delete auc function matplotlib

aucCaculate.py

@@ -7,4 +7,4 @@ if __name__ == "__main__":
     test = pd.read_csv(DIRECTORY_PATH + "test.csv",header = None)
     test_label = test[0].apply(lambda x: x[0]).values
     predict = pd.read_csv(DIRECTORY_PATH + "model.out",header = None)[0].values
-    get_roc_curve(test_label,predict,DIRECTORY_PATH+"auc_test.JPG")
+    get_roc_curve(test_label,predict)

eda/ml_tools/rocCurve.py

 import pandas as pd
 from sklearn import metrics
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
 from sklearn.metrics import auc
 import argparse
 
@@ -12,7 +12,7 @@ parser.add_argument('output_photo',help='The filename of the output_photo')
 args = parser.parse_args()
 
 
-def get_roc_curve(label,pred,output):
+def get_roc_curve(label,pred):
 	"""
 	计算二分类问题的roc和auc
 	"""
@@ -24,15 +24,15 @@ def get_roc_curve(label,pred,output):
 
 		fpr, tpr, thresholds = metrics.roc_curve(y, p)
 
-		plt.plot(fpr,tpr,marker = 'o')
-		plt.xlabel('False positive rate')
-		plt.ylabel('True positive rate')
-		plt.title('roc_cureve')
+		# plt.plot(fpr,tpr,marker = 'o')
+		# plt.xlabel('False positive rate')
+		# plt.ylabel('True positive rate')
+		# plt.title("roc_curev")
 
 		AUC = auc(fpr, tpr)
 		AUC = "auc={}".format(AUC)
-		plt.text(0.5,0.8,AUC,color='blue',ha='center')
-		plt.savefig(output)
+		# plt.text(0.5,0.8,AUC,color='blue',ha='center')
+		# 		# plt.savefig(output)
 		print(AUC)
 	except:
 		print("the format of the file must be the n*1")
@@ -41,4 +41,4 @@ def get_roc_curve(label,pred,output):
 
 
 if __name__ == "__main__":
-	get_roc_curve(args.test_label,args.test_pred,args.output_photo)
+	get_roc_curve(args.test_label,args.test_pred)