Merge pull request #27 from lias-laboratory/26-rebase-the-test-suit-on-scikit-learn-guidelines

26 rebase the test suit on scikit learn guidelines.

Updated test suite to match sklearn compliance guidelines. Update the minor version to reflect the change. New version is now 1.4.1

Author: quentinhaenn

.coverage

@@ -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()

examples/plot_benchmark_custom.py

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

src/radius_clustering/__init__.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)