optuna-hyperparameter-tuner

name	optuna-hyperparameter-tuner
description	Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.
allowed-tools	Read, Grep, Write, Bash, Edit, Glob, WebFetch
graph	{"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:hyperparameter-tuning-experiment-management","skill-area:data-science-experimentation"],"roles":["role:ml-engineer","role:data-scientist"],"workflows":["workflow:ml-model-lifecycle","workflow:experiment-design"]}

Optuna Hyperparameter Tuner

Optimize hyperparameters using Optuna with advanced search strategies, pruning, and visualization.

Overview

This skill provides comprehensive capabilities for hyperparameter optimization using Optuna, the state-of-the-art hyperparameter optimization framework. It supports various samplers, pruners, multi-objective optimization, and integration with popular ML frameworks.

Capabilities

Search Strategies

• Tree-structured Parzen Estimator (TPE) - default, efficient
• CMA-ES - for continuous parameters
• Grid search - exhaustive
• Random search - baseline
• NSGAII - multi-objective optimization
• QMC (Quasi-Monte Carlo) - low-discrepancy sampling

Pruning Strategies

• Median pruning - early stop underperformers
• Hyperband (ASHA) - aggressive resource allocation
• Percentile pruning - threshold-based
• Successive Halving - efficient resource use
• Wilcoxon pruning - statistical comparison

Multi-Objective Optimization

• Pareto front optimization
• Multiple objective functions
• Constraint handling
• Trade-off visualization

Study Management

• Study persistence (SQLite, PostgreSQL, MySQL)
• Study resumption
• Parallel/distributed optimization
• Trial importance analysis
• Parameter relationship analysis

Visualization

• Optimization history
• Parameter importance
• Parallel coordinate plots
• Slice plots
• Contour plots

Prerequisites

Installation

pip install optuna>=3.0.0

Optional Dependencies

# Database backends
pip install optuna[mysql]    # MySQL support
pip install optuna[postgresql]  # PostgreSQL support

# Visualization
pip install optuna-dashboard  # Web dashboard
pip install plotly           # Interactive plots

# Framework integrations
pip install optuna-integration[sklearn]
pip install optuna-integration[pytorch]
pip install optuna-integration[tensorflow]

Usage Patterns

Basic Optimization

import optuna

def objective(trial):
    # Suggest hyperparameters
    learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True)
    n_estimators = trial.suggest_int('n_estimators', 50, 500)
    max_depth = trial.suggest_int('max_depth', 3, 15)
    subsample = trial.suggest_float('subsample', 0.5, 1.0)

    # Train model
    model = XGBClassifier(
        learning_rate=learning_rate,
        n_estimators=n_estimators,
        max_depth=max_depth,
        subsample=subsample,
        random_state=42
    )

    # Cross-validation
    score = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy').mean()

    return score

# Create study
study = optuna.create_study(
    direction='maximize',
    study_name='xgboost-tuning',
    storage='sqlite:///optuna.db',
    load_if_exists=True
)

# Optimize
study.optimize(objective, n_trials=100, timeout=3600)

# Best results
print(f"Best trial: {study.best_trial.number}")
print(f"Best value: {study.best_value:.4f}")
print(f"Best params: {study.best_params}")

With Pruning

import optuna
from optuna.pruners import MedianPruner

def objective_with_pruning(trial):
    # Suggest hyperparameters
    learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True)
    n_epochs = trial.suggest_int('n_epochs', 10, 100)

    # Create model
    model = create_model(learning_rate)

    # Training loop with pruning
    for epoch in range(n_epochs):
        train_loss = train_one_epoch(model)
        val_accuracy = evaluate(model)

        # Report intermediate value
        trial.report(val_accuracy, epoch)

        # Prune if unpromising
        if trial.should_prune():
            raise optuna.TrialPruned()

    return val_accuracy

# Create study with pruner
study = optuna.create_study(
    direction='maximize',
    pruner=MedianPruner(n_startup_trials=5, n_warmup_steps=10)
)

study.optimize(objective_with_pruning, n_trials=100)

Multi-Objective Optimization

import optuna

def multi_objective(trial):
    # Hyperparameters
    learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True)
    model_size = trial.suggest_categorical('model_size', ['small', 'medium', 'large'])

    # Train model
    model = create_model(learning_rate, model_size)
    train(model)

    # Multiple objectives
    accuracy = evaluate_accuracy(model)
    inference_time = measure_inference_time(model)

    return accuracy, inference_time  # maximize accuracy, minimize time

# Create multi-objective study
study = optuna.create_study(
    directions=['maximize', 'minimize'],
    study_name='pareto-optimization'
)

study.optimize(multi_objective, n_trials=100)

# Get Pareto front
pareto_front = study.best_trials
for trial in pareto_front:
    print(f"Accuracy: {trial.values[0]:.4f}, Time: {trial.values[1]:.4f}")

Scikit-learn Integration

import optuna
from optuna.integration import OptunaSearchCV

# Define parameter distributions
param_distributions = {
    'n_estimators': optuna.distributions.IntDistribution(50, 500),
    'max_depth': optuna.distributions.IntDistribution(3, 15),
    'learning_rate': optuna.distributions.FloatDistribution(1e-5, 1e-1, log=True),
    'subsample': optuna.distributions.FloatDistribution(0.5, 1.0)
}

