oxirs-arq 0.2.4

//! Performance prediction models

use serde::{Deserialize, Serialize};
use std::collections::HashMap;

pub struct PerformancePredictor {
    /// Historical performance data
    historical_data: VecDeque<PerformanceDataPoint>,
    /// Regression models
    regression_models: HashMap<String, RegressionModel>,
    /// Neural network predictor
    neural_predictor: NeuralNetworkPredictor,
    /// Ensemble predictor
    ensemble_predictor: EnsemblePredictor,
}

/// Performance data point for prediction
#[derive(Debug, Clone)]
pub struct PerformanceDataPoint {
    pub timestamp: SystemTime,
    pub query_features: QueryFeatures,
    pub execution_time: Duration,
    pub memory_usage: usize,
    pub success: bool,
    pub error_category: Option<String>,
}

/// Query features for ML prediction
#[derive(Debug, Clone)]
pub struct QueryFeatures {
    pub pattern_count: usize,
    pub join_count: usize,
    pub filter_count: usize,
    pub union_count: usize,
    pub optional_count: usize,
    pub graph_patterns: usize,
    pub path_expressions: usize,
    pub aggregations: usize,
    pub subqueries: usize,
    pub services: usize,
    pub estimated_cardinality: usize,
    pub complexity_score: f64,
    pub index_coverage: f64,
}

/// Regression model for performance prediction
#[derive(Debug, Clone)]
pub struct RegressionModel {
    pub model_type: RegressionType,
    pub coefficients: Vec<f64>,
    pub intercept: f64,
    pub r_squared: f64,
    pub confidence_intervals: Vec<(f64, f64)>,
}

/// Types of regression models
#[derive(Debug, Clone)]
pub enum RegressionType {
    Linear,
    Polynomial,
    Exponential,
    Logarithmic,
    PowerLaw,
}

/// Neural network predictor
#[derive(Debug, Clone)]
pub struct NeuralNetworkPredictor {
    pub layers: Vec<NeuralLayer>,
    pub activation_function: ActivationFunction,
    pub training_accuracy: f64,
    pub validation_accuracy: f64,
}

/// Neural network layer
#[derive(Debug, Clone)]
pub struct NeuralLayer {
    pub weights: Vec<Vec<f64>>,
    pub biases: Vec<f64>,
    pub layer_type: LayerType,
}

/// Types of neural network layers
#[derive(Debug, Clone)]
pub enum LayerType {
    Dense,
    Dropout,
    Activation,
    Normalization,
}

/// Activation functions
#[derive(Debug, Clone)]
pub enum ActivationFunction {
    Relu,
    Sigmoid,
    Tanh,
    Swish,
    Gelu,
}

/// Ensemble predictor combining multiple models
#[derive(Debug, Clone)]
pub struct EnsemblePredictor {
    pub models: Vec<PredictorModel>,
    pub weights: Vec<f64>,
    pub ensemble_method: EnsembleMethod,
    pub meta_learner: Option<Box<RegressionModel>>,
}

/// Individual predictor models
#[derive(Debug, Clone)]
pub enum PredictorModel {
    Regression(RegressionModel),
    NeuralNetwork(NeuralNetworkPredictor),
    DecisionTree(DecisionTreeModel),
    RandomForest(RandomForestModel),
}

/// Decision tree model
#[derive(Debug, Clone)]
pub struct DecisionTreeModel {
    pub tree_depth: usize,
    pub feature_splits: HashMap<usize, f64>,
    pub prediction_accuracy: f64,
}

/// Random forest model
#[derive(Debug, Clone)]
pub struct RandomForestModel {
    pub trees: Vec<DecisionTreeModel>,
    pub feature_importance: HashMap<usize, f64>,
    pub oob_accuracy: f64,
}

/// Ensemble methods
#[derive(Debug, Clone)]
pub enum EnsembleMethod {
    Voting,
    Averaging,
    Stacking,
    Boosting,
}

/// Workload classification and analysis
#[derive(Debug, Clone)]

impl PerformancePredictor {
    pub fn new() -> Self {
        Self {
            historical_data: VecDeque::new(),
            regression_models: HashMap::new(),
            neural_predictor: NeuralNetworkPredictor::new(),
            ensemble_predictor: EnsemblePredictor::new(),
        }
    }

    pub fn add_data_point(&mut self, data_point: PerformanceDataPoint) -> Result<()> {
        self.historical_data.push_back(data_point);
        if self.historical_data.len() > 10000 {
            self.historical_data.pop_front();
        }
        Ok(())
    }

    pub fn predict(&self, features: &QueryFeatures) -> Result<PerformancePrediction> {
        // Implementation would use ML models to predict performance
        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(100),
            predicted_memory_usage: 1024 * 1024,
            confidence_interval: (Duration::from_millis(80), Duration::from_millis(120)),
            risk_assessment: RiskLevel::Low,
            optimization_suggestions: vec!["Consider adding an index".to_string()],
        })
    }
}

impl NeuralNetworkPredictor {
    pub fn new() -> Self {
        Self {
            layers: Vec::new(),
            activation_function: ActivationFunction::Relu,
            training_accuracy: 0.0,
            validation_accuracy: 0.0,
        }
    }
}

impl EnsemblePredictor {
    pub fn new() -> Self {
        Self {
            models: Vec::new(),
            weights: Vec::new(),
            ensemble_method: EnsembleMethod::Averaging,
            meta_learner: None,
        }
    }
}