ensemble_vc.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Pipeline\n",
    "\n",
    "> This pipeline uses each match's outcome to learn which player shall make it to the round of 16"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The model trains on data recorded in the prior 3 years of the testing year\n",
    "- Post training, it predicts the first round of the tournament in two ways:\n",
    "    - Walkovers (given manually) for players who compete with players who don't have statistics\n",
    "    - model's predictions for matches where both players have statistics\n",
    "- Testing data is reconstructed to predict winners of the second round\n",
    "- Only relies on model for prediction of winners of second round\n",
    "- Testing data is reconstructed to predict winners of the third round. This is also the last loop of testing.\n",
    "- Only relies on model for prediction of winners of third round\n",
    "- Compare with the actual results and winners of third round"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from difflib import get_close_matches\n",
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score\n",
    ")\n",
    "from sklearn.ensemble import VotingClassifier\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_ON = [2015, 2018]\n",
    "TEST_ON = 2018\n",
    "mod1 = MLPClassifier(activation='logistic', solver='adam', \n",
    "                      learning_rate='adaptive', max_iter=10000, warm_start=True,\n",
    "                      hidden_layer_sizes=(128,50,2))\n",
    "mod2 = svm.SVC(kernel='rbf', C=1000, probability=True)\n",
    "\n",
    "MODEL = VotingClassifier([('mlp',mod1),('svm',mod2)], voting='hard',weights=[2,1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = ['aces_per_match', 'double_faults_per_match', 'break_points_faced_per_match',\n",
    "         'break_points_opportunities_per_match', 'height (cm)',\n",
    "         'matches_played', '1st_serve',\n",
    "         'return_games_won', '1st_serve_points_won', '2nd_serve_points_won',\n",
    "         'service_games_won', '1st_serve_return_points_won',\n",
    "         '2nd_serve_return_points_won']\n",
    "\n",
    "# DO NOT CHANGE THIS: It conatins the order of matches so that the match seeds are intact\n",
    "actual_seed = [48, 43, 34, 42, 35, 53, 57, 40, 52, 49, 63, 54, 55, 59, 46,\n",
    "               58, 39, 60, 41, 33, 61, 56, 50, 62, 51, 44, 38, 37, 45, 36,\n",
    "               32, 47, 8, 10, 9, 15, 21, 25, 6, 7, 14, 24, 20, 18, 13, 11,\n",
    "               26, 19, 16, 4, 3, 2, 28, 5, 12, 1, 17, 27, 30, 22, 29, 0,\n",
    "               23, 31,]\n",
    "\n",
    "\n",
    "def change_name(name: str) -> str:\n",
    "    \"\"\"change order of name from first last to last first\n",
    "\n",
    "    Args:\n",
    "        name (str): name of player as first last\n",
    "\n",
    "    Returns:\n",
    "        str: last first name\n",
    "    \"\"\"\n",
    "    split_names = name.split()\n",
    "    f_name = split_names.pop(0)\n",
    "    split_names.append(f_name)\n",
    "    final_name = \" \".join(split_names)\n",
    "    return final_name\n",
    "\n",
    "\n",
    "def stat_df(year: int) -> pd.DataFrame:\n",
    "    \"\"\"returns stats of players participating in the next given year's AO\n",
    "\n",
    "    Args:\n",
    "        year (int): year of stats needed\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: stats of players participating in the next given year's AO\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(f'./data/aus-open-player-stats-{year}.csv')\n",
    "\n",
    "    df.loc[df.aces == 0, 'aces'] = -np.inf\n",
    "\n",
    "    per_match_cols = ['aces', 'double_faults',\n",
    "                      'break_points_opportunities', 'break_points_faced']\n",
    "    for col in per_match_cols:\n",
    "        df[f'{col}_per_match'] = df[col] / df.matches_played\n",
    "\n",
    "    percent_cols = [\"1st_serve\", \"1st_serve_points_won\", \"2nd_serve_points_won\", \"break_points_saved\",\n",
    "                    \"service_games_won\", \"1st_serve_return_points_won\", \"2nd_serve_return_points_won\",\n",
    "                    \"break_points_converted\", \"return_games_won\", 'total_points_won', 'return_points_won']\n",
    "    for col in percent_cols:\n",
    "        df[col] = df[col].apply(lambda x: round(float(x.strip('%')) / 100, 2))\n",
    "\n",
    "    per_match_cols = [f'{x}_per_match' for x in per_match_cols]\n",
    "    per_match_cols.extend(percent_cols)\n",
    "    per_match_cols.extend(['name', 'year', 'height (cm)', 'matches_played'])\n",
    "    return df[per_match_cols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_df(year: int) -> pd.DataFrame:\n",
    "    \"\"\"provides training data for the given year\n",
    "\n",
    "    Args:\n",
    "        year (int): year of the tournament\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: training data for the given year with the player's stats in the previous year\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(f'./data/m{year}.csv')\n",
    "    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True).dt.year\n",
    "    df = df[['Round', 'Winner', 'Loser', 'WRank', 'LRank', 'Date']]\n",
    "\n",
    "    even = df.iloc[::2,].copy(deep=True)\n",
    "    odd = df.iloc[1::2,].copy(deep=True)\n",
    "    even.rename(columns={'Winner': 'player_0', 'Loser': 'player_1',\n",
    "                'WRank': 'rank_0', 'LRank': 'rank_1'}, inplace=True)\n",
    "    odd.rename(columns={'Winner': 'player_1', 'Loser': 'player_0',\n",
    "                'WRank': 'rank_1', 'LRank': 'rank_0'}, inplace=True)\n",
    "    even['winner'] = 0\n",
    "    odd['winner'] = 1\n",
    "\n",
    "\n",
    "    final_df = pd.concat([even, odd]).sort_index()\n",
    "    final_df['rank'] = final_df['rank_0'] - final_df['rank_1']\n",
    "    # final_df.drop(columns=['rank'], inplace=True)\n",
    "\n",
    "    names_to_lookup = set(final_df.player_0)\n",
    "    names_to_lookup.update(set(final_df.player_1))\n",
    "\n",
    "    df2 = stat_df(year - 1)\n",
    "\n",
    "    df2['new_name'] = df2['name'].apply(change_name)\n",
    "    df2['other_name'] = df2['new_name'].apply(\n",
    "        lambda x: get_close_matches(x, names_to_lookup, 1, cutoff=0.5)[0])\n",
    "\n",
    "    df2 = df2.drop(columns=['new_name'])\n",
    "\n",
    "    final_df[stats] = final_df.apply(lambda x: df2.loc[df2.other_name == x.player_0,stats].copy(deep=True).iloc[0,] - df2.loc[df2.other_name == x.player_1,stats].copy(deep=True).iloc[0,], axis=1)\n",
    "    return final_df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Round</th>\n",
       "      <th>player_0</th>\n",
       "      <th>player_1</th>\n",
       "      <th>rank_0</th>\n",
       "      <th>rank_1</th>\n",
       "      <th>Date</th>\n",
       "      <th>winner</th>\n",
       "      <th>rank</th>\n",
       "      <th>aces_per_match</th>\n",
       "      <th>double_faults_per_match</th>\n",
       "      <th>...</th>\n",
       "      <th>break_points_opportunities_per_match</th>\n",
       "      <th>height (cm)</th>\n",
       "      <th>matches_played</th>\n",
       "      <th>1st_serve</th>\n",
       "      <th>return_games_won</th>\n",
       "      <th>1st_serve_points_won</th>\n",
       "      <th>2nd_serve_points_won</th>\n",
       "      <th>service_games_won</th>\n",
       "      <th>1st_serve_return_points_won</th>\n",
       "      <th>2nd_serve_return_points_won</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Round</td>\n",
       "      <td>Berankis R.</td>\n",
       "      <td>Sijsling I.</td>\n",
       "      <td>85.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.551724</td>\n",
       "      <td>-1.706897</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.116711</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Round</td>\n",
       "      <td>Brown D.</td>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-1.868132</td>\n",
       "      <td>-0.817582</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.432967</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Round</td>\n",
       "      <td>Anderson K.</td>\n",
       "      <td>Schwartzman D.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>9.104343</td>\n",
       "      <td>2.211335</td>\n",
       "      <td>...</td>\n",
       "      <td>4.626059</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Round</td>\n",
       "      <td>Coric B.</td>\n",
       "      <td>Chardy J.</td>\n",
       "      <td>91.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-7.867821</td>\n",
       "      <td>-4.334382</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.924469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Round</td>\n",
       "      <td>Lacko L.</td>\n",
       "      <td>Gonzalez M.</td>\n",
       "      <td>99.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.426471</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>...</td>\n",
       "      <td>1.036765</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</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>122</th>\n",
       "      <td>Quarterfinals</td>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.753917</td>\n",
       "      <td>-0.888479</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.495853</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Quarterfinals</td>\n",
       "      <td>Raonic M.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>10.661367</td>\n",
       "      <td>0.880763</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.586645</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Semifinals</td>\n",
       "      <td>Federer R.</td>\n",
       "      <td>Wawrinka S.</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-10.566952</td>\n",
       "      <td>-3.987179</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.730057</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Semifinals</td>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.480672</td>\n",
       "      <td>1.415126</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.892437</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>The Final</td>\n",
       "      <td>Federer R.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.484163</td>\n",
       "      <td>-3.124434</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.446833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>381 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Round     player_0        player_1  rank_0  rank_1  Date  winner  \\\n",
       "0        1st Round  Berankis R.     Sijsling I.    85.0    84.0  2015       0   \n",
       "1        1st Round     Brown D.     Dimitrov G.    90.0    11.0  2015       1   \n",
       "2        1st Round  Anderson K.  Schwartzman D.    15.0    59.0  2015       0   \n",
       "3        1st Round     Coric B.       Chardy J.    91.0    31.0  2015       1   \n",
       "4        1st Round     Lacko L.     Gonzalez M.    99.0    98.0  2015       0   \n",
       "..             ...          ...             ...     ...     ...   ...     ...   \n",
       "122  Quarterfinals  Dimitrov G.       Goffin D.    15.0    11.0  2017       0   \n",
       "123  Quarterfinals    Raonic M.        Nadal R.     3.0     9.0  2017       1   \n",
       "124     Semifinals   Federer R.     Wawrinka S.    17.0     4.0  2017       0   \n",
