diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index d5cd6ab38..e879511d4 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -3,6 +3,7 @@ name: RecBole tests on: - pull_request + jobs: build: @@ -22,8 +23,9 @@ jobs: python -m pip install --upgrade pip pip install pytest pip install dgl + pip install xgboost if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - + # Use "python -m pytest" instead of "pytest" to fix imports - name: Test metrics run: | @@ -39,7 +41,4 @@ jobs: python -m pytest -v tests/config/test_config.py export PYTHONPATH=. python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting - + diff --git a/.gitignore b/.gitignore index db5a25afc..49788101f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ +# Saved models +/saved* +*.pth + .vscode/ .idea/ *.pyc diff --git a/README.md b/README.md index bc742a81b..99731fb04 100644 --- a/README.md +++ b/README.md @@ -66,6 +66,11 @@ for reference. **11/03/2020**: We release the first version of RecBole **v0.1.1**. +## Blogs + +[RecBole小白入门系列博客（一）——快速安装和简单上手](https://blog.csdn.net/Turinger_2000/article/details/110414642) + +[RecBole小白入门系列博客（二） ——General类模型运行流程](https://blog.csdn.net/Turinger_2000/article/details/110395198) ## Installation RecBole works with the following operating systems: @@ -182,9 +187,9 @@ We will keep improving our implementations, and update these test results. ## RecBole Major Releases | Releases | Date | Features | |-----------|--------|-------------------------| +| v0.1.2 | 12/06/2020 | Basic RecBole | | v0.1.1 | 11/03/2020 | Basic RecBole | - ## Contributing Please let us know if you encounter a bug or have any suggestions by [filing an issue](https://github.com/RUCAIBox/RecBole/issues). diff --git a/asset/time_test_result/Context-aware_recommendation.md b/asset/time_test_result/Context-aware_recommendation.md index 64782e201..fa518892a 100644 --- a/asset/time_test_result/Context-aware_recommendation.md +++ b/asset/time_test_result/Context-aware_recommendation.md @@ -1,191 +1,191 @@ -## Time and memory cost of context-aware recommendation models - -### Datasets information: - -| Dataset | #Interaction | #Feature Field | #Feature | -| ------- | ------------: | --------------: | --------: | -| ml-1m | 1,000,209 | 5 | 134 | -| Criteo | 2,292,530 | 39 | 2,572,192 | -| Avazu | 4,218,938 | 21 | 1,326,631 | - -### Device information - -``` -OS: Linux -Python Version: 3.8.3 -PyTorch Version: 1.7.0 -cudatoolkit Version: 10.1 -GPU: TITAN RTX（24GB） -Machine Specs: 32 CPU machine, 64GB RAM -``` - -### 1) ml-1m dataset: - -#### Time and memory cost on ml-1m dataset: - -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | -| --------- | -----------------: | -----------------: | -----------: | -| LR | 18.34 | 2.18 | 0.82 | -| DIN | 20.37 | 2.26 | 1.16 | -| DSSM | 21.93 | 2.24 | 0.95 | -| FM | 19.33 | 2.34 | 0.83 | -| DeepFM | 20.42 | 2.27 | 0.91 | -| Wide&Deep | 26.13 | 2.95 | 0.89 | -| NFM | 23.36 | 2.26 | 0.89 | -| AFM | 20.08 | 2.26 | 0.92 | -| AutoInt | 22.41 | 2.34 | 0.94 | -| DCN | 28.33 | 2.97 | 0.93 | -| FNN(DNN) | 19.51 | 2.21 | 0.91 | -| PNN | 22.29 | 2.23 | 0.91 | -| FFM | 22.98 | 2.47 | 0.87 | -| FwFM | 23.38 | 2.50 | 0.85 | -| xDeepFM | 24.40 | 2.30 | 1.06 | - -#### Config file of ml-1m dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: user_id -ITEM_ID_FIELD: item_id -LABEL_FIELD: label -threshold: - rating: 4.0 -drop_filter_field : True -load_col: - inter: [user_id, item_id, rating] - item: [item_id, release_year, genre] - user: [user_id, age, gender, occupation] - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -eval_setting: RO_RS -group_by_user: False -valid_metric: AUC -metrics: ['AUC', 'LogLoss'] -``` - -Other parameters (including model parameters) are default value. - -### 2）Criteo dataset: - -#### Time and memory cost on Criteo dataset: - -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | -| --------- | -------------------------: | ---------------------------: | ---------------: | -| LR | 7.65 | 0.61 | 1.11 | -| DIN | - | - | - | -| DSSM | - | - | - | -| FM | 9.77 | 0.73 | 1.45 | -| DeepFM | 13.64 | 0.83 | 1.72 | -| Wide&Deep | 13.58 | 0.80 | 1.72 | -| NFM | 13.36 | 0.75 | 1.72 | -| AFM | 19.40 | 1.02 | 2.34 | -| AutoInt | 19.40 | 0.98 | 2.06 | -| DCN | 16.25 | 0.78 | 1.67 | -| FNN(DNN) | 10.03 | 0.64 | 1.63 | -| PNN | 12.92 | 0.72 | 1.85 | -| FFM | - | - | Out of Memory | -| FwFM | 1175.24 | 8.90 | 2.12 | -| xDeepFM | 32.27 | 1.34 | 2.25 | - -Note: Criteo dataset is not suitable for DIN model and DSSM model. -#### Config file of Criteo dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: ~ -ITEM_ID_FIELD: ~ -LABEL_FIELD: label - -load_col: - inter: '*' - -highest_val: - index: 2292530 - -fill_nan: True -normalize_all: True -min_item_inter_num: 0 -min_user_inter_num: 0 - -drop_filter_field : True - - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -eval_setting: RO_RS -group_by_user: False -valid_metric: AUC -metrics: ['AUC', 'LogLoss'] -``` - -Other parameters (including model parameters) are default value. - -### 3）Avazu dataset: - -#### Time and memory cost on Avazu dataset: - -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | -| --------- | -------------------------: | ---------------------------: | ---------------: | -| LR | 9.30 | 0.76 | 1.42 | -| DIN | - | - | - | -| DSSM | - | - | - | -| FM | 25.68 | 0.94 | 2.60 | -| DeepFM | 28.41 | 1.19 | 2.66 | -| Wide&Deep | 27.58 | 0.97 | 2.66 | -| NFM | 30.46 | 1.06 | 2.66 | -| AFM | 31.03 | 1.06 | 2.69 | -| AutoInt | 38.11 | 1.41 | 2.84 | -| DCN | 30.78 | 0.96 | 2.64 | -| FNN(DNN) | 23.53 | 0.84 | 2.60 | -| PNN | 25.86 | 0.90 | 2.68 | -| FFM | - | - | Out of Memory | -| FwFM | 336.75 | 7.49 | 2.63 | -| xDeepFM | 54.88 | 1.45 | 2.89 | - -Note: Avazu dataset is not suitable for DIN model and DSSM model. -#### Config file of Avazu dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: ~ -ITEM_ID_FIELD: ~ -LABEL_FIELD: label -fill_nan: True -normalize_all: True - -load_col: - inter: '*' - -lowest_val: - timestamp: 14102931 -drop_filter_field : False - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -eval_setting: RO_RS -group_by_user: False -valid_metric: AUC -metrics: ['AUC', 'LogLoss'] -``` - -Other parameters (including model parameters) are default value. - - - - - - - +## Time and memory cost of context-aware recommendation models + +### Datasets information: + +| Dataset | #Interaction | #Feature Field | #Feature | +| ------- | ------------: | --------------: | --------: | +| ml-1m | 1,000,209 | 5 | 134 | +| Criteo | 2,292,530 | 39 | 2,572,192 | +| Avazu | 4,218,938 | 21 | 1,326,631 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -----------------: | -----------------: | -----------: | +| LR | 18.34 | 2.18 | 0.82 | +| DIN | 20.37 | 2.26 | 1.16 | +| DSSM | 21.93 | 2.24 | 0.95 | +| FM | 19.33 | 2.34 | 0.83 | +| DeepFM | 20.42 | 2.27 | 0.91 | +| Wide&Deep | 26.13 | 2.95 | 0.89 | +| NFM | 23.36 | 2.26 | 0.89 | +| AFM | 20.08 | 2.26 | 0.92 | +| AutoInt | 22.41 | 2.34 | 0.94 | +| DCN | 28.33 | 2.97 | 0.93 | +| FNN(DNN) | 19.51 | 2.21 | 0.91 | +| PNN | 22.29 | 2.23 | 0.91 | +| FFM | 22.98 | 2.47 | 0.87 | +| FwFM | 23.38 | 2.50 | 0.85 | +| xDeepFM | 24.40 | 2.30 | 1.06 | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +LABEL_FIELD: label +threshold: + rating: 4.0 +drop_filter_field : True +load_col: + inter: [user_id, item_id, rating] + item: [item_id, release_year, genre] + user: [user_id, age, gender, occupation] + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + +Other parameters (including model parameters) are default value. + +### 2）Criteo dataset: + +#### Time and memory cost on Criteo dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| LR | 7.65 | 0.61 | 1.11 | +| DIN | - | - | - | +| DSSM | - | - | - | +| FM | 9.77 | 0.73 | 1.45 | +| DeepFM | 13.64 | 0.83 | 1.72 | +| Wide&Deep | 13.58 | 0.80 | 1.72 | +| NFM | 13.36 | 0.75 | 1.72 | +| AFM | 19.40 | 1.02 | 2.34 | +| AutoInt | 19.40 | 0.98 | 2.06 | +| DCN | 16.25 | 0.78 | 1.67 | +| FNN(DNN) | 10.03 | 0.64 | 1.63 | +| PNN | 12.92 | 0.72 | 1.85 | +| FFM | - | - | Out of Memory | +| FwFM | 1175.24 | 8.90 | 2.12 | +| xDeepFM | 32.27 | 1.34 | 2.25 | + +Note: Criteo dataset is not suitable for DIN model and DSSM model. +#### Config file of Criteo dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: ~ +ITEM_ID_FIELD: ~ +LABEL_FIELD: label + +load_col: + inter: '*' + +highest_val: + index: 2292530 + +fill_nan: True +normalize_all: True +min_item_inter_num: 0 +min_user_inter_num: 0 + +drop_filter_field : True + + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + +Other parameters (including model parameters) are default value. + +### 3）Avazu dataset: + +#### Time and memory cost on Avazu dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| LR | 9.30 | 0.76 | 1.42 | +| DIN | - | - | - | +| DSSM | - | - | - | +| FM | 25.68 | 0.94 | 2.60 | +| DeepFM | 28.41 | 1.19 | 2.66 | +| Wide&Deep | 27.58 | 0.97 | 2.66 | +| NFM | 30.46 | 1.06 | 2.66 | +| AFM | 31.03 | 1.06 | 2.69 | +| AutoInt | 38.11 | 1.41 | 2.84 | +| DCN | 30.78 | 0.96 | 2.64 | +| FNN(DNN) | 23.53 | 0.84 | 2.60 | +| PNN | 25.86 | 0.90 | 2.68 | +| FFM | - | - | Out of Memory | +| FwFM | 336.75 | 7.49 | 2.63 | +| xDeepFM | 54.88 | 1.45 | 2.89 | + +Note: Avazu dataset is not suitable for DIN model and DSSM model. +#### Config file of Avazu dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: ~ +ITEM_ID_FIELD: ~ +LABEL_FIELD: label +fill_nan: True +normalize_all: True + +load_col: + inter: '*' + +lowest_val: + timestamp: 14102931 +drop_filter_field : False + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + +Other parameters (including model parameters) are default value. + + + + + + + diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 63f432e1b..373836e50 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -1,225 +1,225 @@ -## Time and memory cost of sequential recommendation models - -### Datasets information: - -| Dataset | #User | #Item | #Interaction | Sparsity | -| ---------- | -------: | ------: | ------------: | --------: | -| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | -| DIGINETICA | 59,425 | 42,116 | 547,416 | 0.9998 | -| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | - -### Device information - -``` -OS: Linux -Python Version: 3.8.3 -PyTorch Version: 1.7.0 -cudatoolkit Version: 10.1 -GPU: TITAN RTX（24GB） -Machine Specs: 32 CPU machine, 64GB RAM -``` - -### 1) ml-1m dataset: - -#### Time and memory cost on ml-1m dataset: - -| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | -| ---------------- | -----------------: | -----------------: | -----------: | -| Improved GRU-Rec | 7.78 | 0.11 | 1.27 | -| SASRec | 17.78 | 0.12 | 1.84 | -| NARM | 8.29 | 0.11 | 1.29 | -| FPMC | 7.51 | 0.11 | 1.18 | -| STAMP | 7.32 | 0.11 | 1.20 | -| Caser | 44.85 | 0.12 | 1.14 | -| NextItNet | 16433.27 | 96.31 | 1.86 | -| TransRec | 10.08 | 0.16 | 8.18 | -| S3Rec | - | - | - | -| GRU4RecF | 10.20 | 0.15 | 1.80 | -| SASRecF | 18.84 | 0.17 | 1.78 | -| BERT4Rec | 36.09 | 0.34 | 1.97 | -| FDSA | 31.86 | 0.19 | 2.32 | -| SRGNN | 327.38 | 2.19 | 1.21 | -| GCSAN | 335.27 | 0.02 | 1.58 | -| KSR | - | - | - | -| GRU4RecKG | - | - | - | - -#### Config file of ml-1m dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: user_id -ITEM_ID_FIELD: item_id -TIME_FIELD: timestamp -NEG_PREFIX: neg_ -ITEM_LIST_LENGTH_FIELD: item_length -LIST_SUFFIX: _list -MAX_ITEM_LIST_LENGTH: 20 -POSITION_FIELD: position_id -load_col: - inter: [user_id, item_id, timestamp] -min_user_inter_num: 0 -min_item_inter_num: 0 - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -valid_metric: MRR@10 -eval_setting: TO_LS,full -training_neg_sample_num: 0 -``` - -Other parameters (including model parameters) are default value. - -**NOTE :** - -1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . - -2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: - -``` -load_col: - inter: [user_id, item_id, timestamp] - item: [item_id, genre] -``` - -### 2）DIGINETICA dataset: - -#### Time and memory cost on DIGINETICA dataset: - -| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | -| ---------------- | -----------------: | -----------------: | -----------: | -| Improved GRU-Rec | 4.10 | 1.05 | 4.02 | -| SASRec | 8.36 | 1.21 | 4.43 | -| NARM | 4.30 | 1.08 | 4.09 | -| FPMC | 2.98 | 1.08 | 4.08 | -| STAMP | 4.27 | 1.04 | 3.88 | -| Caser | 17.15 | 1.18 | 3.94 | -| NextItNet | 6150.49 | 947.66 | 4.54 | -| TransRec | - | - | Out of Memory | -| S3Rec | - | - | - | -| GRU4RecF | 4.79 | 1.17 | 4.83 | -| SASRecF | 8.66 | 1.29 | 5.11 | -| BERT4Rec | 16.80 | 3.54 | 7.97 | -| FDSA | 13.44 | 1.47 | 5.66 | -| SRGNN | 88.59 | 15.37 | 4.01 | -| GCSAN | 96.69 | 17.11 | 4.25 | -| KSR | - | - | - | -| GRU4RecKG | - | - | - | - -#### Config file of DIGINETICA dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: session_id -ITEM_ID_FIELD: item_id -TIME_FIELD: timestamp -NEG_PREFIX: neg_ -ITEM_LIST_LENGTH_FIELD: item_length -LIST_SUFFIX: _list -MAX_ITEM_LIST_LENGTH: 20 -POSITION_FIELD: position_id -load_col: - inter: [session_id, item_id, timestamp] -min_user_inter_num: 6 -min_item_inter_num: 1 - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -valid_metric: MRR@10 -eval_setting: TO_LS,full -training_neg_sample_num: 0 -``` - -Other parameters (including model parameters) are default value. - -**NOTE :** - -1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . - -2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: - -``` -load_col: - inter: [session_id, item_id, timestamp] - item: [item_id, item_category] -``` - -### 3）Yelp dataset: - -#### Time and memory cost on Yelp dataset: - -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | -| ---------------- | -----------------: | -----------------: | -----------: | -| Improved GRU-Rec | 44.31 | 2.74 | 7.92 | -| SASRec | 75.51 | 3.11 | 8.32 | -| NARM | 45.65 | 2.76 | 7.98 | -| FPMC | 21.05 | 3.05 | 8.22 | -| STAMP | 42.08 | 2.72 | 7.77 | -| Caser | 147.15 | 2.89 | 7.87 | -| NextItNet | 45019.38 | 1670.76 | 8.44 | -| TransRec | - | - | Out of Memory | -| S3Rec | - | - | - | -| GRU4RecF | - | - | Out of Memory | -| SASRecF | - | - | Out of Memory | -| BERT4Rec | 193.74 | 8.43 | 16.57 | -| FDSA | - | - | Out of Memory | -| SRGNN | 825.11 | 33.20 | 7.90 | -| GCSAN | 837.23 | 33.00 | 8.14 | -| KSR | - | - | - | -| GRU4RecKG | - | - | - | - -#### Config file of DIGINETICA dataset: - -``` -# dataset config -field_separator: "\t" -seq_separator: " " -USER_ID_FIELD: session_id -ITEM_ID_FIELD: item_id -TIME_FIELD: timestamp -NEG_PREFIX: neg_ -ITEM_LIST_LENGTH_FIELD: item_length -LIST_SUFFIX: _list -MAX_ITEM_LIST_LENGTH: 20 -POSITION_FIELD: position_id -load_col: - inter: [session_id, item_id, timestamp] -min_user_inter_num: 6 -min_item_inter_num: 1 - -# training and evaluation -epochs: 500 -train_batch_size: 2048 -eval_batch_size: 2048 -valid_metric: MRR@10 -eval_setting: TO_LS,full -training_neg_sample_num: 0 -``` - -Other parameters (including model parameters) are default value. - -**NOTE :** - -1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . - -2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: - -``` -load_col: - inter: [session_id, item_id, timestamp] - item: [item_id, item_category] -``` - - - - - - - +## Time and memory cost of sequential recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | +| ---------- | -------: | ------: | ------------: | --------: | +| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | +| DIGINETICA | 59,425 | 42,116 | 547,416 | 0.9998 | +| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 7.78 | 0.11 | 1.27 | +| SASRec | 17.78 | 0.12 | 1.84 | +| NARM | 8.29 | 0.11 | 1.29 | +| FPMC | 7.51 | 0.11 | 1.18 | +| STAMP | 7.32 | 0.11 | 1.20 | +| Caser | 44.85 | 0.12 | 1.14 | +| NextItNet | 16433.27 | 96.31 | 1.86 | +| TransRec | 10.08 | 0.16 | 8.18 | +| S3Rec | - | - | - | +| GRU4RecF | 10.20 | 0.15 | 1.80 | +| SASRecF | 18.84 | 0.17 | 1.78 | +| BERT4Rec | 36.09 | 0.34 | 1.97 | +| FDSA | 31.86 | 0.19 | 2.32 | +| SRGNN | 327.38 | 2.19 | 1.21 | +| GCSAN | 335.27 | 0.02 | 1.58 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [user_id, item_id, timestamp] +min_user_inter_num: 0 +min_item_inter_num: 0 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +Other parameters (including model parameters) are default value. + +**NOTE :** + +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . + +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: + +``` +load_col: + inter: [user_id, item_id, timestamp] + item: [item_id, genre] +``` + +### 2）DIGINETICA dataset: + +#### Time and memory cost on DIGINETICA dataset: + +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 4.10 | 1.05 | 4.02 | +| SASRec | 8.36 | 1.21 | 4.43 | +| NARM | 4.30 | 1.08 | 4.09 | +| FPMC | 2.98 | 1.08 | 4.08 | +| STAMP | 4.27 | 1.04 | 3.88 | +| Caser | 17.15 | 1.18 | 3.94 | +| NextItNet | 6150.49 | 947.66 | 4.54 | +| TransRec | - | - | Out of Memory | +| S3Rec | - | - | - | +| GRU4RecF | 4.79 | 1.17 | 4.83 | +| SASRecF | 8.66 | 1.29 | 5.11 | +| BERT4Rec | 16.80 | 3.54 | 7.97 | +| FDSA | 13.44 | 1.47 | 5.66 | +| SRGNN | 88.59 | 15.37 | 4.01 | +| GCSAN | 96.69 | 17.11 | 4.25 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of DIGINETICA dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: session_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [session_id, item_id, timestamp] +min_user_inter_num: 6 +min_item_inter_num: 1 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +Other parameters (including model parameters) are default value. + +**NOTE :** + +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . + +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: + +``` +load_col: + inter: [session_id, item_id, timestamp] + item: [item_id, item_category] +``` + +### 3）Yelp dataset: + +#### Time and memory cost on Yelp dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 44.31 | 2.74 | 7.92 | +| SASRec | 75.51 | 3.11 | 8.32 | +| NARM | 45.65 | 2.76 | 7.98 | +| FPMC | 21.05 | 3.05 | 8.22 | +| STAMP | 42.08 | 2.72 | 7.77 | +| Caser | 147.15 | 2.89 | 7.87 | +| NextItNet | 45019.38 | 1670.76 | 8.44 | +| TransRec | - | - | Out of Memory | +| S3Rec | - | - | - | +| GRU4RecF | - | - | Out of Memory | +| SASRecF | - | - | Out of Memory | +| BERT4Rec | 193.74 | 8.43 | 16.57 | +| FDSA | - | - | Out of Memory | +| SRGNN | 825.11 | 33.20 | 7.90 | +| GCSAN | 837.23 | 33.00 | 8.14 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of DIGINETICA dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: session_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [session_id, item_id, timestamp] +min_user_inter_num: 6 +min_item_inter_num: 1 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +Other parameters (including model parameters) are default value. + +**NOTE :** + +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . + +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: + +``` +load_col: + inter: [session_id, item_id, timestamp] + item: [item_id, item_category] +``` + + + + + + + diff --git a/conda/conda_release.sh b/conda/conda_release.sh new file mode 100644 index 000000000..a8b0e3efa --- /dev/null +++ b/conda/conda_release.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +conda-build --python 3.6 . +printf "python 3.6 version is released \n" +conda-build --python 3.7 . +printf "python 3.7 version is released \n" +conda-build --python 3.8 . +printf "python 3.8 version is released \n" diff --git a/recbole/__init__.py b/recbole/__init__.py index 182dd10a5..83c915e76 100644 --- a/recbole/__init__.py +++ b/recbole/__init__.py @@ -2,4 +2,4 @@ from __future__ import print_function from __future__ import division -__version__ = '0.1.1' +__version__ = '0.1.2' diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 381164ae7..04e51adfa 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -19,7 +19,7 @@ import torch from logging import getLogger -from recbole.evaluator import loss_metrics, topk_metrics +from recbole.evaluator import group_metrics, individual_metrics from recbole.utils import get_model, Enum, EvaluatorType, ModelType, InputType, \ general_arguments, training_arguments, evaluation_arguments, dataset_arguments @@ -194,6 +194,13 @@ def _get_model_and_dataset(self, model, dataset): return final_model, final_model_class, final_dataset + def _update_internal_config_dict(self, file): + with open(file, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + return config_dict + def _load_internal_config_dict(self, model, model_class, dataset): current_path = os.path.dirname(os.path.realpath(__file__)) overall_init_file = os.path.join(current_path, '../properties/overall.yaml') @@ -201,65 +208,45 @@ def _load_internal_config_dict(self, model, model_class, dataset): sample_init_file = os.path.join(current_path, '../properties/dataset/sample.yaml') dataset_init_file = os.path.join(current_path, '../properties/dataset/' + dataset + '.yaml') + quick_start_config_path = os.path.join(current_path, '../properties/quick_start_config/') + context_aware_init = os.path.join(quick_start_config_path, 'context-aware.yaml') + context_aware_on_ml_100k_init = os.path.join(quick_start_config_path, 'context-aware_ml-100k.yaml') + DIN_init = os.path.join(quick_start_config_path, 'sequential_DIN.yaml') + DIN_on_ml_100k_init = os.path.join(quick_start_config_path, 'sequential_DIN_on_ml-100k.yaml') + sequential_init = os.path.join(quick_start_config_path, 'sequential.yaml') + special_sequential_on_ml_100k_init = os.path.join(quick_start_config_path, 'special_sequential_on_ml-100k.yaml') + sequential_embedding_model_init = os.path.join(quick_start_config_path, 'sequential_embedding_model.yaml') + knowledge_base_init = os.path.join(quick_start_config_path, 'knowledge_base.yaml') + self.internal_config_dict = dict() for file in [overall_init_file, model_init_file, sample_init_file, dataset_init_file]: if os.path.isfile(file): - with open(file, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if file == dataset_init_file: - self.parameters['Dataset'] += [key for key in config_dict.keys() if - key not in self.parameters['Dataset']] - if config_dict is not None: - self.internal_config_dict.update(config_dict) + config_dict = self._update_internal_config_dict(file) + if file == dataset_init_file: + self.parameters['Dataset'] += [key for key in config_dict.keys() if + key not in self.parameters['Dataset']] + self.internal_config_dict['MODEL_TYPE'] = model_class.type if self.internal_config_dict['MODEL_TYPE'] == ModelType.GENERAL: pass - elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT: - self.internal_config_dict.update({ - 'eval_setting': 'RO_RS', - 'group_by_user': False, - 'training_neg_sample_num': 0, - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + elif self.internal_config_dict['MODEL_TYPE'] in {ModelType.CONTEXT, ModelType.XGBOOST}: + self._update_internal_config_dict(context_aware_init) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'threshold': {'rating': 4}, - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year', 'class']}, - }) - + self._update_internal_config_dict(context_aware_on_ml_100k_init) elif self.internal_config_dict['MODEL_TYPE'] == ModelType.SEQUENTIAL: if model == 'DIN': - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS, uni100', - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + self._update_internal_config_dict(DIN_init) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year']}, - }) - + self._update_internal_config_dict(DIN_on_ml_100k_init) + elif model in ['GRU4RecKG', 'KSR']: + self._update_internal_config_dict(sequential_embedding_model_init) else: - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS,full', - }) + self._update_internal_config_dict(sequential_init) if dataset == 'ml-100k' and model in ['GRU4RecF', 'SASRecF', 'FDSA', 'S3Rec']: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'item': ['item_id', 'release_year', 'class']}, - }) + self._update_internal_config_dict(special_sequential_on_ml_100k_init) elif self.internal_config_dict['MODEL_TYPE'] == ModelType.KNOWLEDGE: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'kg': ['head_id', 'relation_id', 'tail_id'], - 'link': ['item_id', 'entity_id']} - }) + self._update_internal_config_dict(knowledge_base_init) def _get_final_config_dict(self): final_config_dict = dict() @@ -290,12 +277,12 @@ def _set_default_parameters(self): eval_type = None for metric in self.final_config_dict['metrics']: - if metric.lower() in loss_metrics: + if metric.lower() in individual_metrics: if eval_type is not None and eval_type == EvaluatorType.RANKING: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: eval_type = EvaluatorType.INDIVIDUAL - if metric.lower() in topk_metrics: + if metric.lower() in group_metrics: if eval_type is not None and eval_type == EvaluatorType.INDIVIDUAL: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: diff --git a/recbole/config/eval_setting.py b/recbole/config/eval_setting.py index 27d910c5a..030a1136f 100644 --- a/recbole/config/eval_setting.py +++ b/recbole/config/eval_setting.py @@ -57,7 +57,7 @@ class EvalSetting(object): Usually records are sorted by timestamp, or shuffled. split_args (dict): Args about splitting. - usually records are splitted by ratio (eg. 8:1:1), + usually records are split by ratio (eg. 8:1:1), or by 'leave one out' strategy, which means the last purchase record of one user is used for evaluation. @@ -173,7 +173,7 @@ def sort_by(self, field, ascending=None): Args: field (str or list of str): Name or list of names ascending (bool or list of bool): Sort ascending vs. descending. Specify list for multiple sort orders. - If this is a list of bools, must match the length of the field + If this is a list of bool, must match the length of the field """ if not isinstance(field, list): field = [field] diff --git a/recbole/data/dataloader/__init__.py b/recbole/data/dataloader/__init__.py index 18e6f0674..90ffa311f 100644 --- a/recbole/data/dataloader/__init__.py +++ b/recbole/data/dataloader/__init__.py @@ -4,3 +4,5 @@ from recbole.data.dataloader.context_dataloader import * from recbole.data.dataloader.sequential_dataloader import * from recbole.data.dataloader.knowledge_dataloader import * +from recbole.data.dataloader.xgboost_dataloader import * +from recbole.data.dataloader.user_dataloader import * diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index 6b21009cb..e5464606d 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -60,6 +60,8 @@ def __init__(self, config, dataset, self.history_item_matrix = self.dataset.history_item_matrix self.history_user_matrix = self.dataset.history_user_matrix self.inter_matrix = self.dataset.inter_matrix + self.get_user_feature = self.dataset.get_user_feature + self.get_item_feature = self.dataset.get_item_feature for dataset_attr in self.dataset._dataloader_apis: try: @@ -80,7 +82,7 @@ def setup(self): pass def data_preprocess(self): - """This function is used to do some data preprocess, such as pre-neg-sampling and pre-data-augmentation. + """This function is used to do some data preprocess, such as pre-data-augmentation. By default, it will do nothing. """ pass @@ -129,22 +131,11 @@ def set_batch_size(self, batch_size): self.batch_size = batch_size self.logger.warning('Batch size is changed to {}'.format(batch_size)) - def get_user_feature(self): - """It is similar to :meth:`~recbole.data.dataset.dataset.Dataset.get_user_feature`, but it will return an - :class:`~recbole.data.interaction.Interaction` of user feature instead of a :class:`pandas.DataFrame`. + def upgrade_batch_size(self, batch_size): + """Upgrade the batch_size of the dataloader, if input batch_size is bigger than current batch_size. - Returns: - Interaction: The interaction of user feature. - """ - user_df = self.dataset.get_user_feature() - return self._dataframe_to_interaction(user_df) - - def get_item_feature(self): - """It is similar to :meth:`~recbole.data.dataset.dataset.Dataset.get_item_feature`, but it will return an - :class:`~recbole.data.interaction.Interaction` of item feature instead of a :class:`pandas.DataFrame`. - - Returns: - Interaction: The interaction of item feature. + Args: + batch_size (int): the new batch_size of dataloader. """ - item_df = self.dataset.get_item_feature() - return self._dataframe_to_interaction(item_df) + if self.batch_size < batch_size: + self.set_batch_size(batch_size) diff --git a/recbole/data/dataloader/context_dataloader.py b/recbole/data/dataloader/context_dataloader.py index a42ff8be4..9ca4fc4df 100644 --- a/recbole/data/dataloader/context_dataloader.py +++ b/recbole/data/dataloader/context_dataloader.py @@ -12,11 +12,13 @@ ################################################ """ -from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, \ + GeneralFullDataLoader class ContextDataLoader(GeneralDataLoader): - """:class:`ContextDataLoader` is inherit from :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + """:class:`ContextDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, and didn't add/change anything at all. """ pass @@ -28,3 +30,11 @@ class ContextNegSampleDataLoader(GeneralNegSampleDataLoader): and didn't add/change anything at all. """ pass + + +class ContextFullDataLoader(GeneralFullDataLoader): + """:class:`ContextFullDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralFullDataLoader`, + and didn't add/change anything at all. + """ + pass diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 58627fa3e..baea05fe5 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -13,12 +13,11 @@ """ import numpy as np -import pandas as pd import torch -from tqdm import tqdm from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader from recbole.data.dataloader.neg_sample_mixin import NegSampleMixin, NegSampleByMixin +from recbole.data.interaction import Interaction, cat_interactions from recbole.utils import DataLoaderType, InputType @@ -50,7 +49,7 @@ def _shuffle(self): def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): @@ -70,33 +69,36 @@ class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): - self.uid2index, self.uid2items_num = None, None + self.uid_field = dataset.uid_field + self.iid_field = dataset.iid_field + self.uid_list, self.uid2index, self.uid2items_num = None, None, None super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) def setup(self): if self.user_inter_in_one_batch: - self.uid2index, self.uid2items_num = self.dataset.uid2index + uid_field = self.dataset.uid_field + user_num = self.dataset.user_num + self.dataset.sort(by=uid_field, ascending=True) + self.uid_list = [] + start, end = dict(), dict() + for i, uid in enumerate(self.dataset.inter_feat[uid_field].numpy()): + if uid not in start: + self.uid_list.append(uid) + start[uid] = i + end[uid] = i + self.uid2index = np.array([None] * user_num) + self.uid2items_num = np.zeros(user_num, dtype=np.int64) + for uid in self.uid_list: + self.uid2index[uid] = slice(start[uid], end[uid] + 1) + self.uid2items_num[uid] = end[uid] - start[uid] + 1 + self.uid_list = np.array(self.uid_list) self._batch_size_adaptation() - def data_preprocess(self): - if self.user_inter_in_one_batch: - new_inter_num = 0 - new_inter_feat = [] - new_uid2index = [] - for uid, index in self.uid2index: - new_inter_feat.append(self._neg_sampling(self.dataset.inter_feat[index])) - new_num = len(new_inter_feat[-1]) - new_uid2index.append((uid, slice(new_inter_num, new_inter_num + new_num))) - new_inter_num += new_num - self.dataset.inter_feat = pd.concat(new_inter_feat, ignore_index=True) - self.uid2index = np.array(new_uid2index) - else: - self.dataset.inter_feat = self._neg_sampling(self.dataset.inter_feat) - def _batch_size_adaptation(self): if self.user_inter_in_one_batch: inters_num = sorted(self.uid2items_num * self.times, reverse=True) @@ -108,82 +110,80 @@ def _batch_size_adaptation(self): batch_num = i new_batch_size += inters_num[i] self.step = batch_num - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) else: batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times - self.step = batch_num if self.real_time else new_batch_size - self.set_batch_size(new_batch_size) + self.step = batch_num + self.upgrade_batch_size(new_batch_size) @property def pr_end(self): if self.user_inter_in_one_batch: - return len(self.uid2index) + return len(self.uid_list) else: return len(self.dataset) def _shuffle(self): if self.user_inter_in_one_batch: - new_index = np.random.permutation(len(self.uid2index)) - self.uid2index = self.uid2index[new_index] - self.uid2items_num = self.uid2items_num[new_index] + np.random.shuffle(self.uid_list) else: self.dataset.shuffle() def _next_batch_data(self): if self.user_inter_in_one_batch: - sampling_func = self._neg_sampling if self.real_time else (lambda x: x) - cur_data = [] - for uid, index in self.uid2index[self.pr: self.pr + self.step]: - cur_data.append(sampling_func(self.dataset[index])) - cur_data = pd.concat(cur_data, ignore_index=True) - pos_len_list = self.uid2items_num[self.pr: self.pr + self.step] + uid_list = self.uid_list[self.pr: self.pr + self.step] + data_list = [] + for uid in uid_list: + index = self.uid2index[uid] + data_list.append(self._neg_sampling(self.dataset[index])) + cur_data = cat_interactions(data_list) + pos_len_list = self.uid2items_num[uid_list] user_len_list = pos_len_list * self.times + cur_data.set_additional_info(list(pos_len_list), list(user_len_list)) self.pr += self.step - return self._dataframe_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) + return cur_data else: - cur_data = self.dataset[self.pr: self.pr + self.step] + cur_data = self._neg_sampling(self.dataset[self.pr: self.pr + self.step]) self.pr += self.step - if self.real_time: - cur_data = self._neg_sampling(cur_data) - return self._dataframe_to_interaction(cur_data) + return cur_data def _neg_sampling(self, inter_feat): - uid_field = self.config['USER_ID_FIELD'] - iid_field = self.config['ITEM_ID_FIELD'] - uids = inter_feat[uid_field].to_list() + uids = inter_feat[self.uid_field] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) - return self.sampling_func(uid_field, iid_field, neg_iids, inter_feat) - - def _neg_sample_by_pair_wise_sampling(self, uid_field, iid_field, neg_iids, inter_feat): - inter_feat.insert(len(inter_feat.columns), self.neg_item_id, neg_iids) - - if self.dataset.item_feat is not None: - neg_prefix = self.config['NEG_PREFIX'] - neg_item_feat = self.dataset.item_feat.add_prefix(neg_prefix) - inter_feat = pd.merge(inter_feat, neg_item_feat, - on=self.neg_item_id, how='left', suffixes=('_inter', '_item')) - + return self.sampling_func(inter_feat, neg_iids) + + def _neg_sample_by_pair_wise_sampling(self, inter_feat, neg_iids): + inter_feat = inter_feat.repeat(self.times) + neg_item_feat = Interaction({self.iid_field: neg_iids}) + neg_item_feat = self.dataset.join(neg_item_feat) + neg_item_feat.add_prefix(self.neg_prefix) + inter_feat.update(neg_item_feat) return inter_feat - def _neg_sample_by_point_wise_sampling(self, uid_field, iid_field, neg_iids, inter_feat): + def _neg_sample_by_point_wise_sampling(self, inter_feat, neg_iids): pos_inter_num = len(inter_feat) - - new_df = pd.concat([inter_feat] * self.times, ignore_index=True) - new_df[iid_field].values[pos_inter_num:] = neg_iids - - labels = np.zeros(pos_inter_num * self.times, dtype=np.int64) - labels[: pos_inter_num] = 1 - new_df[self.label_field] = labels - - return new_df + new_data = inter_feat.repeat(self.times) + new_data[self.iid_field][pos_inter_num:] = neg_iids + new_data = self.dataset.join(new_data) + labels = torch.zeros(pos_inter_num * self.times) + labels[: pos_inter_num] = 1.0 + new_data.update(Interaction({self.label_field: labels})) + return new_data def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. + """ + return self.uid2items_num[self.uid_list] + + def get_user_len_list(self): + """ + Returns: + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ - return self.uid2items_num + return self.uid2items_num[self.uid_list] * self.times class GeneralFullDataLoader(NegSampleMixin, AbstractDataLoader): @@ -207,88 +207,91 @@ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'full': raise ValueError('neg_sample strategy in GeneralFullDataLoader() should be `full`') - self.uid2index, self.uid2items_num = dataset.uid2index + + uid_field = dataset.uid_field + iid_field = dataset.iid_field + user_num = dataset.user_num + self.uid_list = [] + self.uid2items_num = np.zeros(user_num, dtype=np.int64) + self.uid2swap_idx = np.array([None] * user_num) + self.uid2rev_swap_idx = np.array([None] * user_num) + self.uid2history_item = np.array([None] * user_num) + + dataset.sort(by=uid_field, ascending=True) + last_uid = None + positive_item = set() + uid2used_item = sampler.used_ids + for uid, iid in zip(dataset.inter_feat[uid_field].numpy(), dataset.inter_feat[iid_field].numpy()): + if uid != last_uid: + self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) + last_uid = uid + self.uid_list.append(uid) + positive_item = set() + positive_item.add(iid) + self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) + self.uid_list = torch.tensor(self.uid_list) + self.user_df = dataset.join(Interaction({uid_field: self.uid_list})) super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - def data_preprocess(self): - self.user_tensor, tmp_pos_idx, tmp_used_idx, self.pos_len_list, self.neg_len_list = \ - self._neg_sampling(self.uid2index, show_progress=True) - tmp_pos_len_list = [sum(self.pos_len_list[_: _ + self.step]) for _ in range(0, self.pr_end, self.step)] - tot_item_num = self.dataset.item_num - tmp_used_len_list = [sum( - [tot_item_num - x for x in self.neg_len_list[_: _ + self.step]] - ) for _ in range(0, self.pr_end, self.step)] - self.pos_idx = list(torch.split(tmp_pos_idx, tmp_pos_len_list)) - self.used_idx = list(torch.split(tmp_used_idx, tmp_used_len_list)) - for i in range(len(self.pos_idx)): - self.pos_idx[i] -= i * tot_item_num * self.step - for i in range(len(self.used_idx)): - self.used_idx[i] -= i * tot_item_num * self.step + def _set_user_property(self, uid, used_item, positive_item): + if uid is None: + return + history_item = used_item - positive_item + positive_item_num = len(positive_item) + self.uid2items_num[uid] = positive_item_num + swap_idx = torch.tensor(sorted(set(range(positive_item_num)) ^ positive_item)) + self.uid2swap_idx[uid] = swap_idx + self.uid2rev_swap_idx[uid] = swap_idx.flip(0) + self.uid2history_item[uid] = torch.tensor(list(history_item), dtype=torch.int64) def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.dataset.item_num, 1) new_batch_size = batch_num * self.dataset.item_num self.step = batch_num - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) @property def pr_end(self): - return len(self.uid2index) + return len(self.uid_list) def _shuffle(self): self.logger.warnning('GeneralFullDataLoader can\'t shuffle') def _next_batch_data(self): - if not self.real_time: - slc = slice(self.pr, self.pr + self.step) - idx = self.pr // self.step - cur_data = self.user_tensor[slc], self.pos_idx[idx], self.used_idx[idx], \ - self.pos_len_list[slc], self.neg_len_list[slc] - else: - cur_data = self._neg_sampling(self.uid2index[self.pr: self.pr + self.step]) + user_df = self.user_df[self.pr: self.pr + self.step] + cur_data = self._neg_sampling(user_df) self.pr += self.step return cur_data - def _neg_sampling(self, uid2index, show_progress=False): - uid_field = self.dataset.uid_field - iid_field = self.dataset.iid_field - tot_item_num = self.dataset.item_num - - start_idx = 0 - pos_len_list = [] - neg_len_list = [] - - pos_idx = [] - used_idx = [] - - iter_data = tqdm(uid2index) if show_progress else uid2index - for uid, index in iter_data: - pos_item_id = self.dataset.inter_feat[iid_field][index].values - pos_idx.extend([_ + start_idx for _ in pos_item_id]) - pos_num = len(pos_item_id) - pos_len_list.append(pos_num) + def _neg_sampling(self, user_df): + uid_list = list(user_df[self.dataset.uid_field]) + pos_len_list = self.uid2items_num[uid_list] + user_len_list = np.full(len(uid_list), self.item_num) + user_df.set_additional_info(pos_len_list, user_len_list) - used_item_id = self.sampler.used_ids[uid] - used_idx.extend([_ + start_idx for _ in used_item_id]) - used_num = len(used_item_id) + history_item = self.uid2history_item[uid_list] + history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) + history_col = torch.cat(list(history_item)) - neg_num = tot_item_num - used_num - neg_len_list.append(neg_num) - - start_idx += tot_item_num - - user_df = pd.DataFrame({uid_field: np.array(uid2index[:, 0], dtype=np.int)}) - user_interaction = self._dataframe_to_interaction(self.join(user_df)) - - return user_interaction, \ - torch.LongTensor(pos_idx), torch.LongTensor(used_idx), \ - pos_len_list, neg_len_list + swap_idx = self.uid2swap_idx[uid_list] + rev_swap_idx = self.uid2rev_swap_idx[uid_list] + swap_row = torch.cat([torch.full_like(swap, i) for i, swap in enumerate(swap_idx)]) + swap_col_after = torch.cat(list(swap_idx)) + swap_col_before = torch.cat(list(rev_swap_idx)) + return user_df, (history_row, history_col), swap_row, swap_col_after, swap_col_before def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. + """ + return self.uid2items_num[self.uid_list] + + def get_user_len_list(self): + """ + Returns: + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ - return self.uid2items_num + return np.full(self.pr_end, self.item_num) diff --git a/recbole/data/dataloader/knowledge_dataloader.py b/recbole/data/dataloader/knowledge_dataloader.py index f4718ad20..632b283bf 100644 --- a/recbole/data/dataloader/knowledge_dataloader.py +++ b/recbole/data/dataloader/knowledge_dataloader.py @@ -13,6 +13,7 @@ """ from recbole.data.dataloader import AbstractDataLoader, GeneralNegSampleDataLoader +from recbole.data.interaction import Interaction from recbole.utils import InputType, KGDataLoaderState @@ -63,24 +64,17 @@ def pr_end(self): return len(self.dataset.kg_feat) def _shuffle(self): - self.dataset.kg_feat = self.dataset.kg_feat.sample(frac=1).reset_index(drop=True) + self.dataset.kg_feat.shuffle() def _next_batch_data(self): - cur_data = self.dataset.kg_feat[self.pr: self.pr + self.step] + cur_data = self._neg_sampling(self.dataset.kg_feat[self.pr: self.pr + self.step]) self.pr += self.step - if self.real_time: - cur_data = self._neg_sampling(cur_data) - return self._dataframe_to_interaction(cur_data) - - def data_preprocess(self): - """Do neg-sampling before training/evaluation. - """ - self.dataset.kg_feat = self._neg_sampling(self.dataset.kg_feat) + return cur_data def _neg_sampling(self, kg_feat): - hids = kg_feat[self.hid_field].to_list() + hids = kg_feat[self.hid_field] neg_tids = self.sampler.sample_by_entity_ids(hids, self.neg_sample_num) - kg_feat.insert(len(kg_feat.columns), self.neg_tid_field, neg_tids) + kg_feat.update(Interaction({self.neg_tid_field: neg_tids})) return kg_feat @@ -129,65 +123,51 @@ def __init__(self, config, dataset, sampler, kg_sampler, neg_sample_args, # using kg_sampler and dl_format is pairwise self.kg_dataloader = KGDataLoader(config, dataset, kg_sampler, - batch_size=batch_size, dl_format=InputType.PAIRWISE, shuffle=shuffle) + batch_size=batch_size, dl_format=InputType.PAIRWISE, shuffle=True) - self.main_dataloader = self.general_dataloader + self.state = None super().