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do_ids.py
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do_ids.py
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import tensorflow as tf
from argparse import ArgumentParser
from libs.DataHandler import IDS
from libs.ExperimentWrapper import ExperimentWrapper
from libs.constants import add_standard_arguments, ALARM_SMALL, ALARM_BIG
# Reduce the hunger of TF when we're training on a GPU
try:
tf.config.experimental.set_memory_growth(tf.config.list_physical_devices("GPU")[0], True)
except IndexError:
tf.config.run_functions_eagerly(True)
pass # No GPUs available
# Configuration
this_parse = ArgumentParser(description="Train ARGUE on CovType")
add_standard_arguments(this_parse)
this_args = this_parse.parse_args()
experiment_config = [
IDS(
random_state=this_args.random_seed, y_normal=['0', '1', '2', '3', '4', '5'],
y_anomalous=['Bot', 'BruteForce', 'Infiltration', 'WebAttacks'],
n_train_anomalies=this_args.n_train_anomalies, p_pollution=this_args.p_contamination
)
]
DIM_TARGET = [150, 120, 80, 60, 40, 20]
DIM_ALARM = ALARM_BIG
BATCH_SIZE = 8192
A4RGUE_CLUSTERS = 2
if __name__ == '__main__':
this_experiment = ExperimentWrapper(
save_prefix="IDS", data_setup=experiment_config,
random_seed=this_args.random_seed, out_path=this_args.model_path,
p_contamination=this_args.p_contamination
)
# Fails on DAGMM:
# Errors may have originated from an input operation.
# Input Source operations connected to node GMM_energy/triangular_solve/MatrixTriangularSolve:
# GMM/Cholesky (defined at /app/baselines/dagmm_v2/gmm.py:53)
# REPEN: very high runtimes
this_experiment.do_everything(
dim_target=DIM_TARGET, dim_alarm=DIM_ALARM,
learning_rate=this_args.learning_rate, batch_size=BATCH_SIZE, n_epochs=this_args.n_epochs,
out_path=this_args.result_path, evaluation_split=this_args.data_split, train_dagmm=False, train_repen=False,
train_devnet=this_args.n_train_anomalies > 0, a4rgue_clusters=A4RGUE_CLUSTERS
)