dssm train

recommend-model-produce/src/main/python/models/dssm/data/data_process.sh

@@ -1,24 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#!/bin/bash
-
-wget https://paddlerec.bj.bcebos.com/dssm%2Fbq.tar.gz
-tar xzf dssm%2Fbq.tar.gz
-rm -f dssm%2Fbq.tar.gz
-mv bq/train.txt ./raw_data.txt
-python3 preprocess.py
-mkdir big_train
-mv train.txt ./big_train
-mkdir big_test
-mv test.txt ./big_test

recommend-model-produce/src/main/python/models/dssm/data/prepare_dump_data.sh

@@ -1,27 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#!/bin/bash
-
-
-cat train/train.txt | awk -F'\t' 'BEGIN{OFS="\t"}{print NR, "item_"NR, $0}' > data_with_lineid
-for i in 20210803 20210804
-do
-    for j in 1
-    do
-        mkdir -p train_with_insid/$i/$j
-        cp data_with_lineid train_with_insid/$i/$j
-        mkdir -p test_with_insid/$i/$j
-        cp data_with_lineid test_with_insid/$i/$j
-    done
-done

recommend-model-produce/src/main/python/models/dssm/data/preprocess.py

@@ -1,131 +0,0 @@
-#encoding=utf-8
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import jieba
-import numpy as np
-import random
-
-f = open("./raw_data.txt", "r", encoding="utf-8")
-lines = f.readlines()
-f.close()
-
-#建立字典
-word_dict = {}
-for line in lines:
-    line = line.strip().split("\t")
-    text = line[0].strip("") + " " + line[1].strip("")
-    text = jieba.cut(text)
-    for word in text:
-        if word in word_dict:
-            continue
-        else:
-            word_dict[word] = len(word_dict) + 1
-
-f = open("./raw_data.txt", "r", encoding="utf-8")
-lines = f.readlines()
-f.close()
-
-lines = [line.strip().split("\t") for line in lines]
-
-#建立以query为key，以负例为value的字典
-neg_dict = {}
-for line in lines:
-    if line[2] == "0":
-        if line[0] in neg_dict:
-            neg_dict[line[0]].append(line[1])
-        else:
-            neg_dict[line[0]] = [line[1]]
-
-#建立以query为key，以正例为value的字典
-pos_dict = {}
-for line in lines:
-    if line[2] == "1":
-        if line[0] in pos_dict:
-            pos_dict[line[0]].append(line[1])
-        else:
-            pos_dict[line[0]] = [line[1]]
-
-print("build dict done")
-#划分训练集和测试集
-query_list = list(pos_dict.keys())
-#print(len(query_list))
-np.random.seed(107)
-np.random.shuffle(query_list)
-train_query = query_list[:11600]
-test_query = query_list[11600:]
-
-#获得训练集
-train_set = []
-for query in train_query:
-    for pos in pos_dict[query]:
-        if query not in neg_dict:
-            continue
-        for neg in neg_dict[query]:
-            train_set.append([query, pos, neg])
-random.shuffle(train_set)
-print("get train_set done")
-
-#获得测试集
-test_set = []
-for query in test_query:
-    for pos in pos_dict[query]:
-        test_set.append([query, pos, 1])
-    if query not in neg_dict:
-        continue
-    for neg in neg_dict[query]:
-        test_set.append([query, neg, 0])
-random.shuffle(test_set)
-print("get test_set done")
-
-#训练集中的query,pos,neg转化为词袋
-f = open("train.txt", "w", encoding="utf-8")
-for line in train_set:
-    query = jieba.cut(line[0].strip())
-    pos = jieba.cut(line[1].strip())
-    neg = jieba.cut(line[2].strip())
-    query_token = [0] * (len(word_dict) + 1)
-    for word in query:
-        query_token[word_dict[word]] = 1
-    pos_token = [0] * (len(word_dict) + 1)
-    for word in pos:
-        pos_token[word_dict[word]] = 1
-    neg_token = [0] * (len(word_dict) + 1)
-    for word in neg:
-        neg_token[word_dict[word]] = 1
-    f.write(','.join([str(x) for x in query_token]) + "\t" + ','.join([
-        str(x) for x in pos_token
-    ]) + "\t" + ','.join([str(x) for x in neg_token]) + "\n")
-f.close()
-
-#测试集中的query和pos转化为词袋
-f = open("test.txt", "w", encoding="utf-8")
-fa = open("label.txt", "w", encoding="utf-8")
-for line in test_set:
-    query = jieba.cut(line[0].strip())
-    pos = jieba.cut(line[1].strip())
-    label = line[2]
-    query_token = [0] * (len(word_dict) + 1)
-    for word in query:
-        query_token[word_dict[word]] = 1
-    pos_token = [0] * (len(word_dict) + 1)
-    for word in pos:
-        pos_token[word_dict[word]] = 1
-    f.write(','.join([str(x) for x in query_token]) + "\t" + ','.join(
-        [str(x) for x in pos_token]) + "\n")
-    fa.write(str(label) + "\n")
-f.close()
-fa.close()

recommend-model-produce/src/main/python/models/dssm/data/test/test.txt

@@ -1,5 +0,0 @@
-djise-19293414-39429345-1789989892    1,3,0,2,4
-agsse-19290414-08429345-1709989892    1,3,0,2,4
-sdfsg-192980914-300345-1789969892    1,1,1,1,1
-gasrew-803414-139429345-1789989892    1,3,0,2,4
-gewt-9293414-429345-1789989852    2,3,2,2,4