__init__(config, dataset, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - @property - def pr(self): - """Pointer of :class:`KnowledgeBasedDataLoader`. It would be affect by self.state. - """ - return self.main_dataloader.pr - - @pr.setter - def pr(self, value): - self.main_dataloader.pr = value - def __iter__(self): - if not hasattr(self, 'state') or not hasattr(self, 'main_dataloader'): - raise ValueError('The dataloader\'s state and main_dataloader must be set ' - 'when using the kg based dataloader') - return super().__iter__() + if self.state is None: + raise ValueError('The dataloader\'s state must be set when using the kg based dataloader, ' + 'you should call set_mode() before __iter__()') + if self.state == KGDataLoaderState.KG: + return self.kg_dataloader.__iter__() + elif self.state == KGDataLoaderState.RS: + return self.general_dataloader.__iter__() + elif self.state == KGDataLoaderState.RSKG: + self.kg_dataloader.__iter__() + self.general_dataloader.__iter__() + return self def _shuffle(self): - if self.state == KGDataLoaderState.RSKG: - self.general_dataloader._shuffle() - self.kg_dataloader._shuffle() - else: - self.main_dataloader._shuffle() + pass def __next__(self): - if self.pr >= self.pr_end: - if self.state == KGDataLoaderState.RSKG: - self.general_dataloader.pr = 0 - self.kg_dataloader.pr = 0 - else: - self.pr = 0 + if self.general_dataloader.pr >= self.general_dataloader.pr_end: + self.general_dataloader.pr = 0 + self.kg_dataloader.pr = 0 raise StopIteration() return self._next_batch_data() def __len__(self): - return len(self.main_dataloader) + return len(self.general_dataloader) @property def pr_end(self): - return self.main_dataloader.pr_end + return self.general_dataloader.pr_end def _next_batch_data(self): - if self.state == KGDataLoaderState.KG: - return self.kg_dataloader._next_batch_data() - elif self.state == KGDataLoaderState.RS: - return self.general_dataloader._next_batch_data() - elif self.state == KGDataLoaderState.RSKG: - if self.kg_dataloader.pr >= self.kg_dataloader.pr_end: - self.kg_dataloader.pr = 0 - kg_data = self.kg_dataloader._next_batch_data() - rec_data = self.general_dataloader._next_batch_data() - rec_data.update(kg_data) - return rec_data + try: + kg_data = self.kg_dataloader.__next__() + except StopIteration: + kg_data = self.kg_dataloader.__next__() + rec_data = self.general_dataloader.__next__() + rec_data.update(kg_data) + return rec_data def set_mode(self, state): """Set the mode of :class:`KnowledgeBasedDataLoader`, it can be set to three states: @@ -204,11 +184,3 @@ def set_mode(self, state): if state not in set(KGDataLoaderState): raise NotImplementedError('kg data loader has no state named [{}]'.format(self.state)) self.state = state - if self.state == KGDataLoaderState.RS: - self.main_dataloader = self.general_dataloader - elif self.state == KGDataLoaderState.KG: - self.main_dataloader = self.kg_dataloader - else: # RSKG - kgpr = self.kg_dataloader.pr_end - rspr = self.general_dataloader.pr_end - self.main_dataloader = self.general_dataloader if rspr < kgpr else self.kg_dataloader diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 2187227de..9ea6d05ed 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -49,11 +49,6 @@ def setup(self): """ self._batch_size_adaptation() - def data_preprocess(self): - """Do neg-sampling before training/evaluation. - """ - raise NotImplementedError('Method [data_preprocess] should be implemented.') - def _batch_size_adaptation(self): """Adjust the batch size to ensure that each positive and negative interaction can be in a batch. """ @@ -72,10 +67,17 @@ def _neg_sampling(self, inter_feat): def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ raise NotImplementedError('Method [get_pos_len_list] should be implemented.') + def get_user_len_list(self): + """ + Returns: + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + raise NotImplementedError('Method [get_user_len_list] should be implemented.') + class NegSampleByMixin(NegSampleMixin): """:class:`NegSampleByMixin` is an abstract class which can sample negative examples by ratio. @@ -92,12 +94,11 @@ class NegSampleByMixin(NegSampleMixin): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'by': raise ValueError('neg_sample strategy in GeneralInteractionBasedDataLoader() should be `by`') - if dl_format == InputType.PAIRWISE and neg_sample_args['by'] != 1: - raise ValueError('Pairwise dataloader can only neg sample by 1') self.user_inter_in_one_batch = (sampler.phase != 'train') and (config['eval_type'] != EvaluatorType.INDIVIDUAL) self.neg_sample_by = neg_sample_args['by'] @@ -109,16 +110,16 @@ def __init__(self, config, dataset, sampler, neg_sample_args, self.label_field = config['LABEL_FIELD'] dataset.set_field_property(self.label_field, FeatureType.FLOAT, FeatureSource.INTERACTION, 1) elif dl_format == InputType.PAIRWISE: - self.times = 1 + self.times = self.neg_sample_by self.sampling_func = self._neg_sample_by_pair_wise_sampling - neg_prefix = config['NEG_PREFIX'] + self.neg_prefix = config['NEG_PREFIX'] iid_field = config['ITEM_ID_FIELD'] - self.neg_item_id = neg_prefix + iid_field + self.neg_item_id = self.neg_prefix + iid_field columns = [iid_field] if dataset.item_feat is None else dataset.item_feat.columns for item_feat_col in columns: - neg_item_feat_col = neg_prefix + item_feat_col + neg_item_feat_col = self.neg_prefix + item_feat_col dataset.copy_field_property(neg_item_feat_col, item_feat_col) else: raise ValueError('`neg sampling by` with dl_format [{}] not been implemented'.format(dl_format)) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 0013b1132..88c643b65 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -16,7 +16,8 @@ import torch from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader -from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin +from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin, NegSampleMixin +from recbole.data.interaction import Interaction, cat_interactions from recbole.utils import DataLoaderType, FeatureSource, FeatureType, InputType @@ -50,37 +51,25 @@ def __init__(self, config, dataset, self.max_item_list_len = config['MAX_ITEM_LIST_LENGTH'] list_suffix = config['LIST_SUFFIX'] - self.item_list_field = self.iid_field + list_suffix - self.time_list_field = self.time_field + list_suffix - self.position_field = config['POSITION_FIELD'] - self.target_iid_field = self.iid_field - self.target_time_field = self.time_field - self.item_list_length_field = config['ITEM_LIST_LENGTH_FIELD'] - for field in dataset.inter_feat: - if field not in [self.uid_field, self.iid_field, self.time_field]: + if field != self.uid_field: + list_field = field + list_suffix + setattr(self, f'{field}_list_field', list_field) ftype = dataset.field2type[field] - setattr(self, f'{field}_list_field', field + list_suffix) - if dataset.field2type[field] == FeatureType.TOKEN: - dataset.set_field_property(getattr(self, f'{field}_list_field'), FeatureType.TOKEN_SEQ, - FeatureSource.INTERACTION, - self.max_item_list_len) - elif dataset.field2type[field] == FeatureType.FLOAT: - dataset.set_field_property(getattr(self, f'{field}_list_field'), FeatureType.FLOAT_SEQ, - FeatureSource.INTERACTION, - self.max_item_list_len) + + if ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ]: + list_ftype = FeatureType.TOKEN_SEQ + else: + list_ftype = FeatureType.FLOAT_SEQ + + if ftype in [FeatureType.TOKEN_SEQ, FeatureType.FLOAT_SEQ]: + list_len = (self.max_item_list_len, dataset.field2seqlen[field]) else: - raise NotImplementedError('Field with ftype [{}] is not implemented for sequential model'.format(ftype)) - - dataset.set_field_property(self.item_list_field, FeatureType.TOKEN_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - dataset.set_field_property(self.time_list_field, FeatureType.FLOAT_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - if self.position_field: - dataset.set_field_property(self.position_field, FeatureType.TOKEN_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - dataset.set_field_property(self.target_iid_field, FeatureType.TOKEN, FeatureSource.INTERACTION, 1) - dataset.set_field_property(self.target_time_field, FeatureType.FLOAT, FeatureSource.INTERACTION, 1) + list_len = self.max_item_list_len + + dataset.set_field_property(list_field, list_ftype, FeatureSource.INTERACTION, list_len) + + self.item_list_length_field = config['ITEM_LIST_LENGTH_FIELD'] dataset.set_field_property(self.item_list_length_field, FeatureType.TOKEN, FeatureSource.INTERACTION, 1) self.uid_list, self.item_list_index, self.target_index, self.item_list_length = \ @@ -93,85 +82,68 @@ def __init__(self, config, dataset, def data_preprocess(self): """Do data augmentation before training/evaluation. """ - self.pre_processed_data = self.augmentation(self.uid_list, self.item_list_field, - self.target_index, self.item_list_length) + self.pre_processed_data = self.augmentation(self.item_list_index, self.target_index, self.item_list_length) @property def pr_end(self): return len(self.uid_list) def _shuffle(self): - new_index = np.random.permutation(len(self.item_list_index)) if self.real_time: + new_index = torch.randperm(self.pr_end) self.uid_list = self.uid_list[new_index] self.item_list_index = self.item_list_index[new_index] self.target_index = self.target_index[new_index] self.item_list_length = self.item_list_length[new_index] else: - new_data = {} - for key, value in self.pre_processed_data.items(): - new_data[key] = value[new_index] - self.pre_processed_data = new_data + self.pre_processed_data.shuffle() def _next_batch_data(self): - cur_index = slice(self.pr, self.pr + self.step) + cur_data = self._get_processed_data(slice(self.pr, self.pr + self.step)) + self.pr += self.step + return cur_data + + def _get_processed_data(self, index): if self.real_time: - cur_data = self.augmentation(self.uid_list[cur_index], - self.item_list_index[cur_index], - self.target_index[cur_index], - self.item_list_length[cur_index]) + cur_data = self.augmentation(self.item_list_index[index], + self.target_index[index], + self.item_list_length[index]) else: - cur_data = {} - for key, value in self.pre_processed_data.items(): - cur_data[key] = value[cur_index] - self.pr += self.step - return self._dict_to_interaction(cur_data) + cur_data = self.pre_processed_data[index] + return cur_data - def augmentation(self, uid_list, item_list_index, target_index, item_list_length): + def augmentation(self, item_list_index, target_index, item_list_length): """Data augmentation. Args: - uid_list (np.ndarray): user id list. - item_list_index (np.ndarray): the index of history items list in interaction. - target_index (np.ndarray): the index of items to be predicted in interaction. - item_list_length (np.ndarray): history list length. + item_list_index (numpy.ndarray): the index of history items list in interaction. + target_index (numpy.ndarray): the index of items to be predicted in interaction. + item_list_length (numpy.ndarray): history list length. Returns: dict: the augmented data. """ new_length = len(item_list_index) + new_data = self.dataset.inter_feat[target_index] new_dict = { - self.uid_field: uid_list, - self.item_list_field: np.zeros((new_length, self.max_item_list_len), dtype=np.int64), - self.time_list_field: np.zeros((new_length, self.max_item_list_len)), - self.target_iid_field: self.dataset.inter_feat[self.iid_field][target_index].values, - self.target_time_field: self.dataset.inter_feat[self.time_field][target_index].values, - self.item_list_length_field: item_list_length, + self.item_list_length_field: torch.tensor(item_list_length), } - for field in self.dataset.inter_feat: - if field not in [self.uid_field, self.iid_field, self.time_field]: - new_dict[field] = self.dataset.inter_feat[field][target_index].values - """Add extra field feature for interaction""" - ftype = self.dataset.field2type[field] - if ftype == FeatureType.TOKEN or ftype == FeatureType.FLOAT: - field_value = self.dataset.inter_feat[field] - dtype = np.int64 if ftype == FeatureType.TOKEN else np.float32 - new_dict[getattr(self, f'{field}_list_field')] = np.zeros((new_length, self.max_item_list_len), - dtype=dtype) - for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): - new_dict[getattr(self, f'{field}_list_field')][i][:length] = field_value[index] - else: - raise NotImplementedError('Field with ftype [{}] is not implemented for sequential model'.format(ftype)) - if self.position_field: - new_dict[self.position_field] = np.tile(np.arange(self.max_item_list_len), (new_length, 1)) - - iid_value = self.dataset.inter_feat[self.iid_field].values - time_value = self.dataset.inter_feat[self.time_field].values - for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): - new_dict[self.item_list_field][i][:length] = iid_value[index] - new_dict[self.time_list_field][i][:length] = time_value[index] - return new_dict + for field in self.dataset.inter_feat: + if field != self.uid_field: + list_field = getattr(self, f'{field}_list_field') + list_len = self.dataset.field2seqlen[list_field] + shape = (new_length, list_len) if isinstance(list_len, int) else (new_length,) + list_len + list_ftype = self.dataset.field2type[list_field] + dtype = torch.int64 if list_ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ] else torch.float64 + new_dict[list_field] = torch.zeros(shape, dtype=dtype) + + value = self.dataset.inter_feat[field] + for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): + new_dict[list_field][i][:length] = value[index] + + new_data.update(Interaction(new_dict)) + return new_data class SequentialNegSampleDataLoader(NegSampleByMixin, SequentialDataLoader): @@ -191,45 +163,29 @@ class SequentialNegSampleDataLoader(NegSampleByMixin, SequentialDataLoader): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - def data_preprocess(self): - """Do data augmentation and neg-sampling before training/evaluation. - """ - self.pre_processed_data = self.augmentation(self.uid_list, self.item_list_field, - self.target_index, self.item_list_length) - self.pre_processed_data = self._neg_sampling(self.pre_processed_data) - def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times - self.step = batch_num if self.real_time else new_batch_size - self.set_batch_size(new_batch_size) + self.step = batch_num + self.upgrade_batch_size(new_batch_size) def _next_batch_data(self): - cur_index = slice(self.pr, self.pr + self.step) - if self.real_time: - cur_data = self.augmentation(self.uid_list[cur_index], - self.item_list_index[cur_index], - self.target_index[cur_index], - self.item_list_length[cur_index]) - cur_data = self._neg_sampling(cur_data) - else: - cur_data = {} - for key, value in self.pre_processed_data.items(): - cur_data[key] = value[cur_index] + cur_data = self._get_processed_data(slice(self.pr, self.pr + self.step)) + cur_data = self._neg_sampling(cur_data) self.pr += self.step if self.user_inter_in_one_batch: cur_data_len = len(cur_data[self.uid_field]) pos_len_list = np.ones(cur_data_len // self.times, dtype=np.int64) user_len_list = pos_len_list * self.times - return self._dict_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) - else: - return self._dict_to_interaction(cur_data) + cur_data.set_additional_info(list(pos_len_list), list(user_len_list)) + return cur_data def _neg_sampling(self, data): if self.user_inter_in_one_batch: @@ -238,41 +194,44 @@ def _neg_sampling(self, data): for i in range(data_len): uids = data[self.uid_field][i: i + 1] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) - cur_data = {field: data[field][i: i + 1] for field in data} + cur_data = data[i: i + 1] data_list.append(self.sampling_func(cur_data, neg_iids)) - return {field: np.concatenate([d[field] for d in data_list]) - for field in data} + return cat_interactions(data_list) else: uids = data[self.uid_field] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) return self.sampling_func(data, neg_iids) def _neg_sample_by_pair_wise_sampling(self, data, neg_iids): - data[self.neg_item_id] = neg_iids - return data + new_data = data.repeat(self.times) + new_data.update(Interaction({self.neg_item_id: neg_iids})) + return new_data def _neg_sample_by_point_wise_sampling(self, data, neg_iids): - new_data = {} - for key, value in data.items(): - if key == self.target_iid_field: - new_data[key] = np.concatenate([value, neg_iids]) - else: - new_data[key] = np.concatenate([value] * self.times) - pos_len = len(data[self.target_iid_field]) - total_len = len(new_data[self.target_iid_field]) - new_data[self.label_field] = np.zeros(total_len, dtype=np.int) - new_data[self.label_field][:pos_len] = 1 + pos_inter_num = len(data) + new_data = data.repeat(self.times) + new_data[self.iid_field][pos_inter_num:] = neg_iids + labels = torch.zeros(pos_inter_num * self.times) + labels[: pos_inter_num] = 1.0 + new_data.update(Interaction({self.label_field: labels})) return new_data def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return np.ones(self.pr_end, dtype=np.int64) + def get_user_len_list(self): + """ + Returns: + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + return np.full(self.pr_end, self.times) -class SequentialFullDataLoader(SequentialDataLoader): + +class SequentialFullDataLoader(NegSampleMixin, SequentialDataLoader): """:class:`SequentialFullDataLoader` is a sequential-dataloader with full sort. In order to speed up calculation, this dataloader would only return then user part of interactions, positive items and used items. It would not return negative items. @@ -291,24 +250,41 @@ class SequentialFullDataLoader(SequentialDataLoader): def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): - super().__init__(config, dataset, + super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) + def _batch_size_adaptation(self): + pass + + def _neg_sampling(self, inter_feat): + pass + def _shuffle(self): self.logger.warnning('SequentialFullDataLoader can\'t shuffle') def _next_batch_data(self): interaction = super()._next_batch_data() - tot_item_num = self.dataset.item_num inter_num = len(interaction) - pos_idx = used_idx = interaction[self.target_iid_field] + torch.arange(inter_num) * tot_item_num - pos_len_list = [1] * inter_num - neg_len_list = [tot_item_num - 1] * inter_num - return interaction, pos_idx, used_idx, pos_len_list, neg_len_list + pos_len_list = np.ones(inter_num, dtype=np.int64) + user_len_list = np.full(inter_num, self.item_num) + interaction.set_additional_info(pos_len_list, user_len_list) + scores_row = torch.arange(inter_num).repeat(2) + padding_idx = torch.zeros(inter_num, dtype=torch.int64) + positive_idx = interaction[self.iid_field] + scores_col_after = torch.cat((padding_idx, positive_idx)) + scores_col_before = torch.cat((positive_idx, padding_idx)) + return interaction, None, scores_row, scores_col_after, scores_col_before def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + """ + return np.ones(self.pr_end, dtype=np.int64) + + def get_user_len_list(self): + """ + Returns: + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ - return np.ones(self.pr_end, dtype=np.int64) \ No newline at end of file + return np.full(self.pr_end, self.item_num) diff --git a/recbole/data/dataloader/user_dataloader.py b/recbole/data/dataloader/user_dataloader.py index 73d92aa51..e96849835 100644 --- a/recbole/data/dataloader/user_dataloader.py +++ b/recbole/data/dataloader/user_dataloader.py @@ -3,16 +3,18 @@ # @Email : chenyushuo@ruc.edu.cn # UPDATE -# @Time : 2020/9/23 -# @Author : Yushuo Chen -# @email : chenyushuo@ruc.edu.cn +# @Time : 2020/9/23, 2020/12/28 +# @Author : Yushuo Chen, Xingyu Pan +# @email : chenyushuo@ruc.edu.cn, panxy@ruc.edu.cn """ recbole.data.dataloader.user_dataloader ################################################ """ +import torch from recbole.data.dataloader import AbstractDataLoader +from recbole.data.interaction import Interaction from recbole.utils.enum_type import DataLoaderType, InputType @@ -36,6 +38,7 @@ class UserDataLoader(AbstractDataLoader): def __init__(self, config, dataset, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): self.uid_field = dataset.uid_field + self.user_list = Interaction({self.uid_field: torch.arange(dataset.user_num)}) super().__init__(config=config, dataset=dataset, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) @@ -50,12 +53,12 @@ def setup(self): @property def pr_end(self): - return len(self.dataset.user_feat) + return len(self.user_list) def _shuffle(self): - self.dataset.user_feat = self.dataset.user_feat.sample(frac=1).reset_index(drop=True) + self.user_list.shuffle() def _next_batch_data(self): - cur_data = self.dataset.user_feat[[self.uid_field]][self.pr: self.pr + self.step] + cur_data = self.user_list[self.pr: self.pr + self.step] self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data diff --git a/recbole/data/dataloader/xgboost_dataloader.py b/recbole/data/dataloader/xgboost_dataloader.py new file mode 100644 index 000000000..17c9ab2b0 --- /dev/null +++ b/recbole/data/dataloader/xgboost_dataloader.py @@ -0,0 +1,40 @@ +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +# UPDATE: +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +""" +recbole.data.dataloader.xgboost_dataloader +################################################ +""" + +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, \ + GeneralFullDataLoader + + +class XgboostDataLoader(GeneralDataLoader): + """:class:`XgboostDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostNegSampleDataLoader(GeneralNegSampleDataLoader): + """:class:`XgboostNegSampleDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralNegSampleDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostFullDataLoader(GeneralFullDataLoader): + """:class:`XgboostFullDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralFullDataLoader`, + and didn't add/change anything at all. + """ + pass diff --git a/recbole/data/dataset/__init__.py b/recbole/data/dataset/__init__.py index 2bdb7f4ad..58026c332 100644 --- a/recbole/data/dataset/__init__.py +++ b/recbole/data/dataset/__init__.py @@ -3,4 +3,5 @@ from recbole.data.dataset.kg_dataset import KnowledgeBasedDataset from recbole.data.dataset.social_dataset import SocialDataset from recbole.data.dataset.kg_seq_dataset import Kg_Seq_Dataset +from recbole.data.dataset.xgboost_dataset import XgboostDataset from recbole.data.dataset.customized_dataset import * diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 7612d4052..38c343fc4 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -23,11 +23,10 @@ import torch import torch.nn.utils.rnn as rnn_utils from scipy.sparse import coo_matrix -from sklearn.impute import SimpleImputer -from recbole.utils import FeatureSource, FeatureType from recbole.data.interaction import Interaction from recbole.data.utils import dlapi +from recbole.utils import FeatureSource, FeatureType class Dataset(object): @@ -78,17 +77,18 @@ class Dataset(object): time_field (str or None): The same as ``config['TIME_FIELD']``. - inter_feat (:class:`pandas.DataFrame`): Internal data structure stores the interaction features. + inter_feat (:class:`Interaction`): Internal data structure stores the interaction features. It's loaded from file ``.inter``. - user_feat (:class:`pandas.DataFrame` or None): Internal data structure stores the user features. + user_feat (:class:`Interaction` or None): Internal data structure stores the user features. It's loaded from file ``.user`` if existed. - item_feat (:class:`pandas.DataFrame` or None): Internal data structure stores the item features. + item_feat (:class:`Interaction` or None): Internal data structure stores the item features. It's loaded from file ``.item`` if existed. - feat_list (list): A list contains all the features (:class:`pandas.DataFrame`), including additional features. + feat_name_list (list): A list contains all the features' name (:class:`str`), including additional features. """ + def __init__(self, config, saved_dataset=None): self.config = config self.dataset_name = config['dataset'] @@ -111,12 +111,12 @@ def _from_scratch(self): self._get_field_from_config() self._load_data(self.dataset_name, self.dataset_path) self._data_processing() + self._change_feat_format() def _get_preset(self): """Initialization useful inside attributes. """ self.dataset_path = self.config['data_path'] - self._fill_nan_flag = self.config['fill_nan'] self.field2type = {} self.field2source = {} @@ -134,6 +134,10 @@ def _get_field_from_config(self): self.label_field = self.config['LABEL_FIELD'] self.time_field = self.config['TIME_FIELD'] + if (self.uid_field is None) ^ (self.iid_field is None): + raise ValueError('USER_ID_FIELD and ITEM_ID_FIELD need to be set at the same time ' + 'or not set at the same time.') + self.logger.debug('uid_field: {}'.format(self.uid_field)) self.logger.debug('iid_field: {}'.format(self.iid_field)) @@ -147,7 +151,7 @@ def _data_processing(self): - Normalization - Preloading weights initialization """ - self.feat_list = self._build_feat_list() + self.feat_name_list = self._build_feat_name_list() if self.benchmark_filename_list is None: self._data_filtering() @@ -175,23 +179,24 @@ def _data_filtering(self): self._filter_by_inter_num() self._reset_index() - def _build_feat_list(self): + def _build_feat_name_list(self): """Feat list building. - Any feat loaded by Dataset can be found in ``feat_list`` + Any feat loaded by Dataset can be found in ``feat_name_list`` Returns: - builded feature list. + built feature name list. Note: Subclasses can inherit this method to add new feat. """ - feat_list = [feat for feat in [self.inter_feat, self.user_feat, self.item_feat] if feat is not None] + feat_name_list = [feat_name for feat_name in ['inter_feat', 'user_feat', 'item_feat'] + if getattr(self, feat_name, None) is not None] if self.config['additional_feat_suffix'] is not None: for suf in self.config['additional_feat_suffix']: - if hasattr(self, '{}_feat'.format(suf)): - feat_list.append(getattr(self, '{}_feat'.format(suf))) - return feat_list + if getattr(self, '{}_feat'.format(suf), None) is not None: + feat_name_list.append('{}_feat'.format(suf)) + return feat_name_list def _restore_saved_dataset(self, saved_dataset): """Restore saved dataset from ``saved_dataset``. @@ -310,7 +315,7 @@ def _load_additional_feat(self, token, dataset_path): For those additional features, e.g. pretrained entity embedding, user can set them as ``config['additional_feat_suffix']``, then they will be loaded and stored in - :attr:`feat_list`. See :doc:`../user_guide/data/data_args` for details. + :attr:`feat_name_list`. See :doc:`../user_guide/data/data_args` for details. Args: token (str): dataset name. @@ -374,7 +379,7 @@ def _load_feat(self, filepath, source): pandas.DataFrame: Loaded feature Note: - For sequence features, ``seqlen`` will be loaded, but data in DataFrame will not be cutted off. + For sequence features, ``seqlen`` will be loaded, but data in DataFrame will not be cut off. Their length is limited only after calling :meth:`~_dict_to_interaction` or :meth:`~_dataframe_to_interaction` """ @@ -431,24 +436,16 @@ def _load_feat(self, filepath, source): def _user_item_feat_preparation(self): """Sort :attr:`user_feat` and :attr:`item_feat` by ``user_id`` or ``item_id``. - Missing values will be filled. + Missing values will be filled later. """ - flag = False if self.user_feat is not None: new_user_df = pd.DataFrame({self.uid_field: np.arange(self.user_num)}) self.user_feat = pd.merge(new_user_df, self.user_feat, on=self.uid_field, how='left') - flag = True self.logger.debug('ordering user features by user id.') if self.item_feat is not None: new_item_df = pd.DataFrame({self.iid_field: np.arange(self.item_num)}) self.item_feat = pd.merge(new_item_df, self.item_feat, on=self.iid_field, how='left') - flag = True self.logger.debug('ordering item features by user id.') - if flag: - # CANNOT be removed - # user/item feat has been updated, thus feat_list should be updated too. - self.feat_list = self._build_feat_list() - self._fill_nan_flag = True def _preload_weight_matrix(self): """Transfer preload weight features into :class:`numpy.ndarray` with shape ``[id_token_length]`` @@ -457,30 +454,27 @@ def _preload_weight_matrix(self): preload_fields = self.config['preload_weight'] if preload_fields is None: return - drop_flag = self.config['drop_preload_weight'] - if drop_flag is None: - drop_flag = True - self.logger.debug('preload weight matrix for {}, drop=[{}]'.format(preload_fields, drop_flag)) + self.logger.debug('preload weight matrix for {}'.format(preload_fields)) for preload_id_field in preload_fields: preload_value_field = preload_fields[preload_id_field] if preload_id_field not in self.field2source: - raise ValueError('prelaod id field [{}] not exist'.format(preload_id_field)) + raise ValueError('preload id field [{}] not exist'.format(preload_id_field)) if preload_value_field not in self.field2source: - raise ValueError('prelaod value field [{}] not exist'.format(preload_value_field)) + raise ValueError('preload value field [{}] not exist'.format(preload_value_field)) pid_source = self.field2source[preload_id_field] pv_source = self.field2source[preload_value_field] if pid_source != pv_source: - raise ValueError('preload id field [{}] is from source [{}],' - 'while prelaod value field [{}] is from source [{}], which should be the same'.format( - preload_id_field, pid_source, preload_value_field, pv_source - )) - for feat in self.feat_list: + raise ValueError(f'preload id field [{preload_id_field}] is from source [{pid_source}],' + f'while preload value field [{preload_value_field}] is from source [{pv_source}], ' + f'which should be the same.') + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if preload_id_field in feat: id_ftype = self.field2type[preload_id_field] if id_ftype != FeatureType.TOKEN: - raise ValueError('prelaod id field [{}] should be type token, but is [{}]'.format( + raise ValueError('preload id field [{}] should be type token, but is [{}]'.format( preload_id_field, id_ftype )) value_ftype = self.field2type[preload_value_field] @@ -508,9 +502,6 @@ def _preload_weight_matrix(self): value_ftype)) continue self._preloaded_weight[preload_id_field] = matrix - if drop_flag: - self._del_col(preload_id_field) - self._del_col(preload_value_field) def _fill_nan(self): """Missing value imputation. @@ -521,23 +512,18 @@ def _fill_nan(self): For fields with type :obj:`~recbole.utils.enum_type.FeatureType.FLOAT`, missing value will be filled by the average of original data. - For sequence features, missing value will be filled by ``[0]``. + For sequence features, missing value will be filled by ``[0]``. """ self.logger.debug('Filling nan') - if not self._fill_nan_flag: - return - - most_freq = SimpleImputer(missing_values=np.nan, strategy='most_frequent', copy=False) - aveg = SimpleImputer(missing_values=np.nan, strategy='mean', copy=False) - - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) for field in feat: ftype = self.field2type[field] if ftype == FeatureType.TOKEN: - feat[field] = most_freq.fit_transform(feat[field].values.reshape(-1, 1)) + feat[field].fillna(value=0, inplace=True) elif ftype == FeatureType.FLOAT: - feat[field] = aveg.fit_transform(feat[field].values.reshape(-1, 1)) + feat[field].fillna(value=feat[field].mean(), inplace=True) elif ftype.value.endswith('seq'): feat[field] = feat[field].apply(lambda x: [0] if (not isinstance(x, np.ndarray) and (not isinstance(x, list))) @@ -561,7 +547,7 @@ def _normalize(self): for field in fields: ftype = self.field2type[field] if field not in self.field2type: - raise ValueError('Field [{}] doesn\'t exist'.format(field)) + raise ValueError('Field [{}] does not exist'.format(field)) elif ftype != FeatureType.FLOAT and ftype != FeatureType.FLOAT_SEQ: self.logger.warning('{} is not a FLOAT/FLOAT_SEQ feat, which will not be normalized.'.format(field)) elif self.config['normalize_all']: @@ -571,7 +557,8 @@ def _normalize(self): self.logger.debug('Normalized fields: {}'.format(fields)) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) for field in feat: if field not in fields: continue @@ -580,15 +567,19 @@ def _normalize(self): lst = feat[field].values mx, mn = max(lst), min(lst) if mx == mn: - raise ValueError('All the same value in [{}] from [{}_feat]'.format(field, feat)) - feat[field] = (lst - mn) / (mx - mn) + self.logger.warning('All the same value in [{}] from [{}_feat]'.format(field, feat)) + feat[field] = 1.0 + else: + feat[field] = (lst - mn) / (mx - mn) elif ftype == FeatureType.FLOAT_SEQ: split_point = np.cumsum(feat[field].agg(len))[:-1] lst = feat[field].agg(np.concatenate) mx, mn = max(lst), min(lst) if mx == mn: - raise ValueError('All the same value in [{}] from [{}_feat]'.format(field, feat)) - lst = (lst - mn) / (mx - mn) + self.logger.warning('All the same value in [{}] from [{}_feat]'.format(field, feat)) + lst = 1.0 + else: + lst = (lst - mn) / (mx - mn) lst = np.split(lst, split_point) feat[field] = lst @@ -643,16 +634,34 @@ def _filter_by_inter_num(self): Lower bound is also called k-core filtering, which means this method will filter loops until all the users and items has at least k interactions. """ + if self.uid_field is None or self.iid_field is None: + return + + max_user_inter_num = self.config['max_user_inter_num'] + min_user_inter_num = self.config['min_user_inter_num'] + max_item_inter_num = self.config['max_item_inter_num'] + min_item_inter_num = self.config['min_item_inter_num'] + + if max_user_inter_num is None and min_user_inter_num is None: + user_inter_num = Counter() + else: + user_inter_num = Counter(self.inter_feat[self.uid_field].values) + + if max_item_inter_num is None and min_item_inter_num is None: + item_inter_num = Counter() + else: + item_inter_num = Counter(self.inter_feat[self.iid_field].values) + while True: ban_users = self._get_illegal_ids_by_inter_num(field=self.uid_field, feat=self.user_feat, - max_num=self.config['max_user_inter_num'], - min_num=self.config['min_user_inter_num']) + inter_num=user_inter_num, + max_num=max_user_inter_num, min_num=min_user_inter_num) ban_items = self._get_illegal_ids_by_inter_num(field=self.iid_field, feat=self.item_feat, - max_num=self.config['max_item_inter_num'], - min_num=self.config['min_item_inter_num']) + inter_num=item_inter_num, + max_num=max_item_inter_num, min_num=min_item_inter_num) if len(ban_users) == 0 and len(ban_items) == 0: - return + break if self.user_feat is not None: dropped_user = self.user_feat[self.uid_field].isin(ban_users) @@ -663,19 +672,25 @@ def _filter_by_inter_num(self): self.item_feat.drop(self.item_feat.index[dropped_item], inplace=True) dropped_inter = pd.Series(False, index=self.inter_feat.index) - if self.uid_field: - dropped_inter |= self.inter_feat[self.uid_field].isin(ban_users) - if self.iid_field: - dropped_inter |= self.inter_feat[self.iid_field].isin(ban_items) - self.logger.debug('[{}] dropped interactions'.format(len(dropped_inter))) - self.inter_feat.drop(self.inter_feat.index[dropped_inter], inplace=True) - - def _get_illegal_ids_by_inter_num(self, field, feat, max_num=None, min_num=None): + user_inter = self.inter_feat[self.uid_field] + item_inter = self.inter_feat[self.iid_field] + dropped_inter |= user_inter.isin(ban_users) + dropped_inter |= item_inter.isin(ban_items) + + user_inter_num -= Counter(user_inter[dropped_inter].values) + item_inter_num -= Counter(item_inter[dropped_inter].values) + + dropped_index = self.inter_feat.index[dropped_inter] + self.logger.debug('[{}] dropped interactions'.format(len(dropped_index))) + self.inter_feat.drop(dropped_index, inplace=True) + + def _get_illegal_ids_by_inter_num(self, field, feat, inter_num, max_num=None, min_num=None): """Given inter feat, return illegal ids, whose inter num out of [min_num, max_num] Args: field (str): field name of user_id or item_id. feat (pandas.DataFrame): interaction feature. + inter_num (Counter): interaction number counter. max_num (int, optional): max number of interaction. Defaults to ``None``. min_num (int, optional): min number of interaction. Defaults to ``None``. @@ -686,16 +701,9 @@ def _get_illegal_ids_by_inter_num(self, field, feat, max_num=None, min_num=None) field, max_num, min_num )) - if field is None: - return set() - if max_num is None and min_num is None: - return set() - max_num = max_num or np.inf min_num = min_num or -1 - ids = self.inter_feat[field].values - inter_num = Counter(ids) ids = {id_ for id_ in inter_num if inter_num[id_] < min_num or inter_num[id_] > max_num} if feat is not None: @@ -716,14 +724,12 @@ def _filter_by_field_value(self): if not filter_field: return - if self.config['drop_filter_field']: - for field in set(filter_field): - self._del_col(field) def _reset_index(self): - """Reset index for all feats in :attr:`feat_list`. + """Reset index for all feats in :attr:`feat_name_list`. """ - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if feat.empty: raise ValueError('Some feat is empty, please check the filtering settings.') feat.reset_index(drop=True, inplace=True) @@ -732,8 +738,8 @@ def _drop_by_value(self, val, cmp): """Drop illegal rows by value. Args: - val (float): value that compared to. - cmp (function): return False if a row need to be droped + val (dict): value that compared to. + cmp (Callable): return False if a row need to be dropped Returns: field names that used to compare with val. @@ -748,23 +754,26 @@ def _drop_by_value(self, val, cmp): raise ValueError('field [{}] not defined in dataset'.format(field)) if self.field2type[field] not in {FeatureType.FLOAT, FeatureType.FLOAT_SEQ}: raise ValueError('field [{}] is not float-like field in dataset, which can\'t be filter'.format(field)) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if field in feat: feat.drop(feat.index[cmp(feat[field].values, val[field])], inplace=True) filter_field.append(field) return filter_field - def _del_col(self, field): + def _del_col(self, feat, field): """Delete columns Args: - field (str): field name to be droped. + feat (pandas.DataFrame or Interaction): the feat contains field. + field (str): field name to be dropped. """ self.logger.debug('delete column [{}]'.format(field)) - for feat in self.feat_list: - if field in feat: - feat.drop(columns=field, inplace=True) - for dct in [self.field2id_token, self.field2seqlen, self.field2source, self.field2type]: + if isinstance(feat, Interaction): + feat.drop(column=field) + else: + feat.drop(columns=field, inplace=True) + for dct in [self.field2id_token, self.field2token_id, self.field2seqlen, self.field2source, self.field2type]: if field in dct: del dct[field] @@ -777,7 +786,7 @@ def _set_label_by_threshold(self): Note: Key of ``config['threshold']`` if a field name. - This field will be droped after label generation. + This field will be dropped after label generation. """ threshold = self.config['threshold'] if threshold is None: @@ -794,7 +803,7 @@ def _set_label_by_threshold(self): self.inter_feat[self.label_field] = (self.inter_feat[field] >= value).astype(int) else: raise ValueError('field [{}] not in inter_feat'.format(field)) - self._del_col(field) + self._del_col(self.inter_feat, field) def _get_fields_in_same_space(self): """Parsing ``config['fields_in_same_space']``. See :doc:`../user_guide/data/data_args` for detail arg setting. @@ -916,6 +925,13 @@ def _remap(self, remap_list): split_point = np.cumsum(feat[field].agg(len))[:-1] feat[field] = np.split(new_ids, split_point) + def _change_feat_format(self): + """Change feat format from :class:`pandas.DataFrame` to :class:`Interaction`. + """ + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) + setattr(self, feat_name, self._dataframe_to_interaction(feat)) + @dlapi.set() def num(self, field): """Given ``field``, for token-like fields, return the number of different tokens after remapping, @@ -1024,10 +1040,10 @@ def token2id(self, field, tokens): Args: field (str): Field of external tokens. - tokens (str, list or np.ndarray): External tokens. + tokens (str, list or numpy.ndarray): External tokens. Returns: - int or np.ndarray: The internal ids of external tokens. + int or numpy.ndarray: The internal ids of external tokens. """ if isinstance(tokens, str): if