wehub-resource-sync
213 lines
6.8 KiB
Markdown
213 lines
6.8 KiB
Markdown
# Graph Convolutional Matrix Completion
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Paper link: [https://arxiv.org/abs/1706.02263](https://arxiv.org/abs/1706.02263)
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Author's code: [https://github.com/riannevdberg/gc-mc](https://github.com/riannevdberg/gc-mc)
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The implementation does not handle side-channel features and mini-epoching and thus achieves
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slightly worse performance when using node features.
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Credit: Jiani Zhang ([@jennyzhang0215](https://github.com/jennyzhang0215))
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## Dependencies
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* PyTorch 1.2+
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* pandas
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* torchtext 0.9+ (if using user and item contents as node features)
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* spacy (if using user and item contents as node features)
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- You will also need to run `python -m spacy download en_core_web_sm`
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## Data
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Supported datasets: ml-100k, ml-1m, ml-10m
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## How to run
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### Train with full-graph
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ml-100k, no feature
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```bash
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python3 train.py --data_name=ml-100k --use_one_hot_fea --gcn_agg_accum=stack
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```
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Results: RMSE=0.9088 (0.910 reported)
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ml-100k, with feature
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```bash
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python3 train.py --data_name=ml-100k --gcn_agg_accum=stack
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```
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Results: RMSE=0.9448 (0.905 reported)
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ml-1m, no feature
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```bash
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python3 train.py --data_name=ml-1m --gcn_agg_accum=sum --use_one_hot_fea
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```
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Results: RMSE=0.8377 (0.832 reported)
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ml-10m, no feature
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```bash
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python3 train.py --data_name=ml-10m --gcn_agg_accum=stack --gcn_dropout=0.3 \
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--train_lr=0.001 --train_min_lr=0.0001 --train_max_iter=15000 \
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--use_one_hot_fea --gen_r_num_basis_func=4
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```
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Results: RMSE=0.7800 (0.777 reported)
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Testbed: EC2 p3.2xlarge instance(Amazon Linux 2)
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### Train with minibatch on a single GPU
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ml-100k, no feature
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```bash
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python3 train_sampling.py --data_name=ml-100k \
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--use_one_hot_fea \
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--gcn_agg_accum=stack \
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--gpu 0
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```
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ml-100k, no feature with mix_cpu_gpu run, for mix_cpu_gpu run with no feature, the W_r is stored in CPU by default other than in GPU.
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```bash
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python3 train_sampling.py --data_name=ml-100k \
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--use_one_hot_fea \
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--gcn_agg_accum=stack \
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--mix_cpu_gpu \
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--gpu 0
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```
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Results: RMSE=0.9380
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ml-100k, with feature
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```bash
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python3 train_sampling.py --data_name=ml-100k \
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--gcn_agg_accum=stack \
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--train_max_epoch 90 \
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--gpu 0
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```
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Results: RMSE=0.9574
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ml-1m, no feature
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```bash
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python3 train_sampling.py --data_name=ml-1m \
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--gcn_agg_accum=sum \
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--use_one_hot_fea \
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--train_max_epoch 160 \
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--gpu 0
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```
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ml-1m, no feature with mix_cpu_gpu run
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```bash
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python3 train_sampling.py --data_name=ml-1m \
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--gcn_agg_accum=sum \
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--use_one_hot_fea \
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--train_max_epoch 60 \
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--mix_cpu_gpu \
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--gpu 0
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```
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Results: RMSE=0.8632
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ml-10m, no feature
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```bash
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python3 train_sampling.py --data_name=ml-10m \
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--gcn_agg_accum=stack \
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--gcn_dropout=0.3 \
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--train_lr=0.001 \
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--train_min_lr=0.0001 \
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--train_max_epoch=60 \
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--use_one_hot_fea \
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--gen_r_num_basis_func=4 \
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--gpu 0
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```
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ml-10m, no feature with mix_cpu_gpu run
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```bash
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python3 train_sampling.py --data_name=ml-10m \
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--gcn_agg_accum=stack \
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--gcn_dropout=0.3 \
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--train_lr=0.001 \
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--train_min_lr=0.0001 \
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--train_max_epoch=60 \
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--use_one_hot_fea \
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--gen_r_num_basis_func=4 \
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--mix_cpu_gpu \
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--gpu 0
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```
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Results: RMSE=0.8050
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Testbed: EC2 p3.2xlarge instance
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### Train with minibatch on multi-GPU
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ml-100k, no feature
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```bash
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python train_sampling.py --data_name=ml-100k \
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--gcn_agg_accum=stack \
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--train_max_epoch 30 \
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--train_lr 0.02 \
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--use_one_hot_fea \
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--gpu 0,1,2,3,4,5,6,7
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```
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ml-100k, no feature with mix_cpu_gpu run
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```bash
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python train_sampling.py --data_name=ml-100k \
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--gcn_agg_accum=stack \
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--train_max_epoch 30 \
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--train_lr 0.02 \
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--use_one_hot_fea \
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--mix_cpu_gpu \
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--gpu 0,1,2,3,4,5,6,7
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```
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Result: RMSE=0.9397
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ml-100k, with feature
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```bash
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python train_sampling.py --data_name=ml-100k \
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--gcn_agg_accum=stack \
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--train_max_epoch 30 \
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--gpu 0,1,2,3,4,5,6,7
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```
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Result: RMSE=0.9655
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ml-1m, no feature
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```bash
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python train_sampling.py --data_name=ml-1m \
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--gcn_agg_accum=sum \
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--train_max_epoch 40 \
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--use_one_hot_fea \
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--gpu 0,1,2,3,4,5,6,7
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```
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ml-1m, no feature with mix_cpu_gpu run
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```bash
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python train_sampling.py --data_name=ml-1m \
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--gcn_agg_accum=sum \
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--train_max_epoch 40 \
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--use_one_hot_fea \
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--mix_cpu_gpu \
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--gpu 0,1,2,3,4,5,6,7
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```
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Results: RMSE=0.8621
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ml-10m, no feature
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```bash
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python train_sampling.py --data_name=ml-10m \
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--gcn_agg_accum=stack \
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--gcn_dropout=0.3 \
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--train_lr=0.001 \
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--train_min_lr=0.0001 \
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--train_max_epoch=30 \
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--use_one_hot_fea \
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--gen_r_num_basis_func=4 \
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--gpu 0,1,2,3,4,5,6,7
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```
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ml-10m, no feature with mix_cpu_gpu run
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```bash
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python train_sampling.py --data_name=ml-10m \
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--gcn_agg_accum=stack \
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--gcn_dropout=0.3 \
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--train_lr=0.001 \
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--train_min_lr=0.0001 \
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--train_max_epoch=30 \
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--use_one_hot_fea \
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--gen_r_num_basis_func=4 \
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--mix_cpu_gpu \
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--gpu 0,1,2,3,4,5,6,7
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```
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Results: RMSE=0.8084
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Testbed: EC2 p3.16xlarge instance
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### Train with minibatch on CPU
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ml-100k, no feature
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```bash
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python3 train_sampling.py --data_name=ml-100k \
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--use_one_hot_fea \
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--gcn_agg_accum=stack \
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--gpu -1
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```
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Testbed: EC2 r5.xlarge instance
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