fix ml train (#1092)

* fix sklearn params

* fix sklearn params

* fix metric order

ppcls/utils/metrics.py

@@ -25,7 +25,10 @@ from sklearn.preprocessing import binarize
 
 import numpy as np
 
-__all__ = ["multi_hot_encode", "hamming_distance", "accuracy_score", "precision_recall_fscore", "mean_average_precision"]
+__all__ = [
+    "multi_hot_encode", "hamming_distance", "accuracy_score",
+    "precision_recall_fscore", "mean_average_precision"
+]
 
 
 def multi_hot_encode(logits, threshold=0.5):
@@ -33,7 +36,7 @@ def multi_hot_encode(logits, threshold=0.5):
     Encode logits to multi-hot by elementwise for multilabel
     """
 
-    return binarize(logits, threshold)
+    return binarize(logits, threshold=threshold)
 
 
 def hamming_distance(output, target):
@@ -70,7 +73,8 @@ def accuracy_score(output, target, base="sample"):
         tps = mcm[:, 1, 1]
         fps = mcm[:, 0, 1]
 
-        accuracy = (sum(tps) + sum(tns)) / (sum(tps) + sum(tns) + sum(fns) + sum(fps))
+        accuracy = (sum(tps) + sum(tns)) / (
+            sum(tps) + sum(tns) + sum(fns) + sum(fps))
 
     return accuracy
 
@@ -84,7 +88,8 @@ def precision_recall_fscore(output, target):
         fscores:
     """
 
-    precisions, recalls, fscores, _ = precision_recall_fscore_support(target, output)
+    precisions, recalls, fscores, _ = precision_recall_fscore_support(target,
+                                                                      output)
 
     return precisions, recalls, fscores
 

tools/program.py

@@ -172,8 +172,8 @@ def create_metric(out,
         metric_names.append(ham_dist_name)
         metric_names.append(accuracy_name)
 
-        fetch_list.append(accuracy)
         fetch_list.append(ham_dist)
+        fetch_list.append(accuracy)
 
     # multi cards' eval
     if not use_xpu: