AutoGluon_Baseball_Pitch_Outcome_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9mR5Q0NAhOd"
      },
      "source": [
        "# AutoGluon for Sports Analytics: Baseball Pitch Outcome Prediction\n",
        "\n",
        "Name: John Hodge\n",
        "\n",
        "Date: 04/19/24"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Riyfxj09C6aa"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "Welcome to our tutorial on leveraging AutoGluon for sports analytics, specifically for the task of predicting baseball pitch types. AutoGluon, a powerful automated machine learning (AutoML) tool, simplifies the process of model building and deployment, making it accessible even to those with limited machine learning expertise. In this tutorial, we will guide you through the steps of installing dependencies, preparing your dataset, defining the prediction task, training the model, and making predictions. This hands-on guide aims to provide you with practical experience in using AutoGluon for real-world sports data, helping you unlock new insights and enhance your analytical capabilities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VpQ23WOAhOe"
      },
      "source": [
        "## Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XShb2JnuAhOe",
        "outputId": "3b4386e0-356a-45fd-f136-4040084ead2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install autogluon"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eTmgc4sVAhOe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from autogluon.tabular import TabularDataset, TabularPredictor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V17q4JsoAhOe"
      },
      "source": [
        "Check to see whether a CUDA-based GPU is available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMNhbPkPAhOf",
        "outputId": "7b2a961e-442d-4341-cb6d-ff9be8c87f44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU is not available\n"
          ]
        }
      ],
      "source": [
        "# Check to see whether a GPU is available\n",
        "if torch.cuda.is_available():\n",
        "    gpu_available = True\n",
        "    print(\"GPU is available\")\n",
        "else:\n",
        "    gpu_available = False\n",
        "    print(\"GPU is not available\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yisA7OsoAhOf"
      },
      "source": [
        "## Load and Prepare Data\n",
        "\n",
        "We'll start by loading data from a CSV file. For this example, assume your CSV has columns for PitchType, Balls, Strikes, and PreviousPitchType. You might need to adjust the column names based on your actual data.\n",
        "\n",
        "Data preparation is a crucial step in any machine learning pipeline. For this tutorial, our data consists of various features related to baseball pitches, such as PitchType, Balls, Strikes, and PreviousPitchType.\n",
        "\n",
        "Effective data preparation enhances model accuracy by ensuring that the input data is suitably formatted and cleaned. This might include handling missing values, encoding categorical variables, and normalizing or scaling numerical features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39DW-8AZAhOf",
        "outputId": "a8d5e505-2d34-4068-cd51-a79ed35bbceb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    Balls  Strikes  PitchType Outcome PreviousPitchType\n",
            "1       0        1   Changeup    ball          Fastball\n",
            "2       1        1     Slider     hit          Changeup\n",
            "3       0        0   Fastball  strike            Slider\n",
            "4       0        1   Changeup  strike          Fastball\n",
            "5       0        2   Fastball  strike          Changeup\n",
            "6       0        0   Fastball     hit          Fastball\n",
            "7       0        0   Fastball  strike          Fastball\n",
            "8       0        1     Slider  strike          Fastball\n",
            "9       0        2  Curveball    ball            Slider\n",
            "10      1        2  Curveball  strike         Curveball\n"
          ]
        }
      ],
      "source": [
        "data_path = 'data/baseball_pitch_data.csv'\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv(data_path)\n",
        "\n",
        "# Create a new column 'PreviousPitchType' with the 'PitchType' from the previous row\n",
        "df['PreviousPitchType'] = df['PitchType'].shift(1)\n",
        "\n",
        "# Save the updated DataFrame back to CSV if needed\n",
        "# df.to_csv('/path/to/your/updated_baseball_pitch_data.csv', index=False)\n",
        "\n",
        "# Display the updated DataFrame to verify\n",
        "# print(df.head(10))\n",
        "\n",
        "# Truncate the DataFrame to N rows\n",
        "num_truncation_rows = -1 # Number of rows to truncate to (-1 for all rows)\n",
        "df_truncated = df[1:num_truncation_rows]\n",
        "\n",
        "# Load data\n",
        "data = TabularDataset(df_truncated)\n",
        "\n",
        "# Preview data\n",
        "print(data.head(10))\n",
        "\n",
        "# Optionally, drop rows with missing values\n",
        "data = data.dropna(subset=['PitchType', 'Balls', 'Strikes', 'PreviousPitchType'])\n",
        "\n",
        "# Split data into training and testing datasets\n",
        "train_data = data.sample(frac=0.8, random_state=42)  # 80% for training\n",
