AutoML_AutoGluon_Regression_Classification_Sample.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## AutoGluon AutoML Sample Code"
      ],
      "metadata": {
        "id": "ts6ve60tmtfP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "PhUI9_iS2T5C"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install AutoGluon\n",
        "!pip install autogluon.tabular"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuhjV5xn5WNy",
        "outputId": "9ae30d2b-d73e-4a93-bc46-69a8ef45288a"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regression"
      ],
      "metadata": {
        "id": "ixzdHrLxPYVG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load Data"
      ],
      "metadata": {
        "id": "KAp6OFfEnDJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the label column\n",
        "label = 'price_usd'"
      ],
      "metadata": {
        "id": "dvUEQn3UDqXl"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load the initial unprocessed data\n",
        "cars = pd.read_csv('cars.csv')"
      ],
      "metadata": {
        "id": "SkLd30Sl2gTj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cars.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em7sc0tn532m",
        "outputId": "cc9b62c0-2d73-438c-ca27-33396fe7b926"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38531 entries, 0 to 38530\n",
            "Data columns (total 19 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   manufacturer_name  38531 non-null  object \n",
            " 1   model_name         38531 non-null  object \n",
            " 2   transmission       38531 non-null  object \n",
            " 3   color              38531 non-null  object \n",
            " 4   odometer_value     38531 non-null  int64  \n",
            " 5   year_produced      38531 non-null  int64  \n",
            " 6   engine_fuel        38531 non-null  object \n",
            " 7   engine_has_gas     38531 non-null  bool   \n",
            " 8   engine_type        38531 non-null  object \n",
            " 9   engine_capacity    38521 non-null  float64\n",
            " 10  body_type          38531 non-null  object \n",
            " 11  has_warranty       38531 non-null  bool   \n",
            " 12  state              38531 non-null  object \n",
            " 13  drivetrain         38531 non-null  object \n",
            " 14  price_usd          38531 non-null  float64\n",
            " 15  is_exchangeable    38531 non-null  bool   \n",
            " 16  number_of_photos   38531 non-null  float64\n",
            " 17  up_counter         38531 non-null  float64\n",
            " 18  duration_listed    38531 non-null  float64\n",
            "dtypes: bool(3), float64(5), int64(2), object(9)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data to test and train\n",
        "from sklearn.model_selection import train_test_split\n",
        "cars_train, cars_test = train_test_split(cars, test_size=0.2, random_state=42)\n",
        "cars_test_labels = cars_test[label]\n",
        "cars_test_nolabel = cars_test.drop([label], axis=1)\n",
        "cars_train_labels = cars_train[label]\n",
        "cars_train_nolabel = cars_train.drop([label], axis=1)\n",
        "cars_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "-qvFVaZK6HrY",
        "outputId": "e5e8f56d-d81d-4507-d081-7d4a43765768"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      manufacturer_name      model_name transmission   color  odometer_value  \\\n",
              "37935           Peugeot             307   mechanical  silver          320000   \n",
              "28568               BMW             525    automatic  violet          400000   \n",
              "24311         Chevrolet         Captiva   mechanical    grey           79000   \n",
              "19393              Jeep  Grand Cherokee    automatic   other          500000   \n",
              "2540               Opel           Astra   mechanical  silver          280000   \n",
              "\n",
              "       year_produced engine_fuel  engine_has_gas engine_type  engine_capacity  \\\n",
              "37935           2005    gasoline           False    gasoline              1.6   \n",
              "28568           1997      diesel           False      diesel              2.5   \n",
              "24311           2015      diesel           False      diesel              2.2   \n",
              "19393           2004      diesel           False      diesel              2.7   \n",
              "2540            2007      diesel           False      diesel              1.7   \n",
              "\n",
              "       body_type  has_warranty  state drivetrain  price_usd  is_exchangeable  \\\n",
              "37935  universal         False  owned      front     7280.0            False   \n",
              "28568  universal         False  owned       rear     2380.0             True   \n",
              "24311        suv         False  owned        all    20300.0            False   \n",
              "19393        suv         False  owned        all     9100.0            False   \n",
              "2540   universal         False  owned      front     7000.0             True   \n",
              "\n",
              "       number_of_photos  up_counter  duration_listed  \n",
              "37935              10.0         1.0             45.5  \n",
              "28568               2.5         0.5              2.5  \n",
              "24311               4.5         6.5             30.5  \n",
