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, Metric

from src.prediction import evaluate_downsampling

if __name__ == "__main__":

MAX_WORKERS = {

Dataset.UTIResistance: {

ClassifierKind.GBT: 30,

ClassifierKind.SVM: 8,

ClassifierKind.RF: 30,

ClassifierKind.LR: 8,

}

}

grid = [

Namespace(**args)

for args in list(

ParameterGrid({"dataset": [Dataset.fast()], "kind": [*ClassifierKind]})

)

]

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

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

if idx is None:

raise ValueError("Not in array job. $SLURM_ARRAY_TASK_ID is undefined.")

args = grid[int(idx)]

evaluate_downsampling(

classifier=args.kind,

dataset=args.dataset,

downsample=True,

n_reps=200,

n_runs=10,

max_workers=8,

)