Add command line arguments for summary

src/summary.py

@@ -1,23 +1,46 @@
+import argparse
 from Results import Summary
 
+
+def parse_arguments():
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "-m",
+        "--model",
+        type=str,
+        required=True,
+        help="model name",
+    )
+    ap.add_argument(
+        "-s",
+        "--score",
+        type=str,
+        required=True,
+        help="score name {accuracy, f1_micro, f1_macro, all}",
+    )
+    args = ap.parse_args()
+    return (
+        args.score,
+        args.model,
+    )
+
+
+(
+    score,
+    model,
+) = parse_arguments()
+
+all_metrics = ["accuracy", "f1-macro", "f1-micro"]
+
+metrics = all_metrics if score == "all" else [score]
+
 summary = Summary()
 summary.acquire()
 
-for metric in ["accuracy", "f1-macro", "f1-micro"]:
+for metric in metrics:
-    for model in ["STree", "ODTE"]:
     title = f"BEST RESULT of {metric} for {model}"
-        best = summary.best_result(
+    best = summary.best_result(criterion="model", value=model, score=metric)
-            criterion="model", value=model, score=metric
+    summary.show_result(data=best, title=title)
-        )
-        summary.show_result(data=best, title=title) if best != {} else print(
-            "No best result for {} {}".format(model, metric)
-        )
     summary.show_result(
-    summary.best_result(score="accuracy"), title="BEST RESULT accuracy"
+        summary.best_result(score=metric), title=f"BEST RESULT of {metric}"
-)
-summary.show_result(
-    summary.best_result(score="f1-macro"), title="BEST RESULT f1_macro"
-)
-summary.show_result(
-    summary.best_result(score="f1-micro"), title="BEST RESULT f1_micro"
     )