Clean up nested duplicate: v2/v2/kintsugi_vendor/kintsugi/bdi/drift_classifier.py

v2/v2/kintsugi_vendor/kintsugi/bdi/drift_classifier.py

@@ -1,147 +0,0 @@
-"""BDI drift classification from coherence score changes."""
-
-from dataclasses import dataclass
-from typing import List
-
-from .coherence import CoherenceScore
-
-
-@dataclass(frozen=True)
-class DriftClassification:
-    category: str  # healthy_adaptation | stale_beliefs | intention_drift | values_tension
-    confidence: float
-    evidence: tuple  # frozen needs immutable
-    recommendation: str
-
-
-_VALID_CATEGORIES = {
-    "healthy_adaptation",
-    "stale_beliefs",
-    "intention_drift",
-    "values_tension",
-}
-
-
-class BDIDriftClassifier:
-    """Classifies drift from coherence score deltas."""
-
-    def __init__(self) -> None:
-        pass
-
-    def classify(
-        self,
-        coherence_before: CoherenceScore,
-        coherence_after: CoherenceScore,
-        time_delta_days: float,
-    ) -> DriftClassification:
-        overall_delta = coherence_after.overall - coherence_before.overall
-        bd_delta = (
-            coherence_after.belief_desire_alignment
-            - coherence_before.belief_desire_alignment
-        )
-        di_delta = (
-            coherence_after.desire_intention_alignment
-            - coherence_before.desire_intention_alignment
-        )
-        bi_delta = (
-            coherence_after.belief_intention_alignment
-            - coherence_before.belief_intention_alignment
-        )
-
-        new_issues = set(coherence_after.issues) - set(coherence_before.issues)
-        evidence_items: List[str] = []
-
-        # Healthy adaptation: overall improved or stable, no new issues
-        if overall_delta >= 0 and not new_issues:
-            evidence_items.append(f"Overall coherence delta: +{overall_delta:.4f}")
-            return DriftClassification(
-                category="healthy_adaptation",
-                confidence=min(1.0, 0.6 + overall_delta),
-                evidence=tuple(evidence_items),
-                recommendation="Continue current trajectory. BDI coherence is stable or improving.",
-            )
-
-        # Stale beliefs: belief-related scores dropped + large time delta
-        if (bd_delta < -0.05 or bi_delta < -0.05) and time_delta_days > 60:
-            evidence_items.append(f"Belief-desire delta: {bd_delta:.4f}")
-            evidence_items.append(f"Belief-intention delta: {bi_delta:.4f}")
-            evidence_items.append(f"Time since last check: {time_delta_days:.0f} days")
-            return DriftClassification(
-                category="stale_beliefs",
-                confidence=min(1.0, 0.5 + abs(bd_delta) + abs(bi_delta)),
-                evidence=tuple(evidence_items),
-                recommendation="Schedule a belief review session. Several beliefs may be outdated.",
-            )
-
-        # Intention drift: intention alignment dropped
-        if di_delta < -0.05 or bi_delta < -0.05:
-            evidence_items.append(f"Desire-intention delta: {di_delta:.4f}")
-            evidence_items.append(f"Belief-intention delta: {bi_delta:.4f}")
-            return DriftClassification(
-                category="intention_drift",
-                confidence=min(1.0, 0.5 + abs(di_delta) + abs(bi_delta)),
-                evidence=tuple(evidence_items),
-                recommendation="Review active intentions. Some may no longer serve current desires or beliefs.",
-            )
-
-        # Values tension: desire alignment dropped
-        if bd_delta < -0.05:
-            evidence_items.append(f"Belief-desire delta: {bd_delta:.4f}")
-            return DriftClassification(
-                category="values_tension",
-                confidence=min(1.0, 0.5 + abs(bd_delta)),
-                evidence=tuple(evidence_items),
-                recommendation="Facilitate a values alignment session. Desires may have shifted away from core beliefs.",
-            )
-
-        # Default fallback
-        evidence_items.append(f"Overall delta: {overall_delta:.4f}")
-        if new_issues:
-            evidence_items.append(f"New issues: {len(new_issues)}")
-        return DriftClassification(
-            category="values_tension",
-            confidence=0.4,
-            evidence=tuple(evidence_items),
-            recommendation="Review BDI coherence. Minor tensions detected across layers.",
-        )
-
-    def classify_from_events(self, events: List[dict]) -> DriftClassification:
-        """Classify drift from a list of drift event dicts."""
-        if not events:
-            return DriftClassification(
-                category="healthy_adaptation",
-                confidence=0.5,
-                evidence=("No events provided.",),
-                recommendation="No drift events to classify.",
-            )
-
-        category_counts: dict = {}
-        evidence_items: List[str] = []
-        for event in events:
-            cat = event.get("category", "values_tension")
-            category_counts[cat] = category_counts.get(cat, 0) + 1
-            desc = event.get("description", "")
-            if desc:
-                evidence_items.append(desc)
-
-        # Pick the most frequent category
-        dominant = max(category_counts, key=lambda k: category_counts[k])
-        if dominant not in _VALID_CATEGORIES:
-            dominant = "values_tension"
-
-        total = sum(category_counts.values())
-        confidence = category_counts[dominant] / total if total else 0.5
-
-        recommendations = {
-            "healthy_adaptation": "Continue current trajectory.",
-            "stale_beliefs": "Schedule a belief review session.",
-            "intention_drift": "Review active intentions for relevance.",
-            "values_tension": "Facilitate a values alignment session.",
-        }
-
-        return DriftClassification(
-            category=dominant,
-            confidence=round(confidence, 4),
-            evidence=tuple(evidence_items[:5]),
-            recommendation=recommendations.get(dominant, "Review BDI coherence."),
-        )