <td>0.165396</td>\n",
       "      <td>50.424018</td>\n",
       "      <td>8.854308</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.837504</td>\n",
       "      <td>44.927427</td>\n",
       "      <td>7.739291</td>\n",
       "      <td>8.587003</td>\n",
       "      <td>-0.812784</td>\n",
       "      <td>-1.864497</td>\n",
       "      <td>8.712548</td>\n",
       "      <td>3.115661</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>-0.857336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34120</th>\n",
       "      <td>7.943434</td>\n",
       "      <td>2.119192</td>\n",
       "      <td>0.668939</td>\n",
       "      <td>-0.108838</td>\n",
       "      <td>3.519444</td>\n",
       "      <td>-0.752273</td>\n",
       "      <td>-2.646717</td>\n",
       "      <td>-0.239899</td>\n",
       "      <td>4.748737</td>\n",
       "      <td>2.023737</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.318937</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>1.549462</td>\n",
       "      <td>-3.102063</td>\n",
       "      <td>-0.163057</td>\n",
       "      <td>2.194803</td>\n",
       "      <td>0.936170</td>\n",
       "      <td>1.131746</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.270817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34121</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>-13.222222</td>\n",
       "      <td>-1.555556</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>7.888889</td>\n",
       "      <td>-5.666667</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34122</th>\n",
       "      <td>11.562500</td>\n",
       "      <td>0.105882</td>\n",
       "      <td>-0.462500</td>\n",
       "      <td>5.988725</td>\n",
       "      <td>-2.290441</td>\n",
       "      <td>3.325735</td>\n",
       "      <td>-2.981618</td>\n",
       "      <td>-0.444853</td>\n",
       "      <td>14.360049</td>\n",
       "      <td>2.738235</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.163988</td>\n",
       "      <td>15.541391</td>\n",
       "      <td>3.165961</td>\n",
       "      <td>6.399239</td>\n",
       "      <td>-0.235968</td>\n",
       "      <td>1.033951</td>\n",
       "      <td>2.439572</td>\n",
       "      <td>1.734998</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34123</th>\n",
       "      <td>14.319876</td>\n",
       "      <td>2.615528</td>\n",
       "      <td>0.181435</td>\n",
       "      <td>-1.183126</td>\n",
       "      <td>4.634886</td>\n",
       "      <td>0.755970</td>\n",
       "      <td>-4.278778</td>\n",
       "      <td>-0.465769</td>\n",
       "      <td>-0.199724</td>\n",
       "      <td>0.900621</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.290306</td>\n",
       "      <td>-1.081816</td>\n",
       "      <td>0.171272</td>\n",
       "      <td>-2.687507</td>\n",
       "      <td>-0.291850</td>\n",
       "      <td>3.934419</td>\n",
       "      <td>0.635217</td>\n",
       "      <td>2.044453</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.327804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34124</th>\n",
       "      <td>1.485139</td>\n",
       "      <td>1.955521</td>\n",
       "      <td>0.851936</td>\n",
       "      <td>-6.183726</td>\n",
       "      <td>4.278153</td>\n",
       "      <td>1.206724</td>\n",
       "      <td>1.456567</td>\n",
       "      <td>0.990607</td>\n",
       "      <td>7.802800</td>\n",
       "      <td>0.466353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.986824</td>\n",
       "      <td>9.315395</td>\n",
       "      <td>1.329519</td>\n",
       "      <td>2.675363</td>\n",
       "      <td>1.128111</td>\n",
       "      <td>1.145925</td>\n",
       "      <td>2.033833</td>\n",
       "      <td>1.313774</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.283616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34125</th>\n",
       "      <td>-17.053252</td>\n",
       "      <td>-3.658537</td>\n",
       "      <td>1.589024</td>\n",
       "      <td>6.770325</td>\n",
       "      <td>-7.794309</td>\n",
       "      <td>-3.156098</td>\n",
       "      <td>-2.624390</td>\n",
       "      <td>0.245935</td>\n",
       "      <td>-2.657317</td>\n",
       "      <td>-1.751626</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.238946</td>\n",
       "      <td>-12.718699</td>\n",
       "      <td>-3.021089</td>\n",
       "      <td>-3.180534</td>\n",
       "      <td>-0.452081</td>\n",
       "      <td>-6.441720</td>\n",
       "      <td>-2.408329</td>\n",
       "      <td>-3.962368</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.055203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34126</th>\n",
       "      <td>-4.834565</td>\n",
       "      <td>-0.771899</td>\n",
       "      <td>-0.200055</td>\n",
       "      <td>-2.747093</td>\n",
       "      <td>-2.286489</td>\n",
       "      <td>2.390407</td>\n",
       "      <td>3.032987</td>\n",
       "      <td>0.520543</td>\n",
       "      <td>2.404001</td>\n",
       "      <td>-2.872051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361796</td>\n",
       "      <td>0.049202</td>\n",
       "      <td>-2.998521</td>\n",
       "      <td>2.318179</td>\n",
       "      <td>0.504089</td>\n",
       "      <td>-1.272786</td>\n",
       "      <td>-1.120343</td>\n",
       "      <td>-1.062686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34127</th>\n",
       "      <td>24.666667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-2.111111</td>\n",
       "      <td>5.222222</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>-6.944444</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>-20.666667</td>\n",
       "      <td>-2.611111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>-22.903846</td>\n",
       "      <td>-3.576923</td>\n",
       "      <td>-4.980769</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>2.769231</td>\n",
       "      <td>-4.750000</td>\n",
       "      <td>-0.750000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.730359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34129</th>\n",
       "      <td>-23.943478</td>\n",
       "      <td>-3.600564</td>\n",
       "      <td>1.186151</td>\n",
       "      <td>-7.350483</td>\n",
       "      <td>-3.306441</td>\n",
       "      <td>0.584461</td>\n",
       "      <td>-7.259420</td>\n",
       "      <td>-1.356924</td>\n",
       "      <td>11.193639</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.083469</td>\n",
       "      <td>11.818631</td>\n",
       "      <td>2.686179</td>\n",
       "      <td>2.382453</td>\n",
       "      <td>-1.005014</td>\n",
       "      <td>-2.684553</td>\n",
       "      <td>3.128388</td>\n",
       "      <td>-0.030149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34130</th>\n",
       "      <td>-36.780556</td>\n",
       "      <td>-5.243576</td>\n",
       "      <td>0.384375</td>\n",
       "      <td>8.224306</td>\n",
       "      <td>-13.922396</td>\n",
       "      <td>-7.107639</td>\n",
       "      <td>-4.015799</td>\n",
       "      <td>-0.518576</td>\n",
       "      <td>-14.027604</td>\n",
       "      <td>-1.814062</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.271763</td>\n",
       "      <td>-9.669180</td>\n",
       "      <td>-2.183908</td>\n",
       "      <td>1.353210</td>\n",
       "      <td>0.053839</td>\n",
       "      <td>-9.404856</td>\n",
       "      <td>-2.884535</td>\n",
       "      <td>-5.607461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.707095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34133</th>\n",
       "      <td>-7.288982</td>\n",
       "      <td>0.455066</td>\n",
       "      <td>-0.088146</td>\n",
       "      <td>-4.683637</td>\n",
       "      <td>1.603191</td>\n",
       "      <td>-2.437867</td>\n",
       "      <td>-2.181611</td>\n",
       "      <td>0.245061</td>\n",
       "      <td>-5.361322</td>\n",
       "      <td>-0.591439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145358</td>\n",
       "      <td>-4.334556</td>\n",
       "      <td>-0.550908</td>\n",
       "      <td>0.365162</td>\n",
       "      <td>0.153291</td>\n",
       "      <td>-1.146078</td>\n",
       "      <td>-1.144960</td>\n",
       "      <td>-1.042645</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>0.116789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34136</th>\n",
       "      <td>-15.666667</td>\n",
       "      <td>-4.523810</td>\n",
       "      <td>0.353175</td>\n",
       "      <td>5.095238</td>\n",
       "      <td>-5.468254</td>\n",
       "      <td>1.924603</td>\n",
       "      <td>2.174603</td>\n",
       "      <td>0.551587</td>\n",
       "      <td>9.369048</td>\n",
       "      <td>-1.805556</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.141204</td>\n",
       "      <td>-9.194537</td>\n",
       "      <td>-2.598245</td>\n",
       "      <td>-3.866989</td>\n",
       "      <td>-1.114929</td>\n",
       "      <td>-7.598430</td>\n",
       "      <td>-1.259863</td>\n",
       "      <td>-4.356239</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.853023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34138</th>\n",
       "      <td>-10.662760</td>\n",
       "      <td>-0.723698</td>\n",
       "      <td>1.511719</td>\n",
       "      <td>-3.970313</td>\n",
       "      <td>-1.268750</td>\n",
       "      <td>-0.272917</td>\n",
       "      <td>3.866927</td>\n",
       "      <td>-0.132552</td>\n",
       "      <td>-15.766667</td>\n",
       "      <td>-1.258333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.156203</td>\n",
       "      <td>-13.920927</td>\n",
       "      <td>-0.715006</td>\n",
       "      <td>-5.207425</td>\n",
       "      <td>-0.038440</td>\n",
       "      <td>0.217732</td>\n",
       "      <td>-1.888127</td>\n",
       "      <td>-0.863972</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>0.183922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34139</th>\n",
       "      <td>17.757576</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>-4.257576</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>1.287879</td>\n",
       "      <td>5.893939</td>\n",
       "      <td>-6.227273</td>\n",
       "      <td>-0.606061</td>\n",
       "      <td>4.863636</td>\n",
       "      <td>-1.651515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974107</td>\n",
       "      <td>15.687245</td>\n",
       "      <td>0.466454</td>\n",
       "      <td>10.102806</td>\n",
       "      <td>0.781335</td>\n",
       "      <td>4.007143</td>\n",
       "      <td>0.734694</td>\n",
       "      <td>2.608036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.712187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34140</th>\n",
       "      <td>2.866667</td>\n",
       "      <td>3.163667</td>\n",
       "      <td>0.851000</td>\n",
       "      <td>-0.375333</td>\n",
       "      <td>3.054333</td>\n",
       "      <td>-3.953667</td>\n",
       "      <td>-6.377333</td>\n",
       "      <td>-0.484333</td>\n",
       "      <td>-10.507000</td>\n",
       "      <td>-1.217667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535724</td>\n",
