ウォークフォワード処理と潜在ファクターモデルの識別チェック

# cv.py


```py
"""Walk-forward orchestration for latent factor models."""

from __future__ import annotations

import gc
import hashlib
import inspect
import json
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import UTC, datetime
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import polars as pl
import yaml
from ml4t.diagnostic.metrics import cross_sectional_ic
from threadpoolctl import threadpool_limits

from case_studies.utils.backtest_loaders import get_rebalance_step, thin_to_rebalance_dates
from case_studies.utils.folds import fold_seed
from case_studies.utils.latent_factors.cae import run_cae_fold
from case_studies.utils.latent_factors.ipca import run_ipca_fold
from case_studies.utils.latent_factors.library_bridge import configure_latent_torch_runtime
from case_studies.utils.latent_factors.panel import (
    prepare_panel_data,
    prepare_ragged_panel_data,
    rank_normalize_cross_section,
)
from case_studies.utils.latent_factors.pca import run_pca_fold
from case_studies.utils.latent_factors.sae import run_sae_fold
from case_studies.utils.latent_factors.sdf import run_sdf_fold
from case_studies.utils.latent_factors.versions import latent_model_version
from case_studies.utils.persistent_panel import PERSISTENT_PANEL_MODELS
from utils.modeling import RANDOM_SEED, seed_everything

_MODEL_RUNNERS = {
    "pca": run_pca_fold,
    "ipca": run_ipca_fold,
    "cae": run_cae_fold,
    "sdf": run_sdf_fold,
    "sae": run_sae_fold,
}

TEMPORAL_FEATURE_ASSEMBLY = "fold_scoped_v1"

# Runner arguments the caller supplies directly, which a preset must not overwrite.
# Every latent-factor preset declares n_factors, so before this set covered it the
# N_FACTORS notebook parameter was a no-op in every configured notebook.
_CALLER_SUPPLIED_RUNNER_ARGS = frozenset({"device", "n_factors"})
_EPOCH_MODELS = frozenset({"cae", "sae"})


def merge_preset_into_runner_kwargs(
    kwargs: dict[str, Any],
    *,
    preset: dict[str, Any],
    allowed: set[str],
    model_name: str,
) -> dict[str, Any]:
    """Fold a model's preset into runner kwargs the caller already filled in.

    Mutates and returns ``kwargs``. A preset value is applied only when the runner
    accepts that argument and the caller did not supply it.
    """
    explicit = set(_CALLER_SUPPLIED_RUNNER_ARGS)
    if model_name in _EPOCH_MODELS:
        explicit.add("n_epochs")
    kwargs.update(
        {key: value for key, value in preset.items() if key in allowed and key not in explicit}
    )
    return kwargs


def _numpy_serializer(obj: Any) -> Any:
    if isinstance(obj, np.ndarray):
        return obj.tolist()
    if isinstance(obj, (np.integer,)):
        return int(obj)
    if isinstance(obj, (np.floating,)):
        return float(obj)
    if isinstance(obj, np.bool_):
        return bool(obj)
    raise TypeError(f"Object of type {type(obj)} is not JSON serializable")


def _save_fold_extras(path: Path, fold_extras: list[dict]) -> None:
    path.write_text(json.dumps(fold_extras, default=_numpy_serializer, indent=1))


def load_fold_extras(case_study_id: str, training_hash: str) -> list[dict] | None:
    from utils.paths import get_case_study_dir

    extras_path = (
        get_case_study_dir(case_study_id)
        / "run_log"
        / "training"
        / training_hash
        / "fold_extras.json"
    )
    if not extras_path.exists():
        return None
    return json.loads(extras_path.read_text())


def _expected_latent_checkpoints(
    model_name: str,
    *,
    n_epochs: int,
    model_kwargs: dict[str, Any],
    include_internal_aliases: bool = False,
) -> tuple[int, ...]:
    """Resolve physical checkpoints and, when requested, fitted-state aliases."""
    from case_studies.utils.latent_factors.common import resolve_checkpoint_epochs

    if model_name in {"pca", "ipca"}:
        return (0,)
    if model_name in {"cae", "sae"}:
        physical = resolve_checkpoint_epochs(
            n_epochs,
            checkpoint_interval=model_kwargs.get("checkpoint_interval", 5),
            checkpoint_epochs=model_kwargs.get("checkpoint_epochs"),
        )
        if model_name == "cae" and include_internal_aliases:
            return tuple(sorted({0, *physical}))
        return tuple(physical)
    if model_name == "sdf":
        n_epochs_unc = int(model_kwargs.get("n_epochs_unc", 256))
        n_epochs_cond = int(model_kwargs.get("n_epochs_cond", 1024))
        physical = resolve_checkpoint_epochs(
            max(n_epochs_unc, n_epochs_cond),
            checkpoint_interval=model_kwargs.get("checkpoint_interval"),
            checkpoint_epochs=model_kwargs.get("checkpoint_epochs"),
        )
        labels: set[int] = {-3, -2, -1, 0} if include_internal_aliases else set()
        labels.update(epoch for epoch in physical if epoch <= n_epochs_unc)
        labels.update(n_epochs_unc + epoch for epoch in physical if epoch <= n_epochs_cond)
        return tuple(sorted(labels))
    raise ValueError(f"Unsupported latent-factor model: {model_name!r}")


def _build_expected_latent_training_spec(
    *,
    model_name: str,
    label_col: str,
    n_factors: int,
    n_epochs: int,
    model_kwargs: dict[str, Any],
    feature_names: list[str],
    splits: list[dict[str, Any]],
    task_type: str,
    class_values: list | None,
    eval_label_col: str | None,
    input_digest: str,
    macro_digest: str | None,
    runtime_spec: dict[str, Any],
    temporal_feature_assembly: str | None = None,
    temporal_feature_digest: str | None = None,
    macro_context_spec: dict[str, Any] | None = None,
    input_data_spec: dict[str, Any] | None = None,
) -> tuple[dict[str, Any], tuple[int, ...]]:
    """Build the exact identity expected from the registration path."""
