change test file

tensnsorflow/feature_test.py

@@ -226,7 +226,7 @@ def feature_engineer():
 
     train = rdd.map(
         lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9],
-                   x[10], x[11], x[12], x[13]))
+                   x[10], x[11], x[12], x[13],x[14]))
     f = time.time()
     spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list",
                                       "tag1_list", "tag2_list", "tag3_list", "tag4_list",
@@ -243,7 +243,7 @@ def feature_engineer():
 
     # test = rdd.filter(lambda x: x[0] == validate_date).map(
     #     lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9],
-    #                x[10], x[11], x[12], x[13]))
+    #                x[10], x[11], x[12], x[13],x[14]))
     #
     # spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list",
     #                                  "tag1_list", "tag2_list", "tag3_list", "tag4_list",
@@ -320,7 +320,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
     # print("native csv")
     # native_pre.toPandas().to_csv(local_path+"native.csv", header=True)
     # spark.createDataFrame(rdd.filter(lambda x: x[0] == 0)
-    #                       .map(lambda x: (x[1],x[2],x[6],x[7],x[8],x[9],x[10],x[11],x[12],x[13],x[14],x[15],x[16]))) \
+    #                       .map(lambda x: (x[1],x[2],x[6],x[7],x[8],x[9],x[10],x[11],x[12],x[13],x[14],x[15],x[16],x[17]))) \
     #     .toDF("y","z","app_list", "level2_list", "level3_list","tag1_list", "tag2_list", "tag3_list", "tag4_list",
     #           "tag5_list", "tag6_list", "tag7_list", "ids","number").repartition(100).write.format("tfrecords") \
     #         .save(path=path+"native/", mode="overwrite")
@@ -335,7 +335,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
 
     spark.createDataFrame(rdd.filter(lambda x: x[0] == 1)
                           .map(
-        lambda x: (x[1], x[2], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13], x[14], x[15], x[16]))) \
+        lambda x: (x[1], x[2], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13], x[14], x[15], x[16],x[17]))) \
         .toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list", "tag4_list",
               "tag5_list", "tag6_list", "tag7_list", "ids","number").repartition(100).write.format("tfrecords") \
         .save(path=path + "test_nearby/", mode="overwrite")