change test file

tensnsorflow/feature_test.py

@@ -309,7 +309,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
     native_pre = spark.createDataFrame(rdd.filter(lambda x:x[0] == 0).map(lambda x:(x[3],x[4],x[5])))\
         .toDF("city","uid","cid_id")
     print("native csv")
-    native_pre.repartion(1).write.format('com.databricks.spark.csv').save(path + "native/", header='true')
+    native_pre.repartition(1).write.format('com.databricks.spark.csv').save(path + "native/", 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]))) \
         .toDF("y","z","app_list", "level2_list", "level3_list","tag1_list", "tag2_list", "tag3_list", "tag4_list",
@@ -322,7 +322,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
     nearby_pre = spark.createDataFrame(rdd.filter(lambda x: x[0] == 1).map(lambda x: (x[3], x[4], x[5]))) \
         .toDF("city", "uid", "cid_id")
     print("nearby csv")
-    nearby_pre.repartion(1).write.format('com.databricks.spark.csv').save(path + "nearby/", header='true')
+    nearby_pre.repartition(1).write.format('com.databricks.spark.csv').save(path + "nearby/", header='true')
 
     spark.createDataFrame(rdd.filter(lambda x: x[0] == 1)
                           .map(
@@ -376,7 +376,7 @@ if __name__ == '__main__':
           "where e.stat_date >= '2019-06-10'"
 
     df = spark.sql(sql)
-    df.repartion(1).write.format('com.databricks.spark.csv').save(path + "native/a.csv", header='true')
+    df.repartition(1).write.format('com.databricks.spark.csv').save(path + "native/a.csv", header='true')
 
 
     spark.stop()