    from case_studies.utils.registry import build_training_spec

    try:
        spec = build_training_spec(
            "latent_factors",
            model_name,
            label_col,
            n_folds=len(splits),
            n_epochs=n_epochs,
        )
    except FileNotFoundError:
        spec = {
            "config_name": model_name,
            "family": "latent_factors",
            "feature_sets": ["financial", "model_based"],
            "label": label_col,
            "library": "pytorch",
            "n_epochs": n_epochs,
            "params": {"n_factors": n_factors},
            "seed": 42,
        }

    checkpoints = _expected_latent_checkpoints(
        model_name,
        n_epochs=n_epochs,
        model_kwargs=model_kwargs,
    )
    fold_extras: list[dict[str, Any]] = []
    if model_name in {"cae", "sae", "sdf"}:
        expected_extra: dict[str, Any] = {"checkpoint_epochs": list(checkpoints)}
        if model_name == "sdf":
            expected_extra["output_mode"] = model_kwargs.get("output_mode", "beta_network")
            expected_extra["expected_return_mapper"] = model_kwargs.get(
                "expected_return_mapper", "linear"
            )
        fold_extras = [expected_extra]
    expected = _apply_latent_factor_runtime_spec(
        spec=spec,
        model_name=model_name,
        n_factors=n_factors,
        n_epochs=n_epochs,
        model_kwargs=model_kwargs,
        fold_extras=fold_extras,
        feature_names=feature_names,
        splits=splits,
        task_type=task_type,
        class_values=class_values,
        eval_label_col=eval_label_col,
        input_digest=input_digest,
        macro_digest=macro_digest,
        runtime_spec=runtime_spec,
        temporal_feature_assembly=temporal_feature_assembly,
        temporal_feature_digest=temporal_feature_digest,
        macro_context_spec=macro_context_spec,
        input_data_spec=input_data_spec,
    )
    return expected, checkpoints


def _expected_latent_prediction_keys(
    dataset: pl.DataFrame,
    *,
    splits: list[dict[str, Any]],
    label_col: str,
    eval_label_col: str | None,
    date_col: str,
    entity_col: str,
) -> pl.DataFrame:
    """Return the exact validation key surface implied by the dated input."""
    frames: list[pl.DataFrame] = []
    for split in splits:
        frame = dataset.filter(
            (pl.col(date_col) >= _to_naive_timestamp(split["val_start"]))
            & (pl.col(date_col) <= _to_naive_timestamp(split["val_end"]))
            & pl.col(label_col).is_finite()
        )
        if eval_label_col:
            frame = frame.filter(pl.col(eval_label_col).is_finite())
        frames.append(
            frame.select(date_col, entity_col).with_columns(
                pl.lit(int(split["fold"]), dtype=pl.Int64).alias("fold_id")
            )
        )
    if not frames:
        return pl.DataFrame()
    return _normalize_prediction_keys(pl.concat(frames).unique(), date_col, entity_col)


def _normalize_prediction_keys(
    frame: pl.DataFrame,
    date_col: str,
    entity_col: str,
) -> pl.DataFrame:
    key_cols = [date_col, entity_col, "fold_id"]
    return (
        frame.select(key_cols)
        .with_columns(
            pl.col(date_col).cast(pl.Datetime("us")),
            pl.col(entity_col).cast(pl.String),
            pl.col("fold_id").cast(pl.Int64),
        )
        .sort(key_cols)
    )


def _load_registered_latent_factor(
    case_study_id: str,
    *,
    model_name: str,
    training_spec: dict[str, Any],
    prediction_split: str,
    expected_checkpoints: tuple[int, ...],
    expected_keys: pl.DataFrame,
    date_col: str,
    entity_col: str,
    eval_label_col: str | None,
) -> tuple[str, pl.DataFrame, pl.DataFrame] | None:
    """Replay one exact, complete registry cohort and validate its metrics."""
    from case_studies.utils import registry

    training_hash = registry.training_hash_from_spec(training_spec)
    runs = registry.load_training_runs(
        case_study_id,
        family="latent_factors",
        label=training_spec["label"],
    )
    if runs.height == 0 or "training_hash" not in runs.columns:
        return None
    matching = runs.filter(pl.col("training_hash") == training_hash)
    if matching.height == 0:
        return None
    if matching.height != 1 or json.loads(matching["spec_json"][0]) != training_spec:
        raise ValueError(f"Cached {model_name} training identity is inconsistent")

    prediction_sets = registry.load_prediction_sets(
        case_study_id,
        training_hash=training_hash,
        split=prediction_split,
    )
    required_metadata = {"prediction_hash", "checkpoint_value", "checkpoint_kind"}
    missing_metadata = required_metadata - set(prediction_sets.columns)
    if missing_metadata:
        raise ValueError(
            f"Cached {model_name} checkpoint metadata is missing {sorted(missing_metadata)}"
        )
    if prediction_sets.height != len(expected_checkpoints):
        raise ValueError(
            f"Cached {model_name} checkpoint count {prediction_sets.height} does not match "
            f"expected {len(expected_checkpoints)}"
        )
    if prediction_sets["checkpoint_value"].null_count():
        raise ValueError(f"Cached {model_name} has a null checkpoint value")
    if prediction_sets.filter(pl.col("checkpoint_kind") != "epoch").height:
        raise ValueError(f"Cached {model_name} contains a non-epoch checkpoint")
    observed = tuple(sorted(int(value) for value in prediction_sets["checkpoint_value"]))
    if len(observed) != len(set(observed)) or observed != expected_checkpoints:
        raise ValueError(
            f"Cached {model_name} checkpoints {observed} do not match "
            f"expected {expected_checkpoints}"
        )

    key_cols = [date_col, entity_col, "fold_id"]
    metrics_rows: list[dict[str, Any]] = []
    frames: list[pl.DataFrame] = []
    for row in prediction_sets.sort("checkpoint_value").iter_rows(named=True):
        epoch = int(row["checkpoint_value"])
        path = (
            registry.prediction_dir(case_study_id, row["prediction_hash"]) / "predictions.parquet"
        )
        if not path.exists():
            raise FileNotFoundError(path)
        predictions = pl.read_parquet(path)
        required_cols = {date_col, entity_col, "fold_id", "y_true", "y_score", "epoch"}
        if eval_label_col:
            required_cols.add("eval_actual")
        missing_cols = required_cols - set(predictions.columns)
        if missing_cols:
            raise ValueError(
                f"Cached {model_name} checkpoint {epoch} schema is missing {sorted(missing_cols)}"
            )
        if predictions.select(pl.col(list(required_cols)).null_count()).row(0) != (0,) * len(
            required_cols
        ):
            raise ValueError(f"Cached {model_name} checkpoint {epoch} contains nulls")
        if predictions["epoch"].n_unique() != 1 or int(predictions["epoch"][0]) != epoch:
            raise ValueError(f"Cached {model_name} checkpoint {epoch} has contradictory epoch data")
        actual_keys = _normalize_prediction_keys(predictions, date_col, entity_col)
        if actual_keys.n_unique() != predictions.height:
            raise ValueError(f"Cached {model_name} checkpoint {epoch} has duplicate keys")
        if not actual_keys.equals(expected_keys):
            raise ValueError(
                f"Cached {model_name} checkpoint {epoch} key or fold coverage is incomplete"
            )

        actual_col = "eval_actual" if eval_label_col else "y_true"
        metric = cross_sectional_ic(
            predictions,
            predictions,
            pred_col="y_score",
            ret_col=actual_col,
            date_col=date_col,
