diff --git a/models/recall/youtube_dnn/README.md b/models/recall/youtube_dnn/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d7ae92e268dcafded93d4298639ed47271302d44 --- /dev/null +++ b/models/recall/youtube_dnn/README.md @@ -0,0 +1,146 @@ +# Youtebe-DNN + +以下是本例的简要目录结构及说明： + +``` +├── data #样例数据 + ├── train + ├── data.txt + ├── test + ├── data.txt +├── generate_ramdom_data # 随机训练数据生成文件 +├── __init__.py +├── README.md # 文档 +├── model.py #模型文件 +├── config.yaml #配置文件 +├── data_prepare.sh #一键数据处理脚本 +├── reader.py #reader +├── infer.py # 预测程序 +``` + +注：在阅读该示例前，建议您先了解以下内容： + +[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md) + + +--- +## 内容 + +- [模型简介](#模型简介) +- [数据准备](#数据准备) +- [运行环境](#运行环境) +- [快速开始](#快速开始) +- [论文复现](#论文复现) +- [进阶使用](#进阶使用) +- [FAQ](#FAQ) + +## 模型简介 +[《Deep Neural Networks for YouTube Recommendations》](https://link.zhihu.com/?target=https%3A//static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) 这篇论文是google的YouTube团队在推荐系统上DNN方面的尝试，是经典的向量化召回模型，主要通过模型来学习用户和物品的兴趣向量，并通过内积来计算用户和物品之间的相似性，从而得到最终的候选集。YouTube采取了两层深度网络完成整个推荐过程： + +1.第一层是**Candidate Generation Model**完成候选视频的快速筛选，这一步候选视频集合由百万降低到了百的量级。 + +2.第二层是用**Ranking Model**完成几百个候选视频的精排。 + +本项目在paddlepaddle上完成YouTube dnn的召回部分Candidate Generation Model，分别获得用户和物品的向量表示，从而后续可以通过其他方法（如用户和物品的余弦相似度）给用户推荐物品。 + +由于原论文没有开源数据集，本项目随机构造数据验证网络的正确性。 + +本项目支持功能 + +训练：单机CPU、单机单卡GPU、本地模拟参数服务器训练、增量训练，配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md) + +预测：单机CPU、单机单卡GPU；配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md) + +## 数据处理 +调用python generate_ramdom_data.py生成随机训练数据，每行数据格式如下： +``` +#watch_vec;search_vec;other_feat;label +0.01,0.02,...,0.09;0.01,0.02,...,0.09;0.01,0.02,...,0.09;20 +``` +方便起见，我们提供了一键式数据生成脚本： +``` +sh data_prepare.sh +``` + +## 运行环境 + +PaddlePaddle>=1.7.2 + +python 2.7/3.5/3.6/3.7 + +PaddleRec >=0.1 + +os : windows/linux/macos + +## 快速开始 + +### 单机训练 + +``` +mode: [cpu_single_train] + +runner: +- name: cpu_single_train + class: train + device: cpu # if use_gpu, set it to gpu + epochs: 20 + save_checkpoint_interval: 1 + save_inference_interval: 1 + save_checkpoint_path: "increment_youtubednn" + save_inference_path: "inference_youtubednn" + save_inference_feed_varnames: ["watch_vec", "search_vec", "other_feat"] # feed vars of save inference + save_inference_fetch_varnames: ["l3.tmp_2"] + print_interval: 1 +``` + +### 单机预测 +通过计算每个用户和每个物品的余弦相似度，给每个用户推荐topk视频： + +cpu infer: +``` +python infer.py --test_epoch 19 --inference_model_dir ./inference_youtubednn --increment_model_dir ./increment_youtubednn --watch_vec_size 64 --search_vec_size 64 --other_feat_size 64 --topk 5 +``` + +gpu infer: +``` +python infer.py --use_gpu 1 --test_epoch 19 --inference_model_dir ./inference_youtubednn --increment_model_dir ./increment_youtubednn --watch_vec_size 64 --search_vec_size 64 --other_feat_size 64 --topk 5 +``` +### 运行 +``` +python -m paddlerec.run -m paddlerec.models.recall.w2v +``` + +### 结果展示 + +样例数据训练结果展示： + +``` +Running SingleStartup. +Running SingleRunner. +batch: 1, acc: [0.03125] +batch: 2, acc: [0.0625] +batch: 3, acc: [0.] +... +epoch 0 done, use time: 0.0605320930481, global metrics: acc=[0.] +... +epoch 19 done, use time: 0.33447098732, global metrics: acc=[0.] +``` + +样例数据预测结果展示: +``` +user:0, top K videos:[40, 31, 4, 33, 93] +user:1, top K videos:[35, 57, 58, 40, 17] +user:2, top K videos:[35, 17, 