NBA_PlayerAwards.ipynb

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    "# Predicting the NBA Awards with Machine Learning\n",
    "\n",
    "The goal of this analysis is to predict the [NBA Awards](https://www.basketball-reference.com/awards/) for a specific year by applying machine learning algorithms on player performance data and award voting data from other years. NBA Awards considered in this analysis are:\n",
    "- [NBA Most Valuable Player](https://www.basketball-reference.com/awards/mvp.html) (MVP)\n",
    "- [NBA Rookie of the Year](https://www.basketball-reference.com/awards/roy.html) (ROY) \n",
    "- [NBA Defensive Player of the Year](https://www.basketball-reference.com/awards/dpoy.html) (DPOY) \n",
    "- [NBA Sixth Man of the Year](https://www.basketball-reference.com/awards/smoy.html) (SMOY) \n",
    "\n",
    "The analysis is based on the [Scikit-learn](http://scikit-learn.org) machine learning package, NBA data are taken from [basketball-reference.com](https://www.basketball-reference.com).\n",
    "\n",
    "## Import modules and libraries:"
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      "Using TensorFlow backend.\n"
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      "Author: gmalim\n",
      "\n",
      "last updated: Mon Feb 04 2019\n",
      "\n",
      "CPython 3.6.5\n",
      "IPython 7.2.0\n",
      "\n",
      "numpy 1.16.1\n",
      "pandas 0.24.0\n",
      "sklearn 0.20.2\n",
      "xgboost 0.81\n",
      "matplotlib 3.0.2\n",
      "seaborn 0.9.0\n",
      "\n",
      "compiler   : GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)\n",
      "system     : Darwin\n",
      "release    : 15.6.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 2\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "import NBAanalysissetup # See NBAanalysissetup.py\n",
    "\n",
    "from NBAanalysissetup import MyModel, MyCM\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy             as np\n",
    "import pandas            as pd\n",
    "import seaborn           as sns\n",
    "\n",
    "import operator\n",
    "import os\n",
    "import time\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
    "\n",
    "from sklearn.neighbors        import KNeighborsRegressor\n",
    "from sklearn.linear_model     import Ridge, Lasso, ElasticNet, SGDRegressor, PassiveAggressiveRegressor\n",
    "from sklearn.linear_model     import RidgeCV, LassoCV, ElasticNetCV\n",
    "from sklearn.svm              import SVR\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.tree             import DecisionTreeRegressor\n",
    "from sklearn.ensemble         import RandomForestRegressor, ExtraTreesRegressor \n",
    "from sklearn.ensemble         import GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.neural_network   import MLPRegressor\n",
    "\n",
    "from xgboost                  import XGBRegressor\n",
    "\n",
    "from sklearn.model_selection import validation_curve, LeaveOneGroupOut, GridSearchCV\n",
    "\n",
    "from sklearn.metrics import explained_variance_score, median_absolute_error, mean_absolute_error \n",
    "from sklearn.metrics import mean_squared_error, mean_squared_log_error, r2_score\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from keras.models                import Sequential\n",
    "from keras.layers                import Dense, Dropout\n",
    "from keras                       import regularizers\n",
    "\n",
    "import ipywidgets\n",
    "\n",
    "from bokeh.models   import ColumnDataSource, LabelSet, HoverTool\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.palettes import RdBu \n",
    "from bokeh.io       import output_notebook, push_notebook, show\n",
    "\n",
    "output_notebook() # to enable Bokeh for Jupyter\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -a \"Author: gmalim\" \n",
    "print(\"\")\n",
    "%watermark -u -n\n",
    "print(\"\")\n",
    "%watermark -v -p numpy,pandas,sklearn,xgboost,matplotlib,seaborn\n",
    "print(\"\")\n",
    "%watermark -m\n",
    "\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User input\n",
    "\n",
    "Choose the NBA Award you want to predict:\n",
    "1. NBA Most Valuable Player (MVP)\n",
    "2. NBA Rookie of the Year (ROY)\n",
    "3. NBA Defensive Player of the Year (DPOY)\n",
    "4. NBA Sixth Man of the Year (SMOY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> NBA Award used in analysis: MVP\n"
     ]
    }
   ],
   "source": [
    "awards = {1:'MVP', 2:'ROY', 3:'DPOY', 4:'SMOY'}\n",
    "\n",
    "award = awards[1] # Enter an integer from the awards dict\n",
    "\n",
    "print(\"--> NBA Award used in analysis: {}\".format(award))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter the first and last year for which data has been scraped, and choose the year you want to predict. The years that are not selected are used for cross-validation and training of the ML algorithms. NBA data from 2000-2018 from [basketball-reference.com](https://www.basketball-reference.com) have been saved as csv-files in the [data](data) directory of this repository using the scraper functions in [*NBAanalysissetup.py*](NBAanalysissetup.py). Data from other years can be obtained by using [*Basketball_Reference_scraper.py*](Basketball_Reference_scraper.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Test year = 2019 (MVP has not been awarded yet)\n",
      "--> Train years = 2010-2018\n"
     ]
    }
   ],
   "source": [
    "first_year = 2010 # Enter first year for which data has been scraped\n",
    "last_year  = 2019 # Enter last year for which data has been scraped\n",
    "test_year  = 2019 # Enter any year for which data has been scraped\n",
    "\n",
    "#%store -r test_year # get test_year that was stored in another notebook\n",
    "\n",
    "# Check if corresponding data files exist:\n",
    "\n",
    "checkfile = \"data/NBA_{}_{}-{}.csv\".format(award, test_year-1, test_year)\n",
    "if not os.path.exists(checkfile):\n",
    "    print(\"--> {} does not exist!\".format(checkfile))\n",
    "\n",
    "# Check if award has been awarded yet for test_year:\n",
    " \n",
    "shellcommand = \"wc -l data/NBA_{}_{}-{}.csv | awk '{{print $1}}'\".format(award, test_year-1, test_year)\n",
    "n = !{shellcommand}\n",
    "n = int(n[0])\n",
    "test_year_truth_is_known = False if n == 1 else True\n",
    "\n",
    "if test_year_truth_is_known:\n",
    "    print(\"--> Test year = {} ({} has been awarded)\".format(test_year, award))\n",
    "else:\n",
    "    print(\"--> Test year = {} ({} has not been awarded yet)\".format(test_year, award))\n",
    "    \n",
    "train_years = list(range(first_year, last_year+1))\n",
    "train_years.remove(test_year)\n",
    "\n",
    "first_train_year = train_years[0]\n",
    "last_train_year  = train_years[-1]\n",
    "    \n",
    "if (first_train_year < test_year < last_train_year):\n",
    "    print(\"--> Train years = {}-{} except {}\".format(first_train_year, last_train_year, test_year))\n",
    "else:\n",
    "    print(\"--> Train years = {}-{}\".format(first_train_year, last_train_year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose the minimum number of games a player has to have played (ROY, SMOY) or started (MVP, DPOY) per season to be included in the analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Minimum number of games = 35\n"
     ]
    }
   ],
   "source": [
    "min_num_games = 35 # Enter an integer between 0 and 82\n",
    "\n",
    "print(\"--> Minimum number of games =\", min_num_games)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing\n",
    "\n",
    "Load NBA player performance data and NBA Award voting data into dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Loading train year 2009-2010 ...\n",
      "--> Loading train year 2010-2011 ...\n",
      "--> Loading train year 2011-2012 ...\n",
      "--> Loading train year 2012-2013 ...\n",
      "--> Loading train year 2013-2014 ...\n",
      "--> Loading train year 2014-2015 ...\n",
      "--> Loading train year 2015-2016 ...\n",
      "--> Loading train year 2016-2017 ...\n",
      "--> Loading train year 2017-2018 ...\n",
      "--> Loading test  year 2018-2019 ...\n"
     ]
    }
   ],
   "source": [
    "df_train, df_test = NBAanalysissetup.loaddata_allyears(train_years, test_year, award)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print table of players with award votes in *test_year*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> The NBA MVP for 2019 has not been awarded yet\n"
     ]
    }
   ],
   "source": [
    "if test_year_truth_is_known:\n",
    "    print(\"--> Players with {} votes in {} :\".format(award, test_year))\n",
    "    #df_tmp = df_train[(df_train['YEAR'] == print_year) & (df_train['G'] > 81)]\n",
    "    df_tmp = df_test[df_test['AVS'] > 0]\n",
    "    cm = sns.diverging_palette(240, 10, n=9, as_cmap=True)\n",
    "    df_tmp_style = df_tmp.style.background_gradient(cmap=cm, axis=0)\n",
    "    display(df_tmp_style)\n",
    "else:\n",
    "    print(\"--> The NBA {} for {} has not been awarded yet\".format(award, test_year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select only players which have played/started at least *min_num_games* number of games in a season:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> # of players in train set = 4315\n",
      "--> # of players in test  set = 496\n",
      "\n",
      "--> # of players in train set = 1387\n",
      "--> # of players in test  set = 119\n"
     ]
    }
   ],
   "source": [
    "#print(\"--> # of players in train set =\", df_train.shape[0])\n",
    "#print(\"--> # of players in test  set =\", df_test .shape[0])\n",
    "#print(\"\")\n",
    "\n",
    "#df_train = df_train[df_train['AVS'] > 0]\n",
    "#df_test  = df_test [df_test ['AVS'] > 0]\n",
    "\n",
    "print(\"--> # of players in train set =\", df_train.shape[0])\n",
    "print(\"--> # of players in test  set =\", df_test .shape[0])\n",
    "print(\"\")\n",
    "\n",
    "if (award == 'ROY') or (award == 'SMOY'):\n",
    "    df_train = df_train[df_train['G'] >= min_num_games]\n",
    "    df_test  = df_test [df_test ['G'] >= min_num_games]\n",
    "else:\n",
    "    df_train = df_train[(((df_train['GS'] >= min_num_games) & (df_train['YEAR'] >= 1982)) | \\\n",
    "                         ((df_train['G']  >= min_num_games) & (df_train['YEAR'] <  1982)))]\n",
    "    df_test  = df_test [(((df_test ['GS'] >= min_num_games) & (df_test ['YEAR'] >= 1982)) | \\\n",
    "                         ((df_test ['G']  >= min_num_games) & (df_test ['YEAR'] <  1982)))]\n",
    "    \n",
    "print(\"--> # of players in train set =\", df_train.shape[0])\n",
    "print(\"--> # of players in test  set =\", df_test .shape[0])\n",
    "\n",
    "# Reset dataframe indices:\n",
    "\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_test  = df_test .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NaN handling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> # of players with NaNs in train set = 73\n",
      "--> # of players with NaNs in test  set = 5\n",
      "\n",
      "--> # of players with NaNs in train set = 0\n",
      "--> # of players with NaNs in test  set = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"--> # of players with NaNs in train set =\", df_train.shape[0] - df_train.dropna().shape[0])\n",
    "print(\"--> # of players with NaNs in test  set =\", df_test .shape[0] - df_test .dropna().shape[0])\n",
    "print(\"\")\n",
    "\n",
    "# Replace NaNs with 0s in the following columns:\n",
    "\n",
    "df_train[['FG%', '3P%', '2P%', 'FT%', 'eFG%', 'TS%', '3PAr', 'FTr']] = \\\n",
    "df_train[['FG%', '3P%', '2P%', 'FT%', 'eFG%', 'TS%', '3PAr', 'FTr']].fillna(value=0)\n",
    "df_test [['FG%', '3P%', '2P%', 'FT%', 'eFG%', 'TS%', '3PAr', 'FTr']] = \\\n",
    "df_test [['FG%', '3P%', '2P%', 'FT%', 'eFG%', 'TS%', '3PAr', 'FTr']].fillna(value=0)\n",
    "\n",
    "print(\"--> # of players with NaNs in train set =\", df_train.shape[0] - df_train.dropna().shape[0])\n",
    "print(\"--> # of players with NaNs in test  set =\", df_test .shape[0] - df_test .dropna().shape[0])\n",
    "print(\"\")\n",
    "\n",
    "# Remove remaining players with NaNs, if necessary:\n",
    "\n",
    "if (df_train.shape[0] - df_train.dropna().shape[0] != 0):\n",
    "    #print(\"--> Players in train set with NaNs:\")\n",
    "    #print(df_train[df_train.isnull().any(axis=1)])\n",
    "    #print(\"\")\n",
    "    df_train.dropna(inplace=True)\n",
    "    print(\"--> # of players with NaNs in train set =\", df_train.shape[0] - df_train.dropna().shape[0])\n",
    "\n",
    "if (df_test.shape[0] - df_test.dropna().shape[0] != 0):\n",
    "    #print(\"--> Players in test set with NaNs:\")\n",
    "    #print(df_test[df_test.isnull().any(axis=1)])\n",
    "    #print(\"\")\n",
    "    df_test.dropna(inplace=True)\n",
    "    print(\"--> # of players with NaNs in test  set =\", df_test.shape[0] - df_test.dropna().shape[0])\n",
    "    \n",
    "# Reset dataframe indices:\n",
    "\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_test  = df_test .reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Feature Matrix (*X*) and Target Vector (*y*) dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Train data set : # of players = 1387, # of features = 18\n",
      "--> Test  data set : # of players =  119, # of features = 18\n",
      "\n",
      "--> Model features : TW/TOT, G/TOT, GS/G, MP/G, 2P/48, 2P%, 3P/48, 3P%, FT/48, FT%, USG%, ORB%, DRB%, AST%, TOV%, STL%, BLK%, PF/48\n"
     ]
    }
   ],
   "source": [
    "# Feature Matrix dataframes:\n",
    "\n",
    "X_train = df_train.drop(['AVS', 'YEAR'], axis=1) # AVS and YEAR are dropped: AVS is the target variable (see below),\n",
    "X_test  = df_test .drop(['AVS', 'YEAR'], axis=1) # YEAR is only used for cross-validation (see CV section).\n",
    "\n",
    "X_list = [X_train, X_test]\n",
    "\n",
    "for X in X_list:\n",
    "    \n",
    "    # Remove features with no predictive power:\n",
    "    \n",
    "    X.drop(['Player', 'Pos', 'Age', 'Tm', 'TC'], axis=1, inplace=True)\n",
    "        \n",
    "    # Remove features that are correlated with other features:\n",
    "    \n",
    "    X.drop(['TW', 'G', 'GS', 'MP',\n",
    "            'FG', 'FG/48', 'FGA', 'FGA/48', 'FG%', 'eFG%',\n",
    "            '2P', '2PA', '2PA/48', \n",
    "            '3P', '3PA', '3PA/48', '3PAr',\n",
    "            'FT', 'FTA', 'FTA/48', 'FTr',\n",
    "            'PTS', 'PTS/48', 'TS%', 'PF',\n",
    "            'TRB', 'TRB/48', 'TRB%', \n",
    "            'ORB',    'DRB',    'AST',    'TOV',    'STL',    'BLK', \n",
    "            'ORB/48', 'DRB/48', 'AST/48', 'TOV/48', 'STL/48', 'BLK/48', \n",
    "#            'ORB%',   'DRB%',   'AST%',   'TOV%',   'STL%',   'BLK%', \n",
    "            'OWS', 'DWS', 'WS', 'OWS/48', 'DWS/48', 'WS/48', \n",
    "            'OBPM', 'DBPM', 'BPM', 'VORP', \n",
    "            'EFF', 'GSC', 'PER'],                    \n",
    "           axis=1, inplace=True)\n",
    "    \n",
    "# Target Vector dataframes: \n",
    "\n",
