0_harness/evaluation/eval_formula.py

"""eval_formula.py — the shared evaluation core.

Runs ONE formula module over the test set and returns raw metrics, both
per-cluster and pooled. This is the common engine called by evaluate.py
(on each reference baseline and on a submitted formula.py).

A formula module must expose the v2 contract:
  USED_INPUTS, LAW_CONSTANTS, OTHER_CONSTANTS, LOCAL_FITTABLE,
  predict(X, **params)   and (Type II) fit(X_fit, y_fit, **law_constants).

`run_formula` performs NO score normalisation and NO judging — it only
executes the formula and measures error. The reference-relative score
and the judge channels live in evaluate.py.
"""
from __future__ import annotations

import csv
import random
import signal
import time
from pathlib import Path

import numpy as np


# --------------------------------------------------------------------------
# data loading
# --------------------------------------------------------------------------

def load_csv(path: Path) -> tuple[list[str], list[list[str]]]:
    with path.open("r", newline="") as fh:
        reader = csv.reader(fh)
        header = next(reader)
        rows = [list(r) for r in reader]
    return header, rows


def group_by_cluster(header: list[str], rows: list[list[str]]) -> dict[int, list[list[str]]]:
    gid = header.index("group_id")
    out: dict[int, list[list[str]]] = {}
    for r in rows:
        out.setdefault(int(float(r[gid])), []).append(r)
    return out


def load_clusters(task_root: Path) -> dict:
    """Type II loader. Load test_fit.csv + test_test.csv, grouped by cluster.

    Returns a dict with: fit_header, test_header, fit_by_cluster,
    test_by_cluster, cluster_ids (sorted, present in both).
    """
    fit_header,  fit_rows  = load_csv(task_root / "data" / "test_fit.csv")
    test_header, test_rows = load_csv(task_root / "data" / "test_test.csv")
    fit_by_cluster  = group_by_cluster(fit_header,  fit_rows)
    test_by_cluster = group_by_cluster(test_header, test_rows)
    cluster_ids = sorted(set(fit_by_cluster) & set(test_by_cluster))
    return {
        "fit_header": fit_header, "test_header": test_header,
        "fit_by_cluster": fit_by_cluster, "test_by_cluster": test_by_cluster,
        "cluster_ids": cluster_ids,
    }


def load_flat(task_root: Path) -> dict:
    """Type I loader. Load train.csv + test.csv as flat tables (no clusters).

    The reference / submission formulas predict directly on test.csv.
    train.csv is carried for completeness (the SR system trains on it) but
    the v2 score does not use it — there is no naive baseline.
    """
    train_header, train_rows = load_csv(task_root / "data" / "train.csv")
    test_header,  test_rows  = load_csv(task_root / "data" / "test.csv")
    return {
        "train_header": train_header, "train_rows": train_rows,
        "test_header": test_header,   "test_rows": test_rows,
    }


# --------------------------------------------------------------------------
# helpers
# --------------------------------------------------------------------------

def _to_array(rows: list[list[str]], header: list[str], cols: list[str]) -> np.ndarray:
    idx = [header.index(c) for c in cols]
    return np.array([[float(row[i]) for i in idx] for row in rows], dtype=float)


def _col(rows: list[list[str]], header: list[str], name: str) -> np.ndarray:
    i = header.index(name)
    return np.array([float(r[i]) for r in rows], dtype=float)


def metrics(y_true: np.ndarray, y_pred: np.ndarray) -> dict:
    """Full metric menu so any task-declared `metric` can be selected later."""
    y_true = np.asarray(y_true, dtype=float)
    y_pred = np.asarray(y_pred, dtype=float)
    mask = np.isfinite(y_pred) & np.isfinite(y_true)
    n = int(mask.sum())
    if n == 0:
        return {"rmse": None, "mae": None, "mse": None, "r2": None, "smape": None, "n_finite": 0}
    yt, yp = y_true[mask], y_pred[mask]
    err = yp - yt
    mse = float(np.mean(err ** 2))
    rmse = float(np.sqrt(mse))
    mae = float(np.mean(np.abs(err)))
    ss_tot = float(np.sum((yt - yt.mean()) ** 2))
    r2 = float(1.0 - float(np.sum(err ** 2)) / ss_tot) if ss_tot > 0 else None
    denom = (np.abs(yt) + np.abs(yp)) / 2.0
    safe = denom > 0
    smape = (float(np.mean(np.where(safe, np.abs(err) / np.where(safe, denom, 1.0), 0.0)))
             if safe.any() else None)
    return {"rmse": rmse, "mae": mae, "mse": mse, "r2": r2, "smape": smape, "n_finite": n}


class _Timeout(Exception):
    pass


def _timeout_handler(signum, frame):  # noqa: ARG001
    raise _Timeout()


# --------------------------------------------------------------------------
# core
# --------------------------------------------------------------------------

def run_formula(mod, clusters: dict, target_name: str,
                fit_timeout_seconds: int | None = None,
                seed: int | None = None) -> dict:
    """Execute one formula module over every test cluster.