            entity_col=entity_col,
            method="spearman",
            min_obs=5,
        )
        registry_metrics = registry.load_prediction_metrics(
            case_study_id,
            prediction_hash=row["prediction_hash"],
        )
        required_daily_metrics = {"ic_mean_daily", "ic_std_daily"}
        if registry_metrics.height != 1 or not required_daily_metrics <= set(
            registry_metrics.columns
        ):
            raise ValueError(
                f"Cached {model_name} checkpoint {epoch} has invalid daily registry metrics"
            )
        comparisons = {
            "daily mean": (registry_metrics["ic_mean_daily"][0], float(metric["ic_mean"])),
            "daily std": (registry_metrics["ic_std_daily"][0], float(metric["ic_std"])),
        }
        mismatches = {
            name: values
            for name, values in comparisons.items()
            if values[0] is None
            or not np.isclose(float(values[0]), values[1], atol=1e-12, rtol=0.0)
        }
        if mismatches:
            raise ValueError(
                f"Cached {model_name} checkpoint {epoch} daily metric mismatch: {mismatches}"
            )

        for fold_id in expected_keys["fold_id"].unique().sort():
            fold_metric = cross_sectional_ic(
                predictions.filter(pl.col("fold_id") == fold_id),
                predictions.filter(pl.col("fold_id") == fold_id),
                pred_col="y_score",
                ret_col=actual_col,
                date_col=date_col,
                entity_col=entity_col,
                method="spearman",
                min_obs=5,
            )
            metrics_rows.append(
                {
                    "epoch": epoch,
                    "fold_id": int(fold_id),
                    "ic_mean": float(fold_metric["ic_mean"]),
                    "n_scored_dates": int(fold_metric["n_periods"]),
                }
            )
        frames.append(predictions)

    return training_hash, pl.DataFrame(metrics_rows), pl.concat(frames)


def run_latent_factor_cv(
    panel_data: dict | None,
    splits: list[dict[str, Any]],
    *,
    models: list[str],
    n_factors: int = 5,
    n_epochs: int = 50,
    model_kwargs: dict[str, dict[str, Any]] | None = None,
    save_dir: Path | None = None,
    use_cache: bool = True,
    force_retrain: bool = False,
    random_state: int | None = None,
    dataset: pl.DataFrame | None = None,
    feature_names: list[str] | None = None,
    label_col: str | None = None,
    date_col: str = "timestamp",
    entity_col: str = "symbol",
    case_study_id: str | None = None,
    notebook: str = "latent_factors",
    eval_label_col: str | None = None,
    task_type: str = "regression",
    class_values: list | None = None,
    prediction_split: str = "validation",
    macro_panel: pl.DataFrame | None = None,
    macro_context_spec: dict[str, Any] | None = None,
    input_data_spec: dict[str, Any] | None = None,
    persistent_entities: bool = True,
    checkpoint_selection_policy: str | None = None,
    reporting_epoch: int | None = None,
    score_dates: str = "auto",
    score_cadence: str | None = None,
    score_rebalance_step: int | None = None,
    device: str = "cpu",
    num_threads: int = 8,
    deterministic_algorithms: bool = True,
    temporal_by_fold: pd.DataFrame | None = None,
    temporal_keys: list[str] | None = None,
    temporal_feature_names: list[str] | None = None,
    fold_workers: int = 1,
    checkpoint_surface: str = "physical",
) -> dict[str, Any]:
    """Run walk-forward latent factor CV from the raw dated dataset."""
    del panel_data
    if dataset is None or feature_names is None or label_col is None:
        raise ValueError(
            "run_latent_factor_cv requires dataset, feature_names, and label_col. "
            "Pre-built latent-factor panels are no longer supported."
        )
    if isinstance(fold_workers, bool) or not isinstance(fold_workers, int) or fold_workers < 1:
        raise ValueError("fold_workers must be a positive integer")
    if fold_workers > 1 and models != ["ipca"]:
        raise ValueError("parallel fold execution is currently supported only for IPCA-only runs")
    if checkpoint_surface not in {"fitted_state", "physical"}:
        raise ValueError("checkpoint_surface must be 'physical' or 'fitted_state'")

    model_kwargs = model_kwargs or {}
    runtime_spec = configure_latent_torch_runtime(
        device,
        seed=random_state if random_state is not None else RANDOM_SEED,
        num_threads=num_threads,
        deterministic_algorithms=deterministic_algorithms,
    )
    input_digest = _latent_input_digest(
        dataset,
        feature_names=feature_names,
        label_col=label_col,
        eval_label_col=eval_label_col,
        date_col=date_col,
        entity_col=entity_col,
        splits=splits,
    )
    macro_digest = (
        _frame_digest(_filter_dataset_for_splits(macro_panel, splits=splits, date_col=date_col))
        if macro_panel is not None
        else None
    )
    expected_prediction_keys = _expected_latent_prediction_keys(
        dataset,
        splits=splits,
        label_col=label_col,
        eval_label_col=eval_label_col,
        date_col=date_col,
        entity_col=entity_col,
    )
    has_fold_temporal = bool(
        temporal_by_fold is not None and temporal_keys and temporal_feature_names
    )
    temporal_feature_assembly = TEMPORAL_FEATURE_ASSEMBLY if has_fold_temporal else None
    temporal_feature_digest = (
        _frame_digest(
            _temporal_digest_frame(temporal_by_fold, temporal_keys, temporal_feature_names)
        )
        if has_fold_temporal
        else None
    )
    metric_policy = _resolve_metric_policy(
        case_study_id=case_study_id,
        label_col=label_col,
        checkpoint_selection_policy=checkpoint_selection_policy,
        reporting_epoch=reporting_epoch,
        score_dates=score_dates,
        score_cadence=score_cadence,
        score_rebalance_step=score_rebalance_step,
    )
    model_results: list[dict[str, Any]] = []
    all_predictions: dict[str, pl.DataFrame] = {}
    fold_metrics: dict[str, pl.DataFrame] = {}
    all_extras: dict[str, list[dict]] = {}

    if save_dir is not None:
        log_path = save_dir / "latent_factors.log"
    elif case_study_id:
        from utils.paths import get_case_study_dir

        log_path = get_case_study_dir(case_study_id) / "run_log" / "latent_factors.log"
    else:
        log_path = Path("/tmp/latent_factors.log")
    log_path.parent.mkdir(parents=True, exist_ok=True)
    log_file = open(log_path, "w")  # noqa: SIM115

    def log(message: str) -> None:
        line = f"[{datetime.now(UTC):%H:%M:%S}] {message}"
        log_file.write(line + "\n")
        log_file.flush()
        print(message)

    from utils.paths import display_path

    log(f"Latent factor CV: {len(models)} models × {len(splits)} folds")
    log(f"Log file: {display_path(log_path)}")
    log(
        "Scoring: "
        f"dates={metric_policy['score_dates']} "
        f"cadence={metric_policy['score_cadence'] or '-'} "
        f"step={metric_policy['score_rebalance_step']} "
        f"checkpoint_selection={metric_policy['checkpoint_selection_policy']} "
        f"reporting_epoch={metric_policy['reporting_epoch'] if metric_policy['reporting_epoch'] is not None else 'last'}"
    )

    seed_everything(random_state if random_state is not None else RANDOM_SEED)

    active_models: list[str] = []
    state: dict[str, dict[str, Any]] = {}
    started_at: dict[str, str] = {}
    started_clock: dict[str, float] = {}
    model_dirs: dict[str, Path | None] = {}

    for model_name in models:
        if model_name not in _MODEL_RUNNERS:
