"""Experiment grid: every model config for the main table, ablation, and confound.

Defined as TrainConfig factories (not loose YAML) so the grid is typed and the
runner can iterate deterministically. Epochs are tuned to the small cohort
(198 subjects) — early stopping on val Macro-F1 usually halts well before the cap.
"""
from __future__ import annotations

from trifuse.training.config import TrainConfig

TRI = ("axial", "coronal", "sagittal")


def main_grid() -> list[TrainConfig]:
    return [
        # --- tabular ---
        TrainConfig(name="xgboost", model="xgboost", modality="tabular"),
        TrainConfig(name="tabular_mlp", model="tabular_mlp", modality="tabular",
                    epochs=80, batch_size=16, lr=1e-3),
        # --- CNN ---
        TrainConfig(name="resnet50", model="resnet50", modality="slice2d",
                    epochs=60, batch_size=16, lr=3e-4, backbone_lr=3e-5,
                    freeze_epochs=5),
        TrainConfig(name="densenet2p5d", model="densenet2p5d", modality="slice25d",
                    n_slices=9, planes=("axial",), epochs=60, batch_size=8,
                    lr=3e-4, backbone_lr=3e-5, freeze_epochs=5),
        TrainConfig(name="resnet3d", model="resnet3d", modality="vol3d",
                    vol_size=(128, 128, 128), epochs=100, batch_size=8,
                    lr=1e-4),
        # --- transformers ---
        TrainConfig(name="vit_b16", model="vit2d", modality="slice25d",
                    n_slices=9, planes=("axial",), epochs=60, batch_size=8,
                    lr=3e-4, backbone_lr=1e-5, freeze_epochs=8, unfreeze_last_n=2),
        TrainConfig(name="swin3d", model="swin3d", modality="vol3d",
                    vol_size=(96, 112, 112), epochs=80, batch_size=8,
                    lr=1e-4),
        # --- recent hybrids (retrained on our folds) ---
        TrainConfig(name="hcct", model="hcct", modality="vol3d",
                    vol_size=(96, 112, 112), epochs=80, batch_size=8,
                    lr=1e-4),
        TrainConfig(name="vswin_lite", model="vswin_lite", modality="vol3d",
                    vol_size=(96, 112, 112), epochs=80, batch_size=8,
                    lr=1e-4),
        # --- multimodal baseline ---
        TrainConfig(name="densenet_latefusion", model="densenet_latefusion",
                    modality="multimodal", n_slices=9, planes=("axial",),
                    epochs=60, batch_size=8, lr=3e-4, backbone_lr=3e-5, freeze_epochs=5),
        # --- proposed ---
        TrainConfig(name="trifuse_ad", model="trifuse", modality="multimodal",
                    n_slices=9, planes=TRI, epochs=70, batch_size=6,
                    lr=3e-4, backbone_lr=3e-5, freeze_epochs=6, grad_accum=2),
    ]


def ablation_grid() -> list[TrainConfig]:
    """A1-A5 + full, all sharing TriFuse-AD's training recipe."""
    base = dict(modality="multimodal", n_slices=9, epochs=70, batch_size=6,
                lr=3e-4, backbone_lr=3e-5, freeze_epochs=6, grad_accum=2)
    return [
        TrainConfig(name="abl_A1_axial", model="trifuse", planes=("axial",), **base),
        TrainConfig(name="abl_A2_meanpool", model="trifuse_meanpool", planes=TRI, **base),
        TrainConfig(name="abl_A3_nometa", model="trifuse_nometa", planes=TRI, **base),
        TrainConfig(name="abl_A4_concat", model="trifuse_concat", planes=TRI, **base),
        TrainConfig(name="abl_A5_weightedce", model="trifuse", planes=TRI,
                    loss="weighted_ce", **base),
        TrainConfig(name="abl_full", model="trifuse", planes=TRI, **base),
    ]