optirs_core/streaming/adaptive_streaming/
drift_detection.rs

1// Drift detection and adaptation for streaming data
2//
3// This module provides comprehensive drift detection capabilities including
4// statistical methods, distribution-based approaches, model-based detection,
5// and ensemble methods for identifying concept drift in streaming data.
6
7use super::config::*;
8use super::optimizer::{Adaptation, AdaptationPriority, AdaptationType, StreamingDataPoint};
9
10use scirs2_core::numeric::Float;
11use serde::{Deserialize, Serialize};
12use std::collections::{HashMap, VecDeque};
13use std::time::{Duration, Instant};
14
15/// Enhanced drift detector with multiple detection methods
16pub struct EnhancedDriftDetector<A: Float + Send + Sync> {
17    /// Configuration for drift detection
18    config: DriftConfig,
19    /// Current detection method
20    detection_method: DriftDetectionMethod,
21    /// Statistical test implementations
22    statistical_tests: HashMap<StatisticalMethod, Box<dyn StatisticalTest<A>>>,
23    /// Distribution comparison methods
24    distribution_methods: HashMap<DistributionMethod, Box<dyn DistributionComparator<A>>>,
25    /// Model-based detectors
26    model_detectors: HashMap<ModelType, Box<dyn ModelBasedDetector<A>>>,
27    /// Ensemble voting strategy
28    ensemble_strategy: Option<VotingStrategy>,
29    /// Detection history
30    detection_history: VecDeque<DriftEvent<A>>,
31    /// False positive tracker
32    false_positive_tracker: FalsePositiveTracker<A>,
33    /// Reference window for comparison
34    reference_window: VecDeque<StreamingDataPoint<A>>,
35    /// Current drift state
36    drift_state: DriftState,
37    /// Last detection timestamp
38    last_detection: Option<Instant>,
39    /// Sensitivity adjustment factor
40    sensitivity_factor: A,
41}
42
43/// Drift event information
44#[derive(Debug, Clone)]
45pub struct DriftEvent<A: Float + Send + Sync> {
46    /// Event timestamp
47    pub timestamp: Instant,
48    /// Drift severity level
49    pub severity: DriftSeverity,
50    /// Detection confidence
51    pub confidence: A,
52    /// Detection method that triggered
53    pub detection_method: String,
54    /// Statistical significance
55    pub p_value: Option<A>,
56    /// Drift magnitude estimate
57    pub magnitude: A,
58    /// Affected features (if applicable)
59    pub affected_features: Vec<usize>,
60}
61
62/// Drift severity levels
63#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
64pub enum DriftSeverity {
65    /// Minor drift that may not require immediate action
66    Minor,
67    /// Moderate drift requiring attention
68    Moderate,
69    /// Major drift requiring significant adaptation
70    Major,
71    /// Critical drift requiring immediate response
72    Critical,
73}
74
75/// Current drift detection state
76#[derive(Debug, Clone, PartialEq, Eq)]
77pub enum DriftState {
78    /// Normal operation, no drift detected
79    Stable,
80    /// Warning level - potential drift detected
81    Warning,
82    /// Drift confirmed
83    Drift,
84    /// Recovering from drift
85    Recovery,
86}
87
88/// False positive tracking for drift detection
89pub struct FalsePositiveTracker<A: Float + Send + Sync> {
90    /// Recent false positive events
91    false_positives: VecDeque<Instant>,
92    /// True positive events
93    true_positives: VecDeque<Instant>,
94    /// Current false positive rate
95    current_fp_rate: A,
96    /// Target false positive rate
97    target_fp_rate: A,
98}
99
100/// Trait for statistical drift detection tests
101pub trait StatisticalTest<A: Float + Send + Sync>: Send + Sync {
102    /// Performs the statistical test for drift
103    fn test_for_drift(
104        &mut self,
105        reference: &[A],
106        current: &[A],
107    ) -> Result<DriftTestResult<A>, String>;
108
109    /// Updates test parameters based on historical performance
110    fn update_parameters(&mut self, performance_feedback: A) -> Result<(), String>;
111
112    /// Resets the test state
113    fn reset(&mut self);
114}
115
116/// Result of a drift detection test
117#[derive(Debug, Clone)]
118pub struct DriftTestResult<A: Float + Send + Sync> {
119    /// Whether drift was detected
120    pub drift_detected: bool,
121    /// Statistical significance (p-value)
122    pub p_value: A,
123    /// Test statistic value
124    pub test_statistic: A,
125    /// Confidence in the result
126    pub confidence: A,
127    /// Additional test-specific metadata
128    pub metadata: HashMap<String, A>,
129}
130
131/// Trait for distribution-based drift detection
132pub trait DistributionComparator<A: Float + Send + Sync>: Send + Sync {
133    /// Compares two distributions for drift
134    fn compare_distributions(
135        &self,
136        reference: &[A],
137        current: &[A],
138    ) -> Result<DistributionComparison<A>, String>;
139
140    /// Gets the threshold for drift detection
141    fn get_threshold(&self) -> A;
142
143    /// Updates threshold based on performance
144    fn update_threshold(&mut self, new_threshold: A);
145}
146
147/// Result of distribution comparison
148#[derive(Debug, Clone)]
149pub struct DistributionComparison<A: Float + Send + Sync> {
150    /// Distance/divergence measure
151    pub distance: A,
152    /// Threshold for drift detection
153    pub threshold: A,
154    /// Whether drift was detected
155    pub drift_detected: bool,
156    /// Comparison confidence
157    pub confidence: A,
158}
159
160/// Trait for model-based drift detection
161pub trait ModelBasedDetector<A: Float + Send + Sync>: Send + Sync {
162    /// Updates the model with new data
163    fn update_model(&mut self, data: &[StreamingDataPoint<A>]) -> Result<(), String>;
164
165    /// Detects drift based on model performance
166    fn detect_drift(
167        &mut self,
168        data: &[StreamingDataPoint<A>],
169    ) -> Result<ModelDriftResult<A>, String>;
170
171    /// Resets the model
172    fn reset_model(&mut self) -> Result<(), String>;
173}
174
175/// Result of model-based drift detection
176#[derive(Debug, Clone)]
177pub struct ModelDriftResult<A: Float + Send + Sync> {
178    /// Whether drift was detected
179    pub drift_detected: bool,
180    /// Model performance degradation
181    pub performance_degradation: A,
182    /// Drift confidence
183    pub confidence: A,
184    /// Feature importance changes
185    pub feature_importance_changes: Vec<A>,
186}
187
188impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum + 'static> EnhancedDriftDetector<A> {
189    /// Creates a new enhanced drift detector
190    pub fn new(config: &StreamingConfig) -> Result<Self, String> {
191        let drift_config = config.drift_config.clone();
192
193        let mut statistical_tests: HashMap<StatisticalMethod, Box<dyn StatisticalTest<A>>> =
194            HashMap::new();
195        let mut distribution_methods: HashMap<
196            DistributionMethod,
197            Box<dyn DistributionComparator<A>>,
198        > = HashMap::new();
199        let mut model_detectors: HashMap<ModelType, Box<dyn ModelBasedDetector<A>>> =
200            HashMap::new();
201
202        // Initialize statistical tests
203        statistical_tests.insert(
204            StatisticalMethod::ADWIN,
205            Box::new(ADWINTest::new(drift_config.sensitivity)?),
206        );
207        statistical_tests.insert(
208            StatisticalMethod::DDM,
209            Box::new(DDMTest::new(drift_config.sensitivity)?),
210        );
211        statistical_tests.insert(
212            StatisticalMethod::PageHinkley,
213            Box::new(PageHinkleyTest::new(drift_config.sensitivity)?),
214        );
215
216        // Initialize distribution methods
217        distribution_methods.insert(
218            DistributionMethod::KLDivergence,
219            Box::new(KLDivergenceComparator::new(drift_config.sensitivity)?),
220        );
221        distribution_methods.insert(
222            DistributionMethod::JSDivergence,
223            Box::new(JSDivergenceComparator::new(drift_config.sensitivity)?),
224        );
225
226        // Initialize model detectors
227        model_detectors.insert(ModelType::Linear, Box::new(LinearModelDetector::new()?));
228
229        let ensemble_strategy = match &drift_config.detection_method {
230            DriftDetectionMethod::Ensemble {
231                voting_strategy, ..
