optirs-nas 0.3.1

// Bayesian optimization for architecture search

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use scirs2_core::random::Random;
use scirs2_core::RngExt;
use std::collections::VecDeque;
use std::fmt::Debug;

#[allow(unused_imports)]
use crate::error::Result;
use crate::nas_engine::{OptimizerArchitecture, SearchResult, SearchSpaceConfig};
use crate::EvaluationMetric;

use super::random::RandomSearch;
use super::{SearchStrategy, SearchStrategyStatistics};

/// Bayesian optimization for architecture search
pub struct BayesianOptimization<T: Float + Debug + Send + Sync + 'static> {
    gaussian_process: GaussianProcess<T>,
    acquisition_function: AcquisitionFunction<T>,
    observed_architectures: Vec<OptimizerArchitecture<T>>,
    observed_performances: Vec<T>,
    kernel: GPKernel<T>,
    statistics: SearchStrategyStatistics<T>,
    exploration_factor: T,
}

/// Gaussian Process for Bayesian optimization
#[derive(Debug)]
pub struct GaussianProcess<T: Float + Debug + Send + Sync + 'static> {
    kernel_matrix: Array2<T>,
    inverse_kernel: Array2<T>,
    noise_variance: T,
    length_scales: Array1<T>,
    signal_variance: T,
}

/// Acquisition functions for Bayesian optimization
#[derive(Debug)]
pub struct AcquisitionFunction<T: Float + Debug + Send + Sync + 'static> {
    function_type: AcquisitionType,
    explorationweight: T,
    current_best: T,
}

/// Types of acquisition functions
#[derive(Debug, Clone, Copy)]
pub enum AcquisitionType {
    /// Expected Improvement
    EI,
    /// Upper Confidence Bound
    UCB,
    /// Probability of Improvement
    PI,
    /// Thompson Sampling
    Thompson,
    /// Information Gain
    InfoGain,
}

/// Gaussian Process kernels
#[derive(Debug)]
pub struct GPKernel<T: Float + Debug + Send + Sync + 'static> {
    _kerneltype: KernelType,
    hyperparameters: Array1<T>,
}

/// Kernel types for GP
#[derive(Debug, Clone, Copy)]
pub enum KernelType {
    RBF,
    Matern32,
    Matern52,
    Linear,
    Polynomial,
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    BayesianOptimization<T>
{
    pub fn new(
        kerneltype: KernelType,
        acquisition_type: AcquisitionType,
        exploration_factor: f64,
    ) -> Self {
        Self {
            gaussian_process: GaussianProcess::new(kerneltype),
            acquisition_function: AcquisitionFunction::new(
                acquisition_type,
                scirs2_core::numeric::NumCast::from(exploration_factor)
                    .unwrap_or_else(|| T::zero()),
            ),
            observed_architectures: Vec::new(),
            observed_performances: Vec::new(),
            kernel: GPKernel::new(kerneltype),
            statistics: SearchStrategyStatistics::default(),
            exploration_factor: scirs2_core::numeric::NumCast::from(exploration_factor)
                .unwrap_or_else(|| T::zero()),
        }
    }

    fn encode_architecture(&self, architecture: &OptimizerArchitecture<T>) -> Array1<T> {
        // Simple encoding: component types and hyperparameter values
        let mut encoding = Vec::new();

        for component_str in &architecture.components {
            // Encode component type as one-hot (use a simple hash of the string as placeholder)
            let hash = component_str
                .bytes()
                .fold(0u8, |acc, b| acc.wrapping_add(b));
            encoding.push(scirs2_core::numeric::NumCast::from(hash).unwrap_or_else(|| T::zero()));
        }

        // Encode hyperparameters from the architecture's hyperparameters map
        for &value in architecture.hyperparameters.values() {
            encoding.push(value);
        }

        // Pad to fixed size
        encoding.resize(64, T::zero());
        Array1::from_vec(encoding)
    }

    fn fit_gp(&mut self) -> Result<()> {
        if self.observed_architectures.len() < 2 {
            return Ok(());
        }

        // Encode all observed architectures
        let encoded_archs: Vec<Array1<T>> = self
            .observed_architectures
            .iter()
            .map(|arch| self.encode_architecture(arch))
            .collect();

        // Fit Gaussian Process
        self.gaussian_process
            .fit(&encoded_archs, &self.observed_performances)?;

        Ok(())
    }

    fn suggest_next_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
    ) -> Result<OptimizerArchitecture<T>> {
        if self.observed_architectures.len() < 5 {
            // Use random search for initial points
            let mut random_search = RandomSearch::<T>::new(Some(42));
            random_search.initialize(searchspace)?;
            return random_search.generate_architecture(searchspace, &VecDeque::new());
        }