       "      <td>0.434958</td>\n",
       "      <td>0.283374</td>\n",
       "      <td>0.114639</td>\n",
       "      <td>0.492302</td>\n",
       "      <td>1.357597</td>\n",
       "      <td>0.227441</td>\n",
       "      <td>0.631608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34141</th>\n",
       "      <td>15.658611</td>\n",
       "      <td>0.610278</td>\n",
       "      <td>-2.438333</td>\n",
       "      <td>4.606944</td>\n",
       "      <td>1.618889</td>\n",
       "      <td>0.805278</td>\n",
       "      <td>2.307778</td>\n",
       "      <td>-0.737778</td>\n",
       "      <td>7.865278</td>\n",
       "      <td>1.181111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.520536</td>\n",
       "      <td>9.083750</td>\n",
       "      <td>0.574792</td>\n",
       "      <td>3.948839</td>\n",
       "      <td>-0.268661</td>\n",
       "      <td>2.534792</td>\n",
       "      <td>1.441607</td>\n",
       "      <td>1.631607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.278235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34142</th>\n",
       "      <td>18.895833</td>\n",
       "      <td>-0.145833</td>\n",
       "      <td>-0.656250</td>\n",
       "      <td>5.281250</td>\n",
       "      <td>2.989583</td>\n",
       "      <td>1.989583</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>-0.052083</td>\n",
       "      <td>24.458333</td>\n",
       "      <td>6.093750</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329114</td>\n",
       "      <td>7.639662</td>\n",
       "      <td>1.406962</td>\n",
       "      <td>2.376582</td>\n",
       "      <td>-0.378270</td>\n",
       "      <td>0.838819</td>\n",
       "      <td>1.417511</td>\n",
       "      <td>1.033966</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.521972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34143</th>\n",
       "      <td>-6.820943</td>\n",
       "      <td>5.979015</td>\n",
       "      <td>1.771441</td>\n",
       "      <td>0.574498</td>\n",
       "      <td>-1.982729</td>\n",
       "      <td>-3.448421</td>\n",
       "      <td>2.151415</td>\n",
       "      <td>0.226445</td>\n",
       "      <td>-15.656090</td>\n",
       "      <td>-6.242792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.709534</td>\n",
       "      <td>-22.314864</td>\n",
       "      <td>-7.877532</td>\n",
       "      <td>1.030867</td>\n",
       "      <td>0.780622</td>\n",
       "      <td>-3.127224</td>\n",
       "      <td>-6.801938</td>\n",
       "      <td>-4.011065</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.012581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34144</th>\n",
       "      <td>26.106589</td>\n",
       "      <td>2.637597</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>7.503876</td>\n",
       "      <td>4.001938</td>\n",
       "      <td>1.653101</td>\n",
       "      <td>-5.015504</td>\n",
       "      <td>-0.108527</td>\n",
       "      <td>46.581395</td>\n",
       "      <td>8.112403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.717697</td>\n",
       "      <td>25.229329</td>\n",
       "      <td>5.778408</td>\n",
       "      <td>9.017458</td>\n",
       "      <td>0.542454</td>\n",
       "      <td>5.144467</td>\n",
       "      <td>4.628472</td>\n",
       "      <td>4.506437</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.116734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34145</th>\n",
       "      <td>52.684524</td>\n",
       "      <td>0.465030</td>\n",
       "      <td>-0.764881</td>\n",
       "      <td>6.930804</td>\n",
       "      <td>8.859375</td>\n",
       "      <td>8.774554</td>\n",
       "      <td>14.485119</td>\n",
       "      <td>2.885417</td>\n",
       "      <td>50.959821</td>\n",
       "      <td>10.066964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.210134</td>\n",
       "      <td>46.052692</td>\n",
       "      <td>8.709631</td>\n",
       "      <td>10.788813</td>\n",
       "      <td>-0.452274</td>\n",
       "      <td>8.832555</td>\n",
       "      <td>8.892710</td>\n",
       "      <td>8.229462</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.893484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34146</th>\n",
       "      <td>20.863095</td>\n",
       "      <td>3.675595</td>\n",
       "      <td>-0.645833</td>\n",
       "      <td>-0.726190</td>\n",
       "      <td>10.077381</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>-15.261905</td>\n",
       "      <td>0.788690</td>\n",
       "      <td>2.976190</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164683</td>\n",
       "      <td>11.803571</td>\n",
       "      <td>0.680556</td>\n",
       "      <td>1.793651</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>4.797619</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>3.496032</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.888489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34147</th>\n",
       "      <td>-26.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>-6.500000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-65.000000</td>\n",
       "      <td>-12.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.296296</td>\n",
       "      <td>-30.185185</td>\n",
       "      <td>-0.148148</td>\n",
       "      <td>-18.018519</td>\n",
       "      <td>-0.574074</td>\n",
       "      <td>-3.870370</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.870370</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.580448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27148 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       serve_rating_1year  aces_1year  double_faults_1year  first_serve_1year  \\\n",
       "0               22.680874   -1.353279            -0.859836           8.912022   \n",
       "2               -6.668889   -1.328148             0.091852          -2.805926   \n",
       "3               15.666667    1.666667             2.666667          -0.000000   \n",
       "4              -17.928571   -5.785714            -0.428571           3.142857   \n",
       "6               12.351282   -5.203846            -0.489744           7.125321   \n",
       "7              -18.353409   -4.653409            -0.875758           2.303788   \n",
       "8               70.226190    7.142857            -1.130952           5.142857   \n",
       "9               12.619211    0.216533             0.910997           5.292173   \n",
       "10             -28.925223   -4.390253             2.524330           0.473437   \n",
       "11              -2.760000    1.307415            -0.039048          -1.938231   \n",
       "12               3.183059   -6.966392            -2.358852           8.609417   \n",
       "13             -15.372953    0.937427            -0.627339          -4.621053   \n",
       "14               0.458225   -0.296050             0.237635          -3.945671   \n",
       "16              48.040292    0.343161            -2.199782           8.098888   \n",
       "17              -8.989354   -1.820860            -0.403192          -0.158523   \n",
       "18             -18.981481    5.064815             0.981481          -3.296296   \n",
       "19             -41.194203   -4.696377            -1.065217          -6.203623   \n",
       "22              -7.166667   -0.273810             0.059524           0.936508   \n",
       "23             -11.643411   -0.515504             0.957364           0.290698   \n",
       "24             -17.962963    0.169753            -0.922840          -7.986111   \n",
       "25              -5.536765   -1.281863             1.568627           0.377451   \n",
       "26              -1.942308    3.147436             0.262821           3.839744   \n",
       "27             -17.757687   -3.441624             0.347050          -0.428061   \n",
       "28             -59.000000   -6.941176             0.029412          -1.264706   \n",
       "29              66.250000    3.250000            -7.250000           5.750000   \n",
       "30             -21.912863   -0.929704             0.114954           4.408653   \n",
       "31             -17.857143   -0.571429            -1.357143          -4.071429   \n",
       "32               0.000000    0.000000             0.000000           0.000000   \n",
       "33              13.535380   -0.739181            -2.824561           8.489766   \n",
       "34               0.000000    0.000000             0.000000           0.000000   \n",
       "...                   ...         ...                  ...                ...   \n",
       "34112           19.490643    1.328363            -0.362865          -1.978947   \n",
       "34113           -8.917708   -1.684375            -0.636458          -0.900000   \n",
       "34114          -29.523770   -5.727643             1.334153           3.622619   \n",
       "34115          -27.282813   -2.324890             2.789886          -8.875360   \n",
       "34116           45.023611    6.227778            -1.484722          -7.474653   \n",
       "34117           17.829545   -1.429924            -0.056818           5.017045   \n",
       "34118          -36.429762   -3.016270            -0.027778          -7.359524   \n",
       "34119            3.106831   -4.228223             0.270624           1.608743   \n",
       "34120            7.943434    2.119192             0.668939          -0.108838   \n",
       "34121           39.000000    3.500000             1.500000           4.500000   \n",
       "34122           11.562500    0.105882            -0.462500           5.988725   \n",
       "34123           14.319876    2.615528             0.181435          -1.183126   \n",
       "34124            1.485139    1.955521             0.851936          -6.183726   \n",
       "34125          -17.053252   -3.658537             1.589024           6.770325   \n",
       "34126           -4.834565   -0.771899            -0.200055          -2.747093   \n",
       "34127           24.666667    0.166667             0.166667          -2.111111   \n",
       "34129          -23.943478   -3.600564             1.186151          -7.350483   \n",
       "34130          -36.780556   -5.243576             0.384375           8.224306   \n",
       "34133           -7.288982    0.455066            -0.088146          -4.683637   \n",
       "34136          -15.666667   -4.523810             0.353175           5.095238   \n",
       "34138          -10.662760   -0.723698             1.511719          -3.970313   \n",
       "34139           17.757576    0.090909            -4.257576           2.833333   \n",
       "34140            2.866667    3.163667             0.851000          -0.375333   \n",
       "34141           15.658611    0.610278            -2.438333           4.606944   \n",
       "34142           18.895833   -0.145833            -0.656250           5.281250   \n",
       "34143           -6.820943    5.979015             1.771441           0.574498   \n",
       "34144           26.106589    2.637597            -0.023256           7.503876   \n",
       "34145           52.684524    0.465030            -0.764881           6.930804   \n",
