ruff formatter

Author: quentinhaenn

docs/source/conf.py

@@ -9,26 +9,26 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath("../.."))
 
-project = 'Radius Clustering'
-copyright = '2024, Haenn Quentin, Chardin Brice, Baron Mickaël'
-author = 'Haenn Quentin, Chardin Brice, Baron Mickaël'
-release = '1.0'
+project = "Radius Clustering"
+copyright = "2024, Haenn Quentin, Chardin Brice, Baron Mickaël"
+author = "Haenn Quentin, Chardin Brice, Baron Mickaël"
+release = "1.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.viewcode',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.viewcode",
     "sphinx-prompt",
-    'sphinx.ext.napoleon',
+    "sphinx.ext.napoleon",
     "sphinx_remove_toctrees",
     "sphinxcontrib.email",
-    'sphinx_gallery.gen_gallery',
-    'sphinx_copybutton',
+    "sphinx_gallery.gen_gallery",
+    "sphinx_copybutton",
     "sphinx.ext.intersphinx",
     "sphinx_design",
 ]
@@ -43,22 +43,20 @@
 # Conf of numpydoc
 numpydoc_class_members_toctree = False
 
-templates_path = ['_templates']
+templates_path = ["_templates"]
 exclude_patterns = []
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'pydata_sphinx_theme'
-#html_static_path = ['_static']
+html_theme = "pydata_sphinx_theme"
+# html_static_path = ['_static']
 
 html_logo = "./images/logo-lias.jpg"
 
 html_short_title = "Radius Clustering"
 
-html_sidebars = {
-    "**": []
-}
+html_sidebars = {"**": []}
 
 html_theme_options = {
     "icon_links_label": "Icon Links",
@@ -87,16 +85,15 @@
 sg_examples_dir = "../../examples"
 sg_gallery_dir = "auto_examples"
 sphinx_gallery_conf = {
-    "doc_module"              : "radius_clustering",
-    "backreferences_dir"      : os.path.join("modules", "generated"),
-    "show_memory"             : False,
-    "examples_dirs"           : [sg_examples_dir],
-    "gallery_dirs"            : [sg_gallery_dir],
-
+    "doc_module": "radius_clustering",
+    "backreferences_dir": os.path.join("modules", "generated"),
+    "show_memory": False,
+    "examples_dirs": [sg_examples_dir],
+    "gallery_dirs": [sg_gallery_dir],
     # avoid generating too many cross links
     "inspect_global_variables": False,
-    "remove_config_comments"  : True,
-    "plot_gallery"            : "True",
-    "recommender"             : {"enable": True, "n_examples": 4, "min_df": 12},
-    "reset_modules"           : ("matplotlib", "seaborn"),
+    "remove_config_comments": True,
+    "plot_gallery": "True",
+    "recommender": {"enable": True, "n_examples": 4, "min_df": 12},
+    "reset_modules": ("matplotlib", "seaborn"),
 }

examples/plot_iris_example.py

@@ -19,7 +19,7 @@
 # SPDX-License-Identifier: MIT
 
 
-#%%
+# %%
 # Load the Iris dataset
 # ---------------------
 #
@@ -34,11 +34,11 @@
 
 # Load the Iris dataset
 iris = datasets.load_iris()
-X = iris['data']
+X = iris["data"]
 y = iris.target
 
 
-#%%
+# %%
 # Visualize the Iris dataset
 # --------------------------
 #
@@ -52,13 +52,13 @@
 pca = PCA(n_components=3)
 iris_reduced = pca.fit_transform(X)
 fig = plt.figure(figsize=(8, 6))
-ax = fig.add_subplot(111, projection='3d', elev=48, azim=134)
+ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
 ax.scatter(
     iris_reduced[:, 0],
     iris_reduced[:, 1],
     iris_reduced[:, 2],
     c=y,
-    cmap='Dark2',
+    cmap="Dark2",
     s=40,
 )
 # Set plot labels
@@ -74,7 +74,7 @@
 
 plt.show()
 