            log(f"  WARNING: unknown model {model_name!r}, skipping")
            continue
        if model_name == "pca" and not persistent_entities:
            raise ValueError("PCA requires persistent entity IDs for the current dataset")

        model_dir = save_dir / model_name if save_dir is not None else None
        model_dirs[model_name] = model_dir
        model_temporal_feature_assembly = temporal_feature_assembly if model_name != "pca" else None
        model_temporal_feature_digest = temporal_feature_digest if model_name != "pca" else None
        model_macro_context = macro_context_spec if model_name == "sdf" else None
        if use_cache and not force_retrain and case_study_id:
            training_spec, expected_checkpoints = _build_expected_latent_training_spec(
                model_name=model_name,
                label_col=label_col,
                n_factors=n_factors,
                n_epochs=n_epochs,
                model_kwargs=model_kwargs.get(model_name, {}),
                feature_names=feature_names,
                splits=splits,
                task_type=task_type,
                class_values=class_values,
                eval_label_col=eval_label_col,
                input_digest=input_digest,
                macro_digest=macro_digest,
                runtime_spec=runtime_spec,
                temporal_feature_assembly=model_temporal_feature_assembly,
                temporal_feature_digest=model_temporal_feature_digest,
                macro_context_spec=model_macro_context,
                input_data_spec=input_data_spec,
            )
            registered = _load_registered_latent_factor(
                case_study_id,
                model_name=model_name,
                training_spec=training_spec,
                prediction_split=prediction_split,
                expected_checkpoints=expected_checkpoints,
                expected_keys=expected_prediction_keys,
                date_col=date_col,
                entity_col=entity_col,
                eval_label_col=eval_label_col,
            )
            if registered is not None:
                training_hash, metrics_df, preds_df = registered
                best_epoch, mean_ic = _select_reporting_epoch(
                    metrics_df,
                    checkpoint_selection_policy=metric_policy["checkpoint_selection_policy"],
                    reporting_epoch=metric_policy["reporting_epoch"],
                )
                model_results.append(
                    {
                        "model_name": model_name,
                        "mean_ic": round(mean_ic, 4),
                        "best_epoch": best_epoch,
                        "n_folds": int(metrics_df["fold_id"].n_unique()),
                        "elapsed_s": 0.0,
                        "started_at": None,
                    }
                )
                all_predictions[model_name] = preds_df
                fold_metrics[model_name] = metrics_df
                all_extras[model_name] = load_fold_extras(case_study_id, training_hash) or []
                log(f"  {model_name}: loaded exact registry cohort (IC={mean_ic:+.4f})")
                continue
        if (
            use_cache
            and not force_retrain
            and model_dir is not None
            and model_macro_context is None
            and (model_dir / "predictions.parquet").exists()
            and (model_dir / "fold_metrics.parquet").exists()
        ):
            preds_df = pl.read_parquet(model_dir / "predictions.parquet")
            metrics_df = pl.read_parquet(model_dir / "fold_metrics.parquet")
            expected_cache_checkpoints = set(
                _expected_latent_checkpoints(
                    model_name,
                    n_epochs=n_epochs,
                    model_kwargs=model_kwargs.get(model_name, {}),
                    include_internal_aliases=checkpoint_surface == "fitted_state",
                )
            )
            expected_cache_surface = {
                (int(split["fold"]), checkpoint)
                for split in splits
                for checkpoint in expected_cache_checkpoints
            }
            cached_prediction_surface = set(
                preds_df.select("fold_id", "epoch").unique().iter_rows()
            )
            cached_metric_surface = set(metrics_df.select("fold_id", "epoch").unique().iter_rows())
            if (
                cached_prediction_surface != expected_cache_surface
                or cached_metric_surface != expected_cache_surface
            ):
                log(f"  {model_name}: cache checkpoint surface mismatch, retraining")
            elif "n_scored_dates" not in metrics_df.columns:
                # Written before fold ICs recorded the dates they scored, so the epoch IC
                # cannot be averaged over decision dates from it.
                log(f"  {model_name}: cache predates dated fold ICs, retraining")
            else:
                best_epoch, mean_ic = _select_reporting_epoch(
                    metrics_df,
                    checkpoint_selection_policy=metric_policy["checkpoint_selection_policy"],
                    reporting_epoch=metric_policy["reporting_epoch"],
                )
                model_results.append(
                    {
                        "model_name": model_name,
                        "mean_ic": round(mean_ic, 4),
                        "best_epoch": best_epoch,
                        "n_folds": int(metrics_df["fold_id"].n_unique())
                        if metrics_df.height > 0
                        else 0,
                        "elapsed_s": 0.0,
                        "started_at": None,
                    }
                )
                all_predictions[model_name] = preds_df
                fold_metrics[model_name] = metrics_df
                all_extras[model_name] = []
                log(f"  {model_name}: loaded cache (best IC={mean_ic:+.4f})")
                continue

        active_models.append(model_name)
        started_at[model_name] = datetime.now(UTC).isoformat()
        started_clock[model_name] = time.perf_counter()
        state[model_name] = {
            "fold_ics": [],
            "pred_frames": [],
            "pred_files": [],
            "fold_extras": [],
        }
        log(f"  {model_name} (K={n_factors}):")

    need_pca_inputs = bool(PERSISTENT_PANEL_MODELS & set(active_models))
    need_ragged_inputs = any(
        model_name not in PERSISTENT_PANEL_MODELS for model_name in active_models
    )

    def runner_kwargs(
        model_name: str,
        model_input: dict[str, Any],
        fold_id: int,
    ) -> dict[str, Any]:
        runner = _MODEL_RUNNERS[model_name]
        kwargs: dict[str, Any] = {"n_factors": n_factors}
        if model_name in {"cae", "sae"}:
            kwargs["n_epochs"] = n_epochs
        if model_name in {"cae", "sae", "sdf"}:
            kwargs["log_fn"] = log
            kwargs["device"] = runtime_spec["device"]
        if model_name in {"cae", "sae"}:
            kwargs["task_type"] = task_type
            if (
                task_type == "classification"
                and model_input.get("factor_returns_train") is not None
            ):
                kwargs["factor_returns_train"] = model_input["factor_returns_train"]
        if model_name == "sdf" and model_input.get("macro_train") is not None:
            kwargs["macro_train"] = model_input["macro_train"]
            kwargs["macro_val"] = model_input["macro_val"]
        model_dir = model_dirs[model_name]
        if model_dir is not None and "artifact_dir" in inspect.signature(runner).parameters:
            kwargs["artifact_dir"] = model_dir / "artifacts" / f"fold_{fold_id}"
        if model_name in model_kwargs:
