/
args.py
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/
args.py
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import sys
import argparse
def get_args():
"""
Code modified from:
https://github.com/ignavier/golem/blob/main/src/utils/config.py
"""
parser = argparse.ArgumentParser()
# configurations for dataset
parser.add_argument(
"--n",
type=int,
default=3000,
help="Number of samples.",
)
parser.add_argument(
"--d",
type=int,
default=20,
help="Number of nodes.",
)
parser.add_argument(
"--graph_type",
type=str,
default="erdos-renyi",
help="Type of graph ('erdos-renyi', 'barabasi-albert').",
)
parser.add_argument(
"--degree",
type=int,
default=3,
help="Degree of graph.",
)
parser.add_argument(
"--sem_type",
type=str,
default="gauss",
help="Type of noise ['gauss', 'exp', 'gumbel', 'mnonr'].",
)
parser.add_argument(
"--dataset_type",
type=str,
default="nonlinear_3",
help="Choose between nonlinear_1, nonlinear_2, nonlinear_3.",
)
parser.add_argument(
"--x_dim",
type=int,
default=1,
help="Dimension of vector for X.",
)
# configurations for model
parser.add_argument(
"--hidden_size",
type=int,
default=16,
help="Hidden size for NonLinerTransformer.",
)
parser.add_argument(
"--layer",
type=int,
default=3,
help="Number of MLP layers for both encoder and decoder.",
)
parser.add_argument(
"--latent_dim",
type=int,
default=1,
help="Latent dimension for CAE.",
)
parser.add_argument(
"--lambda_sparsity",
type=float,
default=1.0,
help="Coefficient of L1 penalty.",
)
parser.add_argument(
"--psp",
action="store_true",
help="Whether to use proportional sparsity penalty.",
)
# configurations for training
parser.add_argument(
"--learning_rate",
type=float,
default=1e-3,
help="Learning rate.",
)
parser.add_argument(
"--max_iters",
type=int,
default=20,
help="Upper bound of iterations for ALM optimization.",
)
parser.add_argument(
"--min_iters",
type=int,
default=5,
help="Lower bound of iterations for early stopping.",
)
parser.add_argument(
"--epochs",
type=int,
default=300,
help="Number of epochs for training in each iterations.",
)
parser.add_argument(
"--init_rho",
type=float,
default=1.0,
help="Initial value for rho.",
)
parser.add_argument(
"--rho_thres",
type=float,
default=1e18,
help="Threshold for rho.",
)
parser.add_argument(
"--beta",
type=float,
default=10.0,
help="Multiplication to amplify rho each time.",
)
parser.add_argument(
"--gamma",
type=float,
default=0.25,
help="Threshold for judge h(A).",
)
parser.add_argument(
"--min_h",
type=float,
default=1e-12,
help="Lower bound of h for early stopping.",
)
parser.add_argument(
"--max_h",
type=float,
default=1e-7,
help="Upper bound of h for early stopping.",
)
parser.add_argument(
"--early_stopping",
action="store_true",
help="Whether to use early stopping.",
)
parser.add_argument(
"--mse_thres",
type=float,
default=1.15,
help="Threshold ratio between reconstruction loss for early stopping",
)
# configurations for others
parser.add_argument(
"--seed_data",
type=int,
default=0,
help="Random seed for generating dataset.",
)
parser.add_argument(
"--seed_model",
type=int,
default=0,
help="Random seed for initializing model.",
)
parser.add_argument(
"--graph_thres",
type=float,
default=0.3,
help="Threshold to filter out small values in graph",
)
parser.add_argument(
"--base_dir",
type=str,
default="runs",
help="Base output folder.",
)
parser.add_argument(
"--cuda",
type=int,
default=-1,
help="Whether to use GPU for training. (-1 for CPU, and 0,...,n for GPU)",
)
parser.add_argument(
"--log_level",
type=str,
default="DEBUG",
help="log level (INFO, DEBUG).",
)
# parse arguments
return parser.parse_args(args=sys.argv[1:])