Add show result

src/Results.py

@@ -684,6 +684,38 @@ class Summary:
             )
         )
 
+    def show_result(self, file_name: str, metric: float = 0.0) -> None:
+        def whites(n: int) -> str:
+            return " " * n + "*"
+
+        result = StubReport(os.path.join(Folders.results, file_name))
+        length = 80
+        print("*" * length)
+        print("*" + whites(length - 2))
+        print(f"* {file_name:60s}" + whites(length - 63))
+        print("*" + whites(length - 2))
+        print(
+            f"* Date : {result.data['date']:15s} Time: "
+            f"{result.data['time']:18s} Time Spent: "
+            f"{result.data['duration']:9,.2f} secs." + whites(length - 78)
+        )
+        print(
+            f"* Model: {result.data['model']:15s} Score: "
+            f"{result.data['score_name']:15s} "
+            f"  Stratified: {str(result.data['stratified']):15s}"
+            + whites(length - 78)
+        )
+        seeds = str(result.data["seeds"])
+        seeds_len = len(seeds)
+        print(
+            f"* Seeds: {seeds:{seeds_len}s} Platform: "
+            f"{result.data['platform']:17s} " + whites(length - 79)
+        )
+        if metric != 0.0:
+            print(f"* Metric: {metric:10.7f}" + whites(length - 21))
+        print("*" + whites(length - 2))
+        print("*" * length)
+
     def best_result(
         self, criterion=None, value=None, score="accuracy"
     ) -> dict:
@@ -694,4 +726,8 @@ class Summary:
             if criterion is None or value is None
             else [x for x in haystack if x[criterion] == value]
         )
-        return sorted(haystack, key=lambda x: x["metric"], reverse=True)[0]
+        return (
+            sorted(haystack, key=lambda x: x["metric"], reverse=True)[0]
+            if len(haystack) > 0
+            else {}
+        )

src/Summary.py

@@ -2,6 +2,14 @@ from Results import Summary
 
 summary = Summary()
 summary.acquire()
-print(summary.best_result())
-print(summary.best_result(criterion="model", value="ODTE"))
-summary.list()
+
+for metric in ["accuracy", "f1_macro", "f1_micro"]:
+    for model in ["STree", "ODTE"]:
+        best = summary.best_result(
+            criterion="model", value=model, score=metric
+        )
+        summary.show_result(
+            best["file"], best["metric"]
+        ) if best != {} else print(
+            "No best result for {} {}".format(model, metric)
+        )