model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# from helpers import flatten_performance_df,get_performances_by_team,stack_df,win_loss_error_rate\n",
    "import xgboost as xgb\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.decomposition import PCA\n",
    "from constants import MONGO_NAME, MONGO_PW, MONGO_DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client = pymongo.MongoClient(\n",
    "#     f\"mongodb+srv://{MONGO_NAME}:{MONGO_PW}@cluster0.sfhws.mongodb.net/{MONGO_DB}?retryWrites=true&w=majority\"\n",
    "# )\n",
    "# db = client.superteam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# player_performances = db.playerPerformances.find({})\n",
    "# team_performances = db.teamPerformances.find({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# player_performance_df = pd.DataFrame(list(player_performances)).set_index('_id')\n",
    "# team_performance_df = pd.DataFrame(list(team_performances)).set_index('_id')\n",
    "# player_performance_df = flatten_performance_df(player_performance_df)\n",
    "# team_performance_df = flatten_performance_df(team_performance_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# team_game_ids = list(set(team_performance_df.GAME_ID))\n",
    "# player_game_ids = list(set(player_performance_df.GAME_ID))\n",
    "# game_ids = list(set(team_game_ids) & set(player_game_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_performances_by_team(performance_df, type):\n",
    "#     if type == \"team\":\n",
    "#         i = 6\n",
    "#     if type == \"player\":\n",
    "#         i = 9\n",
    "#     team_list = performance_df[\"TEAM_ABBREVIATION\"].astype(\"category\").cat.categories\n",
    "#     team_1_performances = performance_df[\n",
    "#         performance_df[\"TEAM_ABBREVIATION\"] == team_list[0]\n",
    "#     ]\n",
    "#     team_2_performances = performance_df[\n",
    "#         performance_df[\"TEAM_ABBREVIATION\"] == team_list[1]\n",
    "#     ]\n",
    "#     team_1_performances = (\n",
    "#         team_1_performances.iloc[:, i:]\n",
    "#         .apply(pd.to_numeric)\n",
    "#         .sort_values(\"MIN\", ascending=False)\n",
    "#         .reset_index(drop=True)\n",
    "#     )\n",
    "#     team_2_performances = (\n",
    "#         team_2_performances.iloc[:, i:]\n",
    "#         .apply(pd.to_numeric)\n",
    "#         .sort_values(\"MIN\", ascending=False)\n",
    "#         .reset_index(drop=True)\n",
    "#     )\n",
    "#     return team_1_performances, team_2_performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "    #  'START_POSITION',\n",
    "    \"PCT_FGA_2PT\",\n",
    "    \"PCT_AST_2PM\",\n",
    "    \"PCT_PTS_2PT\",\n",
    "    \"AST_PCT\",\n",
    "    \"PCT_FG3M\",\n",
    "    \"PCT_BLKA\",\n",
    "    \"PCT_BLK\",\n",
    "    \"FG3_PCT\",\n",
    "    \"PCT_PTS\",\n",
    "    \"PCT_FGM\",\n",
    "    \"PCT_REB\",\n",
    "    \"PCT_FGA\",\n",
    "    \"E_USG_PCT\",\n",
    "    \"REB_PCT\",\n",
    "    \"PCT_PTS_OFF_TOV\",\n",
    "    \"PCT_DREB\",\n",
    "    \"OPP_OREB_PCT\",\n",
    "    \"PCT_UAST_3PM\",\n",
    "    \"PCT_TOV\",\n",
    "    \"DREB_PCT\",\n",
    "    \"PCT_FTM\",\n",
    "    \"OPP_TOV_PCT\",\n",
    "    \"PCT_UAST_2PM\",\n",
    "    \"PCT_AST_3PM\",\n",
    "    \"USG_PCT\",\n",
    "    \"PCT_AST\",\n",
    "    \"FG_PCT\",\n",
    "    \"EFG_PCT\",\n",
    "    \"TS_PCT\",\n",
    "    \"PCT_OREB\",\n",
    "    \"PCT_PTS_2PT_MR\",\n",
    "    \"PCT_PF\",\n",
    "    \"FT_PCT\",\n",
    "    \"PCT_PTS_PAINT\",\n",
    "    \"PCT_PTS_FT\",\n",
    "    \"PCT_PFD\",\n",
    "    \"PCT_FGA_3PT\",\n",
    "    \"OPP_EFG_PCT\",\n",
    "    \"CFG_PCT\",\n",
    "    \"TM_TOV_PCT\",\n",
    "    \"PCT_UAST_FGM\",\n",
    "    \"PCT_PTS_3PT\",\n",
    "    \"OREB_PCT\",\n",
    "    \"PCT_PTS_FB\",\n",
    "    \"PCT_AST_FGM\",\n",
    "    \"UFG_PCT\",\n",
    "    \"PCT_FG3A\",\n",
    "    \"PCT_STL\",\n",
    "    \"DFG_PCT\",\n",
    "    \"OREB\",\n",
    "    \"AST\",\n",
    "    \"REB\",\n",
    "    \"DFGA\",\n",
    "    \"SAST\",\n",
    "    \"OPP_PTS_2ND_CHANCE\",\n",
    "    \"PFD\",\n",
    "    \"TO\",\n",
    "    \"FG3A\",\n",
    "    \"STL\",\n",
    "    \"POSS\",\n",
    "    \"PASS\",\n",
    "    \"UFGM\",\n",
    "    \"FG3M\",\n",
    "    \"PTS\",\n",
    "    \"UFGA\",\n",
    "    \"DRBC\",\n",
    "    \"OPP_PTS_PAINT\",\n",
    "    \"FTM\",\n",
    "    \"ORBC\",\n",
    "    \"BLKA\",\n",
    "    \"PTS_FB\",\n",
    "    \"CFGA\",\n",
    "    \"PTS_PAINT\",\n",
    "    \"TCHS\",\n",
    "    \"CFGM\",\n",
    "    \"PLUS_MINUS\",\n",
    "    \"DFGM\",\n",
    "    \"OPP_PTS_OFF_TOV\",\n",
    "    \"PTS_OFF_TOV\",\n",
    "    \"FGA\",\n",
    "    \"FTA\",\n",
    "    \"PTS_2ND_CHANCE\",\n",
    "    \"FGM\",\n",
    "    \"PF\",\n",
    "    \"DREB\",\n",
    "    \"BLK\",\n",
    "    \"RBC\",\n",
    "    \"OPP_PTS_FB\",\n",
    "    \"FTAST\",\n",
    "    \"E_OFF_RATING\",\n",
    "    \"OFF_RATING\",\n",
    "    \"E_NET_RATING\",\n",
    "    \"E_DEF_RATING\",\n",
    "    \"NET_RATING\",\n",
    "    \"DEF_RATING\",\n",
    "    \"E_PACE\",\n",
    "    \"AST_RATIO\",\n",
    "    \"DIST\",\n",
    "    \"AST_TOV\",\n",
    "    \"FTA_RATE\",\n",
    "    \"OPP_FTA_RATE\",\n",
    "    \"MIN\",\n",
    "    \"PACE_PER40\",\n",
    "    \"PACE\",\n",
    "    \"PIE\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tqdm import tqdm  # For progress tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def win_loss_error_rate(test_predictions, test_labels):\n",
    "    win_loss_predictions = np.where(test_predictions > 0, 1, 0)\n",
    "    win_loss_truth = np.where(test_labels.to_numpy() > 0, 1, 0)\n",
    "    return abs(win_loss_predictions - win_loss_truth).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the player data\n",