    "y_train = df_train['AVS'] # target = AVS, a floating variable introduced to indicate players' award vote share\n",
    "y_test  = df_test ['AVS']\n",
    "\n",
    "n_players_train = X_train.shape[0]\n",
    "n_players_test  = X_test .shape[0]\n",
    "\n",
    "n_features    = X_train.shape[1] # equals X_test.shape[1]\n",
    "feature_names = X_train.columns  # equals X_test.columns\n",
    "\n",
    "print(\"--> Train data set : # of players = {:4}, # of features = {}\".format(n_players_train, n_features))\n",
    "print(\"--> Test  data set : # of players = {:4}, # of features = {}\".format(n_players_test,  n_features))\n",
    "print(\"\")\n",
    "print(\"--> Model features : \", end = '')\n",
    "print(*feature_names, sep=', ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Award Voting Share (AVS) distribution in training and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> # of players in train set with AVS > 0 = 121\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "\n",
    "plt.hist(y_train, range=(0, 1), bins=20, histtype=\"stepfilled\", color='b', lw=2, alpha=0.5, label='training data')\n",
    "if test_year_truth_is_known:\n",
    "    plt.hist(y_test,  range=(0, 1), bins=20, histtype=\"stepfilled\", color='r', lw=2, alpha=0.5, label='test data')\n",
    "    \n",
    "plt.xlabel(\"{} Voting Share\".format(award))\n",
    "plt.ylabel(\"# of players\")\n",
    "plt.yscale('log')\n",
    "plt.grid(True)\n",
    "if test_year_truth_is_known:\n",
    "    plt.title(\"{} Voting Share distributions in test and training data\".format(award))\n",
    "    plt.legend(loc='upper right')\n",
    "else:\n",
    "    plt.title(\"{} Voting Share distribution in training data\".format(award))\n",
    "\n",
    "print(\"--> # of players in train set with AVS > 0 =\", len(df_train[df_train['AVS'] > 0]))\n",
    "if test_year_truth_is_known:\n",
    "    print(\"--> # of players in test set with AVS > 0 =\", len(df_test [df_test ['AVS'] > 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore relationship between AVS and other features in training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color=iter(plt.cm.tab20(np.linspace(0, 1, len(X_train.columns))))    \n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "for ifeature, feature in enumerate(X_train.columns):\n",
    "\n",
    "    plt.subplot(6, 4, ifeature+1)\n",
    "\n",
    "    plt.title(\"{} vs. AVS in training data\".format(feature))\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('AVS')\n",
    "    plt.scatter(X_train[feature], y_train, color=next(color), alpha=0.5)\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale feature dataframes to accommodate ML algorithms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = X_train.columns # This is no longer available after transformation with scaler\n",
    "\n",
    "#X_scaler = StandardScaler(with_mean=True, with_std=True) # Scaler with zero mean and unit variance\n",
    "#X_scaler = MinMaxScaler() # Scales features to range 0 to 1 (or -1 to 1 if there are negative values)\n",
    "X_scaler = RobustScaler() # Similar method to the MinMaxScaler but more robust to outliers\n",
    "\n",
    "X_train = X_scaler.fit_transform(X_train) # Fit scaler to training data and transform X_train\n",
    "X_test  = X_scaler.transform(X_test)      # Transform X_test using fitted scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning: Regression\n",
    "\n",
    "Define a Deep Neural Network using Keras-TensorFlow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "from keras import backend as K\n",
    "\n",
    "def keras_metric_f1score(y_true, y_pred):\n",
    "    \n",
    "    y_true_np = K.eval(y_true)\n",
    "    y_pred_np = K.eval(y_pred)\n",
    "    \n",
    "    CM = MyCM(confusion_matrix(y_true_np, y_pred_np))\n",
    "\n",
    "    return CM.f1\n",
    "'''\n",
    "\n",
    "def myDNN(optimizer='adam',\n",
    "          loss='binary_crossentropy', \n",
    "          act_hidden='relu',\n",
    "          init='glorot_uniform', \n",
    "          n_input=1,\n",
    "          n_nodes=100,\n",
    "          l1_alpha=0,\n",
    "          l2_alpha=0,\n",
    "          do_rate=0):\n",
    "    \n",
    "    # Define model:\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Dense(n_nodes, \n",
    "                    activation           = act_hidden,\n",
    "                    kernel_initializer   = init, \n",
    "                    kernel_regularizer   = regularizers.l1_l2(l1_alpha, l2_alpha),\n",
    "                    bias_regularizer     = regularizers.l1_l2(l1_alpha, l2_alpha),\n",
    "                    activity_regularizer = regularizers.l1_l2(l1_alpha, l2_alpha),\n",
    "                    input_dim            = n_input))\n",
    "    #model.add(Dropout(do_rate))\n",
    "    model.add(Dense(n_nodes, \n",
    "                    activation           = act_hidden,\n",
    "                    kernel_initializer   = init, \n",
    "                    kernel_regularizer   = regularizers.l1_l2(l1_alpha, l2_alpha),\n",
    "                    bias_regularizer     = regularizers.l1_l2(l1_alpha, l2_alpha),\n",
    "                    activity_regularizer = regularizers.l1_l2(l1_alpha, l2_alpha)))\n",
    "    #model.add(Dropout(do_rate))\n",
    "    model.add(Dense(1, kernel_initializer=init, activation='sigmoid'))\n",
    "    \n",
    "    # Compile model:\n",
    "    \n",
    "    model.compile(optimizer=optimizer, loss=loss) #, metrics=['binary_accuracy', keras_metric_f1score])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select ML regressors, set hyper-parameters and instantiate models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> ML regressors implemented in this analysis:\n",
      "\n",
      " 0 : K-Nearest Neighbours Regressor\n",
      " 1 : Ridge Regressor\n",
      " 2 : Lasso Regressor\n",
      " 3 : ElasticNet Regressor\n",
      " 4 : Support Vector Machine Regressor\n",
      " 5 : Stochastic Gradient Descent Regressor\n",
      " 6 : Passive Aggressive Regressor\n",
      " 7 : Neural Network Regressor\n",
      " 8 : Deep Neural Network Regressor\n",
      " 9 : Gaussian Process Regressor\n",
      "10 : Decision Tree Regressor\n",
      "11 : Random Forest Regressor\n",
      "12 : Extra Randomized Trees Regressor\n",
      "13 : Adaptive Boosted Decision Tree Regressor\n",
      "14 : Gradient Boosted Decision Tree Regressor\n",
      "15 : Extreme Gradient Boosted Decision Tree Regressor\n"
     ]
    }
   ],
   "source": [
    "rseed = 666 # Set random_state of model (where applicable) to specific seed, to tune hyper-parameters\n",
    "\n",
    "models = []\n",
    "\n",
    "estimator_name = 'K-Nearest Neighbours Regressor'\n",
    "n_n = 5\n",
    "w   = 'uniform' # 'uniform', 'distance'\n",
    "estimator = KNeighborsRegressor(n_neighbors=n_n, weights=w)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Ridge Regressor' # Shrinkage => Feature coefficient suppression\n",
    "a = 0.1 # regularization factor (higher alpha -> stronger regularization i.e. opposite of C parameter)\n",
    "estimator = Ridge(alpha=a, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Lasso Regressor' # Sparsity => Feature selection\n",
    "a = 1e-05\n",
    "estimator = Lasso(alpha=a, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'ElasticNet Regressor' # Shrinkage & Sparsity\n",
    "a = 1e-04\n",
    "l_r = 0.5\n",
    "estimator = ElasticNet(alpha=a, l1_ratio=l_r, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Support Vector Machine Regressor'\n",
    "#estimator_name = 'Linear Support Vector Machine Regressor'\n",
    "C = 0.1\n",
    "e = 0.1\n",
    "k = 'poly' # 'linear'\n",
    "d = 3 # only used by poly kernel\n",
    "estimator = SVR(C=C, epsilon=e, kernel=k, degree=d, cache_size=4000)\n",
    "#l = 'epsilon_insensitive' # i.e. l1 regularizarion -> Sparsity\n",
    "#l = 'squared_epsilon_insensitive' # i.e. l2 regularizarion -> Shrinkage \n",
    "#estimator = LinearSVR(C=C, loss=l, dual=False, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Stochastic Gradient Descent Regressor'\n",
    "l = 'squared_loss' # 'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'\n",
    "p = 'l2' # 'l1', 'elasticnet'\n",
    "a = 0.01\n",
    "l_r = 0.15 # only for elasticnet penalty\n",
    "estimator = SGDRegressor(loss=l, penalty=p, alpha=a, l1_ratio=l_r, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Passive Aggressive Regressor'\n",
    "C = 0.001\n",
    "l = 'epsilon_insensitive' # 'squared_epsilon_insensitive'\n",
    "e = 0.1\n",
    "estimator = PassiveAggressiveRegressor(C=C, loss=l, epsilon=e, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Neural Network Regressor'\n",
    "a = 1.0 # l2 regularization factor\n",
    "estimator = MLPRegressor(alpha=a, #hidden_layer_sizes=(100, 100, 100, 100, 100), \n",
    "                         activation='relu', solver='adam', random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Deep Neural Network Regressor'\n",
    "l2_a = 0.0001\n",
    "e = 100\n",
    "b = 10\n",
    "estimator = KerasRegressor(build_fn=myDNN, n_input=n_features, l2_alpha=l2_a,\n",
    "                           verbose=0, epochs=e, batch_size=b)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Gaussian Process Regressor' # (CV is slowwwwwwww)\n",
    "estimator = GaussianProcessRegressor(random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Decision Tree Regressor'\n",
    "m_d = None   # i.e. nodes are expanded until leafs are pure \n",
    "m_f = 'auto' # The number of features to consider when looking for the best split. \n",
    "#              'auto' => max_features=n_features \n",
    "estimator = DecisionTreeRegressor(max_depth=m_d, max_features=m_f, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Random Forest Regressor'\n",
    "n_e = 100\n",
    "m_d = None\n",
    "m_f = 'auto'\n",
    "estimator = RandomForestRegressor(n_estimators=n_e, max_depth=m_d, max_features=m_f, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Extra Randomized Trees Regressor'\n",
    "n_e = 100\n",
    "m_d = None\n",
    "m_f = 'auto'\n",
    "estimator = ExtraTreesRegressor(n_estimators=n_e, max_depth=m_d, max_features=m_f, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Adaptive Boosted Decision Tree Regressor'\n",
    "n_e = 100\n",
    "l_r = 0.1\n",
    "estimator = AdaBoostRegressor(n_estimators=n_e, learning_rate=l_r, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Gradient Boosted Decision Tree Regressor'\n",
    "n_e = 100\n",
    "m_d = 3\n",
    "l_r = 0.1\n",
    "estimator = GradientBoostingRegressor(n_estimators=n_e, max_depth=m_d, learning_rate=l_r, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "estimator_name = 'Extreme Gradient Boosted Decision Tree Regressor'\n",
    "n_e = 100\n",
    "m_d = 3\n",
    "l_r = 0.1\n",
    "estimator = XGBRegressor(n_estimators=n_e, max_depth=m_d, learning_rate=l_r, random_state=rseed)\n",
    "models.append(MyModel(estimator_name, estimator))\n",
    "\n",
    "print(\"--> ML regressors implemented in this analysis:\")\n",
    "print(\"\")\n",
    "\n",
    "model_names = []\n",
    "\n",
    "for counter, model in enumerate(models, 0):\n",
    "    print(\"{:2} : {}\".format(counter, model.estimator_name))\n",
    "    #print(\"     Model parameters : \", mymodel.estimator.get_params())\n",
    "    model_names.append(model.estimator_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-validation\n",
    "\n",
    "Cross-validate the model using training data and the *LeaveOneGroupOut* cross-validation scheme in which a group is defined as a single NBA season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "logo = LeaveOneGroupOut()\n",
    "\n",
    "cv_groups = df_train['YEAR'] # Players in the same group (i.e. a single NBA season) have identical YEAR variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use *GridSearchCV* to tune hyperparameters:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "regressor = models[4].estimator # Lasso Regressor\n",
    "\n",
    "cv_logo = logo.split(X_train, y_train, groups=cv_groups)\n",
    "\n",
    "scoring_list = ['explained_variance', 'neg_median_absolute_error', 'neg_mean_absolute_error', \n",
    "                'neg_mean_squared_error', 'r2']\n",
    "\n",
    "min_logspace = -4\n",
    "max_logspace =  4\n",
    "logspace = np.logspace(min_logspace, max_logspace, (abs(min_logspace)+abs(max_logspace)+1))\n",
    "\n",
    "min_linspace = 0.1\n",
    "max_linspace = 0.9\n",
    "linspace = np.linspace(min_linspace, max_linspace, 9)\n",
    "\n",
    "#param_grid = dict(alpha=logspace)\n",
    "#param_grid = dict(l1_ratio=linspace)\n",
    "#param_grid = dict(alpha=logspace, l1_ratio=linspace)\n",
    "#param_grid = dict(C=logspace)\n",
    "param_grid = dict(epsilon=logspace)\n",
    "\n",
    "refit_parameter = 'neg_mean_squared_error'\n",
    "\n",
    "gridsearchCV = GridSearchCV(estimator=regressor, param_grid=param_grid, return_train_score=False,\n",
    "                            cv=cv_logo, scoring=scoring_list, refit=refit_parameter, n_jobs=-1)\n",
    "gridsearchCV.fit(X_train, y_train)        \n",
    "\n",
    "best_score = gridsearchCV.best_score_                                  \n",
    "#best_est   = gridsearchCV.best_estimator_.alpha\n",
    "#best_est = gridsearchCV.best_estimator_.l1_ratio\n",
    "#best_est   = gridsearchCV.best_estimator_.C\n",
    "best_est   = gridsearchCV.best_estimator_.epsilon\n",
    "\n",
    "print(\"best_score ({}) = {}\".format(refit_parameter, best_score))\n",
    "#print(\"best alpha    = {}\".format(best_est))\n",
    "#print(\"best l1_ratio = {}\".format(best_est))\n",
    "#print(\"best C = {}\".format(best_est))\n",
    "print(\"best epsilon = {}\".format(best_est))\n",
    "print(\"\")\n",
    "\n",
    "scores = gridsearchCV.cv_results_\n",
    "\n",
    "#print(\"--> alpha    :\", logspace)\n",
    "#print(\"--> l1_ratio :\", linspace)\n",
    "#print(\"--> C :\", logspace)\n",
    "print(\"--> epsilon :\", logspace)\n",
    "\n",
    "for scorer in sorted(scoring_list):\n",
    "    print(\"--> {} scores:\".format(scorer), scores['mean_test_{}'.format(scorer)])\n",
    "    best_index = np.nonzero(scores['rank_test_{}'.format(scorer)] == 1)[0][0]\n",
    "    print(\"--> best {} score index =\".format(scorer), best_index)\n",
    "    best_score = scores['mean_test_{}'.format(scorer)][best_index]\n",
    "    print(\"--> best {} score =\".format(scorer), best_score)\n",
    "    print(\"\")\n",
    "'''    \n",
    "print(\"*****************\")\n",
    "    \n",
    "cv_logo = logo.split(X_train, y_train, groups=cv_groups)\n",
    "    \n",
    "#regr = RidgeCV(cv=cv_logo)\n",
    "#regr = LassoCV(cv=cv_logo, random_state=rseed)\n",
    "regr = ElasticNetCV(cv=cv_logo, random_state=rseed)\n",
    "regr.fit(X_train, y_train)\n",
    "print(regr.alpha_) \n",
    "print(regr.l1_ratio_) \n",
    "''';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate and plot validation curves for specific models:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "model = models[1]\n",
    "\n",
    "regressor = model.estimator\n",
    "\n",
    "print(\"--> Plotting validation curves for\", model.estimator_name, \"...\")\n",
    "\n",
    "min_logspace = -3\n",
    "max_logspace =  1\n",
    "param_range = np.logspace(min_logspace, max_logspace, (abs(min_logspace)+abs(max_logspace)+1))\n",
    "#param_range = [2,4,6,8,10] #[3, 7, 11, 15]\n",
    "#param_name = 'n_neighbors'\n",
    "param_name = 'alpha'\n",
    "#param_name = 'C'\n",
    "#param_name = 'epsilon'\n",
    "#param_name = 'max_depth'\n",
    "\n",
    "plt.figure(figsize=(20, 7))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "scoring_metric = 'explained_variance'\n",
    "\n",
    "cv_logo = logo.split(X_train, y_train, groups=cv_groups)\n",
    "\n",
    "train_scores, test_scores = validation_curve(regressor, X_train, y_train, param_name=param_name, n_jobs=-1,\n",
    "                                             param_range=param_range, cv=cv_logo, scoring=scoring_metric)\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std  = np.std (train_scores, axis=1)\n",
    "test_scores_mean  = np.mean(test_scores,  axis=1)\n",
    "test_scores_std   = np.std (test_scores,  axis=1)\n",
    "\n",
    "plt.title(\"{} validation curves\".format(model.estimator_name))\n",
    "plt.xlabel(param_name)\n",
    "plt.ylabel(scoring_metric)\n",
    "plt.ylim(0.0, 1.05)\n",
    "plt.semilogx(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=2)\n",
    "#plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=2)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, \n",
    "                 alpha=0.2, color=\"darkorange\", lw=2)\n",
    "plt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=2)\n",
    "#plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=2)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, \n",
    "                 alpha=0.2, color=\"navy\", lw=2)\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"best\");\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "scoring_metric = 'r2'\n",
    "\n",
    "cv_logo = logo.split(X_train, y_train, groups=cv_groups)\n",
    "\n",
    "train_scores, test_scores = validation_curve(regressor, X_train, y_train, param_name=param_name, n_jobs=-1,\n",
    "                                             param_range=param_range, cv=cv_logo, scoring=scoring_metric)\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std  = np.std (train_scores, axis=1)\n",
    "test_scores_mean  = np.mean(test_scores,  axis=1)\n",
    "test_scores_std   = np.std (test_scores,  axis=1)\n",
    "\n",
    "plt.title(\"{} validation curves\".format(model.estimator_name))\n",
    "plt.xlabel(param_name)\n",
    "plt.ylabel(scoring_metric)\n",
    "plt.ylim(0.0, 1.05)\n",
    "plt.semilogx(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=2)\n",
    "#plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=2)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, \n",
    "                 alpha=0.2, color=\"darkorange\", lw=2)\n",
    "plt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=2)\n",
    "#plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=2)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, \n",
    "                 alpha=0.2, color=\"navy\", lw=2)\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot predicted vs true AVS distributions and calculate regression metrics for all cross-validation groups for all models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Cross-validating K-Nearest Neighbours Regressor ...\n",
      "--> Cross-validating Ridge Regressor ...\n",
      "--> Cross-validating Lasso Regressor ...\n",
      "--> Cross-validating ElasticNet Regressor ...\n",
      "--> Cross-validating Support Vector Machine Regressor ...\n",
      "--> Cross-validating Stochastic Gradient Descent Regressor ...\n",
      "--> Cross-validating Passive Aggressive Regressor ...\n",
      "--> Cross-validating Neural Network Regressor ...\n",
      "--> Cross-validating Deep Neural Network Regressor ...\n",
      "--> Cross-validating Gaussian Process Regressor ...\n",
      "--> Cross-validating Decision Tree Regressor ...\n",
      "--> Cross-validating Random Forest Regressor ...\n",
      "--> Cross-validating Extra Randomized Trees Regressor ...\n",
      "--> Cross-validating Adaptive Boosted Decision Tree Regressor ...\n",
      "--> Cross-validating Gradient Boosted Decision Tree Regressor ...\n",
      "--> Cross-validating Extreme Gradient Boosted Decision Tree Regressor ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> Regression metrics for all models (mean and std over all years):\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "            background-color:  #ea8e94;\n",
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       "            background-color:  #da3b46;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col2 {\n",
       "            background-color:  #da3b46;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col1 {\n",
       "            background-color:  #f0b2b7;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col2 {\n",
       "            background-color:  #f0b1b5;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col1 {\n",
       "            background-color:  #417ca8;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col2 {\n",
       "            background-color:  #417ca8;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col1 {\n",
       "            background-color:  #eea5aa;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col2 {\n",
       "            background-color:  #eea5aa;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col1 {\n",
       "            background-color:  #e46f78;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col2 {\n",
       "            background-color:  #e47179;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col1 {\n",
       "            background-color:  #f0b1b5;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col2 {\n",
       "            background-color:  #f0b1b5;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col1 {\n",
       "            background-color:  #f1b3b8;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col2 {\n",
       "            background-color:  #f1b3b8;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col1 {\n",
       "            background-color:  #f2b9bd;\n",
       "            color:  #000000;\n",
       "        }    #T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col2 {\n",
       "            background-color:  #f2b9bd;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Estimator</th>        <th class=\"col_heading level0 col1\" >R2 score</th>        <th class=\"col_heading level0 col2\" >Explained variance score</th>        <th class=\"col_heading level0 col3\" >Median absolute error</th>        <th class=\"col_heading level0 col4\" >Mean absolute error</th>        <th class=\"col_heading level0 col5\" >Mean squared error</th>        <th class=\"col_heading level0 col6\" >Mean squared log error</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col0\" class=\"data row0 col0\" >K-Nearest Neighbours Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col1\" class=\"data row0 col1\" >0.545</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col2\" class=\"data row0 col2\" >0.545</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col4\" class=\"data row0 col4\" >0.0145</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col5\" class=\"data row0 col5\" >0.00472</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col0\" class=\"data row1 col0\" >Ridge Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col1\" class=\"data row1 col1\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col2\" class=\"data row1 col2\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col3\" class=\"data row1 col3\" >0.0272</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col4\" class=\"data row1 col4\" >0.0445</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col5\" class=\"data row1 col5\" >0.00791</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col0\" class=\"data row2 col0\" >Lasso Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col1\" class=\"data row2 col1\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col2\" class=\"data row2 col2\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col3\" class=\"data row2 col3\" >0.0272</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col4\" class=\"data row2 col4\" >0.0445</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col5\" class=\"data row2 col5\" >0.00791</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col0\" class=\"data row3 col0\" >ElasticNet Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col1\" class=\"data row3 col1\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col2\" class=\"data row3 col2\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col3\" class=\"data row3 col3\" >0.0273</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col4\" class=\"data row3 col4\" >0.0443</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col5\" class=\"data row3 col5\" >0.00791</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row3_col6\" class=\"data row3 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col0\" class=\"data row4 col0\" >Support Vector Machine Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col1\" class=\"data row4 col1\" >0.427</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col2\" class=\"data row4 col2\" >0.546</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col3\" class=\"data row4 col3\" >0.0423</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col4\" class=\"data row4 col4\" >0.0533</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col5\" class=\"data row4 col5\" >0.00594</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row4_col6\" class=\"data row4 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col0\" class=\"data row5 col0\" >Stochastic Gradient Descent Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col1\" class=\"data row5 col1\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col2\" class=\"data row5 col2\" >0.237</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col3\" class=\"data row5 col3\" >0.0277</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col4\" class=\"data row5 col4\" >0.0439</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col5\" class=\"data row5 col5\" >0.00791</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col0\" class=\"data row6 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col1\" class=\"data row6 col1\" >0.191</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col2\" class=\"data row6 col2\" >0.193</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col3\" class=\"data row6 col3\" >0.0335</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col4\" class=\"data row6 col4\" >0.05</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col5\" class=\"data row6 col5\" >0.00838</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row6_col6\" class=\"data row6 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col0\" class=\"data row7 col0\" >Neural Network Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col1\" class=\"data row7 col1\" >0.527</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col2\" class=\"data row7 col2\" >0.527</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col3\" class=\"data row7 col3\" >0.00675</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col4\" class=\"data row7 col4\" >0.0238</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col5\" class=\"data row7 col5\" >0.00491</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row7_col6\" class=\"data row7 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col0\" class=\"data row8 col0\" >Deep Neural Network Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col1\" class=\"data row8 col1\" >0.711</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col2\" class=\"data row8 col2\" >0.711</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col3\" class=\"data row8 col3\" >0.000131</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col4\" class=\"data row8 col4\" >0.00975</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col5\" class=\"data row8 col5\" >0.003</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row8_col6\" class=\"data row8 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col0\" class=\"data row9 col0\" >Gaussian Process Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col1\" class=\"data row9 col1\" >0.475</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col2\" class=\"data row9 col2\" >0.478</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col3\" class=\"data row9 col3\" >0.000424</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col4\" class=\"data row9 col4\" >0.0154</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col5\" class=\"data row9 col5\" >0.00545</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row9_col6\" class=\"data row9 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col0\" class=\"data row10 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col1\" class=\"data row10 col1\" >-0.013</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col2\" class=\"data row10 col2\" >-0.0129</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col3\" class=\"data row10 col3\" >2.15e-05</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col4\" class=\"data row10 col4\" >0.0198</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col5\" class=\"data row10 col5\" >0.0105</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row10_col6\" class=\"data row10 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col0\" class=\"data row11 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col1\" class=\"data row11 col1\" >0.499</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col2\" class=\"data row11 col2\" >0.499</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col3\" class=\"data row11 col3\" >2.17e-05</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col4\" class=\"data row11 col4\" >0.0171</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col5\" class=\"data row11 col5\" >0.00519</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row11_col6\" class=\"data row11 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col0\" class=\"data row12 col0\" >Extra Randomized Trees Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col1\" class=\"data row12 col1\" >0.604</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col2\" class=\"data row12 col2\" >0.604</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col3\" class=\"data row12 col3\" >1.59e-05</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col4\" class=\"data row12 col4\" >0.0162</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col5\" class=\"data row12 col5\" >0.00411</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row12_col6\" class=\"data row12 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col0\" class=\"data row13 col0\" >Adaptive Boosted Decision Tree Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col1\" class=\"data row13 col1\" >0.477</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col2\" class=\"data row13 col2\" >0.479</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col3\" class=\"data row13 col3\" >0.00543</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col4\" class=\"data row13 col4\" >0.0227</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col5\" class=\"data row13 col5\" >0.00542</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row13_col6\" class=\"data row13 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col0\" class=\"data row14 col0\" >Gradient Boosted Decision Tree Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col1\" class=\"data row14 col1\" >0.471</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col2\" class=\"data row14 col2\" >0.471</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col3\" class=\"data row14 col3\" >0.00138</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col4\" class=\"data row14 col4\" >0.0177</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col5\" class=\"data row14 col5\" >0.00548</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row14_col6\" class=\"data row14 col6\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col0\" class=\"data row15 col0\" >Extreme Gradient Boosted Decision Tree Regressor</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col1\" class=\"data row15 col1\" >0.46</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col2\" class=\"data row15 col2\" >0.46</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col3\" class=\"data row15 col3\" >0.00138</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col4\" class=\"data row15 col4\" >0.0182</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col5\" class=\"data row15 col5\" >0.0056</td>\n",
       "                        <td id=\"T_a5d3965c_2899_11e9_a556_d49a200443c6row15_col6\" class=\"data row15 col6\" >nan</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x126651b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = np.array(X_train) # To accommodate X_train = X_training[train_index] and X_test = X_training[test_index]\n",
    "y_train = np.array(y_train) # To accommodate y_train = y_training[train_index] and y_test = y_training[test_index]\n",
    "\n",
    "logo = LeaveOneGroupOut()\n",
    "\n",
    "cv_groups = df_train['YEAR'] # Players in the same group (i.e. a single NBA season) have identical YEAR variables\n",
    "\n",
    "cv_metrics = []\n",
    "\n",
    "color=iter(plt.cm.tab20(np.linspace(0, 1, 20)))    \n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "for imodel, model in enumerate(models):\n",
    "    \n",
    "    model.reset()\n",
    "    \n",
    "    regressor      = model.estimator\n",
    "    regressor_name = model.estimator_name\n",
    "    \n",
    "    cv_y_predictions = []\n",
    "    cv_y_truths      = []\n",
    "    cv_importances   = []\n",
    "    \n",
    "    print(\"--> Cross-validating\", regressor_name, \"...\")\n",
    "    \n",
    "    for train_index, test_index in logo.split(X_train, y_train, groups=cv_groups):\n",
    "\n",
    "        X_cvtrain = X_train[train_index]\n",
    "        X_cvtest  = X_train[test_index]\n",
    "        y_cvtrain = y_train[train_index]\n",
    "        y_cvtest  = y_train[test_index]\n",
    "                       \n",
    "        regressor.fit(X_cvtrain, y_cvtrain)\n",
    "    \n",
    "        y_cvprediction = regressor.predict(X_cvtest) \n",
    "        y_cvtruth      = y_cvtest.tolist()\n",
    "             \n",
    "        cv_y_predictions = np.append(cv_y_predictions, y_cvprediction)\n",
    "        cv_y_truths      = np.append(cv_y_truths     , y_cvtruth)\n",
    "        \n",
    "        if hasattr(model.estimator, \"feature_importances_\"):\n",
    "            importances = regressor.feature_importances_\n",
    "            cv_importances.append(importances)\n",
    "    \n",
    "    cv_metrics.append({\n",
    "        'Estimator'                : regressor_name, \n",
    "        'R2 score'                 : r2_score(cv_y_truths, cv_y_predictions),\n",
    "        'Explained variance score' : explained_variance_score(cv_y_truths, cv_y_predictions),\n",
    "        'Median absolute error'    : median_absolute_error(cv_y_truths, cv_y_predictions),\n",
    "        'Mean absolute error'      : mean_absolute_error(cv_y_truths, cv_y_predictions),\n",
    "        'Mean squared error'       : mean_squared_error(cv_y_truths, cv_y_predictions),\n",
    "        # 'Mean squared log error'   : mean_squared_log_error(cv_y_truths, cv_y_predictions)\n",
    "    })\n",
    "    \n",
    "    model.cv_importances = cv_importances        \n",
    "    model.cv_evs         = explained_variance_score(cv_y_truths, cv_y_predictions)\n",
    "    \n",
    "    plt.subplot(4, 4, imodel+1)\n",
    "\n",
    "    plt.title(regressor_name)\n",
    "    plt.xlabel(\"True AVS\")\n",
    "    plt.ylabel(\"Predicted AVS\")\n",
    "    plt.xlim(-0.05, 1.05)\n",
    "    plt.ylim(-0.05, 1.05)\n",
    "    plt.grid(True)\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=1, color='black', alpha=.3)\n",
    "    plt.scatter(cv_y_truths, cv_y_predictions, color=next(color), edgecolor='k')\n",
    "    plt.tight_layout()\n",
    "       \n",
    "plt.show()\n",
    "    \n",
    "print(\"\")\n",
    "print(\"--> Regression metrics for all models (mean and std over all years):\")\n",
    "\n",
    "columnorder = ['Estimator', 'R2 score', 'Explained variance score', 'Median absolute error', \n",
    "               'Mean absolute error', 'Mean squared error', 'Mean squared log error']\n",
    "\n",
    "pd.set_option('precision', 3)\n",
    "#pd.set_option('display.float_format', '{:6.3f}'.format) # doesn't seem to work with display(df)\n",
    "df_cv_metrics = pd.DataFrame(cv_metrics, columns=columnorder)\n",
    "cm = sns.diverging_palette(240, 10, n=9, as_cmap=True)\n",
    "subset = ['R2 score', 'Explained variance score']\n",
    "df_cv_metrics_style = df_cv_metrics.style.background_gradient(subset=subset, cmap=cm, axis=0)\n",
    "display(df_cv_metrics_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion: \n",
    "\n",
    "Unfortunately none of the regression models tested in this analysis are able to accurately predict the NBA Award voting shares. This is due to the erratic relationship between AVS and other features in the analysis (and to a lesser degree to the small sample size of players with award votes). Nevertheless, models with an Explained-Variance Score greater than 0.3 are retained to make a final prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> K-Nearest Neighbours Regressor                   is accepted\n",
      "--> Ridge Regressor                                  is rejected\n",
      "--> Lasso Regressor                                  is rejected\n",
      "--> ElasticNet Regressor                             is rejected\n",
      "--> Support Vector Machine Regressor                 is accepted\n",
      "--> Stochastic Gradient Descent Regressor            is rejected\n",
      "--> Passive Aggressive Regressor                     is rejected\n",
      "--> Neural Network Regressor                         is accepted\n",
      "--> Deep Neural Network Regressor                    is accepted\n",
      "--> Gaussian Process Regressor                       is accepted\n",
      "--> Decision Tree Regressor                          is rejected\n",
      "--> Random Forest Regressor                          is accepted\n",
      "--> Extra Randomized Trees Regressor                 is accepted\n",
      "--> Adaptive Boosted Decision Tree Regressor         is accepted\n",
      "--> Gradient Boosted Decision Tree Regressor         is accepted\n",
      "--> Extreme Gradient Boosted Decision Tree Regressor is accepted\n"
     ]
    }
   ],
   "source": [
    "EVS_threshold = 0.3\n",
    "\n",
    "rejected_models = []\n",
    "\n",
    "for model in models:\n",
    "    if model.cv_evs < EVS_threshold:\n",
    "        rejected_models.append(model)\n",
    "        print(\"--> {:48} is rejected\".format(model.estimator_name))\n",
    "    else:\n",
    "        print(\"--> {:48} is accepted\".format(model.estimator_name))\n",
    "\n",
    "#models = [x for x in models if x not in rejected_models]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate feature importances of all fitted Decision Tree ensemble models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Feature importances  of all models (mean +/- std over all CV years):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decision Tree Regressor</th>\n",
       "      <th>Random Forest Regressor</th>\n",
       "      <th>Extra Randomized Trees Regressor</th>\n",
       "      <th>Adaptive Boosted Decision Tree Regressor</th>\n",
       "      <th>Gradient Boosted Decision Tree Regressor</th>\n",
       "      <th>Extreme Gradient Boosted Decision Tree Regressor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TW/TOT</th>\n",
       "      <td>9.65 $\\pm$ 7.76</td>\n",
       "      <td>10.07 $\\pm$ 2.92</td>\n",
       "      <td>12.13 $\\pm$ 1.33</td>\n",
       "      <td>16.33 $\\pm$ 2.53</td>\n",
       "      <td>12.51 $\\pm$ 3.69</td>\n",
       "      <td>17.63 $\\pm$ 3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G/TOT</th>\n",
       "      <td>3.74 $\\pm$ 4.87</td>\n",
       "      <td>3.06 $\\pm$ 0.76</td>\n",
       "      <td>3.31 $\\pm$ 0.41</td>\n",
       "      <td>3.24 $\\pm$ 1.09</td>\n",
       "      <td>2.35 $\\pm$ 1.59</td>\n",
       "      <td>4.31 $\\pm$ 1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS/G</th>\n",
       "      <td>0.00 $\\pm$ 0.00</td>\n",
       "      <td>0.02 $\\pm$ 0.01</td>\n",
       "      <td>0.07 $\\pm$ 0.05</td>\n",
       "      <td>0.00 $\\pm$ 0.00</td>\n",
       "      <td>0.01 $\\pm$ 0.02</td>\n",
       "      <td>0.04 $\\pm$ 0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP/G</th>\n",
       "      <td>7.57 $\\pm$ 5.96</td>\n",
       "      <td>6.82 $\\pm$ 1.69</td>\n",
       "      <td>6.87 $\\pm$ 0.90</td>\n",
       "      <td>8.10 $\\pm$ 2.51</td>\n",
       "      <td>7.76 $\\pm$ 3.21</td>\n",
       "      <td>7.05 $\\pm$ 1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2P/48</th>\n",
       "      <td>0.83 $\\pm$ 0.67</td>\n",
       "      <td>1.40 $\\pm$ 0.30</td>\n",
       "      <td>2.36 $\\pm$ 0.21</td>\n",
       "      <td>0.98 $\\pm$ 0.36</td>\n",
       "      <td>1.12 $\\pm$ 0.37</td>\n",
       "      <td>6.57 $\\pm$ 1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2P%</th>\n",
       "      <td>14.71 $\\pm$ 7.31</td>\n",
       "      <td>10.83 $\\pm$ 3.11</td>\n",
       "      <td>8.81 $\\pm$ 1.13</td>\n",
       "      <td>6.20 $\\pm$ 2.48</td>\n",
       "      <td>13.36 $\\pm$ 4.32</td>\n",
       "      <td>6.29 $\\pm$ 0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P/48</th>\n",
       "      <td>5.85 $\\pm$ 2.16</td>\n",
       "      <td>6.64 $\\pm$ 2.37</td>\n",
       "      <td>5.26 $\\pm$ 1.13</td>\n",
       "      <td>2.48 $\\pm$ 0.57</td>\n",
       "      <td>6.36 $\\pm$ 1.69</td>\n",
       "      <td>7.75 $\\pm$ 1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P%</th>\n",
       "      <td>0.13 $\\pm$ 0.18</td>\n",
       "      <td>2.57 $\\pm$ 0.49</td>\n",
       "      <td>3.06 $\\pm$ 0.40</td>\n",
       "      <td>6.07 $\\pm$ 1.98</td>\n",
       "      <td>2.79 $\\pm$ 1.69</td>\n",
       "      <td>2.34 $\\pm$ 1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/48</th>\n",
       "      <td>10.28 $\\pm$ 4.05</td>\n",
       "      <td>12.94 $\\pm$ 2.92</td>\n",
       "      <td>18.39 $\\pm$ 2.09</td>\n",
       "      <td>12.74 $\\pm$ 1.46</td>\n",
       "      <td>12.90 $\\pm$ 3.44</td>\n",
       "      <td>13.48 $\\pm$ 2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT%</th>\n",
       "      <td>3.36 $\\pm$ 1.34</td>\n",
       "      <td>2.14 $\\pm$ 0.45</td>\n",
       "      <td>2.35 $\\pm$ 0.41</td>\n",
       "      <td>0.68 $\\pm$ 0.54</td>\n",
       "      <td>1.47 $\\pm$ 1.07</td>\n",
       "      <td>1.32 $\\pm$ 0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USG%</th>\n",
       "      <td>29.98 $\\pm$ 4.32</td>\n",
       "      <td>26.53 $\\pm$ 2.81</td>\n",
       "      <td>12.06 $\\pm$ 0.90</td>\n",
       "      <td>27.84 $\\pm$ 3.31</td>\n",
       "      <td>28.02 $\\pm$ 2.48</td>\n",
       "      <td>13.07 $\\pm$ 1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORB%</th>\n",
       "      <td>1.03 $\\pm$ 1.32</td>\n",
       "      <td>0.83 $\\pm$ 0.14</td>\n",
       "      <td>2.01 $\\pm$ 0.36</td>\n",
       "      <td>0.55 $\\pm$ 0.42</td>\n",
       "      <td>0.59 $\\pm$ 0.34</td>\n",
       "      <td>0.81 $\\pm$ 0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB%</th>\n",
       "      <td>2.09 $\\pm$ 1.91</td>\n",
       "      <td>1.31 $\\pm$ 0.36</td>\n",
       "      <td>2.90 $\\pm$ 0.36</td>\n",
       "      <td>1.41 $\\pm$ 0.81</td>\n",
       "      <td>0.56 $\\pm$ 0.79</td>\n",
       "      <td>1.28 $\\pm$ 0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AST%</th>\n",
       "      <td>4.72 $\\pm$ 4.17</td>\n",
       "      <td>3.27 $\\pm$ 0.82</td>\n",
       "      <td>5.87 $\\pm$ 0.91</td>\n",
       "      <td>6.60 $\\pm$ 2.93</td>\n",
       "      <td>3.61 $\\pm$ 1.53</td>\n",
       "      <td>5.51 $\\pm$ 1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOV%</th>\n",
       "      <td>0.81 $\\pm$ 1.13</td>\n",
       "      <td>1.37 $\\pm$ 0.20</td>\n",
       "      <td>2.08 $\\pm$ 0.15</td>\n",
       "      <td>0.49 $\\pm$ 0.23</td>\n",
       "      <td>0.42 $\\pm$ 0.20</td>\n",
       "      <td>1.05 $\\pm$ 0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STL%</th>\n",
       "      <td>1.37 $\\pm$ 1.10</td>\n",
       "      <td>1.92 $\\pm$ 0.33</td>\n",
       "      <td>3.45 $\\pm$ 0.51</td>\n",
       "      <td>0.70 $\\pm$ 0.31</td>\n",
       "      <td>1.09 $\\pm$ 0.32</td>\n",
       "      <td>5.42 $\\pm$ 0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLK%</th>\n",
       "      <td>0.97 $\\pm$ 1.02</td>\n",
       "      <td>1.61 $\\pm$ 0.35</td>\n",
       "      <td>2.82 $\\pm$ 0.50</td>\n",
       "      <td>0.35 $\\pm$ 0.18</td>\n",
       "      <td>0.92 $\\pm$ 0.58</td>\n",
       "      <td>1.67 $\\pm$ 0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF/48</th>\n",
       "      <td>2.92 $\\pm$ 3.43</td>\n",
       "      <td>6.68 $\\pm$ 2.14</td>\n",
       "      <td>6.19 $\\pm$ 0.78</td>\n",
       "      <td>5.24 $\\pm$ 2.06</td>\n",
       "      <td>4.16 $\\pm$ 1.97</td>\n",
       "      <td>4.42 $\\pm$ 0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Decision Tree Regressor Random Forest Regressor  \\\n",
       "TW/TOT         9.65 $\\pm$ 7.76        10.07 $\\pm$ 2.92   \n",
       "G/TOT          3.74 $\\pm$ 4.87         3.06 $\\pm$ 0.76   \n",
       "GS/G           0.00 $\\pm$ 0.00         0.02 $\\pm$ 0.01   \n",
       "MP/G           7.57 $\\pm$ 5.96         6.82 $\\pm$ 1.69   \n",
       "2P/48          0.83 $\\pm$ 0.67         1.40 $\\pm$ 0.30   \n",
       "2P%           14.71 $\\pm$ 7.31        10.83 $\\pm$ 3.11   \n",
       "3P/48          5.85 $\\pm$ 2.16         6.64 $\\pm$ 2.37   \n",