              "19393               6.5        39.5            151.0  \n",
              "2540                4.0         3.0             33.0  "
            ],
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              "\n",
              "  <div id=\"df-d98e3204-8969-4532-9153-7d8a0de9988e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>manufacturer_name</th>\n",
              "      <th>model_name</th>\n",
              "      <th>transmission</th>\n",
              "      <th>color</th>\n",
              "      <th>odometer_value</th>\n",
              "      <th>year_produced</th>\n",
              "      <th>engine_fuel</th>\n",
              "      <th>engine_has_gas</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>engine_capacity</th>\n",
              "      <th>body_type</th>\n",
              "      <th>has_warranty</th>\n",
              "      <th>state</th>\n",
              "      <th>drivetrain</th>\n",
              "      <th>price_usd</th>\n",
              "      <th>is_exchangeable</th>\n",
              "      <th>number_of_photos</th>\n",
              "      <th>up_counter</th>\n",
              "      <th>duration_listed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37935</th>\n",
              "      <td>Peugeot</td>\n",
              "      <td>307</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>silver</td>\n",
              "      <td>320000</td>\n",
              "      <td>2005</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>False</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>1.6</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>front</td>\n",
              "      <td>7280.0</td>\n",
              "      <td>False</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28568</th>\n",
              "      <td>BMW</td>\n",
              "      <td>525</td>\n",
              "      <td>automatic</td>\n",
              "      <td>violet</td>\n",
              "      <td>400000</td>\n",
              "      <td>1997</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.5</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>rear</td>\n",
              "      <td>2380.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24311</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Captiva</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>grey</td>\n",
              "      <td>79000</td>\n",
              "      <td>2015</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.2</td>\n",
              "      <td>suv</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>all</td>\n",
              "      <td>20300.0</td>\n",
              "      <td>False</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>30.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19393</th>\n",
              "      <td>Jeep</td>\n",
              "      <td>Grand Cherokee</td>\n",
              "      <td>automatic</td>\n",
              "      <td>other</td>\n",
              "      <td>500000</td>\n",
              "      <td>2004</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.7</td>\n",
              "      <td>suv</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>all</td>\n",
              "      <td>9100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>39.5</td>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2540</th>\n",
              "      <td>Opel</td>\n",
              "      <td>Astra</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>silver</td>\n",
              "      <td>280000</td>\n",
              "      <td>2007</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>1.7</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>front</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>True</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.0</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Import AutoGluon Libraries\n",
        "from autogluon.tabular import TabularDataset, TabularPredictor\n",
        "# Create an instance and then fit the train data\n",
        "regression = TabularPredictor(label=label, path='.').fit(cars_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVv4vdK66hxi",
        "outputId": "11140f04-6555-42ef-a789-5de2f298dc81"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: path already exists! This predictor may overwrite an existing predictor! path=\".\"\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"./\"\n",
            "AutoGluon Version:  0.7.0\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Sat Apr 29 09:15:28 UTC 2023\n",
            "Train Data Rows:    30824\n",
            "Train Data Columns: 18\n",
            "Label Column: price_usd\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).\n",
            "\tLabel info (max, min, mean, stddev): (70000.0, 1.4, 9307.82389, 9010.97979)\n",
            "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    12636.53 MB\n",
            "\tTrain Data (Original)  Memory Usage: 19.15 MB (0.2% 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",
            "\t\t\tNote: Converting 4 features to boolean dtype as they only contain 2 unique values.\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",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('bool', [])   : 3 | ['engine_has_gas', 'has_warranty', 'is_exchangeable']\n",
            "\t\t('float', [])  : 4 | ['engine_capacity', 'number_of_photos', 'up_counter', 'duration_listed']\n",
            "\t\t('int', [])    : 2 | ['odometer_value', 'year_produced']\n",
