optirs-core 0.3.1

// Drift detection and adaptation for streaming data
//
// This module provides comprehensive drift detection capabilities including
// statistical methods, distribution-based approaches, model-based detection,
// and ensemble methods for identifying concept drift in streaming data.

use super::config::*;
use super::optimizer::{Adaptation, AdaptationPriority, AdaptationType, StreamingDataPoint};

use scirs2_core::numeric::Float;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Enhanced drift detector with multiple detection methods
pub struct EnhancedDriftDetector<A: Float + Send + Sync> {
    /// Configuration for drift detection
    config: DriftConfig,
    /// Current detection method
    detection_method: DriftDetectionMethod,
    /// Statistical test implementations
    statistical_tests: HashMap<StatisticalMethod, Box<dyn StatisticalTest<A>>>,
    /// Distribution comparison methods
    distribution_methods: HashMap<DistributionMethod, Box<dyn DistributionComparator<A>>>,
    /// Model-based detectors
    model_detectors: HashMap<ModelType, Box<dyn ModelBasedDetector<A>>>,
    /// Ensemble voting strategy
    ensemble_strategy: Option<VotingStrategy>,
    /// Detection history
    detection_history: VecDeque<DriftEvent<A>>,
    /// False positive tracker
    false_positive_tracker: FalsePositiveTracker<A>,
    /// Reference window for comparison
    reference_window: VecDeque<StreamingDataPoint<A>>,
    /// Current drift state
    drift_state: DriftState,
    /// Last detection timestamp
    last_detection: Option<Instant>,
    /// Sensitivity adjustment factor
    sensitivity_factor: A,
}

/// Drift event information
#[derive(Debug, Clone)]
pub struct DriftEvent<A: Float + Send + Sync> {
    /// Event timestamp
    pub timestamp: Instant,
    /// Drift severity level
    pub severity: DriftSeverity,
    /// Detection confidence
    pub confidence: A,
    /// Detection method that triggered
    pub detection_method: String,
    /// Statistical significance
    pub p_value: Option<A>,
    /// Drift magnitude estimate
    pub magnitude: A,
    /// Affected features (if applicable)
    pub affected_features: Vec<usize>,
}

/// Drift severity levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum DriftSeverity {
    /// Minor drift that may not require immediate action
    Minor,
    /// Moderate drift requiring attention
    Moderate,
    /// Major drift requiring significant adaptation
    Major,
    /// Critical drift requiring immediate response
    Critical,
}

/// Current drift detection state
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DriftState {
    /// Normal operation, no drift detected
    Stable,
    /// Warning level - potential drift detected
    Warning,
    /// Drift confirmed
    Drift,
    /// Recovering from drift
    Recovery,
}

/// False positive tracking for drift detection
pub struct FalsePositiveTracker<A: Float + Send + Sync> {
    /// Recent false positive events
    false_positives: VecDeque<Instant>,
    /// True positive events
    true_positives: VecDeque<Instant>,
    /// Current false positive rate
    current_fp_rate: A,
    /// Target false positive rate
    target_fp_rate: A,
}

/// Trait for statistical drift detection tests
pub trait StatisticalTest<A: Float + Send + Sync>: Send + Sync {
    /// Performs the statistical test for drift
    fn test_for_drift(
        &mut self,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String>;

    /// Updates test parameters based on historical performance
    fn update_parameters(&mut self, performance_feedback: A) -> Result<(), String>;

    /// Resets the test state
    fn reset(&mut self);
}

/// Result of a drift detection test
#[derive(Debug, Clone)]
pub struct DriftTestResult<A: Float + Send + Sync> {
    /// Whether drift was detected
    pub drift_detected: bool,
    /// Statistical significance (p-value)
    pub p_value: A,
    /// Test statistic value
    pub test_statistic: A,
    /// Confidence in the result
    pub confidence: A,
    /// Additional test-specific metadata
    pub metadata: HashMap<String, A>,
}

/// Trait for distribution-based drift detection
pub trait DistributionComparator<A: Float + Send + Sync>: Send + Sync {
    /// Compares two distributions for drift
    fn compare_distributions(
        &self,
        reference: &[A],
        current: &[A],
    ) -> Result<DistributionComparison<A>, String>;

    /// Gets the threshold for drift detection
    fn get_threshold(&self) -> A;

