Benjamin Admin
cfafa31ea2
Roadmap item 5. GAP analysis and measure-prioritisation are the SAME computation: Required − Known = the Capability Delta. The Capability Delta Engine (RS-005) computes it once; renderers read that ONE delta. Interview Renderer (missing info → questions) was already built; this adds the Roadmap/Management Renderer (missing capabilities → measures ranked by regulatory leverage). - compliance/optimization/: regulatory_leverage() + select_within_budget() (pure leverage math) + roadmap_from_delta(assessment, ...) — the keystone binding optimization to the RS-005 delta (dependency optimization → transition_reasoning, acyclic; the delta engine stays hermetic). leverage(measure) = number of regulatory requirements it closes at once (e.g. patch management → CRA+MaschinenVO+IEC62443+ISO27001 = 4). No new corpus, no new meta-model class (freeze v1.0). - Welt-1 honesty: percentages are exact count ratios over the IDENTIFIED requirements (the known delta), never "% gesetzeskonform". - reference suite: "Regulatory Optimization" section runs the SAME convergence delta → ranked measures + budget answer + the management sentence "of N identified requirements you close M with the top-K measures (X%) — highest regulatory leverage". - ADR-003: Capability Delta Engine — one delta, many renderers; rename Gap → Capability Delta. 13 optimization tests (31 with transition+company), mypy --strict clean, check-loc 0. Product code with no app caller + ADR/reference = non-runtime → no deploy (ADR-001). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
49 lines
2.3 KiB
Python
49 lines
2.3 KiB
Python
"""Schemas for the Regulatory Optimization Engine.
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These DTOs are *derived views* (computed-not-stored): nothing here is persisted; every value
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is recomputed from the input each call. No new meta-model class, no graph (freeze v1.0).
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Python 3.9 compatible (no `|` unions).
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"""
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from __future__ import annotations
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from typing import List
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from pydantic import BaseModel, Field
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class RankedMeasure(BaseModel):
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"""One measure (a capability to implement) ranked by its regulatory leverage."""
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capability_id: str
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covers: List[str] = Field(default_factory=list) # the in-scope requirements it satisfies
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leverage: int = 0 # = len(covers): how many it closes at once
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cumulative_requirements: int = 0 # running total of requirements closed (ranked order)
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cumulative_coverage: float = 0.0 # cumulative_requirements / total_requirements (0..1)
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class OptimizationPlan(BaseModel):
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"""Measures ranked by regulatory leverage — greatest regulatory effect first.
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`total_requirements` counts the IDENTIFIED requirements in scope (the known delta from the
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patterns), NOT a company's total legal duties. The percentages are exact count ratios over
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this identified set — never a compliance verdict (Welt-1 discipline).
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"""
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in_scope_requirements: List[str] = Field(default_factory=list) # the distinct requirement keys counted
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total_measures: int = 0 # number of distinct measures (delta capabilities)
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total_requirements: int = 0 # Sum of leverage = identified requirements closable
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ranked_measures: List[RankedMeasure] = Field(default_factory=list)
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headline: str = "" # "N identifizierte Anforderungen -> M Massnahmen ..."
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class BudgetPlan(BaseModel):
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"""The budget answer: with a budget of K measures, which K and how much do they close?"""
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budget: int = 0
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selected_capabilities: List[str] = Field(default_factory=list)
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requirements_closed: int = 0
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total_requirements: int = 0
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coverage_ratio: float = 0.0 # requirements_closed / total_requirements (0..1)
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headline: str = ""
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