def test_peakvi():
data = synthetic_iid(run_setup_anndata=False)
PEAKVI.setup_anndata(
data,
batch_key="batch",
)
vae = PEAKVI(
data,
model_depth=False,
)
vae.train(1, save_best=False)
vae = PEAKVI(
data,
region_factors=False,
)
vae.train(1, save_best=False)
vae = PEAKVI(data, )
vae.train(3)
vae.get_elbo(indices=vae.validation_indices)
vae.get_accessibility_estimates()
vae.get_accessibility_estimates(normalize_cells=True)
vae.get_accessibility_estimates(normalize_regions=True)
vae.get_library_size_factors()
vae.get_region_factors()
vae.get_reconstruction_error(indices=vae.validation_indices)
vae.get_latent_representation()
vae.differential_accessibility(groupby="labels", group1="label_1")
def test_peakvi_online_update(save_path):
n_latent = 5
adata1 = synthetic_iid()
PEAKVI.setup_anndata(adata1, batch_key="batch", labels_key="labels")
model = PEAKVI(adata1, n_latent=n_latent)
model.train(1, save_best=False)
dir_path = os.path.join(save_path, "saved_model/")
model.save(dir_path, overwrite=True)
# also test subset var option
adata2 = synthetic_iid()
adata2.obs["batch"] = adata2.obs.batch.cat.rename_categories(
["batch_2", "batch_3"])
model2 = PEAKVI.load_query_data(adata2, dir_path)
model2.train(max_epochs=1, weight_decay=0.0, save_best=False)
model2.get_latent_representation()
# encoder linear layer equal for peak features
one = (model.module.z_encoder.encoder.fc_layers[0]
[0].weight.detach().cpu().numpy()[:, :adata1.shape[1]])
two = (model2.module.z_encoder.encoder.fc_layers[0]
[0].weight.detach().cpu().numpy()[:, :adata1.shape[1]])
np.testing.assert_equal(one, two)
assert (np.sum(model2.module.z_encoder.encoder.fc_layers[0]
[0].weight.grad.cpu().numpy()[:, :adata1.shape[1]]) == 0)
# test options
adata1 = synthetic_iid()
PEAKVI.setup_anndata(adata1, batch_key="batch", labels_key="labels")
model = PEAKVI(
adata1,
n_latent=n_latent,
encode_covariates=True,
)
model.train(1, check_val_every_n_epoch=1, save_best=False)
dir_path = os.path.join(save_path, "saved_model/")
model.save(dir_path, overwrite=True)
adata2 = synthetic_iid()
adata2.obs["batch"] = adata2.obs.batch.cat.rename_categories(
["batch_2", "batch_3"])
model2 = PEAKVI.load_query_data(adata2, dir_path, freeze_expression=True)
model2.train(max_epochs=1, weight_decay=0.0, save_best=False)
# deactivate no grad decorator
model2.get_latent_representation()
# pytorch lightning zeros the grad, so this will get a grad to inspect
single_pass_for_online_update(model2)
grad = model2.module.z_encoder.encoder.fc_layers[0][0].weight.grad.cpu(
).numpy()
# expression part has zero grad
assert np.sum(grad[:, :-4]) == 0
# categorical part has non-zero grad
assert np.count_nonzero(grad[:, -4:]) > 0
# do not freeze expression
model3 = PEAKVI.load_query_data(
adata2,
dir_path,
freeze_expression=False,
freeze_decoder_first_layer=False,
)
model3.train(max_epochs=1, save_best=False, weight_decay=0.0)
model3.get_latent_representation()
single_pass_for_online_update(model3)
grad = model3.module.z_encoder.encoder.fc_layers[0][0].weight.grad.cpu(
).numpy()
# linear layer weight in encoder layer has non-zero grad
assert np.count_nonzero(grad[:, :-4]) != 0
grad = model3.module.z_decoder.px_decoder.fc_layers[0][0].weight.grad.cpu(
).numpy()
# linear layer weight in decoder layer has non-zero grad
assert np.count_nonzero(grad[:, :-4]) != 0