def test_gimvi():
adata_seq = synthetic_iid()
adata_spatial = synthetic_iid()
model = GIMVI(adata_seq, adata_spatial, n_latent=10)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
model.get_latent_representation()
model.get_imputed_values()
adata_spatial.var_names += "asdf"
with pytest.raises(ValueError):
model = GIMVI(adata_seq, adata_spatial)
def test_gimvi():
adata_seq = synthetic_iid()
adata_spatial = synthetic_iid()
model = GIMVI(adata_seq, adata_spatial, n_latent=10)
assert hasattr(model.module, "library_log_means_0") and not hasattr(
model.module, "library_log_means_1"
)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
model.get_latent_representation()
model.get_imputed_values()
adata_spatial.var_names += "asdf"
with pytest.raises(ValueError):
model = GIMVI(adata_seq, adata_spatial)
def test_gimvi_model_library_size():
adata_seq = synthetic_iid()
adata_spatial = synthetic_iid()
model = GIMVI(
adata_seq, adata_spatial, model_library_size=[True, True], n_latent=10
)
assert hasattr(model.module, "library_log_means_0") and hasattr(
model.module, "library_log_means_1"
)
model.train(1, check_val_every_n_epoch=1, train_size=0.5)
model.get_latent_representation()
model.get_imputed_values()
def test_save_and_load(save_path, legacy=False):
prefix = "GIMVI_"
adata = synthetic_iid()
GIMVI.setup_anndata(
adata,
batch_key="batch",
)
adata2 = synthetic_iid()
GIMVI.setup_anndata(
adata2,
batch_key="batch",
)
# GIMVI
model = GIMVI(adata, adata2)
model.train(3, train_size=0.5)
z1 = model.get_latent_representation([adata])
z2 = model.get_latent_representation([adata])
np.testing.assert_array_equal(z1, z2)
if legacy:
legacy_save(model,
save_path,
overwrite=True,
save_anndata=True,
prefix=prefix)
else:
model.save(save_path,
overwrite=True,
save_anndata=True,
prefix=prefix)
model = GIMVI.load(save_path, prefix=prefix)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
tmp_adata2 = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
GIMVI.load(save_path,
adata_seq=tmp_adata,
adata_spatial=tmp_adata2,
prefix=prefix)
model = GIMVI.load(save_path,
adata_seq=adata,
adata_spatial=adata2,
prefix=prefix)
z2 = model.get_latent_representation([adata])
np.testing.assert_array_equal(z1, z2)
model = GIMVI.load(
save_path,
adata_seq=adata,
adata_spatial=adata2,
use_gpu=False,
prefix=prefix,
)
z2 = model.get_latent_representation([adata])
np.testing.assert_almost_equal(z1, z2, decimal=3)
assert model.is_trained is True
def test_saving_and_loading(save_path):
adata = synthetic_iid()
# GIMVI
model = GIMVI(adata, adata)
model.train(3, train_size=0.5)
z1 = model.get_latent_representation([adata])
z2 = model.get_latent_representation([adata])
np.testing.assert_array_equal(z1, z2)
model.save(save_path, overwrite=True, save_anndata=True)
model = GIMVI.load(save_path)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
GIMVI.load(save_path, tmp_adata, tmp_adata)
model = GIMVI.load(save_path, adata, adata)
z2 = model.get_latent_representation([adata])
np.testing.assert_array_equal(z1, z2)
assert model.is_trained is True