a2980bdca2
* feat(epistemic): populate normative_detail on TurnEvent and ChatResponse Adds normative_detail_from_verdicts() to core.epistemic_state and wires it into both the stub and main ChatResponse/TurnEvent construction sites. The field carries a sorted comma-separated list of violated boundary or commitment IDs when normative clearance is VIOLATED or SUPPRESSED; empty string otherwise. * docs(ADR-0142): ratify epistemic state taxonomy — 14-state vocabulary + normative clearance axis Formalises the six-subsystem Framing 1 audit findings into a first-class decision. Accepts the 14-state taxonomy and companion 4-value normative clearance axis. Documents Phase 3 deliverables already landed and defers structured provenance + cross-subsystem transition machinery to ADR-0144. * feat(recognition): output contract + ADR-0143 Adds recognition/outcome.py: RecognitionOutcome, FeatureBundle, BoundFeature, EvidenceSpan, NegativeEvidence, the three typed refusal classes (ShapeRefusal, FeatureEvidenceRefusal, FeatureConsistencyRefusal), and RecognitionProvenance. Frozen dataclasses, JSON-serializable, byte-deterministic invariants enforced in __post_init__. ADR-0143 commits to Mechanism D (multi-resolution anti-unification over token sequences) and defines the two-phase acceptance test. * feat(recognition): derive phase1 anti-unifier
359 lines
13 KiB
Python
359 lines
13 KiB
Python
"""Deterministic Phase 1 anti-unification over taught token sequences."""
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from __future__ import annotations
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import hashlib
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import json
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from dataclasses import dataclass
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from typing import Any, Iterable, Mapping, Sequence
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from recognition.outcome import (
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EVIDENCED,
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UNDETERMINED,
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BoundFeature,
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EvidenceSpan,
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FeatureBundle,
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FeatureEvidence,
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NegativeEvidence,
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RecognitionOutcome,
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RecognitionProvenance,
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ShapeRefusal,
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)
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TokenSequence = Sequence[str]
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Scalar = str | int | float
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@dataclass(frozen=True, slots=True)
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class Constant:
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token: str
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def as_dict(self) -> dict[str, str]:
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return {"kind": "constant", "token": self.token}
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@dataclass(frozen=True, slots=True)
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class TypedSlot:
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feature_name: str
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slot_type: str
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min_width: int = 1
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max_width: int = 1
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ignored_prefix_tokens: tuple[str, ...] = ()
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def as_dict(self) -> dict[str, Any]:
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return {
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"feature_name": self.feature_name,
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"ignored_prefix_tokens": list(self.ignored_prefix_tokens),
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"kind": "typed_slot",
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"max_width": self.max_width,
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"min_width": self.min_width,
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"slot_type": self.slot_type,
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}
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PatternElement = Constant | TypedSlot
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@dataclass(frozen=True, slots=True)
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class DerivedRecognizer:
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pattern: tuple[PatternElement, ...]
