quantrs2_device/performance_analytics_dashboard/

analytics.rs

//! Analytics Engines for Performance Dashboard
//!
//! This module contains the analytics engines that power the dashboard's
//! statistical analysis, trend detection, anomaly detection, and performance prediction.

use super::config::{AnalyticsConfig, PredictionConfig};
use super::data::{
    AnomalyDetectionResults, PerformancePredictions, RealtimeMetrics, StatisticalAnalysisResults,
    TrendAnalysisResults, TrendDirection,
};
use crate::DeviceResult;
use scirs2_core::ndarray::{Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, SystemTime};

/// Statistical analyzer for metrics
pub struct StatisticalAnalyzer {
    config: AnalyticsConfig,
    analysis_cache: HashMap<String, CachedAnalysis>,
    computation_history: VecDeque<AnalysisRecord>,
}

/// Trend analyzer for performance trends
pub struct TrendAnalyzer {
    config: AnalyticsConfig,
    trend_models: HashMap<String, TrendModel>,
    forecast_accuracy: HashMap<String, f64>,
}

/// Anomaly detector for performance anomalies
pub struct AnomalyDetector {
    config: AnalyticsConfig,
    detection_models: HashMap<String, AnomalyDetectionModel>,
    baseline_statistics: HashMap<String, BaselineStatistics>,
    anomaly_history: VecDeque<HistoricalAnomaly>,
}

/// Performance predictor for forecasting
pub struct PerformancePredictor {
    config: PredictionConfig,
    prediction_models: HashMap<String, PredictionModelInstance>,
    feature_engineering: FeatureEngineeringPipeline,
    model_registry: ModelRegistry,
}

/// Cached analysis result
#[derive(Debug, Clone)]
pub struct CachedAnalysis {
    pub timestamp: SystemTime,
    pub analysis_type: String,
    pub results: HashMap<String, f64>,
    pub validity_period: Duration,
}

/// Analysis record for history tracking
#[derive(Debug, Clone)]
pub struct AnalysisRecord {
    pub timestamp: SystemTime,
    pub analysis_type: String,
    pub computation_time: Duration,
    pub data_points: usize,
    pub success: bool,
}

/// Trend model for time series analysis
#[derive(Debug, Clone)]
pub struct TrendModel {
    pub model_type: TrendModelType,
    pub parameters: Vec<f64>,
    pub last_update: SystemTime,
    pub accuracy: f64,
    pub trend_direction: TrendDirection,
}

/// Trend model types
#[derive(Debug, Clone, PartialEq)]
pub enum TrendModelType {
    Linear,
    Exponential,
    Polynomial { degree: usize },
    MovingAverage { window: usize },
    ExponentialSmoothing { alpha: f64 },
    ARIMA { p: usize, d: usize, q: usize },
    Custom(String),
}

/// Anomaly detection model
#[derive(Debug, Clone)]
pub struct AnomalyDetectionModel {
    pub model_type: AnomalyModelType,
    pub sensitivity: f64,
    pub baseline: BaselineStatistics,
    pub detection_threshold: f64,
    pub last_training: SystemTime,
}

/// Anomaly detection model types
#[derive(Debug, Clone, PartialEq)]
pub enum AnomalyModelType {
    StatisticalOutlier,
    IsolationForest,
    LocalOutlierFactor,
    OneClassSVM,
    AutoEncoder,
    LSTM,
    Custom(String),
}

/// Baseline statistics for anomaly detection
#[derive(Debug, Clone)]
pub struct BaselineStatistics {
    pub mean: f64,
    pub std_dev: f64,
    pub percentiles: HashMap<String, f64>,
    pub distribution_type: String,
    pub seasonal_patterns: Vec<f64>,
}

/// Historical anomaly record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalAnomaly {
    pub timestamp: SystemTime,
    pub metric_name: String,
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub value: f64,
    pub expected_value: f64,
    pub deviation: f64,
    pub resolution: Option<AnomalyResolution>,
}

/// Anomaly types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum AnomalyType {
    Outlier,
    Drift,
    Spike,
    Drop,
    PatternChange,
    SeasonalDeviation,
    Custom(String),
}

/// Anomaly severity levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Anomaly resolution information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyResolution {
    pub resolution_time: SystemTime,
    pub resolution_method: String,
    pub root_cause: Option<String>,
    pub corrective_action: Option<String>,
}

/// Current anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Anomaly {
    pub id: String,
    pub timestamp: SystemTime,
    pub metric_name: String,
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub current_value: f64,
    pub expected_value: f64,
    pub confidence: f64,
    pub impact_assessment: ImpactAssessment,
}

