feat: Phase X X2 calibration improvement tracker schema (CIT-) -- aggregates MBL records; VALID_CIT_TREND_DIRECTIONS (4), MIN_BATCHES_FOR_TREND=2, IMPROVEMENT_THRESHOLD=0.05; BatchHitRateEntry helper; trend from first→latest delta; insufficient_data→N/A; 49 tests (#1009)

Co-authored-by: OpenCode <opencode@example.com>

Author: cschanhniem

docs/research/NEXT_100_PR_MAP.md

@@ -289,7 +289,7 @@ Track whether the pipeline actually improves across batches by capturing per-bat
 | ID | Task | Why it matters | Priority |
 |----|------|----------------|----------|
 | X1 | Add multi-batch learning record schema (MBL-) (complete). — src/openamp_foundry/evidence/multi_batch_learning_record.py: VALID_MBL_QUALITY_GRADES (5: A-D/N/A), VALID_BATCH_LEARNING_STATUSES (3), GRADE_A_HIT_RATE=0.40; hit_rate auto-computed from n_confirmed_hits/n_candidates_tested; grade auto-derived; no_wet_lab_data forces N/A grade; whr_ids list; dry_lab_only=True; 61 tests in tests/evidence/test_multi_batch_learning_record.py. | Per-batch snapshot of prediction quality (hit rate, AUROC, n_confirmed_hits) after wet-lab feedback; enables cross-batch comparison of whether the pipeline is learning; feeds into calibration improvement tracker. | C |
-| X2 | Add calibration improvement tracker schema (CIT-). | Aggregates MBL records across batches; computes hit-rate trend direction (improving/stable/degrading/insufficient_data); minimum 2 batches required; flags when calibration is not producing measurable improvement. | C |
+| X2 | Add calibration improvement tracker schema (CIT-) (complete). — src/openamp_foundry/evidence/calibration_improvement_tracker.py: VALID_CIT_TREND_DIRECTIONS (4: improving/stable/degrading/insufficient_data), VALID_CIT_SUMMARY_GRADES (5: A-D/N/A), MIN_BATCHES_FOR_TREND=2, IMPROVEMENT_THRESHOLD=0.05; BatchHitRateEntry helper; trend auto-computed from first→latest hit_rate delta; insufficient_data forces N/A grade; dry_lab_only=True; 49 tests. | Aggregates MBL records across batches; computes hit-rate trend direction (improving/stable/degrading/insufficient_data); minimum 2 batches required; flags when calibration is not producing measurable improvement. | C |
 | X3 | Add learning progress report schema (LPR-). | Human-readable summary of what the pipeline has learned from all batches to date; references CIT- for trend data; includes which candidate features proved predictive vs not; links to calibration decision logs. | C |
 | X4 | Add recalibration confidence certificate schema (RCC-). | Asserts with what confidence the current calibration weights are reliable based on cohort size, quality, and consistency across batches; A/B/C/D grade; prevents overconfident calibration claims. | C |
 | X5 | Add Phase X learning gate (XLG-). | Top-level gate asserting MBL + CIT + LPR + RCC all present; overall verdict: learning_verified/learning_in_progress/learning_not_started; closes Phase X; no calibration improvement claim is credible without passing this gate. | C |

