basketball.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're using Google Colab, you may simply run all the cells. Otherwise, if this is your first time running this notebook, run only the cell below to create a virtual environment and change the kernel to the virtual environment, before running all the cells\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching any missing dependencies...\n",
      "All dependencies are installed.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "try:\n",
    "    from google.colab import drive\n",
    "    colab = True\n",
    "except:\n",
    "    colab = False\n",
    "\n",
    "print(\"Fetching any missing dependencies...\")\n",
    "if colab:\n",
    "    %pip install torch_xla\n",
    "    %pip install -r requirements.txt -q\n",
    "else:\n",
    "    if not os.path.exists(\"venv\"):\n",
    "        !python3.11 -m venv venv\n",
    "        !venv/bin/pip install ipykernel\n",
    "        !PYDEVD_DISABLE_FILE_VALIDATION=1 venv/bin/python -m ipykernel install --user --name=venv\n",
    "    !venv/bin/pip install -r requirements.txt -q\n",
    "print(\"All dependencies are installed.\")\n",
    "\n",
    "import bronte\n",
    "\n",
    "# Preprocessing\n",
    "import os\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from copy import deepcopy\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer, IterativeImputer\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "from sklearn.pipeline import Pipeline\n",
    "from ydata_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ETL Pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x376 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca3673e8b91746caad960d06e724b958",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "543c2462fad041d7b3f293629043241c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "152f530ce3434d83bff97ad562810596",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be961270e8c3471ba4f7b498badb062c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5ee9098e5de4638a9e8d752d5ff2c77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_data_sources(filepaths):\n",
    "    data = {}\n",
    "    for name, path_or_paths in filepaths.items():\n",
    "        if isinstance(path_or_paths, str):  # If it's a single string, read that CSV\n",
    "            data[name] = [pd.read_csv(path_or_paths)]\n",
    "        else:  # If it's a list of strings, read each CSV into a list of dataframes\n",
    "            data[name] = [pd.read_csv(path) for path in path_or_paths]\n",
    "    return data\n",
    "\n",
    "\n",
    "def preprocess_aux_data(df):\n",
    "    if \"Player\" in df.columns:\n",
    "        df[\"playerNameI\"] = df[\"Player\"].apply(\n",
    "            lambda x: x[0] + \". \" + \" \".join(x.split(\" \")[1:])\n",
    "        )\n",
    "        df = df.drop(\"Player\", axis=1)\n",
    "    if \"Tm\" in df.columns:\n",
    "        df[\"teamTricode\"] = df[\"Tm\"]\n",
    "        df = df.drop(\"Tm\", axis=1)\n",
    "    return df\n",
    "\n",
    "\n",
    "def merge_main_data(dfs, key):\n",
    "    main_df = dfs[0]\n",
    "    for secondary_df in dfs[1:]:\n",
    "        main_df = pd.merge(main_df, secondary_df, on=key, how=\"left\")\n",
    "    return main_df\n",
    "\n",
    "\n",
    "def merge_aux_data(main_df, dfs, key):\n",
    "    merged_dfs = pd.concat(dfs, ignore_index=True)\n",
    "    merged_dfs = merged_dfs.drop_duplicates(subset=key)\n",
    "\n",
    "    merged_main_df = main_df.merge(merged_dfs, on=key, how=\"left\", suffixes=(\"\", \"_y\"))\n",
    "    return merged_main_df.drop(\n",
    "        [col for col in merged_main_df.columns if col.endswith(\"_y\")], axis=1\n",
    "    )\n",
    "\n",
    "\n",
    "def plot_court(data, title, path=\"./\", color=\"black\"):\n",
    "    fig = plt.figure(figsize=(4, 3.76))\n",
    "    ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "    # 3PT lines + Arc\n",
    "    ax.plot([-220, -220], [0, 140], linewidth=2, color=color)\n",
    "    ax.plot([220, 220], [0, 140], linewidth=2, color=color)\n",
    "    x = np.linspace(-220, 220, 400)\n",
    "    ax.plot(x, 140 + (np.sqrt(1 - (x**2 / 220**2)) * 155), linewidth=2, color=color)\n",
    "\n",
    "    # Lane + Key\n",
    "    ax.plot([-80, -80], [0, 190], linewidth=2, color=color)\n",
    "    ax.plot([80, 80], [0, 190], linewidth=2, color=color)\n",
    "    ax.plot([-60, -60], [0, 190], linewidth=2, color=color)\n",
    "    ax.plot([60, 60], [0, 190], linewidth=2, color=color)\n",
    "    ax.plot([-80, 80], [190, 190], linewidth=2, color=color)\n",
    "\n",
    "    # Free Throw Circle + Rim + Backboard\n",