-#%%
+# %%
 # Compute Clustering with Radius Clustering
 # -----------------------------------------
 #
@@ -87,7 +87,7 @@
 rad.fit(X)
 t_rad = time.time() - t0
 
-#%%
+# %%
 # Compute KMeans Clustering for Comparison
 # ----------------------------------------
 #
@@ -100,14 +100,15 @@
 k_means.fit(X)
 t_kmeans = time.time() - t0
 
-#%% Establishing parity between clusters
+# %% Establishing parity between clusters
 # --------------------------------------
 #
 # We want to have the same color for the same cluster in both plots.
 # We can achieve this by matching the cluster labels of the Radius clustering and the KMeans clustering.
 # First we define a function to retrieve the cluster centers from the Radius clustering and KMeans clustering and
 # match them pairwise.
 
+
 def get_order_labels(kmeans, rad, data):
     centers1_cpy = kmeans.cluster_centers_.copy()
     centers2_cpy = data[rad.centers_].copy()
@@ -133,6 +134,7 @@ def get_order_labels(kmeans, rad, data):
         order.append(int(match_label[0]))
     return order
 
+
 from sklearn.metrics.pairwise import pairwise_distances_argmin
 
 rad_centers_index = np.array(rad.centers_)
@@ -146,7 +148,7 @@ def get_order_labels(kmeans, rad, data):
 kmeans_labels = pairwise_distances_argmin(X, kmeans_centers)
 rad_labels = pairwise_distances_argmin(X, rad_centers_coordinates)
 
-#%%
+# %%
 # Plotting the results and the difference
 # ---------------------------------------
 
@@ -162,7 +164,7 @@ def get_order_labels(kmeans, rad, data):
     iris_reduced[:, 1],
     iris_reduced[:, 2],
     c=kmeans_labels,
-    cmap='Dark2',
+    cmap="Dark2",
     s=40,
 )
 # adapting center coordinates to the 3D plot
@@ -214,7 +216,9 @@ def get_order_labels(kmeans, rad, data):
     different += (kmeans_labels == k) != (rad_labels == k)
 
 identical = np.logical_not(different)
-ax.scatter(iris_reduced[identical, 0], iris_reduced[identical, 1], color="#bbbbbb", marker=".")
+ax.scatter(
+    iris_reduced[identical, 0], iris_reduced[identical, 1], color="#bbbbbb", marker="."
+)
 ax.scatter(iris_reduced[different, 0], iris_reduced[different, 1], color="m")
 ax.set_title("Difference")
 ax.set_xticks(())
@@ -223,7 +227,7 @@ def get_order_labels(kmeans, rad, data):
 
 plt.show()
 
-#%%
+# %%
 # Another difference plot
 # -----------------------
 #
@@ -250,7 +254,7 @@ def get_order_labels(kmeans, rad, data):
 k_means.fit(X)
 t_kmeans = time.time() - t0
 
-#%%
+# %%
 # Reapllying the same process as before
 # --------------------------------------
 
@@ -265,7 +269,7 @@ def get_order_labels(kmeans, rad, data):
 kmeans_labels = pairwise_distances_argmin(X, kmeans_centers)
 rad_labels = pairwise_distances_argmin(X, rad_centers_coordinates)
 
-#%%
+# %%
 # Plotting the results and the difference
 # ---------------------------------------
 
@@ -281,7 +285,7 @@ def get_order_labels(kmeans, rad, data):
     wine_reduced[:, 1],
     wine_reduced[:, 2],
     c=kmeans_labels,
-    cmap='Dark2',
+    cmap="Dark2",
     s=40,
 )
 # adapting center coordinates to the 3D plot
@@ -298,7 +302,9 @@ def get_order_labels(kmeans, rad, data):
 ax.set_yticks(())
 ax.set_zticks(())
 
-ax.text3D(60.0, 80.0, 0.0, "train time: %.2fs\ninertia: %f" % (t_kmeans, k_means.inertia_))
+ax.text3D(
+    60.0, 80.0, 0.0, "train time: %.2fs\ninertia: %f" % (t_kmeans, k_means.inertia_)
+)
 
