Fix pydocstyle linting in new PR; clean up pyproject.toml

Author: pchlenski

.github/workflows/test.yml

@@ -40,10 +40,10 @@ jobs:
 
       # Code quality checks
       - name: Check code formatting with Black
-        run: black --check manify/ --line-length 120
+        run: black --check manify/
 
       - name: Check import ordering with isort
-        run: isort --check-only --diff manify/
+        run: isort --check-only manify/
 
       - name: Run pylint
         run: pylint manify/
@@ -54,11 +54,11 @@ jobs:
 
       # Unit testing
       - name: Run unit tests & collect coverage
-        run: BEARTYPE_ENABLE=true pytest tests --cov=manify --cov-report=xml:coverage.xml
+        run: BEARTYPE_ENABLE=true pytest tests --cov --cov-report=xml
 
       # Check docstrings are in Google style      
       - name: Check docstrings are in Google style
-        run: pydocstyle --add-ignore=D107 --convention=google manify/
+        run: pydocstyle manify/
         continue-on-error: true
 
       # Code coverage

manify/clustering/fuzzy_kmeans.py

@@ -1,4 +1,6 @@
-"""The Riemannian Fuzzy K-Means algorithm is a clustering algorithm that operates on Riemannian manifolds.
+"""Riemannian Fuzzy K-Means.
+
+The Riemannian Fuzzy K-Means algorithm is a clustering algorithm that operates on Riemannian manifolds.
 Compared to a straightforward extension of K-Means or Fuzzy K-Means to Riemannian manifolds,
 it offers significant acceleration while achieving lower loss. For more details,
 please refer to the paper: https://openreview.net/forum?id=9VmOgMN4Ie

manify/optimizers/_adan.py

@@ -1,4 +1,6 @@
-"""This file contains code adapted from the Adan optimizer implementation in PyTorch:
+"""Adaptive Nesterov Momentum Algorithm (Adan) Optimizer.
+
+This file contains code adapted from the Adan optimizer implementation in PyTorch:
 https://github.com/sail-sg/Adan
 
 The remainder of this file is unmodified from the original source, including the license:

manify/optimizers/radan.py

@@ -1,4 +1,6 @@
-"""Radan is the Riemannian version of the Adaptive Nesterov Momentum algorithm.
+"""Riemannian Adan (Radan).
+
+Radan is the Riemannian version of the Adaptive Nesterov Momentum algorithm.
 This code is compatible with both Geoopt and Manify libraries, and is designed for Riemannian Fuzzy K-Means.
 We recommend using the parameters `[0.7, 0.99, 0.99]` for best performance.**
 
@@ -50,23 +52,29 @@
 
 
 class RiemannianAdan(OptimMixin, _adan.Adan):
-    """
-    Riemannian Adan with the same API as :class:`adan.Adan`.
+    """Riemannian Adan with the same API as :class:`adan.Adan`.
+
+    Attributes:
+        param_groups: iterable of parameter groups, each containing parameters to optimize and optimization options
+        _default_manifold: the default manifold used for optimization if not specified in parameters
 
     Args:
-    params : iterable
-        iterable of parameters to optimize or dicts defining parameter groups
-    lr : float (optional)
-        learning rate (default: 1e-3)
-    betas : Tuple[float, float, float] (optional)
-        coefficients used for computing (default: (0.98, 0.92, 0.99))
-    eps : float (optional)
-        term added to the denominator to improve numerical stability (default: 1e-8)
-    weight_decay : float (optional)
-        weight decay (L2 penalty) (default: 0)
+        params: iterable of parameters to optimize or dicts defining parameter groups
+        lr: learning rate (default: 1e-3)
+        betas: coefficients used for computing (default: (0.98, 0.92, 0.99))
+        eps: term added to the denominator to improve numerical stability (default: 1e-8)
+        weight_decay: weight decay (L2 penalty) (default: 0)
     """
 
     def step(self, closure: Callable | None = None) -> Float[torch.Tensor, ""] | None:
+        """Performs a single optimization step.
+
+        Args:
+            closure: A closure that reevaluates the model and returns the loss.
+
+        Returns:
+            The loss value if closure is provided, otherwise None.
+        """
         loss = None
         if closure is not None:
             loss = closure()
@@ -152,6 +160,14 @@ def step(self, closure: Callable | None = None) -> Float[torch.Tensor, ""] | Non
 
