def loss_aegmm(x_true: tf.Tensor,
x_pred: tf.Tensor,
z: tf.Tensor,
gamma: tf.Tensor,
w_energy: float = .1,
w_cov_diag: float = .005
) -> tf.Tensor:
"""
Loss function used for OutlierAEGMM.
Parameters
----------
x_true
Batch of instances.
x_pred
Batch of reconstructed instances by the autoencoder.
z
Latent space values.
gamma
Membership prediction for mixture model components.
w_energy
Weight on sample energy loss term.
w_cov_diag
Weight on covariance regularizing loss term.
Returns
-------
Loss value.
"""
recon_loss = tf.reduce_mean((x_true - x_pred) ** 2)
phi, mu, cov, L, log_det_cov = gmm_params(z, gamma)
sample_energy, cov_diag = gmm_energy(z, phi, mu, cov, L, log_det_cov, return_mean=True)
loss = recon_loss + w_energy * sample_energy + w_cov_diag * cov_diag
return loss
def test_gmm_params_energy():
phi, mu, cov, L, log_det_cov = gmm_params(z, gamma)
assert phi.numpy().shape[0] == K == log_det_cov.shape[0]
assert mu.numpy().shape == (K, D)
assert cov.numpy().shape == L.numpy().shape == (K, D, D)
for _ in range(cov.numpy().shape[0]):
assert (np.diag(cov[_].numpy()) >= 0.).all()
assert (np.diag(L[_].numpy()) >= 0.).all()
sample_energy, cov_diag = gmm_energy(z,
phi,
mu,
cov,
L,
log_det_cov,
return_mean=True)
assert sample_energy.numpy().shape == cov_diag.numpy().shape == ()
sample_energy, _ = gmm_energy(z,
phi,
mu,
cov,
L,
log_det_cov,
return_mean=False)
assert sample_energy.numpy().shape[0] == N
def fit(self,
X: np.ndarray,
loss_fn: tf.keras.losses = loss_aegmm,
w_energy: float = .1,
w_cov_diag: float = .005,
optimizer: tf.keras.optimizers = tf.keras.optimizers.Adam(learning_rate=1e-4),
epochs: int = 20,
batch_size: int = 64,
verbose: bool = True,
log_metric: Tuple[str, "tf.keras.metrics"] = None,
callbacks: tf.keras.callbacks = None,
) -> None:
"""
Train AEGMM model.
Parameters
----------
X
Training batch.
loss_fn
Loss function used for training.
w_energy
Weight on sample energy loss term if default `loss_aegmm` loss fn is used.
w_cov_diag
Weight on covariance regularizing loss term if default `loss_aegmm` loss fn is used.
optimizer
Optimizer used for training.
epochs
Number of training epochs.
batch_size
Batch size used for training.
verbose
Whether to print training progress.
log_metric
Additional metrics whose progress will be displayed if verbose equals True.
callbacks
Callbacks used during training.
"""
# train arguments
args = [self.aegmm, loss_fn, X]
kwargs = {'optimizer': optimizer,
'epochs': epochs,
'batch_size': batch_size,
'verbose': verbose,
'log_metric': log_metric,
'callbacks': callbacks,
'loss_fn_kwargs': {'w_energy': w_energy,
'w_cov_diag': w_cov_diag}
}
# train
trainer(*args, **kwargs)
# set GMM parameters
x_recon, z, gamma = self.aegmm(X)
self.phi, self.mu, self.cov, self.L, self.log_det_cov = gmm_params(z, gamma)
def loss_vaegmm(x_true: tf.Tensor,
x_pred: tf.Tensor,
z: tf.Tensor,
gamma: tf.Tensor,
w_recon: float = 1e-7,
w_energy: float = .1,
w_cov_diag: float = .005,
cov_full: tf.Tensor = None,
cov_diag: tf.Tensor = None,
sim: float = .05) -> tf.Tensor:
"""
Loss function used for OutlierVAEGMM.
Parameters
----------
x_true
Batch of instances.
x_pred
Batch of reconstructed instances by the variational autoencoder.
z
Latent space values.
gamma
Membership prediction for mixture model components.
w_recon
Weight on elbo loss term.
w_energy
Weight on sample energy loss term.
w_cov_diag
Weight on covariance regularizing loss term.
cov_full
Full covariance matrix.
cov_diag
Diagonal (variance) of covariance matrix.
sim
Scale identity multiplier.
Returns
-------
Loss value.
"""
recon_loss = elbo(x_true,
x_pred,
cov_full=cov_full,
cov_diag=cov_diag,
sim=sim)
phi, mu, cov, L, log_det_cov = gmm_params(z, gamma)
sample_energy, cov_diag = gmm_energy(z, phi, mu, cov, L, log_det_cov)
loss = w_recon * recon_loss + w_energy * sample_energy + w_cov_diag * cov_diag
return loss
def fit(self,
X: np.ndarray,
loss_fn: tf.keras.losses = loss_vaegmm,
w_recon: float = 1e-7,
w_energy: float = .1,
w_cov_diag: float = .005,
optimizer: tf.keras.optimizers = tf.keras.optimizers.Adam(learning_rate=1e-4),
cov_elbo: dict = dict(sim=.05),
epochs: int = 20,
batch_size: int = 64,
verbose: bool = True,
log_metric: Tuple[str, "tf.keras.metrics"] = None,
callbacks: tf.keras.callbacks = None,
) -> None:
"""
Train VAEGMM model.
Parameters
----------
X
Training batch.
loss_fn
Loss function used for training.
w_recon
Weight on elbo loss term if default `loss_vaegmm`.
w_energy
Weight on sample energy loss term if default `loss_vaegmm` loss fn is used.
w_cov_diag
Weight on covariance regularizing loss term if default `loss_vaegmm` loss fn is used.
optimizer
Optimizer used for training.
cov_elbo
Dictionary with covariance matrix options in case the elbo loss function is used.
Either use the full covariance matrix inferred from X (dict(cov_full=None)),
only the variance (dict(cov_diag=None)) or a float representing the same standard deviation
for each feature (e.g. dict(sim=.05)).
epochs
Number of training epochs.
batch_size
Batch size used for training.
verbose
Whether to print training progress.
log_metric
Additional metrics whose progress will be displayed if verbose equals True.
callbacks
Callbacks used during training.
"""
# train arguments
args = [self.vaegmm, loss_fn, X]
kwargs = {'optimizer': optimizer,
'epochs': epochs,
'batch_size': batch_size,
'verbose': verbose,
'log_metric': log_metric,
'callbacks': callbacks,
'loss_fn_kwargs': {'w_recon': w_recon,
'w_energy': w_energy,
'w_cov_diag': w_cov_diag}
}
# initialize covariance matrix if default vaegmm loss fn is used
use_elbo = loss_fn.__name__ == 'loss_vaegmm'
cov_elbo_type, cov = [*cov_elbo][0], [*cov_elbo.values()][0]
if use_elbo and cov_elbo_type in ['cov_full', 'cov_diag']:
cov = tfp.stats.covariance(X.reshape(X.shape[0], -1))
if cov_elbo_type == 'cov_diag': # infer standard deviation from covariance matrix
cov = tf.math.sqrt(tf.linalg.diag_part(cov))
if use_elbo:
kwargs['loss_fn_kwargs'][cov_elbo_type] = tf.dtypes.cast(cov, tf.float32)
# train
trainer(*args, **kwargs)
# set GMM parameters
x_recon, z, gamma = self.vaegmm(X)
self.phi, self.mu, self.cov, self.L, self.log_det_cov = gmm_params(z, gamma)