        "test_data = data.drop(train_data.index)  # Remaining 20% for testing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rte8CpqEAhOf"
      },
      "source": [
        "## Define the Prediction Task\n",
        "\n",
        "With AutoGluon, you specify what column you're predicting. In this case, it's PitchType. AutoGluon will automatically handle feature processing. In supervised learning, specifying the target variable correctly is crucial because the model learns to predict this variable based on other input features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9lDkFldAhOf",
        "outputId": "223a8fbd-9d8c-40ff-d246-3c08e9ae68a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Summary statistics of training data:\n",
            "               Balls        Strikes\n",
            "count  302558.000000  302558.000000\n",
            "mean        0.811121       0.874097\n",
            "std         0.942289       0.806010\n",
            "min         0.000000       0.000000\n",
            "25%         0.000000       0.000000\n",
            "50%         1.000000       1.000000\n",
            "75%         1.000000       2.000000\n",
            "max         3.000000       2.000000\n"
          ]
        }
      ],
      "source": [
        "label = 'Outcome'\n",
        "print(\"Summary statistics of training data:\")\n",
        "print(train_data.describe())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xem6kGT1AhOf"
      },
      "source": [
        "## Train the Model\n",
        "\n",
        "Now, train a model using AutoGluon. This can automatically handle categorical features, missing data, and more, but it’s still pivotal to understand how the training process works and what options you have available. AutoGluon automates many decisions and tasks such as feature selection, model selection, and hyperparameter tuning. It uses ensemble techniques to ensure robust predictions.\n",
        "\n",
        "More information on [AutoGluon presets](https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "GPAnB_7qAhOf",
        "outputId": "10ce6ca7-38f6-4219-be57-84e0f87c94e9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Presets specified: ['good_quality']\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting dynamic_stacking from 'auto' to True. Reason: Enable dynamic_stacking when use_bag_holdout is disabled. (use_bag_holdout=False)\n",
            "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=1\n",
            "Note: `save_bag_folds=False`! This will greatly reduce peak disk usage during fit (by ~8x), but runs the risk of an out-of-memory error during model refit if memory is small relative to the data size.\n",
            "\tYou can avoid this risk by setting `save_bag_folds=True`.\n",
            "Dynamic stacking is enabled (dynamic_stacking=True). AutoGluon will try to determine whether the input data is affected by stacked overfitting and enable or disable stacking as a consequence.\n",
            "Detecting stacked overfitting by sub-fitting AutoGluon on the input data. That is, copies of AutoGluon will be sub-fit on subset(s) of the data. Then, the holdout validation data is used to detect stacked overfitting.\n",
            "Sub-fit(s) time limit is: 3600 seconds.\n",
            "Starting holdout-based sub-fit for dynamic stacking. Context path is: autogluon_pitchtype_models/ds_sub_fit/sub_fit_ho.\n",
            "Running the sub-fit in a ray process to avoid memory leakage.\n",
            "Spend 1063 seconds for the sub-fit(s) during dynamic stacking.\n",
            "Time left for full fit of AutoGluon: 2537 seconds.\n",
            "Starting full fit now with num_stack_levels 0.\n",
            "Beginning AutoGluon training ... Time limit = 2537s\n",
            "AutoGluon will save models to \"autogluon_pitchtype_models\"\n",
            "=================== System Info ===================\n",
            "AutoGluon Version:  1.1.0\n",
            "Python Version:     3.9.0\n",
            "Operating System:   Darwin\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   Darwin Kernel Version 23.2.0: Wed Nov 15 21:54:05 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T6031\n",
            "CPU Count:          14\n",
            "Memory Avail:       4.08 GB / 36.00 GB (11.3%)\n",
            "Disk Space Avail:   700.51 GB / 926.35 GB (75.6%)\n",
            "===================================================\n",
            "Train Data Rows:    302558\n",
            "Train Data Columns: 4\n",
            "Label Column:       Outcome\n",
            "Problem Type:       multiclass\n",
            "Preprocessing data ...\n",
            "Train Data Class Count: 3\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    4208.08 MB\n",
            "\tTrain Data (Original)  Memory Usage: 41.90 MB (1.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tStage 5 Generators:\n",
            "\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('int', [])    : 2 | ['Balls', 'Strikes']\n",
            "\t\t('object', []) : 2 | ['PitchType', 'PreviousPitchType']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', []) : 2 | ['PitchType', 'PreviousPitchType']\n",
            "\t\t('int', [])      : 2 | ['Balls', 'Strikes']\n",
            "\t0.3s = Fit runtime\n",
            "\t4 features in original data used to generate 4 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 5.19 MB (0.1% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.51s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "User-specified model hyperparameters to be fit:\n",
            "{\n",
            "\t'NN_TORCH': {},\n",
            "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n",
            "\t'CAT': {},\n",
            "\t'XGB': {},\n",
            "\t'FASTAI': {},\n",
            "\t'RF': [{'criterion': 'gini', 'max_depth': 15, 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'max_depth': 15, 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'max_depth': 15, 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "\t'XT': [{'criterion': 'gini', 'max_depth': 15, 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'max_depth': 15, 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'max_depth': 15, 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
            "}\n",
            "Fitting 11 L1 models ...\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 2536.49s of the 2536.49s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.76%)\n",
            "\t0.5525\t = Validation score   (accuracy)\n",
            "\t107.83s\t = Training   runtime\n",
            "\t1.23s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 2427.26s of the 2427.26s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.24%)\n",
            "\tWarning: Exception caused LightGBMXT_BAG_L1 to fail during training (ImportError)... Skipping this model.\n",
            "\t\t\u001b[36mray::_ray_fit()\u001b[39m (pid=8517, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/__init__.py\", line 8, in <module>\n",
            "    from .basic import Booster, Dataset, Sequence, register_logger\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 247, in <module>\n",
            "    _LIB = _load_lib()\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 232, in _load_lib\n",
            "    lib = ctypes.cdll.LoadLibrary(lib_path[0])\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 460, in LoadLibrary\n",
            "    return self._dlltype(name)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 382, in __init__\n",
            "    self._handle = _dlopen(self._name, mode)\n",
            "OSError: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "\u001b[36mray::_ray_fit()\u001b[39m (pid=8517, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 404, in _ray_fit\n",
            "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 855, in fit\n",
            "    out = self._fit(**kwargs)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/tabular/models/lgb/lgb_model.py\", line 98, in _fit\n",
            "    try_import_lightgbm()  # raise helpful error message if LightGBM isn't installed\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/common/utils/try_import.py\", line 86, in try_import_lightgbm\n",
            "    raise ImportError(\"`import lightgbm` failed. If you are using Mac OSX, \" \"Please try 'brew install libomp'. Detailed info: {}\".format(str(e)))\n",
            "ImportError: `import lightgbm` failed. If you are using Mac OSX, Please try 'brew install libomp'. Detailed info: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "Fitting model: LightGBM_BAG_L1 ... Training model for up to 2426.01s of the 2426.01s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.14%)\n",
            "\tWarning: Exception caused LightGBM_BAG_L1 to fail during training (ImportError)... Skipping this model.\n",
            "\t\t\u001b[36mray::_ray_fit()\u001b[39m (pid=8559, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/__init__.py\", line 8, in <module>\n",
            "    from .basic import Booster, Dataset, Sequence, register_logger\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 247, in <module>\n",
            "    _LIB = _load_lib()\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 232, in _load_lib\n",
            "    lib = ctypes.cdll.LoadLibrary(lib_path[0])\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 460, in LoadLibrary\n",
            "    return self._dlltype(name)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 382, in __init__\n",
            "    self._handle = _dlopen(self._name, mode)\n",
            "OSError: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "\u001b[36mray::_ray_fit()\u001b[39m (pid=8559, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 404, in _ray_fit\n",
            "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 855, in fit\n",
            "    out = self._fit(**kwargs)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/tabular/models/lgb/lgb_model.py\", line 98, in _fit\n",
            "    try_import_lightgbm()  # raise helpful error message if LightGBM isn't installed\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/common/utils/try_import.py\", line 86, in try_import_lightgbm\n",
            "    raise ImportError(\"`import lightgbm` failed. If you are using Mac OSX, \" \"Please try 'brew install libomp'. Detailed info: {}\".format(str(e)))\n",
            "ImportError: `import lightgbm` failed. If you are using Mac OSX, Please try 'brew install libomp'. Detailed info: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "2024-04-19 14:36:16,829\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:16,830\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:16,831\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:16,832\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:16,833\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:16,833\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 2424.92s of the 2424.92s of remaining time.\n",