    "    theta = np.linspace(0, 2 * np.pi, 100)\n",
    "    ax.plot(60 * np.cos(theta), 60 * np.sin(theta) + 190, linewidth=2, color=color)\n",
    "    ax.plot(15 * np.cos(theta), 15 * np.sin(theta) + 60, linewidth=2, color=color)\n",
    "    ax.plot([-30, 30], [40, 40], linewidth=2, color=color)\n",
    "\n",
    "    # Remove ticks and set limits\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xlim(-250, 250)\n",
    "    ax.set_ylim(0, 470)\n",
    "\n",
    "    # Plays\n",
    "    hb = ax.hexbin(\n",
    "        *(data[0], data[1] + 60),\n",
    "        gridsize=(30, 30),\n",
    "        extent=(-300, 300, 0, 940),\n",
    "        bins=\"log\" if len(data[0]) > 100 else None,\n",
    "        cmap=\"Oranges\",\n",
    "    )\n",
    "    # legend\n",
    "    cb = plt.colorbar(hb, ax=ax)\n",
    "    cb.set_label(\"Counts\", fontsize=16)\n",
    "\n",
    "    # if path/court doesn't exist, create it\n",
    "    loc = \"\".join([path, \"court/\"])\n",
    "    if not os.path.exists(loc):\n",
    "        os.makedirs(loc)\n",
    "\n",
    "    # Title\n",
    "    plt.title(title, fontsize=18, fontweight=\"bold\", y=1.05)\n",
    "    plt.savefig(\"\".join([loc, title, \".png\"]), dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Load data\n",
    "path = \"./data/\"\n",
    "filepaths = {\"games\": [\"plays-2022-23.csv\"], \"players\": [\"players-2022-23.csv\"]}\n",
    "# prepend path to filepaths, which is a list of lists\n",
    "data_sources = load_data_sources(\n",
    "    {\n",
    "        name: [os.path.join(path, path_or_paths) for path_or_paths in paths]\n",
    "        for name, paths in filepaths.items()\n",
    "    }\n",
    ")\n",
    "\n",
    "# Ensure sources are loaded\n",
    "assert len(data_sources) > 0 and all(\n",
    "    [len(df) > 0 for df_list in data_sources.values() for df in df_list]\n",
    ")\n",
    "\n",
    "# Combine sources\n",
    "df = pd.DataFrame()\n",
    "main_key = \"game_id\"\n",
    "aux_key = [\"playerNameI\", \"teamTricode\"]\n",
    "for name, dfs in data_sources.items():\n",
    "    dfs = [preprocess_aux_data(df) for df in dfs]\n",
    "    if name == \"games\":\n",
    "        df = merge_main_data(dfs, main_key)\n",
    "    else:\n",
    "        df = merge_aux_data(df, dfs, aux_key)\n",
    "\n",
    "# EDA\n",
    "plot_court((df[\"xLegacy\"], df[\"yLegacy\"]), \"All Play Locations\", path)\n",
    "\n",
    "profile = ProfileReport(df, explorative=True)\n",
    "profile.to_file(\"\".join([path, \"profile.html\"]))\n",
    "profile.to_notebook_iframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>scoreHome</th>\n",
       "      <th>scoreAway</th>\n",
       "      <th>xLegacy</th>\n",
       "      <th>yLegacy</th>\n",
       "      <th>shotDistance</th>\n",
       "      <th>isFieldGoal</th>\n",
       "      <th>shotVal</th>\n",
       "      <th>scoreVal</th>\n",
       "      <th>Rk</th>\n",
       "      <th>...</th>\n",
       "      <th>home</th>\n",
       "      <th>away</th>\n",
       "      <th>playerNameI</th>\n",
       "      <th>teamTricode</th>\n",
       "      <th>description</th>\n",
       "      <th>actionType</th>\n",
       "      <th>subType</th>\n",
       "      <th>location</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Player-additional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.704005</td>\n",
       "      <td>15.46612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.020971</td>\n",
       "      <td>0.630374</td>\n",
       "      <td>340.271141</td>\n",
       "      <td>...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>SAC</td>\n",
       "      <td>A. Sengun</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Jump Ball Sengun vs. Sabonis: Tip to Huerter</td>\n",
       "      <td>Jump Ball</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>h</td>\n",
       "      <td>SG</td>\n",
       "      <td>antetgi01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>SAC</td>\n",
       "      <td>D. Sabonis</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Sabonis 5' Turnaround Hook Shot (2 PTS) (Huert...</td>\n",
       "      <td>Made Shot</td>\n",
       "      <td>Turnaround Hook Shot</td>\n",
       "      <td>v</td>\n",
       "      <td>C</td>\n",
       "      <td>sabondo01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.945005</td>\n",
       "      <td>25.547078</td>\n",
       "      <td>25.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>SAC</td>\n",
       "      <td>J. Green</td>\n",
       "      <td>HOU</td>\n",
       "      <td>MISS Green 9' Driving Floating Bank Jump Shot</td>\n",
       "      <td>Missed Shot</td>\n",
