Activate additional ruff rules

Author: ffl096

conftest.py

@@ -0,0 +1,30 @@
+"""Configure our testing suite."""
+
+import networkx as nx
+import numpy as np
+import pytest
+import toponetx as tnx
+
+
+@pytest.fixture(autouse=True)
+def doctest_default_imports(doctest_namespace) -> None:
+    """Add default imports to the doctest namespace.
+
+    This fixture adds the following default imports to every doctest, so that their use
+    is consistent across all doctests without boilerplate imports polluting the
+    doctests themselves:
+
+    .. code-block:: python
+
+        import numpy as np
+        import networkx as nx
+        import toponetx as tnx
+
+    Parameters
+    ----------
+    doctest_namespace : dict
+        The namespace of the doctest.
+    """
+    doctest_namespace["np"] = np
+    doctest_namespace["nx"] = nx
+    doctest_namespace["tnx"] = tnx

docs/conf.py

@@ -3,7 +3,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "TopoEmbedX"
-copyright = "2022-2023, PyT-Team, Inc."
+copyright = "2022-2023, PyT-Team, Inc."  # noqa: A001
 author = "PyT-Team Authors"
 language = "en"
 

pyproject.toml

@@ -113,20 +113,49 @@ select = [
     "E", # code style
     "W", # warnings
     "I", # import order
+    "D", # pydocstyle rules
     "UP", # pyupgrade rules
+    "YTT", # flake8-2020 rules
+    "S", # bandit rules
+    "BLE", # blind except
     "B", # bugbear rules
+    "A", # builtin shadowing
+    "COM", # comma rules
+    "C4", # comprehensions
+    "DTZ", # datetime rules
+    "T10", # debugger calls
+    "FA", # future annotations
+    "ISC", # implicit str concatenation
+    "ICN", # import conventions
+    "LOG", # logging rules
+    "G", # logging format rules
     "PIE", # pie rules
     "Q", # quote rules
+    "RSE", # raise rules
     "RET", # return rules
+    "SLOT", # slot rules
     "SIM", # code simplifications
+    "TID", # tidy imports
+    "TC", # type checking rules
+    "PTH", # use pathlib
+    "PD", # pandas rules
+    "PLC", # pylint conventions
+    "PLE", # pylint errors
+    "FLY", # flynt
     "NPY", # numpy rules
     "PERF", # performance rules
+    "FURB", # refurb
     "RUF", # miscellaneous rules
 ]
-ignore = ["E501"] # line too long
+ignore = [
+    "E501",    # line too long
+    "COM812",  # trailing commas; conflict with `ruff format`
+    "ISC001",  # implicitly single-line str concat; conflict with `ruff format`
+]
 
 [tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["F401", "F403"]
+"__init__.py" = ["F403"]
+"test/**.py" = ["S101"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"

test/test_neighborhood.py

@@ -29,21 +29,21 @@ def test_neighborhood_from_complex_matrix_dimension_cell_complex(self):
         cc2 = tnx.classes.CellComplex([[0, 1, 2], [1, 2, 3]])
 
         ind, A = tex.neighborhood.neighborhood_from_complex(cc1)
-        assert A.todense().shape == tuple([9, 9])
+        assert A.todense().shape == (9, 9)
         assert len(ind) == 9
 
         ind, A = tex.neighborhood.neighborhood_from_complex(cc2)
-        assert A.todense().shape == tuple([4, 4])
+        assert A.todense().shape == (4, 4)
         assert len(ind) == 4
 
         ind, A = tex.neighborhood.neighborhood_from_complex(
             cc1, neighborhood_type="coadj"
         )
-        assert A.todense().shape == tuple([9, 9])
+        assert A.todense().shape == (9, 9)
         assert len(ind) == 9
 
         ind, A = tex.neighborhood.neighborhood_from_complex(
             cc2, neighborhood_type="coadj"
         )
-        assert A.todense().shape == tuple([4, 4])
+        assert A.todense().shape == (4, 4)
         assert len(ind) == 4

test/test_tutorials.py

@@ -1,13 +1,20 @@
 """Unit tests for the tutorials."""
 