    /// Updates threshold based on performance
    fn update_threshold(&mut self, new_threshold: A);
}

/// Result of distribution comparison
#[derive(Debug, Clone)]
pub struct DistributionComparison<A: Float + Send + Sync> {
    /// Distance/divergence measure
    pub distance: A,
    /// Threshold for drift detection
    pub threshold: A,
    /// Whether drift was detected
    pub drift_detected: bool,
    /// Comparison confidence
    pub confidence: A,
}

/// Trait for model-based drift detection
pub trait ModelBasedDetector<A: Float + Send + Sync>: Send + Sync {
    /// Updates the model with new data
    fn update_model(&mut self, data: &[StreamingDataPoint<A>]) -> Result<(), String>;

    /// Detects drift based on model performance
    fn detect_drift(
        &mut self,
        data: &[StreamingDataPoint<A>],
    ) -> Result<ModelDriftResult<A>, String>;

    /// Resets the model
    fn reset_model(&mut self) -> Result<(), String>;
}

/// Result of model-based drift detection
#[derive(Debug, Clone)]
pub struct ModelDriftResult<A: Float + Send + Sync> {
    /// Whether drift was detected
    pub drift_detected: bool,
    /// Model performance degradation
    pub performance_degradation: A,
    /// Drift confidence
    pub confidence: A,
    /// Feature importance changes
    pub feature_importance_changes: Vec<A>,
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum + 'static> EnhancedDriftDetector<A> {
    /// Creates a new enhanced drift detector
    pub fn new(config: &StreamingConfig) -> Result<Self, String> {
        let drift_config = config.drift_config.clone();

        let mut statistical_tests: HashMap<StatisticalMethod, Box<dyn StatisticalTest<A>>> =
            HashMap::new();
        let mut distribution_methods: HashMap<
            DistributionMethod,
            Box<dyn DistributionComparator<A>>,
        > = HashMap::new();
        let mut model_detectors: HashMap<ModelType, Box<dyn ModelBasedDetector<A>>> =
            HashMap::new();

        // Initialize statistical tests
        statistical_tests.insert(
            StatisticalMethod::ADWIN,
            Box::new(ADWINTest::new(drift_config.sensitivity)?),
        );
        statistical_tests.insert(
            StatisticalMethod::DDM,
            Box::new(DDMTest::new(drift_config.sensitivity)?),
        );
        statistical_tests.insert(
            StatisticalMethod::PageHinkley,
            Box::new(PageHinkleyTest::new(drift_config.sensitivity)?),
        );

        // Initialize distribution methods
        distribution_methods.insert(
            DistributionMethod::KLDivergence,
            Box::new(KLDivergenceComparator::new(drift_config.sensitivity)?),
        );
        distribution_methods.insert(
            DistributionMethod::JSDivergence,
            Box::new(JSDivergenceComparator::new(drift_config.sensitivity)?),
        );

        // Initialize model detectors
        model_detectors.insert(ModelType::Linear, Box::new(LinearModelDetector::new()?));

        let ensemble_strategy = match &drift_config.detection_method {
            DriftDetectionMethod::Ensemble {
                voting_strategy, ..
            } => Some(voting_strategy.clone()),
            _ => None,
        };

        let false_positive_tracker = FalsePositiveTracker::new();

        Ok(Self {
            config: drift_config.clone(),
            detection_method: drift_config.detection_method,
            statistical_tests,
            distribution_methods,
            model_detectors,
            ensemble_strategy,
            detection_history: VecDeque::with_capacity(1000),
            false_positive_tracker,
            reference_window: VecDeque::with_capacity(drift_config.window_size),
            drift_state: DriftState::Stable,
            last_detection: None,
            sensitivity_factor: A::one(),
        })
    }

    /// Detects drift in the given batch of data
    pub fn detect_drift(&mut self, batch: &[StreamingDataPoint<A>]) -> Result<bool, String> {
        if !self.config.enable_detection || batch.len() < self.config.min_samples {
            return Ok(false);
        }

        // Update reference window
        self.update_reference_window(batch)?;

        // Check if we have enough data for comparison
        if self.reference_window.len() < self.config.window_size / 2 {
            return Ok(false);
        }

        // Extract features for comparison
        let current_features = self.extract_features(batch)?;
        let reference_features = self.extract_reference_features()?;

