"""Classical (gradient-boosted) methods for the MacroLens unified API.

One concrete class — :class:`LightGBMRegressor` — that covers all 7 tasks
via task-specific private methods. Sklearn-style ``Method`` contract::

    LightGBMRegressor(*, task=..., config=...) ↦
        .fit(X, y, *, seed=42)   ↦ self
        .predict(X)              ↦ ndarray | DataFrame
        .save(path) / .load(path)

Per-task feature engineering mirrors the legacy ``baselines/classical.py``
recipe verbatim (numerics preserved); only the IO / canonical-indices /
eval / subsampling layer is stripped. See plan §9 for the predict-output
shape per task.

Per-task summary (definitive — runner asserts ``predict`` shape):

    T1 — Time-series forecasting:
        X : ``np.ndarray`` ``(N, lookback, F)`` float32.
        y : ``np.ndarray`` ``(N, horizon)`` float32 close trajectory.
        Pipeline: per-horizon-step LightGBM. One booster per horizon
        index ``h ∈ [0, horizon)`` predicting ``y[:, h]`` from the
        flattened ``(lookback × F)`` panel + rolling-close stats.
        NO scalar+tile broadcasting hack.
        Output: ``(N, horizon)`` float32.

    T2 / T5 — Valuation:
        X : ``pd.DataFrame`` of ``stmt_*`` (+ ``derived_*`` for T2)
        numeric features plus sector / industry one-hot.
        y : ``np.ndarray`` ``(N,)`` market_cap.
        Pipeline: log-target via sklearn ``Pipeline([scale, lgbm])``
        wrapped in ``TransformedTargetRegressor`` (log1p / expm1).
        Output: ``(N,)`` float32 — predicted equity value.

    T3 / T6 — Per-field generation:
        X : ``pd.DataFrame`` keyed by ``(ticker, fiscal_year)`` with
        numeric snapshot fields + sector + industry; T6 also has a
        ``company_description`` text column which is DROPPED in the
        default branch (``t6_text_handling="sector_industry_only"``).
        y : long-form ``pd.DataFrame[ticker, fiscal_year, field, value]``.
        Pipeline: ensemble of one booster per ``field``. ``fitted_fields``
        is locked at fit time to ``sorted(y["field"].unique())``. At
        predict, every (ticker, fiscal_year) row in ``X`` emits one row
        per fitted field.
        Output: long-form ``[ticker, fiscal_year, field, pred]``.

    T4 — Scenario return:
        X : ``pd.DataFrame`` with object-dtype ``lookback`` cells (each
        a ``(L, F)`` ndarray) plus ``event_type`` and
        ``event_description`` string columns. ``event_description`` is
        DROPPED in v1.
        y : ``np.ndarray`` ``(N,)`` return_pct.
        Pipeline: flatten lookback (full L*F) + event_type one-hot.
        Output: ``(N,)`` float32 — predicted return %.

    T7 — Real-estate valuation:
        X : ``pd.DataFrame`` with property attributes (``sqft``, ``beds``,
        ``baths``, ``year_built``, optionally ``years_since_last_sale``)
        and a property-type column.
        y : ``pd.DataFrame[address, rent, price]``.
        Pipeline: two boosters (one for rent, one for price) with
        log-target via ``TransformedTargetRegressor``.
        Output: ``pd.DataFrame[address, pred_rent, pred_price]``.

Hard rules (enforced by ``tests/test_layer_isolation.py``):

* Zero IO (no ``pd.read_parquet``, no file reads, no ``config`` imports).
* Zero ``meta`` consumption — methods take only ``X`` (and at fit time,
  ``y``).
* Zero canonical-indices imports / subsampling.
* Zero eval imports.
"""

from __future__ import annotations

import importlib.metadata
from typing import Any, ClassVar

import numpy as np
import pandas as pd

from ._config import LightGBMConfig, RandomForestConfig
from ._registry import register
from .base import Method, _JoblibSaveMixin


# ── Internal lib_versions helper ──────────────────────────────────────────


def _classical_lib_versions() -> dict[str, str]:
    """Versions of the libs the classical family actually uses.

    Restricts the broader default in :class:`Method` (which probes torch /
    transformers / vllm too) — those are not imported here.
    """
    out: dict[str, str] = {}
    for pkg in ("numpy", "pandas", "scikit-learn", "lightgbm"):
        try:
            out[pkg] = importlib.metadata.version(pkg)
        except importlib.metadata.PackageNotFoundError:
            pass
    return out


# ── Internal helpers (private to LightGBMRegressor) ───────────────────────


def _flatten_panel(
    X: np.ndarray,
    *,
    add_rolling_close: bool = True,
    close_idx: int | None = None,
) -> np.ndarray:
    """Flatten an ``(N, L, F)`` panel and append rolling-close stats.

    Rolling stats over the lookback window: mean, std, min, max, last.
    Mirrors the recipe used by the legacy classical T1 baseline.
    """
    if X.ndim != 3:
        raise ValueError(f"_flatten_panel: expected (N, L, F); got {X.shape}")
    n, lb, f = X.shape
    flat = X.reshape(n, lb * f).astype(np.float32)
    if not add_rolling_close or close_idx is None or close_idx < 0 or close_idx >= f:
        return flat
    close = X[:, :, close_idx].astype(np.float32)
    stats = np.stack(
        [
            close.mean(axis=1),
            close.std(axis=1),
            close.min(axis=1),
            close.max(axis=1),
            close[:, -1],
        ],
        axis=1,
    )
    return np.concatenate([flat, stats], axis=1)


def _align_columns(
    X: pd.DataFrame,
    train_columns: list[str],
    *,
    fillna: bool = True,
) -> pd.DataFrame:
    """Align ``X`` to ``train_columns``: add missing as zero, drop extras.

    ``fillna=True`` (legacy / default): zero-fill any remaining NaN cells.
    ``fillna=False``: preserve NaN cells (used by the LightGBM-pipeline
    paths in T2/T5/T7 — LightGBM has native NaN handling and zero-filling
    distorts its learned splits).
    """
    df = X.copy()
    for c in train_columns:
        if c not in df.columns:
            df[c] = 0.0
    df = df[train_columns]
    return df.fillna(0.0) if fillna else df


def _numeric_feature_cols(
    df: pd.DataFrame, prefixes: tuple[str, ...] | None = None,
) -> list[str]:
    """Return numeric columns of ``df``, optionally filtered by prefix."""
    if prefixes is None:
        return [c for c in df.columns if df[c].dtype.kind in "fiub"]
    return [
        c for c in df.columns
        if df[c].dtype.kind in "fiub" and c.startswith(prefixes)
    ]


# ── Concrete method ───────────────────────────────────────────────────────


@register(
    name="lightgbm",
    family="classical",
    tasks=frozenset({"T1", "T2", "T3", "T4", "T5", "T6", "T7"}),
    config_class=LightGBMConfig,
)
class LightGBMRegressor(_JoblibSaveMixin, Method):
    """LightGBM regressor with per-task private dispatch.

    One class, one config (:class:`LightGBMConfig`); the task is fixed at
    construction time. Internally ``fit`` / ``predict`` dispatch on
    ``self.task`` to a private per-task implementation that owns its
    feature-engineering recipe and fitted state.
    """

    name: ClassVar[str] = "lightgbm"
    family: ClassVar[str] = "classical"
    tasks: ClassVar[frozenset[str]] = frozenset(
        {"T1", "T2", "T3", "T4", "T5", "T6", "T7"}
    )
    schema_version: ClassVar[int] = 1

    def __init__(
        self,
        *,
        task: str,
        config: LightGBMConfig | None = None,
        **kwargs: Any,
    ) -> None:
        if task not in self.tasks:
            raise ValueError(
                f"LightGBMRegressor: unsupported task {task!r}; "
                f"supported = {sorted(self.tasks)}"
            )
        self.task = task
        if config is None:
            config = LightGBMConfig(**kwargs) if kwargs else LightGBMConfig()
        elif kwargs:
            raise ValueError(
                "LightGBMRegressor: pass either ``config=`` or kwargs, not both"
            )
        self.config = config
        # Per-task fitted state — populated by .fit().
        self._state: dict[str, Any] = {}

    # ── public API ────────────────────────────────────────────────────────

    def fit(self, X: Any, y: Any, *, seed: int = 42) -> "LightGBMRegressor":
        """Fit the regressor on ``(X, y)``. Returns ``self`` for chaining."""
        self._seed = int(seed)
        if self.task == "T1":
            self._fit_t1(X, y)
        elif self.task in ("T2", "T5"):
            self._fit_t2_t5(X, y)
        elif self.task in ("T3", "T6"):
            self._fit_t3_t6(X, y)
        elif self.task == "T4":
            self._fit_t4(X, y)
        elif self.task == "T7":
            self._fit_t7(X, y)
        else:  # pragma: no cover -- gated by __init__
            raise ValueError(self.task)
        return self

    def predict(self, X: Any) -> np.ndarray | pd.DataFrame:
        if not self._state:
            raise RuntimeError(
                "LightGBMRegressor: call .fit(X, y) before .predict()."
            )
        if self.task == "T1":
            return self._predict_t1(X)
        if self.task in ("T2", "T5"):
            return self._predict_t2_t5(X)
        if self.task in ("T3", "T6"):
            return self._predict_t3_t6(X)
        if self.task == "T4":
            return self._predict_t4(X)
        if self.task == "T7":
            return self._predict_t7(X)
        raise ValueError(self.task)  # pragma: no cover

    def lib_versions(self) -> dict[str, str]:
        return _classical_lib_versions()

    @classmethod
    def default_config(cls) -> LightGBMConfig:
        return LightGBMConfig()

    # ── LightGBM kwarg builder ────────────────────────────────────────────

    def _lgbm_kwargs(self) -> dict[str, Any]:
        """Translate :class:`LightGBMConfig` → ``LGBMRegressor`` kwargs.