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teaching_set_id: str
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constant_features: Mapping[str, Scalar]
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absent_features: Mapping[str, Scalar]
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def as_dict(self) -> dict[str, Any]:
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return {
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"absent_features": dict(sorted(self.absent_features.items())),
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"constant_features": dict(sorted(self.constant_features.items())),
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"pattern": [_pattern_element_as_dict(element) for element in self.pattern],
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"teaching_set_id": self.teaching_set_id,
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}
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def to_json(self) -> str:
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return json.dumps(self.as_dict(), ensure_ascii=False, separators=(",", ":"), sort_keys=True)
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@classmethod
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def from_json(cls, payload: str) -> "DerivedRecognizer":
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raw = json.loads(payload)
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return cls(
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pattern=tuple(_pattern_element_from_dict(element) for element in raw["pattern"]),
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teaching_set_id=str(raw["teaching_set_id"]),
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constant_features=dict(raw["constant_features"]),
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absent_features=dict(raw["absent_features"]),
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)
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def derive_recognizer(examples: Sequence[tuple[TokenSequence, FeatureBundle]]) -> DerivedRecognizer:
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if not examples:
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raise ValueError("derive_recognizer requires at least one teaching example")
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normalized = tuple((tuple(tokens), bundle) for tokens, bundle in examples)
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teaching_set_id = _teaching_set_id(tokens for tokens, _bundle in normalized)
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feature_names = _feature_names(normalized)
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slot_names = _slot_feature_names(normalized, feature_names)
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absent_features = _absent_uniform_features(normalized, feature_names, slot_names)
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relation = _uniform_feature_value(normalized, "relation")
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relation_token = str(relation)
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anchors = tuple(_single_token_index(tokens, relation_token) for tokens, _bundle in normalized)
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prefix_widths = tuple(anchor for anchor in anchors)
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suffix_widths = tuple(len(tokens) - anchor - 1 for (tokens, _bundle), anchor in zip(normalized, anchors))
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if min(prefix_widths) < 1:
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raise ValueError("agent slot must contain at least one token")
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if set(suffix_widths) != {2}:
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raise ValueError("Phase 1 expects count and unit slots after the relation anchor")
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constant_features = {"relation": relation}
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ignored_prefix_tokens = _ignored_prefix_tokens(normalized, "agent")
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pattern: tuple[PatternElement, ...] = (
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TypedSlot(
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feature_name="agent",
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slot_type=_slot_type(normalized, "agent"),
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min_width=min(prefix_widths),
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max_width=max(prefix_widths),
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ignored_prefix_tokens=ignored_prefix_tokens,
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),
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Constant(relation_token),
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TypedSlot(feature_name="count", slot_type=_slot_type(normalized, "count")),
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TypedSlot(feature_name="unit", slot_type=_slot_type(normalized, "unit")),
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)
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return DerivedRecognizer(
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pattern=pattern,
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teaching_set_id=teaching_set_id,
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constant_features=constant_features,
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absent_features=absent_features,
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)
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def recognize(recognizer: DerivedRecognizer, token_sequence: TokenSequence) -> RecognitionOutcome:
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tokens = tuple(token_sequence)
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provenance = RecognitionProvenance(
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mechanism="anti_unification",
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teaching_set_id=recognizer.teaching_set_id,
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resolution_level="chunk",
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replay_seed="",
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)
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matches = _match_pattern(recognizer.pattern, tokens)
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if matches is None:
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return RecognitionOutcome(
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state=UNDETERMINED,
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provenance=RecognitionProvenance(
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mechanism="anti_unification",
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teaching_set_id=recognizer.teaching_set_id,
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resolution_level="shape",
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replay_seed="",
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),
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refusal_reason=ShapeRefusal(
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reason=f"shape_mismatch:{_shape_description(recognizer.pattern)}"
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),
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)
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feature_evidence: dict[str, tuple[Scalar, FeatureEvidence]] = {}
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for feature_name, value in recognizer.constant_features.items():
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feature_evidence[feature_name] = (value, _constant_evidence(str(value), tokens))
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for feature_name, value in recognizer.absent_features.items():
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feature_evidence[feature_name] = (
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value,
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NegativeEvidence(
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scope_start=0,
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scope_end=len(tokens),
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description=f"{feature_name}={value!r} evidenced by absence of taught counter-marker",
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),
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)
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for slot, span in matches.items():
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value, evidence = _lift_slot(slot, tokens, span)
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feature_evidence[slot.feature_name] = (value, evidence)
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proposition = FeatureBundle.from_mapping(feature_evidence)
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return RecognitionOutcome(
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state=EVIDENCED,
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provenance=provenance,
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proposition=proposition,
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refusal_reason=None,
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)
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def _feature_names(examples: Sequence[tuple[tuple[str, ...], FeatureBundle]]) -> tuple[str, ...]:
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names = tuple(feature.name for feature in examples[0][1].features)
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for _tokens, bundle in examples[1:]:
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if tuple(feature.name for feature in bundle.features) != names:
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raise ValueError("all teaching bundles must expose the same feature set")
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return names
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def _slot_feature_names(
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examples: Sequence[tuple[tuple[str, ...], FeatureBundle]], feature_names: tuple[str, ...]
