def test_cortex(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
trainer_cortex_vae = UnsupervisedTrainer(vae, cortex_dataset, train_size=0.5, use_cuda=use_cuda)
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.train_set.ll()
trainer_cortex_vae.train_set.differential_expression_stats()
trainer_cortex_vae.corrupt_posteriors(corruption='binomial')
trainer_cortex_vae.corrupt_posteriors()
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.uncorrupt_posteriors()
trainer_cortex_vae.train_set.imputation_benchmark(n_samples=1, show_plot=False,
title_plot='imputation', save_path=save_path)
svaec = SCANVI(cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels)
trainer_cortex_svaec = JointSemiSupervisedTrainer(svaec, cortex_dataset,
n_labelled_samples_per_class=3,
use_cuda=use_cuda)
trainer_cortex_svaec.train(n_epochs=1)
trainer_cortex_svaec.labelled_set.accuracy()
trainer_cortex_svaec.full_dataset.ll()
svaec = SCANVI(cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels)
trainer_cortex_svaec = AlternateSemiSupervisedTrainer(svaec, cortex_dataset,
n_labelled_samples_per_class=3,
use_cuda=use_cuda)
trainer_cortex_svaec.train(n_epochs=1, lr=1e-2)
trainer_cortex_svaec.unlabelled_set.accuracy()
data_train, labels_train = trainer_cortex_svaec.labelled_set.raw_data()
data_test, labels_test = trainer_cortex_svaec.unlabelled_set.raw_data()
compute_accuracy_svc(data_train, labels_train, data_test, labels_test,
param_grid=[{'C': [1], 'kernel': ['linear']}])
compute_accuracy_rf(data_train, labels_train, data_test, labels_test,
param_grid=[{'max_depth': [3], 'n_estimators': [10]}])
cls = Classifier(cortex_dataset.nb_genes, n_labels=cortex_dataset.n_labels)
cls_trainer = ClassifierTrainer(cls, cortex_dataset)
cls_trainer.train(n_epochs=1)
cls_trainer.train_set.accuracy()
print("Transferring labels from scVI")
true_labels = labels.ravel()
scVI_labels = transfer_nn_labels(latent, labels, batch_indices)
# train scANVI
print("Training scANVI")
# scanvi = SCANVI(gene_dataset.nb_genes, gene_dataset.n_batches, gene_dataset.n_labels, n_latent=10)
scanvi = SCANVI(gene_dataset.nb_genes,
gene_dataset.n_batches,
gene_dataset.n_labels,
n_latent=10,
reconstruction_loss='nb')
scanvi.load_state_dict(trainer.model.state_dict(), strict=False)
trainer_scanvi = AlternateSemiSupervisedTrainer(scanvi,
gene_dataset,
n_epochs_classifier=5,
lr_classification=5 * 1e-3)
labelled = np.where(gene_dataset.batch_indices == 0)[0]
# np.random.shuffle(labelled)
unlabelled = np.where(gene_dataset.batch_indices == 1)[0]
# np.random.shuffle(unlabelled)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(indices=labelled)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=unlabelled)
# file_name = '%s/scanvi.pkl' % save_path
# if os.path.isfile(file_name):
# print("loaded model from: " + file_name)
# trainer_scanvi.model.load_state_dict(torch.load(file_name))
# trainer_scanvi.model.eval()
# else:
def test_cortex(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
trainer_cortex_vae = UnsupervisedTrainer(
vae, cortex_dataset, train_size=0.5, use_cuda=use_cuda
)
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.train_set.reconstruction_error()
trainer_cortex_vae.train_set.differential_expression_stats()
trainer_cortex_vae.train_set.generate_feature_correlation_matrix(
n_samples=2, correlation_type="pearson"
)
trainer_cortex_vae.train_set.generate_feature_correlation_matrix(
n_samples=2, correlation_type="spearman"
)
trainer_cortex_vae.train_set.imputation(n_samples=1)
trainer_cortex_vae.test_set.imputation(n_samples=5)
trainer_cortex_vae.corrupt_posteriors(corruption="binomial")
trainer_cortex_vae.corrupt_posteriors()
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.uncorrupt_posteriors()
trainer_cortex_vae.train_set.imputation_benchmark(
n_samples=1, show_plot=False, title_plot="imputation", save_path=save_path
)
trainer_cortex_vae.train_set.generate_parameters()
n_cells, n_genes = (
len(trainer_cortex_vae.train_set.indices),
cortex_dataset.nb_genes,
)
n_samples = 3
(dropout, means, dispersions,) = trainer_cortex_vae.train_set.generate_parameters()
assert dropout.shape == (n_cells, n_genes) and means.shape == (n_cells, n_genes)
assert dispersions.shape == (n_cells, n_genes)
(dropout, means, dispersions,) = trainer_cortex_vae.train_set.generate_parameters(
n_samples=n_samples
)
assert dropout.shape == (n_samples, n_cells, n_genes)
assert means.shape == (n_samples, n_cells, n_genes,)
(dropout, means, dispersions,) = trainer_cortex_vae.train_set.generate_parameters(
n_samples=n_samples, give_mean=True
)
assert dropout.shape == (n_cells, n_genes) and means.shape == (n_cells, n_genes)
full = trainer_cortex_vae.create_posterior(
vae, cortex_dataset, indices=np.arange(len(cortex_dataset))
)
x_new, x_old = full.generate(n_samples=10)