tokens in self.field2token_id[field]: @@ -1045,10 +1061,10 @@ def id2token(self, field, ids): Args: field (str): Field of internal ids. - ids (int, list, np.ndarray or torch.Tensor): Internal ids. + ids (int, list, numpy.ndarray or torch.Tensor): Internal ids. Returns: - str or np.ndarray: The external tokens of internal ids. + str or numpy.ndarray: The external tokens of internal ids. """ try: return self.field2id_token[field][ids] @@ -1096,7 +1112,7 @@ def avg_actions_of_users(self): Returns: numpy.float64: Average number of users' interaction records. """ - return np.mean(self.inter_feat.groupby(self.uid_field).size()) + return np.mean(list(Counter(self.inter_feat[self.uid_field].numpy()).values())) @property def avg_actions_of_items(self): @@ -1105,7 +1121,7 @@ def avg_actions_of_items(self): Returns: numpy.float64: Average number of items' interaction records. """ - return np.mean(self.inter_feat.groupby(self.iid_field).size()) + return np.mean(list(Counter(self.inter_feat[self.iid_field].numpy()).values())) @property def sparsity(self): @@ -1116,31 +1132,6 @@ def sparsity(self): """ return 1 - self.inter_num / self.user_num / self.item_num - @property - def uid2index(self): - """Sort ``self.inter_feat``, - and get the mapping of user_id and index of its interaction records. - - Returns: - tuple: - - ``numpy.ndarray`` of tuple ``(uid, slice)``, - interaction records between slice are all belong to the same uid. - - ``numpy.ndarray`` of int, - representing number of interaction records of each user. - """ - self._check_field('uid_field') - self.sort(by=self.uid_field, ascending=True) - uid_list = [] - start, end = dict(), dict() - for i, uid in enumerate(self.inter_feat[self.uid_field].values): - if uid not in start: - uid_list.append(uid) - start[uid] = i - end[uid] = i - index = [(uid, slice(start[uid], end[uid] + 1)) for uid in uid_list] - uid2items_num = [end[uid] - start[uid] + 1 for uid in uid_list] - return np.array(index), np.array(uid2items_num) - def _check_field(self, *field_names): """Given a name of attribute, check if it's exist. @@ -1155,15 +1146,15 @@ def join(self, df): """Given interaction feature, join user/item feature into it. Args: - df (pandas.DataFrame): Interaction feature to be joint. + df (Interaction): Interaction feature to be joint. Returns: - pandas.DataFrame: Interaction feature after joining operation. + Interaction: Interaction feature after joining operation. """ if self.user_feat is not None and self.uid_field in df: - df = pd.merge(df, self.user_feat, on=self.uid_field, how='left', suffixes=('_inter', '_user')) + df.update(self.user_feat[df[self.uid_field]]) if self.item_feat is not None and self.iid_field in df: - df = pd.merge(df, self.item_feat, on=self.iid_field, how='left', suffixes=('_inter', '_item')) + df.update(self.item_feat[df[self.iid_field]]) return df def __getitem__(self, index, join=True): @@ -1195,15 +1186,40 @@ def copy(self, new_inter_feat): whose interaction feature is updated with ``new_inter_feat``, and all the other attributes the same. Args: - new_inter_feat (pandas.DataFrame): The new interaction feature need to be updated. + new_inter_feat (Interaction): The new interaction feature need to be updated. Returns: :class:`~Dataset`: the new :class:`~Dataset` object, whose interaction feature has been updated. """ nxt = copy.copy(self) - nxt.inter_feat = pd.DataFrame(new_inter_feat) + nxt.inter_feat = new_inter_feat return nxt + def _drop_unused_col(self): + """Drop columns which are loaded for data preparation but not used in model. + """ + unused_col = self.config['unused_col'] + if unused_col is None: + return + + for feat_name, unused_fields in unused_col.items(): + feat = getattr(self, feat_name + '_feat') + for field in unused_fields: + if field not in feat: + self.logger.warning('field [{}] is not in [{}_feat], which can not be set in `unused_col`'.format( + field, feat_name)) + continue + self._del_col(feat, field) + + def _grouped_index(self, group_by_list): + index = {} + for i, key in enumerate(group_by_list): + if key not in index: + index[key] = [i] + else: + index[key].append(i) + return index.values() + def _calcu_split_ids(self, tot, ratios): """Given split ratios, and total number, calculate the number of each part after splitting. @@ -1230,7 +1246,7 @@ def split_by_ratio(self, ratios, group_by=None): Defaults to ``None`` Returns: - list: List of :class:`~Dataset`, whose interaction features has been splitted. + list: List of :class:`~Dataset`, whose interaction features has been split. Note: Other than the first one, each part is rounded down. @@ -1244,15 +1260,16 @@ def split_by_ratio(self, ratios, group_by=None): split_ids = self._calcu_split_ids(tot=tot_cnt, ratios=ratios) next_index = [range(start, end) for start, end in zip([0] + split_ids, split_ids + [tot_cnt])] else: - grouped_inter_feat_index = self.inter_feat.groupby(by=group_by).groups.values() - next_index = [[] for i in range(len(ratios))] + grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) + next_index = [[] for _ in range(len(ratios))] for grouped_index in grouped_inter_feat_index: tot_cnt = len(grouped_index) split_ids = self._calcu_split_ids(tot=tot_cnt, ratios=ratios) for index, start, end in zip(next_index, [0] + split_ids, split_ids + [tot_cnt]): index.extend(grouped_index[start: end]) - next_df = [self.inter_feat.loc[index].reset_index(drop=True) for index in next_index] + self._drop_unused_col() + next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds @@ -1260,13 +1277,13 @@ def _split_index_by_leave_one_out(self, grouped_index, leave_one_num): """Split indexes by strategy leave one out. Args: - grouped_index (pandas.DataFrameGroupBy): Index to be splitted. + grouped_index (list of list of int): Index to be split. leave_one_num (int): Number of parts whose length is expected to be ``1``. Returns: - list: List of index that has been splitted. + list: List of index that has been split. """ - next_index = [[] for i in range(leave_one_num + 1)] + next_index = [[] for _ in range(leave_one_num + 1)] for index in grouped_index: index = list(index) tot_cnt = len(index) @@ -1287,32 +1304,34 @@ def leave_one_out(self, group_by, leave_one_num=1): Defaults to ``1``. Returns: - list: List of :class:`~Dataset`, whose interaction features has been splitted. + list: List of :class:`~Dataset`, whose interaction features has been split. """ self.logger.debug('leave one out, group_by=[{}], leave_one_num=[{}]'.format(group_by, leave_one_num)) if group_by is None: raise ValueError('leave one out strategy require a group field') - grouped_inter_feat_index = self.inter_feat.groupby(by=group_by).groups.values() + grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) next_index = self._split_index_by_leave_one_out(grouped_inter_feat_index, leave_one_num) - next_df = [self.inter_feat.loc[index].reset_index(drop=True) for index in next_index] + + self._drop_unused_col() + next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds def shuffle(self): """Shuffle the interaction records inplace. """ - self.inter_feat = self.inter_feat.sample(frac=1).reset_index(drop=True) + self.inter_feat.shuffle() def sort(self, by, ascending=True): """Sort the interaction records inplace. Args: - by (str): Field that as the key in the sorting process. - ascending (bool, optional): Results are ascending if ``True``, otherwise descending. + by (str or list of str): Field that as the key in the sorting process. + ascending (bool or list of bool, optional): Results are ascending if ``True``, otherwise descending. Defaults to ``True`` """ - self.inter_feat.sort_values(by=by, ascending=ascending, inplace=True, ignore_index=True) + self.inter_feat.sort(by=by, ascending=ascending) def build(self, eval_setting): """Processing dataset according to evaluation setting, including Group, Order and Split. @@ -1323,7 +1342,7 @@ def build(self, eval_setting): Object contains evaluation settings, which guide the data processing procedure. Returns: - list: List of builded :class:`Dataset`. + list: List of built :class:`Dataset`. """ if self.benchmark_filename_list is not None: cumsum = list(np.cumsum(self.file_size_list)) @@ -1379,22 +1398,22 @@ def save(self, filepath): def get_user_feature(self): """ Returns: - pandas.DataFrame: user features + Interaction: user features """ if self.user_feat is None: self._check_field('uid_field') - return pd.DataFrame({self.uid_field: np.arange(self.user_num)}) + return Interaction({self.uid_field: torch.arange(self.user_num)}) else: return self.user_feat def get_item_feature(self): """ Returns: - pandas.DataFrame: item features + Interaction: item features """ if self.item_feat is None: self._check_field('iid_field') - return pd.DataFrame({self.iid_field: np.arange(self.item_num)}) + return Interaction({self.iid_field: torch.arange(self.item_num)}) else: return self.item_feat @@ -1409,7 +1428,7 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', else ``matrix[src, tgt] = df_feat[value_field][src, tgt]``. Args: - df_feat (pandas.DataFrame): Feature where src and tgt exist. + df_feat (Interaction): Feature where src and tgt exist. source_field (str): Source field target_field (str): Target field form (str, optional): Sparse matrix format. Defaults to ``coo``. @@ -1419,14 +1438,14 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', Returns: scipy.sparse: Sparse matrix in form ``coo`` or ``csr``. """ - src = df_feat[source_field].values - tgt = df_feat[target_field].values + src = df_feat[source_field] + tgt = df_feat[target_field] if value_field is None: data = np.ones(len(df_feat)) else: - if value_field not in df_feat.columns: + if value_field not in df_feat: raise ValueError('value_field [{}] should be one of `df_feat`\'s features.'.format(value_field)) - data = df_feat[value_field].values + data = df_feat[value_field] mat = coo_matrix((data, (src, tgt)), shape=(self.num(source_field), self.num(target_field))) if form == 'coo': @@ -1436,7 +1455,7 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', else: raise NotImplementedError('sparse matrix format [{}] has not been implemented.'.format(form)) - def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_field=None): + def _create_graph(self, tensor_feat, source_field, target_field, form='dgl', value_field=None): """Get graph that describe relations between two fields. Source and target should be token-like fields. @@ -1447,7 +1466,7 @@ def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_f Currently, we support graph in `DGL`_ and `PyG`_. Args: - df_feat (pandas.DataFrame): Feature where src and tgt exist. + tensor_feat (Interaction): Feature where src and tgt exist. source_field (str): Source field target_field (str): Target field form (str, optional): Library of graph data structure. Defaults to ``dgl``. @@ -1463,7 +1482,6 @@ def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_f .. _PyG: https://github.com/rusty1s/pytorch_geometric """ - tensor_feat = self._dataframe_to_interaction(df_feat) src = tensor_feat[source_field] tgt = tensor_feat[target_field] @@ -1501,7 +1519,7 @@ def inter_matrix(self, form='coo', value_field=None): scipy.sparse: Sparse matrix in form ``coo`` or ``csr``. """ if not self.uid_field or not self.iid_field: - raise ValueError('dataset doesn\'t exist uid/iid, thus can not converted to sparse matrix') + raise ValueError('dataset does not exist uid/iid, thus can not converted to sparse matrix.') return self._create_sparse_matrix(self.inter_feat, self.uid_field, self.iid_field, form, value_field) def _history_matrix(self, row, value_field=None): @@ -1529,13 +1547,13 @@ def _history_matrix(self, row, value_field=None): """ self._check_field('uid_field', 'iid_field') - user_ids, item_ids = self.inter_feat[self.uid_field].values, self.inter_feat[self.iid_field].values + user_ids, item_ids = self.inter_feat[self.uid_field].numpy(), self.inter_feat[self.iid_field].numpy() if value_field is None: values = np.ones(len(self.inter_feat)) else: - if value_field not in self.inter_feat.columns: + if value_field not in self.inter_feat: raise ValueError('value_field [{}] should be one of `inter_feat`\'s features.'.format(value_field)) - values = self.inter_feat[value_field].values + values = self.inter_feat[value_field].numpy() if row == 'user': row_num, max_col_num = self.user_num, self.item_num @@ -1550,7 +1568,7 @@ def _history_matrix(self, row, value_field=None): col_num = np.max(history_len) if col_num > max_col_num * 0.2: - self.logger.warning('max value of {}\'s history interaction records has reached {}% of the total'.format( + self.logger.warning('max value of {}\'s history interaction records has reached {}% of the total.'.format( row, col_num / max_col_num * 100, )) @@ -1628,8 +1646,7 @@ def get_preload_weight(self, field): raise ValueError('field [{}] not in preload_weight'.format(field)) return self._preloaded_weight[field] - @dlapi.set() - def _dataframe_to_interaction(self, data, *args): + def _dataframe_to_interaction(self, data): """Convert :class:`pandas.DataFrame` to :class:`~recbole.data.interaction.Interaction`. Args: @@ -1638,37 +1655,18 @@ def _dataframe_to_interaction(self, data, *args): Returns: :class:`~recbole.data.interaction.Interaction`: Converted data. """ - data = data.to_dict(orient='list') - return self._dict_to_interaction(data, *args) - - @dlapi.set() - def _dict_to_interaction(self, data, *args): - """Convert :class:`dict` to :class:`~recbole.data.interaction.Interaction`. - - Args: - data (dict): data to be converted. - - Returns: - :class:`~recbole.data.interaction.Interaction`: Converted data. - """ + new_data = {} for k in data: + value = data[k].values ftype = self.field2type[k] if ftype == FeatureType.TOKEN: - data[k] = torch.LongTensor(data[k]) + new_data[k] = torch.LongTensor(value) elif ftype == FeatureType.FLOAT: - data[k] = torch.FloatTensor(data[k]) + new_data[k] = torch.FloatTensor(value) elif ftype == FeatureType.TOKEN_SEQ: - if isinstance(data[k], np.ndarray): - data[k] = torch.LongTensor(data[k][:, :self.field2seqlen[k]]) - else: - seq_data = [torch.LongTensor(d[:self.field2seqlen[k]]) for d in data[k]] - data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) + seq_data = [torch.LongTensor(d[:self.field2seqlen[k]]) for d in value] + new_data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) elif ftype == FeatureType.FLOAT_SEQ: - if isinstance(data[k], np.ndarray): - data[k] = torch.FloatTensor(data[k][:, :self.field2seqlen[k]]) - else: - seq_data = [torch.FloatTensor(d[:self.field2seqlen[k]]) for d in data[k]] - data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) - else: - raise ValueError('Illegal ftype [{}]'.format(ftype)) - return Interaction(data, *args) + seq_data = [torch.FloatTensor(d[:self.field2seqlen[k]]) for d in value] + new_data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) + return Interaction(new_data) diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index 06855c310..ea27412ed 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -16,9 +16,8 @@ from collections import Counter import numpy as np -import pandas as pd -from scipy.sparse import coo_matrix import torch +from scipy.sparse import coo_matrix from recbole.data.dataset import Dataset from recbole.data.utils import dlapi @@ -59,10 +58,11 @@ class KnowledgeBasedDataset(Dataset): Note: :attr:`entity_field` doesn't exist exactly. It's only a symbol, - representing entitiy features. E.g. it can be written into ``config['fields_in_same_space']``. + representing entity features. E.g. it can be written into ``config['fields_in_same_space']``. ``[UI-Relation]`` is a special relation token. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -123,11 +123,11 @@ def __str__(self): 'The number of items that have been linked to KG: {}'.format(len(self.item2entity))] return '\n'.join(info) - def _build_feat_list(self): - feat_list = super()._build_feat_list() + def _build_feat_name_list(self): + feat_name_list = super()._build_feat_name_list() if self.kg_feat is not None: - feat_list.append(self.kg_feat) - return feat_list + feat_name_list.append('kg_feat') + return feat_name_list def _restore_saved_dataset(self, saved_dataset): raise NotImplementedError() @@ -382,7 +382,7 @@ def head_entities(self): Returns: numpy.ndarray: List of head entities of kg triplets. """ - return self.kg_feat[self.head_entity_field].values + return self.kg_feat[self.head_entity_field].numpy() @property @dlapi.set() @@ -391,7 +391,7 @@ def tail_entities(self): Returns: numpy.ndarray: List of tail entities of kg triplets. """ - return self.kg_feat[self.tail_entity_field].values + return self.kg_feat[self.tail_entity_field].numpy() @property @dlapi.set() @@ -400,7 +400,7 @@ def relations(self): Returns: numpy.ndarray: List of relations of kg triplets. """ - return self.kg_feat[self.relation_field].values + return self.kg_feat[self.relation_field].numpy() @property @dlapi.set() @@ -419,7 +419,7 @@ def kg_graph(self, form='coo', value_field=None): else ``graph[src, tgt] = self.kg_feat[value_field][src, tgt]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. @@ -447,11 +447,11 @@ def kg_graph(self, form='coo', value_field=None): def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): user_num = self.user_num - hids = self.kg_feat[self.head_entity_field].values + user_num - tids = self.kg_feat[self.tail_entity_field].values + user_num + hids = self.head_entities + user_num + tids = self.tail_entities + user_num - uids = self.inter_feat[self.uid_field].values - iids = self.inter_feat[self.iid_field].values + user_num + uids = self.inter_feat[self.uid_field].numpy() + iids = self.inter_feat[self.iid_field].numpy() + user_num ui_rel_num = len(uids) ui_rel_id = self.relation_num - 1 @@ -463,7 +463,7 @@ def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): if not show_relation: data = np.ones(len(src)) else: - kg_rel = self.kg_feat[self.relation_field].values + kg_rel = self.kg_feat[self.relation_field].numpy() ui_rel = np.full(2 * ui_rel_num, ui_rel_id, dtype=kg_rel.dtype) data = np.concatenate([ui_rel, kg_rel]) node_num = self.entity_num + self.user_num @@ -478,8 +478,8 @@ def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): def _create_ckg_graph(self, form='dgl', show_relation=False): user_num = self.user_num - kg_tensor = self._dataframe_to_interaction(self.kg_feat) - inter_tensor = self._dataframe_to_interaction(self.inter_feat) + kg_tensor = self.kg_feat + inter_tensor = self.inter_feat head_entity = kg_tensor[self.head_entity_field] + user_num tail_entity = kg_tensor[self.tail_entity_field] + user_num @@ -524,7 +524,7 @@ def ckg_graph(self, form='coo', value_field=None): or ``graph[src, tgt] = [UI-Relation]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. diff --git a/recbole/data/dataset/kg_seq_dataset.py b/recbole/data/dataset/kg_seq_dataset.py index 90a34800c..c1c2e3102 100644 --- a/recbole/data/dataset/kg_seq_dataset.py +++ b/recbole/data/dataset/kg_seq_dataset.py @@ -16,5 +16,6 @@ class Kg_Seq_Dataset(SequentialDataset, KnowledgeBasedDataset): Inherit from :class:`~recbole.data.dataset.sequential_dataset.SequentialDataset` and :class:`~recbole.data.dataset.kg_dataset.KnowledgeBasedDataset`. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) diff --git a/recbole/data/dataset/sequential_dataset.py b/recbole/data/dataset/sequential_dataset.py index 4f2af4139..96272d742 100644 --- a/recbole/data/dataset/sequential_dataset.py +++ b/recbole/data/dataset/sequential_dataset.py @@ -12,10 +12,10 @@ ############################### """ -import numpy as np -import pandas as pd import copy +import numpy as np + from recbole.data.dataset import Dataset @@ -34,6 +34,7 @@ class SequentialDataset(Dataset): item_list_length (numpy.ndarray): List of item sequences' length after augmentation. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -60,7 +61,7 @@ def prepare_data_augmentation(self): See :class:`SequentialDataset`'s attributes for details. Note: - Actually, we do not realy generate these new item sequences. + Actually, we do not really generate these new item sequences. One user's item sequence is stored only once in memory. We store the index (slice) of each item sequence after augmentation, which saves memory and accelerates a lot. @@ -75,7 +76,7 @@ def prepare_data_augmentation(self): last_uid = None uid_list, item_list_index, target_index, item_list_length = [], [], [], [] seq_start = 0 - for i, uid in enumerate(self.inter_feat[self.uid_field].values): + for i, uid in enumerate(self.inter_feat[self.uid_field].numpy()): if last_uid != uid: last_uid = uid seq_start = i @@ -99,8 +100,10 @@ def leave_one_out(self, group_by, leave_one_num=1): raise ValueError('leave one out strategy require a group field') self.prepare_data_augmentation() - grouped_index = pd.DataFrame(self.uid_list).groupby(by=0).groups.values() + grouped_index = self._grouped_index(self.uid_list) next_index = self._split_index_by_leave_one_out(grouped_index, leave_one_num) + + self._drop_unused_col() next_ds = [] for index in next_index: ds = copy.copy(self) diff --git a/recbole/data/dataset/social_dataset.py b/recbole/data/dataset/social_dataset.py index 9cf4a93c9..ebeb8ebcb 100644 --- a/recbole/data/dataset/social_dataset.py +++ b/recbole/data/dataset/social_dataset.py @@ -14,9 +14,6 @@ import os -import numpy as np -from scipy.sparse import coo_matrix - from recbole.data.dataset import Dataset from recbole.data.utils import dlapi from recbole.utils import FeatureSource @@ -37,6 +34,7 @@ class SocialDataset(Dataset): net_feat (pandas.DataFrame): Internal data structure stores the network features. It's loaded from file ``.net``. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -56,13 +54,13 @@ def _load_data(self, token, dataset_path): super()._load_data(token, dataset_path) self.net_feat = self._load_net(self.dataset_name, self.dataset_path) - def _build_feat_list(self): - feat_list = super()._build_feat_list() + def _build_feat_name_list(self): + feat_name_list = super()._build_feat_name_list() if self.net_feat is not None: - feat_list.append(self.net_feat) - return feat_list + feat_name_list.append('net_feat') + return feat_name_list - def _load_net(self, dataset_name, dataset_path): + def _load_net(self, dataset_name, dataset_path): net_file_path = os.path.join(dataset_path, '{}.{}'.format(dataset_name, 'net')) if os.path.isfile(net_file_path): net_feat = self._load_feat(net_file_path, FeatureSource.NET) @@ -71,7 +69,7 @@ def _load_net(self, dataset_name, dataset_path): return net_feat else: raise ValueError('File {} not exist'.format(net_file_path)) - + def _get_fields_in_same_space(self): """Parsing ``config['fields_in_same_space']``. See :doc:`../user_guide/data/data_args` for detail arg setting. @@ -83,7 +81,7 @@ def _get_fields_in_same_space(self): """ fields_in_same_space = super()._get_fields_in_same_space() fields_in_same_space = [_ for _ in fields_in_same_space if (self.source_field not in _) and - (self.target_field not in _)] + (self.target_field not in _)] for field_set in fields_in_same_space: if self.uid_field in field_set: field_set.update({self.source_field, self.target_field}) @@ -98,7 +96,7 @@ def net_graph(self, form='coo', value_field=None): else ``graph[src, tgt] = self.net_feat[value_field][src, tgt]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. diff --git a/recbole/data/dataset/xgboost_dataset.py b/recbole/data/dataset/xgboost_dataset.py new file mode 100644 index 000000000..eb044b8e4 --- /dev/null +++ b/recbole/data/dataset/xgboost_dataset.py @@ -0,0 +1,95 @@ +# @Time : 2020/12/17 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +""" +recbole.data.xgboost_dataset +########################## +""" + +from recbole.data.dataset import Dataset +from recbole.utils import FeatureType + + +class XgboostDataset(Dataset): + """:class:`XgboostDataset` is based on :class:`~recbole.data.dataset.dataset.Dataset`, + and + + Attributes: + + """ + + def __init__(self, config, saved_dataset=None): + super().__init__(config, saved_dataset=saved_dataset) + + def _judge_token_and_convert(self, feat): + # get columns whose type is token + col_list = [] + for col_name in feat: + if col_name == self.uid_field or col_name == self.iid_field: + continue + if self.field2type[col_name] == FeatureType.TOKEN: + col_list.append(col_name) + elif self.field2type[col_name] in {FeatureType.TOKEN_SEQ, FeatureType.FLOAT_SEQ}: + feat = feat.drop([col_name], axis=1, inplace=False) + + # get hash map + for col in col_list: + self.hash_map[col] = dict({}) + self.hash_count[col] = 0 + + del_col = [] + for col in self.hash_map: + if col in feat.keys(): + for value in feat[col]: + # print(value) + if value not in self.hash_map[col]: + self.hash_map[col][value] = self.hash_count[col] + self.hash_count[col] = self.hash_count[col] + 1 + if self.hash_count[col] > self.config['token_num_threshold']: + del_col.append(col) + break + + for col in del_col: + del self.hash_count[col] + del self.hash_map[col] + col_list.remove(col) + self.convert_col_list.extend(col_list) + + # transform the original data + for col in self.hash_map.keys(): + if col in feat.keys(): + feat[col] = feat[col].map(self.hash_map[col]) + + return feat + + def _convert_token_to_hash(self): + """Convert the data of token type to hash form + + """ + self.hash_map = {} + self.hash_count = {} + self.convert_col_list = [] + if self.config['convert_token_to_onehot']: + for feat_name in ['inter_feat', 'user_feat', 'item_feat']: + feat = getattr(self, feat_name) + if feat is not None: + feat = self._judge_token_and_convert(feat) + setattr(self, feat_name, feat) + + def _from_scratch(self): + """Load dataset from scratch. + Initialize attributes firstly, then load data from atomic files, pre-process the dataset lastly. + """ + self.logger.debug('Loading {} from scratch'.format(self.__class__)) + + self._get_preset() + self._get_field_from_config() + self._load_data(self.dataset_name, self.dataset_path) + self._data_processing() + self._convert_token_to_hash() + self._change_feat_format() + + def __getitem__(self, index, join=True): + df = self.inter_feat[index] + return self.join(df) if join else df diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 1787bd30b..3e5360d6b 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -13,6 +13,7 @@ """ import numpy as np +import torch class Interaction(object): @@ -81,13 +82,20 @@ class Interaction(object): def __init__(self, interaction, pos_len_list=None, user_len_list=None): self.interaction = interaction + self.pos_len_list = self.user_len_list = None + self.set_additional_info(pos_len_list, user_len_list) + for k in self.interaction: + if not isinstance(self.interaction[k], torch.Tensor): + raise ValueError('interaction [{}] should only contains torch.Tensor'.format(interaction)) + self.length = -1 + for k in self.interaction: + self.length = max(self.length, self.interaction[k].shape[0]) + + def set_additional_info(self, pos_len_list=None, user_len_list=None): self.pos_len_list = pos_len_list self.user_len_list = user_len_list if (self.pos_len_list is None) ^ (self.user_len_list is None): raise ValueError('pos_len_list and user_len_list should be both None or valued.') - for k in self.interaction: - self.length = self.interaction[k].shape[0] - break def __iter__(self): return self.interaction.__iter__() @@ -101,13 +109,17 @@ def __getitem__(self, index): ret[k] = self.interaction[k][index] return Interaction(ret) + def __contains__(self, item): + return item in self.interaction + def __len__(self): return self.length def __str__(self): info = ['The batch_size of interaction: {}'.format(self.length)] for k in self.interaction: - temp_str = " {}, {}, {}".format(k, self.interaction[k].shape, self.interaction[k].device.type) + inter = self.interaction[k] + temp_str = " {}, {}, {}, {}".format(k, inter.shape, inter.device.type, inter.dtype) info.append(temp_str) info.append('\n') return '\n'.join(info) @@ -115,6 +127,14 @@ def __str__(self): def __repr__(self): return self.__str__() + @property + def columns(self): + """ + Returns: + list of str: The columns of interaction. + """ + return list(self.interaction.keys()) + def to(self, device, selected_field=None): """Transfer Tensors in this Interaction object to the specified device. @@ -124,20 +144,21 @@ def to(self, device, selected_field=None): with keys in selected_field will be sent to device. Returns: - Interaction: a copyed Interaction object with Tensors which are sented to + Interaction: a coped Interaction object with Tensors which are sent to the specified device. """ ret = {} if isinstance(selected_field, str): selected_field = [selected_field] - try: + + if selected_field is not None: selected_field = set(selected_field) for k in self.interaction: if k in selected_field: ret[k] = self.interaction[k].to(device) else: ret[k] = self.interaction[k] - except: + else: for k in self.interaction: ret[k] = self.interaction[k].to(device) return Interaction(ret) @@ -146,7 +167,7 @@ def cpu(self): """Transfer Tensors in this Interaction object to cpu. Returns: - Interaction: a copyed Interaction object with Tensors which are sented to cpu. + Interaction: a coped Interaction object with Tensors which are sent to cpu. """ ret = {} for k in self.interaction: @@ -214,8 +235,114 @@ def repeat_interleave(self, repeats, dim=0): def update(self, new_inter): """Similar to ``dict.update()`` + + Args: + new_inter (Interaction): current interaction will be updated by new_inter. """ for k in new_inter.interaction: self.interaction[k] = new_inter.interaction[k] - self.pos_len_list = new_inter.pos_len_list - self.user_len_list = new_inter.user_len_list + if new_inter.pos_len_list is not None: + self.pos_len_list = new_inter.pos_len_list + if new_inter.user_len_list is not None: + self.user_len_list = new_inter.user_len_list + + def drop(self, column): + """Drop column in interaction. + + Args: + column (str): the column to be dropped. + """ + if column not in self.interaction: + raise ValueError('column [{}] is not in [{}]'.format(column, self)) + del self.interaction[column] + + def _reindex(self, index): + """Reset the index of interaction inplace. + + Args: + index: the new index of current interaction. + """ + for k in self.interaction: + self.interaction[k] = self.interaction[k][index] + if self.pos_len_list is not None: + self.pos_len_list = self.pos_len_list[index] + if self.user_len_list is not None: + self.user_len_list = self.user_len_list[index] + + def shuffle(self): + """Shuffle current interaction inplace. + """ + index = torch.randperm(self.length) + self._reindex(index) + + def sort(self, by, ascending=True): + """Sort the current interaction inplace. + + Args: + by (str or list of str): Field that as the key in the sorting process. + ascending (bool or list of bool, optional): Results are ascending if ``True``, otherwise descending. + Defaults to ``True`` + """ + if isinstance(by, str): + if by not in self.interaction: + raise ValueError('[{}] is not exist in interaction [{}]'.format(by, self)) + by = [by] + elif isinstance(by, (list, tuple)): + for b in by: + if b not in self.interaction: + raise ValueError('[{}] is not exist in interaction [{}]'.format(b, self)) + else: + raise TypeError('wrong type of by [{}]'.format(by)) + + if isinstance(ascending, bool): + ascending = [ascending] + elif isinstance(ascending, (list, tuple)): + for a in ascending: + if not isinstance(a, bool): + raise TypeError('wrong type of ascending [{}]'.format(ascending)) + else: + raise TypeError('wrong type of ascending [{}]'.format(ascending)) + + if len(by) != len(ascending): + if len(ascending) == 1: + ascending = ascending * len(by) + else: + raise ValueError('by [{}] and ascending [{}] should have same length'.format(by, ascending)) + + for b, a in zip(by[::-1], ascending[::-1]): + index = np.argsort(self.interaction[b], kind='stable') + if not a: + index = index[::-1] + self._reindex(index) + + def add_prefix(self, prefix): + """Add prefix to current interaction's columns. + + Args: + prefix (str): The prefix to be added. + """ + self.interaction = {prefix + key: value for key, value in self.interaction.items()} + + +def cat_interactions(interactions): + """Concatenate list of interactions to single interaction. + + Args: + interactions (list of :class:`Interaction`): List of interactions to be concatenated. + + Returns: + :class:`Interaction`: Concatenated interaction. + """ + if not isinstance(interactions, (list, tuple)): + raise TypeError('interactions [{}] should be list or tuple'.format(interactions)) + if len(interactions) == 0: + raise ValueError('interactions [{}] should have some interactions'.format(interactions)) + + columns_set = set(interactions[0].columns) + for inter in interactions: + if columns_set != set(inter.columns): + raise ValueError('interactions [{}] should have some interactions'.format(interactions)) + + new_inter = {col: torch.cat([inter[col] for inter in interactions]) + for col in columns_set} + return Interaction(new_inter) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 810c96e0d..fa8c4abf3 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -13,13 +13,13 @@ """ import copy -import os import importlib +import os from recbole.config import EvalSetting +from recbole.data.dataloader import * from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType -from recbole.data.dataloader import * def create_dataset(config): @@ -31,9 +31,10 @@ def create_dataset(config): Returns: Dataset: Constructed dataset. """ - try: - return getattr(importlib.import_module('recbole.data.dataset'), config['model'] + 'Dataset')(config) - except AttributeError: + dataset_module = importlib.import_module('recbole.data.dataset') + if hasattr(dataset_module, config['model'] + 'Dataset'): + return getattr(dataset_module, config['model'] + 'Dataset')(config) + else: model_type = config['MODEL_TYPE'] if model_type == ModelType.SEQUENTIAL: from .dataset import SequentialDataset @@ -44,6 +45,9 @@ def create_dataset(config): elif model_type == ModelType.SOCIAL: from .dataset import SocialDataset return SocialDataset(config) + elif model_type == ModelType.XGBOOST: + from .dataset import XgboostDataset + return XgboostDataset(config) else: from .dataset import Dataset return Dataset(config) @@ -85,18 +89,23 @@ def data_preparation(config, dataset, save=False): if es.split_args['strategy'] != 'loo' and model_type == ModelType.SEQUENTIAL: raise ValueError('Sequential models require "loo" split strategy.') - builded_datasets = dataset.build(es) - train_dataset, valid_dataset, test_dataset = builded_datasets + built_datasets = dataset.build(es) + train_dataset, valid_dataset, test_dataset = built_datasets phases = ['train', 'valid', 'test'] + sampler = None if save: - save_datasets(config['checkpoint_dir'], name=phases, dataset=builded_datasets) + save_datasets(config['checkpoint_dir'], name=phases, dataset=built_datasets) kwargs = {} if config['training_neg_sample_num']: - es.neg_sample_by(config['training_neg_sample_num']) + if dataset.label_field in dataset.inter_feat: + raise ValueError(f'`training_neg_sample_num` should be 0 ' + f'if inter_feat have label_field [{dataset.label_field}].') + train_distribution = config['training_neg_sample_distribution'] or 'uniform' + es.neg_sample_by(by=config['training_neg_sample_num'], distribution=train_distribution) if model_type != ModelType.SEQUENTIAL: - sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) + sampler = Sampler(phases, built_datasets, es.neg_sample_args['distribution']) else: sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) kwargs['sampler'] = sampler.set_phase('train') @@ -117,9 +126,16 @@ def data_preparation(config, dataset, save=False): kwargs = {} if len(es_str) > 1 and getattr(es, es_str[1], None): + if dataset.label_field in dataset.inter_feat: + raise ValueError(f'It can not validate with `{es_str[1]}` ' + f'when inter_feat have label_field [{dataset.label_field}].') getattr(es, es_str[1])() - if 'sampler' not in locals(): - sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) + if sampler is None: + if model_type != ModelType.SEQUENTIAL: + sampler = Sampler(phases, built_datasets, es.neg_sample_args['distribution']) + else: + sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) + sampler.set_distribution(es.neg_sample_args['distribution']) kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')] kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) valid_data, test_data = dataloader_construct( @@ -163,7 +179,7 @@ def dataloader_construct(name, config, eval_setting, dataset, if len(dataset) != len(batch_size): raise ValueError('dataset {} and batch_size {} should have the same length'.format(dataset, batch_size)) - kwargs_list = [{} for i in range(len(dataset))] + kwargs_list = [{} for _ in range(len(dataset))] for key, value in kwargs.items(): key = [key] * len(dataset) if not isinstance(value, list): @@ -179,11 +195,11 @@ def dataloader_construct(name, config, eval_setting, dataset, logger.info(eval_setting) logger.info('batch_size = [{}], shuffle = [{}]\n'.format(batch_size, shuffle)) - DataLoader = get_data_loader(name, config, eval_setting) + dataloader = get_data_loader(name, config, eval_setting) try: ret = [ - DataLoader( + dataloader( config=config, dataset=ds, batch_size=bs, @@ -233,7 +249,12 @@ def get_data_loader(name, config, eval_setting): type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. """ register_table = { - 'DIN': _get_DIN_data_loader + 'DIN': _get_DIN_data_loader, + "MultiDAE": _get_AE_data_loader, + "MultiVAE": _get_AE_data_loader, + 'MacridVAE': _get_AE_data_loader, + 'CDAE': _get_AE_data_loader, + 'ENMF': _get_AE_data_loader } if config['model'] in register_table: @@ -254,7 +275,7 @@ def get_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'by': return ContextNegSampleDataLoader elif neg_sample_strategy == 'full': - raise NotImplementedError('context model\'s full_sort has not been implemented') + return ContextFullDataLoader elif model_type == ModelType.SEQUENTIAL: if neg_sample_strategy == 'none': return SequentialDataLoader @@ -262,6 +283,13 @@ def get_data_loader(name, config, eval_setting): return SequentialNegSampleDataLoader elif neg_sample_strategy == 'full': return SequentialFullDataLoader + elif model_type == ModelType.XGBOOST: + if neg_sample_strategy == 'none': + return XgboostDataLoader + elif neg_sample_strategy == 'by': + return XgboostNegSampleDataLoader + elif neg_sample_strategy == 'full': + return XgboostFullDataLoader elif model_type == ModelType.KNOWLEDGE: if neg_sample_strategy == 'by': if name == 'train': @@ -299,6 +327,29 @@ def _get_DIN_data_loader(name, config, eval_setting): return SequentialFullDataLoader +def _get_AE_data_loader(name, config, eval_setting): + """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. + + Args: + name (str): The stage of dataloader. It can only take two values: 'train' or 'evaluation'. + config (Config): An instance object of Config, used to record parameter information. + eval_setting (EvalSetting): An instance object of EvalSetting, used to record evaluation settings. + + Returns: + type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. + """ + neg_sample_strategy = eval_setting.neg_sample_args['strategy'] + if name == "train": + return UserDataLoader + else: + if neg_sample_strategy == 'none': + return GeneralDataLoader + elif neg_sample_strategy == 'by': + return GeneralNegSampleDataLoader + elif neg_sample_strategy == 'full': + return GeneralFullDataLoader + + class DLFriendlyAPI(object): """A Decorator class, which helps copying :class:`Dataset` methods to :class:`DataLoader`. @@ -306,13 +357,15 @@ class DLFriendlyAPI(object): E.g. if ``train_data`` is an object of :class:`DataLoader`, and :meth:`~recbole.data.dataset.dataset.Dataset.num` is a method of :class:`~recbole.data.dataset.dataset.Dataset`, - Cause it has been decorated, :meth:`~recbole.data.dataset.dataset.Dataset.num` can be called directly by ``train_data``. + Cause it has been decorated, :meth:`~recbole.data.dataset.dataset.Dataset.num` can be called directly by + ``train_data``. See the example of :meth:`set` for details. Attributes: dataloader_apis (set): Register table that saves all the method names of DataLoader Friendly APIs. """ + def __init__(self): self.dataloader_apis = set() @@ -330,9 +383,11 @@ def set(self): def dataset_meth(): ... """ + def decorator(f): self.dataloader_apis.add(f.