       "125     Semifinals  Dimitrov G.        Nadal R.    15.0     9.0  2017       1   \n",
       "126      The Final   Federer R.        Nadal R.    17.0     9.0  2017       0   \n",
       "\n",
       "     rank  aces_per_match  double_faults_per_match  ...  \\\n",
       "0     1.0       -5.551724                -1.706897  ...   \n",
       "1    79.0       -1.868132                -0.817582  ...   \n",
       "2   -44.0        9.104343                 2.211335  ...   \n",
       "3    60.0       -7.867821                -4.334382  ...   \n",
       "4     1.0        2.426471                 0.558824  ...   \n",
       "..    ...             ...                      ...  ...   \n",
       "122   4.0        0.753917                -0.888479  ...   \n",
       "123  -6.0       10.661367                 0.880763  ...   \n",
       "124  13.0      -10.566952                -3.987179  ...   \n",
       "125   6.0        4.480672                 1.415126  ...   \n",
       "126   8.0       -2.484163                -3.124434  ...   \n",
       "\n",
       "     break_points_opportunities_per_match  height (cm)  matches_played  \\\n",
       "0                               -5.116711        -16.0            32.0   \n",
       "1                               -2.432967          5.0           -22.0   \n",
       "2                                4.626059         33.0            27.0   \n",
       "3                               -4.924469          0.0            10.0   \n",
       "4                                1.036765         15.0            26.0   \n",
       "..                                    ...          ...             ...   \n",
       "122                             -1.495853         11.0             4.0   \n",
       "123                             -5.586645         11.0            20.0   \n",
       "124                            -10.730057          2.0            25.0   \n",
       "125                             -1.892437          6.0            18.0   \n",
       "126                            -11.446833          0.0            35.0   \n",
       "\n",
       "     1st_serve  return_games_won  1st_serve_points_won  2nd_serve_points_won  \\\n",
       "0         0.00             -0.03                 -0.05                  0.00   \n",
       "1         0.04             -0.06                 -0.04                 -0.03   \n",
       "2         0.16              0.03                  0.15                  0.17   \n",
       "3        -0.05              0.05                 -0.02                  0.02   \n",
       "4        -0.02              0.09                  0.03                 -0.07   \n",
       "..         ...               ...                   ...                   ...   \n",
       "122       0.03             -0.07                  0.01                  0.05   \n",
       "123      -0.05             -0.21                  0.13                  0.00   \n",
       "124       0.04              0.09                  0.06                 -0.01   \n",
       "125      -0.05             -0.19                  0.05                 -0.01   \n",
       "126      -0.06             -0.09                  0.12                  0.01   \n",
       "\n",
       "     service_games_won  1st_serve_return_points_won  \\\n",
       "0                -0.04                         0.02   \n",
       "1                -0.02                        -0.04   \n",
       "2                 0.44                         0.03   \n",
       "3                 0.01                         0.02   \n",
       "4                -0.02                         0.07   \n",
       "..                 ...                          ...   \n",
       "122               0.05                         0.01   \n",
       "123               0.11                        -0.04   \n",
       "124               0.03                         0.05   \n",
       "125               0.03                        -0.01   \n",
       "126               0.09                         0.01   \n",
       "\n",
       "     2nd_serve_return_points_won  \n",
       "0                          -0.08  \n",
       "1                           0.02  \n",
       "2                           0.10  \n",
       "3                           0.02  \n",
       "4                           0.04  \n",
       "..                           ...  \n",
       "122                        -0.05  \n",
       "123                        -0.07  \n",
       "124                         0.04  \n",
       "125                        -0.07  \n",
       "126                        -0.02  \n",
       "\n",
       "[381 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame()\n",
    "for year in range(TRAIN_ON[0],TRAIN_ON[1]):\n",
    "    final_df = pd.concat([final_df, year_df(year)])\n",
    "final_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.replace([np.inf, -np.inf], np.nan).dropna()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>player_0</th>\n",
       "      <th>player_1</th>\n",
       "      <th>rank_0</th>\n",
       "      <th>rank_1</th>\n",
       "      <th>Date</th>\n",
       "      <th>winner</th>\n",
       "      <th>rank</th>\n",
       "      <th>aces_per_match</th>\n",
       "      <th>double_faults_per_match</th>\n",
       "      <th>break_points_faced_per_match</th>\n",
       "      <th>...</th>\n",
       "      <th>service_games_won</th>\n",
       "      <th>1st_serve_return_points_won</th>\n",
       "      <th>2nd_serve_return_points_won</th>\n",
       "      <th>Round_1st Round</th>\n",
       "      <th>Round_2nd Round</th>\n",
       "      <th>Round_3rd Round</th>\n",
       "      <th>Round_4th Round</th>\n",
       "      <th>Round_Quarterfinals</th>\n",