        // Perform drift detection based on configured method
        let detection_method = self.detection_method.clone();
        let drift_result = match detection_method {
            DriftDetectionMethod::Statistical(method) => {
                self.detect_statistical_drift(&method, &reference_features, &current_features)?
            }
            DriftDetectionMethod::Distribution(method) => {
                self.detect_distribution_drift(&method, &reference_features, &current_features)?
            }
            DriftDetectionMethod::ModelBased(model_type) => {
                self.detect_model_drift(&model_type, batch)?
            }
            DriftDetectionMethod::Ensemble {
                methods,
                voting_strategy,
            } => self.detect_ensemble_drift(
                &methods,
                &voting_strategy,
                &reference_features,
                &current_features,
                batch,
            )?,
        };

        // Update drift state and history
        if drift_result.drift_detected {
            self.handle_drift_detection(drift_result)?;
            Ok(true)
        } else {
            self.update_drift_state(false);
            Ok(false)
        }
    }

    /// Updates the reference window with new data
    fn update_reference_window(&mut self, batch: &[StreamingDataPoint<A>]) -> Result<(), String> {
        for data_point in batch {
            if self.reference_window.len() >= self.config.window_size {
                self.reference_window.pop_front();
            }
            self.reference_window.push_back(data_point.clone());
        }
        Ok(())
    }

    /// Extracts features from a batch of data points
    fn extract_features(&self, batch: &[StreamingDataPoint<A>]) -> Result<Vec<A>, String> {
        let mut features = Vec::new();

        for data_point in batch {
            features.extend(data_point.features.iter().cloned());
        }

        Ok(features)
    }

    /// Extracts reference features from the reference window
    fn extract_reference_features(&self) -> Result<Vec<A>, String> {
        let reference_data: Vec<_> = self
            .reference_window
            .iter()
            .take(self.reference_window.len() / 2)
            .collect();

        let mut features = Vec::new();
        for data_point in reference_data {
            features.extend(data_point.features.iter().cloned());
        }

        Ok(features)
    }

    /// Performs statistical drift detection
    fn detect_statistical_drift(
        &mut self,
        method: &StatisticalMethod,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String> {
        if let Some(test) = self.statistical_tests.get_mut(method) {
            let mut result = test.test_for_drift(reference, current)?;

            // Apply sensitivity factor
            result.confidence = result.confidence * self.sensitivity_factor;
            result.drift_detected = result.p_value
                < A::from(self.config.significance_level).expect("unwrap failed")
                    * self.sensitivity_factor;

            Ok(result)
        } else {
            Err(format!("Statistical method {:?} not implemented", method))
        }
    }

    /// Performs distribution-based drift detection
    fn detect_distribution_drift(
        &mut self,
        method: &DistributionMethod,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String> {
        if let Some(comparator) = self.distribution_methods.get(method) {
            let comparison = comparator.compare_distributions(reference, current)?;

            let result = DriftTestResult {
                drift_detected: comparison.drift_detected,
                p_value: A::one() - comparison.confidence, // Convert confidence to p-value like measure
                test_statistic: comparison.distance,
                confidence: comparison.confidence * self.sensitivity_factor,
                metadata: HashMap::new(),
            };

            Ok(result)
        } else {
            Err(format!("Distribution method {:?} not implemented", method))
        }
    }

    /// Performs model-based drift detection
    fn detect_model_drift(
        &mut self,
        model_type: &ModelType,
        batch: &[StreamingDataPoint<A>],
    ) -> Result<DriftTestResult<A>, String> {
        if let Some(detector) = self.model_detectors.get_mut(model_type) {
            let model_result = detector.detect_drift(batch)?;

            let result = DriftTestResult {
                drift_detected: model_result.drift_detected,
                p_value: A::one() - model_result.confidence,
                test_statistic: model_result.performance_degradation,
                confidence: model_result.confidence * self.sensitivity_factor,
                metadata: HashMap::new(),
            };

            Ok(result)
        } else {
            Err(format!("Model type {:?} not implemented", model_type))
        }
    }

    /// Performs ensemble drift detection
    fn detect_ensemble_drift(
        &mut self,
        methods: &[DriftDetectionMethod],
        voting_strategy: &VotingStrategy,
        reference: &[A],
        current: &[A],
        batch: &[StreamingDataPoint<A>],
    ) -> Result<DriftTestResult<A>, String> {
        let mut results = Vec::new();

