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feat: add rectification event decision contract v2

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Jesse_Chen
2026-08-15 00:56:06 +08:00
parent 21fce9513c
commit 83fef19779
24 changed files with 3637 additions and 562 deletions
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scripts/rectification/scoring_service.py
+107 -20
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@@ -3,16 +3,54 @@ from __future__ import annotations
import hashlib
import json
from collections import defaultdict
from collections.abc import Callable, Sequence
from datetime import date, timedelta
from functools import lru_cache
from typing import Any, Callable, Sequence
from typing import Any
from scripts.active_rectification_event_engine import compute_candidate_static_contexts, compute_event_candidate_rows
from scripts.active_rectification_events import CandidateScoreRow
from scripts.rectification.contracts import LifeEvent, RectificationRequest
from scripts.rectification.contracts import LifeEvent, RectificationRequest, is_scoreable_event
ALGORITHM_VERSION = "rectification-v5-matrix-scoring-2"
INPUT_CONTRACT_VERSION = "rectification-calculation-spec-v4"
PRECISION_WEIGHTS = {
"day": 1.0,
"month": 0.8,
"quarter": 0.65,
"year": 0.5,
"range": 0.35,
}
_ENGINE_KIND_BY_NATIVE_KIND: dict[str, tuple[str, str]] = {
"education_start": ("education", "education_milestone"),
"education_completion": ("education", "education_milestone"),
"education_interruption": ("education", "education_milestone"),
"education_change": ("education", "education_milestone"),
"education_milestone": ("education", "education_milestone"),
"career_entry": ("career", "career_change"),
"career_change": ("career", "career_change"),
"promotion": ("career", "career_change"),
"career_pressure": ("career", "career_change"),
"career_exit": ("career", "career_change"),
"business_start": ("career", "career_change"),
"relationship_start": ("relationship", "relationship_start"),
"relationship_commitment": ("relationship", "relationship_start"),
"relationship_separation": ("relationship", "relationship_change"),
"relationship_end": ("relationship", "relationship_change"),
"relationship_change": ("relationship", "relationship_change"),
"relocation": ("relocation", "relocation"),
"foreign_move": ("relocation", "relocation"),
"return": ("relocation", "relocation"),
"home_change": ("relocation", "relocation"),
"finance_gain": ("finance", "finance_change"),
"finance_loss": ("finance", "finance_change"),
"income_change": ("finance", "finance_change"),
"asset_change": ("finance", "finance_change"),
"finance_change": ("finance", "finance_change"),
"self_health_event": ("health_pressure", "self_health_event"),
"pressure_period": ("health_pressure", "self_health_event"),
}
def _parse(value: str) -> date:
@@ -58,6 +96,7 @@ def sample_event_dates(event: LifeEvent) -> list[str]:
def _legacy_request(request: RectificationRequest, event: LifeEvent, sampled_date: str) -> dict[str, Any]:
engine_domain, _ = _ENGINE_KIND_BY_NATIVE_KIND[event["event_kind"]]
return {
"birth_date": request["birth_date"],
"start_time": request["start_time"],
@@ -66,8 +105,8 @@ def _legacy_request(request: RectificationRequest, event: LifeEvent, sampled_dat
"lon": request["lon"],
"tz": request["tz"],
"events": [{
"id": event["id"], "domain": event["domain"],
"event_kind": event.get("event_kind", event["domain"]),
"id": event["id"], "domain": engine_domain,
"event_kind": event["event_kind"],
"date": sampled_date, "precision": "day", "summary": event.get("summary", ""),
}],
}
@@ -82,57 +121,103 @@ def _cached_rows(serialized: str) -> tuple[CandidateScoreRow, ...]:
return tuple(compute_event_candidate_rows(json.loads(serialized)))
_RELATIONSHIP_SUPPORT_RULES = (
_SUPPORT_RULES = (
"functional_benefic_auxiliary",
"arudha_auxiliary",
"ashtakavarga_target_house_support_auxiliary",
"shadbala_sthana_drik_naisargika_support_auxiliary",
"controlled_transit_jupiter_domain_house",
)
_RELATIONSHIP_CHANGE_RULES = (
_PRESSURE_RULES = (
"functional_malefic_auxiliary",
"ashtakavarga_target_house_pressure_auxiliary",
"shadbala_sthana_drik_naisargika_pressure_auxiliary",
"controlled_transit_saturn_domain_house",
)
_KIND_SEMANTICS: dict[str, tuple[int, float]] = {
"education_start": (1, 1.0),