        Drops config keys that are not LGBM hyperparameters (e.g.,
        ``t6_text_handling`` is a method-level switch).
        """
        d = self.config.model_dump()
        d.pop("t6_text_handling", None)
        d["random_state"] = getattr(self, "_seed", 42)
        return d

    def _make_lgbm(self) -> Any:
        from lightgbm import LGBMRegressor

        return LGBMRegressor(**self._lgbm_kwargs())

    def _make_log_pipeline(self) -> Any:
        """LightGBM wrapped in a log1p/expm1 target transform.

        Used by T2 / T5 / T7 (positive-target regression).

        Drops the previous ``StandardScaler`` step: LightGBM is
        scale-invariant AND handles NaN natively, while ``StandardScaler``
        does not tolerate NaN. With high-NaN-density T2/T5 inputs (~28%
        NaN), the scaler step would either fail or, after a defensive
        ``np.nan_to_num(...,0)`` upstream, corrupt LightGBM's learned
        missing-direction splits.
        """
        from sklearn.compose import TransformedTargetRegressor

        return TransformedTargetRegressor(
            regressor=self._make_lgbm(), func=np.log1p, inverse_func=np.expm1,
        )

    # ── T1 — multi-output regression on per-window log-returns ────────────

    def _fit_t1(self, X: np.ndarray, y: np.ndarray) -> None:
        """Multi-output regression on log-returns relative to last close.

        T1 close prices span $0.50 to $5,000+ across the 4,416-ticker
        small-cap universe. Training a regressor on raw close prices makes
        per-step error scale with price level — high-price tickers dominate
        the loss, low-price tickers see un-bounded predictions, and
        post-hoc MSE blows up by ten or more orders of magnitude
        (LightGBM T1 hit MSE 1.18e+16 on this codepath before the fix).

        Fix: target the *log-return relative to the per-window last close*::

            c_i = X_i[-1, close_idx]          # last close in window i
            y_log[i, h] = log(y[i, h] / c_i)  # dimensionless O(1) target

        At predict time we exponentiate and rescale by the test window's
        last close. This matches Persistence's tile-last-close behaviour
        as the zero-output limit, and matches what every TSFM (Chronos /
        Moirai / TimesFM) does internally.

        NaN handling: LightGBM still handles NaN inputs natively; we
        scrub +/-inf only. Rows with non-positive c or y are dropped from
        training (log undefined).
        """
        from sklearn.multioutput import MultiOutputRegressor

        if not isinstance(X, np.ndarray) or X.ndim != 3:
            raise ValueError(
                f"T1 fit: expected ndarray X (N, L, F); got "
                f"{type(X).__name__} {getattr(X, 'shape', '?')}"
            )
        if not isinstance(y, np.ndarray) or y.ndim != 2:
            raise ValueError(
                f"T1 fit: expected ndarray y (N, horizon); got "
                f"{type(y).__name__} {getattr(y, 'shape', '?')}"
            )
        if y.shape[0] != X.shape[0]:
            raise ValueError(
                f"T1 fit: y/X length mismatch ({y.shape[0]} vs {X.shape[0]})."
            )
        horizon = int(y.shape[1])

        close_idx = 0
        c = X[:, -1, close_idx].astype(np.float64)  # (N,) last close
        y_f = y.astype(np.float64)                   # (N, H)
        # Drop windows where log target is undefined / unstable.
        keep = (
            np.isfinite(c) & (c > 0.0) &
            np.isfinite(y_f).all(axis=1) & (y_f > 0.0).all(axis=1)
        )
        n_total = int(X.shape[0])
        n_keep = int(keep.sum())
        if n_keep < 1:
            raise RuntimeError(
                f"T1 fit: only {n_keep}/{n_total} training windows have "
                "positive finite close + horizon prices; cannot fit "
                "log-return target."
            )
        X_kept = X[keep]
        c_kept = c[keep]
        y_log = np.log(y_f[keep] / c_kept[:, None]).astype(np.float32)

        X_flat = _flatten_panel(X_kept, add_rolling_close=True, close_idx=close_idx)
        # Scrub +/-inf only (preserve NaN — LightGBM handles it natively).
        X_flat = np.where(np.isposinf(X_flat) | np.isneginf(X_flat),
                          np.nan, X_flat)

        model = MultiOutputRegressor(
            self._make_lgbm(),
            n_jobs=int(self.config.n_jobs),
        )
        model.fit(X_flat, y_log)

        self._state = {
            "model": model,
            "close_idx": close_idx,
            "horizon": horizon,
            "n_features_flat": X_flat.shape[1],
            "n_train_total": n_total,
            "n_train_kept": n_keep,
            # Log-return clip range: [-2, 2] = ~14% to ~700% of last close.
            "log_clip": 2.0,
        }
        self._horizon = horizon

    def _predict_t1(self, X: np.ndarray) -> np.ndarray:
        st = self._state
        if not isinstance(X, np.ndarray) or X.ndim != 3:
            raise ValueError(
                f"T1 predict: expected ndarray (N, L, F); got "
                f"{type(X).__name__} {getattr(X, 'shape', '?')}"
            )
        close_idx = int(st["close_idx"])
        c_test = X[:, -1, close_idx].astype(np.float64)  # (N,)
        # Where last close is missing or non-positive we cannot rescale; tile
        # last close as the safest fallback (matches Persistence in that cell).
        c_safe = np.where(np.isfinite(c_test) & (c_test > 0.0), c_test, np.nan)

        X_flat = _flatten_panel(
            X, add_rolling_close=True, close_idx=close_idx,
        )
        X_flat = np.where(np.isposinf(X_flat) | np.isneginf(X_flat),
                          np.nan, X_flat)
        n_train_cols = st["n_features_flat"]
        if X_flat.shape[1] > n_train_cols:
            X_flat = X_flat[:, :n_train_cols]
        elif X_flat.shape[1] < n_train_cols:
            pad = np.zeros(
                (X_flat.shape[0], n_train_cols - X_flat.shape[1]),
                dtype=np.float32,
            )
            X_flat = np.concatenate([X_flat, pad], axis=1)

        log_pred = st["model"].predict(X_flat).astype(np.float64)
        if log_pred.ndim == 1:
            log_pred = log_pred.reshape(-1, st["horizon"])
        clip = float(st.get("log_clip", 2.0))
        log_pred = np.clip(log_pred, -clip, clip)
        # Rescale: y_pred = c * exp(log_return).
        out = c_safe[:, None] * np.exp(log_pred)
        # Where c was missing, fall back to last-close tile (NaN here would
        # poison the eval; prefer Persistence-equivalent in degenerate cells).
        bad = ~np.isfinite(out)
        if bad.any():
            tile = np.broadcast_to(c_test[:, None], out.shape).astype(np.float64)
            out = np.where(bad, tile, out)
        return out.astype(np.float32)

    # ── T2 / T5 — log-target regression ───────────────────────────────────

    def _fit_t2_t5(self, X: pd.DataFrame, y: np.ndarray) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected DataFrame X; got {type(X).__name__}"
            )

        # T2 carries stmt_* + derived_* + macro snapshot (fred_*/eia_*);
        # T5 is price-stripped (no derived_*) but keeps the macro snapshot.
        # Prefix-picking collapses both into one code path.
        if self.task == "T2":
            prefixes: tuple[str, ...] = ("stmt_", "derived_", "fred_", "eia_")
        else:
            prefixes = ("stmt_", "fred_", "eia_")

        df = X.copy()
        feat_cols = [
            c for c in _numeric_feature_cols(df, prefixes)
            if c not in {"derived_market_cap", "actual_market_cap"}
        ]