            merge_preset_into_runner_kwargs(
                kwargs,
                preset=model_kwargs[model_name],
                allowed=set(inspect.signature(runner).parameters),
                model_name=model_name,
            )
        return kwargs

    def fit_fold(
        model_name: str,
        model_input: dict[str, Any],
        fold_id: int,
    ) -> tuple[dict[int, np.ndarray], dict[str, Any], float]:
        fold_started = time.perf_counter()
        result = _MODEL_RUNNERS[model_name](
            model_input["chars_train"],
            model_input["returns_train"],
            model_input["chars_val"],
            model_input["returns_val"],
            **runner_kwargs(model_name, model_input, fold_id),
        )
        if isinstance(result[0], dict):
            checkpoint_preds, extra = result
        else:
            predictions_arr, extra = result
            checkpoint_preds = {0: predictions_arr}
        return checkpoint_preds, extra, time.perf_counter() - fold_started

    def record_fold(
        *,
        split: dict[str, Any],
        model_name: str,
        model_input: dict[str, Any],
        checkpoint_preds: dict[int, np.ndarray],
        extra: dict[str, Any],
        fold_elapsed: float,
    ) -> None:
        state[model_name]["fold_extras"].append({"fold_id": split["fold"], **extra})
        if checkpoint_surface == "physical":
            physical = set(
                _expected_latent_checkpoints(
                    model_name,
                    n_epochs=n_epochs,
                    model_kwargs=model_kwargs.get(model_name, {}),
                )
            )
            checkpoint_preds = {
                epoch: predictions
                for epoch, predictions in checkpoint_preds.items()
                if epoch in physical
            }
            if not checkpoint_preds:
                raise ValueError(f"{model_name} produced no physical checkpoints")
        checkpoint_ics: dict[int, float] = {}
        fold_frames: list[pl.DataFrame] = []
        for epoch, predictions in checkpoint_preds.items():
            frame = _build_prediction_frame(
                predictions=predictions,
                returns_val=model_input["returns_val"],
                eval_returns_val=model_input.get("eval_returns_val"),
                val_dates=model_input["val_dates"],
                val_entities=model_input["val_entities"],
                fold_id=split["fold"],
                model_name=model_name,
                epoch=epoch,
            )
            scored_frame = _score_prediction_frame(
                frame,
                score_dates=metric_policy["score_dates"],
                score_cadence=metric_policy["score_cadence"],
                score_rebalance_step=metric_policy["score_rebalance_step"],
            )
            ic, n_scored_dates = _compute_frame_ic(scored_frame)
            checkpoint_ics[epoch] = ic
            state[model_name]["fold_ics"].append(
                {
                    "fold_id": split["fold"],
                    "epoch": epoch,
                    "ic_mean": round(ic, 4),
                    "n_train": model_input["n_train_periods"],
                    "n_test": model_input["n_val_periods"],
                    "n_scored_dates": n_scored_dates,
                }
            )
            if frame is not None:
                fold_frames.append(frame)
        best_epoch, reported_ic = _select_epoch_from_values(
            checkpoint_ics,
            checkpoint_selection_policy=metric_policy["checkpoint_selection_policy"],
            reporting_epoch=metric_policy["reporting_epoch"],
        )
        log(
            f"      fold {split['fold']}: reported_epoch={best_epoch}, "
            f"IC={reported_ic:+.4f}, {fold_elapsed:.1f}s"
        )
        # This fold's predictions leave memory here. They used to be kept for the whole run
        # in `pred_frames` and, separately, rebuilt from `checkpoint_preds` to be written -
        # every fold of every model resident while the later folds were still fitting, and
        # every prediction frame built twice.
        fold_path = _write_incremental_fold(
            model_dir=model_dirs[model_name],
            fold_id=split["fold"],
            frames=fold_frames,
        )
        if fold_path is None:
            state[model_name]["pred_frames"].extend(fold_frames)
        else:
            state[model_name]["pred_files"].append(fold_path)
        fold_frames.clear()

    if fold_workers > 1 and active_models:
        prepared_folds: list[tuple[dict[str, Any], dict[str, Any]]] = []
        for split in splits:
            fold_inputs = _prepare_fold_inputs(
                dataset=dataset,
                split=split,
                feature_names=feature_names,
                label_col=label_col,
                date_col=date_col,
                entity_col=entity_col,
                eval_label_col=eval_label_col,
                macro_panel=macro_panel,
                need_pca_inputs=False,
                need_ragged_inputs=True,
                temporal_by_fold=temporal_by_fold,
                temporal_keys=temporal_keys,
                temporal_feature_names=temporal_feature_names,
            )
            if fold_inputs is None:
                log(f"    Fold {split['fold']}: skipped (insufficient train/validation dates)")
                continue
            model_input = fold_inputs["ragged"]
            log(
                f"    Fold {split['fold']}: ragged "
                f"train={model_input['n_train_periods']}, "
                f"val={model_input['n_val_periods']}, "
                f"max_N={model_input['chars_train'].shape[1]}"
            )
            prepared_folds.append((split, model_input))
        if not prepared_folds:
            raise RuntimeError("IPCA parallel execution produced no eligible folds")
        prepared_folds.sort(key=lambda item: int(item[0]["fold"]))
        worker_count = min(fold_workers, len(prepared_folds))
        log(f"    IPCA parallel execution: {worker_count} workers, BLAS threads=1")
        completed: dict[int, tuple[dict[int, np.ndarray], dict[str, Any], float]] = {}
        with (
            threadpool_limits(limits=1, user_api="blas"),
            ThreadPoolExecutor(max_workers=worker_count, thread_name_prefix="ipca-fold") as pool,
        ):
            futures = {
                pool.submit(fit_fold, "ipca", model_input, int(split["fold"])): int(split["fold"])
                for split, model_input in prepared_folds
            }
            for future in as_completed(futures):
                fold_id = futures[future]
                checkpoint_preds, extra, fold_elapsed = future.result()
                completed[fold_id] = (checkpoint_preds, extra, fold_elapsed)
                log(
                    f"      fold {fold_id}: fit complete, "
                    f"iterations={extra.get('iterations', '?')}, "
                    f"converged={extra.get('converged', False)}, {fold_elapsed:.1f}s"
                )
        ordered_extras = [
            {"fold_id": int(split["fold"]), **completed[int(split["fold"])][1]}
            for split, _ in prepared_folds
        ]
        _require_fit_convergence("ipca", ordered_extras)
        for split, model_input in prepared_folds:
            checkpoint_preds, extra, fold_elapsed = completed[int(split["fold"])]
            record_fold(
                split=split,
                model_name="ipca",
                model_input=model_input,
                checkpoint_preds=checkpoint_preds,
                extra=extra,
                fold_elapsed=fold_elapsed,
            )
    else:
        for split in splits:
            if not active_models:
