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>
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
"""
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C3 — Structural coherence.
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Uses graph analysis to check that the entity relationship graph is
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well-connected and has meaningful community structure.
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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 Dict, List, Optional
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from markitect.prompts.dependencies.models import DependencyGraph
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@dataclass
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class CoherenceReport:
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"""Results from coherence analysis."""
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connected_components: int = 0
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largest_component_size: int = 0
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modularity: float = 0.0
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community_count: int = 0
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cohesion: float = 0.0
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coupling: float = 0.0
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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": "C3",
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"connected_components": self.connected_components,
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"largest_component_size": self.largest_component_size,
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"modularity": round(self.modularity, 4),
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"community_count": self.community_count,
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"cohesion": round(self.cohesion, 4),
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"coupling": round(self.coupling, 4),
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"entity_count": self.entity_count,
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}
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def check_coherence(
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graph: Optional[DependencyGraph] = None,
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entity_count: int = 0,
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) -> CoherenceReport:
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"""Check structural coherence of the entity relationship graph.
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Args:
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graph: The entity relationship graph. If ``None``, returns
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a report with zero values.
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entity_count: Total number of entities (for context).
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Returns:
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:class:`CoherenceReport` with connectivity and community metrics.
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"""
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if graph is None or len(graph.nodes) == 0:
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return CoherenceReport(entity_count=entity_count)
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try:
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from markitect.analysis.graph import (
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connected_components,
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modularity_score,
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detect_communities,
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cohesion_coupling,
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)
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except ImportError:
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return CoherenceReport(entity_count=entity_count)
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components = connected_components(graph)
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communities = detect_communities(graph, seed=42)
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mod = modularity_score(graph, communities=communities)
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cc = cohesion_coupling(graph, communities=communities)
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return CoherenceReport(
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connected_components=len(components),
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largest_component_size=len(components[0]) if components else 0,
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modularity=mod,
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community_count=len(communities),
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cohesion=cc["cohesion"],
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coupling=cc["coupling"],
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entity_count=entity_count or len(graph.nodes),
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)
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