    "player_data = pd.read_csv(\"data/player_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing games: 100%|██████████| 9830/9830 [00:08<00:00, 1155.55it/s]\n"
     ]
    }
   ],
   "source": [
    "# Define parameters\n",
    "player_count = 5  # Number of players to consider per team\n",
    "target = \"PLUS_MINUS\"\n",
    "game_ids = list(set(player_data.GAME_ID))\n",
    "\n",
    "# Placeholder for processed rows\n",
    "data_rows = []\n",
    "\n",
    "# Process each game\n",
    "for game_id in tqdm(game_ids, desc=\"Processing games\"):\n",
    "    # Filter data for the current game\n",
    "    game_data = player_data[player_data.GAME_ID == game_id]\n",
    "\n",
    "    # Identify the two teams\n",
    "    team_abbr = game_data[\"TEAM_ABBREVIATION\"].unique()\n",
    "    if len(team_abbr) != 2:\n",
    "        continue  # Skip games without exactly two teams\n",
    "\n",
    "    # Get top players based on minutes played\n",
    "    team_1_data = (\n",
    "        game_data[game_data[\"TEAM_ABBREVIATION\"] == team_abbr[0]]\n",
    "        .sort_values(by=\"MIN\", ascending=False)\n",
    "        .head(player_count)\n",
    "    )\n",
    "    team_2_data = (\n",
    "        game_data[game_data[\"TEAM_ABBREVIATION\"] == team_abbr[1]]\n",
    "        .sort_values(by=\"MIN\", ascending=False)\n",
    "        .head(player_count)\n",
    "    )\n",
    "\n",
    "    # Ensure both teams have the required number of players\n",
    "    if len(team_1_data) < player_count or len(team_2_data) < player_count:\n",
    "        continue\n",
    "\n",
    "    # Compute the target (plus-minus)\n",
    "    team_1_plus_minus = team_1_data[\"PTS\"].sum() - team_2_data[\"PTS\"].sum()\n",
    "\n",
    "    # Flatten the features for both teams\n",
    "    team_1_features = team_1_data[features].fillna(0).values.flatten()\n",
    "    team_2_features = team_2_data[features].fillna(0).values.flatten()\n",
    "\n",
    "    # Generate column names dynamically\n",
    "    feature_columns = [\n",
    "        f\"team1_player{i+1}_{feature}\"\n",
    "        for i in range(player_count)\n",
    "        for feature in features\n",
    "    ] + [\n",
    "        f\"team2_player{i+1}_{feature}\"\n",
    "        for i in range(player_count)\n",
    "        for feature in features\n",
    "    ]\n",
    "\n",
    "    # Create original row\n",
    "    data_rows.append(\n",
    "        {\n",
    "            \"features\": np.concatenate([team_1_features, team_2_features]),\n",
    "            \"plus_minus\": team_1_plus_minus,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Create reordered row\n",
    "    data_rows.append(\n",
    "        {\n",
    "            \"features\": np.concatenate([team_2_features, team_1_features]),\n",
    "            \"plus_minus\": -team_1_plus_minus,\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert rows to DataFrame\n",
    "data_df = pd.DataFrame(data_rows)\n",
    "\n",
    "data_df = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(data_df[\"features\"].tolist(), columns=feature_columns),\n",
    "        data_df[[\"plus_minus\"]],\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "data_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling and splitting the data\n",
    "X = data_df.drop(columns=[\"plus_minus\"])\n",
    "y = data_df[\"plus_minus\"]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_scaled, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "# Final output\n",
    "final_data = pd.concat(\n",
    "    [pd.DataFrame(X_scaled, columns=X.columns), y.reset_index(drop=True)], axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:16.02219\tvalidation_1-rmse:16.35190\n",
      "[1]\tvalidation_0-rmse:15.93993\tvalidation_1-rmse:16.27437\n",
      "[2]\tvalidation_0-rmse:15.85832\tvalidation_1-rmse:16.19732\n",
      "[3]\tvalidation_0-rmse:15.77837\tvalidation_1-rmse:16.12203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/eywa/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [21:34:03] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:740: \n",
      "Parameters: { \"single_precision_histogram\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\tvalidation_0-rmse:15.69923\tvalidation_1-rmse:16.04734\n",
      "[5]\tvalidation_0-rmse:15.62087\tvalidation_1-rmse:15.97246\n",
      "[6]\tvalidation_0-rmse:15.54389\tvalidation_1-rmse:15.89863\n",
      "[7]\tvalidation_0-rmse:15.46732\tvalidation_1-rmse:15.82705\n",
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      "[10]\tvalidation_0-rmse:15.24442\tvalidation_1-rmse:15.61692\n",
      "[11]\tvalidation_0-rmse:15.17201\tvalidation_1-rmse:15.54825\n",
      "[12]\tvalidation_0-rmse:15.10016\tvalidation_1-rmse:15.48056\n",
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      "[14]\tvalidation_0-rmse:14.95848\tvalidation_1-rmse:15.34589\n",
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      "[1953]\tvalidation_0-rmse:1.37615\tvalidation_1-rmse:4.93660\n",
      "[1954]\tvalidation_0-rmse:1.37515\tvalidation_1-rmse:4.93598\n",
      "[1955]\tvalidation_0-rmse:1.37409\tvalidation_1-rmse:4.93521\n",
      "[1956]\tvalidation_0-rmse:1.37331\tvalidation_1-rmse:4.93465\n",
      "[1957]\tvalidation_0-rmse:1.37224\tvalidation_1-rmse:4.93404\n",
      "[1958]\tvalidation_0-rmse:1.37142\tvalidation_1-rmse:4.93342\n",
      "[1959]\tvalidation_0-rmse:1.37066\tvalidation_1-rmse:4.93284\n",
      "[1960]\tvalidation_0-rmse:1.36975\tvalidation_1-rmse:4.93225\n",
      "[1961]\tvalidation_0-rmse:1.36878\tvalidation_1-rmse:4.93169\n",
      "[1962]\tvalidation_0-rmse:1.36775\tvalidation_1-rmse:4.93116\n",
      "[1963]\tvalidation_0-rmse:1.36698\tvalidation_1-rmse:4.93070\n",
      "[1964]\tvalidation_0-rmse:1.36614\tvalidation_1-rmse:4.93007\n",
      "[1965]\tvalidation_0-rmse:1.36524\tvalidation_1-rmse:4.92948\n",
      "[1966]\tvalidation_0-rmse:1.36433\tvalidation_1-rmse:4.92885\n",
      "[1967]\tvalidation_0-rmse:1.36358\tvalidation_1-rmse:4.92839\n",