       "      <th>Round_Semifinals</th>\n",
       "      <th>Round_The Final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berankis R.</td>\n",
       "      <td>Sijsling I.</td>\n",
       "      <td>85.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.551724</td>\n",
       "      <td>-1.706897</td>\n",
       "      <td>-3.454907</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brown D.</td>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-1.868132</td>\n",
       "      <td>-0.817582</td>\n",
       "      <td>-1.215385</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson K.</td>\n",
       "      <td>Schwartzman D.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>9.104343</td>\n",
       "      <td>2.211335</td>\n",
       "      <td>4.269068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coric B.</td>\n",
       "      <td>Chardy J.</td>\n",
       "      <td>91.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-7.867821</td>\n",
       "      <td>-4.334382</td>\n",
       "      <td>-5.548387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lacko L.</td>\n",
       "      <td>Gonzalez M.</td>\n",
       "      <td>99.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.426471</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>1.632353</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>122</th>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.753917</td>\n",
       "      <td>-0.888479</td>\n",
       "      <td>-1.460829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Raonic M.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>10.661367</td>\n",
       "      <td>0.880763</td>\n",
       "      <td>-7.456280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Federer R.</td>\n",
       "      <td>Wawrinka S.</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-10.566952</td>\n",
       "      <td>-3.987179</td>\n",
       "      <td>-9.774217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.480672</td>\n",
       "      <td>1.415126</td>\n",
       "      <td>-2.722689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Federer R.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.484163</td>\n",
       "      <td>-3.124434</td>\n",
       "      <td>-10.697964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        player_0        player_1  rank_0  rank_1  Date  winner  rank  \\\n",
       "0    Berankis R.     Sijsling I.    85.0    84.0  2015       0   1.0   \n",
       "1       Brown D.     Dimitrov G.    90.0    11.0  2015       1  79.0   \n",
       "2    Anderson K.  Schwartzman D.    15.0    59.0  2015       0 -44.0   \n",
       "3       Coric B.       Chardy J.    91.0    31.0  2015       1  60.0   \n",
       "4       Lacko L.     Gonzalez M.    99.0    98.0  2015       0   1.0   \n",
       "..           ...             ...     ...     ...   ...     ...   ...   \n",
       "122  Dimitrov G.       Goffin D.    15.0    11.0  2017       0   4.0   \n",
       "123    Raonic M.        Nadal R.     3.0     9.0  2017       1  -6.0   \n",
       "124   Federer R.     Wawrinka S.    17.0     4.0  2017       0  13.0   \n",
       "125  Dimitrov G.        Nadal R.    15.0     9.0  2017       1   6.0   \n",
       "126   Federer R.        Nadal R.    17.0     9.0  2017       0   8.0   \n",
       "\n",
       "     aces_per_match  double_faults_per_match  break_points_faced_per_match  \\\n",
       "0         -5.551724                -1.706897                     -3.454907   \n",
       "1         -1.868132                -0.817582                     -1.215385   \n",
       "2          9.104343                 2.211335                      4.269068   \n",
       "3         -7.867821                -4.334382                     -5.548387   \n",
       "4          2.426471                 0.558824                      1.632353   \n",
       "..              ...                      ...                           ...   \n",
       "122        0.753917                -0.888479                     -1.460829   \n",
       "123       10.661367                 0.880763                     -7.456280   \n",
       "124      -10.566952                -3.987179                     -9.774217   \n",
       "125        4.480672                 1.415126                     -2.722689   \n",
       "126       -2.484163                -3.124434                    -10.697964   \n",
       "\n",
       "     ...  service_games_won  1st_serve_return_points_won  \\\n",
       "0    ...              -0.04                         0.02   \n",
       "1    ...              -0.02                        -0.04   \n",
       "2    ...               0.44                         0.03   \n",
       "3    ...               0.01                         0.02   \n",
       "4    ...              -0.02                         0.07   \n",
       "..   ...                ...                          ...   \n",
       "122  ...               0.05                         0.01   \n",
       "123  ...               0.11                        -0.04   \n",
       "124  ...               0.03                         0.05   \n",
       "125  ...               0.03                        -0.01   \n",
       "126  ...               0.09                         0.01   \n",
       "\n",
       "     2nd_serve_return_points_won  Round_1st Round  Round_2nd Round  \\\n",
       "0                          -0.08                1                0   \n",
       "1                           0.02                1                0   \n",
       "2                           0.10                1                0   \n",
       "3                           0.02                1                0   \n",
