feat(rectification): add adaptive question selector

scripts/active_rectification_questions.py

@@ -5,9 +5,18 @@ from __future__ import annotations
 
 import argparse
 import json
+import sys
 from datetime import datetime, timedelta
+from pathlib import Path
 from typing import Any
 
+
+SCRIPTS_DIR = Path(__file__).resolve().parent
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from active_rectification_selector import select_next_questions
+
 OPTIONS = [
     {"key": "A", "label": "明确有，且时间大致吻合", "score": 2},
     {"key": "B", "label": "有类似，但时间略偏或不够重大", "score": 1},
@@ -15,6 +24,18 @@ OPTIONS = [
     {"key": "D", "label": "不确定 / 不记得", "score": 0},
 ]
 
+QUESTION_RELIABILITY = {
+    "education": 1.0,
+    "residence": 0.95,
+    "relationship": 0.95,
+    "career": 0.9,
+    "career_learning": 0.88,
+    "health_pressure": 0.72,
+    "public_work": 0.82,
+    "fine_timing": 0.45,
+}
+
+
 QUESTION_TEMPLATES = [
     ("education_environment_shift", 1, "education", ["D24", "D4", "Dasha"], "age_16_to_18", "16-18岁附近，是否有明显学业、学校、专业方向或学习环境变化？", "middle_candidate_cluster", "against_D24_sensitive_cluster"),
     ("residence_relocation_shift", 1, "residence", ["D4", "12H", "Rahu/Ketu", "Transit"], "age_20_to_24", "20-24岁附近，是否有搬家、离乡、长期异地、住宿或居住结构变化？", "D4_relocation_cluster", "against_D4_relocation_cluster"),
@@ -69,6 +90,26 @@ def _candidate_scan(
         samples.append(sample)
     has_true_recast = all("varga_lagna" in sample for sample in samples)
     has_kp_recast = all("kp_cusps" in sample for sample in samples)
+    minute_scan = None
+    if lat is not None and lon is not None and tz is not None:
+        try:
+            from candidate_time_sensitivity_scan import scan_candidate_times
+        except ModuleNotFoundError:  # pragma: no cover - package import
+            from scripts.candidate_time_sensitivity_scan import scan_candidate_times
+        minute_scan = scan_candidate_times(
+            {
+                "year": center.year,
+                "month": center.month,
+                "day": center.day,
+                "hour": center.hour,
+                "minute": center.minute,
+                "lat": lat,
+                "lon": lon,
+                "tz": tz,
+            },
+            uncertainty_minutes=uncertainty_minutes,
+            step_minutes=step_minutes,
+        )
     computed_layers = ["time_range", "candidate_cluster", "question_sensitivity_map"]
     blocked_layers = ["true_varga_recast", "true_kp_cusp_recast", "true_arudha_recast"]
     if has_true_recast:
@@ -84,6 +125,7 @@ def _candidate_scan(
         "candidate_count": candidate_count,
         "cluster_labels": ["early_candidate_cluster", "middle_candidate_cluster", "late_candidate_cluster"],
         "samples": samples,
+        "minute_scan": minute_scan,
         "sensitivity_summary": {
             "method": "minute_feature_scan_v2",
             "high_value_layers": ["D2", "D4", "D9", "D10", "D24", "D30", "D60", "UL", "A7", "A10", "KP_cusp"],
@@ -201,6 +243,18 @@ def build_questionnaire(
             "window": window,
             "prompt": prompt,
             "options": OPTIONS,
+            "positive_cluster": yes_bias,
+            "negative_cluster": no_bias,
+            "factual_reliability": QUESTION_RELIABILITY.get(domain, 0.75),
+            "why_this_question": [
+                f"用于区分 {yes_bias} 与 {no_bias}，并核对 {', '.join(sensitivity)} 在候选窗口内的差异。"
+            ],
+            "answer_impact": {
+                "A": f"提高 {yes_bias} 的优先级。",
+                "B": f"弱支持 {yes_bias}。",
+                "C": f"提高 {no_bias} 的相对权重。",
+                "D": "保持中性并转问其他可核验主题。",
+            },
             "scoring_map": {
                 "A": {"effect": "support", "cluster": yes_bias, "points": 2},
                 "B": {"effect": "weak_support", "cluster": yes_bias, "points": 1},
@@ -208,18 +262,23 @@ def build_questionnaire(
                 "D": {"effect": "neutral", "cluster": "neutral", "points": 0},
             },
         })
+    candidate_scan = _candidate_scan(
+        _parse_time(birth_time),
+        uncertainty_minutes,
+        step_minutes,
+        lat=lat,
+        lon=lon,
+        tz=tz,
+        ayanamsa=ayanamsa,
+    )
+    selection = select_next_questions(
+        {"questions": questions, "candidate_scan": candidate_scan}, {}, limit=1
+    )
     return {
         "scope": "active_birth_time_rectification_questionnaire",
         "schema_version": 1,
-        "candidate_scan": _candidate_scan(
-            _parse_time(birth_time),
-            uncertainty_minutes,
-            step_minutes,
-            lat=lat,
-            lon=lon,
-            tz=tz,
-            ayanamsa=ayanamsa,
-        ),
+        "candidate_scan": candidate_scan,
+        "selection": selection,
         "workflow": [
             "candidate_time_scan",
             "varga_arudha_kp_sensitivity_diff",
@@ -357,6 +416,16 @@ def score_answers(
                 "downgrade_reasons": downgrade_reasons,
             }
         )
+    next_round_selection = select_next_questions(
+        {
+            "questions": questions,
+            "candidate_scan": questionnaire.get("candidate_scan")
+            if isinstance(questionnaire.get("candidate_scan"), dict)
+            else None,
+        },
+        {item["id"]: item["answer"] for item in applied},
+        limit=1,
+    )
     return {
         "scope": "active_birth_time_rectification_scoring",
         "schema_version": 1,
@@ -412,6 +481,7 @@ def score_answers(
         ],
         "next_round": next_round,
         "next_round_questions": [question for question in unanswered if question.get("round") == next_round],
+        "next_round_selection": next_round_selection,
         "applied_scoring": applied,
         "unknown_question_ids": unknown_ids,
         "invalid_answers": invalid_answers,