       "34146           20.863095    3.675595            -0.645833          -0.726190   \n",
       "34147          -26.000000   -2.000000             2.000000           6.500000   \n",
       "\n",
       "       first_serve_point_won_1year  second_serve_point_won_1year  \\\n",
       "0                        -0.228962                      4.176776   \n",
       "2                        -0.319630                     -1.348519   \n",
       "3                         4.333333                      0.333333   \n",
       "4                        -8.142857                      7.500000   \n",
       "6                        -1.542949                      6.682372   \n",
       "7                        -7.685985                      0.936742   \n",
       "8                        23.380952                     13.190476   \n",
       "9                         1.937366                      2.542619   \n",
       "10                       -8.280506                     -3.186012   \n",
       "11                       -1.163401                     -2.767347   \n",
       "12                       -3.659661                      8.769414   \n",
       "13                       -3.463158                     -3.182164   \n",
       "14                        2.145996                      2.669318   \n",
       "16                        8.354838                      8.379346   \n",
       "17                       -2.315164                     -0.375871   \n",
       "18                        0.027778                    -14.629630   \n",
       "19                       -9.830435                     -2.342754   \n",
       "22                       -1.757937                     -5.333333   \n",
       "23                       -1.531008                     -3.980620   \n",
       "24                       -2.825617                     -2.270062   \n",
       "25                       -2.544118                      1.301471   \n",
       "26                        1.570513                     -8.096154   \n",
       "27                       -3.258813                     -0.933733   \n",
       "28                      -12.000000                     -9.235294   \n",
       "29                       23.500000                     -7.750000   \n",
       "30                       -5.380052                     -8.326539   \n",
       "31                        0.214286                     -0.785714   \n",
       "32                        0.000000                      0.000000   \n",
       "33                       -3.667251                      3.020468   \n",
       "34                        0.000000                      0.000000   \n",
       "...                            ...                           ...   \n",
       "34112                     5.754971                      1.038889   \n",
       "34113                    -3.576042                      2.856250   \n",
       "34114                    -8.289770                     -0.242718   \n",
       "34115                    -2.175160                     -0.517317   \n",
       "34116                    16.486111                      4.448264   \n",
       "34117                     2.914773                      2.784091   \n",
       "34118                    -5.317857                     -3.999603   \n",
       "34119                    -1.445853                     11.275585   \n",
       "34120                     3.519444                     -0.752273   \n",
       "34121                     2.500000                     11.500000   \n",
       "34122                    -2.290441                      3.325735   \n",
       "34123                     4.634886                      0.755970   \n",
       "34124                     4.278153                      1.206724   \n",
       "34125                    -7.794309                     -3.156098   \n",
       "34126                    -2.286489                      2.390407   \n",
       "34127                     5.222222                      3.833333   \n",
       "34129                    -3.306441                      0.584461   \n",
       "34130                   -13.922396                     -7.107639   \n",
       "34133                     1.603191                     -2.437867   \n",
       "34136                    -5.468254                      1.924603   \n",
       "34138                    -1.268750                     -0.272917   \n",
       "34139                     1.287879                      5.893939   \n",
       "34140                     3.054333                     -3.953667   \n",
       "34141                     1.618889                      0.805278   \n",
       "34142                     2.989583                      1.989583   \n",
       "34143                    -1.982729                     -3.448421   \n",
       "34144                     4.001938                      1.653101   \n",
       "34145                     8.859375                      8.774554   \n",
       "34146                    10.077381                      3.142857   \n",
       "34147                    -6.500000                     -1.500000   \n",
       "\n",
       "       bp_saved_1year  service_game_played_1year  return_rating_1year  \\\n",
       "0           -0.974317                   0.684973            -6.111202   \n",
       "2           -1.214815                  -1.078889            -6.271481   \n",
       "3           14.666667                   4.666667            33.000000   \n",
       "4           -4.071429                  -2.071429            13.285714   \n",
       "6            8.243269                  -0.016987            16.787179   \n",
       "7            2.662879                  -1.681818             6.138258   \n",
       "8           13.833333                   1.797619            68.202381   \n",
       "9            3.370908                  -0.091845            15.568988   \n",
       "10          -7.716369                  -1.606324             5.387798   \n",
       "11           1.696871                   0.855238           -24.072517   \n",
       "12          -7.880327                  -1.177961            60.143971   \n",
       "13          -5.368567                   1.063596           -30.667690   \n",
       "14          -1.399729                  -0.385119            30.328084   \n",
       "16           1.881370                  -0.666527            43.985568   \n",
       "17          -0.249014                  -0.573862            -1.164510   \n",
       "18         -12.518519                   0.953704           -14.055556   \n",
       "19         -16.426812                  -1.119565            -1.856522   \n",
       "22           6.837302                  -3.035714           -43.638889   \n",
       "23          -6.360465                   0.344961            -7.930233   \n",
       "24           6.654321                  -0.507716           -10.188272   \n",
       "25           2.990196                  -0.291667             5.004902   \n",
       "26         -20.608974                   0.358974           -38.435897   \n",
       "27          -4.512689                  -1.165750            -4.230334   \n",
       "28          -5.411765                  -0.970588            -3.852941   \n",
       "29         -38.250000                  -7.000000           -46.000000   \n",
       "30          -3.036150                  -1.622880           -30.937979   \n",
       "31         -16.214286                   0.285714            31.357143   \n",
       "32           0.000000                   0.000000             0.000000   \n",
       "33           0.237719                   0.438304           -11.528070   \n",
       "34           0.000000                   0.000000             0.000000   \n",
       "...               ...                        ...                  ...   \n",
       "34112       -4.066082                   0.152924            13.700000   \n",
       "34113       -5.985417                   2.568750             7.085417   \n",
       "34114       -7.529836                  -0.468978             5.017827   \n",
       "34115        2.905442                   0.626901            14.551190   \n",
       "34116       13.433333                  -0.184375            26.193056   \n",
       "34117        5.378788                  -0.630682            27.994318   \n",
       "34118       -2.807540                  -1.080159           -19.117460   \n",
       "34119        4.949383                   0.165396            50.424018   \n",
       "34120       -2.646717                  -0.239899             4.748737   \n",
       "34121      -11.000000                  -2.500000            -4.000000   \n",
       "34122       -2.981618                  -0.444853            14.360049   \n",
       "34123       -4.278778                  -0.465769            -0.199724   \n",
       "34124        1.456567                   0.990607             7.802800   \n",
       "34125       -2.624390                   0.245935            -2.657317   \n",
       "34126        3.032987                   0.520543             2.404001   \n",
       "34127       -6.944444                   1.333333           -20.666667   \n",
       "34129       -7.259420                  -1.356924            11.193639   \n",
       "34130       -4.015799                  -0.518576           -14.027604   \n",
       "34133       -2.181611                   0.245061            -5.361322   \n",
       "34136        2.174603                   0.551587             9.369048   \n",
       "34138        3.866927                  -0.132552           -15.766667   \n",
       "34139       -6.227273                  -0.606061             4.863636   \n",
       "34140       -6.377333                  -0.484333           -10.507000   \n",
       "34141        2.307778                  -0.737778             7.865278   \n",
       "34142        4.333333                  -0.052083            24.458333   \n",
       "34143        2.151415                   0.226445           -15.656090   \n",
       "34144       -5.015504                  -0.108527            46.581395   \n",
       "34145       14.485119                   2.885417            50.959821   \n",
       "34146      -15.261905                   0.788690             2.976190   \n",
       "34147       65.000000                   3.500000           -65.000000   \n",
       "\n",
       "       first_serve_return_point_won_1year          ...           \\\n",
       "0                               -2.769672          ...            \n",
       "2                               -1.893704          ...            \n",
       "3                                6.333333          ...            \n",
       "4                                2.428571          ...            \n",
       "6                                2.906731          ...            \n",
       "7                                1.996591          ...            \n",
       "8                               11.892857          ...            \n",
       "9                                4.435193          ...            \n",
       "10                               6.242336          ...            \n",