       "4                           0.04                1                0   \n",
       "..                           ...              ...              ...   \n",
       "122                        -0.05                0                0   \n",
       "123                        -0.07                0                0   \n",
       "124                         0.04                0                0   \n",
       "125                        -0.07                0                0   \n",
       "126                        -0.02                0                0   \n",
       "\n",
       "     Round_3rd Round  Round_4th Round  Round_Quarterfinals  Round_Semifinals  \\\n",
       "0                  0                0                    0                 0   \n",
       "1                  0                0                    0                 0   \n",
       "2                  0                0                    0                 0   \n",
       "3                  0                0                    0                 0   \n",
       "4                  0                0                    0                 0   \n",
       "..               ...              ...                  ...               ...   \n",
       "122                0                0                    1                 0   \n",
       "123                0                0                    1                 0   \n",
       "124                0                0                    0                 1   \n",
       "125                0                0                    0                 1   \n",
       "126                0                0                    0                 0   \n",
       "\n",
       "     Round_The Final  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "..               ...  \n",
       "122                0  \n",
       "123                0  \n",
       "124                0  \n",
       "125                0  \n",
       "126                1  \n",
       "\n",
       "[314 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.get_dummies(final_df, columns=['Round'])\n",
    "final_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = final_df.drop(columns=['winner','player_0','player_1','rank_0','rank_1'])\n",
    "y_train = final_df['winner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;mlp&#x27;,\n",
       "                              MLPClassifier(activation=&#x27;logistic&#x27;,\n",
       "                                            hidden_layer_sizes=(128, 50, 2),\n",
       "                                            learning_rate=&#x27;adaptive&#x27;,\n",
       "                                            max_iter=10000, warm_start=True)),\n",
       "                             (&#x27;svm&#x27;, SVC(C=1000, probability=True))],\n",
       "                 weights=[2, 1])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;mlp&#x27;,\n",
       "                              MLPClassifier(activation=&#x27;logistic&#x27;,\n",
       "                                            hidden_layer_sizes=(128, 50, 2),\n",
       "                                            learning_rate=&#x27;adaptive&#x27;,\n",
       "                                            max_iter=10000, warm_start=True)),\n",
       "                             (&#x27;svm&#x27;, SVC(C=1000, probability=True))],\n",
       "                 weights=[2, 1])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(activation=&#x27;logistic&#x27;, hidden_layer_sizes=(128, 50, 2),\n",
       "              learning_rate=&#x27;adaptive&#x27;, max_iter=10000, warm_start=True)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1000, probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('mlp',\n",
       "                              MLPClassifier(activation='logistic',\n",
       "                                            hidden_layer_sizes=(128, 50, 2),\n",
       "                                            learning_rate='adaptive',\n",
       "                                            max_iter=10000, warm_start=True)),\n",
       "                             ('svm', SVC(C=1000, probability=True))],\n",
       "                 weights=[2, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MODEL\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We get data of players participating in 2019, \n",
    "# and their stats in 2018\n",
    "main_df = year_df(TEST_ON)\n",
    "main_df = pd.get_dummies(main_df, columns=['Round'])\n",
    "cols_to_have = main_df.columns\n",
    "main_df = main_df.loc[:63,].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.index = actual_seed\n",
    "main_df.sort_index(inplace=True)\n",
    "test = main_df.replace([np.inf,-np.inf],np.nan)\n",
    "test = test.dropna()\n",
    "\n",
    "# # test.drop(columns=['player_0','player_1','rank_0','rank_1'], inplace=True)\n",
    "X_test = test.drop(columns=['winner','player_0','player_1','rank_0','rank_1'])\n",
    "y_test = test['winner']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First Round (Round of 64) prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7254901960784313"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Round Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_64 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.reset_index(drop=True)\n",
    "test['prediction'] = round_64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this dataframe helps in including the manual and model predictions\n",
    "middle_man = main_df.join(test[['player_0', 'player_1',\n",
    "                                'rank_0', 'rank_1', 'prediction']]\n",
    "                          .set_index(['player_0', 'player_1',\n",
