[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

.pre-commit-config.yaml

@@ -1,79 +1,79 @@
 fail_fast: false
 default_language_version:
-    python: python3
+  python: python3
 default_stages:
-    - commit
-    - push
+  - commit
+  - push
 minimum_pre_commit_version: 2.16.0
 repos:
-    - repo: https://github.com/psf/black
-      rev: 24.4.2
-      hooks:
-          - id: black
-    - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v4.0.0-alpha.8
-      hooks:
-          - id: prettier
-    - repo: https://github.com/asottile/blacken-docs
-      rev: 1.16.0
-      hooks:
-          - id: blacken-docs
-    - repo: https://github.com/PyCQA/isort
-      rev: 5.13.2
-      hooks:
-          - id: isort
-    - repo: https://github.com/asottile/yesqa
-      rev: v1.5.0
-      hooks:
-          - id: yesqa
-            additional_dependencies:
-                - flake8-tidy-imports
-                - flake8-docstrings
-                - flake8-rst-docstrings
-                - flake8-comprehensions
-                - flake8-bugbear
-                - flake8-blind-except
-    - repo: https://github.com/pre-commit/pre-commit-hooks
-      rev: v4.6.0
-      hooks:
-          - id: detect-private-key
-          - id: check-ast
-          - id: end-of-file-fixer
-          - id: mixed-line-ending
-            args: [--fix=lf]
-          - id: trailing-whitespace
-          - id: check-case-conflict
-    - repo: https://github.com/PyCQA/autoflake
-      rev: v2.3.1
-      hooks:
-          - id: autoflake
-            args:
-                - --in-place
-                - --remove-all-unused-imports
-                - --remove-unused-variable
-                - --ignore-init-module-imports
-    - repo: https://github.com/PyCQA/flake8
-      rev: 7.0.0
-      hooks:
-          - id: flake8
-            additional_dependencies:
-                - flake8-tidy-imports
-                - flake8-docstrings
-                - flake8-rst-docstrings
-                - flake8-comprehensions
-                - flake8-bugbear
-                - flake8-blind-except
-    - repo: https://github.com/asottile/pyupgrade
-      rev: v3.15.2
-      hooks:
-          - id: pyupgrade
-            args: [--py3-plus, --py38-plus, --keep-runtime-typing]
-    - repo: local
-      hooks:
-          - id: forbid-to-commit
-            name: Don't commit rej files
-            entry: |
-                Cannot commit .rej files. These indicate merge conflicts that arise during automated template updates.
-                Fix the merge conflicts manually and remove the .rej files.
-            language: fail
-            files: '.*\.rej$'
+  - repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+      - id: black
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v4.0.0-alpha.8
+    hooks:
+      - id: prettier
+  - repo: https://github.com/asottile/blacken-docs
+    rev: 1.16.0
+    hooks:
+      - id: blacken-docs
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+  - repo: https://github.com/asottile/yesqa
+    rev: v1.5.0
+    hooks:
+      - id: yesqa
+        additional_dependencies:
+          - flake8-tidy-imports
+          - flake8-docstrings
+          - flake8-rst-docstrings
+          - flake8-comprehensions
+          - flake8-bugbear
+          - flake8-blind-except
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: detect-private-key
+      - id: check-ast
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: [--fix=lf]
+      - id: trailing-whitespace
+      - id: check-case-conflict
+  - repo: https://github.com/PyCQA/autoflake
+    rev: v2.3.1
+    hooks:
+      - id: autoflake
+        args:
+          - --in-place
+          - --remove-all-unused-imports
+          - --remove-unused-variable
+          - --ignore-init-module-imports
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.0.0
+    hooks:
+      - id: flake8
+        additional_dependencies:
+          - flake8-tidy-imports
+          - flake8-docstrings
+          - flake8-rst-docstrings
+          - flake8-comprehensions
+          - flake8-bugbear
+          - flake8-blind-except
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.15.2
+    hooks:
+      - id: pyupgrade
+        args: [--py3-plus, --py38-plus, --keep-runtime-typing]
+  - repo: local
+    hooks:
+      - id: forbid-to-commit
+        name: Don't commit rej files
+        entry: |
+          Cannot commit .rej files. These indicate merge conflicts that arise during automated template updates.
+          Fix the merge conflicts manually and remove the .rej files.
+        language: fail
+        files: '.*\.rej$'

.readthedocs.yaml

@@ -1,16 +1,16 @@
 # https://docs.readthedocs.io/en/stable/config-file/v2.html
 version: 2
 build:
-    os: ubuntu-20.04
-    tools:
-        python: "3.10"
+  os: ubuntu-20.04
+  tools:
+    python: "3.10"
 sphinx:
-    configuration: docs/conf.py
-    # disable this for more lenient docs builds
-    fail_on_warning: false
+  configuration: docs/conf.py
+  # disable this for more lenient docs builds
+  fail_on_warning: false
 python:
-    install:
-        - method: pip
-          path: .
-          extra_requirements:
-              - doc
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - doc

docs/index.md.rej

@@ -1,9 +1,9 @@
 diff a/docs/index.md b/docs/index.md	(rejected hunks)
 @@ -8,7 +8,6 @@
- 
+
  api.md
  changelog.md
 -template_usage.md
  contributing.md
  references.md
- 
+

pyproject.toml.rej

@@ -6,7 +6,7 @@ diff a/pyproject.toml b/pyproject.toml	(rejected hunks)
 -    "session-info"
 +    "session-info",
  ]
- 
+
  [project.optional-dependencies]
  dev = [
 -    # CLI for bumping the version number
@@ -36,5 +36,5 @@ diff a/pyproject.toml b/pyproject.toml	(rejected hunks)
 -    "pytest-cov",
 +    "coverage",
  ]
- 
+
  [tool.coverage.run]

src/simple_scvi/_mymodel.py

@@ -4,12 +4,8 @@
 from anndata import AnnData
 from scvi import REGISTRY_KEYS
 from scvi.data import AnnDataManager
-from scvi.data.fields import (
-    CategoricalJointObsField,
-    CategoricalObsField,
-    LayerField,
-    NumericalJointObsField,
-)
+from scvi.data.fields import (CategoricalJointObsField, CategoricalObsField,
+                              LayerField, NumericalJointObsField)
 from scvi.model._utils import _init_library_size
 from scvi.model.base import BaseModelClass, UnsupervisedTrainingMixin, VAEMixin
 from scvi.utils import setup_anndata_dsp
@@ -57,7 +53,9 @@ def __init__(
     ):
         super().__init__(adata)
 
-        library_log_means, library_log_vars = _init_library_size(self.adata_manager, self.summary_stats["n_batch"])
+        library_log_means, library_log_vars = _init_library_size(
+            self.adata_manager, self.summary_stats["n_batch"]
+        )
 
         # self.summary_stats provides information about anndata dimensions and other tensor info
 
@@ -108,9 +106,15 @@ def setup_anndata(
             LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
             CategoricalObsField(REGISTRY_KEYS.BATCH_KEY, batch_key),
             CategoricalObsField(REGISTRY_KEYS.LABELS_KEY, labels_key),
-            CategoricalJointObsField(REGISTRY_KEYS.CAT_COVS_KEY, categorical_covariate_keys),
-            NumericalJointObsField(REGISTRY_KEYS.CONT_COVS_KEY, continuous_covariate_keys),
+            CategoricalJointObsField(
+                REGISTRY_KEYS.CAT_COVS_KEY, categorical_covariate_keys
+            ),
+            NumericalJointObsField(
+                REGISTRY_KEYS.CONT_COVS_KEY, continuous_covariate_keys
+            ),
         ]
-        adata_manager = AnnDataManager(fields=anndata_fields, setup_method_args=setup_method_args)
+        adata_manager = AnnDataManager(
+            fields=anndata_fields, setup_method_args=setup_method_args
+        )
         adata_manager.register_fields(adata, **kwargs)
         cls.register_manager(adata_manager)

src/simple_scvi/_mymodule.py

@@ -59,8 +59,12 @@ def __init__(
         # this is needed to comply with some requirement of the VAEMixin class
         self.latent_distribution = "normal"
 
-        self.register_buffer("library_log_means", torch.from_numpy(library_log_means).float())
-        self.register_buffer("library_log_vars", torch.from_numpy(library_log_vars).float())
+        self.register_buffer(
+            "library_log_means", torch.from_numpy(library_log_means).float()
+        )
+        self.register_buffer(
+            "library_log_vars", torch.from_numpy(library_log_vars).float()
+        )
 
         # setup the parameters of your generative model, as well as your inference model
         self.px_r = torch.nn.Parameter(torch.randn(n_input))
@@ -129,7 +133,9 @@ def generative(self, z, library):
         px_scale, _, px_rate, px_dropout = self.decoder("gene", z, library)
         px_r = torch.exp(self.px_r)
 