__name__) return f + return decorator diff --git a/recbole/evaluator/__init__.py b/recbole/evaluator/__init__.py index 61b72ad22..73f4cad5f 100644 --- a/recbole/evaluator/__init__.py +++ b/recbole/evaluator/__init__.py @@ -1,4 +1,4 @@ from recbole.evaluator.abstract_evaluator import * -from recbole.evaluator.loss_evaluator import * +from recbole.evaluator.proxy_evaluator import * from recbole.evaluator.metrics import * -from recbole.evaluator.topk_evaluator import * +from recbole.evaluator.evaluators import * diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index b47d4f419..45254c9a6 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -3,18 +3,27 @@ # @Author : Kaiyuan Li # @email : tsotfsk@outlook.com +# UPDATE +# @Time : 2020/10/21, 2020/12/18 +# @Author : Kaiyuan Li, Zhichao Feng +# @email : tsotfsk@outlook.com, fzcbupt@gmail.com + """ recbole.evaluator.abstract_evaluator ##################################### """ +import numpy as np +import torch +from torch.nn.utils.rnn import pad_sequence + -class AbstractEvaluator(object): - """:class:`AbstractEvaluator` is an abstract object which supports +class BaseEvaluator(object): + """:class:`BaseEvaluator` is an object which supports the evaluation of the model. It is called by :class:`Trainer`. - Note: - If you want to inherit this class and implement your own evalautor class, + Note: + If you want to inherit this class and implement your own evaluator class, you must implement the following functions. Args: @@ -22,25 +31,106 @@ class AbstractEvaluator(object): """ - def __init__(self, config): - self.metrics = config['metrics'] - - def _check_args(self): - """check the correct of the setting""" - raise NotImplementedError + def __init__(self, config, metrics): + self.metrics = metrics + self.full = ('full' in config['eval_setting']) + self.precision = config['metric_decimal_place'] - def collect(self): + def collect(self, *args): """get the intermediate results for each batch, it is called at the end of each batch""" raise NotImplementedError - def evaluate(self): + def evaluate(self, *args): """calculate the metrics of all batches, it is called at the end of each epoch""" raise NotImplementedError - def metrics_info(self): - """get metrics result""" - raise NotImplementedError - - def _calculate_metrics(self): + def _calculate_metrics(self, *args): """ to calculate the metrics""" raise NotImplementedError + + +class GroupedEvaluator(BaseEvaluator): + """:class:`GroupedEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new group-based metric, + you may need to inherit this class + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, scores_tensor, user_len_list): + """padding scores_tensor. It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + scores_list = torch.split(scores_tensor, user_len_list, dim=0) + padding_score = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items + return padding_score + + def full_sort_collect(self, scores_tensor, user_len_list): + """it is called when evaluation sample distribution is `full`. + + """ + return scores_tensor.view(len(user_len_list), -1) + + def get_score_matrix(self, scores_tensor, user_len_list): + """get score matrix. + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + user_len_list(list): number of all items + + """ + if self.full: + scores_matrix = self.full_sort_collect(scores_tensor, user_len_list) + else: + scores_matrix = self.sample_collect(scores_tensor, user_len_list) + return scores_matrix + + +class IndividualEvaluator(BaseEvaluator): + """:class:`IndividualEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new non-group-based metric, + you may need to inherit this class + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + self._check_args() + + def sample_collect(self, true_scores, pred_scores): + """It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + return torch.stack((true_scores, pred_scores.detach()), dim=1) + + def full_sort_collect(self, true_scores, pred_scores): + """it is called when evaluation sample distribution is `full`. + + """ + raise NotImplementedError('full sort can\'t use IndividualEvaluator') + + def get_score_matrix(self, true_scores, pred_scores): + """get score matrix + + Args: + true_scores (tensor): the label of predicted items + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + """ + if self.full: + scores_matrix = self.full_sort_collect(true_scores, pred_scores) + else: + scores_matrix = self.sample_collect(true_scores, pred_scores) + + return scores_matrix + + def _check_args(self): + if self.full: + raise NotImplementedError('full sort can\'t use IndividualEvaluator') diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py new file mode 100644 index 000000000..eb7cb6871 --- /dev/null +++ b/recbole/evaluator/evaluators.py @@ -0,0 +1,357 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/08/04 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/08/04, 2020/08/11, 2020/12/18 +# @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng +# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com + + +from collections import ChainMap + +import numpy as np +import torch + +from recbole.evaluator.abstract_evaluator import GroupedEvaluator, IndividualEvaluator +from recbole.evaluator.metrics import metrics_dict + +# These metrics are typical in topk recommendations +topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} +# These metrics are typical in loss recommendations +loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} +# For GAUC +rank_metrics = {metric.lower(): metric for metric in ['GAUC']} + +# group-based metrics +group_metrics = ChainMap(topk_metrics, rank_metrics) +# not group-based metrics +individual_metrics = ChainMap(loss_metrics) + + +class TopKEvaluator(GroupedEvaluator): + r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which + contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users. Some of them are also limited to k. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.topk = config['topk'] + self._check_args() + + def collect(self, interaction, scores_tensor): + """collect the topk intermediate result of one batch, this function mainly + implements padding and TopK finding. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + user_len_list = interaction.user_len_list + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + + # get topk + _, topk_idx = torch.topk(scores_matrix, max(self.topk), dim=-1) # n_users x k + + return topk_idx + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + topk_idx = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + assert len(pos_len_list) == len(topk_idx) + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(pos_len_list, topk_idx) + for metric, value in zip(self.metrics, result_list): + for k in self.topk: + key = '{}@{}'.format(metric, k) + metric_dict[key] = round(value[k - 1], self.precision) + + return metric_dict + + def _check_args(self): + + # Check topk: + if isinstance(self.topk, (int, list)): + if isinstance(self.topk, int): + self.topk = [self.topk] + for topk in self.topk: + if topk <= 0: + raise ValueError('topk must be a positive integer or a list of positive integers, ' + 'but get `{}`'.format(topk)) + else: + raise TypeError('The topk must be a integer, list') + + def _calculate_metrics(self, pos_len_list, topk_index): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (numpy.ndarray): a list of users' positive items + topk_index (numpy.ndarray): a matrix which contains the index of the topk items for users + + Returns: + numpy.ndarray: a matrix which contains the metrics result + + """ + pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(pos_idx_matrix, pos_len_list) + result_list.append(result) # n_users x len(metrics) x len(ranks) + result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) + return result + + def __str__(self): + msg = 'The TopK Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class RankEvaluator(GroupedEvaluator): + r"""Rank Evaluator is mainly used in ranking tasks except for topk tasks. Now, we support one + rank metric containing `'GAUC'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users except for top-k metrics. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def get_user_pos_len_list(self, interaction, scores_tensor): + """get number of positive items and all items in test set of each user + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + Returns: + list: number of positive items, + list: number of all items + """ + pos_len_list = torch.Tensor(interaction.pos_len_list).to(scores_tensor.device) + user_len_list = interaction.user_len_list + return pos_len_list, user_len_list + + def average_rank(self, scores): + """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. + + Args: + scores(tensor): an ordered tensor, with size of `(N, )` + + Returns: + torch.Tensor: average_rank + + Example: + >>> average_rank(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + + Reference: + https://github.com/scipy/scipy/blob/v0.17.1/scipy/stats/stats.py#L5262-L5352 + + """ + length, width = scores.shape + device = scores.device + true_tensor = torch.full((length, 1), True, dtype=torch.bool, device=device) + + obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) + # bias added to dense + bias = torch.arange(0, length, device=device).repeat(width).reshape(width, -1). \ + transpose(1, 0).reshape(-1) + dense = obs.view(-1).cumsum(0) + bias + + # cumulative counts of each unique value + count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] + # get average rank + avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) + + return avg_rank + + def collect(self, interaction, scores_tensor): + """collect the rank intermediate result of one batch, this function mainly implements ranking + and calculating the sum of rank for positive items. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + desc_scores, desc_index = torch.sort(scores_matrix, dim=-1, descending=True) + + # get the index of positive items in the ranking list + pos_index = (desc_index < pos_len_list.reshape(-1, 1)) + + avg_rank = self.average_rank(desc_scores) + pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)).sum(axis=-1).reshape(-1, 1) + + return pos_rank_sum + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'GAUC:0.9286}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + user_len_list = eval_data.get_user_len_list() + pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + assert len(pos_len_list) == len(pos_rank_sum) + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, self.precision) + + return metric_dict + + def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (numpy.ndarray): a list of users' positive items + topk_index (numpy.ndarray): a matrix which contains the index of the topk items for users + + Returns: + numpy.ndarray: a matrix which contains the metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(user_len_list, pos_len_list, pos_rank_sum) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Rank Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([rank_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class LossEvaluator(IndividualEvaluator): + r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four + loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. + + Note: + The metrics used do not calculate group-based metrics which considers the metrics scores averaged + across users. They are also not limited to k. Instead, they calculate the scores on the entire + prediction results regardless the users. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.label_field = config['LABEL_FIELD'] + + def collect(self, interaction, pred_scores): + """collect the loss intermediate result of one batch, this function mainly + implements concatenating preds and trues. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + Returns: + tensor : a batch of scores with a size of `(N, 2)` + + """ + true_scores = interaction[self.label_field].to(pred_scores.device) + assert len(true_scores) == len(pred_scores) + return self.get_score_matrix(true_scores, pred_scores) + + def evaluate(self, batch_matrix_list, *args): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + + Returns: + dict: such as {'AUC': 0.83} + + """ + concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + trues = concat[:, 0] + preds = concat[:, 1] + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(trues, preds) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, self.precision) + return metric_dict + + def _calculate_metrics(self, trues, preds): + """get metrics result + + Args: + trues (numpy.ndarray): the true scores' list + preds (numpy.ndarray): the predict scores' list + + Returns: + list: a list of metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(trues, preds) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Loss Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ + ']' + return msg + + +metric_eval_bind = [ + (topk_metrics, TopKEvaluator), + (loss_metrics, LossEvaluator), + (rank_metrics, RankEvaluator) +] diff --git a/recbole/evaluator/loss_evaluator.py b/recbole/evaluator/loss_evaluator.py deleted file mode 100644 index 54822ca46..000000000 --- a/recbole/evaluator/loss_evaluator.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04 2020/08/09 -# @Author : Kaiyuan Li Zhichao Feng -# @email : tsotfsk@outlook.com fzcbupt@gmail.com - -""" -recbole.evaluator.loss_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict - -# These metrics are typical in loss recommendations -loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} - - -class LossEvaluator(AbstractEvaluator): - r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four - loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. - - Note: - The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. - They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless the users. - - """ - def __init__(self, config): - super().__init__(config) - - self.label_field = config['LABEL_FIELD'] - self._check_args() - - def collect(self, interaction, pred_scores): - """collect the loss intermediate result of one batch, this function mainly - implements concatenating preds and trues. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - pred_scores (tensor): the tensor of model output with a size of `(N, )` - - Returns: - tensor : a batch of socres with a size of `(N, 2)` - - """ - true_scores = interaction[self.label_field].to(pred_scores.device) - assert len(true_scores) == len(pred_scores) - return torch.stack((true_scores, pred_scores.detach()), dim=1) - - def evaluate(self, batch_matrix_list, *args): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - - Returns: - dict: such as {'AUC': 0.83} - - """ - concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - trues = concat[:, 0] - preds = concat[:, 1] - - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(trues, preds) - for metric, value in zip(self.metrics, result_list): - key = '{}'.format(metric) - metric_dict[key] = round(value, 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in loss_metrics: - raise ValueError("There is no loss metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - def metrics_info(self, trues, preds): - """get metrics result - - Args: - trues (np.ndarray): the true scores' list - preds (np.ndarray): the predict scores' list - - Returns: - list: a list of metrics result - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(trues, preds) - result_list.append(result) - return result_list - - def _calculate_metrics(self, trues, preds): - return self.metrics_info(trues, preds) - - def __str__(self): - mesg = 'The Loss Evaluator Info:\n' + '\tMetrics:[' + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + ']' - return mesg diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 976ac9880..b788fa0eb 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/08/21, 2020/9/16 +# @Time : 2020/08/12, 2020/12/21, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -16,13 +16,14 @@ from logging import getLogger import numpy as np -from recbole.evaluator.utils import _binary_clf_curve from sklearn.metrics import auc as sk_auc -from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error +from sklearn.metrics import mean_absolute_error, mean_squared_error -# TopK Metrics # +from recbole.evaluator.utils import _binary_clf_curve +# TopK Metrics # + def hit_(pos_index, pos_len): r"""Hit_ (also known as hit ratio at :math:`N`) is a way of calculating how many 'hits' you have in an n-sized list of ranked items. @@ -85,7 +86,7 @@ def map_(pos_index, pos_len): actual_len = np.where(pos_len > len_rank, len_rank, pos_len) result = np.zeros_like(pos_index, dtype=np.float) for row, lens in enumerate(actual_len): - ranges = np.arange(1, pos_index.shape[1]+1) + ranges = np.arange(1, pos_index.shape[1] + 1) ranges[lens:] = ranges[lens - 1] result[row] = sum_pre[row] / ranges return result @@ -100,7 +101,7 @@ def recall_(pos_index, pos_len): .. math:: \mathrm {Recall@K} = \frac{|Rel_u\cap Rec_u|}{Rel_u} - :math:`Rel_u` is the set of items relavent to user :math:`U`, + :math:`Rel_u` is the set of items relevant to user :math:`U`, :math:`Rec_u` is the top K items recommended to users. We obtain the result by calculating the average :math:`Recall@K` of each user. @@ -128,7 +129,6 @@ def ndcg_(pos_index, pos_len): :math:`U^{te}` is for all users in the test set. """ - len_rank = np.full_like(pos_len, pos_index.shape[1]) idcg_len = np.where(pos_len > len_rank, len_rank, pos_len) @@ -156,7 +156,7 @@ def precision_(pos_index, pos_len): .. math:: \mathrm {Precision@K} = \frac{|Rel_u \cap Rec_u|}{Rec_u} - :math:`Rel_u` is the set of items relavent to user :math:`U`, + :math:`Rel_u` is the set of items relevant to user :math:`U`, :math:`Rec_u` is the top K items recommended to users. We obtain the result by calculating the average :math:`Precision@K` of each user. @@ -164,8 +164,59 @@ def precision_(pos_index, pos_len): return pos_index.cumsum(axis=1) / np.arange(1, pos_index.shape[1] + 1) -# CTR Metrics # +def gauc_(user_len_list, pos_len_list, pos_rank_sum): + r"""GAUC_ (also known as Group Area Under Curve) is used to evaluate the two-class model, referring to + the area under the ROC curve grouped by user. + + .. _GAUC: https://dl.acm.org/doi/10.1145/3219819.3219823 + + Note: + It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC. + It is also not limited to k. Due to our padding for `scores_tensor` in `RankEvaluator` with + `-np.inf`, the padding value will influence the ranks of origin items. Therefore, we use + descending sort here and make an identity transformation to the formula of `AUC`, which is + shown in `auc_` function. For readability, we didn't do simplification in the code. + + .. math:: + \mathrm {GAUC} = \frac {{{M} \times {(M+N+1)} - \frac{M \times (M+1)}{2}} - + \sum\limits_{i=1}^M rank_{i}} {{M} \times {N}} + :math:`M` is the number of positive samples. + :math:`N` is the number of negative samples. + :math:`rank_i` is the descending rank of the ith positive sample. + + """ + neg_len_list = user_len_list - pos_len_list + + # check positive and negative samples + any_without_pos = np.any(pos_len_list == 0) + any_without_neg = np.any(neg_len_list == 0) + non_zero_idx = np.full(len(user_len_list), True, dtype=np.bool) + if any_without_pos: + logger = getLogger() + logger.warning("No positive samples in some users, " + "true positive value should be meaningless, " + "these users have been removed from GAUC calculation") + non_zero_idx *= (pos_len_list != 0) + if any_without_neg: + logger = getLogger() + logger.warning("No negative samples in some users, " + "false positive value should be meaningless, " + "these users have been removed from GAUC calculation") + non_zero_idx *= (neg_len_list != 0) + if any_without_pos or any_without_neg: + item_list = user_len_list, neg_len_list, pos_len_list, pos_rank_sum + user_len_list, neg_len_list, pos_len_list, pos_rank_sum = \ + map(lambda x: x[non_zero_idx], item_list) + + pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) + user_auc = pair_num / (neg_len_list * pos_len_list) + result = (user_auc * pos_len_list).sum() / pos_len_list.sum() + + return result + + +# CTR Metrics # def auc_(trues, preds): r"""AUC_ (also known as Area Under Curve) is used to evaluate the two-class model, referring to the area under the ROC curve @@ -179,11 +230,11 @@ def auc_(trues, preds): .. math:: \mathrm {AUC} = \frac{\sum\limits_{i=1}^M rank_{i} - - {{M} \times {(M+1)}}} {{M} \times {N}} + - \frac {{M} \times {(M+1)}}{2}} {{{M} \times {N}}} :math:`M` is the number of positive samples. :math:`N` is the number of negative samples. - :math:`rank_i` is the rank of the ith positive sample. + :math:`rank_i` is the ascending rank of the ith positive sample. """ fps, tps = _binary_clf_curve(trues, preds) @@ -300,5 +351,6 @@ def log_loss_(trues, preds): 'rmse': rmse_, 'mae': mae_, 'logloss': log_loss_, - 'auc': auc_ + 'auc': auc_, + 'gauc': gauc_ } diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py new file mode 100644 index 000000000..237e8c29e --- /dev/null +++ b/recbole/evaluator/proxy_evaluator.py @@ -0,0 +1,104 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +# UPDATE +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +from collections import ChainMap + +from recbole.evaluator.evaluators import metric_eval_bind, group_metrics, individual_metrics + + +class ProxyEvaluator(object): + r"""ProxyEvaluator is used to assign the corresponding evaluator according to the evaluation metrics, + for example, TopkEvaluator for top-k metrics, and summarize the results of all evaluators. + + """ + + def __init__(self, config): + self.config = config + self.valid_metrics = ChainMap(group_metrics, individual_metrics) + self.metrics = self.config['metrics'] + self._check_args() + self.evaluators = self.build() + + def build(self): + """assign evaluators according to metrics. + + Returns: + list: a list of evaluators. + + """ + evaluator_list = [] + metrics_list = [metric.lower() for metric in self.metrics] + for metrics, evaluator in metric_eval_bind: + used_metrics = [metric for metric in metrics_list if metric in metrics] + if used_metrics: + evaluator_list.append(evaluator(self.config, used_metrics)) + return evaluator_list + + def collect(self, interaction, scores): + """collect the all used evaluators' intermediate result of one batch. + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores (tensor): the tensor of model output with size of `(N, )` + + """ + results = [] + for evaluator in self.evaluators: + results.append(evaluator.collect(interaction, scores)) + return results + + def merge_batch_result(self, batch_matrix_list): + """merge all the intermediate result got in `self.collect` for used evaluators separately. + + Args: + batch_matrix_list (list): the results of all batches not separated + + Returns: + dict: used evaluators' results of all batches + + """ + matrix_dict = {} + for collect_list in batch_matrix_list: + for i, value in enumerate(collect_list): + matrix_dict.setdefault(i, []).append(value) + + return matrix_dict + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'GAUC': 0.9236}`` + + """ + matrix_dict = self.merge_batch_result(batch_matrix_list) + result_dict = {} + for i, evaluator in enumerate(self.evaluators): + res = evaluator.evaluate(matrix_dict[i], eval_data) + result_dict.update(res) + return result_dict + + def _check_args(self): + + # Check metrics + if isinstance(self.metrics, (str, list)): + if isinstance(self.metrics, str): + self.metrics = [self.metrics] + else: + raise TypeError('metrics must be str or list') + + # Convert metric to lowercase + for m in self.metrics: + if m.lower() not in self.valid_metrics: + raise ValueError("There is no metric named {}!".format(m)) diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py deleted file mode 100644 index b62789b0f..000000000 --- a/recbole/evaluator/topk_evaluator.py +++ /dev/null @@ -1,151 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04, 2020/08/11 -# @Author : Kaiyuan Li, Yupeng Hou -# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn - -""" -recbole.evaluator.topk_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict -from torch.nn.utils.rnn import pad_sequence - -# These metrics are typical in topk recommendations -topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} - - -class TopKEvaluator(AbstractEvaluator): - r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which - contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. - - Note: - The metrics used calculate group-based metrics which considers the metrics scores averaged - across users. Some of them are also limited to k. - - """ - def __init__(self, config): - super().__init__(config) - - self.topk = config['topk'] - self._check_args() - - def collect(self, interaction, scores_tensor, full=False): - """collect the topk intermediate result of one batch, this function mainly - implements padding and TopK finding. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - full (bool, optional): whether it is full sort. Default: False. - - """ - user_len_list = interaction.user_len_list - if full is True: - scores_matrix = scores_tensor.view(len(user_len_list), -1) - else: - scores_list = torch.split(scores_tensor, user_len_list, dim=0) - scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # nusers x items - - # get topk - _, topk_index = torch.topk(scores_matrix, max(self.topk), dim=-1) # nusers x k - - return topk_index - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` - - """ - pos_len_list = eval_data.get_pos_len_list() - topk_index = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - assert len(pos_len_list) == len(topk_index) - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(pos_len_list, topk_index) - for metric, value in zip(self.metrics, result_list): - for k in self.topk: - key = '{}@{}'.format(metric, k) - metric_dict[key] = round(value[k - 1], 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in topk_metrics: - raise ValueError("There is no user grouped topk metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - # Check topk: - if isinstance(self.topk, (int, list)): - if isinstance(self.topk, int): - self.topk = [self.topk] - for topk in self.topk: - if topk <= 0: - raise ValueError('topk must be a positive integer or a list of positive integers, but get `{}`'.format(topk)) - else: - raise TypeError('The topk must be a integer, list') - - def metrics_info(self, pos_idx, pos_len): - """get metrics result - - Args: - pos_idx (np.ndarray): the bool index of all users' topk items that indicating the postive items are - topk items or not - pos_len (list): the length of all users' postivite items - - Returns: - list: a list of matrix which record the results from `1` to `max(topk)` - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(pos_idx, pos_len) - result_list.append(result) - return result_list - - def _calculate_metrics(self, pos_len_list, topk_index): - """integrate the results of each batch and evaluate the topk metrics by users - - Args: - pos_len_list (list): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users - - Returns: - np.ndarray: a matrix which contains the metrics result - - """ - - pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) - result_list = self.metrics_info(pos_idx_matrix, pos_len_list) # n_users x len(metrics) x len(ranks) - result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) - return result - - def __str__(self): - mesg = 'The TopK Evaluator Info:\n' + '\tMetrics:[' + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) \ - + '], TopK:[' + ', '.join(map(str, self.topk)) +']' - return mesg diff --git a/recbole/evaluator/utils.py b/recbole/evaluator/utils.py index 4f14b2ae8..392873493 100644 --- a/recbole/evaluator/utils.py +++ b/recbole/evaluator/utils.py @@ -14,7 +14,6 @@ """ import itertools -from enum import Enum import numpy as np import torch @@ -54,20 +53,20 @@ def trunc(scores, method): """Round the scores by using the given method Args: - scores (np.ndarray): scores + scores (numpy.ndarray): scores method (str): one of ['ceil', 'floor', 'around'] Raises: NotImplementedError: method error Returns: - np.ndarray: processed scores + numpy.ndarray: processed scores """ try: cut_method = getattr(np, method) except NotImplementedError: - raise NotImplementedError("module 'numpy' has no fuction named '{}'".format(method)) + raise NotImplementedError("module 'numpy' has no function named '{}'".format(method)) scores = cut_method(scores) return scores @@ -76,11 +75,11 @@ def cutoff(scores, threshold): """cut of the scores based on threshold Args: - scores (np.ndarray): scores + scores (numpy.ndarray): scores threshold (float): between 0 and 1 Returns: - np.ndarray: processed scores + numpy.ndarray: processed scores """ return np.where(scores > threshold, 1, 0) @@ -93,7 +92,7 @@ def _binary_clf_curve(trues, preds): preds (numpy.ndarray): the predict scores' list Returns: - fps (np.ndarray): A count of false positives, at index i being the number of negative + fps (numpy.ndarray): A count of false positives, at index i being the number of negative samples assigned a score >= thresholds[i] preds (numpy.ndarray): An increasing count of true positives, at index i being the number of positive samples assigned a score >= thresholds[i]. diff --git a/recbole/model/abstract_recommender.py b/recbole/model/abstract_recommender.py index 18983b90a..93c6dfb57 100644 --- a/recbole/model/abstract_recommender.py +++ b/recbole/model/abstract_recommender.py @@ -13,18 +13,24 @@ ################################## """ +from logging import getLogger + import numpy as np import torch import torch.nn as nn -from recbole.utils import ModelType, InputType, FeatureSource, FeatureType from recbole.model.layers import FMEmbedding, FMFirstOrderLinear +from recbole.utils import ModelType, InputType, FeatureSource, FeatureType class AbstractRecommender(nn.Module): r"""Base class for all models """ + def __init__(self): + self.logger = getLogger() + super(AbstractRecommender, self).__init__() + def calculate_loss(self, interaction): r"""Calculate the training loss for a batch data. @@ -110,7 +116,7 @@ def __init__(self, config, dataset): self.n_items = dataset.num(self.ITEM_ID) def gather_indexes(self, output, gather_index): - """Gathers the vectors at the spexific positions over a minibatch""" + """Gathers the vectors at the specific positions over a minibatch""" gather_index = gather_index.view(-1, 1, 1).expand(-1, -1, output.shape[-1]) output_tensor = output.gather(dim=1, index=gather_index) return output_tensor.squeeze(1) @@ -341,6 +347,15 @@ def double_tower_embed_input_fields(self, interaction): return first_sparse_embedding, first_dense_embedding, second_sparse_embedding, second_dense_embedding + def concat_embed_input_fields(self, interaction): + sparse_embedding, dense_embedding = self.embed_input_fields(interaction) + all_embeddings = [] + if sparse_embedding is not None: + all_embeddings.append(sparse_embedding) + if dense_embedding is not None and len(dense_embedding.shape) == 3: + all_embeddings.append(dense_embedding) + return torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + def embed_input_fields(self, interaction): """Embed the whole feature columns. diff --git a/recbole/model/context_aware_recommender/afm.py b/recbole/model/context_aware_recommender/afm.py index 5c615e42c..e1fe1c03c 100644 --- a/recbole/model/context_aware_recommender/afm.py +++ b/recbole/model/context_aware_recommender/afm.py @@ -16,8 +16,8 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import AttLayer from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import AttLayer class AFM(ContextRecommender): @@ -99,15 +99,7 @@ def afm_layer(self, infeature): return att_pooling def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - afm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + afm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.sigmoid(self.first_order_linear(interaction) + self.afm_layer(afm_all_embeddings)) return output.squeeze() diff --git a/recbole/model/context_aware_recommender/autoint.py b/recbole/model/context_aware_recommender/autoint.py index b22e331ee..893d5d8f2 100644 --- a/recbole/model/context_aware_recommender/autoint.py +++ b/recbole/model/context_aware_recommender/autoint.py @@ -13,12 +13,12 @@ """ import torch -import torch.nn.functional as F import torch.nn as nn +import torch.nn.functional as F from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class AutoInt(ContextRecommender): @@ -95,15 +95,7 @@ def autoint_layer(self, infeature): return att_output def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - autoint_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + autoint_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.first_order_linear(interaction) + self.autoint_layer(autoint_all_embeddings) return self.sigmoid(output.squeeze(1)) diff --git a/recbole/model/context_aware_recommender/dcn.py b/recbole/model/context_aware_recommender/dcn.py index 3c9c66ecd..396130803 100644 --- a/recbole/model/context_aware_recommender/dcn.py +++ b/recbole/model/context_aware_recommender/dcn.py @@ -22,9 +22,9 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.loss import RegLoss -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers +from recbole.model.loss import RegLoss class DCN(ContextRecommender): @@ -32,6 +32,7 @@ class DCN(ContextRecommender): automatically construct limited high-degree cross features, and learns the corresponding weights. """ + def __init__(self, config, dataset): super(DCN, self).__init__(config, dataset) @@ -43,13 +44,10 @@ def __init__(self, config, dataset): # define layers and loss # init weight and bias of each cross layer - self.cross_layer_parameter = [nn.Parameter(torch.empty(self.num_feature_field * self.embedding_size, - device=self.device)) - for _ in range(self.cross_layer_num * 2)] - self.cross_layer_w = nn.ParameterList( - self.cross_layer_parameter[:self.cross_layer_num]) - self.cross_layer_b = nn.ParameterList( - self.cross_layer_parameter[self.cross_layer_num:]) + self.cross_layer_w = nn.ParameterList(nn.Parameter(torch.randn(self.num_feature_field * self.embedding_size) + .to(self.device)) for _ in range(self.cross_layer_num)) + self.cross_layer_b = nn.ParameterList(nn.Parameter(torch.zeros(self.num_feature_field * self.embedding_size) + .to(self.device)) for _ in range(self.cross_layer_num)) # size of mlp hidden layer size_list = [self.embedding_size * self.num_feature_field] + self.mlp_hidden_size @@ -97,16 +95,7 @@ def cross_network(self, x_0): return x_l def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - - dcn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + dcn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = dcn_all_embeddings.shape[0] dcn_all_embeddings = dcn_all_embeddings.view(batch_size, -1) diff --git a/recbole/model/context_aware_recommender/deepfm.py b/recbole/model/context_aware_recommender/deepfm.py index 5ff10298b..f1791564e 100644 --- a/recbole/model/context_aware_recommender/deepfm.py +++ b/recbole/model/context_aware_recommender/deepfm.py @@ -16,12 +16,11 @@ Huifeng Guo et al. "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction." in IJCAI 2017. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import BaseFactorizationMachine, MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine, MLPLayers class DeepFM(ContextRecommender): @@ -29,6 +28,7 @@ class DeepFM(ContextRecommender): Also DeepFM can be seen as a combination of FNN and FM. """ + def __init__(self, config, dataset): super(DeepFM, self).__init__(config, dataset) @@ -56,20 +56,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - deepfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + deepfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = deepfm_all_embeddings.shape[0] y_fm = self.first_order_linear(interaction) + self.fm(deepfm_all_embeddings) - y_deep = self.deep_predict_layer( - self.mlp_layers(deepfm_all_embeddings.view(batch_size, -1))) + y_deep = self.deep_predict_layer(self.mlp_layers(deepfm_all_embeddings.view(batch_size, -1))) y = self.sigmoid(y_fm + y_deep) return y.squeeze() diff --git a/recbole/model/context_aware_recommender/dssm.py b/recbole/model/context_aware_recommender/dssm.py index 8261be2eb..346c0f23a 100644 --- a/recbole/model/context_aware_recommender/dssm.py +++ b/recbole/model/context_aware_recommender/dssm.py @@ -12,13 +12,12 @@ PS Huang et al. "Learning Deep Structured Semantic Models for Web Search using Clickthrough Data" in CIKM 2013. """ - import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class DSSM(ContextRecommender): @@ -26,6 +25,7 @@ class DSSM(ContextRecommender): and uses cosine distance to calculate the distance between the two semantic vectors. """ + def __init__(self, config, dataset): super(DSSM, self).__init__(config, dataset) @@ -43,7 +43,7 @@ def __init__(self, config, dataset): self.item_mlp_layers = MLPLayers(item_size_list, self.dropout_prob, activation='tanh', bn=True) self.loss = nn.BCELoss() - self.sigmod = nn.Sigmoid() + self.sigmoid = nn.Sigmoid() # parameters initialization self.apply(self._init_weights) @@ -86,7 +86,7 @@ def forward(self, interaction): item_dnn_out = self.item_mlp_layers(embed_item.view(batch_size, -1)) score = torch.cosine_similarity(user_dnn_out, item_dnn_out, dim=1) - sig_score = self.sigmod(score) + sig_score = self.sigmoid(score) return sig_score.squeeze() def calculate_loss(self, interaction): diff --git a/recbole/model/context_aware_recommender/ffm.py b/recbole/model/context_aware_recommender/ffm.py index 617860ea1..3863d1bfe 100644 --- a/recbole/model/context_aware_recommender/ffm.py +++ b/recbole/model/context_aware_recommender/ffm.py @@ -8,7 +8,7 @@ FFM ##################################################### Reference: - Yuchin Juan et al. "Field-aware Factorization Machines for CTR Prediction" in RecSys 2016. + Yuchin Juan et al. "Field-aware Factorization Machines for CTR Prediction" in RecSys 2016. Reference code: https://github.com/rixwew/pytorch-fm @@ -37,19 +37,20 @@ def __init__(self, config, dataset): super(FFM, self).__init__(config, dataset) # load parameters info - self.fields = config['fields'] # a dict; key: field_id; value: feature_list + self.fields = config['fields'] # a dict; key: field_id; value: feature_list self.sigmoid = nn.Sigmoid() self.feature2id = {} self.feature2field = {} - + self.feature_names = (self.token_field_names, self.float_field_names, self.token_seq_field_names) self.feature_dims = (self.token_field_dims, self.float_field_dims, self.token_seq_field_dims) self._get_feature2field() self.num_fields = len(set(self.feature2field.values())) # the number of fields - self.ffm = FieldAwareFactorizationMachine(self.feature_names, self.feature_dims, self.feature2id, self.feature2field, self.num_fields, self.embedding_size, self.device) + self.ffm = FieldAwareFactorizationMachine(self.feature_names, self.feature_dims, self.feature2id, + self.feature2field, self.num_fields, self.embedding_size, self.device) self.loss = nn.BCELoss() # parameters initialization @@ -73,7 +74,7 @@ def _get_feature2field(self): for name in names: self.feature2id[name] = fea_id fea_id += 1 - + if self.fields is None: field_id = 0 for key, value in self.feature2id.items(): @@ -96,25 +97,25 @@ def get_ffm_input(self, interaction): for tn in self.token_field_names: token_ffm_input.append(torch.unsqueeze(interaction[tn], 1)) if len(token_ffm_input) > 0: - token_ffm_input = torch.cat(token_ffm_input, dim=1) # [batch_size, num_token_features] + token_ffm_input = torch.cat(token_ffm_input, dim=1) # [batch_size, num_token_features] float_ffm_input = [] if self.float_field_names is not None: for fn in self.float_field_names: float_ffm_input.append(torch.unsqueeze(interaction[fn], 1)) if len(float_ffm_input) > 0: - float_ffm_input = torch.cat(float_ffm_input, dim=1) # [batch_size, num_float_features] + float_ffm_input = torch.cat(float_ffm_input, dim=1) # [batch_size, num_float_features] token_seq_ffm_input = [] if self.token_seq_field_names is not None: for tsn in self.token_seq_field_names: - token_seq_ffm_input.append(interaction[tsn]) # a list + token_seq_ffm_input.append(interaction[tsn]) # a list - return (token_ffm_input, float_ffm_input, token_seq_ffm_input) + return token_ffm_input, float_ffm_input, token_seq_ffm_input def forward(self, interaction): ffm_input = self.get_ffm_input(interaction) ffm_output = torch.sum(torch.sum(self.ffm(ffm_input), dim=1), dim=1, keepdim=True) output = self.sigmoid(self.first_order_linear(interaction) + ffm_output) - + return output.squeeze() def calculate_loss(self, interaction): @@ -144,7 +145,8 @@ def __init__(self, feature_names, feature_dims, feature2id, feature2field, num_f self.feature2id = feature2id self.feature2field = feature2field - self.num_features = len(self.token_feature_names) + len(self.float_feature_names) + len(self.token_seq_feature_names) + self.num_features = len(self.token_feature_names) + len(self.float_feature_names) \ + + len(self.token_seq_feature_names) self.num_fields = num_fields self.embed_dim = embed_dim self.device = device @@ -201,7 +203,7 @@ def forward(self, input_x): token_input_x_emb = self._emb_token_ffm_input(token_ffm_input) float_input_x_emb = self._emb_float_ffm_input(float_ffm_input) token_seq_input_x_emb = self._emb_token_seq_ffm_input(token_seq_ffm_input) - + input_x_emb = self._get_input_x_emb(token_input_x_emb, float_input_x_emb, token_seq_input_x_emb) output = list() @@ -241,7 +243,8 @@ def _emb_token_ffm_input(self, token_ffm_input): token_input_x_emb = [] if len(self.token_feature_names) > 0: token_input_x = token_ffm_input + token_ffm_input.new_tensor(self.token_offsets).unsqueeze(0) - token_input_x_emb = [self.token_embeddings[i](token_input_x) for i in range(self.num_fields)] # [num_fields: [batch_size, num_token_features, emb_dim]] + token_input_x_emb = [self.token_embeddings[i](token_input_x) + for i in range(self.num_fields)] # [num_fields: [batch_size, num_token_features, emb_dim]] return token_input_x_emb @@ -249,8 +252,9 @@ def _emb_float_ffm_input(self, float_ffm_input): # get float field-aware embeddings float_input_x_emb = [] if len(self.float_feature_names) > 0: - index = torch.arange(0, self.num_float_features).unsqueeze(0).expand_as(float_ffm_input).long().to(self.device) # [batch_size, num_float_features] - float_input_x_emb = [torch.mul(self.float_embeddings[i](index), float_ffm_input.unsqueeze(2)) for i in range(self.num_fields)] # [num_fields: [batch_size, num_float_features, emb_dim]] + index = torch.arange(0, self.num_float_features).unsqueeze(0).expand_as(float_ffm_input).long().to(self.device) # [batch_size, num_float_features] + float_input_x_emb = [torch.mul(self.float_embeddings[i](index), float_ffm_input.unsqueeze(2)) + for i in range(self.num_fields)] # [num_fields: [batch_size, num_float_features, emb_dim]] return float_input_x_emb @@ -276,6 +280,6 @@ def _emb_token_seq_ffm_input(self, token_seq_ffm_input): result = result.unsqueeze(1) # [batch_size, 1, embed_dim] token_seq_result.append(result) - token_seq_input_x_emb.append(torch.cat(token_seq_result, dim=1)) # [num_fields: batch_size, num_token_seq_features, embed_dim] + token_seq_input_x_emb.append(torch.cat(token_seq_result, dim=1)) # [num_fields: batch_size, num_token_seq_features, embed_dim] return token_seq_input_x_emb diff --git a/recbole/model/context_aware_recommender/fm.py b/recbole/model/context_aware_recommender/fm.py index ae64f9bf5..c43b2aa21 100644 --- a/recbole/model/context_aware_recommender/fm.py +++ b/recbole/model/context_aware_recommender/fm.py @@ -16,12 +16,11 @@ Steffen Rendle et al. "Factorization Machines." in ICDM 2010. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_ -from recbole.model.layers import BaseFactorizationMachine from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine class FM(ContextRecommender): @@ -46,15 +45,7 @@ def _init_weights(self, module): xavier_normal_(module.weight.data) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fm_all_embeddings = torch.cat(all_embeddings, dim=1) + fm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] y = self.sigmoid(self.first_order_linear(interaction) + self.fm(fm_all_embeddings)) return y.squeeze() diff --git a/recbole/model/context_aware_recommender/fnn.py b/recbole/model/context_aware_recommender/fnn.py index 70cda0373..d8e2a1987 100644 --- a/recbole/model/context_aware_recommender/fnn.py +++ b/recbole/model/context_aware_recommender/fnn.py @@ -11,12 +11,11 @@ Weinan Zhang1 et al. "Deep Learning over Multi-field Categorical Data" in ECIR 2016 """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class FNN(ContextRecommender): @@ -59,15 +58,7 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fnn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + fnn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = fnn_all_embeddings.shape[0] output = self.predict_layer(self.mlp_layers(fnn_all_embeddings.view(batch_size, -1))) diff --git a/recbole/model/context_aware_recommender/fwfm.py b/recbole/model/context_aware_recommender/fwfm.py index 5de226476..5c1280f2d 100644 --- a/recbole/model/context_aware_recommender/fwfm.py +++ b/recbole/model/context_aware_recommender/fwfm.py @@ -35,20 +35,20 @@ def __init__(self, config, dataset): # load parameters info self.dropout_prob = config['dropout_prob'] - self.fields = config['fields'] # a dict; key: field_id; value: feature_list + self.fields = config['fields'] # a dict; key: field_id; value: feature_list self.num_features = self.num_feature_field - + self.dropout_layer = nn.Dropout(p=self.dropout_prob) self.sigmoid = nn.Sigmoid() self.feature2id = {} self.feature2field = {} - + self.feature_names = (self.token_field_names, self.token_seq_field_names, self.float_field_names) self.feature_dims = (self.token_field_dims, self.token_seq_field_dims, self.float_field_dims) self._get_feature2field() - self.num_fields = len(set(self.feature2field.values())) # the number of fields + self.num_fields = len(set(self.feature2field.values())) # the number of fields self.num_pair = self.num_fields * self.num_fields self.loss = nn.BCELoss() @@ -71,11 +71,10 @@ def _get_feature2field(self): fea_id = 0 for names in self.feature_names: if names is not None: - print(names) for name in names: self.feature2id[name] = fea_id fea_id += 1 - + if self.fields is None: field_id = 0 for key, value in self.feature2id.items(): @@ -101,33 +100,27 @@ def fwfm_layer(self, infeature): """ # get r(Fi, Fj) batch_size = infeature.shape[0] - para = torch.randn(self.num_fields*self.num_fields*self.embedding_size).expand(batch_size, self.num_fields*self.num_fields*self.embedding_size).to(self.device) # [batch_size*num_pairs*emb_dim] - para = torch.reshape(para, (batch_size, self.num_fields, self.num_fields, self.embedding_size)) - r = nn.Parameter(para, requires_grad=True) # [batch_size, num_fields, num_fields, emb_dim] + para = torch.randn(self.num_fields * self.num_fields * self.embedding_size).\ + expand(batch_size, self.num_fields * self.num_fields * self.embedding_size).\ + to(self.device) # [batch_size*num_pairs*emb_dim] + para = para.reshape(batch_size, self.num_fields, self.num_fields, self.embedding_size) + r = nn.Parameter(para, requires_grad=True) # [batch_size, num_fields, num_fields, emb_dim] - fwfm_inter = list() # [batch_size, num_fields, emb_dim] + fwfm_inter = list() # [batch_size, num_fields, emb_dim] for i in range(self.num_features - 1): for j in range(i + 1, self.num_features): Fi, Fj = self.feature2field[i], self.feature2field[j] fwfm_inter.append(infeature[:, i] * infeature[:, j] * r[:, Fi, Fj]) fwfm_inter = torch.stack(fwfm_inter, dim=1) - fwfm_inter = torch.sum(fwfm_inter, dim=1) # [batch_size, emb_dim] - fwfm_inter = self.dropout_layer(fwfm_inter) + fwfm_inter = torch.sum(fwfm_inter, dim=1) # [batch_size, emb_dim] + fwfm_inter = self.dropout_layer(fwfm_inter) fwfm_output = torch.sum(fwfm_inter, dim=1, keepdim=True) # [batch_size, 1] return fwfm_output def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fwfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + fwfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.sigmoid(self.first_order_linear(interaction) + self.fwfm_layer(fwfm_all_embeddings)) diff --git a/recbole/model/context_aware_recommender/lr.py b/recbole/model/context_aware_recommender/lr.py index e8d35f347..0c3a6a759 100644 --- a/recbole/model/context_aware_recommender/lr.py +++ b/recbole/model/context_aware_recommender/lr.py @@ -27,6 +27,7 @@ class LR(ContextRecommender): Z = \sum_{i} {w_i}{x_i} """ + def __init__(self, config, dataset): super(LR, self).__init__(config, dataset) diff --git a/recbole/model/context_aware_recommender/nfm.py b/recbole/model/context_aware_recommender/nfm.py index d967b9daf..4e0e1750f 100644 --- a/recbole/model/context_aware_recommender/nfm.py +++ b/recbole/model/context_aware_recommender/nfm.py @@ -11,18 +11,18 @@ He X, Chua T S. "Neural factorization machines for sparse predictive analytics" in SIGIR 2017 """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import BaseFactorizationMachine, MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine, MLPLayers class NFM(ContextRecommender): """ NFM replace the fm part as a mlp to model the feature interaction. """ + def __init__(self, config, dataset): super(NFM, self).__init__(config, dataset) @@ -51,18 +51,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - nfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + nfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] bn_nfm_all_embeddings = self.bn(self.fm(nfm_all_embeddings)) - output = self.sigmoid(self.predict_layer(self.mlp_layers(bn_nfm_all_embeddings)) + self.first_order_linear(interaction)) + output = self.predict_layer(self.mlp_layers(bn_nfm_all_embeddings)) + self.first_order_linear(interaction) + output = self.sigmoid(output) return output.squeeze() def calculate_loss(self, interaction): diff --git a/recbole/model/context_aware_recommender/pnn.py b/recbole/model/context_aware_recommender/pnn.py index 13933fe2a..2c0b4946b 100644 --- a/recbole/model/context_aware_recommender/pnn.py +++ b/recbole/model/context_aware_recommender/pnn.py @@ -20,8 +20,8 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class PNN(ContextRecommender): @@ -64,7 +64,7 @@ def __init__(self, config, dataset): def reg_loss(self): """Calculate the L2 normalization loss of model parameters. - Including weight matrixes of mlp layers. + Including weight matrices of mlp layers. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] @@ -84,15 +84,7 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - pnn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + pnn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = pnn_all_embeddings.shape[0] # linear part linear_part = pnn_all_embeddings.view(batch_size, -1) # [batch_size,num_field*embed_dim] @@ -104,7 +96,7 @@ def forward(self, interaction): if self.use_outer: outer_product = self.outer_product(pnn_all_embeddings).view(batch_size, -1) # [batch_size,num_pairs] output.append(outer_product) - output = torch.cat(output, dim=1) # [batch_size,d] + output = torch.cat(output, dim=1) # [batch_size,d] output = self.predict_layer(self.mlp_layers(output)) # [batch_size,1] output = self.sigmoid(output) @@ -125,6 +117,7 @@ class InnerProductLayer(nn.Module): product or inner product between feature vectors. """ + def __init__(self, num_feature_field, device): """ Args: @@ -159,7 +152,7 @@ def forward(self, feat_emb): class OuterProductLayer(nn.Module): - """OutterProduct Layer used in PNN. This implemention is + """OuterProduct Layer used in PNN. This implementation is adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets. """ diff --git a/recbole/model/context_aware_recommender/widedeep.py b/recbole/model/context_aware_recommender/widedeep.py index 72a36a7f8..e1c820efd 100644 --- a/recbole/model/context_aware_recommender/widedeep.py +++ b/recbole/model/context_aware_recommender/widedeep.py @@ -11,12 +11,11 @@ Heng-Tze Cheng et al. "Wide & Deep Learning for Recommender Systems." in RecSys 2016. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class WideDeep(ContextRecommender): @@ -54,20 +53,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - widedeep_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + widedeep_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = widedeep_all_embeddings.shape[0] fm_output = self.first_order_linear(interaction) - deep_output = self.deep_predict_layer( - self.mlp_layers(widedeep_all_embeddings.view(batch_size, -1))) + deep_output = self.deep_predict_layer(self.mlp_layers(widedeep_all_embeddings.view(batch_size, -1))) output = self.sigmoid(fm_output + deep_output) return output.squeeze() diff --git a/recbole/model/context_aware_recommender/xdeepfm.py b/recbole/model/context_aware_recommender/xdeepfm.py index 2a5d60326..b0c2f1e1a 100644 --- a/recbole/model/context_aware_recommender/xdeepfm.py +++ b/recbole/model/context_aware_recommender/xdeepfm.py @@ -23,10 +23,9 @@ import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from logging import getLogger -from recbole.model.layers import MLPLayers, activation_layer from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers, activation_layer class xDeepFM(ContextRecommender): @@ -49,9 +48,8 @@ def __init__(self, config, dataset): if not self.direct: self.cin_layer_size = list(map(lambda x: int(x // 2 * 2), temp_cin_size)) if self.cin_layer_size[:-1] != temp_cin_size[:-1]: - logger = getLogger() - logger.warning('Layer size of CIN should be even except for the last layer when direct is True.' - 'It is changed to {}'.format(self.cin_layer_size)) + self.logger.warning('Layer size of CIN should be even except for the last layer when direct is True.' + 'It is changed to {}'.format(self.cin_layer_size)) # Create a convolutional layer for each CIN layer self.conv1d_list = [] @@ -102,7 +100,7 @@ def reg_loss(self, parameters): def calculate_reg_loss(self): """Calculate the final L2 normalization loss of model parameters. - Including weight matrixes of mlp layers, linear layer and convolutional layers. + Including weight matrices of mlp layers, linear layer and convolutional layers. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] @@ -171,15 +169,8 @@ def compressed_interaction_network(self, input_features, activation='identity'): return result def forward(self, interaction): - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - # Get the output of CIN. - xdeepfm_input = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + xdeepfm_input = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] cin_output = self.compressed_interaction_network(xdeepfm_input) cin_output = self.cin_linear(cin_output) diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py new file mode 100644 index 000000000..a09da2cdb --- /dev/null +++ b/recbole/model/exlib_recommender/xgboost.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +r""" +recbole.model.exlib_recommender.xgboost +############################# +""" + +import xgboost as xgb +from recbole.utils import ModelType, InputType + + +class xgboost(xgb.Booster): + r"""xgboost is inherited from xgb.Booster + + """ + type = ModelType.XGBOOST + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + super().__init__(params=None, cache=(), model_file=None) + + def to(self, device): + return self diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 7766bf1c3..039c0f955 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -6,8 +6,13 @@ from recbole.model.general_recommender.gcmc import GCMC from recbole.model.general_recommender.itemknn import ItemKNN from recbole.model.general_recommender.lightgcn import LightGCN +from recbole.model.general_recommender.line import LINE +from recbole.model.general_recommender.macridvae import MacridVAE +from recbole.model.general_recommender.multidae import MultiDAE +from recbole.model.general_recommender.multivae import MultiVAE from recbole.model.general_recommender.nais import NAIS from recbole.model.general_recommender.neumf import NeuMF from recbole.model.general_recommender.ngcf import NGCF from recbole.model.general_recommender.pop import Pop from recbole.model.general_recommender.spectralcf import SpectralCF +from recbole.model.general_recommender.cdae import CDAE \ No newline at end of file diff --git a/recbole/model/general_recommender/bpr.py b/recbole/model/general_recommender/bpr.py index 586f83df2..684929251 100644 --- a/recbole/model/general_recommender/bpr.py +++ b/recbole/model/general_recommender/bpr.py @@ -19,10 +19,10 @@ import torch import torch.nn as nn -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss +from recbole.utils import InputType class BPR(GeneralRecommender): diff --git a/recbole/model/general_recommender/cdae.py b/recbole/model/general_recommender/cdae.py new file mode 100644 index 000000000..11eef117d --- /dev/null +++ b/recbole/model/general_recommender/cdae.py @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/12 +# @Author : Xingyu Pan +# @Email : panxy@ruc.edu.cn + +r""" +CDAE +################################################ +Reference: + Yao Wu et al., Collaborative denoising auto-encoders for top-n recommender systems. WSDM 2016. + +Reference code: + https://github.com/jasonyaw/CDAE +""" + +import torch +import torch.nn as nn + +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType + + +class CDAE(GeneralRecommender): + r"""Collaborative Denoising Auto-Encoder (CDAE) is a recommendation model + for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. + We implement the the CDAE model with only user dataloader. + """ + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + super(CDAE, self).__init__(config, dataset) + + self.reg_weight_1 = config['reg_weight_1'] + self.reg_weight_2 = config['reg_weight_2'] + self.loss_type = config['loss_type'] + self.hid_activation = config['hid_activation'] + self.out_activation = config['out_activation'] + self.embedding_size = config['embedding_size'] + self.corruption_ratio = config['corruption_ratio'] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + if self.hid_activation == 'sigmoid': + self.h_act = nn.Sigmoid() + elif self.hid_activation == 'relu': + self.h_act = nn.ReLU() + elif self.hid_activation == 'tanh': + self.h_act = nn.Tanh() + else: + raise ValueError('Invalid hidden layer activation function') + + if self.out_activation == 'sigmoid': + self.o_act = nn.Sigmoid() + elif self.out_activation == 'relu': + self.o_act = nn.Sigmoid() + else: + raise ValueError('Invalid output layer activation function') + + self.dropout = nn.Dropout(p=self.corruption_ratio) + + self.h_user = nn.Embedding(self.n_users, self.embedding_size) + self.h_item = nn.Linear(self.n_items, self.embedding_size) + self.out_layer = nn.Linear(self.embedding_size, self.n_items) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def forward(self, x_items, x_users): + h_i = self.dropout(x_items) + h_i = self.h_item(h_i) + h_u = self.h_user(x_users) + h = torch.add(h_u, h_i) + h = self.h_act(h) + out = self.out_layer(h) + return self.o_act(out) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def calculate_loss(self, interaction): + x_users = interaction[self.USER_ID] + x_items = self.get_rating_matrix(x_users) + predict = self.forward(x_items, x_users) + + if self.loss_type == 'MSE': + loss_func = nn.MSELoss(reduction='sum') + elif self.loss_type == 'BCE': + loss_func = nn.BCELoss(reduction='sum') + else: + raise ValueError('Invalid loss_type, loss_type must in [MSE, BCE]') + + loss = loss_func(predict, x_items) + # l1-regularization + loss += self.reg_weight_1 * (self.h_user.weight.norm(p=1) + self.h_item.weight.norm(p=1)) + # l2-regularization + loss += self.reg_weight_2 * (self.h_user.weight.norm() + self.h_item.weight.norm()) + + return loss + + def predict(self, interaction): + users = interaction[self.USER_ID] + predict_items = interaction[self.ITEM_ID] + + items = self.get_rating_matrix(users) + scores = self.forward(items, users) + + return scores[[users, predict_items]] + + def full_sort_predict(self, interaction): + users = interaction[self.USER_ID] + + items = self.get_rating_matrix(users) + predict = self.forward(items, users) + return predict.view(-1) diff --git a/recbole/model/general_recommender/convncf.py b/recbole/model/general_recommender/convncf.py index 2ee9714e1..e90808f1d 100644 --- a/recbole/model/general_recommender/convncf.py +++ b/recbole/model/general_recommender/convncf.py @@ -16,13 +16,12 @@ import torch import torch.nn as nn -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers, CNNLayers +from recbole.utils import InputType class ConvNCFBPRLoss(nn.Module): - """ ConvNCFBPRLoss, based on Bayesian Personalized Ranking, Shape: @@ -38,9 +37,10 @@ class ConvNCFBPRLoss(nn.Module): >>> output = loss(pos_score, neg_score) >>> output.backward() """ + def __init__(self): super(ConvNCFBPRLoss, self).__init__() - + def forward(self, pos_score, neg_score): distance = pos_score - neg_score loss = torch.sum(torch.log((1 + torch.exp(-distance)))) @@ -94,7 +94,7 @@ def forward(self, user, item): def reg_loss(self): r"""Calculate the L2 normalization loss of model parameters. - Including embedding matrixes and weight matrixes of model. + Including embedding matrices and weight matrices of model. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] @@ -118,7 +118,7 @@ def calculate_loss(self, interaction): pos_item_score = self.forward(user, pos_item) neg_item_score = self.forward(user, neg_item) - + loss = self.loss(pos_item_score, neg_item_score) opt_loss = loss + self.reg_loss() diff --git a/recbole/model/general_recommender/dgcf.py b/recbole/model/general_recommender/dgcf.py index 56d688e8d..28109b4c8 100644 --- a/recbole/model/general_recommender/dgcf.py +++ b/recbole/model/general_recommender/dgcf.py @@ -18,17 +18,18 @@ https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering """ -import numpy as np import random as rd + +import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType def sample_cor_samples(n_users, n_items, cor_batch_size): @@ -44,7 +45,7 @@ def sample_cor_samples(n_users, n_items, cor_batch_size): Note: We have to sample some embedded representations out of all nodes. - Becasue we have no way to store cor-distance for each pair. + Because we have no way to store cor-distance for each pair. """ cor_users = rd.sample(list(range(n_users)), cor_batch_size) cor_items = rd.sample(list(range(n_items)), cor_batch_size) @@ -117,9 +118,9 @@ def _build_sparse_tensor(self, indices, values, size): def _get_ego_embeddings(self): # concat of user embeddings and item embeddings - user_embd = self.user_embedding.weight - item_embd = self.item_embedding.weight - ego_embeddings = torch.cat([user_embd, item_embd], dim=0) + user_emb = self.user_embedding.weight + item_emb = self.item_embedding.weight + ego_embeddings = torch.cat([user_emb, item_emb], dim=0) return ego_embeddings def build_matrix(self, A_values): @@ -148,7 +149,7 @@ def build_matrix(self, A_values): try: assert not torch.isnan(d_values).any() except AssertionError: - print("d_values", torch.min(d_values), torch.max(d_values)) + self.logger.info("d_values", torch.min(d_values), torch.max(d_values)) d_values = 1.0 / torch.sqrt(d_values) head_term = torch.sparse.mm(self.head2edge_mat, d_values) @@ -169,7 +170,8 @@ def forward(self): layer_embeddings = [] # split the input embedding table - # .... ego_layer_embeddings is a (n_factors)-leng list of embeddings [n_users+n_items, embed_size/n_factors] + # .... ego_layer_embeddings is a (n_factors)-length list of embeddings + # [n_users+n_items, embed_size/n_factors] ego_layer_embeddings = torch.chunk(ego_embeddings, self.n_factors, 1) for t in range(0, self.n_iterations): iter_embeddings = [] @@ -194,18 +196,18 @@ def forward(self): # get the factor-wise embeddings # .... head_factor_embeddings is a dense tensor with the size of [all_h_list, embed_size/n_factors] # .... analogous to tail_factor_embeddings - head_factor_embedings = torch.index_select(factor_embeddings, dim=0, index=self.all_h_list) - tail_factor_embedings = torch.index_select(ego_layer_embeddings[i], dim=0, index=self.all_t_list) + head_factor_embeddings = torch.index_select(factor_embeddings, dim=0, index=self.all_h_list) + tail_factor_embeddings = torch.index_select(ego_layer_embeddings[i], dim=0, index=self.all_t_list) # .... constrain the vector length # .... make the following attentive weights within the range of (0,1) # to adapt to torch version - head_factor_embedings = F.normalize(head_factor_embedings, p=2, dim=1) - tail_factor_embedings = F.normalize(tail_factor_embedings, p=2, dim=1) + head_factor_embeddings = F.normalize(head_factor_embeddings, p=2, dim=1) + tail_factor_embeddings = F.normalize(tail_factor_embeddings, p=2, dim=1) # get the attentive weights # .... A_factor_values is a dense tensor with the size of [num_edge, 1] - A_factor_values = torch.sum(head_factor_embedings * torch.tanh(tail_factor_embedings), + A_factor_values = torch.sum(head_factor_embeddings * torch.tanh(tail_factor_embeddings), dim=1, keepdim=True) # update the attentive weights @@ -243,18 +245,18 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - # cul regularizer + # cul regularized u_ego_embeddings = self.user_embedding(user) - posi_ego_embeddings = self.item_embedding(pos_item) - negi_ego_embeddings = self.item_embedding(neg_item) - reg_loss = self.reg_loss(u_ego_embeddings, posi_ego_embeddings, negi_ego_embeddings) + pos_ego_embeddings = self.item_embedding(pos_item) + neg_ego_embeddings = self.item_embedding(neg_item) + reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) if self.n_factors > 1 and self.cor_weight > 1e-9: cor_users, cor_items = sample_cor_samples(self.n_users, self.n_items, self.cor_batch_size) diff --git a/recbole/model/general_recommender/dmf.py b/recbole/model/general_recommender/dmf.py index ce04bbdf0..07b5d88e1 100644 --- a/recbole/model/general_recommender/dmf.py +++ b/recbole/model/general_recommender/dmf.py @@ -20,9 +20,9 @@ import torch.nn as nn from torch.nn.init import normal_ -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers +from recbole.utils import InputType class DMF(GeneralRecommender): @@ -102,7 +102,8 @@ def forward(self, user, item): # Following lines construct tensor of shape [B,n_users] using the tensor of shape [B,H] col_indices = self.history_user_id[item].flatten() - row_indices = torch.arange(item.shape[0]).to(self.device).repeat_interleave(self.history_user_id.shape[1], dim=0) + row_indices = torch.arange(item.shape[0]).to(self.device). \ + repeat_interleave(self.history_user_id.shape[1], dim=0) matrix_01 = torch.zeros(1).to(self.device).repeat(item.shape[0], self.n_users) matrix_01.index_put_((row_indices, col_indices), self.history_user_value[item].flatten()) item = self.item_linear(matrix_01) @@ -170,7 +171,8 @@ def get_item_embedding(self): col = interaction_matrix.col i = torch.LongTensor([row, col]) data = torch.FloatTensor(interaction_matrix.data) - item_matrix = torch.sparse.FloatTensor(i, data, torch.Size(interaction_matrix.shape)).to(self.device).transpose(0, 1) + item_matrix = torch.sparse.FloatTensor(i, data, torch.Size(interaction_matrix.shape)).to(self.device).\ + transpose(0, 1) item = torch.sparse.mm(item_matrix, self.item_linear.weight.t()) item = self.item_fc_layers(item) diff --git a/recbole/model/general_recommender/fism.py b/recbole/model/general_recommender/fism.py index 56744a32a..39763e929 100644 --- a/recbole/model/general_recommender/fism.py +++ b/recbole/model/general_recommender/fism.py @@ -14,13 +14,12 @@ https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model """ -from logging import getLogger - import torch import torch.nn as nn +from torch.nn.init import normal_ + from recbole.model.abstract_recommender import GeneralRecommender from recbole.utils import InputType -from torch.nn.init import normal_ class FISM(GeneralRecommender): @@ -37,9 +36,7 @@ def __init__(self, config, dataset): # load dataset info self.LABEL = config['LABEL_FIELD'] - self.logger = getLogger() - - # get all users's history interaction information.the history item + # get all users' history interaction information.the history item # matrix is padding by the maximum number of a user's interactions self.history_item_matrix, self.history_lens, self.mask_mat = self.get_history_info(dataset) @@ -57,7 +54,6 @@ def __init__(self, config, dataset): 'you need to increase it \n\t\t\tuntil the error disappears. For example, ' + \ 'you can append it in the command line such as `--split_to=5`') - # define layers and loss # construct source and destination item embedding matrix self.item_src_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) @@ -92,7 +88,7 @@ def reg_loss(self): Returns: torch.Tensor: reg loss - """ + """ reg_1, reg_2 = self.reg_weights loss_1 = reg_1 * self.item_src_embedding.weight.norm(2) loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2) @@ -132,7 +128,7 @@ def user_forward(self, user_input, item_num, user_bias, repeats=None, pred_slc=N Args: user_input (torch.Tensor): user input tensor - item_num (torch.Tensor): user hitory interaction lens + item_num (torch.Tensor): user history interaction lens repeats (int, optional): the number of items to be evaluated pred_slc (torch.Tensor, optional): continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items @@ -180,7 +176,8 @@ def full_sort_predict(self, interaction): else: output = [] for mask in self.group: - tmp_output = self.user_forward(user_input[:item_num], item_num, user_bias, repeats=len(mask), pred_slc=mask) + tmp_output = self.user_forward(user_input[:item_num], item_num, user_bias, + repeats=len(mask), pred_slc=mask) output.append(tmp_output) output = torch.cat(output, dim=0) scores.append(output) diff --git a/recbole/model/general_recommender/gcmc.py b/recbole/model/general_recommender/gcmc.py index b6619b39d..133dbe53d 100644 --- a/recbole/model/general_recommender/gcmc.py +++ b/recbole/model/general_recommender/gcmc.py @@ -19,15 +19,16 @@ https://github.com/riannevdberg/gc-mc """ - import math + +import numpy as np +import scipy.sparse as sp import torch import torch.nn as nn -import scipy.sparse as sp -import numpy as np -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.layers import SparseDropout +from recbole.utils import InputType class GCMC(GeneralRecommender): @@ -78,11 +79,10 @@ def __init__(self, config, dataset): torch.Size([self.n_items, self.num_all])).to(self.device) else: features = torch.eye(self.num_all).to(self.device) - self.user_features, self.item_features = torch.split( - features, [self.n_users, self.n_items]) + self.user_features, self.item_features = torch.split(features, [self.n_users, self.n_items]) self.input_dim = self.user_features.shape[1] - # adj matrixs for each relation are stored in self.support + # adj matrices for each relation are stored in self.support self.Graph = self.get_norm_adj_mat().to(self.device) self.support = [self.Graph] @@ -91,8 +91,8 @@ def __init__(self, config, dataset): if self.accum == 'stack': div = self.gcn_output_dim // len(self.support) if self.gcn_output_dim % len(self.support) != 0: - print("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits).\n""" - % (self.gcn_output_dim, len(self.support) * div, len(self.support))) + self.logger.warning("HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits)." + % (self.gcn_output_dim, len(self.support) * div, len(self.support))) self.gcn_output_dim = len(self.support) * div # define layers and loss @@ -145,14 +145,12 @@ def get_norm_adj_mat(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - # add epsilon to avoid Devide by zero Warning + # add epsilon to avoid divide by zero Warning diag = np.array(sumArr.flatten())[0] + 1e-7 diag = np.power(diag, -0.5) D = sp.diags(diag) @@ -210,14 +208,15 @@ def full_sort_predict(self, interaction): class GcEncoder(nn.Module): - """Graph Convolutional Encoder + r"""Graph Convolutional Encoder GcEncoder take as input an :math:`N \times D` feature matrix :math:`X` and a graph adjacency matrix :math:`A`, and produce an :math:`N \times E` node embedding matrix; Note that :math:`N` denotes the number of nodes, :math:`D` the number of input features, and :math:`E` the embedding size. """ - def __init__(self, accum, num_user, num_item, support, input_dim, gcn_output_dim, dense_output_dim, drop_prob, device, + def __init__(self, accum, num_user, num_item, support, + input_dim, gcn_output_dim, dense_output_dim, drop_prob, device, sparse_feature=True, act_dense=lambda x: x, share_user_item_weights=True, bias=False): super(GcEncoder, self).__init__() self.num_users = num_user @@ -246,58 +245,62 @@ def __init__(self, accum, num_user, num_item, support, input_dim, gcn_output_dim # gcn layer if self.accum == 'sum': - self.weights_u = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), - requires_grad=True) - for _ in range(self.num_support)]) + self.weights_u = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) if share_user_item_weights: self.weights_v = self.weights_u else: - self.weights_v = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), - requires_grad=True) for _ in range(self.num_support)]) + self.weights_v = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) else: assert self.gcn_output_dim % self.num_support == 0, 'output_dim must be multiple of num_support for stackGC' self.sub_hidden_dim = self.gcn_output_dim // self.num_support - self.weights_u = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), - requires_grad=True) - for _ in range(self.num_support)]) + self.weights_u = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) if share_user_item_weights: self.weights_v = self.weights_u else: - self.weights_v = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), - requires_grad=True) for _ in range(self.num_support)]) + self.weights_v = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) # dense layer - self.dense_layer_u = nn.Linear( - self.gcn_output_dim, self.dense_output_dim, bias=self.bias) + self.dense_layer_u = nn.Linear(self.gcn_output_dim, self.dense_output_dim, bias=self.bias) if share_user_item_weights: self.dense_layer_v = self.dense_layer_u else: - self.dense_layer_v = nn.Linear( - self.gcn_output_dim, self.dense_output_dim, bias=self.bias) + self.dense_layer_v = nn.Linear(self.gcn_output_dim, self.dense_output_dim, bias=self.bias) self._init_weights() def _init_weights(self): - init_range = math.sqrt((self.num_support + 1) / - (self.input_dim + self.gcn_output_dim)) + init_range = math.sqrt((self.num_support + 1) / (self.input_dim + self.gcn_output_dim)) for w in range(self.num_support): self.weights_u[w].data.uniform_(-init_range, init_range) if not self.share_weights: for w in range(self.num_support): self.weights_v[w].data.uniform_(-init_range, init_range) - dense_init_range = math.sqrt( - (self.num_support + 1) / (self.dense_output_dim + self.gcn_output_dim)) - self.dense_layer_u.weight.data.uniform_( - -dense_init_range, dense_init_range) + dense_init_range = math.sqrt((self.num_support + 1) / (self.dense_output_dim + self.gcn_output_dim)) + self.dense_layer_u.weight.data.uniform_(-dense_init_range, dense_init_range) if not self.share_weights: - self.dense_layer_v.weight.data.uniform_( - -dense_init_range, dense_init_range) + self.dense_layer_v.weight.data.uniform_(-dense_init_range, dense_init_range) if self.bias: self.dense_layer_u.bias.data.fill_(0) @@ -374,7 +377,7 @@ def forward(self, user_X, item_X): class BiDecoder(nn.Module): - """Bilinear decoder + """Bi-linear decoder BiDecoder takes pairs of node embeddings and predicts respective entries in the adjacency matrix. """ @@ -390,18 +393,16 @@ def __init__(self, input_dim, output_dim, drop_prob, device, self.dropout_prob = drop_prob self.dropout = nn.Dropout(p=self.dropout_prob) - self.weights = nn.ParameterList( - [nn.Parameter(orthogonal([self.input_dim, self.input_dim]).to(self.device)) - for _ in range(self.num_weights)]) - self.dense_layer = nn.Linear( - self.num_weights, self.output_dim, bias=False) + self.weights = nn.ParameterList([ + nn.Parameter(orthogonal([self.input_dim, self.input_dim]).to(self.device)) + for _ in range(self.num_weights) + ]) + self.dense_layer = nn.Linear(self.num_weights, self.output_dim, bias=False) self._init_weights() def _init_weights(self): - dense_init_range = math.sqrt( - self.output_dim / (self.num_weights + self.output_dim)) - self.dense_layer.weight.data.uniform_( - -dense_init_range, dense_init_range) + dense_init_range = math.sqrt(self.output_dim / (self.num_weights + self.output_dim)) + self.dense_layer.weight.data.uniform_(-dense_init_range, dense_init_range) def forward(self, u_inputs, i_inputs, users, items=None): u_inputs = self.dropout(u_inputs) @@ -434,25 +435,6 @@ def forward(self, u_inputs, i_inputs, users, items=None): return output -class SparseDropout(nn.Module): - """ - This is a Module that execute Dropout on Pytorch sparse tensor. - """ - - def __init__(self, p=0.5): - super(SparseDropout, self).__init__() - # p is ratio of dropout - # convert to keep probability - self.kprob = 1 - p - - def forward(self, x): - mask = ((torch.rand(x._values().size()) + - self.kprob).floor()).type(torch.bool) - rc = x._indices()[:, mask] - val = x._values()[mask] * (1.0 / self.kprob) - return torch.sparse.FloatTensor(rc, val, x.shape) - - def orthogonal(shape, scale=1.1): """ Initialization function for weights in class GCMC. diff --git a/recbole/model/general_recommender/itemknn.py b/recbole/model/general_recommender/itemknn.py index c98c075dd..6a0c202df 100644 --- a/recbole/model/general_recommender/itemknn.py +++ b/recbole/model/general_recommender/itemknn.py @@ -15,8 +15,8 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType, ModelType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.utils import InputType, ModelType class ComputeSimilarity: @@ -130,7 +130,6 @@ def compute_similarity(self, block_size=100): class ItemKNN(GeneralRecommender): - r"""ItemKNN is a basic model that compute item similarity with the interaction matrix. """ diff --git a/recbole/model/general_recommender/lightgcn.py b/recbole/model/general_recommender/lightgcn.py index 4cf3604d9..17affbb27 100644 --- a/recbole/model/general_recommender/lightgcn.py +++ b/recbole/model/general_recommender/lightgcn.py @@ -23,10 +23,10 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class LightGCN(GeneralRecommender): @@ -45,19 +45,16 @@ def __init__(self, config, dataset): super(LightGCN, self).__init__(config, dataset) # load dataset info - self.interaction_matrix = dataset.inter_matrix( - form='coo').astype(np.float32) + self.interaction_matrix = dataset.inter_matrix(form='coo').astype(np.float32) # load parameters info self.latent_dim = config['embedding_size'] # int type:the embedding size of lightGCN self.n_layers = config['n_layers'] # int type:the layer num of lightGCN - self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalizaton + self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization # define layers and loss - self.user_embedding = torch.nn.Embedding( - num_embeddings=self.n_users, embedding_dim=self.latent_dim) - self.item_embedding = torch.nn.Embedding( - num_embeddings=self.n_items, embedding_dim=self.latent_dim) + self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) + self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() @@ -88,14 +85,12 @@ def get_norm_adj_mat(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - # add epsilon to avoid Devide by zero Warning + # add epsilon to avoid divide by zero Warning diag = np.array(sumArr.flatten())[0] + 1e-7 diag = np.power(diag, -0.5) D = sp.diags(diag) @@ -125,14 +120,12 @@ def forward(self): embeddings_list = [all_embeddings] for layer_idx in range(self.n_layers): - all_embeddings = torch.sparse.mm( - self.norm_adj_matrix, all_embeddings) + all_embeddings = torch.sparse.mm(self.norm_adj_matrix, all_embeddings) embeddings_list.append(all_embeddings) lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) - user_all_embeddings, item_all_embeddings = torch.split( - lightgcn_all_embeddings, [self.n_users, self.n_items]) + user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_loss(self, interaction): @@ -146,21 +139,20 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss u_ego_embeddings = self.user_embedding(user) - posi_ego_embeddings = self.item_embedding(pos_item) - negi_ego_embeddings = self.item_embedding(neg_item) + pos_ego_embeddings = self.item_embedding(pos_item) + neg_ego_embeddings = self.item_embedding(neg_item) - reg_loss = self.reg_loss( - u_ego_embeddings, posi_ego_embeddings, negi_ego_embeddings) + reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss @@ -184,7 +176,6 @@ def full_sort_predict(self, interaction): u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate - scores = torch.matmul( - u_embeddings, self.restore_item_e.transpose(0, 1)) + scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py new file mode 100644 index 000000000..66ee1a919 --- /dev/null +++ b/recbole/model/general_recommender/line.py @@ -0,0 +1,182 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/8 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +LINE +################################################ +Reference: + Jian Tang et al. "LINE: Large-scale Information Network Embedding." in WWW 2015. + +Reference code: + https://github.com/shenweichen/GraphEmbedding +""" + +import random + +import numpy as np +import torch +import torch.nn as nn + +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType + + +class NegSamplingLoss(nn.Module): + def __init__(self): + super(NegSamplingLoss, self).__init__() + + def forward(self, score, sign): + return -torch.mean(torch.sigmoid(sign * score)) + + +class LINE(GeneralRecommender): + r"""LINE is a graph embedding model. + + We implement the model to train users and items embedding for recommendation. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(LINE, self).