automl-optimizer

# AutoML Optimizer

## Overview

Automates the tedious process of hyperparameter tuning and model selection. Instead of manually trying different configurations, define a search space and let AutoML find the optimal configuration through intelligent exploration.

## Why AutoML?

**Manual Tuning Problems**:
- Time-consuming (hours/days of trial and error)
- Subjective (depends on intuition)
- Incomplete (can't try all combinations)
- Not reproducible (hard to document search process)

**AutoML Benefits**:
- ✅ Systematic exploration of search space
- ✅ Intelligent sampling (Bayesian optimization)
- ✅ All experiments tracked automatically
- ✅ Find optimal configuration faster
- ✅ Reproducible (search process documented)

## AutoML Strategies

### Strategy 1: Hyperparameter Optimization (Optuna)

```python
from specweave import OptunaOptimizer

# Define search space
def objective(trial):
    # Suggest hyperparameters
    params = {
        'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
        'max_depth': trial.suggest_int('max_depth', 3, 10),
        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),
        'subsample': trial.suggest_float('subsample', 0.5, 1.0),
        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0)
    }
    
    # Train model
    model = XGBClassifier(**params)
    
    # Cross-validation score
    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')
    
    return scores.mean()

# Run optimization
optimizer = OptunaOptimizer(
    objective=objective,
    n_trials=100,
    direction='maximize',
    increment="0042"
)

best_params = optimizer.optimize()

# Creates:
# - .specweave/increments/0042.../experiments/optuna-study/
#   ├── study.db (Optuna database)
#   ├── optimization_history.png
#   ├── param_importances.png
#   ├── parallel_coordinate.png
#   └── best_params.json
```

**Optimization Report**:
```markdown
# Optuna Optimization Report

## Search Space
-