       "      <td>Driving Floating Bank Jump Shot</td>\n",
       "      <td>h</td>\n",
       "      <td>SG</td>\n",
       "      <td>greenja05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.505627</td>\n",
       "      <td>-11.304633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440331</td>\n",
       "      <td>1.58787</td>\n",
       "      <td>311.0</td>\n",
       "      <td>...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>SAC</td>\n",
       "      <td>K. Martin Jr.</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Martin Jr. REBOUND (Off:1 Def:0)</td>\n",
       "      <td>Rebound</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>h</td>\n",
       "      <td>SF</td>\n",
       "      <td>martike04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>SAC</td>\n",
       "      <td>K. Martin Jr.</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Martin Jr. 3' Putback Layup (2 PTS)</td>\n",
       "      <td>Made Shot</td>\n",
       "      <td>Putback Layup Shot</td>\n",
       "      <td>h</td>\n",
       "      <td>SF</td>\n",
       "      <td>martike04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  period  scoreHome  scoreAway xLegacy yLegacy shotDistance isFieldGoal  \\\n",
       "0    1.0  10.704005   15.46612     0.0     0.0          0.0         0.0   \n",
       "1    1.0        0.0        2.0    -9.0    53.0          5.0         1.0   \n",
       "2    1.0   9.945005  25.547078    25.0    86.0          9.0         1.0   \n",
       "3    1.0  -8.505627 -11.304633     0.0     0.0          0.0         0.0   \n",
       "4    1.0        2.0        2.0    23.0    17.0          3.0         1.0   \n",
       "\n",
       "    shotVal  scoreVal          Rk  ... home away    playerNameI teamTricode  \\\n",
       "0  2.020971  0.630374  340.271141  ...  HOU  SAC      A. Sengun         HOU   \n",
       "1       2.0       2.0       428.0  ...  HOU  SAC     D. Sabonis         SAC   \n",
       "2       2.0       0.0       177.0  ...  HOU  SAC       J. Green         HOU   \n",
       "3  2.440331   1.58787       311.0  ...  HOU  SAC  K. Martin Jr.         HOU   \n",
       "4       2.0       2.0       311.0  ...  HOU  SAC  K. Martin Jr.         HOU   \n",
       "\n",
       "                                         description   actionType  \\\n",
       "0       Jump Ball Sengun vs. Sabonis: Tip to Huerter    Jump Ball   \n",
       "1  Sabonis 5' Turnaround Hook Shot (2 PTS) (Huert...    Made Shot   \n",
       "2      MISS Green 9' Driving Floating Bank Jump Shot  Missed Shot   \n",
       "3                   Martin Jr. REBOUND (Off:1 Def:0)      Rebound   \n",
       "4                Martin Jr. 3' Putback Layup (2 PTS)    Made Shot   \n",
       "\n",
       "                           subType location Pos Player-additional  \n",
       "0                          Unknown        h  SG         antetgi01  \n",
       "1             Turnaround Hook Shot        v   C         sabondo01  \n",
       "2  Driving Floating Bank Jump Shot        h  SG         greenja05  \n",
       "3                          Unknown        h  SF         martike04  \n",
       "4               Putback Layup Shot        h  SF         martike04  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep columns that contain at least one letter\n",
    "df = df[[col for col in df.columns if any(char.isalpha() for char in col)]]\n",
    "\n",
    "# remove unnamed columns\n",
    "df = df.loc[:, ~df.columns.str.contains(\"^Unnamed\")]\n",
    "\n",
    "# remove commas\n",
    "for col in df.columns:\n",
    "    if df[col].dtype == \"object\":\n",
    "        try:\n",
    "            df[col] = pd.to_numeric(df[col].str.replace(\",\", \"\"))\n",
    "        except ValueError:\n",
    "            pass\n",
    "\n",
    "# Drop rows where player is missing\n",
    "df = df.dropna(subset=[\"playerNameI\"])\n",
    "\n",
    "# Update numerical_features and categorical_features lists\n",
    "numerical_features = df.select_dtypes(include=[\"number\"]).columns.tolist()\n",
    "categorical_features = df.select_dtypes(include=[\"object\"]).columns.tolist()\n",
    "\n",
    "# Define transformers\n",
    "numerical_transformers = Pipeline(\n",
    "    steps=[\n",
    "        (\n",
    "            \"imputer\",\n",
    "            IterativeImputer(\n",
    "                estimator=BayesianRidge(),\n",
    "                # max_iter=25,\n",
    "                random_state=42,\n",
    "                sample_posterior=True,\n",
    "                skip_complete=True,\n",
    "                n_nearest_features=max(len(numerical_features) // 5, 5),\n",
