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Zhongwei Li
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skills/hyperparameter-tuner/assets/README.md
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skills/hyperparameter-tuner/assets/README.md
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# Assets
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Bundled resources for hyperparameter-tuner skill
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- [ ] hyperparameter_space_template.json: Template for defining the hyperparameter search space.
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- [ ] example_data.csv: Example dataset for training the model.
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- [ ] visualization_template.html: Template for visualizing the hyperparameter tuning results.
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{
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"_comment": "Template for defining the hyperparameter search space. This file should be used as a guide for creating your own hyperparameter configuration.",
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"algorithm": {
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"_comment": "The search algorithm to use. Options: 'grid', 'random', 'bayesian'.",
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"type": "string",
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"default": "random",
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"enum": ["grid", "random", "bayesian"]
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},
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"objective": {
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"_comment": "The metric to optimize. The plugin will attempt to maximize this metric.",
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"type": "string",
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"default": "val_loss"
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},
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"max_trials": {
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"_comment": "The maximum number of trials to run. Each trial will explore a different set of hyperparameters.",
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"type": "integer",
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"default": 10
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},
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"hyperparameters": {
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"_comment": "A dictionary of hyperparameters to search. Each key is the name of the hyperparameter, and the value is a dictionary defining the search space for that hyperparameter.",
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"type": "object",
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"properties": {
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"learning_rate": {
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"_comment": "Example: Learning rate for a neural network.",
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"type": "number",
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"distribution": "loguniform",
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"min": 0.0001,
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"max": 0.1
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},
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"num_layers": {
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"_comment": "Example: Number of layers in a neural network.",
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"type": "integer",
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"distribution": "uniform",
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"min": 2,
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"max": 6
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},
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"dropout_rate": {
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"_comment": "Example: Dropout rate for regularization.",
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"type": "number",
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"distribution": "uniform",
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"min": 0.0,
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"max": 0.5
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},
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"batch_size": {
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"_comment": "Example: Batch size for training.",
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"type": "integer",
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"distribution": "categorical",
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"values": [32, 64, 128, 256]
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},
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"optimizer": {
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"_comment": "Example: Optimization algorithm to use",
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"type": "string",
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"distribution": "categorical",
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"values": ["adam", "sgd", "rmsprop"]
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}
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},
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"required": ["learning_rate", "num_layers"]
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},
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"early_stopping": {
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"_comment": "Parameters for early stopping. If enabled, the tuning process will stop if the objective metric does not improve for a specified number of epochs.",
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"type": "object",
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"properties": {
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"monitor": {
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"_comment": "The metric to monitor for early stopping.",
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"type": "string",
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"default": "val_loss"
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},
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"patience": {
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"_comment": "The number of epochs with no improvement after which training will be stopped.",
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"type": "integer",
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"default": 3
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},
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"enabled": {
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"_comment": "Whether early stopping is enabled.",
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"type": "boolean",
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"default": true
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}
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},
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"required": ["monitor", "patience", "enabled"]
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}
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}
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skills/hyperparameter-tuner/assets/visualization_template.html
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skills/hyperparameter-tuner/assets/visualization_template.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Hyperparameter Tuning Results</title>
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<style>
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/* Basic styling for a modern look */
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body {
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font-family: sans-serif;
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margin: 0;
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padding: 20px;
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background-color: #f4f4f4;
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color: #333;
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}
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.container {
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max-width: 960px;
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margin: 0 auto;
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background-color: #fff;
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padding: 20px;
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border-radius: 8px;
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box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
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}
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h1 {
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text-align: center;
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color: #007bff;
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}
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h2 {
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margin-top: 20px;
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color: #555;
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}
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table {
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width: 100%;
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border-collapse: collapse;
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margin-top: 10px;
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}
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th, td {
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border: 1px solid #ddd;
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padding: 8px;
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text-align: left;
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}
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th {
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background-color: #f2f2f2;
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font-weight: bold;
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}
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.best-params {
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margin-top: 20px;
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padding: 15px;
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background-color: #e6f7ff;
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border: 1px solid #b3d9ff;
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border-radius: 5px;
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}
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.best-params h3 {
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color: #007bff;
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margin-bottom: 10px;
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}
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/* Responsive design */
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@media (max-width: 600px) {
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body {
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padding: 10px;
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}
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.container {
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padding: 10px;
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}
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table {
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font-size: 0.8em;
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}
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>Hyperparameter Tuning Results</h1>
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<div class="best-params">
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<h3>Best Hyperparameters</h3>
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<p><strong>Metric:</strong> {{best_metric}}</p>
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<p><strong>Value:</strong> {{best_metric_value}}</p>
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<p><strong>Parameters:</strong> {{best_parameters}}</p>
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</div>
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<h2>Trial Results</h2>
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<table>
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<thead>
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<tr>
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<th>Trial</th>
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<th>Parameters</th>
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<th>Metric Value</th>
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</tr>
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</thead>
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<tbody>
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{{trial_results}}
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</tbody>
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</table>
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<h2>Visualization</h2>
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<img src="{{visualization_url}}" alt="Hyperparameter Tuning Visualization">
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</div>
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</body>
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</html>
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