feat: Add benchmark/blobs_benchmark_visualiser.py

benchmark/blobs_benchmark_visualiser.py

@@ -0,0 +1,62 @@
+# © Crown Copyright GCHQ
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Visualise the results of ``blobs_benchmark.py``."""
+
+import json
+
+
+# Function to print metrics table for each sample size
+def print_metrics_table(data: dict, sample_size: str) -> None:
+    """
+    Print a table for the given sample size with methods as rows and metrics as columns.
+
+    :param sample_size: The sample size for which to print the table.
+    """
+    # Define header
+    header = (
+        f"| {'Method':^15} | {'unweighted_mmd':^15} | {'unweighted_ksd':^15} | "
+        f"{'weighted_mmd':^15} | {'weighted_ksd':^15} | {'time':^10} |"
+    )
+    separator = "-" * len(header)
+
+    # Print table for the current sample size
+    print(f"\nSample Size: {sample_size}")
+    print(separator)
+    print(header)
+    print(separator)
+
+    for method, metrics in data[sample_size].items():
+        print(
+            f"| {method:^15} | {metrics['unweighted_mmd']:^15.8f} | "
+            f"{metrics['unweighted_ksd']:^15.8f} | {metrics['weighted_mmd']:^15.8f} | "
+            f"{metrics['weighted_ksd']:^15.8f} | {metrics['time']:^10.3f} |"
+        )
+
+    print(separator)
+
+
+def main() -> None:
+    """Load the data and print metrics in table format per sample size."""
+    # Load the JSON data
+    with open("coreset_comparison_results.json", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # Print tables for each sample size
+    for sample_size in data.keys():
+        print_metrics_table(data, sample_size)
+
+
+if __name__ == "__main__":
+    main()