       "3P%            0.13 $\\pm$ 0.18         2.57 $\\pm$ 0.49   \n",
       "FT/48         10.28 $\\pm$ 4.05        12.94 $\\pm$ 2.92   \n",
       "FT%            3.36 $\\pm$ 1.34         2.14 $\\pm$ 0.45   \n",
       "USG%          29.98 $\\pm$ 4.32        26.53 $\\pm$ 2.81   \n",
       "ORB%           1.03 $\\pm$ 1.32         0.83 $\\pm$ 0.14   \n",
       "DRB%           2.09 $\\pm$ 1.91         1.31 $\\pm$ 0.36   \n",
       "AST%           4.72 $\\pm$ 4.17         3.27 $\\pm$ 0.82   \n",
       "TOV%           0.81 $\\pm$ 1.13         1.37 $\\pm$ 0.20   \n",
       "STL%           1.37 $\\pm$ 1.10         1.92 $\\pm$ 0.33   \n",
       "BLK%           0.97 $\\pm$ 1.02         1.61 $\\pm$ 0.35   \n",
       "PF/48          2.92 $\\pm$ 3.43         6.68 $\\pm$ 2.14   \n",
       "\n",
       "       Extra Randomized Trees Regressor  \\\n",
       "TW/TOT                 12.13 $\\pm$ 1.33   \n",
       "G/TOT                   3.31 $\\pm$ 0.41   \n",
       "GS/G                    0.07 $\\pm$ 0.05   \n",
       "MP/G                    6.87 $\\pm$ 0.90   \n",
       "2P/48                   2.36 $\\pm$ 0.21   \n",
       "2P%                     8.81 $\\pm$ 1.13   \n",
       "3P/48                   5.26 $\\pm$ 1.13   \n",
       "3P%                     3.06 $\\pm$ 0.40   \n",
       "FT/48                  18.39 $\\pm$ 2.09   \n",
       "FT%                     2.35 $\\pm$ 0.41   \n",
       "USG%                   12.06 $\\pm$ 0.90   \n",
       "ORB%                    2.01 $\\pm$ 0.36   \n",
       "DRB%                    2.90 $\\pm$ 0.36   \n",
       "AST%                    5.87 $\\pm$ 0.91   \n",
       "TOV%                    2.08 $\\pm$ 0.15   \n",
       "STL%                    3.45 $\\pm$ 0.51   \n",
       "BLK%                    2.82 $\\pm$ 0.50   \n",
       "PF/48                   6.19 $\\pm$ 0.78   \n",
       "\n",
       "       Adaptive Boosted Decision Tree Regressor  \\\n",
       "TW/TOT                         16.33 $\\pm$ 2.53   \n",
       "G/TOT                           3.24 $\\pm$ 1.09   \n",
       "GS/G                            0.00 $\\pm$ 0.00   \n",
       "MP/G                            8.10 $\\pm$ 2.51   \n",
       "2P/48                           0.98 $\\pm$ 0.36   \n",
       "2P%                             6.20 $\\pm$ 2.48   \n",
       "3P/48                           2.48 $\\pm$ 0.57   \n",
       "3P%                             6.07 $\\pm$ 1.98   \n",
       "FT/48                          12.74 $\\pm$ 1.46   \n",
       "FT%                             0.68 $\\pm$ 0.54   \n",
       "USG%                           27.84 $\\pm$ 3.31   \n",
       "ORB%                            0.55 $\\pm$ 0.42   \n",
       "DRB%                            1.41 $\\pm$ 0.81   \n",
       "AST%                            6.60 $\\pm$ 2.93   \n",
       "TOV%                            0.49 $\\pm$ 0.23   \n",
       "STL%                            0.70 $\\pm$ 0.31   \n",
       "BLK%                            0.35 $\\pm$ 0.18   \n",
       "PF/48                           5.24 $\\pm$ 2.06   \n",
       "\n",
       "       Gradient Boosted Decision Tree Regressor  \\\n",
       "TW/TOT                         12.51 $\\pm$ 3.69   \n",
       "G/TOT                           2.35 $\\pm$ 1.59   \n",
       "GS/G                            0.01 $\\pm$ 0.02   \n",
       "MP/G                            7.76 $\\pm$ 3.21   \n",
       "2P/48                           1.12 $\\pm$ 0.37   \n",
       "2P%                            13.36 $\\pm$ 4.32   \n",
       "3P/48                           6.36 $\\pm$ 1.69   \n",
       "3P%                             2.79 $\\pm$ 1.69   \n",
       "FT/48                          12.90 $\\pm$ 3.44   \n",
       "FT%                             1.47 $\\pm$ 1.07   \n",
       "USG%                           28.02 $\\pm$ 2.48   \n",
       "ORB%                            0.59 $\\pm$ 0.34   \n",
       "DRB%                            0.56 $\\pm$ 0.79   \n",
       "AST%                            3.61 $\\pm$ 1.53   \n",
       "TOV%                            0.42 $\\pm$ 0.20   \n",
       "STL%                            1.09 $\\pm$ 0.32   \n",
       "BLK%                            0.92 $\\pm$ 0.58   \n",
       "PF/48                           4.16 $\\pm$ 1.97   \n",
       "\n",
       "       Extreme Gradient Boosted Decision Tree Regressor  \n",
       "TW/TOT                                 17.63 $\\pm$ 3.23  \n",
       "G/TOT                                   4.31 $\\pm$ 1.22  \n",
       "GS/G                                    0.04 $\\pm$ 0.07  \n",
       "MP/G                                    7.05 $\\pm$ 1.65  \n",
       "2P/48                                   6.57 $\\pm$ 1.81  \n",
       "2P%                                     6.29 $\\pm$ 0.88  \n",
       "3P/48                                   7.75 $\\pm$ 1.67  \n",
       "3P%                                     2.34 $\\pm$ 1.01  \n",
       "FT/48                                  13.48 $\\pm$ 2.45  \n",
       "FT%                                     1.32 $\\pm$ 0.87  \n",
       "USG%                                   13.07 $\\pm$ 1.49  \n",
       "ORB%                                    0.81 $\\pm$ 0.47  \n",
       "DRB%                                    1.28 $\\pm$ 0.68  \n",
       "AST%                                    5.51 $\\pm$ 1.11  \n",
       "TOV%                                    1.05 $\\pm$ 0.76  \n",
       "STL%                                    5.42 $\\pm$ 0.89  \n",
       "BLK%                                    1.67 $\\pm$ 0.66  \n",
       "PF/48                                   4.42 $\\pm$ 0.81  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_importances = pd.DataFrame() #index=feature_names, columns=model_names)\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    if hasattr(model.estimator, \"feature_importances_\"):\n",
    "    \n",
    "        #print(\"--> Calculating feature importances for\", model.estimator_name, \"...\")\n",
    "        \n",
    "        cv_importances = model.cv_importances\n",
    "        \n",
    "        df_cvimportances = pd.DataFrame(cv_importances, columns=feature_names)\n",
    "                        \n",
    "        mean_cvimportances = np.mean(df_cvimportances, axis=0)\n",
    "        std_cvimportances  = np.std (df_cvimportances, axis=0)\n",
    "        \n",
    "        for i in range(n_features):\n",
    "            df_importances.loc[feature_names[i], model.estimator_name] = \\\n",
    "                \"{:.2f} $\\pm$ {:.2f}\".format((mean_cvimportances[i]*100), (std_cvimportances[i]*100))\n",
    "\n",
    "#print(\"\")\n",
    "print(\"--> Feature importances  of all models (mean +/- std over all CV years):\")\n",
    "df_importances.dropna(axis=1, how='all', inplace=True)\n",
    "display(df_importances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training and predictions\n",
    "\n",
    "Fit all models to training data and use fitted models to predict the players' AVS in *test_year*. Predicted vs true AVS distributions are plotted and regression metrics are calculated if award voting has been completed for *test_year*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Fitting K-Nearest Neighbours Regressor ...\n",
      "--> Fitting Ridge Regressor ...\n",
      "--> Fitting Lasso Regressor ...\n",
      "--> Fitting ElasticNet Regressor ...\n",
      "--> Fitting Support Vector Machine Regressor ...\n",
      "--> Fitting Stochastic Gradient Descent Regressor ...\n",
      "--> Fitting Passive Aggressive Regressor ...\n",
      "--> Fitting Neural Network Regressor ...\n",
      "--> Fitting Deep Neural Network Regressor ...\n",
      "--> Fitting Gaussian Process Regressor ...\n",
      "--> Fitting Decision Tree Regressor ...\n",
      "--> Fitting Random Forest Regressor ...\n",
      "--> Fitting Extra Randomized Trees Regressor ...\n",
      "--> Fitting Adaptive Boosted Decision Tree Regressor ...\n",
      "--> Fitting Gradient Boosted Decision Tree Regressor ...\n",
      "--> Fitting Extreme Gradient Boosted Decision Tree Regressor ...\n"
     ]
    }
   ],
   "source": [
    "if test_year_truth_is_known:\n",
    "    color=iter(plt.cm.tab20(np.linspace(0, 1, 20)))    \n",
    "    plt.figure(figsize=(20, 20))\n",
    "\n",
    "regression_metrics = []\n",
    "\n",
    "for imodel, model in enumerate(models):\n",
    "    \n",
    "    regressor      = model.estimator\n",
    "    regressor_name = model.estimator_name\n",
    "    \n",
    "    print(\"--> Fitting\", regressor_name, \"...\")\n",
    "    \n",
    "    regressor.fit(X_train, y_train)          # Fit model to training data\n",
    "\n",
    "    y_prediction = regressor.predict(X_test) # Predict test data\n",
    "    \n",
    "    model.set_y_prediction(y_prediction)\n",
    "    \n",
    "    if test_year_truth_is_known:\n",
    "\n",
    "        #y_score = regressor.score(X_test, y_test) # i.e. r2 score\n",
    "        #model.set_y_score(y_score)\n",
    "    \n",
    "        y_truth = y_test.tolist()\n",
    "        model.set_y_truth(y_truth)\n",
    "                \n",
    "        r2_s                 = r2_score(y_truth, y_prediction)\n",
    "        explained_variance_s = explained_variance_score(y_truth, y_prediction)\n",
    "        median_absolute_e    = median_absolute_error(y_truth, y_prediction)\n",
    "        mean_absolute_e      = mean_absolute_error(y_truth, y_prediction)\n",
    "        mean_squared_e       = mean_squared_error(y_truth, y_prediction)\n",
    "        # mean_squared_log_e   = mean_squared_log_error(y_truth, y_prediction)\n",
    "        \n",
    "        regression_metrics.append({\n",
    "            'Estimator'                : regressor_name, \n",
    "            'R2 Score'                 : r2_s                 .round(3),\n",
    "            'Explained Variance Score' : explained_variance_s .round(3),\n",
    "            'Median Absolute Error'    : median_absolute_e    .round(3),\n",
    "            'Mean Absolute Error'      : mean_absolute_e      .round(3),\n",
    "            'Mean Squared Error'       : mean_squared_e       .round(3),\n",
    "            # 'Mean Squared Log Error'   : mean_squared_log_e   .round(3)\n",
    "        })\n",
    "        \n",
    "        plt.subplot(4, 4, imodel+1)\n",
    "\n",
    "        plt.title(regressor_name)\n",
    "        plt.xlabel(\"True AVS\")\n",
    "        plt.ylabel(\"Predicted AVS\")\n",
    "        plt.xlim(-0.05, 1.05)\n",
    "        plt.ylim(-0.05, 1.05)\n",
    "        plt.grid(True)\n",
    "        plt.plot([0, 1], [0, 1], linestyle='--', lw=1, color='black', alpha=.3)\n",
    "        plt.scatter(y_truth, y_prediction, color=next(color), edgecolor='k')\n",
    "        plt.tight_layout()\n",
    "    \n",
    "if test_year_truth_is_known:\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\")\n",
    "    print(\"--> Regression metrics for all models:\")\n",
    "    columnorder = ['Estimator', 'R2 Score', 'Explained Variance Score', 'Median Absolute Error', \n",
    "                   'Mean Absolute Error', 'Mean Squared Error'] #, 'Mean Squared Log Error']\n",
    "    df_regression_metrics = pd.DataFrame(regression_metrics, columns=columnorder)\n",
    "    cm = sns.diverging_palette(240, 10, n=9, as_cmap=True)\n",
    "    df_regression_metrics_style = df_regression_metrics.style.background_gradient( \\\n",
    "        subset=['R2 Score', 'Explained Variance Score'], cmap=cm, axis=0)\n",
    "    display(df_regression_metrics_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List NBA Award candidates for all models, and add predicted AVS and corresponding ranks to *df_test*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Predicted top-10 MVP vote shares in 2019 for K-Nearest Neighbours Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.661\n",
      "---->   2: Kevin Durant             =  0.506\n",
      "---->   3: LeBron James             =  0.390\n",
      "---->   4: Kawhi Leonard            =  0.228\n",
      "---->   5: Nikola Jokic             =  0.172\n",
      "---->   6: Anthony Davis            =  0.103\n",
      "---->   7: Jimmy Butler             =  0.098\n",
      "---->   8: Russell Westbrook        =  0.089\n",
      "---->   9: Giannis Antetokounmpo    =  0.086\n",
      "---->  10: Damian Lillard           =  0.045\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Ridge Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.307\n",
      "---->   2: Giannis Antetokounmpo    =  0.222\n",
      "---->   3: Stephen Curry            =  0.196\n",
      "---->   4: Joel Embiid              =  0.195\n",
      "---->   5: LeBron James             =  0.186\n",
      "---->   6: Kevin Durant             =  0.186\n",
      "---->   7: Paul George              =  0.185\n",
      "---->   8: Kawhi Leonard            =  0.176\n",
      "---->   9: Damian Lillard           =  0.169\n",
      "---->  10: Anthony Davis            =  0.165\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Lasso Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.307\n",
      "---->   2: Giannis Antetokounmpo    =  0.221\n",
      "---->   3: Stephen Curry            =  0.196\n",
      "---->   4: Joel Embiid              =  0.195\n",
      "---->   5: LeBron James             =  0.185\n",
      "---->   6: Kevin Durant             =  0.185\n",
      "---->   7: Paul George              =  0.184\n",
      "---->   8: Kawhi Leonard            =  0.175\n",
      "---->   9: Damian Lillard           =  0.168\n",
      "---->  10: Anthony Davis            =  0.164\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for ElasticNet Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.307\n",
      "---->   2: Giannis Antetokounmpo    =  0.217\n",
      "---->   3: Joel Embiid              =  0.192\n",
      "---->   4: Stephen Curry            =  0.191\n",
      "---->   5: Kevin Durant             =  0.181\n",
      "---->   6: LeBron James             =  0.180\n",
      "---->   7: Paul George              =  0.179\n",
      "---->   8: Kawhi Leonard            =  0.170\n",
      "---->   9: Damian Lillard           =  0.166\n",
      "---->  10: Anthony Davis            =  0.158\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Support Vector Machine Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.685\n",
      "---->   2: Giannis Antetokounmpo    =  0.449\n",
      "---->   3: Kevin Durant             =  0.279\n",
      "---->   4: Joel Embiid              =  0.250\n",
      "---->   5: Kawhi Leonard            =  0.243\n",
      "---->   6: LeBron James             =  0.233\n",
      "---->   7: Nikola Jokic             =  0.190\n",
      "---->   8: Damian Lillard           =  0.160\n",
      "---->   9: Paul George              =  0.159\n",
      "---->  10: Kyrie Irving             =  0.142\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Stochastic Gradient Descent Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.304\n",
      "---->   2: Giannis Antetokounmpo    =  0.214\n",
      "---->   3: Joel Embiid              =  0.190\n",
      "---->   4: Kevin Durant             =  0.172\n",
