Jesse_Chen
7dc97a49ee
Keep representative-time cards off while method coverage is still open. New cases bind Skill 10.0.9; existing 10.0.8 packages stay hashed. Co-authored-by: Cursor <cursoragent@cursor.com>
123 lines
5.9 KiB
Python
123 lines
5.9 KiB
Python
from __future__ import annotations
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from typing import Any
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from uuid import NAMESPACE_URL, uuid5
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from scripts.rectification.candidate_feature_service import build_candidate_feature_snapshot
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from scripts.rectification.contracts import EVENT_CONTRACT_VERSION, RectificationRequest
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from scripts.rectification.decision_policy import (
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EXECUTION_LEDGER_VERSION,
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POLICY_VERSION,
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build_candidate_decisions,
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build_decision_receipt,
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build_execution_ledger,
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)
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from scripts.rectification.diagnostics_service import run_diagnostics
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from scripts.rectification.scoring_service import (
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ALGORITHM_VERSION,
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build_event_contribution_matrix,
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calculation_spec,
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score_from_matrix,
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scoreable_request,
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sha256,
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)
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def candidate_features(request: RectificationRequest) -> dict[str, Any]:
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spec = calculation_spec(request)
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spec_hash = sha256(spec)
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scoring_request = scoreable_request(request)
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return {
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"algorithm_version": ALGORITHM_VERSION,
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"event_contract_version": EVENT_CONTRACT_VERSION,
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"decision_policy_version": POLICY_VERSION,
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"calculation_spec": spec,
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"calculation_spec_hash": spec_hash,
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"candidate_feature_snapshot": build_candidate_feature_snapshot(scoring_request, spec_hash),
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"can_confirm_exact_minute": False,
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}
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def score_candidates(request: RectificationRequest) -> dict[str, Any]:
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scoring_request = scoreable_request(request)
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built = build_event_contribution_matrix(scoring_request)
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rows = score_from_matrix(scoring_request, built)
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spec = calculation_spec(request)
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spec_hash = sha256(spec)
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diagnostic_values = run_diagnostics(scoring_request, rows, built)
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fingerprint = sha256(request)
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result_id = str(uuid5(NAMESPACE_URL, f"{ALGORITHM_VERSION}:{fingerprint}"))
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candidate_decisions = build_candidate_decisions(rows, result_id=result_id)
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decision_receipt = build_decision_receipt(request, candidate_decisions, built, diagnostic_values)
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execution_ledger = build_execution_ledger(request, built, diagnostic_values, candidate_decisions)
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representative = candidate_decisions[0] if candidate_decisions else None
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return {
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"result_id": result_id,
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"algorithm_version": ALGORITHM_VERSION,
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"event_contract_version": EVENT_CONTRACT_VERSION,
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"decision_policy_version": POLICY_VERSION,
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"calculation_spec": spec,
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"calculation_spec_hash": spec_hash,
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"candidate_scores": [{
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"time": row["time"],
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"score": row["score"],
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"supporting_event_ids": [item["event_id"] for item in row["evidence"] if item["points"] > 0],
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"conflicting_event_ids": [item["event_id"] for item in row["evidence"] if item["points"] < 0],
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} for row in rows],
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"candidate_decisions": candidate_decisions,
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"candidate_decision_receipt": decision_receipt,
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"decision_receipt": decision_receipt,
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"execution_ledger_version": EXECUTION_LEDGER_VERSION,
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"execution_ledger": execution_ledger,
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"event_contribution_matrix": built["matrix"],
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"candidate_feature_snapshot": build_candidate_feature_snapshot(
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scoring_request, spec_hash, built.get("static_contexts")
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),
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"diagnostics": diagnostic_values,
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"robustness": {
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"neighbor_support_minutes": diagnostic_values.get("neighbor_support_minutes", 0),
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"leave_one_out_retention_rate": diagnostic_values.get("leave_one_event_out_retention_rate", 0),
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"leave_one_domain_out_retention_rate": diagnostic_values.get("leave_one_domain_out_retention_rate", 0),
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"date_sensitivity_retention_rate": diagnostic_values.get("date_sensitivity_retention_rate", 0),
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},
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"missing_layers": built["missing_layers"],
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"display_allowed": decision_receipt["display_allowed"],
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"selection_allowed": decision_receipt["selection_allowed"],
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"acceptance_allowed": decision_receipt["acceptance_allowed"],
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"propose_allowed": decision_receipt["propose_allowed"],
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"confirmation_allowed": decision_receipt["confirmation_allowed"],
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"representative_candidate_id": representative["candidate_id"] if representative else None,
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"representative_time": representative["time"] if representative else None,
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"overall_confidence": decision_receipt["overall_confidence"],
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"margin_percent": decision_receipt["margin_percent"],
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"can_confirm_exact_minute": decision_receipt["confirmation_allowed"],
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}
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def diagnostics(request: RectificationRequest) -> dict[str, Any]:
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scored = score_candidates(request)
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return {
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"result_id": scored["result_id"],
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"algorithm_version": scored["algorithm_version"],
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"event_contract_version": scored["event_contract_version"],
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"decision_policy_version": scored["decision_policy_version"],
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"calculation_spec_hash": scored["calculation_spec_hash"],
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"candidate_decisions": scored["candidate_decisions"],
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"candidate_decision_receipt": scored["candidate_decision_receipt"],
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"decision_receipt": scored["decision_receipt"],
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"execution_ledger_version": scored["execution_ledger_version"],
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"execution_ledger": scored["execution_ledger"],
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"diagnostics": scored["diagnostics"],
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"missing_layers": scored["missing_layers"],
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"display_allowed": scored["display_allowed"],
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"selection_allowed": scored["selection_allowed"],
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"acceptance_allowed": scored["acceptance_allowed"],
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"propose_allowed": scored["propose_allowed"],
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"confirmation_allowed": scored["confirmation_allowed"],
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"representative_candidate_id": scored["representative_candidate_id"],
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"representative_time": scored["representative_time"],
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"overall_confidence": scored["overall_confidence"],
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"margin_percent": scored["margin_percent"],
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"can_confirm_exact_minute": scored["confirmation_allowed"],
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}
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