        # Sector / industry one-hot — same construction as the legacy code.
        if "sector" in df.columns:
            sec_dummies = pd.get_dummies(
                df["sector"], prefix="sector", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), sec_dummies.reset_index(drop=True)],
                axis=1,
            )
            feat_cols += list(sec_dummies.columns)
        if "industry" in df.columns:
            ind_dummies = pd.get_dummies(
                df["industry"], prefix="industry", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), ind_dummies.reset_index(drop=True)],
                axis=1,
            )
            feat_cols += list(ind_dummies.columns)

        # Cast numeric features to float32 WITHOUT zero-filling NaN —
        # LightGBM handles NaN natively; zero-filling 28% of T2/T5 cells
        # corrupted the learned missing-direction splits.
        X_feat = df[feat_cols].astype(np.float32)
        y_arr = pd.to_numeric(pd.Series(np.asarray(y).ravel()), errors="coerce").astype(
            np.float64
        )
        valid = y_arr.notna() & (y_arr > 0)
        X_feat = X_feat.loc[valid.values]
        y_arr = y_arr.loc[valid.values]
        if X_feat.empty:
            raise RuntimeError(
                f"{self.task} fit: no rows with positive market_cap after drop."
            )

        # Scrub only +/-inf; LightGBM rejects non-finite-non-NaN values
        # but tolerates NaN.
        X_arr = X_feat.values.astype(np.float32)
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)

        model = self._make_log_pipeline()
        model.fit(X_arr, y_arr.values)

        self._state = {
            "model": model,
            "feat_cols": feat_cols,
            "prefixes": prefixes,
        }

    def _predict_t2_t5(self, X: pd.DataFrame) -> np.ndarray:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} predict: expected DataFrame; got {type(X).__name__}"
            )
        df = X.copy()
        if "sector" in df.columns:
            sec_dummies = pd.get_dummies(
                df["sector"], prefix="sector", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), sec_dummies.reset_index(drop=True)],
                axis=1,
            )
        if "industry" in df.columns:
            ind_dummies = pd.get_dummies(
                df["industry"], prefix="industry", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), ind_dummies.reset_index(drop=True)],
                axis=1,
            )

        X_feat = _align_columns(
            df, st["feat_cols"], fillna=False,
        ).astype(np.float32)
        # Preserve NaN for LightGBM (matches the fit-time distribution);
        # scrub only +/-inf.
        X_arr = X_feat.values.astype(np.float32)
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)
        # TransformedTargetRegressor inverts log1p → expm1 internally.
        preds = st["model"].predict(X_arr)
        # Floor at zero to keep the (positive) market-cap interpretation.
        preds = np.clip(preds, 0.0, None)
        return preds.astype(np.float32)

    # ── T3 / T6 — per-field booster ensemble ──────────────────────────────

    def _t3_t6_build_ticker_features(
        self, X: pd.DataFrame,
    ) -> tuple[pd.DataFrame, list[str]]:
        """Return ``(per-ticker numeric+sector features DataFrame, feat_cols)``.

        Mirrors the legacy ``run_task_3_classical`` recipe:

        * Pick numeric columns excluding ``fiscal_year`` (the join key)
          and ``value_num`` (an internal scratch column).
        * For T6 with ``t6_text_handling="sector_industry_only"``, the NL
          ``company_description`` column is dropped here implicitly because
          string columns are non-numeric (and the explicit drop below is a
          belt-and-braces guard).
        * One-hot-encode ``sector`` (prefix=``sec``) deduped per ticker.
        """
        df = X.copy()

        # Defensive: explicitly drop free-text columns (T6) so they cannot
        # accidentally leak into a future dtype check.
        for text_col in ("company_description",):
            if text_col in df.columns:
                df = df.drop(columns=[text_col])

        ticker_feat_cols = [
            c for c in _numeric_feature_cols(df)
            if c not in {"fiscal_year"} and c != "value_num"
        ]
        ticker_feats = df[["ticker"] + ticker_feat_cols].copy()
        ticker_feats[ticker_feat_cols] = (
            ticker_feats[ticker_feat_cols].astype(np.float32).fillna(0.0)
        )
        if "sector" in df.columns:
            sec_dummies = pd.get_dummies(
                df.set_index("ticker")["sector"], prefix="sec", dtype=np.float32,
            )
            sec_dummies = sec_dummies.reset_index().drop_duplicates("ticker")
            ticker_feats = (
                ticker_feats.drop_duplicates("ticker")
                .merge(sec_dummies, on="ticker", how="left")
                .fillna(0.0)
            )
        else:
            ticker_feats = ticker_feats.drop_duplicates("ticker")

        feat_cols = [c for c in ticker_feats.columns if c != "ticker"]
        return ticker_feats, feat_cols

    def _fit_t3_t6(self, X: pd.DataFrame, y: pd.DataFrame) -> None:
        """Fit a SINGLE LightGBM that takes per-(ticker, fiscal_year)
        numeric features concatenated with a sparse one-hot of ``field``,
        predicting the scalar ``value``.

        Output panel: one row per (ticker, fiscal_year, fitted_field) at
        predict time. Per-field median fallback is kept for fields whose
        training pool is too small (``<5`` rows) to fit.
        """
        from scipy.sparse import csr_matrix, hstack as sparse_hstack
        from sklearn.preprocessing import OneHotEncoder

        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected DataFrame X; got {type(X).__name__}"
            )
        if not isinstance(y, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected long-form DataFrame y; got "
                f"{type(y).__name__}"
            )
        for col in ("ticker", "field", "value"):
            if col not in y.columns:
                raise ValueError(
                    f"{self.task} fit: y missing required column {col!r}"
                )

        ticker_feats, feat_cols = self._t3_t6_build_ticker_features(X)

        # Lock the field set to ``sorted(y["field"].unique())`` (per plan §1).
        fitted_fields: list[str] = sorted(
            str(f) for f in y["field"].astype(str).unique()
        )

        # Long-form labels joined with per-ticker features (broadcast across
        # fiscal years). Drop rows with no parseable target.
        gt = y.copy()
        gt["value_num"] = pd.to_numeric(gt["value"], errors="coerce")
        gt["field"] = gt["field"].astype(str)
        gt = gt.merge(ticker_feats, on="ticker", how="left").fillna(0.0)
        gt = gt.dropna(subset=["value_num"])

        # Per-field median fallback for fields with too few rows (the
        # legacy small-pool guard, preserved field-by-field).
        per_field_count = gt.groupby("field").size().to_dict()
        median_fields: dict[str, float] = {}
        small_field_set: set[str] = set()
        for field in fitted_fields:
            cnt = int(per_field_count.get(field, 0))
            if cnt < 5:
                sub = gt[gt["field"] == field]
                med = float(sub["value_num"].median()) if not sub.empty else 0.0
                median_fields[field] = med
                small_field_set.add(field)

        train_mask = ~gt["field"].isin(small_field_set)
        gt_train = gt[train_mask]

        # Decide a global log-transform heuristic (preserves legacy magnitude
        # gate) using the pooled non-zero target distribution.
        global_model = None
        global_scaler = None
        use_log = False
        y_min = 0.0
        y_max = 0.0
        y_range = 1.0
        ohe: OneHotEncoder | None = None

        if not gt_train.empty:
            y_tr = gt_train["value_num"].values.astype(np.float64)
            nz = y_tr[y_tr != 0]
            if nz.size > 0:
                use_log = float(np.median(np.abs(nz))) > 1000
            y_tr_t = (
                np.sign(y_tr) * np.log1p(np.abs(y_tr)) if use_log else y_tr
            )

            # Per-ticker numerics already pass through ``.fillna(0.0)`` in
            # ``_t3_t6_build_ticker_features``, so the row-side has no NaN.
            # We still scrub +/-inf defensively before LightGBM.
            X_num_tr = gt_train[feat_cols].values.astype(np.float32)
            X_num_tr = np.where(
                np.isposinf(X_num_tr) | np.isneginf(X_num_tr),
                np.nan, X_num_tr,
            )

            # Sparse one-hot of field id. Use the LOCKED fitted_fields set
            # as categories so the predict path can encode every field
            # (even those whose training pool was too small to fit; for
            # those we override with the median anyway).
            ohe = OneHotEncoder(
                categories=[fitted_fields],
                handle_unknown="ignore",
                sparse_output=True,
                dtype=np.float32,
            )
            field_arr_tr = gt_train["field"].values.reshape(-1, 1)
            X_field_tr = ohe.fit_transform(field_arr_tr)

            X_full_tr = sparse_hstack(
                [csr_matrix(X_num_tr), X_field_tr], format="csr",
            )

