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add fibinet
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README.md
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| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)
| 排序 | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) | | 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) | | 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) | | 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
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# fibinet
以下是本例的简要目录结构及说明：
```
├── data #样例数据
├── sample_data
├── train
├── sample_train.txt
├── download.sh
├── run.sh
├── get_slot_data.py
├── __init__.py
├── README.md # 文档
├── model.py #模型文件
├── config.yaml #配置文件
```
## 简介
[《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf)是新浪微博机器学习团队发表在RecSys19上的一篇论文，文章指出当前的许多通过特征组合进行CTR预估的工作主要使用特征向量的内积或哈达玛积来计算交叉特征，这种方法忽略了特征本身的重要程度。提出通过使用Squeeze-Excitation network (SENET) 结构动态学习特征的重要性以及使用一个双线性函数来更好的建模交叉特征。
本项目在paddlepaddle上实现FibiNET的网络结构，并在开源数据集Criteo上验证模型效果。
## 数据下载及预处理
数据地址：[Criteo]( https://fleet.bj.bcebos.com/ctr_data.tar.gz)
（1）将原始训练集按9:1划分为训练集和验证集
（2）数值特征（连续特征）进行归一化处理
执行run.sh生成训练集和测试集
```
sh run.sh
```
## 环境
PaddlePaddle 1.7.2
python3.7
PaddleRec
## 单机训练
CPU环境
在config.yaml文件中设置好设备，epochs等。
```
# select runner by name
mode: [single_cpu_train, single_cpu_infer]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: single_cpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment_model" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 10
phases: [phase1]
```
## 单机预测
CPU环境
在config.yaml文件中设置好epochs、device等参数。
```
- name: single_cpu_infer
class: infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu #选择预测的设备
init_model_path: "increment_dnn" # load model path
phases: [phase2]
```
## 运行
```
python -m paddlerec.run -m paddlerec.models.rank.fibinet
```
## 模型效果
在样例数据上测试模型
训练：
```
Running SingleStartup.
W0623 12:03:35.130075 509 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0
W0623 12:03:35.134771 509 device_context.cc:245] device: 0, cuDNN Version: 7.3.
Running SingleRunner.
batch: 100, AUC: [0.6449976], BATCH_AUC: [0.69029814]
batch: 200, AUC: [0.6769844], BATCH_AUC: [0.70255003]
batch: 300, AUC: [0.67131597], BATCH_AUC: [0.68954499]
batch: 400, AUC: [0.68129822], BATCH_AUC: [0.70892718]
batch: 500, AUC: [0.68242937], BATCH_AUC: [0.69269376]
batch: 600, AUC: [0.68741928], BATCH_AUC: [0.72034578]
...
batch: 1400, AUC: [0.84607023], BATCH_AUC: [0.93358024]
batch: 1500, AUC: [0.84796116], BATCH_AUC: [0.95302841]
batch: 1600, AUC: [0.84949111], BATCH_AUC: [0.92868531]
batch: 1700, AUC: [0.85113661], BATCH_AUC: [0.95452616]
batch: 1800, AUC: [0.85260467], BATCH_AUC: [0.92847032]
epoch 3 done, use time: 1618.1106688976288
```
预测
```
load persistables from increment_model/3
batch: 20, AUC: [0.85304064], BATCH_AUC: [0.94178556]
batch: 40, AUC: [0.85304544], BATCH_AUC: [0.95207907]
batch: 60, AUC: [0.85303907], BATCH_AUC: [0.94782551]
batch: 80, AUC: [0.85298773], BATCH_AUC: [0.93987691]
...
batch: 1780, AUC: [0.866046], BATCH_AUC: [0.96424594]
batch: 1800, AUC: [0.86633785], BATCH_AUC: [0.96900967]
batch: 1820, AUC: [0.86662365], BATCH_AUC: [0.96759972]
```
models/rank/fibinet/__init__.py 0 → 100644
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# 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.
models/rank/fibinet/config.yaml 0 → 100644
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# 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.
