def test_nb_not_zinb():
synthetic_dataset = SyntheticDataset()
svaec = SVAEC(synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
reconstruction_loss="nb")
infer_synthetic_svaec = JointSemiSupervisedVariationalInference(
svaec, synthetic_dataset, use_cuda=use_cuda)
infer_synthetic_svaec.train(n_epochs=1)
def test_synthetic_2():
synthetic_dataset = SyntheticDataset()
vaec = VAEC(synthetic_dataset.nb_genes, synthetic_dataset.n_batches,
synthetic_dataset.n_labels)
infer_synthetic_vaec = JointSemiSupervisedVariationalInference(
vaec,
synthetic_dataset,
use_cuda=use_cuda,
early_stopping_metric='ll',
frequency=1,
save_best_state_metric='accuracy',
on='labelled')
infer_synthetic_vaec.train(n_epochs=20)
infer_synthetic_vaec.svc_rf(unit_test=True)
def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
svaec = SVAEC(synthetic_dataset.nb_genes, synthetic_dataset.n_batches,
synthetic_dataset.n_labels)
infer_synthetic_svaec = JointSemiSupervisedVariationalInference(
svaec, synthetic_dataset, use_cuda=use_cuda)
infer_synthetic_svaec.train(n_epochs=1)
infer_synthetic_svaec.entropy_batch_mixing('labelled')
infer_synthetic_svaec.show_t_sne('labelled', n_samples=50)
infer_synthetic_svaec.show_t_sne('unlabelled',
n_samples=50,
color_by='labels')
infer_synthetic_svaec.show_t_sne('labelled',
n_samples=50,
color_by='batches and labels')
infer_synthetic_svaec.clustering_scores('labelled')
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_errors('test', rate=0.5)
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')
def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
svaec = SVAEC(synthetic_dataset.nb_genes, synthetic_dataset.n_batches,
synthetic_dataset.n_labels)
infer_synthetic_svaec = JointSemiSupervisedVariationalInference(
svaec, synthetic_dataset, use_cuda=use_cuda)
infer_synthetic_svaec.fit(n_epochs=1)
infer_synthetic_svaec.entropy_batch_mixing('labelled')
vaec = VAEC(synthetic_dataset.nb_genes, synthetic_dataset.n_batches,
synthetic_dataset.n_labels)
infer_synthetic_vaec = JointSemiSupervisedVariationalInference(
vaec,
synthetic_dataset,
use_cuda=use_cuda,
early_stopping_metric='ll',
frequency=1,
save_best_state_metric='accuracy',
on='labelled')
infer_synthetic_vaec.fit(n_epochs=20)
infer_synthetic_vaec.svc_rf(unit_test=True)
infer_synthetic_vaec.show_t_sne('labelled', n_samples=50)