SimonBlanke / Hyperactive

    #    - Preserve solutions near each reference point

def main():

    # === NSGAIIISampler Example ===

    # Many-objective Optimization with NSGA-III

    nsga_iii_theory()

    # Load dataset

    X, y = load_breast_cancer(return_X_y=True)

    print(

        f"Dataset: Breast cancer classification ({X.shape[0]} samples, {X.shape[1]} features)"

    )

    # Create many-objective experiment

    experiment = ManyObjectiveExperiment(X, y)

    # Many-objective Problem (4 objectives):

    #   Objective 1: Maximize classification accuracy

    #   Objective 2: Minimize model complexity (tree depth)

    #   Objective 3: Minimize training time

    #   Objective 4: Maximize interpretability (smaller trees)

    #   → Complex trade-offs between multiple conflicting goals

    # Define search space

    param_space = {

        "max_depth": (1, 20),  # Tree depth

        "min_samples_split": (2, 50),  # Minimum samples to split

        "min_samples_leaf": (1, 20),  # Minimum samples per leaf

        "max_leaf_nodes": (10, 200),  # Maximum leaf nodes

        "criterion": ["gini", "entropy"],  # Split criterion

    }

    # Search Space:

    # for param, space in param_space.items():

    #   print(f"  {param}: {space}")

    # Configure NSGAIIISampler

    optimizer = NSGAIIISampler(

        param_space=param_space,

        n_trials=60,  # More trials needed for many objectives

        random_state=42,

        experiment=experiment,

        population_size=24,  # Larger population for many objectives

        mutation_prob=0.1,  # Mutation probability

        crossover_prob=0.9,  # Crossover probability

    )

    # NSGAIIISampler Configuration:

    # n_trials: configured above

    # population_size: larger for many objectives

    # mutation_prob: mutation probability

    # crossover_prob: crossover probability

    # Selection: Reference point-based diversity preservation

    # Note: NSGA-III is designed for 3+ objectives.

    # For 2 objectives, NSGA-II is typically preferred.

    # This example demonstrates the interface for many-objective problems.

    # Run optimization

    # Running NSGA-III many-objective optimization...

    try:

        best_params = optimizer.solve()

        # Results

        print("\n=== Results ===")

        print(f"Best parameters: {best_params}")

        print(f"Best score: {optimizer.best_score_:.4f}")

        print()

        # NSGA-III produces a diverse set of solutions across 4D Pareto front:

        #  High accuracy, complex, slower models

        #  Balanced accuracy/complexity trade-offs

        #  Fast, simple, interpretable models

        #  Various combinations optimizing different objectives

    except Exception as e:

        print(f"Many-objective optimization example: {e}")

        print("Note: This demonstrates the interface for many-objective problems.")

        return None, None

    # NSGA-III vs NSGA-II for Many Objectives:

    #

    # NSGA-II Limitations (3+ objectives):

    #  Most solutions become non-dominated

    #  Crowding distance loses effectiveness

    #  Selection pressure decreases

    #  Uneven distribution in objective space

    # NSGA-III Advantages:

    #  Reference points guide search directions

    #  Maintains diversity across all objectives

    #  Better selection pressure in many objectives

    #  Structured exploration of objective space

    #  Adaptive normalization handles different scales

    # Reference Point Mechanism:

    #  Systematic placement on normalized hyperplane

    #  Each point represents a different objective priority

    #  Solutions associated with nearest reference points

    #  Selection maintains balance across all points

    #  Prevents clustering in limited objective regions

    # Many-objective Problem Characteristics:

    #

    # Challenges:

    #  Exponential growth of non-dominated solutions

    #  Difficulty visualizing high-dimensional trade-offs

    #  User preference articulation becomes complex

    #  Increased computational requirements

    # Best Use Cases:

    #  Engineering design with multiple constraints

    #  Multi-criteria decision making (3+ criteria)

    #  Resource allocation problems

    #  System optimization with conflicting requirements

    #  When objective interactions are complex

    #

    # Algorithm Selection Guide:

    #

    # Use NSGA-III when:

    #    3 or more objectives

    #    Objectives are truly conflicting

    #    Comprehensive trade-off analysis needed

    #    Reference point guidance is beneficial

    #

    # Use NSGA-II when:

    #    2 objectives

    #    Simpler Pareto front structure

    #    Established performance for bi-objective problems

    #

    # Use single-objective methods when:

    #    Can formulate as weighted combination

    #    Clear primary objective with constraints

    #    Computational efficiency is critical

    if "best_params" in locals():

        return best_params, optimizer.best_score_

    else:

        return None, None

if __name__ == "__main__":