       "11                              -2.518912          ...            \n",
       "12                              10.854823          ...            \n",
       "13                              -6.365789          ...            \n",
       "14                               5.997321          ...            \n",
       "16                               7.936815          ...            \n",
       "17                               0.871784          ...            \n",
       "18                              -9.685185          ...            \n",
       "19                              -1.342754          ...            \n",
       "22                              -7.261905          ...            \n",
       "23                              -1.930233          ...            \n",
       "24                              -0.179012          ...            \n",
       "25                               6.514706          ...            \n",
       "26                              -7.243590          ...            \n",
       "27                              -0.693537          ...            \n",
       "28                               0.823529          ...            \n",
       "29                               0.250000          ...            \n",
       "30                              -4.218786          ...            \n",
       "31                               7.285714          ...            \n",
       "32                               0.000000          ...            \n",
       "33                              -2.181579          ...            \n",
       "34                               0.000000          ...            \n",
       "...                                   ...          ...            \n",
       "34112                           -0.122222          ...            \n",
       "34113                            0.193750          ...            \n",
       "34114                            1.105787          ...            \n",
       "34115                            1.781212          ...            \n",
       "34116                            4.938194          ...            \n",
       "34117                            6.143939          ...            \n",
       "34118                           -2.252778          ...            \n",
       "34119                            8.854308          ...            \n",
       "34120                            2.023737          ...            \n",
       "34121                            3.500000          ...            \n",
       "34122                            2.738235          ...            \n",
       "34123                            0.900621          ...            \n",
       "34124                            0.466353          ...            \n",
       "34125                           -1.751626          ...            \n",
       "34126                           -2.872051          ...            \n",
       "34127                           -2.611111          ...            \n",
       "34129                            2.833333          ...            \n",
       "34130                           -1.814062          ...            \n",
       "34133                           -0.591439          ...            \n",
       "34136                           -1.805556          ...            \n",
       "34138                           -1.258333          ...            \n",
       "34139                           -1.651515          ...            \n",
       "34140                           -1.217667          ...            \n",
       "34141                            1.181111          ...            \n",
       "34142                            6.093750          ...            \n",
       "34143                           -6.242792          ...            \n",
       "34144                            8.112403          ...            \n",
       "34145                           10.066964          ...            \n",
       "34146                            0.666667          ...            \n",
       "34147                          -12.500000          ...            \n",
       "\n",
       "       service_game_played_20years  return_rating_20years  \\\n",
       "0                         0.037864              -1.631293   \n",
       "2                        -0.891805             -12.771137   \n",
       "3                         3.142857              20.714286   \n",
       "4                        -0.760106              11.217407   \n",
       "6                        -0.334195              24.155651   \n",
       "7                        -1.380606               8.924113   \n",
       "8                         0.658095              42.002381   \n",
       "9                         0.268349              14.370963   \n",
       "10                       -2.017434               2.654034   \n",
       "11                        0.695082             -17.435662   \n",
       "12                       -1.181322              59.924116   \n",
       "13                        1.308291             -25.285201   \n",
       "14                       -0.599471              27.540083   \n",
       "16                       -0.357638              42.849211   \n",
       "17                       -0.053983              -5.769948   \n",
       "18                        1.346667              -7.763333   \n",
       "19                       -1.119565              -1.856522   \n",
       "22                       -1.698718             -22.342735   \n",
       "23                        0.344961              -7.930233   \n",
       "24                       -0.507716             -10.188272   \n",
       "25                       -0.291667               5.004902   \n",
       "26                        0.358974             -38.435897   \n",
       "27                       -1.165750              -4.230334   \n",
       "28                       -0.970588              -3.852941   \n",
       "29                       -7.000000             -46.000000   \n",
       "30                       -1.622880             -30.937979   \n",
       "31                        0.285714              31.357143   \n",
       "32                        0.000000               0.000000   \n",
       "33                        0.438304             -11.528070   \n",
       "34                        0.000000               0.000000   \n",
       "...                            ...                    ...   \n",
       "34112                     0.354167              15.980882   \n",
       "34113                     0.949371              11.403459   \n",
       "34114                    -0.917059               7.812599   \n",
       "34115                     0.583730              11.592345   \n",
       "34116                     0.239747              10.739969   \n",
       "34117                     0.241987              31.984295   \n",
       "34118                    -0.617334             -18.293965   \n",
       "34119                    -0.837504              44.927427   \n",
       "34120                    -0.318937               0.002912   \n",
       "34121                     0.555556             -13.222222   \n",
       "34122                    -0.163988              15.541391   \n",
       "34123                    -0.290306              -1.081816   \n",
       "34124                     0.986824               9.315395   \n",
       "34125                    -0.238946             -12.718699   \n",
       "34126                     0.361796               0.049202   \n",
       "34127                     0.538462             -22.903846   \n",
       "34129                    -1.083469              11.818631   \n",
       "34130                    -0.271763              -9.669180   \n",
       "34133                     0.145358              -4.334556   \n",
       "34136                    -1.141204              -9.194537   \n",
       "34138                    -0.156203             -13.920927   \n",
       "34139                     0.974107              15.687245   \n",
       "34140                     0.535724               0.434958   \n",
       "34141                    -0.520536               9.083750   \n",
       "34142                    -0.329114               7.639662   \n",
       "34143                     0.709534             -22.314864   \n",
       "34144                     0.717697              25.229329   \n",
       "34145                    -0.210134              46.052692   \n",
       "34146                     0.164683              11.803571   \n",
       "34147                    -0.296296             -30.185185   \n",
       "\n",
       "       first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                                 -2.059026              2.284791   \n",
       "2                                 -2.217613             -4.413200   \n",
       "3                                  2.142857             17.285714   \n",
       "4                                 -0.002116              1.797011   \n",
       "6                                  3.991762              6.441762   \n",
       "7                                  2.322814             -1.377749   \n",
       "8                                  7.269762             14.875952   \n",
       "9                                  3.861322              0.921584   \n",
       "10                                 3.187368             -3.777288   \n",
       "11                                -1.833359             -5.655868   \n",
       "12                                12.098007             12.726353   \n",
       "13                                -6.110167             -6.432567   \n",
       "14                                 5.663394              5.387453   \n",
       "16                                 7.384862             10.514026   \n",
       "17                                 0.070507             -2.054217   \n",
       "18                                -8.060000             16.538333   \n",
       "19                                -1.342754              0.431884   \n",
       "22                                -4.038034             -7.508547   \n",
       "23                                -1.930233             -1.589147   \n",
       "24                                -0.179012             -6.567901   \n",
       "25                                 6.514706             -2.110294   \n",
       "26                                -7.243590             -3.128205   \n",
       "27                                -0.693537             -4.606158   \n",
       "28                                 0.823529             -5.735294   \n",
       "29                                 0.250000            -22.500000   \n",
       "30                                -4.218786            -10.944048   \n",
       "31                                 7.285714             -0.142857   \n",
       "32                                 0.000000              0.000000   \n",
       "33                                -2.181579             -4.554094   \n",
       "34                                 0.000000              0.000000   \n",
       "...                                     ...                   ...   \n",
       "34112                              0.601961              9.165196   \n",
