BGCL/
├── datasets/
│ └── wsdrean/
│ ├── cold: sample data for cold start
│ │ ├── rt: data of response time
│ │ └── tp: data of response throughput
│ └── sparsity: sample data for sparsity
│ ├── rt: data of response time
│ └── tp: data of response throughput
├── utils/
│ ├── attention.py: aggregating the feature of neighbors
│ ├── batchutils.py: some utils
│ ├── combiner.py: implementation of the combiner
│ └── encoder.py: together with aggregator to form the decomposer
│
├── bgcl.py: implementation of the bgcl
├── run.py: run the model
├── train.py: train the model
├── test.py: test the model
└── README.md
- Python == 3.6.9
- torchvision == 0.4.2
- numpy == 1.17.3
- scikit-learn == 0.21.3
- epochs: the number of epochs to train
- test_epochs: the number of epochs to test
- lr: learning rate
- optimizer: the optimizer
- momentum: the momentum of optimizer
- lr: learning rate
- embed_dim: embedding dimension
- droprate: dropout rate in layer
- temp: the temp
- batch_size: batch size for training
- test_batch_size: batch size for testing
- n_size: the size of neighbor
- dataset: the prefix of dataset
- density: the density of dataset
- cold_start_density: the density of cold_start
- p: the dropout rate
- a: the similiarity setting
- mode: the mode of experiment (sparsity or cold)
- coldStartType: the cold start type
- cold_start_density: the density of cold_start
python run.py
unzip the dataset first
example: unzip the rt_user_allData_0.8_0.7_0.500_0.005.7z to get rt_user_allData_0.8_0.7_0.500_0.005.p for running
Dataset: wsdream
Density: 0.005
-------------------- Hyperparams --------------------
dropout rate: 0.5
dimension of embedding: 128
type of optimizer: Adam
learning rate: 0.001
p: 0.8
a: 0.7
datatype: rt
mode: cold
coldStartType: user
cold_start_density: 0.500
...
2022-12-06 21:10:51.528380 Training: [200 epoch, 50 batch] loss: 4.67270, the best RMSE/MAE: 1.85315 / 0.71128
2022-12-06 21:10:52.077927 Training: [200 epoch, 60 batch] loss: 4.13545, the best RMSE/MAE: 1.85315 / 0.71128
2022-12-06 21:10:52.606055 Training: [200 epoch, 70 batch] loss: 4.26554, the best RMSE/MAE: 1.85315 / 0.71128
<Test> RMSE: 1.86849, MAE: 0.71469
The best RMSE/MAE: 1.85315 / 0.71128
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent