Backport PR #1317 on branch 0.14.x (update black version)

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
     - repo: https://github.com/psf/black
-      rev: 21.10b0
+      rev: 22.1.0
       hooks:
           - id: black
     - repo: https://github.com/PyCQA/flake8

docs/conf.py

@@ -98,9 +98,9 @@
 
 
 # General information about the project.
-project = u"scvi-tools"
-copyright = u"2021, Yosef Lab, UC Berkeley"
-author = u"Romain Lopez, Adam Gayoso, Pierre Boyeau, Galen Xing"
+project = "scvi-tools"
+copyright = "2021, Yosef Lab, UC Berkeley"
+author = "Romain Lopez, Adam Gayoso, Pierre Boyeau, Galen Xing"
 
 # The version info for the project you're documenting, acts as replacement
 # for |version| and |release|, also used in various other places throughout

pyproject.toml

@@ -31,7 +31,7 @@ version = "0.14.5"
 
 [tool.poetry.dependencies]
 anndata = ">=0.7.5"
-black = {version = ">=20.8b1", optional = true}
+black = {version = ">=22.1", optional = true}
 codecov = {version = ">=2.0.8", optional = true}
 docrep = ">=0.3.2"
 flake8 = {version = ">=3.7.7", optional = true}
@@ -70,6 +70,7 @@ sphinx-panels = {version = "*", optional = true}
 sphinx-tabs = {version = "*", optional = true}
 sphinx_copybutton = {version = "<=0.3.1", optional = true}
 torch = ">=1.8.0"
+torchmetrics = ">=0.6.0"
 tqdm = ">=4.56.0"
 typing_extensions = {version = "*", python = "<3.8", optional = true}
 

scvi/distributions/_negative_binomial.py

@@ -304,7 +304,7 @@ def mean(self):
 
     @property
     def variance(self):
-        return self.mean + (self.mean ** 2) / self.theta
+        return self.mean + (self.mean**2) / self.theta
 
     def sample(
         self, sample_shape: Union[torch.Size, Tuple] = torch.Size()

scvi/model/_metrics.py

@@ -44,7 +44,7 @@ def nearest_neighbor_overlap(x1, x2, k=100):
     set_2 = set(np.where(kmatrix_2.A.flatten() == 1)[0])
     fold_enrichment = (
         len(set_1.intersection(set_2))
-        * n_samples ** 2
+        * n_samples**2
         / (float(len(set_1)) * len(set_2))
     )
     return spearman_correlation, fold_enrichment

scvi/model/_totalvi.py

@@ -1174,7 +1174,7 @@ def _get_totalvi_protein_priors(adata, batch_mask, n_cells=100):
 
         # average distribution over cells
         batch_avg_mu = np.mean(mus)
-        batch_avg_scale = np.sqrt(np.sum(np.square(scales)) / (n_cells ** 2))
+        batch_avg_scale = np.sqrt(np.sum(np.square(scales)) / (n_cells**2))
 
         batch_avg_mus.append(batch_avg_mu)
         batch_avg_scales.append(batch_avg_scale)

scvi/module/_amortizedlda.py

@@ -38,7 +38,7 @@ def logistic_normal_approximation(
     """
     K = alpha.shape[0]
     mu = torch.log(alpha) - torch.log(alpha).sum() / K
-    sigma = torch.sqrt((1 - 2 / K) / alpha + torch.sum(1 / alpha) / K ** 2)
+    sigma = torch.sqrt((1 - 2 / K) / alpha + torch.sum(1 / alpha) / K**2)
     return mu, sigma
 
 

scvi/module/_multivae.py

@@ -335,7 +335,7 @@ def inference(
 
         ## Sample from the average distribution
         qzp_m = (qzm_acc + qzm_expr) / 2
-        qzp_v = (qzv_acc + qzv_expr) / (2 ** 0.5)
+        qzp_v = (qzv_acc + qzv_expr) / (2**0.5)
         zp = Normal(qzp_m, qzp_v.sqrt()).rsample()
 
         ## choose the correct latent representation based on the modality