[FutureWarn] Fix FutureWarning in TAHIN example. (dmlc#7418)

examples/pytorch/TAHIN/utils.py

@@ -1,40 +1,25 @@
-import numpy as np
-import torch
-from sklearn.metrics import (
-    accuracy_score,
-    average_precision_score,
-    f1_score,
-    log_loss,
-    ndcg_score,
-    roc_auc_score,
-)
-
-
-def evaluate_auc(pred, label):
-    res = roc_auc_score(y_score=pred, y_true=label)
-    return res
-
-
-def evaluate_acc(pred, label):
-    res = []
-    for _value in pred:
-        if _value >= 0.5:
-            res.append(1)
-        else:
-            res.append(0)
-    return accuracy_score(y_pred=res, y_true=label)
-
-
-def evaluate_f1_score(pred, label):
-    res = []
-    for _value in pred:
-        if _value >= 0.5:
-            res.append(1)
-        else:
-            res.append(0)
-    return f1_score(y_pred=res, y_true=label)
-
-
-def evaluate_logloss(pred, label):
-    res = log_loss(y_true=label, y_pred=pred, eps=1e-7, normalize=True)
-    return res
+from sklearn.metrics import accuracy_score, f1_score, log_loss, roc_auc_score
+
+
+def evaluate_auc(pred, label):
+    res = roc_auc_score(y_score=pred, y_true=label)
+    return res
+
+
+def evaluate_acc(pred, label):
+    res = []
+    for _value in pred:
+        res.append(1 if _value >= 0.5 else 0)
+    return accuracy_score(y_pred=res, y_true=label)
+
+
+def evaluate_f1_score(pred, label):
+    res = []
+    for _value in pred:
+        res.append(1 if _value >= 0.5 else 0)
+    return f1_score(y_pred=res, y_true=label)
+
+
+def evaluate_logloss(pred, label):
+    res = log_loss(y_true=label, y_pred=pred, normalize=True)
+    return res