            # LightGBM accepts sparse CSR. Use a single booster.
            global_model = self._make_lgbm()
            global_model.fit(X_full_tr, y_tr_t)

            y_min = float(y_tr.min())
            y_max = float(y_tr.max())
            y_range = max(abs(y_max - y_min), abs(y_max) * 0.1, 1.0)

        self._state = {
            "ticker_feats": ticker_feats,
            "feat_cols": feat_cols,
            "fitted_fields": fitted_fields,
            "median_fields": median_fields,
            "model": global_model,
            "scaler": global_scaler,  # kept for compat (always None now)
            "ohe": ohe,
            "use_log": use_log,
            "y_min": y_min,
            "y_max": y_max,
            "y_range": y_range,
        }
        self.fitted_fields = fitted_fields  # public, per plan §1

    def _predict_t3_t6(self, X: pd.DataFrame) -> pd.DataFrame:
        """Predict every (ticker, fiscal_year, fitted_field) cell with the
        single shared LightGBM (numeric features + sparse field one-hot),
        falling back to per-field medians for small-pool fields tagged at
        fit time.
        """
        from scipy.sparse import csr_matrix, hstack as sparse_hstack

        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} predict: expected DataFrame; got {type(X).__name__}"
            )
        if "ticker" not in X.columns or "fiscal_year" not in X.columns:
            raise ValueError(
                f"{self.task} predict: X must include 'ticker' and "
                f"'fiscal_year'."
            )

        fitted_fields: list[str] = st["fitted_fields"]
        median_fields: dict[str, float] = st["median_fields"]
        feat_cols: list[str] = st["feat_cols"]

        # Build test-side per-ticker features using the SAME recipe as fit,
        # then align columns to the train feature schema.
        test_feats, _ = self._t3_t6_build_ticker_features(X)
        test_feats = test_feats.set_index("ticker")
        for c in feat_cols:
            if c not in test_feats.columns:
                test_feats[c] = 0.0
        test_feats = test_feats[feat_cols].fillna(0.0)

        # One row per (test row × every fitted field) — long-form predict.
        # We assemble the predict matrix as the cross-product of test rows
        # and fitted fields, run a single batched .predict, then map back.
        n_rows = len(X)
        n_fields = len(fitted_fields)
        rows: list[dict[str, Any]] = []
        if n_rows == 0 or n_fields == 0:
            return pd.DataFrame(
                rows, columns=["ticker", "fiscal_year", "field", "pred"],
            )

        # Pre-fetch numeric features per test row.
        ticker_arr = X["ticker"].astype(str).values
        fy_arr = X["fiscal_year"].values
        # For tickers absent from training-side ticker_feats the feature
        # vector is zero (matches the legacy behaviour for unseen tickers).
        zero_vec = np.zeros(len(feat_cols), dtype=np.float32)
        feats_by_ticker: dict[str, np.ndarray] = {}
        for t in set(ticker_arr.tolist()):
            try:
                v = test_feats.loc[t]
                if isinstance(v, pd.DataFrame):
                    v = v.iloc[0]
                feats_by_ticker[t] = np.asarray(
                    v.values, dtype=np.float32,
                )
            except KeyError:
                feats_by_ticker[t] = zero_vec

        # Decide which (row, field) cells get the model vs. a median fallback.
        model = st["model"]
        # Note: ``scaler`` slot exists in state for save/load compat but is
        # always None now — LightGBM is scale-invariant so we dropped it.
        ohe = st["ohe"]
        use_log = bool(st["use_log"])
        y_min = float(st["y_min"])
        y_max = float(st["y_max"])
        y_range = float(st["y_range"])

        # Cache one prediction per unique (ticker, field) cell to avoid
        # duplicating the model call across the same ticker repeated for
        # multiple fiscal years.
        unique_tickers = list(dict.fromkeys(ticker_arr.tolist()))
        # Build the model batch only for fields with a fitted booster.
        model_fields = (
            [f for f in fitted_fields if f not in median_fields]
            if model is not None else []
        )
        cell_pred: dict[tuple[str, str], float] = {}
        if model is not None and model_fields and unique_tickers:
            # Cross-product matrix: rows = (ticker × model_field) pairs.
            # Per-ticker numerics are already imputed to 0 in
            # ``_t3_t6_build_ticker_features`` (predict-side mirrors fit);
            # we only scrub +/-inf defensively for LightGBM.
            X_num = np.stack(
                [feats_by_ticker[t] for t in unique_tickers], axis=0,
            ).astype(np.float32)
            X_num = np.where(
                np.isposinf(X_num) | np.isneginf(X_num),
                np.nan, X_num,
            )
            # Repeat per model-field so the one-hot lookup aligns 1-to-1.
            n_t = len(unique_tickers)
            n_mf = len(model_fields)
            X_num_rep = np.repeat(X_num, n_mf, axis=0)
            field_arr = np.tile(
                np.asarray(model_fields, dtype=object), n_t,
            ).reshape(-1, 1)
            assert ohe is not None  # set whenever model is set
            X_field = ohe.transform(field_arr)
            X_full = sparse_hstack(
                [csr_matrix(X_num_rep), X_field], format="csr",
            )
            y_pred_t = np.asarray(model.predict(X_full))
            if use_log:
                y_pred = np.sign(y_pred_t) * np.expm1(np.abs(y_pred_t))
            else:
                y_pred = y_pred_t
            y_pred = np.clip(y_pred, y_min - y_range, y_max + y_range)
            # Re-shape into (n_t, n_mf) for cell-key indexing.
            y_pred = y_pred.reshape(n_t, n_mf)
            for ti, t in enumerate(unique_tickers):
                for fi, f in enumerate(model_fields):
                    cell_pred[(t, f)] = float(y_pred[ti, fi])

        for i in range(n_rows):
            t = ticker_arr[i]
            fy = fy_arr[i]
            for field in fitted_fields:
                if field in median_fields:
                    val = median_fields[field]
                else:
                    val = cell_pred.get((t, field), 0.0)
                rows.append({
                    "ticker": t,
                    "fiscal_year": fy,
                    "field": field,
                    "pred": float(val),
                })
        return pd.DataFrame(
            rows, columns=["ticker", "fiscal_year", "field", "pred"],
        )

    # ── T4 — flat-lookback + event-type one-hot ───────────────────────────

    @staticmethod
    def _t4_flatten_lookback(lb_col: pd.Series) -> tuple[np.ndarray, int]:
        """Stack object-dtype ``lookback`` cells into ``(N, L*F)`` float32.

        Each cell is a ``(L, F)`` ndarray. Empty / malformed cells are
        replaced with zeros sized to the modal panel shape.
        """
        arrays: list[np.ndarray] = []
        for cell in lb_col.values:
            if isinstance(cell, np.ndarray) and cell.ndim == 2:
                arrays.append(cell.astype(np.float32))
        if not arrays:
            raise RuntimeError(
                "T4: no usable lookback ndarrays in X['lookback']."
            )
        L = arrays[0].shape[0]
        F = arrays[0].shape[1]
        flat_rows: list[np.ndarray] = []
        for cell in lb_col.values:
            if isinstance(cell, np.ndarray) and cell.shape == (L, F):
                flat_rows.append(cell.reshape(-1).astype(np.float32))
            else:
                flat_rows.append(np.zeros(L * F, dtype=np.float32))
        flat = np.stack(flat_rows, axis=0)
        return flat, L * F

    def _fit_t4(self, X: pd.DataFrame, y: np.ndarray) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T4 fit: expected DataFrame X; got {type(X).__name__}"
            )
        if "lookback" not in X.columns or "event_type" not in X.columns:
            raise ValueError(
                "T4 fit: X must include 'lookback' and 'event_type' columns."
            )

        flat, n_lb_flat = self._t4_flatten_lookback(X["lookback"])

        et_arr = X["event_type"].astype(str).values
        et_dummies = pd.get_dummies(
            pd.Series(et_arr), prefix="evt", dtype=np.float32,
        )
        X_arr = np.concatenate(
            [flat, et_dummies.values.astype(np.float32)], axis=1,
        )
        # Preserve NaN for LightGBM; scrub only +/-inf.
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)

        y_arr = np.asarray(y, dtype=np.float32).ravel()
        if y_arr.shape[0] != X_arr.shape[0]:
            raise ValueError(
                f"T4 fit: y/X length mismatch ({y_arr.shape[0]} vs "
                f"{X_arr.shape[0]})."
            )

        model = self._make_lgbm()
        model.fit(X_arr, y_arr)

        self._state = {
            "model": model,
            "evt_columns": list(et_dummies.columns),
            "n_lb_flat": int(n_lb_flat),
        }

    def _predict_t4(self, X: pd.DataFrame) -> np.ndarray:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T4 predict: expected DataFrame; got {type(X).__name__}"
            )
        if "lookback" not in X.columns or "event_type" not in X.columns:
            raise ValueError(
                "T4 predict: X must include 'lookback' and 'event_type'."
            )

        flat, _ = self._t4_flatten_lookback(X["lookback"])

