jesse-uxandClaude Code
932f2fffba
Preserve closed confirmation gates and previously-exposed dataset boundaries. Add auditable 900-trial sensitivity results, current scorer freshness checks, and the v5 collection protocol. Co-Authored-By: Claude Code <noreply@anthropic.com>
212 lines
11 KiB
Python
212 lines
11 KiB
Python
#!/usr/bin/env python3
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"""Reported-centre sensitivity on exposed public cases, without oracle answers."""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Any
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ROOT = Path(__file__).resolve().parents[2]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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from scripts.active_rectification_event_engine import (
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AYANAMSA, NODE_MODE, compute_candidate_static_contexts,
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)
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from scripts.minute_rectification_blind_eval import _opaque_winner, implementation_sha256
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from scripts.minute_rectification_holdout_validator import validate
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from scripts.rectification.candidate_contrast import select_signature_representatives
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from scripts.rectification.scoring_service import build_event_contribution_matrix, score_from_matrix
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from scripts.research.cluster_width_lib import SEPARATION_LEAD, still_valid_public
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from scripts.research.probe_supply_after_six import request_from_case
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from scripts.research.sealed_holdout_rerun import DATASET, file_sha256, opaque_order
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OFFSETS = (-30, -20, -15, -10, -8, -5, -3, 0, 3, 5, 8, 10, 15, 20, 30)
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RADII = (15, 30, 60)
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MINUTE_STEP = 1
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PRODUCTION_FILES = [
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"scripts/rectification/scoring_service.py",
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"scripts/rectification/dasha_transition_proximity.py",
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"scripts/rectification/candidate_contrast.py",
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"scripts/rectification/case_holdout.py",
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"scripts/rectification/contracts.py",
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"scripts/rectification/event_probes.py",
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]
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RESEARCH_FILES = [
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"scripts/research/reported_offset_sweep.py",
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"scripts/research/cluster_width_lib.py",
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"scripts/research/probe_supply_after_six.py",
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"scripts/research/sealed_holdout_rerun.py",
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"scripts/minute_rectification_blind_eval.py",
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]
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def shifted_window(case: dict[str, Any], offset: int, radius: int) -> tuple[dict[str, Any], list[datetime]]:
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if radius not in (*RADII, 120):
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raise ValueError("unsupported_radius")
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true_at = datetime.fromisoformat(f"{case['birth']['date']}T{case['birth']['time']}:00")
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reported_at = true_at + timedelta(minutes=offset)
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start = reported_at - timedelta(minutes=radius)
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end = reported_at + timedelta(minutes=radius)
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candidates = [start + timedelta(minutes=i) for i in range(0, radius * 2 + 1, MINUTE_STEP)]
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# Reuse only the established event normalization, then replace the window.
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# The ranker sees neither the truth label nor the simulated offset.
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request = request_from_case(case)
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request.update({
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"birth_date": start.date().isoformat(),
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"start_time": start.strftime("%H:%M"),
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"end_time": end.strftime("%H:%M"),
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"minute_step": MINUTE_STEP,
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"ayanamsa": AYANAMSA,
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"node_mode": NODE_MODE,
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})
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return request, candidates
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def score_window(request: dict[str, Any], contexts: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Keep auxiliary transition anchors on each candidate's actual date.
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The production matrix's transition-proximity helper accepts one birth date
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per call, unlike the static chart layer which reads candidate_at. Grouping
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is only an offline adapter; it does not change the production scorer.
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"""
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by_date: dict[str, list[dict[str, Any]]] = {}
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for context in contexts:
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by_date.setdefault(context["candidate_at"].date().isoformat(), []).append(context)
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by_time = {}
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for candidate_date, group in by_date.items():
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dated_request = {**request, "birth_date": candidate_date}
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built = build_event_contribution_matrix(dated_request, static_contexts=group)
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by_time.update({row["time"]: row for row in score_from_matrix(dated_request, built)})
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return [by_time[context["candidate_at"].strftime("%H:%M")] for context in contexts]
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def delivery_moments(public: list[dict[str, Any]], candidates: list[datetime]) -> list[datetime]:
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"""Initial delivery envelope in date-aware order; no truth-based narrowing."""