88, 40, 9] +user:3, top K videos:[73, 35, 39, 58, 38] +user:4, top K videos:[40, 31, 57, 4, 73] +user:5, top K videos:[38, 9, 7, 88, 22] +user:6, top K videos:[35, 73, 14, 58, 28] +user:7, top K videos:[35, 73, 58, 38, 56] +user:8, top K videos:[38, 40, 9, 35, 99] +user:9, top K videos:[88, 73, 9, 35, 28] +user:10, top K videos:[35, 52, 28, 54, 73] +``` + +## 进阶使用 + +## FAQ diff --git a/models/recall/youtube_dnn/config.yaml b/models/recall/youtube_dnn/config.yaml index f77755150cd84b5a5c30a3736aa15315ce239364..048963f558b54cc7cfedf505f30bca589852a597 100644 --- a/models/recall/youtube_dnn/config.yaml +++ b/models/recall/youtube_dnn/config.yaml @@ -17,11 +17,10 @@ workspace: "models/recall/youtube_dnn" dataset: - name: dataset_train - batch_size: 5 - type: DataLoader - #type: QueueDataset + batch_size: 32 + type: DataLoader # or QueueDataset data_path: "{workspace}/data/train" - data_converter: "{workspace}/random_reader.py" + data_converter: "{workspace}/reader.py" hyper_parameters: watch_vec_size: 64 @@ -30,22 +29,23 @@ hyper_parameters: output_size: 100 layers: [128, 64, 32] optimizer: - class: adam - learning_rate: 0.001 - strategy: async + class: SGD + learning_rate: 0.01 -mode: train_runner +mode: [cpu_single_train] runner: -- name: train_runner +- name: cpu_single_train class: train device: cpu - epochs: 3 - save_checkpoint_interval: 2 - save_inference_interval: 4 - save_checkpoint_path: "increment" - save_inference_path: "inference" - print_interval: 10 + epochs: 20 + save_checkpoint_interval: 1 + save_inference_interval: 1 + save_checkpoint_path: "increment_youtubednn" + save_inference_path: "inference_youtubednn" + save_inference_feed_varnames: ["watch_vec", "search_vec", "other_feat"] # feed vars of save inference + save_inference_fetch_varnames: ["l3.tmp_2"] + print_interval: 1 phase: - name: train diff --git a/models/recall/youtube_dnn/data/test/data.txt b/models/recall/youtube_dnn/data/test/data.txt new file mode 100644 index 0000000000000000000000000000000000000000..64aff676b129b695ec331e5ea1a94439e16bf4d8 --- /dev/null +++ b/models/recall/youtube_dnn/data/test/data.txt @@ -0,0 +1,64 @@ 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diff --git a/models/recall/youtube_dnn/data/train/samll_data.txt b/models/recall/youtube_dnn/data/train/samll_data.txt deleted file mode 100644 index c3c4cf5f84f66594e76603cce1f18d211ebd05a7..0000000000000000000000000000000000000000 --- a/models/recall/youtube_dnn/data/train/samll_data.txt +++ /dev/null @@ -1,100 +0,0 @@ -4764,174,1 -4764,2958,0 -4764,452,0 -4764,1946,0 -4764,3208,0 -2044,2237,1 -2044,1998,0 -2044,328,0 -2044,1542,0 -2044,1932,0 -4276,65,1 -4276,3247,0 -4276,942,0 -4276,3666,0 -4276,2222,0 -3933,682,1 -3933,2451,0 -3933,3695,0 -3933,1643,0 -3933,3568,0 -1151,1265,1 -1151,118,0 -1151,2532,0 -1151,2083,0 -1151,2350,0 -1757,876,1 -1757,201,0 -1757,3633,0 -1757,1068,0 -1757,2549,0 -3370,276,1 -3370,2435,0 -3370,606,0 -3370,910,0 -3370,2146,0 -5137,1018,1 -5137,2163,0 -5137,3167,0 -5137,2315,0 -5137,3595,0 -3933,2831,1 -3933,2881,0 -3933,2949,0 -3933,3660,0 -3933,417,0 -3102,999,1 -3102,1902,0 -3102,2161,0 -3102,3042,0 -3102,1113,0 -2022,336,1 -2022,1672,0 -2022,2656,0 -2022,3649,0 -2022,883,0 -2664,655,1 -2664,3660,0 -2664,1711,0 -2664,3386,0 -2664,1668,0 -25,701,1 -25,32,0 -25,2482,0 -25,3177,0 -25,2767,0 -1738,1643,1 -1738,2187,0 -1738,228,0 -1738,650,0 -1738,3101,0 -5411,1241,1 -5411,2546,0 -5411,3019,0 -5411,3618,0 -5411,1674,0 -638,579,1 -638,3512,0 -638,783,0 -638,2111,0 -638,1880,0 -3554,200,1 -3554,2893,0 -3554,2428,0 -3554,969,0 -3554,2741,0 -4283,1074,1 -4283,3056,0 -4283,2032,0 -4283,405,0 -4283,1505,0 -5111,200,1 -5111,3488,0 -5111,477,0 -5111,2790,0 -5111,40,0 -3964,515,1 -3964,1528,0 -3964,2173,0 -3964,1701,0 -3964,2832,0 diff --git a/models/recall/youtube_dnn/data_prepare.sh b/models/recall/youtube_dnn/data_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..b69cae28ecc87fcd1586407ce51d3ca56219919d --- /dev/null +++ b/models/recall/youtube_dnn/data_prepare.sh @@ -0,0 +1 @@ +python generate_ramdom_data.py diff --git a/models/recall/youtube_dnn/generate_ramdom_data.py b/models/recall/youtube_dnn/generate_ramdom_data.py new file mode 100644 index 0000000000000000000000000000000000000000..cfdb53ede1c5281c910792fffdc1dce14d8ced59 --- /dev/null +++ b/models/recall/youtube_dnn/generate_ramdom_data.py @@ -0,0 +1,40 @@ +# 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 paddle +import numpy as np + +# Build a random data set. +sample_size = 100 +batch_size = 32 +watch_vec_size = 64 +search_vec_size = 64 +other_feat_size = 64 +output_size = 100 + +watch_vecs = np.random.rand(batch_size * sample_size, watch_vec_size).tolist() +search_vecs = np.random.rand(batch_size * sample_size, + search_vec_size).tolist() +other_vecs = np.random.rand(batch_size * sample_size, other_feat_size).tolist() +labels = np.random.randint( + output_size, size=(batch_size * sample_size)).tolist() + +output_path = "./data/train/data.txt" +with open(output_path, 'w') as fout: + for i in range(batch_size * sample_size): + _str_ = ','.join(map(str, watch_vecs[i])) + ";" + ','.join( + map(str, search_vecs[i])) + ";" + ','.join( + map(str, other_vecs[i])) + ";" + str(labels[i]) + fout.write(_str_) + fout.write("\n") diff --git a/models/recall/youtube_dnn/infer.py b/models/recall/youtube_dnn/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff4915d86943d8ebfb2a56a9c5d2106a81aa33a --- /dev/null +++ b/models/recall/youtube_dnn/infer.py @@ -0,0 +1,126 @@ +# 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 copy +import numpy as np +import argparse +import paddle.fluid as fluid +import pandas as pd +from paddle.fluid.incubate.fleet.utils import utils + + +def parse_args(): + parser = argparse.ArgumentParser("PaddlePaddle Youtube DNN infer example") + parser.add_argument( + '--use_gpu', type=int, default='0', help='whether use gpu') + parser.add_argument( + "--batch_size", type=int, default=32, help="batch_size") + parser.add_argument( + "--test_epoch", type=int, default=19, help="test_epoch") + parser.add_argument( + '--inference_model_dir', + type=str, + default='./inference_youtubednn', + help='inference_model_dir') + parser.add_argument( + '--increment_model_dir', + type=str, + default='./increment_youtubednn', + help='persistable_model_dir') + parser.add_argument( + '--watch_vec_size', type=int, default=64, help='watch_vec_size') + parser.add_argument( + '--search_vec_size', type=int, default=64, help='search_vec_size') + parser.add_argument( + '--other_feat_size', type=int, default=64, help='other_feat_size') + parser.add_argument('--topk', type=int, default=5, help='topk') + args = parser.parse_args() + return args + + +def infer(args): + video_save_path = os.path.join(args.increment_model_dir, + str(args.test_epoch), "l4_weight") + video_vec, = utils.load_var("l4_weight", [32, 100], 'float32', + video_save_path) + + place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + cur_model_path = os.path.join(args.inference_model_dir, + str(args.test_epoch)) + + user_vec = None + with fluid.scope_guard(fluid.Scope()): + infer_program, feed_target_names, fetch_vars = fluid.io.load_inference_model( + cur_model_path, exe) + # Build a random data set. + sample_size = 100 + watch_vecs = [] + search_vecs = [] + other_feats = [] + + for i in range(sample_size): + watch_vec = np.random.rand(1, args.watch_vec_size) + search_vec = np.random.rand(1, args.search_vec_size) + other_feat = np.random.rand(1, args.other_feat_size) + watch_vecs.append(watch_vec) + search_vecs.append(search_vec) + other_feats.append(other_feat) + + for i in range(sample_size): + l3 = exe.run(infer_program, + feed={ + "watch_vec": watch_vecs[i].astype('float32'), + "search_vec": search_vecs[i].astype('float32'), + "other_feat": other_feats[i].astype('float32'), + }, + return_numpy=True, + fetch_list=fetch_vars) + if user_vec is not None: + user_vec = np.concatenate([user_vec, l3[0]], axis=0) + else: + user_vec = l3[0] + + # get topk result + user_video_sim_list = [] + for i in range(user_vec.shape[0]): + for j in range(video_vec.shape[1]): + user_video_sim = cos_sim(user_vec[i], video_vec[:, j]) + user_video_sim_list.append(user_video_sim) + + tmp_list = copy.deepcopy(user_video_sim_list) + tmp_list.sort() + max_sim_index = [ + user_video_sim_list.index(one) + for one in tmp_list[::-1][:args.topk] + ] + + print("user:{0}, top K videos:{1}".format(i, max_sim_index)) + user_video_sim_list = [] + + +def cos_sim(vector_a, vector_b): + vector_a = np.mat(vector_a) + vector_b = np.mat(vector_b) + num = float(vector_a * vector_b.T) + denom = np.linalg.norm(vector_a) * np.linalg.norm(vector_b) + cos = num / (denom + 1e-4) + sim = 0.5 + 0.5 * cos + return sim + + +if __name__ == "__main__": + args = parse_args() + infer(args) diff --git a/models/recall/youtube_dnn/random_reader.py b/models/recall/youtube_dnn/reader.py similarity index 75% rename from models/recall/youtube_dnn/random_reader.py rename to models/recall/youtube_dnn/reader.py index a81269ca2efdb3cf0b928ec27e9d19ff80d77aeb..3ba78a8e9289c95d99f82e07cbc7316a37ee8ace 100644 --- a/models/recall/youtube_dnn/random_reader.py +++ b/models/recall/youtube_dnn/reader.py @@ -39,13 +39,17 @@ class Reader(ReaderBase): """ This function needs to be implemented by the user, based on data format """ - + features = line.rstrip().split(";") + watch_vec = features[0].split(',') + search_vec = features[1].split(',') + other_feat = features[2].split(',') + label = features[3] + assert (len(watch_vec) == self.watch_vec_size) + assert (len(search_vec) == self.search_vec_size) + assert (len(other_feat) == self.other_feat_size) feature_name = ["watch_vec", "search_vec", "other_feat", "label"] yield list( - zip(feature_name, [ - np.random.rand(self.watch_vec_size).tolist() - ] + [np.random.rand(self.search_vec_size).tolist()] + [ - np.random.rand(self.other_feat_size).tolist() - ] + [[np.random.randint(self.output_size)]])) + zip(feature_name, [watch_vec] + [search_vec] + [other_feat] + + [label])) return reader