232            } => Some(voting_strategy.clone()),
233            _ => None,
234        };
235
236        let false_positive_tracker = FalsePositiveTracker::new();
237
238        Ok(Self {
239            config: drift_config.clone(),
240            detection_method: drift_config.detection_method,
241            statistical_tests,
242            distribution_methods,
243            model_detectors,
244            ensemble_strategy,
245            detection_history: VecDeque::with_capacity(1000),
246            false_positive_tracker,
247            reference_window: VecDeque::with_capacity(drift_config.window_size),
248            drift_state: DriftState::Stable,
249            last_detection: None,
250            sensitivity_factor: A::one(),
251        })
252    }
253
254    /// Detects drift in the given batch of data
255    pub fn detect_drift(&mut self, batch: &[StreamingDataPoint<A>]) -> Result<bool, String> {
256        if !self.config.enable_detection || batch.len() < self.config.min_samples {
257            return Ok(false);
258        }
259
260        // Update reference window
261        self.update_reference_window(batch)?;
262
263        // Check if we have enough data for comparison
264        if self.reference_window.len() < self.config.window_size / 2 {
265            return Ok(false);
266        }
267
268        // Extract features for comparison
269        let current_features = self.extract_features(batch)?;
270        let reference_features = self.extract_reference_features()?;
271
272        // Perform drift detection based on configured method
273        let detection_method = self.detection_method.clone();
274        let drift_result = match detection_method {
275            DriftDetectionMethod::Statistical(method) => {
276                self.detect_statistical_drift(&method, &reference_features, &current_features)?
277            }
278            DriftDetectionMethod::Distribution(method) => {
279                self.detect_distribution_drift(&method, &reference_features, &current_features)?
280            }
281            DriftDetectionMethod::ModelBased(model_type) => {
282                self.detect_model_drift(&model_type, batch)?
283            }
284            DriftDetectionMethod::Ensemble {
285                methods,
286                voting_strategy,
287            } => self.detect_ensemble_drift(
288                &methods,
289                &voting_strategy,
290                &reference_features,
291                &current_features,
292                batch,
293            )?,
294        };
295
296        // Update drift state and history
297        if drift_result.drift_detected {
298            self.handle_drift_detection(drift_result)?;
299            Ok(true)
300        } else {
301            self.update_drift_state(false);
302            Ok(false)
303        }
304    }
305
306    /// Updates the reference window with new data
307    fn update_reference_window(&mut self, batch: &[StreamingDataPoint<A>]) -> Result<(), String> {
308        for data_point in batch {
309            if self.reference_window.len() >= self.config.window_size {
310                self.reference_window.pop_front();
311            }
312            self.reference_window.push_back(data_point.clone());
313        }
314        Ok(())
315    }
316
317    /// Extracts features from a batch of data points
318    fn extract_features(&self, batch: &[StreamingDataPoint<A>]) -> Result<Vec<A>, String> {
319        let mut features = Vec::new();
320
321        for data_point in batch {
322            features.extend(data_point.features.iter().cloned());
323        }
324
325        Ok(features)
326    }
327
328    /// Extracts reference features from the reference window
329    fn extract_reference_features(&self) -> Result<Vec<A>, String> {
330        let reference_data: Vec<_> = self
331            .reference_window
332            .iter()
333            .take(self.reference_window.len() / 2)
334            .collect();
335
336        let mut features = Vec::new();
337        for data_point in reference_data {
338            features.extend(data_point.features.iter().cloned());
339        }
340
341        Ok(features)
342    }
343
344    /// Performs statistical drift detection
345    fn detect_statistical_drift(
346        &mut self,
347        method: &StatisticalMethod,
348        reference: &[A],
349        current: &[A],
350    ) -> Result<DriftTestResult<A>, String> {
351        if let Some(test) = self.statistical_tests.get_mut(method) {
352            let mut result = test.test_for_drift(reference, current)?;
353
354            // Apply sensitivity factor
355            result.confidence = result.confidence * self.sensitivity_factor;
356            result.drift_detected = result.p_value
357                < A::from(self.config.significance_level).expect("unwrap failed")
358                    * self.sensitivity_factor;
