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029e02e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | """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,
}
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