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ffm-baseline
Commit 9c21d9b3 authored by 张彦钊's avatar 张彦钊
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change test file

parent 41fc8e97
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Showing with 136 additions and 100 deletions
eda/esmm/Model_pipline/feature_engineering.py
View file @ 9c21d9b3
......@@ -89,6 +89,19 @@ def get_pre_number():
def feature_engineer():
apps_number, app_list_map, level2_number, leve2_map, level3_number, leve3_map = get_map()
app_list_map["app_list"] = 16
leve3_map["level3_ids"] = 17
leve3_map["search_tag3"] = 18
leve2_map["level2_ids"] = 19
leve2_map["tag1"] = 20
leve2_map["tag2"] = 21
leve2_map["tag3"] = 22
leve2_map["tag4"] = 23
leve2_map["tag5"] = 24
leve2_map["tag6"] = 25
leve2_map["tag7"] = 26
leve2_map["search_tag2"] = 27
unique_values = []
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select distinct stat_date from esmm_train_data_dwell"
......@@ -151,7 +164,7 @@ def feature_engineer():
validate_date = con_sql(db, sql)[0].values.tolist()[0]
print("validate_date:" + validate_date)
temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
start = (temp - datetime.timedelta(days=100)).strftime("%Y-%m-%d")
start = (temp - datetime.timedelta(days=3)).strftime("%Y-%m-%d")
print(start)
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC')
......@@ -162,95 +175,102 @@ def feature_engineer():
unique_values.extend(get_unique(db, sql))
features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "hospital_id",
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time"]
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
"app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
"search_tag2", "search_tag3"]
unique_values.extend(features)
print("unique_values length")
print(len(unique_values))
print("特征维度：")
print(apps_number + level2_number + level3_number + len(unique_values))
temp = list(range(16 + apps_number + level2_number + level3_number,
16 + apps_number + level2_number + level3_number + len(unique_values)))
temp = list(range(28 + apps_number + level2_number + level3_number,
28 + apps_number + level2_number + level3_number + len(unique_values)))
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," \
"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," \
"ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7," \
"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 " \
"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.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.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.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.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.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 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 " \
"where e.stat_date >= '{}'".format(start)
df = spark.sql(sql)
df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
"tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])
df = df.na.fill(dict(zip(features, features)))
rdd = df.select("stat_date", "y", "z", "app_list", "level2_ids", "level3_ids",
"tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
"ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
"hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
"maintain_time", "recover_time").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),
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[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[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[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)]))
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
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]))
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") \
.repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
h = time.time()
print("train tfrecord done")
print((h - f) / 60)
print("训练集样本总量：")
print(rdd.count())
get_pre_number()
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]))
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") \
.repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
print("va tfrecord done")
rdd.unpersist()
# 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," \
# "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," \
# "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 " \
# "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.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.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.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.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.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 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 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)
#
# df = spark.sql(sql)
#
# df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
# "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
# "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])
#
# df = df.na.fill(dict(zip(features, features)))
#
# rdd = df.select("stat_date", "y", "z", "app_list", "level2_ids", "level3_ids",
# "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
# "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
# "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
# "maintain_time", "recover_time", "search_tag2", "search_tag3")\
# .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),
# 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[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[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[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)],
# app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map)
# ))
#
#
# 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
#
# 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[15]))
# 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", "search_tag2_list","search_tag3_list") \
# .repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
# h = time.time()
# print("train tfrecord done")
# print((h - f) / 60)
#
# print("训练集样本总量：")
# print(rdd.count())
#
# get_pre_number()
#
# 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[15]))
#
# 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", "search_tag2_list","search_tag3_list") \
# .repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
#
# print("va tfrecord done")
#
# rdd.unpersist()
return validate_date, value_map, app_list_map, leve2_map, leve3_map