      "---->   5: LeBron James             =  0.160\n",
      "---->   6: Stephen Curry            =  0.160\n",
      "---->   7: Kawhi Leonard            =  0.156\n",
      "---->   8: Damian Lillard           =  0.154\n",
      "---->   9: Paul George              =  0.152\n",
      "---->  10: Nikola Jokic             =  0.149\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Passive Aggressive Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.263\n",
      "---->   2: Giannis Antetokounmpo    =  0.228\n",
      "---->   3: Joel Embiid              =  0.195\n",
      "---->   4: Kevin Durant             =  0.193\n",
      "---->   5: Nikola Jokic             =  0.167\n",
      "---->   6: LeBron James             =  0.164\n",
      "---->   7: Kawhi Leonard            =  0.157\n",
      "---->   8: Damian Lillard           =  0.153\n",
      "---->   9: Stephen Curry            =  0.145\n",
      "---->  10: Russell Westbrook        =  0.144\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Neural Network Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.553\n",
      "---->   2: Giannis Antetokounmpo    =  0.432\n",
      "---->   3: Kevin Durant             =  0.342\n",
      "---->   4: Kawhi Leonard            =  0.283\n",
      "---->   5: Joel Embiid              =  0.264\n",
      "---->   6: Nikola Jokic             =  0.250\n",
      "---->   7: LeBron James             =  0.229\n",
      "---->   8: Damian Lillard           =  0.214\n",
      "---->   9: Paul George              =  0.180\n",
      "---->  10: Anthony Davis            =  0.167\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Deep Neural Network Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.717\n",
      "---->   2: Kevin Durant             =  0.572\n",
      "---->   3: Paul George              =  0.449\n",
      "---->   4: Kawhi Leonard            =  0.448\n",
      "---->   5: Nikola Jokic             =  0.408\n",
      "---->   6: Joel Embiid              =  0.326\n",
      "---->   7: Giannis Antetokounmpo    =  0.312\n",
      "---->   8: Anthony Davis            =  0.244\n",
      "---->   9: Damian Lillard           =  0.236\n",
      "---->  10: Ben Simmons              =  0.068\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Gaussian Process Regressor:\n",
      "\n",
      "---->   1: Kevin Durant             =  0.310\n",
      "---->   2: Damian Lillard           =  0.207\n",
      "---->   3: James Harden             =  0.198\n",
      "---->   4: Kawhi Leonard            =  0.175\n",
      "---->   5: Jimmy Butler             =  0.098\n",
      "---->   6: Anthony Davis            =  0.091\n",
      "---->   7: LeBron James             =  0.075\n",
      "---->   8: Kyrie Irving             =  0.067\n",
      "---->   9: Paul George              =  0.037\n",
      "---->  10: Nikola Jokic             =  0.032\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Decision Tree Regressor:\n",
      "\n",
      "---->   1: Buddy Hield              =  0.922\n",
      "---->   2: James Harden             =  0.879\n",
      "---->   3: Devin Booker             =  0.425\n",
      "---->   3: Joel Embiid              =  0.425\n",
      "---->   5: LeBron James             =  0.330\n",
      "---->   5: Kawhi Leonard            =  0.330\n",
      "---->   7: Kevin Durant             =  0.112\n",
      "---->   8: Damian Lillard           =  0.080\n",
      "---->   9: Blake Griffin            =  0.074\n",
      "---->  10: Giannis Antetokounmpo    =  0.020\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Random Forest Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.537\n",
      "---->   2: Buddy Hield              =  0.476\n",
      "---->   3: Giannis Antetokounmpo    =  0.422\n",
      "---->   4: Joel Embiid              =  0.407\n",
      "---->   5: Kevin Durant             =  0.351\n",
      "---->   6: LeBron James             =  0.349\n",
      "---->   7: Kawhi Leonard            =  0.265\n",
      "---->   8: Stephen Curry            =  0.255\n",
      "---->   9: Anthony Davis            =  0.226\n",
      "---->  10: Blake Griffin            =  0.142\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Extra Randomized Trees Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.707\n",
      "---->   2: Giannis Antetokounmpo    =  0.400\n",
      "---->   3: Joel Embiid              =  0.357\n",
      "---->   4: Kevin Durant             =  0.339\n",
      "---->   5: Kawhi Leonard            =  0.324\n",
      "---->   6: LeBron James             =  0.243\n",
      "---->   7: Stephen Curry            =  0.202\n",
      "---->   8: Anthony Davis            =  0.152\n",
      "---->   9: Nikola Jokic             =  0.138\n",
      "---->  10: Russell Westbrook        =  0.126\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Adaptive Boosted Decision Tree Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.590\n",
      "---->   2: Joel Embiid              =  0.382\n",
      "---->   3: Kevin Durant             =  0.276\n",
      "---->   4: LeBron James             =  0.209\n",
      "---->   5: Kawhi Leonard            =  0.201\n",
      "---->   6: Giannis Antetokounmpo    =  0.179\n",
      "---->   7: Stephen Curry            =  0.156\n",
      "---->   8: Anthony Davis            =  0.153\n",
      "---->   9: Devin Booker             =  0.126\n",
      "---->  10: Damian Lillard           =  0.109\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Gradient Boosted Decision Tree Regressor:\n",
      "\n",
      "---->   1: Buddy Hield              =  0.605\n",
      "---->   2: James Harden             =  0.556\n",
      "---->   3: Stephen Curry            =  0.447\n",
      "---->   4: Joel Embiid              =  0.435\n",
      "---->   5: Giannis Antetokounmpo    =  0.384\n",
      "---->   6: Kevin Durant             =  0.348\n",
      "---->   7: LeBron James             =  0.291\n",
      "---->   8: Kawhi Leonard            =  0.260\n",
      "---->   9: Anthony Davis            =  0.249\n",
      "---->  10: Devin Booker             =  0.140\n",
      "\n",
      "==> Predicted top-10 MVP vote shares in 2019 for Extreme Gradient Boosted Decision Tree Regressor:\n",
      "\n",
      "---->   1: James Harden             =  0.629\n",
      "---->   2: Joel Embiid              =  0.545\n",
      "---->   3: Buddy Hield              =  0.499\n",
      "---->   4: Stephen Curry            =  0.375\n",
      "---->   5: Giannis Antetokounmpo    =  0.339\n",
      "---->   6: LeBron James             =  0.309\n",
      "---->   7: Kevin Durant             =  0.299\n",
      "---->   8: Kawhi Leonard            =  0.256\n",
      "---->   9: Anthony Davis            =  0.244\n",
      "---->  10: Devin Booker             =  0.171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "player_index_dict = {}\n",
    "\n",
    "for i in range(n_players_test):\n",
    "    player_name = df_test.iat[i,0]\n",
    "    player_index_dict[player_name] = i\n",
    "\n",
    "printlimit = 10\n",
    "    \n",
    "for imodel, model in enumerate(models):\n",
    "            \n",
    "    model_acronym = ''.join(c for c in model.estimator_name if c.isupper())\n",
    "    column_name_score = '{} score'.format(model_acronym)\n",
    "    column_name_rank  = '{} rank' .format(model_acronym)\n",
    "    \n",
    "    df_rank = pd.DataFrame(columns=['Player', column_name_rank], index=range(0,n_players_test))\n",
    "\n",
    "    regressor    = model.estimator\n",
    "    y_prediction = model.y_prediction\n",
    "    \n",
    "    df_test[column_name_score] = y_prediction\n",
    "    \n",
    "    player_score_dict = {}\n",
    "    for i in range(n_players_test):\n",
    "        player_score_dict[df_test.iat[i,0]] = y_prediction[i]\n",
    "\n",
    "    sorted_player_score_dict = sorted(player_score_dict.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    \n",
    "    print(\"==> Predicted top-{} {} vote shares in {} for {}:\".format( \\\n",
    "        printlimit, award, test_year, model.estimator_name))\n",
    "    print(\"\")\n",
    "    \n",
    "    previous_rank  = 0\n",
    "    previous_score = 1e10\n",
    "    rank_increment = 0\n",
    "    \n",
    "    for i, (key, value) in enumerate(dict(sorted_player_score_dict).items(), 1):\n",
    "        \n",
    "        if (value < previous_score):\n",
    "            rank = previous_rank + 1 + rank_increment\n",
    "            rank_increment = 0\n",
    "        else:\n",
    "            rank = previous_rank\n",
    "            rank_increment += 1\n",
    "        previous_rank  = rank\n",
    "        previous_score = value\n",
    "        \n",
    "        df_rank.loc[player_index_dict[key], 'Player'        ] = key\n",
    "        df_rank.loc[player_index_dict[key], column_name_rank] = rank\n",
    "        \n",
    "        if (i <= printlimit):\n",
    "            print(\"----> {:3}: {:24} = {:6.3f}\".format(rank, key, value))\n",
    "        \n",
    "    df_test = pd.merge(df_test, df_rank, how='right', left_on=['Player'], right_on=['Player'])    \n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create NBA Award candidate list with all AVS predictions and prediction ranks, sorted according to the median predicted rank over all selected models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Predicted MVP candidate top-10 in order of the median predicted rank over all selected models:\n"
     ]
    },
    {
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       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col5 {\n",
       "            background-color:  #6796b9;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col7 {\n",
       "            background-color:  #719dbe;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col9 {\n",
       "            background-color:  #b6cdde;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col11 {\n",
       "            background-color:  #ed9da3;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col13 {\n",
       "            background-color:  #6494b8;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col15 {\n",
       "            background-color:  #417ca8;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col17 {\n",
       "            background-color:  #6a98ba;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col19 {\n",
       "            background-color:  #417ca8;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col21 {\n",
       "            background-color:  #417ca8;\n",
       "            color:  #000000;\n",
       "        }    #T_c28db426_2899_11e9_b08b_d49a200443c6row9_col23 {\n",
       "            background-color:  #4b83ac;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_c28db426_2899_11e9_b08b_d49a200443c6\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Player</th>        <th class=\"col_heading level0 col1\" >Tm</th>        <th class=\"col_heading level0 col2\" >G</th>        <th class=\"col_heading level0 col3\" >KNNR score</th>        <th class=\"col_heading level0 col4\" >KNNR rank</th>        <th class=\"col_heading level0 col5\" >SVMR score</th>        <th class=\"col_heading level0 col6\" >SVMR rank</th>        <th class=\"col_heading level0 col7\" >NNR score</th>        <th class=\"col_heading level0 col8\" >NNR rank</th>        <th class=\"col_heading level0 col9\" >DNNR score</th>        <th class=\"col_heading level0 col10\" >DNNR rank</th>        <th class=\"col_heading level0 col11\" >GPR score</th>        <th class=\"col_heading level0 col12\" >GPR rank</th>        <th class=\"col_heading level0 col13\" >RFR score</th>        <th class=\"col_heading level0 col14\" >RFR rank</th>        <th class=\"col_heading level0 col15\" >ERTR score</th>        <th class=\"col_heading level0 col16\" >ERTR rank</th>        <th class=\"col_heading level0 col17\" >ABDTR score</th>        <th class=\"col_heading level0 col18\" >ABDTR rank</th>        <th class=\"col_heading level0 col19\" >GBDTR score</th>        <th class=\"col_heading level0 col20\" >GBDTR rank</th>        <th class=\"col_heading level0 col21\" >EGBDTR score</th>        <th class=\"col_heading level0 col22\" >EGBDTR rank</th>        <th class=\"col_heading level0 col23\" >Median AVS</th>        <th class=\"col_heading level0 col24\" >Median rank</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col0\" class=\"data row0 col0\" >James Harden</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col1\" class=\"data row0 col1\" >HOU</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col2\" class=\"data row0 col2\" >49</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col3\" class=\"data row0 col3\" >0.661</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col5\" class=\"data row0 col5\" >0.685</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col7\" class=\"data row0 col7\" >0.553</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col8\" class=\"data row0 col8\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col9\" class=\"data row0 col9\" >0.717</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col10\" class=\"data row0 col10\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col11\" class=\"data row0 col11\" >0.198</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col12\" class=\"data row0 col12\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col13\" class=\"data row0 col13\" >0.537</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col14\" class=\"data row0 col14\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col15\" class=\"data row0 col15\" >0.707</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col16\" class=\"data row0 col16\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col17\" class=\"data row0 col17\" >0.59</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col18\" class=\"data row0 col18\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col19\" class=\"data row0 col19\" >0.556</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col20\" class=\"data row0 col20\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col21\" class=\"data row0 col21\" >0.629</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col22\" class=\"data row0 col22\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col23\" class=\"data row0 col23\" >0.609</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row0_col24\" class=\"data row0 col24\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col0\" class=\"data row1 col0\" >Kevin Durant</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col1\" class=\"data row1 col1\" >GSW</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col2\" class=\"data row1 col2\" >52</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col3\" class=\"data row1 col3\" >0.506</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col5\" class=\"data row1 col5\" >0.279</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col6\" class=\"data row1 col6\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col7\" class=\"data row1 col7\" >0.342</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col8\" class=\"data row1 col8\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col9\" class=\"data