generated from mathias/template-go-web
mathiasandClaude Sonnet 4.6
e11e7d2524
internal/eval: three pure-Go diagnostics on frozen embeddings: LinearProbe(emb, y, λ) → val_vol_r2 (OOS R², closed-form ridge, Cholesky) Silhouette(emb, labels) → mean silhouette (Euclidean, multi-label, errors on <2 classes) EffectiveRank(emb) → Roy effective rank (Jacobi eigenvalues → entropy → exp(H)) cmd/eval/main.go: CLI driver reading embeddings.json (exported by train.py with EXPORT_EMBEDDINGS=1), standardises per-dim, dispatches to -metric flag. task eval:probe / eval:silhouette / eval:collapse wired in Taskfile. 8/8 tests pass (red-green: perfect clusters, rank-1, full-rank, noise, constant target, single-label error). Pure stdlib, no external deps. Closes #4. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
139 lines
3.4 KiB
Go
139 lines
3.4 KiB
Go
package eval_test
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import (
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"math"
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"math/rand"
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"testing"
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"gitea.d-ma.be/mathias/jepa-fx-risk/internal/eval"
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)
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func seededRNG(seed int64) *rand.Rand {
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return rand.New(rand.NewSource(seed))
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}
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// ── LinearProbe (val_vol_r2) ──────────────────────────────────────────────────
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func TestLinearProbe_Perfect(t *testing.T) {
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n := 50
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emb := make([][]float64, n)
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y := make([]float64, n)
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for i := range emb {
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emb[i] = []float64{float64(i)}
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y[i] = float64(i)
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}
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r2 := eval.LinearProbe(emb, y, 1e-3)
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if r2 < 0.99 {
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t.Fatalf("perfect predictor: want R²≥0.99, got %.4f", r2)
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}
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}
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func TestLinearProbe_ConstantTarget(t *testing.T) {
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n := 40
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emb := make([][]float64, n)
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y := make([]float64, n)
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for i := range emb {
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emb[i] = []float64{float64(i), float64(i * i)}
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y[i] = 3.0
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}
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r2 := eval.LinearProbe(emb, y, 1e-3)
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if r2 > 0.01 {
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t.Fatalf("constant target: want R²≤0.01, got %.4f", r2)
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}
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}
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func TestLinearProbe_NoiseEmbedding(t *testing.T) {
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rng := seededRNG(42)
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n := 80
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emb := make([][]float64, n)
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y := make([]float64, n)
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for i := range emb {
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emb[i] = []float64{rng.NormFloat64(), rng.NormFloat64()}
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y[i] = float64(i)
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}
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r2 := eval.LinearProbe(emb, y, 1e-3)
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if r2 > 0.10 {
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t.Fatalf("noise embedding: want R²<0.10, got %.4f", r2)
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}
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}
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// ── Silhouette ────────────────────────────────────────────────────────────────
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func TestSilhouette_PerfectClusters(t *testing.T) {
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emb := make([][]float64, 40)
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labels := make([]int, 40)
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for i := range emb {
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if i < 20 {
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emb[i] = []float64{0.0, 0.0}
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labels[i] = 0
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} else {
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emb[i] = []float64{1000.0, 1000.0}
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labels[i] = 1
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}
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}
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sil, err := eval.Silhouette(emb, labels)
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if err != nil {
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t.Fatal(err)
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}
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if sil < 0.95 {
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t.Fatalf("perfect clusters: want sil≥0.95, got %.4f", sil)
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}
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}
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func TestSilhouette_SingleLabel(t *testing.T) {
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emb := [][]float64{{1, 2}, {3, 4}, {5, 6}}
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labels := []int{0, 0, 0}
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_, err := eval.Silhouette(emb, labels)
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if err == nil {
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t.Fatal("expected error for single-label input")
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}
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}
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func TestSilhouette_RandomClusters(t *testing.T) {
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rng := seededRNG(7)
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n := 60
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emb := make([][]float64, n)
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labels := make([]int, n)
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for i := range emb {
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emb[i] = []float64{rng.NormFloat64(), rng.NormFloat64()}
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labels[i] = i % 2
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}
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sil, err := eval.Silhouette(emb, labels)
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if err != nil {
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t.Fatal(err)
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}
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if math.Abs(sil) > 0.30 {
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t.Fatalf("random clusters: want |sil|≤0.30, got %.4f", sil)
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}
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}
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// ── EffectiveRank ─────────────────────────────────────────────────────────────
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func TestEffectiveRank_Rank1(t *testing.T) {
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emb := make([][]float64, 30)
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for i := range emb {
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emb[i] = []float64{1.0, 2.0, 3.0, 4.0}
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}
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er := eval.EffectiveRank(emb)
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if er > 1.5 {
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t.Fatalf("rank-1 matrix: want erank≤1.5, got %.4f", er)
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}
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}
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func TestEffectiveRank_FullRank(t *testing.T) {
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rng := seededRNG(99)
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dim := 8
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emb := make([][]float64, 200)
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for i := range emb {
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row := make([]float64, dim)
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for j := range row {
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row[j] = rng.NormFloat64()
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}
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emb[i] = row
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}
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er := eval.EffectiveRank(emb)
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if er < float64(dim)*0.7 {
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t.Fatalf("full-rank: want erank≥%.1f, got %.4f", float64(dim)*0.7, er)
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}
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}
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