def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
synthetic_dataset.cell_types = np.array(["A", "B", "C"])
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(
svaec, synthetic_dataset, use_cuda=use_cuda
)
trainer_synthetic_svaec.train(n_epochs=1)
trainer_synthetic_svaec.labelled_set.entropy_batch_mixing()
trainer_synthetic_svaec.full_dataset.knn_purity()
trainer_synthetic_svaec.labelled_set.show_t_sne(n_samples=5)
trainer_synthetic_svaec.unlabelled_set.show_t_sne(n_samples=5, color_by="labels")
trainer_synthetic_svaec.labelled_set.show_t_sne(
n_samples=5, color_by="batches and labels"
)
trainer_synthetic_svaec.labelled_set.clustering_scores()
trainer_synthetic_svaec.labelled_set.clustering_scores(prediction_algorithm="gmm")
trainer_synthetic_svaec.unlabelled_set.unsupervised_classification_accuracy()
trainer_synthetic_svaec.unlabelled_set.differential_expression_score(
synthetic_dataset.labels.ravel() == 1,
synthetic_dataset.labels.ravel() == 2,
n_samples=2,
M_permutation=10,
)
trainer_synthetic_svaec.unlabelled_set.one_vs_all_degenes(
n_samples=2, M_permutation=10
)
def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
synthetic_dataset.cell_types = np.array(['A', 'B', 'C'])
svaec = SCANVI(synthetic_dataset.nb_genes, synthetic_dataset.n_batches,
synthetic_dataset.n_labels)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(svaec,
synthetic_dataset,
use_cuda=use_cuda)
trainer_synthetic_svaec.train(n_epochs=1)
trainer_synthetic_svaec.labelled_set.entropy_batch_mixing()
trainer_synthetic_svaec.full_dataset.knn_purity(verbose=True)
trainer_synthetic_svaec.labelled_set.show_t_sne(n_samples=5)
trainer_synthetic_svaec.unlabelled_set.show_t_sne(n_samples=5,
color_by='labels')
trainer_synthetic_svaec.labelled_set.show_t_sne(
n_samples=5, color_by='batches and labels')
trainer_synthetic_svaec.labelled_set.clustering_scores()
trainer_synthetic_svaec.labelled_set.clustering_scores(
prediction_algorithm='gmm')
trainer_synthetic_svaec.unlabelled_set.unsupervised_classification_accuracy(
)
trainer_synthetic_svaec.unlabelled_set.differential_expression_score(
'B', 'C', genes=['2', '4'], M_sampling=2, M_permutation=10)
trainer_synthetic_svaec.unlabelled_set.differential_expression_table(
M_sampling=2, M_permutation=10)
def test_synthetic_2():
synthetic_dataset = SyntheticDataset()
vaec = VAEC(synthetic_dataset.nb_genes, synthetic_dataset.n_batches, synthetic_dataset.n_labels)
trainer_synthetic_vaec = JointSemiSupervisedTrainer(vaec, synthetic_dataset, use_cuda=use_cuda, frequency=1,
early_stopping_kwargs={'early_stopping_metric': 'll',
'on': 'labelled_set',
'save_best_state_metric': 'll'})
trainer_synthetic_vaec.train(n_epochs=2)
def test_nb_not_zinb():
synthetic_dataset = SyntheticDataset()
svaec = SCANVI(synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
labels_groups=[0, 0, 1],
reconstruction_loss="nb")
trainer_synthetic_svaec = JointSemiSupervisedTrainer(svaec, synthetic_dataset, use_cuda=use_cuda)
trainer_synthetic_svaec.train(n_epochs=1)
def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
synthetic_dataset.cell_types = np.array(["A", "B", "C"])
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(svaec,
synthetic_dataset,
use_cuda=use_cuda)
trainer_synthetic_svaec.train(n_epochs=1)
trainer_synthetic_svaec.labelled_set.entropy_batch_mixing()
with tempfile.TemporaryDirectory() as temp_dir:
posterior_save_path = os.path.join(temp_dir, "posterior_data")
original_post = trainer_synthetic_svaec.labelled_set.sequential()
original_post.save_posterior(posterior_save_path)
new_svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
new_post = load_posterior(posterior_save_path,
model=new_svaec,
use_cuda=False)
assert np.array_equal(new_post.indices, original_post.indices)
assert np.array_equal(new_post.gene_dataset.X,
original_post.gene_dataset.X)
assert np.array_equal(new_post.gene_dataset.labels,
original_post.gene_dataset.labels)
trainer_synthetic_svaec.full_dataset.knn_purity()
trainer_synthetic_svaec.labelled_set.show_t_sne(n_samples=5)
trainer_synthetic_svaec.unlabelled_set.show_t_sne(n_samples=5,
color_by="labels")
trainer_synthetic_svaec.labelled_set.show_t_sne(
n_samples=5, color_by="batches and labels")
trainer_synthetic_svaec.labelled_set.clustering_scores()
trainer_synthetic_svaec.labelled_set.clustering_scores(
prediction_algorithm="gmm")
trainer_synthetic_svaec.unlabelled_set.unsupervised_classification_accuracy(
)
trainer_synthetic_svaec.unlabelled_set.differential_expression_score(
synthetic_dataset.labels.ravel() == 1,
synthetic_dataset.labels.ravel() == 2,
n_samples=2,
M_permutation=10,
)
trainer_synthetic_svaec.unlabelled_set.one_vs_all_degenes(n_samples=2,
M_permutation=10)
def test_hierarchy():
synthetic_dataset = SyntheticDataset()
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
ontology=[
np.array([[1, 1, 0], [0, 0, 1]]),
np.array([[1, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 1]])
],
use_ontology=True,
reconstruction_loss="zinb",
n_layers=3,
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(svaec,
synthetic_dataset,
use_cuda=use_cuda)
trainer_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,
)
trainer_synthetic_vaec = JointSemiSupervisedTrainer(
vaec,
synthetic_dataset,
use_cuda=use_cuda,
frequency=1,
early_stopping_kwargs={
"early_stopping_metric": "reconstruction_error",
"on": "labelled_set",
"save_best_state_metric": "reconstruction_error",
},
)
trainer_synthetic_vaec.train(n_epochs=2)
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()
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()