    "            ),\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "categorical_transformers = Pipeline(\n",
    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))]\n",
    ")\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numerical_transformers, numerical_features),\n",
    "        (\"cat\", categorical_transformers, categorical_features),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipeline = Pipeline(steps=[(\"preprocessor\", preprocessor)])\n",
    "\n",
    "# Apply the transformations\n",
    "client = Client(n_workers=os.cpu_count())\n",
    "with joblib.parallel_backend(\"dask\"):\n",
    "    df = pd.DataFrame(\n",
    "        pipeline.fit_transform(df), columns=numerical_features + categorical_features\n",
    "    )\n",
    "client.close()\n",
    "\n",
    "# Drop rows with missing or duplicate values\n",
    "df = df.dropna()\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x376 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x376 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x376 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if \"clock\" in df.columns:\n",
    "    # Let's convert the clock str to number of seconds\n",
    "    minute = df[\"clock\"].apply(lambda x: int(x.split(\":\")[0]))\n",
    "    second = df[\"clock\"].apply(lambda x: float(x.split(\":\")[1]))\n",
    "    time = minute * 60 + second\n",
    "    df[\"time\"] = max(time) - time  # this reverses the clock so that it counts up\n",
    "\n",
    "# Create new columns for various checks\n",
    "df[\"is_rebound\"] = df[\"actionType\"] == \"Rebound\"\n",
    "df[\"is_field_goal_attempt\"] = df[\"isFieldGoal\"] == 1\n",
    "\n",
    "# Calculate shifted columns in one go to reduce redundant operations\n",
    "df_next = df[[\"teamTricode\", \"is_rebound\", \"is_field_goal_attempt\", \"time\"]].shift(-1)\n",
    "df_next_next = df[[\"teamTricode\", \"is_field_goal_attempt\", \"time\"]].shift(-2)\n",
    "\n",
    "# Create conditions for better readability\n",
    "df[\"led_to_off_rebound\"] = (\n",
    "    df_next[\"is_rebound\"] & (df_next[\"teamTricode\"] == df[\"teamTricode\"])\n",
    ") & (df[\"is_field_goal_attempt\"] & (df[\"scoreVal\"] == 0))\n",
    "\n",
    "df[\"led_to_transition_opp\"] = (\n",
    "    df_next_next[\"is_field_goal_attempt\"]\n",
    "    & (df_next_next[\"teamTricode\"] != df[\"teamTricode\"])\n",
    "    & ((df_next_next[\"time\"] - df[\"time\"]) <= 5)\n",
    ") & (df[\"is_field_goal_attempt\"] & (df[\"scoreVal\"] == 0))\n",
    "\n",
    "del df_next\n",
    "del df_next_next\n",
    "\n",
    "# Handle the last play of the game\n",
    "df.loc[\n",
    "    df[\"teamTricode\"].shift(-1).isnull(),\n",
    "    [\"led_to_off_rebound\", \"led_to_transition_opp\"],\n",
    "] = False\n",
    "\n",
    "# Define aggregation functions\n",
    "agg_funcs = {\n",
    "    \"num_shots\": (\"is_field_goal_attempt\", \"sum\"),\n",
    "    \"num_made\": (\"scoreVal\", \"sum\"),\n",
    "    \"num_off_rebounds\": (\"led_to_off_rebound\", \"sum\"),\n",
    "    \"num_transition_opps\": (\"led_to_transition_opp\", \"sum\"),\n",
    "}\n",
    "\n",
    "# Apply aggregation functions\n",
    "for col_agg, (col, func) in agg_funcs.items():\n",
    "    df[col_agg] = df.groupby([\"xLegacy\", \"yLegacy\"])[col].transform(func)\n",
    "\n",
    "# Calculate total possessions for each player\n",
    "df[\"Possessions\"] = df[\"FGA\"] + 0.44 * df[\"FTA\"] - df[\"ORB\"] + df[\"TOV\"]\n",
    "\n",
    "# Create a column to track turnovers that led to live ball situations\n",
    "df[\"is_turnover\"] = (df[\"actionType\"] == \"Turnover\") & df[\"led_to_transition_opp\"]\n",
    "\n",
    "# Group by playerNameI and calculate turnovers\n",
    "turnovers = (\n",
    "    df.groupby(\"playerNameI\")\n",
    "    .agg(is_turnover_live_ball=(\"is_turnover\", \"sum\"), TOV_total=(\"TOV\", \"sum\"))\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# Merge the turnover data with the main dataframe\n",
    "df = df.merge(turnovers, on=\"playerNameI\", how=\"left\")\n",
    "\n",
    "# Calculate the rates\n",
    "off_rebound_rate = df[\"num_off_rebounds\"] / df[\"num_shots\"]\n",
    "transition_opp_rate = df[\"num_transition_opps\"] / df[\"num_shots\"]\n",
    "live_to_rate = df[\"is_turnover_live_ball\"] / df[\"Possessions\"]\n",
    "dead_to_rate = (df[\"TOV_total\"] - df[\"is_turnover_live_ball\"]) / df[\"Possessions\"]\n",
    "shot_rate = df[\"num_made\"] / df[\"num_shots\"]\n",
    "\n",
    "PPP = df[\"scoreVal\"] / df[\"Possessions\"]\n",
    "rates = off_rebound_rate + dead_to_rate - transition_opp_rate - live_to_rate\n",