"education_completion": (1, 1.2),
"education_interruption": (-1, 1.0),
"education_change": (0, 1.0),
"career_entry": (1, 1.0),
"career_change": (0, 1.0),
"promotion": (1, 1.2),
"career_pressure": (-1, 1.0),
"career_exit": (-1, 1.2),
"business_start": (1, 1.2),
"relationship_start": (1, 1.0),
"relationship_commitment": (1, 1.2),
"relationship_separation": (-1, 1.0),
"relationship_end": (-1, 1.2),
"relationship_change": (-1, 1.0),
"relocation": (0, 1.0),
"foreign_move": (1, 1.0),
"return": (1, 0.8),
"home_change": (0, 1.0),
"finance_gain": (1, 1.0),
"finance_loss": (-1, 1.0),
"income_change": (0, 1.0),
"asset_change": (0, 1.0),
"self_health_event": (-1, 1.0),
"pressure_period": (-1, 1.2),
}
def _relationship_kind_factor(event_kind: str, rule_ids: Sequence[str]) -> float:
if event_kind not in {"relationship_start", "relationship_change"}:
return 1.0
support = sum(any(rule.endswith(marker) for marker in _RELATIONSHIP_SUPPORT_RULES) for rule in rule_ids)
change = sum(any(rule.endswith(marker) for marker in _RELATIONSHIP_CHANGE_RULES) for rule in rule_ids)
direction = support - change if event_kind == "relationship_start" else change - support
return max(0.8, min(1.2, 1 + 0.08 * direction))
def precision_weight(precision: str) -> float:
return PRECISION_WEIGHTS[precision]
def _event_kind_factor(event_kind: str, rule_ids: Sequence[str]) -> float:
direction, intensity = _KIND_SEMANTICS.get(event_kind, (0, 1.0))
support = sum(any(rule.endswith(marker) for marker in _SUPPORT_RULES) for rule in rule_ids)
pressure = sum(any(rule.endswith(marker) for marker in _PRESSURE_RULES) for rule in rule_ids)
semantic_signal = direction * (support - pressure) * intensity
return max(0.8, min(1.2, 1 + 0.08 * semantic_signal))
def _kind_adjusted_evidence(event: LifeEvent, evidence: dict[str, Any]) -> dict[str, Any]:
if event["domain"] != "relationship":
return evidence
event_kind = event["event_kind"]
rules = list(evidence["rule_ids"])
direction, _ = _KIND_SEMANTICS.get(event_kind, (0, 1.0))
semantic_label = "support" if direction > 0 else "pressure" if direction < 0 else "change"
return {
**evidence,
"rule_ids": [*rules, f"event_kind_profile:{event_kind}"],
"points": round(float(evidence["points"]) * _relationship_kind_factor(event_kind, rules), 4),
"rule_ids": [*rules, f"event_kind_profile:{event_kind}:{semantic_label}"],
"points": round(
float(evidence["points"])
* _event_kind_factor(event_kind, rules)
* precision_weight(event["precision"]),
4,
),
}
def scoreable_request(request: RectificationRequest) -> RectificationRequest:
return {**request, "events": [event for event in request["events"] if is_scoreable_event(event)]}
def build_event_contribution_matrix(
request: RectificationRequest,
row_provider: Callable[[dict[str, Any]], Sequence[CandidateScoreRow]] | None = None,
) -> dict[str, Any]:
static_contexts = None if row_provider is not None else compute_candidate_static_contexts(request)
scoring_request = scoreable_request(request)
if not scoring_request["events"]:
return {
"candidate_times": [], "matrix": {}, "date_sensitivity": [],
"missing_layers": [], "static_contexts": None,
}
static_contexts = None if row_provider is not None else compute_candidate_static_contexts(scoring_request)
provider = row_provider or (lambda value: compute_event_candidate_rows(value, static_contexts=static_contexts))
matrix: dict[str, dict[str, dict[str, Any]]] = defaultdict(dict)
missing_layers: set[str] = set()
date_sensitivity: list[dict[str, Any]] = []
candidate_grid: list[str] | None = None
for event in request["events"]:
for event in scoring_request["events"]:
samples = sample_event_dates(event)
sample_rows = []
for sampled in samples:
rows = list(provider(_legacy_request(request, event, sampled)))
rows = list(provider(_legacy_request(scoring_request, event, sampled)))
sample_rows.append([
{**row, "score": adjusted["points"], "evidence": [adjusted]}
for row in rows
@@ -184,6 +269,8 @@ def score_from_matrix(request: RectificationRequest, built: dict[str, Any]) -> l
for candidate_time in built["candidate_times"]:
evidence = []
for event in request["events"]:
if not is_scoreable_event(event):
continue
contribution = built["matrix"][event["id"]][candidate_time]
evidence.append({
"event_id": event["id"], "domain": event["domain"], "candidate_time": candidate_time,