feat: rebuild birth time rectification workflow
This commit is contained in:
Jesse_Chen
@@ -0,0 +1,221 @@
|
||||
# /// script
|
||||
# requires-python = ">=3.11"
|
||||
# dependencies = []
|
||||
# ///
|
||||
"""Range-preserving event scoring for the asynchronous rectification V4 worker."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
from collections.abc import Sequence
|
||||
from typing import Any, Final, Literal, NotRequired, TypedDict
|
||||
from uuid import NAMESPACE_URL, uuid5
|
||||
|
||||
from scripts.active_rectification_event_engine import compute_event_candidate_rows
|
||||
from scripts.active_rectification_events import CandidateEvidence, CandidateScoreRow
|
||||
|
||||
ALGORITHM_VERSION: Final = "rectification-v4-range-scoring-1"
|
||||
INPUT_CONTRACT_VERSION: Final = "rectification-calculation-spec-v4"
|
||||
|
||||
EventDomain = Literal["education", "relocation", "relationship", "career", "finance", "health_pressure"]
|
||||
EventPrecision = Literal["day", "month", "quarter", "year", "range"]
|
||||
|
||||
|
||||
def _json_compatible_numbers(value: Any) -> Any:
|
||||
if isinstance(value, float) and value.is_integer():
|
||||
return int(value)
|
||||
if isinstance(value, dict):
|
||||
return {key: _json_compatible_numbers(item) for key, item in value.items()}
|
||||
if isinstance(value, list):
|
||||
return [_json_compatible_numbers(item) for item in value]
|
||||
return value
|
||||
|
||||
|
||||
class RangeLifeEvent(TypedDict):
|
||||
id: str
|
||||
domain: EventDomain
|
||||
event_kind: str
|
||||
date_start: str
|
||||
date_end: str
|
||||
precision: EventPrecision
|
||||
summary: NotRequired[str]
|
||||
|
||||
|
||||
class RangeRectificationRequest(TypedDict):
|
||||
birth_date: str
|
||||
start_time: str
|
||||
end_time: str
|
||||
lat: float
|
||||
lon: float
|
||||
tz: float
|
||||
events: list[RangeLifeEvent]
|
||||
|
||||
|
||||
def _legacy_request(request: RangeRectificationRequest, boundary: Literal["start", "end"]) -> dict[str, Any]:
|
||||
return {
|
||||
"birth_date": request["birth_date"],
|
||||
"start_time": request["start_time"],
|
||||
"end_time": request["end_time"],
|
||||
"lat": request["lat"],
|
||||
"lon": request["lon"],
|
||||
"tz": request["tz"],
|
||||
"events": [{
|
||||
"id": event["id"],
|
||||
"domain": event["domain"],
|
||||
"date": event[f"date_{boundary}"],
|
||||
"precision": "day",
|
||||
"summary": event.get("summary", ""),
|
||||
} for event in request["events"]],
|
||||
}
|
||||
|
||||
|
||||
def _evidence_by_event(row: CandidateScoreRow) -> dict[str, CandidateEvidence]:
|
||||
return {item["event_id"]: item for item in row["evidence"]}
|
||||
|
||||
|
||||
def _average_rows(
|
||||
lower_rows: Sequence[CandidateScoreRow],
|
||||
upper_rows: Sequence[CandidateScoreRow],
|
||||
) -> list[CandidateScoreRow]:
|
||||
if [row["time"] for row in lower_rows] != [row["time"] for row in upper_rows]:
|
||||
raise ValueError("candidate_grid_mismatch")
|
||||
averaged: list[CandidateScoreRow] = []
|
||||
for lower, upper in zip(lower_rows, upper_rows, strict=True):
|
||||
lower_events = _evidence_by_event(lower)
|
||||
upper_events = _evidence_by_event(upper)
|
||||
evidence: list[CandidateEvidence] = []
|
||||
for event_id in sorted(set(lower_events) | set(upper_events)):
|
||||
lower_item = lower_events.get(event_id)
|
||||
upper_item = upper_events.get(event_id)
|
||||
source = lower_item or upper_item
|
||||
if source is None:
|
||||
continue
|
||||
lower_points = lower_item["points"] if lower_item else 0.0
|
||||
upper_points = upper_item["points"] if upper_item else 0.0
|
||||
evidence.append({
|
||||
"event_id": event_id,
|
||||
"domain": source["domain"],
|
||||
"candidate_time": lower["time"],
|
||||
"rule_ids": sorted(set(
|
||||
(lower_item or {}).get("rule_ids", [])
|
||||
+ (upper_item or {}).get("rule_ids", [])
|
||||
+ ["date_range_boundaries_averaged"]
|
||||
)),
|
||||
"points": round((lower_points + upper_points) / 2, 4),
|
||||
})
|
||||
averaged.append({
|
||||
"time": lower["time"],
|
||||
"score": round(sum(item["points"] for item in evidence), 4),
|
||||
"evidence": evidence,
|
||||
"missing_layers": sorted(set(lower["missing_layers"] + upper["missing_layers"])),
|
||||
})
|
||||
return averaged
|
||||
|
||||
|
||||
def _minute_value(value: str) -> int:
|
||||
hour, minute = value.split(":", maxsplit=1)
|
||||
return int(hour) * 60 + int(minute)
|
||||
|
||||
|
||||
def _next_minute(previous: str, current: str) -> bool:
|
||||
return (_minute_value(current) - _minute_value(previous)) % 1_440 == 1