    "df[\"PPP*rates\"] = PPP * rates\n",
    "\n",
    "cols_to_remove = [\n",
    "    \"is_rebound\",\n",
    "    \"is_turnover\",\n",
    "    \"is_field_goal_attempt\",\n",
    "    \"led_to_off_rebound\",\n",
    "    \"led_to_transition_opp\",\n",
    "    \"clock\",\n",
    "    \"num_shots\",\n",
    "    \"num_made\",\n",
    "    \"num_off_rebounds\",\n",
    "    \"num_transition_opps\",\n",
    "    \"Possessions\",\n",
    "    \"description\",\n",
    "    \"is_turnover_live_ball\",\n",
    "    \"TOV_total\",\n",
    "    \"Player-additional\",\n",
    "]\n",
    "\n",
    "# Drop the listed columns\n",
    "df.drop(columns=cols_to_remove, inplace=True, errors=\"ignore\")\n",
    "\n",
    "# Coerce scoreVal to int\n",
    "df[\"scoreVal\"] = pd.Series(np.rint(df[\"scoreVal\"].astype(float)))\n",
    "\n",
    "# Plot the top plays by PPP*rates\n",
    "for percent in [1e-1, 1, 1e1]:\n",
    "    for col in [\"PPP*rates\"]:\n",
    "        filtered = df[df[\"xLegacy\"] != 0]\n",
    "        filtered = filtered[filtered[\"yLegacy\"] != 0]\n",
    "        sorted = filtered.sort_values(by=col, ascending=False).reset_index(drop=True)\n",
    "        sorted[\"rank\"] = (sorted.index + 1) / len(sorted) * 100\n",
    "        sorted = sorted[sorted[\"rank\"] <= percent]\n",
    "        plot_court(\n",
    "            (sorted[\"xLegacy\"], sorted[\"yLegacy\"]),\n",
    "            f\"Top {percent}% of Play Locations by {col}\",\n",
    "            path,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split game IDs into chunks of size 8\n",
    "unique_keys = df[main_key].unique()\n",
    "chunk_size = 8  # roughly 4000 rows per 8 games (500 rows per game)\n",
    "chunks = [\n",
    "    unique_keys[i : i + chunk_size] for i in range(0, len(unique_keys), chunk_size)\n",
    "]\n",
    "\n",
    "# Make each chunk into a table\n",
    "bronte.flush()  # flush the database\n",
    "for index, chunk in enumerate(chunks):\n",
    "    table = df[df[main_key].isin(chunk)]\n",
    "    bronte.load(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Options\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_options = {}\n",
    "arch_options = {}\n",
    "\n",
    "# runs\n",
    "task_options[\"restart\"] = True  # clear checkpoints before training, restarts training\n",
    "task_options[\"resume\"] = True  # resume from best checkpoint, enables transfer learning\n",
    "task_options[\"importance\"] = False\n",
    "\n",
    "# data\n",
    "task_options[\"db_path\"] = \"data/data.db\"\n",
    "task_options[\"table_prefix\"] = \"bronte_\"\n",
    "task_options[\"timeseries\"] = False\n",
    "task_options[\"samples\"] = 2  # max number of tables to use for training\n",
    "task_options[\n",
    "    \"sample_size\"\n",
    "] = 0  # fraction of each table to use or number of rows, 0 for all\n",
    "task_options[\"max_corr\"] = 1.0  # maximum correlation between features\n",
    "task_options[\"task\"] = \"\"\n",
    "\n",
    "# performance\n",
    "task_options[\"cpu\"] = True  # Unless only available, if False\n",
    "task_options[\"autocast\"] = True\n",
    "task_options[\"n_workers\"] = -1\n",
    "task_options[\"batch_size\"] = 192\n",
    "task_options[\"drop_last\"] = True  # if smaller\n",
    "task_options[\"accumulate\"] = 5  # set <=1 to disable gradient accumulation\n",
    "task_options[\"use_checkpoint\"] = not task_options[\n",
    "    \"cpu\"\n",
    "]  # checkpoint.checkpoint, not .pt files\n",
    "task_options[\"tune_trials\"] = (\n",
    "    0 if task_options[\"cpu\"] else 1000\n",
    ")  # set <=0 to disable tuning\n",
    "\n",
    "# hyperparameters\n",
    "arch_options[\"epochs\"] = 1\n",
    "arch_options[\"lr\"] = 1e-3\n",
    "arch_options[\"patience\"] = 0\n",
    "arch_options[\"factor\"] = 0.6\n",
    "arch_options[\"betas\"] = (0.9, 0.75)\n",
    "arch_options[\"eps\"] = 1e-9\n",
    "arch_options[\"decay\"] = 3e-3\n",
    "\n",
    "# gradient\n",
    "arch_options[\"norm_clip\"] = 1.0  # clip gradient norms at this value\n",
    "arch_options[\"init_scale\"] = 2**16\n",
    "arch_options[\"growth_factor\"] = 2\n",
    "arch_options[\"backoff_factor\"] = 0.5\n",
    "arch_options[\"growth_interval\"] = 2000\n",
    "arch_options[\"grad_scaling\"] = not task_options[\"cpu\"]  # flag\n",
    "\n",
    "# finetuning\n",
    "task_options[\"prev\"] = []  # list of previous targets (name of base model)\n",
    "arch_options[\"freeze\"] = []  # list of layers to freeze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specific\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dict_copy(orig):\n",
    "    dup = {}\n",