recommend-model-produce/src/main/python/models/dssm/data/train/train.txt

@@ -1,12 +0,0 @@
-4,iLife-pages/user-videos-share-recommend,weixin_openid_otjoB5ZEwY3adBGBmuqG-SufzoFk,42909003,41390716,1,2	0	41390716	26303874	1522430,38,39,6022,182,194,15,42,30,23,27,2,4,5,1,1,84,87,51,81,85,46,0,0,0,0,0,0,81,0,0,19,18,10,20,19,10,21,19,10,19,18,11,15,16,18,10,24,20,9,22,20,9,22,22,8,22,23,9,23,20,22,23,0.84,0.87,0.51,0.81,0.85,0.46,0.0,0.0,0.0,0.0,0.0,0.0,0.81,0.0,0.0,0.19,0.18,0.1,0.2,0.19,0.1,0.21,0.19,0.1,0.19,0.18,0.11,0.15,0.16,0.18,0.1,0.24,0.2,0.09,0.22,0.2,0.09,0.22,0.22,0.08,0.22,0.23,0.09,0.23,0.2,0.22,0.23,0.092959,0.091848,0.988048,0.09036,0.080075,0.886174,0.0,0.0,0.0,0.0,0.0,0.0,0.739653,0.0,0.0,0.060194,0.052371,0.870042,0.060402,0.050624,0.838121,0.061695,0.05276,0.855184,0.054098,0.050241,0.928705,0.523529,0.509136,0.521871,0.512282,0.071686,0.053487,0.746133,0.0721,0.052187,0.723817,0.068376,0.051588,0.754474,0.061676,0.055182,0.894708,0.557429,0.519987,0.523791,0.551456	2607705,17,43,5192,290,633,27,48,30,23,27,2,1,5,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,20,19,15,18,17,13,17,15,12,13,13,13,14,13,6,20,16,15,18,17,15,20,18,14,19,17,12,21,18,21,18,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12,0.2,0.19,0.15,0.18,0.17,0.13,0.17,0.15,0.12,0.13,0.13,0.13,0.14,0.13,0.06,0.2,0.16,0.15,0.18,0.17,0.15,0.2,0.18,0.14,0.19,0.17,0.12,0.21,0.18,0.21,0.18,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.047823,0.057223,1.196572,0.047257,0.051353,1.086678,0.047737,0.04767,0.998596,0.048139,0.047478,0.986266,0.519413,0.488597,0.482026,0.480801,0.052013,0.048886,0.939885,0.052909,0.0479,0.905328,0.05444,0.048373,0.888563,0.052461,0.04908,0.935544,0.52888,0.510421,0.5156,0.518859
-0,vlog-pages/user-videos-share-recommend,weixin_openid_o0w175cHrvB_U6Uccfor5Rxa8mro,41321247,42281529,6,9	0	42281529	35932119	3229119,32,43,3905,290,633,15,48,37,25,27,2,8,1,1,2,19,84,89,20,80,85,0,0,0,0,0,0,83,0,0,9,23,24,14,24,23,12,21,21,18,12,9,22,23,22,4,20,16,15,18,17,15,20,18,14,19,17,12,21,18,21,18,0.19,0.84,0.89,0.2,0.8,0.85,0.0,0.0,0.0,0.0,0.0,0.0,0.8300000000000001,0.0,0.0,0.09,0.23,0.24,0.14,0.24,0.23,0.12,0.21,0.21,0.18,0.12,0.09,0.22,0.23,0.22,0.04,0.2,0.16,0.15,0.18,0.17,0.15,0.2,0.18,0.14,0.19,0.17,0.12,0.21,0.18,0.21,0.18,0.035971,0.073639,2.047187,0.039561,0.067458,1.705158,0.0,0.0,0.0,0.0,0.0,0.0,0.788233,0.0,0.0,0.046257,0.068202,1.474438,0.046972,0.064333,1.369609,0.047723,0.060435,1.26638,0.053114,0.047451,0.893388,0.743416,0.685791,0.623261,0.457025,0.052013,0.048886,0.939885,0.052909,0.0479,0.905328,0.05444,0.048373,0.888563,0.052461,0.04908,0.935544,0.52888,0.510421,0.5156,0.518859	2562749,18,66,816,290,633,27,48,37,25,27,2,1,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,17,23,6,19,22,4,20,23,5,23,22,20,21,20,18,2,14,23,10,23,23,14,17,17,18,21,15,23,21,12,18,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.17,0.23,0.06,0.19,0.22,0.04,0.2,0.23,0.05,0.23,0.22,0.2,0.21,0.2,0.18,0.02,0.14,0.23,0.1,0.23,0.23,0.14,0.17,0.17,0.18,0.21,0.15,0.23,0.21,0.12,0.18,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03656,0.051036,1.395947,0.040474,0.054355,1.342978,0.040838,0.054466,1.333723,0.044014,0.054005,1.227002,0.575852,0.593366,0.585771,0.571369,0.036312,0.046736,1.287086,0.044015,0.051758,1.175908,0.045745,0.047662,1.041892,0.05189,0.05361,1.033158,0.561924,0.543471,0.476901,0.525163
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