--- a
+++ b/AICare-baselines/metrics/binary_classification_metrics.py
@@ -0,0 +1,34 @@
+import torch
+from torchmetrics import AUROC, Accuracy, AveragePrecision, Precision, Recall
+from torchmetrics.classification import BinaryF1Score, ConfusionMatrix
+import numpy as np
+from sklearn import metrics as sklearn_metrics
+
+def minpse(preds, labels):
+    precisions, recalls, thresholds = sklearn_metrics.precision_recall_curve(labels, preds)
+    minpse_score = np.max([min(x, y) for (x, y) in zip(precisions, recalls)])
+    return minpse_score
+
+def get_binary_metrics(preds, labels):
+    accuracy = Accuracy(task="binary", threshold=0.5)
+    auroc = AUROC(task="binary")
+    auprc = AveragePrecision(task="binary")
+    f1 = BinaryF1Score()
+
+    # convert labels type to int
+    labels = labels.type(torch.int)
+    accuracy(preds, labels)
+    auroc(preds, labels)
+    auprc(preds, labels)
+    f1(preds, labels)
+
+    minpse_score = minpse(preds, labels) 
+
+    # return a dictionary
+    return {
+        "accuracy": accuracy.compute().item(),
+        "auroc": auroc.compute().item(),
+        "auprc": auprc.compute().item(),
+        "f1": f1.compute().item(),
+        "minpse": minpse_score,
+    }