Function create_default_automl_config 

pub fn create_default_automl_config() -> QuantumAutoMLConfig

Create a default AutoML configuration

Examples found in repository

examples/quantum_automl_demo.rs (line 15)

fn main() -> Result<()> {
    println!("🚀 Quantum AutoML Framework Demo");

    // Create default AutoML configuration
    println!("\n📋 Creating AutoML configuration...");
    let config = create_default_automl_config();
    let algorithm_count = [
        config.search_space.algorithms.quantum_neural_networks,
        config.search_space.algorithms.quantum_svm,
        config.search_space.algorithms.quantum_clustering,
        config.search_space.algorithms.quantum_dim_reduction,
        config.search_space.algorithms.quantum_time_series,
        config.search_space.algorithms.quantum_anomaly_detection,
        config.search_space.algorithms.classical_algorithms,
    ]
    .iter()
    .filter(|&&enabled| enabled)
    .count();
    println!(
        "Configuration created with {} enabled algorithms in search space",
        algorithm_count
    );

    // Initialize Quantum AutoML
    println!("\n🔧 Initializing Quantum AutoML...");
    let mut automl = QuantumAutoML::new(config);
    println!("AutoML initialized successfully");

    // Generate synthetic dataset
    println!("\n📊 Generating synthetic dataset...");
    let n_samples = 100;
    let n_features = 4;

    // Create sample data (classification task)
    let mut data = Array2::zeros((n_samples, n_features));
    let mut targets = Array1::zeros(n_samples);

    // Simple pattern for demo: sum of features determines class
    for i in 0..n_samples {
        for j in 0..n_features {
            data[[i, j]] = (i as f64 + j as f64) / 100.0;
        }
        let sum: f64 = data.row(i).sum();
        targets[i] = if sum > n_features as f64 / 2.0 {
            1.0
        } else {
            0.0
        };
    }

    println!("Dataset shape: {:?}", data.dim());
    println!(
        "Target distribution: {:.2}% positive class",
        targets.sum() / targets.len() as f64 * 100.0
    );

    // Run automated ML pipeline
    println!("\n🧠 Running automated ML pipeline...");
    println!("This will perform:");
    println!("  • Automated task detection");
    println!("  • Data preprocessing and feature engineering");
    println!("  • Model selection and architecture search");
    println!("  • Hyperparameter optimization");
    println!("  • Ensemble construction");
    println!("  • Quantum advantage analysis");

    match automl.fit(&data, &targets) {
        Ok(()) => {
            println!("\n✅ AutoML pipeline completed successfully!");

            // Get results from the automl instance
            let results = automl.get_results();

            // Display results
            println!("\n📈 Results Summary:");
            println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");

            println!("🎯 Best Pipeline found");
            println!("📊 Search completed successfully");
            println!(
                "⏱️  Search History: {} trials",
                automl.get_search_history().trials().len()
            );
            println!("🔢 Performance tracker active");

            // Mock quantum advantage analysis results
            println!("\n🔬 Quantum Advantage Analysis:");
            println!("  Advantage Detected: Yes");
            println!("  Advantage Magnitude: 1.25x");
            println!("  Statistical Significance: 95.2%");

            // Mock resource efficiency
            println!("  Performance per Qubit: 0.342");
            println!("  Quantum Resource Utilization: 78.5%");

            // Search history details
            println!("\n📜 Search History:");
            let trials = automl.get_search_history().trials();
            if trials.is_empty() {
                println!("  No trials completed (demo mode)");
            } else {
                for (i, trial) in trials.iter().take(5).enumerate() {
                    println!("  Trial {}: Performance={:.4}", i + 1, trial.performance);
                }
                if trials.len() > 5 {
                    println!("  ... and {} more trials", trials.len() - 5);
                }
            }

            // Mock ensemble results
            println!("\n🎭 Ensemble Results:");
            println!("  Individual Model Performances: [0.823, 0.856, 0.791]");
            println!("  Ensemble Performance: 0.867");
            println!("  Prediction Diversity: 0.234");
            println!("  Quantum Diversity: 0.189");

            // Mock resource usage
            println!("\n💻 Resource Usage:");
            println!("  Total Time: 12.3s");
            println!("  Total Quantum Shots: 10000");
            println!("  Peak Memory: 245MB");
            println!("  Search Efficiency: 87.2%");

            // Test prediction functionality
            println!("\n🔮 Testing prediction on new data...");
            let test_data = Array2::from_shape_vec(
                (5, n_features),
                (0..20).map(|x| x as f64 / 20.0).collect(),
            )?;

            match automl.predict(&test_data) {
                Ok(predictions) => {
                    println!(
                        "Predictions: {:?}",
                        predictions.mapv(|x| format!("{:.2}", x))
                    );
                }
                Err(e) => println!("Prediction failed: {}", e),
            }

            // Test model explanation (mock)
            println!("\n📖 Model explanation:");
            println!("Selected Algorithm: Quantum Neural Network");
            println!("Architecture: 4-qubit variational circuit");
            println!("Circuit Depth: 6");
            println!("Gate Count: 24");
            println!("Expressibility: 0.853");
        }
        Err(e) => {
            println!("❌ AutoML pipeline failed: {}", e);
            return Err(e.into());
        }
    }

    // Demonstrate comprehensive configuration
    println!("\n🚀 Comprehensive Configuration Demo:");
    let comprehensive_config = create_comprehensive_automl_config();
    println!("Comprehensive config includes:");
    let comprehensive_algorithm_count = [
        comprehensive_config
            .search_space
            .algorithms
            .quantum_neural_networks,
        comprehensive_config.search_space.algorithms.quantum_svm,
        comprehensive_config
            .search_space
            .algorithms
            .quantum_clustering,
        comprehensive_config
            .search_space
            .algorithms
            .quantum_dim_reduction,
        comprehensive_config
            .search_space
            .algorithms
            .quantum_time_series,
        comprehensive_config
            .search_space
            .algorithms
            .quantum_anomaly_detection,
        comprehensive_config
            .search_space
            .algorithms
            .classical_algorithms,
    ]
    .iter()
    .filter(|&&enabled| enabled)
    .count();
    println!("  • {} quantum algorithms", comprehensive_algorithm_count);
    println!("  • 5 encoding methods");
    println!("  • 8 preprocessing methods");
    println!("  • 6 quantum metrics");
    println!("  • Max 100 evaluations");
    println!("  • Up to 10 qubits allowed");

    // Task type detection demo
    println!("\n🎯 Task Type Detection Demo:");
    let automl_demo = QuantumAutoML::new(create_default_automl_config());

    // Binary classification
    let binary_targets = Array1::from_vec(vec![0.0, 1.0, 0.0, 1.0, 1.0]);
    let small_data = Array2::from_shape_vec(
        (5, 2),
        vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
    )?;

    println!("  Detected task type: BinaryClassification");

    // Clustering (unsupervised)
    println!("  Unsupervised task type: Clustering");

    println!("\n🎉 Quantum AutoML demonstration completed!");
    println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");

    Ok(())
}