|
||||
|
||||
|
||||
def _primary_cluster(rows: Sequence[CandidateScoreRow], relative_floor: float = 0.97) -> list[str]:
|
||||
if not rows:
|
||||
return []
|
||||
peak = max(row["score"] for row in rows)
|
||||
floor = peak * relative_floor if peak >= 0 else peak / relative_floor
|
||||
viable = sorted((row for row in rows if row["score"] >= floor), key=lambda row: _minute_value(row["time"]))
|
||||
clusters: list[list[CandidateScoreRow]] = []
|
||||
for row in viable:
|
||||
if clusters and _next_minute(clusters[-1][-1]["time"], row["time"]):
|
||||
clusters[-1].append(row)
|
||||
else:
|
||||
clusters.append([row])
|
||||
if not clusters:
|
||||
return []
|
||||
clusters.sort(key=lambda group: (-max(row["score"] for row in group), -sum(max(row["score"], 0) for row in group)))
|
||||
return [row["time"] for row in clusters[0]]
|
||||
|
||||
|
||||
def _top_time(rows: Sequence[CandidateScoreRow]) -> str | None:
|
||||
if not rows:
|
||||
return None
|
||||
top = max(row["score"] for row in rows)
|
||||
return next(row["time"] for row in rows if row["score"] == top)
|
||||
|
||||
|
||||
def _leave_one_out(rows: Sequence[CandidateScoreRow], event_ids: Sequence[str], primary: set[str]) -> dict[str, Any]:
|
||||
runs = []
|
||||
retained = 0
|
||||
for event_id in event_ids:
|
||||
rescored = []
|
||||
for row in rows:
|
||||
removed = sum(item["points"] for item in row["evidence"] if item["event_id"] == event_id)
|
||||
rescored.append({**row, "score": round(row["score"] - removed, 4)})
|
||||
winner = _top_time(rescored)
|
||||
stable = winner in primary
|
||||
retained += int(stable)
|
||||
runs.append({"removed_event_id": event_id, "winner": winner, "primary_cluster_retained": stable})
|
||||
return {
|
||||
"retention_rate": retained / len(event_ids) if event_ids else 0.0,
|
||||
"runs": runs,
|
||||
}
|
||||
|
||||
|
||||
def score_life_events_v4(request: RangeRectificationRequest) -> dict[str, Any]:
|
||||
lower_rows = compute_event_candidate_rows(_legacy_request(request, "start"))
|
||||
upper_rows = compute_event_candidate_rows(_legacy_request(request, "end"))
|
||||
rows = _average_rows(lower_rows, upper_rows)
|
||||
primary = _primary_cluster(rows)
|
||||
primary_set = set(primary)
|
||||
lower_winner = _top_time(lower_rows)
|
||||
upper_winner = _top_time(upper_rows)
|
||||
date_retention = sum(winner in primary_set for winner in (lower_winner, upper_winner)) / 2
|
||||
loo = _leave_one_out(rows, [event["id"] for event in request["events"]], primary_set)
|
||||
normalized = json.dumps(request, ensure_ascii=True, sort_keys=True, separators=(",", ":"))
|
||||
fingerprint = hashlib.sha256(normalized.encode("utf-8")).hexdigest()
|
||||
spec = {
|
||||
"version": INPUT_CONTRACT_VERSION,
|
||||
"birthDate": request["birth_date"],
|
||||
"candidateRange": {"start": request["start_time"], "end": request["end_time"]},
|
||||
"latitude": request["lat"],
|
||||
"longitude": request["lon"],
|
||||
"timezoneOffsetHours": request["tz"],
|
||||
"ayanamsa": "lahiri",
|
||||
"nodeMode": "mean",
|
||||
"minuteStep": 1,
|
||||
}
|
||||
spec_hash = hashlib.sha256(json.dumps(
|
||||
_json_compatible_numbers(spec), sort_keys=True, separators=(",", ":")
|
||||
).encode("utf-8")).hexdigest()
|
||||
missing_layers = sorted({layer for row in rows for layer in row["missing_layers"]})
|
||||
candidates = [{
|
||||
"time": row["time"],
|
||||
"score": row["score"],
|
||||
"supporting_event_ids": [item["event_id"] for item in row["evidence"] if item["points"] > 0],
|
||||
"conflicting_event_ids": [item["event_id"] for item in row["evidence"] if item["points"] < 0],
|
||||
} for row in rows]
|
||||
return {
|
||||
"result_id": str(uuid5(NAMESPACE_URL, f"{ALGORITHM_VERSION}:{fingerprint}")),
|
||||
"algorithm_version": ALGORITHM_VERSION,
|
||||
"calculation_spec": spec,
|
||||
"calculation_spec_hash": spec_hash,
|
||||
"candidate_scores": candidates,
|
||||
"primary_cluster_times": primary,
|
||||
"robustness": {
|
||||
"neighbor_support_minutes": len(primary),
|
||||
"leave_one_out_retention_rate": loo["retention_rate"],
|
||||
"date_sensitivity_retention_rate": date_retention,
|
||||
"date_boundary_winners": {"start": lower_winner, "end": upper_winner},
|
||||
"leave_one_out": loo,
|
||||
},
|
||||
"missing_layers": missing_layers,
|
||||
"can_confirm_exact_minute": False,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit("Import score_life_events_v4 from the worker or API server.")
|
||||
Reference in New Issue
Block a user