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ffm-baseline
Commit 6b6c865e authored by 张彦钊's avatar 张彦钊
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change test file

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tensnsorflow/multi_hot.py
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...@@ -188,89 +188,89 @@ def feature_engineer(): ...@@ -188,89 +188,89 @@ def feature_engineer():
28 + apps_number + level2_number + level3_number + len(unique_values))) 28 + apps_number + level2_number + level3_number + len(unique_values)))
value_map = dict(zip(unique_values, temp)) value_map = dict(zip(unique_values, temp))
sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \ # sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
"u.channel,c.top,cut.time,dl.app_list,feat.level3_ids,doctor.hospital_id," \ # "u.channel,c.top,cut.time,dl.app_list,feat.level3_ids,doctor.hospital_id," \
"wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \ # "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
"ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \ # "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
"k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \ # "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
"from jerry_test.esmm_train_data_dwell e left join jerry_test.user_feature u on e.device_id = u.device_id " \ # "from jerry_test.esmm_train_data_dwell e left join jerry_test.user_feature u on e.device_id = u.device_id " \
"left join jerry_test.cid_type_top c on e.device_id = c.device_id " \ # "left join jerry_test.cid_type_top c on e.device_id = c.device_id " \
"left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \ # "left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \
"left join jerry_test.device_app_list dl on e.device_id = dl.device_id " \ # "left join jerry_test.device_app_list dl on e.device_id = dl.device_id " \
"left join jerry_test.diary_feat feat on e.cid_id = feat.diary_id " \ # "left join jerry_test.diary_feat feat on e.cid_id = feat.diary_id " \
"left join jerry_test.knowledge k on feat.level2 = k.level2_id " \ # "left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
"left join jerry_test.wiki_tag wiki on e.device_id = wiki.device_id " \ # "left join jerry_test.wiki_tag wiki on e.device_id = wiki.device_id " \
"left join jerry_test.question_tag question on e.device_id = question.device_id " \ # "left join jerry_test.question_tag question on e.device_id = question.device_id " \
"left join jerry_test.search_tag search on e.device_id = search.device_id " \ # "left join jerry_test.search_tag search on e.device_id = search.device_id " \
"left join jerry_test.budan_tag budan on e.device_id = budan.device_id " \ # "left join jerry_test.budan_tag budan on e.device_id = budan.device_id " \
"left join jerry_test.order_tag ot on e.device_id = ot.device_id " \ # "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.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.cart_tag cart on e.device_id = cart.device_id " \
"left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \ # "left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \
"left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \ # "left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \
"left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \ # "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
"where e.stat_date >= '{}'".format(start) # "where e.stat_date >= '{}'".format(start)
#
df = spark.sql(sql) # df = spark.sql(sql)
#
df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer", # df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids", # "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
"tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7","search_tag2","search_tag3"]) # "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7","search_tag2","search_tag3"])
#
df = df.na.fill(dict(zip(features, features))) # df = df.na.fill(dict(zip(features, features)))
#
rdd = df.select("stat_date", "y", "z", "app_list", "level2_ids", "level3_ids", # rdd = df.select("stat_date", "y", "z", "app_list", "level2_ids", "level3_ids",
"tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7", # "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
"ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time", # "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
"hospital_id", "treatment_method", "price_min", "price_max", "treatment_time", # "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
"maintain_time", "recover_time","search_tag2","search_tag3")\ # "maintain_time", "recover_time","search_tag2","search_tag3")\
.rdd.repartition(200).map( # .rdd.repartition(200).map(
lambda x: (x[0], float(x[1]), float(x[2]), app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map), # lambda x: (x[0], float(x[1]), float(x[2]), app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map),
app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map), app_list_func(x[7], leve2_map), # app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map), app_list_func(x[7], leve2_map),
app_list_func(x[8], leve2_map), app_list_func(x[9], leve2_map), app_list_func(x[10], leve2_map), # app_list_func(x[8], leve2_map), app_list_func(x[9], leve2_map), app_list_func(x[10], leve2_map),
app_list_func(x[11], leve2_map), app_list_func(x[12], leve2_map), # app_list_func(x[11], leve2_map), app_list_func(x[12], leve2_map),
[value_map.get(x[0], 1), value_map.get(x[13], 2), value_map.get(x[14], 3), value_map.get(x[15], 4), # [value_map.get(x[0], 1), value_map.get(x[13], 2), value_map.get(x[14], 3), value_map.get(x[15], 4),