        // Collect results from all methods
        for method in methods {
            let result = match method {
                DriftDetectionMethod::Statistical(stat_method) => {
                    self.detect_statistical_drift(stat_method, reference, current)?
                }
                DriftDetectionMethod::Distribution(dist_method) => {
                    self.detect_distribution_drift(dist_method, reference, current)?
                }
                DriftDetectionMethod::ModelBased(model_type) => {
                    self.detect_model_drift(model_type, batch)?
                }
                DriftDetectionMethod::Ensemble { .. } => {
                    // Avoid recursive ensemble calls
                    continue;
                }
            };
            results.push(result);
        }

        // Apply voting strategy
        let ensemble_result = self.apply_voting_strategy(voting_strategy, &results)?;
        Ok(ensemble_result)
    }

    /// Applies the ensemble voting strategy
    fn apply_voting_strategy(
        &self,
        strategy: &VotingStrategy,
        results: &[DriftTestResult<A>],
    ) -> Result<DriftTestResult<A>, String> {
        if results.is_empty() {
            return Err("No results to vote on".to_string());
        }

        let drift_detected = match strategy {
            VotingStrategy::Majority => {
                let positive_votes = results.iter().filter(|r| r.drift_detected).count();
                positive_votes > results.len() / 2
            }
            VotingStrategy::Weighted { weights } => {
                if weights.len() != results.len() {
                    return Err("Number of weights doesn't match number of results".to_string());
                }

                let weighted_score: f64 = results
                    .iter()
                    .zip(weights.iter())
                    .map(|(result, &weight)| weight * if result.drift_detected { 1.0 } else { 0.0 })
                    .sum();

                let total_weight: f64 = weights.iter().sum();
                weighted_score / total_weight > 0.5
            }
            VotingStrategy::Unanimous => results.iter().all(|r| r.drift_detected),
            VotingStrategy::Threshold { min_votes } => {
                let positive_votes = results.iter().filter(|r| r.drift_detected).count();
                positive_votes >= *min_votes
            }
        };

        // Aggregate confidence and p-values
        let avg_confidence = results.iter().map(|r| r.confidence).sum::<A>()
            / A::from(results.len()).expect("unwrap failed");

        let avg_p_value = results.iter().map(|r| r.p_value).sum::<A>()
            / A::from(results.len()).expect("unwrap failed");

        let avg_test_statistic = results.iter().map(|r| r.test_statistic).sum::<A>()
            / A::from(results.len()).expect("unwrap failed");

        Ok(DriftTestResult {
            drift_detected,
            p_value: avg_p_value,
            test_statistic: avg_test_statistic,
            confidence: avg_confidence,
            metadata: HashMap::new(),
        })
    }

    /// Handles drift detection event
    fn handle_drift_detection(&mut self, result: DriftTestResult<A>) -> Result<(), String> {
        let severity = self.classify_drift_severity(&result);

        let drift_event = DriftEvent {
            timestamp: Instant::now(),
            severity: severity.clone(),
            confidence: result.confidence,
            detection_method: format!("{:?}", self.detection_method),
            p_value: Some(result.p_value),
            magnitude: result.test_statistic,
            affected_features: Vec::new(), // Could be computed based on feature-wise analysis
        };

        // Store in history
        if self.detection_history.len() >= 1000 {
            self.detection_history.pop_front();
        }
        self.detection_history.push_back(drift_event);

        // Update drift state
        self.update_drift_state(true);
        self.last_detection = Some(Instant::now());

        // Update false positive tracker if enabled
        if self.config.enable_false_positive_tracking {
            self.false_positive_tracker.record_detection(true)?;
        }

        Ok(())
    }

    /// Classifies drift severity based on test results
    fn classify_drift_severity(&self, result: &DriftTestResult<A>) -> DriftSeverity {
        let confidence = result.confidence.to_f64().unwrap_or(0.0);
        let p_value = result.p_value.to_f64().unwrap_or(1.0);

        if p_value < 0.001 && confidence > 0.95 {
            DriftSeverity::Critical
        } else if p_value < 0.01 && confidence > 0.9 {
            DriftSeverity::Major
        } else if p_value < 0.05 && confidence > 0.8 {
            DriftSeverity::Moderate
        } else {
            DriftSeverity::Minor
        }
    }