      "[1968]\tvalidation_0-rmse:1.36284\tvalidation_1-rmse:4.92802\n",
      "[1969]\tvalidation_0-rmse:1.36189\tvalidation_1-rmse:4.92740\n",
      "[1970]\tvalidation_0-rmse:1.36093\tvalidation_1-rmse:4.92689\n",
      "[1971]\tvalidation_0-rmse:1.35998\tvalidation_1-rmse:4.92630\n",
      "[1972]\tvalidation_0-rmse:1.35911\tvalidation_1-rmse:4.92562\n",
      "[1973]\tvalidation_0-rmse:1.35833\tvalidation_1-rmse:4.92506\n",
      "[1974]\tvalidation_0-rmse:1.35755\tvalidation_1-rmse:4.92453\n",
      "[1975]\tvalidation_0-rmse:1.35665\tvalidation_1-rmse:4.92393\n",
      "[1976]\tvalidation_0-rmse:1.35569\tvalidation_1-rmse:4.92333\n",
      "[1977]\tvalidation_0-rmse:1.35477\tvalidation_1-rmse:4.92284\n",
      "[1978]\tvalidation_0-rmse:1.35387\tvalidation_1-rmse:4.92248\n",
      "[1979]\tvalidation_0-rmse:1.35309\tvalidation_1-rmse:4.92192\n",
      "[1980]\tvalidation_0-rmse:1.35226\tvalidation_1-rmse:4.92138\n",
      "[1981]\tvalidation_0-rmse:1.35126\tvalidation_1-rmse:4.92084\n",
      "[1982]\tvalidation_0-rmse:1.35029\tvalidation_1-rmse:4.92028\n",
      "[1983]\tvalidation_0-rmse:1.34943\tvalidation_1-rmse:4.91963\n",
      "[1984]\tvalidation_0-rmse:1.34860\tvalidation_1-rmse:4.91911\n",
      "[1985]\tvalidation_0-rmse:1.34762\tvalidation_1-rmse:4.91854\n",
      "[1986]\tvalidation_0-rmse:1.34685\tvalidation_1-rmse:4.91806\n",
      "[1987]\tvalidation_0-rmse:1.34596\tvalidation_1-rmse:4.91755\n",
      "[1988]\tvalidation_0-rmse:1.34514\tvalidation_1-rmse:4.91700\n",
      "[1989]\tvalidation_0-rmse:1.34429\tvalidation_1-rmse:4.91652\n",
      "[1990]\tvalidation_0-rmse:1.34340\tvalidation_1-rmse:4.91599\n",
      "[1991]\tvalidation_0-rmse:1.34253\tvalidation_1-rmse:4.91533\n",
      "[1992]\tvalidation_0-rmse:1.34163\tvalidation_1-rmse:4.91469\n",
      "[1993]\tvalidation_0-rmse:1.34073\tvalidation_1-rmse:4.91411\n",
      "[1994]\tvalidation_0-rmse:1.33996\tvalidation_1-rmse:4.91343\n",
      "[1995]\tvalidation_0-rmse:1.33905\tvalidation_1-rmse:4.91285\n",
      "[1996]\tvalidation_0-rmse:1.33830\tvalidation_1-rmse:4.91232\n",
      "[1997]\tvalidation_0-rmse:1.33755\tvalidation_1-rmse:4.91176\n",
      "[1998]\tvalidation_0-rmse:1.33686\tvalidation_1-rmse:4.91117\n",
      "[1999]\tvalidation_0-rmse:1.33607\tvalidation_1-rmse:4.91065\n",
      "[2000]\tvalidation_0-rmse:1.33524\tvalidation_1-rmse:4.91003\n",
      "[2001]\tvalidation_0-rmse:1.33446\tvalidation_1-rmse:4.90965\n",
      "[2002]\tvalidation_0-rmse:1.33358\tvalidation_1-rmse:4.90905\n",
      "[2003]\tvalidation_0-rmse:1.33277\tvalidation_1-rmse:4.90868\n",
      "[2004]\tvalidation_0-rmse:1.33190\tvalidation_1-rmse:4.90815\n",
      "[2005]\tvalidation_0-rmse:1.33103\tvalidation_1-rmse:4.90769\n",
      "[2006]\tvalidation_0-rmse:1.33025\tvalidation_1-rmse:4.90721\n",
      "[2007]\tvalidation_0-rmse:1.32914\tvalidation_1-rmse:4.90656\n",
      "[2008]\tvalidation_0-rmse:1.32833\tvalidation_1-rmse:4.90617\n",
      "[2009]\tvalidation_0-rmse:1.32742\tvalidation_1-rmse:4.90556\n",
      "[2010]\tvalidation_0-rmse:1.32649\tvalidation_1-rmse:4.90498\n",
      "[2011]\tvalidation_0-rmse:1.32569\tvalidation_1-rmse:4.90430\n",
      "[2012]\tvalidation_0-rmse:1.32479\tvalidation_1-rmse:4.90361\n",
      "[2013]\tvalidation_0-rmse:1.32381\tvalidation_1-rmse:4.90303\n",
      "[2014]\tvalidation_0-rmse:1.32296\tvalidation_1-rmse:4.90248\n",
      "[2015]\tvalidation_0-rmse:1.32231\tvalidation_1-rmse:4.90204\n",
      "[2016]\tvalidation_0-rmse:1.32150\tvalidation_1-rmse:4.90156\n",
      "[2017]\tvalidation_0-rmse:1.32069\tvalidation_1-rmse:4.90091\n",
      "[2018]\tvalidation_0-rmse:1.31996\tvalidation_1-rmse:4.90039\n",
      "[2019]\tvalidation_0-rmse:1.31906\tvalidation_1-rmse:4.89986\n",
      "[2020]\tvalidation_0-rmse:1.31811\tvalidation_1-rmse:4.89930\n",
      "[2021]\tvalidation_0-rmse:1.31722\tvalidation_1-rmse:4.89877\n",
      "[2022]\tvalidation_0-rmse:1.31624\tvalidation_1-rmse:4.89806\n",
      "[2023]\tvalidation_0-rmse:1.31528\tvalidation_1-rmse:4.89748\n",
      "[2024]\tvalidation_0-rmse:1.31457\tvalidation_1-rmse:4.89710\n",
      "[2025]\tvalidation_0-rmse:1.31377\tvalidation_1-rmse:4.89663\n",
      "[2026]\tvalidation_0-rmse:1.31285\tvalidation_1-rmse:4.89606\n",
      "[2027]\tvalidation_0-rmse:1.31215\tvalidation_1-rmse:4.89550\n",
      "[2028]\tvalidation_0-rmse:1.31135\tvalidation_1-rmse:4.89515\n",
      "[2029]\tvalidation_0-rmse:1.31049\tvalidation_1-rmse:4.89456\n",
      "[2030]\tvalidation_0-rmse:1.30961\tvalidation_1-rmse:4.89397\n",
      "[2031]\tvalidation_0-rmse:1.30888\tvalidation_1-rmse:4.89341\n",
      "[2032]\tvalidation_0-rmse:1.30814\tvalidation_1-rmse:4.89298\n",
      "[2033]\tvalidation_0-rmse:1.30736\tvalidation_1-rmse:4.89246\n",
      "[2034]\tvalidation_0-rmse:1.30642\tvalidation_1-rmse:4.89196\n",
      "[2035]\tvalidation_0-rmse:1.30562\tvalidation_1-rmse:4.89150\n",
      "[2036]\tvalidation_0-rmse:1.30497\tvalidation_1-rmse:4.89102\n",
      "[2037]\tvalidation_0-rmse:1.30425\tvalidation_1-rmse:4.89053\n",
      "[2038]\tvalidation_0-rmse:1.30319\tvalidation_1-rmse:4.88992\n",
      "[2039]\tvalidation_0-rmse:1.30243\tvalidation_1-rmse:4.88941\n",
      "[2040]\tvalidation_0-rmse:1.30163\tvalidation_1-rmse:4.88892\n",
      "[2041]\tvalidation_0-rmse:1.30085\tvalidation_1-rmse:4.88839\n",
      "[2042]\tvalidation_0-rmse:1.30005\tvalidation_1-rmse:4.88784\n",
      "[2043]\tvalidation_0-rmse:1.29924\tvalidation_1-rmse:4.88726\n",
      "[2044]\tvalidation_0-rmse:1.29839\tvalidation_1-rmse:4.88658\n",
      "[2045]\tvalidation_0-rmse:1.29742\tvalidation_1-rmse:4.88596\n",
      "[2046]\tvalidation_0-rmse:1.29657\tvalidation_1-rmse:4.88536\n",
      "[2047]\tvalidation_0-rmse:1.29593\tvalidation_1-rmse:4.88485\n",
      "[2048]\tvalidation_0-rmse:1.29496\tvalidation_1-rmse:4.88431\n",
      "[2049]\tvalidation_0-rmse:1.29409\tvalidation_1-rmse:4.88364\n",