# workspace
workspace: "paddlerec.models.rank.fibinet"
# list of dataset
dataset:
- name: dataloader_train # name of dataset to distinguish different datasets
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
- name: dataset_infer # name
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
# hyper parameters of user-defined network
hyper_parameters:
# optimizer config
optimizer:
class: Adam
learning_rate: 0.001
strategy: async
# user-defined <key, value> pairs
sparse_inputs_slots: 27
sparse_feature_number: 1000001
sparse_feature_dim: 9
dense_input_dim: 13
bilinear_type: 'all'
reduction_ratio: 3
dropout_rate: 0.5
# select runner by name
mode: [single_cpu_train, single_cpu_infer]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: single_cpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment_model" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 100
phases: [phase1]
- name: single_gpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: gpu
save_checkpoint_interval: 1 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment_model" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 100
phases: [phase1]
- name: single_cpu_infer
class: infer
# device to run training or infer
device: cpu
init_model_path: "increment_model" # load model path
phases: [phase2]
- name: single_gpu_infer
class: infer
# device to run training or infer
device: gpu
init_model_path: "increment_model" # load model path
phases: [phase2]
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataloader_train # select dataset by name
thread_num: 8
- name: phase2
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_infer # select dataset by name
thread_num: 8
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wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
tar -zxvf ctr_data.tar.gz
mv ./raw_data ./train_data_full
mkdir train_data && cd train_data
cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
mv ./test_data ./test_data_full
mkdir test_data && cd test_data
cp ../test_data_full/part-220 ./ && cd ..
echo "Complete data download."
echo "Full Train data stored in ./train_data_full "
echo "Full Test data stored in ./test_data_full "
echo "Rapid Verification train data stored in ./train_data "
echo "Rapid Verification test data stored in ./test_data "
models/rank/fibinet/data/get_slot_data.py 0 → 100644
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# Copyright (c) 2019 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.fluid.incubate.data_generator as dg
cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
hash_dim_ = 1000001
continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
class CriteoDataset(dg.MultiSlotDataGenerator):
"""
DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
"""
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.rstrip('\n').split('\t')
dense_feature = []
sparse_feature = []
for idx in continuous_range_:
if features[idx] == "":
dense_feature.append(0.0)
else:
dense_feature.append(
(float(features[idx]) - cont_min_[idx - 1]) /
cont_diff_[idx - 1])
for idx in categorical_range_:
sparse_feature.append(
[hash(str(idx) + features[idx]) % hash_dim_])
label = [int(features[0])]
process_line = dense_feature, sparse_feature, label
feature_name = ["dense_feature"]
for idx in categorical_range_:
feature_name.append("C" + str(idx - 13))
feature_name.append("label")
s = "click:" + str(label[0])
for i in dense_feature:
s += " dense_feature:" + str(i)
for i in range(1, 1 + len(categorical_range_)):
s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
print(s.strip())
yield None
return reader
d = CriteoDataset()
d.run_from_stdin()
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sh download.sh
mkdir slot_train_data_full
for i in `ls ./train_data_full`
do
cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
done
mkdir slot_test_data_full
for i in `ls ./test_data_full`
do
cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
done
mkdir slot_train_data
for i in `ls ./train_data`
do
cat train_data/$i | python get_slot_data.py > slot_train_data/$i
done
mkdir slot_test_data
for i in `ls ./test_data`
do
cat test_data/$i | python get_slot_data.py > slot_test_data/$i
done
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click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.08 dense_feature:0.207421875 dense_feature:0.028 dense_feature:0.35 dense_feature:0.08 dense_feature:0.082 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.08 1:737395 2:210498 3:903564 4:286224 5:286835 6:906818 7:906116 8:67180 9:27346 10:51086 11:142177 12:95024 13:157883 14:873363 15:600281 16:812592 17:228085 18:35900 19:880474 20:984402 21:100885 22:26235 23:410878 24:798162 25:499868 26:306163
click:1 dense_feature:0.0 dense_feature:0.932006633499 dense_feature:0.02 dense_feature:0.14 dense_feature:0.0395625 dense_feature:0.328 dense_feature:0.98 dense_feature:0.12 dense_feature:1.886 dense_feature:0.0 dense_feature:1.8 dense_feature:0.0 dense_feature:0.14 1:715353 2:761523 3:432904 4:892267 5:515218 6:948614 7:266726 8:67180 9:27346 10:266081 11:286126 12:789480 13:49621 14:255651 15:47663 16:79797 17:342789 18:616331 19:880474 20:984402 21:242209 22:26235 23:669531 24:26284 25:269955 26:187951