    "    for key, value in orig.items():\n",
    "        try:\n",
    "            dup[key] = deepcopy(value)\n",
    "        except:\n",
    "            dup[key] = value\n",
    "    return dup\n",
    "\n",
    "\n",
    "db = sqlite3.connect(task_options[\"db_path\"])\n",
    "n_cols = pd.read_sql_query(\n",
    "    f'select count() from pragma_table_info(\\'{task_options[\"table_prefix\"]}1\\')', db\n",
    ").values[0][0]\n",
    "n_rows = pd.read_sql_query(\n",
    "    f'select count(*) from {task_options[\"table_prefix\"]}1', db\n",
    ").values[0][0]\n",
    "db.close()\n",
    "\n",
    "# arch\n",
    "ffn_opts = dict_copy(arch_options)\n",
    "ffn_opts[\"arch\"] = \"ffn\"\n",
    "ffn_opts[\"fc_layers\"] = [n_cols * 2 // 3] * 3\n",
    "ffn_opts[\"fc_dropout\"] = np.repeat(0.5, len(ffn_opts[\"fc_layers\"]))\n",
    "\n",
    "rnn_opts = dict_copy(ffn_opts)\n",
    "rnn_opts[\"importance\"] = False\n",
    "rnn_opts[\"arch\"] = \"rnn\"\n",
    "rnn_opts[\"rnn_layers\"] = 2\n",
    "rnn_opts[\"rnn_dropout\"] = 0.8\n",
    "rnn_opts[\"rnn_type\"] = \"GRU\"  # 'GRU' (or 'LSTM', a bit buggy)\n",
    "rnn_opts[\"rnn_seq_len\"] = 1  # 128\n",
    "rnn_opts[\"rnn_bidi\"] = task_options[\"timeseries\"]\n",
    "rnn_opts[\"attention\"] = \"concat\"  # 'dot', 'general', or 'concat', or None\n",
    "\n",
    "# task\n",
    "reg_opts = dict_copy(task_options)\n",
    "reg_opts[\"task\"] = \"reg\"\n",
    "reg_opts[\"criterion\"] = \"huber\"\n",
    "reg_opts[\"targets\"] = [\"PPP*rates\"]\n",
    "reg_opts[\"num_out\"] = 1\n",
    "\n",
    "class_opts = dict_copy(task_options)\n",
    "class_opts[\"task\"] = \"class\"\n",
    "class_opts[\"criterion\"] = \"cross\"\n",
    "class_opts[\"targets\"] = [\"scoreVal\"]\n",
    "class_opts[\"max_clusters\"] = 4  # max number of classes\n",
    "\n",
    "# multi-task\n",
    "multi_reg_opts = dict_copy(reg_opts)\n",
    "multi_reg_opts[\"targets\"].append(\"scoreVal\")\n",
    "multi_reg_opts[\"samples\"] = 1\n",
    "\n",
    "multi_class_opts = dict_copy(class_opts)\n",
    "multi_class_opts[\"targets\"].append(\"PPP*rates\")\n",
    "multi_class_opts[\"samples\"] = 1\n",
    "\n",
    "# fine-tune\n",
    "fine_reg_opts = dict_copy(reg_opts)\n",
    "fine_reg_opts[\"targets\"] = [\"scoreVal\"]\n",
    "fine_reg_opts[\"prev\"].append(reg_opts[\"targets\"])\n",
    "fine_reg_opts[\"samples\"] = 1\n",
    "\n",
    "fine_class_opts = dict_copy(class_opts)\n",
    "fine_class_opts[\"targets\"] = [\"PPP*rates\"]\n",
    "fine_class_opts[\"prev\"].append(class_opts[\"targets\"])\n",
    "fine_class_opts[\"samples\"] = 1\n",
    "\n",
    "models = {\n",
    "    \"tasks\": {\n",
    "        \"single\": [\n",
    "            reg_opts,\n",
    "            class_opts,\n",
    "        ],\n",
    "        \"multi\": [\n",
    "            multi_reg_opts,\n",
    "            multi_class_opts,\n",
    "        ],\n",
    "        \"fine\": [\n",
    "            fine_reg_opts,\n",
    "            fine_class_opts,\n",
    "        ],\n",
    "    },\n",
    "    \"archs\": [ffn_opts, rnn_opts],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting ['PPP*rates'] with models/reg rnn/[['PPP*rates']]/0/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([-0.2239,  0.5996,  0.5307,  0.1633,  0.4538,  0.5274,  0.4166,  0.0165,\n",
      "        -0.2953,  0.5489])\n",
      "\n",
      "Predicting ['PPP*rates'] with models/reg rnn/[['PPP*rates']]/1/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([ 0.3298,  0.2610,  1.0315,  0.2501, -0.3085, -0.8600, -0.6476, -0.1305,\n",
      "        -0.6742,  0.6632])\n",
      "\n",
      "Predicting ['scoreVal'] with models/reg rnn/[['PPP*rates'], ['scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([-0.9129, -0.8247, -0.5114, -1.3111, -0.4404, -1.2107, -0.4260, -0.0558,\n",
      "        -0.9624, -0.3545])\n",
      "\n",
      "Predicting ['scoreVal'] with models/reg rnn/[['scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([-0.2839, -0.7785,  0.1924, -0.4514, -0.4626, -0.5743, -0.0553, -0.6118,\n",
      "        -0.2278, -0.2189])\n",
      "\n",
      "Predicting ['PPP*rates', 'scoreVal'] with models/reg rnn/[['PPP*rates', 'scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([1.2182, 1.4659, 1.4797, 0.9212, 1.8864, 1.4856, 1.5967, 1.4549, 1.3713,\n",
      "        1.3434])\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([-1.6310, -1.4015, -1.7951, -0.6128, -1.8843, -1.4316, -1.3876, -1.6786,\n",
      "        -1.1479, -2.0316])\n",
      "\n",
      "Predicting ['scoreVal'] with models/class ffn/[['scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([[0.1141, 0.2528, 0.2651, 0.3680],\n",
