c3e9153cf3
Reference distribution builder: - BuildReferences() tokenises samples, computes per-domain centroid imprints - Per-key variance for Mahalanobis distance normalisation Imprint comparator: - Compare() returns cosine, KL divergence, Mahalanobis per domain - Classify() picks best domain with confidence margin - Symmetric KL with epsilon smoothing, component-weighted Cross-domain anomaly detection: - DetectAnomalies() flags model vs imprint domain disagreements - AnomalyStats tracks rate and confusion pair counts 17 new tests, all race-clean. Phase 2b complete. Co-Authored-By: Virgil <virgil@lethean.io>
60 lines
1.8 KiB
Go
60 lines
1.8 KiB
Go
package reversal
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// AnomalyResult flags a potential domain mismatch between model classification
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// and imprint-based classification.
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type AnomalyResult struct {
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Text string `json:"text"`
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ModelDomain string `json:"model_domain"` // domain from 1B model
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ImprintDomain string `json:"imprint_domain"` // domain from imprint comparison
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Confidence float64 `json:"confidence"` // imprint classification margin
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IsAnomaly bool `json:"is_anomaly"` // true when domains disagree
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}
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// AnomalyStats holds aggregate anomaly detection metrics.
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type AnomalyStats struct {
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Total int `json:"total"`
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Anomalies int `json:"anomalies"`
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Rate float64 `json:"rate"` // anomalies / total
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ByPair map[string]int `json:"by_pair"` // "model->imprint": count
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}
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// DetectAnomalies compares 1B model domain tags against imprint-based classification.
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// Returns per-sample results and aggregate stats.
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// Samples with empty Domain are skipped.
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func (rs *ReferenceSet) DetectAnomalies(tokeniser *Tokeniser, samples []ClassifiedText) ([]AnomalyResult, *AnomalyStats) {
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stats := &AnomalyStats{ByPair: make(map[string]int)}
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var results []AnomalyResult
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for _, s := range samples {
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if s.Domain == "" {
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continue
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}
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tokens := tokeniser.Tokenise(s.Text)
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imp := NewImprint(tokens)
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cls := rs.Classify(imp)
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isAnomaly := s.Domain != cls.Domain
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result := AnomalyResult{
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Text: s.Text,
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ModelDomain: s.Domain,
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ImprintDomain: cls.Domain,
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Confidence: cls.Confidence,
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IsAnomaly: isAnomaly,
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}
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results = append(results, result)
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stats.Total++
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if isAnomaly {
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stats.Anomalies++
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key := s.Domain + "->" + cls.Domain
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stats.ByPair[key]++
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}
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}
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if stats.Total > 0 {
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stats.Rate = float64(stats.Anomalies) / float64(stats.Total)
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}
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return results, stats
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}
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