                break
            # The fold's number is an input to the fit, not a label on it: renumbering
            # the windows reseeds every one of them. See `folds.fold_seed`.
            seed_everything(fold_seed(RANDOM_SEED, int(split["fold"])))
            fold_inputs = _prepare_fold_inputs(
                dataset=dataset,
                split=split,
                feature_names=feature_names,
                label_col=label_col,
                date_col=date_col,
                entity_col=entity_col,
                eval_label_col=eval_label_col,
                macro_panel=macro_panel,
                need_pca_inputs=need_pca_inputs,
                need_ragged_inputs=need_ragged_inputs,
                temporal_by_fold=temporal_by_fold,
                temporal_keys=temporal_keys,
                temporal_feature_names=temporal_feature_names,
            )
            if fold_inputs is None:
                log(f"    Fold {split['fold']}: skipped (insufficient train/validation dates)")
                continue
            display_input = fold_inputs["ragged"] or fold_inputs["pca"]
            input_kind = "ragged" if fold_inputs["ragged"] is not None else "persistent"
            log(
                f"    Fold {split['fold']}: {input_kind} "
                f"train={display_input['n_train_periods']}, "
                f"val={display_input['n_val_periods']}, "
                f"max_N={display_input['chars_train'].shape[1]}"
            )
            for model_name in active_models:
                model_input = fold_inputs["pca"] if model_name == "pca" else fold_inputs["ragged"]
                checkpoint_preds, extra, fold_elapsed = fit_fold(
                    model_name,
                    model_input,
                    int(split["fold"]),
                )
                record_fold(
                    split=split,
                    model_name=model_name,
                    model_input=model_input,
                    checkpoint_preds=checkpoint_preds,
                    extra=extra,
                    fold_elapsed=fold_elapsed,
                )

    for model_name in active_models:
        fold_ics_df = pl.DataFrame(state[model_name]["fold_ics"])
        # Read back in the order the folds were fitted, which is the order the accumulating
        # list produced.
        if state[model_name]["pred_files"]:
            preds_df = pl.read_parquet(state[model_name]["pred_files"])
        elif state[model_name]["pred_frames"]:
            preds_df = pl.concat(state[model_name]["pred_frames"])
        else:
            preds_df = pl.DataFrame()
        best_epoch, mean_ic = _select_reporting_epoch(
            fold_ics_df,
            checkpoint_selection_policy=metric_policy["checkpoint_selection_policy"],
            reporting_epoch=metric_policy["reporting_epoch"],
        )
        elapsed = time.perf_counter() - started_clock[model_name]

        model_results.append(
            {
                "model_name": model_name,
                "mean_ic": round(mean_ic, 4),
                "best_epoch": best_epoch,
                "n_folds": int(fold_ics_df["fold_id"].n_unique()) if fold_ics_df.height > 0 else 0,
                "elapsed_s": round(elapsed, 1),
                "started_at": started_at[model_name],
            }
        )

        all_predictions[model_name] = preds_df
        fold_metrics[model_name] = fold_ics_df
        all_extras[model_name] = state[model_name]["fold_extras"]

        _require_fit_convergence(model_name, state[model_name]["fold_extras"])

        model_dir = model_dirs[model_name]
        if model_dir is not None:
            model_dir.mkdir(parents=True, exist_ok=True)
            preds_df.write_parquet(model_dir / "predictions.parquet")
            fold_ics_df.write_parquet(model_dir / "fold_metrics.parquet")

        if case_study_id and preds_df.height > 0:
            model_temporal_feature_assembly = (
                temporal_feature_assembly if model_name != "pca" else None
            )
            model_temporal_feature_digest = temporal_feature_digest if model_name != "pca" else None
            model_macro_context = macro_context_spec if model_name == "sdf" else None
            training_hash = _register_model_predictions(
                case_study_id=case_study_id,
                model_name=model_name,
                label_col=label_col,
                n_epochs=n_epochs,
                n_factors=n_factors,
                notebook=notebook,
                prediction_split=prediction_split,
                task_type=task_type,
                class_values=class_values,
                eval_label_col=eval_label_col,
                started_at=started_at[model_name],
                elapsed=elapsed,
                model_kwargs=model_kwargs.get(model_name, {}),
                fold_extras=state[model_name]["fold_extras"],
                fold_ics_df=fold_ics_df,
                preds_df=preds_df,
                feature_names=feature_names,
                splits=splits,
                input_digest=input_digest,
                macro_digest=macro_digest,
                runtime_spec=runtime_spec,
                temporal_feature_assembly=model_temporal_feature_assembly,
                temporal_feature_digest=model_temporal_feature_digest,
                macro_context_spec=model_macro_context,
                input_data_spec=input_data_spec,
            )
            if state[model_name]["fold_extras"]:
                from utils.paths import get_case_study_dir

                extras_dir = (
                    get_case_study_dir(case_study_id) / "run_log" / "training" / training_hash
                )
                extras_dir.mkdir(parents=True, exist_ok=True)
                _save_fold_extras(extras_dir / "fold_extras.json", state[model_name]["fold_extras"])

        # Named for the policy that produced it. Under `fixed` this is the configured reporting
        # epoch and not an arg-max over checkpoints, so calling it "best" asserted a selection
        # that did not happen - and it read as one, printing epoch 50 for a curve peaking at 20.
        selection = metric_policy["checkpoint_selection_policy"]
        epoch_label = "best epoch" if selection == "validation_ic" else "reporting epoch"
        log(f"    -> {epoch_label}={best_epoch} ({selection}), IC={mean_ic:+.4f} ({elapsed:.1f}s)")
        gc.collect()

    if model_results:
        model_results.sort(key=lambda row: row["mean_ic"], reverse=True)
        best = model_results[0]
    else:
        best = {"model_name": "none", "mean_ic": 0.0}

    log(f"  Best: {best['model_name']} (IC={best['mean_ic']:+.4f})")
    log_file.close()

    return {
        "model_results": model_results,
        "best_model": best["model_name"],
        "best_ic": best["mean_ic"],
        "all_predictions": all_predictions,
        "fold_metrics": fold_metrics,
        "fold_extras": all_extras,
    }


def _frame_digest(frame: pl.DataFrame) -> str:
    """Return a stable digest of an ordered Polars frame and its schema."""
    columns = list(frame.columns)
    key_cols = [column for column in ("fold", "timestamp", "symbol") if column in columns]
    ordered = frame.sort(key_cols) if key_cols else frame
    row_hashes = ordered.hash_rows(seed=42).to_numpy()
    schema = [(column, str(ordered.schema[column])) for column in columns]
    digest = hashlib.sha256(json.dumps(schema, separators=(",", ":")).encode())
    digest.update(row_hashes.tobytes())
    return digest.hexdigest()


def _latent_input_digest(
    dataset: pl.DataFrame,
    *,
    feature_names: list[str],
    label_col: str,
    eval_label_col: str | None,
    date_col: str,
    entity_col: str,
    splits: list[dict[str, Any]],
) -> str:
    columns = [date_col, entity_col, *feature_names, label_col]
    if eval_label_col:
        columns.append(eval_label_col)
    dataset = _filter_dataset_for_splits(dataset, splits=splits, date_col=date_col)
    return _frame_digest(dataset.select(columns))


def _filter_dataset_for_splits(
    dataset: pl.DataFrame,
    *,
    splits: list[dict[str, Any]],
    date_col: str,
) -> pl.DataFrame:
    """Keep only rows used by at least one declared train or validation window."""
    filter_col = pl.col(date_col)
    if (
        hasattr(dataset[date_col].dtype, "time_zone")
        and dataset[date_col].dtype.time_zone is not None
    ):
        filter_col = filter_col.dt.replace_time_zone(None)

    used = pl.lit(False)
    for split in splits:
        train = filter_col.is_between(
            _to_naive_timestamp(split["train_start"]),
            _to_naive_timestamp(split["train_end"]),
            closed="both",
        )
        validation = filter_col.is_between(
            _to_naive_timestamp(split["val_start"]),
            _to_naive_timestamp(split["val_end"]),
            closed="both",
        )
        used = used | train | validation
    return dataset.filter(used)


# Every latent-factor model that iterates towards a fit. `pca` is deliberately absent: it is
# a deterministic decomposition with nothing to converge, so asking it for a determination
# would refuse every PCA cohort.
_CONVERGENCE_GUARDED_MODELS = frozenset({"ipca", "cae", "sae", "sdf"})


def _require_fit_convergence(
    model_name: str,
    fold_extras: list[dict[str, Any]],
) -> None:
    """Refuse to register any iterative latent-factor cohort that did not converge.