            "\t\t('object', []) : 9 | ['manufacturer_name', 'model_name', 'transmission', 'color', 'engine_fuel', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])  : 8 | ['manufacturer_name', 'model_name', 'color', 'engine_fuel', 'engine_type', ...]\n",
            "\t\t('float', [])     : 4 | ['engine_capacity', 'number_of_photos', 'up_counter', 'duration_listed']\n",
            "\t\t('int', [])       : 2 | ['odometer_value', 'year_produced']\n",
            "\t\t('int', ['bool']) : 4 | ['transmission', 'engine_has_gas', 'has_warranty', 'is_exchangeable']\n",
            "\t0.5s = Fit runtime\n",
            "\t18 features in original data used to generate 18 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 1.88 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.54s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
            "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Automatically generating train/validation split with holdout_frac=0.08110563197508434, Train Rows: 28324, Val Rows: 2500\n",
            "Fitting 11 L1 models ...\n",
            "Fitting model: KNeighborsUnif ...\n",
            "\t-6917.6481\t = Validation score   (-root_mean_squared_error)\n",
            "\t9.07s\t = Training   runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist ...\n",
            "\t-6981.0807\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.09s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: LightGBMXT ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_set's rmse: 2241.7\n",
            "[2000]\tvalid_set's rmse: 2232.31\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\t-2228.8838\t = Validation score   (-root_mean_squared_error)\n",
            "\t18.3s\t = Training   runtime\n",
            "\t0.7s\t = Validation runtime\n",
            "Fitting model: LightGBM ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_set's rmse: 2232.79\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\t-2230.3857\t = Validation score   (-root_mean_squared_error)\n",
            "\t5.43s\t = Training   runtime\n",
            "\t0.39s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE ...\n",
            "\t-2726.4501\t = Validation score   (-root_mean_squared_error)\n",
            "\t52.97s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: CatBoost ...\n",
            "\tWarning: Exception caused CatBoost to fail during training (ImportError)... Skipping this model.\n",
            "\t\t`import catboost` failed. A quick tip is to install via `pip install autogluon.tabular[catboost]==0.7.0`.\n",
            "Fitting model: ExtraTreesMSE ...\n",
            "\t-2836.2089\t = Validation score   (-root_mean_squared_error)\n",
            "\t23.84s\t = Training   runtime\n",
            "\t0.31s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI ...\n",
            "\t-2057.7063\t = Validation score   (-root_mean_squared_error)\n",
            "\t52.4s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: XGBoost ...\n",
            "\t-2447.5727\t = Validation score   (-root_mean_squared_error)\n",
            "\t68.25s\t = Training   runtime\n",
            "\t0.27s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch ...\n",
            "\t-2474.5213\t = Validation score   (-root_mean_squared_error)\n",
            "\t43.36s\t = Training   runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge ...\n",
            "\t-2227.7071\t = Validation score   (-root_mean_squared_error)\n",
            "\t4.93s\t = Training   runtime\n",
            "\t0.27s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t-2002.1358\t = Validation score   (-root_mean_squared_error)\n",
            "\t0.54s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 287.67s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"./\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the performance using test data\n",
        "regression_pred = regression.predict(cars_test_nolabel)\n",
        "regression_perf = regression.evaluate_predictions(y_true=cars_test_labels, y_pred=regression_pred, auxiliary_metrics=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waaqGM8l6rBO",
        "outputId": "d30b4ab4-3cd1-40b8-9b7e-a3f0eeaa88e4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluation: root_mean_squared_error on test data: -2147.154850074437\n",
            "\tNote: Scores are always higher_is_better. This metric score can be multiplied by -1 to get the metric value.\n",
            "Evaluations on test data:\n",
            "{\n",
            "    \"root_mean_squared_error\": -2147.154850074437,\n",
            "    \"mean_squared_error\": -4610273.950198177,\n",
            "    \"mean_absolute_error\": -1239.6310273428235,\n",
            "    \"r2\": 0.9424819021983564,\n",
            "    \"pearsonr\": 0.9709425795189421,\n",
            "    \"median_absolute_error\": -755.84765625\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show the leaderboard\n",
        "regression.leaderboard(cars_test, silent=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "4joCA8dP6zrO",
        "outputId": "c5861db3-0d00-4e83-cf2d-25e96bb81481"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  model   score_test    score_val  pred_time_test  \\\n",
              "0   WeightedEnsemble_L2 -2147.154850 -2002.135818        8.098982   \n",
              "1       NeuralNetFastAI -2234.256113 -2057.706346        0.533169   \n",
              "2              LightGBM -2259.305488 -2230.385708        2.321411   \n",
              "3         LightGBMLarge -2275.738581 -2227.707052        1.120335   \n",