      "        [0.1207, 0.2584, 0.2530, 0.3679],\n",
      "        [0.1194, 0.2368, 0.2545, 0.3894],\n",
      "        [0.1232, 0.2541, 0.2497, 0.3729],\n",
      "        [0.1209, 0.2178, 0.2708, 0.3905],\n",
      "        [0.1072, 0.2345, 0.2544, 0.4039],\n",
      "        [0.1086, 0.2358, 0.2555, 0.4001],\n",
      "        [0.1255, 0.2389, 0.2516, 0.3840],\n",
      "        [0.1458, 0.2785, 0.2466, 0.3292],\n",
      "        [0.1177, 0.2286, 0.2641, 0.3896]])\n",
      "\n",
      "Predicting ['scoreVal'] with models/class ffn/[['scoreVal']]/1/model.pt...\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([[0.1705, 0.2782, 0.2735, 0.2778],\n",
      "        [0.2247, 0.2315, 0.2245, 0.3194],\n",
      "        [0.1834, 0.2849, 0.2659, 0.2658],\n",
      "        [0.2254, 0.2398, 0.2809, 0.2539],\n",
      "        [0.2181, 0.2497, 0.2653, 0.2669],\n",
      "        [0.1893, 0.2872, 0.2602, 0.2634],\n",
      "        [0.2019, 0.2656, 0.2748, 0.2577],\n",
      "        [0.2221, 0.2718, 0.2464, 0.2598],\n",
      "        [0.1939, 0.2949, 0.2576, 0.2536],\n",
      "        [0.1905, 0.2997, 0.2823, 0.2274]])\n",
      "\n",
      "Predicting ['PPP*rates'] with models/class ffn/[['scoreVal'], ['PPP*rates']]/0/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([[0.4148, 0.5852],\n",
      "        [0.3837, 0.6163],\n",
      "        [0.3864, 0.6136],\n",
      "        [0.4170, 0.5830],\n",
      "        [0.4089, 0.5911],\n",
      "        [0.4195, 0.5805],\n",
      "        [0.4264, 0.5736],\n",
      "        [0.3847, 0.6153],\n",
      "        [0.4557, 0.5443],\n",
      "        [0.4038, 0.5962]])\n",
      "\n",
      "Predicting ['PPP*rates'] with models/reg ffn/[['PPP*rates']]/0/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([1.4562, 0.4087, 1.3567, 1.2857, 1.1598, 0.3557, 0.8959, 1.6803, 0.8350,\n",
      "        1.4412])\n",
      "\n",
      "Predicting ['PPP*rates'] with models/reg ffn/[['PPP*rates']]/1/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([-1.2894, -1.2008, -1.2774, -0.8273, -0.7817, -1.2183, -0.2693, -0.9190,\n",
      "        -0.8343, -0.7900])\n",
      "\n",
      "Predicting ['scoreVal'] with models/reg ffn/[['PPP*rates'], ['scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([ 0.2157,  0.2096, -1.0911, -0.9490,  0.1283, -0.9638, -0.7357,  1.1852,\n",
      "        -0.7361,  0.5029])\n",
      "\n",
      "Predicting ['PPP*rates', 'scoreVal'] with models/reg ffn/[['PPP*rates', 'scoreVal']]/0/model.pt...\n",
      "\n",
      "Head of PPP*rates predictions:\n",
      "tensor([ 0.8411,  0.4457, -0.0284,  0.9092,  0.3135,  0.4383, -0.2590,  0.5081,\n",
      "         0.6654,  0.7398])\n",
      "\n",
      "Head of scoreVal predictions:\n",
      "tensor([1.0593, 0.8839, 0.3958, 0.8597, 0.5132, 0.8337, 1.4565, 1.4940, 0.5717,\n",
      "        1.4965])\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{\"models/reg rnn/[['PPP*rates']]/0/model.pt\": {'0': [tensor([-2.2390e-01,  5.9958e-01,  5.3071e-01,  1.6328e-01,  4.5385e-01,\n",
       "            5.2744e-01,  4.1663e-01,  1.6479e-02, -2.9525e-01,  5.4894e-01,\n",
       "            9.3043e-01, -4.1988e-02,  9.9638e-01,  7.7705e-01, -2.8610e-01,\n",
       "            3.7303e-01,  7.2112e-01, -1.9205e-01,  2.0119e-01,  1.2860e+00,\n",
       "           -2.9672e-01,  4.7592e-01,  6.4100e-01, -7.4911e-01,  6.0371e-01,\n",
       "            3.3165e-01,  1.7170e-01, -1.0343e+00,  1.1186e+00,  2.6400e-01,\n",
       "            6.0758e-03,  1.3092e+00,  2.7257e-01,  5.6509e-01,  4.8671e-02,\n",
       "            1.4289e-01,  6.7189e-01,  4.4762e-01,  4.4510e-01, -1.8951e-01,\n",
       "            4.9016e-01,  1.4022e+00,  6.8242e-01,  7.6968e-01,  3.0746e-03,\n",
       "            6.7700e-01,  3.8662e-01,  1.3039e+00, -7.4563e-02,  9.0533e-01,\n",
       "            6.1771e-01,  7.6868e-01,  8.4750e-01,  3.7894e-02,  5.1113e-01,\n",
       "            3.0484e-01,  9.2660e-01, -5.6551e-02,  5.3627e-01,  3.5903e-01,\n",
       "            4.4742e-02,  2.0352e-01,  1.2556e-02,  1.1538e-01,  2.2783e-01,\n",
       "            1.3778e-01,  5.9144e-01,  1.4846e+00,  8.6952e-01,  2.1578e-01,\n",
       "            5.3368e-01,  8.2382e-01,  4.2383e-01, -1.2398e-01,  7.4564e-01,\n",
       "            5.2797e-01, -2.2420e-01,  3.1623e-01,  1.2959e-02,  5.6287e-01,\n",
       "            8.7984e-01,  3.8021e-01,  2.3142e-01,  6.2657e-01,  4.1168e-01,\n",
       "            2.5460e-01, -2.5523e-02,  2.3474e-01,  5.5718e-01,  3.5695e-02,\n",
       "            5.4735e-01, -7.8123e-01,  1.8795e-01, -3.5128e-01, -3.9917e-02,\n",
