def test_extra_covariates_transfer(): adata = synthetic_iid() adata.obs["cont1"] = np.random.normal(size=(adata.shape[0],)) adata.obs["cont2"] = np.random.normal(size=(adata.shape[0],)) adata.obs["cat1"] = np.random.randint(0, 5, size=(adata.shape[0],)) adata.obs["cat2"] = np.random.randint(0, 5, size=(adata.shape[0],)) setup_anndata( adata, batch_key="batch", labels_key="labels", protein_expression_obsm_key="protein_expression", protein_names_uns_key="protein_names", continuous_covariate_keys=["cont1", "cont2"], categorical_covariate_keys=["cat1", "cat2"], ) bdata = synthetic_iid() bdata.obs["cont1"] = np.random.normal(size=(bdata.shape[0],)) bdata.obs["cont2"] = np.random.normal(size=(bdata.shape[0],)) bdata.obs["cat1"] = 0 bdata.obs["cat2"] = 1 transfer_anndata_setup(adata_source=adata, adata_target=bdata) # give it a new category del bdata.uns["_scvi"] bdata.obs["cat1"] = 6 transfer_anndata_setup( adata_source=adata, adata_target=bdata, extend_categories=True ) assert bdata.uns["_scvi"]["extra_categoricals"]["mappings"]["cat1"][-1] == 6
def _validate_anndata(self, adata: Optional[AnnData] = None, copy_if_view: bool = True): """Validate anndata has been properly registered, transfer if necessary.""" if adata is None: adata = self.adata if adata.is_view: if copy_if_view: logger.info("Received view of anndata, making copy.") adata = adata.copy() else: raise ValueError("Please run `adata = adata.copy()`") if "_scvi" not in adata.uns_keys(): logger.info("Input adata not setup with scvi. " + "attempting to transfer anndata setup") transfer_anndata_setup(self.scvi_setup_dict_, adata) is_nonneg_int = _check_nonnegative_integers( get_from_registry(adata, _CONSTANTS.X_KEY)) if not is_nonneg_int: logger.warning( "Make sure the registered X field in anndata contains unnormalized count data." ) _check_anndata_setup_equivalence(self.scvi_setup_dict_, adata) return adata
def load( cls, dir_path: str, adata: Optional[AnnData] = None, use_gpu: Optional[bool] = None, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. adata AnnData organized in the same way as data used to train model. It is not necessary to run :func:`~scvi.data.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. If None, will check for and load anndata saved with the model. use_gpu Whether to load model on GPU. Returns ------- Model with loaded state dictionaries. Examples -------- >>> vae = SCVI.load(adata, save_path) >>> vae.get_latent_representation() """ load_adata = adata is None if use_gpu is None: use_gpu = torch.cuda.is_available() map_location = torch.device("cpu") if use_gpu is False else None ( scvi_setup_dict, attr_dict, var_names, model_state_dict, new_adata, ) = _load_saved_files(dir_path, load_adata, map_location=map_location) adata = new_adata if new_adata is not None else adata _validate_var_names(adata, var_names) transfer_anndata_setup(scvi_setup_dict, adata) model = _initialize_model(cls, adata, attr_dict, use_gpu) # set saved attrs for loaded model for attr, val in attr_dict.items(): setattr(model, attr, val) model.module.load_state_dict(model_state_dict) if use_gpu: model.module.cuda() model.module.eval() model._validate_anndata(adata) return model
def _transfer_model(model, adata): adata = adata.copy() attr_dict = model._get_user_attributes() attr_dict = {a[0]: a[1] for a in attr_dict if a[0][-1] == "_"} scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") transfer_anndata_setup(scvi_setup_dict, adata, extend_categories=True) adata.uns["_scvi"]["summary_stats"]["n_labels"] = scvi_setup_dict[ "summary_stats" ]["n_labels"] new_model = _initialize_model(model.__class__, adata, attr_dict, use_cuda=True) for attr, val in attr_dict.items(): setattr(new_model, attr, val) model.model.cuda() new_model.model.cuda() new_state_dict = model.model.state_dict() load_state_dict = model.model.state_dict().copy() new_state_dict = new_model.model.state_dict() for key, load_ten in load_state_dict.items(): new_ten = new_state_dict[key] if new_ten.size() == load_ten.size(): continue else: dim_diff = new_ten.size()[-1] - load_ten.size()[-1] fixed_ten = torch.cat([load_ten, new_ten[..., -dim_diff:]], dim=-1) load_state_dict[key] = fixed_ten new_model.model.load_state_dict(load_state_dict) new_model.model.eval() new_model.is_trained_ = False return new_model, adata
