yonluandClaude Opus 4.8
65048401e8
Consolidates in-progress work across autotag, explore, and library: - autotag: beets/Picard-informed scoring engine — ID-first matching, VA handling, recommendation tiers, and a merged distance/rank cascade, with an eval harness for regression tracking. - explore: offline MusicBrainz dump import/incremental refresh replaces the legacy tier crawl; index-first local search with fuzzy matching and a dedicated ranker; disk-free guards for dump downloads. - library: artist-credit extraction and matching. - lyrics: owned-library lyric search (FTS) with LRCLIB backfill. Also: rewrite README to be user-focused, and migrate upstream to git.ljones.me/yonlu/yellowjacket. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
103 lines
2.4 KiB
Go
103 lines
2.4 KiB
Go
package explore
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import (
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"bytes"
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"math"
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"testing"
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)
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func TestDefaultModelScoreOrdering(t *testing.T) {
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m := DefaultModel()
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// A popular, in-library exact match must outscore an obscure
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// substring match.
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strong := RankFeatures{NameMatch: 1.0, LogPopularity: 0.9, InLibrary: 1.0}
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weak := RankFeatures{NameMatch: 0.2, LogPopularity: 0.1}
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if m.Score(strong) <= m.Score(weak) {
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t.Errorf("strong candidate %.3f should outscore weak %.3f",
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m.Score(strong), m.Score(weak))
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}
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}
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func TestUpdateMovesTowardLabel(t *testing.T) {
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m := DefaultModel()
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// A candidate the user repeatedly clicks should see its predicted
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// probability rise after training on positive labels.
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f := RankFeatures{NameMatch: 0.4, LogPopularity: 0.2}
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before := m.Probability(f)
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for range 50 {
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m.Update(Sample{Features: f, Label: 1.0}, 0.1)
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}
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after := m.Probability(f)
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if after <= before {
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t.Errorf("probability should rise toward positive label: before=%.4f after=%.4f",
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before, after)
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}
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}
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func TestUpdateLearnsNegative(t *testing.T) {
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m := DefaultModel()
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f := RankFeatures{NameMatch: 0.9, LogPopularity: 0.9}
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before := m.Probability(f)
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// Shown repeatedly, never clicked — probability should fall.
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for range 50 {
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m.Update(Sample{Features: f, Label: 0.0}, 0.1)
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}
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after := m.Probability(f)
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if after >= before {
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t.Errorf("probability should fall toward negative label: before=%.4f after=%.4f",
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before, after)
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}
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}
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func TestMatchStrengthTiers(t *testing.T) {
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if matchStrength(true, false, false, false) != 1.0 {
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t.Error("exact match should be 1.0")
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}
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if matchStrength(false, false, false, false) != 0.0 {
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t.Error("no match should be 0.0")
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}
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// Tiers must be strictly ordered.
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exact := matchStrength(true, false, false, false)
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prefix := matchStrength(false, true, false, false)
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word := matchStrength(false, false, true, false)
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sub := matchStrength(false, false, false, true)
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if !(exact > prefix && prefix > word && word > sub) {
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t.Errorf("tiers not strictly ordered: %v %v %v %v", exact, prefix, word, sub)
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}
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}
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func TestModelRoundTrip(t *testing.T) {
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m := DefaultModel()
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m.Bias = 0.123
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m.Weights[0] = 0.777
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var buf bytes.Buffer
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if err := SaveModel(&buf, m); err != nil {
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t.Fatalf("SaveModel: %v", err)
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}
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got, err := LoadModel(&buf)
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if err != nil {
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t.Fatalf("LoadModel: %v", err)
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}
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if math.Abs(got.Bias-m.Bias) > 1e-9 || math.Abs(got.Weights[0]-m.Weights[0]) > 1e-9 {
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t.Errorf("round trip mismatch: got %+v want %+v", got, m)
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}
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}
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