    "                                      'rank_0', 'rank_1']),\n",
    "                          on=['player_0', 'player_1', 'rank_0', 'rank_1'])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Force a certian prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "middle_man.loc[(middle_man.aces_per_match == np.inf) |\n",
    "               (middle_man.aces_per_match == -np.inf), 'prediction'] = middle_man.loc[(middle_man.aces_per_match == np.inf) |\n",
    "                                                                                      (middle_man.aces_per_match == -np.inf),].apply(lambda x: 1 if x.aces_per_match == -np.inf else 0, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "middle_man['w_name'] = middle_man.apply(lambda x: x.player_0 if x.prediction == 0 else x.player_1, axis=1)\n",
    "middle_man['w_rank'] = middle_man.apply(lambda x: x.rank_0 if x.prediction == 0 else x.rank_1, axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2nd Round predictions (Round of 32)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_32 = middle_man[['w_name','w_rank']].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_even = round_32.iloc[::2,].copy(deep=True).reset_index(drop=True)\n",
    "r_32_odd = round_32.iloc[1::2,].copy(deep=True).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_even.columns = ['player_0','rank_0']\n",
    "r_32_odd.columns = ['player_1','rank_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_merge = pd.concat([r_32_even,r_32_odd], axis=1)\n",
    "r_32_merge['Round'] = '2nd Round'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m names_to_lookup\u001b[39m.\u001b[39mupdate(middle_man\u001b[39m.\u001b[39mplayer_1)\n\u001b[1;32m      6\u001b[0m df2[\u001b[39m'\u001b[39m\u001b[39mnew_name\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df2[\u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mapply(change_name)\n\u001b[0;32m----> 7\u001b[0m df2[\u001b[39m'\u001b[39m\u001b[39mother_name\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df2[\u001b[39m'\u001b[39;49m\u001b[39mnew_name\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49mapply(\n\u001b[1;32m      8\u001b[0m     \u001b[39mlambda\u001b[39;49;00m x: get_close_matches(x, names_to_lookup, \u001b[39m1\u001b[39;49m, cutoff\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m)[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m     10\u001b[0m df2 \u001b[39m=\u001b[39m df2\u001b[39m.\u001b[39mdrop(columns\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mnew_name\u001b[39m\u001b[39m'\u001b[39m])\n",
      "File \u001b[0;32m~/Documents/Spring23/CS490/Predicting-Brackets-Grand-Slam-Tennis-Tournaments/venv/lib/python3.11/site-packages/pandas/core/series.py:4771\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4661\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply\u001b[39m(\n\u001b[1;32m   4662\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   4663\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4666\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   4667\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m Series:\n\u001b[1;32m   4668\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   4669\u001b[0m \u001b[39m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4670\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4769\u001b[0m \u001b[39m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4770\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4771\u001b[0m     \u001b[39mreturn\u001b[39;00m SeriesApply(\u001b[39mself\u001b[39;49m, func, convert_dtype, args, kwargs)\u001b[39m.\u001b[39;49mapply()\n",
      "File \u001b[0;32m~/Documents/Spring23/CS490/Predicting-Brackets-Grand-Slam-Tennis-Tournaments/venv/lib/python3.11/site-packages/pandas/core/apply.py:1123\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1120\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_str()\n\u001b[1;32m   1122\u001b[0m \u001b[39m# self.f is Callable\u001b[39;00m\n\u001b[0;32m-> 1123\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_standard()\n",
      "File \u001b[0;32m~/Documents/Spring23/CS490/Predicting-Brackets-Grand-Slam-Tennis-Tournaments/venv/lib/python3.11/site-packages/pandas/core/apply.py:1174\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1172\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1173\u001b[0m         values \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39mastype(\u001b[39mobject\u001b[39m)\u001b[39m.\u001b[39m_values\n\u001b[0;32m-> 1174\u001b[0m         mapped \u001b[39m=\u001b[39m lib\u001b[39m.\u001b[39;49mmap_infer(\n\u001b[1;32m   1175\u001b[0m             values,\n\u001b[1;32m   1176\u001b[0m             f,\n\u001b[1;32m   1177\u001b[0m             convert\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvert_dtype,\n\u001b[1;32m   1178\u001b[0m         )\n\u001b[1;32m   1180\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(mapped) \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(mapped[\u001b[39m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1181\u001b[0m     \u001b[39m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1182\u001b[0m     \u001b[39m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1183\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_constructor_expanddim(\u001b[39mlist\u001b[39m(mapped), index\u001b[39m=\u001b[39mobj\u001b[39m.\u001b[39mindex)\n",