__init__(config, dataset) + + self.embedding_size = config['embedding_size'] + self.order = config['order'] + self.second_order_loss_weight = config['second_order_loss_weight'] + self.training_neg_sample_num = config['training_neg_sample_num'] + + self.interaction_feat = dataset.dataset.inter_feat + + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + + if self.order == 2: + self.user_context_embedding = nn.Embedding(self.n_users, self.embedding_size) + self.item_context_embedding = nn.Embedding(self.n_items, self.embedding_size) + + self.loss_fct = NegSamplingLoss() + + self.used_ids = self.get_used_ids() + self.random_list = self.get_user_id_list() + np.random.shuffle(self.random_list) + self.random_pr = 0 + self.random_list_length = len(self.random_list) + + self.apply(xavier_normal_initialization) + + def get_used_ids(self): + cur = np.array([set() for _ in range(self.n_items)]) + for iid, uid in zip(self.interaction_feat[self.USER_ID].numpy(), + self.interaction_feat[self.ITEM_ID].numpy()): + cur[iid].add(uid) + return cur + + def sampler(self, key_ids): + + key_ids = np.array(key_ids.cpu()) + key_num = len(key_ids) + total_num = key_num + value_ids = np.zeros(total_num, dtype=np.int64) + check_list = np.arange(total_num) + key_ids = np.tile(key_ids, 1) + while len(check_list) > 0: + value_ids[check_list] = self.random_num(len(check_list)) + check_list = np.array([i for i, used, v in + zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) + if v in used]) + + return torch.tensor(value_ids, device=self.device) + + def random_num(self, num): + value_id = [] + self.random_pr %= self.random_list_length + while True: + if self.random_pr + num <= self.random_list_length: + value_id.append(self.random_list[self.random_pr: self.random_pr + num]) + self.random_pr += num + break + else: + value_id.append(self.random_list[self.random_pr:]) + num -= self.random_list_length - self.random_pr + self.random_pr = 0 + np.random.shuffle(self.random_list) + return np.concatenate(value_id) + + def get_user_id_list(self): + return np.arange(1, self.n_users) + + def forward(self, h, t): + + h_embedding = self.user_embedding(h) + t_embedding = self.item_embedding(t) + + return torch.sum(h_embedding.mul(t_embedding), dim=1) + + def context_forward(self, h, t, field): + + if field == "uu": + h_embedding = self.user_embedding(h) + t_embedding = self.item_context_embedding(t) + else: + h_embedding = self.item_embedding(h) + t_embedding = self.user_context_embedding(t) + + return torch.sum(h_embedding.mul(t_embedding), dim=1) + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + pos_item = interaction[self.ITEM_ID] + neg_item = interaction[self.NEG_ITEM_ID] + + score_pos = self.forward(user, pos_item) + + ones = torch.ones(len(score_pos), device=self.device) + + if self.order == 1: + if random.random() < 0.5: + score_neg = self.forward(user, neg_item) + else: + neg_user = self.sampler(pos_item) + score_neg = self.forward(neg_user, pos_item) + return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) + + else: + # randomly train i-i relation and u-u relation with u-i relation + if random.random() < 0.5: + score_neg = self.forward(user, neg_item) + score_pos_con = self.context_forward(user, pos_item, 'uu') + score_neg_con = self.context_forward(user, neg_item, 'uu') + else: + # sample negative user for item + neg_user = self.sampler(pos_item) + score_neg = self.forward(neg_user, pos_item) + score_pos_con = self.context_forward(pos_item, user, 'ii') + score_neg_con = self.context_forward(pos_item, neg_user, 'ii') + + return self.loss_fct(ones, score_pos) \ + + self.loss_fct(-1 * ones, score_neg) \ + + self.loss_fct(ones, score_pos_con) * self.second_order_loss_weight \ + + self.loss_fct(-1 * ones, score_neg_con) * self.second_order_loss_weight + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + scores = self.forward(user, item) + + return scores + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + # get user embedding from storage variable + u_embeddings = self.user_embedding(user) + i_embedding = self.item_embedding.weight + # dot with all item embedding to accelerate + scores = torch.matmul(u_embeddings, i_embedding.transpose(0, 1)) + + return scores.view(-1) diff --git a/recbole/model/general_recommender/macridvae.py b/recbole/model/general_recommender/macridvae.py new file mode 100644 index 000000000..a761648ab --- /dev/null +++ b/recbole/model/general_recommender/macridvae.py @@ -0,0 +1,205 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/23 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MacridVAE +################################################ +Reference: + Jianxin Ma et al. "Learning Disentangled Representations for Recommendation." in NeurIPS 2019. + +Reference code: + https://jianxinma.github.io/disentangle-recsys.html +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.distributions.one_hot_categorical import OneHotCategorical + +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss +from recbole.utils import InputType + + +class MacridVAE(GeneralRecommender): + r"""MacridVAE is a item-based model collaborative filtering model that learn disentangled representations from user + behavior and simultaneously rank all items for user. + + We implement the model following the original author + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MacridVAE, self).__init__(config, dataset) + + self.layers = config['encoder_hidden_size'] + self.embedding_size = config['embedding_size'] + self.drop_out = config['drop_out'] + self.kfac = config['kfac'] + self.tau = config['tau'] + self.nogb = config['nogb'] + self.anneal_cap = config['anneal_cap'] + self.total_anneal_steps = config['total_anneal_steps'] + self.regs = config['reg_weights'] + self.std = config['std'] + + self.update = 0 + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + self.encode_layer_dims = [self.n_items] + self.layers + [self.embedding_size * 2] + + self.encoder = self.mlp_layers(self.encode_layer_dims) + + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + self.k_embedding = nn.Embedding(self.kfac, self.embedding_size) + + self.l2_loss = EmbLoss() + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layers(self, layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + epsilon = torch.zeros_like(std).normal_(mean=0, std=self.std) + return mu + epsilon * std + else: + return mu + + def forward(self, rating_matrix): + + cores = F.normalize(self.k_embedding.weight, dim=1) + items = F.normalize(self.item_embedding.weight, dim=1) + + rating_matrix = F.normalize(rating_matrix) + rating_matrix = F.dropout(rating_matrix, self.drop_out, training=self.training) + + cates_logits = torch.matmul(items, cores.transpose(0, 1)) / self.tau + + if self.nogb: + cates = torch.softmax(cates_logits, dim=1) + else: + cates_dist = OneHotCategorical(logits=cates_logits) + cates_sample = cates_dist.sample() + cates_mode = torch.softmax(cates_logits, dim=1) + cates = (self.training * cates_sample + + (1 - self.training) * cates_mode) + + probs = None + mulist = [] + logvarlist = [] + for k in range(self.kfac): + cates_k = cates[:, k].reshape(1, -1) + # encoder + x_k = rating_matrix * cates_k + h = self.encoder(x_k) + mu = h[:, :self.embedding_size] + mu = F.normalize(mu, dim=1) + logvar = h[:, self.embedding_size:] + + mulist.append(mu) + logvarlist.append(logvar) + + z = self.reparameterize(mu, logvar) + + # decoder + z_k = F.normalize(z, dim=1) + logits_k = torch.matmul(z_k, items.transpose(0, 1)) / self.tau + probs_k = torch.exp(logits_k) + probs_k = probs_k * cates_k + probs = (probs_k if (probs is None) else (probs + probs_k)) + + logits = torch.log(probs) + + return logits, mulist, logvarlist + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + self.update += 1 + if self.total_anneal_steps > 0: + anneal = min(self.anneal_cap, 1. * self.update / self.total_anneal_steps) + else: + anneal = self.anneal_cap + + z, mu, logvar = self.forward(rating_matrix) + kl_loss = None + for i in range(self.kfac): + kl_ = -0.5 * torch.mean(torch.sum(1 + logvar[i] - logvar[i].exp(), dim=1)) + kl_loss = (kl_ if (kl_loss is None) else (kl_loss + kl_)) + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + if self.regs[0] != 0 or self.regs[1] != 0: + return ce_loss + kl_loss * anneal + self.reg_loss() + + return ce_loss + kl_loss * anneal + + def reg_loss(self): + r"""Calculate the L2 normalization loss of model parameters. + Including embedding matrices and weight matrices of model. + + Returns: + loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] + """ + reg_1, reg_2 = self.regs[:2] + loss_1 = reg_1 * self.item_embedding.weight.norm(2) + loss_2 = reg_1 * self.k_embedding.weight.norm(2) + loss_3 = 0 + for name, parm in self.encoder.named_parameters(): + if name.endswith('weight'): + loss_3 = loss_3 + reg_2 * parm.norm(2) + return loss_1 + loss_2 + loss_3 + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/model/general_recommender/multidae.py b/recbole/model/general_recommender/multidae.py new file mode 100644 index 000000000..3b3658b64 --- /dev/null +++ b/recbole/model/general_recommender/multidae.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/14 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MultiDAE +################################################ +Reference: + Dawen Liang et al. "Variational Autoencoders for Collaborative Filtering." in WWW 2018. + +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import MLPLayers +from recbole.utils import InputType + + +class MultiDAE(GeneralRecommender): + r"""MultiDAE is a item-based model collaborative filtering model that simultaneously rank all items for user . + + We implement the the MultiDAE model with only user dataloader. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MultiDAE, self).__init__(config, dataset) + + self.layers = config["mlp_hidden_size"] + self.lat_dim = config['latent_dimension'] + self.drop_out = config['dropout_prob'] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim] + self.decode_layer_dims = [self.lat_dim] + self.encode_layer_dims[::-1][1:] + + self.encoder = MLPLayers(self.encode_layer_dims, activation='tanh') + self.decoder = self.mlp_layers(self.decode_layer_dims) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layers(self, layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def forward(self, rating_matrix): + + h = F.normalize(rating_matrix) + + h = F.dropout(h, self.drop_out, training=self.training) + + h = self.encoder(h) + return self.decoder(h) + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + z = self.forward(rating_matrix) + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + return ce_loss + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/model/general_recommender/multivae.py b/recbole/model/general_recommender/multivae.py new file mode 100644 index 000000000..83495fa12 --- /dev/null +++ b/recbole/model/general_recommender/multivae.py @@ -0,0 +1,141 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/14 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MultiVAE +################################################ +Reference: + Dawen Liang et al. "Variational Autoencoders for Collaborative Filtering." in WWW 2018. + +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType + + +class MultiVAE(GeneralRecommender): + r"""MultiVAE is a item-based model collaborative filtering model that simultaneously rank all items for user . + + We implement the MultiVAE model with only user dataloader. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MultiVAE, self).__init__(config, dataset) + + self.layers = config["mlp_hidden_size"] + self.lat_dim = config['latent_dimension'] + self.drop_out = config['dropout_prob'] + self.anneal_cap = config['anneal_cap'] + self.total_anneal_steps = config["total_anneal_steps"] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + self.update = 0 + + self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim] + self.decode_layer_dims = [int(self.lat_dim / 2)] + self.encode_layer_dims[::-1][1:] + + self.encoder = self.mlp_layers(self.encode_layer_dims) + self.decoder = self.mlp_layers(self.decode_layer_dims) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layers(self, layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + epsilon = torch.zeros_like(std).normal_(mean=0, std=0.01) + return mu + epsilon * std + else: + return mu + + def forward(self, rating_matrix): + + h = F.normalize(rating_matrix) + + h = F.dropout(h, self.drop_out, training=self.training) + + h = self.encoder(h) + + mu = h[:, :int(self.lat_dim / 2)] + logvar = h[:, int(self.lat_dim / 2):] + + z = self.reparameterize(mu, logvar) + z = self.decoder(z) + return z, mu, logvar + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + rating_matrix = self.get_rating_matrix(user) + + self.update += 1 + if self.total_anneal_steps > 0: + anneal = min(self.anneal_cap, 1. * self.update / self.total_anneal_steps) + else: + anneal = self.anneal_cap + + z, mu, logvar = self.forward(rating_matrix) + + # KL loss + kl_loss = -0.5 * torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)) * anneal + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + return ce_loss + kl_loss + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/model/general_recommender/nais.py b/recbole/model/general_recommender/nais.py index 9c408a3b9..084ee0e88 100644 --- a/recbole/model/general_recommender/nais.py +++ b/recbole/model/general_recommender/nais.py @@ -18,14 +18,13 @@ https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model """ -from logging import getLogger - import torch import torch.nn as nn +from torch.nn.init import constant_, normal_, xavier_normal_ + from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers from recbole.utils import InputType -from torch.nn.init import constant_, normal_, xavier_normal_ class NAIS(GeneralRecommender): @@ -45,9 +44,8 @@ def __init__(self, config, dataset): # load dataset info self.LABEL = config['LABEL_FIELD'] - self.logger = getLogger() - # get all users's history interaction information.the history item + # get all users' history interaction information.the history item # matrix is padding by the maximum number of a user's interactions self.history_item_matrix, self.history_lens, self.mask_mat = self.get_history_info(dataset) @@ -76,7 +74,7 @@ def __init__(self, config, dataset): self.item_dst_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.bias = nn.Parameter(torch.zeros(self.n_items)) if self.algorithm == 'concat': - self.mlp_layers = MLPLayers([self.embedding_size*2, self.weight_size]) + self.mlp_layers = MLPLayers([self.embedding_size * 2, self.weight_size]) elif self.algorithm == 'prod': self.mlp_layers = MLPLayers([self.embedding_size, self.weight_size]) else: @@ -89,7 +87,7 @@ def __init__(self, config, dataset): self.logger.info('use pretrain from [{}]...'.format(self.pretrain_path)) self._load_pretrain() else: - self.logger.info('unuse pretrain...') + self.logger.info('unused pretrain...') self.apply(self._init_weights) def _init_weights(self, module): @@ -143,7 +141,7 @@ def reg_loss(self): Returns: torch.Tensor: reg loss - """ + """ reg_1, reg_2, reg_3 = self.reg_weights loss_1 = reg_1 * self.item_src_embedding.weight.norm(2) loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2) @@ -177,9 +175,9 @@ def mask_softmax(self, similarity, logits, bias, item_num, batch_mask_mat): """softmax the unmasked user history items and get the final output Args: - similarity (torch.Tensor): the similarity between the histoy items and target items + similarity (torch.Tensor): the similarity between the history items and target items logits (torch.Tensor): the initial weights of the history items - item_num (torch.Tensor): user hitory interaction lengths + item_num (torch.Tensor): user history interaction lengths bias (torch.Tensor): bias batch_mask_mat (torch.Tensor): the mask of user history interactions @@ -189,7 +187,7 @@ def mask_softmax(self, similarity, logits, bias, item_num, batch_mask_mat): """ exp_logits = torch.exp(logits) # batch_size x max_len - exp_logits = batch_mask_mat * exp_logits # batch_size x max_len + exp_logits = batch_mask_mat * exp_logits # batch_size x max_len exp_sum = torch.sum(exp_logits, dim=1, keepdim=True) exp_sum = torch.pow(exp_sum, self.beta) weights = torch.div(exp_logits, exp_sum) @@ -203,9 +201,9 @@ def softmax(self, similarity, logits, item_num, bias): """softmax the user history features and get the final output Args: - similarity (torch.Tensor): the similarity between the histoy items and target items + similarity (torch.Tensor): the similarity between the history items and target items logits (torch.Tensor): the initial weights of the history items - item_num (torch.Tensor): user hitory interaction lengths + item_num (torch.Tensor): user history interaction lengths bias (torch.Tensor): bias Returns: @@ -241,7 +239,7 @@ def user_forward(self, user_input, item_num, repeats=None, pred_slc=None): Args: user_input (torch.Tensor): user input tensor - item_num (torch.Tensor): user hitory interaction lens + item_num (torch.Tensor): user history interaction lens repeats (int, optional): the number of items to be evaluated pred_slc (torch.Tensor, optional): continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items diff --git a/recbole/model/general_recommender/neumf.py b/recbole/model/general_recommender/neumf.py index bd95a4bab..8784c4cd7 100644 --- a/recbole/model/general_recommender/neumf.py +++ b/recbole/model/general_recommender/neumf.py @@ -19,9 +19,9 @@ import torch.nn as nn from torch.nn.init import normal_ -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers +from recbole.utils import InputType class NeuMF(GeneralRecommender): @@ -57,10 +57,10 @@ def __init__(self, config, dataset): self.item_mf_embedding = nn.Embedding(self.n_items, self.mf_embedding_size) self.user_mlp_embedding = nn.Embedding(self.n_users, self.mlp_embedding_size) self.item_mlp_embedding = nn.Embedding(self.n_items, self.mlp_embedding_size) - self.mlp_layers = MLPLayers([2 * self.mlp_embedding_size] + self.mlp_hidden_size) + self.mlp_layers = MLPLayers([2 * self.mlp_embedding_size] + self.mlp_hidden_size, self.dropout_prob) self.mlp_layers.logger = None # remove logger to use torch.save() if self.mf_train and self.mlp_train: - self.predict_layer = nn.Linear(self.mf_embedding_size + self.mlp_hidden_size[-1], 1, self.dropout_prob) + self.predict_layer = nn.Linear(self.mf_embedding_size + self.mlp_hidden_size[-1], 1) elif self.mf_train: self.predict_layer = nn.Linear(self.mf_embedding_size, 1) elif self.mlp_train: @@ -107,9 +107,9 @@ def forward(self, user, item): user_mlp_e = self.user_mlp_embedding(user) item_mlp_e = self.item_mlp_embedding(item) if self.mf_train: - mf_output = torch.mul(user_mf_e, item_mf_e) # [batch_size, embedding_size] + mf_output = torch.mul(user_mf_e, item_mf_e) # [batch_size, embedding_size] if self.mlp_train: - mlp_output = self.mlp_layers(torch.cat((user_mlp_e, item_mlp_e), -1)) # [batch_size, layers[-1]] + mlp_output = self.mlp_layers(torch.cat((user_mlp_e, item_mlp_e), -1)) # [batch_size, layers[-1]] if self.mf_train and self.mlp_train: output = self.sigmoid(self.predict_layer(torch.cat((mf_output, mlp_output), -1))) elif self.mf_train: diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py index 6416d0e25..d02ccd499 100644 --- a/recbole/model/general_recommender/ngcf.py +++ b/recbole/model/general_recommender/ngcf.py @@ -25,42 +25,11 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss -from recbole.model.layers import BiGNNLayer from recbole.model.init import xavier_normal_initialization - - -def sparse_dropout(x, rate, noise_shape): - r"""This is a function that execute Dropout on Pytorch sparse tensor. - - A random dropout will be applied to the input sparse tensor. - - Note: - input tensor SHOULD be a sparse float tensor. - we suggest to use '._nnz()' as the shape of sparse tensor for an easy calling. - - Args: - x (torch.sparse.FloatTensor): The input sparse tensor. - rate (float): Dropout rate which should in [0,1]. - noise_shape(tuple): Shape of the input sparse tensor. suggest '._nnz()' - - Returns: - torch.sparse.FloatTensor: The result sparse tensor after dropout. - - """ - random_tensor = 1 - rate - random_tensor += torch.rand(noise_shape).to(x.device) - dropout_mask = torch.floor(random_tensor).type(torch.bool) - i = x._indices() - v = x._values() - - i = i[:, dropout_mask] - v = v[dropout_mask] - - out = torch.sparse.FloatTensor(i, v, x.shape).to(x.device) - return out * (1. / (1 - rate)) +from recbole.model.layers import BiGNNLayer, SparseDropout +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class NGCF(GeneralRecommender): @@ -84,6 +53,7 @@ def __init__(self, config, dataset): self.reg_weight = config['reg_weight'] # define layers and loss + self.sparse_dropout = SparseDropout(self.node_dropout) self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.GNNlayers = torch.nn.ModuleList() @@ -126,7 +96,7 @@ def get_norm_adj_mat(self): A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - diag = np.array(sumArr.flatten())[0] + 1e-7 # add epsilon to avoid Devide by zero Warning + diag = np.array(sumArr.flatten())[0] + 1e-7 # add epsilon to avoid divide by zero Warning diag = np.power(diag, -0.5) D = sp.diags(diag) L = D * A * D @@ -162,9 +132,8 @@ def get_ego_embeddings(self): return ego_embeddings def forward(self): - # A_hat: spare tensor with shape of [n_items+n_users,n_items+n_users] - A_hat = sparse_dropout(self.norm_adj_matrix, self.node_dropout, - self.norm_adj_matrix._nnz()) if self.node_dropout != 0 else self.norm_adj_matrix + + A_hat = self.sparse_dropout(self.norm_adj_matrix) if self.node_dropout != 0 else self.norm_adj_matrix all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for gnn in self.GNNlayers: @@ -190,14 +159,14 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss - reg_loss = self.reg_loss(u_embeddings, posi_embeddings, negi_embeddings) # L2 regularization of embeddings + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) # L2 regularization of embeddings return mf_loss + self.reg_weight * reg_loss diff --git a/recbole/model/general_recommender/pop.py b/recbole/model/general_recommender/pop.py index 879283d5a..9a7e75c0a 100644 --- a/recbole/model/general_recommender/pop.py +++ b/recbole/model/general_recommender/pop.py @@ -14,8 +14,8 @@ import torch -from recbole.utils import InputType, ModelType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.utils import InputType, ModelType class Pop(GeneralRecommender): @@ -36,7 +36,6 @@ def forward(self): pass def calculate_loss(self, interaction): - item = interaction[self.ITEM_ID] self.item_cnt[item, :] = self.item_cnt[item, :] + 1 @@ -45,7 +44,6 @@ def calculate_loss(self, interaction): return torch.nn.Parameter(torch.zeros(1)) def predict(self, interaction): - item = interaction[self.ITEM_ID] result = torch.true_divide(self.item_cnt[item, :], self.max_cnt) return result.squeeze() diff --git a/recbole/model/general_recommender/spectralcf.py b/recbole/model/general_recommender/spectralcf.py index f15715097..537b452b5 100644 --- a/recbole/model/general_recommender/spectralcf.py +++ b/recbole/model/general_recommender/spectralcf.py @@ -18,10 +18,10 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class SpectralCF(GeneralRecommender): @@ -98,10 +98,8 @@ def get_laplacian_matrix(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix @@ -172,14 +170,13 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - reg_loss = self.reg_loss( - u_embeddings, posi_embeddings, negi_embeddings) + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss diff --git a/recbole/model/knowledge_aware_recommender/cfkg.py b/recbole/model/knowledge_aware_recommender/cfkg.py index 489c79e69..01261f09a 100644 --- a/recbole/model/knowledge_aware_recommender/cfkg.py +++ b/recbole/model/knowledge_aware_recommender/cfkg.py @@ -14,9 +14,9 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType class CFKG(KnowledgeRecommender): diff --git a/recbole/model/knowledge_aware_recommender/cke.py b/recbole/model/knowledge_aware_recommender/cke.py index 21738a653..180aea446 100644 --- a/recbole/model/knowledge_aware_recommender/cke.py +++ b/recbole/model/knowledge_aware_recommender/cke.py @@ -14,10 +14,10 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class CKE(KnowledgeRecommender): diff --git a/recbole/model/knowledge_aware_recommender/kgat.py b/recbole/model/knowledge_aware_recommender/kgat.py index 13dd1a01e..02f4571d6 100644 --- a/recbole/model/knowledge_aware_recommender/kgat.py +++ b/recbole/model/knowledge_aware_recommender/kgat.py @@ -13,17 +13,16 @@ https://github.com/xiangwang1223/knowledge_graph_attention_network """ -import copy +import numpy as np +import scipy.sparse as sp import torch import torch.nn as nn import torch.nn.functional as F -import numpy as np -import scipy.sparse as sp -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class Aggregator(nn.Module): @@ -99,7 +98,7 @@ def __init__(self, config, dataset): self.reg_weight = config['reg_weight'] # generate intermediate data - self.A_in = self.init_graph() # init the attention matrix by the structure of ckg + self.A_in = self.init_graph() # init the attention matrix by the structure of ckg # define layers and loss self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) @@ -128,7 +127,7 @@ def init_graph(self): adj_list = [] for rel_type in range(1, self.n_relations, 1): edge_idxs = self.ckg.filter_edges(lambda edge: edge.data['relation_id'] == rel_type) - sub_graph = dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True).\ + sub_graph = dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True). \ adjacency_matrix(transpose=False, scipy_fmt='coo').astype('float') rowsum = np.array(sub_graph.sum(1)) d_inv = np.power(rowsum, -1).flatten() @@ -184,13 +183,13 @@ def calculate_loss(self, interaction): user_all_embeddings, entity_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = entity_all_embeddings[pos_item] - negi_embeddings = entity_all_embeddings[neg_item] + pos_embeddings = entity_all_embeddings[pos_item] + neg_embeddings = entity_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - reg_loss = self.reg_loss(u_embeddings, posi_embeddings, negi_embeddings) + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss @@ -268,7 +267,7 @@ def update_attentive_A(self): # Current PyTorch version does not support softmax on SparseCUDA, temporarily move to CPU to calculate softmax A_in = torch.sparse.FloatTensor(indices, kg_score, self.matrix_size).cpu() A_in = torch.sparse.softmax(A_in, dim=1).to(self.device) - self.A_in = copy.copy(A_in) + self.A_in = A_in def predict(self, interaction): user = interaction[self.USER_ID] diff --git a/recbole/model/knowledge_aware_recommender/kgcn.py b/recbole/model/knowledge_aware_recommender/kgcn.py index ad48a7b86..5b2d7b655 100644 --- a/recbole/model/knowledge_aware_recommender/kgcn.py +++ b/recbole/model/knowledge_aware_recommender/kgcn.py @@ -14,14 +14,14 @@ https://github.com/hwwang55/KGCN """ +import numpy as np import torch import torch.nn as nn -import numpy as np -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss +from recbole.utils import InputType class KGCN(KnowledgeRecommender): @@ -86,7 +86,7 @@ def construct_adj(self, kg_graph): - adj_relation(torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size] """ - # print('constructing knowledge graph ...') + # self.logger.info('constructing knowledge graph ...') # treat the KG as an undirected graph kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): @@ -100,7 +100,7 @@ def construct_adj(self, kg_graph): kg_dict[tail] = [] kg_dict[tail].append((head, relation)) - # print('constructing adjacency matrix ...') + # self.logger.info('constructing adjacency matrix ...') # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] @@ -153,10 +153,8 @@ def get_neighbors(self, items): relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) - neighbor_entities = torch.reshape(torch.index_select( - self.adj_entity, 0, index), (self.batch_size, -1)) - neighbor_relations = torch.reshape(torch.index_select( - self.adj_relation, 0, index), (self.batch_size, -1)) + neighbor_entities = torch.index_select(self.adj_entity, 0, index).reshape(self.batch_size, -1) + neighbor_relations = torch.index_select(self.adj_relation, 0, index).reshape(self.batch_size, -1) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations @@ -178,20 +176,18 @@ def mix_neighbor_vectors(self, neighbor_vectors, neighbor_relations, user_embedd """ avg = False if not avg: - user_embeddings = torch.reshape(user_embeddings, - (self.batch_size, 1, 1, self.embedding_size)) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, + self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] - user_relation_scores_normalized = self.softmax( - user_relation_scores) # [batch_size, -1, n_neighbor] + user_relation_scores_normalized = self.softmax(user_relation_scores) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = torch.unsqueeze(user_relation_scores_normalized, dim=-1) # [batch_size, -1, n_neighbor, 1] neighbors_aggregated = torch.mean(user_relation_scores_normalized * neighbor_vectors, dim=2) # [batch_size, -1, dim] else: - neighbors_aggregated = torch.mean( - neighbor_vectors, dim=2) # [batch_size, -1, dim] + neighbors_aggregated = torch.mean(neighbor_vectors, dim=2) # [batch_size, -1, dim] return neighbors_aggregated def aggregate(self, user_embeddings, entities, relations): @@ -221,33 +217,27 @@ def aggregate(self, user_embeddings, entities, relations): shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] - neighbor_vectors = torch.reshape( - entity_vectors[hop + 1], shape) - neighbor_relations = torch.reshape( - relation_vectors[hop], shape) + neighbor_vectors = entity_vectors[hop + 1].reshape(shape) + neighbor_relations = relation_vectors[hop].reshape(shape) neighbors_agg = self.mix_neighbor_vectors(neighbor_vectors, neighbor_relations, user_embeddings) # [batch_size, -1, dim] if self.aggregator_class == 'sum': - output = torch.reshape( - self_vectors + neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = (self_vectors + neighbors_agg).reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'neighbor': - output = torch.reshape( - neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = neighbors_agg.reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'concat': # [batch_size, -1, dim * 2] output = torch.cat([self_vectors, neighbors_agg], dim=-1) - output = torch.reshape( - output, (-1, self.embedding_size * 2)) # [-1, dim * 2] + output = output.reshape(-1, self.embedding_size * 2) # [-1, dim * 2] else: raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) # [batch_size, -1, dim] - output = torch.reshape( - output, [self.batch_size, -1, self.embedding_size]) + output = output.reshape(self.batch_size, -1, self.embedding_size) if i == self.n_iter - 1: vector = self.Tanh(output) @@ -257,8 +247,7 @@ def aggregate(self, user_embeddings, entities, relations): entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter - item_embeddings = torch.reshape( - entity_vectors[0], (self.batch_size, self.embedding_size)) + item_embeddings = entity_vectors[0].reshape(self.batch_size, self.embedding_size) return item_embeddings diff --git a/recbole/model/knowledge_aware_recommender/kgnnls.py b/recbole/model/knowledge_aware_recommender/kgnnls.py index 8d18f30de..5749be261 100644 --- a/recbole/model/knowledge_aware_recommender/kgnnls.py +++ b/recbole/model/knowledge_aware_recommender/kgnnls.py @@ -15,15 +15,16 @@ https://github.com/hwwang55/KGNN-LS """ +import random + +import numpy as np import torch import torch.nn as nn -import numpy as np -import random -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss +from recbole.utils import InputType class KGNNLS(KnowledgeRecommender): @@ -62,14 +63,12 @@ def __init__(self, config, dataset): self.adj_entity, self.adj_relation = adj_entity.to( self.device), adj_relation.to(self.device) - inter_feat = dataset.dataset.inter_feat.values - pos_users = torch.from_numpy(inter_feat[:, 0]) - pos_items = torch.from_numpy(inter_feat[:, 1]) + inter_feat = dataset.dataset.inter_feat + pos_users = inter_feat[dataset.dataset.uid_field] + pos_items = inter_feat[dataset.dataset.iid_field] pos_label = torch.ones(pos_items.shape) - pos_interaction_table, self.offset = self.get_interaction_table( - pos_users, pos_items, pos_label) - self.interaction_table = self.sample_neg_interaction( - pos_interaction_table, self.offset) + pos_interaction_table, self.offset = self.get_interaction_table(pos_users, pos_items, pos_label) + self.interaction_table = self.sample_neg_interaction(pos_interaction_table, self.offset) # define function self.softmax = nn.Softmax(dim=-1) @@ -145,7 +144,7 @@ def construct_adj(self, kg_graph): - adj_relation (torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size] """ - # print('constructing knowledge graph ...') + # self.logger.info('constructing knowledge graph ...') # treat the KG as an undirected graph kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): @@ -159,20 +158,16 @@ def construct_adj(self, kg_graph): kg_dict[tail] = [] kg_dict[tail].append((head, relation)) - # print('constructing adjacency matrix ...') + # self.logger.info('constructing adjacency matrix ...') # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] - adj_entity = np.zeros( - [entity_num, self.neighbor_sample_size], dtype=np.int64) - adj_relation = np.zeros( - [entity_num, self.neighbor_sample_size], dtype=np.int64) + adj_entity = np.zeros([entity_num, self.neighbor_sample_size], dtype=np.int64) + adj_relation = np.zeros([entity_num, self.neighbor_sample_size], dtype=np.int64) for entity in range(entity_num): if entity not in kg_dict.keys(): - adj_entity[entity] = np.array( - [entity] * self.neighbor_sample_size) - adj_relation[entity] = np.array( - [0] * self.neighbor_sample_size) + adj_entity[entity] = np.array([entity] * self.neighbor_sample_size) + adj_relation[entity] = np.array([0] * self.neighbor_sample_size) continue neighbors = kg_dict[entity] @@ -183,10 +178,8 @@ def construct_adj(self, kg_graph): else: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=True) - adj_entity[entity] = np.array( - [neighbors[i][0] for i in sampled_indices]) - adj_relation[entity] = np.array( - [neighbors[i][1] for i in sampled_indices]) + adj_entity[entity] = np.array([neighbors[i][0] for i in sampled_indices]) + adj_relation[entity] = np.array([neighbors[i][1] for i in sampled_indices]) return torch.from_numpy(adj_entity), torch.from_numpy(adj_relation) @@ -212,10 +205,8 @@ def get_neighbors(self, items): relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) - neighbor_entities = torch.reshape(torch.index_select( - self.adj_entity, 0, index), (self.batch_size, -1)) - neighbor_relations = torch.reshape(torch.index_select( - self.adj_relation, 0, index), (self.batch_size, -1)) + neighbor_entities = torch.index_select(self.adj_entity, 0, index).reshape(self.batch_size, -1) + neighbor_relations = torch.index_select(self.adj_relation, 0, index).reshape(self.batch_size, -1) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations @@ -244,17 +235,13 @@ def aggregate(self, user_embeddings, entities, relations): for i in range(self.n_iter): entity_vectors_next_iter = [] for hop in range(self.n_iter - i): - shape = (self.batch_size, -1, - self.neighbor_sample_size, self.embedding_size) + shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] - neighbor_vectors = torch.reshape( - entity_vectors[hop + 1], shape) - neighbor_relations = torch.reshape( - relation_vectors[hop], shape) + neighbor_vectors = entity_vectors[hop + 1].reshape(shape) + neighbor_relations = relation_vectors[hop].reshape(shape) # mix_neighbor_vectors - user_embeddings = torch.reshape(user_embeddings, - (self.batch_size, 1, 1, self.embedding_size)) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = torch.unsqueeze(self.softmax(user_relation_scores), @@ -263,24 +250,19 @@ def aggregate(self, user_embeddings, entities, relations): dim=2) # [batch_size, -1, dim] if self.aggregator_class == 'sum': - output = torch.reshape( - self_vectors + neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = (self_vectors + neighbors_agg).reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'neighbor': - output = torch.reshape( - neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = neighbors_agg.reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'concat': # [batch_size, -1, dim * 2] output = torch.cat([self_vectors, neighbors_agg], dim=-1) - output = torch.reshape( - output, (-1, self.embedding_size * 2)) # [-1, dim * 2] + output = output.reshape(-1, self.embedding_size * 2) # [-1, dim * 2] else: - raise Exception("Unknown aggregator: " + - self.aggregator_class) + raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) # [batch_size, -1, dim] - output = torch.reshape( - output, [self.batch_size, -1, self.embedding_size]) + output = output.reshape(self.batch_size, -1, self.embedding_size) if i == self.n_iter - 1: vector = self.Tanh(output) @@ -290,8 +272,7 @@ def aggregate(self, user_embeddings, entities, relations): entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter - res = torch.reshape( - entity_vectors[0], (self.batch_size, self.embedding_size)) + res = entity_vectors[0].reshape(self.batch_size, self.embedding_size) return res def label_smoothness_predict(self, user_embeddings, user, entities, relations): @@ -320,8 +301,7 @@ def label_smoothness_predict(self, user_embeddings, user, entities, relations): for entities_per_iter in entities: users = torch.unsqueeze(user, dim=1) # [batch_size, 1] - user_entity_concat = users * self.offset + \ - entities_per_iter # [batch_size, n_neighbor^i] + user_entity_concat = users * self.offset + entities_per_iter # [batch_size, n_neighbor^i] # the first one in entities is the items to be held out if holdout_item_for_user is None: @@ -340,10 +320,9 @@ def lookup_interaction_table(x, _): holdout_mask = (holdout_item_for_user - user_entity_concat).bool() # True if the entity is a labeled item reset_mask = (initial_label - 0.5).bool() - reset_mask = torch.logical_and( - reset_mask, holdout_mask) # remove held-out items - initial_label = holdout_mask.float() * initial_label + torch.logical_not( - holdout_mask).float() * 0.5 # label initialization + reset_mask = torch.logical_and(reset_mask, holdout_mask) # remove held-out items + initial_label = holdout_mask.float() * initial_label + \ + torch.logical_not(holdout_mask).float() * 0.5 # label initialization reset_masks.append(reset_mask) entity_labels.append(initial_label) @@ -357,24 +336,21 @@ def lookup_interaction_table(x, _): for hop in range(self.n_iter - i): masks = reset_masks[hop] self_labels = entity_labels[hop] - neighbor_labels = torch.reshape(entity_labels[hop + 1], - [self.batch_size, -1, self.neighbor_sample_size]) - neighbor_relations = torch.reshape(relation_vectors[hop], - [self.batch_size, -1, self.neighbor_sample_size, - self.embedding_size]) + neighbor_labels = entity_labels[hop + 1].reshape(self.batch_size, -1, self.neighbor_sample_size) + neighbor_relations = relation_vectors[hop].reshape(self.batch_size, -1, self.neighbor_sample_size, + self.embedding_size) # mix_neighbor_labels - user_embeddings = torch.reshape(user_embeddings, - [self.batch_size, 1, 1, self.embedding_size]) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, + self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] - user_relation_scores_normalized = self.softmax( - user_relation_scores) # [batch_size, -1, n_neighbor] + user_relation_scores_normalized = self.softmax(user_relation_scores) # [batch_size, -1, n_neighbor] neighbors_aggregated_label = torch.mean(user_relation_scores_normalized * neighbor_labels, dim=2) # [batch_size, -1, dim] # [batch_size, -1] - output = masks.float() * self_labels + torch.logical_not(masks).float() * \ - neighbors_aggregated_label + output = masks.float() * self_labels + \ + torch.logical_not(masks).float() * neighbors_aggregated_label entity_labels_next_iter.append(output) entity_labels = entity_labels_next_iter diff --git a/recbole/model/knowledge_aware_recommender/ktup.py b/recbole/model/knowledge_aware_recommender/ktup.py index 72d7034ed..c3277efc6 100644 --- a/recbole/model/knowledge_aware_recommender/ktup.py +++ b/recbole/model/knowledge_aware_recommender/ktup.py @@ -18,10 +18,10 @@ import torch.nn.functional as F from torch.autograd import Variable -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbMarginLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbMarginLoss +from recbole.utils import InputType class KTUP(KnowledgeRecommender): @@ -74,7 +74,7 @@ def __init__(self, config, dataset): self.relation_norm_embedding.weight.data = normalize_rel_norm_emb def _masked_softmax(self, logits): - probs = F.softmax(logits, dim=len(logits.shape)-1) + probs = F.softmax(logits, dim=len(logits.shape) - 1) return probs def convert_to_one_hot(self, indices, num_classes): @@ -127,7 +127,8 @@ def st_gumbel_softmax(self, logits, temperature=1.0): return y def _get_preferences(self, user_e, item_e, use_st_gumbel=False): - pref_probs = torch.matmul(user_e + item_e, torch.t(self.pref_embedding.weight + self.relation_embedding.weight)) / 2 + pref_probs = torch.matmul(user_e + item_e, + torch.t(self.pref_embedding.weight + self.relation_embedding.weight)) / 2 if use_st_gumbel: # todo: different torch versions may cause the st_gumbel_softmax to report errors, wait to be test pref_probs = self.st_gumbel_softmax(pref_probs) @@ -209,7 +210,8 @@ def calculate_kg_loss(self, interaction): loss = self.kg_weight * (kg_loss + orthogonal_loss + reg_loss) entity = torch.cat([h, pos_t, neg_t]) entity = entity[entity < self.n_items] - align_loss = self.align_weight * alignLoss(self.item_embedding(entity), self.entity_embedding(entity), self.L1_flag) + align_loss = self.align_weight * alignLoss(self.item_embedding(entity), self.entity_embedding(entity), + self.L1_flag) return loss, align_loss diff --git a/recbole/model/knowledge_aware_recommender/mkr.py b/recbole/model/knowledge_aware_recommender/mkr.py index 7354ced6e..3b8503f80 100644 --- a/recbole/model/knowledge_aware_recommender/mkr.py +++ b/recbole/model/knowledge_aware_recommender/mkr.py @@ -16,10 +16,10 @@ import torch import torch.nn as nn -from recbole.utils import InputType -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import MLPLayers +from recbole.utils import InputType class MKR(KnowledgeRecommender): @@ -38,8 +38,8 @@ def __init__(self, config, dataset): self.LABEL = config['LABEL_FIELD'] self.embedding_size = config['embedding_size'] self.kg_embedding_size = config['kg_embedding_size'] - self.L = config['low_layers_num'] # the number of low layers - self.H = config['high_layers_num'] # the number of high layers + self.L = config['low_layers_num'] # the number of low layers + self.H = config['high_layers_num'] # the number of high layers self.reg_weight = config['reg_weight'] self.use_inner_product = config['use_inner_product'] self.dropout_prob = config['dropout_prob'] @@ -79,20 +79,22 @@ def forward(self, user_indices=None, item_indices=None, head_indices=None, relation_indices=None, tail_indices=None): self.item_embeddings = self.item_embeddings_lookup(item_indices) self.head_embeddings = self.entity_embeddings_lookup(head_indices) - self.item_embeddings, self.head_embeddings = self.cc_unit([self.item_embeddings, self.head_embeddings]) # calculate feature interactions between items and entities + self.item_embeddings, self.head_embeddings = self.cc_unit( + [self.item_embeddings, self.head_embeddings]) # calculate feature interactions between items and entities if user_indices is not None: # RS self.user_embeddings = self.user_embeddings_lookup(user_indices) self.user_embeddings = self.user_mlp(self.user_embeddings) - - if self.use_inner_product: # get scores by inner product. - self.scores = torch.sum(self.user_embeddings * self.item_embeddings, 1) # [batch_size] - else: # get scores by mlp layers - self.user_item_concat = torch.cat([self.user_embeddings, self.item_embeddings], 1) # [batch_size, emb_dim*2] + + if self.use_inner_product: # get scores by inner product. + self.scores = torch.sum(self.user_embeddings * self.item_embeddings, 1) # [batch_size] + else: # get scores by mlp layers + self.user_item_concat = torch.cat([self.user_embeddings, self.item_embeddings], + 1) # [batch_size, emb_dim*2] self.user_item_concat = self.rs_mlp(self.user_item_concat) - - self.scores = torch.squeeze(self.rs_pred_mlp(self.user_item_concat)) # [batch_size] + + self.scores = torch.squeeze(self.rs_pred_mlp(self.user_item_concat)) # [batch_size] self.scores_normalized = torch.sigmoid(self.scores) outputs = [self.user_embeddings, self.item_embeddings, self.scores, self.scores_normalized] @@ -101,16 +103,17 @@ def forward(self, user_indices=None, item_indices=None, head_indices=None, self.tail_embeddings = self.entity_embeddings_lookup(tail_indices) self.relation_embeddings = self.relation_embeddings_lookup(relation_indices) self.tail_embeddings = self.tail_mlp(self.tail_embeddings) - - self.head_relation_concat = torch.cat([self.head_embeddings, self.relation_embeddings], 1) # [batch_size, emb_dim*2] + + self.head_relation_concat = torch.cat([self.head_embeddings, self.relation_embeddings], + 1) # [batch_size, emb_dim*2] self.head_relation_concat = self.kge_mlp(self.head_relation_concat) - self.tail_pred = self.kge_pred_mlp(self.head_relation_concat) # [batch_size, 1] + self.tail_pred = self.kge_pred_mlp(self.head_relation_concat) # [batch_size, 1] self.tail_pred = torch.sigmoid(self.tail_pred) self.scores_kge = torch.sigmoid(torch.sum(self.tail_embeddings * self.tail_pred, 1)) self.rmse = torch.mean( torch.sqrt(torch.sum(torch.pow(self.tail_embeddings - - self.tail_pred, 2), 1) / self.embedding_size)) + self.tail_pred, 2), 1) / self.embedding_size)) outputs = [self.head_embeddings, self.tail_embeddings, self.scores_kge, self.rmse] return outputs @@ -179,6 +182,7 @@ class CrossCompressUnit(nn.Module): r"""This is Cross&Compress Unit for MKR model to model feature interactions between items and entities. """ + def __init__(self, dim): super(CrossCompressUnit, self).