 # MDS
 ax = fig.add_subplot(1, 3, 2, projection="3d", elev=48, azim=134, roll=0)
@@ -333,7 +339,9 @@ def get_order_labels(kmeans, rad, data):
     different += (kmeans_labels == k) != (rad_labels == k)
 
 identical = np.logical_not(different)
-ax.scatter(wine_reduced[identical, 0], wine_reduced[identical, 1], color="#bbbbbb", marker=".")
+ax.scatter(
+    wine_reduced[identical, 0], wine_reduced[identical, 1], color="#bbbbbb", marker="."
+)
 ax.scatter(wine_reduced[different, 0], wine_reduced[different, 1], color="m")
 ax.set_title("Difference")
 ax.set_xticks(())
@@ -342,7 +350,7 @@ def get_order_labels(kmeans, rad, data):
 
 plt.show()
 
-#%%
+# %%
 # Conclusion
 # ----------
 #

radius_clustering/__init__.py

@@ -1,7 +1,7 @@
 # Import the main clustering class
 from .radius_clustering import RadiusClustering
 
-__all__ = ['RadiusClustering']
+__all__ = ["RadiusClustering"]
 
 # Optionally, you can set a version number for your package
-__version__ = '1.0.0'
+__version__ = "1.0.0"

setup.py

@@ -7,20 +7,24 @@
         "radius_clustering.utils._emos",
         ["radius_clustering/utils/emos.pyx", "radius_clustering/utils/main-emos.c"],
         include_dirs=[np.get_include(), "radius_clustering/utils"],
-        extra_compile_args = ["-std=c99", "-O3", "-march=native", "-ffast-math", "-DREP"],
+        extra_compile_args=["-std=c99", "-O3", "-march=native", "-ffast-math", "-DREP"],
     ),
     Extension(
         "radius_clustering.utils._mds_approx",
-        ["radius_clustering/utils/mds.pyx", "radius_clustering/utils/mds_core.cpp", "radius_clustering/utils/random_manager.cpp"],
+        [
+            "radius_clustering/utils/mds.pyx",
+            "radius_clustering/utils/mds_core.cpp",
+            "radius_clustering/utils/random_manager.cpp",
+        ],
         include_dirs=[np.get_include(), "radius_clustering/utils"],
         language="c++",
         extra_compile_args=["-std=c++11", "-O3", "-march=native", "-ffast-math"],
         extra_link_args=["-std=c++11", "-fopenmp"],
-    )
+    ),
 ]
 
 setup(
-    ext_modules = cythonize(extensions, language_level=3),
+    ext_modules=cythonize(extensions, language_level=3),
     include_dirs=[np.get_include()],
     package_data={"radius_clustering": ["utils/*.pyx", "utils/*.h"]},
-)
+)

tests/test_rad.py

@@ -9,20 +9,23 @@ def test_from_import():
 def test_radius_clustering_approx():
     from radius_clustering import RadiusClustering
     from sklearn import datasets
+
     # Load the Iris dataset
-    iris = datasets.fetch_openml(name='iris', version=1, parser='auto')
-    X = iris['data']  # Use dictionary-style access instead of attribute access
+    iris = datasets.fetch_openml(name="iris", version=1, parser="auto")
+    X = iris["data"]  # Use dictionary-style access instead of attribute access
 
     graph_mds_api_consistent = RadiusClustering(manner="approx", threshold=1.43)
 
     result_api_consistent = graph_mds_api_consistent.fit_predict(X)
 
+
 def test_radius_clustering_exact():
     from radius_clustering import RadiusClustering
     from sklearn import datasets
+
     # Load the Iris dataset
-    iris = datasets.fetch_openml(name='iris', version=1, parser='auto')
-    X = iris['data']  # Use dictionary-style access instead of attribute access
+    iris = datasets.fetch_openml(name="iris", version=1, parser="auto")
+    X = iris["data"]  # Use dictionary-style access instead of attribute access
 
     graph_mds_api_consistent = RadiusClustering(manner="exact", threshold=1.43)