        // Generate candidate architectures
        let num_candidates = 100;
        let mut candidates = Vec::new();
        let mut random_search = RandomSearch::<T>::new(Some(42));
        random_search.initialize(searchspace)?;

        for _ in 0..num_candidates {
            candidates.push(random_search.generate_architecture(searchspace, &VecDeque::new())?);
        }

        // Evaluate acquisition function for each candidate
        let mut best_architecture = candidates[0].clone();
        let mut best_acquisition = T::neg_infinity();

        for candidate in candidates {
            let encoded = self.encode_architecture(&candidate);
            let (mean, variance) = self.gaussian_process.predict(&encoded)?;
            let acquisition_value = self.acquisition_function.evaluate(mean, variance);

            if acquisition_value > best_acquisition {
                best_acquisition = acquisition_value;
                best_architecture = candidate;
            }
        }

        Ok(best_architecture)
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    SearchStrategy<T> for BayesianOptimization<T>
{
    fn initialize(&mut self, _searchspace: &SearchSpaceConfig) -> Result<()> {
        self.observed_architectures.clear();
        self.observed_performances.clear();
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        _history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        let architecture = self.suggest_next_architecture(searchspace)?;
        self.statistics.total_architectures_generated += 1;
        Ok(architecture)
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        for result in results {
            if let Some(&performance) = result
                .evaluation_results
                .metric_scores
                .get(&EvaluationMetric::FinalPerformance)
            {
                self.observed_architectures
                    .push(result.architecture.clone());
                self.observed_performances.push(performance);

                // Update statistics
                if performance > self.statistics.best_performance {
                    self.statistics.best_performance = performance;
                }

                // Refit GP
                self.fit_gp()?;
            }
        }

        // Update average performance
        if !self.observed_performances.is_empty() {
            let sum: T = self.observed_performances.iter().cloned().sum();
            self.statistics.average_performance = sum
                / T::from(self.observed_performances.len())
                    .expect("conversion from usize to T failed");
        }

        Ok(())
    }

    fn name(&self) -> &str {
        "BayesianOptimization"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate = self.exploration_factor;
        stats.exploitation_rate = T::one() - self.exploration_factor;
        stats
    }
}

// Implementation stubs for supporting components
impl<T: Float + Debug + Default + Send + Sync> GaussianProcess<T> {
    fn new(_kerneltype: KernelType) -> Self {
        Self {
            kernel_matrix: Array2::zeros((0, 0)),
            inverse_kernel: Array2::zeros((0, 0)),
            noise_variance: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
            length_scales: Array1::ones(1),
            signal_variance: T::one(),
        }
    }

    fn fit(&mut self, _x: &[Array1<T>], _y: &[T]) -> Result<()> {
        // Simplified GP fitting
        Ok(())
    }

    fn predict(&self, _x: &Array1<T>) -> Result<(T, T)> {
        // Simplified prediction - return mean and variance
        Ok((
            scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
        ))
    }
}

impl<T: Float + Debug + Default + Send + Sync> AcquisitionFunction<T> {
    fn new(function_type: AcquisitionType, explorationweight: T) -> Self {
        Self {
            function_type,
            explorationweight,
            current_best: T::zero(),
        }
    }

    fn evaluate(&self, mean: T, variance: T) -> T {
        match self.function_type {
            AcquisitionType::UCB => mean + self.explorationweight * variance.sqrt(),
            AcquisitionType::EI => {
                // Simplified Expected Improvement
                let std_dev = variance.sqrt();
                if std_dev > scirs2_core::numeric::NumCast::from(1e-8).unwrap_or_else(|| T::zero())
                {
                    let z = (mean - self.current_best) / std_dev;
                    // Simplified calculation without proper CDF/PDF
                    z * std_dev
                } else {
                    T::zero()
                }
            }
            AcquisitionType::PI => {
                // Simplified Probability of Improvement
                if variance > scirs2_core::numeric::NumCast::from(1e-8).unwrap_or_else(|| T::zero())
                {
                    let z = (mean - self.current_best) / variance.sqrt();
                    // Simplified - would need proper CDF
                    if z > T::zero() {
                        T::one()
                    } else {
                        T::zero()
                    }
                } else {
                    T::zero()
                }
            }
            AcquisitionType::Thompson => {
                // Thompson sampling - sample from posterior
                mean + variance.sqrt()
                    * T::from(scirs2_core::random::Random::default().random::<f64>())
                        .expect("thompson sampling conversion failed")
            }
            AcquisitionType::InfoGain => {
                // Information gain - simplified as entropy
                variance.ln()
            }
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> GPKernel<T> {
    fn new(kerneltype: KernelType) -> Self {
        Self {
            _kerneltype: kerneltype,
            hyperparameters: Array1::ones(2), // length_scale and signal_variance
        }
    }
}