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valid = still_valid_public(public, {}, lead=SEPARATION_LEAD)
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clocks = {str(value)[:5] for row in valid for value in (row.get("cluster_times") or [row["time"]])}
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return [candidate for candidate in candidates if candidate.strftime("%H:%M") in clocks]
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def reveal_metrics(
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rows: list[dict[str, Any]], candidates: list[datetime], delivery: list[datetime],
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truth: datetime, benchmark_id: str, case_id: str,
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) -> dict[str, Any]:
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ordered = opaque_order(benchmark_id, case_id, rows)
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predicted_clock = _opaque_winner(benchmark_id, case_id, rows)
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by_clock = {candidate.strftime("%H:%M"): candidate for candidate in candidates}
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if len(by_clock) != len(candidates):
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raise ValueError("ambiguous_candidate_clock")
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predicted = by_clock[predicted_clock]
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within = min(candidates) <= truth <= max(candidates)
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in_candidates = truth in candidates
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rank = next((i for i, row in enumerate(ordered, 1) if by_clock[row["time"]] == truth), None)
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covered = bool(delivery) and min(delivery) <= truth <= max(delivery)
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return {
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"truth_in_window": within,
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"truth_in_candidates": in_candidates,
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"true_rank": rank,
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"top_1_hit": predicted == truth,
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"top_1_minute_error": abs((predicted - truth).total_seconds()) / 60,
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"delivery_covers_truth": covered,
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"candidate_count": len(candidates),
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"delivery_width_minutes": int((max(delivery) - min(delivery)).total_seconds() / 60) + 1 if delivery else 0,
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}
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def summarize(trials: list[dict[str, Any]], radii: tuple[int, ...], offsets: tuple[int, ...]) -> list[dict[str, Any]]:
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result = []
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for radius in radii:
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for offset in offsets:
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group = [row for row in trials if row["radius_minutes"] == radius and row["offset_minutes"] == offset]
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count = len(group)
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result.append({
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"radius_minutes": radius, "offset_minutes": offset, "trial_count": count,
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**{key: round(sum(row[source] for row in group) / count, 4) if count else None for key, source in (
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("truth_in_window_rate", "truth_in_window"),
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("top_1_rate", "top_1_hit"),
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("delivery_coverage_rate", "delivery_covers_truth"),
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("mean_absolute_minute_error", "top_1_minute_error"),
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)},
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})
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return result
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def run(dataset: Path = DATASET, radii: tuple[int, ...] = RADII, offsets: tuple[int, ...] = OFFSETS) -> dict[str, Any]:
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manifest = json.loads(dataset.read_text(encoding="utf-8"))
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frozen_files = manifest["frozen_scoring"]["files"]
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scoring_files = sorted(set(frozen_files + PRODUCTION_FILES))
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starting_hash = implementation_sha256(scoring_files)
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validation = validate(dataset)
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invalid = validation["invalid_cases"]
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trials = []
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for index, case in enumerate(manifest["cases"], 1):
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if case["case_id"] in invalid:
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continue
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# Reuse static chart calculations, not window-dependent scores or ranks.
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contexts_by_moment: dict[datetime, dict[str, Any]] = {}
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for radius in radii:
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for offset in offsets:
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request, candidates = shifted_window(case, offset, radius)
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missing = [moment for moment in candidates if moment not in contexts_by_moment]
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if missing:
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contexts_by_moment.update(zip(missing, compute_candidate_static_contexts(request, candidates=missing), strict=True))
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contexts = [contexts_by_moment[moment] for moment in candidates]
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rows = score_window(request, contexts)
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public = select_signature_representatives(rows, contexts)
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delivery = delivery_moments(public, candidates)
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# All scores and the delivery envelope are locked before reveal.
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truth = datetime.fromisoformat(f"{case['birth']['date']}T{case['birth']['time']}:00")
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trials.append({
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"case_ordinal": index, "offset_minutes": offset, "radius_minutes": radius,
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**reveal_metrics(rows, candidates, delivery, truth, manifest["benchmark_id"], case["case_id"]),
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})
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if implementation_sha256(scoring_files) != starting_hash:
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raise ValueError("scorer_changed_during_sweep")
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return {
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"scope": "reported_offset_sensitivity_not_product_accuracy",
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"specification": {
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"ayanamsa": AYANAMSA, "node_mode": NODE_MODE,
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"radii_minutes": list(radii), "offsets_minutes": list(offsets), "minute_step": MINUTE_STEP,
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"dataset": dataset.relative_to(ROOT).as_posix(), "dataset_sha256": file_sha256(dataset),
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"dataset_benchmark_id": manifest["benchmark_id"],
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"implementation_sha256": implementation_sha256(frozen_files),
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"implementation_sha256_prefix": implementation_sha256(frozen_files)[:16],
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"production_scoring_files": scoring_files, "production_scoring_sha256": starting_hash,
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"research_files": RESEARCH_FILES,
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"research_implementation_sha256": implementation_sha256(RESEARCH_FILES),
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"hash_scope": "explicit_identity_file_sets_not_a_transitive_dependency_lock",
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"scorer": "native_event_contribution_matrix_not_shadow_fact_ranker",
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"delivery": "initial_signature_clusters_peak_gap_lt_8_envelope_no_answers_no_elimination",
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"rank": "score_desc_then_sha256(benchmark_id:case_id:candidate_time)",
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"grid": "every_minute_inclusive_not_production_two_minute_sampling",
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"cross_midnight": "date_aware_candidates_distance_and_matrix_grouped_by_candidate_date",
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"evaluator_sha256": file_sha256(Path(__file__)),
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"replay_revision": "candidate_date_grouped_v2",
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"supersedes": "initial_sweep_invalidated_cross_midnight_transition_anchor",
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"is_blind_evaluation": False, "truth_hidden_from_ranker": True,
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"results_previously_seen": True, "must_not_use_for_tuning": True,
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},
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"excluded_cases": invalid, "case_count": validation["valid_public_aa_cases"],
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"trial_count": len(trials), "trials": trials,
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"summary": summarize(trials, radii, offsets),
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"widening_geometry": {
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"scored_radii": list(radii),
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"radius_120_scored": 120 in radii,
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"truth_in_window_condition": "abs(reported_offset_minutes) <= radius_minutes",
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"radius_15_first_integer_minute_outside": 16,
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"all_tested_offsets_within_radius_30_60_120": max(map(abs, offsets)) <= 30,
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"boundary": "Geometry rescues candidate inclusion only, not ranking or delivery coverage; no population frequency without a reported-offset distribution.",
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},
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}
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--json", action="store_true")
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args = parser.parse_args()
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print(json.dumps(run(), ensure_ascii=False, indent=2))
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