      "File \u001b[0;32m~/Documents/Spring23/CS490/Predicting-Brackets-Grand-Slam-Tennis-Tournaments/venv/lib/python3.11/site-packages/pandas/_libs/lib.pyx:2924\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "Cell \u001b[0;32mIn[22], line 8\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      4\u001b[0m names_to_lookup\u001b[39m.\u001b[39mupdate(middle_man\u001b[39m.\u001b[39mplayer_1)\n\u001b[1;32m      6\u001b[0m df2[\u001b[39m'\u001b[39m\u001b[39mnew_name\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df2[\u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mapply(change_name)\n\u001b[1;32m      7\u001b[0m df2[\u001b[39m'\u001b[39m\u001b[39mother_name\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df2[\u001b[39m'\u001b[39m\u001b[39mnew_name\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mapply(\n\u001b[0;32m----> 8\u001b[0m     \u001b[39mlambda\u001b[39;00m x: get_close_matches(x, names_to_lookup, \u001b[39m1\u001b[39;49m, cutoff\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m)[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m     10\u001b[0m df2 \u001b[39m=\u001b[39m df2\u001b[39m.\u001b[39mdrop(columns\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mnew_name\u001b[39m\u001b[39m'\u001b[39m])\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "df2 = stat_df(2018)\n",
    "\n",
    "names_to_lookup = set(middle_man.player_0)\n",
    "names_to_lookup.update(middle_man.player_1)\n",
    "\n",
    "df2['new_name'] = df2['name'].apply(change_name)\n",
    "df2['other_name'] = df2['new_name'].apply(\n",
    "    lambda x: get_close_matches(x, names_to_lookup, 1, cutoff=0.5)[0])\n",
    "\n",
    "df2 = df2.drop(columns=['new_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_merge[stats] = r_32_merge.apply(lambda x: df2.loc[df2.other_name == x.player_0,stats].copy(deep=True).iloc[0,] - df2.loc[df2.other_name == x.player_1,stats].copy(deep=True).iloc[0,], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_merge['rank'] = r_32_merge['rank_0'] - r_32_merge['rank_1']\n",
    "r_32_merge = pd.get_dummies(r_32_merge, columns=['Round'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_merge[['Round_1st Round','Round_3rd Round', 'Round_4th Round', 'Round_Quarterfinals','Round_Semifinals','Round_The Final']] = 0\n",
    "r_32_merge['Date'] = pd.to_datetime('2019')\n",
    "r_32_merge['Date'] = pd.to_datetime(r_32_merge['Date'], yearfirst=True).dt.year"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = r_32_merge.drop(columns=['player_0', 'player_1', 'rank_0', 'rank_1'])\n",
    "r_32_pred = model.predict(X_test[X_train.columns])\n",
    "r_32_merge['prediction'] = r_32_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_32_merge['w_name'] = r_32_merge.apply(lambda x: x.player_0 if x.prediction == 0 else x.player_1, axis=1)\n",
    "r_32_merge['w_rank'] = r_32_merge.apply(lambda x: x.rank_0 if x.prediction == 0 else x.rank_1, axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3rd Round predictions (Round of 16)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16 = r_32_merge[['w_name','w_rank']].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_even = r_16.iloc[::2,].copy(deep=True).reset_index(drop=True)\n",
    "r_16_odd = r_16.iloc[1::2,].copy(deep=True).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_even.columns = ['player_0','rank_0']\n",
    "r_16_odd.columns = ['player_1','rank_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_merge = pd.concat([r_16_even,r_16_odd], axis=1)\n",
    "r_16_merge['Round'] = '3rd Round'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_merge[stats] = r_16_merge.apply(lambda x: df2.loc[df2.other_name == x.player_0,stats].copy(deep=True).iloc[0,] - df2.loc[df2.other_name == x.player_1,stats].copy(deep=True).iloc[0,], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_merge['rank'] = r_16_merge['rank_0'] - r_16_merge['rank_1']\n",
    "r_16_merge = pd.get_dummies(r_16_merge, columns=['Round'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_merge[['Round_1st Round','Round_2nd Round', 'Round_4th Round', 'Round_Quarterfinals','Round_Semifinals','Round_The Final']] = 0\n",
    "r_16_merge['Date'] = pd.to_datetime('2019')\n",
    "r_16_merge['Date'] = pd.to_datetime(r_16_merge['Date'], yearfirst=True).dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = r_16_merge.drop(columns=['player_0','player_1','rank_0','rank_1'])\n",
    "r_16_pred = model.predict(X_test[X_train.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_16_merge['prediction']= r_16_pred"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Contenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>player_0</th>\n",
       "      <th>player_1</th>\n",
       "      <th>rank_0</th>\n",
       "      <th>rank_1</th>\n",
       "      <th>w_name</th>\n",
       "      <th>w_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Djokovic N.</td>\n",
       "      <td>Shapovalov D.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Djokovic N.</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>Medvedev D.</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Medvedev D.</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fognini F.</td>\n",
       "      <td>Carreno Busta P.</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Fognini F.</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kohlschreiber P.</td>\n",
       "      <td>Nishikori K.</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Nishikori K.</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zverev A.</td>\n",
       "      <td>Simon G.</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Zverev A.</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chung H.</td>\n",
       "      <td>Raonic M.</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Raonic M.</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Coric B.</td>\n",
       "      <td>Cecchinato M.</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Coric B.</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pouille L.</td>\n",
