{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Quick Visualization for Hyperparameter Optimization Analysis\n\nOptuna provides various visualization features in :mod:`optuna.visualization` to analyze optimization results visually.\n\nThis tutorial walks you through this module by visualizing the history of lightgbm model for breast cancer dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import lightgbm as lgb\nimport numpy as np\nimport sklearn.datasets\nimport sklearn.metrics\nfrom sklearn.model_selection import train_test_split\n\nimport optuna\nfrom optuna.visualization import plot_contour\nfrom optuna.visualization import plot_edf\nfrom optuna.visualization import plot_intermediate_values\nfrom optuna.visualization import plot_optimization_history\nfrom optuna.visualization import plot_parallel_coordinate\nfrom optuna.visualization import plot_param_importances\nfrom optuna.visualization import plot_slice\n\nSEED = 42\n\nnp.random.seed(SEED)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define the objective function.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n    data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)\n    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)\n    dtrain = lgb.Dataset(train_x, label=train_y)\n    dvalid = lgb.Dataset(valid_x, label=valid_y)\n\n    param = {\n        \"objective\": \"binary\",\n        \"metric\": \"auc\",\n        \"verbosity\": -1,\n        \"boosting_type\": \"gbdt\",\n        \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.4, 1.0),\n        \"bagging_freq\": trial.suggest_int(\"bagging_freq\", 1, 7),\n        \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 5, 100),\n    }\n\n    # Add a callback for pruning.\n    pruning_callback = optuna.integration.LightGBMPruningCallback(trial, \"auc\")\n    gbm = lgb.train(\n        param, dtrain, valid_sets=[dvalid], verbose_eval=False, callbacks=[pruning_callback]\n    )\n\n    preds = gbm.predict(valid_x)\n    pred_labels = np.rint(preds)\n    accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels)\n    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "study = optuna.create_study(\n    direction=\"maximize\",\n    sampler=optuna.samplers.TPESampler(seed=SEED),\n    pruner=optuna.pruners.MedianPruner(n_warmup_steps=10),\n)\nstudy.optimize(objective, n_trials=100, timeout=600)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot functions\nVisualize the optimization history. See :func:`~optuna.visualization.plot_optimization_history` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_optimization_history(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize the learning curves of the trials. See :func:`~optuna.visualization.plot_intermediate_values` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_intermediate_values(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize high-dimensional parameter relationships. See :func:`~optuna.visualization.plot_parallel_coordinate` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_parallel_coordinate(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Select parameters to visualize.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_parallel_coordinate(study, params=[\"bagging_freq\", \"bagging_fraction\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize hyperparameter relationships. See :func:`~optuna.visualization.plot_contour` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_contour(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Select parameters to visualize.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_contour(study, params=[\"bagging_freq\", \"bagging_fraction\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize individual hyperparameters as slice plot. See :func:`~optuna.visualization.plot_slice` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_slice(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Select parameters to visualize.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_slice(study, params=[\"bagging_freq\", \"bagging_fraction\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize parameter importances. See :func:`~optuna.visualization.plot_param_importances` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_param_importances(study)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize empirical distribution function. See :func:`~optuna.visualization.plot_edf` for the details.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_edf(study)"
      ]
    }
  ],
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