examples/FairGBM-python-notebooks/FairGBM_example_for_equalized_odds_[google_colab].ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Install notebook requirements."
      ],
      "metadata": {
        "id": "guyJqK-R_pNq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kk_0kz8ejiur",
        "outputId": "d1b4b372-e921-42ad-df1b-60d1cee94a84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: fairgbm in /usr/local/lib/python3.10/dist-packages (0.9.14)\n",
            "Requirement already satisfied: fairlearn in /usr/local/lib/python3.10/dist-packages (0.9.0)\n",
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            "Requirement already satisfied: wget in /usr/local/lib/python3.10/dist-packages (3.2)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fairgbm) (1.23.5)\n",
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            "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna->hyperparameter-tuning) (4.5.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna->hyperparameter-tuning) (2.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna->hyperparameter-tuning) (2.1.3)\n"
          ]
        }
      ],
      "source": [
        "%pip install fairgbm fairlearn scikit-learn hyperparameter-tuning seaborn matplotlib wget"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "c73em4AWlBks"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from fairgbm import FairGBMClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2shmRM-q-shH"
      },
      "source": [
        "Set-up useful global constants."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "HscI2bfhjrC3"
      },
      "outputs": [],
      "source": [
        "SEED = 42\n",
        "\n",
        "TEST_SIZE = 0.2\n",
        "VALIDATION_SIZE = 0.2\n",
        "\n",
        "PERF_METRIC = \"accuracy\"\n",
        "DISP_METRIC = \"equalized_odds_diff\"    # measures disparity, lower is better\n",
        "FAIR_METRIC = \"equalized_odds_ratio\"   # similar but higher is better\n",
        "\n",
        "N_JOBS = max(2, os.cpu_count() - 2)\n",
        "\n",
        "np.random.seed(SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "kps_mg9dj565"
      },
      "outputs": [],
      "source": [
        "SENSITIVE_COL = \"sex\"\n",
        "sensitive_col_map = {\"Male\": 0, \"Female\": 1}\n",
        "\n",
        "sensitive_col_inverse = {val: key for key, val in sensitive_col_map.items()}\n",
        "\n",
        "POS_LABEL = \">50K\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load UCI Adult dataset.**"
      ],
      "metadata": {
        "id": "dkk1W4Xe_0-W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "4VnMP7vrk9Xp"
      },
      "outputs": [],
      "source": [
        "from fairlearn.datasets import fetch_adult\n",
        "\n",
        "X, Y = fetch_adult(\n",
        "    as_frame=True,\n",
        "    return_X_y=True,\n",
        ")\n",
        "\n",
        "# Map labels and sensitive column to numeric data\n",
        "Y = np.array(Y == POS_LABEL, dtype=int)\n",
        "S = np.array([sensitive_col_map[elem] for elem in X[SENSITIVE_COL]], dtype=int)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split in train/test/validation."
      ],
      "metadata": {
        "id": "rSryXTc1_8fj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UiwJNjYSlNXz"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_other, y_train, y_other, s_train, s_other = train_test_split(\n",
        "    X, Y, S,\n",
        "    test_size=TEST_SIZE + VALIDATION_SIZE,\n",
        "    stratify=Y, random_state=SEED,\n",
        ")\n",
        "\n",
        "X_val, X_test, y_val, y_test, s_val, s_test = train_test_split(\n",
        "    X_other, y_other, s_other,\n",
        "    test_size=TEST_SIZE / (TEST_SIZE + VALIDATION_SIZE),\n",
        "    stratify=y_other, random_state=SEED,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get a YAML file detailing the hyperparameter search space.\n",
        "\n",
        "This example file should cover a wide range of uses, but you can tailor it to your own use-case."
      ],
      "metadata": {
        "id": "FAjMdjsO__dq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OSBJdvVWl_F0",
        "outputId": "b4828f21-4ba3-45de-a306-b312ee5e78ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'fairgbm_hyperparam_space (1).yaml'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import wget\n",
        "fairgbm_hyperparam_space_url = \"https://raw.githubusercontent.com/feedzai/fairgbm/main-fairgbm/examples/FairGBM-python-notebooks/fairgbm_equalized-odds.yaml\"\n",
        "fairgbm_hyperparam_space_path = \"fairgbm_hyperparam_space.yaml\"\n",
        "wget.download(fairgbm_hyperparam_space_url, fairgbm_hyperparam_space_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set-up hyperparameter tuner using the `hyperparameter-tuning` package (essentially provides useful evaluation boilerplate code on top of `optuna`)."