    /// Updates the current drift state
    fn update_drift_state(&mut self, drift_detected: bool) {
        self.drift_state = match (&self.drift_state, drift_detected) {
            (DriftState::Stable, true) => DriftState::Warning,
            (DriftState::Warning, true) => DriftState::Drift,
            (DriftState::Drift, false) => DriftState::Recovery,
            (DriftState::Recovery, false) => DriftState::Stable,
            (state, _) => state.clone(),
        };
    }

    /// Computes adaptation for drift sensitivity
    pub fn compute_sensitivity_adaptation(&mut self) -> Result<Option<Adaptation<A>>, String> {
        // Check if sensitivity should be adjusted based on false positive rate
        if self.config.enable_false_positive_tracking {
            let current_fp_rate = self.false_positive_tracker.current_fp_rate;
            let target_fp_rate = A::from(0.05).expect("unwrap failed"); // 5% target false positive rate

            if (current_fp_rate - target_fp_rate).abs() > A::from(0.02).expect("unwrap failed") {
                let adjustment = if current_fp_rate > target_fp_rate {
                    // Too many false positives, decrease sensitivity
                    -A::from(0.1).expect("unwrap failed")
                } else {
                    // Too few detections (potentially missing true positives), increase sensitivity
                    A::from(0.1).expect("unwrap failed")
                };

                let adaptation = Adaptation {
                    adaptation_type: AdaptationType::DriftSensitivity,
                    magnitude: adjustment,
                    target_component: "drift_detector".to_string(),
                    parameters: HashMap::new(),
                    priority: AdaptationPriority::Normal,
                    timestamp: Instant::now(),
                };

                return Ok(Some(adaptation));
            }
        }

        Ok(None)
    }

    /// Applies sensitivity adaptation
    pub fn apply_sensitivity_adaptation(
        &mut self,
        adaptation: &Adaptation<A>,
    ) -> Result<(), String> {
        if adaptation.adaptation_type == AdaptationType::DriftSensitivity {
            self.sensitivity_factor = (self.sensitivity_factor + adaptation.magnitude)
                .max(A::from(0.1).expect("unwrap failed"))
                .min(A::from(2.0).expect("unwrap failed"));
        }
        Ok(())
    }

    /// Checks if drift is currently detected
    pub fn is_drift_detected(&self) -> bool {
        matches!(self.drift_state, DriftState::Drift | DriftState::Warning)
    }

    /// Gets the current drift state
    pub fn get_drift_state(&self) -> &DriftState {
        &self.drift_state
    }

    /// Gets recent drift events
    pub fn get_recent_drift_events(&self, count: usize) -> Vec<&DriftEvent<A>> {
        self.detection_history.iter().rev().take(count).collect()
    }

    /// Resets the drift detector
    pub fn reset(&mut self) -> Result<(), String> {
        self.detection_history.clear();
        self.reference_window.clear();
        self.drift_state = DriftState::Stable;
        self.last_detection = None;
        self.sensitivity_factor = A::one();

        // Reset all detection methods
        for test in self.statistical_tests.values_mut() {
            test.reset();
        }

        for detector in self.model_detectors.values_mut() {
            detector.reset_model()?;
        }

        Ok(())
    }

    /// Gets diagnostic information
    pub fn get_diagnostics(&self) -> DriftDiagnostics {
        DriftDiagnostics {
            current_state: self.drift_state.clone(),
            detection_count: self.detection_history.len(),
            false_positive_rate: self
                .false_positive_tracker
                .current_fp_rate
                .to_f64()
                .unwrap_or(0.0),
            sensitivity_factor: self.sensitivity_factor.to_f64().unwrap_or(1.0),
            last_detection_time: self.last_detection,
            reference_window_size: self.reference_window.len(),
        }
    }
}

impl<A: Float + Send + Sync + Send + Sync> FalsePositiveTracker<A> {
    fn new() -> Self {
        Self {
            false_positives: VecDeque::new(),
            true_positives: VecDeque::new(),
            current_fp_rate: A::zero(),
            target_fp_rate: A::from(0.05).expect("unwrap failed"),
        }
    }

    fn record_detection(&mut self, is_true_positive: bool) -> Result<(), String> {
        let now = Instant::now();

        if is_true_positive {
            self.true_positives.push_back(now);
        } else {
            self.false_positives.push_back(now);
        }

        // Keep only recent events (last hour)
        let cutoff = now - Duration::from_secs(3600);
        self.false_positives.retain(|&time| time > cutoff);
        self.true_positives.retain(|&time| time > cutoff);