/// Impact assessment for anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImpactAssessment {
    pub affected_systems: Vec<String>,
    pub impact_severity: String,
    pub estimated_duration: Option<Duration>,
    pub business_impact: Option<String>,
}

/// Prediction model instance
#[derive(Debug, Clone)]
pub struct PredictionModelInstance {
    pub model_id: String,
    pub model_type: String,
    pub model_parameters: HashMap<String, f64>,
    pub training_data_size: usize,
    pub last_training: SystemTime,
    pub prediction_accuracy: f64,
    pub feature_importance: HashMap<String, f64>,
}

/// Feature engineering pipeline
#[derive(Debug, Clone)]
pub struct FeatureEngineeringPipeline {
    pub transformations: Vec<FeatureTransformation>,
    pub feature_selection: FeatureSelectionMethod,
    pub preprocessing_steps: Vec<PreprocessingStep>,
}

/// Feature transformation types
#[derive(Debug, Clone)]
pub enum FeatureTransformation {
    Normalization,
    Standardization,
    LogTransform,
    PolynomialFeatures { degree: usize },
    InteractionFeatures,
    TemporalFeatures,
    Custom(String),
}

/// Feature selection methods
#[derive(Debug, Clone)]
pub enum FeatureSelectionMethod {
    VarianceThreshold { threshold: f64 },
    UnivariateSelection { k: usize },
    RecursiveFeatureElimination { n_features: usize },
    LassoRegularization { alpha: f64 },
    MutualInformation,
    Custom(String),
}

/// Preprocessing steps
#[derive(Debug, Clone)]
pub enum PreprocessingStep {
    MissingValueImputation { method: String },
    OutlierRemoval { method: String },
    DataSmoothing { window_size: usize },
    Detrending,
    SeasonalDecomposition,
    Custom(String),
}

/// Model registry for managing prediction models
#[derive(Debug, Clone)]
pub struct ModelRegistry {
    pub registered_models: HashMap<String, ModelMetadata>,
    pub active_models: Vec<String>,
    pub model_performance_history: HashMap<String, Vec<ModelPerformanceRecord>>,
}

/// Model metadata
#[derive(Debug, Clone)]
pub struct ModelMetadata {
    pub model_id: String,
    pub model_name: String,
    pub model_type: String,
    pub version: String,
    pub created_at: SystemTime,
    pub last_updated: SystemTime,
    pub performance_metrics: HashMap<String, f64>,
    pub hyperparameters: HashMap<String, f64>,
}

/// Model performance record
#[derive(Debug, Clone)]
pub struct ModelPerformanceRecord {
    pub timestamp: SystemTime,
    pub accuracy: f64,
    pub precision: f64,
    pub recall: f64,
    pub f1_score: f64,
    pub custom_metrics: HashMap<String, f64>,
}

/// Anomaly patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyPatterns {
    pub recurring_patterns: Vec<RecurringPattern>,
    pub correlation_patterns: Vec<CorrelationPattern>,
    pub temporal_patterns: Vec<TemporalPattern>,
}

/// Recurring anomaly pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecurringPattern {
    pub pattern_id: String,
    pub frequency: String,
    pub affected_metrics: Vec<String>,
    pub pattern_strength: f64,
    pub next_predicted_occurrence: Option<SystemTime>,
}

/// Correlation pattern between anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationPattern {
    pub pattern_id: String,
    pub primary_metric: String,
    pub correlated_metrics: Vec<String>,
    pub correlation_strength: f64,
    pub time_lag: Option<Duration>,
}

/// Temporal pattern in anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalPattern {
    pub pattern_id: String,
    pub time_of_day: Option<String>,
    pub day_of_week: Option<String>,
    pub seasonal_component: Option<String>,
    pub pattern_confidence: f64,
}

/// Root cause analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RootCauseAnalysis {
    pub probable_causes: Vec<ProbableCause>,
    pub causal_chains: Vec<CausalChain>,
    pub correlation_analysis: CorrelationAnalysisResult,
    pub recommendation_score: f64,
}

/// Probable cause of anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProbableCause {
    pub cause_id: String,
    pub cause_description: String,
    pub confidence: f64,
    pub supporting_evidence: Vec<String>,
    pub recommended_actions: Vec<String>,
}

/// Causal chain analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CausalChain {
    pub chain_id: String,
    pub events: Vec<CausalEvent>,
    pub chain_probability: f64,
}