359
360            Ok(result)
361        } else {
362            Err(format!("Statistical method {:?} not implemented", method))
363        }
364    }
365
366    /// Performs distribution-based drift detection
367    fn detect_distribution_drift(
368        &mut self,
369        method: &DistributionMethod,
370        reference: &[A],
371        current: &[A],
372    ) -> Result<DriftTestResult<A>, String> {
373        if let Some(comparator) = self.distribution_methods.get(method) {
374            let comparison = comparator.compare_distributions(reference, current)?;
375
376            let result = DriftTestResult {
377                drift_detected: comparison.drift_detected,
378                p_value: A::one() - comparison.confidence, // Convert confidence to p-value like measure
379                test_statistic: comparison.distance,
380                confidence: comparison.confidence * self.sensitivity_factor,
381                metadata: HashMap::new(),
382            };
383
384            Ok(result)
385        } else {
386            Err(format!("Distribution method {:?} not implemented", method))
387        }
388    }
389
390    /// Performs model-based drift detection
391    fn detect_model_drift(
392        &mut self,
393        model_type: &ModelType,
394        batch: &[StreamingDataPoint<A>],
395    ) -> Result<DriftTestResult<A>, String> {
396        if let Some(detector) = self.model_detectors.get_mut(model_type) {
397            let model_result = detector.detect_drift(batch)?;
398
399            let result = DriftTestResult {
400                drift_detected: model_result.drift_detected,
401                p_value: A::one() - model_result.confidence,
402                test_statistic: model_result.performance_degradation,
403                confidence: model_result.confidence * self.sensitivity_factor,
404                metadata: HashMap::new(),
405            };
406
407            Ok(result)
408        } else {
409            Err(format!("Model type {:?} not implemented", model_type))
410        }
411    }
412
413    /// Performs ensemble drift detection
414    fn detect_ensemble_drift(
415        &mut self,
416        methods: &[DriftDetectionMethod],
417        voting_strategy: &VotingStrategy,
418        reference: &[A],
419        current: &[A],
420        batch: &[StreamingDataPoint<A>],
421    ) -> Result<DriftTestResult<A>, String> {
422        let mut results = Vec::new();
423
424        // Collect results from all methods
425        for method in methods {
426            let result = match method {
427                DriftDetectionMethod::Statistical(stat_method) => {
428                    self.detect_statistical_drift(stat_method, reference, current)?
429                }
430                DriftDetectionMethod::Distribution(dist_method) => {
431                    self.detect_distribution_drift(dist_method, reference, current)?
432                }
433                DriftDetectionMethod::ModelBased(model_type) => {
434                    self.detect_model_drift(model_type, batch)?
435                }
436                DriftDetectionMethod::Ensemble { .. } => {
437                    // Avoid recursive ensemble calls
438                    continue;
439                }
440            };
441            results.push(result);
442        }
443
444        // Apply voting strategy
445        let ensemble_result = self.apply_voting_strategy(voting_strategy, &results)?;
446        Ok(ensemble_result)
447    }
448
449    /// Applies the ensemble voting strategy
450    fn apply_voting_strategy(
451        &self,
452        strategy: &VotingStrategy,
453        results: &[DriftTestResult<A>],
454    ) -> Result<DriftTestResult<A>, String> {
455        if results.is_empty() {
456            return Err("No results to vote on".to_string());
457        }
458
459        let drift_detected = match strategy {
460            VotingStrategy::Majority => {
461                let positive_votes = results.iter().filter(|r| r.drift_detected).count();
462                positive_votes > results.len() / 2
463            }
464            VotingStrategy::Weighted { weights } => {
465                if weights.len() != results.len() {
466                    return Err("Number of weights doesn't match number of results".to_string());
467                }
468
469                let weighted_score: f64 = results
470                    .iter()
471                    .zip(weights.iter())
472                    .map(|(result, &weight)| weight * if result.drift_detected { 1.0 } else { 0.0 })
473                    .sum();
474
475                let total_weight: f64 = weights.iter().sum();
476                weighted_score / total_weight > 0.5
477            }
478            VotingStrategy::Unanimous => results.iter().all(|r| r.drift_detected),
479            VotingStrategy::Threshold { min_votes } => {