# Create search
search = OptunaSearchCV(
    XGBClassifier(random_state=42),
    param_distributions,
    n_trials=100,
    cv=5,
    scoring='accuracy',
    study=study,  # Optional: use existing study
    n_jobs=-1
)

# Fit
search.fit(X_train, y_train)

# Results
print(f"Best score: {search.best_score_:.4f}")
print(f"Best params: {search.best_params_}")

PyTorch Integration

import optuna
from optuna.integration import PyTorchLightningPruningCallback

def objective(trial):
    # Hyperparameters
    lr = trial.suggest_float('lr', 1e-5, 1e-1, log=True)
    hidden_size = trial.suggest_int('hidden_size', 32, 256)
    dropout = trial.suggest_float('dropout', 0.1, 0.5)

    # Create model
    model = LightningModel(
        hidden_size=hidden_size,
        dropout=dropout,
        lr=lr
    )

    # Create trainer with pruning callback
    trainer = pl.Trainer(
        max_epochs=100,
        callbacks=[
            PyTorchLightningPruningCallback(trial, monitor='val_accuracy')
        ]
    )

    trainer.fit(model, train_loader, val_loader)

    return trainer.callback_metrics['val_accuracy'].item()

Distributed Optimization

import optuna

# Create shared study with database storage
study = optuna.create_study(
    study_name='distributed-study',
    storage='postgresql://user:pass@host:5432/optuna',
    direction='maximize',
    load_if_exists=True
)

# Run on multiple workers (each worker runs this)
study.optimize(objective, n_trials=25)  # Each worker does 25 trials

# Results are automatically aggregated
print(f"Total trials: {len(study.trials)}")

Integration with Babysitter SDK

Task Definition Example

const hyperparameterTuningTask = defineTask({
  name: 'optuna-hyperparameter-tuning',
  description: 'Optimize hyperparameters using Optuna',

  inputs: {
    studyName: { type: 'string', required: true },
    direction: { type: 'string', default: 'maximize' },
    nTrials: { type: 'number', default: 100 },
    timeout: { type: 'number' },
    parameterSpace: { type: 'object', required: true },
    objectiveScript: { type: 'string', required: true },
    sampler: { type: 'string', default: 'tpe' },
    pruner: { type: 'string', default: 'median' }
  },

  outputs: {
    bestValue: { type: 'number' },
    bestParams: { type: 'object' },
    nTrialsCompleted: { type:  },
    : { :  }
  },

   () {
     {
      : ,
      : ,
      : {
        : ,
        : {
          : ,
          : inputs.,
          : inputs.,
          : inputs.,
          : inputs.,
          : inputs.,
          : inputs.,
          : inputs.,
          : inputs.
        }
      },
      : {
        : ,
        : 
      }
    };
  }
});

MCP Server Integration

Using optuna-mcp (Official)

{
  "mcpServers": {
    "optuna": {
      "command": "uvx",
      "args": ["optuna-mcp"],
      "env": {
        "OPTUNA_STORAGE": "sqlite:///optuna.db"
      }
    }
  }
}

Available MCP Tools

• optuna_create_study - Create new optimization study
• optuna_get_study - Retrieve study information
• optuna_list_studies - List all studies
• optuna_get_best_trial - Get best trial from study
• optuna_get_trials - List trials in study
• optuna_visualize - Generate visualization
• optuna_suggest_params - Get parameter suggestions

Sampler Selection Guide

Sampler	Use Case	Pros	Cons
TPESampler	Default, most cases	Efficient, handles conditionals	May miss global optimum
CmaEsSampler	Continuous parameters	Good for correlated params	Only continuous
GridSampler	Small discrete spaces	Exhaustive	Exponential complexity
RandomSampler	Baseline, parallel	Simple, embarrassingly parallel	Inefficient
NSGAIISampler	Multi-objective	Pareto optimization	Slower convergence
QMCSampler	Space exploration	Low discrepancy	Not adaptive

Pruner Selection Guide

Pruner	Use Case	Aggressiveness
MedianPruner	Default, safe	Moderate
HyperbandPruner	Deep learning	Aggressive
SuccessiveHalvingPruner	Resource-efficient	High
PercentilePruner	Configurable threshold	Variable
NopPruner	No pruning needed	None

Visualization

Generate Visualizations

import optuna.visualization as vis

# Optimization history
fig = vis.plot_optimization_history(study)
fig.write_html('optimization_history.html')

# Parameter importance
fig = vis.plot_param_importances(study)
fig.write_html('param_importance.html')

# Parallel coordinate
fig = vis.plot_parallel_coordinate(study)
fig.write_html('parallel_coordinate.html')

# Contour plot (2 params)
fig = vis.plot_contour(study, params=['learning_rate', 'max_depth'])
fig.write_html('contour.html')

# Slice plot
fig = vis.plot_slice(study)
fig.write_html('slice.html')

Optuna Dashboard

# Launch dashboard
optuna-dashboard sqlite:///optuna.db

# Access at http://localhost:8080

Best Practices

1. Start with TPE: Use default sampler unless you have specific needs
2. Use Pruning: Enable early stopping for iterative algorithms
3. Persist Studies: Use database storage for resumability
4. Log Intermediate Values: Enable pruning and progress tracking
5. Set Timeouts: Prevent runaway optimization
6. Analyze Importance: Focus on high-impact parameters
7. Use Conditional Parameters: Model dependencies between params

References

• Optuna Documentation
• Optuna MCP Server
• Optuna GitHub
• Optuna Dashboard
• Optuna Examples