def __init__(self, n_input: int, n_batch: int = 0, n_labels: int = 0,
n_hidden: int = 128, n_latent: int = 10, n_layers: int = 1,
dropout_rate: float = 0.1, dispersion: str = "gene",
log_variational: bool = True, reconstruction_loss: str = "zinb",
y_prior=None, labels_groups: Sequence[int] = None, use_labels_groups: bool = False,
classifier_parameters: dict = dict()):
super().__init__(n_input, n_hidden=n_hidden, n_latent=n_latent, n_layers=n_layers,
dropout_rate=dropout_rate, n_batch=n_batch, dispersion=dispersion,
log_variational=log_variational, reconstruction_loss=reconstruction_loss)
self.n_labels = n_labels
# Classifier takes n_latent as input
cls_parameters = {"n_layers": n_layers, "n_hidden": n_hidden, "dropout_rate": dropout_rate}
cls_parameters.update(classifier_parameters)
self.classifier = Classifier(n_latent, n_labels=n_labels, **cls_parameters)
self.encoder_z2_z1 = Encoder(n_latent, n_latent, n_cat_list=[self.n_labels], n_layers=n_layers,
n_hidden=n_hidden, dropout_rate=dropout_rate)
self.decoder_z1_z2 = Decoder(n_latent, n_latent, n_cat_list=[self.n_labels], n_layers=n_layers,
n_hidden=n_hidden)
self.y_prior = torch.nn.Parameter(
y_prior if y_prior is not None else (1 / n_labels) * torch.ones(1, n_labels), requires_grad=False
)
self.use_labels_groups = use_labels_groups
self.labels_groups = np.array(labels_groups) if labels_groups is not None else None
if self.use_labels_groups:
assert labels_groups is not None, "Specify label groups"
unique_groups = np.unique(self.labels_groups)
self.n_groups = len(unique_groups)
assert (unique_groups == np.arange(self.n_groups)).all()
self.classifier_groups = Classifier(n_latent, n_hidden, self.n_groups, n_layers, dropout_rate)
self.groups_index = torch.nn.ParameterList([torch.nn.Parameter(
torch.tensor((self.labels_groups == i).astype(np.uint8), dtype=torch.uint8), requires_grad=False
) for i in range(self.n_groups)])
def __init__(self,
n_input,
n_labels,
n_hidden=128,
n_latent=10,
n_layers=1,
dropout_rate=0.1,
n_batch=0,
y_prior=None,
use_cuda=False):
super(SVAEC, self).__init__()
self.n_labels = n_labels
self.n_input = n_input
self.y_prior = y_prior if y_prior is not None else (
1 / self.n_labels) * torch.ones(self.n_labels)
# Automatically desactivate if useless
self.n_batch = 0 if n_batch == 1 else n_batch
self.z_encoder = Encoder(n_input,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dropout_rate=dropout_rate)
self.l_encoder = Encoder(n_input,
n_hidden=n_hidden,
n_latent=1,
n_layers=1,
dropout_rate=dropout_rate)
self.decoder = DecoderSCVI(n_latent,
n_input,
n_hidden=n_hidden,
n_layers=n_layers,
dropout_rate=dropout_rate,
n_batch=n_batch)
self.dispersion = 'gene'
self.px_r = torch.nn.Parameter(torch.randn(n_input, ))
# Classifier takes n_latent as input
self.classifier = Classifier(n_latent, n_hidden, self.n_labels,
n_layers, dropout_rate)
self.encoder_z2_z1 = Encoder(n_input=n_latent,
n_cat=self.n_labels,
n_latent=n_latent,
n_layers=n_layers)
self.decoder_z1_z2 = Decoder(n_latent,
n_latent,
n_cat=self.n_labels,
n_layers=n_layers)
self.use_cuda = use_cuda and torch.cuda.is_available()
if self.use_cuda:
self.cuda()
self.y_prior = self.y_prior.cuda()
def __init__(self,
n_input,
n_batch,
n_labels,
n_hidden=128,
n_latent=10,
n_layers=1,
dropout_rate=0.1,
y_prior=None,
logreg_classifier=False,
dispersion="gene",
log_variational=True,
reconstruction_loss="zinb"):
super(SVAEC, self).__init__(n_input,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dropout_rate=dropout_rate,
n_batch=n_batch,
dispersion=dispersion,
log_variational=log_variational,
reconstruction_loss=reconstruction_loss)
self.n_labels = n_labels
self.n_latent_layers = 2
# Classifier takes n_latent as input
if logreg_classifier:
self.classifier = LinearLogRegClassifier(n_latent, self.n_labels)
else:
self.classifier = Classifier(n_latent, n_hidden, self.n_labels,
n_layers, dropout_rate)
self.encoder_z2_z1 = Encoder(n_latent,
n_latent,
n_cat_list=[self.n_labels],
n_layers=n_layers,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
self.decoder_z1_z2 = Decoder(n_latent,
n_latent,
n_cat_list=[self.n_labels],
n_layers=n_layers,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
self.y_prior = torch.nn.Parameter(y_prior if y_prior is not None else
(1 / self.n_labels) *
torch.ones(self.n_labels),
requires_grad=False)
def __init__(self,
n_input,
n_batch,
n_labels,
n_hidden=128,
n_latent=10,
n_layers=1,
dropout_rate=0.1,
y_prior=None,
logreg_classifier=False,
dispersion="gene",
log_variational=True,
reconstruction_loss="zinb",
labels_groups=None,
use_labels_groups=False):
super(SVAEC, self).__init__(n_input,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dropout_rate=dropout_rate,
n_batch=n_batch,
dispersion=dispersion,
log_variational=log_variational,
reconstruction_loss=reconstruction_loss)
self.n_labels = n_labels
self.n_latent_layers = 2
# Classifier takes n_latent as input
if logreg_classifier:
self.classifier = LinearLogRegClassifier(n_latent, self.n_labels)
else:
self.classifier = Classifier(n_latent, n_hidden, self.n_labels,
n_layers, dropout_rate)
self.encoder_z2_z1 = Encoder(n_latent,
n_latent,
n_cat_list=[self.n_labels],
n_layers=n_layers,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
self.decoder_z1_z2 = Decoder(n_latent,
n_latent,
n_cat_list=[self.n_labels],
n_layers=n_layers,
n_hidden=n_hidden,
dropout_rate=dropout_rate)
self.y_prior = torch.nn.Parameter(y_prior if y_prior is not None else
(1 / n_labels) *
torch.ones(1, n_labels),
requires_grad=False)
self.use_labels_groups = use_labels_groups
self.labels_groups = np.array(
labels_groups) if labels_groups is not None else None
if self.use_labels_groups:
assert labels_groups is not None, "Specify label groups"
unique_groups = np.unique(self.labels_groups)
self.n_groups = len(unique_groups)
assert (unique_groups == np.arange(self.n_groups)).all()
self.classifier_groups = Classifier(n_latent, n_hidden,
self.n_groups, n_layers,
dropout_rate)
self.groups_index = torch.nn.ParameterList([
torch.nn.Parameter(torch.tensor(
(self.labels_groups == i).astype(np.uint8),
dtype=torch.uint8),
requires_grad=False)
for i in range(self.n_groups)
])