480                let positive_votes = results.iter().filter(|r| r.drift_detected).count();
481                positive_votes >= *min_votes
482            }
483        };
484
485        // Aggregate confidence and p-values
486        let avg_confidence = results.iter().map(|r| r.confidence).sum::<A>()
487            / A::from(results.len()).expect("unwrap failed");
488
489        let avg_p_value = results.iter().map(|r| r.p_value).sum::<A>()
490            / A::from(results.len()).expect("unwrap failed");
491
492        let avg_test_statistic = results.iter().map(|r| r.test_statistic).sum::<A>()
493            / A::from(results.len()).expect("unwrap failed");
494
495        Ok(DriftTestResult {
496            drift_detected,
497            p_value: avg_p_value,
498            test_statistic: avg_test_statistic,
499            confidence: avg_confidence,
500            metadata: HashMap::new(),
501        })
502    }
503
504    /// Handles drift detection event
505    fn handle_drift_detection(&mut self, result: DriftTestResult<A>) -> Result<(), String> {
506        let severity = self.classify_drift_severity(&result);
507
508        let drift_event = DriftEvent {
509            timestamp: Instant::now(),
510            severity: severity.clone(),
511            confidence: result.confidence,
512            detection_method: format!("{:?}", self.detection_method),
513            p_value: Some(result.p_value),
514            magnitude: result.test_statistic,
515            affected_features: Vec::new(), // Could be computed based on feature-wise analysis
516        };
517
518        // Store in history
519        if self.detection_history.len() >= 1000 {
520            self.detection_history.pop_front();
521        }
522        self.detection_history.push_back(drift_event);
523
524        // Update drift state
525        self.update_drift_state(true);
526        self.last_detection = Some(Instant::now());
527
528        // Update false positive tracker if enabled
529        if self.config.enable_false_positive_tracking {
530            self.false_positive_tracker.record_detection(true)?;
531        }
532
533        Ok(())
534    }
535
536    /// Classifies drift severity based on test results
537    fn classify_drift_severity(&self, result: &DriftTestResult<A>) -> DriftSeverity {
538        let confidence = result.confidence.to_f64().unwrap_or(0.0);
539        let p_value = result.p_value.to_f64().unwrap_or(1.0);
540
541        if p_value < 0.001 && confidence > 0.95 {
542            DriftSeverity::Critical
543        } else if p_value < 0.01 && confidence > 0.9 {
544            DriftSeverity::Major
545        } else if p_value < 0.05 && confidence > 0.8 {
546            DriftSeverity::Moderate
547        } else {
548            DriftSeverity::Minor
549        }
550    }
551
552    /// Updates the current drift state
553    fn update_drift_state(&mut self, drift_detected: bool) {
554        self.drift_state = match (&self.drift_state, drift_detected) {
555            (DriftState::Stable, true) => DriftState::Warning,
556            (DriftState::Warning, true) => DriftState::Drift,
557            (DriftState::Drift, false) => DriftState::Recovery,
558            (DriftState::Recovery, false) => DriftState::Stable,
559            (state, _) => state.clone(),
560        };
561    }
562
563    /// Computes adaptation for drift sensitivity
564    pub fn compute_sensitivity_adaptation(&mut self) -> Result<Option<Adaptation<A>>, String> {
565        // Check if sensitivity should be adjusted based on false positive rate
566        if self.config.enable_false_positive_tracking {
567            let current_fp_rate = self.false_positive_tracker.current_fp_rate;
568            let target_fp_rate = A::from(0.05).expect("unwrap failed"); // 5% target false positive rate
569
570            if (current_fp_rate - target_fp_rate).abs() > A::from(0.02).expect("unwrap failed") {
571                let adjustment = if current_fp_rate > target_fp_rate {
572                    // Too many false positives, decrease sensitivity
573                    -A::from(0.1).expect("unwrap failed")
574                } else {
575                    // Too few detections (potentially missing true positives), increase sensitivity
576                    A::from(0.1).expect("unwrap failed")
577                };
578
579                let adaptation = Adaptation {
580                    adaptation_type: AdaptationType::DriftSensitivity,
581                    magnitude: adjustment,
582                    target_component: "drift_detector".to_string(),
583                    parameters: HashMap::new(),
584                    priority: AdaptationPriority::Normal,
585                    timestamp: Instant::now(),
586                };
587
588                return Ok(Some(adaptation));
589            }
590        }
591
592        Ok(None)
