统计icon数据

eda/esmm/Model_pipline/feature_engineering.py

@@ -319,10 +319,10 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
     h = time.time()
     print((h-f)/60)
 
-    native_pre = spark.createDataFrame(rdd.filter(lambda x: x[0] == 1).map(lambda x: (x[3], x[4], x[5]))) \
+    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")
-    native_pre.toPandas().to_csv(local_path + "nearby.csv", header=True)
+    nearby_pre.toPandas().to_csv(local_path + "nearby.csv", header=True)
 
     spark.createDataFrame(rdd.filter(lambda x: x[0] == 1)
                           .map(