    `_require_ipca_convergence` below reads a ``converged`` flag that only the IPCA branch of
    `library_bridge` wrote, so its name was accurate and its coverage was one model of four.
    An SDF, CAE or SAE fit that never identified registered anyway: nothing compared the
    objective at the last step with the one before it, nothing required the terminal Sharpe
    to be finite, its predictions entered the population, and `require_complete` and the
    notebook's IC table both passed. The failure was invisible at the point where it
    happened and showed up only as results that were quietly meaningless.

    The determination is not the same statement for every model, and `library_bridge`'s
    ``convergence_criterion`` records which one was applied: IPCA reports whether its
    alternating least squares settled within ``tol``, and the three gradient-descent models
    report a finite terminal objective, plus a finite terminal Sharpe for the SDF. What the
    guard requires is identical either way - a determination exists, and it is positive.
    """
    if model_name not in _CONVERGENCE_GUARDED_MODELS:
        return
    if model_name == "ipca":
        _require_ipca_convergence(model_name, fold_extras)
        return

    # An absent flag is a runner that stopped writing the determination, which is how this
    # guard silently loses a model. It is refused separately from a negative flag so the
    # message says which of the two happened. IPCA keeps the older reading, where an absent
    # flag counts as a fit that did not settle, because its stored extras were written
    # under it.
    undetermined = [int(extra["fold_id"]) for extra in fold_extras if "converged" not in extra]
    if undetermined:
        raise RuntimeError(
            f"{model_name} recorded no convergence determination for folds {undetermined}; "
            "refusing to register predictions from a fit that was never checked"
        )
    failed = [int(extra["fold_id"]) for extra in fold_extras if not extra["converged"]]
    if failed:
        raise RuntimeError(
            f"{model_name} did not converge for folds "
            f"{failed}; refusing to register predictions from a fit that did not identify"
        )


def _require_ipca_convergence(
    model_name: str,
    fold_extras: list[dict[str, Any]],
) -> None:
    if model_name != "ipca":
        return
    failed = [int(extra["fold_id"]) for extra in fold_extras if not extra.get("converged", False)]
    if failed:
        raise RuntimeError(
            "IPCA did not converge for folds "
            f"{failed}; refusing to register predictions from an incomplete ALS fit"
        )


def _prepare_fold_inputs(
    *,
    dataset: pl.DataFrame,
    split: dict[str, Any],
    feature_names: list[str],
    label_col: str,
    date_col: str,
    entity_col: str,
    eval_label_col: str | None,
    macro_panel: pl.DataFrame | None,
    need_pca_inputs: bool,
    need_ragged_inputs: bool = True,
    temporal_by_fold: pd.DataFrame | None = None,
    temporal_keys: list[str] | None = None,
    temporal_feature_names: list[str] | None = None,
) -> dict[str, Any] | None:
    if temporal_by_fold is not None and temporal_keys and temporal_feature_names:
        dataset = _replace_fold_temporal_features(
            dataset=dataset,
            temporal_by_fold=temporal_by_fold,
            temporal_keys=temporal_keys,
            temporal_feature_names=temporal_feature_names,
            fold_id=int(split["fold"]),
        )
    fold_dataset = _filter_dataset_window(
        dataset,
        date_col=date_col,
        start=split["train_start"],
        end=split["val_end"],
    )
    ragged_inputs = None
    if need_ragged_inputs:
        ragged_dataset = fold_dataset
        if macro_panel is not None:
            macro_start = macro_panel.select(pl.col(date_col).min()).item()
            ragged_dataset = ragged_dataset.filter(pl.col(date_col) >= macro_start)
        ragged_panel = prepare_ragged_panel_data(
            ragged_dataset,
            feature_names=feature_names,
            label_col=label_col,
            date_col=date_col,
            entity_col=entity_col,
            eval_label_col=eval_label_col,
            macro_panel=macro_panel,
        )
        ragged_panel["chars"] = rank_normalize_cross_section(ragged_panel["chars"])

        ragged_train_mask = _date_mask(
            ragged_panel["dates"], split["train_start"], split["train_end"]
        )
        ragged_val_mask = _date_mask(ragged_panel["dates"], split["val_start"], split["val_end"])
        ragged_inputs = {
            "chars_train": ragged_panel["chars"][ragged_train_mask],
            "returns_train": ragged_panel["returns"][ragged_train_mask],
            "chars_val": ragged_panel["chars"][ragged_val_mask],
            "returns_val": ragged_panel["returns"][ragged_val_mask],
            "factor_returns_train": (
                ragged_panel["eval_returns"][ragged_train_mask]
                if ragged_panel.get("eval_returns") is not None
                else None
            ),
            "eval_returns_val": (
                ragged_panel["eval_returns"][ragged_val_mask]
                if ragged_panel.get("eval_returns") is not None
                else None
            ),
            "val_dates": ragged_panel["dates"][ragged_val_mask],
            "val_entities": ragged_panel["entities"][ragged_val_mask],
            "macro_train": (
                ragged_panel["macro"][ragged_train_mask]
                if ragged_panel.get("macro") is not None
                else None
            ),
            "macro_val": (
                ragged_panel["macro"][ragged_val_mask]
                if ragged_panel.get("macro") is not None
                else None
            ),
            "n_train_periods": int(ragged_train_mask.sum()),
            "n_val_periods": int(ragged_val_mask.sum()),
        }

    persistent_inputs = None
    if need_pca_inputs:
        train_dataset = _filter_dataset_window(
            fold_dataset,
            date_col=date_col,
            start=split["train_start"],
            end=split["train_end"],
        )
        persistent_panel = prepare_panel_data(
            fold_dataset,
            feature_names=feature_names,
            label_col=label_col,
            date_col=date_col,
            entity_col=entity_col,
            eligibility_dataset=train_dataset,
            eval_label_col=eval_label_col,
        )
        persistent_train_mask = _date_mask(
            persistent_panel["dates"],
            split["train_start"],
            split["train_end"],
        )
        persistent_val_mask = _date_mask(
            persistent_panel["dates"],
            split["val_start"],
            split["val_end"],
        )
        persistent_inputs = {
            "chars_train": persistent_panel["chars"][persistent_train_mask],
            "returns_train": persistent_panel["returns"][persistent_train_mask],
            "chars_val": persistent_panel["chars"][persistent_val_mask],
            "returns_val": persistent_panel["returns"][persistent_val_mask],
            "eval_returns_val": (
                persistent_panel["eval_returns"][persistent_val_mask]
                if persistent_panel.get("eval_returns") is not None
                else None
            ),
            "val_dates": persistent_panel["dates"][persistent_val_mask],
            "val_entities": np.broadcast_to(
                persistent_panel["entities"][None, :],
                (int(persistent_val_mask.sum()), len(persistent_panel["entities"])),
            ).copy(),
            "macro_train": None,
            "macro_val": None,
            "n_train_periods": int(persistent_train_mask.sum()),
            "n_val_periods": int(persistent_val_mask.sum()),
        }

    available_input = ragged_inputs or persistent_inputs
    if available_input is None:
        raise ValueError("At least one latent-factor input representation is required")
    if available_input["n_train_periods"] < 10 or available_input["n_val_periods"] < 3:
        return None

    return {"ragged": ragged_inputs, "pca": persistent_inputs}


def _replace_fold_temporal_features(
    *,
    dataset: pl.DataFrame,
    temporal_by_fold: pd.DataFrame,
    temporal_keys: list[str],
    temporal_feature_names: list[str],
    fold_id: int,
) -> pl.DataFrame:
    """Replace the schema-placeholder columns with one fold's learned features."""