       "34113                              1.970755              5.833019   \n",
       "34114                              1.261038             -0.280566   \n",
       "34115                              1.918882              3.961730   \n",
       "34116                              2.270792              2.006561   \n",
       "34117                              5.411538             10.759615   \n",
       "34118                             -3.154905             -5.488134   \n",
       "34119                              7.739291              8.587003   \n",
       "34120                              1.549462             -3.102063   \n",
       "34121                             -1.555556              3.333333   \n",
       "34122                              3.165961              6.399239   \n",
       "34123                              0.171272             -2.687507   \n",
       "34124                              1.329519              2.675363   \n",
       "34125                             -3.021089             -3.180534   \n",
       "34126                             -2.998521              2.318179   \n",
       "34127                             -3.576923             -4.980769   \n",
       "34129                              2.686179              2.382453   \n",
       "34130                             -2.183908              1.353210   \n",
       "34133                             -0.550908              0.365162   \n",
       "34136                             -2.598245             -3.866989   \n",
       "34138                             -0.715006             -5.207425   \n",
       "34139                              0.466454             10.102806   \n",
       "34140                              0.283374              0.114639   \n",
       "34141                              0.574792              3.948839   \n",
       "34142                              1.406962              2.376582   \n",
       "34143                             -7.877532              1.030867   \n",
       "34144                              5.778408              9.017458   \n",
       "34145                              8.709631             10.788813   \n",
       "34146                              0.680556              1.793651   \n",
       "34147                             -0.148148            -18.018519   \n",
       "\n",
       "       return_games_played_20years  service_point_won_20years  \\\n",
       "0                         0.099016                   5.118173   \n",
       "2                        -0.862751                  -2.844315   \n",
       "3                         3.000000                   1.000000   \n",
       "4                        -0.557619                  -5.830450   \n",
       "6                        -0.786303                   0.790709   \n",
       "7                        -1.212381                  -2.773853   \n",
       "8                         0.642381                  11.011429   \n",
       "9                         0.439661                   3.069863   \n",
       "10                       -2.050648                  -6.983664   \n",
       "11                        0.719789                  -1.955712   \n",
       "12                       -1.500216                   0.895951   \n",
       "13                        1.131968                  -1.232124   \n",
       "14                       -0.629917                   1.070907   \n",
       "16                       -0.264745                  10.867629   \n",
       "17                        0.250268                  -1.848756   \n",
       "18                        1.470000                  -4.775000   \n",
       "19                       -1.018841                  -9.797101   \n",
       "22                       -1.421795                  -2.129915   \n",
       "23                        0.472868                  -3.003876   \n",
       "24                       -0.632716                  -3.925926   \n",
       "25                       -0.323529                  -1.372549   \n",
       "26                        0.480769                  -0.250000   \n",
       "27                       -1.253664                  -3.394140   \n",
       "28                       -1.117647                 -13.176471   \n",
       "29                       -8.000000                  19.000000   \n",
       "30                       -1.366725                  -5.497648   \n",
       "31                        0.000000                  -4.428571   \n",
       "32                        0.000000                   0.000000   \n",
       "33                        0.411988                   0.936550   \n",
       "34                        0.000000                   0.000000   \n",
       "...                            ...                        ...   \n",
       "34112                     0.368627                   3.966912   \n",
       "34113                     0.732390                  -0.650943   \n",
       "34114                    -0.974649                  -6.352350   \n",
       "34115                     0.771866                  -4.823175   \n",
       "34116                     0.103276                   8.889290   \n",
       "34117                     0.345833                   5.558974   \n",
       "34118                    -0.235731                  -7.218753   \n",
       "34119                    -0.812784                  -1.864497   \n",
       "34120                    -0.163057                   2.194803   \n",
       "34121                     0.833333                   7.888889   \n",
       "34122                    -0.235968                   1.033951   \n",
       "34123                    -0.291850                   3.934419   \n",
       "34124                     1.128111                   1.145925   \n",
       "34125                    -0.452081                  -6.441720   \n",
       "34126                     0.504089                  -1.272786   \n",
       "34127                     0.480769                   2.769231   \n",
       "34129                    -1.005014                  -2.684553   \n",
       "34130                     0.053839                  -9.404856   \n",
       "34133                     0.153291                  -1.146078   \n",
       "34136                    -1.114929                  -7.598430   \n",
       "34138                    -0.038440                   0.217732   \n",
       "34139                     0.781335                   4.007143   \n",
       "34140                     0.492302                   1.357597   \n",
       "34141                    -0.268661                   2.534792   \n",
       "34142                    -0.378270                   0.838819   \n",
       "34143                     0.780622                  -3.127224   \n",
       "34144                     0.542454                   5.144467   \n",
       "34145                    -0.452274                   8.832555   \n",
       "34146                     0.325397                   4.797619   \n",
       "34147                    -0.574074                  -3.870370   \n",
       "\n",
       "       return_point_won_20years  total_point_won_20years       h2h  \\\n",
       "0                     -1.148676                 1.866266 -1.000000   \n",
       "2                     -2.289802                -2.225344  1.000000   \n",
       "3                      2.571429                 1.857143 -1.000000   \n",
       "4                      2.368942                -1.484709 -1.000000   \n",
       "6                      4.116092                 2.654502 -0.200000   \n",
       "7                      2.779481                -0.078052  1.000000   \n",
       "8                      6.719048                 8.235476 -1.000000   \n",
       "9                      3.633980                 2.877679 -0.000000   \n",
       "10                     1.720767                -2.407857 -0.000000   \n",
       "11                    -2.947333                -2.154163  0.000000   \n",
       "12                    12.855204                 6.047258 -1.000000   \n",
       "13                    -4.869222                -2.768531  0.500000   \n",
       "14                     5.971693                 2.989335 -0.333333   \n",
       "16                     7.906587                 8.417840 -1.000000   \n",
       "17                    -0.579861                -1.283191  0.142857   \n",
       "18                    -7.723333                -5.661667 -1.000000   \n",
       "19                    -0.043478                -4.345652  1.000000   \n",
       "22                    -5.091453                -3.359402  0.000000   \n",
       "23                    -2.310078                -2.193798  0.333333   \n",
       "24                    -0.391975                -1.966049 -1.000000   \n",
       "25                     2.769608                 0.627451  1.000000   \n",
       "26                    -9.634615                -4.897436 -1.000000   \n",
       "27                    -0.009770                -1.534911 -0.333333   \n",
       "28                     0.352941                -5.941176  0.000000   \n",
       "29                    -4.750000                 5.500000  1.000000   \n",
       "30                    -5.081707                -5.383595  1.000000   \n",
       "31                     8.071429                 1.357143  1.000000   \n",
       "32                     0.000000                 0.000000  1.000000   \n",
       "33                    -1.727485                -0.585088  1.000000   \n",
       "34                     0.000000                 0.000000  1.000000   \n",
       "...                         ...                      ...       ...   \n",
       "34112                  1.522794                 2.501471  1.000000   \n",
       "34113                  1.630189                 0.652201  0.333333   \n",
       "34114                  1.773932                -2.037261  0.333333   \n",
       "34115                  1.970888                -1.208108 -0.600000   \n",
       "34116                  2.454088                 5.330413 -0.000000   \n",
       "34117                  6.017628                 4.774038  0.333333   \n",
       "34118                 -3.449184                -4.952832  1.000000   \n",
       "34119                  8.712548                 3.115661 -0.142857   \n",
       "34120                  0.936170                 1.131746  0.333333   \n",
       "34121                 -5.666667                 1.500000  1.000000   \n",
       "34122                  2.439572                 1.734998 -0.600000   \n",
       "34123                  0.635217                 2.044453 -0.000000   \n",
       "34124                  2.033833                 1.313774 -0.000000   \n",
       "34125                 -2.408329                -3.962368 -1.000000   \n",
       "34126                 -1.120343                -1.062686  1.000000   \n",
       "34127                 -4.750000                -0.750000 -0.333333   \n",
       "34129                  3.128388                -0.030149  1.000000   \n",
       "34130                 -2.884535                -5.607461  1.000000   \n",
