def test_saving_and_loading(save_path):
def test_save_load_model(cls, adata, save_path):
model = cls(adata, latent_distribution="normal")
model.train(1, train_size=0.2)
z1 = model.get_latent_representation(adata)
test_idx1 = model.validation_indices
model.save(save_path, overwrite=True, save_anndata=True)
model = cls.load(save_path)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
cls.load(save_path, tmp_adata)
model = cls.load(save_path, adata)
z2 = model.get_latent_representation()
test_idx2 = model.validation_indices
np.testing.assert_array_equal(z1, z2)
np.testing.assert_array_equal(test_idx1, test_idx2)
assert model.is_trained is True
save_path = os.path.join(save_path, "tmp")
adata = synthetic_iid()
for cls in [SCVI, LinearSCVI, TOTALVI]:
print(cls)
test_save_load_model(cls, adata, save_path)
# AUTOZI
model = AUTOZI(adata, latent_distribution="normal")
model.train(1, train_size=0.5)
ab1 = model.get_alphas_betas()
model.save(save_path, overwrite=True, save_anndata=True)
model = AUTOZI.load(save_path)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
AUTOZI.load(save_path, tmp_adata)
model = AUTOZI.load(save_path, adata)
ab2 = model.get_alphas_betas()
np.testing.assert_array_equal(ab1["alpha_posterior"],
ab2["alpha_posterior"])
np.testing.assert_array_equal(ab1["beta_posterior"], ab2["beta_posterior"])
assert model.is_trained is True
# SCANVI
model = SCANVI(adata, "label_0")
model.train(max_epochs=1, train_size=0.5)
p1 = model.predict()
model.save(save_path, overwrite=True, save_anndata=True)
model = SCANVI.load(save_path)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
SCANVI.load(save_path, tmp_adata)
model = SCANVI.load(save_path, adata)
p2 = model.predict()
np.testing.assert_array_equal(p1, p2)
assert model.is_trained is True
def test_saving_and_loading(save_path):
def test_save_load_model(cls, adata, save_path):
model = cls(adata, latent_distribution="normal")
model.train(1)
z1 = model.get_latent_representation(adata)
test_idx1 = model.test_indices
model.save(save_path, overwrite=True)
model = cls.load(adata, save_path)
z2 = model.get_latent_representation()
test_idx2 = model.test_indices
np.testing.assert_array_equal(z1, z2)
np.testing.assert_array_equal(test_idx1, test_idx2)
assert model.is_trained is True
save_path = os.path.join(save_path, "tmp")
adata = synthetic_iid()
for cls in [SCVI, LinearSCVI, TOTALVI]:
print(cls)
test_save_load_model(cls, adata, save_path)
# AUTOZI
model = AUTOZI(adata, latent_distribution="normal")
model.train(1)
ab1 = model.get_alphas_betas()
model.save(save_path, overwrite=True)
model = AUTOZI.load(adata, save_path)
ab2 = model.get_alphas_betas()
np.testing.assert_array_equal(ab1["alpha_posterior"],
ab2["alpha_posterior"])
np.testing.assert_array_equal(ab1["beta_posterior"], ab2["beta_posterior"])
assert model.is_trained is True
# SCANVI
model = SCANVI(adata, "undefined_0")
model.train(n_epochs_unsupervised=1, n_epochs_semisupervised=1)
p1 = model.predict()
model.save(save_path, overwrite=True)
model = SCANVI.load(adata, save_path)
p2 = model.predict()
np.testing.assert_array_equal(p1, p2)
assert model.is_trained is True
# GIMVI
model = GIMVI(adata, adata)
model.train(1)
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)
model = GIMVI.load(adata, adata, save_path)
z2 = model.get_latent_representation([adata])
np.testing.assert_array_equal(z1, z2)
assert model.is_trained is True