src/openamp_foundry/evidence/calibration_improvement_tracker.py

@@ -0,0 +1,191 @@
+"""CIT- calibration improvement tracker schema.
+
+Aggregates MBL- records across batches and computes whether the pipeline's
+prediction quality is trending in the right direction. Requires at least 2
+batches with wet-lab data to compute a trend. Flags when calibration is not
+producing measurable improvement.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+VALID_CIT_TREND_DIRECTIONS: frozenset[str] = frozenset({
+    "improving",
+    "stable",
+    "degrading",
+    "insufficient_data",
+})
+
+VALID_CIT_SUMMARY_GRADES: frozenset[str] = frozenset({
+    "A",
+    "B",
+    "C",
+    "D",
+    "N/A",
+})
+
+MIN_BATCHES_FOR_TREND: int = 2
+IMPROVEMENT_THRESHOLD: float = 0.05
+DEGRADATION_THRESHOLD: float = -0.05
+
+
+@dataclass
+class BatchHitRateEntry:
+    batch_id: str
+    mbl_id: str
+    hit_rate: float
+    quality_grade: str
+
+
+@dataclass
+class CalibrationImprovementTracker:
+    cit_id: str
+    pipeline_version: str
+    batch_entries: list[BatchHitRateEntry]
+    n_batches: int
+    n_batches_with_data: int
+    first_batch_hit_rate: float
+    latest_batch_hit_rate: float
+    hit_rate_delta: float
+    trend_direction: str
+    summary_grade: str
+    dry_lab_only: bool
+    limitations: list[str]
+    created_at: str
+
+
+def validate_calibration_improvement_tracker(cit: CalibrationImprovementTracker) -> None:
+    if not cit.cit_id.startswith("CIT-"):
+        raise ValueError(f"cit_id must start with 'CIT-': {cit.cit_id!r}")
+    if not cit.pipeline_version:
+        raise ValueError("pipeline_version must be non-empty")
+    for entry in cit.batch_entries:
+        if not (0.0 <= entry.hit_rate <= 1.0):
+            raise ValueError(
+                f"hit_rate must be in [0, 1] for batch {entry.batch_id!r}: {entry.hit_rate}"
+            )
+    if cit.n_batches != len(cit.batch_entries):
+        raise ValueError("n_batches must equal len(batch_entries)")
+    entries_with_data = [e for e in cit.batch_entries if e.quality_grade != "N/A"]
+    if cit.n_batches_with_data != len(entries_with_data):
+        raise ValueError("n_batches_with_data mismatch")
+    if cit.trend_direction not in VALID_CIT_TREND_DIRECTIONS:
+        raise ValueError(
+            f"trend_direction {cit.trend_direction!r} not in VALID_CIT_TREND_DIRECTIONS"
+        )
+    if cit.summary_grade not in VALID_CIT_SUMMARY_GRADES:
+        raise ValueError(
+            f"summary_grade {cit.summary_grade!r} not in VALID_CIT_SUMMARY_GRADES"
+        )
+    if cit.trend_direction == "insufficient_data" and cit.summary_grade != "N/A":
+        raise ValueError(
+            "summary_grade must be 'N/A' when trend_direction='insufficient_data'"
+        )
+    if not cit.dry_lab_only:
+        raise ValueError("dry_lab_only must be True")
+    if not cit.limitations:
+        raise ValueError("limitations must be non-empty")
+    if not cit.created_at:
+        raise ValueError("created_at must be non-empty")
+
+
+def _compute_trend(
+    entries_with_data: list[BatchHitRateEntry],
+) -> tuple[float, float, float, str]:
+    if len(entries_with_data) < MIN_BATCHES_FOR_TREND:
+        first = entries_with_data[0].hit_rate if entries_with_data else 0.0
+        latest = entries_with_data[-1].hit_rate if entries_with_data else 0.0
+        return first, latest, 0.0, "insufficient_data"
+    first = entries_with_data[0].hit_rate
+    latest = entries_with_data[-1].hit_rate
+    delta = round(latest - first, 6)
+    if delta >= IMPROVEMENT_THRESHOLD:
+        direction = "improving"
+    elif delta <= DEGRADATION_THRESHOLD:
+        direction = "degrading"
+    else:
+        direction = "stable"
+    return first, latest, delta, direction
+
+
+def _compute_summary_grade(
+    trend: str,
+    latest_hit_rate: float,
+) -> str:
+    if trend == "insufficient_data":
+        return "N/A"
+    if trend == "improving" and latest_hit_rate >= 0.25:
+        return "A"
+    if trend in ("improving", "stable") and latest_hit_rate >= 0.10:
+        return "B"
+    if trend == "stable" or latest_hit_rate >= 0.05:
+        return "C"
+    return "D"
+
+
+def build_calibration_improvement_tracker(
+    *,
+    cit_id: str,
+    pipeline_version: str,
+    batch_entry_dicts: list[dict],
+    limitations: list[str],
+    created_at: str,
+) -> CalibrationImprovementTracker:
+    """Build a CalibrationImprovementTracker.
+
+    batch_entry_dicts: list of dicts with keys:
+        batch_id, mbl_id, hit_rate, quality_grade
+    """
+    entries = [
+        BatchHitRateEntry(
+            batch_id=d["batch_id"],
+            mbl_id=d["mbl_id"],
+            hit_rate=float(d["hit_rate"]),
+            quality_grade=d["quality_grade"],
+        )
+        for d in batch_entry_dicts
+    ]
+    entries_with_data = [e for e in entries if e.quality_grade != "N/A"]
+    first_hr, latest_hr, delta, trend = _compute_trend(entries_with_data)
+    grade = _compute_summary_grade(trend, latest_hr)
+    cit = CalibrationImprovementTracker(
+        cit_id=cit_id,
+        pipeline_version=pipeline_version,
+        batch_entries=entries,
+        n_batches=len(entries),
+        n_batches_with_data=len(entries_with_data),
+        first_batch_hit_rate=first_hr,
+        latest_batch_hit_rate=latest_hr,
+        hit_rate_delta=delta,
+        trend_direction=trend,
+        summary_grade=grade,
+        dry_lab_only=True,
+        limitations=limitations,
+        created_at=created_at,
+    )
+    validate_calibration_improvement_tracker(cit)
+    return cit
+
+
+def format_calibration_improvement_tracker(cit: CalibrationImprovementTracker) -> str:
+    lines = [
+        f"Calibration Improvement Tracker — {cit.cit_id}",
+        f"Pipeline: {cit.pipeline_version}",
+        f"Trend: {cit.trend_direction}  |  Grade: {cit.summary_grade}",
+        f"Batches: {cit.n_batches} total, {cit.n_batches_with_data} with data",
+        f"Hit rate: first={cit.first_batch_hit_rate:.1%}  "
+        f"latest={cit.latest_batch_hit_rate:.1%}  "
+        f"delta={cit.hit_rate_delta:+.1%}",
+    ]
+    if cit.batch_entries:
+        lines.append("Batch history:")
+        for entry in cit.batch_entries:
+            lines.append(
+                f"  {entry.batch_id} ({entry.mbl_id}): "
+                f"hr={entry.hit_rate:.1%}  grade={entry.quality_grade}"
+            )
+    lines.append(f"Created: {cit.created_at}")
+    lines.append(f"Limitations: {'; '.join(cit.limitations)}")
+    lines.append(f"dry_lab_only: {cit.dry_lab_only}")
+    return "\n".join(lines)