change test file

tensnsorflow/multi.py

@@ -71,11 +71,11 @@ def feature_engineer():
         .map(lambda x: (app_list_func(x[0], app_list_map),[value_map[x[2]],value_map[x[1]]], x[3],x[4]))
 
     spark.createDataFrame(test).toDF("level2_ids","ids","y","z")\
-        .repartition(1).write.format("tfrecords").option("recordType", "SequenceExample").save(path=path+"va/", mode="overwrite")
+        .repartition(1).write.format("tfrecords").save(path=path+"va/", mode="overwrite")
 
     print("va write done")
     spark.createDataFrame(train).toDF("level2_ids","ids","y","z") \
-        .repartition(1).write.format("tfrecords").option("recordType", "SequenceExample").save(path=path+"tr/", mode="overwrite")
+        .repartition(1).write.format("tfrecords").save(path=path+"tr/", mode="overwrite")
 
     print("done")
     rdd.unpersist()
@@ -103,7 +103,7 @@ def get_predict(date,value_map,app_list_map):
 
     spark.createDataFrame(rdd.filter(lambda x: x[4] == 0).map(lambda x: (x[0],x[5],x[6],x[7]))) \
         .toDF("level2_ids","y","z","ids").repartition(1).write.format("tfrecords") \
-            .option("recordType", "SequenceExample").save(path=path+"native/", mode="overwrite")
+            .save(path=path+"native/", mode="overwrite")
 
     nearby_pre = spark.createDataFrame(rdd.filter(lambda x: x[4] == 1).map(lambda x:(x[1],x[2],x[3]))) \
         .toDF("city", "uid", "cid_id")
@@ -111,7 +111,7 @@ def get_predict(date,value_map,app_list_map):
     nearby_pre.toPandas().to_csv(local_path+"nearby.csv", header=True)
     spark.createDataFrame(rdd.filter(lambda x: x[4] == 1).map(lambda x: (x[0], x[5], x[6], x[7]))) \
         .toDF("level2_ids","y","z","ids").repartition(1).write.format("tfrecords") \
-        .option("recordType", "SequenceExample").save(path=path+"nearby/", mode="overwrite")
+        .save(path=path+"nearby/", mode="overwrite")
 
     rdd.unpersist()
 
@@ -135,15 +135,15 @@ if __name__ == '__main__':
         .set("spark.driver.maxResultSize", "8g").set("spark.sql.avro.compression.codec","snappy")
 
     spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
-    # ti = pti.TiContext(spark)
-    # ti.tidbMapDatabase("jerry_test")
-    # # ti.tidbMapDatabase("eagle")
-    # spark.sparkContext.setLogLevel("WARN")
-    # path = "hdfs:///strategy/esmm/"
-    # local_path = "/home/gmuser/esmm/"
-    #
-    # validate_date, value_map, app_list_map = feature_engineer()
-    # get_predict(validate_date, value_map, app_list_map)
+    ti = pti.TiContext(spark)
+    ti.tidbMapDatabase("jerry_test")
+    # ti.tidbMapDatabase("eagle")
+    spark.sparkContext.setLogLevel("WARN")
+    path = "hdfs:///strategy/esmm/"
+    local_path = "/home/gmuser/esmm/"
+
+    validate_date, value_map, app_list_map = feature_engineer()
+    get_predict(validate_date, value_map, app_list_map)
 
     df = spark.read.format("tfrecords").option("recordType", "Example").load("/strategy/va.tfrecord")
     df.show(1)