Guard demo.sh against removed results/ artifacts

scripts/demo.sh

@@ -32,13 +32,23 @@ banner() {
     echo "============================================================"
 }
 
+# The artifact sections below read committed results/* files. Those are no
+# longer tracked, so each section is guarded: a missing artifact prints a
+# skip line instead of aborting the demo under `set -e`.
+have() {
+    if [ -f "$1" ]; then return 0; fi
+    echo "  (skipped -- $1 not present; regenerate it with the pipeline)"
+    return 1
+}
+
 banner "Day 1 -- temporal-split fix is committed"
 echo "Pre-fix (random split, leaked future txns):"
 echo "    train_test_split(X, y, test_size=0.2, stratify=y)"
 echo "Post-fix (per-source temporal split):"
 grep -n "def temporal_split_per_source" src/train.py | head -1
 echo ""
 echo "Honest sparkov_test AUC (from results/baseline_metrics.json):"
+if have results/baseline_metrics.json; then
 python - <<'PY'
 import json
 b = json.load(open("results/baseline_metrics.json"))
@@ -47,20 +57,24 @@ auc = held.get("our_auc")
 ag = held.get("baselines", {}).get("AutoGluon", {})
 print(f"  sparkov_test_auc = {auc}  (vs AutoGluon {ag.get('baseline_auc', 'n/a')}, delta {ag.get('delta', 'n/a')})")
 PY
+fi
 
 banner "Day 2 -- Pandas == Dask determinism (1K rows)"
 python -m pytest tests/test_features_determinism.py -q --disable-warnings 2>&1 | tail -3
 
 banner "Day 2 -- MLflow registry rollback latency (cached results)"
+if have results/registry_rollback_times.csv; then
 python - <<'PY'
 import pandas as pd
 df = pd.read_csv("results/registry_rollback_times.csv")
 print(df[["iteration", "alias_flip_seconds", "audit_tag_seconds", "total_seconds"]].to_string(index=False))
 print(f"\nMedian alias-flip: {df['alias_flip_seconds'].median() * 1000:.1f} ms")
 print(f"Max alias-flip:    {df['alias_flip_seconds'].max() * 1000:.1f} ms")
 PY
+fi
 
 banner "Day 3 -- 30-day synthetic drift replay (cached results)"
+if have results/drift_replay_summary.json; then
 python - <<'PY'
 import json, pandas as pd
 summary = json.load(open("results/drift_replay_summary.json"))
@@ -71,33 +85,32 @@ print(f"Detected fires:  {summary.get('detected_fire_days', '<see per_day.csv>')
 print(f"Precision:       {summary.get('precision', 'n/a')}")
 print(f"Recall:          {summary.get('recall', 'n/a')}")
 PY
+fi
 
 banner "Day 3 -- auto-retrain events"
+if have results/drift_retrain_events.csv; then
 python - <<'PY'
 import pandas as pd
 df = pd.read_csv("results/drift_retrain_events.csv")
 cols = ["triggered_on_day", "shadow_auprc", "prod_auprc", "promote_decision",
         "new_model_version", "seconds_end_to_end"]
 print(df[cols].to_string(index=False))
 PY
+fi
 
 banner "Day 6 -- Sentinel champion vs naive notebook vs Claude Opus 4.6"
+if have results/day06/frontier_comparison.csv; then
 python - <<'PY'
 import pandas as pd
 df = pd.read_csv("results/day06/frontier_comparison.csv")
 df_disp = df[["strategy", "auc", "auprc", "f1_at_0_5", "latency_s_per_query", "cost_usd_at_1k_qps_per_day"]]
 df_disp.columns = ["strategy", "AUC", "AUPRC", "F1@0.5", "latency/q (s)", "$/day @ 1k qps"]
 print(df_disp.to_string(index=False))
 PY
+fi
 
 banner "Sprint complete -- 7 days, 31 tests passing, AutoGluon gap closed"
-echo "Sources:"
-echo "  results/baseline_metrics.json        (Day 1 honest AUC)"
-echo "  results/throughput_speedup.csv       (Day 2 Pandas vs Dask)"
-echo "  results/registry_rollback_times.csv  (Day 2 alias-flip)"
-echo "  results/drift_replay_summary.json    (Day 3 30-day replay)"
-echo "  results/drift_retrain_events.csv     (Day 3 auto-retrain)"
-echo "  results/day05/                       (Day 5 sweep + fix)"
-echo "  results/day06/                       (Day 6 frontier + ablation)"
+echo "Artifacts are regenerated into results/ by the pipeline; none are"
+echo "tracked in the repo. Run 'dvc repro' and the src/ harnesses to rebuild."
 echo ""
 echo "Open the dashboard: streamlit run app.py -> sidebar 'Ops'"