Release 0.20.0 (#1888)

* Update pyproject.toml * update black * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update _trainingplans.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
scverse · Feb 1, 2023 · 486e3e4 · 486e3e4
1 parent 3792568
commit 486e3e4
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Showing 22 changed files with 29 additions and 27 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ default_stages:
 minimum_pre_commit_version: 2.16.0
 repos:
     - repo: https://github.com/psf/black
-      rev: 22.12.0
+      rev: 23.1.0
       hooks:
           - id: black
     - repo: https://github.com/pre-commit/mirrors-prettier

diff --git a/pyproject.toml b/pyproject.toml
@@ -5,7 +5,7 @@ requires = ["hatchling"]
 
 [project]
 name = "scvi-tools"
-version = "0.20.0b2"
+version = "0.20.0"
 description = "Deep probabilistic analysis of single-cell omics data."
 readme = "README.md"
 requires-python = ">=3.8"

diff --git a/scvi/_settings.py b/scvi/_settings.py
@@ -58,7 +58,6 @@ def __init__(
         dl_pin_memory_gpu_training: bool = False,
         jax_preallocate_gpu_memory: bool = False,
     ):
-
         self.seed = seed
         self.batch_size = batch_size
         if progress_bar_style not in ["rich", "tqdm"]:

diff --git a/scvi/autotune/_manager.py b/scvi/autotune/_manager.py
@@ -190,7 +190,6 @@ def _validate_search_space(self, search_space: dict, use_defaults: bool) -> dict
         # add defaults if requested
         _search_space = {}
         if use_defaults:
-
             # parse defaults into tune sample functions
             for param, metadata in self._defaults.items():
                 sample_fn = getattr(tune, metadata["fn"])

diff --git a/scvi/data/_built_in_data/_seqfish.py b/scvi/data/_built_in_data/_seqfish.py
@@ -15,7 +15,6 @@ def _load_seqfishplus(
     save_path: str = "data/",
     tissue_region: str = "subventricular cortex",
 ) -> anndata.AnnData:
-
     if tissue_region == "subventricular cortex":
         file_prefix = "cortex_svz"
     elif tissue_region == "olfactory bulb":

diff --git a/scvi/data/_built_in_data/_synthetic.py b/scvi/data/_built_in_data/_synthetic.py
@@ -16,7 +16,6 @@ def _generate_synthetic(
     n_labels: int = 3,
     sparse: bool = False,
 ) -> AnnData:
-
     data = np.random.negative_binomial(5, 0.3, size=(batch_size * n_batches, n_genes))
     mask = np.random.binomial(n=1, p=0.7, size=(batch_size * n_batches, n_genes))
     data = data * mask  # We put the batch index first

diff --git a/scvi/data/_preprocessing.py b/scvi/data/_preprocessing.py
@@ -103,7 +103,6 @@ def poisson_gene_selection(
     obs_frac_zeross = []
     exp_frac_zeross = []
     for b in np.unique(batch_info):
-
         ad = adata[batch_info == b]
         data = ad.layers[layer] if layer is not None else ad.X
 

diff --git a/scvi/dataloaders/_ann_dataloader.py b/scvi/dataloaders/_ann_dataloader.py
@@ -121,7 +121,6 @@ def __init__(
         iter_ndarray: bool = False,
         **data_loader_kwargs,
     ):
-
         if adata_manager.adata is None:
             raise ValueError(
                 "Please run register_fields() on your AnnDataManager object first."

diff --git a/scvi/distributions/_negative_binomial.py b/scvi/distributions/_negative_binomial.py
@@ -430,7 +430,6 @@ def __init__(
         scale: Optional[torch.Tensor] = None,
         validate_args: bool = False,
     ):
-
         super().__init__(
             total_count=total_count,
             probs=probs,
@@ -527,7 +526,6 @@ def __init__(
         theta2: Optional[torch.Tensor] = None,
         validate_args: bool = False,
     ):
-
         (
             self.mu1,
             self.theta1,

diff --git a/scvi/external/gimvi/_task.py b/scvi/external/gimvi/_task.py
@@ -34,7 +34,7 @@ def training_step(self, batch, batch_idx, optimizer_idx=0):
             loss_output_objs = []
             n_obs = 0
             zs = []
-            for (i, tensors) in enumerate(batch):
+            for i, tensors in enumerate(batch):
                 n_obs += tensors[REGISTRY_KEYS.X_KEY].shape[0]
                 self.loss_kwargs.update(dict(kl_weight=self.kl_weight, mode=i))
                 inference_kwargs = dict(mode=i)
@@ -76,7 +76,7 @@ def training_step(self, batch, batch_idx, optimizer_idx=0):
         # this condition will not be met unless self.adversarial_classifier is not False
         if optimizer_idx == 1:
             zs = []
-            for (i, tensors) in enumerate(batch):
+            for i, tensors in enumerate(batch):
                 inference_inputs = self.module._get_inference_input(tensors)
                 inference_inputs.update({"mode": i})
                 outputs = self.module.inference(**inference_inputs)

diff --git a/scvi/model/_totalvi.py b/scvi/model/_totalvi.py
@@ -897,7 +897,10 @@ def _get_denoised_samples(
             generative_kwargs = dict(transform_batch=transform_batch)
             inference_kwargs = dict(n_samples=n_samples)
             with torch.inference_mode():
-                inference_outputs, generative_outputs, = self.module.forward(
+                (
+                    inference_outputs,
+                    generative_outputs,
+                ) = self.module.forward(
                     tensors,
                     inference_kwargs=inference_kwargs,
                     generative_kwargs=generative_kwargs,

diff --git a/scvi/model/base/_base_model.py b/scvi/model/base/_base_model.py
@@ -639,7 +639,12 @@ def load(
         load_adata = adata is None
         _, _, device = parse_use_gpu_arg(use_gpu)
 
-        (attr_dict, var_names, model_state_dict, new_adata,) = _load_saved_files(
+        (
+            attr_dict,
+            var_names,
+            model_state_dict,
+            new_adata,
+        ) = _load_saved_files(
             dir_path,
             load_adata,
             map_location=device,

diff --git a/scvi/model/base/_pyromixin.py b/scvi/model/base/_pyromixin.py
@@ -284,7 +284,6 @@ def _get_posterior_samples(
             description="Sampling global variables, sample: ",
             disable=not show_progress,
         ):
-
             # generate new sample
             samples_ = self._get_one_posterior_sample(
                 args, kwargs, return_sites=return_sites, return_observed=return_observed

diff --git a/scvi/model/base/_rnamixin.py b/scvi/model/base/_rnamixin.py
@@ -583,7 +583,6 @@ def get_latent_library_size(
             else:
                 ql = outputs["ql"]
                 if ql is None:
-
                     raise RuntimeError(
                         "The module for this model does not compute the posterior distribution "
                         "for the library size. Set `give_mean` to False to use the observed library size instead."

diff --git a/scvi/model/base/_utils.py b/scvi/model/base/_utils.py
@@ -135,7 +135,6 @@ def _initialize_model(cls, adata, attr_dict):
 
 
 def _validate_var_names(adata, source_var_names):
-
     user_var_names = adata.var_names.astype(str)
     if not np.array_equal(source_var_names, user_var_names):
         warnings.warn(

diff --git a/scvi/module/_amortizedlda.py b/scvi/module/_amortizedlda.py
@@ -101,7 +101,6 @@ def __init__(
     def _get_fn_args_from_batch(
         tensor_dict: Dict[str, torch.Tensor]
     ) -> Union[Iterable, dict]:
-
         x = tensor_dict[REGISTRY_KEYS.X_KEY]
         library = torch.sum(x, dim=1)
         return (x, library), {}

diff --git a/scvi/module/_totalvae.py b/scvi/module/_totalvae.py
@@ -660,7 +660,10 @@ def sample(self, tensors, n_samples=1):
         """Sample from the generative model."""
         inference_kwargs = dict(n_samples=n_samples)
         with torch.inference_mode():
-            inference_outputs, generative_outputs, = self.forward(
+            (
+                inference_outputs,
+                generative_outputs,
+            ) = self.forward(
                 tensors,
                 inference_kwargs=inference_kwargs,
                 compute_loss=False,

diff --git a/scvi/module/_vae.py b/scvi/module/_vae.py
@@ -494,7 +494,10 @@ def sample(
             tensor with shape (n_cells, n_genes, n_samples)
         """
         inference_kwargs = dict(n_samples=n_samples)
-        _, generative_outputs, = self.forward(
+        (
+            _,
+            generative_outputs,
+        ) = self.forward(
             tensors,
             inference_kwargs=inference_kwargs,
             compute_loss=False,

diff --git a/scvi/train/_trainingplans.py b/scvi/train/_trainingplans.py
@@ -1168,8 +1168,9 @@ def jit_training_step(
         **kwargs,
     ):
         """Jit training step."""
-        # state can't be passed here
+
         def loss_fn(params):
+            # state can't be passed here
             vars_in = {"params": params, **state.state}
             outputs, new_model_state = state.apply_fn(
                 vars_in, batch, rngs=rngs, mutable=list(state.state.keys()), **kwargs

diff --git a/tests/autotune/test_types.py b/tests/autotune/test_types.py
@@ -14,7 +14,6 @@ def __init__(self, n_train: Tunable[int] = 1000, n_val: Tunable[int] = 100):
 
 
 class DummyModel(TunableMixin, DummyTrainingMixin):
-
     _data_splitter_cls = DummyDataSplitter
 
     def __init__(

diff --git a/tests/core/test_differential.py b/tests/core/test_differential.py
@@ -50,7 +50,6 @@ def test_features():
 
 
 def test_differential_computation(save_path):
-
     n_latent = 5
     adata = synthetic_iid()
     SCVI.setup_anndata(
@@ -111,7 +110,11 @@ def m1_domain_fn_test(samples):
     )
 
     # Test query features
-    obs_col, group1, _, = _prepare_obs(
+    (
+        obs_col,
+        group1,
+        _,
+    ) = _prepare_obs(
         idx1="(labels == 'label_1') & (batch == 'batch_1')", idx2=None, adata=adata
     )
     assert (obs_col == group1).sum() == adata.obs.loc[

diff --git a/tests/models/test_pyro.py b/tests/models/test_pyro.py
@@ -93,7 +93,6 @@ def _get_fn_args_from_batch(tensor_dict):
         return (x, y, ind_x), {}
 
     def forward(self, x, y, ind_x):
-
         obs_plate = self.create_plates(x, y, ind_x)
 
         sigma = pyro.sample("sigma", dist.Exponential(self.one))
@@ -114,7 +113,6 @@ def forward(self, x, y, ind_x):
 
 class BayesianRegressionModule(PyroBaseModuleClass):
     def __init__(self, **kwargs):
-
         super().__init__()
         self._model = BayesianRegressionPyroModel(**kwargs)
         self._guide = AutoNormal(
@@ -465,7 +463,6 @@ def test_pyro_bayesian_train_sample_mixin_with_local_full_data():
 
 class FunctionBasedPyroModule(PyroBaseModuleClass):
     def __init__(self, n_input: int, n_latent: int, n_hidden: int, n_layers: int):
-
         super().__init__()
         self.n_input = n_input
         self.n_latent = n_latent