add data_limit to evaluation

chebai/result/classification.py

@@ -46,6 +46,7 @@ def evaluate_model(
     collate = data_module.reader.COLLATER()
 
     data_list = data_module.load_processed_data("test", filename)
+    data_list = data_list[: data_module.data_limit]
     preds_list = []
     labels_list = []
     if buffer_dir is not None:
@@ -125,18 +126,11 @@ def load_results_from_buffer(buffer_dir, device):
 
 def print_metrics(preds, labels, device, classes=None, top_k=10, markdown_output=False):
     """Prints relevant metrics, including micro and macro F1, recall and precision, best k classes and worst classes."""
-    f1_macro = MultilabelF1Score(preds.shape[1], average="macro").to(device=device)
     f1_micro = MultilabelF1Score(preds.shape[1], average="micro").to(device=device)
-
     my_f1_macro = MacroF1(preds.shape[1]).to(device=device)
 
-    print(
-        f"Macro-F1 (torchmetrics, unadjusted) on test set with {preds.shape[1]} classes: {f1_macro(preds, labels):3f}"
-    )
-    print(f"Macro-F1 (my implementation): {my_f1_macro(preds, labels)}")
-    print(
-        f"Micro-F1 on test set with {preds.shape[1]} classes: {f1_micro(preds, labels):3f}"
-    )
+    print(f"Macro-F1: {my_f1_macro(preds, labels):3f}")
+    print(f"Micro-F1: {f1_micro(preds, labels):3f}")
     precision_macro = MultilabelPrecision(preds.shape[1], average="macro").to(
         device=device
     )
@@ -156,7 +150,7 @@ def print_metrics(preds, labels, device, classes=None, top_k=10, markdown_output
         )
         print(f"| --- | --- | --- | --- | --- | --- | --- |")
         print(
-            f"| | {f1_macro(preds, labels):3f} | {f1_micro(preds, labels):3f} | {precision_macro(preds, labels):3f} | "
+            f"| | {my_f1_macro(preds, labels):3f} | {f1_micro(preds, labels):3f} | {precision_macro(preds, labels):3f} | "
             f"{precision_micro(preds, labels):3f} | {recall_macro(preds, labels):3f} | "
             f"{recall_micro(preds, labels):3f} |"
         )