- rm -rf ../../origin_deep_cluster_odps_8.tar.gz
tar -cvzf ../../origin_deep_cluster_odps_8.tar.gz *
sudo docker run -ti --name hengsong2 -v /Users/songge/Desktop/beifen/hengsong.lhs:/home/hengsong --net=host reg.docker.alibaba-inc.com/zhiji/imgtoolkit_video:nightly-dev bash
- docker 的 java 环境
source /etc/profile
JAVA_HOME=/home/hengsong/jdk1.8.0_221
JRE_HOME=PATH:
JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export JAVA_HOME
export JRE_HOME
export PATH
export CLASSPATH
- 给阔姐的表
graph_embedding.jl_jingyan_query_related_video_pool
where type_biz=2
body里的 item_id, ugc_old_memo/s":"feedback","text/s": video_url， video_id 字段
create table hs_jingyan_query_related_video_pool_ugc as
select id, coalesce(CONCAT('http://cloud.video.taobao.com', get_json_object(body, '.entities.k3.play_url/s')))as video_url, coalesce(get_json_object(body, '
.entities.k2.video_id/l')) as video_id, coalesce(get_json_object(body, '
.entities.k0.item_id/l') )as item_id, coalesce(get_json_object(body, '
.entities.k1.text/s')) as text
from graph_embedding.jl_jingyan_query_related_video_pool where ds=max_pt('graph_embedding.jl_jingyan_query_related_video_pool') and type_biz=2;
graph_embedding.hs_jingyan_query_related_video_pool_ugc
- 得到负采样
hs_tmp_dssm_6
hs_tmp_dssm_index_1
hs_tmp_dssm_item_id_1
create table hs_tmp_36 as select item_id from hs_tmp_dssm_6;
create table hs_tmp_dssm_item_id_1 as select int(rand() * 135525) as randint, item_id from hs_tmp_36;
(5 * ) insert into table hs_tmp_dssm_item_id_1 select int(rand() * 135525) as randint, item_id from hs_tmp_36;
hs_tmp_dssm_8
create table hs_tmp_dssm_8 as
select a.index, b.item_id from
(select index from hs_tmp_dssm_index_1)a join (select * from hs_tmp_dssm_item_id_1)b on a.index == b.randint % 5421;
select index, count(*) as freq from hs_tmp_dssm_8 group by index order by freq desc limit 10;
去重
hs_tmp_dssm_9->hs_tmp_dssm_10
create table hs_tmp_dssm_9 as select a.*, b.index as indexb, b.item_id as item_idb from (select * from hs_tmp_dssm_8)a left join (select * from hs_tmp_dssm_6)b on a.index=b.index and a.item_id=b.item_id;
create table hs_tmp_dssm_10 as select index, item_id, 0 as label from hs_tmp_dssm_9 where indexb is NULL and item_idb is NULL;
create table hs_tmp_dssm_11 as select index, item_id, 1 as label from hs_tmp_dssm_6;
得到样本集合
positive samples : hs_tmp_dssm_11
negetive samples : hs_tmp_dssm_10
乱序
insert into table hs_tmp_dssm_11 select * from hs_tmp_10;
create table hs_tmp_38 as select int(rand() * 11000000000) as id, * from hs_tmp_dssm_11;
create table hs_tmp_dssm_12 as select index, item_id, label from hs_tmp_38 order by id;
create table hs_tmp_dssm_13 as select * from hs_tmp_38 order by id;
下面这个更好：
drop table hs_tmp_dssm_12;
yes
create table hs_tmp_dssm_12 lifecycle 30 as select * from hs_tmp_dssm_13 DISTRIBUTE by random();
drop table hs_tmp_dssm_13;
yes
create table hs_tmp_dssm_13 lifecycle 30 as select * from hs_tmp_dssm_12 DISTRIBUTE by random();
+------------+------------+
| label | freq |
+------------+------------+
| 0 | 5828333140 |
| 1 | 1171862133 |
+------------+------------+
drop table if exists graph_embedding.zj_xhs_dssm_pos_neg_sample_info_shuffle_;
create table if not exists graph_embedding.zj_xhs_dssm_pos_neg_sample_info_shuffle_ LIFECYCLE 30
as select * from graph_embedding.zj_xhs_dssm_pos_neg_sample_info_ DISTRIBUTE by random();
取得query_ws和title_ws字段：
create table hs_tmp_39 as select distinct index, se_keyword_ws from hs_tmp_dssm_3;
create table hs_tmp_40 as select distinct item_id, title_ws from hs_tmp_dssm_3;
create table hs_tmp_41 as
select a.index, a.se_keyword_ws, b.item_id, b.label from (select * from hs_tmp_39)a join (select * from hs_tmp_dssm_12)b on a.index == b.index;
create table hs_tmp_42 as
select a.title_ws, b.* from (select * from hs_tmp_40)a join (select * from hs_tmp_41)b on a.item_id == b.item_id;
训练数据：hs_train_data_dssm_1 测试数据：hs_test_data_dssm_1
drop table if exists hs_train_data_dssm_1;
yes
drop table if exists hs_test_data_dssm_1;
yes
PAI -name split -project algo_public
-DinputTableName=graph_embedding.hs_tmp_42
-Doutput1TableName=graph_embedding.hs_train_data_dssm_1
-Doutput2TableName=graph_embedding.hs_test_data_dssm_1
-Dfraction=0.8
-DmemSizePerCore=4096
-DcoreNum=300
;
判断title补全长度
create table hs_title_length as select REGEXP_COUNT(title_ws, ' ') as title_len, REGEXP_COUNT(se_keyword_ws, ' ') as query_len from hs_tmp_42;
总数量：7000195273
25： 129186737
30： 43367246
10: 0
取20更好一点
pai -name tensorflow140 -Dscript="file:///home/hengsong/origin_deep_cluster_odps_8.tar.gz" -DentryFile="train_v4.py" -Dcluster='{"worker":{"count":30, "cpu":200, "memory":4000}, "ps":{"count":30, "cpu":200, "memory":5000}}' -Dtables="odps://graph_embedding/tables/hs_train_data_dssm_1,odps://graph_embedding/tables/hs_test_data_dssm_1" -DcheckpointDir="oss://bucket-automl/hengsong/?role_arn=acs:ram::1293303983251548:role/graph2018&host=cn-hangzhou.oss-internal.aliyun-inc.com" -DuserDefinedParameters="--learning_rate=1e-2 --batch_size=2048 --is_save_model=True --attention_type=1 --num_epochs=100 --ckpt=hs_ugc_video.ckpt" -DuseSparseClusterSchema=True;
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