Lars
e9e094c6a4
- Added new functions for BMI and goal weight/body fat percentage retrieval in `body_metrics.py`. - Introduced training frequency and inter-session gap calculations in `activity_metrics.py`. - Updated placeholder registrations to include new metrics for nutrition and activity. - Improved data handling in `placeholder_resolver.py` for better integration of new metrics. - Enhanced documentation across modules to reflect the new functionalities. These updates improve the accuracy and comprehensiveness of health and fitness assessments within the application.
102 lines
5.0 KiB
Python
102 lines
5.0 KiB
Python
"""
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Placeholder registration: nutrition_score
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Focus-gewichteter Ernährungs-Meta-Score (separates Modul, um nutrition_part_c schlank zu halten).
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"""
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from placeholder_registry import (
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PlaceholderMetadata,
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MissingValuePolicy,
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EvidenceType,
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OutputType,
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PlaceholderType,
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register_placeholder,
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)
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nutrition_score_metadata = PlaceholderMetadata(
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key="nutrition_score",
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category="Ernährung",
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description="Ernährungs-Score (0–100), gewichtet nach Focus Areas",
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_int",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="calculate_nutrition_score",
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source_tables=[
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"nutrition_log",
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"weight_log",
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"user_focus_area_weights",
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"focus_area_definitions",
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],
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semantic_contract=(
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"Gewichteter Score 0–100 aus Komponenten, die nur einfließen, wenn der Nutzer "
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"passende Ernährungs-Focus-Gewichte gesetzt hat (z. B. protein_intake, "
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"calorie_balance, macro_consistency). Nutzt u. a. Protein-Adequacy, "
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"Makro-Konsistenz, Kalorien-Adhärenz (über Energiebilanz) und Makro-Balance."
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),
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business_meaning=(
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"Verdichteter KPI für Prompts: passt die dokumentierte Ernährung zur "
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"gewichteten strategischen Priorität des Nutzers?"
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),
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unit="score (0-100)",
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time_window="composite (7d / 28d je Komponente)",
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.SCORE,
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format_hint="Ganzzahl; bei fehlender Ernährungs-Gewichtung oft nicht verfügbar",
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example_output="72",
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minimum_data_requirements=(
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"Mindestens eine Ernährungs-Focus-Komponente mit Gewicht > 0; "
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"sowie je nach Komponente ausreichende nutrition_log-/weight_log-Abdeckung."
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),
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quality_filter_policy=None,
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confidence_logic=(
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"Kein separates Confidence-Feld im Resolver; fehlende Komponenten werden "
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"aus der Gewichtung ausgeschlossen. total_nutrition_weight == 0 ergibt keinen Score."
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),
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="not_applicable",
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legacy_display="nicht verfügbar",
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),
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known_limitations=(
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"Abhängig von user_focus_area_weights; ohne Ernährungs-Fokus liefert die "
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"Funktion None. Kalorien-Adhärenz nutzt vereinfachte Heuristik (goal_type-TODO). "
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"_score_macro_balance nutzt noch zeilenbasierte 28d-Abfrage (langfristig an "
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"Tagesaggregation angleichen)."
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),
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layer_1_decision="Data Layer (nutrition_metrics.calculate_nutrition_score)",
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layer_2a_decision="Placeholder Resolver (_safe_int)",
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layer_2b_reuse_possible=True,
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architecture_alignment="Phase 0c: Berechnung in nutrition_metrics",
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issue_53_alignment="Layer 1 als Quelle; Komponenten nutzen weitere Layer-1-Funktionen",
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evidence={},
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)
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nutrition_score_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("description", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("semantic_contract", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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nutrition_score_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("placeholder_type", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("minimum_data_requirements", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("confidence_logic", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("known_limitations", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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nutrition_score_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(nutrition_score_metadata)
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