              "4            LightGBMXT -2279.571050 -2228.883765        3.975284   \n",
              "5               XGBoost -2363.747409 -2447.572692        1.648315   \n",
              "6        NeuralNetTorch -2564.763022 -2474.521302        0.139015   \n",
              "7       RandomForestMSE -2728.001026 -2726.450087        4.636035   \n",
              "8         ExtraTreesMSE -2780.698443 -2836.208947        2.254777   \n",
              "9        KNeighborsDist -6864.161938 -6981.080693        0.107051   \n",
              "10       KNeighborsUnif -6920.489506 -6917.648092        0.110673   \n",
              "\n",
              "    pred_time_val    fit_time  pred_time_test_marginal  \\\n",
              "0        1.469437  124.960373                 0.009769   \n",
              "1        0.076809   52.397183                 0.533169   \n",
              "2        0.385301    5.429170                 2.321411   \n",
              "3        0.269171    4.932309                 1.120335   \n",
              "4        0.696380   18.298086                 3.975284   \n",
              "5        0.271994   68.253038                 1.648315   \n",
              "6        0.040705   43.359291                 0.139015   \n",
              "7        0.374502   52.967184                 4.636035   \n",
              "8        0.308768   23.843520                 2.254777   \n",
              "9        0.082602    0.087760                 0.107051   \n",
              "10       0.038632    9.070331                 0.110673   \n",
              "\n",
              "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              "0                 0.001070           0.544334            2       True   \n",
              "1                 0.076809          52.397183            1       True   \n",
              "2                 0.385301           5.429170            1       True   \n",
              "3                 0.269171           4.932309            1       True   \n",
              "4                 0.696380          18.298086            1       True   \n",
              "5                 0.271994          68.253038            1       True   \n",
              "6                 0.040705          43.359291            1       True   \n",
              "7                 0.374502          52.967184            1       True   \n",
              "8                 0.308768          23.843520            1       True   \n",
              "9                 0.082602           0.087760            1       True   \n",
              "10                0.038632           9.070331            1       True   \n",
              "\n",
              "    fit_order  \n",
              "0          11  \n",
              "1           7  \n",
              "2           4  \n",
              "3          10  \n",
              "4           3  \n",
              "5           8  \n",
              "6           9  \n",
              "7           5  \n",
              "8           6  \n",
              "9           2  \n",
              "10          1  "
            ],
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              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score_test</th>\n",
              "      <th>score_val</th>\n",
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              "      <th>pred_time_val</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>-2147.154850</td>\n",
              "      <td>-2002.135818</td>\n",
              "      <td>8.098982</td>\n",
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              "      <td>0.009769</td>\n",
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              "      <td>2</td>\n",
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              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NeuralNetFastAI</td>\n",
              "      <td>-2234.256113</td>\n",
              "      <td>-2057.706346</td>\n",
              "      <td>0.533169</td>\n",
              "      <td>0.076809</td>\n",
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              "      <th>2</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>-2259.305488</td>\n",
              "      <td>-2230.385708</td>\n",
              "      <td>2.321411</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBMLarge</td>\n",
              "      <td>-2275.738581</td>\n",
              "      <td>-2227.707052</td>\n",
              "      <td>1.120335</td>\n",
              "      <td>0.269171</td>\n",
              "      <td>4.932309</td>\n",
              "      <td>1.120335</td>\n",
              "      <td>0.269171</td>\n",
              "      <td>4.932309</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBMXT</td>\n",
              "      <td>-2279.571050</td>\n",
              "      <td>-2228.883765</td>\n",
              "      <td>3.975284</td>\n",
              "      <td>0.696380</td>\n",
              "      <td>18.298086</td>\n",
              "      <td>3.975284</td>\n",
              "      <td>0.696380</td>\n",
              "      <td>18.298086</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>-2363.747409</td>\n",
              "      <td>-2447.572692</td>\n",
              "      <td>1.648315</td>\n",
              "      <td>0.271994</td>\n",
              "      <td>68.253038</td>\n",
              "      <td>1.648315</td>\n",
              "      <td>0.271994</td>\n",