       "34133                 -1.144960                -1.042645 -0.090909   \n",
       "34136                 -1.259863                -4.356239  0.333333   \n",
       "34138                 -1.888127                -0.863972 -0.600000   \n",
       "34139                  0.734694                 2.608036  1.000000   \n",
       "34140                  0.227441                 0.631608  1.000000   \n",
       "34141                  1.441607                 1.631607  1.000000   \n",
       "34142                  1.417511                 1.033966 -0.000000   \n",
       "34143                 -6.801938                -4.011065  0.333333   \n",
       "34144                  4.628472                 4.506437 -1.000000   \n",
       "34145                  8.892710                 8.229462 -1.000000   \n",
       "34146                  3.888889                 3.496032 -1.000000   \n",
       "34147                 -3.000000                -3.870370  0.000000   \n",
       "\n",
       "       minutes_loser_winner  \n",
       "0                  0.051257  \n",
       "2                 -0.000953  \n",
       "3                 -0.654459  \n",
       "4                  0.461002  \n",
       "6                 -0.270004  \n",
       "7                 -0.012852  \n",
       "8                 -0.988188  \n",
       "9                  0.276389  \n",
       "10                 0.833793  \n",
       "11                 0.866020  \n",
       "12                -0.826643  \n",
       "13                 0.524411  \n",
       "14                -0.340478  \n",
       "16                -0.440061  \n",
       "17                -0.108866  \n",
       "18                 0.901450  \n",
       "19                 0.917861  \n",
       "22                 0.145420  \n",
       "23                -0.135211  \n",
       "24                 0.186507  \n",
       "25                -0.094218  \n",
       "26                 0.886092  \n",
       "27                -0.001228  \n",
       "28                 0.425738  \n",
       "29                -0.796990  \n",
       "30                 0.710295  \n",
       "31                 0.920333  \n",
       "32                 0.967373  \n",
       "33                -0.233179  \n",
       "34                 0.000000  \n",
       "...                     ...  \n",
       "34112              0.000000  \n",
       "34113              0.000000  \n",
       "34114              0.455270  \n",
       "34115             -0.000000  \n",
       "34116             -0.147449  \n",
       "34117             -0.492202  \n",
       "34118              0.138200  \n",
       "34119             -0.857336  \n",
       "34120             -0.270817  \n",
       "34121              0.000000  \n",
       "34122             -0.000000  \n",
       "34123             -0.327804  \n",
       "34124             -0.283616  \n",
       "34125              0.055203  \n",
       "34126              0.150237  \n",
       "34127              0.730359  \n",
       "34129              0.301961  \n",
       "34130              0.707095  \n",
       "34133              0.116789  \n",
       "34136              0.853023  \n",
       "34138              0.183922  \n",
       "34139              0.712187  \n",
       "34140              0.000000  \n",
       "34141             -0.278235  \n",
       "34142             -0.521972  \n",
       "34143              0.012581  \n",
       "34144              0.116734  \n",
       "34145             -0.893484  \n",
       "34146             -0.888489  \n",
       "34147             -0.580448  \n",
       "\n",
       "[27148 rows x 32 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, LabelBinarizer\n",
    "from sklearn.preprocessing import label_binarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLabelBinarizer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, classes):\n",
    "        self.classes = classes\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        enc = label_binarize(X, classes=self.classes)\n",
    "        return enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 1, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"round_csv\"])),\n",
    "        (\"cat_encoder\", CustomLabelBinarizer(index_round_csv)),\n",
    "    ])\n",
    "\n",
    "round_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 8)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0],\n",
       "       [0, 0, 1, 0],\n",
       "       [0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, 0],\n",
       "       [0, 1, 0, 0],\n",
       "       [0, 1, 0, 0]])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"surface\"])),\n",
    "        (\"cat_encoder\", CustomLabelBinarizer(index_surface)),\n",
    "    ])\n",
    "\n",
    "surface_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 4)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawSize_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"draw_size\"])),\n",
    "        (\"cat_encoder\", CustomLabelBinarizer(index_draw_size)),\n",
    "    ])\n",
    "\n",
    "drawSize_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 9)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawSize_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0],\n",
       "       [1, 0, 0, 0],\n",
       "       [1, 0, 0, 0],\n",
       "       ...,\n",
       "       [1, 0, 0, 0],\n",
       "       [1, 0, 0, 0],\n",
       "       [1, 0, 0, 0]])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levelTour_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"level_tour\"])),\n",
    "        (\"cat_encoder\", CustomLabelBinarizer(index_level_tour)),\n",
    "    ])\n",
    "\n",
    "levelTour_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 4)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levelTour_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 61.  ,  27.52,  55.  ],\n",
       "       [ 65.  ,  29.49,  80.  ],\n",
       "       [ 68.  ,  29.72,  81.  ],\n",
       "       ...,\n",
       "       [ 63.  ,  30.13,  88.  ],\n",
       "       [ 60.  ,  29.64,  91.  ],\n",
       "       [ 45.  ,  27.82, 100.  ]])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "weather_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"temp_weather\", \"press_weather\", \"humid_weather\"])),\n",
    "        ('imputer', Imputer(strategy=\"mean\")),\n",
    "    ])\n",
    "\n",
    "weather_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 3)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2016.],\n",
       "       [2016.],\n",
       "       [2016.],\n",
       "       ...,\n",
       "       [2016.],\n",
       "       [2016.],\n",
       "       [2016.]])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "year_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"year_atp\"])),\n",
    "        ('imputer', Imputer(strategy=\"most_frequent\")),\n",
    "    ])\n",
    "\n",
    "year_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 1)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "year_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLabelBinarizer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, sparse_output=False):\n",
    "        self.sparse_output = sparse_output\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        enc = LabelBinarizer(sparse_output=self.sparse_output)\n",
    "        return enc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1],\n",
       "       ...,\n",
       "       [1],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "door_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"door\"])),\n",
    "        (\"cat_encoder\", CustomLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "door_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 1)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "door_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "class fillna(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, val):\n",
    "        self.val = val\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):        \n",
    "        return X.fillna(self.val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 2)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncertainty_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"uncertainty_1year\", \"uncertainty_20years\"])),\n",
    "        (\"cat_encoder\", fillna(10)),\n",
    "    ])\n",
    "\n",
    "uncertainty_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 2)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"count_match_1year\", \"count_match_20years\"])),\n",
    "        (\"cat_encoder\", fillna(0)),\n",
    "    ])\n",
    "\n",
    "count_pipeline.fit_transform(dataset).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "preprocess_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"stats_pipeline\", stats_pipeline),\n",
    "        (\"round_pipeline\", round_pipeline),\n",
    "        (\"surface_pipeline\", surface_pipeline),\n",
    "        (\"drawSize_pipeline\", drawSize_pipeline),\n",
    "        (\"levelTour_pipeline\", levelTour_pipeline),\n",
    "        (\"weather_pipeline\", levelTour_pipeline),\n",
    "        (\"year_pipeline\", levelTour_pipeline),\n",
    "        (\"door_pipeline\", levelTour_pipeline),\n",
    "        (\"uncertainty_pipeline\", levelTour_pipeline),\n",
    "        (\"count_pipeline\", count_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148, 75)"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.680874</td>\n",
       "      <td>-1.353279</td>\n",
       "      <td>-0.859836</td>\n",
       "      <td>8.912022</td>\n",
       "      <td>-0.228962</td>\n",
       "      <td>4.176776</td>\n",
       "      <td>-0.974317</td>\n",
       "      <td>0.684973</td>\n",
       "      <td>-6.111202</td>\n",
       "      <td>-2.769672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.668889</td>\n",
       "      <td>-1.328148</td>\n",
       "      <td>0.091852</td>\n",
       "      <td>-2.805926</td>\n",
       "      <td>-0.319630</td>\n",
       "      <td>-1.348519</td>\n",
       "      <td>-1.214815</td>\n",
       "      <td>-1.078889</td>\n",
       "      <td>-6.271481</td>\n",
       "      <td>-1.893704</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-17.928571</td>\n",
       "      <td>-5.785714</td>\n",
       "      <td>-0.428571</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>-8.142857</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>-4.071429</td>\n",
       "      <td>-2.071429</td>\n",
       "      <td>13.285714</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.351282</td>\n",
       "      <td>-5.203846</td>\n",
       "      <td>-0.489744</td>\n",
       "      <td>7.125321</td>\n",
       "      <td>-1.542949</td>\n",
       "      <td>6.682372</td>\n",
       "      <td>8.243269</td>\n",
       "      <td>-0.016987</td>\n",
       "      <td>16.787179</td>\n",
       "      <td>2.906731</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5          6   \\\n",
       "0  22.680874 -1.353279 -0.859836  8.912022 -0.228962  4.176776  -0.974317   \n",
       "1  -6.668889 -1.328148  0.091852 -2.805926 -0.319630 -1.348519  -1.214815   \n",
       "2  15.666667  1.666667  2.666667 -0.000000  4.333333  0.333333  14.666667   \n",
