diff --git a/package/samplers/whale_optimization/README.md b/package/samplers/whale_optimization/README.md
index c9ed91e6..82f06d7b 100644
--- a/package/samplers/whale_optimization/README.md
+++ b/package/samplers/whale_optimization/README.md
@@ -13,6 +13,8 @@ license: MIT License
## Example
+[](https://colab.research.google.com/github/optuna/optunahub-registry/blob/main/package/samplers/whale_optimization/example.ipynb)
+
```python
from __future__ import annotations
@@ -32,12 +34,7 @@ if __name__ == "__main__":
y = trial.suggest_float("y", -10, 10)
return x**2 + y**2
- sampler = WhaleOptimizationSampler(
- {
- "x": optuna.distributions.FloatDistribution(-10, 10),
- "y": optuna.distributions.FloatDistribution(-10, 10),
- }
- )
+ sampler = WhaleOptimizationSampler()
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=100)
optuna.visualization.matplotlib.plot_optimization_history(study)
diff --git a/package/samplers/whale_optimization/example.ipynb b/package/samplers/whale_optimization/example.ipynb
new file mode 100644
index 00000000..63d0451d
--- /dev/null
+++ b/package/samplers/whale_optimization/example.ipynb
@@ -0,0 +1,188 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Example usage of `WhaleOptimizationSampler` 🐳\n",
+ "\n",
+ "This notebook demonstrates a simple usage of [`WhaleOptimizationSampler`](https://hub.optuna.org/samplers/whale_optimization/) in [OptunaHub](https://hub.optuna.org/). \n",
+ "- `WhaleOptimizationSampler` is an implementation of the [Whale Optimization Algorithm (WOA)](https://www.sciencedirect.com/science/article/abs/pii/S0965997816300163). \n",
+ "- WOA is a nature-inspired meta-heuristic optimization algorithm that mimics the bubble-net feeding behavior of humpback whales.\n",
+ "\n",
+ "## Step 1: Imports\n",
+ "\n",
+ "This example requires the following two modules:\n",
+ "- `optuna`: A hyperparameter optimization framework\n",
+ "- `oputnahub`: A module for loading additional components of Optuna"
+ ],
+ "metadata": {
+ "id": "u3QqXvzOFAeE"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "pws57WUlE1te"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install --quiet --progress-bar off optuna optunahub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import optuna\n",
+ "import optunahub"
+ ],
+ "metadata": {
+ "id": "Wx2Ov0CyF9lB"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 2: Define objective function\n",
+ "\n",
+ "As a demonstration, we use a simple quadratic function as an objective function."
+ ],
+ "metadata": {
+ "id": "LdYS50siFywV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def objective(trial: optuna.trial.Trial) -> float:\n",
+ " x = trial.suggest_float(\"x\", -10, 10)\n",
+ " y = trial.suggest_float(\"y\", -10, 10)\n",
+ " return x**2 + y**2"
+ ],
+ "metadata": {
+ "id": "lNInSywME95H"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 3: Load `WhaleOptimizationSampler`\n",
+ "\n",
+ "With `optunahub.load_module`, you can use modules in [Optunanub](https://hub.optuna.org/) in your code. \n",
+ "In this case, a module defined in [samplers/whale_optimization](https://github.com/optuna/optunahub-registry/tree/main/package/samplers/whale_optimization) is loaded, and you can instantiate `WhaleOptimizationSampler` from the module."
+ ],
+ "metadata": {
+ "id": "yIEfLon-GlvV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "mod = optunahub.load_module(\"samplers/whale_optimization\")\n",
+ "sampler = mod.WhaleOptimizationSampler()"
+ ],
+ "metadata": {
+ "id": "0nH6__u5F5uQ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 4: Run optimization\n",
+ "\n",
+ "You can optimize the objective function with `WhaleOptimizationSampler` as usual."
+ ],
+ "metadata": {
+ "id": "Vx2XljcNG0o_"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "study = optuna.create_study(sampler=sampler)\n",
+ "study.optimize(objective, n_trials=100)"
+ ],
+ "metadata": {
+ "id": "zWEKYXYaG3Sg"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "optuna.visualization.plot_optimization_history(study)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 542
+ },
+ "id": "NuhMdbUqG5U2",
+ "outputId": "c0d4fbd4-6b27-4dc3-a5f5-c05f5a857d4c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/recipes/002_registration.py b/recipes/002_registration.py
index f69ecb47..eb3f1ec0 100644
--- a/recipes/002_registration.py
+++ b/recipes/002_registration.py
@@ -18,7 +18,7 @@
| ├── __init__.py
| ├── README.md
| ├── LICENSE
-| ├── (example.py)
+| ├── (example.py, example.ipynb)
| ├── (requirements.txt)
| └── (images)
| ├── (figure1.png)
@@ -36,7 +36,7 @@
- `__init__.py`: An initialization file. This file must implement your algorithm or import its implementation from another file, e.g., `YOUR_ALGORITHM_NAME.py`.
- `README.md`: A description of your algorithm. This file is used to create an `web page of OptunaHub `_. Let me explain the format of the `README.md` file later.
- `LICENSE`: A license file. This file must contain the license of your algorithm. It should be the MIT license in the alpha version of OptunaHub.
-- `example.py`: This is optional. This file should contain a simple example of how to use your algorithm (Example: `example.py for Simulated Annealing Sampler `_).
+- `example.py`, `example.ipynb`: This is optional. This file should contain a simple example of how to use your algorithm (Example: `example.py for Simulated Annealing Sampler `_). You can provide examples in both formats.
- `requirements.txt`: This is optional. A file that contains the additional dependencies of your algorithm. If there are no additional dependencies other than Optuna and OptunaHub, you do not need to create this file.
- `images`: This is optional. A directory that contains images. Only relative references to images in this directory are allowed in README.md, e.g., ````, and absolute paths to images are not allowed. The first image that appears in README.md will be used as the thumbnail.