code/experiments/result_schema.py

"""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


# ── Shared sub-schemas ────────────────────────────────────────────────────


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


# ── Per-task metric schemas ───────────────────────────────────────────────


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


# ── Family-level container ────────────────────────────────────────────────


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 dispatch map ─────────────────────────────────────────────────────


TASK_RESULT_TYPES: dict[str, type[pydantic.BaseModel]] = {
    "T1": T1Metrics,
    "T2": T2Metrics,
    "T3": T3Metrics,
    "T4": T4Metrics,
    "T5": T5Metrics,
    "T6": T6Metrics,
    "T7": T7Metrics,
}