特征工程优化

train/train_service.py

@@ -177,6 +177,10 @@ def addDays(n, format="%Y%m%d"):
 
 if __name__ == '__main__':
 
+    trainDays = int(sys.argv[1])
+    isTest = False
+    if (len(sys.argv) >= 3 and "test" == sys.argv[2]):
+        isTest = True
     curTime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
     print("train_service执行时间:{}".format(curTime))
     splitTimestamp = int(time.mktime(time.strptime(addDays(-1), "%Y%m%d")))
@@ -189,6 +193,11 @@ if __name__ == '__main__':
     print("读取数据...")
     timestmp1 = int(round(time.time()))
     df_train = loadData(data_path_train)
+    print(df_train.dtypes)
+
+    df_test = df_train.loc[df_train['timestamp']>=splitTimestamp]
+    if isTest:
+        df_train = df_train.loc[df_train['timestamp'] < splitTimestamp]
     # df_test = loadData(data_path_test)
     timestmp2 = int(round(time.time()))
     print("读取数据耗时s:{}".format(timestmp2 - timestmp1))
@@ -202,20 +211,20 @@ if __name__ == '__main__':
     print(datasColumns)
 
     df_train = df_train[datasColumns + ["label"]]
-    # df_test = df_test[datasColumns + ["label"]]
+    df_test = df_test[datasColumns + ["label"]]
 
     trainSize = df_train["label"].count()
     print("trainSize:{}".format(trainSize))
-    # testSize = df_test["label"].count()
-    # print("trainSize:{},testSize{}".format(trainSize,testSize))
+    testSize = df_test["label"].count()
+    print("trainSize:{},testSize{}".format(trainSize,testSize))
 
     # 数据类型转换
     df_train = csvTypeConvert(datasColumns,df_train,data_vocab)
-    # df_test = csvTypeConvert(datasColumns,df_test,data_vocab)
+    df_test = csvTypeConvert(datasColumns,df_test,data_vocab)
 
     # 获取训练数据
     train_data = getDataSet(df_train,shuffleSize=trainSize,)
-    # test_data = getDataSet(df_test,shuffleSize=testSize)
+    test_data = getDataSet(df_test,shuffleSize=testSize)
 
 
     print("train start...")
@@ -224,7 +233,7 @@ if __name__ == '__main__':
     timestmp4 = int(round(time.time()))
     print("train end...耗时h:{}".format((timestmp4 - timestmp3)/60/60))
 
-    # evaluate(model,test_data)
+    evaluate(model,test_data)
 
 
     # predict(model_file,test_data)