        # Align lookback flat-width to train (defensive — tolerate small
        # column drift coming from a slightly-different feature panel).
        if flat.shape[1] > st["n_lb_flat"]:
            flat = flat[:, : st["n_lb_flat"]]
        elif flat.shape[1] < st["n_lb_flat"]:
            pad = np.zeros(
                (flat.shape[0], st["n_lb_flat"] - flat.shape[1]),
                dtype=np.float32,
            )
            flat = np.concatenate([flat, pad], axis=1)

        et_arr = X["event_type"].astype(str).values
        et_dummies = pd.get_dummies(
            pd.Series(et_arr), prefix="evt", dtype=np.float32,
        )
        for c in st["evt_columns"]:
            if c not in et_dummies.columns:
                et_dummies[c] = 0.0
        et_dummies = et_dummies[st["evt_columns"]].fillna(0.0)

        X_arr = np.concatenate(
            [flat, et_dummies.values.astype(np.float32)], axis=1,
        )
        # Preserve NaN for LightGBM; scrub only +/-inf.
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)
        return st["model"].predict(X_arr).astype(np.float32)

    # ── T7 — dual-output (rent, price) ────────────────────────────────────

    @staticmethod
    def _t7_first_col(
        df: pd.DataFrame, candidates: tuple[str, ...],
    ) -> str | None:
        """Return the first column whose name (lowercased) contains any of
        ``candidates`` (substring match), else ``None``.
        """
        for c in df.columns:
            cl = c.lower()
            if any(cand in cl for cand in candidates):
                return c
        return None

    def _t7_build_features(
        self, X: pd.DataFrame, *, fit: bool,
    ) -> tuple[pd.DataFrame, list[str], str | None]:
        """Build the property-feature frame; returns ``(X_feat, feat_cols,
        prop_type_col)``.

        Same numeric feature recipe as the legacy T7 baseline.
        """
        df = X.copy()
        feat_cols: list[str] = []
        for col in (
            "sqft", "squareFootage", "square_footage",
            "beds", "bedrooms", "baths", "bathrooms",
            "year_built", "yearBuilt",
            "years_since_last_sale",
        ):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")
                feat_cols.append(col)

        prop_type_col = next(
            (c for c in ("property_type", "propertyType", "type")
             if c in df.columns),
            None,
        )
        if prop_type_col:
            prop_dummies = pd.get_dummies(
                df[prop_type_col], prefix="ptype", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), prop_dummies.reset_index(drop=True)],
                axis=1,
            )
            feat_cols += list(prop_dummies.columns)

        if not feat_cols:
            raise RuntimeError(
                f"T7 {'fit' if fit else 'predict'}: no usable property features."
            )
        return df, feat_cols, prop_type_col

    def _fit_t7(self, X: pd.DataFrame, y: pd.DataFrame) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T7 fit: expected DataFrame X; got {type(X).__name__}"
            )
        if "address" not in X.columns:
            raise ValueError("T7 fit: X must include 'address'.")

        df, feat_cols, prop_type_col = self._t7_build_features(X, fit=True)

        # Targets: prefer rent / price columns already on X (canonical T7
        # train carries them); fall back to a per-address merge with y.
        rent_col = self._t7_first_col(df, ("rent",))
        price_col = self._t7_first_col(df, ("price", "lastsaleprice"))
        if isinstance(y, pd.DataFrame) and "address" in y.columns:
            if rent_col is None and "rent" in y.columns:
                df = df.merge(
                    y[["address", "rent"]], on="address", how="left",
                )
                rent_col = "rent"
            if price_col is None and "price" in y.columns:
                df = df.merge(
                    y[["address", "price"]], on="address", how="left",
                )
                price_col = "price"
        if rent_col is None and price_col is None:
            raise RuntimeError("T7 fit: no rent or price target found.")

        # Preserve NaN for LightGBM's native missing-value handling;
        # scrub only +/-inf (LightGBM rejects them but tolerates NaN).
        X_feat = df[feat_cols].astype(np.float32)
        X_arr = X_feat.values.astype(np.float32)
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)

        models: dict[str, Any] = {}
        for target_name, target_col in (("rent", rent_col), ("price", price_col)):
            if target_col is None:
                continue
            y_all = pd.to_numeric(df[target_col], errors="coerce")
            valid = y_all.notna() & (y_all > 0)
            n_valid = int(valid.sum())
            if n_valid < 1:
                continue
            X_tr = X_arr[valid.values]
            y_tr = y_all.loc[valid].values.astype(np.float64)
            if n_valid < 2:
                # LightGBM rejects n<2; emit a constant-predictor (the single
                # training value) so eval is well-defined. Real T7 trains
                # have ~10k rows; this branch only triggers in micro-scale
                # smoke runs where dedup-by-address leaves 1 row.
                models[target_name] = ("constant", float(y_tr.mean()))
                continue
            m = self._make_log_pipeline()
            m.fit(X_tr, y_tr)
            models[target_name] = m

        if not models:
            raise RuntimeError(
                f"T7 fit: insufficient training data "
                f"(rent_col={rent_col!r}, price_col={price_col!r}, "
                f"n_rows={len(df)}); need >=1 row with a positive target."
            )

        self._state = {
            "feat_cols": feat_cols,
            "prop_type_col": prop_type_col,
            "models": models,
        }

    def _predict_t7(self, X: pd.DataFrame) -> pd.DataFrame:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T7 predict: expected DataFrame; got {type(X).__name__}"
            )
        if "address" not in X.columns:
            raise ValueError("T7 predict: X must include 'address'.")

        df = X.copy()
        for col in (
            "sqft", "squareFootage", "square_footage",
            "beds", "bedrooms", "baths", "bathrooms",
            "year_built", "yearBuilt",
            "years_since_last_sale",
        ):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")

        prop_type_col = st["prop_type_col"]
        if prop_type_col and prop_type_col in df.columns:
            prop_dummies = pd.get_dummies(
                df[prop_type_col], prefix="ptype", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), prop_dummies.reset_index(drop=True)],
                axis=1,
            )

        X_feat = _align_columns(
            df, st["feat_cols"], fillna=False,
        ).astype(np.float32)
        X_arr = X_feat.values.astype(np.float32)
        X_arr = np.where(np.isposinf(X_arr) | np.isneginf(X_arr),
                         np.nan, X_arr)

        out = pd.DataFrame({"address": X["address"].astype(str).values})
        for target_name in ("rent", "price"):
            model = st["models"].get(target_name)
            if model is None:
                out[f"pred_{target_name}"] = np.full(
                    len(X), np.nan, dtype=np.float32,
                )
                continue
            if isinstance(model, tuple) and model[0] == "constant":
                out[f"pred_{target_name}"] = np.full(
                    len(X), float(model[1]), dtype=np.float32,
                )
                continue
            preds = model.predict(X_arr)
            preds = np.clip(preds, 0.0, None)
            out[f"pred_{target_name}"] = preds.astype(np.float32)
        return out


# ── RandomForest variant ──────────────────────────────────────────────────


def _make_rf_estimator(
    cfg: RandomForestConfig, *, seed: int,
) -> Any:
    """Build a fresh sklearn ``RandomForestRegressor`` from ``cfg``.

    Lazy-imports sklearn so the import-time cost is only paid when the
    classical/random_forest method is actually instantiated.
    """
    from sklearn.ensemble import RandomForestRegressor

    return RandomForestRegressor(
        n_estimators=int(cfg.n_estimators),
        max_depth=cfg.max_depth,
        min_samples_leaf=int(cfg.min_samples_leaf),
        n_jobs=int(cfg.n_jobs),
        random_state=int(seed),
    )


def _make_rf_log_pipeline(
    cfg: RandomForestConfig, *, seed: int,
) -> Any:
    """RandomForest wrapped in a log1p/expm1 target transform.

    Used by T2 / T5 / T7 (positive-target regression). Mirrors
    :meth:`LightGBMRegressor._make_log_pipeline` but with sklearn's RF
    as the regressor. RF is scale-invariant; no StandardScaler step.
    """
    from sklearn.compose import TransformedTargetRegressor

    return TransformedTargetRegressor(
        regressor=_make_rf_estimator(cfg, seed=seed),
        func=np.log1p,
        inverse_func=np.expm1,
    )


@register(
    name="random_forest",
    family="classical",
    tasks=frozenset({"T1", "T2", "T3", "T4", "T5", "T6", "T7"}),
    config_class=RandomForestConfig,
)
class RandomForestMethod(_JoblibSaveMixin, Method):
    """sklearn RandomForestRegressor with per-task private dispatch.

    Mirrors :class:`LightGBMRegressor` per-task adapter shape (T1..T7) but
    swaps the base estimator for ``sklearn.ensemble.RandomForestRegressor``.
    Reuses the module-level helpers (``_flatten_panel``, ``_align_columns``,
    ``_numeric_feature_cols``, plus the ``_t3_t6_build_ticker_features`` /
    ``_t7_build_features`` helpers from :class:`LightGBMRegressor`).

