Merge pull request #3 from NKI-CCB/pre-commit-ci-update-config

[pre-commit.ci] pre-commit autoupdate

Author: saroudant

.pre-commit-config.yaml

@@ -7,11 +7,11 @@ default_stages:
 minimum_pre_commit_version: 2.16.0
 repos:
     - repo: https://github.com/psf/black
-      rev: 22.12.0
+      rev: 23.3.0
       hooks:
           - id: black
     - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v3.0.0-alpha.4
+      rev: v3.0.0-alpha.6
       hooks:
           - id: prettier
     - repo: https://github.com/asottile/blacken-docs
@@ -43,8 +43,8 @@ repos:
             args: [--fix=lf]
           - id: trailing-whitespace
           - id: check-case-conflict
-    - repo: https://github.com/myint/autoflake
-      rev: v2.0.0
+    - repo: https://github.com/PyCQA/autoflake
+      rev: v2.0.2
       hooks:
           - id: autoflake
             args:

sobolev_alignment/scvi_model_search.py

@@ -194,10 +194,6 @@ def _objective_function(params):
 def split_dataset(data_an, test_size=0.1):
     """Split between training and testing"""
     train_data_df, test_data_df = train_test_split(data_an.to_df(), test_size=test_size)
-    train_data_an = data_an[
-        train_data_df.index,
-    ]
-    test_data_an = data_an[
-        test_data_df.index,
-    ]
+    train_data_an = data_an[train_data_df.index,]
+    test_data_an = data_an[test_data_df.index,]
     return train_data_an, test_data_an

sobolev_alignment/sobolev_alignment.py

@@ -329,7 +329,6 @@ def _train_krr(
         frob_norm_source: bool = False,
         lib_size_norm: bool = False,
     ):
-
         if n_krr_clfs == 1:
             self.approximate_krr_regressions_[data_source] = self._train_one_krr(
                 data_source=data_source,
@@ -702,7 +701,6 @@ def _memmap_log_processing(
         unit_std: bool = False,
         frob_norm_source: bool = False,
     ):
-
         # Save embedding
         if save_mmap is not None and type(save_mmap) == str:
             self._save_mmap = save_mmap

tests/test_krr_approx.py

@@ -176,7 +176,6 @@ def test_laplacian_falkon_fit(self, fit_falkon_laplacian_ridge, valid_input, val
         assert pearson_corr[0] > pearson_threshold
 
     def test_ridge_coef_sklearn_fit(self, fit_sklearn_laplacian_ridge, input, valid_input):
-
         pred_reconstruct = fit_sklearn_laplacian_ridge.kernel_(
             valid_input, input[fit_sklearn_laplacian_ridge.ridge_samples_idx_, :]
         )
@@ -186,7 +185,6 @@ def test_ridge_coef_sklearn_fit(self, fit_sklearn_laplacian_ridge, input, valid_
         )
 
     def test_ridge_coef_falkon_fit(self, fit_falkon_laplacian_ridge, input, valid_input):
-
         pred_reconstruct = fit_falkon_laplacian_ridge.kernel_(valid_input, fit_falkon_laplacian_ridge.anchors())
         pred_reconstruct = pred_reconstruct.matmul(fit_falkon_laplacian_ridge.sample_weights_)
         np.testing.assert_array_almost_equal(