def test_special_dataset_size(self):
gene_dataset = GeneExpressionDataset()
x = np.random.randint(1, 100, (17 * 2, 10))
y = np.random.randint(1, 100, (17 * 2, 10))
gene_dataset.populate_from_data(x)
protein_data = CellMeasurement(
name="protein_expression",
data=y,
columns_attr_name="protein_names",
columns=np.arange(10),
)
gene_dataset.initialize_cell_measurement(protein_data)
# Test UnsupervisedTrainer
vae = VAE(
gene_dataset.nb_genes,
n_batch=gene_dataset.n_batches,
n_labels=gene_dataset.n_labels,
)
trainer = UnsupervisedTrainer(
vae,
gene_dataset,
train_size=0.5,
use_cuda=False,
data_loader_kwargs={"batch_size": 8},
)
trainer.train(n_epochs=1)
# Test JVATrainer
jvae = JVAE(
[gene_dataset.nb_genes, gene_dataset.nb_genes],
gene_dataset.nb_genes,
[slice(None)] * 2,
["zinb", "zinb"],
[True, True],
n_batch=1,
)
cls = Classifier(gene_dataset.nb_genes, n_labels=2, logits=True)
trainer = JVAETrainer(
jvae,
cls,
[gene_dataset, gene_dataset],
train_size=0.5,
use_cuda=False,
data_loader_kwargs={"batch_size": 8},
)
trainer.train(n_epochs=1)
totalvae = TOTALVI(gene_dataset.nb_genes,
len(gene_dataset.protein_names))
trainer = TotalTrainer(
totalvae,
gene_dataset,
train_size=0.5,
use_cuda=False,
data_loader_kwargs={"batch_size": 8},
early_stopping_kwargs=None,
)
trainer.train(n_epochs=1)
def test_special_dataset_size(self):
gene_dataset = GeneExpressionDataset()
x = np.random.randint(1, 100, (17 * 2, 10))
gene_dataset.populate_from_data(x)
# Test UnsupervisedTrainer
vae = VAE(
gene_dataset.nb_genes,
n_batch=gene_dataset.n_batches,
n_labels=gene_dataset.n_labels,
)
trainer = UnsupervisedTrainer(
vae,
gene_dataset,
train_size=0.5,
use_cuda=False,
data_loader_kwargs={"batch_size": 8},
)
trainer.train(n_epochs=1)
# Test JVATrainer
jvae = JVAE(
[gene_dataset.nb_genes, gene_dataset.nb_genes],
gene_dataset.nb_genes,
[slice(None)] * 2,
["zinb", "zinb"],
[True, True],
n_batch=1,
)
cls = Classifier(gene_dataset.nb_genes, n_labels=2, logits=True)
trainer = JVAETrainer(
jvae,
cls,
[gene_dataset, gene_dataset],
train_size=0.5,
use_cuda=False,
data_loader_kwargs={"batch_size": 8},
)
trainer.train(n_epochs=1)
n_layers_decoder_individual=0,
n_layers_decoder_shared=0,
n_layers_encoder_individual=1,
n_layers_encoder_shared=1,
dim_hidden_encoder=64,
dim_hidden_decoder_shared=64,
dropout_rate_encoder=0.2,
dropout_rate_decoder=0.2,
n_batch=n_batches,
n_latent=n_latent,
)
discriminator = Classifier(n_latent, 32, 2, 3, logits=True)
trainer = JVAETrainer(model,
discriminator,
datasets,
0.95,
frequency=1,
kappa=kappa)
trainer.train(n_epochs=200)
_, Imputed = trainer.get_imputed_values(normalized=True)
Imputed = np.reshape(
Imputed[:, np.argwhere(i == Common_data.gene_names)[0]], -1)
Imp_Genes[i] = Imputed
gimVI_time.append(tm.time() - start)
Imp_Genes = Imp_Genes.fillna(0)
Imp_Genes.to_csv('Results/gimVI_LeaveOneOut.csv')
gimVI_time = pd.DataFrame(gimVI_time)
gimVI_time.to_csv('Results/gimVI_Time.csv', index=False)
### New genes