First best results of datasets list

src/Results.py

@@ -659,6 +659,7 @@ class Summary:
     def __init__(self) -> None:
         self.results = Files().get_all_results()
         self.data = []
+        self.datasets = {}
 
     def acquire(self) -> None:
         """Get all results"""
@@ -684,6 +685,7 @@ class Summary:
                 file=result,
                 metric=report.accuracy / BEST_ACCURACY_STREE,
             )
+            self.datasets[result] = report.lines
             self.data.append(entry)
 
     def list(self) -> None:
@@ -768,10 +770,9 @@ class Summary:
         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] = (
+            for dataset in self.datasets[entry["file"]]:
+                if dataset["score"] < best_results[dataset["dataset"]][0]:
+                    best_results[dataset["dataset"]] = (
                         dataset["score"],
                         dataset["hyperparameters"],
                         entry["file"],

src/best.py

@@ -1,4 +1,5 @@
 import argparse
+import json
 from Results import Summary
 from Utils import EnvDefault
 
@@ -27,8 +28,15 @@ metrics = all_metrics if score == "all" else [score]
 summary = Summary()
 summary.acquire()
 
+nl = 50
+num = 100
 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}")
+        print(f"{key:30s} {item[2]:{nl}s}")
+        print("-" * num)
+        print(f"{item[0]:30.7f} {json.dumps(item[1]):{nl}s}")
+        print("-" * num)
+        print(f"{item[3]:{nl+num}s}")
+        print("*" * num)