      "[2050]\tvalidation_0-rmse:1.29325\tvalidation_1-rmse:4.88312\n",
      "[2051]\tvalidation_0-rmse:1.29232\tvalidation_1-rmse:4.88249\n",
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      "[2053]\tvalidation_0-rmse:1.29067\tvalidation_1-rmse:4.88146\n",
      "[2054]\tvalidation_0-rmse:1.28992\tvalidation_1-rmse:4.88090\n",
      "[2055]\tvalidation_0-rmse:1.28904\tvalidation_1-rmse:4.88045\n",
      "[2056]\tvalidation_0-rmse:1.28836\tvalidation_1-rmse:4.87997\n",
      "[2057]\tvalidation_0-rmse:1.28750\tvalidation_1-rmse:4.87951\n",
      "[2058]\tvalidation_0-rmse:1.28686\tvalidation_1-rmse:4.87898\n",
      "[2059]\tvalidation_0-rmse:1.28600\tvalidation_1-rmse:4.87838\n",
      "[2060]\tvalidation_0-rmse:1.28524\tvalidation_1-rmse:4.87800\n",
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      "[2064]\tvalidation_0-rmse:1.28200\tvalidation_1-rmse:4.87584\n",
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      "[2069]\tvalidation_0-rmse:1.27776\tvalidation_1-rmse:4.87315\n",
      "[2070]\tvalidation_0-rmse:1.27685\tvalidation_1-rmse:4.87266\n",
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      "[2099]\tvalidation_0-rmse:1.25411\tvalidation_1-rmse:4.85886\n",
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      "[9826]\tvalidation_0-rmse:0.12206\tvalidation_1-rmse:4.36821\n",
      "[9827]\tvalidation_0-rmse:0.12203\tvalidation_1-rmse:4.36821\n",
      "[9828]\tvalidation_0-rmse:0.12201\tvalidation_1-rmse:4.36820\n",
      "[9829]\tvalidation_0-rmse:0.12198\tvalidation_1-rmse:4.36820\n",
      "[9830]\tvalidation_0-rmse:0.12196\tvalidation_1-rmse:4.36820\n",
      "[9831]\tvalidation_0-rmse:0.12193\tvalidation_1-rmse:4.36820\n",
      "[9832]\tvalidation_0-rmse:0.12192\tvalidation_1-rmse:4.36820\n",
      "[9833]\tvalidation_0-rmse:0.12189\tvalidation_1-rmse:4.36819\n",
      "[9834]\tvalidation_0-rmse:0.12186\tvalidation_1-rmse:4.36819\n",
      "[9835]\tvalidation_0-rmse:0.12184\tvalidation_1-rmse:4.36819\n",
      "[9836]\tvalidation_0-rmse:0.12181\tvalidation_1-rmse:4.36819\n",
      "[9837]\tvalidation_0-rmse:0.12176\tvalidation_1-rmse:4.36818\n",
      "[9838]\tvalidation_0-rmse:0.12172\tvalidation_1-rmse:4.36818\n",
      "[9839]\tvalidation_0-rmse:0.12171\tvalidation_1-rmse:4.36818\n",
      "[9840]\tvalidation_0-rmse:0.12168\tvalidation_1-rmse:4.36818\n",
      "[9841]\tvalidation_0-rmse:0.12164\tvalidation_1-rmse:4.36817\n",
      "[9842]\tvalidation_0-rmse:0.12162\tvalidation_1-rmse:4.36816\n",
      "[9843]\tvalidation_0-rmse:0.12160\tvalidation_1-rmse:4.36816\n",
      "[9844]\tvalidation_0-rmse:0.12158\tvalidation_1-rmse:4.36816\n",
      "[9845]\tvalidation_0-rmse:0.12155\tvalidation_1-rmse:4.36816\n",
      "[9846]\tvalidation_0-rmse:0.12154\tvalidation_1-rmse:4.36815\n",
      "[9847]\tvalidation_0-rmse:0.12153\tvalidation_1-rmse:4.36815\n",
      "[9848]\tvalidation_0-rmse:0.12152\tvalidation_1-rmse:4.36815\n",
      "[9849]\tvalidation_0-rmse:0.12150\tvalidation_1-rmse:4.36815\n",
      "[9850]\tvalidation_0-rmse:0.12148\tvalidation_1-rmse:4.36815\n",
      "[9851]\tvalidation_0-rmse:0.12145\tvalidation_1-rmse:4.36814\n",
      "[9852]\tvalidation_0-rmse:0.12142\tvalidation_1-rmse:4.36814\n",
      "[9853]\tvalidation_0-rmse:0.12140\tvalidation_1-rmse:4.36813\n",
      "[9854]\tvalidation_0-rmse:0.12139\tvalidation_1-rmse:4.36813\n",
      "[9855]\tvalidation_0-rmse:0.12138\tvalidation_1-rmse:4.36813\n",
      "[9856]\tvalidation_0-rmse:0.12135\tvalidation_1-rmse:4.36813\n",
      "[9857]\tvalidation_0-rmse:0.12134\tvalidation_1-rmse:4.36813\n",
      "[9858]\tvalidation_0-rmse:0.12130\tvalidation_1-rmse:4.36812\n",
      "[9859]\tvalidation_0-rmse:0.12127\tvalidation_1-rmse:4.36811\n",
      "[9860]\tvalidation_0-rmse:0.12122\tvalidation_1-rmse:4.36810\n",
      "[9861]\tvalidation_0-rmse:0.12119\tvalidation_1-rmse:4.36810\n",
      "[9862]\tvalidation_0-rmse:0.12118\tvalidation_1-rmse:4.36810\n",
      "[9863]\tvalidation_0-rmse:0.12117\tvalidation_1-rmse:4.36810\n",
      "[9864]\tvalidation_0-rmse:0.12116\tvalidation_1-rmse:4.36809\n",
      "[9865]\tvalidation_0-rmse:0.12113\tvalidation_1-rmse:4.36809\n",
      "[9866]\tvalidation_0-rmse:0.12112\tvalidation_1-rmse:4.36809\n",
      "[9867]\tvalidation_0-rmse:0.12107\tvalidation_1-rmse:4.36809\n",
      "[9868]\tvalidation_0-rmse:0.12104\tvalidation_1-rmse:4.36808\n",
      "[9869]\tvalidation_0-rmse:0.12101\tvalidation_1-rmse:4.36807\n",
      "[9870]\tvalidation_0-rmse:0.12100\tvalidation_1-rmse:4.36807\n",
      "[9871]\tvalidation_0-rmse:0.12096\tvalidation_1-rmse:4.36807\n",
      "[9872]\tvalidation_0-rmse:0.12092\tvalidation_1-rmse:4.36806\n",
      "[9873]\tvalidation_0-rmse:0.12091\tvalidation_1-rmse:4.36806\n",
      "[9874]\tvalidation_0-rmse:0.12089\tvalidation_1-rmse:4.36806\n",
      "[9875]\tvalidation_0-rmse:0.12086\tvalidation_1-rmse:4.36806\n",
      "[9876]\tvalidation_0-rmse:0.12083\tvalidation_1-rmse:4.36807\n",
      "[9877]\tvalidation_0-rmse:0.12080\tvalidation_1-rmse:4.36806\n",
      "[9878]\tvalidation_0-rmse:0.12079\tvalidation_1-rmse:4.36806\n",
      "[9879]\tvalidation_0-rmse:0.12075\tvalidation_1-rmse:4.36806\n",
      "[9880]\tvalidation_0-rmse:0.12074\tvalidation_1-rmse:4.36806\n",
      "[9881]\tvalidation_0-rmse:0.12071\tvalidation_1-rmse:4.36806\n",
      "[9882]\tvalidation_0-rmse:0.12067\tvalidation_1-rmse:4.36806\n",
      "[9883]\tvalidation_0-rmse:0.12065\tvalidation_1-rmse:4.36805\n",
      "[9884]\tvalidation_0-rmse:0.12064\tvalidation_1-rmse:4.36805\n",
      "[9885]\tvalidation_0-rmse:0.12063\tvalidation_1-rmse:4.36805\n",
      "[9886]\tvalidation_0-rmse:0.12061\tvalidation_1-rmse:4.36804\n",
      "[9887]\tvalidation_0-rmse:0.12060\tvalidation_1-rmse:4.36804\n",
      "[9888]\tvalidation_0-rmse:0.12059\tvalidation_1-rmse:4.36804\n",
      "[9889]\tvalidation_0-rmse:0.12056\tvalidation_1-rmse:4.36804\n",
      "[9890]\tvalidation_0-rmse:0.12053\tvalidation_1-rmse:4.36804\n",