__init__() self.dim = dim @@ -194,7 +198,7 @@ def forward(self, inputs): e = torch.unsqueeze(e, 1) # [batch_size, dim, dim] c_matrix = torch.matmul(v, e) - c_matrix_transpose = c_matrix.permute(0,2,1) + c_matrix_transpose = c_matrix.permute(0, 2, 1) # [batch_size * dim, dim] c_matrix = c_matrix.view(-1, self.dim) c_matrix_transpose = c_matrix_transpose.contiguous().view(-1, self.dim) diff --git a/recbole/model/knowledge_aware_recommender/ripplenet.py b/recbole/model/knowledge_aware_recommender/ripplenet.py index 65c0e9508..aa87b264d 100644 --- a/recbole/model/knowledge_aware_recommender/ripplenet.py +++ b/recbole/model/knowledge_aware_recommender/ripplenet.py @@ -12,15 +12,16 @@ in CIKM 2018. """ +import collections + +import numpy as np import torch import torch.nn as nn -import numpy as np -import collections -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class RippleNet(KnowledgeRecommender): @@ -112,12 +113,12 @@ def _build_ripple_set(self): # we simply copy the ripple set of the last hop here if len(memories_h) == 0: if h == 0: - # print("user {} without 1-hop kg facts, fill with padding".format(user)) + # self.logger.info("user {} without 1-hop kg facts, fill with padding".format(user)) # raise AssertionError("User without facts in 1st hop") n_padding += 1 - memories_h = [0 for i in range(self.n_memory)] - memories_r = [0 for i in range(self.n_memory)] - memories_t = [0 for i in range(self.n_memory)] + memories_h = [0 for _ in range(self.n_memory)] + memories_r = [0 for _ in range(self.n_memory)] + memories_t = [0 for _ in range(self.n_memory)] memories_h = torch.LongTensor(memories_h).to(self.device) memories_r = torch.LongTensor(memories_r).to(self.device) memories_t = torch.LongTensor(memories_t).to(self.device) @@ -135,7 +136,7 @@ def _build_ripple_set(self): memories_r = torch.LongTensor(memories_r).to(self.device) memories_t = torch.LongTensor(memories_t).to(self.device) ripple_set[user].append((memories_h, memories_r, memories_t)) - print("{} among {} users are padded".format(n_padding, len(self.user_dict))) + self.logger.info("{} among {} users are padded".format(n_padding, len(self.user_dict))) return ripple_set def forward(self, interaction): @@ -161,7 +162,7 @@ def forward(self, interaction): head_ent = torch.cat(memories_h[i], dim=0) relation = torch.cat(memories_r[i], dim=0) tail_ent = torch.cat(memories_t[i], dim=0) - # print("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) + # self.logger.info("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) # [batch size * n_memory, dim] self.h_emb_list.append(self.entity_embedding(head_ent)) @@ -259,7 +260,8 @@ def _key_addressing_full(self): r"""Conduct reasoning for specific item and user ripple set Returns: - o_list (dict -> torch.cuda.FloatTensor): list of torch.cuda.FloatTensor n_hop * [batch_size, n_item, embedding_size] + o_list (dict -> torch.cuda.FloatTensor): list of torch.cuda.FloatTensor + n_hop * [batch_size, n_item, embedding_size] """ o_list = [] for hop in range(self.n_hop): @@ -332,7 +334,7 @@ def full_sort_predict(self, interaction): head_ent = torch.cat(memories_h[i], dim=0) relation = torch.cat(memories_r[i], dim=0) tail_ent = torch.cat(memories_t[i], dim=0) - # print("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) + # self.logger.info("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) # [batch size * n_memory, dim] self.h_emb_list.append(self.entity_embedding(head_ent)) diff --git a/recbole/model/layers.py b/recbole/model/layers.py index 81cbe75aa..5e2a9107f 100644 --- a/recbole/model/layers.py +++ b/recbole/model/layers.py @@ -15,15 +15,16 @@ Common Layers in recommender system """ -from logging import getLogger -import numpy as np import copy import math + +import numpy as np import torch import torch.nn as nn import torch.nn.functional as fn from torch.nn.init import normal_ -from recbole.utils import ModelType, InputType, FeatureType + +from recbole.utils import FeatureType class MLPLayers(nn.Module): @@ -32,8 +33,8 @@ class MLPLayers(nn.Module): Args: - layers(list): a list contains the size of each layer in mlp layers - dropout(float): probability of an element to be zeroed. Default: 0 - - activation(str): activation function after each layer in mlp layers. Default: 'relu' - candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none' + - activation(str): activation function after each layer in mlp layers. Default: 'relu'. + candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none' Shape: @@ -50,7 +51,7 @@ class MLPLayers(nn.Module): >>> torch.Size([128, 16]) """ - def __init__(self, layers, dropout=0, activation='relu', bn=False, init_method=None): + def __init__(self, layers, dropout=0., activation='relu', bn=False, init_method=None): super(MLPLayers, self).__init__() self.layers = layers self.dropout = dropout @@ -214,14 +215,14 @@ def __init__(self, in_dim, att_dim): self.h = nn.Parameter(torch.randn(att_dim), requires_grad=True) def forward(self, infeatures): - att_singal = self.w(infeatures) # [batch_size, M, att_dim] - att_singal = fn.relu(att_singal) # [batch_size, M, att_dim] + att_signal = self.w(infeatures) # [batch_size, M, att_dim] + att_signal = fn.relu(att_signal) # [batch_size, M, att_dim] - att_singal = torch.mul(att_singal, self.h) # [batch_size, M, att_dim] - att_singal = torch.sum(att_singal, dim=2) # [batch_size, M] - att_singal = fn.softmax(att_singal, dim=1) # [batch_size, M] + att_signal = torch.mul(att_signal, self.h) # [batch_size, M, att_dim] + att_signal = torch.sum(att_signal, dim=2) # [batch_size, M] + att_signal = fn.softmax(att_signal, dim=1) # [batch_size, M] - return att_singal + return att_signal class Dice(nn.Module): @@ -271,11 +272,11 @@ def __init__(self, mask_mat, att_hidden_size=(80, 40), activation='sigmoid', sof self.dense = nn.Linear(self.att_hidden_size[-1], 1) def forward(self, queries, keys, keys_length): - embbedding_size = queries.shape[-1] # H + embedding_size = queries.shape[-1] # H hist_len = keys.shape[1] # T queries = queries.repeat(1, hist_len) - queries = queries.view(-1, hist_len, embbedding_size) + queries = queries.view(-1, hist_len, embedding_size) # MLP Layer input_tensor = torch.cat([queries, keys, queries - keys, queries * keys], dim=-1) @@ -295,7 +296,7 @@ def forward(self, queries, keys, keys_length): output = output.masked_fill(mask=mask, value=torch.tensor(mask_value)) output = output.unsqueeze(1) - output = output / (embbedding_size ** 0.5) + output = output / (embedding_size ** 0.5) # get the weight of each user's history list about the target item if self.softmax_stag: @@ -319,6 +320,7 @@ class VanillaAttention(nn.Module): weights (torch.Tensor): the attention weights """ + def __init__(self, hidden_dim, attn_dim): super().__init__() self.projection = nn.Sequential( @@ -348,6 +350,7 @@ class MultiHeadAttention(nn.Module): hidden_states (torch.Tensor): the output of the multi-head self-attention layer """ + def __init__(self, n_heads, hidden_size, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps): super(MultiHeadAttention, self).__init__() if hidden_size % n_heads != 0: @@ -420,6 +423,7 @@ class FeedForward(nn.Module): hidden_states (torch.Tensor): the output of the point-wise feed-forward layer """ + def __init__(self, hidden_size, inner_size, hidden_dropout_prob, hidden_act, layer_norm_eps): super(FeedForward, self).__init__() self.dense_1 = nn.Linear(hidden_size, inner_size) @@ -473,16 +477,18 @@ class TransformerLayer(nn.Module): attention_mask (torch.Tensor): the attention mask for the multi-head self-attention sublayer Returns: - feedforward_output (torch.Tensor): the output of the point-wise feed-forward sublayer, is the output of the transformer layer + feedforward_output (torch.Tensor): The output of the point-wise feed-forward sublayer, + is the output of the transformer layer. """ + def __init__(self, n_heads, hidden_size, intermediate_size, hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps): super(TransformerLayer, self).__init__() self.multi_head_attention = MultiHeadAttention(n_heads, hidden_size, - hidden_dropout_prob, attn_dropout_prob, layer_norm_eps) + hidden_dropout_prob, attn_dropout_prob, layer_norm_eps) self.feed_forward = FeedForward(hidden_size, intermediate_size, - hidden_dropout_prob, hidden_act, layer_norm_eps) + hidden_dropout_prob, hidden_act, layer_norm_eps) def forward(self, hidden_states, attention_mask): attention_output = self.multi_head_attention(hidden_states, attention_mask) @@ -504,6 +510,7 @@ class TransformerEncoder(nn.Module): - layer_norm_eps(float): a value added to the denominator for numerical stability. Default: 1e-12 """ + def __init__(self, n_layers=2, n_heads=2, @@ -523,13 +530,13 @@ def __init__(self, def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): """ Args: - hidden_states (torch.Tensor): the input of the TrandformerEncoder + hidden_states (torch.Tensor): the input of the TransformerEncoder attention_mask (torch.Tensor): the attention mask for the input hidden_states output_all_encoded_layers (Bool): whether output all transformer layers' output Returns: - all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer layers' output, - otherwise return a list only consists of the output of last transformer layer. + all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer + layers' output, otherwise return a list only consists of the output of last transformer layer. """ all_encoder_layers = [] @@ -626,7 +633,7 @@ def embed_float_fields(self, float_fields, type, embed=True): return float_embedding def embed_token_fields(self, token_fields, type): - """Get the embedding of toekn fields + """Get the embedding of token fields Args: token_fields(torch.Tensor): input, [batch_size, max_item_length, num_token_field] @@ -754,8 +761,11 @@ def embed_input_fields(self, user_idx, item_idx): token_seq_fields_embedding[type]], dim=-2) dense_embedding[type] = float_fields_embedding[type] - # sparse_embedding[type] shape: [batch_size, max_item_length, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding[type] shape: [batch_size, max_item_length, num_float_field] or [batch_size, max_item_length, num_float_field, embed_dim] or None + # sparse_embedding[type] + # shape: [batch_size, max_item_length, num_token_seq_field+num_token_field, embed_dim] or None + # dense_embedding[type] + # shape: [batch_size, max_item_length, num_float_field] + # or [batch_size, max_item_length, num_float_field, embed_dim] or None return sparse_embedding, dense_embedding def forward(self, user_idx, item_idx): @@ -857,7 +867,8 @@ def __init__(self, channels, kernels, strides, activation='relu', init_method=No cnn_modules = [] for i in range(self.num_of_nets): - cnn_modules.append(nn.Conv2d(self.channels[i], self.channels[i + 1], self.kernels[i], stride=self.strides[i])) + cnn_modules.append( + nn.Conv2d(self.channels[i], self.channels[i + 1], self.kernels[i], stride=self.strides[i])) if self.activation.lower() == 'sigmoid': cnn_modules.append(nn.Sigmoid()) elif self.activation.lower() == 'tanh': @@ -1041,3 +1052,25 @@ def forward(self, interaction): total_fields_embedding.append(token_seq_fields_embedding) return torch.sum(torch.cat(total_fields_embedding, dim=1), dim=1) + self.bias # [batch_size, output_dim] + + +class SparseDropout(nn.Module): + """ + This is a Module that execute Dropout on Pytorch sparse tensor. + """ + + def __init__(self, p=0.5): + super(SparseDropout, self).__init__() + # p is ratio of dropout + # convert to keep probability + self.kprob = 1 - p + + def forward(self, x): + if not self.training: + return x + + mask = ((torch.rand(x._values().size()) + + self.kprob).floor()).type(torch.bool) + rc = x._indices()[:, mask] + val = x._values()[mask] * (1.0 / self.kprob) + return torch.sparse.FloatTensor(rc, val, x.shape) diff --git a/recbole/model/loss.py b/recbole/model/loss.py index ebfc84ca1..9727261f3 100644 --- a/recbole/model/loss.py +++ b/recbole/model/loss.py @@ -14,13 +14,11 @@ Common Loss in recommender system """ - import torch import torch.nn as nn class BPRLoss(nn.Module): - """ BPRLoss, based on Bayesian Personalized Ranking Args: @@ -39,6 +37,7 @@ class BPRLoss(nn.Module): >>> output = loss(pos_score, neg_score) >>> output.backward() """ + def __init__(self, gamma=1e-10): super(BPRLoss, self).__init__() self.gamma = gamma @@ -52,6 +51,7 @@ class RegLoss(nn.Module): """ RegLoss, L2 regularization on model parameters """ + def __init__(self): super(RegLoss, self).__init__() @@ -69,6 +69,7 @@ class EmbLoss(nn.Module): """ EmbLoss, regularization on embeddings """ + def __init__(self, norm=2): super(EmbLoss, self).__init__() self.norm = norm diff --git a/recbole/model/sequential_recommender/bert4rec.py b/recbole/model/sequential_recommender/bert4rec.py index 365434166..f808e1ff9 100644 --- a/recbole/model/sequential_recommender/bert4rec.py +++ b/recbole/model/sequential_recommender/bert4rec.py @@ -50,8 +50,8 @@ def __init__(self, config, dataset): self.mask_item_length = int(self.mask_ratio * self.max_seq_length) # define layers and loss - self.item_embedding = nn.Embedding(self.n_items+1, self.hidden_size, padding_idx=0) # mask token add 1 - self.position_embedding = nn.Embedding(self.max_seq_length+1, self.hidden_size) # add mask_token at the last + self.item_embedding = nn.Embedding(self.n_items + 1, self.hidden_size, padding_idx=0) # mask token add 1 + self.position_embedding = nn.Embedding(self.max_seq_length + 1, self.hidden_size) # add mask_token at the last self.trm_encoder = TransformerEncoder(n_layers=self.n_layers, n_heads=self.n_heads, hidden_size=self.hidden_size, inner_size=self.inner_size, hidden_dropout_prob=self.hidden_dropout_prob, @@ -99,8 +99,8 @@ def _neg_sample(self, item_set): def _padding_sequence(self, sequence, max_length): pad_len = max_length - len(sequence) - sequence = [0]*pad_len + sequence - sequence = sequence[-max_length:] # truncate according to the max_length + sequence = [0] * pad_len + sequence + sequence = sequence[-max_length:] # truncate according to the max_length return sequence def reconstruct_train_data(self, item_seq): @@ -237,7 +237,7 @@ def predict(self, interaction): test_item = interaction[self.ITEM_ID] item_seq = self.reconstruct_test_data(item_seq, item_seq_len) seq_output = self.forward(item_seq) - seq_output = self.gather_indexes(seq_output, item_seq_len-1) # [B H] + seq_output = self.gather_indexes(seq_output, item_seq_len - 1) # [B H] test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores @@ -247,7 +247,7 @@ def full_sort_predict(self, interaction): item_seq_len = interaction[self.ITEM_SEQ_LEN] item_seq = self.reconstruct_test_data(item_seq, item_seq_len) seq_output = self.forward(item_seq) - seq_output = self.gather_indexes(seq_output, item_seq_len-1) # [B H] + seq_output = self.gather_indexes(seq_output, item_seq_len - 1) # [B H] test_items_emb = self.item_embedding.weight[:self.n_items] # delete masked token scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, item_num] return scores diff --git a/recbole/model/sequential_recommender/caser.py b/recbole/model/sequential_recommender/caser.py index 0cb018ba4..7203685e3 100644 --- a/recbole/model/sequential_recommender/caser.py +++ b/recbole/model/sequential_recommender/caser.py @@ -25,8 +25,8 @@ from torch.nn import functional as F from torch.nn.init import normal_, xavier_normal_, constant_ -from recbole.model.loss import RegLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import RegLoss, BPRLoss class Caser(SequentialRecommender): @@ -61,7 +61,10 @@ def __init__(self, config, dataset): # horizontal conv layer lengths = [i + 1 for i in range(self.max_seq_length)] - self.conv_h = nn.ModuleList([nn.Conv2d(in_channels=1, out_channels=self.n_h, kernel_size=(i, self.embedding_size)) for i in lengths]) + self.conv_h = nn.ModuleList([ + nn.Conv2d(in_channels=1, out_channels=self.n_h, kernel_size=(i, self.embedding_size)) + for i in lengths + ]) # fully-connected layer self.fc1_dim_v = self.n_v * self.embedding_size @@ -95,7 +98,7 @@ def _init_weights(self, module): def forward(self, user, item_seq): # Embedding Look-up - # use unsqueeze() to get a 4-D input for convolution layers. (batchsize * 1 * max_length * embedding_size) + # use unsqueeze() to get a 4-D input for convolution layers. (batch_size * 1 * max_length * embedding_size) item_seq_emb = self.item_embedding(item_seq).unsqueeze(1) user_emb = self.user_embedding(user).squeeze(1) @@ -155,7 +158,7 @@ def calculate_loss(self, interaction): loss = self.loss_fct(logits, pos_items) reg_loss = self.reg_loss([self.user_embedding.weight, self.item_embedding.weight, - self.conv_v.weight,self.fc1.weight, self.fc2.weight]) + self.conv_v.weight, self.fc1.weight, self.fc2.weight]) loss = loss + self.reg_weight * reg_loss + self.reg_loss_conv_h() return loss diff --git a/recbole/model/sequential_recommender/din.py b/recbole/model/sequential_recommender/din.py index 161454684..f8f1e6b51 100644 --- a/recbole/model/sequential_recommender/din.py +++ b/recbole/model/sequential_recommender/din.py @@ -24,9 +24,9 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.utils import InputType -from recbole.model.layers import MLPLayers, SequenceAttLayer, ContextSeqEmbLayer from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.layers import MLPLayers, SequenceAttLayer, ContextSeqEmbLayer +from recbole.utils import InputType class DIN(SequentialRecommender): @@ -61,15 +61,10 @@ def __init__(self, config, dataset): # self.dnn_list = [(3 * self.num_feature_field['item'] + self.num_feature_field['user']) # * self.embedding_size] + self.mlp_hidden_size num_item_feature = len(self.item_feat.interaction.keys()) - self.dnn_list = [ - (3 * num_item_feature) * self.embedding_size - ] + self.mlp_hidden_size - self.att_list = [ - 4 * num_item_feature * self.embedding_size - ] + self.mlp_hidden_size - - mask_mat = torch.arange(self.max_seq_length).to(self.device).view( - 1, -1) # init mask + self.dnn_list = [3 * num_item_feature * self.embedding_size] + self.mlp_hidden_size + self.att_list = [4 * num_item_feature * self.embedding_size] + self.mlp_hidden_size + + mask_mat = torch.arange(self.max_seq_length).to(self.device).view(1, -1) # init mask self.attention = SequenceAttLayer(mask_mat, self.att_list, activation='Sigmoid', diff --git a/recbole/model/sequential_recommender/fdsa.py b/recbole/model/sequential_recommender/fdsa.py index 41bbae25d..bae7192ab 100644 --- a/recbole/model/sequential_recommender/fdsa.py +++ b/recbole/model/sequential_recommender/fdsa.py @@ -17,14 +17,14 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer, VanillaAttention +from recbole.model.loss import BPRLoss class FDSA(SequentialRecommender): r""" FDSA is similar with the GRU4RecF implemented in RecBole, which uses two different Transformer encoders to - encode items and features respectively and concatenates the two subparts's outputs as the final output. + encode items and features respectively and concatenates the two subparts' outputs as the final output. """ diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py new file mode 100644 index 000000000..30f95b29e --- /dev/null +++ b/recbole/model/sequential_recommender/fossil.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 20:00 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +FOSSIL +################################################ + +Reference: + Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in ICDM 2016. + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class FOSSIL(SequentialRecommender): + r""" + FOSSIL uses similarity of the items as main purpose and uses high MC as a way of sequential preference improve of + ability of sequential recommendation + + """ + + def __init__(self, config, dataset): + super(FOSSIL, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameters + self.embedding_size = config["embedding_size"] + self.order_len = config["order_len"] + assert self.order_len <= self.max_seq_length, "order_len can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.alpha = config["alpha"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_lambda = nn.Embedding(self.n_users, self.order_len) + self.lambda_ = nn.Parameter(torch.zeros(self.order_len)).to(self.device) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self.init_weights) + + def inverse_seq_item_embedding(self, seq_item_embedding, seq_item_len): + """ + inverse seq_item_embedding like this: + simple to 2-dim + [1,2,3,0,0,0] -- ??? -- >> [0,0,0,1,2,3] + first: [0,0,0,0,0,0] concat [1,2,3,0,0,0] + using gather_indexes: to get one by one + first get 3,then 2,last 1 + """ + zeros = torch.zeros_like(seq_item_embedding, dtype=torch.float).to(self.device) + # batch_size * seq_len * embedding_size + item_embedding_zeros = torch.cat([zeros, seq_item_embedding], dim=1) + # batch_size * 2_mul_seq_len * embedding_size + embedding_list = list() + for i in range(self.order_len): + embedding = self.gather_indexes(item_embedding_zeros, + self.max_seq_length + seq_item_len - self.order_len + i) + embedding_list.append(embedding.unsqueeze(1)) + short_item_embedding = torch.cat(embedding_list, dim=1) + # batch_size * short_len * embedding_size + + return short_item_embedding + + def reg_loss(self, user_embedding, item_embedding, seq_output): + + reg_1 = self.reg_weight + loss_1 = reg_1 * torch.norm(user_embedding, p=2) \ + + reg_1 * torch.norm(item_embedding, p=2) \ + + reg_1 * torch.norm(seq_output, p=2) + + return loss_1 + + def init_weights(self, module): + + if isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, seq_item_len, user): + + seq_item_embedding = self.item_embedding(seq_item) + + high_order_seq_item_embedding = self.inverse_seq_item_embedding(seq_item_embedding, seq_item_len) + # batch_size * order_len * embedding + + high_order = self.get_high_order_Markov(high_order_seq_item_embedding, user) + similarity = self.get_similarity(seq_item_embedding, seq_item_len) + + return high_order + similarity + + def get_high_order_Markov(self, high_order_item_embedding, user): + """ + + in order to get the inference of past items and the user's taste to the current predict item + """ + + user_lambda = self.user_lambda(user).unsqueeze(dim=2) + # batch_size * order_len * 1 + lambda_ = self.lambda_.unsqueeze(dim=0).unsqueeze(dim=2) + # 1 * order_len * 1 + lambda_ = torch.add(user_lambda, lambda_) + # batch_size * order_len * 1 + high_order_item_embedding = torch.mul(high_order_item_embedding, lambda_) + # batch_size * order_len * embedding_size + high_order_item_embedding = high_order_item_embedding.sum(dim=1) + # batch_size * embedding_size + + return high_order_item_embedding + + def get_similarity(self, seq_item_embedding, seq_item_len): + """ + in order to get the inference of past items to the current predict item + """ + coeff = torch.pow(seq_item_len.unsqueeze(1), -self.alpha).float() + # batch_size * 1 + similarity = torch.mul(coeff, seq_item_embedding.sum(dim=1)) + # batch_size * embedding_size + + return similarity + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(seq_item, seq_item_len, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + + user_lambda = self.user_lambda(user) + pos_items_embedding = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_lambda, pos_items_embedding, seq_output) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_lambda, pos_items_embedding, seq_output) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, item_seq_len, user) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(item_seq, item_seq_len, user) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/model/sequential_recommender/fpmc.py b/recbole/model/sequential_recommender/fpmc.py index ce1ba3373..6654b59d1 100644 --- a/recbole/model/sequential_recommender/fpmc.py +++ b/recbole/model/sequential_recommender/fpmc.py @@ -20,9 +20,9 @@ from torch import nn from torch.nn.init import xavier_normal_ -from recbole.utils import InputType -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss +from recbole.utils import InputType class FPMC(SequentialRecommender): @@ -66,19 +66,18 @@ def _init_weights(self, module): xavier_normal_(module.weight.data) def forward(self, user, item_seq, item_seq_len, next_item): - item_last_click_index = item_seq_len - 1 item_last_click = torch.gather(item_seq, dim=1, index=item_last_click_index.unsqueeze(1)) - item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] + item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] user_emb = self.UI_emb(user) - user_emb = torch.unsqueeze(user_emb, dim=1) # [b,1,emb] + user_emb = torch.unsqueeze(user_emb, dim=1) # [b,1,emb] iu_emb = self.IU_emb(next_item) - iu_emb = torch.unsqueeze(iu_emb, dim=1) # [b,n,emb] in here n = 1 + iu_emb = torch.unsqueeze(iu_emb, dim=1) # [b,n,emb] in here n = 1 il_emb = self.IL_emb(next_item) - il_emb = torch.unsqueeze(il_emb, dim=1) # [b,n,emb] in here n = 1 + il_emb = torch.unsqueeze(il_emb, dim=1) # [b,n,emb] in here n = 1 # This is the core part of the FPMC model,can be expressed by a combination of a MF and a FMC model # MF @@ -119,13 +118,13 @@ def full_sort_predict(self, interaction): user_emb = self.UI_emb(user) all_iu_emb = self.IU_emb.weight - mf = torch.matmul(user_emb, all_iu_emb.transpose(0,1)) + mf = torch.matmul(user_emb, all_iu_emb.transpose(0, 1)) all_il_emb = self.IL_emb.weight item_last_click_index = item_seq_len - 1 item_last_click = torch.gather(item_seq, dim=1, index=item_last_click_index.unsqueeze(1)) item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] - fmc = torch.matmul(item_seq_emb, all_il_emb.transpose(0,1)) + fmc = torch.matmul(item_seq_emb, all_il_emb.transpose(0, 1)) fmc = torch.squeeze(fmc, dim=1) score = mf + fmc return score diff --git a/recbole/model/sequential_recommender/gcsan.py b/recbole/model/sequential_recommender/gcsan.py index e527c7c3b..597ba2361 100644 --- a/recbole/model/sequential_recommender/gcsan.py +++ b/recbole/model/sequential_recommender/gcsan.py @@ -14,16 +14,16 @@ """ import math -import numpy as np +import numpy as np import torch from torch import nn from torch.nn import Parameter from torch.nn import functional as F -from recbole.model.loss import EmbLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.layers import TransformerEncoder +from recbole.model.loss import EmbLoss, BPRLoss class GNN(nn.Module): @@ -72,16 +72,16 @@ def GNNCell(self, A, hidden): # [batch_size, max_session_len, embedding_size * 2] inputs = torch.cat([input_in, input_out], 2) - # gi.size equals to gh.size, shape of [batch_size, max_session_len, embdding_size * 3] + # gi.size equals to gh.size, shape of [batch_size, max_session_len, embedding_size * 3] gi = F.linear(inputs, self.w_ih, self.b_ih) gh = F.linear(hidden, self.w_hh, self.b_hh) # (batch_size, max_session_len, embedding_size) i_r, i_i, i_n = gi.chunk(3, 2) h_r, h_i, h_n = gh.chunk(3, 2) - resetgate = torch.sigmoid(i_r + h_r) - inputgate = torch.sigmoid(i_i + h_i) - newgate = torch.tanh(i_n + resetgate * h_n) - hy = (1 - inputgate) * hidden + inputgate * newgate + reset_gate = torch.sigmoid(i_r + h_r) + input_gate = torch.sigmoid(i_i + h_i) + new_gate = torch.tanh(i_n + reset_gate * h_n) + hy = (1 - input_gate) * hidden + input_gate * new_gate return hy def forward(self, A, hidden): @@ -91,7 +91,7 @@ def forward(self, A, hidden): class GCSAN(SequentialRecommender): - r"""GCSAN captures rich local dependencies via graph nerual network, + r"""GCSAN captures rich local dependencies via graph neural network, and learns long-range dependencies by applying the self-attention mechanism. Note: diff --git a/recbole/model/sequential_recommender/gru4rec.py b/recbole/model/sequential_recommender/gru4rec.py index d59d50fcf..94d542174 100644 --- a/recbole/model/sequential_recommender/gru4rec.py +++ b/recbole/model/sequential_recommender/gru4rec.py @@ -17,13 +17,12 @@ """ - import torch from torch import nn from torch.nn.init import xavier_uniform_, xavier_normal_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class GRU4Rec(SequentialRecommender): @@ -70,7 +69,7 @@ def __init__(self, config, dataset): def _init_weights(self, module): if isinstance(module, nn.Embedding): xavier_normal_(module.weight) - elif isinstance(module,nn.GRU): + elif isinstance(module, nn.GRU): xavier_uniform_(self.gru_layers.weight_hh_l0) xavier_uniform_(self.gru_layers.weight_ih_l0) @@ -92,8 +91,8 @@ def calculate_loss(self, interaction): neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) neg_items_emb = self.item_embedding(neg_items) - pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' diff --git a/recbole/model/sequential_recommender/gru4recf.py b/recbole/model/sequential_recommender/gru4recf.py index 3e4abd855..608c6448f 100644 --- a/recbole/model/sequential_recommender/gru4recf.py +++ b/recbole/model/sequential_recommender/gru4recf.py @@ -17,9 +17,9 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss -from recbole.model.layers import FeatureSeqEmbLayer from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import FeatureSeqEmbLayer +from recbole.model.loss import BPRLoss class GRU4RecF(SequentialRecommender): @@ -34,7 +34,7 @@ class GRU4RecF(SequentialRecommender): (3) Weighted sum of outputs from two different RNNs. We implemented the optimal parallel version(2), which uses different RNNs to - encode items and features respectively and concatenates the two subparts's + encode items and features respectively and concatenates the two subparts' outputs as the final output. The different RNN encoders are trained simultaneously. """ @@ -106,12 +106,12 @@ def forward(self, item_seq, item_seq_len): feat_num, embedding_size = table_shape[-2], table_shape[-1] feature_emb = feature_table.view(table_shape[:-2] + (feat_num * embedding_size,)) - feature_gru_output, _ = self.feature_gru_layers(feature_emb) # [B Len H] + feature_gru_output, _ = self.feature_gru_layers(feature_emb) # [B Len H] output_concat = torch.cat((item_gru_output, feature_gru_output), -1) # [B Len 2*H] output = self.dense_layer(output_concat) output = self.gather_indexes(output, item_seq_len - 1) # [B H] - return output # [B H] + return output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] @@ -120,10 +120,10 @@ def calculate_loss(self, interaction): pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] - pos_items_emb = self.item_embedding(pos_items) # [B H] - neg_items_emb = self.item_embedding(neg_items) # [B H] - pos_score = torch.sum(seq_output*pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output*neg_items_emb, dim=-1) # [B] + pos_items_emb = self.item_embedding(pos_items) # [B H] + neg_items_emb = self.item_embedding(neg_items) # [B H] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' diff --git a/recbole/model/sequential_recommender/hgn.py b/recbole/model/sequential_recommender/hgn.py new file mode 100644 index 000000000..009b455ab --- /dev/null +++ b/recbole/model/sequential_recommender/hgn.py @@ -0,0 +1,199 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 16:36 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HGN +################################################ + +Reference: + Chen Ma et al. "Hierarchical Gating Networks for Sequential Recommendation."in SIGKDD 2019 + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_uniform_, constant_, normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HGN(SequentialRecommender): + r""" + HGN sets feature gating and instance gating to get the important feature and item for predicting the next item + + """ + + def __init__(self, config, dataset): + super(HGN, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.reg_weight = config["reg_weight"] + self.pool_type = config["pooling_type"] + + if self.pool_type not in ["max", "average"]: + raise NotImplementedError("Make sure 'loss_type' in ['max', 'average']!") + + # define the layers and loss function + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + + # define the module feature gating need + self.w1 = nn.Linear(self.embedding_size, self.embedding_size) + self.w2 = nn.Linear(self.embedding_size, self.embedding_size) + self.b = nn.Parameter(torch.zeros(self.embedding_size), requires_grad=True).to(self.device) + + # define the module instance gating need + self.w3 = nn.Linear(self.embedding_size, 1, bias=False) + self.w4 = nn.Linear(self.embedding_size, self.max_seq_length, bias=False) + + # define item_embedding for prediction + self.item_embedding_for_prediction = nn.Embedding(self.n_items, self.embedding_size) + + self.sigmoid = nn.Sigmoid() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def reg_loss(self, user_embedding, item_embedding, seq_item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.w1.weight, p=2) + reg_1 * torch.norm(self.w2.weight, p=2) + reg_1 * torch.norm( + self.w3.weight, p=2) + reg_1 * torch.norm(self.w4.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + reg_2 * torch.norm( + seq_item_embedding, p=2) + + return loss_1 + loss_2 + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 1 / self.embedding_size) + elif isinstance(module, nn.Linear): + xavier_uniform_(module.weight.data) + if module.bias is not None: + constant_(module.bias.data, 0) + + def feature_gating(self, seq_item_embedding, user_embedding): + """ + + choose the features that will be sent to the next stage(more important feature, more focus) + """ + + batch_size, seq_len, embedding_size = seq_item_embedding.size() + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.w1(seq_item_embedding) + # batch_size * seq_len * embedding_size + user_embedding = self.w2(user_embedding) + # batch_size * embedding_size + user_embedding = user_embedding.unsqueeze(1).repeat(1, seq_len, 1) + # batch_size * seq_len * embedding_size + + user_item = self.sigmoid(seq_item_embedding + user_embedding + self.b) + # batch_size * seq_len * embedding_size + + user_item = torch.mul(seq_item_embedding_value, user_item) + # batch_size * seq_len * embedding_size + + return user_item + + def instance_gating(self, user_item, user_embedding): + """ + + choose the last click items that will influence the prediction( more important more chance to get attention) + """ + + user_embedding_value = user_item + + user_item = self.w3(user_item) + # batch_size * seq_len * 1 + + user_embedding = self.w4(user_embedding).unsqueeze(2) + # batch_size * seq_len * 1 + + instance_score = self.sigmoid(user_item + user_embedding).squeeze(-1) + # batch_size * seq_len * 1 + output = torch.mul(instance_score.unsqueeze(2), user_embedding_value) + # batch_size * seq_len * embedding_size + + if self.pool_type == "average": + output = torch.div(output.sum(dim=1), instance_score.sum(dim=1).unsqueeze(1)) + # batch_size * embedding_size + else: + # for max_pooling + index = torch.max(instance_score, dim=1)[1] + # batch_size * 1 + output = self.gather_indexes(output, index) + # batch_size * seq_len * embedding_size ==>> batch_size * embedding_size + + return output + + def forward(self, seq_item, user): + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + feature_gating = self.feature_gating(seq_item_embedding, user_embedding) + instance_gating = self.instance_gating(feature_gating, user_embedding) + # batch_size * embedding_size + item_item = torch.sum(seq_item_embedding, dim=1) + # batch_size * embedding_size + + return user_embedding + instance_gating + item_item + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_embedding = self.item_embedding(seq_item) + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding_for_prediction(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding_for_prediction.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.item_embedding_for_prediction(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.item_embedding_for_prediction.