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.06 dense_feature:0.14125 dense_feature:0.076 dense_feature:0.05 dense_feature:0.22 dense_feature:0.208 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.06 1:737395 2:952384 3:511141 4:271077 5:286835 6:948614 7:903547 8:507110 9:27346 10:56047 11:612953 12:747707 13:977426 14:671506 15:158148 16:833738 17:342789 18:427155 19:880474 20:537425 21:916237 22:26235 23:468277 24:676936 25:751788 26:363967
click:0 dense_feature:0.0 dense_feature:0.124378109453 dense_feature:0.02 dense_feature:0.04 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.024 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:210127 2:286436 3:183920 4:507656 5:286835 6:906818 7:199553 8:67180 9:502607 10:708281 11:809876 12:888238 13:375164 14:202774 15:459895 16:475933 17:555571 18:847163 19:26230 20:26229 21:808836 22:191474 23:410878 24:315120 25:26224 26:26223
click:0 dense_feature:0.1 dense_feature:0.0149253731343 dense_feature:0.34 dense_feature:0.32 dense_feature:0.016421875 dense_feature:0.098 dense_feature:0.04 dense_feature:0.96 dense_feature:0.202 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.32 1:230803 2:817085 3:539110 4:388629 5:286835 6:948614 7:586040 8:67180 9:27346 10:271155 11:176640 12:827381 13:36881 14:202774 15:397299 16:411672 17:342789 18:474060 19:880474 20:984402 21:216871 22:26235 23:761351 24:787115 25:884722 26:904135
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.13 dense_feature:0.04 dense_feature:0.246203125 dense_feature:0.108 dense_feature:0.05 dense_feature:0.04 dense_feature:0.03 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:737395 2:64837 3:259267 4:336976 5:515218 6:154084 7:847938 8:67180 9:27346 10:708281 11:776766 12:964800 13:324323 14:873363 15:212708 16:637238 17:681378 18:895034 19:673458 20:984402 21:18600 22:26235 23:410878 24:787115 25:884722 26:355412
click:0 dense_feature:0.0 dense_feature:0.028192371476 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0245625 dense_feature:0.016 dense_feature:0.04 dense_feature:0.12 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:554760 3:661483 4:263696 5:938478 6:906818 7:786926 8:67180 9:27346 10:245862 11:668197 12:745676 13:432600 14:413795 15:751427 16:272410 17:342789 18:422136 19:26230 20:26229 21:452501 22:26235 23:51381 24:776636 25:26224 26:26223
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click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.01 dense_feature:0.16 dense_feature:0.206765625 dense_feature:0.328 dense_feature:0.13 dense_feature:0.16 dense_feature:0.176 dense_feature:0.0 dense_feature:0.7 dense_feature:0.0 dense_feature:0.16 1:737395 2:552854 3:606082 4:267619 5:286835 6:948614 7:918889 8:67180 9:27346 10:708281 11:400024 12:972010 13:66330 14:255651 15:432931 16:650209 17:506108 18:212910 19:26230 20:26229 21:107726 22:26235 23:410878 24:718419 25:26224 26:26223
models/rank/fibinet/model.py 0 → 100644
浏览文件 @ 66c7f380
# 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.fluid as fluid
import itertools
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_fleet_mode().upper(
) == "PSLIB" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number")
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim")
self.learning_rate = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
def _SENETLayer(self, inputs, filed_size, reduction_ratio=3):
reduction_size = max(1, filed_size // reduction_ratio)
Z = fluid.layers.reduce_mean(inputs, dim=-1)
A_1 = fluid.layers.fc(
input=Z,
size=reduction_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='W_1')
A_2 = fluid.layers.fc(
input=A_1,
size=filed_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='W_2')
V = fluid.layers.elementwise_mul(
inputs, y=fluid.layers.unsqueeze(
input=A_2, axes=[2]))
return fluid.layers.split(V, num_or_sections=filed_size, dim=1)
def _BilinearInteraction(self,
inputs,
filed_size,
embedding_size,
bilinear_type="interaction"):
if bilinear_type == "all":
p = [
fluid.layers.elementwise_mul(
fluid.layers.fc(
input=v_i,
size=embedding_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act=None,
name=None),
fluid.layers.squeeze(
input=v_j, axes=[1]))
for v_i, v_j in itertools.combinations(inputs, 2)
]
else:
raise NotImplementedError
return fluid.layers.concat(input=p, axis=1)
def _DNNLayer(self, inputs, dropout_rate=0.5):
deep_input = inputs
for i, hidden_unit in enumerate([400, 400, 400]):
fc_out = fluid.layers.fc(
input=deep_input,
size=hidden_unit,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='d_' + str(i))
fc_out = fluid.layers.dropout(fc_out, dropout_prob=dropout_rate)
deep_input = fc_out
return deep_input
def net(self, input, is_infer=False):
self.sparse_inputs = self._sparse_data_var[1:]
self.dense_input = self._dense_data_var[0]
self.label_input = self._sparse_data_var[0]
emb = []
for data in self.sparse_inputs:
feat_emb = fluid.embedding(