    `seed`, if given, fixes the global NumPy / Python RNG before the run so
    a stochastic submission `fit()` is reproducible. evaluate.py runs each
    Type II submission under several seeds and reports mean / std.

    Returns:
      {
        "per_cluster": {cid: {"metrics": {...}, "failed": bool, "error": str|None}},
        "pooled":      {...metrics over all clusters' test rows...},
        "n_clusters_fitted": int,
        "n_clusters_failed": int,
        "max_fit_seconds": float,   # slowest per-cluster fit() wall-time
      }

    A cluster is `failed` if fit() raises / times out, or predict() returns
    non-finite. Failed clusters are excluded from the pooled metric here;
    the reference-relative score for failed clusters is evaluate.py's job.
    `max_fit_seconds` lets evaluate.py derive the fit_timeout cap from the
    reference bank's measured fit cost.
    """
    fit_header  = clusters["fit_header"]
    test_header = clusters["test_header"]
    fit_by_cluster  = clusters["fit_by_cluster"]
    test_by_cluster = clusters["test_by_cluster"]
    cluster_ids = clusters["cluster_ids"]

    if seed is not None:
        np.random.seed(seed)
        random.seed(seed)

    used = list(mod.USED_INPUTS)
    LAW  = dict(mod.LAW_CONSTANTS)
    is_type_ii = bool(mod.LOCAL_FITTABLE)

    per_cluster: dict[int, dict] = {}
    pooled_pred, pooled_true = [], []
    n_failed = 0
    max_fit_seconds = 0.0

    for cid in cluster_ids:
        fr = fit_by_cluster[cid]
        tr = test_by_cluster[cid]
        try:
            if used:
                X_fit  = _to_array(fr, fit_header,  used)
                X_test = _to_array(tr, test_header, used)
            else:
                X_fit  = np.zeros((len(fr), 0), dtype=float)
                X_test = np.zeros((len(tr), 0), dtype=float)
            y_fit  = _col(fr, fit_header,  target_name)
            y_test = _col(tr, test_header, target_name)

            if is_type_ii:
                if fit_timeout_seconds:
                    signal.signal(signal.SIGALRM, _timeout_handler)
                    signal.alarm(int(fit_timeout_seconds))
                t0 = time.perf_counter()
                try:
                    local = mod.fit(X_fit, y_fit, **LAW)
                finally:
                    if fit_timeout_seconds:
                        signal.alarm(0)
                max_fit_seconds = max(max_fit_seconds, time.perf_counter() - t0)
            else:
                local = {}

            y_pred = np.asarray(mod.predict(X_test, **LAW, **local), dtype=float)
            if not np.all(np.isfinite(y_pred)):
                raise RuntimeError("predict returned non-finite values")

            m = metrics(y_test, y_pred)
            per_cluster[cid] = {"metrics": m, "failed": False, "error": None}
            pooled_pred.append(y_pred)
            pooled_true.append(y_test)
        except _Timeout:
            n_failed += 1
            per_cluster[cid] = {"metrics": None, "failed": True,
                                "error": f"fit() exceeded {fit_timeout_seconds}s"}
        except Exception as exc:  # noqa: BLE001
            n_failed += 1
            per_cluster[cid] = {"metrics": None, "failed": True,
                                "error": f"{type(exc).__name__}: {exc}"}

    pooled = (metrics(np.concatenate(pooled_true), np.concatenate(pooled_pred))
              if pooled_pred else None)

    return {
        "per_cluster": per_cluster,
        "pooled": pooled,
        "n_clusters_fitted": len(cluster_ids) - n_failed,
        "n_clusters_failed": n_failed,
        "max_fit_seconds": max_fit_seconds,
    }


# --------------------------------------------------------------------------
# core — Type I (flat, no clusters, no fit)
# --------------------------------------------------------------------------

def run_formula_flat(mod, flat: dict, target_name: str) -> dict:
    """Execute one Type I formula on the flat test set.

    Type I: LOCAL_FITTABLE is empty, there is no fit() — predict() is called
    once on the whole test set with only LAW_CONSTANTS.

    Returns:
      {"pooled": {...metrics...} | None, "failed": bool, "error": str|None}
    """
    test_header = flat["test_header"]
    test_rows   = flat["test_rows"]
    used = list(mod.USED_INPUTS)
    LAW  = dict(mod.LAW_CONSTANTS)

    try:
        X_test = (_to_array(test_rows, test_header, used) if used
                  else np.zeros((len(test_rows), 0), dtype=float))
        y_test = _col(test_rows, test_header, target_name)
        y_pred = np.asarray(mod.predict(X_test, **LAW), dtype=float)
        if not np.all(np.isfinite(y_pred)):
            raise RuntimeError("predict returned non-finite values")
        return {"pooled": metrics(y_test, y_pred), "failed": False, "error": None}
    except Exception as exc:  # noqa: BLE001
        return {"pooled": None, "failed": True,
                "error": f"{type(exc).__name__}: {exc}"}