def load( cls, dir_path: str, adata: Optional[AnnData] = None, use_cuda: bool = False, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. adata AnnData organized in the same way as data used to train model. It is not necessary to run :func:`~scvi.data.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. If None, will check for and load anndata saved with the model. use_cuda Whether to load model on GPU. Returns ------- Model with loaded state dictionaries. Examples -------- >>> vae = SCVI.load(adata, save_path) >>> vae.get_latent_representation() """ model_path = os.path.join(dir_path, "model_params.pt") setup_dict_path = os.path.join(dir_path, "attr.pkl") adata_path = os.path.join(dir_path, "adata.h5ad") varnames_path = os.path.join(dir_path, "var_names.csv") if os.path.exists(adata_path) and adata is None: adata = read(adata_path) elif not os.path.exists(adata_path) and adata is None: raise ValueError( "Save path contains no saved anndata and no adata was passed.") var_names = np.genfromtxt(varnames_path, delimiter=",", dtype=str) user_var_names = adata.var_names.astype(str) if not np.array_equal(var_names, user_var_names): logger.warning( "var_names for adata passed in does not match var_names of " "adata used to train the model. For valid results, the vars " "need to be the same and in the same order as the adata used to train the model." ) with open(setup_dict_path, "rb") as handle: attr_dict = pickle.load(handle) scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") transfer_anndata_setup(scvi_setup_dict, adata) if "init_params_" not in attr_dict.keys(): raise ValueError( "No init_params_ were saved by the model. Check out the " "developers guide if creating custom models.") # get the parameters for the class init signiture init_params = attr_dict.pop("init_params_") # update use_cuda from the saved model use_cuda = use_cuda and torch.cuda.is_available() init_params["use_cuda"] = use_cuda # grab all the parameters execept for kwargs (is a dict) non_kwargs = { k: v for k, v in init_params.items() if not isinstance(v, dict) } # expand out kwargs kwargs = {k: v for k, v in init_params.items() if isinstance(v, dict)} kwargs = {k: v for (i, j) in kwargs.items() for (k, v) in j.items()} model = cls(adata, **non_kwargs, **kwargs) for attr, val in attr_dict.items(): setattr(model, attr, val) if use_cuda: model.model.load_state_dict(torch.load(model_path)) model.model.cuda() else: device = torch.device("cpu") model.model.load_state_dict( torch.load(model_path, map_location=device)) model.model.eval() model._validate_anndata(adata) return model
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_transfer_anndata_setup(): # test transfer_anndata function adata1 = synthetic_iid(run_setup_anndata=False) adata2 = synthetic_iid(run_setup_anndata=False) adata2.X = adata1.X setup_anndata(adata1) transfer_anndata_setup(adata1, adata2) np.testing.assert_array_equal(adata1.obs["_scvi_local_l_mean"], adata2.obs["_scvi_local_l_mean"]) # test if layer was used initially, again used in transfer setup adata1 = synthetic_iid(run_setup_anndata=False) adata2 = synthetic_iid(run_setup_anndata=False) raw_counts = adata1.X.copy() adata1.layers["raw"] = raw_counts adata2.layers["raw"] = raw_counts zeros = np.zeros_like(adata1.X) ones = np.ones_like(adata1.X) adata1.X = zeros adata2.X = ones setup_anndata(adata1, layer="raw") transfer_anndata_setup(adata1, adata2) np.testing.assert_array_equal(adata1.obs["_scvi_local_l_mean"], adata2.obs["_scvi_local_l_mean"]) # test that an unknown batch throws an error adata1 = synthetic_iid() adata2 = synthetic_iid(run_setup_anndata=False) adata2.obs["batch"] = [2] * adata2.n_obs with pytest.raises(ValueError): transfer_anndata_setup(adata1, adata2) # TODO: test that a batch with wrong dtype throws an error # adata1 = synthetic_iid() # adata2 = synthetic_iid(run_setup_anndata=False) # adata2.obs["batch"] = ["0"] * adata2.n_obs # with pytest.raises(ValueError): # transfer_anndata_setup(adata1, adata2) # test that an unknown label throws an error adata1 = synthetic_iid() adata2 = synthetic_iid(run_setup_anndata=False) adata2.obs["labels"] = ["label_123"] * adata2.n_obs with pytest.raises(ValueError): transfer_anndata_setup(adata1, adata2) # test that correct mapping was applied adata1 = synthetic_iid() adata2 = synthetic_iid(run_setup_anndata=False) adata2.obs["labels"] = ["label_1"] * adata2.n_obs transfer_anndata_setup(adata1, adata2) labels_mapping = adata1.uns["_scvi"]["categorical_mappings"][ "_scvi_labels"]["mapping"] correct_label = np.where(labels_mapping == "label_1")[0][0] adata2.obs["_scvi_labels"][0] == correct_label # test that transfer_anndata_setup correctly looks for adata.obs['batch'] adata1 = synthetic_iid() adata2 = synthetic_iid(run_setup_anndata=False) del adata2.obs["batch"] with pytest.raises(KeyError): transfer_anndata_setup(adata1, adata2) # test that transfer_anndata_setup assigns same batch and label to cells # if the original anndata was also same batch and label adata1 = synthetic_iid(run_setup_anndata=False) setup_anndata(adata1) adata2 = synthetic_iid(run_setup_anndata=False) del adata2.obs["batch"] transfer_anndata_setup(adata1, adata2) assert adata2.obs["_scvi_batch"][0] == 0 assert adata2.obs["_scvi_labels"][0] == 0
def load( cls, dir_path: str, adata: Optional[AnnData] = None, use_gpu: Optional[Union[str, int, bool]] = None, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. adata AnnData organized in the same way as data used to train model. It is not necessary to run :func:`~scvi.data.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. If None, will check for and load anndata saved with the model. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). Returns ------- Model with loaded state dictionaries. Examples -------- >>> vae = SCVI.load(save_path, adata) >>> vae.get_latent_representation() """ load_adata = adata is None use_gpu, device = parse_use_gpu_arg(use_gpu) ( scvi_setup_dict, attr_dict, var_names, model_state_dict, new_adata, ) = _load_saved_files(dir_path, load_adata, map_location=device) adata = new_adata if new_adata is not None else adata _validate_var_names(adata, var_names) transfer_anndata_setup(scvi_setup_dict, adata) model = _initialize_model(cls, adata, attr_dict) # set saved attrs for loaded model for attr, val in attr_dict.items(): setattr(model, attr, val) # some Pyro modules with AutoGuides may need one training step try: model.module.load_state_dict(model_state_dict) except RuntimeError as err: if isinstance(model.module, PyroBaseModuleClass): logger.info("Preparing underlying module for load") model.train(max_steps=1) pyro.clear_param_store() model.module.load_state_dict(model_state_dict) else: raise err model.to_device(device) model.module.eval() model._validate_anndata(adata) return model
def load( cls, dir_path: str, prefix: Optional[str] = None, adata: Optional[AnnData] = None, use_gpu: Optional[Union[str, int, bool]] = None, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. prefix Prefix of saved file names. adata AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved `scvi` setup dictionary. If None, will check for and load anndata saved with the model. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). Returns ------- Model with loaded state dictionaries. Examples -------- >>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_.... """ load_adata = adata is None use_gpu, device = parse_use_gpu_arg(use_gpu) ( attr_dict, var_names, model_state_dict, new_adata, ) = _load_saved_files(dir_path, load_adata, map_location=device, prefix=prefix) adata = new_adata if new_adata is not None else adata scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") # Filter out keys that are no longer populated by setup_anndata. # TODO(jhong): remove hack with setup_anndata refactor. deprecated_keys = {"local_l_mean", "local_l_var"} scvi_setup_dict["data_registry"] = { k: v for k, v in scvi_setup_dict["data_registry"].items() if k not in deprecated_keys } _validate_var_names(adata, var_names) transfer_anndata_setup(scvi_setup_dict, adata) model = _initialize_model(cls, adata, attr_dict) # set saved attrs for loaded model for attr, val in attr_dict.items(): setattr(model, attr, val) # some Pyro modules with AutoGuides may need one training step try: model.module.load_state_dict(model_state_dict) except RuntimeError as err: if isinstance(model.module, PyroBaseModuleClass): old_history = model.history_.copy() logger.info("Preparing underlying module for load") model.train(max_steps=1) model.history_ = old_history pyro.clear_param_store() model.module.load_state_dict(model_state_dict) else: raise err model.to_device(device) model.module.eval() model._validate_anndata(adata) return model
def load_query_data( cls, adata: AnnData, reference_model: Union[str, BaseModelClass], use_cuda: bool = True, freeze_dropout: bool = False, freeze_expression: bool = True, freeze_batchnorm_encoder: bool = True, freeze_batchnorm_decoder: bool = False, ): """ Online update of a reference model with scArches algorithm [Lotfollahi20]_. Parameters ---------- adata AnnData organized in the same way as data used to train model. It is not necessary to run :func:`~scvi.data.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. reference_model Either an already instantiated model of the same class, or a path to saved outputs for reference model. use_cuda Whether to load model on GPU. freeze_dropout Whether to freeze dropout during training freeze_expression Freeze neurons corersponding to expression in first layer freeze_batchnorm_encoder Whether to freeze batchnorm weight and bias during training for encoder freeze_batchnorm_decoder Whether to freeze batchnorm weight and bias during training for decoder """ use_cuda = use_cuda and torch.cuda.is_available() if isinstance(reference_model, str): map_location = torch.device("cpu") if use_cuda is False else None ( scvi_setup_dict, attr_dict, var_names, load_state_dict, _, ) = _load_saved_files(reference_model, load_adata=False, map_location=map_location) else: attr_dict = reference_model._get_user_attributes() attr_dict = {a[0]: a[1] for a in attr_dict if a[0][-1] == "_"} scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") var_names = reference_model.adata.var_names load_state_dict = reference_model.model.state_dict().copy() _validate_var_names(adata, var_names) transfer_anndata_setup(scvi_setup_dict, adata, extend_categories=True) # for scanvi, any new labels in query cannot be used to extend the model adata.uns["_scvi"]["summary_stats"]["n_labels"] = scvi_setup_dict[ "summary_stats"]["n_labels"] model = _initialize_model(cls, adata, attr_dict, use_cuda) # set saved attrs for loaded model for attr, val in attr_dict.items(): setattr(model, attr, val) if use_cuda: model.model.cuda() # model tweaking new_state_dict = model.model.state_dict() for key, load_ten in load_state_dict.items(): new_ten = new_state_dict[key] if new_ten.size() == load_ten.size(): continue # new categoricals changed size else: dim_diff = new_ten.size()[-1] - load_ten.size()[-1] fixed_ten = torch.cat([load_ten, new_ten[..., -dim_diff:]], dim=-1) load_state_dict[key] = fixed_ten model.model.load_state_dict(load_state_dict) model.model.eval() _set_params_online_update( model.model, freeze_batchnorm_encoder=freeze_batchnorm_encoder, freeze_batchnorm_decoder=freeze_batchnorm_decoder, freeze_dropout=freeze_dropout, freeze_expression=freeze_expression, ) model.is_trained_ = False return model
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])
