tegwick
11585e6968
Five concern checks: Redundancy (embedding/word overlap), Coverage (FCA gap analysis), Coherence (graph connectivity), Consistency (cycle detection), Granularity (Shannon entropy). Orchestrator runs all or selected checks, CLI `markitect infospace check` command added. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""
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C2 — Coverage completeness.
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Uses FCA and cross-tabulation to detect structural coverage gaps:
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attribute combinations (domain × VSM system) with no entities.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from markitect.infospace.models import EntityMeta
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from markitect.analysis.fca import FormalContext, find_empty_cells, find_gap_concepts
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@dataclass
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class CoverageReport:
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"""Results from coverage analysis."""
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coverage_ratio: float = 0.0
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empty_cells: List[dict] = field(default_factory=list)
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gap_concepts: List[dict] = field(default_factory=list)
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domain_counts: Dict[str, int] = field(default_factory=dict)
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entity_count: int = 0
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def to_dict(self) -> dict:
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return {
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"concern": "C2",
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"coverage_ratio": round(self.coverage_ratio, 4),
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"empty_cells": self.empty_cells,
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"gap_concepts_count": len(self.gap_concepts),
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"domain_counts": self.domain_counts,
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"entity_count": self.entity_count,
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}
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def _extract_attributes(entity: EntityMeta) -> set[str]:
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"""Extract FCA attributes from an entity."""
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attrs: set[str] = set()
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if entity.domain:
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attrs.add(f"domain:{entity.domain}")
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if entity.source_chapter:
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attrs.add(f"chapter:{entity.source_chapter}")
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return attrs
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def check_coverage(
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entities: List[EntityMeta],
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extra_attributes: Optional[Dict[str, set[str]]] = None,
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) -> CoverageReport:
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"""Check coverage completeness using FCA gap analysis.
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Args:
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entities: Entity metadata list.
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extra_attributes: Optional ``{slug: {attr, ...}}`` to merge
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with auto-extracted attributes (e.g. VSM mappings).
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Returns:
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:class:`CoverageReport` with gaps and coverage ratio.
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"""
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n = len(entities)
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if n == 0:
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return CoverageReport()
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# Build entity → attributes mapping
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entity_attrs: Dict[str, set[str]] = {}
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for e in entities:
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attrs = _extract_attributes(e)
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if extra_attributes and e.slug in extra_attributes:
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attrs.update(extra_attributes[e.slug])
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entity_attrs[e.slug] = attrs
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# Domain counts
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domain_counts: Dict[str, int] = {}
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for e in entities:
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d = e.domain or "(unspecified)"
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domain_counts[d] = domain_counts.get(d, 0) + 1
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# Build FCA context
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context = FormalContext.from_dict(entity_attrs)
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# Cross-tabulation: domain × chapter
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domains = sorted({a for attrs in entity_attrs.values() for a in attrs if a.startswith("domain:")})
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chapters = sorted({a for attrs in entity_attrs.values() for a in attrs if a.startswith("chapter:")})
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empty = []
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if domains and chapters:
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raw_empty = find_empty_cells(context, domains, chapters)
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empty = [{"dimension_a": a, "dimension_b": b} for a, b in raw_empty]
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# FCA gap concepts
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gaps = find_gap_concepts(context)
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gap_dicts = [
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{"intent": sorted(g.intent), "extent_size": g.extent_size}
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for g in gaps
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if g.intent_size <= 4 # Only report manageable gaps
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]
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# Coverage ratio: populated cells / total possible cells
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total_cells = len(domains) * len(chapters) if domains and chapters else 1
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populated = total_cells - len(empty)
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ratio = populated / total_cells if total_cells > 0 else 0.0
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return CoverageReport(
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coverage_ratio=ratio,
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empty_cells=empty,
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gap_concepts=gap_dicts,
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domain_counts=domain_counts,
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entity_count=n,
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)
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