| """Pydantic v2 result schemas for MacroLens task runners. |
| |
| These models replace the legacy ``TypedDict`` shapes used pre-Phase-4. The |
| orchestrator (:mod:`experiments.run_all`) persists results as |
| :class:`macrolens.RunRecord` (full reproducibility envelope); these |
| per-task models capture the *metric content* of a single record's |
| ``metrics`` field and are used by post-hoc tools (``gen_tables.py``, |
| ``analysis.py``) that need a typed handle on the per-task metric set. |
| |
| Every model: |
| |
| * Uses ``model_config = ConfigDict(extra="forbid", frozen=True)`` so unknown |
| keys raise at construction and instances are hashable. |
| * Allows every metric to be ``None`` — runners that legitimately skip a |
| metric (e.g., a deterministic naive method that does not report CRPS) |
| emit ``None``, not a sentinel string. |
| * Adds T5/T6/T7 (the legacy schema was missing T5/T6/T7). |
| """ |
|
|
| from __future__ import annotations |
|
|
| import pydantic |
|
|
|
|
| |
|
|
|
|
| class BootstrapCI(pydantic.BaseModel): |
| """Bootstrap 95% CI for a scalar metric (matches ``MetricValue``).""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| mean: float | None = None |
| ci_lo: float | None = None |
| ci_hi: float | None = None |
| std: float | None = None |
|
|
|
|
| class MultiSeedStats(pydantic.BaseModel): |
| """Mean +/- std over the headline T1 multi-seed subset.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| seed_mean: float | None = None |
| seed_std: float | None = None |
| per_seed: dict[int, float] | None = None |
|
|
|
|
| |
|
|
|
|
| class T1Metrics(pydantic.BaseModel): |
| """T1 — Contextual Time-Series Forecasting.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T1" |
| horizon: int | None = None |
| granularity: str = "daily" |
| seed: int = 42 |
| mse: float | None = None |
| mae: float | None = None |
| rmse: float | None = None |
| directional_accuracy: float | None = None |
| mse_ci: BootstrapCI | None = None |
| mae_ci: BootstrapCI | None = None |
| da_ci: BootstrapCI | None = None |
| multiseed: MultiSeedStats | None = None |
| n_instances: int | None = None |
| inference_time_sec: float | None = None |
| train_time_sec: float | None = None |
|
|
|
|
| class T2Metrics(pydantic.BaseModel): |
| """T2 — Point-in-Time Equity Valuation.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T2" |
| granularity: str = "daily" |
| seed: int = 42 |
| mape: float | None = None |
| median_ape: float | None = None |
| rank_correlation: float | None = None |
| rank_p_value: float | None = None |
| mape_ci: BootstrapCI | None = None |
| n_predictions: int | None = None |
| n_tickers: int | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| class T3Metrics(pydantic.BaseModel): |
| """T3 — Statement Generation (per-field MAPE + balance equation).""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T3" |
| granularity: str = "daily" |
| seed: int = 42 |
| overall_mape: float | None = None |
| per_field_mape: dict[str, float] | None = None |
| balance_equation_accuracy: float | None = None |
| balance_equation_checked: int | None = None |
| success_rate: float | None = None |
| n_fields_matched: int | None = None |
| n_field_misses: int | None = None |
| n_tickers: int | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| class T4Metrics(pydantic.BaseModel): |
| """T4 — Scenario-Conditioned Return Forecasting.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T4" |
| granularity: str = "daily" |
| seed: int = 42 |
| return_mae_pct: float | None = None |
| directional_accuracy: float | None = None |
| ci_calibration_95: float | None = None |
| return_mae_ci: BootstrapCI | None = None |
| n_predictions: int | None = None |
| n_scenarios: int | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| class T5Metrics(pydantic.BaseModel): |
| """T5 — Private-Company Valuation (no market prices).""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T5" |
| granularity: str = "daily" |
| seed: int = 42 |
| mape: float | None = None |
| median_ape: float | None = None |
| rank_correlation: float | None = None |
| rank_p_value: float | None = None |
| mape_ci: BootstrapCI | None = None |
| n_predictions: int | None = None |
| n_tickers: int | None = None |
| gap_vs_t2: float | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| class T6Metrics(pydantic.BaseModel): |
| """T6 — Generator Evaluation (NL description -> XBRL).""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T6" |
| granularity: str = "daily" |
| seed: int = 42 |
| overall_mape: float | None = None |
| per_field_mape: dict[str, float] | None = None |
| success_rate: float | None = None |
| n_fields_matched: int | None = None |
| n_field_misses: int | None = None |
| n_tickers: int | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| class T7Metrics(pydantic.BaseModel): |
| """T7 — Real-Estate Valuation.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| method_id: str |
| task: str = "T7" |
| granularity: str = "daily" |
| seed: int = 42 |
| rent_MAPE: float | None = None |
| price_MAPE: float | None = None |
| rent_median_APE: float | None = None |
| price_median_APE: float | None = None |
| rent_n_valid: int | None = None |
| price_n_valid: int | None = None |
| n_predictions: int | None = None |
| inference_time_sec: float | None = None |
|
|
|
|
| |
|
|
|
|
| class FamilyResults(pydantic.BaseModel): |
| """Container emitted by each family's ``run_all_*()`` function.""" |
|
|
| model_config = pydantic.ConfigDict(extra="forbid", frozen=True) |
|
|
| family: str |
| panel_version: str | None = None |
| methods: dict[str, list[dict]] = pydantic.Field(default_factory=dict) |
|
|
|
|
| |
|
|
|
|
| TASK_RESULT_TYPES: dict[str, type[pydantic.BaseModel]] = { |
| "T1": T1Metrics, |
| "T2": T2Metrics, |
| "T3": T3Metrics, |
| "T4": T4Metrics, |
| "T5": T5Metrics, |
| "T6": T6Metrics, |
| "T7": T7Metrics, |
| } |
|
|