       "3 -17.928571 -5.785714 -0.428571  3.142857 -8.142857  7.500000  -4.071429   \n",
       "4  12.351282 -5.203846 -0.489744  7.125321 -1.542949  6.682372   8.243269   \n",
       "\n",
       "         7          8         9   ...     65   66   67   68   69   70   71  \\\n",
       "0  0.684973  -6.111202 -2.769672  ...    0.0  1.0  0.0  0.0  0.0  1.0  0.0   \n",
       "1 -1.078889  -6.271481 -1.893704  ...    1.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "2  4.666667  33.000000  6.333333  ...    1.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "3 -2.071429  13.285714  2.428571  ...    1.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "4 -0.016987  16.787179  2.906731  ...    1.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "\n",
       "    72     73     74  \n",
       "0  0.0  110.0  243.0  \n",
       "1  0.0   71.0  130.0  \n",
       "2  0.0    3.0    7.0  \n",
       "3  0.0   10.0  200.0  \n",
       "4  0.0   75.0  148.0  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    75\n",
       "dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train).isnull().any().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27148,)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = dataset[\"result\"]\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### predset preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "testset = test_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.fit_transform(testset)\n",
    "y_test = testset[\"result\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.6572112727942531, total=   2.1s\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.6500920810313076, total=   2.1s\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.6497237569060773, total=   2.1s\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.6469613259668509, total=   2.2s\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.6546961325966851, total=   2.0s\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.6580110497237569, total=   2.0s\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.6554327808471455, total=   2.2s\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.6432123779701603, total=   2.1s\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.6651317001289372, total=   2.2s\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.6492908454595689, total=   2.1s\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.6405156537753223, total=   2.0s\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.6388581952117863, total=   2.3s\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.6408839779005525, total=   2.5s\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.6430281819856327, total=   2.4s\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.6454227297844907, total=   2.4s\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV]  n_neighbors=4, weights=distance, score=0.660036832412523, total=   2.3s\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV]  n_neighbors=4, weights=distance, score=0.658195211786372, total=   2.3s\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=distance, score=0.6604051565377532, total=   2.4s\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=distance, score=0.6607109965002763, total=   2.3s\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=distance, score=0.6503960213667342, total=   2.3s\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV]  n_neighbors=5, weights=uniform, score=0.6524861878453039, total=   2.3s\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV]  n_neighbors=5, weights=uniform, score=0.6583793738489871, total=   2.5s\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV]  n_neighbors=5, weights=uniform, score=0.6511970534069982, total=   2.4s\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV]  n_neighbors=5, weights=uniform, score=0.6662368760361024, total=   2.4s\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV]  n_neighbors=5, weights=distance, score=0.6681399631675875, total=   2.4s\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV]  n_neighbors=5, weights=uniform, score=0.6527905691655922, total=   2.6s\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV]  n_neighbors=5, weights=distance, score=0.6596685082872928, total=   2.4s\n",
      "[CV]  n_neighbors=5, weights=distance, score=0.6596685082872928, total=   2.3s\n",
      "[CV]  n_neighbors=5, weights=distance, score=0.6754466752624793, total=   2.1s\n",
      "[CV]  n_neighbors=5, weights=distance, score=0.6605268005157487, total=   2.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid=[{'weights': ['uniform', 'distance'], 'n_neighbors': [3, 4, 5]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3, n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 5, 'weights': 'distance'}"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.664689848239281"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(n_jobs=-1, weights='uniform', n_neighbors=3)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8195078827169588"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_knn_pred = knn_clf.predict(X_train)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_train, y_knn_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8375564129443569"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "forest_clf.fit(X_train, y_train)\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10, n_jobs=-1)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.836"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf_pred = forest_clf.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, forest_clf_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saved the model on the disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "PATH = 'models/'\n",
    "\n",
    "def save_model(obj, name, path=PATH):\n",
    "    file = open(path + name, 'wb')\n",
    "    pickle.dump(obj, file)\n",
    "    file.close()\n",
    "\n",
    "save_model(forest_clf, \"forest_clf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe901579710>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe8dee27710>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=100, high=500),\n",
    "        'max_features': randint(low=5, high=15),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestClassifier(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42, n_jobs=-1)\n",
    "rnd_search.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 14, 'n_estimators': 314}"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.74523258e-03, 1.59797413e-03, 1.28461142e-03, 8.79527716e-04,\n",
       "       1.29364500e-03, 1.16683467e-03, 9.87311221e-04, 7.13805457e-05,\n",
       "       1.66380151e-03, 1.74338495e-03, 1.27144517e-03, 5.40166838e-04,\n",
       "       1.48346600e-03, 7.26739057e-04, 1.21437177e-03, 1.34053176e-03,\n",
       "       1.27236443e-03, 9.35057097e-04, 1.05660824e-03, 5.63673761e-05,\n",
       "       4.86983061e-05, 8.37696766e-04, 8.02484718e-04, 6.47551913e-04,\n",
       "       7.43099759e-05, 8.43781548e-04, 8.31686466e-04, 6.68131734e-04,\n",
       "       7.64290157e-05, 8.62079098e-04, 8.06828381e-04, 6.53943354e-04,\n",
       "       6.78264716e-05, 8.62368260e-04, 7.52145664e-04, 6.47294889e-04,\n",
       "       5.51692092e-05, 8.54230546e-04, 7.88949216e-04, 6.95053883e-04,\n",
       "       6.44439995e-05, 3.13948044e-02, 3.76958812e-02])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = rnd_search.best_estimator_.feature_importances_\n",
    "#sorted(zip(feature_importan)ces, list(dataset.columns.values)), reverse=True))\n",
    "feature_importances[32:] ## useless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rnd_pred = rnd_search.predict(X_train)\n",
    "\n",
    "accuracy_score(y_train, rnd_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(rnd_search, \"rnd_search\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### predset\n",
    "\n",
    "match beetween years 2017 and 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "valset = predset.copy()\n",
    "\n",
    "valset.insert(valset.shape[1], \"result\", np.random.randint(2, size=valset.shape[0]))\n",
    "valset[\"result\"].value_counts()\n",
    "\n",
    "X_val = preprocess_pipeline.fit_transform(valset)\n",
    "y_val = valset[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8548258441903749"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf_pred = forest_clf.predict(X_val)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_val, forest_clf_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 85% of prediction accuracy , the almosts bookmakers are about 60% with odds modulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86838606753523"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rnd_pred = rnd_search.predict(X_val)\n",
    "\n",
    "accuracy_score(y_val, rnd_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test with only Common Opponent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = stats_pipeline.fit_transform(dataset)\n",
    "y_train = dataset[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = stats_pipeline.fit_transform(test_set)\n",
    "y_test = test_set[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = stats_pipeline.fit_transform(valset)\n",
    "y_val = valset[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe8da26f2b0>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe8da26f438>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=100, high=500),\n",
    "        'max_features': randint(low=5, high=15),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestClassifier(random_state=42)\n",
    "rnd_search_co = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42, n_jobs=-1)\n",
    "rnd_search_co.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 443}"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_co.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = rnd_search_co.best_estimator_.feature_importances_\n",
    "#sorted(zip(feature_importan)ces, list(dataset.columns.values)), reverse=True))\n",
    "feature_importances[32:] ## useless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8045714285714286"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rnd_pred_co = rnd_search_co.predict(X_test)\n",
    "accuracy_score(y_test, rnd_pred_co)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8516352034033502"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_pred_co = rnd_search_co.predict(X_val)\n",
    "accuracy_score(y_val, rnd_pred_co)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(rnd_search_co, \"rnd_search_co\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction pre-match 11/10/18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "PATH = 'models/'\n",
    "\n",
    "def load_model(name, path=PATH):\n",
    "    file = open(path + name, 'rb')\n",
    "    obj = pickle.load(file)\n",
    "    file.close()\n",
    "    return obj\n",
    "\n",
    "model = load_model(\"rnd_search_co\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import psycopg2 as pg\n",
    "import requests as req\n",
    "\n",
    "URL = 'postgresql://dataset:dataset1747@35.204.62.91:5432/dataset-dump1'\n",
    "engine = create_engine(URL)\n",
    "\n",
    "def get_data_prediction(date, winner, looser):\n",