      ],
      "metadata": {
        "id": "OhxmUw4LAfJZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xfn5gv-MlVoY"
      },
      "outputs": [],
      "source": [
        "from hpt.tuner import ObjectiveFunction, OptunaTuner\n",
        "import optuna.logging\n",
        "optuna.logging.set_verbosity(optuna.logging.WARN)   # Turn off optuna log notes.\n",
        "\n",
        "obj_func = ObjectiveFunction(\n",
        "    X_train=X_train, y_train=y_train, s_train=s_train,\n",
        "    X_val=X_val, y_val=y_val, s_val=s_val,\n",
        "    X_test=X_test, y_test=y_test, s_test=s_test,\n",
        "    hyperparameter_space=fairgbm_hyperparam_space_path,\n",
        "    eval_metric=PERF_METRIC,\n",
        "    other_eval_metric=FAIR_METRIC,\n",
        "    threshold=0.50,\n",
        ")\n",
        "\n",
        "tuner = OptunaTuner(\n",
        "    objective_function=obj_func,\n",
        "    direction=\"maximize\",\n",
        "    seed=SEED,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "80c7716af87f4953b37a5824666a80c8",
            "63890facec3a4fe1b055624b49d5e9ee",
            "06093cbc4f2347a09e0702dcff468236",
            "40b10589d01b40ab9c7e2a8587ca6fc2",
            "7e37081a99544de0898402f7d3f0afb1",
            "e07f68e528ae45dbb25ce97b11e2fd87",
            "2d702bdd80f04ccab055629ac1854bea",
            "2918e5a43e6a4284a45e31a21e64e10e",
            "e6183d5d0efa4406ac64e3d5336e5bcc",
            "c4d71adf52944b138f6f15c635b6f858",
            "d4ead5036eb0441f82c2a7722340ece1"
          ]
        },
        "id": "mAcWKDLUlXIS",
        "outputId": "25504cfc-5dfd-4c9b-9070-c0f83826bea0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80c7716af87f4953b37a5824666a80c8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best model is {'classpath': 'fairgbm.FairGBMClassifier', 'n_jobs': 1, 'boosting_type': 'gbdt', 'enable_bundle': False, 'n_estimators': 174, 'num_leaves': 6, 'min_child_samples': 52, 'max_depth': 5, 'learning_rate': 0.17141223842861147, 'reg_alpha': 0.03343187778948064, 'reg_lambda': 0.032412497842132154, 'constraint_type': 'FNR,FPR', 'multiplier_learning_rate': 0.07446344965283483}\n",
            "CPU times: user 4min 54s, sys: 1.31 s, total: 4min 55s\n",
            "Wall time: 3min 22s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "N_TRIALS = 20\n",
        "tuner.optimize(n_trials=N_TRIALS, n_jobs=N_JOBS, show_progress_bar=True)\n",
        "print(f\"Best model is {obj_func.best_trial.hyperparameters}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTsQrWJQqTPQ"
      },
      "source": [
        "## Plot results and select best model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6pP_Caf6qn6X"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(palette=\"colorblind\", style=\"whitegrid\", rc={\"grid.linestyle\": \"--\", \"figure.dpi\": 200, \"figure.figsize\": (4,3)})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g41U3A9zr34R"
      },
      "source": [
        "Model selection (on validation):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "5O6OS5MZm-UP"
      },
      "outputs": [],
      "source": [
        "# Choose some relative weight to give to the performance metric\n",
        "# (or select model based on some other criterion)\n",
        "TRADEOFF_WEIGHT = 0.5\n",
        "\n",
        "# Model selection is done on validation data\n",
        "val_results = obj_func.get_results(\"validation\")\n",
        "\n",
        "# Get index of best model in the results table\n",
        "# > note: IDs and indices may differ as trials are ran asynchronously\n",
        "best_model_idx = np.argmax(\n",
        "    val_results[PERF_METRIC] * TRADEOFF_WEIGHT\n",
        "    + (1 - val_results[DISP_METRIC]) * (1 - TRADEOFF_WEIGHT)\n",
        ")\n",
        "\n",
        "best_trial = obj_func.all_results[best_model_idx]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmcmH6azr8XV"
      },
      "source": [