def from_scvi_model( cls, scvi_model: SCVI, adata: Optional[AnnData] = None, restrict_to_batch: Optional[str] = None, doublet_ratio: int = 2, **classifier_kwargs, ): """ Instantiate a SOLO model from an scvi model. Parameters ---------- scvi_model Pre-trained model of :class:`~scvi.model.SCVI`. The adata object used to initialize this model should have only been setup with count data, and optionally a `batch_key`; i.e., no extra covariates or labels, etc. adata Optional anndata to use that is compatible with scvi_model. restrict_to_batch Batch category in `batch_key` used to setup adata for scvi_model to restrict Solo model to. This allows to train a Solo model on one batch of a scvi_model that was trained on multiple batches. doublet_ratio Ratio of generated doublets to produce relative to number of cells in adata or length of indices, if not `None`. **classifier_kwargs Keyword args for :class:`~scvi.module.Classifier` Returns ------- SOLO model """ _validate_scvi_model(scvi_model, restrict_to_batch=restrict_to_batch) orig_adata = scvi_model.adata orig_batch_key = scvi_model.scvi_setup_dict_["categorical_mappings"][ "_scvi_batch"]["original_key"] if adata is not None: transfer_anndata_setup(orig_adata, adata) else: adata = orig_adata if restrict_to_batch is not None: batch_mask = adata.obs[orig_batch_key] == restrict_to_batch if np.sum(batch_mask) == 0: raise ValueError( "Batch category given to restrict_to_batch not found.\n" + "Available categories: {}".format( adata.obs[orig_batch_key].astype( "category").cat.categories)) # indices in adata with restrict_to_batch category batch_indices = np.where(batch_mask)[0] else: # use all indices batch_indices = None # anndata with only generated doublets doublet_adata = cls.create_doublets(adata, indices=batch_indices, doublet_ratio=doublet_ratio) # if scvi wasn't trained with batch correction having the # zeros here does nothing. doublet_adata.obs[orig_batch_key] = ( restrict_to_batch if restrict_to_batch is not None else 0) # if model is using observed lib size, needs to get lib sample # which is just observed lib size on log scale give_mean_lib = not scvi_model.module.use_observed_lib_size # get latent representations and make input anndata latent_rep = scvi_model.get_latent_representation( adata, indices=batch_indices) lib_size = scvi_model.get_latent_library_size(adata, indices=batch_indices, give_mean=give_mean_lib) latent_adata = AnnData( np.concatenate([latent_rep, np.log(lib_size)], axis=1)) latent_adata.obs[LABELS_KEY] = "singlet" orig_obs_names = adata.obs_names latent_adata.obs_names = (orig_obs_names[batch_indices] if batch_indices is not None else orig_obs_names) logger.info("Creating doublets, preparing SOLO model.") f = io.StringIO() with redirect_stdout(f): setup_anndata(doublet_adata, batch_key=orig_batch_key) doublet_latent_rep = scvi_model.get_latent_representation( doublet_adata) doublet_lib_size = scvi_model.get_latent_library_size( doublet_adata, give_mean=give_mean_lib) doublet_adata = AnnData( np.concatenate([doublet_latent_rep, np.log(doublet_lib_size)], axis=1)) doublet_adata.obs[LABELS_KEY] = "doublet" full_adata = latent_adata.concatenate(doublet_adata) setup_anndata(full_adata, labels_key=LABELS_KEY) return cls(full_adata, **classifier_kwargs)
def load( cls, dir_path: str, adata_seq: Optional[AnnData] = None, adata_spatial: Optional[AnnData] = None, use_gpu: Optional[Union[str, int, bool]] = None, ): """ Instantiate a model from the saved output. Parameters ---------- adata_seq AnnData organized in the same way as data used to train model. It is not necessary to run :func:`~scvi.data.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. AnnData must be registered via :func:`~scvi.data.setup_anndata`. adata_spatial AnnData organized in the same way as data used to train model. If None, will check for and load anndata saved with the model. dir_path Path to saved outputs. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). Returns ------- Model with loaded state dictionaries. Examples -------- >>> vae = GIMVI.load(adata_seq, adata_spatial, save_path) >>> vae.get_latent_representation() """ model_path = os.path.join(dir_path, "model_params.pt") setup_dict_path = os.path.join(dir_path, "attr.pkl") seq_data_path = os.path.join(dir_path, "adata_seq.h5ad") spatial_data_path = os.path.join(dir_path, "adata_spatial.h5ad") seq_var_names_path = os.path.join(dir_path, "var_names_seq.csv") spatial_var_names_path = os.path.join(dir_path, "var_names_spatial.csv") if adata_seq is None and os.path.exists(seq_data_path): adata_seq = read(seq_data_path) elif adata_seq is None and not os.path.exists(seq_data_path): raise ValueError( "Save path contains no saved anndata and no adata was passed." ) if adata_spatial is None and os.path.exists(spatial_data_path): adata_spatial = read(spatial_data_path) elif adata_spatial is None and not os.path.exists(spatial_data_path): raise ValueError( "Save path contains no saved anndata and no adata was passed." ) adatas = [adata_seq, adata_spatial] seq_var_names = np.genfromtxt(seq_var_names_path, delimiter=",", dtype=str) spatial_var_names = np.genfromtxt( spatial_var_names_path, delimiter=",", dtype=str ) var_names = [seq_var_names, spatial_var_names] for i, adata in enumerate(adatas): saved_var_names = var_names[i] user_var_names = adata.var_names.astype(str) if not np.array_equal(saved_var_names, user_var_names): warnings.warn( "var_names for adata passed in does not match var_names of " "adata used to train the model. For valid results, the vars " "need to be the same and in the same order as the adata used to train the model." ) with open(setup_dict_path, "rb") as handle: attr_dict = pickle.load(handle) scvi_setup_dicts = attr_dict.pop("scvi_setup_dicts_") transfer_anndata_setup(scvi_setup_dicts["seq"], adata_seq) transfer_anndata_setup(scvi_setup_dicts["spatial"], adata_spatial) # get the parameters for the class init signiture init_params = attr_dict.pop("init_params_") # new saving and loading, enable backwards compatibility if "non_kwargs" in init_params.keys(): # grab all the parameters execept for kwargs (is a dict) non_kwargs = init_params["non_kwargs"] kwargs = init_params["kwargs"] # expand out kwargs kwargs = {k: v for (i, j) in kwargs.items() for (k, v) in j.items()} else: # grab all the parameters execept for kwargs (is a dict) non_kwargs = { k: v for k, v in init_params.items() if not isinstance(v, dict) } kwargs = {k: v for k, v in init_params.items() if isinstance(v, dict)} kwargs = {k: v for (i, j) in kwargs.items() for (k, v) in j.items()} model = cls(adata_seq, adata_spatial, **non_kwargs, **kwargs) for attr, val in attr_dict.items(): setattr(model, attr, val) _, device = parse_use_gpu_arg(use_gpu) model.module.load_state_dict(torch.load(model_path, map_location=device)) model.module.eval() model.to_device(device) return model
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 load_query_data( cls, adata: AnnData, reference_model: Union[str, BaseModelClass], inplace_subset_query_vars: bool = False, use_gpu: Optional[Union[str, int, bool]] = None, unfrozen: bool = False, freeze_dropout: bool = False, freeze_expression: bool = True, freeze_decoder_first_layer: bool = True, freeze_batchnorm_encoder: bool = True, freeze_batchnorm_decoder: bool = False, freeze_classifier: bool = True, ): """ Online update of a reference model with scArches algorithm [Lotfollahi21]_. Parameters ---------- adata AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved `scvi` setup dictionary. reference_model Either an already instantiated model of the same class, or a path to saved outputs for reference model. inplace_subset_query_vars Whether to subset and rearrange query vars inplace based on vars used to train reference model. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). unfrozen Override all other freeze options for a fully unfrozen model freeze_dropout Whether to freeze dropout during training freeze_expression Freeze neurons corersponding to expression in first layer freeze_decoder_first_layer Freeze neurons corersponding to first layer in decoder freeze_batchnorm_encoder Whether to freeze batchnorm weight and bias during training for encoder freeze_batchnorm_decoder Whether to freeze batchnorm weight and bias during training for decoder freeze_classifier Whether to freeze classifier completely. Only applies to `SCANVI`. """ use_gpu, device = parse_use_gpu_arg(use_gpu) if