def __init__(self,
name: str = 'LSTM-ED',
num_epochs: int = 10,
batch_size: int = 20,
lr: float = 1e-3,
hidden_size: int = 5,
sequence_length: int = 30,
train_gaussian_percentage: float = 0.25,
n_layers: tuple = (1, 1),
use_bias: tuple = (True, True),
dropout: tuple = (0, 0),
seed: int = None,
gpu: int = None,
details=True,
contamination=0.05):
deepBase.__init__(self, __name__, name, seed, details=details)
PyTorchUtils.__init__(self, seed, gpu)
self.num_epochs = num_epochs
self.batch_size = batch_size
self.lr = lr
self.hidden_size = hidden_size
self.sequence_length = sequence_length
self.train_gaussian_percentage = train_gaussian_percentage
self.n_layers = n_layers
self.use_bias = use_bias
self.dropout = dropout
self.lstmed = None
self.mean, self.cov = None, None
self.contamination = contamination
def __init__(self, len_in=1, len_out=10, num_epochs=10, lr=1e-3, batch_size=1,
seed: int=None, gpu: int=None, details=True,contamination=0.05):
deepBase.__init__(self, __name__, 'LSTM-AD', seed, details=details)
PyTorchUtils.__init__(self, seed, gpu)
self.num_epochs = num_epochs
self.lr = lr
self.batch_size = batch_size
self.len_in = len_in
self.len_out = len_out
self.mean, self.cov = None, None
self.contamination=contamination
def __init__(self,
num_epochs=10,
lambda_energy=0.1,
lambda_cov_diag=0.005,
lr=1e-3,
batch_size=50,
gmm_k=3,
normal_percentile=80,
sequence_length=30,
autoencoder_type=AutoEncoderModule,
autoencoder_args=None,
hidden_size: int = 5,
seed: int = None,
gpu: int = None,
details=True,
contamination=0.05):
_name = 'LSTM-DAGMM' if autoencoder_type == LSTMEDModule else 'DAGMM'
deepBase.__init__(self, __name__, _name, seed, details=details)
PyTorchUtils.__init__(self, seed, gpu)
self.num_epochs = num_epochs
self.lambda_energy = lambda_energy
self.lambda_cov_diag = lambda_cov_diag
self.lr = lr
self.batch_size = batch_size
self.sequence_length = sequence_length
self.gmm_k = gmm_k # Number of Gaussian mixtures
self.normal_percentile = normal_percentile # Up to which percentile data should be considered normal
self.autoencoder_type = autoencoder_type
if autoencoder_type == AutoEncoderModule:
self.autoencoder_args = ({'sequence_length': self.sequence_length})
elif autoencoder_type == LSTMEDModule:
self.autoencoder_args = ({
'n_layers': (1, 1),
'use_bias': (True, True),
'dropout': (0.0, 0.0)
})
self.autoencoder_args.update({'seed': seed, 'gpu': gpu})
if autoencoder_args is not None:
self.autoencoder_args.update(autoencoder_args)
self.hidden_size = hidden_size
self.dagmm, self.optimizer, self.train_energy, self._threshold = None, None, None, None
self.contamination = contamination
def __init__(self,
name: str = 'AutoEncoder',
num_epochs: int = 10,
batch_size: int = 20,
lr: float = 1e-3,
hidden_size: int = 5,
sequence_length: int = 30,
train_gaussian_percentage: float = 0.25,
seed: int = None,
gpu: int = None,
details=True,
contamination=0.05):
deepBase.__init__(self, __name__, name, seed, details=details)
PyTorchUtils.__init__(self, seed, gpu)
self.num_epochs = num_epochs
self.batch_size = batch_size
self.lr = lr
self.hidden_size = hidden_size
self.sequence_length = sequence_length
self.train_gaussian_percentage = train_gaussian_percentage
self.contamination = contamination
self.aed = None
self.mean, self.cov = None, None