/// Causal event in a chain
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CausalEvent {
    pub event_id: String,
    pub event_description: String,
    pub timestamp: SystemTime,
    pub event_impact: f64,
}

/// Correlation analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationAnalysisResult {
    pub metric_correlations: HashMap<String, f64>,
    pub time_lag_correlations: HashMap<String, (Duration, f64)>,
    pub cross_correlations: HashMap<String, Vec<f64>>,
}

/// Seasonal patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalPatterns {
    pub daily_patterns: HashMap<String, Vec<f64>>,
    pub weekly_patterns: HashMap<String, Vec<f64>>,
    pub monthly_patterns: HashMap<String, Vec<f64>>,
    pub seasonal_strength: HashMap<String, f64>,
}

/// Change point detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangePointDetection {
    pub change_points: Vec<ChangePoint>,
    pub change_point_probabilities: Vec<f64>,
    pub structural_breaks: Vec<StructuralBreak>,
}

/// Change point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangePoint {
    pub timestamp: SystemTime,
    pub metric_name: String,
    pub change_magnitude: f64,
    pub confidence: f64,
    pub change_type: String,
}

/// Structural break
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StructuralBreak {
    pub breakpoint_time: SystemTime,
    pub variable_name: String,
    pub magnitude: f64,
    pub confidence_level: f64,
    pub break_type: String,
}

/// Forecasting results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ForecastingResults {
    pub forecasts: HashMap<String, Forecast>,
    pub forecast_accuracy: HashMap<String, ForecastAccuracy>,
    pub uncertainty_bounds: HashMap<String, (Vec<f64>, Vec<f64>)>,
}

/// Forecast data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Forecast {
    pub predicted_values: Vec<f64>,
    pub prediction_timestamps: Vec<SystemTime>,
    pub confidence_intervals: Vec<(f64, f64)>,
    pub model_used: String,
}

/// Forecast accuracy metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ForecastAccuracy {
    pub mae: f64,   // Mean Absolute Error
    pub mse: f64,   // Mean Squared Error
    pub rmse: f64,  // Root Mean Squared Error
    pub mape: f64,  // Mean Absolute Percentage Error
    pub smape: f64, // Symmetric Mean Absolute Percentage Error
}

impl StatisticalAnalyzer {
    pub fn new(config: AnalyticsConfig) -> Self {
        Self {
            config,
            analysis_cache: HashMap::new(),
            computation_history: VecDeque::new(),
        }
    }

    pub async fn analyze_metrics(
        &mut self,
        metrics: &RealtimeMetrics,
    ) -> DeviceResult<StatisticalAnalysisResults> {
        // Check cache first
        if let Some(cached) = self.get_cached_analysis("descriptive_stats") {
            if self.is_cache_valid(&cached) {
                return Ok(self.build_results_from_cache(&cached));
            }
        }

        // Perform new analysis
        let start_time = SystemTime::now();
        let results = self.perform_statistical_analysis(metrics).await?;
        let computation_time = start_time.elapsed().unwrap_or(Duration::from_secs(0));

        // Cache results
        self.cache_analysis("descriptive_stats", &results, computation_time);

        // Record computation history
        self.record_analysis("descriptive_stats", computation_time, 1, true);

        Ok(results)
    }

    async fn perform_statistical_analysis(
        &self,
        metrics: &RealtimeMetrics,
    ) -> DeviceResult<StatisticalAnalysisResults> {
        // Simplified implementation - real implementation would use SciRS2
        Ok(StatisticalAnalysisResults::default())
    }

    fn get_cached_analysis(&self, analysis_type: &str) -> Option<&CachedAnalysis> {
        self.analysis_cache.get(analysis_type)
    }

    fn is_cache_valid(&self, cached: &CachedAnalysis) -> bool {
        SystemTime::now()
            .duration_since(cached.timestamp)
            .unwrap_or(Duration::from_secs(0))
            < cached.validity_period
    }

    fn build_results_from_cache(&self, _cached: &CachedAnalysis) -> StatisticalAnalysisResults {
        StatisticalAnalysisResults::default()
    }

    fn cache_analysis(
        &mut self,
        analysis_type: &str,
        _results: &StatisticalAnalysisResults,
        _computation_time: Duration,
    ) {
        let cached = CachedAnalysis {
            timestamp: SystemTime::now(),
            analysis_type: analysis_type.to_string(),
            results: HashMap::new(),
            validity_period: Duration::from_secs(300), // 5 minutes
        };
        self.analysis_cache
            .insert(analysis_type.to_string(), cached);
    }

    fn record_analysis(
        &mut self,
        analysis_type: &str,
        computation_time: Duration,
        data_points: usize,
        success: bool,
    ) {
        let record = AnalysisRecord {
            timestamp: SystemTime::now(),
            analysis_type: analysis_type.to_string(),
            computation_time,
            data_points,
            success,
        };

        self.computation_history.push_back(record);

