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converter_to_csv.py
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converter_to_csv.py
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from src.utils import npz_to_csv_converter
if __name__ == "__main__":
percentages = [0.05, 0.1, 0.3]
deltas_large = [0.5, 1.0, 2.0, 5.0, 10.0]
betas = [0.0, 0.5, 1.0]
index_dl = 3
index_beta = 0
index_per = 2
dl = deltas_large[index_dl]
beta = betas[index_beta]
p = percentages[index_per]
dictionaries_to_convert = []
dictionaries_to_convert.extend(
[
{
"loss_name": "L2",
"alpha_min": 0.01,
"alpha_max": 100000,
"alpha_pts": 100,
"delta_small": 0.1,
"delta_large": dl,
"percentage": p,
"beta": beta,
"experiment_type": "reg_param optimal",
}
for dl in deltas_large
]
)
dictionaries_to_convert.extend(
[
{
"loss_name": "L1",
"alpha_min": 0.01,
"alpha_max": 100000,
"alpha_pts": 100,
"delta_small": 0.1,
"delta_large": dl,
"percentage": p,
"beta": beta,
"experiment_type": "reg_param optimal",
}
for dl in deltas_large
]
)
dictionaries_to_convert.extend(
[
{
"loss_name": "Huber",
"alpha_min": 0.01,
"alpha_max": 100000,
"alpha_pts": 100,
"delta_small": 0.1,
"delta_large": dl,
"percentage": p,
"beta": beta,
"experiment_type": "reg_param huber_param optimal",
}
for dl in deltas_large
]
)
dictionaries_to_convert.extend(
[
{
"alpha_min": 0.01,
"alpha_max": 1000,
"alpha_pts": 46,
"delta_small": 0.1,
"delta_large": dl,
"percentage": p,
"beta": beta,
"experiment_type": "BO",
}
for dl in deltas_large
]
)
for d in dictionaries_to_convert:
npz_to_csv_converter(**d)