deploy: cabc92a

Author: quentinhaenn

_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "results = benchmark_radius_clustering()\n\n# Plot the results\nfig, axs = plt.subplot_mosaic(\n    [\n        [\"time\", \"time\", \"time\", \"time\"],\n        [\"iris\", \"wine\", \"breast_cancer\", \"vehicle\"],\n        [\"glass\", \"ionosphere\", \"synthetic\", \"yeast\"],\n    ],\n    layout=\"constrained\",\n    figsize=(12, 8),\n)\nfig.suptitle(\"Benchmark of Radius Clustering Solvers\", fontsize=16)\n\naxs['time'].set_yscale('log')  # Use logarithmic scale for better visibility\nfor algo, algo_results in results.items():\n    # Plot execution time\n    axs['time'].plot(\n        DATASETS.keys(),\n        algo_results[\"time\"],\n        marker='o',\n        label=algo,\n    )\n    # Plot number of clusters\n\nfor i, (name, (dataset, _)) in enumerate(DATASETS.items()):\n    axs[name].bar(\n        results.keys(),\n        [results[algo][\"clusters\"][i] for algo in results.keys()],\n        label=name,\n    )\n    axs[name].axhline(\n        y=len(set(dataset.target)),  # Number of unique classes in the dataset\n        label=\"True number of clusters\",\n        color='r',\n        linestyle='--',\n    )\n    axs[name].set_title(name)\n    axs[name].set_xlabel(\"Algorithms\")\n\naxs[\"iris\"].set_ylabel(\"Number of clusters\")\naxs[\"glass\"].set_ylabel(\"Number of clusters\")\n\naxs['time'].set_title(\"Execution Time (log scale)\")\naxs['time'].set_xlabel(\"Datasets\")\naxs['time'].set_ylabel(\"Time (seconds)\")\naxs['time'].legend(title=\"Algorithms\")\nplt.tight_layout()\nplt.show()"
+        "results = benchmark_radius_clustering()\n\n# Plot the results\nfig, axs = plt.subplot_mosaic(\n    [\n        [\"time\", \"time\", \"time\", \"time\"],\n        [\"iris\", \"wine\", \"breast_cancer\", \"vehicle\"],\n        [\"glass\", \"ionosphere\", \"synthetic\", \"yeast\"],\n    ],\n    layout=\"constrained\",\n    figsize=(12, 8),\n)\nfig.suptitle(\"Benchmark of Radius Clustering Solvers\", fontsize=16)\n\naxs['time'].set_yscale('log')  # Use logarithmic scale for better visibility\n\nalgorithms = list(results.keys())\ndataset_names = list(DATASETS.keys())\nn_algos = len(algorithms)\nx_indices = np.arange(len(dataset_names))  # the label locations\nbar_width = 0.8 / n_algos  # the width of the bars, with some padding\n\nfor i, algo in enumerate(algorithms):\n    times = results[algo][\"time\"]\n    # Calculate position for each bar in the group to center them\n    position = x_indices - (n_algos * bar_width / 2) + (i * bar_width) + bar_width / 2\n    axs['time'].bar(position, times, bar_width, label=algo)\n\nfor i, (name, (dataset, _)) in enumerate(DATASETS.items()):\n    axs[name].bar(\n        results.keys(),\n        [results[algo][\"clusters\"][i] for algo in results.keys()],\n        label=name,\n    )\n    axs[name].axhline(\n        y=len(set(dataset.target)),  # Number of unique classes in the dataset\n        label=\"True number of clusters\",\n        color='r',\n        linestyle='--',\n    )\n    axs[name].set_title(name)\n\naxs[\"iris\"].set_ylabel(\"Number of clusters\")\naxs[\"glass\"].set_ylabel(\"Number of clusters\")\n\naxs['time'].set_title(\"Execution Time (log scale)\")\naxs['time'].set_xlabel(\"Datasets\")\naxs['time'].set_ylabel(\"Time (seconds)\")\naxs['time'].set_xticks(x_indices)\naxs['time'].set_xticklabels(dataset_names)\naxs['time'].legend(title=\"Algorithms\")\nplt.tight_layout()\nplt.show()"
       ]
     },
     {

_downloads/40d009986931086fe7524a6c6c645a2b/plot_benchmark_custom.ipynb

@@ -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()

_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -251,7 +251,7 @@ Run the benchmark and plot the results
 --------------------------------------
 We run the benchmark and plot the results for each dataset.
 
-.. GENERATED FROM PYTHON SOURCE LINES 170-223
+.. GENERATED FROM PYTHON SOURCE LINES 170-227
 
 .. code-block:: Python
 
@@ -272,15 +272,18 @@ We run the benchmark and plot the results for each dataset.
     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(
@@ -295,14 +298,15 @@ We run the benchmark and plot the results for each dataset.
             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()
@@ -321,13 +325,13 @@ We run the benchmark and plot the results for each dataset.
 
  .. code-block:: none
 
-    /home/runner/work/radius_clustering/radius_clustering/examples/plot_benchmark_custom.py:219: UserWarning: The figure layout has changed to tight
+    /home/runner/work/radius_clustering/radius_clustering/examples/plot_benchmark_custom.py:223: UserWarning: The figure layout has changed to tight
       plt.tight_layout()
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 224-231
+.. GENERATED FROM PYTHON SOURCE LINES 228-235
 
 Conclusion
 ----------
@@ -340,7 +344,7 @@ The difference plot can be very useful to see where the two clustering algorithm
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (13 minutes 0.891 seconds)
+   **Total running time of the script:** (13 minutes 0.295 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_benchmark_custom.py: