auc caculation

aucCaculate.py

+from eda.ml_tools.rocCurve import get_roc_curve
+import pandas as pd
+from config import *
+
+
+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")

diaryTraining.py

@@ -3,10 +3,10 @@ import xlearn as xl
 from config import *
 
 
-print("start training")
+print("Start training")
 ffm_model = xl.create_ffm()
 ffm_model.setTrain(DIRECTORY_PATH + "data.csv")
-ffm_model.setValidate(DIRECTORY_PATH + "data.csv")
+ffm_model.setValidate(DIRECTORY_PATH + "validation.csv")
 
 
 param = {'task':'binary', 'lr':0.03,
@@ -14,9 +14,13 @@ param = {'task':'binary', 'lr':0.03,
 
 ffm_model.fit(param, DIRECTORY_PATH + "model.out")
 
-ffm_model.setTest(DIRECTORY_PATH + "data.csv")
+print("predicting")
+ffm_model.setTest(DIRECTORY_PATH + "test.csv")
 ffm_model.setSigmoid()
 ffm_model.predict(DIRECTORY_PATH + "model.out",
                   DIRECTORY_PATH + "output.txt")
 
-print("end")
+
+
+
+

eda/ml_tools/roc_curve.py → eda/ml_tools/rocCurve.py

@@ -21,11 +21,14 @@ def get_roc_curve(label,pred,output):
 		pred_label = pd.read_table(pred)
 		y = test_label.values
 		p = pred_label.values
+
 		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')
+
 		AUC = auc(fpr, tpr)
 		AUC = "auc={}".format(AUC)
 		plt.text(0.5,0.8,AUC,color='blue',ha='center')
@@ -36,5 +39,6 @@ def get_roc_curve(label,pred,output):
 		print("the test_label must be 0 or 1")
 		print("the test_pred must be at [0,1]")
 
+
 if __name__ == "__main__":
 	get_roc_curve(args.test_label,args.test_pred,args.output_photo)

processData.py

@@ -10,15 +10,18 @@ exposure, click, click_device_id = fetch_data(
 # 求曝光表和点击表的差集合
 print("曝光表处理前的样本个数")
 print(exposure.shape)
+
 exposure = exposure.append(click)
 exposure = exposure.append(click)
 subset = click.columns.tolist()
 exposure = exposure.drop_duplicates(subset=subset,keep=False)
 print("差集后曝光表个数")
 print(exposure.shape)
+
 exposure = exposure.loc[exposure["device_id"].isin(click_device_id)]
 print("去除未点击用户后曝光表个数")
 print(exposure.shape)
+
 # 打标签
 click["y"] = 1
 exposure["y"] = 0
@@ -65,6 +68,7 @@ test = data.loc[:test_number]
 print("测试集大小")
 print(test.shape[0])
 test.to_csv(DIRECTORY_PATH + "test.csv",index = False,header = None)
+
 validation = data.loc[(test_number+1):(test_number+validation_number)]
 print("验证集大小")
 print(validation.shape[0])