-        return dict(px_scale=px_scale, px_r=px_r, px_rate=px_rate, px_dropout=px_dropout)
+        return dict(
+            px_scale=px_scale, px_r=px_r, px_rate=px_rate, px_dropout=px_dropout
+        )
 
     def loss(
         self,
@@ -151,20 +157,28 @@ def loss(
         mean = torch.zeros_like(qz_m)
         scale = torch.ones_like(qz_v)
 
-        kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(dim=1)
+        kl_divergence_z = kl(Normal(qz_m, torch.sqrt(qz_v)), Normal(mean, scale)).sum(
+            dim=1
+        )
 
         batch_index = tensors[REGISTRY_KEYS.BATCH_KEY]
         n_batch = self.library_log_means.shape[1]
-        local_library_log_means = F.linear(one_hot(batch_index, n_batch), self.library_log_means)
-        local_library_log_vars = F.linear(one_hot(batch_index, n_batch), self.library_log_vars)
+        local_library_log_means = F.linear(
+            one_hot(batch_index, n_batch), self.library_log_means
+        )
+        local_library_log_vars = F.linear(
+            one_hot(batch_index, n_batch), self.library_log_vars
+        )
 
         kl_divergence_l = kl(
             Normal(ql_m, torch.sqrt(ql_v)),
             Normal(local_library_log_means, torch.sqrt(local_library_log_vars)),
         ).sum(dim=1)
 
         reconst_loss = (
-            -ZeroInflatedNegativeBinomial(mu=px_rate, theta=px_r, zi_logits=px_dropout).log_prob(x).sum(dim=-1)
+            -ZeroInflatedNegativeBinomial(mu=px_rate, theta=px_r, zi_logits=px_dropout)
+            .log_prob(x)
+            .sum(dim=-1)
         )
 
         kl_local_for_warmup = kl_divergence_z
@@ -174,8 +188,12 @@ def loss(
 
         loss = torch.mean(reconst_loss + weighted_kl_local)
 
-        kl_local = dict(kl_divergence_l=kl_divergence_l, kl_divergence_z=kl_divergence_z)
-        return LossOutput(loss=loss, reconstruction_loss=reconst_loss, kl_local=kl_local)
+        kl_local = dict(
+            kl_divergence_l=kl_divergence_l, kl_divergence_z=kl_divergence_z
+        )
+        return LossOutput(
+            loss=loss, reconstruction_loss=reconst_loss, kl_local=kl_local
+        )
 
     @torch.no_grad()
     def sample(
@@ -216,10 +234,14 @@ def sample(
         px_rate = generative_outputs["px_rate"]
         px_dropout = generative_outputs["px_dropout"]
 
-        dist = ZeroInflatedNegativeBinomial(mu=px_rate, theta=px_r, zi_logits=px_dropout)
+        dist = ZeroInflatedNegativeBinomial(
+            mu=px_rate, theta=px_r, zi_logits=px_dropout
+        )
 
         if n_samples > 1:
-            exprs = dist.sample().permute([1, 2, 0])  # Shape : (n_cells_batch, n_genes, n_samples)
+            exprs = dist.sample().permute(
+                [1, 2, 0]
+            )  # Shape : (n_cells_batch, n_genes, n_samples)
         else:
             exprs = dist.sample()
 
@@ -249,11 +271,23 @@ def marginal_ll(self, tensors: TensorDict, n_mc_samples: int):
 
             # Log-probabilities
             n_batch = self.library_log_means.shape[1]
-            local_library_log_means = F.linear(one_hot(batch_index, n_batch), self.library_log_means)
-            local_library_log_vars = F.linear(one_hot(batch_index, n_batch), self.library_log_vars)
-            p_l = Normal(local_library_log_means, local_library_log_vars.sqrt()).log_prob(library).sum(dim=-1)
-
-            p_z = Normal(torch.zeros_like(qz_m), torch.ones_like(qz_v)).log_prob(z).sum(dim=-1)
+            local_library_log_means = F.linear(
+                one_hot(batch_index, n_batch), self.library_log_means
+            )
+            local_library_log_vars = F.linear(
+                one_hot(batch_index, n_batch), self.library_log_vars
+            )
+            p_l = (
+                Normal(local_library_log_means, local_library_log_vars.sqrt())
+                .log_prob(library)
+                .sum(dim=-1)
+            )
+
+            p_z = (
+                Normal(torch.zeros_like(qz_m), torch.ones_like(qz_v))
+                .log_prob(z)
+                .sum(dim=-1)
+            )
             p_x_zl = -reconst_loss
             q_z_x = Normal(qz_m, qz_v.sqrt()).log_prob(z).sum(dim=-1)
             q_l_x = Normal(ql_m, ql_v.sqrt()).log_prob(library).sum(dim=-1)