def test_new_setup_compat():
adata = synthetic_iid()
adata.obs["cat1"] = np.random.randint(0, 5, size=(adata.shape[0], ))
adata.obs["cat2"] = np.random.randint(0, 5, size=(adata.shape[0], ))
adata.obs["cont1"] = np.random.normal(size=(adata.shape[0], ))
adata.obs["cont2"] = np.random.normal(size=(adata.shape[0], ))
adata2 = adata.copy()
adata3 = adata.copy()
SCVI.setup_anndata(
adata,
batch_key="batch",
labels_key="labels",
categorical_covariate_keys=["cat1", "cat2"],
continuous_covariate_keys=["cont1", "cont2"],
)
model = SCVI(adata)
adata_manager = model.adata_manager
model.view_anndata_setup(hide_state_registries=True)
field_registries = adata_manager.registry[_constants._FIELD_REGISTRIES_KEY]
field_registries_legacy_subset = {
k: v
for k, v in field_registries.items() if k in LEGACY_REGISTRY_KEYS
}
# Backwards compatibility test.
adata2_manager = manager_from_setup_dict(SCVI, adata2, LEGACY_SETUP_DICT)
np.testing.assert_equal(
field_registries_legacy_subset,
adata2_manager.registry[_constants._FIELD_REGISTRIES_KEY],
)
# Test transfer.
adata3_manager = adata_manager.transfer_setup(adata3)
np.testing.assert_equal(
field_registries,
adata3_manager.registry[_constants._FIELD_REGISTRIES_KEY],
)
def test_scvi(save_path):
n_latent = 5
# Test with size factor.
adata = synthetic_iid()
adata.obs["size_factor"] = np.random.randint(1, 5, size=(adata.shape[0], ))
SCVI.setup_anndata(
adata,
batch_key="batch",
labels_key="labels",
size_factor_key="size_factor",
)
model = SCVI(adata, n_latent=n_latent)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
# Test with observed lib size.
adata = synthetic_iid()
SCVI.setup_anndata(
adata,
batch_key="batch",
labels_key="labels",
)
model = SCVI(adata, n_latent=n_latent)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
# Test without observed lib size.
model = SCVI(adata,
n_latent=n_latent,
var_activation=Softplus(),
use_observed_lib_size=False)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
# tests __repr__
print(model)
# test view_registry
model.view_anndata_setup()
model.view_anndata_setup(hide_state_registries=True)
assert model.is_trained is True
z = model.get_latent_representation()
assert z.shape == (adata.shape[0], n_latent)
assert len(model.history["elbo_train"]) == 2
model.get_elbo()
model.get_marginal_ll(n_mc_samples=3)
model.get_reconstruction_error()
model.get_normalized_expression(transform_batch="batch_1")
adata2 = synthetic_iid()
# test view_registry with different anndata before transfer setup
with pytest.raises(ValueError):
model.view_anndata_setup(adata=adata2)
model.view_anndata_setup(adata=adata2, hide_state_registries=True)
# test get methods with different anndata
model.get_elbo(adata2)
model.get_marginal_ll(adata2, n_mc_samples=3)
model.get_reconstruction_error(adata2)
latent = model.get_latent_representation(adata2, indices=[1, 2, 3])
assert latent.shape == (3, n_latent)
denoised = model.get_normalized_expression(adata2)
assert denoised.shape == adata.shape
# test view_registry with different anndata after transfer setup
model.view_anndata_setup(adata=adata2)
model.view_anndata_setup(adata=adata2, hide_state_registries=True)
denoised = model.get_normalized_expression(adata2,
indices=[1, 2, 3],
transform_batch="batch_1")
denoised = model.get_normalized_expression(
adata2, indices=[1, 2, 3], transform_batch=["batch_0", "batch_1"])
assert denoised.shape == (3, adata2.n_vars)
sample = model.posterior_predictive_sample(adata2)
assert sample.shape == adata2.shape
sample = model.posterior_predictive_sample(adata2,
indices=[1, 2, 3],
gene_list=["1", "2"])
assert sample.shape == (3, 2)
sample = model.posterior_predictive_sample(adata2,
indices=[1, 2, 3],
gene_list=["1", "2"],
n_samples=3)
assert sample.shape == (3, 2, 3)
model.get_feature_correlation_matrix(correlation_type="pearson")
model.get_feature_correlation_matrix(
adata2,
indices=[1, 2, 3],
correlation_type="spearman",
rna_size_factor=500,
n_samples=5,
)
model.get_feature_correlation_matrix(
adata2,
indices=[1, 2, 3],
correlation_type="spearman",
rna_size_factor=500,
n_samples=5,
transform_batch=["batch_0", "batch_1"],
)
params = model.get_likelihood_parameters()
assert params["mean"].shape == adata.shape
assert (params["mean"].shape == params["dispersions"].shape ==
params["dropout"].shape)
params = model.get_likelihood_parameters(adata2, indices=[1, 2, 3])
assert params["mean"].shape == (3, adata.n_vars)
params = model.get_likelihood_parameters(adata2,
indices=[1, 2, 3],
n_samples=3,
give_mean=True)
assert params["mean"].shape == (3, adata.n_vars)
model.get_latent_library_size()
model.get_latent_library_size(adata2, indices=[1, 2, 3])
# test transfer_anndata_setup
adata2 = synthetic_iid()
model._validate_anndata(adata2)
model.get_elbo(adata2)
# test automatic transfer_anndata_setup + on a view
adata = synthetic_iid()
SCVI.setup_anndata(
adata,
batch_key="batch",
labels_key="labels",
)
model = SCVI(adata)
adata2 = synthetic_iid()
model.get_elbo(adata2[:10])
# test mismatched categories raises ValueError
adata2 = synthetic_iid()
adata2.obs.labels.cat.rename_categories(["a", "b", "c"], inplace=True)
with pytest.raises(ValueError):
model.get_elbo(adata2)
# test differential expression
model.differential_expression(groupby="labels", group1="label_1")
model.differential_expression(groupby="labels",
group1="label_1",
group2="label_2",
mode="change")
model.differential_expression(groupby="labels")
model.differential_expression(idx1=[0, 1, 2], idx2=[3, 4, 5])
model.differential_expression(idx1=[0, 1, 2])
# transform batch works with all different types
a = synthetic_iid()
batch = np.zeros(a.n_obs)
batch[:64] += 1
a.obs["batch"] = batch
SCVI.setup_anndata(
a,
batch_key="batch",
)
m = SCVI(a)
m.train(1, train_size=0.5)
m.get_normalized_expression(transform_batch=1)
m.get_normalized_expression(transform_batch=[0, 1])
# test get_likelihood_parameters() when dispersion=='gene-cell'
model = SCVI(adata, dispersion="gene-cell")
model.get_likelihood_parameters()
# test train callbacks work
a = synthetic_iid()
SCVI.setup_anndata(
a,
batch_key="batch",
labels_key="labels",
)
m = scvi.model.SCVI(a)
lr_monitor = LearningRateMonitor()
m.train(
callbacks=[lr_monitor],
max_epochs=10,
check_val_every_n_epoch=1,
log_every_n_steps=1,
plan_kwargs={"reduce_lr_on_plateau": True},
)
assert "lr-Adam" in m.history.keys()