593    }
594
595    /// Applies sensitivity adaptation
596    pub fn apply_sensitivity_adaptation(
597        &mut self,
598        adaptation: &Adaptation<A>,
599    ) -> Result<(), String> {
600        if adaptation.adaptation_type == AdaptationType::DriftSensitivity {
601            self.sensitivity_factor = (self.sensitivity_factor + adaptation.magnitude)
602                .max(A::from(0.1).expect("unwrap failed"))
603                .min(A::from(2.0).expect("unwrap failed"));
604        }
605        Ok(())
606    }
607
608    /// Checks if drift is currently detected
609    pub fn is_drift_detected(&self) -> bool {
610        matches!(self.drift_state, DriftState::Drift | DriftState::Warning)
611    }
612
613    /// Gets the current drift state
614    pub fn get_drift_state(&self) -> &DriftState {
615        &self.drift_state
616    }
617
618    /// Gets recent drift events
619    pub fn get_recent_drift_events(&self, count: usize) -> Vec<&DriftEvent<A>> {
620        self.detection_history.iter().rev().take(count).collect()
621    }
622
623    /// Resets the drift detector
624    pub fn reset(&mut self) -> Result<(), String> {
625        self.detection_history.clear();
626        self.reference_window.clear();
627        self.drift_state = DriftState::Stable;
628        self.last_detection = None;
629        self.sensitivity_factor = A::one();
630
631        // Reset all detection methods
632        for test in self.statistical_tests.values_mut() {
633            test.reset();
634        }
635
636        for detector in self.model_detectors.values_mut() {
637            detector.reset_model()?;
638        }
639
640        Ok(())
641    }
642
643    /// Gets diagnostic information
644    pub fn get_diagnostics(&self) -> DriftDiagnostics {
645        DriftDiagnostics {
646            current_state: self.drift_state.clone(),
647            detection_count: self.detection_history.len(),
648            false_positive_rate: self
649                .false_positive_tracker
650                .current_fp_rate
651                .to_f64()
652                .unwrap_or(0.0),
653            sensitivity_factor: self.sensitivity_factor.to_f64().unwrap_or(1.0),
654            last_detection_time: self.last_detection,
655            reference_window_size: self.reference_window.len(),
656        }
657    }
658}
659
660impl<A: Float + Send + Sync + Send + Sync> FalsePositiveTracker<A> {
661    fn new() -> Self {
662        Self {
663            false_positives: VecDeque::new(),
664            true_positives: VecDeque::new(),
665            current_fp_rate: A::zero(),
666            target_fp_rate: A::from(0.05).expect("unwrap failed"),
667        }
668    }
669
670    fn record_detection(&mut self, is_true_positive: bool) -> Result<(), String> {
671        let now = Instant::now();
672
673        if is_true_positive {
674            self.true_positives.push_back(now);
675        } else {
676            self.false_positives.push_back(now);
677        }
678
679        // Keep only recent events (last hour)
680        let cutoff = now - Duration::from_secs(3600);
681        self.false_positives.retain(|&time| time > cutoff);
682        self.true_positives.retain(|&time| time > cutoff);
683
684        // Update false positive rate
685        let total_detections = self.false_positives.len() + self.true_positives.len();
686        if total_detections > 0 {
687            self.current_fp_rate = A::from(self.false_positives.len()).expect("unwrap failed")
688                / A::from(total_detections).expect("unwrap failed");
689        }
690
691        Ok(())
692    }
693}
694
695/// Diagnostic information for drift detection
696#[derive(Debug, Clone)]
697pub struct DriftDiagnostics {
698    pub current_state: DriftState,
699    pub detection_count: usize,
700    pub false_positive_rate: f64,
701    pub sensitivity_factor: f64,
702    pub last_detection_time: Option<Instant>,
703    pub reference_window_size: usize,
704}
705
706// Simplified implementations of detection methods
707// In practice, these would be more sophisticated
708
709struct ADWINTest<A: Float + Send + Sync> {
710    sensitivity: A,
711    window: VecDeque<A>,
712}
713
714impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> ADWINTest<A> {
715    fn new(sensitivity: f64) -> Result<Self, String> {
716        Ok(Self {
717            sensitivity: A::from(sensitivity).expect("unwrap failed"),
718            window: VecDeque::new(),
719        })
720    }
721}
722
723impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A>
724    for ADWINTest<A>
725{
726    fn test_for_drift(
727        &mut self,
728        reference: &[A],
729        current: &[A],
730    ) -> Result<DriftTestResult<A>, String> {
731        // Simplified ADWIN implementation
732        let ref_mean =
733            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
