def test_autozi():
data = synthetic_iid(n_batches=1)
for disp_zi in ["gene", "gene-label"]:
autozivae = AUTOZI(
data,
dispersion=disp_zi,
zero_inflation=disp_zi,
)
autozivae.train(1, plan_kwargs=dict(lr=1e-2), check_val_every_n_epoch=1)
assert len(autozivae.history["elbo_train"]) == 1
assert len(autozivae.history["elbo_validation"]) == 1
autozivae.get_elbo(indices=autozivae.validation_indices)
autozivae.get_reconstruction_error(indices=autozivae.validation_indices)
autozivae.get_marginal_ll(indices=autozivae.validation_indices, n_mc_samples=3)
autozivae.get_alphas_betas()
def test_autozi():
data = synthetic_iid(n_batches=1)
for disp_zi in ["gene", "gene-label"]:
autozivae = AUTOZI(
data,
dispersion=disp_zi,
zero_inflation=disp_zi,
)
autozivae.train(1, lr=1e-2, frequency=1)
assert len(autozivae.history["elbo_train_set"]) == 2
assert len(autozivae.history["elbo_test_set"]) == 2
autozivae.get_elbo(indices=autozivae.test_indices)
autozivae.get_reconstruction_error(indices=autozivae.test_indices)
autozivae.get_marginal_ll(indices=autozivae.test_indices)
autozivae.get_alphas_betas()
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, PEAKVI]:
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_autozi():
data = synthetic_iid(n_batches=1)
for disp_zi in ["gene", "gene-label"]:
autozivae = AUTOZI(
data,
dispersion=disp_zi,
zero_inflation=disp_zi,
)
autozivae.train(1, lr=1e-2)
autozivae.get_elbo(indices=autozivae.test_indices)
autozivae.get_reconstruction_error(indices=autozivae.test_indices)
autozivae.get_marginal_ll(indices=autozivae.test_indices)
autozivae.get_alphas_betas()
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
def test_autozi():
data = synthetic_iid(n_batches=1, run_setup_anndata=False)
AUTOZI.setup_anndata(
data,
batch_key="batch",
labels_key="labels",
)
for disp_zi in ["gene", "gene-label"]:
autozivae = AUTOZI(
data,
dispersion=disp_zi,
zero_inflation=disp_zi,
)
autozivae.train(1,
plan_kwargs=dict(lr=1e-2),
check_val_every_n_epoch=1)
assert len(autozivae.history["elbo_train"]) == 1
assert len(autozivae.history["elbo_validation"]) == 1
autozivae.get_elbo(indices=autozivae.validation_indices)
autozivae.get_reconstruction_error(
indices=autozivae.validation_indices)
autozivae.get_marginal_ll(indices=autozivae.validation_indices,
n_mc_samples=3)
autozivae.get_alphas_betas()
# Model library size.
for disp_zi in ["gene", "gene-label"]:
autozivae = AUTOZI(
data,
dispersion=disp_zi,
zero_inflation=disp_zi,
use_observed_lib_size=False,
)
autozivae.train(1,
plan_kwargs=dict(lr=1e-2),
check_val_every_n_epoch=1)
assert hasattr(autozivae.module, "library_log_means") and hasattr(
autozivae.module, "library_log_vars")
assert len(autozivae.history["elbo_train"]) == 1
assert len(autozivae.history["elbo_validation"]) == 1
autozivae.get_elbo(indices=autozivae.validation_indices)
autozivae.get_reconstruction_error(
indices=autozivae.validation_indices)
autozivae.get_marginal_ll(indices=autozivae.validation_indices,
n_mc_samples=3)
autozivae.get_alphas_betas()
def test_save_load_autozi(legacy=False):
prefix = "AUTOZI_"
model = AUTOZI(adata, latent_distribution="normal")
model.train(1, train_size=0.5)
ab1 = model.get_alphas_betas()
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 = AUTOZI.load(save_path, prefix=prefix)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
AUTOZI.load(save_path, adata=tmp_adata, prefix=prefix)
model = AUTOZI.load(save_path, adata=adata, prefix=prefix)
assert "test" in adata.uns["_scvi"]["data_registry"]
assert adata.uns["_scvi"]["data_registry"]["test"] == dict(
attr_name="obs", attr_key="cont1")
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
def test_saving_and_loading(save_path):
def legacy_save(
model,
dir_path,
prefix=None,
overwrite=False,
save_anndata=False,
**anndata_write_kwargs,
):
if not os.path.exists(dir_path) or overwrite:
os.makedirs(dir_path, exist_ok=overwrite)
else:
raise ValueError(
"{} already exists. Please provide an unexisting directory for saving."