        "Plot test results:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "zUOVqKL2npYH",
        "outputId": "9f1b8304-465a-438e-ccea-a6143fdfd52d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_results = obj_func.get_results(\"test\")\n",
        "best_trial_results = test_results.iloc[best_model_idx]\n",
        "\n",
        "# Plot best model with a red star\n",
        "sns.scatterplot(\n",
        "    x=[best_trial_results[PERF_METRIC]],\n",
        "    y=[best_trial_results[DISP_METRIC]],\n",
        "    color=\"red\",\n",
        "    marker=\"*\",\n",
        "    s=100,\n",
        "    label=\"best on validation\",\n",
        "    zorder=10,\n",
        ")\n",
        "\n",
        "# Plot all other points\n",
        "sns.scatterplot(\n",
        "    data=test_results.drop(index=best_trial.id),\n",
        "    x=PERF_METRIC,\n",
        "    y=DISP_METRIC,\n",
        ")\n",
        "\n",
        "ax_kwargs = dict(\n",
        "    title=\"Fairness-Accuracy Trade-Off (test)\",\n",
        "    xlabel=PERF_METRIC + r\"$\\rightarrow$\",\n",
        "    ylabel=r\"equalized odds (diff.) $\\leftarrow$\" if DISP_METRIC == \"equalized_odds_diff\" else DISP_METRIC,\n",
        ")\n",
        "\n",
        "# You may need to change these ranges to see the models\n",
        "plt.ylim(0, 0.08)\n",
        "plt.xlim(0.83, 0.9)\n",
        "\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.gca().set(**ax_kwargs)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYdaFZ3suSc7"
      },
      "source": [
        "By default, `hyperparameter_tuning` will train, evalaute, and discard all models. You can re-create the best model with:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "Dn-x5T2UvJel",
        "outputId": "fbb6b974-1d10-4dfc-bdb2-eed3416ee3eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.59 s, sys: 27.1 ms, total: 6.62 s\n",
            "Wall time: 6.61 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FairGBMClassifier(constraint_type='FNR,FPR', enable_bundle=False,\n",
              "                  learning_rate=0.016029234981291163, max_depth=6,\n",
              "                  min_child_samples=63,\n",
              "                  multiplier_learning_rate=0.05220004751596058,\n",
              "                  n_estimators=1045, n_jobs=1, num_leaves=7,\n",
              "                  reg_alpha=0.00015636059199278671,\n",
              "                  reg_lambda=0.0014516067928004718)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>FairGBMClassifier(constraint_type=&#x27;FNR,FPR&#x27;, enable_bundle=False,\n",
              "                  learning_rate=0.016029234981291163, max_depth=6,\n",
              "                  min_child_samples=63,\n",
              "                  multiplier_learning_rate=0.05220004751596058,\n",
              "                  n_estimators=1045, n_jobs=1, num_leaves=7,\n",
              "                  reg_alpha=0.00015636059199278671,\n",
              "                  reg_lambda=0.0014516067928004718)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FairGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>FairGBMClassifier(constraint_type=&#x27;FNR,FPR&#x27;, enable_bundle=False,\n",
              "                  learning_rate=0.016029234981291163, max_depth=6,\n",
              "                  min_child_samples=63,\n",
              "                  multiplier_learning_rate=0.05220004751596058,\n",
              "                  n_estimators=1045, n_jobs=1, num_leaves=7,\n",
              "                  reg_alpha=0.00015636059199278671,\n",
              "                  reg_lambda=0.0014516067928004718)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "%%time\n",
        "fairgbm_clf = obj_func.reconstruct_model(trial_results=best_trial)\n",
        "fairgbm_clf"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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