734        let cur_mean =
735            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");
736
737        let difference = (ref_mean - cur_mean).abs();
738        let threshold = self.sensitivity;
739
740        let drift_detected = difference > threshold;
741
742        Ok(DriftTestResult {
743            drift_detected,
744            p_value: if drift_detected {
745                A::from(0.01).expect("unwrap failed")
746            } else {
747                A::from(0.5).expect("unwrap failed")
748            },
749            test_statistic: difference,
750            confidence: if drift_detected {
751                A::from(0.9).expect("unwrap failed")
752            } else {
753                A::from(0.1).expect("unwrap failed")
754            },
755            metadata: HashMap::new(),
756        })
757    }
758
759    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
760        Ok(())
761    }
762
763    fn reset(&mut self) {
764        self.window.clear();
765    }
766}
767
768struct DDMTest<A: Float + Send + Sync> {
769    sensitivity: A,
770    error_rate: A,
771    std_dev: A,
772}
773
774impl<A: Float + Default + Send + Sync + std::iter::Sum> DDMTest<A> {
775    fn new(sensitivity: f64) -> Result<Self, String> {
776        Ok(Self {
777            sensitivity: A::from(sensitivity).expect("unwrap failed"),
778            error_rate: A::zero(),
779            std_dev: A::zero(),
780        })
781    }
782}
783
784impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A> for DDMTest<A> {
785    fn test_for_drift(
786        &mut self,
787        reference: &[A],
788        current: &[A],
789    ) -> Result<DriftTestResult<A>, String> {
790        // Simplified DDM implementation
791        let ref_mean =
792            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
793        let cur_mean =
794            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");
795
796        let difference = (ref_mean - cur_mean).abs();
797        let drift_detected = difference > self.sensitivity;
798
799        Ok(DriftTestResult {
800            drift_detected,
801            p_value: if drift_detected {
802                A::from(0.02).expect("unwrap failed")
803            } else {
804                A::from(0.6).expect("unwrap failed")
805            },
806            test_statistic: difference,
807            confidence: if drift_detected {
808                A::from(0.85).expect("unwrap failed")
809            } else {
810                A::from(0.15).expect("unwrap failed")
811            },
812            metadata: HashMap::new(),
813        })
814    }
815
816    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
817        Ok(())
818    }
819
820    fn reset(&mut self) {
821        self.error_rate = A::zero();
822        self.std_dev = A::zero();
823    }
824}
825
826struct PageHinkleyTest<A: Float + Send + Sync> {
827    sensitivity: A,
828    cumulative_sum: A,
829}
830
831impl<A: Float + Default + Send + Sync + std::iter::Sum> PageHinkleyTest<A> {
832    fn new(sensitivity: f64) -> Result<Self, String> {
833        Ok(Self {
834            sensitivity: A::from(sensitivity).expect("unwrap failed"),
835            cumulative_sum: A::zero(),
836        })
837    }
838}
839
840impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalTest<A>
841    for PageHinkleyTest<A>
842{
843    fn test_for_drift(
844        &mut self,
845        reference: &[A],
846        current: &[A],
847    ) -> Result<DriftTestResult<A>, String> {
848        // Simplified Page-Hinkley test
849        let ref_mean =
850            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
851        let cur_mean =
852            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");
853
854        let difference = cur_mean - ref_mean;
855        self.cumulative_sum = self.cumulative_sum + difference;
856
857        let drift_detected = self.cumulative_sum.abs() > self.sensitivity;
858
859        Ok(DriftTestResult {
860            drift_detected,
861            p_value: if drift_detected {
862                A::from(0.015).expect("unwrap failed")
863            } else {
864                A::from(0.7).expect("unwrap failed")
865            },
866            test_statistic: self.cumulative_sum,
867            confidence: if drift_detected {
868                A::from(0.88).expect("unwrap failed")
869            } else {
870                A::from(0.12).expect("unwrap failed")
871            },
872            metadata: HashMap::new(),
873        })
874    }
875
876    fn update_parameters(&mut self, _performance_feedback: A) -> Result<(), String> {
877        Ok(())
878    }
879
880    fn reset(&mut self) {
881        self.cumulative_sum = A::zero();
882    }
883}
884
885struct KLDivergenceComparator<A: Float + Send + Sync> {
886    threshold: A,
887}
888
889impl<A: Float + Send + Sync + Send + Sync> KLDivergenceComparator<A> {