      "[9891]\tvalidation_0-rmse:0.12051\tvalidation_1-rmse:4.36804\n",
      "[9892]\tvalidation_0-rmse:0.12046\tvalidation_1-rmse:4.36803\n",
      "[9893]\tvalidation_0-rmse:0.12044\tvalidation_1-rmse:4.36803\n",
      "[9894]\tvalidation_0-rmse:0.12042\tvalidation_1-rmse:4.36803\n",
      "[9895]\tvalidation_0-rmse:0.12040\tvalidation_1-rmse:4.36803\n",
      "[9896]\tvalidation_0-rmse:0.12038\tvalidation_1-rmse:4.36802\n",
      "[9897]\tvalidation_0-rmse:0.12036\tvalidation_1-rmse:4.36802\n",
      "[9898]\tvalidation_0-rmse:0.12035\tvalidation_1-rmse:4.36802\n",
      "[9899]\tvalidation_0-rmse:0.12032\tvalidation_1-rmse:4.36802\n",
      "[9900]\tvalidation_0-rmse:0.12030\tvalidation_1-rmse:4.36801\n",
      "[9901]\tvalidation_0-rmse:0.12025\tvalidation_1-rmse:4.36801\n",
      "[9902]\tvalidation_0-rmse:0.12020\tvalidation_1-rmse:4.36800\n",
      "[9903]\tvalidation_0-rmse:0.12018\tvalidation_1-rmse:4.36800\n",
      "[9904]\tvalidation_0-rmse:0.12016\tvalidation_1-rmse:4.36799\n",
      "[9905]\tvalidation_0-rmse:0.12014\tvalidation_1-rmse:4.36799\n",
      "[9906]\tvalidation_0-rmse:0.12012\tvalidation_1-rmse:4.36799\n",
      "[9907]\tvalidation_0-rmse:0.12008\tvalidation_1-rmse:4.36799\n",
      "[9908]\tvalidation_0-rmse:0.12007\tvalidation_1-rmse:4.36798\n",
      "[9909]\tvalidation_0-rmse:0.12004\tvalidation_1-rmse:4.36798\n",
      "[9910]\tvalidation_0-rmse:0.12002\tvalidation_1-rmse:4.36798\n",
      "[9911]\tvalidation_0-rmse:0.11998\tvalidation_1-rmse:4.36797\n",
      "[9912]\tvalidation_0-rmse:0.11997\tvalidation_1-rmse:4.36796\n",
      "[9913]\tvalidation_0-rmse:0.11995\tvalidation_1-rmse:4.36796\n",
      "[9914]\tvalidation_0-rmse:0.11993\tvalidation_1-rmse:4.36795\n",
      "[9915]\tvalidation_0-rmse:0.11990\tvalidation_1-rmse:4.36795\n",
      "[9916]\tvalidation_0-rmse:0.11988\tvalidation_1-rmse:4.36794\n",
      "[9917]\tvalidation_0-rmse:0.11987\tvalidation_1-rmse:4.36795\n",
      "[9918]\tvalidation_0-rmse:0.11986\tvalidation_1-rmse:4.36795\n",
      "[9919]\tvalidation_0-rmse:0.11981\tvalidation_1-rmse:4.36794\n",
      "[9920]\tvalidation_0-rmse:0.11979\tvalidation_1-rmse:4.36794\n",
      "[9921]\tvalidation_0-rmse:0.11976\tvalidation_1-rmse:4.36793\n",
      "[9922]\tvalidation_0-rmse:0.11975\tvalidation_1-rmse:4.36793\n",
      "[9923]\tvalidation_0-rmse:0.11971\tvalidation_1-rmse:4.36793\n",
      "[9924]\tvalidation_0-rmse:0.11968\tvalidation_1-rmse:4.36792\n",
      "[9925]\tvalidation_0-rmse:0.11967\tvalidation_1-rmse:4.36791\n",
      "[9926]\tvalidation_0-rmse:0.11965\tvalidation_1-rmse:4.36791\n",
      "[9927]\tvalidation_0-rmse:0.11963\tvalidation_1-rmse:4.36791\n",
      "[9928]\tvalidation_0-rmse:0.11961\tvalidation_1-rmse:4.36791\n",
      "[9929]\tvalidation_0-rmse:0.11958\tvalidation_1-rmse:4.36790\n",
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      "[9932]\tvalidation_0-rmse:0.11953\tvalidation_1-rmse:4.36790\n",
      "[9933]\tvalidation_0-rmse:0.11952\tvalidation_1-rmse:4.36790\n",
      "[9934]\tvalidation_0-rmse:0.11948\tvalidation_1-rmse:4.36789\n",
      "[9935]\tvalidation_0-rmse:0.11945\tvalidation_1-rmse:4.36789\n",
      "[9936]\tvalidation_0-rmse:0.11941\tvalidation_1-rmse:4.36788\n",
      "[9937]\tvalidation_0-rmse:0.11940\tvalidation_1-rmse:4.36788\n",
      "[9938]\tvalidation_0-rmse:0.11939\tvalidation_1-rmse:4.36788\n",
      "[9939]\tvalidation_0-rmse:0.11935\tvalidation_1-rmse:4.36788\n",
      "[9940]\tvalidation_0-rmse:0.11931\tvalidation_1-rmse:4.36787\n",
      "[9941]\tvalidation_0-rmse:0.11928\tvalidation_1-rmse:4.36787\n",
      "[9942]\tvalidation_0-rmse:0.11927\tvalidation_1-rmse:4.36787\n",
      "[9943]\tvalidation_0-rmse:0.11925\tvalidation_1-rmse:4.36786\n",
      "[9944]\tvalidation_0-rmse:0.11923\tvalidation_1-rmse:4.36786\n",
      "[9945]\tvalidation_0-rmse:0.11920\tvalidation_1-rmse:4.36786\n",
      "[9946]\tvalidation_0-rmse:0.11918\tvalidation_1-rmse:4.36785\n",
      "[9947]\tvalidation_0-rmse:0.11917\tvalidation_1-rmse:4.36785\n",
      "[9948]\tvalidation_0-rmse:0.11915\tvalidation_1-rmse:4.36786\n",
      "[9949]\tvalidation_0-rmse:0.11911\tvalidation_1-rmse:4.36785\n",
      "[9950]\tvalidation_0-rmse:0.11909\tvalidation_1-rmse:4.36785\n",
      "[9951]\tvalidation_0-rmse:0.11908\tvalidation_1-rmse:4.36784\n",
      "[9952]\tvalidation_0-rmse:0.11905\tvalidation_1-rmse:4.36784\n",
      "[9953]\tvalidation_0-rmse:0.11902\tvalidation_1-rmse:4.36784\n",
      "[9954]\tvalidation_0-rmse:0.11899\tvalidation_1-rmse:4.36783\n",
      "[9955]\tvalidation_0-rmse:0.11898\tvalidation_1-rmse:4.36783\n",
      "[9956]\tvalidation_0-rmse:0.11895\tvalidation_1-rmse:4.36783\n",
      "[9957]\tvalidation_0-rmse:0.11893\tvalidation_1-rmse:4.36783\n",
      "[9958]\tvalidation_0-rmse:0.11888\tvalidation_1-rmse:4.36783\n",
      "[9959]\tvalidation_0-rmse:0.11884\tvalidation_1-rmse:4.36782\n",
      "[9960]\tvalidation_0-rmse:0.11883\tvalidation_1-rmse:4.36781\n",
      "[9961]\tvalidation_0-rmse:0.11879\tvalidation_1-rmse:4.36782\n",
      "[9962]\tvalidation_0-rmse:0.11876\tvalidation_1-rmse:4.36781\n",
      "[9963]\tvalidation_0-rmse:0.11875\tvalidation_1-rmse:4.36782\n",
      "[9964]\tvalidation_0-rmse:0.11873\tvalidation_1-rmse:4.36782\n",
      "[9965]\tvalidation_0-rmse:0.11871\tvalidation_1-rmse:4.36782\n",
      "[9966]\tvalidation_0-rmse:0.11870\tvalidation_1-rmse:4.36782\n",
      "[9967]\tvalidation_0-rmse:0.11869\tvalidation_1-rmse:4.36782\n",
      "[9968]\tvalidation_0-rmse:0.11867\tvalidation_1-rmse:4.36781\n",
      "[9969]\tvalidation_0-rmse:0.11864\tvalidation_1-rmse:4.36780\n",
      "[9970]\tvalidation_0-rmse:0.11862\tvalidation_1-rmse:4.36780\n",
      "[9971]\tvalidation_0-rmse:0.11859\tvalidation_1-rmse:4.36780\n",
      "[9972]\tvalidation_0-rmse:0.11856\tvalidation_1-rmse:4.36780\n",
      "[9973]\tvalidation_0-rmse:0.11853\tvalidation_1-rmse:4.36779\n",
      "[9974]\tvalidation_0-rmse:0.11851\tvalidation_1-rmse:4.36778\n",