......@@ -260,7 +280,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
"u.device_type,u.manufacturer,u.channel,c.top,e.device_id,e.cid_id,cut.time," \
"dl.app_list,e.hospital_id,feat.level3_ids," \
"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," \
"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 " \
"from jerry_test.esmm_pre_data e " \
"left join jerry_test.user_feature u on e.device_id = u.device_id " \
......@@ -275,48 +295,57 @@ 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"
"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 " \
"limit 600000"
features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "hospital_id",
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time"]
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
"app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
"search_tag2", "search_tag3"]
df = spark.sql(sql)
df = df.drop_duplicates(["ucity_id", "device_id", "cid_id"])
df = df.na.fill(dict(zip(features, features)))
f = time.time()
rdd = df.select("label", "y", "z", "ucity_id", "device_id", "cid_id", "app_list", "level2_ids", "level3_ids",
"tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
"ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
"hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
"maintain_time", "recover_time") \
"maintain_time", "recover_time", "search_tag2", "search_tag3") \
.rdd.repartition(200).map(lambda x: (x[0], float(x[1]), float(x[2]), x[3], x[4], x[5],
app_list_func(x[6], app_list_map), app_list_func(x[7], leve2_map),
app_list_func(x[8], leve3_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[13], leve2_map),
app_list_func(x[14], leve2_map), app_list_func(x[15], leve2_map),
[value_map.get(date,1), value_map.get(x[16],2),
value_map.get(x[17],3), value_map.get(x[18], 4),
[value_map.get(date, 1), value_map.get(x[16], 2),
value_map.get(x[17], 3), value_map.get(x[18], 4),
value_map.get(x[19], 5), value_map.get(x[20], 6),
value_map.get(x[21], 7), value_map.get(x[22], 8),
value_map.get(x[23], 9), value_map.get(x[24], 10),
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)
]))
value_map.get(x[29], 15)], app_list_func(x[30], leve2_map),
app_list_func(x[31], leve3_map)))
rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
println(rdd.count())
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)
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",
"tag5_list", "tag6_list", "tag7_list", "ids").repartition(1).write.format("tfrecords") \
.save(path=path+"native/", mode="overwrite")
.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]))) \
.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_list","search_tag3_list") \
.repartition(1).write.format("tfrecords").save(path=path+"native/", mode="overwrite")
print("native tfrecord done")
h = time.time()
print((h-f)/60)
......@@ -327,11 +356,11 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
nearby_pre.toPandas().to_csv(local_path + "nearby.csv", header=True)
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]))) \
.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]))) \
.toDF("y", "z", "app_list", "level2_list", "level3_list", "tag1_list", "tag2_list", "tag3_list", "tag4_list",
"tag5_list", "tag6_list", "tag7_list", "ids").repartition(1).write.format("tfrecords") \
.save(path=path + "nearby/", mode="overwrite")
"tag5_list", "tag6_list", "tag7_list", "ids", "search_tag2_list","search_tag3_list") \
.repartition(1).write.format("tfrecords").save(path=path + "nearby/", mode="overwrite")
print("nearby tfrecord done")
......
eda/esmm/Model_pipline/train.py
View file @ 9c21d9b3
......@@ -63,7 +63,9 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
"tag4_list": tf.VarLenFeature(tf.int64),
"tag5_list": tf.VarLenFeature(tf.int64),
"tag6_list": tf.VarLenFeature(tf.int64),
"tag7_list": tf.VarLenFeature(tf.int64)
"tag7_list": tf.VarLenFeature(tf.int64),
"search_tag2_list": tf.VarLenFeature(tf.int64),
"search_tag3_list": tf.VarLenFeature(tf.int64)
}
parsed = tf.parse_single_example(record, features)
y = parsed.pop('y')
......@@ -131,6 +133,8 @@ def model_fn(features, labels, mode, params):
tag5_list = features['tag5_list']
tag6_list = features['tag6_list']
tag7_list = features['tag7_list']
search_tag2_list = features['search_tag2_list']
search_tag3_list = features['search_tag3_list']
if FLAGS.task_type != "infer":
y = labels['y']
......@@ -149,10 +153,13 @@ def model_fn(features, labels, mode, params):
tag5 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag5_list, sp_weights=None, combiner="sum")
tag6 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag6_list, sp_weights=None, combiner="sum")
tag7 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag7_list, sp_weights=None, combiner="sum")
search_tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag2_list, sp_weights=None, combiner="sum")
search_tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag3_list, sp_weights=None, combiner="sum")
# x_concat = tf.reshape(embedding_id,shape=[-1, common_dims]) # None * (F * K)
x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1,
tag2, tag3, tag4, tag5, tag6, tag7], axis=1)
tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1)
with tf.name_scope("CVR_Task"):
if mode == tf.estimator.ModeKeys.TRAIN:
......
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