row1 col9\" >0.572</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col10\" class=\"data row1 col10\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col11\" class=\"data row1 col11\" >0.31</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col12\" class=\"data row1 col12\" >1</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col13\" class=\"data row1 col13\" >0.351</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col14\" class=\"data row1 col14\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col15\" class=\"data row1 col15\" >0.339</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col16\" class=\"data row1 col16\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col17\" class=\"data row1 col17\" >0.276</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col18\" class=\"data row1 col18\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col19\" class=\"data row1 col19\" >0.348</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col20\" class=\"data row1 col20\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col21\" class=\"data row1 col21\" >0.299</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col22\" class=\"data row1 col22\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col23\" class=\"data row1 col23\" >0.341</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row1_col24\" class=\"data row1 col24\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col0\" class=\"data row2 col0\" >Joel Embiid</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col1\" class=\"data row2 col1\" >PHI</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col2\" class=\"data row2 col2\" >49</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col3\" class=\"data row2 col3\" >0.016</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col4\" class=\"data row2 col4\" >13</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col5\" class=\"data row2 col5\" >0.25</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col6\" class=\"data row2 col6\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col7\" class=\"data row2 col7\" >0.264</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col8\" class=\"data row2 col8\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col9\" class=\"data row2 col9\" >0.326</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col10\" class=\"data row2 col10\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col11\" class=\"data row2 col11\" >0.00242</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col12\" class=\"data row2 col12\" >29</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col13\" class=\"data row2 col13\" >0.407</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col14\" class=\"data row2 col14\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col15\" class=\"data row2 col15\" >0.357</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col16\" class=\"data row2 col16\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col17\" class=\"data row2 col17\" >0.382</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col18\" class=\"data row2 col18\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col19\" class=\"data row2 col19\" >0.435</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col20\" class=\"data row2 col20\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col21\" class=\"data row2 col21\" >0.545</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col22\" class=\"data row2 col22\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col23\" class=\"data row2 col23\" >0.341</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row2_col24\" class=\"data row2 col24\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col0\" class=\"data row3 col0\" >Giannis Antetokounmpo</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col1\" class=\"data row3 col1\" >MIL</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col2\" class=\"data row3 col2\" >48</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col3\" class=\"data row3 col3\" >0.0864</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col4\" class=\"data row3 col4\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col5\" class=\"data row3 col5\" >0.449</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col6\" class=\"data row3 col6\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col7\" class=\"data row3 col7\" >0.432</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col8\" class=\"data row3 col8\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col9\" class=\"data row3 col9\" >0.312</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col10\" class=\"data row3 col10\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col11\" class=\"data row3 col11\" >0.00864</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col12\" class=\"data row3 col12\" >16</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col13\" class=\"data row3 col13\" >0.422</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col14\" class=\"data row3 col14\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col15\" class=\"data row3 col15\" >0.4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col16\" class=\"data row3 col16\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col17\" class=\"data row3 col17\" >0.179</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col18\" class=\"data row3 col18\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col19\" class=\"data row3 col19\" >0.384</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col20\" class=\"data row3 col20\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col21\" class=\"data row3 col21\" >0.339</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col22\" class=\"data row3 col22\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col23\" class=\"data row3 col23\" >0.361</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row3_col24\" class=\"data row3 col24\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col0\" class=\"data row4 col0\" >Kawhi Leonard</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col1\" class=\"data row4 col1\" >TOR</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col2\" class=\"data row4 col2\" >39</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col3\" class=\"data row4 col3\" >0.228</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col4\" class=\"data row4 col4\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col5\" class=\"data row4 col5\" >0.243</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col6\" class=\"data row4 col6\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col7\" class=\"data row4 col7\" >0.283</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col8\" class=\"data row4 col8\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col9\" class=\"data row4 col9\" >0.448</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col10\" class=\"data row4 col10\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col11\" class=\"data row4 col11\" >0.175</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col12\" class=\"data row4 col12\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col13\" class=\"data row4 col13\" >0.265</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col14\" class=\"data row4 col14\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col15\" class=\"data row4 col15\" >0.324</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col16\" class=\"data row4 col16\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col17\" class=\"data row4 col17\" >0.201</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col18\" class=\"data row4 col18\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col19\" class=\"data row4 col19\" >0.26</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col20\" class=\"data row4 col20\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col21\" class=\"data row4 col21\" >0.256</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col22\" class=\"data row4 col22\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col23\" class=\"data row4 col23\" >0.258</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row4_col24\" class=\"data row4 col24\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col0\" class=\"data row5 col0\" >LeBron James</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col1\" class=\"data row5 col1\" >LAL</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col2\" class=\"data row5 col2\" >35</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col3\" class=\"data row5 col3\" >0.39</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col4\" class=\"data row5 col4\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col5\" class=\"data row5 col5\" >0.233</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col6\" class=\"data row5 col6\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col7\" class=\"data row5 col7\" >0.229</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col8\" class=\"data row5 col8\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col9\" class=\"data row5 col9\" >0.0524</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col10\" class=\"data row5 col10\" >11</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col11\" class=\"data row5 col11\" >0.0748</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col12\" class=\"data row5 col12\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col13\" class=\"data row5 col13\" >0.349</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col14\" class=\"data row5 col14\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col15\" class=\"data row5 col15\" >0.243</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col16\" class=\"data row5 col16\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col17\" class=\"data row5 col17\" >0.209</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col18\" class=\"data row5 col18\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col19\" class=\"data row5 col19\" >0.291</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col20\" class=\"data row5 col20\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col21\" class=\"data row5 col21\" >0.309</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col22\" class=\"data row5 col22\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col23\" class=\"data row5 col23\" >0.238</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row5_col24\" class=\"data row5 col24\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col0\" class=\"data row6 col0\" >Anthony Davis</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col1\" class=\"data row6 col1\" >NOP</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col2\" class=\"data row6 col2\" >41</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col3\" class=\"data row6 col3\" >0.103</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col4\" class=\"data row6 col4\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col5\" class=\"data row6 col5\" >0.101</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col6\" class=\"data row6 col6\" >14</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col7\" class=\"data row6 col7\" >0.167</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col8\" class=\"data row6 col8\" >10</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col9\" class=\"data row6 col9\" >0.244</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col10\" class=\"data row6 col10\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col11\" class=\"data row6 col11\" >0.091</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col12\" class=\"data row6 col12\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col13\" class=\"data row6 col13\" >0.226</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col14\" class=\"data row6 col14\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col15\" class=\"data row6 col15\" >0.152</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col16\" class=\"data row6 col16\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col17\" class=\"data row6 col17\" >0.153</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col18\" class=\"data row6 col18\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col19\" class=\"data row6 col19\" >0.249</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col20\" class=\"data row6 col20\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col21\" class=\"data row6 col21\" >0.244</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col22\" class=\"data row6 col22\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col23\" class=\"data row6 col23\" >0.16</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row6_col24\" class=\"data row6 col24\" >8.5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col0\" class=\"data row7 col0\" >Nikola Jokic</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col1\" class=\"data row7 col1\" >DEN</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col2\" class=\"data row7 col2\" >51</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col3\" class=\"data row7 col3\" >0.172</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col4\" class=\"data row7 col4\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col5\" class=\"data row7 col5\" >0.19</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col6\" class=\"data row7 col6\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col7\" class=\"data row7 col7\" >0.25</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col8\" class=\"data row7 col8\" >6</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col9\" class=\"data row7 col9\" >0.408</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col10\" class=\"data row7 col10\" >5</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col11\" class=\"data row7 col11\" >0.0319</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col12\" class=\"data row7 col12\" >10</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col13\" class=\"data row7 col13\" >0.0265</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col14\" class=\"data row7 col14\" >16</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col15\" class=\"data row7 col15\" >0.138</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col16\" class=\"data row7 col16\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col17\" class=\"data row7 col17\" >0.0455</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col18\" class=\"data row7 col18\" >17</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col19\" class=\"data row7 col19\" >0.0313</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col20\" class=\"data row7 col20\" >18</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col21\" class=\"data row7 col21\" >0.0607</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col22\" class=\"data row7 col22\" >13</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col23\" class=\"data row7 col23\" >0.0995</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row7_col24\" class=\"data row7 col24\" >9.5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col0\" class=\"data row8 col0\" >Stephen Curry</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col1\" class=\"data row8 