        // Keep only last 1000 records
        if self.computation_history.len() > 1000 {
            self.computation_history.pop_front();
        }
    }
}

impl TrendAnalyzer {
    pub fn new(config: AnalyticsConfig) -> Self {
        Self {
            config,
            trend_models: HashMap::new(),
            forecast_accuracy: HashMap::new(),
        }
    }

    pub async fn analyze_trends(
        &mut self,
        historical_data: &HashMap<String, Vec<f64>>,
    ) -> DeviceResult<TrendAnalysisResults> {
        let mut trend_directions = HashMap::new();
        let mut trend_strengths = HashMap::new();

        for (metric_name, values) in historical_data {
            if values.len() < self.config.trend_window_size {
                continue;
            }

            let (direction, strength) = self.calculate_trend(values)?;
            trend_directions.insert(metric_name.clone(), direction);
            trend_strengths.insert(metric_name.clone(), strength);
        }

        Ok(TrendAnalysisResults {
            trend_directions,
            trend_strengths,
            seasonal_patterns:
                crate::performance_analytics_dashboard::data::SeasonalPatterns::default(),
            change_points:
                crate::performance_analytics_dashboard::data::ChangePointDetection::default(),
            forecasts: crate::performance_analytics_dashboard::data::ForecastingResults::default(),
        })
    }

    fn calculate_trend(&self, values: &[f64]) -> DeviceResult<(TrendDirection, f64)> {
        if values.len() < 2 {
            return Ok((TrendDirection::Stable, 0.0));
        }

        // Simple linear trend calculation
        let n = values.len() as f64;
        let x_mean = (n - 1.0) / 2.0;
        let y_mean = values.iter().sum::<f64>() / n;

        let mut numerator = 0.0;
        let mut denominator = 0.0;

        for (i, &y) in values.iter().enumerate() {
            let x = i as f64;
            numerator += (x - x_mean) * (y - y_mean);
            denominator += (x - x_mean).powi(2);
        }

        if denominator == 0.0 {
            return Ok((TrendDirection::Stable, 0.0));
        }

        let slope = numerator / denominator;
        let strength = slope.abs();

        let direction = if slope > 0.01 {
            TrendDirection::Increasing
        } else if slope < -0.01 {
            TrendDirection::Decreasing
        } else {
            TrendDirection::Stable
        };

        Ok((direction, strength))
    }
}

impl AnomalyDetector {
    pub fn new(config: AnalyticsConfig) -> Self {
        Self {
            config,
            detection_models: HashMap::new(),
            baseline_statistics: HashMap::new(),
            anomaly_history: VecDeque::new(),
        }
    }

    pub async fn detect_anomalies(
        &mut self,
        metrics: &RealtimeMetrics,
    ) -> DeviceResult<AnomalyDetectionResults> {
        let mut current_anomalies = Vec::new();

        // Check device metrics for anomalies
        if let Some(anomaly) = self
            .check_metric_anomaly("fidelity", metrics.device_metrics.fidelity)
            .await?
        {
            current_anomalies.push(anomaly);
        }

        if let Some(anomaly) = self
            .check_metric_anomaly("error_rate", metrics.device_metrics.error_rate)
            .await?
        {
            current_anomalies.push(anomaly);
        }

        Ok(AnomalyDetectionResults {
            current_anomalies: current_anomalies
                .into_iter()
                .map(|a| crate::performance_analytics_dashboard::data::Anomaly {
                    timestamp: a
                        .timestamp
                        .duration_since(std::time::UNIX_EPOCH)
                        .unwrap_or_default()
                        .as_secs(),
                    metric_name: a.metric_name,
                    anomaly_score: a.confidence,
                    anomaly_type: format!("{:?}", a.anomaly_type),
                    description: format!("Anomaly detected with confidence {:.2}", a.confidence),
                })
                .collect(),
            anomaly_history: vec![], // Simplified conversion
            anomaly_patterns:
                crate::performance_analytics_dashboard::data::AnomalyPatterns::default(),
            root_cause_analysis:
                crate::performance_analytics_dashboard::data::RootCauseAnalysis::default(),
        })
    }

    async fn check_metric_anomaly(
        &mut self,
        metric_name: &str,
        value: f64,
    ) -> DeviceResult<Option<Anomaly>> {
        // Get or create baseline for this metric
        let baseline = if let Some(baseline) = self.baseline_statistics.get(metric_name) {
            baseline.clone()
        } else {
            let new_baseline = self.create_baseline(metric_name);
            self.baseline_statistics
                .insert(metric_name.to_string(), new_baseline.clone());
            new_baseline
        };