            "2024-04-19 14:36:16,834\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,950\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,951\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,952\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,952\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,953\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,953\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:36:21,954\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "\t0.5511\t = Validation score   (accuracy)\n",
            "\t2.4s\t = Training   runtime\n",
            "\t4.22s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 2418.21s of the 2418.21s of remaining time.\n",
            "\t0.5511\t = Validation score   (accuracy)\n",
            "\t2.12s\t = Training   runtime\n",
            "\t4.23s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ... Training model for up to 2411.79s of the 2411.79s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.17%)\n",
            "\t0.5497\t = Validation score   (accuracy)\n",
            "\t6.2s\t = Training   runtime\n",
            "\t0.13s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 2404.66s of the 2404.66s of remaining time.\n",
            "\t0.5511\t = Validation score   (accuracy)\n",
            "\t1.85s\t = Training   runtime\n",
            "\t4.24s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 2398.5s of the 2398.5s of remaining time.\n",
            "\t0.5511\t = Validation score   (accuracy)\n",
            "\t1.87s\t = Training   runtime\n",
            "\t4.23s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ... Training model for up to 2392.33s of the 2392.33s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.49%)\n",
            "\t0.5521\t = Validation score   (accuracy)\n",
            "\t15.57s\t = Training   runtime\n",
            "\t0.39s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 2375.69s of the 2375.69s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.03%)\n",
            "\t0.5524\t = Validation score   (accuracy)\n",
            "\t188.14s\t = Training   runtime\n",
            "\t1.01s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 2186.52s of the 2186.52s of remaining time.\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (8 workers, per: cpus=1, gpus=0, memory=1.22%)\n",
            "\tWarning: Exception caused LightGBMLarge_BAG_L1 to fail during training (ImportError)... Skipping this model.\n",
            "\t\t\u001b[36mray::_ray_fit()\u001b[39m (pid=8608, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/__init__.py\", line 8, in <module>\n",
            "    from .basic import Booster, Dataset, Sequence, register_logger\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 247, in <module>\n",
            "    _LIB = _load_lib()\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/basic.py\", line 232, in _load_lib\n",
            "    lib = ctypes.cdll.LoadLibrary(lib_path[0])\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 460, in LoadLibrary\n",
            "    return self._dlltype(name)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/ctypes/__init__.py\", line 382, in __init__\n",
            "    self._handle = _dlopen(self._name, mode)\n",
            "OSError: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "\u001b[36mray::_ray_fit()\u001b[39m (pid=8608, ip=127.0.0.1)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 404, in _ray_fit\n",
            "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 855, in fit\n",
            "    out = self._fit(**kwargs)\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/tabular/models/lgb/lgb_model.py\", line 98, in _fit\n",
            "    try_import_lightgbm()  # raise helpful error message if LightGBM isn't installed\n",
            "  File \"/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/autogluon/common/utils/try_import.py\", line 86, in try_import_lightgbm\n",
            "    raise ImportError(\"`import lightgbm` failed. If you are using Mac OSX, \" \"Please try 'brew install libomp'. Detailed info: {}\".format(str(e)))\n",
            "ImportError: `import lightgbm` failed. If you are using Mac OSX, Please try 'brew install libomp'. Detailed info: dlopen(/Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n",
            "  Referenced from: <C3EB28DD-60B6-3334-AFA2-72BBBF9DBAEF> /Users/johnhodge/opt/anaconda3/envs/py3_env/lib/python3.9/site-packages/lightgbm/lib/lib_lightgbm.so\n",
            "  Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)\n",
            "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 2185.38s of remaining time.\n",
            "\tEnsemble Weights: {'NeuralNetFastAI_BAG_L1': 0.941, 'CatBoost_BAG_L1': 0.059}\n",
            "\t0.5525\t = Validation score   (accuracy)\n",
            "\t2.45s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 354.11s ... Best model: \"WeightedEnsemble_L2\"\n",
            "Automatically performing refit_full as a post-fit operation (due to `.fit(..., refit_full=True)`\n",
            "Refitting models via `predictor.refit_full` using all of the data (combined train and validation)...\n",