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) -> frozenset[str]:
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slots = []
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for name in feature_names:
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evidences = tuple(_feature(bundle, name).evidence for _tokens, bundle in examples)
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if all(isinstance(evidence, EvidenceSpan) for evidence in evidences):
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values = tuple(_feature(bundle, name).value for _tokens, bundle in examples)
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if len(set(values)) > 1:
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slots.append(name)
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return frozenset(slots)
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def _absent_uniform_features(
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examples: Sequence[tuple[tuple[str, ...], FeatureBundle]],
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feature_names: tuple[str, ...],
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slot_names: frozenset[str],
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) -> dict[str, Scalar]:
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absent: dict[str, Scalar] = {}
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for name in feature_names:
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if name in slot_names or name == "relation":
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continue
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feature_values = tuple(_feature(bundle, name).value for _tokens, bundle in examples)
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if len(set(feature_values)) == 1:
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absent[name] = feature_values[0]
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return absent
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def _uniform_feature_value(examples: Sequence[tuple[tuple[str, ...], FeatureBundle]], name: str) -> Scalar:
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values = tuple(_feature(bundle, name).value for _tokens, bundle in examples)
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if len(set(values)) != 1:
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raise ValueError(f"feature must be uniform in Phase 1: {name}")
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return values[0]
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def _feature(bundle: FeatureBundle, name: str) -> BoundFeature:
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feature = bundle.get(name)
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if feature is None:
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raise ValueError(f"missing feature in teaching bundle: {name}")
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return feature
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def _slot_type(examples: Sequence[tuple[tuple[str, ...], FeatureBundle]], name: str) -> str:
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values = tuple(_feature(bundle, name).value for _tokens, bundle in examples)
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if all(isinstance(value, int) for value in values):
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return "int"
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if all(isinstance(value, float) for value in values):
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return "float"
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return "str"
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def _ignored_prefix_tokens(examples: Sequence[tuple[tuple[str, ...], FeatureBundle]], name: str) -> tuple[str, ...]:
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ignored = set()
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for tokens, bundle in examples:
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evidence = _feature(bundle, name).evidence
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if isinstance(evidence, EvidenceSpan):
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ignored.update(token.lower() for token in tokens[: evidence.start])
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return tuple(sorted(ignored))
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def _single_token_index(tokens: tuple[str, ...], token: str) -> int:
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indexes = [index for index, candidate in enumerate(tokens) if candidate == token]
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if len(indexes) != 1:
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raise ValueError(f"constant anchor must occur exactly once: {token!r}")
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return indexes[0]
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def _teaching_set_id(token_sequences: Iterable[tuple[str, ...]]) -> str:
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canonical = json.dumps(sorted(token_sequences), ensure_ascii=False, separators=(",", ":"))
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return hashlib.sha256(canonical.encode("utf-8")).hexdigest()
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def _match_pattern(pattern: tuple[PatternElement, ...], tokens: tuple[str, ...]) -> dict[TypedSlot, tuple[int, int]] | None:
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def walk(index: int, cursor: int, spans: dict[TypedSlot, tuple[int, int]]) -> dict[TypedSlot, tuple[int, int]] | None:
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if index == len(pattern):
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return spans if cursor == len(tokens) else None
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element = pattern[index]
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if isinstance(element, Constant):
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if cursor < len(tokens) and tokens[cursor] == element.token:
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return walk(index + 1, cursor + 1, spans)
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return None
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remaining_min = _minimum_width(pattern[index + 1 :])
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max_end = min(cursor + element.max_width, len(tokens) - remaining_min)
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for end in range(cursor + element.min_width, max_end + 1):
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next_spans = dict(spans)
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next_spans[element] = (cursor, end)