              "      <td>68.253038</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NeuralNetTorch</td>\n",
              "      <td>-2564.763022</td>\n",
              "      <td>-2474.521302</td>\n",
              "      <td>0.139015</td>\n",
              "      <td>0.040705</td>\n",
              "      <td>43.359291</td>\n",
              "      <td>0.139015</td>\n",
              "      <td>0.040705</td>\n",
              "      <td>43.359291</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForestMSE</td>\n",
              "      <td>-2728.001026</td>\n",
              "      <td>-2726.450087</td>\n",
              "      <td>4.636035</td>\n",
              "      <td>0.374502</td>\n",
              "      <td>52.967184</td>\n",
              "      <td>4.636035</td>\n",
              "      <td>0.374502</td>\n",
              "      <td>52.967184</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ExtraTreesMSE</td>\n",
              "      <td>-2780.698443</td>\n",
              "      <td>-2836.208947</td>\n",
              "      <td>2.254777</td>\n",
              "      <td>0.308768</td>\n",
              "      <td>23.843520</td>\n",
              "      <td>2.254777</td>\n",
              "      <td>0.308768</td>\n",
              "      <td>23.843520</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>KNeighborsDist</td>\n",
              "      <td>-6864.161938</td>\n",
              "      <td>-6981.080693</td>\n",
              "      <td>0.107051</td>\n",
              "      <td>0.082602</td>\n",
              "      <td>0.087760</td>\n",
              "      <td>0.107051</td>\n",
              "      <td>0.082602</td>\n",
              "      <td>0.087760</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>KNeighborsUnif</td>\n",
              "      <td>-6920.489506</td>\n",
              "      <td>-6917.648092</td>\n",
              "      <td>0.110673</td>\n",
              "      <td>0.038632</td>\n",
              "      <td>9.070331</td>\n",
              "      <td>0.110673</td>\n",
              "      <td>0.038632</td>\n",
              "      <td>9.070331</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30bee4a1-491a-4aff-877d-ca7ff3cfb794')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show the best model\n",
        "regression.get_model_best()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6T_2U56W68om",
        "outputId": "5c67861d-90a5-4621-a5ff-4e39fef3ca3f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WeightedEnsemble_L2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification"
      ],
      "metadata": {
        "id": "fpZI3H82pilp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load Data"
      ],
      "metadata": {
        "id": "aGLLLCt6pmHC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the label column\n",
        "label = 'Survived'"
      ],
      "metadata": {
        "id": "yb1UIAaxpqll"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load the initial unprocessed data\n",
        "titanic = pd.read_csv('titanic.csv')"
      ],
      "metadata": {
        "id": "toakSgkjpsxd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEkygWQqpyfH",
        "outputId": "9091ff9d-0e61-4527-cef7-9e5179a3b148"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data to test and train\n",
        "from sklearn.model_selection import train_test_split\n",
        "df_train, df_test = train_test_split(titanic, test_size=0.2, random_state=42)\n",
        "titanic_test_labels = df_test[label]\n",
        "titanic_test_nolabel = df_test.drop([label], axis=1)\n",
        "titanic_train_labels = df_train[label]\n",
        "titanic_train_nolabel = df_train.drop([label], axis=1)\n",
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "oEExbbFnp19C",
        "outputId": "f2fc3ce6-27f3-405d-f958-8eeaef1d5d97"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                Name  \\\n",
              "331          332         0       1                 Partner, Mr. Austen   \n",
              "733          734         0       2          Berriman, Mr. William John   \n",
              "382          383         0       3                  Tikkanen, Mr. Juho   \n",
              "704          705         0       3             Hansen, Mr. Henrik Juul   \n",
              "813          814         0       3  Andersson, Miss. Ebba Iris Alfrida   \n",
              "\n",
              "        Sex   Age  SibSp  Parch             Ticket     Fare Cabin Embarked  \n",
              "331    male  45.5      0      0             113043  28.5000  C124        S  \n",
              "733    male  23.0      0      0              28425  13.0000   NaN        S  \n",
              "382    male  32.0      0      0  STON/O 2. 3101293   7.9250   NaN        S  \n",
              "704    male  26.0      1      0             350025   7.8542   NaN        S  \n",
              "813  female   6.0      4      2             347082  31.2750   NaN        S  "
            ],
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              "      <td>Andersson, Miss. Ebba Iris Alfrida</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>31.2750</td>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import AutoGluon Libraries\n",
        "from autogluon.tabular import TabularDataset, TabularPredictor\n",
        "# Create an instance and then fit the train data\n",
        "classification = TabularPredictor(label=label, path='.').fit(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTOHF1WDqAWy",
