refractor the process data file

aucCaculate.py

@@ -2,9 +2,8 @@ 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 = 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 + "output.txt",header = None)[0].values
-    get_roc_curve(test_label,predict)
+    predict = pd.read_csv(DIRECTORY_PATH + "output.txt", header=None)[0].values
+    get_roc_curve(test_label, predict, "1")

config.py

 
 DIRECTORY_PATH = '/home/zhangyanzhao/'
+VALIDATION_DATE = '2018-08-05'
+TEST_DATE = '2018-08-06'
+DATA_START_DATE = '2018-07-06'
+DATA_END_DATE = '2018-08-06'
 
 
 # processData.py

diaryTraining.py

-
 import xlearn as xl
 from config import *
 
-
 print("Start training")
 ffm_model = xl.create_ffm()
-ffm_model.setTrain(DIRECTORY_PATH + "data.csv")
+ffm_model.setTrain(DIRECTORY_PATH + "train.csv")
 ffm_model.setValidate(DIRECTORY_PATH + "validation.csv")
 
-
-param = {'task':'binary', 'lr':0.03,
-         'lambda':0.002, 'metric':'auc'}
+param = {'task': 'binary', 'lr': 0.03, 'lambda': 0.002, 'metric': 'auc'}
 
 ffm_model.fit(param, DIRECTORY_PATH + "model.out")
 
@@ -19,8 +15,3 @@ ffm_model.setTest(DIRECTORY_PATH + "test.csv")
 ffm_model.setSigmoid()
 ffm_model.predict(DIRECTORY_PATH + "model.out",
                   DIRECTORY_PATH + "output.txt")
-
-
-
-
-

eda/ml_tools/rocCurve.py

 import pandas as pd
 from sklearn import metrics
-# import matplotlib.pyplot as plt
 from sklearn.metrics import auc
 # import argparse
 
-
-# parser = argparse.ArgumentParser()
-# parser.add_argument('test_label',help='The filename of the test_label')
-# parser.add_argument('test_pred',help='The filename of the test_pred')
-# # parser.add_argument('output_photo',help='The filename of the output_photo')
-# args = parser.parse_args()
-
-
-def get_roc_curve(label,pred):
+def get_roc_curve(y,pred,pos_label):
 	"""
 	计算二分类问题的roc和auc
 	"""
-
-	test_label = pd.read_table(label)
-	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_curev")
-
-	AUC = auc(fpr, tpr)
-	AUC = "auc={}".format(AUC)
-	# plt.text(0.5,0.8,AUC,color='blue',ha='center')
-	# 		# plt.savefig(output)
+	fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label)
+	AUC = metrics.auc(fpr, tpr)
 	print(AUC)
 
 
-#
-# if __name__ == "__main__":
-# 	get_roc_curve(args.test_label,args.test_pred)

processData.py

@@ -5,7 +5,7 @@ import pandas as pd
 from config import *
 
 exposure, click, click_device_id = fetch_data(
-    start_date='2018-08-03', end_date='2018-08-06')
+    start_date=DATA_START_DATE, end_date=DATA_END_DATE)
 
 # 求曝光表和点击表的差集合
 print("曝光表处理前的样本个数")
@@ -14,7 +14,7 @@ print(exposure.shape)
 exposure = exposure.append(click)
 exposure = exposure.append(click)
 subset = click.columns.tolist()
-exposure = exposure.drop_duplicates(subset=subset,keep=False)
+exposure = exposure.drop_duplicates(subset=subset, keep=False)
 print("差集后曝光表个数")
 print(exposure.shape)
 
@@ -33,23 +33,22 @@ print(exposure.shape[0])
 
 # 合并点击表和曝光表
 data = click.append(exposure)
-data = data.sort_values(by="stat_date",ascending=False)
+data = data.sort_values(by="stat_date", ascending=False)
 print("前两行数据")
 print(data.head(2))
 print("后两行数据")
 print(data.tail(2))
-test_number = data[data["stat_date"]=='2018-08-06'].shape[0]
-validation_number = data[data["stat_date"]=='2018-08-05'].shape[0]
-data = data.drop("stat_date",axis=1)
+test_number = data[data["stat_date"] == TEST_DATE].shape[0]
+validation_number = data[data["stat_date"] == VALIDATION_DATE].shape[0]
+data = data.drop("stat_date", axis=1)
 
 # 数值是0的特征会被ffm格式删除，经过下面的处理后，没有数值是0的特征
-data.loc[data["hour"]==0,["hour"]] = 24
-data.loc[data["minute"]==0,["minute"]] = 60
+data.loc[data["hour"] == 0, ["hour"]] = 24
+data.loc[data["minute"] == 0, ["minute"]] = 60
 data["hour"] = data["hour"].astype("category")
 data["minute"] = data["minute"].astype("category")
 print(data.head(2))
 
-
 print("Start ffm transform")
 start = time.time()
 ffm_train = FFMFormatPandas()
@@ -57,9 +56,10 @@ data = ffm_train.fit_transform(data, y='y')
 print("done transform ffm")
 end = time.time()
 print("ffm转化数据耗时：")
-print(end-start)
-data.to_csv(DIRECTORY_PATH + "data.csv",index=False)
-data = pd.read_csv(DIRECTORY_PATH + "data.csv",header=None)
+print(end - start)
+
+data.to_csv(DIRECTORY_PATH + "data{0}-{1}.csv".format(DATA_START_DATE, DATA_END_DATE), index=False)
+data = pd.read_csv(DIRECTORY_PATH + "data{0}-{1}.csv".format(DATA_START_DATE, DATA_END_DATE), header=None)
 print("数据集大小")
 print(data.shape)
 print(data.head(2))
@@ -67,13 +67,14 @@ print(data.head(2))
 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)
+test.to_csv(DIRECTORY_PATH + "test{0}.csv".format(TEST_DATE), index=False, header=None)
 
-validation = data.loc[(test_number+1):(test_number+validation_number)]
+validation = data.loc[(test_number + 1):(test_number + validation_number)]
 print("验证集大小")
 print(validation.shape[0])
-validation.to_csv(DIRECTORY_PATH + "validation.csv",index = False,header = None)
-train = data.loc[(test_number+validation_number+1):]
+validation.to_csv(DIRECTORY_PATH + "validation{0}.csv".format(VALIDATION_DATE), index=False, header=None)
+train = data.loc[(test_number + validation_number + 1):]
 print("训练集大小")
 print(train.shape[0])
-train.to_csv(DIRECTORY_PATH + "train.csv",index = False,header = None)
+# TODO validation date is not the end of train date
\ No newline at end of file
+train.to_csv(DIRECTORY_PATH + "train{0}-{1}.csv".format(DATA_START_DATE, VALIDATION_DATE), index=False, header=None)