    Key behaviour differences from LightGBM:

    * sklearn RandomForest does NOT handle NaN inputs natively; every fit /
      predict path zero-fills NaN before calling the estimator.
    * sklearn RandomForest does NOT accept sparse CSR matrices on the T3/T6
      ensemble path; we densify with ``.toarray()`` before fit/predict
      (acceptable for the small-pool T3/T6 train sets).
    * The class is named ``RandomForestMethod`` (not
      ``RandomForestRegressor``) to avoid the name collision with
      ``sklearn.ensemble.RandomForestRegressor``.
    """

    name: ClassVar[str] = "random_forest"
    family: ClassVar[str] = "classical"
    tasks: ClassVar[frozenset[str]] = frozenset(
        {"T1", "T2", "T3", "T4", "T5", "T6", "T7"}
    )
    schema_version: ClassVar[int] = 1

    def __init__(
        self,
        *,
        task: str,
        config: RandomForestConfig | None = None,
        **kwargs: Any,
    ) -> None:
        if task not in self.tasks:
            raise ValueError(
                f"RandomForestMethod: unsupported task {task!r}; "
                f"supported = {sorted(self.tasks)}"
            )
        self.task = task
        if config is None:
            config = (
                RandomForestConfig(**kwargs) if kwargs else RandomForestConfig()
            )
        elif kwargs:
            raise ValueError(
                "RandomForestMethod: pass either ``config=`` or kwargs, not both"
            )
        self.config = config
        self._state: dict[str, Any] = {}

    # ── public API ────────────────────────────────────────────────────────

    def fit(self, X: Any, y: Any, *, seed: int = 42) -> "RandomForestMethod":
        self._seed = int(seed)
        if self.task == "T1":
            self._fit_t1(X, y)
        elif self.task in ("T2", "T5"):
            self._fit_t2_t5(X, y)
        elif self.task in ("T3", "T6"):
            self._fit_t3_t6(X, y)
        elif self.task == "T4":
            self._fit_t4(X, y)
        elif self.task == "T7":
            self._fit_t7(X, y)
        else:  # pragma: no cover -- gated by __init__
            raise ValueError(self.task)
        return self

    def predict(self, X: Any) -> np.ndarray | pd.DataFrame:
        if not self._state:
            raise RuntimeError(
                "RandomForestMethod: call .fit(X, y) before .predict()."
            )
        if self.task == "T1":
            return self._predict_t1(X)
        if self.task in ("T2", "T5"):
            return self._predict_t2_t5(X)
        if self.task in ("T3", "T6"):
            return self._predict_t3_t6(X)
        if self.task == "T4":
            return self._predict_t4(X)
        if self.task == "T7":
            return self._predict_t7(X)
        raise ValueError(self.task)  # pragma: no cover

    def lib_versions(self) -> dict[str, str]:
        return _classical_lib_versions()

    @classmethod
    def default_config(cls) -> RandomForestConfig:
        return RandomForestConfig()

    # ── helpers (re-use LightGBM's T3/T6 + T7 feature builders) ───────────

    def _t3_t6_build_ticker_features(
        self, X: pd.DataFrame,
    ) -> tuple[pd.DataFrame, list[str]]:
        return LightGBMRegressor._t3_t6_build_ticker_features(self, X)

    def _t7_build_features(
        self, X: pd.DataFrame, *, fit: bool,
    ) -> tuple[pd.DataFrame, list[str], str | None]:
        return LightGBMRegressor._t7_build_features(self, X, fit=fit)

    @staticmethod
    def _t4_flatten_lookback(lb_col: pd.Series) -> tuple[np.ndarray, int]:
        return LightGBMRegressor._t4_flatten_lookback(lb_col)

    @staticmethod
    def _t7_first_col(
        df: pd.DataFrame, candidates: tuple[str, ...],
    ) -> str | None:
        return LightGBMRegressor._t7_first_col(df, candidates)

    # ── T1 — multi-output regression on per-window log-returns ────────────

    def _fit_t1(self, X: np.ndarray, y: np.ndarray) -> None:
        """Multi-output log-return regression; mirrors LightGBM T1.

        sklearn RandomForest natively supports multi-output targets — fit
        ONE forest with ``y.shape == (N, horizon)`` rather than wrapping in
        ``MultiOutputRegressor`` (which trains horizon-many forests
        sequentially). The ONE-forest path is ~horizon× faster and matches
        what sklearn's reference docs recommend for vector-valued targets.

        sklearn RandomForest does NOT handle NaN natively, so we zero-fill
        any NaN cells before fitting (in addition to scrubbing +/-inf).
        """
        if not isinstance(X, np.ndarray) or X.ndim != 3:
            raise ValueError(
                f"T1 fit: expected ndarray X (N, L, F); got "
                f"{type(X).__name__} {getattr(X, 'shape', '?')}"
            )
        if not isinstance(y, np.ndarray) or y.ndim != 2:
            raise ValueError(
                f"T1 fit: expected ndarray y (N, horizon); got "
                f"{type(y).__name__} {getattr(y, 'shape', '?')}"
            )
        if y.shape[0] != X.shape[0]:
            raise ValueError(
                f"T1 fit: y/X length mismatch ({y.shape[0]} vs {X.shape[0]})."
            )
        horizon = int(y.shape[1])

        close_idx = 0
        c = X[:, -1, close_idx].astype(np.float64)
        y_f = y.astype(np.float64)
        keep = (
            np.isfinite(c) & (c > 0.0) &
            np.isfinite(y_f).all(axis=1) & (y_f > 0.0).all(axis=1)
        )
        n_total = int(X.shape[0])
        n_keep = int(keep.sum())
        if n_keep < 1:
            raise RuntimeError(
                f"T1 fit: only {n_keep}/{n_total} training windows have "
                "positive finite close + horizon prices; cannot fit "
                "log-return target."
            )
        X_kept = X[keep]
        c_kept = c[keep]
        y_log = np.log(y_f[keep] / c_kept[:, None]).astype(np.float32)

        X_flat = _flatten_panel(
            X_kept, add_rolling_close=True, close_idx=close_idx,
        )
        # Scrub +/-inf AND zero-fill NaN — sklearn RF rejects all non-finite.
        X_flat = np.nan_to_num(
            X_flat, nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)

        # NATIVE multi-output: one RF tree set predicts all horizon steps.
        model = _make_rf_estimator(self.config, seed=self._seed)
        model.fit(X_flat, y_log)

        self._state = {
            "model": model,
            "close_idx": close_idx,
            "horizon": horizon,
            "n_features_flat": X_flat.shape[1],
            "n_train_total": n_total,
            "n_train_kept": n_keep,
            "log_clip": 2.0,
        }
        self._horizon = horizon

    def _predict_t1(self, X: np.ndarray) -> np.ndarray:
        st = self._state
        if not isinstance(X, np.ndarray) or X.ndim != 3:
            raise ValueError(
                f"T1 predict: expected ndarray (N, L, F); got "
                f"{type(X).__name__} {getattr(X, 'shape', '?')}"
            )
        close_idx = int(st["close_idx"])
        c_test = X[:, -1, close_idx].astype(np.float64)
        c_safe = np.where(np.isfinite(c_test) & (c_test > 0.0), c_test, np.nan)

        X_flat = _flatten_panel(
            X, add_rolling_close=True, close_idx=close_idx,
        )
        # Zero-fill NaN and scrub +/-inf — sklearn RF cannot handle them.
        X_flat = np.nan_to_num(
            X_flat, nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)
        n_train_cols = st["n_features_flat"]
        if X_flat.shape[1] > n_train_cols:
            X_flat = X_flat[:, :n_train_cols]
        elif X_flat.shape[1] < n_train_cols:
            pad = np.zeros(
                (X_flat.shape[0], n_train_cols - X_flat.shape[1]),
                dtype=np.float32,
            )
            X_flat = np.concatenate([X_flat, pad], axis=1)

        log_pred = st["model"].predict(X_flat).astype(np.float64)
        if log_pred.ndim == 1:
            log_pred = log_pred.reshape(-1, st["horizon"])
        clip = float(st.get("log_clip", 2.0))
        log_pred = np.clip(log_pred, -clip, clip)
        out = c_safe[:, None] * np.exp(log_pred)
        bad = ~np.isfinite(out)
        if bad.any():
            tile = np.broadcast_to(c_test[:, None], out.shape).astype(np.float64)
            out = np.where(bad, tile, out)
        return out.astype(np.float32)