        // Simple statistical anomaly detection
        let z_score = (value - baseline.mean) / baseline.std_dev;
        let threshold = 3.0; // 3-sigma rule

        if z_score.abs() > threshold {
            let anomaly = Anomaly {
                id: format!(
                    "{}-{}",
                    metric_name,
                    SystemTime::now()
                        .duration_since(std::time::UNIX_EPOCH)
                        .unwrap()
                        .as_secs()
                ),
                timestamp: SystemTime::now(),
                metric_name: metric_name.to_string(),
                anomaly_type: if z_score > 0.0 {
                    AnomalyType::Spike
                } else {
                    AnomalyType::Drop
                },
                severity: if z_score.abs() > 5.0 {
                    AnomalySeverity::Critical
                } else {
                    AnomalySeverity::High
                },
                current_value: value,
                expected_value: baseline.mean,
                confidence: (z_score.abs() - threshold) / threshold,
                impact_assessment: ImpactAssessment {
                    affected_systems: vec![metric_name.to_string()],
                    impact_severity: "Medium".to_string(),
                    estimated_duration: Some(Duration::from_secs(15 * 60)),
                    business_impact: Some("Performance degradation".to_string()),
                },
            };

            Ok(Some(anomaly))
        } else {
            Ok(None)
        }
    }

    fn create_baseline(&self, metric_name: &str) -> BaselineStatistics {
        // Default baseline statistics
        let (mean, std_dev) = match metric_name {
            "fidelity" => (0.95, 0.02),
            "error_rate" => (0.01, 0.005),
            "coherence_time" => (100.0, 10.0),
            _ => (0.0, 1.0),
        };

        BaselineStatistics {
            mean,
            std_dev,
            percentiles: HashMap::new(),
            distribution_type: "normal".to_string(),
            seasonal_patterns: Vec::new(),
        }
    }
}

impl PerformancePredictor {
    pub fn new(config: PredictionConfig) -> Self {
        Self {
            config,
            prediction_models: HashMap::new(),
            feature_engineering: FeatureEngineeringPipeline {
                transformations: Vec::new(),
                feature_selection: FeatureSelectionMethod::VarianceThreshold { threshold: 0.01 },
                preprocessing_steps: Vec::new(),
            },
            model_registry: ModelRegistry {
                registered_models: HashMap::new(),
                active_models: Vec::new(),
                model_performance_history: HashMap::new(),
            },
        }
    }

    pub async fn predict_performance(
        &mut self,
        historical_data: &HashMap<String, Vec<f64>>,
    ) -> DeviceResult<PerformancePredictions> {
        // Simplified prediction implementation
        Ok(PerformancePredictions::default())
    }
}

// Default implementations
impl Default for SeasonalPatterns {
    fn default() -> Self {
        Self {
            daily_patterns: HashMap::new(),
            weekly_patterns: HashMap::new(),
            monthly_patterns: HashMap::new(),
            seasonal_strength: HashMap::new(),
        }
    }
}

impl Default for ChangePointDetection {
    fn default() -> Self {
        Self {
            change_points: Vec::new(),
            change_point_probabilities: Vec::new(),
            structural_breaks: Vec::new(),
        }
    }
}

impl Default for ForecastingResults {
    fn default() -> Self {
        Self {
            forecasts: HashMap::new(),
            forecast_accuracy: HashMap::new(),
            uncertainty_bounds: HashMap::new(),
        }
    }
}

impl Default for AnomalyPatterns {
    fn default() -> Self {
        Self {
            recurring_patterns: Vec::new(),
            correlation_patterns: Vec::new(),
            temporal_patterns: Vec::new(),
        }
    }
}

impl Default for RootCauseAnalysis {
    fn default() -> Self {
        Self {
            probable_causes: Vec::new(),
            causal_chains: Vec::new(),
            correlation_analysis: CorrelationAnalysisResult {
                metric_correlations: HashMap::new(),
                time_lag_correlations: HashMap::new(),
                cross_correlations: HashMap::new(),
            },
            recommendation_score: 0.0,
        }
    }
}