-import glob
 import subprocess
 import tempfile
+from pathlib import Path
 
 import pytest
 
 
 def _exec_tutorial(path):
+    """Execute the notebooks for testing in the given directory.
+
+    Parameters
+    ----------
+    path : str
+        Path to the tutorials directory.
+    """
     with tempfile.NamedTemporaryFile(suffix=".ipynb") as tmp_file:
         args = [
             "jupyter",
@@ -21,14 +28,20 @@ def _exec_tutorial(path):
             tmp_file.name,
             path,
         ]
-        subprocess.check_call(args)
+        subprocess.check_call(args)  # noqa: S603
 
 
 TUTORIALS_DIR = "tutorials"
-paths = sorted(glob.glob(f"{TUTORIALS_DIR}/*.ipynb"))
+paths = sorted(Path(TUTORIALS_DIR).glob("*.ipynb"))
 
 
 @pytest.mark.parametrize("path", paths)
 def test_tutorial(path):
-    """Test the tutorials."""
+    """Test the tutorials.
+
+    Parameters
+    ----------
+    path : str
+        Path to the tutorials directory.
+    """
     _exec_tutorial(path)

topoembedx/__init__.py

@@ -1,8 +1,3 @@
 """Initialize the library with modules and other content."""
 
-from .classes.cell2vec import Cell2Vec
-from .classes.cell_diff2vec import CellDiff2Vec
-from .classes.deepcell import DeepCell
-from .classes.higher_order_laplacian_eigenmaps import HigherOrderLaplacianEigenmaps
-from .classes.hoglee import HOGLEE
-from .classes.hope import HOPE
+from .classes import *

topoembedx/classes/__init__.py

@@ -1 +1,9 @@
 """Initialize the classes module of TopoEmbedX."""
+
+from .cell2vec import *
+from .cell_diff2vec import *
+from .deepcell import *
+from .higher_order_laplacian_eigenmaps import *
+from .hoglee import *
+from .hope import *
+from .random_walks import *

topoembedx/classes/cell2vec.py

@@ -111,7 +111,7 @@ def fit(
         neighborhood_type : {"adj", "coadj"}, default="adj"
             The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
         neighborhood_dim : dict
-            The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+            The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
             In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
             - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
             neighborhood_dim["rank"].

topoembedx/classes/cell_diff2vec.py

@@ -91,7 +91,7 @@ def fit(
         neighborhood_type : {"adj", "coadj"}, default="adj"
             The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
         neighborhood_dim : dict
-            The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+            The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
             In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
             - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
             neighborhood_dim["rank"].

topoembedx/classes/deepcell.py

@@ -91,7 +91,7 @@ def fit(
         neighborhood_type : {"adj", "coadj"}, default="adj"
             The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
         neighborhood_dim : dict
-            The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+            The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
             In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
             - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
             neighborhood_dim["rank"].

topoembedx/classes/higher_order_laplacian_eigenmaps.py

@@ -56,7 +56,7 @@ def fit(
         neighborhood_type : {"adj", "coadj"}, default="adj"
             The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
         neighborhood_dim : dict
-            The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+            The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
             In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
             - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
             neighborhood_dim["rank"].

topoembedx/classes/hoglee.py

@@ -48,7 +48,7 @@ def fit(
         neighborhood_type : {"adj", "coadj"}, default="adj"
             The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
         neighborhood_dim : dict
-            The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+            The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
             In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
             - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
             neighborhood_dim["rank"].