input=data,
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=fluid.ParamAttr(
name='dis_emb',
learning_rate=5,
initializer=fluid.initializer.Xavier(
fan_in=self.sparse_feature_dim,
fan_out=self.sparse_feature_dim)),
is_sparse=True)
emb.append(feat_emb)
concat_emb = fluid.layers.concat(emb, axis=1)
filed_size = len(self.sparse_inputs)
bilinear_type = envs.get_global_env("hyper_parameters.bilinear_type")
reduction_ratio = envs.get_global_env(
"hyper_parameters.reduction_ratio")
dropout_rate = envs.get_global_env("hyper_parameters.dropout_rate")
senet_output = self._SENETLayer(concat_emb, filed_size,
reduction_ratio)
senet_bilinear_out = self._BilinearInteraction(
senet_output, filed_size, self.sparse_feature_dim, bilinear_type)
concat_emb = fluid.layers.split(
concat_emb, num_or_sections=filed_size, dim=1)
bilinear_out = self._BilinearInteraction(
concat_emb, filed_size, self.sparse_feature_dim, bilinear_type)
dnn_input = fluid.layers.concat(
input=[senet_bilinear_out, bilinear_out, self.dense_input], axis=1)
dnn_output = self._DNNLayer(dnn_input, dropout_rate)
y_pred = fluid.layers.fc(
input=dnn_output,
size=1,
param_attr=fluid.initializer.Xavier(uniform=False),
act='sigmoid',
name='logit')
self.predict = y_pred
auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
label=self.label_input,
num_thresholds=2**12,
slide_steps=20)
if is_infer:
self._infer_results["AUC"] = auc
self._infer_results["BATCH_AUC"] = batch_auc
return
self._metrics["AUC"] = auc
self._metrics["BATCH_AUC"] = batch_auc
cost = fluid.layers.log_loss(
input=self.predict,
label=fluid.layers.cast(
x=self.label_input, dtype='float32'))
avg_cost = fluid.layers.reduce_mean(cost)
self._cost = avg_cost
models/rank/readme.md 100755 → 100644
浏览文件 @ 66c7f380
...@@ -37,35 +37,43 @@ ...@@ -37,35 +37,43 @@
| xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) | | xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) |
| DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) | | DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) |
| FGCNN | Feature Generation by CNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)(2019) | | FGCNN | Feature Generation by CNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)(2019) |
| FIBINET | Combining Feature Importance and Bilinear feature Interaction | [《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf)(2019) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
[wide&deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454): [wide&deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454):
<p align="center"> <p align="center">
<img align="center" src="../../doc/imgs/wide&deep.png"> <img align="center" src="../../doc/imgs/wide&deep.png">
<p> <p>
[DeepFM](https://arxiv.org/pdf/1703.04247.pdf): [DeepFM](https://arxiv.org/pdf/1703.04247.pdf):
<p align="center"> <p align="center">
<img align="center" src="../../doc/imgs/deepfm.png"> <img align="center" src="../../doc/imgs/deepfm.png">
<p> <p>
[XDeepFM](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023): [XDeepFM](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023):
<p align="center"> <p align="center">
<img align="center" src="../../doc/imgs/xdeepfm.png"> <img align="center" src="../../doc/imgs/xdeepfm.png">
<p> <p>
[DCN](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754): [DCN](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754):
<p align="center"> <p align="center">
<img align="center" src="../../doc/imgs/dcn.png"> <img align="center" src="../../doc/imgs/dcn.png">
<p> <p>
[DIN](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823): [DIN](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823):
<p align="center"> <p align="center">
<img align="center" src="../../doc/imgs/din.png"> <img align="center" src="../../doc/imgs/din.png">
<p> <p>
[FIBINET](https://arxiv.org/pdf/1905.09433.pdf):
<p align="center">
<img align="center" src="../../doc/imgs/fibinet.png">
<p>
## 使用教程(快速开始) ## 使用教程(快速开始)
使用样例数据快速开始，参考[训练](###训练) & [预测](###预测) 使用样例数据快速开始，参考[训练](###训练) & [预测](###预测)
## 使用教程(复现论文) ## 使用教程(复现论文)
为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。 为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
...@@ -77,6 +85,7 @@ ...@@ -77,6 +85,7 @@
| DIN | 32 | 10 | 100 | | DIN | 32 | 10 | 100 |
| Wide&Deep | 40 | 1 | 40 | | Wide&Deep | 40 | 1 | 40 |
| xDeepFM | 100 | 1 | 10 | | xDeepFM | 100 | 1 | 10 |
| Fibinet | 1000 | 8 | 4 |
### 数据处理 ### 数据处理
参考每个模型目录数据下载&预处理脚本 参考每个模型目录数据下载&预处理脚本
...@@ -116,6 +125,7 @@ python -m paddlerec.run -m ./config.yaml # 以DNN为例 ...@@ -116,6 +125,7 @@ python -m paddlerec.run -m ./config.yaml # 以DNN为例
| Criteo | xDeepFM | 0.48657 | -- | -- | -- | | Criteo | xDeepFM | 0.48657 | -- | -- | -- |
| Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- | | Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- |
| Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- | | Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- |
| Criteo | Fibinet | -- | 0.86662 | -- | -- |
## 分布式 ## 分布式
......
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