            "\tModels trained in this way will have the suffix \"_FULL\" and have NaN validation score.\n",
            "\tThis process is not bound by time_limit, but should take less time than the original `predictor.fit` call.\n",
            "\tTo learn more, refer to the `.refit_full` method docstring which explains how \"_FULL\" models differ from normal models.\n",
            "Fitting 1 L1 models ...\n",
            "Fitting model: NeuralNetFastAI_BAG_L1_FULL ...\n",
            "2024-04-19 14:40:22,161\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,162\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,163\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,164\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,165\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,166\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "2024-04-19 14:40:22,166\tERROR worker.py:406 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
            "\tStopping at the best epoch learned earlier - 13.\n",
            "\t45.87s\t = Training   runtime\n",
            "Fitting model: RandomForestGini_BAG_L1_FULL | Skipping fit via cloning parent ...\n",
            "\t2.4s\t = Training   runtime\n",
            "\t4.22s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr_BAG_L1_FULL | Skipping fit via cloning parent ...\n",
            "\t2.12s\t = Training   runtime\n",
            "\t4.23s\t = Validation runtime\n",
            "Fitting 1 L1 models ...\n",
            "Fitting model: CatBoost_BAG_L1_FULL ...\n",
            "\t0.28s\t = Training   runtime\n",
            "Fitting model: ExtraTreesGini_BAG_L1_FULL | Skipping fit via cloning parent ...\n",
            "\t1.85s\t = Training   runtime\n",
            "\t4.24s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr_BAG_L1_FULL | Skipping fit via cloning parent ...\n",
            "\t1.87s\t = Training   runtime\n",
            "\t4.23s\t = Validation runtime\n",
            "Fitting 1 L1 models ...\n",
            "Fitting model: XGBoost_BAG_L1_FULL ...\n",
            "\t0.73s\t = Training   runtime\n",
            "Fitting 1 L1 models ...\n",
            "Fitting model: NeuralNetTorch_BAG_L1_FULL ...\n",
            "\t67.77s\t = Training   runtime\n",
            "Fitting model: WeightedEnsemble_L2_FULL | Skipping fit via cloning parent ...\n",
            "\tEnsemble Weights: {'NeuralNetFastAI_BAG_L1': 0.941, 'CatBoost_BAG_L1': 0.059}\n",
            "\t2.45s\t = Training   runtime\n",
            "Updated best model to \"WeightedEnsemble_L2_FULL\" (Previously \"WeightedEnsemble_L2\"). AutoGluon will default to using \"WeightedEnsemble_L2_FULL\" for predict() and predict_proba().\n",
            "Refit complete, total runtime = 115.12s ... Best model: \"WeightedEnsemble_L2_FULL\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"autogluon_pitchtype_models\")\n"
          ]
        }
      ],
      "source": [
        "MODELS_DIR = 'autogluon_pitchtype_models'  # Specifies folder to store trained models\n",
        "MODEL_PRESET = 'good_quality'  # Preset for training models\n",
        "\n",
        "if gpu_available:\n",
        "    predictor = TabularPredictor(label=label, path=MODELS_DIR).fit(train_data, presets=MODEL_PRESET,\n",
        "                                                                  num_gpus=1)\n",
        "else:\n",
        "    predictor = TabularPredictor(label=label, path=MODELS_DIR).fit(train_data, presets=MODEL_PRESET)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oqEgzyWAhOg"
      },
      "source": [
        "## Evaluate the Model\n",
        "\n",
        "After training, evaluating your model is crucial to understand its performance and to ensure it generalizes well on unseen data. Model evaluation typically involves calculating performance metrics such as accuracy, precision, recall, and F1-score on a test set that was not used during the training process. Once the model is trained, you can evaluate its performance on the test data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nUjOtHqUAhOg"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.5498016922263352, 'balanced_accuracy': 0.3353163363028917, 'mcc': 0.022533727900984492}\n"
          ]
        }
      ],
      "source": [
        "performance = predictor.evaluate(test_data)\n",
        "print(performance)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BRHZ3amAhOg"
      },
      "source": [
        "## View leaderboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "U_EeTPU6AhOg"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score_test</th>\n",
              "      <th>score_val</th>\n",
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              "      <th>fit_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost_BAG_L1_FULL</td>\n",
              "      <td>0.551071</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.118919</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.726267</td>\n",
              "      <td>0.118919</td>\n",
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              "      <td>NeuralNetTorch_BAG_L1_FULL</td>\n",
              "      <td>0.550225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.223883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67.772540</td>\n",