topoembedx/classes/hope.py

@@ -146,9 +146,8 @@ def fit(
 
         Examples
         --------
-        >>> import toponetx as tnx
         >>> from topoembedx import HOPE
-        >>> ccc = tnx.classes.CombinatorialComplex()
+        >>> ccc = tnx.CombinatorialComplex()
         >>> ccc.add_cell([2, 5], rank=1)
         >>> ccc.add_cell([2, 4], rank=1)
         >>> ccc.add_cell([7, 8], rank=1)

topoembedx/classes/random_walks.py

@@ -43,10 +43,10 @@ def transition_from_adjacency(
     This function generates a transition matrix from an adjacency matrix
     using the following steps:
 
-    1. Add self-loop to the adjaency matrix if self_loop is set to True
-    2. Compute the degree matrix
+    1. Add self-loop to the adjacency matrix if ``self_loop`` is set to ``True``.
+    2. Compute the degree matrix.
     3. Compute the transition matrix by taking the dot product of the inverse of
-       the degree matrix and the adjacency matrix
+       the degree matrix and the adjacency matrix.
 
     Parameters
     ----------
@@ -55,7 +55,7 @@ def transition_from_adjacency(
     sub_sampling : float, default=0.1
         The rate of subsampling.
     self_loop : bool, default=True
-        A flag indicating whether to add self-loop to the adjacency matrix.
+        Whether to add self-loops to the adjacency matrix.
 
     Returns
     -------
@@ -64,7 +64,6 @@ def transition_from_adjacency(
 
     Examples
     --------
-    >>> import numpy as np
     >>> A = np.array([[0, 1, 1, 0], [1, 0, 1, 0], [1, 1, 0, 1], [0, 0, 1, 0]])
     >>> transition_from_adjacency(A)
     array([[0.33333333, 0.33333333, 0.33333333, 0.        ],
@@ -165,7 +164,6 @@ def random_walk(
 
     Examples
     --------
-    >>> import numpy as np
     >>> transition_matrix = np.array(
     ...     [
     ...         [0.0, 1.0, 0.0, 0.0],
@@ -175,10 +173,9 @@ def random_walk(
     ...     ]
     ... )
     >>> states = ["A", "B", "C", "D"]
-    >>> walks = random_walk(
+    >>> random_walk(
     ...     length=3, num_walks=2, states=states, transition_matrix=transition_matrix
     ... )
-    >>> print(walks)
     [['B', 'C', 'D'], ['B', 'C', 'B']]
     """
     rw = RandomWalk(states, transition_matrix)

topoembedx/exceptions.py renamed to topoembedx/exception.py

@@ -1,4 +1,6 @@
-"""Base classes for TopoEmbedX exceptions."""
+"""Base errors and exceptions for TopoEmbedX."""
+
+__all__ = ["TopoEmbedXError", "TopoEmbedXException", "TopoEmbedXNotImplementedError"]
 
 
 class TopoEmbedXException(Exception):

topoembedx/neighborhood.py

@@ -13,9 +13,8 @@ def neighborhood_from_complex(
 ) -> tuple[list, csr_matrix]:
     """Compute the neighborhood of a complex.
 
-    This function returns the indices and matrix for the neighborhood specified
-    by `neighborhood_type`
-    and `neighborhood_dim` for the input complex `domain`.
+    This function returns the indices and matrix for the neighborhood specified by
+    `neighborhood_type` and `neighborhood_dim` for the input complex `domain`.
 
     Parameters
     ----------
@@ -24,7 +23,7 @@ def neighborhood_from_complex(
     neighborhood_type : {"adj", "coadj"}, default="adj"
         The type of neighborhood to compute. "adj" for adjacency matrix, "coadj" for coadjacency matrix.
     neighborhood_dim : dict
-        The integer parmaters needed to specify the neighborhood of the cells to generate the embedding.
+        The integer parameters needed to specify the neighborhood of the cells to generate the embedding.
         In TopoNetX  (co)adjacency neighborhood matrices are specified via one or two parameters.
         - For Cell/Simplicial/Path complexes (co)adjacency matrix is specified by a single parameter, this is precisely
         neighborhood_dim["rank"].