890    fn new(sensitivity: f64) -> Result<Self, String> {
891        Ok(Self {
892            threshold: A::from(sensitivity).expect("unwrap failed"),
893        })
894    }
895}
896
897impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> DistributionComparator<A>
898    for KLDivergenceComparator<A>
899{
900    fn compare_distributions(
901        &self,
902        reference: &[A],
903        current: &[A],
904    ) -> Result<DistributionComparison<A>, String> {
905        // Simplified KL divergence calculation
906        let ref_mean =
907            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
908        let cur_mean =
909            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");
910
911        let distance = (ref_mean - cur_mean).abs();
912        let drift_detected = distance > self.threshold;
913
914        Ok(DistributionComparison {
915            distance,
916            threshold: self.threshold,
917            drift_detected,
918            confidence: if drift_detected {
919                A::from(0.8).expect("unwrap failed")
920            } else {
921                A::from(0.2).expect("unwrap failed")
922            },
923        })
924    }
925
926    fn get_threshold(&self) -> A {
927        self.threshold
928    }
929
930    fn update_threshold(&mut self, new_threshold: A) {
931        self.threshold = new_threshold;
932    }
933}
934
935struct JSDivergenceComparator<A: Float + Send + Sync> {
936    threshold: A,
937}
938
939impl<A: Float + Send + Sync + Send + Sync> JSDivergenceComparator<A> {
940    fn new(sensitivity: f64) -> Result<Self, String> {
941        Ok(Self {
942            threshold: A::from(sensitivity).expect("unwrap failed"),
943        })
944    }
945}
946
947impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> DistributionComparator<A>
948    for JSDivergenceComparator<A>
949{
950    fn compare_distributions(
951        &self,
952        reference: &[A],
953        current: &[A],
954    ) -> Result<DistributionComparison<A>, String> {
955        // Simplified JS divergence calculation
956        let ref_mean =
957            reference.iter().cloned().sum::<A>() / A::from(reference.len()).expect("unwrap failed");
958        let cur_mean =
959            current.iter().cloned().sum::<A>() / A::from(current.len()).expect("unwrap failed");
960
961        let distance = (ref_mean - cur_mean).abs() * A::from(0.5).expect("unwrap failed"); // Simplified
962        let drift_detected = distance > self.threshold;
963
964        Ok(DistributionComparison {
965            distance,
966            threshold: self.threshold,
967            drift_detected,
968            confidence: if drift_detected {
969                A::from(0.75).expect("unwrap failed")
970            } else {
971                A::from(0.25).expect("unwrap failed")
972            },
973        })
974    }
975
976    fn get_threshold(&self) -> A {
977        self.threshold
978    }
979
980    fn update_threshold(&mut self, new_threshold: A) {
981        self.threshold = new_threshold;
982    }
983}
984
985struct LinearModelDetector<A: Float + Send + Sync> {
986    model_performance: A,
987    baseline_performance: A,
988}
989
990impl<A: Float + Default + Send + Sync + Send + Sync> LinearModelDetector<A> {
991    fn new() -> Result<Self, String> {
992        Ok(Self {
993            model_performance: A::zero(),
994            baseline_performance: A::zero(),
995        })
996    }
997}
998
999impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> ModelBasedDetector<A>
1000    for LinearModelDetector<A>
1001{
1002    fn update_model(&mut self, _data: &[StreamingDataPoint<A>]) -> Result<(), String> {
1003        // Simplified model update
1004        Ok(())
1005    }
1006
1007    fn detect_drift(
1008        &mut self,
1009        _data: &[StreamingDataPoint<A>],
1010    ) -> Result<ModelDriftResult<A>, String> {
1011        // Simplified drift detection based on performance degradation
1012        let performance_degradation = self.baseline_performance - self.model_performance;
1013        let drift_detected = performance_degradation > A::from(0.1).expect("unwrap failed");
1014
1015        Ok(ModelDriftResult {
1016            drift_detected,
1017            performance_degradation,
1018            confidence: if drift_detected {
1019                A::from(0.7).expect("unwrap failed")
1020            } else {
1021                A::from(0.3).expect("unwrap failed")
1022            },
1023            feature_importance_changes: Vec::new(),
1024        })
1025    }
1026
1027    fn reset_model(&mut self) -> Result<(), String> {
1028        self.model_performance = A::zero();
1029        self.baseline_performance = A::zero();
1030        Ok(())
1031    }
1032}
optirs_core/streaming/adaptive_streaming/drift_detection.rs

optirs_core/streaming/adaptive_streaming/
drift_detection.rs