    # ── T2 / T5 — log-target regression ───────────────────────────────────

    def _fit_t2_t5(self, X: pd.DataFrame, y: np.ndarray) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected DataFrame X; got {type(X).__name__}"
            )

        if self.task == "T2":
            prefixes: tuple[str, ...] = ("stmt_", "derived_", "fred_", "eia_")
        else:
            prefixes = ("stmt_", "fred_", "eia_")

        df = X.copy()
        feat_cols = [
            c for c in _numeric_feature_cols(df, prefixes)
            if c not in {"derived_market_cap", "actual_market_cap"}
        ]

        if "sector" in df.columns:
            sec_dummies = pd.get_dummies(
                df["sector"], prefix="sector", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), sec_dummies.reset_index(drop=True)],
                axis=1,
            )
            feat_cols += list(sec_dummies.columns)
        if "industry" in df.columns:
            ind_dummies = pd.get_dummies(
                df["industry"], prefix="industry", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), ind_dummies.reset_index(drop=True)],
                axis=1,
            )
            feat_cols += list(ind_dummies.columns)

        X_feat = df[feat_cols].astype(np.float32)
        y_arr = pd.to_numeric(
            pd.Series(np.asarray(y).ravel()), errors="coerce",
        ).astype(np.float64)
        valid = y_arr.notna() & (y_arr > 0)
        X_feat = X_feat.loc[valid.values]
        y_arr = y_arr.loc[valid.values]
        if X_feat.empty:
            raise RuntimeError(
                f"{self.task} fit: no rows with positive market_cap after drop."
            )

        # sklearn RF rejects NaN/inf — zero-fill before fit.
        X_arr = np.nan_to_num(
            X_feat.values.astype(np.float32),
            nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)

        model = _make_rf_log_pipeline(self.config, seed=self._seed)
        model.fit(X_arr, y_arr.values)

        self._state = {
            "model": model,
            "feat_cols": feat_cols,
            "prefixes": prefixes,
        }

    def _predict_t2_t5(self, X: pd.DataFrame) -> np.ndarray:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} predict: expected DataFrame; got {type(X).__name__}"
            )
        df = X.copy()
        if "sector" in df.columns:
            sec_dummies = pd.get_dummies(
                df["sector"], prefix="sector", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), sec_dummies.reset_index(drop=True)],
                axis=1,
            )
        if "industry" in df.columns:
            ind_dummies = pd.get_dummies(
                df["industry"], prefix="industry", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), ind_dummies.reset_index(drop=True)],
                axis=1,
            )

        # Zero-fill NaN at align time (RF can't handle them); also scrub
        # any straggler +/-inf below.
        X_feat = _align_columns(
            df, st["feat_cols"], fillna=True,
        ).astype(np.float32)
        X_arr = np.nan_to_num(
            X_feat.values.astype(np.float32),
            nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)
        preds = st["model"].predict(X_arr)
        preds = np.clip(preds, 0.0, None)
        return preds.astype(np.float32)

    # ── T3 / T6 — per-field booster ensemble (sparse field one-hot) ───────

    def _fit_t3_t6(self, X: pd.DataFrame, y: pd.DataFrame) -> None:
        """Single RandomForest with per-(ticker, fiscal_year) numeric
        features + one-hot of ``field``. sklearn RF does NOT accept sparse
        CSR — we densify with ``.toarray()`` before fit (acceptable for
        the small-pool T3/T6 train sets).
        """
        from sklearn.preprocessing import OneHotEncoder

        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected DataFrame X; got {type(X).__name__}"
            )
        if not isinstance(y, pd.DataFrame):
            raise TypeError(
                f"{self.task} fit: expected long-form DataFrame y; got "
                f"{type(y).__name__}"
            )
        for col in ("ticker", "field", "value"):
            if col not in y.columns:
                raise ValueError(
                    f"{self.task} fit: y missing required column {col!r}"
                )

        ticker_feats, feat_cols = self._t3_t6_build_ticker_features(X)

        fitted_fields: list[str] = sorted(
            str(f) for f in y["field"].astype(str).unique()
        )

        gt = y.copy()
        gt["value_num"] = pd.to_numeric(gt["value"], errors="coerce")
        gt["field"] = gt["field"].astype(str)
        gt = gt.merge(ticker_feats, on="ticker", how="left").fillna(0.0)
        gt = gt.dropna(subset=["value_num"])

        per_field_count = gt.groupby("field").size().to_dict()
        median_fields: dict[str, float] = {}
        small_field_set: set[str] = set()
        for field in fitted_fields:
            cnt = int(per_field_count.get(field, 0))
            if cnt < 5:
                sub = gt[gt["field"] == field]
                med = float(sub["value_num"].median()) if not sub.empty else 0.0
                median_fields[field] = med
                small_field_set.add(field)

        train_mask = ~gt["field"].isin(small_field_set)
        gt_train = gt[train_mask]

        global_model = None
        global_scaler = None
        use_log = False
        y_min = 0.0
        y_max = 0.0
        y_range = 1.0
        ohe: OneHotEncoder | None = None

        if not gt_train.empty:
            y_tr = gt_train["value_num"].values.astype(np.float64)
            nz = y_tr[y_tr != 0]
            if nz.size > 0:
                use_log = float(np.median(np.abs(nz))) > 1000
            y_tr_t = (
                np.sign(y_tr) * np.log1p(np.abs(y_tr)) if use_log else y_tr
            )

            X_num_tr = gt_train[feat_cols].values.astype(np.float32)
            # sklearn RF rejects NaN/inf — zero-fill.
            X_num_tr = np.nan_to_num(
                X_num_tr, nan=0.0, posinf=0.0, neginf=0.0,
            ).astype(np.float32)

            ohe = OneHotEncoder(
                categories=[fitted_fields],
                handle_unknown="ignore",
                sparse_output=True,
                dtype=np.float32,
            )
            field_arr_tr = gt_train["field"].values.reshape(-1, 1)
            X_field_tr = ohe.fit_transform(field_arr_tr).toarray().astype(np.float32)

            X_full_tr = np.concatenate([X_num_tr, X_field_tr], axis=1)

            global_model = _make_rf_estimator(self.config, seed=self._seed)
            global_model.fit(X_full_tr, y_tr_t)

            y_min = float(y_tr.min())
            y_max = float(y_tr.max())
            y_range = max(abs(y_max - y_min), abs(y_max) * 0.1, 1.0)

        self._state = {
            "ticker_feats": ticker_feats,
            "feat_cols": feat_cols,
            "fitted_fields": fitted_fields,
            "median_fields": median_fields,
            "model": global_model,
            "scaler": global_scaler,
            "ohe": ohe,
            "use_log": use_log,
            "y_min": y_min,
            "y_max": y_max,
            "y_range": y_range,
        }
        self.fitted_fields = fitted_fields

    def _predict_t3_t6(self, X: pd.DataFrame) -> pd.DataFrame:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"{self.task} predict: expected DataFrame; got {type(X).__name__}"
            )
        if "ticker" not in X.columns or "fiscal_year" not in X.columns:
            raise ValueError(
                f"{self.task} predict: X must include 'ticker' and "
                f"'fiscal_year'."
            )

        fitted_fields: list[str] = st["fitted_fields"]
        median_fields: dict[str, float] = st["median_fields"]
        feat_cols: list[str] = st["feat_cols"]

        test_feats, _ = self._t3_t6_build_ticker_features(X)
        test_feats = test_feats.set_index("ticker")
        for c in feat_cols:
            if c not in test_feats.columns:
                test_feats[c] = 0.0
        test_feats = test_feats[feat_cols].fillna(0.0)

        n_rows = len(X)
        n_fields = len(fitted_fields)
        rows: list[dict[str, Any]] = []
        if n_rows == 0 or n_fields == 0:
            return pd.DataFrame(
                rows, columns=["ticker", "fiscal_year", "field", "pred"],
            )

        ticker_arr = X["ticker"].astype(str).values
        fy_arr = X["fiscal_year"].values
        zero_vec = np.zeros(len(feat_cols), dtype=np.float32)
        feats_by_ticker: dict[str, np.ndarray] = {}
        for t in set(ticker_arr.tolist()):
            try:
                v = test_feats.loc[t]
                if isinstance(v, pd.DataFrame):
                    v = v.iloc[0]
                feats_by_ticker[t] = np.asarray(
                    v.values, dtype=np.float32,
                )
            except KeyError:
                feats_by_ticker[t] = zero_vec

        model = st["model"]
        ohe = st["ohe"]
        use_log = bool(st["use_log"])
        y_min = float(st["y_min"])
        y_max = float(st["y_max"])
        y_range = float(st["y_range"])