      "[9975]\tvalidation_0-rmse:0.11848\tvalidation_1-rmse:4.36778\n",
      "[9976]\tvalidation_0-rmse:0.11847\tvalidation_1-rmse:4.36777\n",
      "[9977]\tvalidation_0-rmse:0.11845\tvalidation_1-rmse:4.36777\n",
      "[9978]\tvalidation_0-rmse:0.11843\tvalidation_1-rmse:4.36777\n",
      "[9979]\tvalidation_0-rmse:0.11839\tvalidation_1-rmse:4.36777\n",
      "[9980]\tvalidation_0-rmse:0.11837\tvalidation_1-rmse:4.36777\n",
      "[9981]\tvalidation_0-rmse:0.11836\tvalidation_1-rmse:4.36777\n",
      "[9982]\tvalidation_0-rmse:0.11832\tvalidation_1-rmse:4.36777\n",
      "[9983]\tvalidation_0-rmse:0.11830\tvalidation_1-rmse:4.36777\n",
      "[9984]\tvalidation_0-rmse:0.11827\tvalidation_1-rmse:4.36777\n",
      "[9985]\tvalidation_0-rmse:0.11825\tvalidation_1-rmse:4.36777\n",
      "[9986]\tvalidation_0-rmse:0.11824\tvalidation_1-rmse:4.36777\n",
      "[9987]\tvalidation_0-rmse:0.11822\tvalidation_1-rmse:4.36777\n",
      "[9988]\tvalidation_0-rmse:0.11818\tvalidation_1-rmse:4.36776\n",
      "[9989]\tvalidation_0-rmse:0.11817\tvalidation_1-rmse:4.36776\n",
      "[9990]\tvalidation_0-rmse:0.11815\tvalidation_1-rmse:4.36775\n",
      "[9991]\tvalidation_0-rmse:0.11812\tvalidation_1-rmse:4.36776\n",
      "[9992]\tvalidation_0-rmse:0.11808\tvalidation_1-rmse:4.36777\n",
      "[9993]\tvalidation_0-rmse:0.11805\tvalidation_1-rmse:4.36776\n",
      "[9994]\tvalidation_0-rmse:0.11804\tvalidation_1-rmse:4.36777\n",
      "[9995]\tvalidation_0-rmse:0.11801\tvalidation_1-rmse:4.36776\n",
      "[9996]\tvalidation_0-rmse:0.11796\tvalidation_1-rmse:4.36776\n",
      "[9997]\tvalidation_0-rmse:0.11795\tvalidation_1-rmse:4.36775\n",
      "[9998]\tvalidation_0-rmse:0.11793\tvalidation_1-rmse:4.36775\n",
      "[9999]\tvalidation_0-rmse:0.11787\tvalidation_1-rmse:4.36774\n"
     ]
    }
   ],
   "source": [
    "# Train an XGBoost model\n",
    "model = xgb.XGBRegressor(\n",
    "    booster=\"gbtree\",\n",
    "    tree_method=\"hist\",\n",
    "    learning_rate=0.01,\n",
    "    n_estimators=10000,\n",
    "    objective=\"reg:squarederror\",\n",
    "    eval_metric=\"rmse\",\n",
    "    early_stopping_rounds=50,\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "eval_set = [(X_train, y_train), (X_test, y_test)]\n",
    "model = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    eval_set=eval_set,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Most Important Features:\n",
      "                        Feature  Importance\n",
      "75     team1_player1_PLUS_MINUS    0.088324\n",
      "600    team2_player1_PLUS_MINUS    0.084514\n",
      "705    team2_player2_PLUS_MINUS    0.056189\n",
      "618    team2_player1_NET_RATING    0.043328\n",
      "180    team1_player2_PLUS_MINUS    0.041489\n",
      "196  team1_player2_E_NET_RATING    0.023620\n",
      "93     team1_player1_NET_RATING    0.019528\n",
      "285    team1_player3_PLUS_MINUS    0.019187\n",
      "723    team2_player2_NET_RATING    0.018823\n",
      "915    team2_player4_PLUS_MINUS    0.018698\n",
      "Train RMSE: 0.1178691535439527\n",
      "Test RMSE: 4.367742837377589\n",
      "Train Win-Loss Error Rate: 0.012472160356347439\n",
      "Test Win-Loss Error Rate: 0.08246373122932044\n",
      "Train R2 Score: 0.9999464154243469\n",
      "Test R2 Score: 0.929263174533844\n"
     ]
    }
   ],
   "source": [
    "# Print feature importances\n",
    "importances = model.feature_importances_\n",
    "feature_importance_df = pd.DataFrame(\n",
    "    {\"Feature\": X.columns, \"Importance\": importances}\n",
    ").sort_values(by=\"Importance\", ascending=False)\n",
    "\n",
    "print(\"Top 10 Most Important Features:\")\n",
    "print(feature_importance_df.head(10))\n",
    "\n",
    "# Evaluate model performance\n",
    "train_rmse = np.sqrt(((model.predict(X_train) - y_train) ** 2).mean())\n",
    "test_rmse = np.sqrt(((model.predict(X_test) - y_test) ** 2).mean())\n",
    "\n",
    "print(f\"Train RMSE: {train_rmse}\")\n",
    "print(f\"Test RMSE: {test_rmse}\")\n",
    "\n",
    "# Evaluate win-loss error rate\n",
    "train_predictions = model.predict(X_train)\n",
    "test_predictions = model.predict(X_test)\n",
    "train_error_rate = win_loss_error_rate(train_predictions, y_train)\n",
    "test_error_rate = win_loss_error_rate(test_predictions, y_test)\n",
    "\n",
    "print(f\"Train Win-Loss Error Rate: {train_error_rate}\")\n",
    "print(f\"Test Win-Loss Error Rate: {test_error_rate}\")\n",
    "\n",
    "# R2 score\n",
    "train_r2 = r2_score(y_train, train_predictions)\n",
    "test_r2 = r2_score(y_test, test_predictions)\n",
    "\n",
    "print(f\"Train R2 Score: {train_r2}\")\n",
    "print(f\"Test R2 Score: {test_r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model(f\"models/{player_count}_player_model.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_df_list = []\n",
    "# for game_id in tqdm(game_ids):\n",
    "#     game_player_performances = player_performance_df[player_performance_df.GAME_ID==game_id]\n",
    "#     game_team_performances = team_performance_df[team_performance_df.GAME_ID==game_id]\n",
    "#     if len(game_player_performances.TEAM_ABBREVIATION.unique())!=2:\n",
    "#         continue\n",
    "\n",
    "#     a_players,b_players = get_performances_by_team(game_player_performances,\"player\")\n",
    "#     a_team,b_team = get_performances_by_team(game_team_performances,\"team\")\n",
    "\n",
    "#     team_a_feature_df = pd.concat([stack_df(pd.concat([a_players[:player_count],b_players[:player_count]]).reset_index(drop=True))],axis=1)\n",
    "#     team_a_data_df = pd.concat([team_a_feature_df,a_team[target]],axis=1)\n",
    "\n",
    "#     team_b_feature_df = pd.concat([stack_df(pd.concat([b_players[:player_count],a_players[:player_count]]).reset_index(drop=True))],axis=1)\n",
    "#     team_b_data_df = pd.concat([team_b_feature_df,b_team[target]],axis=1)\n",
