from __future__ import annotations

# fmt: off

import sys # isort: skip

from pathlib import Path # isort: skip

ROOT = Path(__file__).resolve().parent.parent # isort: skip

sys.path.append(str(ROOT)) # isort: skip

# fmt: on

import os

import sys

from argparse import Namespace

from pathlib import Path

from sklearn.model_selection import ParameterGrid

from src.enumerables import ClassifierKind, Dataset

from src.prediction import evaluate_downsampling

if __name__ == "__main__":

MAX_WORKERS = 80 if os.environ.get("CC_CLUSTER") == "niagara" else 8

grid = [

Namespace(**args)

for args in list(

ParameterGrid(

{

"dataset": [Dataset.MimicIV],

"kind": [ClassifierKind.SVM],

},

)

)

]

print(f"Total number of combinations: {len(grid)}")

idx = os.environ.get("SLURM_ARRAY_TASK_ID")

if idx is None:

for args in grid:

evaluate_downsampling(

classifier=args.kind,

dataset=args.dataset,

downsample=True,

n_reps=200,

n_runs=10,

)

else:

args = grid[int(idx)]

evaluate_downsampling(

classifier=args.kind,

dataset=args.dataset,

downsample=True,

n_reps=200,

n_runs=10,

max_workers=MAX_WORKERS,

)