    #    - User chooses final solution based on preferences

def main():

    # === NSGAIISampler Example ===

    # Multi-objective Optimization with NSGA-II

    nsga_ii_theory()

    # Load dataset

    X, y = load_digits(return_X_y=True)

    print(f"Dataset: Handwritten digits ({X.shape[0]} samples, {X.shape[1]} features)")

    # Create multi-objective experiment

    experiment = MultiObjectiveExperiment(X, y)

    # Multi-objective Problem:

    #   Objective 1: Maximize classification accuracy

    #   Objective 2: Minimize model complexity

    #   → Trade-off between performance and simplicity

    # Define search space

    param_space = {

        "n_estimators": (10, 200),  # Number of trees

        "max_depth": (1, 20),  # Tree depth (complexity)

        "min_samples_split": (2, 20),  # Minimum samples to split

        "min_samples_leaf": (1, 10),  # Minimum samples per leaf

        "max_features": ["sqrt", "log2", None],  # Feature sampling

    }

    # Search Space:

    # for param, space in param_space.items():

    #   print(f"  {param}: {space}")

    # Configure NSGAIISampler

    optimizer = NSGAIISampler(

        param_space=param_space,

        n_trials=50,  # Population evolves over multiple generations

        random_state=42,

        experiment=experiment,

        population_size=20,  # Population size for genetic algorithm

        mutation_prob=0.1,  # Mutation probability

        crossover_prob=0.9,  # Crossover probability

    )

    # NSGAIISampler Configuration:

    # n_trials: configured above

    # population_size: for genetic algorithm

    # mutation_prob: mutation probability

    # crossover_prob: crossover probability

    # Selection: Non-dominated sorting + crowding distance

    # Note: This example demonstrates the interface.

    # In practice, NSGA-II returns multiple Pareto-optimal solutions.

    # For single-objective problems, consider TPE or GP samplers instead.

    # Run optimization

    # Running NSGA-II multi-objective optimization...

    try:

        best_params = optimizer.solve()

        # Results

        print("\n=== Results ===")

        print(f"Best parameters: {best_params}")

        print(f"Best score: {optimizer.best_score_:.4f}")

        print()

        # NSGA-II typically returns multiple solutions along Pareto front:

        #  High accuracy, high complexity models

        #  Medium accuracy, medium complexity models

        #  Lower accuracy, low complexity models

        #  User selects based on preferences/constraints

    except Exception as e:

        print(f"Multi-objective optimization example: {e}")

        print("Note: This demonstrates the interface for multi-objective problems.")

        return None, None

    # NSGA-II Evolution Process:

    #

    # Generation 1: Random initialization

    #  Diverse population across parameter space

    #  Wide range of accuracy/complexity trade-offs

    # Generations 2-N: Evolutionary improvement

    #  Non-dominated sorting identifies best fronts

    #  Crowding distance maintains solution diversity

    #  Crossover combines good solutions

    #  Mutation explores new parameter regions

    # Final Population: Pareto front approximation

    #  Multiple non-dominated solutions

    #  Represents optimal trade-offs

    #  User chooses based on domain requirements

    # Key Advantages:

    #  Handles multiple conflicting objectives naturally

    #  Finds diverse set of optimal trade-offs

    #  No need to specify objective weights a priori

    #  Provides insight into objective relationships

    #  Robust to objective scaling differences

    # Best Use Cases:

    #  True multi-objective problems (accuracy vs speed, cost vs quality)

    #  When trade-offs between objectives are important

    #  Robustness analysis with multiple criteria

    #  When single objective formulation is unclear

    # Limitations:

    #  More complex than single-objective methods

    #  Requires more evaluations (population-based)

    #  May be overkill for single-objective problems

    #  Final solution selection still required

    # When to Use NSGA-II vs Single-objective Methods:

    # Use NSGA-II when:

    #    Multiple objectives genuinely conflict

    #    Trade-off analysis is valuable

    #    Objective weights are unknown

    #

    # Use TPE/GP when:

    #    Single clear objective

    #    Computational budget is limited

    #    Faster convergence needed

    if "best_params" in locals():

        return best_params, optimizer.best_score_

    else:

        return None, None

if __name__ == "__main__":