26 rebase the test suit on scikit learn guidelines

examples/plot_benchmark_custom.py

@@ -184,15 +184,18 @@ def benchmark_radius_clustering():
 fig.suptitle("Benchmark of Radius Clustering Solvers", fontsize=16)
 
 axs['time'].set_yscale('log')  # Use logarithmic scale for better visibility
-for algo, algo_results in results.items():
-    # Plot execution time
-    axs['time'].plot(
-        DATASETS.keys(),
-        algo_results["time"],
-        marker='o',
-        label=algo,
-    )
-    # Plot number of clusters
+
+algorithms = list(results.keys())
+dataset_names = list(DATASETS.keys())
+n_algos = len(algorithms)
+x_indices = np.arange(len(dataset_names))  # the label locations
+bar_width = 0.8 / n_algos  # the width of the bars, with some padding
+
+for i, algo in enumerate(algorithms):
+    times = results[algo]["time"]
+    # Calculate position for each bar in the group to center them
+    position = x_indices - (n_algos * bar_width / 2) + (i * bar_width) + bar_width / 2
+    axs['time'].bar(position, times, bar_width, label=algo)
 
 for i, (name, (dataset, _)) in enumerate(DATASETS.items()):
     axs[name].bar(
@@ -207,14 +210,15 @@ def benchmark_radius_clustering():
         linestyle='--',
     )
     axs[name].set_title(name)
-    axs[name].set_xlabel("Algorithms")
 
 axs["iris"].set_ylabel("Number of clusters")
 axs["glass"].set_ylabel("Number of clusters")
 
 axs['time'].set_title("Execution Time (log scale)")
 axs['time'].set_xlabel("Datasets")
 axs['time'].set_ylabel("Time (seconds)")
+axs['time'].set_xticks(x_indices)
+axs['time'].set_xticklabels(dataset_names)
 axs['time'].legend(title="Algorithms")
 plt.tight_layout()
 plt.show()

src/radius_clustering/__init__.py

@@ -2,4 +2,4 @@
 from .radius_clustering import RadiusClustering
 
 __all__ = ["RadiusClustering"]
-__version__ = "1.4.0"
+__version__ = "1.4.1"

tests/test_structural.py

@@ -1,18 +1,17 @@
-from logging import getLogger
-
-logger = getLogger(__name__)
-logger.setLevel("INFO")
-
+from sklearn.utils.estimator_checks import parametrize_with_checks
 def test_import():
     import radius_clustering as rad
 
 
 def test_from_import():
     from radius_clustering import RadiusClustering
 
-def test_check_estimator_api_consistency():
-    from radius_clustering import RadiusClustering
-    from sklearn.utils.estimator_checks import check_estimator
 
-    # Check the API consistency of the RadiusClustering estimator
-    check_estimator(RadiusClustering())
+from radius_clustering import RadiusClustering
+
+@parametrize_with_checks([RadiusClustering()])
+def test_check_estimator_api_consistency(estimator, check, request):
+
+    """Check the API consistency of the RadiusClustering estimator
+    """
+    check(estimator)