update pre-commit and add Ruff linting and formatting

.pre-commit-config.yaml

@@ -30,16 +30,16 @@ repos:
     rev: v2.4.1
     hooks:
       - id: codespell
-        args: ["-L", "larg"]
         exclude: >
             (?x)^(
                 .*\.txt|
+                .*\.svg|
                 .*\.ipynb
             )$
 
   # Clang format
   - repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v20.1.7
+    rev: v20.1.8
     hooks:
       - id: clang-format
         types_or: [c++, c, cuda]
@@ -55,15 +55,16 @@ repos:
 
   # Ruff, the Python auto-correcting linter/formatter written in Rust
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
+    rev: v0.12.3
     hooks:
       - id: ruff
-        args: ["--fix", "--show-fixes", "--exclude=__init__.py"]
+        args: ["--fix", "--show-fixes"]
       - id: ruff-format
 
-  # Black format Python and notebooks
-  - repo: https://github.com/psf/black
-    rev: 25.1.0
+  # Format docstrings
+  - repo: https://github.com/DanielNoord/pydocstringformatter
+    rev: v0.7.3
     hooks:
-      - id: black-jupyter
+      - id: pydocstringformatter
+        args: ["--style=numpydoc"]
 ...

pyproject.toml

@@ -1,3 +1,82 @@
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+indent-width = 4
+line-length = 88
+target-version = "py39"
+extend-include = ["*.ipynb"]
+
+lint.select = [
+  "ANN", # flake8-annotations
+  "ARG", # flake8-unused-arguments
+  "B",   # flake8-bugbear
+  "C4",  # flake8-comprehensions
+  "C90", # mccabe
+#  "D",   # pydocstyle
+  "DTZ", # flake8-datetimez
+  "E",   # pycodestyle
+  "EM",  # flake8-errmsg
+  "ERA", # eradicate
+  "F",   # Pyflakes
+  "I",   # isort
+  "ICN", # flake8-import-conventions
+  "N",   # pep8-naming
+  "PD",  # pandas-vet
+  "PGH", # pygrep-hooks
+  "PIE", # flake8-pie
+  "PL",  # Pylint
+  "PLC", # Pylint
+  "PLE", # Pylint
+  "PLR", # Pylint
+  "PLW", # Pylint
+  "PT",  # flake8-pytest-style
+  "Q",   # flake8-quotes
+  "RET", # flake8-return
+  "RUF100",  # Ruff-specific
+  "S",   # flake8-bandit
+  "SIM", # flake8-simplify
+#  "T20", # flake8-print
+  "TID", # flake8-tidy-imports
+  "UP",  # pyupgrade
+  "W",   # pycodestyle
+  "YTT",  # flake8-2020
+]
+
+exclude = [
+    "__init__.py",
+    "setup.py",
+]
+
+lint.ignore = [
+    "S301",  # pickle
+    "S311",  # Standard pseudo-random generators
+]
+
+[tool.ruff.lint.pep8-naming]
+extend-ignore-names = [
+    "X", "X1", "X_train", "X_val", "X_test", "X_predict",
+]
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.format]
+docstring-code-format = true
+docstring-code-line-length = 80
+
+[tool.ruff.lint.extend-per-file-ignores]
+"test/**" = ["S101", "PLR2004", "ANN"]
+"python/example_rmux.py" = ["PLR2004"]
+"python/example_maze.py" = ["PLR2004"]
+"python/notebooks/example_cartpole.ipynb" = ["E501", "SIM115"]
+"python/notebooks/example_maze.ipynb" = ["E501", "PLR2004"]
+"python/notebooks/example_regression.ipynb" = ["PLR2004"]
+"python/notebooks/example_rmux.ipynb" = ["PLR2004"]
+"python/notebooks/example_tuning.ipynb" = ["E501", "ANN201"]
+
+[tool.codespell]
+ignore-words-list = [
+    "larg",
+]

python/example_cartpole.py

@@ -117,7 +117,7 @@
 
 
 def replay(replay_size: int = 5000) -> None:
-    """Performs experience replay updates"""
+    """Performs experience replay updates."""
     batch_size: int = min(len(memory), replay_size)
     batch = random.sample(memory, batch_size)
     for state, action, reward, next_state, done in batch:
@@ -133,7 +133,7 @@ def replay(replay_size: int = 5000) -> None:
 
 
 def egreedy_action(state: np.ndarray) -> int:
-    """Selects an action using an epsilon greedy policy"""
+    """Selects an action using an epsilon greedy policy."""
     if np.random.rand() < epsilon:
         return random.randrange(N_ACTIONS)
     prediction_array = xcs.predict(state.reshape(1, -1))[0]
@@ -143,7 +143,7 @@ def egreedy_action(state: np.ndarray) -> int:
 
 
 def episode() -> tuple[float, int]:
-    """Executes a single episode, saving to memory buffer"""
+    """Executes a single episode, saving to memory buffer."""
     episode_score: float = 0
     episode_steps: int = 0
     state: np.ndarray = env.reset()[0]

python/example_maze.py

@@ -283,7 +283,7 @@ def run_experiment(env: Maze) -> None:
     bar.close()
 