assert x_new.shape == (cortex_dataset.nb_cells, cortex_dataset.nb_genes, 10)
assert x_old.shape == (cortex_dataset.nb_cells, cortex_dataset.nb_genes)
trainer_cortex_vae.train_set.imputation_benchmark(
n_samples=1, show_plot=False, title_plot="imputation", save_path=save_path
)
svaec = SCANVI(
cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels
)
trainer_cortex_svaec = JointSemiSupervisedTrainer(
svaec, cortex_dataset, n_labelled_samples_per_class=3, use_cuda=use_cuda
)
trainer_cortex_svaec.train(n_epochs=1)
trainer_cortex_svaec.labelled_set.accuracy()
trainer_cortex_svaec.full_dataset.reconstruction_error()
svaec = SCANVI(
cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels
)
trainer_cortex_svaec = AlternateSemiSupervisedTrainer(
svaec, cortex_dataset, n_labelled_samples_per_class=3, use_cuda=use_cuda
)
trainer_cortex_svaec.train(n_epochs=1, lr=1e-2)
trainer_cortex_svaec.unlabelled_set.accuracy()
data_train, labels_train = trainer_cortex_svaec.labelled_set.raw_data()
data_test, labels_test = trainer_cortex_svaec.unlabelled_set.raw_data()
compute_accuracy_svc(
data_train,
labels_train,
data_test,
labels_test,
param_grid=[{"C": [1], "kernel": ["linear"]}],
)
compute_accuracy_rf(
data_train,
labels_train,
data_test,
labels_test,
param_grid=[{"max_depth": [3], "n_estimators": [10]}],
)
cls = Classifier(cortex_dataset.nb_genes, n_labels=cortex_dataset.n_labels)
cls_trainer = ClassifierTrainer(cls, cortex_dataset)
cls_trainer.train(n_epochs=1)
cls_trainer.train_set.accuracy()
celltype1)
g.write('vae' + '\t' + rmCellTypes +
("\t%.4f" * 8 + "\t%s" * 8 + "\n") %
tuple(be + list(cell_type2)))
plotUMAP(latent, plotname, 'vae', gene_dataset.cell_types,
rmCellTypes, gene_dataset.batch_indices.ravel())
scanvi = SCANVI(gene_dataset.nb_genes,
2, (gene_dataset.n_labels + 1),
n_hidden=128,
n_latent=10,
n_layers=2,
dispersion='gene')
scanvi.load_state_dict(trainer.model.state_dict(), strict=False)
trainer_scanvi = AlternateSemiSupervisedTrainer(
scanvi,
gene_dataset,
n_epochs_classifier=10,
lr_classification=5 * 1e-3)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=gene_dataset.batch_indices.ravel() == 0)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=gene_dataset.batch_indices.ravel() == 1)
trainer_scanvi.train(n_epochs=10)
scanvi_full = trainer_scanvi.create_posterior(
trainer_scanvi.model,
gene_dataset,
indices=np.arange(len(gene_dataset)))
latent, _, _ = scanvi_full.sequential().get_latent()
acc, cell_type = KNNpurity(latent1, latent2, latent,
batch_indices.ravel(), labels, keys)
f.write('scanvi' + '\t' + rmCellTypes +
def trainSCANVI(gene_dataset,
model_type,
filename,
rep,
nlayers=2,
reconstruction_loss: str = "zinb"):
vae_posterior = trainVAE(gene_dataset,
filename,
rep,
reconstruction_loss=reconstruction_loss)
filename = '../' + filename + '/' + model_type + '.' + reconstruction_loss + '.rep' + str(
rep) + '.pkl'
scanvi = SCANVI(gene_dataset.nb_genes,
gene_dataset.n_batches,
gene_dataset.n_labels,
n_layers=nlayers,
reconstruction_loss=reconstruction_loss)
scanvi.load_state_dict(vae_posterior.model.state_dict(), strict=False)
if model_type == 'scanvi1':
trainer_scanvi = AlternateSemiSupervisedTrainer(
scanvi,
gene_dataset,
classification_ratio=0,
n_epochs_classifier=100,
lr_classification=5 * 1e-3)
labelled = np.where(gene_dataset.batch_indices.ravel() == 0)[0]
labelled = np.random.choice(labelled, len(labelled), replace=False)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=labelled)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=(gene_dataset.batch_indices.ravel() == 1))
elif model_type == 'scanvi2':
trainer_scanvi = AlternateSemiSupervisedTrainer(
scanvi,
gene_dataset,
classification_ratio=0,
n_epochs_classifier=100,
lr_classification=5 * 1e-3)
labelled = np.where(gene_dataset.batch_indices.ravel() == 1)[0]
labelled = np.random.choice(labelled, len(labelled), replace=False)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=labelled)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=(gene_dataset.batch_indices.ravel() == 0))
elif model_type == 'scanvi0':
trainer_scanvi = SemiSupervisedTrainer(scanvi,
gene_dataset,
classification_ratio=0,
n_epochs_classifier=100,
lr_classification=5 * 1e-3)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=(gene_dataset.batch_indices.ravel() < 0))
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=(gene_dataset.batch_indices.ravel() >= 0))
else:
trainer_scanvi = SemiSupervisedTrainer(scanvi,
gene_dataset,
classification_ratio=10,
n_epochs_classifier=100,
lr_classification=5 * 1e-3)
if os.path.isfile(filename):
trainer_scanvi.model.load_state_dict(torch.load(filename))
trainer_scanvi.model.eval()
else:
trainer_scanvi.train(n_epochs=5)
torch.save(trainer_scanvi.model.state_dict(), filename)
return trainer_scanvi