              "      <td>0.223883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67.772540</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForestGini_BAG_L1</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>0.551147</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.107121</td>\n",
              "      <td>4.218421</td>\n",
              "      <td>2.404032</td>\n",
              "      <td>0.107121</td>\n",
              "      <td>4.218421</td>\n",
              "      <td>2.404032</td>\n",
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              "      <td>True</td>\n",
              "      <td>2</td>\n",
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              "      <td>ExtraTreesEntr_BAG_L1</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>0.551147</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.107458</td>\n",
              "      <td>4.225893</td>\n",
              "      <td>1.873344</td>\n",
              "      <td>0.107458</td>\n",
              "      <td>4.225893</td>\n",
              "      <td>1.873344</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ExtraTreesGini_BAG_L1</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>0.551147</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.108862</td>\n",
              "      <td>4.235048</td>\n",
              "      <td>1.852494</td>\n",
              "      <td>0.108862</td>\n",
              "      <td>4.235048</td>\n",
              "      <td>1.852494</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RandomForestEntr_BAG_L1_FULL</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.111210</td>\n",
              "      <td>4.230346</td>\n",
              "      <td>2.115787</td>\n",
              "      <td>0.111210</td>\n",
              "      <td>4.230346</td>\n",
              "      <td>2.115787</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RandomForestEntr_BAG_L1</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>0.551147</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.111474</td>\n",
              "      <td>4.230346</td>\n",
              "      <td>2.115787</td>\n",
              "      <td>0.111474</td>\n",
              "      <td>4.230346</td>\n",
              "      <td>2.115787</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForestGini_BAG_L1_FULL</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.113438</td>\n",
              "      <td>4.218421</td>\n",
              "      <td>2.404032</td>\n",
              "      <td>0.113438</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ExtraTreesEntr_BAG_L1_FULL</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.118939</td>\n",
              "      <td>4.225893</td>\n",
              "      <td>1.873344</td>\n",
              "      <td>0.118939</td>\n",
              "      <td>4.225893</td>\n",
              "      <td>1.873344</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ExtraTreesGini_BAG_L1_FULL</td>\n",
              "      <td>0.550093</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.123439</td>\n",
              "      <td>4.235048</td>\n",
              "      <td>1.852494</td>\n",
              "      <td>0.123439</td>\n",
              "      <td>4.235048</td>\n",
              "      <td>1.852494</td>\n",
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              "      <td>True</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NeuralNetFastAI_BAG_L1_FULL</td>\n",
              "      <td>0.549802</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.329764</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.872117</td>\n",
              "      <td>0.329764</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.872117</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>WeightedEnsemble_L2_FULL</td>\n",
              "      <td>0.549802</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.359136</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.600251</td>\n",
              "      <td>0.002504</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.452347</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>CatBoost_BAG_L1_FULL</td>\n",
              "      <td>0.549524</td>\n",
              "      <td>NaN</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.026868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.275787</td>\n",
              "      <td>0.026868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.275787</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.552522</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.367711</td>\n",
              "      <td>116.478410</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.013465</td>\n",
              "      <td>2.452347</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>NeuralNetFastAI_BAG_L1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.552516</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.227467</td>\n",
              "      <td>107.829761</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.227467</td>\n",
              "      <td>107.829761</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NeuralNetTorch_BAG_L1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.552446</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.008550</td>\n",
              "      <td>188.139505</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.008550</td>\n",