 
-def plot_performance(env: Maze):
+def plot_performance(env: Maze) -> None:
     """Plots learning performance."""
     plt.figure(figsize=(10, 6))
     plt.plot(trials, steps)

python/example_rmux.py

@@ -101,13 +101,12 @@ def execute(self, act: int) -> tuple[bool, float]:
                     reward += 0.1
             else:
                 reward = self.max_payoff
+        elif self.payoff_map:
+            reward = (pos - self.pos_bits) * 0.2
+            if answer == 1:
+                reward += 0.1
         else:
-            if self.payoff_map:
-                reward = (pos - self.pos_bits) * 0.2
-                if answer == 1:
-                    reward += 0.1
-            else:
-                reward = 0
+            reward = 0
         return correct, reward
 
 

python/notebooks/example_cartpole.ipynb

@@ -33,12 +33,12 @@
     "import random\n",
     "from collections import deque\n",
     "\n",
-    "from matplotlib import rcParams\n",
-    "import matplotlib.pyplot as plt\n",
-    "import imageio\n",
     "import gymnasium as gym\n",
+    "import imageio\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from IPython.display import display, Image\n",
+    "from IPython.display import Image, display\n",
+    "from matplotlib import rcParams\n",
     "from tqdm import tqdm\n",
     "\n",
     "import xcsf\n",
@@ -625,12 +625,12 @@
     }
    ],
    "source": [
-    "annotated_frames = list()\n",
+    "annotated_frames = []\n",
     "\n",
     "if SAVE_GIF:\n",
     "    # add score and episode nr\n",
     "    rcParams[\"font.family\"] = \"monospace\"\n",
-    "    bbox = dict(boxstyle=\"round\", fc=\"0.8\")\n",
+    "    bbox = {\"boxstyle\": \"round\", \"fc\": \"0.8\"}\n",
     "    bar = tqdm(total=len(frames), position=0, leave=True)\n",
     "    for i in range(len(frames)):\n",
     "        fig = plt.figure(dpi=90)\n",

python/notebooks/example_classification.ipynb

@@ -21,8 +21,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.datasets import fetch_openml\n",
-    "from sklearn.metrics import ConfusionMatrixDisplay\n",
-    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    classification_report,\n",
+    "    confusion_matrix,\n",
+    ")\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
     "\n",

python/notebooks/example_maze.ipynb

@@ -486,7 +486,7 @@
     }
    ],
    "source": [
-    "def plot_performance(env: Maze):\n",
+    "def plot_performance(env: Maze) -> None:\n",
     "    \"\"\"Plots learning performance.\"\"\"\n",
     "    plt.figure(figsize=(10, 6))\n",
     "    plt.plot(trials, steps)\n",
@@ -534,9 +534,10 @@
    ],
    "source": [
     "import io\n",
-    "from PIL import Image\n",
     "from turtle import Screen, Turtle\n",
+    "\n",
     "from IPython.display import display\n",
+    "from PIL import Image\n",
     "\n",
     "GRID_WIDTH: int = maze.x_size\n",
     "GRID_HEIGHT: int = maze.y_size\n",

python/notebooks/example_regression.ipynb

@@ -21,7 +21,7 @@
     "import graphviz\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from IPython.display import display, Image\n",
+    "from IPython.display import Image, display\n",
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.ensemble import RandomForestRegressor\n",
     "from sklearn.gaussian_process import GaussianProcessRegressor\n",

python/notebooks/example_rmux.ipynb

@@ -109,13 +109,12 @@
     "                    reward += 0.1\n",
     "            else:\n",
     "                reward = self.max_payoff\n",
+    "        elif self.payoff_map:\n",
+    "            reward = (pos - self.pos_bits) * 0.2\n",
+    "            if answer == 1:\n",
+    "                reward += 0.1\n",
     "        else:\n",
-    "            if self.payoff_map:\n",
-    "                reward = (pos - self.pos_bits) * 0.2\n",
-    "                if answer == 1:\n",
-    "                    reward += 0.1\n",
-    "            else:\n",
-    "                reward = 0\n",
+    "            reward = 0\n",
     "        return correct, reward"
    ]
   },

python/notebooks/example_seeding.ipynb

@@ -14,6 +14,7 @@
    "outputs": [],
    "source": [
     "import json\n",
+    "\n",
     "import numpy as np\n",
     "\n",
     "import xcsf\n",

python/notebooks/example_tuning.ipynb

@@ -693,12 +693,12 @@
    "source": [
     "# General parameters can be searched in the usual way\n",
     "\n",
-    "parameters = dict(beta=[0.1, 0.5])\n",
+    "parameters = {\"beta\": [0.1, 0.5]}\n",
     "\n",
     "# EA parameters require specifying a dict,\n",
     "# but individual values can still be set because the other values are not changed.\n",
     "\n",
-    "parameters = dict(ea=[{\"lambda\": 2}, {\"lambda\": 10}])\n",
+    "parameters = {\"ea\": [{\"lambda\": 2}, {\"lambda\": 10}]}\n",
     "\n",
     "# However, for actions, conditions, and predictions, the WHOLE dict must be specified\n",
     "# for each value to try in the search. See Optuna below.\n",
@@ -752,7 +752,7 @@
     }
    ],
    "source": [
-    "def objective(trial):\n",
+    "def objective(trial: optuna.trial.Trial) -> float:\n",
     "    \"\"\"Measure a new hyperparameter combination.\"\"\"\n",
     "    # get new parameters to try\n",
     "    e0: float = trial.suggest_float(\"e0\", 0.08, 0.12, step=0.01)\n",