def cortex_benchmark(n_epochs=250, use_cuda=True, unit_test=False):
cortex_dataset = CortexDataset()
vae = VAE(cortex_dataset.nb_genes)
infer_cortex_vae = VariationalInference(vae, cortex_dataset, use_cuda=use_cuda)
infer_cortex_vae.train(n_epochs=n_epochs)
infer_cortex_vae.ll('test') # assert ~ 1200
infer_cortex_vae.differential_expression('test')
infer_cortex_vae.imputation('test', rate=0.1) # assert ~ 2.3
n_samples = 1000 if not unit_test else 10
infer_cortex_vae.show_t_sne('test', n_samples=n_samples)
return infer_cortex_vae
def test_cortex():
cortex_dataset = CortexDataset()
vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
infer_cortex_vae = VariationalInference(vae,
cortex_dataset,
train_size=0.1,
use_cuda=use_cuda)
infer_cortex_vae.train(n_epochs=1)
infer_cortex_vae.ll('train')
infer_cortex_vae.differential_expression_stats('train')
infer_cortex_vae.differential_expression('test')
infer_cortex_vae.imputation('train', corruption='uniform')
infer_cortex_vae.imputation('test', n_samples=2, corruption='binomial')
svaec = SVAEC(cortex_dataset.nb_genes, cortex_dataset.n_batches,
cortex_dataset.n_labels)
infer_cortex_svaec = JointSemiSupervisedVariationalInference(
svaec,
cortex_dataset,
n_labelled_samples_per_class=50,
use_cuda=use_cuda)
infer_cortex_svaec.train(n_epochs=1)
infer_cortex_svaec.accuracy('labelled')
infer_cortex_svaec.ll('all')
svaec = SVAEC(cortex_dataset.nb_genes,
cortex_dataset.n_batches,
cortex_dataset.n_labels,
logreg_classifier=True)
infer_cortex_svaec = AlternateSemiSupervisedVariationalInference(
svaec,
cortex_dataset,
n_labelled_samples_per_class=50,
use_cuda=use_cuda)
infer_cortex_svaec.train(n_epochs=1, lr=1e-2)
infer_cortex_svaec.accuracy('unlabelled')
infer_cortex_svaec.svc_rf(unit_test=True)
cls = Classifier(cortex_dataset.nb_genes, n_labels=cortex_dataset.n_labels)
infer_cls = ClassifierInference(cls, cortex_dataset)
infer_cls.train(n_epochs=1)
infer_cls.accuracy('train')