value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8), # value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8),
value_map.get(x[20], 9), value_map.get(x[21], 10), # value_map.get(x[20], 9), value_map.get(x[21], 10),
value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13), # value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13),
value_map.get(x[25], 14), value_map.get(x[26], 15)], # value_map.get(x[25], 14), value_map.get(x[26], 15)],
app_list_func(x[27], leve2_map),app_list_func(x[28], leve3_map) # app_list_func(x[27], leve2_map),app_list_func(x[28], leve3_map)
)) # ))
#
#
rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER) # rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
#
# TODO 上线后把下面train fliter 删除，因为最近一天的数据也要作为训练集 # # TODO 上线后把下面train fliter 删除，因为最近一天的数据也要作为训练集
#
train = rdd.map( # train = rdd.map(
lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], # 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[15])) # x[10], x[11], x[12], x[13],x[14],x[15]))
f = time.time() # f = time.time()
spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list", # spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list",
"tag1_list", "tag2_list", "tag3_list", "tag4_list", # "tag1_list", "tag2_list", "tag3_list", "tag4_list",
"tag5_list", "tag6_list", "tag7_list", "ids","search_tag2_list","search_tag3_list") \ # "tag5_list", "tag6_list", "tag7_list", "ids","search_tag2_list","search_tag3_list") \
.repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite") # .repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
h = time.time() # h = time.time()
print("train tfrecord done") # print("train tfrecord done")
print((h - f) / 60) # print((h - f) / 60)
#
print("训练集样本总量：") # print("训练集样本总量：")
print(rdd.count()) # print(rdd.count())
#
get_pre_number() # get_pre_number()
#
test = rdd.filter(lambda x: x[0] == validate_date).map( # 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], # 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[15])) # x[10], x[11], x[12], x[13],x[14],x[15]))
#
spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list", # spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list",
"tag1_list", "tag2_list", "tag3_list", "tag4_list", # "tag1_list", "tag2_list", "tag3_list", "tag4_list",
"tag5_list", "tag6_list", "tag7_list", "ids","search_tag2_list","search_tag3_list") \ # "tag5_list", "tag6_list", "tag7_list", "ids","search_tag2_list","search_tag3_list") \
.repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite") # .repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
#
print("va tfrecord done") # print("va tfrecord done")
#
rdd.unpersist() # rdd.unpersist()
return validate_date, value_map, app_list_map, leve2_map, leve3_map return validate_date, value_map, app_list_map, leve2_map, leve3_map
...@@ -296,7 +296,8 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map): ...@@ -296,7 +296,8 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
"left join jerry_test.sixin_tag sixin on e.device_id = sixin.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.cart_tag cart on e.device_id = cart.device_id " \
"left join jerry_test.knowledge k on feat.level2 = k.level2_id " \ "left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
"left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date" "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
"limit 60000"
features = ["ucity_id", "ccity_name", "device_type", "manufacturer", features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "hospital_id", "channel", "top", "time", "hospital_id",
...@@ -340,9 +341,9 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map): ...@@ -340,9 +341,9 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
# 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) 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], .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],x[18],x[3],x[4],x[5]))) \ x[12],x[13],x[14],x[15],x[16],x[17],x[18]))) \
.toDF("y","z","app_list", "level2_list", "level3_list","tag1_list", "tag2_list", "tag3_list", "tag4_list", .toDF("y","z","app_list", "level2_list", "level3_list","tag1_list", "tag2_list", "tag3_list", "tag4_list",
"tag5_list", "tag6_list", "tag7_list", "ids","search_tag2","search_tag3","city","uid","cid_id")\ "tag5_list", "tag6_list", "tag7_list", "ids","search_tag2","search_tag3")\
.repartition(1).write.format("tfrecords").save(path=path+"native/", mode="overwrite") .repartition(1).write.format("tfrecords").save(path=path+"native/", mode="overwrite")
print("native tfrecord done") print("native tfrecord done")
h = time.time() h = time.time()
...@@ -355,9 +356,9 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map): ...@@ -355,9 +356,9 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
spark.createDataFrame(rdd.filter(lambda x: x[0] == 1) 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], .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],x[18],x[3],x[4],x[5]))) \ x[12],x[13], x[14], x[15], x[16],x[17],x[18]))) \
.toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list", "tag4_list", .toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list", "tag4_list",
"tag5_list", "tag6_list", "tag7_list", "ids","search_tag2", "search_tag3","city","uid","cid_id")\ "tag5_list", "tag6_list", "tag7_list", "ids","search_tag2", "search_tag3")\
.repartition(1).write.format("tfrecords").save(path=path + "nearby/", mode="overwrite") .repartition(1).write.format("tfrecords").save(path=path + "nearby/", mode="overwrite")
print("nearby tfrecord done") print("nearby tfrecord done")
......
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