              "      <td>188.139505</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>XGBoost_BAG_L1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.552122</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.389998</td>\n",
              "      <td>15.567087</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.389998</td>\n",
              "      <td>15.567087</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CatBoost_BAG_L1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.549739</td>\n",
              "      <td>accuracy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.126779</td>\n",
              "      <td>6.196302</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.126779</td>\n",
              "      <td>6.196302</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           model  score_test  score_val eval_metric  \\\n",
              "0            XGBoost_BAG_L1_FULL    0.551071        NaN    accuracy   \n",
              "1     NeuralNetTorch_BAG_L1_FULL    0.550225        NaN    accuracy   \n",
              "2        RandomForestGini_BAG_L1    0.550093   0.551147    accuracy   \n",
              "3          ExtraTreesEntr_BAG_L1    0.550093   0.551147    accuracy   \n",
              "4          ExtraTreesGini_BAG_L1    0.550093   0.551147    accuracy   \n",
              "5   RandomForestEntr_BAG_L1_FULL    0.550093        NaN    accuracy   \n",
              "6        RandomForestEntr_BAG_L1    0.550093   0.551147    accuracy   \n",
              "7   RandomForestGini_BAG_L1_FULL    0.550093        NaN    accuracy   \n",
              "8     ExtraTreesEntr_BAG_L1_FULL    0.550093        NaN    accuracy   \n",
              "9     ExtraTreesGini_BAG_L1_FULL    0.550093        NaN    accuracy   \n",
              "10   NeuralNetFastAI_BAG_L1_FULL    0.549802        NaN    accuracy   \n",
              "11      WeightedEnsemble_L2_FULL    0.549802        NaN    accuracy   \n",
              "12          CatBoost_BAG_L1_FULL    0.549524        NaN    accuracy   \n",
              "13           WeightedEnsemble_L2         NaN   0.552522    accuracy   \n",
              "14        NeuralNetFastAI_BAG_L1         NaN   0.552516    accuracy   \n",
              "15         NeuralNetTorch_BAG_L1         NaN   0.552446    accuracy   \n",
              "16                XGBoost_BAG_L1         NaN   0.552122    accuracy   \n",
              "17               CatBoost_BAG_L1         NaN   0.549739    accuracy   \n",
              "\n",
              "    pred_time_test  pred_time_val    fit_time  pred_time_test_marginal  \\\n",
              "0         0.118919            NaN    0.726267                 0.118919   \n",
              "1         0.223883            NaN   67.772540                 0.223883   \n",
              "2         0.107121       4.218421    2.404032                 0.107121   \n",
              "3         0.107458       4.225893    1.873344                 0.107458   \n",
              "4         0.108862       4.235048    1.852494                 0.108862   \n",
              "5         0.111210       4.230346    2.115787                 0.111210   \n",
              "6         0.111474       4.230346    2.115787                 0.111474   \n",
              "7         0.113438       4.218421    2.404032                 0.113438   \n",
              "8         0.118939       4.225893    1.873344                 0.118939   \n",
              "9         0.123439       4.235048    1.852494                 0.123439   \n",
              "10        0.329764            NaN   45.872117                 0.329764   \n",
              "11        0.359136            NaN   48.600251                 0.002504   \n",
              "12        0.026868            NaN    0.275787                 0.026868   \n",
              "13             NaN       1.367711  116.478410                      NaN   \n",
              "14             NaN       1.227467  107.829761                      NaN   \n",
              "15             NaN       1.008550  188.139505                      NaN   \n",
              "16             NaN       0.389998   15.567087                      NaN   \n",
              "17             NaN       0.126779    6.196302                      NaN   \n",
              "\n",
              "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              "0                      NaN           0.726267            1       True   \n",
              "1                      NaN          67.772540            1       True   \n",
              "2                 4.218421           2.404032            1       True   \n",
              "3                 4.225893           1.873344            1       True   \n",
              "4                 4.235048           1.852494            1       True   \n",
              "5                 4.230346           2.115787            1       True   \n",
              "6                 4.230346           2.115787            1       True   \n",
              "7                 4.218421           2.404032            1       True   \n",
              "8                 4.225893           1.873344            1       True   \n",
              "9                 4.235048           1.852494            1       True   \n",
              "10                     NaN          45.872117            1       True   \n",
              "11                     NaN           2.452347            2       True   \n",
              "12                     NaN           0.275787            1       True   \n",