        // Update false positive rate
        let total_detections = self.false_positives.len() + self.true_positives.len();
        if total_detections > 0 {
            self.current_fp_rate = A::from(self.false_positives.len()).expect("unwrap failed")
                / A::from(total_detections).expect("unwrap failed");
        }

        Ok(())
    }
}

/// Diagnostic information for drift detection
#[derive(Debug, Clone)]
pub struct DriftDiagnostics {
    pub current_state: DriftState,
    pub detection_count: usize,
    pub false_positive_rate: f64,
    pub sensitivity_factor: f64,
    pub last_detection_time: Option<Instant>,
    pub reference_window_size: usize,
}

// Simplified implementations of detection methods
// In practice, these would be more sophisticated

struct ADWINTest<A: Float + Send + Sync> {
    sensitivity: A,
    window: VecDeque<A>,
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> ADWINTest<A> {
    fn new(sensitivity: f64) -> Result<Self, String> {
        Ok(Self {
            sensitivity: A::from(sensitivity).expect("unwrap failed"),
            window: VecDeque::new(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A>
    for ADWINTest<A>
{
    fn test_for_drift(
        &mut self,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String> {
        // Simplified ADWIN implementation
        let ref_mean =
            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
        let cur_mean =
            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");

        let difference = (ref_mean - cur_mean).abs();
        let threshold = self.sensitivity;

        let drift_detected = difference > threshold;

        Ok(DriftTestResult {
            drift_detected,
            p_value: if drift_detected {
                A::from(0.01).expect("unwrap failed")
            } else {
                A::from(0.5).expect("unwrap failed")
            },
            test_statistic: difference,
            confidence: if drift_detected {
                A::from(0.9).expect("unwrap failed")
            } else {
                A::from(0.1).expect("unwrap failed")
            },
            metadata: HashMap::new(),
        })
    }

    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
        Ok(())
    }

    fn reset(&mut self) {
        self.window.clear();
    }
}

struct DDMTest<A: Float + Send + Sync> {
    sensitivity: A,
    error_rate: A,
    std_dev: A,
}

impl<A: Float + Default + Send + Sync + std::iter::Sum> DDMTest<A> {
    fn new(sensitivity: f64) -> Result<Self, String> {
        Ok(Self {
            sensitivity: A::from(sensitivity).expect("unwrap failed"),
            error_rate: A::zero(),
            std_dev: A::zero(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A> for DDMTest<A> {
    fn test_for_drift(
        &mut self,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String> {
        // Simplified DDM implementation
        let ref_mean =
            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
        let cur_mean =
            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");

        let difference = (ref_mean - cur_mean).abs();
        let drift_detected = difference > self.sensitivity;

        Ok(DriftTestResult {
            drift_detected,
            p_value: if drift_detected {
                A::from(0.02).expect("unwrap failed")
            } else {
                A::from(0.6).expect("unwrap failed")
            },
            test_statistic: difference,
            confidence: if drift_detected {
                A::from(0.85).expect("unwrap failed")
            } else {
                A::from(0.15).expect("unwrap failed")
            },
            metadata: HashMap::new(),
        })
    }

    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
        Ok(())
    }

    fn reset(&mut self) {
        self.error_rate = A::zero();
        self.std_dev = A::zero();
    }
}

struct PageHinkleyTest<A: Float + Send + Sync> {
    sensitivity: A,
    cumulative_sum: A,
}

impl<A: Float + Default + Send + Sync + std::iter::Sum> PageHinkleyTest<A> {
    fn new(sensitivity: f64) -> Result<Self, String> {
        Ok(Self {
            sensitivity: A::from(sensitivity).expect("unwrap failed"),
            cumulative_sum: A::zero(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A>
    for PageHinkleyTest<A>
{
    fn test_for_drift(
        &mut self,
        reference: &[A],
        current: &[A],
    ) -> Result<DriftTestResult<A>, String> {
        // Simplified Page-Hinkley test
        let ref_mean =
            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
        let cur_mean =
            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");

        let difference = cur_mean - ref_mean;
        self.cumulative_sum = self.cumulative_sum + difference;

        let drift_detected = self.cumulative_sum.abs() > self.sensitivity;