       "      <td>Thiem D.</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Thiem D.</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cilic M.</td>\n",
       "      <td>Verdasco F.</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Cilic M.</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bautista Agut R.</td>\n",
       "      <td>Khachanov K.</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Khachanov K.</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tsitsipas S.</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Tsitsipas S.</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Monfils G.</td>\n",
       "      <td>Federer R.</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Federer R.</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Anderson K.</td>\n",
       "      <td>Johnson S.</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Anderson K.</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dimitrov G.</td>\n",
       "      <td>Isner J.</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Isner J.</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Edmund K.</td>\n",
       "      <td>Schwartzman D.</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Edmund K.</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>De Minaur A.</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Nadal R.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_0          player_1  rank_0  rank_1        w_name  w_rank\n",
       "0        Djokovic N.     Shapovalov D.     1.0    27.0   Djokovic N.     1.0\n",
       "1          Goffin D.       Medvedev D.    22.0    19.0   Medvedev D.    19.0\n",
       "2         Fognini F.  Carreno Busta P.    13.0    23.0    Fognini F.    13.0\n",
       "3   Kohlschreiber P.      Nishikori K.    32.0     9.0  Nishikori K.     9.0\n",
       "4          Zverev A.          Simon G.     4.0    30.0     Zverev A.     4.0\n",
       "5           Chung H.         Raonic M.    25.0    17.0     Raonic M.    17.0\n",
       "6           Coric B.     Cecchinato M.    12.0    18.0      Coric B.    12.0\n",
       "7         Pouille L.          Thiem D.    31.0     8.0      Thiem D.     8.0\n",
       "8           Cilic M.       Verdasco F.     7.0    28.0      Cilic M.     7.0\n",
       "9   Bautista Agut R.      Khachanov K.    24.0    11.0  Khachanov K.    11.0\n",
       "10      Tsitsipas S.   Basilashvili N.    15.0    20.0  Tsitsipas S.    15.0\n",
       "11        Monfils G.        Federer R.    33.0     3.0    Federer R.     3.0\n",
       "12       Anderson K.        Johnson S.     6.0    34.0   Anderson K.     6.0\n",
       "13       Dimitrov G.          Isner J.    21.0    10.0      Isner J.    10.0\n",
       "14         Edmund K.    Schwartzman D.    14.0    16.0     Edmund K.    14.0\n",
       "15      De Minaur A.          Nadal R.    29.0     2.0      Nadal R.     2.0"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_16_merge['w_name'] = r_16_merge.apply(lambda x: x.player_0 if x.prediction == 0 else x.player_1, axis=1)\n",
    "r_16_merge['w_rank'] = r_16_merge.apply(lambda x: x.rank_0 if x.prediction == 0 else x.rank_1, axis=1)\n",
    "r_16_merge[['player_0','player_1','rank_0','rank_1','w_name','w_rank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Laaksonen H.</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Haase R.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zverev A.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Ramos-Vinolas A.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Sandgren T.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Berrettini M.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  actual  pred\n",
       "0        Laaksonen H.       0     0\n",
       "1            Sousa P.       0     0\n",
       "2            Haase R.       0     0\n",
       "3           Zverev A.       1     1\n",
       "4         Dimitrov G.       1     0\n",
       "..                ...     ...   ...\n",
       "123        Hurkacz H.       0     0\n",
       "124       Mcdonald M.       0     0\n",
       "125  Ramos-Vinolas A.       0     0\n",
       "126       Sandgren T.       0     0\n",
       "127     Berrettini M.       0     0\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(f'./data/m{TEST_ON}.csv')\n",
    "winners = test_df.loc[test_df.Round == '3rd Round','Winner']\n",
    "\n",
    "conf_matrix = pd.DataFrame(columns=['name','actual','pred'])\n",
    "names = set(test_df.Loser)\n",
    "names.update(test_df.Winner)\n",
    "conf_matrix['name'] = pd.Series(list(names))\n",
    "conf_matrix['actual'] = 0\n",
    "conf_matrix['pred'] = 0\n",
    "conf_matrix.loc[conf_matrix['name'].isin(winners),'actual'] = 1\n",
    "pred_winners = r_16_merge['w_name']\n",
    "conf_matrix.loc[conf_matrix['name'].isin(pred_winners),'pred'] = 1\n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,   6],\n",
       "       [  6,  10]])"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(conf_matrix['actual'],conf_matrix['pred'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.90625\n",
      "Precision: 0.625\n",
      "Recall: 0.625\n",
      "F1: 0.625\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(conf_matrix['actual'],conf_matrix['pred'])\n",
    "prec = precision_score(conf_matrix['actual'],conf_matrix['pred'])\n",
    "rec = recall_score(conf_matrix['actual'],conf_matrix['pred'])\n",
    "f1 = f1_score(conf_matrix['actual'],conf_matrix['pred'])\n",
    "print(f'Accuracy: {acc}\\nPrecision: {prec}\\nRecall: {rec}\\nF1: {f1}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When including `rank`: **10/16**\n",
    "\n",
    "When excluding `rank`: **7/16**"
   ]
  }
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