              "13                0.013465           2.452347            2      False   \n",
              "14                1.227467         107.829761            1      False   \n",
              "15                1.008550         188.139505            1      False   \n",
              "16                0.389998          15.567087            1      False   \n",
              "17                0.126779           6.196302            1      False   \n",
              "\n",
              "    fit_order  \n",
              "0          16  \n",
              "1          17  \n",
              "2           2  \n",
              "3           6  \n",
              "4           5  \n",
              "5          12  \n",
              "6           3  \n",
              "7          11  \n",
              "8          15  \n",
              "9          14  \n",
              "10         10  \n",
              "11         18  \n",
              "12         13  \n",
              "13          9  \n",
              "14          1  \n",
              "15          8  \n",
              "16          7  \n",
              "17          4  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictor.leaderboard(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_1TIEmfAhOg"
      },
      "source": [
        "## Make Predictions\n",
        "\n",
        "Once satisfied with your model's performance, the final step is using it to make predictions on new data. This step is where your model is put to the test, providing insights and decisions based on the data it analyzes. Now you can use the model to predict the pitch type:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "hpocASI4AhOg"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6     strike\n",
            "14    strike\n",
            "16    strike\n",
            "19    strike\n",
            "20    strike\n",
            "Name: Outcome, dtype: object\n",
            "        ball       hit    strike\n",
            "6   0.303388  0.118411  0.578201\n",
            "14  0.442720  0.097562  0.459719\n",
            "16  0.405308  0.107504  0.487188\n",
            "19  0.426240  0.102519  0.471241\n",
            "20  0.302796  0.118424  0.578780\n"
          ]
        }
      ],
      "source": [
        "predictions = predictor.predict(test_data)\n",
        "print(predictions.head())\n",
        "\n",
        "# To view the probability of each class\n",
        "probabilities = predictor.predict_proba(test_data)\n",
        "print(probabilities.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEmDQ8w0AhOg"
      },
      "source": [
        "## Insights and Feature Importance\n",
        "\n",
        "AutoGluon provides functionality to understand which features are most important for the predictions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "N-dlj0gSAhOg"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Computing feature importance via permutation shuffling for 4 features using 5000 rows with 5 shuffle sets...\n",
            "\t1.09s\t= Expected runtime (0.22s per shuffle set)\n",
            "\t0.73s\t= Actual runtime (Completed 5 of 5 shuffle sets)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   importance    stddev   p_value  n  p99_high   p99_low\n",
            "PitchType             0.00264  0.001640  0.011373  5  0.006016 -0.000736\n",
            "PreviousPitchType     0.00000  0.000000  0.500000  5  0.000000  0.000000\n",
            "Strikes              -0.00008  0.002292  0.529234  5  0.004639 -0.004799\n",
            "Balls                -0.00024  0.002251  0.588351  5  0.004395 -0.004875\n"
          ]
        }
      ],
      "source": [
        "feature_importance = predictor.feature_importance(data=train_data)\n",
        "print(feature_importance)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The results show that only PitchType is salient in predicting the outcome of the pitch in this data set. The count (balls and strikes) and previous pitch are found to not be salients, which makes sense for the data set based on how the data set was trained using a Markov model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYIqhDdLAhOg"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "In this tutorial, we explored the capabilities of AutoGluon in the context of sports analytics by predicting baseball pitch types. We walked through the entire workflow, from data preparation and model training to evaluation and making predictions. By now, you should have a solid understanding of how AutoGluon can be used to handle complex predictive modeling tasks with ease. We encourage you to apply the knowledge and techniques learned here to your datasets and challenges, exploring further the potential of AutoML to transform your analytical processes. Remember, the key to mastering AutoGluon is continuous experimentation and adaptation to your specific needs. Experiment with AutoGluon's hyperparameters and model configurations to improve performance.\n",
        "\n",
        "## Next steps\n",
        "\n",
        "Run the predictor model again using a data set that contains more complex probabilistic pitch sequences using longer Markov chains."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C_PK9mXUDL9o"
      },
      "outputs": [],
      "source": []
    }
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