def test_scvi_sparse(save_path):
n_latent = 5
adata = synthetic_iid(run_setup_anndata=False)
adata.X = csr_matrix(adata.X)
setup_anndata(adata)
model = SCVI(adata, n_latent=n_latent)
model.train(1, train_size=0.5)
assert model.is_trained is True
z = model.get_latent_representation()
assert z.shape == (adata.shape[0], n_latent)
model.get_elbo()
model.get_marginal_ll(n_mc_samples=3)
model.get_reconstruction_error()
model.get_normalized_expression()
model.differential_expression(groupby="labels", group1="label_1")
def test_scvi(save_path):
n_latent = 5
adata = synthetic_iid()
model = SCVI(adata, n_latent=n_latent)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
model = SCVI(adata, n_latent=n_latent, var_activation=Softplus())
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
# tests __repr__
print(model)
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"]) == 1
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()
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
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(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
model.get_elbo(adata2)
# test automatic transfer_anndata_setup + on a view
adata = synthetic_iid()
model = SCVI(adata)
adata2 = synthetic_iid(run_setup_anndata=False)
model.get_elbo(adata2[:10])
# test that we catch incorrect mappings
adata = synthetic_iid()
adata2 = synthetic_iid(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
adata2.uns["_scvi"]["categorical_mappings"]["_scvi_labels"]["mapping"] = np.array(
["label_4", "label_0", "label_2"]
)
with pytest.raises(ValueError):
model.get_elbo(adata2)
# test that same mapping different order doesn't raise error
adata = synthetic_iid()
adata2 = synthetic_iid(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
adata2.uns["_scvi"]["categorical_mappings"]["_scvi_labels"]["mapping"] = np.array(
["label_1", "label_0", "label_2"]
)
model.get_elbo(adata2) # should automatically transfer setup
# test mismatched categories raises ValueError
adata2 = synthetic_iid(run_setup_anndata=False)
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(run_setup_anndata=False)
batch = np.zeros(a.n_obs)
batch[:64] += 1
a.obs["batch"] = batch
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()
m = scvi.model.SCVI(a)
lr_monitor = LearningRateMonitor()
m.train(
callbacks=[lr_monitor],
max_epochs=10,
log_every_n_steps=1,
plan_kwargs={"reduce_lr_on_plateau": True},
)
assert "lr-Adam" in m.history.keys()
def test_scvi():
n_latent = 5
adata = synthetic_iid()
model = SCVI(adata, n_latent=n_latent)
model.train(1, frequency=1, train_size=0.5)
assert model.is_trained is True
z = model.get_latent_representation()
assert z.shape == (adata.shape[0], n_latent)
# len of history should be 2 since metrics is also run once at the very end after training
assert len(model.history["elbo_train_set"]) == 2
model.get_elbo()
model.get_marginal_ll()
model.get_reconstruction_error()
model.get_normalized_expression(transform_batch="batch_1")
adata2 = synthetic_iid()
model.get_elbo(adata2)
model.get_marginal_ll(adata2)
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
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(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
model.get_elbo(adata2)
# test automatic transfer_anndata_setup + on a view
adata = synthetic_iid()
model = SCVI(adata)
adata2 = synthetic_iid(run_setup_anndata=False)
model.get_elbo(adata2[:10])
# test that we catch incorrect mappings
adata = synthetic_iid()
adata2 = synthetic_iid(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
adata2.uns["_scvi"]["categorical_mappings"]["_scvi_labels"][
"mapping"] = np.array(["label_1", "label_0", "label_2"])
with pytest.raises(ValueError):
model.get_elbo(adata2)
# test mismatched categories raises ValueError
adata2 = synthetic_iid(run_setup_anndata=False)
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(run_setup_anndata=False)
batch = np.zeros(a.n_obs)
batch[:64] += 1
a.obs["batch"] = batch
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])
def test_scvi():
n_latent = 5
adata = synthetic_iid()
model = SCVI(adata, n_latent=n_latent)
model.train(1)
assert model.is_trained is True
z = model.get_latent_representation()
assert z.shape == (adata.shape[0], n_latent)
model.get_elbo()
model.get_marginal_ll()
model.get_reconstruction_error()
model.get_normalized_expression()
adata2 = synthetic_iid()
model.get_elbo(adata2)
model.get_marginal_ll(adata2)
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
denoised = model.get_normalized_expression(adata2,
indices=[1, 2, 3],
transform_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,
)
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(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
model.get_elbo(adata2)
# test automatic transfer_anndata_setup + on a view
adata = synthetic_iid()
model = SCVI(adata)
adata2 = synthetic_iid(run_setup_anndata=False)
model.get_elbo(adata2[:10])
# test that we catch incorrect mappings
adata = synthetic_iid()
adata2 = synthetic_iid(run_setup_anndata=False)
transfer_anndata_setup(adata, adata2)
adata2.uns["_scvi"]["categorical_mappings"]["_scvi_labels"][
"mapping"] = pd.Index(
data=["undefined_1", "undefined_0", "undefined_2"])
with pytest.raises(ValueError):
model.get_elbo(adata2)
# test mismatched categories raises ValueError
adata2 = synthetic_iid(run_setup_anndata=False)
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="undefined_1")
model.differential_expression(groupby="labels",
group1="undefined_1",
group2="undefined_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])