        "outputId": "306bc24d-b494-483a-8755-f3830b04cd85"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: path already exists! This predictor may overwrite an existing predictor! path=\".\"\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"./\"\n",
            "AutoGluon Version:  0.7.0\n",
            "Python Version:     3.10.12\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Sat Apr 29 09:15:28 UTC 2023\n",
            "Train Data Rows:    712\n",
            "Train Data Columns: 11\n",
            "Label Column: Survived\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [0, 1]\n",
            "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11940.56 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.25 MB (0.0% 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",
            "\t\t\tNote: Converting 1 features to boolean dtype as they only contain 2 unique values.\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",
            "\t\tFitting TextSpecialFeatureGenerator...\n",
            "\t\t\tFitting BinnedFeatureGenerator...\n",
            "\t\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\t\tFitting TextNgramFeatureGenerator...\n",
            "\t\t\tFitting CountVectorizer for text features: ['Name']\n",
            "\t\t\tCountVectorizer fit with vocabulary size = 7\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])        : 2 | ['Age', 'Fare']\n",
            "\t\t('int', [])          : 4 | ['PassengerId', 'Pclass', 'SibSp', 'Parch']\n",
            "\t\t('object', [])       : 4 | ['Sex', 'Ticket', 'Cabin', 'Embarked']\n",
            "\t\t('object', ['text']) : 1 | ['Name']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])                    : 3 | ['Ticket', 'Cabin', 'Embarked']\n",
            "\t\t('float', [])                       : 2 | ['Age', 'Fare']\n",
            "\t\t('int', [])                         : 4 | ['PassengerId', 'Pclass', 'SibSp', 'Parch']\n",
            "\t\t('int', ['binned', 'text_special']) : 9 | ['Name.char_count', 'Name.word_count', 'Name.capital_ratio', 'Name.lower_ratio', 'Name.special_ratio', ...]\n",
            "\t\t('int', ['bool'])                   : 1 | ['Sex']\n",
            "\t\t('int', ['text_ngram'])             : 8 | ['__nlp__.henry', '__nlp__.john', '__nlp__.master', '__nlp__.miss', '__nlp__.mr', ...]\n",
            "\t0.6s = Fit runtime\n",
            "\t11 features in original data used to generate 27 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.06 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.72s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 569, Val Rows: 143\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif ...\n",
            "\t0.5874\t = Validation score   (accuracy)\n",
            "\t0.03s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist ...\n",
            "\t0.5944\t = Validation score   (accuracy)\n",
            "\t0.01s\t = Training   runtime\n",
            "\t0.15s\t = Validation runtime\n",
            "Fitting model: LightGBMXT ...\n",
            "\t0.8392\t = Validation score   (accuracy)\n",
            "\t0.96s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: LightGBM ...\n",
            "\t0.8322\t = Validation score   (accuracy)\n",
            "\t1.04s\t = Training   runtime\n",
            "\t0.03s\t = Validation runtime\n",
            "Fitting model: RandomForestGini ...\n",
            "\t0.7972\t = Validation score   (accuracy)\n",
            "\t2.16s\t = Training   runtime\n",
            "\t0.12s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr ...\n",
            "\t0.7972\t = Validation score   (accuracy)\n",
            "\t2.2s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: CatBoost ...\n",
            "\tWarning: Exception caused CatBoost to fail during training (ImportError)... Skipping this model.\n",
            "\t\t`import catboost` failed. A quick tip is to install via `pip install autogluon.tabular[catboost]==0.7.0`.\n",
            "Fitting model: ExtraTreesGini ...\n",
            "\t0.8042\t = Validation score   (accuracy)\n",
            "\t0.77s\t = Training   runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr ...\n",
            "\t0.8182\t = Validation score   (accuracy)\n",
            "\t0.71s\t = Training   runtime\n",
            "\t0.06s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI ...\n",
            "No improvement since epoch 4: early stopping\n",
            "\t0.8112\t = Validation score   (accuracy)\n",
            "\t0.87s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "Fitting model: XGBoost ...\n",
            "\t0.8252\t = Validation score   (accuracy)\n",
            "\t0.65s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch ...\n",
            "\t0.8252\t = Validation score   (accuracy)\n",
            "\t1.96s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge ...\n",
            "\t0.8042\t = Validation score   (accuracy)\n",
            "\t0.7s\t = Training   runtime\n",
            "\t0.01s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.8601\t = Validation score   (accuracy)\n",
            "\t0.76s\t = Training   runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 14.83s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"./\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the performance using test data\n",
        "classification_pred = classification.predict(titanic_test_nolabel)\n",
        "classification_perf = classification.evaluate_predictions(y_true=titanic_test_labels, y_pred=classification_pred, auxiliary_metrics=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPWyHFbcqN1Z",