    "\n",
    "#     data_df_list.append(team_a_data_df)\n",
    "#     data_df_list.append(team_b_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.concat(data_df_list).fillna(0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(100)\n",
    "col_list = [\"feature_{}\".format(i) for i in range(100)]\n",
    "transformed_X = pd.DataFrame(pca.fit_transform(X))\n",
    "transformed_X.columns = col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features, test_features, train_labels, test_labels = train_test_split(\n",
    "    transformed_X, y, test_size=0.2, random_state=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:14.16435\tvalidation_1-rmse:14.32864\n",
      "[1]\tvalidation_0-rmse:14.02975\tvalidation_1-rmse:14.19273\n",
      "[2]\tvalidation_0-rmse:13.89657\tvalidation_1-rmse:14.05817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/main/lib/python3.9/site-packages/xgboost/data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\tvalidation_0-rmse:13.76477\tvalidation_1-rmse:13.92494\n",
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      "[463]\tvalidation_0-rmse:2.44678\tvalidation_1-rmse:2.75530\n",
      "[464]\tvalidation_0-rmse:2.44606\tvalidation_1-rmse:2.75510\n",
      "[465]\tvalidation_0-rmse:2.44540\tvalidation_1-rmse:2.75505\n",
      "[466]\tvalidation_0-rmse:2.44480\tvalidation_1-rmse:2.75494\n",
      "[467]\tvalidation_0-rmse:2.44428\tvalidation_1-rmse:2.75487\n",
      "[468]\tvalidation_0-rmse:2.44363\tvalidation_1-rmse:2.75479\n",
      "[469]\tvalidation_0-rmse:2.44307\tvalidation_1-rmse:2.75457\n",
      "[470]\tvalidation_0-rmse:2.44254\tvalidation_1-rmse:2.75446\n",
      "[471]\tvalidation_0-rmse:2.44196\tvalidation_1-rmse:2.75429\n",
      "[472]\tvalidation_0-rmse:2.44127\tvalidation_1-rmse:2.75412\n",
      "[473]\tvalidation_0-rmse:2.44058\tvalidation_1-rmse:2.75404\n",
      "[474]\tvalidation_0-rmse:2.43990\tvalidation_1-rmse:2.75384\n",
      "[475]\tvalidation_0-rmse:2.43919\tvalidation_1-rmse:2.75371\n",
      "[476]\tvalidation_0-rmse:2.43861\tvalidation_1-rmse:2.75359\n",
      "[477]\tvalidation_0-rmse:2.43805\tvalidation_1-rmse:2.75346\n",
      "[478]\tvalidation_0-rmse:2.43743\tvalidation_1-rmse:2.75341\n",
      "[479]\tvalidation_0-rmse:2.43667\tvalidation_1-rmse:2.75329\n",
      "[480]\tvalidation_0-rmse:2.43587\tvalidation_1-rmse:2.75317\n",
      "[481]\tvalidation_0-rmse:2.43523\tvalidation_1-rmse:2.75308\n",
      "[482]\tvalidation_0-rmse:2.43459\tvalidation_1-rmse:2.75299\n",
      "[483]\tvalidation_0-rmse:2.43391\tvalidation_1-rmse:2.75291\n",
      "[484]\tvalidation_0-rmse:2.43340\tvalidation_1-rmse:2.75284\n",
      "[485]\tvalidation_0-rmse:2.43290\tvalidation_1-rmse:2.75280\n",
      "[486]\tvalidation_0-rmse:2.43229\tvalidation_1-rmse:2.75273\n",
      "[487]\tvalidation_0-rmse:2.43168\tvalidation_1-rmse:2.75262\n",
      "[488]\tvalidation_0-rmse:2.43098\tvalidation_1-rmse:2.75247\n",
      "[489]\tvalidation_0-rmse:2.43041\tvalidation_1-rmse:2.75241\n",
      "[490]\tvalidation_0-rmse:2.42970\tvalidation_1-rmse:2.75238\n",
      "[491]\tvalidation_0-rmse:2.42921\tvalidation_1-rmse:2.75235\n",
      "[492]\tvalidation_0-rmse:2.42859\tvalidation_1-rmse:2.75231\n",
      "[493]\tvalidation_0-rmse:2.42790\tvalidation_1-rmse:2.75219\n",
      "[494]\tvalidation_0-rmse:2.42727\tvalidation_1-rmse:2.75214\n",
      "[495]\tvalidation_0-rmse:2.42660\tvalidation_1-rmse:2.75200\n",
      "[496]\tvalidation_0-rmse:2.42604\tvalidation_1-rmse:2.75206\n",
      "[497]\tvalidation_0-rmse:2.42546\tvalidation_1-rmse:2.75195\n",
      "[498]\tvalidation_0-rmse:2.42500\tvalidation_1-rmse:2.75187\n",
      "[499]\tvalidation_0-rmse:2.42431\tvalidation_1-rmse:2.75183\n",
      "[500]\tvalidation_0-rmse:2.42375\tvalidation_1-rmse:2.75174\n",
      "[501]\tvalidation_0-rmse:2.42323\tvalidation_1-rmse:2.75174\n",
      "[502]\tvalidation_0-rmse:2.42256\tvalidation_1-rmse:2.75168\n",
      "[503]\tvalidation_0-rmse:2.42185\tvalidation_1-rmse:2.75165\n",
      "[504]\tvalidation_0-rmse:2.42117\tvalidation_1-rmse:2.75165\n",
      "[505]\tvalidation_0-rmse:2.42055\tvalidation_1-rmse:2.75150\n",
      "[506]\tvalidation_0-rmse:2.41986\tvalidation_1-rmse:2.75145\n",
      "[507]\tvalidation_0-rmse:2.41928\tvalidation_1-rmse:2.75147\n",
      "[508]\tvalidation_0-rmse:2.41871\tvalidation_1-rmse:2.75131\n",
      "[509]\tvalidation_0-rmse:2.41808\tvalidation_1-rmse:2.75117\n",
      "[510]\tvalidation_0-rmse:2.41751\tvalidation_1-rmse:2.75118\n",
      "[511]\tvalidation_0-rmse:2.41699\tvalidation_1-rmse:2.75094\n",
      "[512]\tvalidation_0-rmse:2.41631\tvalidation_1-rmse:2.75082\n",
      "[513]\tvalidation_0-rmse:2.41558\tvalidation_1-rmse:2.75070\n",
      "[514]\tvalidation_0-rmse:2.41491\tvalidation_1-rmse:2.75055\n",
      "[515]\tvalidation_0-rmse:2.41431\tvalidation_1-rmse:2.75057\n",
      "[516]\tvalidation_0-rmse:2.41374\tvalidation_1-rmse:2.75076\n",
      "[517]\tvalidation_0-rmse:2.41320\tvalidation_1-rmse:2.75073\n",
      "[518]\tvalidation_0-rmse:2.41265\tvalidation_1-rmse:2.75076\n",
      "[519]\tvalidation_0-rmse:2.41203\tvalidation_1-rmse:2.75064\n",
      "[520]\tvalidation_0-rmse:2.41145\tvalidation_1-rmse:2.75053\n",
      "[521]\tvalidation_0-rmse:2.41097\tvalidation_1-rmse:2.75043\n",
      "[522]\tvalidation_0-rmse:2.41041\tvalidation_1-rmse:2.75024\n",
      "[523]\tvalidation_0-rmse:2.40981\tvalidation_1-rmse:2.75028\n",
      "[524]\tvalidation_0-rmse:2.40922\tvalidation_1-rmse:2.75031\n",
      "[525]\tvalidation_0-rmse:2.40867\tvalidation_1-rmse:2.75021\n",
      "[526]\tvalidation_0-rmse:2.40815\tvalidation_1-rmse:2.75020\n",
      "[527]\tvalidation_0-rmse:2.40756\tvalidation_1-rmse:2.75015\n",
      "[528]\tvalidation_0-rmse:2.40702\tvalidation_1-rmse:2.75035\n",
      "[529]\tvalidation_0-rmse:2.40650\tvalidation_1-rmse:2.75023\n",
      "[530]\tvalidation_0-rmse:2.40604\tvalidation_1-rmse:2.75027\n",
