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')