    "    URL = \"https://raw.githubusercontent.com/panaC/tennis-dataset/master/app/dataset/prediction.sql\"\n",
    "    with engine.connect() as conn, conn.begin():\n",
    "        return pd.read_sql(req.get(URL).text.format(date=date, winner=winner, looser=looser), conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data = get_data_prediction('11/10/2018', 'Stefanos Tsitsipas', 'Alex de Minaur')\n",
    "prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.insert(prediction_data.shape[1], \"result\", np.random.randint(2, size=prediction_data.shape[0]))\n",
    "\n",
    "X_pred = stats_pipeline.fit_transform(prediction_data)\n",
    "pred = model.predict(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06320542, 0.93679458]])"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data[\"result\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is 97% of probability that Tsitsipas win because the result is 1 and 1 = first player in fct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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>uncertainty_1year</th>\n",
       "      <th>count_match_1year</th>\n",
       "      <th>serve_rating_1year</th>\n",
       "      <th>aces_1year</th>\n",
       "      <th>double_faults_1year</th>\n",
       "      <th>first_serve_1year</th>\n",
       "      <th>first_serve_point_won_1year</th>\n",
       "      <th>second_serve_point_won_1year</th>\n",
       "      <th>bp_saved_1year</th>\n",
       "      <th>service_game_played_1year</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.067935</td>\n",
       "      <td>13</td>\n",
       "      <td>-15.825</td>\n",
       "      <td>-5.65</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-6.716667</td>\n",
       "      <td>2.075</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>5.975</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>9.790835</td>\n",
       "      <td>3.092286</td>\n",
       "      <td>0.669302</td>\n",
       "      <td>-0.72378</td>\n",
       "      <td>-2.896813</td>\n",
       "      <td>2.132079</td>\n",
       "      <td>-0.335131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   uncertainty_1year  count_match_1year  serve_rating_1year  aces_1year  \\\n",
       "0           0.067935                 13             -15.825       -5.65   \n",
       "\n",
       "   double_faults_1year  first_serve_1year  first_serve_point_won_1year  \\\n",
       "0                  1.6          -6.716667                        2.075   \n",
       "\n",
       "   second_serve_point_won_1year  bp_saved_1year  service_game_played_1year  \\\n",
       "0                      0.516667           5.975                  -0.333333   \n",
       "\n",
       "           ...           return_rating_20years  \\\n",
       "0          ...                        9.790835   \n",
       "\n",
       "   first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                              3.092286              0.669302   \n",
       "\n",
       "   return_games_played_20years  service_point_won_20years  \\\n",
       "0                     -0.72378                  -2.896813   \n",
       "\n",
       "   return_point_won_20years  total_point_won_20years  winner_loser_match_win  \\\n",
       "0                  2.132079                -0.335131                     0.0   \n",
       "\n",
       "   h2h  minutes_loser_winner  \n",
       "0  1.0                  None  \n",
       "\n",
       "[1 rows x 37 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data = get_data_prediction('11/10/2018', 'Kevin Anderson', 'Dominic Thiem')\n",
    "prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.insert(prediction_data.shape[1], \"result\", np.random.randint(2, size=prediction_data.shape[0]))\n",
    "\n",
    "X_pred = stats_pipeline.fit_transform(prediction_data)\n",
    "pred = model.predict(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07900677, 0.92099323]])"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data[\"result\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- warning combine option. I think that 1 year is too short for head2head , add 3 new colonne with may be 10 years combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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>uncertainty_1year</th>\n",
       "      <th>count_match_1year</th>\n",
       "      <th>serve_rating_1year</th>\n",
       "      <th>aces_1year</th>\n",
       "      <th>double_faults_1year</th>\n",
       "      <th>first_serve_1year</th>\n",
       "      <th>first_serve_point_won_1year</th>\n",
       "      <th>second_serve_point_won_1year</th>\n",
       "      <th>bp_saved_1year</th>\n",
       "      <th>service_game_played_1year</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.091912</td>\n",
       "      <td>15</td>\n",
       "      <td>14.346154</td>\n",
       "      <td>4.230769</td>\n",
       "      <td>-1.076923</td>\n",
       "      <td>-2.961538</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>1.115385</td>\n",
       "      <td>3.538462</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>...</td>\n",
       "      <td>3.321875</td>\n",
       "      <td>0.985911</td>\n",
       "      <td>-0.114494</td>\n",
       "      <td>0.206544</td>\n",
       "      <td>3.67691</td>\n",
       "      <td>0.968995</td>\n",
       "      <td>2.11016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   uncertainty_1year  count_match_1year  serve_rating_1year  aces_1year  \\\n",
       "0           0.091912                 15           14.346154    4.230769   \n",
       "\n",
       "   double_faults_1year  first_serve_1year  first_serve_point_won_1year  \\\n",
       "0            -1.076923          -2.961538                     3.153846   \n",
       "\n",
       "   second_serve_point_won_1year  bp_saved_1year  service_game_played_1year  \\\n",
       "0                      1.115385        3.538462                   1.538462   \n",
       "\n",
       "           ...           return_rating_20years  \\\n",
       "0          ...                        3.321875   \n",
       "\n",
       "   first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                              0.985911             -0.114494   \n",
       "\n",
       "   return_games_played_20years  service_point_won_20years  \\\n",
       "0                     0.206544                    3.67691   \n",
       "\n",
       "   return_point_won_20years  total_point_won_20years  winner_loser_match_win  \\\n",
       "0                  0.968995                  2.11016                     1.0   \n",
       "\n",
       "        h2h  minutes_loser_winner  \n",
       "0  0.666667                  None  \n",
       "\n",
       "[1 rows x 37 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data = get_data_prediction('11/10/2018', 'Alexander Zverev', 'Marin Cilic')\n",
    "prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.insert(prediction_data.shape[1], \"result\", np.random.randint(2, size=prediction_data.shape[0]))\n",
    "\n",
    "X_pred = stats_pipeline.fit_transform(prediction_data)\n",
    "pred = model.predict(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.78329571, 0.21670429]])"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data[\"result\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>uncertainty_1year</th>\n",
       "      <th>count_match_1year</th>\n",
       "      <th>serve_rating_1year</th>\n",
       "      <th>aces_1year</th>\n",
       "      <th>double_faults_1year</th>\n",
       "      <th>first_serve_1year</th>\n",
       "      <th>first_serve_point_won_1year</th>\n",
       "      <th>second_serve_point_won_1year</th>\n",
       "      <th>bp_saved_1year</th>\n",
       "      <th>service_game_played_1year</th>\n",
       "      <th>...</th>\n",
       "      <th>return_rating_20years</th>\n",
       "      <th>first_serve_return_point_won_20years</th>\n",
       "      <th>bp_converted_20years</th>\n",
       "      <th>return_games_played_20years</th>\n",
       "      <th>service_point_won_20years</th>\n",
       "      <th>return_point_won_20years</th>\n",
       "      <th>total_point_won_20years</th>\n",
       "      <th>winner_loser_match_win</th>\n",
       "      <th>h2h</th>\n",
       "      <th>minutes_loser_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.15625</td>\n",
       "      <td>8</td>\n",
       "      <td>39.625</td>\n",
       "      <td>20.625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>9.375</td>\n",
       "      <td>-11.6875</td>\n",
       "      <td>21.5625</td>\n",
       "      <td>7.5625</td>\n",
       "      <td>...</td>\n",
       "      <td>-68.709553</td>\n",
       "      <td>-13.003473</td>\n",
       "      <td>-14.844916</td>\n",
       "      <td>2.604565</td>\n",
       "      <td>3.238263</td>\n",
       "      <td>-14.168259</td>\n",
       "      <td>-5.131055</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   uncertainty_1year  count_match_1year  serve_rating_1year  aces_1year  \\\n",
       "0            0.15625                  8              39.625      20.625   \n",
       "\n",
       "   double_faults_1year  first_serve_1year  first_serve_point_won_1year  \\\n",
       "0                  1.0              3.125                        9.375   \n",
       "\n",
       "   second_serve_point_won_1year  bp_saved_1year  service_game_played_1year  \\\n",
       "0                      -11.6875         21.5625                     7.5625   \n",
       "\n",
       "           ...           return_rating_20years  \\\n",
       "0          ...                      -68.709553   \n",
       "\n",
       "   first_serve_return_point_won_20years  bp_converted_20years  \\\n",
       "0                            -13.003473            -14.844916   \n",
       "\n",
       "   return_games_played_20years  service_point_won_20years  \\\n",
       "0                     2.604565                   3.238263   \n",
       "\n",
       "   return_point_won_20years  total_point_won_20years  winner_loser_match_win  \\\n",
       "0                -14.168259                -5.131055                     2.0   \n",
       "\n",
       "        h2h  minutes_loser_winner  \n",
       "0  0.333333                  None  \n",
       "\n",
       "[1 rows x 37 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data = get_data_prediction('11/10/2018', 'Novak Djokovic', 'John Isner')\n",
    "prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data.insert(prediction_data.shape[1], \"result\", np.random.randint(2, size=prediction_data.shape[0]))\n",
    "\n",
    "X_pred = stats_pipeline.fit_transform(prediction_data)\n",
    "pred = model.predict(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.66817156, 0.33182844]])"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_data[\"result\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if prediction_data[\"result\"][0] == 0 then \n",
    "    \n",
    "    pred = 1 = los or pred = 0 = winner \n",
    "\n",
    "else\n",
    "\n",
    "    pred = 1 = win or pred = 0 = los"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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