col1\" >GSW</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col2\" class=\"data row8 col2\" >41</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col3\" class=\"data row8 col3\" >0.0112</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col4\" class=\"data row8 col4\" >14</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col5\" class=\"data row8 col5\" >0.0957</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col6\" class=\"data row8 col6\" >15</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col7\" class=\"data row8 col7\" >0.161</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col8\" class=\"data row8 col8\" >11</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col9\" class=\"data row8 col9\" >0.00108</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col10\" class=\"data row8 col10\" >23</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col11\" class=\"data row8 col11\" >-0.0705</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col12\" class=\"data row8 col12\" >119</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col13\" class=\"data row8 col13\" >0.255</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col14\" class=\"data row8 col14\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col15\" class=\"data row8 col15\" >0.202</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col16\" class=\"data row8 col16\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col17\" class=\"data row8 col17\" >0.156</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col18\" class=\"data row8 col18\" >7</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col19\" class=\"data row8 col19\" >0.447</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col20\" class=\"data row8 col20\" >3</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col21\" class=\"data row8 col21\" >0.375</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col22\" class=\"data row8 col22\" >4</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col23\" class=\"data row8 col23\" >0.158</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row8_col24\" class=\"data row8 col24\" >9.5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c28db426_2899_11e9_b08b_d49a200443c6level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col0\" class=\"data row9 col0\" >Damian Lillard</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col1\" class=\"data row9 col1\" >POR</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col2\" class=\"data row9 col2\" >51</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col3\" class=\"data row9 col3\" >0.045</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col4\" class=\"data row9 col4\" >10</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col5\" class=\"data row9 col5\" >0.16</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col6\" class=\"data row9 col6\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col7\" class=\"data row9 col7\" >0.214</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col8\" class=\"data row9 col8\" >8</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col9\" class=\"data row9 col9\" >0.236</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col10\" class=\"data row9 col10\" >9</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col11\" class=\"data row9 col11\" >0.207</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col12\" class=\"data row9 col12\" >2</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col13\" class=\"data row9 col13\" >0.0771</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col14\" class=\"data row9 col14\" >14</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col15\" class=\"data row9 col15\" >0.118</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col16\" class=\"data row9 col16\" >11</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col17\" class=\"data row9 col17\" >0.109</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col18\" class=\"data row9 col18\" >10</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col19\" class=\"data row9 col19\" >0.0325</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col20\" class=\"data row9 col20\" >17</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col21\" class=\"data row9 col21\" >0.0555</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col22\" class=\"data row9 col22\" >15</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col23\" class=\"data row9 col23\" >0.114</td>\n",
       "                        <td id=\"T_c28db426_2899_11e9_b08b_d49a200443c6row9_col24\" class=\"data row9 col24\" >10</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x121927c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_column_names = []\n",
    "rank_column_names  = []\n",
    "both_column_names  = []\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    if model in rejected_models:\n",
    "        continue\n",
    "    \n",
    "    model_acronym = ''.join(c for c in model.estimator_name if c.isupper())\n",
    "    score_column_names.append(\"{} score\".format(model_acronym))\n",
    "    rank_column_names .append(\"{} rank\" .format(model_acronym))\n",
    "    both_column_names .append(\"{} score\".format(model_acronym))\n",
    "    both_column_names .append(\"{} rank\" .format(model_acronym))\n",
    "\n",
    "all_column_names = ['Player', 'Tm', 'G']\n",
    "all_column_names.extend(both_column_names)\n",
    "\n",
    "df_awardcandidates = df_test.filter(all_column_names, axis=1)\n",
    "\n",
    "'''\n",
    "# Scale all probability scores between 0 and 1:\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "df_awardcandidates[score_column_names] = scaler.fit_transform(df_awardcandidates[score_column_names])\n",
    "\n",
    "# Scale all probability score ranks between 1 and n_players_test:\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(1, n_players_test))\n",
    "\n",
    "df_awardcandidates[rank_column_names] = scaler.fit_transform(df_awardcandidates[rank_column_names])\n",
    "'''\n",
    "\n",
    "# Calculate median predicted AVS and median predicted rank of all non-rejected models:\n",
    "\n",
    "df_test['Median AVS' ] = df_awardcandidates[score_column_names].median(axis=1)\n",
    "df_test['Median rank'] = df_awardcandidates[rank_column_names ].median(axis=1)\n",
    "\n",
    "df_awardcandidates['Median AVS' ] = df_awardcandidates[score_column_names].median(axis=1)\n",
    "df_awardcandidates['Median rank'] = df_awardcandidates[rank_column_names ].median(axis=1)\n",
    "\n",
    "if test_year_truth_is_known:\n",
    "    df_awardcandidates['AVS'] = df_test['AVS']\n",
    "\n",
    "#df_awardcandidates = df_awardcandidates.sort_values('Median AVS',  ascending=False)\n",
    "df_awardcandidates = df_awardcandidates.sort_values('Median rank', ascending=True)\n",
    "\n",
    "df_awardcandidates = df_awardcandidates.reset_index(drop=True)\n",
    "df_awardcandidates.index = np.arange(1, len(df_awardcandidates) + 1)\n",
    "\n",
    "print_limit = 10\n",
    "#print(\"--> Predicted {} candidate top-{} in order of the median predicted AVS over all selected models:\".format(\n",
    "#    award, print_limit))\n",
    "print(\"--> Predicted {} candidate top-{} in order of the median predicted rank over all selected models:\".format(\n",
    "    award, print_limit))\n",
    "subset_scores = ['Median AVS', *score_column_names]\n",
    "cm = sns.diverging_palette(240, 10, n=9, as_cmap=True)\n",
    "df_awardcandidates_style = df_awardcandidates[:print_limit].style.background_gradient(subset=subset_scores, \n",
    "                                                                                      cmap=cm, axis=0)\n",
    "display(df_awardcandidates_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot true AVS versus predicted AVS in the test data set if award voting has been completed for *test_year*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> The NBA MVP for 2019 has not been awarded yet\n"
     ]
    }
   ],
   "source": [
    "if test_year_truth_is_known:\n",
    "    plt.title(\"Truth vs. Prediction\")\n",
    "    plt.xlabel('True AVS')\n",
    "    plt.ylabel('Predicted AVS')\n",
    "    plt.xlim(-0.05, 1.05)\n",
    "    plt.ylim(-0.05, 1.05)\n",
    "    plt.grid(True)\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=1, color='black', alpha=.3)\n",
    "    plt.scatter(df_test['AVS'], df_test['Median AVS'], color='r', edgecolor='k');\n",
    "else:\n",
    "    print(\"--> The NBA {} for {} has not been awarded yet\".format(award, test_year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create interactive Bokeh plot using IPython/Jupyter widgets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> The NBA MVP for 2019 has not been awarded yet\n"
     ]
    }
   ],
   "source": [
    "if not test_year_truth_is_known:\n",
    "    print(\"--> The NBA {} for {} has not been awarded yet\".format(award, test_year))\n",
    "else:\n",
    "\n",
    "    # Create interactive figure tools:\n",
    "\n",
    "    tools = \"pan,wheel_zoom,box_zoom,reset,save\".split(',')\n",
    "    hover = HoverTool(tooltips=[\n",
    "        (\"Player\", \"@Player\"),\n",
    "        (\"Team\",   \"@Tm\"),\n",
    "        (\"Year\",   \"@YEAR\"),\n",
    "        (\"PER\",    \"@PER\"),\n",
    "        (\"VORP\",   \"@VORP\"),\n",
    "        (\"WS/48\",  \"@{WS/48}\"),\n",
    "        (\"PTS/48\", \"@{PTS/48}\")\n",
    "    ])\n",
    "    tools.append(hover)\n",
    "\n",
    "    # Create PTS/48 column:\n",
    "\n",
    "    df_test[['PTS/48']] = df_test[['PTS']].div(df_test['MP'].values, axis=0)\n",
    "    df_test[['PTS/48']] = df_test[['PTS/48']].multiply(48, axis=0)\n",
    "\n",
    "    # Create color column according to AVS:\n",
    "\n",
    "    avs = df_test[\"AVS\"]\n",
    "    low = min(avs)\n",
    "    high = max(avs)\n",
    "    avs_colorindices = [int(10*(x-low)/(high-low)) for x in avs] # colors range from 0 to 10\n",
    "    df_test['avs_colors'] = [RdBu[11][i] for i in avs_colorindices]\n",
    "\n",
    "    # Create Bokeh figure:\n",
    "\n",
    "    p1 = figure(plot_width=700, plot_height=600, tools=tools, toolbar_location=\"above\")\n",
    "    p1.title.text = 'Interactive Bokeh plot using IPython/Jupyter widgets'\n",
    "    p1.background_fill_color = \"#dddddd\"\n",
    "    p1.grid.grid_line_color  = \"white\"\n",
    "\n",
    "    equality_line = p1.line([0, 1], [0, 1], line_dash='dashed', line_width=1, color='black', alpha=.3)\n",
    "\n",
    "    s1 = ColumnDataSource(data=df_test)\n",
    "\n",
    "    c1 = p1.circle(x='AVS', y='Median AVS', source=s1,\n",
    "                   size=12, color='avs_colors', line_color=\"black\", fill_alpha=0.8)\n",
    "\n",
    "    l1 = LabelSet(x='AVS', y='Median AVS', source=s1, \n",
    "                  text=\"Player\", y_offset=8, text_align='center', text_font_size=\"8pt\", text_color=\"#555555\")\n",
    "    p1.add_layout(l1)\n",
    "\n",
    "    bokeh_handle = show(p1, notebook_handle=True)\n",
    "\n",
    "    # Create IPython/Jupyter widgets:\n",
    "\n",
    "    def toggle_playernames(Show_Player_Names):\n",
    "        if Show_Player_Names:\n",
    "            l1.visible = True\n",
    "        else:\n",
    "            l1.visible = False\n",
    "        push_notebook(handle=bokeh_handle)\n",
    "\n",
    "    ipywidgets.interact(toggle_playernames, Show_Player_Names=True)\n",
    "    \n",
    "    def update(X_Axis='AVS', Y_Axis='Median AVS'):    \n",
    "        c1.data_source.data['AVS']        = df_test[X_Axis].tolist()\n",
    "        c1.data_source.data['Median AVS'] = df_test[Y_Axis].tolist()\n",
    "        p1.xaxis.axis_label = X_Axis\n",
    "        p1.yaxis.axis_label = Y_Axis\n",
    "        if (X_Axis=='AVS') and (Y_Axis=='Median AVS'):\n",
    "            equality_line.visible = True\n",
    "        else:\n",
    "            equality_line.visible = False\n",
    "        push_notebook(handle=bokeh_handle)\n",
    "\n",
    "    discrete_columns   = [x for x in df_test.columns if df_test[x].dtype == object]\n",
    "    continuous_columns = [x for x in df_test.columns if x not in discrete_columns]\n",
    "    \n",
    "    ipywidgets.interact(update, X_Axis=continuous_columns, Y_Axis=continuous_columns);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The Top-10 NBA Award candidates for *test_year* in order of the median predicted rank over all models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Predicted Top-10 MVP candidates 2019 :\n",
      "\n",
      " 1) James Harden             (HOU) Median predicted rank:  1.0 (Actual AVS = N.A., Games played: 49)\n",
      " 2) Kevin Durant             (GSW) Median predicted rank:  3.0 (Actual AVS = N.A., Games played: 52)\n",
      " 3) Joel Embiid              (PHI) Median predicted rank:  4.0 (Actual AVS = N.A., Games played: 49)\n",
      " 4) Giannis Antetokounmpo    (MIL) Median predicted rank:  5.0 (Actual AVS = N.A., Games played: 48)\n",
      " 5) Kawhi Leonard            (TOR) Median predicted rank:  5.0 (Actual AVS = N.A., Games played: 39)\n",
      " 6) LeBron James             (LAL) Median predicted rank:  6.0 (Actual AVS = N.A., Games played: 35)\n",
      " 7) Anthony Davis            (NOP) Median predicted rank:  8.5 (Actual AVS = N.A., Games played: 41)\n",
      " 8) Nikola Jokic             (DEN) Median predicted rank:  9.5 (Actual AVS = N.A., Games played: 51)\n",
      " 9) Stephen Curry            (GSW) Median predicted rank:  9.5 (Actual AVS = N.A., Games played: 41)\n",
      "10) Damian Lillard           (POR) Median predicted rank: 10.0 (Actual AVS = N.A., Games played: 51)\n"
     ]
    }
   ],
   "source": [
    "print_nplayers = 10\n",
    "\n",
    "print(\"--> Predicted Top-{} {} candidates {} :\".format(print_nplayers, award, test_year))\n",
    "print(\"\")\n",
    "\n",
    "for index, row in df_awardcandidates.iterrows():\n",
    "    if (index <= print_nplayers):\n",
    "        if test_year_truth_is_known:\n",
    "            #print(\"{:2}) {:24} ({}) Median predicted AVS: {:5.3f} (Actual AVS = {:4.3f}, Games played: {})\".format(\n",
    "            #    index, row['Player'], row['Tm'], row['Median AVS'], row['AVS'], row['G']))\n",
    "            print(\"{:2}) {:24} ({}) Median predicted rank: {:4.1f} (Actual AVS = {:4.3f}, Games played: {})\".format(\n",
    "                index, row['Player'], row['Tm'], row['Median rank'], row['AVS'], row['G']))\n",
    "        else:\n",
    "            #print(\"{:2}) {:24} ({}) Median predicted AVS: {:5.3f} (Actual AVS = N.A., Games played: {})\".format(\n",
    "            #    index, row['Player'], row['Tm'], row['Median MVS'], row['G']))\n",
    "            print(\"{:2}) {:24} ({}) Median predicted rank: {:4.1f} (Actual AVS = N.A., Games played: {})\".format(\n",
    "                index, row['Player'], row['Tm'], row['Median rank'], row['G']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> Total processing time = 449.9 secs\n"
     ]
    }
   ],
   "source": [
    "end = time.time()\n",
    "\n",
    "print(\"--> Total processing time = {:.1f} secs\".format((end - start)))"
   ]
  }
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