        Ok(DriftTestResult {
            drift_detected,
            p_value: if drift_detected {
                A::from(0.015).expect("unwrap failed")
            } else {
                A::from(0.7).expect("unwrap failed")
            },
            test_statistic: self.cumulative_sum,
            confidence: if drift_detected {
                A::from(0.88).expect("unwrap failed")
            } else {
                A::from(0.12).expect("unwrap failed")
            },
            metadata: HashMap::new(),
        })
    }

    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
        Ok(())
    }

    fn reset(&mut self) {
        self.cumulative_sum = A::zero();
    }
}

struct KLDivergenceComparator<A: Float + Send + Sync> {
    threshold: A,
}

impl<A: Float + Send + Sync + Send + Sync> KLDivergenceComparator<A> {
    fn new(sensitivity: f64) -> Result<Self, String> {
        Ok(Self {
            threshold: A::from(sensitivity).expect("unwrap failed"),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> DistributionComparator<A>
    for KLDivergenceComparator<A>
{
    fn compare_distributions(
        &self,
        reference: &[A],
        current: &[A],
    ) -> Result<DistributionComparison<A>, String> {
        // Simplified KL divergence calculation
        let ref_mean =
            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
        let cur_mean =
            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");

        let distance = (ref_mean - cur_mean).abs();
        let drift_detected = distance > self.threshold;

        Ok(DistributionComparison {
            distance,
            threshold: self.threshold,
            drift_detected,
            confidence: if drift_detected {
                A::from(0.8).expect("unwrap failed")
            } else {
                A::from(0.2).expect("unwrap failed")
            },
        })
    }

    fn get_threshold(&self) -> A {
        self.threshold
    }

    fn update_threshold(&mut self, new_threshold: A) {
        self.threshold = new_threshold;
    }
}

struct JSDivergenceComparator<A: Float + Send + Sync> {
    threshold: A,
}

impl<A: Float + Send + Sync + Send + Sync> JSDivergenceComparator<A> {
    fn new(sensitivity: f64) -> Result<Self, String> {
        Ok(Self {
            threshold: A::from(sensitivity).expect("unwrap failed"),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> DistributionComparator<A>
    for JSDivergenceComparator<A>
{
    fn compare_distributions(
        &self,
        reference: &[A],
        current: &[A],
    ) -> Result<DistributionComparison<A>, String> {
        // Simplified JS divergence calculation
        let ref_mean =
            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
        let cur_mean =
            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");

        let distance = (ref_mean - cur_mean).abs() * A::from(0.5).expect("unwrap failed"); // Simplified
        let drift_detected = distance > self.threshold;

        Ok(DistributionComparison {
            distance,
            threshold: self.threshold,
            drift_detected,
            confidence: if drift_detected {
                A::from(0.75).expect("unwrap failed")
            } else {
                A::from(0.25).expect("unwrap failed")
            },
        })
    }

    fn get_threshold(&self) -> A {
        self.threshold
    }

    fn update_threshold(&mut self, new_threshold: A) {
        self.threshold = new_threshold;
    }
}

struct LinearModelDetector<A: Float + Send + Sync> {
    model_performance: A,
    baseline_performance: A,
}

impl<A: Float + Default + Send + Sync + Send + Sync> LinearModelDetector<A> {
    fn new() -> Result<Self, String> {
        Ok(Self {
            model_performance: A::zero(),
            baseline_performance: A::zero(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> ModelBasedDetector<A>
    for LinearModelDetector<A>
{
    fn update_model(&mut self, _data: &[StreamingDataPoint<A>]) -> Result<(), String> {
        // Simplified model update
        Ok(())
    }

    fn detect_drift(
        &mut self,
        _data: &[StreamingDataPoint<A>],
    ) -> Result<ModelDriftResult<A>, String> {
        // Simplified drift detection based on performance degradation
        let performance_degradation = self.baseline_performance - self.model_performance;
        let drift_detected = performance_degradation > A::from(0.1).expect("unwrap failed");

        Ok(ModelDriftResult {
            drift_detected,
            performance_degradation,
            confidence: if drift_detected {
                A::from(0.7).expect("unwrap failed")
            } else {
                A::from(0.3).expect("unwrap failed")
            },
            feature_importance_changes: Vec::new(),
        })
    }

    fn reset_model(&mut self) -> Result<(), String> {
        self.model_performance = A::zero();
        self.baseline_performance = A::zero();
        Ok(())
    }
}