        "outputId": "92210274-3ee6-4a4b-bc63-0b50bba079f6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluation: accuracy on test data: 0.8156424581005587\n",
            "Evaluations on test data:\n",
            "{\n",
            "    \"accuracy\": 0.8156424581005587,\n",
            "    \"balanced_accuracy\": 0.800965250965251,\n",
            "    \"mcc\": 0.616378890163125,\n",
            "    \"f1\": 0.762589928057554,\n",
            "    \"precision\": 0.8153846153846154,\n",
            "    \"recall\": 0.7162162162162162\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show the leaderboard\n",
        "classification.leaderboard(df_test, silent=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "N23kTOx-qRGh",
        "outputId": "d40c6604-9c9e-476f-a23f-0b2e6e0eb26b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n",
              "0        NeuralNetTorch    0.837989   0.825175        0.023059       0.023891   \n",
              "1        ExtraTreesGini    0.837989   0.804196        0.095941       0.075993   \n",
              "2      RandomForestEntr    0.837989   0.797203        0.111319       0.080818   \n",
              "3        ExtraTreesEntr    0.832402   0.818182        0.087354       0.055371   \n",
              "4      RandomForestGini    0.832402   0.797203        0.093761       0.119533   \n",
              "5       NeuralNetFastAI    0.826816   0.811189        0.021128       0.016918   \n",
              "6         LightGBMLarge    0.815642   0.804196        0.014370       0.007372   \n",
              "7   WeightedEnsemble_L2    0.815642   0.860140        0.171219       0.151906   \n",
              "8              LightGBM    0.810056   0.832168        0.007009       0.026812   \n",
              "9               XGBoost    0.810056   0.825175        0.017367       0.007648   \n",
              "10           LightGBMXT    0.804469   0.839161        0.011641       0.029188   \n",
              "11       KNeighborsDist    0.664804   0.594406        0.003794       0.147414   \n",
              "12       KNeighborsUnif    0.642458   0.587413        0.003871       0.124783   \n",
              "\n",
              "    fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n",
              "0   1.964506                 0.023059                0.023891   \n",
              "1   0.773317                 0.095941                0.075993   \n",
              "2   2.196914                 0.111319                0.080818   \n",
              "3   0.714980                 0.087354                0.055371   \n",
              "4   2.161258                 0.093761                0.119533   \n",
              "5   0.867712                 0.021128                0.016918   \n",
              "6   0.701964                 0.014370                0.007372   \n",
              "7   6.742328                 0.004072                0.001091   \n",
              "8   1.035710                 0.007009                0.026812   \n",
              "9   0.646237                 0.017367                0.007648   \n",
              "10  0.957054                 0.011641                0.029188   \n",
              "11  0.014934                 0.003794                0.147414   \n",
              "12  0.032590                 0.003871                0.124783   \n",
              "\n",
              "    fit_time_marginal  stack_level  can_infer  fit_order  \n",
              "0            1.964506            1       True         11  \n",
              "1            0.773317            1       True          7  \n",
              "2            2.196914            1       True          6  \n",
              "3            0.714980            1       True          8  \n",
              "4            2.161258            1       True          5  \n",
              "5            0.867712            1       True          9  \n",
              "6            0.701964            1       True         12  \n",
              "7            0.756142            2       True         13  \n",
              "8            1.035710            1       True          4  \n",
              "9            0.646237            1       True         10  \n",
              "10           0.957054            1       True          3  \n",
              "11           0.014934            1       True          2  \n",
              "12           0.032590            1       True          1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fab9f59f-02a4-4007-9fb7-4a07c2e9a18a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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>model</th>\n",
              "      <th>score_test</th>\n",
              "      <th>score_val</th>\n",
              "      <th>pred_time_test</th>\n",
              "      <th>pred_time_val</th>\n",
              "      <th>fit_time</th>\n",
              "      <th>pred_time_test_marginal</th>\n",
              "      <th>pred_time_val_marginal</th>\n",
              "      <th>fit_time_marginal</th>\n",
              "      <th>stack_level</th>\n",
              "      <th>can_infer</th>\n",
              "      <th>fit_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NeuralNetTorch</td>\n",
              "      <td>0.837989</td>\n",
              "      <td>0.825175</td>\n",
              "      <td>0.023059</td>\n",
              "      <td>0.023891</td>\n",
              "      <td>1.964506</td>\n",