isinstance(reference_model, str): ( attr_dict, var_names, load_state_dict, _, ) = _load_saved_files(reference_model, load_adata=False, map_location=device) else: attr_dict = reference_model._get_user_attributes() attr_dict = {a[0]: a[1] for a in attr_dict if a[0][-1] == "_"} var_names = reference_model.adata.var_names load_state_dict = deepcopy(reference_model.module.state_dict()) scvi_setup_dict = attr_dict.pop("scvi_setup_dict_") if inplace_subset_query_vars: logger.debug("Subsetting query vars to reference vars.") adata._inplace_subset_var(var_names) _validate_var_names(adata, var_names) version_split = scvi_setup_dict["scvi_version"].split(".") if version_split[1] < "8" and version_split[0] == "0": warnings.warn( "Query integration should be performed using models trained with version >= 0.8" ) transfer_anndata_setup(scvi_setup_dict, adata, extend_categories=True) model = _initialize_model(cls, adata, attr_dict) # set saved attrs for loaded model for attr, val in attr_dict.items(): setattr(model, attr, val) model.to_device(device) # model tweaking new_state_dict = model.module.state_dict() for key, load_ten in load_state_dict.items(): new_ten = new_state_dict[key] if new_ten.size() == load_ten.size(): continue # new categoricals changed size else: dim_diff = new_ten.size()[-1] - load_ten.size()[-1] fixed_ten = torch.cat([load_ten, new_ten[..., -dim_diff:]], dim=-1) load_state_dict[key] = fixed_ten model.module.load_state_dict(load_state_dict) model.module.eval() _set_params_online_update( model.module, unfrozen=unfrozen, freeze_decoder_first_layer=freeze_decoder_first_layer, freeze_batchnorm_encoder=freeze_batchnorm_encoder, freeze_batchnorm_decoder=freeze_batchnorm_decoder, freeze_dropout=freeze_dropout, freeze_expression=freeze_expression, freeze_classifier=freeze_classifier, ) model.is_trained_ = False return model
def test_totalvi(save_path): adata = synthetic_iid() n_obs = adata.n_obs n_vars = adata.n_vars n_proteins = adata.obsm["protein_expression"].shape[1] n_latent = 10 model = TOTALVI(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 == (n_obs, n_latent) model.get_elbo() model.get_marginal_ll(n_mc_samples=3) model.get_reconstruction_error() model.get_normalized_expression() model.get_normalized_expression(transform_batch=["batch_0", "batch_1"]) model.get_latent_library_size() model.get_protein_foreground_probability() model.get_protein_foreground_probability(transform_batch=["batch_0", "batch_1"]) post_pred = model.posterior_predictive_sample(n_samples=2) assert post_pred.shape == (n_obs, n_vars + n_proteins, 2) post_pred = model.posterior_predictive_sample(n_samples=1) assert post_pred.shape == (n_obs, n_vars + n_proteins) feature_correlation_matrix1 = model.get_feature_correlation_matrix( correlation_type="spearman" ) feature_correlation_matrix1 = model.get_feature_correlation_matrix( correlation_type="spearman", transform_batch=["batch_0", "batch_1"] ) feature_correlation_matrix2 = model.get_feature_correlation_matrix( correlation_type="pearson" ) assert feature_correlation_matrix1.shape == ( n_vars + n_proteins, n_vars + n_proteins, ) assert feature_correlation_matrix2.shape == ( n_vars + n_proteins, n_vars + n_proteins, ) # model.get_likelihood_parameters() model.get_elbo(indices=model.validation_indices) model.get_marginal_ll(indices=model.validation_indices, n_mc_samples=3) model.get_reconstruction_error(indices=model.validation_indices) adata2 = synthetic_iid() norm_exp = model.get_normalized_expression(adata2, indices=[1, 2, 3]) assert norm_exp[0].shape == (3, adata2.n_vars) assert norm_exp[1].shape == (3, adata2.obsm["protein_expression"].shape[1]) latent_lib_size = model.get_latent_library_size(adata2, indices=[1, 2, 3]) assert latent_lib_size.shape == (3, 1) pro_foreground_prob = model.get_protein_foreground_probability( adata2, indices=[1, 2, 3], protein_list=["1", "2"] ) assert pro_foreground_prob.shape == (3, 2) model.posterior_predictive_sample(adata2) model.get_feature_correlation_matrix(adata2) # model.get_likelihood_parameters(adata2) # test transfer_anndata_setup + view adata2 = synthetic_iid(run_setup_anndata=False) transfer_anndata_setup(adata, adata2) model.get_elbo(adata2[:10]) # test automatic transfer_anndata_setup adata = synthetic_iid() model = TOTALVI(adata) adata2 = synthetic_iid(run_setup_anndata=False) model.get_elbo(adata2) # 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_8"] ) with pytest.raises(ValueError): model.get_elbo(adata2) # test that same mapping different order is okay 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 that we catch missing proteins adata2 = synthetic_iid(run_setup_anndata=False) del adata2.obsm["protein_expression"] with pytest.raises(KeyError): model.get_elbo(adata2) model.differential_expression(groupby="labels", group1="label_1") model.differential_expression(groupby="labels", group1="label_1", group2="label_2") model.differential_expression(idx1=[0, 1, 2], idx2=[3, 4, 5]) model.differential_expression(idx1=[0, 1, 2]) model.differential_expression(groupby="labels") # test with missing proteins adata = scvi.data.pbmcs_10x_cite_seq(save_path=save_path, protein_join="outer") model = TOTALVI(adata) assert model.module.protein_batch_mask is not None model.train(1, train_size=0.5)
def load( cls, dir_path: str, prefix: Optional[str] = None, adata_seq: Optional[AnnData] = None, adata_spatial: Optional[AnnData] = None, use_gpu: Optional[Union[str, int, bool]] = None, ): """ Instantiate a model from the saved output. Parameters ---------- dir_path Path to saved outputs. prefix Prefix of saved file names. adata_seq AnnData organized in the same way as data used to train model. It is not necessary to run :meth:`~scvi.external.GIMVI.setup_anndata`, as AnnData is validated against the saved `scvi` setup dictionary. AnnData must be registered via :meth:`~scvi.external.GIMVI.setup_anndata`. adata_spatial AnnData organized in the same way as data used to train model. If None, will check for and load anndata saved with the model. use_gpu Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False). Returns ------- Model with loaded state dictionaries. Examples -------- >>> vae = GIMVI.load(adata_seq, adata_spatial, save_path) >>> vae.get_latent_representation() """ _, device = parse_use_gpu_arg(use_gpu) ( attr_dict, seq_var_names, spatial_var_names, model_state_dict, loaded_adata_seq, loaded_adata_spatial, ) = _load_saved_gimvi_files( dir_path, adata_seq is None, adata_spatial is None, prefix=prefix, map_location=device, ) adata_seq = loaded_adata_seq or adata_seq adata_spatial = loaded_adata_spatial or adata_spatial adatas = [adata_seq, adata_spatial] var_names = [seq_var_names, spatial_var_names] for i, adata in enumerate(adatas): saved_var_names = var_names[i] user_var_names = adata.var_names.astype(str) if not np.array_equal(saved_var_names, user_var_names): warnings.warn( "var_names for adata passed in does not match var_names of " "adata used to train the model. For valid results, the vars " "need to be the same and in the same order as the adata used to train the model." ) scvi_setup_dicts = attr_dict.pop("scvi_setup_dicts_") transfer_anndata_setup(scvi_setup_dicts["seq"], adata_seq) transfer_anndata_setup(scvi_setup_dicts["spatial"], adata_spatial) # get the parameters for the class init signiture init_params = attr_dict.pop("init_params_") # new saving and loading, enable backwards compatibility if "non_kwargs" in init_params.keys(): # grab all the parameters execept for kwargs (is a dict) non_kwargs = init_params["non_kwargs"] kwargs = init_params["kwargs"] # expand out kwargs kwargs = { k: v for (i, j) in kwargs.items() for (k, v) in j.items() } else: # grab all the parameters execept for kwargs (is a dict) non_kwargs = { k: v for k, v in init_params.items() if not isinstance(v, dict) } kwargs = { k: v for k, v in init_params.items() if isinstance(v, dict) } kwargs = { k: v for (i, j) in kwargs.items() for (k, v) in j.items() } model = cls(adata_seq, adata_spatial, **non_kwargs, **kwargs) for attr, val in attr_dict.items(): setattr(model, attr, val) model.module.load_state_dict(model_state_dict) model.module.eval() model.to_device(device) return model