"""Tabular baselines on demographic + morphometric features.

Allowed features (NO CDR, NO MMSE — those are label-leaking): age, sex, education,
ses, etiv, nwbv, asf. XGBoost is used both as a main baseline (B0) and as the
shortcut/confound probe (age-only, demographic-only, full-structured).
"""
from __future__ import annotations

import numpy as np
import torch
import torch.nn as nn

FEATURES_FULL = ["age", "sex", "education", "ses", "etiv", "nwbv", "asf"]
FEATURES_DEMO = ["age", "sex", "education", "ses"]
FEATURES_AGE = ["age"]


def make_xgb(n_classes: int = 3, seed: int = 0):
    """Return an XGBoost classifier configured for small-n multiclass."""
    from xgboost import XGBClassifier
    return XGBClassifier(
        n_estimators=300, max_depth=3, learning_rate=0.05,
        subsample=0.8, colsample_bytree=0.8, reg_lambda=1.0,
        objective="multi:softprob", num_class=n_classes,
        eval_metric="mlogloss", tree_method="hist",
        random_state=seed, n_jobs=4,
    )


class TabularMLP(nn.Module):
    """Neural tabular baseline (B1) and the metadata branch reference."""

    def __init__(self, n_features: int = 7, n_classes: int = 3, hidden: int = 128,
                 dropout: float = 0.3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_features, 64), nn.GELU(), nn.Dropout(dropout),
            nn.Linear(64, hidden), nn.GELU(), nn.LayerNorm(hidden),
            nn.Dropout(dropout), nn.Linear(hidden, n_classes),
        )

    def forward(self, tab: torch.Tensor, *_unused) -> torch.Tensor:
        return self.net(tab)