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ffm-baseline
Commit 321e3589 authored by 张彦钊's avatar 张彦钊
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change test file

parent 2b18672a
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Showing with 14 additions and 18 deletions
tensnsorflow/feature_test.py
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......@@ -214,10 +214,7 @@ def feature_engineer():
app_list_func(x[11], leve2_map), app_list_func(x[12], leve2_map),
[value_map[x[0]], value_map[x[13]], value_map[x[14]], value_map[x[15]], value_map[x[16]],
value_map[x[17]], value_map[x[18]], value_map[x[19]], value_map[x[20]], value_map[x[21]],
value_map[x[22]], value_map[x[23]], value_map[x[24]], value_map[x[25]], value_map[x[26]]]))\
.zipWithIndex().map(lambda x:(x[0][0],x[0][1],x[0][2],x[0][3],x[0][4],x[0][5],x[0][6],x[0][7],x[0][8],
x[0][9],x[0][10],x[0][11],x[0][12],x[0][13],
x[1]))
value_map[x[22]], value_map[x[23]], value_map[x[24]], value_map[x[25]], value_map[x[26]]]))
rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
......@@ -225,11 +222,11 @@ 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[14]))
x[10], x[11], x[12], x[13]))
f = time.time()
spark.createDataFrame(train).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") \
"tag5_list", "tag6_list", "tag7_list", "ids") \
.repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
h = time.time()
print("train tfrecord done")
......@@ -242,11 +239,11 @@ 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[14]))
x[10], x[11], x[12], x[13]))
spark.createDataFrame(test).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") \
"tag5_list", "tag6_list", "tag7_list", "ids") \
.repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
print("va tfrecord done")
......@@ -276,7 +273,8 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
"left join jerry_test.order_tag ot on e.device_id = ot.device_id " \
"left join jerry_test.sixin_tag sixin on e.device_id = sixin.device_id " \
"left join jerry_test.cart_tag cart on e.device_id = cart.device_id " \
"left join jerry_test.knowledge k on feat.level2 = k.level2_id limit 6000"
"left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
"where label = 1 limit 60000"
features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "hospital_id",
......@@ -305,21 +303,18 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
value_map.get(x[25], 11), value_map.get(x[26], 12),
value_map.get(x[27], 13), value_map.get(x[28], 14),
value_map.get(x[29], 15)
]))\
.zipWithIndex().map(lambda x:(x[0][0],x[0][1],x[0][2],x[0][3],x[0][4],x[0][5],x[0][6],x[0][7],x[0][8],
x[0][9],x[0][10],x[0][11],x[0][12],x[0][13],x[0][14],x[0][15],x[0][16],
x[1]))
]))
rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
# 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.toPandas().to_csv(local_path + "native.csv", header=True)
# 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],x[17]))) \
# .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",
# "tag5_list", "tag6_list", "tag7_list", "ids","number").repartition(1).write.format("tfrecords") \
# "tag5_list", "tag6_list", "tag7_list", "ids").repartition(1).write.format("tfrecords") \
# .save(path=path+"native/", mode="overwrite")
# print("native tfrecord done")
# h = time.time()
......@@ -332,9 +327,9 @@ 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],x[17]))) \
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",
"tag5_list", "tag6_list", "tag7_list", "ids","number").repartition(100).write.format("tfrecords") \
"tag5_list", "tag6_list", "tag7_list", "ids").repartition(100).write.format("tfrecords") \
.save(path=path + "nearby/", mode="overwrite")
print("nearby tfrecord done")
......@@ -355,6 +350,7 @@ if __name__ == '__main__':
spark.sparkContext.setLogLevel("WARN")
path = "hdfs:///strategy/esmm/"
local_path = "/home/gmuser/esmm/"
validate_date, value_map, app_list_map, leve2_map, leve3_map = feature_engineer()
get_predict(validate_date, value_map, app_list_map, leve2_map, leve3_map)
......
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