97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
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"""Registry: Korrelations- und Treiber-Metriken (Data Layer correlations)."""
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from placeholder_registry import (
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PlaceholderMetadata,
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MissingValuePolicy,
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OutputType,
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PlaceholderType,
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register_placeholder,
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)
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from ._evidence import tag_standard_evidence
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CAT = "Korrelationen"
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MVP = lambda reason, disp: MissingValuePolicy(
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available=False, value_raw=None, missing_reason=reason, legacy_display=disp
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)
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def register_korrelationen():
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for key, dl_fn, desc, tables, sem in [
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(
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"correlation_energy_weight_lag",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Energiebilanz ↔ Gewicht",
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["nutrition_log", "weight_log"],
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"correlations.calculate_lag_correlation(pid, 'energy', 'weight')",
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),
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(
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"correlation_protein_lbm",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Protein ↔ Magermasse",
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["nutrition_log", "weight_log", "caliper_log"],
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"correlations.calculate_lag_correlation(pid, 'protein', 'lbm')",
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),
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(
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"correlation_load_hrv",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Trainingslast ↔ HRV",
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["activity_log", "vitals_baseline"],
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"correlations.calculate_lag_correlation(pid, 'training_load', 'hrv')",
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),
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(
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"correlation_load_rhr",
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"calculate_lag_correlation",
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"JSON: Lag-Korrelation Trainingslast ↔ Ruhepuls",
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["activity_log", "vitals_baseline"],
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"correlations.calculate_lag_correlation(pid, 'training_load', 'rhr')",
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),
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(
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"plateau_detected",
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"calculate_plateau_detected",
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"JSON: Platten-Erkennung (Gewicht/Körper)",
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["weight_log", "caliper_log"],
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"correlations.calculate_plateau_detected",
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),
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(
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"top_drivers",
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"calculate_top_drivers",
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"JSON: Top Treiber für Ziel-/Score-Variablen",
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["weight_log", "nutrition_log", "activity_log", "vitals_baseline", "sleep_log"],
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"correlations.calculate_top_drivers",
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),
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]:
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m = PlaceholderMetadata(
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key=key,
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category=CAT,
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description=desc,
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_json",
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data_layer_module="backend/data_layer/correlations.py",
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data_layer_function=dl_fn,
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source_tables=tables,
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semantic_contract=sem,
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business_meaning="Strukturierte Korrelationsausgabe für KI",
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unit="JSON",
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time_window="funktionsintern",
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output_type=OutputType.JSON,
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placeholder_type=PlaceholderType.RAW_DATA,
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format_hint="JSON-String",
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example_output="{}",
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minimum_data_requirements="Ausreichend gekoppelte Zeitreihen",
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quality_filter_policy=None,
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confidence_logic="Wie correlations.*",
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missing_value_policy=MVP("insufficient_data", "{}"),
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known_limitations="Bei wenigen Daten leer oder wenig robust",
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layer_1_decision=f"correlations.{dl_fn}",
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layer_2a_decision="_safe_json",
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layer_2b_reuse_possible=True,
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architecture_alignment="Phase 0c",
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issue_53_alignment="Layer 1",
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evidence={},
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)
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tag_standard_evidence(m)
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register_placeholder(m)
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register_korrelationen()
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