Begin best results per dataset report

src/Results.py

@@ -758,3 +758,23 @@ class Summary:
             if len(haystack) > 0
             else {}
         )
+
+    def best_results_datasets(self, score="accuracy") -> dict:
+        """Get the best results for each dataset"""
+        dt = Datasets()
+        best_results = {}
+        for dataset in dt:
+            best_results[dataset] = (1, "", "", "")
+        haystack = [x for x in self.data if x["score"] == score]
+        # Search for the best results for each dataset
+        for entry in haystack:
+            print(entry)
+            for dataset in entry["results"]:
+                if dataset["score"] < best_results[dataset][0]:
+                    best_results[dataset] = (
+                        dataset["score"],
+                        dataset["hyperparameters"],
+                        entry["file"],
+                        entry["title"],
+                    )
+        return best_results

src/best.py

@@ -0,0 +1,34 @@
+import argparse
+from Results import Summary
+from Utils import EnvDefault
+
+
+def parse_arguments():
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "-s",
+        "--score",
+        type=str,
+        action=EnvDefault,
+        envvar="score",
+        required=True,
+        help="score name {accuracy, f1_micro, f1_macro, all}",
+    )
+    args = ap.parse_args()
+    return (args.score,)
+
+
+(score,) = parse_arguments()
+
+all_metrics = ["accuracy", "f1-macro", "f1-micro"]
+
+metrics = all_metrics if score == "all" else [score]
+
+summary = Summary()
+summary.acquire()
+
+for metric in metrics:
+    title = f"BEST RESULTS of {metric} for datasets"
+    best = summary.best_results_datasets(score=metric)
+    for key, item in best.items():
+        print(f"{key}: {item}")