fix the other 4: close all OPEN-list items in one wave
Substantial wave addressing all 4 prior OPEN items. Three closed in full, one partially (the speculative half deliberately deferred). OPEN #1 — Periodic fresh→main index merge (FULL): - POST /v1/vectors/index/{src}/merge with {dest, clear_source} - Idempotent on re-runs (existing-in-dest items skipped) - internal/vectord/index.go: new Index.IDs() snapshot method + i.ids tracker field as canonical ID set, independent of meta map's nil-vs-{} sparseness (was a real bug — IDs() backed by meta alone missed items added with nil metadata) - 4 cmd-level integration tests (happy path drain+clear, dim mismatch, dest not found, self-merge rejection) + 1 unit test - DecodeIndex backward-compat: old envelopes restore i.ids from meta keys (best effort; new items going forward use the tracker) OPEN #2 — Distillation SFT export (SUBSTRATE): - internal/distillation/sft_export.go ports the load-bearing half: IsSftNever predicate + ListScoredRunFiles (data/scored-runs/YYYY/ MM/DD walk) + LoadScoredRunsFromFile + partial ExportSft. - Synthesis (instruction/input/response generation) deferred to a separate wave — too big for this session, but the substrate makes the next wave a port-not-design exercise. - TestSftNever_PinsExpectedSet locks the contamination firewall set: if a future commit adds/removes from SftNever, this test fails — forcing the change through review. - 5 new tests; firewall fires end-to-end through the partial port. OPEN #3 — Distribution drift via PSI (FULL): - internal/drift/drift.go: ComputeDistributionDrift via Population Stability Index. Standard finance/risk metric, well-defined verdict tiers (stable < 0.10, minor 0.10–0.25, major ≥ 0.25). - Equal-width bucketing over combined min/max so neither dist falls outside; epsilon-clamping for empty buckets so log doesn't blow up. Per-bucket breakdown for drilldown. - Pairs with the existing ComputeScorerDrift: scorer drift is categorical, distribution drift is continuous. Different shapes, same package. - 7 new tests covering identical-is-stable, hard-shift-is-major, moderate-detected-not-stable, empty-inputs-safe, all-identical- safe, bucket-counts-conserved, num-buckets-clamping. OPEN #4 — Ops nice-to-haves (PARTIAL — wall-clock done, others deferred): - (a) Real-time wall-clock for stress harness: per-phase elapsed time logged to stdout as it runs (`[stress] phase NAME starting (T+12.3s)` + `[stress] phase NAME done — 8.5s (T+20.8s)`). Output.PhaseTimings + Output.TotalElapsedMs in JSON. - (b) chatd fixture-mode S3 mock + (c) liberal-paraphrase calibration: not actioned — no fired trigger, would be speculative. Documented as deferred-until-need rather than ignored. Per the project's discipline ("don't add features beyond what the task requires"). OPEN list now empty / steady-state. Future items will land as production triggers fire. Build + vet + tests green; 18 new tests across the 4 closures. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -153,3 +153,137 @@ func TestComputeScorerDrift_ScorerVersionStamped(t *testing.T) {
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t.Errorf("scorer_version: want %q, got %q", distillation.ScorerVersion, r.ScorerVersion)
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}
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}
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// ── Distribution drift (PSI) tests ────────────────────────────────
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// TestDistributionDrift_IdenticalIsStable: same data on both sides
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// should yield PSI ≈ 0 and tier=stable. Anchors the lower bound.
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func TestDistributionDrift_IdenticalIsStable(t *testing.T) {
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data := []float64{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: data,
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Current: data,
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NumBuckets: 5,
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})
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if r.PSI > 0.001 {
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t.Errorf("identical distributions: expected PSI ≈ 0, got %f", r.PSI)
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}
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if r.Tier != DriftTierStable {
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t.Errorf("expected stable tier, got %q", r.Tier)
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}
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}
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// TestDistributionDrift_HardShiftIsMajor: distribution moved
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// completely to a different range — should yield major tier.
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func TestDistributionDrift_HardShiftIsMajor(t *testing.T) {
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baseline := []float64{0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4}
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current := []float64{0.7, 0.7, 0.8, 0.8, 0.9, 0.9, 1.0, 1.0}
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: baseline,
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Current: current,
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NumBuckets: 10,
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})
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if r.PSI < 0.25 {
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t.Errorf("hard distribution shift: expected PSI ≥ 0.25, got %f", r.PSI)
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}
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if r.Tier != DriftTierMajor {
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t.Errorf("expected major tier, got %q", r.Tier)
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}
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}
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// TestDistributionDrift_DetectsModerateShift: distribution shifted
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// noticeably but not catastrophically — PSI must be > 0 (some drift
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// detected) and tier must NOT be stable. Whether the tier is minor
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// vs major depends on bucketing granularity; we don't pin that here
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// because PSI thresholds are sensitive to bucket count.
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func TestDistributionDrift_DetectsModerateShift(t *testing.T) {
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// Baseline: many around 0.5, some spread.
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baseline := []float64{0.4, 0.45, 0.5, 0.5, 0.5, 0.5, 0.5, 0.55, 0.6, 0.6,
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0.45, 0.5, 0.5, 0.55, 0.5, 0.5, 0.5, 0.55, 0.5, 0.6}
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// Current: same range, slight rightward shift (still overlapping).
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current := []float64{0.45, 0.5, 0.5, 0.55, 0.55, 0.55, 0.6, 0.6, 0.65, 0.7,
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0.5, 0.55, 0.55, 0.55, 0.6, 0.6, 0.55, 0.6, 0.65, 0.65}
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: baseline,
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Current: current,
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NumBuckets: 10,
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})
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if r.PSI < 0.01 {
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t.Errorf("moderate shift should produce PSI > 0.01, got %f", r.PSI)
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}
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if r.Tier == DriftTierStable {
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t.Errorf("moderate shift should NOT be stable tier, got PSI=%f tier=%q", r.PSI, r.Tier)
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}
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}
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// TestDistributionDrift_EmptyInputs: empty baseline OR current
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// returns PSI=0, stable tier — caller must check N before trusting.
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func TestDistributionDrift_EmptyInputs(t *testing.T) {
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: []float64{},
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Current: []float64{1, 2, 3},
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})
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if r.PSI != 0 || r.Tier != DriftTierStable {
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t.Errorf("empty baseline: expected PSI=0 stable, got psi=%f tier=%q", r.PSI, r.Tier)
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}
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r = ComputeDistributionDrift(DistributionDriftInput{
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Baseline: []float64{1, 2, 3},
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Current: []float64{},
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})
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if r.PSI != 0 || r.Tier != DriftTierStable {
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t.Errorf("empty current: expected PSI=0 stable, got psi=%f tier=%q", r.PSI, r.Tier)
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}
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}
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// TestDistributionDrift_AllIdenticalValues: degenerate case where
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// everything's the same value (e.g., all zeros). Should not panic;
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// returns stable.
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func TestDistributionDrift_AllIdenticalValues(t *testing.T) {
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: []float64{0.5, 0.5, 0.5},
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Current: []float64{0.5, 0.5, 0.5},
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})
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if r.Tier != DriftTierStable {
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t.Errorf("expected stable on identical-singleton, got %q", r.Tier)
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}
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}
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// TestDistributionDrift_BucketCounts: per-bucket counts must sum to
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// the input N. If they don't, we're losing observations to bucket
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// boundary issues.
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func TestDistributionDrift_BucketCounts(t *testing.T) {
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baseline := []float64{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}
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current := []float64{0.5, 0.5, 0.5, 0.5}
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r := ComputeDistributionDrift(DistributionDriftInput{
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Baseline: baseline,
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Current: current,
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NumBuckets: 5,
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})
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totalB := 0
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totalC := 0
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for _, b := range r.Buckets {
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totalB += b.BaselineCount
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totalC += b.CurrentCount
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}
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if totalB != len(baseline) {
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t.Errorf("baseline bucket counts sum to %d, expected %d", totalB, len(baseline))
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}
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if totalC != len(current) {
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t.Errorf("current bucket counts sum to %d, expected %d", totalC, len(current))
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}
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}
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// TestDistributionDrift_NumBucketsClamping: 0 → default 10; > 100 → 100.
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func TestDistributionDrift_NumBucketsClamping(t *testing.T) {
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in := DistributionDriftInput{Baseline: []float64{1, 2}, Current: []float64{1, 2}}
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in.NumBuckets = 0
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r := ComputeDistributionDrift(in)
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if r.NumBuckets != 10 {
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t.Errorf("0 should default to 10 buckets, got %d", r.NumBuckets)
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}
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in.NumBuckets = 500
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r = ComputeDistributionDrift(in)
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if r.NumBuckets != 100 {
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t.Errorf("500 should clamp to 100 buckets, got %d", r.NumBuckets)
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}
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}
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