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matched = walk(index + 1, end, next_spans)
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if matched is not None:
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return matched
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return None
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return walk(0, 0, {})
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def _minimum_width(pattern: Sequence[PatternElement]) -> int:
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return sum(1 if isinstance(element, Constant) else element.min_width for element in pattern)
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def _lift_slot(slot: TypedSlot, tokens: tuple[str, ...], span: tuple[int, int]) -> tuple[Scalar, EvidenceSpan]:
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raw_tokens = tokens[span[0] : span[1]]
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start = span[0]
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if slot.ignored_prefix_tokens and raw_tokens and raw_tokens[0].lower() in slot.ignored_prefix_tokens:
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raw_tokens = raw_tokens[1:]
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start += 1
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if not raw_tokens:
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raise ValueError(f"slot {slot.feature_name!r} had no evidence tokens after prefix removal")
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text = " ".join(raw_tokens)
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if slot.slot_type == "int":
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if len(raw_tokens) != 1 or not raw_tokens[0].isdigit():
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raise ValueError(f"slot {slot.feature_name!r} expected one integer token")
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return int(raw_tokens[0]), EvidenceSpan(start=start, end=start + 1, text=raw_tokens[0])
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if slot.slot_type == "float":
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if len(raw_tokens) != 1:
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raise ValueError(f"slot {slot.feature_name!r} expected one float token")
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return float(raw_tokens[0]), EvidenceSpan(start=start, end=start + 1, text=raw_tokens[0])
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if slot.feature_name == "unit":
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return _singularize(raw_tokens[-1]), EvidenceSpan(start=span[1] - 1, end=span[1], text=raw_tokens[-1])
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return text, EvidenceSpan(start=start, end=span[1], text=text)
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def _constant_evidence(value: str, tokens: tuple[str, ...]) -> EvidenceSpan:
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for index, token in enumerate(tokens):
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if token == value:
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return EvidenceSpan(start=index, end=index + 1, text=token)
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raise ValueError(f"constant feature had no evidence in matched token sequence: {value!r}")
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def _singularize(token: str) -> str:
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lowered = token.lower()
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if lowered.endswith("ves") and len(lowered) > 3:
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return lowered[:-3] + "f"
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if lowered.endswith("ies") and len(lowered) > 3:
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return lowered[:-3] + "y"
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if lowered.endswith("s") and len(lowered) > 1:
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return lowered[:-1]
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return lowered
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def _shape_description(pattern: tuple[PatternElement, ...]) -> str:
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pieces = []
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for element in pattern:
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if isinstance(element, Constant):
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pieces.append(repr(element.token))
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else:
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pieces.append(f"<{element.feature_name}:{element.slot_type}[{element.min_width},{element.max_width}]>")
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return " ".join(pieces)
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def _pattern_element_as_dict(element: PatternElement) -> dict[str, Any]:
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return element.as_dict()
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def _pattern_element_from_dict(raw: Mapping[str, Any]) -> PatternElement:
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if raw["kind"] == "constant":
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return Constant(token=str(raw["token"]))
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if raw["kind"] == "typed_slot":
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return TypedSlot(
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feature_name=str(raw["feature_name"]),
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slot_type=str(raw["slot_type"]),
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min_width=int(raw["min_width"]),
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max_width=int(raw["max_width"]),
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ignored_prefix_tokens=tuple(str(token) for token in raw.get("ignored_prefix_tokens", ())),
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)
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raise ValueError(f"unknown pattern element kind: {raw['kind']!r}")
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__all__ = [
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"Constant",
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"DerivedRecognizer",
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"TypedSlot",
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"derive_recognizer",
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"recognize",
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]
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