    from utils.modeling import fold_temporal_frame

    fold_temporal = fold_temporal_frame(
        temporal_by_fold, fold_id, temporal_keys=temporal_keys, schema=dataset.schema
    )
    if fold_temporal.is_empty():
        raise ValueError(f"No temporal features found for fold {fold_id}")
    fold_temporal = fold_temporal.unique(subset=temporal_keys, keep="last")

    missing = sorted(set(temporal_feature_names) - set(fold_temporal.columns))
    if missing:
        raise ValueError(f"Fold {fold_id} temporal data is missing features: {missing}")
    return dataset.drop(temporal_feature_names, strict=False).join(
        fold_temporal.select([*temporal_keys, *temporal_feature_names]),
        on=temporal_keys,
        how="left",
    )


def _filter_dataset_window(
    dataset: pl.DataFrame,
    *,
    date_col: str,
    start: Any,
    end: Any,
) -> pl.DataFrame:
    start_ts = _to_naive_timestamp(start)
    end_ts = _to_naive_timestamp(end)
    filter_col = pl.col(date_col)
    if (
        hasattr(dataset[date_col].dtype, "time_zone")
        and dataset[date_col].dtype.time_zone is not None
    ):
        filter_col = filter_col.dt.replace_time_zone(None)
    return dataset.filter((filter_col >= start_ts) & (filter_col <= end_ts))


def _date_mask(dates: np.ndarray, start: Any, end: Any) -> np.ndarray:
    start_dt = np.datetime64(_to_naive_timestamp(start))
    end_dt = np.datetime64(_to_naive_timestamp(end))
    dates_arr = np.asarray(dates, dtype="datetime64[ns]")
    return (dates_arr >= start_dt) & (dates_arr <= end_dt)


def _to_naive_timestamp(value: Any) -> pd.Timestamp:
    ts = pd.Timestamp(value)
    if ts.tz is not None:
        ts = ts.tz_convert("UTC").tz_localize(None)
    return ts


def _temporal_digest_frame(
    temporal_by_fold: Any,
    temporal_keys: list[str],
    temporal_feature_names: list[str],
) -> pl.DataFrame:
    """The columns the temporal digest covers, projected out of whatever form is held.

    The one consumer that spans every fold rather than selecting one, so it is also the only
    place the whole artifact is read - and it reads the hashed columns alone, not the table.
    """
    columns = ["fold", *temporal_keys, *temporal_feature_names]
    if isinstance(temporal_by_fold, pl.LazyFrame):
        return temporal_by_fold.select(columns).collect()
    if isinstance(temporal_by_fold, pl.DataFrame):
        return temporal_by_fold.select(columns)
    return pl.from_pandas(temporal_by_fold.loc[:, columns])


def _resolve_metric_policy(
    *,
    case_study_id: str | None,
    label_col: str | None,
    checkpoint_selection_policy: str | None,
    reporting_epoch: int | None,
    score_dates: str,
    score_cadence: str | None,
    score_rebalance_step: int | None,
) -> dict[str, Any]:
    from utils import CASE_STUDIES_DIR

    setup: dict[str, Any] = {}
    lf_setup: dict[str, Any] = {}
    if case_study_id:
        setup = yaml.safe_load(
            (CASE_STUDIES_DIR / case_study_id / "config" / "setup.yaml").read_text()
        )
        lf_setup = (setup.get("modeling") or {}).get("latent_factors") or {}

    selection_policy = checkpoint_selection_policy or lf_setup.get(
        "checkpoint_selection_policy",
        "fixed",
    )
    if selection_policy not in {"fixed", "validation_ic"}:
        raise ValueError(
            "checkpoint_selection_policy must be 'fixed' or 'validation_ic'; "
            f"got {selection_policy!r}"
        )

    resolved_reporting_epoch = reporting_epoch
    if resolved_reporting_epoch is None and lf_setup.get("reporting_epoch") is not None:
        resolved_reporting_epoch = int(lf_setup["reporting_epoch"])

    score_mode = score_dates
    if score_mode == "auto":
        # IC is defined at every prediction timestamp, then averaged. Portfolio
        # rebalance cadence belongs to the downstream backtest and must not
        # thin the model-evaluation series or checkpoint-selection metric.
        score_mode = lf_setup.get("score_dates") or "all"
    if score_mode not in {"all", "rebalance"}:
        raise ValueError(f"score_dates must be 'auto', 'all', or 'rebalance'; got {score_dates!r}")

    resolved_cadence = score_cadence or lf_setup.get("score_cadence")
    resolved_step = score_rebalance_step
    if resolved_step is None and lf_setup.get("score_rebalance_step") is not None:
        resolved_step = int(lf_setup["score_rebalance_step"])

    if score_mode == "rebalance":
        if resolved_cadence is None:
            resolved_cadence = (setup.get("decision") or {}).get("cadence")
        if resolved_step is None:
            resolved_step = (
                get_rebalance_step(case_study_id, label_col) if case_study_id and label_col else 1
            )
        if not resolved_cadence:
            raise ValueError(
                "score_dates='rebalance' requires a cadence; pass score_cadence or declare "
                "decision.cadence in setup.yaml."
            )
    else:
        resolved_step = 1 if resolved_step is None else int(resolved_step)
        resolved_cadence = resolved_cadence or ""

    return {
        "checkpoint_selection_policy": selection_policy,
        "reporting_epoch": resolved_reporting_epoch,
        "score_dates": score_mode,
        "