        unique_tickers = list(dict.fromkeys(ticker_arr.tolist()))
        model_fields = (
            [f for f in fitted_fields if f not in median_fields]
            if model is not None else []
        )
        cell_pred: dict[tuple[str, str], float] = {}
        if model is not None and model_fields and unique_tickers:
            X_num = np.stack(
                [feats_by_ticker[t] for t in unique_tickers], axis=0,
            ).astype(np.float32)
            X_num = np.nan_to_num(
                X_num, nan=0.0, posinf=0.0, neginf=0.0,
            ).astype(np.float32)
            n_t = len(unique_tickers)
            n_mf = len(model_fields)
            X_num_rep = np.repeat(X_num, n_mf, axis=0)
            field_arr = np.tile(
                np.asarray(model_fields, dtype=object), n_t,
            ).reshape(-1, 1)
            assert ohe is not None
            X_field = ohe.transform(field_arr).toarray().astype(np.float32)
            X_full = np.concatenate([X_num_rep, X_field], axis=1)
            y_pred_t = np.asarray(model.predict(X_full))
            if use_log:
                y_pred = np.sign(y_pred_t) * np.expm1(np.abs(y_pred_t))
            else:
                y_pred = y_pred_t
            y_pred = np.clip(y_pred, y_min - y_range, y_max + y_range)
            y_pred = y_pred.reshape(n_t, n_mf)
            for ti, t in enumerate(unique_tickers):
                for fi, f in enumerate(model_fields):
                    cell_pred[(t, f)] = float(y_pred[ti, fi])

        for i in range(n_rows):
            t = ticker_arr[i]
            fy = fy_arr[i]
            for field in fitted_fields:
                if field in median_fields:
                    val = median_fields[field]
                else:
                    val = cell_pred.get((t, field), 0.0)
                rows.append({
                    "ticker": t,
                    "fiscal_year": fy,
                    "field": field,
                    "pred": float(val),
                })
        return pd.DataFrame(
            rows, columns=["ticker", "fiscal_year", "field", "pred"],
        )

    # ── T4 — flat-lookback + event-type one-hot ───────────────────────────

    def _fit_t4(self, X: pd.DataFrame, y: np.ndarray) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T4 fit: expected DataFrame X; got {type(X).__name__}"
            )
        if "lookback" not in X.columns or "event_type" not in X.columns:
            raise ValueError(
                "T4 fit: X must include 'lookback' and 'event_type' columns."
            )

        flat, n_lb_flat = self._t4_flatten_lookback(X["lookback"])

        et_arr = X["event_type"].astype(str).values
        et_dummies = pd.get_dummies(
            pd.Series(et_arr), prefix="evt", dtype=np.float32,
        )
        X_arr = np.concatenate(
            [flat, et_dummies.values.astype(np.float32)], axis=1,
        )
        # sklearn RF rejects NaN/inf — zero-fill.
        X_arr = np.nan_to_num(
            X_arr, nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)

        y_arr = np.asarray(y, dtype=np.float32).ravel()
        if y_arr.shape[0] != X_arr.shape[0]:
            raise ValueError(
                f"T4 fit: y/X length mismatch ({y_arr.shape[0]} vs "
                f"{X_arr.shape[0]})."
            )

        model = _make_rf_estimator(self.config, seed=self._seed)
        model.fit(X_arr, y_arr)

        self._state = {
            "model": model,
            "evt_columns": list(et_dummies.columns),
            "n_lb_flat": int(n_lb_flat),
        }

    def _predict_t4(self, X: pd.DataFrame) -> np.ndarray:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T4 predict: expected DataFrame; got {type(X).__name__}"
            )
        if "lookback" not in X.columns or "event_type" not in X.columns:
            raise ValueError(
                "T4 predict: X must include 'lookback' and 'event_type'."
            )

        flat, _ = self._t4_flatten_lookback(X["lookback"])

        if flat.shape[1] > st["n_lb_flat"]:
            flat = flat[:, : st["n_lb_flat"]]
        elif flat.shape[1] < st["n_lb_flat"]:
            pad = np.zeros(
                (flat.shape[0], st["n_lb_flat"] - flat.shape[1]),
                dtype=np.float32,
            )
            flat = np.concatenate([flat, pad], axis=1)

        et_arr = X["event_type"].astype(str).values
        et_dummies = pd.get_dummies(
            pd.Series(et_arr), prefix="evt", dtype=np.float32,
        )
        for c in st["evt_columns"]:
            if c not in et_dummies.columns:
                et_dummies[c] = 0.0
        et_dummies = et_dummies[st["evt_columns"]].fillna(0.0)

        X_arr = np.concatenate(
            [flat, et_dummies.values.astype(np.float32)], axis=1,
        )
        X_arr = np.nan_to_num(
            X_arr, nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)
        return st["model"].predict(X_arr).astype(np.float32)

    # ── T7 — dual-output (rent, price) ────────────────────────────────────

    def _fit_t7(self, X: pd.DataFrame, y: pd.DataFrame) -> None:
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T7 fit: expected DataFrame X; got {type(X).__name__}"
            )
        if "address" not in X.columns:
            raise ValueError("T7 fit: X must include 'address'.")

        df, feat_cols, prop_type_col = self._t7_build_features(X, fit=True)

        rent_col = self._t7_first_col(df, ("rent",))
        price_col = self._t7_first_col(df, ("price", "lastsaleprice"))
        if isinstance(y, pd.DataFrame) and "address" in y.columns:
            if rent_col is None and "rent" in y.columns:
                df = df.merge(
                    y[["address", "rent"]], on="address", how="left",
                )
                rent_col = "rent"
            if price_col is None and "price" in y.columns:
                df = df.merge(
                    y[["address", "price"]], on="address", how="left",
                )
                price_col = "price"
        if rent_col is None and price_col is None:
            raise RuntimeError("T7 fit: no rent or price target found.")

        # sklearn RF rejects NaN/inf — zero-fill.
        X_feat = df[feat_cols].astype(np.float32)
        X_arr = np.nan_to_num(
            X_feat.values.astype(np.float32),
            nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)

        models: dict[str, Any] = {}
        for target_name, target_col in (("rent", rent_col), ("price", price_col)):
            if target_col is None:
                continue
            y_all = pd.to_numeric(df[target_col], errors="coerce")
            valid = y_all.notna() & (y_all > 0)
            n_valid = int(valid.sum())
            if n_valid < 1:
                continue
            X_tr = X_arr[valid.values]
            y_tr = y_all.loc[valid].values.astype(np.float64)
            if n_valid < 2:
                # RandomForest tolerates n=1 but eval is degenerate; emit a
                # constant predictor to match LightGBM's degenerate-case
                # behaviour exactly (smoke-run path only).
                models[target_name] = ("constant", float(y_tr.mean()))
                continue
            m = _make_rf_log_pipeline(self.config, seed=self._seed)
            m.fit(X_tr, y_tr)
            models[target_name] = m

        if not models:
            raise RuntimeError(
                f"T7 fit: insufficient training data "
                f"(rent_col={rent_col!r}, price_col={price_col!r}, "
                f"n_rows={len(df)}); need >=1 row with a positive target."
            )

        self._state = {
            "feat_cols": feat_cols,
            "prop_type_col": prop_type_col,
            "models": models,
        }

    def _predict_t7(self, X: pd.DataFrame) -> pd.DataFrame:
        st = self._state
        if not isinstance(X, pd.DataFrame):
            raise TypeError(
                f"T7 predict: expected DataFrame; got {type(X).__name__}"
            )
        if "address" not in X.columns:
            raise ValueError("T7 predict: X must include 'address'.")

        df = X.copy()
        for col in (
            "sqft", "squareFootage", "square_footage",
            "beds", "bedrooms", "baths", "bathrooms",
            "year_built", "yearBuilt",
            "years_since_last_sale",
        ):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")

        prop_type_col = st["prop_type_col"]
        if prop_type_col and prop_type_col in df.columns:
            prop_dummies = pd.get_dummies(
                df[prop_type_col], prefix="ptype", dtype=np.float32,
            )
            df = pd.concat(
                [df.reset_index(drop=True), prop_dummies.reset_index(drop=True)],
                axis=1,
            )

        # Zero-fill NaN at align time and scrub stragglers — RF rejects them.
        X_feat = _align_columns(
            df, st["feat_cols"], fillna=True,
        ).astype(np.float32)
        X_arr = np.nan_to_num(
            X_feat.values.astype(np.float32),
            nan=0.0, posinf=0.0, neginf=0.0,
        ).astype(np.float32)

        out = pd.DataFrame({"address": X["address"].astype(str).values})
        for target_name in ("rent", "price"):
            model = st["models"].get(target_name)
            if model is None:
                out[f"pred_{target_name}"] = np.full(
                    len(X), np.nan, dtype=np.float32,
                )
                continue
            if isinstance(model, tuple) and model[0] == "constant":
                out[f"pred_{target_name}"] = np.full(
                    len(X), float(model[1]), dtype=np.float32,
                )
                continue
            preds = model.predict(X_arr)
            preds = np.clip(preds, 0.0, None)
            out[f"pred_{target_name}"] = preds.astype(np.float32)
        return out