              "      <td>0.023059</td>\n",
              "      <td>0.023891</td>\n",
              "      <td>1.964506</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ExtraTreesGini</td>\n",
              "      <td>0.837989</td>\n",
              "      <td>0.804196</td>\n",
              "      <td>0.095941</td>\n",
              "      <td>0.075993</td>\n",
              "      <td>0.773317</td>\n",
              "      <td>0.095941</td>\n",
              "      <td>0.075993</td>\n",
              "      <td>0.773317</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForestEntr</td>\n",
              "      <td>0.837989</td>\n",
              "      <td>0.797203</td>\n",
              "      <td>0.111319</td>\n",
              "      <td>0.080818</td>\n",
              "      <td>2.196914</td>\n",
              "      <td>0.111319</td>\n",
              "      <td>0.080818</td>\n",
              "      <td>2.196914</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ExtraTreesEntr</td>\n",
              "      <td>0.832402</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>0.087354</td>\n",
              "      <td>0.055371</td>\n",
              "      <td>0.714980</td>\n",
              "      <td>0.087354</td>\n",
              "      <td>0.055371</td>\n",
              "      <td>0.714980</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestGini</td>\n",
              "      <td>0.832402</td>\n",
              "      <td>0.797203</td>\n",
              "      <td>0.093761</td>\n",
              "      <td>0.119533</td>\n",
              "      <td>2.161258</td>\n",
              "      <td>0.093761</td>\n",
              "      <td>0.119533</td>\n",
              "      <td>2.161258</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NeuralNetFastAI</td>\n",
              "      <td>0.826816</td>\n",
              "      <td>0.811189</td>\n",
              "      <td>0.021128</td>\n",
              "      <td>0.016918</td>\n",
              "      <td>0.867712</td>\n",
              "      <td>0.021128</td>\n",
              "      <td>0.016918</td>\n",
              "      <td>0.867712</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LightGBMLarge</td>\n",
              "      <td>0.815642</td>\n",
              "      <td>0.804196</td>\n",
              "      <td>0.014370</td>\n",
              "      <td>0.007372</td>\n",
              "      <td>0.701964</td>\n",
              "      <td>0.014370</td>\n",
              "      <td>0.007372</td>\n",
              "      <td>0.701964</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>0.815642</td>\n",
              "      <td>0.860140</td>\n",
              "      <td>0.171219</td>\n",
              "      <td>0.151906</td>\n",
              "      <td>6.742328</td>\n",
              "      <td>0.004072</td>\n",
              "      <td>0.001091</td>\n",
              "      <td>0.756142</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>0.810056</td>\n",
              "      <td>0.832168</td>\n",
              "      <td>0.007009</td>\n",
              "      <td>0.026812</td>\n",
              "      <td>1.035710</td>\n",
              "      <td>0.007009</td>\n",
              "      <td>0.026812</td>\n",
              "      <td>1.035710</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.810056</td>\n",
              "      <td>0.825175</td>\n",
              "      <td>0.017367</td>\n",
              "      <td>0.007648</td>\n",
              "      <td>0.646237</td>\n",
              "      <td>0.017367</td>\n",
              "      <td>0.007648</td>\n",
              "      <td>0.646237</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LightGBMXT</td>\n",
              "      <td>0.804469</td>\n",
              "      <td>0.839161</td>\n",
              "      <td>0.011641</td>\n",
              "      <td>0.029188</td>\n",
              "      <td>0.957054</td>\n",
              "      <td>0.011641</td>\n",
              "      <td>0.029188</td>\n",
              "      <td>0.957054</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>KNeighborsDist</td>\n",
              "      <td>0.664804</td>\n",
              "      <td>0.594406</td>\n",
              "      <td>0.003794</td>\n",
              "      <td>0.147414</td>\n",
              "      <td>0.014934</td>\n",
              "      <td>0.003794</td>\n",
              "      <td>0.147414</td>\n",
              "      <td>0.014934</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>KNeighborsUnif</td>\n",
              "      <td>0.642458</td>\n",
              "      <td>0.587413</td>\n",
              "      <td>0.003871</td>\n",
              "      <td>0.124783</td>\n",
              "      <td>0.032590</td>\n",
              "      <td>0.003871</td>\n",
              "      <td>0.124783</td>\n",
              "      <td>0.032590</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fab9f59f-02a4-4007-9fb7-4a07c2e9a18a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fab9f59f-02a4-4007-9fb7-4a07c2e9a18a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fab9f59f-02a4-4007-9fb7-4a07c2e9a18a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show the best model\n",
        "classification.get_model_best()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ThS0oJ4MqUkw",
        "outputId": "963a4451-c21d-4e83-d98e-555fedb0b608"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'WeightedEnsemble_L2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    }
  ]
}