l_ai_knowledge/internal/application/service/metric/mrr.go

49 lines
1.2 KiB
Go

package metric
import (
"knowlege-lsxd/internal/types"
)
// MRRMetric calculates Mean Reciprocal Rank for retrieval evaluation
type MRRMetric struct{}
// NewMRRMetric creates a new MRRMetric instance
func NewMRRMetric() *MRRMetric {
return &MRRMetric{}
}
// Compute calculates the Mean Reciprocal Rank score
func (m *MRRMetric) Compute(metricInput *types.MetricInput) float64 {
// Get ground truth and predicted IDs
gts := metricInput.RetrievalGT
ids := metricInput.RetrievalIDs
// Convert ground truth to sets for efficient lookup
gtSets := make([]map[int]struct{}, len(gts))
for i, gt := range gts {
gtSets[i] = make(map[int]struct{})
for _, docID := range gt {
gtSets[i][docID] = struct{}{}
}
}
var sumRR float64 // Sum of reciprocal ranks
// Calculate reciprocal rank for each query
for _, gtSet := range gtSets {
// Find first relevant document in results
for i, predID := range ids {
if _, ok := gtSet[predID]; ok {
// Reciprocal rank is 1/position (1-based)
sumRR += 1.0 / float64(i+1)
break // Only consider first relevant document
}
}
}
// Handle case with no ground truth
if len(gtSets) == 0 {
return 0
}
// Return mean of reciprocal ranks
return sumRR / float64(len(gtSets))
}