     @torch.no_grad()  # type: ignore
     def stabilize_group(self, group: dict[str, Any]) -> None:
+        """Stabilizes the parameters in the group by projecting them onto their respective manifolds.
+
+        Args:
+            group: A dictionary containing the parameters and their states.
+
+        Returns:
+            None
+        """
         for p in group["params"]:
             if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                 continue

pyproject.toml

@@ -1,3 +1,7 @@
+# =============================================================================
+# Project Configuration
+# =============================================================================
+
 [build-system]
 requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
@@ -17,7 +21,6 @@ dependencies = [
     "tqdm",
     "cvxpy",
     "scikit-learn==1.5.1",
-    "geoopt",
     "datasets"
 ]
 
@@ -48,7 +51,22 @@ packages = ["manify"]
 [tool.setuptools.package-data]
 "manify" = ["../data/**/*"]
 
-# Pylint configuration
+# =============================================================================
+# CI/CD Tool Configuration
+# =============================================================================
+
+# Code formatting with Black
+[tool.black]
+line-length = 120
+target-version = ["py310"]
+
+# Import sorting with isort
+[tool.isort]
+profile = "black"
+line_length = 120
+skip = ["manify/optimizers/_adan.py"]  # Use unmodified adan.py code from original repo
+
+# Static analysis with Pylint
 [tool.pylint.master]
 init-hook = "import sys; sys.path.append('.')"
 fail-under = 8.0
@@ -74,18 +92,10 @@ module-rgx = "([a-z_][a-z0-9_]*)"
 [tool.pylint.typecheck]
 generated-members = "torch.*,nn.*"
 
-[tool.pytest.ini_options]
-testpaths = ["tests"]
-addopts = "--jaxtyping-packages=beartype.beartype"
-python_files = "test_*.py"
-
+# Type checking with MyPy
 [tool.mypy]
-# Specify the packages to check
 packages = ["manify"]
-disable_error_code = ["name-defined"] # Needed for jaxtyping compatibility - else vector annotations fail
-exclude = ["manify/optimizers/_adan.py"] # Use unmodified adan.py code from original repo
-
-# Ignore missing imports for external libraries
+exclude = ["manify/optimizers/_adan.py"]  # Use unmodified adan.py code from original repo
 python_version = "3.10"
 disallow_untyped_defs = true
 disallow_incomplete_defs = true
@@ -97,8 +107,28 @@ warn_return_any = true
 warn_unused_ignores = false
 follow_imports = "skip"
 ignore_missing_imports = true
+disable_error_code = ["name-defined"]  # Needed for jaxtyping compatibility - else vector annotations fail
 
-[tool.isort]
-profile = "black"
-line_length = 120
-skip = ["manify/optimizers/_adan.py"] # Use unmodified adan.py code from original repo
+# Unit testing with pytest
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "--jaxtyping-packages=beartype.beartype"
+python_files = "test_*.py"
+
+# Code coverage
+[tool.coverage.run]
+source = ["manify"]
+branch = true
+omit = ["manify/optimizers/_adan.py"]  # Exclude from coverage reporting
+
+[tool.coverage.report]
+fail_under = 80
+
+[tool.coverage.xml]
+output = "coverage.xml"
+
+# Docstring style checking with pydocstyle
+[tool.pydocstyle]
+convention = "google"
+add_ignore = ["D107"]
+match = "(?!_adan).*\\.py$"  # Exclude _adan.py from docstring checks