      "[531]\tvalidation_0-rmse:2.40548\tvalidation_1-rmse:2.75014\n",
      "[532]\tvalidation_0-rmse:2.40506\tvalidation_1-rmse:2.75001\n",
      "[533]\tvalidation_0-rmse:2.40446\tvalidation_1-rmse:2.75001\n",
      "[534]\tvalidation_0-rmse:2.40391\tvalidation_1-rmse:2.74987\n",
      "[535]\tvalidation_0-rmse:2.40326\tvalidation_1-rmse:2.74972\n",
      "[536]\tvalidation_0-rmse:2.40276\tvalidation_1-rmse:2.74969\n",
      "[537]\tvalidation_0-rmse:2.40230\tvalidation_1-rmse:2.74968\n",
      "[538]\tvalidation_0-rmse:2.40173\tvalidation_1-rmse:2.74973\n",
      "[539]\tvalidation_0-rmse:2.40129\tvalidation_1-rmse:2.74966\n",
      "[540]\tvalidation_0-rmse:2.40083\tvalidation_1-rmse:2.74968\n",
      "[541]\tvalidation_0-rmse:2.40034\tvalidation_1-rmse:2.74969\n",
      "[542]\tvalidation_0-rmse:2.39996\tvalidation_1-rmse:2.74968\n",
      "[543]\tvalidation_0-rmse:2.39947\tvalidation_1-rmse:2.74966\n",
      "[544]\tvalidation_0-rmse:2.39901\tvalidation_1-rmse:2.74958\n",
      "[545]\tvalidation_0-rmse:2.39854\tvalidation_1-rmse:2.74971\n",
      "[546]\tvalidation_0-rmse:2.39807\tvalidation_1-rmse:2.74979\n",
      "[547]\tvalidation_0-rmse:2.39762\tvalidation_1-rmse:2.74977\n",
      "[548]\tvalidation_0-rmse:2.39715\tvalidation_1-rmse:2.74973\n",
      "[549]\tvalidation_0-rmse:2.39663\tvalidation_1-rmse:2.74973\n",
      "[550]\tvalidation_0-rmse:2.39617\tvalidation_1-rmse:2.74971\n",
      "[551]\tvalidation_0-rmse:2.39569\tvalidation_1-rmse:2.74988\n",
      "[552]\tvalidation_0-rmse:2.39522\tvalidation_1-rmse:2.74987\n",
      "[553]\tvalidation_0-rmse:2.39472\tvalidation_1-rmse:2.74987\n",
      "[554]\tvalidation_0-rmse:2.39430\tvalidation_1-rmse:2.74992\n",
      "[555]\tvalidation_0-rmse:2.39388\tvalidation_1-rmse:2.74992\n",
      "[556]\tvalidation_0-rmse:2.39337\tvalidation_1-rmse:2.74992\n",
      "[557]\tvalidation_0-rmse:2.39297\tvalidation_1-rmse:2.74995\n",
      "[558]\tvalidation_0-rmse:2.39249\tvalidation_1-rmse:2.75003\n",
      "[559]\tvalidation_0-rmse:2.39205\tvalidation_1-rmse:2.75005\n",
      "[560]\tvalidation_0-rmse:2.39163\tvalidation_1-rmse:2.75006\n",
      "[561]\tvalidation_0-rmse:2.39124\tvalidation_1-rmse:2.75010\n",
      "[562]\tvalidation_0-rmse:2.39079\tvalidation_1-rmse:2.75012\n",
      "[563]\tvalidation_0-rmse:2.39030\tvalidation_1-rmse:2.75005\n",
      "[564]\tvalidation_0-rmse:2.38994\tvalidation_1-rmse:2.75004\n",
      "[565]\tvalidation_0-rmse:2.38951\tvalidation_1-rmse:2.75004\n",
      "[566]\tvalidation_0-rmse:2.38913\tvalidation_1-rmse:2.75005\n",
      "[567]\tvalidation_0-rmse:2.38867\tvalidation_1-rmse:2.75023\n",
      "[568]\tvalidation_0-rmse:2.38816\tvalidation_1-rmse:2.75020\n",
      "[569]\tvalidation_0-rmse:2.38772\tvalidation_1-rmse:2.75034\n",
      "[570]\tvalidation_0-rmse:2.38733\tvalidation_1-rmse:2.75039\n",
      "[571]\tvalidation_0-rmse:2.38683\tvalidation_1-rmse:2.75036\n",
      "[572]\tvalidation_0-rmse:2.38645\tvalidation_1-rmse:2.75040\n",
      "[573]\tvalidation_0-rmse:2.38603\tvalidation_1-rmse:2.75041\n",
      "[574]\tvalidation_0-rmse:2.38559\tvalidation_1-rmse:2.75045\n",
      "[575]\tvalidation_0-rmse:2.38514\tvalidation_1-rmse:2.75044\n",
      "[576]\tvalidation_0-rmse:2.38469\tvalidation_1-rmse:2.75048\n",
      "[577]\tvalidation_0-rmse:2.38423\tvalidation_1-rmse:2.75047\n",
      "[578]\tvalidation_0-rmse:2.38393\tvalidation_1-rmse:2.75047\n",
      "[579]\tvalidation_0-rmse:2.38350\tvalidation_1-rmse:2.75048\n",
      "[580]\tvalidation_0-rmse:2.38303\tvalidation_1-rmse:2.75047\n",
      "[581]\tvalidation_0-rmse:2.38270\tvalidation_1-rmse:2.75048\n",
      "[582]\tvalidation_0-rmse:2.38216\tvalidation_1-rmse:2.75049\n",
      "[583]\tvalidation_0-rmse:2.38174\tvalidation_1-rmse:2.75053\n",
      "[584]\tvalidation_0-rmse:2.38109\tvalidation_1-rmse:2.75046\n",
      "[585]\tvalidation_0-rmse:2.38065\tvalidation_1-rmse:2.75040\n",
      "[586]\tvalidation_0-rmse:2.38005\tvalidation_1-rmse:2.75037\n",
      "[587]\tvalidation_0-rmse:2.37976\tvalidation_1-rmse:2.75038\n",
      "[588]\tvalidation_0-rmse:2.37926\tvalidation_1-rmse:2.75038\n",
      "[589]\tvalidation_0-rmse:2.37867\tvalidation_1-rmse:2.75031\n",
      "[590]\tvalidation_0-rmse:2.37809\tvalidation_1-rmse:2.75026\n",
      "[591]\tvalidation_0-rmse:2.37762\tvalidation_1-rmse:2.75022\n",
      "[592]\tvalidation_0-rmse:2.37702\tvalidation_1-rmse:2.75015\n",
      "[593]\tvalidation_0-rmse:2.37661\tvalidation_1-rmse:2.75016\n"
     ]
    }
   ],
   "source": [
    "n = 1000\n",
    "model = xgb.XGBRegressor(\n",
    "    booster=\"gbtree\",\n",
    "    tree_method=\"hist\",\n",
    "    learning_rate=0.01,\n",
    "    single_precision_histogram=True,\n",
    "    n_estimators=n,\n",
    "    objective=\"reg:squarederror\",\n",
    "    eval_metric=\"rmse\",\n",
    ")\n",
    "eval_set = [(train_features, train_labels), (test_features, test_labels)]\n",
    "model = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    eval_set=eval_set,\n",
    "    verbose=True,\n",
    "    early_stopping_rounds=50,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9638313745291099\n",
      "0.9748136725777435\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(test_features)\n",
    "wler = win_loss_error_rate(predictions, test_labels)\n",
    "r2 = r2_score(test_labels, predictions)\n",
    "print(r2)\n",
    "print(1 - wler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='weight', ylabel='Features'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(\n",
    "    model, max_num_features=20, importance_type=\"weight\", xlabel=\"weight\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save_model(f\"models/{player_count}_player_model.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "c9450414bf23f3dd29b5b05a150ab805ddf54e50a477ec96525535ab209d16c9"
  },
  "kernelspec": {
   "display_name": "Python 3.9.11 ('main')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}