Jesse_Chen
72 lines
2.9 KiB
Python
72 lines
2.9 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 RectificationRequest
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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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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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return {
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"algorithm_version": ALGORITHM_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(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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built = build_event_contribution_matrix(request)
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rows = score_from_matrix(request, built)
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spec = calculation_spec(request)
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spec_hash = sha256(spec)
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diagnostics = run_diagnostics(request, rows, built)
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fingerprint = sha256(request)
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return {
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"result_id": str(uuid5(NAMESPACE_URL, f"{ALGORITHM_VERSION}:{fingerprint}")),
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"algorithm_version": ALGORITHM_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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"event_contribution_matrix": built["matrix"],
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"candidate_feature_snapshot": build_candidate_feature_snapshot(request, spec_hash, built.get("static_contexts")),
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"diagnostics": diagnostics,
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"robustness": {
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"neighbor_support_minutes": diagnostics["neighbor_support_minutes"],
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"leave_one_out_retention_rate": diagnostics["leave_one_event_out_retention_rate"],
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"leave_one_domain_out_retention_rate": diagnostics["leave_one_domain_out_retention_rate"],
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"date_sensitivity_retention_rate": diagnostics["date_sensitivity_retention_rate"],
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},
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"missing_layers": built["missing_layers"],
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"can_confirm_exact_minute": False,
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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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"calculation_spec_hash": scored["calculation_spec_hash"],
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"diagnostics": scored["diagnostics"],
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"missing_layers": scored["missing_layers"],
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"can_confirm_exact_minute": False,
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}
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