[pre-commit.ci] pre-commit autoupdate (#405)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/adrienverge/yamllint.git: v1.30.0 → v1.32.0](https://github.com/adrienverge/yamllint.git/compare/v1.30.0...v1.32.0)
- [github.com/asottile/pyupgrade: v3.3.1 → v3.4.0](asottile/pyupgrade@v3.3.1...v3.4.0)
- [github.com/psf/black: 23.1.0 → 23.3.0](psf/black@23.1.0...23.3.0)
- [github.com/PyCQA/docformatter: v1.5.1 → v1.7.1](PyCQA/docformatter@v1.5.1...v1.7.1)

* [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>

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.30.0
+    rev: v1.32.0
     hooks:
       - id: yamllint
         args: [-c=.yamllint.yml]
@@ -25,18 +25,18 @@ repos:
       - id: add-trailing-comma
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.4.0
     hooks:
       - id: pyupgrade
 
   - repo: https://github.com/psf/black
-    rev: 23.1.0
+    rev: 23.3.0
     hooks:
       - id: black
         args: ["--safe"]
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.5.1
+    rev: v1.7.1
     hooks:
       - id: docformatter
         name: Format docstring

src/nleval/data/network/funcoup.py

@@ -8,7 +8,6 @@ class FunCoup(BaseNDExData):
     The edge weights are PFC values, which is a probabilistic estimation about
     whether a pair of genes are functionally coupled.
 
-
     https://funcoup5.scilifelab.se/help/#Citation
 
     """

src/nleval/feature/base.py

@@ -75,7 +75,7 @@ def isempty(self) -> bool:
 
     @property
     def dim(self):
-        """int: dimension of feature vectors."""
+        """Int: dimension of feature vectors."""
         return self._dim
 
     @dim.setter

src/nleval/graph/base.py

@@ -203,16 +203,16 @@ def num_nodes(self) -> int:
 
     @property
     def size(self):
-        """int: number of nodes in graph."""
+        """Int: number of nodes in graph."""
         return self.num_nodes
 
     @property
     def num_edges(self) -> int:
-        """int: Number of edges."""
+        """Int: Number of edges."""
         raise NotImplementedError
 
     def isempty(self):
-        """bool: true if graph is empty, indicated by empty idmap."""
+        """Bool: true if graph is empty, indicated by empty idmap."""
         return not self.size
 
     def induced_subgraph(self, node_ids: List[str]):

src/nleval/graph/dense.py

@@ -40,7 +40,7 @@ def __getitem__(self, key):
 
     @property
     def num_edges(self) -> int:
-        """int: Number of edges."""
+        """Int: Number of edges."""
         return (self.mat != 0).sum()
 
     @property

src/nleval/graph/sparse.py

@@ -49,12 +49,12 @@ def edge_data(self) -> EdgeData:
 
     @property
     def weighted(self):
-        """bool: Whether weights (3rd column in edgelist) are available."""
+        """Bool: Whether weights (3rd column in edgelist) are available."""
         return self._weighted
 
     @property
     def directed(self):
-        """bool: Indicate whether edges are directed or not."""
+        """Bool: Indicate whether edges are directed or not."""
         return self._directed
 
     @weighted.setter
@@ -69,7 +69,7 @@ def directed(self, val):
 
     @property
     def num_edges(self) -> int:
-        """int: Number of edges."""
+        """Int: Number of edges."""
         return sum(len(nbrs) for nbrs in self.edge_data)
 
     def __getitem__(self, key: Union[str, List[str]]):

src/nleval/label/filters/nonred.py

@@ -11,7 +11,8 @@ class LabelsetNonRedFilter(BaseFilter):
     """Filter out redundant labelsets in a labelset collection.
 
     The detailed procedure can be found in the supplementary data of
-    https://doi.org/10.1093/bioinformatics/btaa150 In brief, given a labelset
+    https://doi.org/10.1093/bioinformatics/btaa150
+    In brief, given a labelset
     collection, a graph of labelsets if first constructed based on the
     redundancy score function of interest. Here, we use the combination of
     Jaccard index and overlap coefficient. Then, for each connected component in

src/nleval/model/label_propagation.py

@@ -151,7 +151,7 @@ def __init__(
 
     @property
     def beta(self) -> float:
-        """float: restart parameter."""
+        """Float: restart parameter."""
         return self._beta
 
     @beta.setter

src/nleval/model_trainer/gnn.py

@@ -61,7 +61,7 @@ def __init__(
 
     @property
     def metric_best(self):
-        """str: Metric used for determining the best model."""
+        """Str: Metric used for determining the best model."""
         return self._metric_best
 
     @metric_best.setter

src/nleval/util/idhandler.py

@@ -122,7 +122,7 @@ def lst(self):
 
     @property
     def size(self):
-        """int: number of IDs in list."""
+        """Int: number of IDs in list."""
         return len(self._lst)
 
     def copy(self):

test/test_graph.py

@@ -758,7 +758,7 @@ def setUpClass(cls):
         cls.case = test_case1()
 
     def check_graph(self, graph):
-        """compare graph with data, true if identical."""
+        """Compare graph with data, true if identical."""
         mat = self.case.data_mat[:, 1:]
         IDlst = [str(int(i)) for i in self.case.data_mat[:, 0]]
         for idx1, node_id1 in enumerate(IDlst):
@@ -1120,15 +1120,7 @@ def test_restrict_to_branch(self):
         self.assertRaises(IDNotExistError, graph.restrict_to_branch, "g")
 
     def test_read_obo(self):
-        r"""
-
-               a
-        /      |       \
-        b      c (x, y)  d
-        |       \      /
-        e [z]       f
-
-        """
+        r"""A /      |       \ b      c (x, y)  d |       \      / e [z]       f."""
         obo_path = osp.join(SAMPLE_DATA_DIR, "toy_ontology.obo")
         with self.subTest(xref_prefix=None):
             # Do not capture xref when xref_prefix is unset