       "           -3.5250e-01, -3.0531e-01,  4.3280e-01,  8.0917e-01, -1.9612e-02,\n",
       "            7.5712e-02,  9.3391e-01, -1.6950e-01, -4.1306e-01, -1.9176e-01,\n",
       "            7.4189e-01, -8.6898e-01, -7.2422e-03,  1.3845e-01,  1.2146e+00,\n",
       "           -2.1572e-01,  6.5023e-01,  9.2735e-01, -2.0163e-01,  8.2640e-01,\n",
       "            8.1283e-02,  1.2373e+00,  3.4352e-01,  3.7259e-01,  5.5730e-02,\n",
       "            7.0176e-01, -3.1932e-01,  6.0406e-01,  2.3433e-01, -4.1209e-02,\n",
       "            1.0068e+00, -5.7000e-01,  4.9375e-01,  6.9391e-01, -4.3222e-01,\n",
       "            4.4810e-01, -2.4807e-01, -2.8057e-01, -7.0819e-01, -6.4476e-01,\n",
       "            2.8321e-01,  1.8873e+00,  4.4996e-01, -1.7200e-02, -5.6250e-02,\n",
       "            4.2756e-01,  2.2025e-03,  4.7965e-01, -7.4756e-03, -4.0314e-01,\n",
       "            4.4321e-01,  5.1124e-01, -3.5364e-01,  9.9842e-02, -8.2352e-02,\n",
       "            5.5768e-01,  5.6208e-01,  6.6057e-01,  1.0236e-01, -4.2404e-01,\n",
       "           -4.0307e-01,  6.4068e-01, -5.5563e-01,  1.1027e+00,  4.4096e-01,\n",
       "            1.1093e+00,  9.9443e-01,  1.1784e+00,  4.6084e-01, -4.6201e-02,\n",
       "           -6.3659e-01, -4.7811e-01,  1.1360e+00,  8.3349e-02,  1.2573e+00,\n",
       "            1.3351e+00, -2.4632e-01,  4.5144e-01,  5.3452e-01,  5.3868e-01,\n",
       "            6.4049e-01,  1.9832e+00, -5.0313e-01, -8.0066e-01,  1.0685e+00,\n",
       "            1.8892e+00,  1.1426e+00,  3.8196e-01, -3.9235e-01,  6.6230e-03,\n",
       "            4.8486e-01, -4.4553e-01,  2.1562e-02, -1.8876e-01,  1.3574e+00,\n",
       "            7.4226e-01,  1.4869e-02,  3.8442e-01,  1.3829e+00,  6.8671e-01,\n",
       "            1.4703e+00,  2.4567e-01,  5.0822e-01, -1.7656e-02,  8.5831e-01,\n",
       "            1.3118e-01, -6.2468e-01,  6.3578e-01,  7.2648e-01,  8.8636e-01,\n",
       "            3.6934e-01,  8.3727e-02,  2.1433e-01, -3.1649e-01,  3.2573e-01,\n",
       "            5.4835e-01,  4.1145e-01,  1.1451e+00,  7.1892e-01,  1.1143e+00,\n",
       "            1.8927e-01,  1.1050e+00,  6.8035e-01,  6.5796e-01,  5.1804e-01,\n",
       "            2.3848e-01,  1.1595e-01, -1.1492e-01,  1.2199e+00,  9.2668e-01,\n",
       "            4.1645e-01,  8.6738e-02,  2.1039e-01,  6.2749e-01, -1.9254e-01,\n",
       "           -6.4920e-01,  9.7166e-02, -3.2151e-01,  1.9393e-01,  1.2517e+00,\n",
       "            3.5851e-01, -6.2068e-01,  1.7953e-01, -2.7331e-01,  3.8930e-01,\n",
       "            1.3459e+00, -9.5715e-02,  2.8825e-01, -2.2795e-01,  4.6159e-01,\n",
       "           -3.8456e-01,  5.9217e-01, -3.5119e-01,  8.7404e-01,  1.9280e-01,\n",
       "            4.1079e-01,  6.7888e-01,  7.0531e-01,  1.1231e+00,  2.4954e-01,\n",
       "            8.1379e-01,  2.5014e-01,  1.7463e-01,  1.7328e-01,  5.0497e-01,\n",
       "            6.8041e-01,  4.2830e-01, -1.2235e-01,  7.4156e-01,  5.8053e-01,\n",
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       "           -1.6864e-01,  5.2154e-01])]},\n",
       " \"models/reg rnn/[['PPP*rates']]/1/model.pt\": {'0': [tensor([ 0.3298,  0.2610,  1.0315,  0.2501, -0.3085, -0.8600, -0.6476, -0.1305,\n",
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       "           -2.2951e-01])]},\n",
       " \"models/reg rnn/[['PPP*rates', 'scoreVal']]/0/model.pt\": {'0': [tensor([[ 1.2182],\n",
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       "            0.5313,  0.5723,  1.5609,  1.1934,  0.2685,  0.7081,  0.2146,  1.0246])]},\n",
       " \"models/reg ffn/[['PPP*rates']]/1/model.pt\": {'0': [tensor([-1.2894, -1.2008, -1.2774, -0.8273, -0.7817, -1.2183, -0.2693, -0.9190,\n",
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       "           -1.1939, -1.2133, -1.2072, -0.8753, -0.7787])]},\n",
       " \"models/reg ffn/[['PPP*rates'], ['scoreVal']]/0/model.pt\": {'0': [tensor([ 2.1572e-01,  2.0961e-01, -1.0911e+00, -9.4900e-01,  1.2829e-01,\n",
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       "            1.3188e+00])]},\n",
       " \"models/reg ffn/[['PPP*rates', 'scoreVal']]/0/model.pt\": {'0': [tensor([[ 8.4113e-01],\n",
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      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bronte.track()  # call once to start tensorboard\n",
    "# bronte.fit(models)\n",
    "bronte.predict()  # no args -> use test split of training data\n",
    "\n",
    "# bronte.track()  # call again to stop tensorboard"
   ]
  }
 ],
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   "gpuType": "T4",
   "provenance": [],
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