Lars
f0e6fd04fb
- Introduced new routes and API endpoints for managing personal reference values. - Updated the SettingsPage to include a section for reference values with navigation to manage them. - Enhanced the backend to support reference values in the data layer and versioning. - Added necessary imports and UI components for a seamless user experience.
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
"""
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Declarative schemas for template-based training evaluation (Layer 1 scaffold).
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Templates select built-in algorithms by id and pass parameters; they do not embed
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executable logic. See resolver and algorithm registry.
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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, Mapping, Optional
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@dataclass(frozen=True)
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class FocusAreaMapping:
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"""Maps a share of one dimension's score to a Focus Area (by stable key)."""
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focus_area_key: str
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weight: float
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@dataclass(frozen=True)
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class DimensionSpec:
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"""
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One evaluation dimension: algorithm + inputs + params + FA mapping.
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inputs: names of keys expected in the flat activity_inputs dict passed to the resolver.
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"""
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key: str
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algorithm_id: str
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inputs: tuple[str, ...]
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params: Mapping[str, Any]
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maps_to: tuple[FocusAreaMapping, ...]
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@dataclass(frozen=True)
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class CalculationTemplate:
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"""Declarative multi-dimensional calculation template (in-code registry)."""
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id: str
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version: str
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label: str
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dimensions: tuple[DimensionSpec, ...]
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@dataclass(frozen=True)
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class TrainingBaseProfile:
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"""
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Conceptual base profile: links a training context to a default template.
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allowed_dimension_keys: if set, dimensions not listed are skipped at resolve time.
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"""
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key: str
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label: str
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default_template_id: str
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allowed_dimension_keys: Optional[frozenset[str]] = None
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@dataclass
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class AlgorithmRunResult:
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"""Output of a single built-in algorithm execution."""
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raw_score: float
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normalized_score: float
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missing_inputs: List[str]
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detail: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DimensionResult:
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"""Per-dimension result after resolution."""
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dimension_key: str
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algorithm_id: str
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raw_score: float
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normalized_score: float
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missing_inputs: List[str]
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evidence: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class TrainingEvaluationResult:
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"""
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Stable structured result for Layer 2 (KI, charts, APIs, future persistence).
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focus_area_contributions: aggregated contribution per focus_area key (additive).
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"""
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template_id: str
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template_version: str
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base_profile_key: Optional[str]
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dimension_results: List[DimensionResult]
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focus_area_contributions: Dict[str, float]
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confidence: str
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evidence: Dict[str, Any]
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trace: Optional[Dict[str, Any]] = None
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def to_serializable(self) -> Dict[str, Any]:
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"""JSON-compatible dict (for APIs / storage)."""
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return {
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"template_id": self.template_id,
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"template_version": self.template_version,
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"base_profile_key": self.base_profile_key,
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"dimension_results": [
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{
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"dimension_key": d.dimension_key,
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"algorithm_id": d.algorithm_id,
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"raw_score": d.raw_score,
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"normalized_score": d.normalized_score,
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"missing_inputs": d.missing_inputs,
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"evidence": d.evidence,
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}
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for d in self.dimension_results
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],
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"focus_area_contributions": dict(self.focus_area_contributions),
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"confidence": self.confidence,
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"evidence": self.evidence,
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"trace": self.trace,
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}
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