.format(dir_path))
file_name_prefix = prefix or ""
if save_anndata:
model.adata.write(
os.path.join(dir_path, f"{file_name_prefix}adata.h5ad"),
**anndata_write_kwargs,
)
model_save_path = os.path.join(dir_path,
f"{file_name_prefix}model_params.pt")
attr_save_path = os.path.join(dir_path, f"{file_name_prefix}attr.pkl")
varnames_save_path = os.path.join(dir_path,
f"{file_name_prefix}var_names.csv")
torch.save(model.module.state_dict(), model_save_path)
var_names = model.adata.var_names.astype(str)
var_names = var_names.to_numpy()
np.savetxt(varnames_save_path, var_names, fmt="%s")
# get all the user attributes
user_attributes = model._get_user_attributes()
# only save the public attributes with _ at the very end
user_attributes = {
a[0]: a[1]
for a in user_attributes if a[0][-1] == "_"
}
with open(attr_save_path, "wb") as f:
pickle.dump(user_attributes, f)
def test_save_load_model(cls, adata, save_path, prefix=None, legacy=False):
if cls is TOTALVI:
cls.setup_anndata(
adata,
batch_key="batch",
labels_key="labels",
protein_expression_obsm_key="protein_expression",
protein_names_uns_key="protein_names",
)
else:
cls.setup_anndata(adata, batch_key="batch", labels_key="labels")
model = cls(adata, latent_distribution="normal")
model.train(1, train_size=0.2)
z1 = model.get_latent_representation(adata)
test_idx1 = model.validation_indices
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 = cls.load(save_path, prefix=prefix)
model.get_latent_representation()
# Load with mismatched genes.
tmp_adata = synthetic_iid(n_genes=200, )
with pytest.raises(ValueError):
cls.load(save_path, adata=tmp_adata, prefix=prefix)
# Load with different batches.
tmp_adata = synthetic_iid()
tmp_adata.obs["batch"] = tmp_adata.obs["batch"].cat.rename_categories(
["batch_2", "batch_3"])
with pytest.raises(ValueError):
cls.load(save_path, adata=tmp_adata, prefix=prefix)
model = cls.load(save_path, adata=adata, prefix=prefix)
assert "batch" in model.adata_manager.data_registry
assert model.adata_manager.data_registry["batch"] == dict(
attr_name="obs", attr_key="_scvi_batch")
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, PEAKVI]:
test_save_load_model(cls,
adata,
save_path,
prefix=f"{cls.__name__}_",
legacy=True)
test_save_load_model(cls, adata, save_path, prefix=f"{cls.__name__}_")
# Test load prioritizes newer save paradigm and thus mismatches legacy save.
with pytest.raises(AssertionError):
test_save_load_model(cls,
adata,
save_path,
prefix=f"{cls.__name__}_",
legacy=True)
# AUTOZI
def test_save_load_autozi(legacy=False):
prefix = "AUTOZI_"
model = AUTOZI(adata, latent_distribution="normal")
model.train(1, train_size=0.5)
ab1 = model.get_alphas_betas()
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 = AUTOZI.load(save_path, prefix=prefix)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
AUTOZI.load(save_path, adata=tmp_adata, prefix=prefix)
model = AUTOZI.load(save_path, adata=adata, prefix=prefix)
assert "batch" in model.adata_manager.data_registry
assert model.adata_manager.data_registry["batch"] == dict(
attr_name="obs", attr_key="_scvi_batch")
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
AUTOZI.setup_anndata(adata, batch_key="batch", labels_key="labels")
test_save_load_autozi(legacy=True)
test_save_load_autozi()
# Test load prioritizes newer save paradigm and thus mismatches legacy save.
with pytest.raises(AssertionError):
test_save_load_autozi(legacy=True)
# SCANVI
def test_save_load_scanvi(legacy=False):
prefix = "SCANVI_"
model = SCANVI(adata)
model.train(max_epochs=1, train_size=0.5)
p1 = model.predict()
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 = SCANVI.load(save_path, prefix=prefix)
model.get_latent_representation()
tmp_adata = scvi.data.synthetic_iid(n_genes=200)
with pytest.raises(ValueError):
SCANVI.load(save_path, adata=tmp_adata, prefix=prefix)
model = SCANVI.load(save_path, adata=adata, prefix=prefix)
assert "batch" in model.adata_manager.data_registry
assert model.adata_manager.data_registry["batch"] == dict(
attr_name="obs", attr_key="_scvi_batch")
p2 = model.predict()
np.testing.assert_array_equal(p1, p2)
assert model.is_trained is True
SCANVI.setup_anndata(adata,
"label_0",
batch_key="batch",
labels_key="labels")
test_save_load_scanvi(legacy=True)
test_save_load_scanvi()
# Test load prioritizes newer save paradigm and thus mismatches legacy save.
with pytest.raises(AssertionError):
test_save_load_scanvi(legacy=True)