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/model/sequential_recommender/hrm.py b/recbole/model/sequential_recommender/hrm.py new file mode 100644 index 000000000..0eb2908d4 --- /dev/null +++ b/recbole/model/sequential_recommender/hrm.py @@ -0,0 +1,172 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 12:08 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HRM +################################################ + +Reference: + Pengfei Wang et al. "Learning Hierarchical Representation Model for Next Basket Recommendation." in SIGIR 2015. + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HRM(SequentialRecommender): + r""" + HRM can well capture both sequential behavior and users’ general taste by involving transaction and + user representations in prediction. + + HRM user max- & average- pooling as a good helper. + """ + + def __init__(self, config, dataset): + super(HRM, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.pooling_type_layer_1 = config["pooling_type_layer_1"] + self.pooling_type_layer_2 = config["pooling_type_layer_2"] + self.high_order = config["high_order"] + assert self.high_order <= self.max_seq_length, "high_order can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.dropout_prob = config["dropout_prob"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def _init_weights(self, module): + + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user, seq_item_len): + + # seq_item=self.inverse_seq_item(seq_item) + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + # batch_size * seq_len * embedding_size + + high_order_item_embedding = seq_item_embedding[:, -self.high_order:, :] + # batch_size * high_order * embedding_size + + user_embedding = self.dropout(self.user_embedding(user)) + # batch_size * embedding_size + + # layer 1 + if self.pooling_type_layer_1 == "max": + high_order_item_embedding = torch.max(high_order_item_embedding, dim=1).values + # batch_size * embedding_size + else: + for idx, len in enumerate(seq_item_len): + if len > self.high_order: + seq_item_len[idx] = self.high_order + high_order_item_embedding = torch.sum(seq_item_embedding, dim=1) + high_order_item_embedding = torch.div(high_order_item_embedding, seq_item_len.unsqueeze(1).float()) + # batch_size * embedding_size + hybrid_user_embedding = self.dropout( + torch.cat([user_embedding.unsqueeze(dim=1), high_order_item_embedding.unsqueeze(dim=1)], dim=1)) + # batch_size * 2_mul_embedding_size + + # layer 2 + if self.pooling_type_layer_2 == "max": + hybrid_user_embedding = torch.max(hybrid_user_embedding, dim=1).values + # batch_size * embedding_size + else: + hybrid_user_embedding = torch.mean(hybrid_user_embedding, dim=1) + # batch_size * embedding_size + + return hybrid_user_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight.t() + logits = torch.matmul(seq_output, test_item_emb) + loss = self.loss_fct(logits, pos_items) + + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + seq_output = seq_output.repeat(1, self.embedding_size) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + + return scores diff --git a/recbole/model/sequential_recommender/ksr.py b/recbole/model/sequential_recommender/ksr.py index c788b9730..6c884b049 100644 --- a/recbole/model/sequential_recommender/ksr.py +++ b/recbole/model/sequential_recommender/ksr.py @@ -14,14 +14,12 @@ """ - import torch from torch import nn from torch.nn.init import xavier_uniform_, xavier_normal_ -from recbole.utils import InputType -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class KSR(SequentialRecommender): @@ -48,13 +46,13 @@ def __init__(self, config, dataset): self.loss_type = config['loss_type'] self.num_layers = config['num_layers'] self.dropout_prob = config['dropout_prob'] - self.gamma = config['gamma'] # Scaling factor + self.gamma = config['gamma'] # Scaling factor self.device = config['device'] self.loss_type = config['loss_type'] self.freeze_kg = config['freeze_kg'] # define layers and loss - self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.entity_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.entity_embedding.weight.requires_grad = not self.freeze_kg @@ -75,31 +73,35 @@ def __init__(self, config, dataset): self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") - + # parameters initialization self.apply(self._init_weights) self.entity_embedding.weight.data.copy_(torch.from_numpy(self.entity_embedding_matrix[:self.n_items])) - self.relation_Matrix = torch.from_numpy(self.relation_embedding_matrix[:self.n_relations]).to(self.device) # [R H] + self.relation_Matrix = torch.from_numpy(self.relation_embedding_matrix[:self.n_relations]).to( + self.device) # [R H] def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, nn.Embedding): xavier_normal_(module.weight) - elif isinstance(module,nn.GRU): + elif isinstance(module, nn.GRU): xavier_uniform_(self.gru_layers.weight_hh_l0) xavier_uniform_(self.gru_layers.weight_ih_l0) - + def _get_kg_embedding(self, head): - """Difference: We generate the embeddings of the tail entities on every relations only for head due to the 1-N problems. """ - head_e = self.entity_embedding(head) # [B H] - relation_Matrix = self.relation_Matrix.repeat(head_e.size()[0], 1, 1) # [B R H] - head_Matrix = torch.unsqueeze(head_e, 1).repeat(1, self.n_relations, 1) # [B R H] + """Difference: + We generate the embeddings of the tail entities on every relations only for head due to the 1-N problems. + """ + head_e = self.entity_embedding(head) # [B H] + relation_Matrix = self.relation_Matrix.repeat(head_e.size()[0], 1, 1) # [B R H] + head_Matrix = torch.unsqueeze(head_e, 1).repeat(1, self.n_relations, 1) # [B R H] tail_Matrix = head_Matrix + relation_Matrix - + return head_e, tail_Matrix - + def _memory_update_cell(self, user_memory, update_memory): - z = torch.sigmoid(torch.mul(user_memory, update_memory).sum(-1).float()).unsqueeze(-1) # [B R 1], the gate vector + z = torch.sigmoid(torch.mul(user_memory, update_memory).sum(-1).float()).unsqueeze( + -1) # [B R 1], the gate vector updated_user_memory = (1.0 - z) * user_memory + z * update_memory return updated_user_memory @@ -108,19 +110,20 @@ def memory_update(self, item_seq, item_seq_len): step_length = item_seq.size()[1] last_item = item_seq_len - 1 # init user memory with 0s - user_memory = torch.zeros(item_seq.size()[0], self.n_relations, self.embedding_size).float().to(self.device) # [B R H] + user_memory = torch.zeros(item_seq.size()[0], self.n_relations, self.embedding_size).float().to( + self.device) # [B R H] last_user_memory = torch.zeros_like(user_memory) - for i in range(step_length): # [len] - _, update_memory = self._get_kg_embedding(item_seq[:, i]) # [B R H] - user_memory = self._memory_update_cell(user_memory, update_memory) # [B R H] + for i in range(step_length): # [len] + _, update_memory = self._get_kg_embedding(item_seq[:, i]) # [B R H] + user_memory = self._memory_update_cell(user_memory, update_memory) # [B R H] last_user_memory[last_item == i] = user_memory[last_item == i].float() return last_user_memory - + def memory_read(self, user_memory): """ define read operator """ attrs = self.relation_Matrix - attentions = nn.functional.softmax(self.gamma * torch.mul(user_memory, attrs).sum(-1).float(), -1) # [B R] - u_m = torch.mul(user_memory, attentions.unsqueeze(-1)).sum(1) # [B H] + attentions = nn.functional.softmax(self.gamma * torch.mul(user_memory, attrs).sum(-1).float(), -1) # [B R] + u_m = torch.mul(user_memory, attentions.unsqueeze(-1)).sum(1) # [B H] return u_m def forward(self, item_seq, item_seq_len): @@ -141,7 +144,7 @@ def forward(self, item_seq, item_seq_len): # combine them together p_u = self.dense_layer_u(torch.cat((seq_output, u_m), -1)) # [B H] return p_u - + def _get_item_comb_embedding(self, item): h_e, _ = self._get_kg_embedding(item) i_e = self.item_embedding(item) @@ -157,13 +160,14 @@ def calculate_loss(self, interaction): neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self._get_item_comb_embedding(pos_items) neg_items_emb = self._get_item_comb_embedding(neg_items) - pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss - else: # self.loss_type = 'CE' - test_items_emb = self.dense_layer_i(torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] - logits = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + else: # self.loss_type = 'CE' + test_items_emb = self.dense_layer_i( + torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] + logits = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss @@ -180,6 +184,7 @@ def full_sort_predict(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, item_seq_len) - test_items_emb = self.dense_layer_i(torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] + test_items_emb = self.dense_layer_i( + torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores diff --git a/recbole/model/sequential_recommender/narm.py b/recbole/model/sequential_recommender/narm.py index 562f74a12..76f5594b7 100644 --- a/recbole/model/sequential_recommender/narm.py +++ b/recbole/model/sequential_recommender/narm.py @@ -24,8 +24,8 @@ from torch import nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class NARM(SequentialRecommender): @@ -52,7 +52,7 @@ def __init__(self, config, dataset): self.a_2 = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.v_t = nn.Linear(self.hidden_size, 1, bias=False) self.ct_dropout = nn.Dropout(self.dropout_probs[1]) - self.b = nn.Linear(2*self.hidden_size, self.embedding_size, bias=False) + self.b = nn.Linear(2 * self.hidden_size, self.embedding_size, bias=False) self.loss_type = config['loss_type'] if self.loss_type == 'BPR': self.loss_fct = BPRLoss() diff --git a/recbole/model/sequential_recommender/nextitnet.py b/recbole/model/sequential_recommender/nextitnet.py index c55851d6f..9b2aba98e 100644 --- a/recbole/model/sequential_recommender/nextitnet.py +++ b/recbole/model/sequential_recommender/nextitnet.py @@ -22,8 +22,8 @@ from torch.nn import functional as F from torch.nn.init import uniform_, xavier_normal_, constant_ -from recbole.model.loss import RegLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import RegLoss, BPRLoss class NextItNet(SequentialRecommender): @@ -54,8 +54,9 @@ def __init__(self, config, dataset): self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) # residual blocks dilations in blocks:[1,2,4,8,1,2,4,8,...] - rb = [ResidualBlock_b(self.residual_channels, self.residual_channels, kernel_size=self.kernel_size, - dilation=dilation) for dilation in self.dilations] + rb = [ResidualBlock_b(self.residual_channels, self.residual_channels, + kernel_size=self.kernel_size, dilation=dilation) + for dilation in self.dilations] self.residual_blocks = nn.Sequential(*rb) # fully-connected layer @@ -82,11 +83,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0.1) def forward(self, item_seq): - item_seq_emb = self.item_embedding(item_seq) # [batch_size, seq_len, embed_size] + item_seq_emb = self.item_embedding(item_seq) # [batch_size, seq_len, embed_size] # Residual locks dilate_outputs = self.residual_blocks(item_seq_emb) hidden = dilate_outputs[:, -1, :].view(-1, self.residual_channels) # [batch_size, embed_size] - seq_output = self.final_layer(hidden) # [batch_size, embedding_size] + seq_output = self.final_layer(hidden) # [batch_size, embedding_size] return seq_output def reg_loss_rb(self): @@ -97,7 +98,7 @@ def reg_loss_rb(self): if self.reg_weight > 0.0: for name, parm in self.residual_blocks.named_parameters(): if name.endswith('weight'): - loss_rb += torch.norm(parm,2) + loss_rb += torch.norm(parm, 2) return self.reg_weight * loss_rb def calculate_loss(self, interaction): @@ -116,7 +117,7 @@ def calculate_loss(self, interaction): test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) - reg_loss = self.reg_loss([self.item_embedding.weight,self.final_layer.weight]) + reg_loss = self.reg_loss([self.item_embedding.weight, self.final_layer.weight]) loss = loss + self.reg_weight * reg_loss + self.reg_loss_rb() return loss @@ -141,14 +142,14 @@ class ResidualBlock_a(nn.Module): r""" Residual block (a) in the paper """ + def __init__(self, in_channel, out_channel, kernel_size=3, dilation=None): super(ResidualBlock_a, self).__init__() - half_channel = out_channel//2 + half_channel = out_channel // 2 self.ln1 = nn.LayerNorm(out_channel, eps=1e-8) self.conv1 = nn.Conv2d(in_channel, half_channel, kernel_size=(1, 1), padding=0) - self.ln2 = nn.LayerNorm(half_channel, eps=1e-8) self.conv2 = nn.Conv2d(half_channel, half_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation) @@ -176,12 +177,13 @@ def forward(self, x): # x: [batch_size, seq_len, embed_size] def conv_pad(self, x, dilation): # x: [batch_size, seq_len, embed_size] r""" Dropout-mask: To avoid the future information leakage problem, this paper proposed a masking-based dropout trick for the 1D dilated convolution to prevent the network from seeing the future items. - Also the One-dimensional transformation is completed in this funtion. + Also the One-dimensional transformation is completed in this function. """ - inputs_pad = x.permute(0, 2, 1) # [batch_size, embed_size, seq_len] + inputs_pad = x.permute(0, 2, 1) # [batch_size, embed_size, seq_len] inputs_pad = inputs_pad.unsqueeze(2) # [batch_size, embed_size, 1, seq_len] - pad = nn.ZeroPad2d(((self.kernel_size - 1) * dilation, 0, 0, 0)) # padding opration args：(left,right,top,bottom) - inputs_pad = pad(inputs_pad) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] + pad = nn.ZeroPad2d( + ((self.kernel_size - 1) * dilation, 0, 0, 0)) # padding operation args：(left,right,top,bottom) + inputs_pad = pad(inputs_pad) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] return inputs_pad @@ -189,22 +191,24 @@ class ResidualBlock_b(nn.Module): r""" Residual block (b) in the paper """ + def __init__(self, in_channel, out_channel, kernel_size=3, dilation=None): super(ResidualBlock_b, self).__init__() self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation) self.ln1 = nn.LayerNorm(out_channel, eps=1e-8) - self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation*2) + self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation * 2) self.ln2 = nn.LayerNorm(out_channel, eps=1e-8) self.dilation = dilation self.kernel_size = kernel_size def forward(self, x): # x: [batch_size, seq_len, embed_size] - x_pad = self.conv_pad(x, self.dilation) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] - out = self.conv1(x_pad).squeeze(2).permute(0, 2, 1) # [batch_size, seq_len+(self.kernel_size-1)*dilations-kernel_size+1, embed_size] + x_pad = self.conv_pad(x, self.dilation) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] + out = self.conv1(x_pad).squeeze(2).permute(0, 2, 1) + # [batch_size, seq_len+(self.kernel_size-1)*dilations-kernel_size+1, embed_size] out = F.relu(self.ln1(out)) - out_pad = self.conv_pad(out, self.dilation*2) + out_pad = self.conv_pad(out, self.dilation * 2) out2 = self.conv2(out_pad).squeeze(2).permute(0, 2, 1) out2 = F.relu(self.ln2(out2)) return out2 + x @@ -212,7 +216,7 @@ def forward(self, x): # x: [batch_size, seq_len, embed_size] def conv_pad(self, x, dilation): r""" Dropout-mask: To avoid the future information leakage problem, this paper proposed a masking-based dropout trick for the 1D dilated convolution to prevent the network from seeing the future items. - Also the One-dimensional transformation is completed in this funtion. + Also the One-dimensional transformation is completed in this function. """ inputs_pad = x.permute(0, 2, 1) inputs_pad = inputs_pad.unsqueeze(2) diff --git a/recbole/model/sequential_recommender/npe.py b/recbole/model/sequential_recommender/npe.py new file mode 100644 index 000000000..29674241a --- /dev/null +++ b/recbole/model/sequential_recommender/npe.py @@ -0,0 +1,115 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 14:56 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +NPE +################################################ + +Reference: + ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in ijcai2018 + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class NPE(SequentialRecommender): + r""" + models a user’s click to an item in two terms: the personal preference of the user for the item, + and the relationships between this item and other items clicked by the user + + """ + + def __init__(self, config, dataset): + super(NPE, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.dropout_prob = config["dropout_prob"] + + # define layers and loss type + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + self.embedding_seq_item = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the module + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user): + + user_embedding = self.dropout(self.relu(self.user_embedding(user))) + # batch_size * embedding_size + seq_item_embedding = self.item_embedding(seq_item).sum(dim=1) + seq_item_embedding = self.dropout(self.relu(seq_item_embedding)) + # batch_size * embedding_size + + return user_embedding + seq_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_embs = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.relu(self.item_embedding(neg_items)) + pos_items_emb = self.relu(pos_items_embs) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.relu(self.item_embedding.weight) + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.relu(self.item_embedding(test_item)) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.relu(self.item_embedding.weight) + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py new file mode 100644 index 000000000..75a44dc68 --- /dev/null +++ b/recbole/model/sequential_recommender/repeatnet.py @@ -0,0 +1,321 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 8:30 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun, Fan xinyan +# @Email : shaoweiqi@ruc.edu.cn, xinyan.fan@ruc.edu.cn + +r""" +RepeatNet +################################################ + +Reference: + Pengjie Ren et al. "RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation." + in AAAI 2019 + +Reference code: + https://github.com/PengjieRen/RepeatNet. + +""" + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_normal_, constant_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.utils import InputType + + +class RepeatNet(SequentialRecommender): + r""" + RepeatNet explores a hybrid encoder with an repeat module and explore module + repeat module is used for finding out the repeat consume in sequential recommendation + explore module is used for exploring new items for recommendation + + """ + + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + + super(RepeatNet, self).__init__(config, dataset) + + # load the dataset information + self.device = config["device"] + + # load parameters + self.embedding_size = config["embedding_size"] + self.hidden_size = config["hidden_size"] + self.joint_train = config["joint_train"] + self.dropout_prob = config["dropout_prob"] + + # define the layers and loss function + self.item_matrix = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.gru = nn.GRU(self.embedding_size, self.hidden_size, batch_first=True) + self.repeat_explore_mechanism = Repeat_Explore_Mechanism(self.device, + hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + dropout_prob=self.dropout_prob) + self.repeat_recommendation_decoder = Repeat_Recommendation_Decoder(self.device, + hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + num_item=self.n_items, + dropout_prob=self.dropout_prob) + self.explore_recommendation_decoder = Explore_Recommendation_Decoder(hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + num_item=self.n_items, + device=self.device, + dropout_prob=self.dropout_prob) + + self.loss_fct = F.nll_loss + + # init the weight of the module + self.apply(self._init_weights) + + def _init_weights(self, module): + + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + elif isinstance(module, nn.Linear): + xavier_normal_(module.weight.data) + if module.bias is not None: + constant_(module.bias.data, 0) + + def forward(self, item_seq, item_seq_len): + + batch_seq_item_embedding = self.item_matrix(item_seq) + # batch_size * seq_len == embedding ==>> batch_size * seq_len * embedding_size + + all_memory, _ = self.gru(batch_seq_item_embedding) + last_memory = self.gather_indexes(all_memory, item_seq_len - 1) + # all_memory: batch_size * item_seq * hidden_size + # last_memory: batch_size * hidden_size + timeline_mask = (item_seq == 0) + + self.repeat_explore = self.repeat_explore_mechanism.forward(all_memory=all_memory, + last_memory=last_memory) + # batch_size * 2 + repeat_recommendation_decoder = self.repeat_recommendation_decoder.forward(all_memory=all_memory, + last_memory=last_memory, + item_seq=item_seq, + mask=timeline_mask) + # batch_size * num_item + explore_recommendation_decoder = self.explore_recommendation_decoder.forward(all_memory=all_memory, + last_memory=last_memory, + item_seq=item_seq, + mask=timeline_mask) + # batch_size * num_item + prediction = repeat_recommendation_decoder * self.repeat_explore[:, 0].unsqueeze(1) \ + + explore_recommendation_decoder * self.repeat_explore[:, 1].unsqueeze(1) + # batch_size * num_item + + return prediction + + def calculate_loss(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + pos_item = interaction[self.POS_ITEM_ID] + prediction = self.forward(item_seq, item_seq_len) + loss = self.loss_fct((prediction + 1e-8).log(), pos_item, ignore_index=0) + if self.joint_train is True: + loss += self.repeat_explore_loss(item_seq, pos_item) + + return loss + + def repeat_explore_loss(self, item_seq, pos_item): + + batch_size = item_seq.size(0) + repeat, explore = torch.zeros(batch_size).to(self.device), torch.ones(batch_size).to(self.device) + index = 0 + for seq_item_ex, pos_item_ex in zip(item_seq, pos_item): + if pos_item_ex in seq_item_ex: + repeat[index] = 1 + explore[index] = 0 + index += 1 + repeat_loss = torch.mul(repeat.unsqueeze(1), torch.log(self.repeat_explore[:, 0] + 1e-8)).mean() + explore_loss = torch.mul(explore.unsqueeze(1), torch.log(self.repeat_explore[:, 1] + 1e-8)).mean() + + return (-repeat_loss - explore_loss) / 2 + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + prediction = self.forward(item_seq, item_seq_len) + + return prediction + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(item_seq, item_seq_len) + # batch_size * num_items + seq_output = seq_output.unsqueeze(-1) + # batch_size * num_items * 1 + scores = self.gather_indexes(seq_output, test_item).squeeze() + + return scores + + +class Repeat_Explore_Mechanism(nn.Module): + + def __init__(self, device, hidden_size, seq_len, dropout_prob): + super(Repeat_Explore_Mechanism, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.device = device + self.seq_len = seq_len + self.Wre = nn.Linear(hidden_size, hidden_size, bias=False) + self.Ure = nn.Linear(hidden_size, hidden_size, bias=False) + self.tanh = nn.Tanh() + self.Vre = nn.Linear(hidden_size, 1, bias=False) + self.Wcre = nn.Linear(hidden_size, 2, bias=False) + + def forward(self, all_memory, last_memory): + """ + calculate the probability of Repeat and explore + """ + all_memory_values = all_memory + + all_memory = self.dropout(self.Ure(all_memory)) + + last_memory = self.dropout(self.Wre(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output_ere = self.tanh(all_memory + last_memory) + + output_ere = self.Vre(output_ere) + alpha_are = nn.Softmax(dim=1)(output_ere) + alpha_are = alpha_are.repeat(1, 1, self.hidden_size) + output_cre = alpha_are * all_memory_values + output_cre = output_cre.sum(dim=1) + + output_cre = self.Wcre(output_cre) + + repeat_explore_mechanism = nn.Softmax(dim=-1)(output_cre) + + return repeat_explore_mechanism + + +class Repeat_Recommendation_Decoder(nn.Module): + + def __init__(self, device, hidden_size, seq_len, num_item, dropout_prob): + super(Repeat_Recommendation_Decoder, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.device = device + self.seq_len = seq_len + self.num_item = num_item + self.Wr = nn.Linear(hidden_size, hidden_size, bias=False) + self.Ur = nn.Linear(hidden_size, hidden_size, bias=False) + self.tanh = nn.Tanh() + self.Vr = nn.Linear(hidden_size, 1) + + def forward(self, all_memory, last_memory, item_seq, mask=None): + """ + calculate the the force of repeat + """ + all_memory = self.dropout(self.Ur(all_memory)) + + last_memory = self.dropout(self.Wr(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output_er = self.tanh(last_memory + all_memory) + + output_er = self.Vr(output_er).squeeze(2) + + if mask is not None: + output_er.masked_fill_(mask, -1e9) + + output_er = nn.Softmax(dim=-1)(output_er) + output_er = output_er.unsqueeze(1) + + map_matrix = build_map(item_seq, self.device, max_index=self.num_item) + output_er = torch.matmul(output_er, map_matrix).squeeze(1).to(self.device) + repeat_recommendation_decoder = output_er.squeeze(1).to(self.device) + + return repeat_recommendation_decoder.to(self.device) + + +class Explore_Recommendation_Decoder(nn.Module): + + def __init__(self, hidden_size, seq_len, num_item, device, dropout_prob): + super(Explore_Recommendation_Decoder, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.seq_len = seq_len + self.num_item = num_item + self.device = device + self.We = nn.Linear(hidden_size, hidden_size) + self.Ue = nn.Linear(hidden_size, hidden_size) + self.tanh = nn.Tanh() + self.Ve = nn.Linear(hidden_size, 1) + self.matrix_for_explore = nn.Linear(2 * self.hidden_size, self.num_item, bias=False) + + def forward(self, all_memory, last_memory, item_seq, mask=None): + """ + calculate the force of explore + """ + all_memory_values, last_memory_values = all_memory, last_memory + + all_memory = self.dropout(self.Ue(all_memory)) + + last_memory = self.dropout(self.We(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output_ee = self.tanh(all_memory + last_memory) + output_ee = self.Ve(output_ee).squeeze(-1) + + if mask is not None: + output_ee.masked_fill_(mask, -1e9) + + output_ee = output_ee.unsqueeze(-1) + + alpha_e = nn.Softmax(dim=1)(output_ee) + alpha_e = alpha_e.repeat(1, 1, self.hidden_size) + output_e = (alpha_e * all_memory_values).sum(dim=1) + output_e = torch.cat([output_e, last_memory_values], dim=1) + output_e = self.dropout(self.matrix_for_explore(output_e)) + + map_matrix = build_map(item_seq, self.device, max_index=self.num_item) + explore_mask = torch.bmm((item_seq > 0).float().unsqueeze(1), map_matrix).squeeze(1) + output_e = output_e.masked_fill(explore_mask.bool(), float('-inf')) + explore_recommendation_decoder = nn.Softmax(1)(output_e) + + return explore_recommendation_decoder + + +def build_map(b_map, device, max_index=None): + """ + project the b_map to the place where it in should be + like this: + item_seq A: [3,4,5] n_items: 6 + after map: A + [0,0,1,0,0,0] + [0,0,0,1,0,0] + [0,0,0,0,1,0] + + batch_size * seq_len ==>> batch_size * seq_len * n_item + + use in RepeatNet: + [3,4,5] matmul [0,0,1,0,0,0] + [0,0,0,1,0,0] ==>>> [0,0,3,4,5,0] it works in the RepeatNet when project the seq item into all items + [0,0,0,0,1,0] + batch_size * 1 * seq_len matmul batch_size * seq_len * n_item ==>> batch_size * 1 * n_item + """ + batch_size, b_len = b_map.size() + if max_index is None: + max_index = b_map.max() + 1 + if torch.cuda.is_available(): + b_map_ = torch.FloatTensor(batch_size, b_len, max_index).fill_(0).to(device) + else: + b_map_ = torch.zeros(batch_size, b_len, max_index) + b_map_.scatter_(2, b_map.unsqueeze(2), 1.) + b_map_.requires_grad = False + return b_map_ diff --git a/recbole/model/sequential_recommender/s3rec.py b/recbole/model/sequential_recommender/s3rec.py index 7a9617886..1fcdbf413 100644 --- a/recbole/model/sequential_recommender/s3rec.py +++ b/recbole/model/sequential_recommender/s3rec.py @@ -23,8 +23,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder +from recbole.model.loss import BPRLoss class S3Rec(SequentialRecommender): @@ -107,7 +107,7 @@ def __init__(self, config, dataset): else: # load pretrained model for finetune pretrained = torch.load(self.pre_model_path) - print('Load pretrained model from', self.pre_model_path) + self.logger.info('Load pretrained model from', self.pre_model_path) self.load_state_dict(pretrained['state_dict']) def _init_weights(self, module): @@ -179,7 +179,7 @@ def forward(self, item_seq, bidirectional=True): trm_output = self.trm_encoder(input_emb, attention_mask, output_all_encoded_layers=True) - seq_output = trm_output[-1] # [B L H] + seq_output = trm_output[-1] # [B L H] return seq_output def pretrain(self, features, masked_item_sequence, pos_items, neg_items, @@ -231,13 +231,12 @@ def pretrain(self, features, masked_item_sequence, pos_items, neg_items, pos_segment_score = self._segment_prediction(segment_context, pos_segment_emb) neg_segment_score = self._segment_prediction(segment_context, neg_segment_emb) sp_distance = torch.sigmoid(pos_segment_score - neg_segment_score) - sp_loss = torch.sum(self.loss_fct(sp_distance, - torch.ones_like(sp_distance, dtype=torch.float32))) + sp_loss = torch.sum(self.loss_fct(sp_distance, torch.ones_like(sp_distance, dtype=torch.float32))) - pretrain_loss = self.aap_weight*aap_loss \ - + self.mip_weight*mip_loss \ - + self.map_weight*map_loss \ - + self.sp_weight*sp_loss + pretrain_loss = self.aap_weight * aap_loss \ + + self.mip_weight * mip_loss \ + + self.map_weight * map_loss \ + + self.sp_weight * sp_loss return pretrain_loss @@ -250,7 +249,7 @@ def _neg_sample(self, item_set): # [ , ] def _padding_zero_at_left(self, sequence): # had truncated according to the max_length pad_len = self.max_seq_length - len(sequence) - sequence = [0]*pad_len + sequence + sequence = [0] * pad_len + sequence return sequence def reconstruct_pretrain_data(self, item_seq, item_seq_len): @@ -260,7 +259,7 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): # We don't need padding for features item_feature_seq = self.item_feat[self.FEATURE_FIELD][item_seq] - 1 - + end_index = item_seq_len.cpu().numpy().tolist() item_seq = item_seq.cpu().numpy().tolist() item_feature_seq = item_feature_seq.cpu().numpy().tolist() @@ -268,7 +267,7 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): # we will padding zeros at the left side # these will be train_instances, after will be reshaped to batch sequence_instances = [] - associated_features = [] # For Associated Attribute Prediction and Masked Attribute Prediction + associated_features = [] # For Associated Attribute Prediction and Masked Attribute Prediction long_sequence = [] for i, end_i in enumerate(end_index): sequence_instances.append(item_seq[i][:end_i]) @@ -343,7 +342,6 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): return associated_features, masked_item_sequence, pos_items, neg_items, \ masked_segment_list, pos_segment_list, neg_segment_list - def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] @@ -390,6 +388,6 @@ def full_sort_predict(self, interaction): item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, bidirectional=False) seq_output = self.gather_indexes(seq_output, item_seq_len - 1) - test_items_emb = self.item_embedding.weight[:self.n_items-1] # delete masked token + test_items_emb = self.item_embedding.weight[:self.n_items - 1] # delete masked token scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores diff --git a/recbole/model/sequential_recommender/sasrec.py b/recbole/model/sequential_recommender/sasrec.py index 564250dbf..274f097d7 100644 --- a/recbole/model/sequential_recommender/sasrec.py +++ b/recbole/model/sequential_recommender/sasrec.py @@ -19,8 +19,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder +from recbole.model.loss import BPRLoss class SASRec(SequentialRecommender): @@ -29,7 +29,7 @@ class SASRec(SequentialRecommender): NOTE: In the author's implementation, the Point-Wise Feed-Forward Network (PFFN) is implemented - by CNN with 1x1 kernel. In this implementation, we follows the original BERT implmentation + by CNN with 1x1 kernel. In this implementation, we follows the original BERT implementation using Fully Connected Layer to implement the PFFN. """ @@ -39,7 +39,7 @@ def __init__(self, config, dataset): # load parameters info self.n_layers = config['n_layers'] self.n_heads = config['n_heads'] - self.hidden_size = config['hidden_size'] # same as embedding_size + self.hidden_size = config['hidden_size'] # same as embedding_size self.inner_size = config['inner_size'] # the dimensionality in feed-forward layer self.hidden_dropout_prob = config['hidden_dropout_prob'] self.attn_dropout_prob = config['attn_dropout_prob'] @@ -50,7 +50,7 @@ def __init__(self, config, dataset): self.loss_type = config['loss_type'] # define layers and loss - self.item_embedding = nn.Embedding(self.n_items, self.hidden_size , padding_idx=0) + self.item_embedding = nn.Embedding(self.n_items, self.hidden_size, padding_idx=0) self.position_embedding = nn.Embedding(self.max_seq_length, self.hidden_size) self.trm_encoder = TransformerEncoder(n_layers=self.n_layers, n_heads=self.n_heads, hidden_size=self.hidden_size, inner_size=self.inner_size, @@ -116,7 +116,7 @@ def forward(self, item_seq, item_seq_len): output_all_encoded_layers=True) output = trm_output[-1] output = self.gather_indexes(output, item_seq_len - 1) - return output # [B H] + return output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] diff --git a/recbole/model/sequential_recommender/sasrecf.py b/recbole/model/sequential_recommender/sasrecf.py index 8c7f90c17..71242d026 100644 --- a/recbole/model/sequential_recommender/sasrecf.py +++ b/recbole/model/sequential_recommender/sasrecf.py @@ -12,8 +12,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer +from recbole.model.loss import BPRLoss class SASRecF(SequentialRecommender): @@ -130,7 +130,7 @@ def forward(self, item_seq, item_seq_len): output_all_encoded_layers=True) output = trm_output[-1] seq_output = self.gather_indexes(output, item_seq_len - 1) - return seq_output # [B H] + return seq_output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] diff --git a/recbole/model/sequential_recommender/shan.py b/recbole/model/sequential_recommender/shan.py new file mode 100644 index 000000000..b9caf8707 --- /dev/null +++ b/recbole/model/sequential_recommender/shan.py @@ -0,0 +1,213 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/20 22:33 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +SHAN +################################################ + +Reference: + Ying, H et al. "Sequential Recommender System based on Hierarchical Attention Network."in IJCAI 2018 + + +""" +import numpy as np +import torch +import torch.nn as nn +from torch.nn.init import normal_, uniform_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class SHAN(SequentialRecommender): + r""" + SHAN exploit the Hierarchical Attention Network to get the long-short term preference + first get the long term purpose and then fuse the long-term with recent items to get long-short term purpose + + """ + + def __init__(self, config, dataset): + + super(SHAN, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.short_item_length = config["short_item_length"] # the length of the short session items + assert self.short_item_length <= self.max_seq_length, "short_item_length can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + + # define layers and loss + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) + + self.long_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + self.long_short_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_short_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + + self.relu = nn.ReLU() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameter of the model + self.apply(self.init_weights) + + def reg_loss(self, user_embedding, item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.long_w.weight, p=2) + reg_1 * torch.norm(self.long_short_w.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + + return loss_1 + loss_2 + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 0.01) + elif isinstance(module, nn.Linear): + uniform_(module.weight.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + elif isinstance(module, nn.Parameter): + uniform_(module.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + print(module.data) + + def forward(self, seq_item, user, seq_item_len): + + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + + # get the mask + mask = seq_item.data.eq(0) + long_term_attention_based_pooling_layer = self.long_term_attention_based_pooling_layer(seq_item_embedding, + user_embedding, mask) + # batch_size * 1 * embedding_size + + short_item_embedding = seq_item_embedding[:, -self.short_item_length:, :] + mask_long_short = mask[:, -self.short_item_length:] + batch_size = mask_long_short.size(0) + x = torch.zeros(size=(batch_size, 1)).eq(1).to(self.device) + mask_long_short = torch.cat([x, mask_long_short], dim=1) + # batch_size * short_item_length * embedding_size + long_short_item_embedding = torch.cat([long_term_attention_based_pooling_layer, short_item_embedding], dim=1) + # batch_size * 1_plus_short_item_length * embedding_size + + long_short_item_embedding = self.long_and_short_term_attention_based_pooling_layer(long_short_item_embedding, + user_embedding, + mask_long_short) + # batch_size * embedding_size + + return long_short_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores + + def long_and_short_term_attention_based_pooling_layer(self, long_short_item_embedding, user_embedding, mask=None): + """ + + fusing the long term purpose with the short-term preference + """ + long_short_item_embedding_value = long_short_item_embedding + + long_short_item_embedding = self.relu(self.long_short_w(long_short_item_embedding) + self.long_short_b) + long_short_item_embedding = torch.matmul(long_short_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + long_short_item_embedding.masked_fill_(mask, -1e9) + long_short_item_embedding = nn.Softmax(dim=-1)(long_short_item_embedding) + long_short_item_embedding = torch.mul(long_short_item_embedding_value, + long_short_item_embedding.unsqueeze(2)).sum(dim=1) + + return long_short_item_embedding + + def long_term_attention_based_pooling_layer(self, seq_item_embedding, user_embedding, mask=None): + """ + + get the long term purpose of user + """ + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.relu(self.long_w(seq_item_embedding) + self.long_b) + user_item_embedding = torch.matmul(seq_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + user_item_embedding.masked_fill_(mask, -1e9) + user_item_embedding = nn.Softmax(dim=1)(user_item_embedding) + user_item_embedding = torch.mul(seq_item_embedding_value, user_item_embedding.unsqueeze(2)).sum(dim=1, + keepdim=True) + # batch_size * 1 * embedding_size + + return user_item_embedding diff --git a/recbole/model/sequential_recommender/srgnn.py b/recbole/model/sequential_recommender/srgnn.py index 665e8791a..91d9e6e1a 100644 --- a/recbole/model/sequential_recommender/srgnn.py +++ b/recbole/model/sequential_recommender/srgnn.py @@ -15,16 +15,16 @@ https://github.com/CRIPAC-DIG/SR-GNN """ -import numpy as np import math +import numpy as np import torch from torch import nn from torch.nn import Parameter from torch.nn import functional as F -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class GNN(nn.Module): @@ -58,7 +58,7 @@ def GNNCell(self, A, hidden): [batch_size, max_session_len, embedding_size] Returns: - torch.FloatTensor:Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size] + torch.FloatTensor: Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size] """ @@ -67,16 +67,16 @@ def GNNCell(self, A, hidden): # [batch_size, max_session_len, embedding_size * 2] inputs = torch.cat([input_in, input_out], 2) - # gi.size equals to gh.size, shape of [batch_size, max_session_len, embdding_size * 3] + # gi.size equals to gh.size, shape of [batch_size, max_session_len, embedding_size * 3] gi = F.linear(inputs, self.w_ih, self.b_ih) gh = F.linear(hidden, self.w_hh, self.b_hh) # (batch_size, max_session_len, embedding_size) i_r, i_i, i_n = gi.chunk(3, 2) h_r, h_i, h_n = gh.chunk(3, 2) - resetgate = torch.sigmoid(i_r + h_r) - inputgate = torch.sigmoid(i_i + h_i) - newgate = torch.tanh(i_n + resetgate * h_n) - hy = (1 - inputgate) * hidden + inputgate * newgate + reset_gate = torch.sigmoid(i_r + h_r) + input_gate = torch.sigmoid(i_i + h_i) + new_gate = torch.tanh(i_n + reset_gate * h_n) + hy = (1 - input_gate) * hidden + input_gate * new_gate return hy def forward(self, A, hidden): @@ -90,7 +90,7 @@ class SRGNN(SequentialRecommender): In addition to considering the connection between the item and the adjacent item, it also considers the connection with other interactive items. - Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connecion matrix A + Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connection matrix A Outgoing edges: === ===== ===== ===== ===== diff --git a/recbole/model/sequential_recommender/stamp.py b/recbole/model/sequential_recommender/stamp.py index 71fbce412..f9982734e 100644 --- a/recbole/model/sequential_recommender/stamp.py +++ b/recbole/model/sequential_recommender/stamp.py @@ -21,8 +21,8 @@ from torch import nn from torch.nn.init import normal_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class STAMP(SequentialRecommender): diff --git a/recbole/model/sequential_recommender/transrec.py b/recbole/model/sequential_recommender/transrec.py index fd4a20c0f..5e431c8bd 100644 --- a/recbole/model/sequential_recommender/transrec.py +++ b/recbole/model/sequential_recommender/transrec.py @@ -15,10 +15,10 @@ import torch from torch import nn -from recbole.utils import InputType from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss, EmbLoss, RegLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss, RegLoss +from recbole.utils import InputType class TransRec(SequentialRecommender): @@ -41,8 +41,8 @@ def __init__(self, config, dataset): self.user_embedding = nn.Embedding(self.n_users, self.embedding_size, padding_idx=0) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) - self.bias = nn.Embedding(self.n_items, 1, padding_idx=0) # Beta popularity bias - self.T = nn.Parameter(torch.zeros(self.embedding_size)) # average user representation 'global' + self.bias = nn.Embedding(self.n_items, 1, padding_idx=0) # Beta popularity bias + self.T = nn.Parameter(torch.zeros(self.embedding_size)) # average user representation 'global' self.bpr_loss = BPRLoss() self.emb_loss = EmbLoss() @@ -52,21 +52,21 @@ def __init__(self, config, dataset): self.apply(xavier_normal_initialization) def _l2_distance(self, x, y): - return torch.sqrt(torch.sum((x - y)**2, dim=-1, keepdim=True)) # [B 1] + return torch.sqrt(torch.sum((x - y) ** 2, dim=-1, keepdim=True)) # [B 1] def gather_last_items(self, item_seq, gather_index): - "Gathers the last_item at the spexific positions over a minibatch" + """Gathers the last_item at the specific positions over a minibatch""" gather_index = gather_index.view(-1, 1) - last_items = item_seq.gather(index=gather_index, dim=1) # [B 1] - return last_items.squeeze(-1) # [B] + last_items = item_seq.gather(index=gather_index, dim=1) # [B 1] + return last_items.squeeze(-1) # [B] def forward(self, user, item_seq, item_seq_len): # the last item at the last position - last_items = self.gather_last_items(item_seq, item_seq_len - 1) # [B] - user_emb = self.user_embedding(user) # [B H] + last_items = self.gather_last_items(item_seq, item_seq_len - 1) # [B] + user_emb = self.user_embedding(user) # [B H] last_items_emb = self.item_embedding(last_items) # [B H] - T = self.T.expand_as(user_emb) # [B H] - seq_output = user_emb + T + last_items_emb # [B H] + T = self.T.expand_as(user_emb) # [B H] + seq_output = user_emb + T + last_items_emb # [B H] return seq_output def calculate_loss(self, interaction): @@ -117,13 +117,13 @@ def full_sort_predict(self, interaction): seq_output = self.forward(user, item_seq, item_seq_len) # [B H] - test_items_emb = self.item_embedding.weight # [item_num H] - test_items_emb = test_items_emb.repeat(seq_output.size(0), 1, 1) # [user_num item_num H] + test_items_emb = self.item_embedding.weight # [item_num H] + test_items_emb = test_items_emb.repeat(seq_output.size(0), 1, 1) # [user_num item_num H] - user_hidden = seq_output.unsqueeze(1).expand_as(test_items_emb) # [user_num item_num H] - test_bias = self.bias.weight # [item_num 1] - test_bias = test_bias.repeat(user_hidden.size(0), 1, 1) # [user_num item_num 1] + user_hidden = seq_output.unsqueeze(1).expand_as(test_items_emb) # [user_num item_num H] + test_bias = self.bias.weight # [item_num 1] + test_bias = test_bias.repeat(user_hidden.size(0), 1, 1) # [user_num item_num 1] - scores = test_bias - self._l2_distance(user_hidden, test_items_emb) # [user_num item_num 1] + scores = test_bias - self._l2_distance(user_hidden, test_items_emb) # [user_num item_num 1] scores = scores.squeeze(-1) # [B n_items] return scores diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index c513530cd..29449c0a5 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -37,12 +37,9 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : False # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ -drop_preload_weight: True normalize_field: ~ normalize_all: True diff --git a/recbole/properties/dataset/sample.yaml b/recbole/properties/dataset/sample.yaml index 8dcb9cc23..ffbd73159 100644 --- a/recbole/properties/dataset/sample.yaml +++ b/recbole/properties/dataset/sample.yaml @@ -33,15 +33,12 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : True # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ -drop_preload_weight: True normalize_field: ~ -normalize_all: True +normalize_all: ~ # Sequential Model Needed ITEM_LIST_LENGTH_FIELD: item_length diff --git a/recbole/properties/model/CDAE.yaml b/recbole/properties/model/CDAE.yaml new file mode 100644 index 000000000..e1acdffbf --- /dev/null +++ b/recbole/properties/model/CDAE.yaml @@ -0,0 +1,7 @@ +loss_type: BCE +hid_activation: relu +out_activation: sigmoid +corruption_ratio: 0.5 +embedding_size: 64 +reg_weight_1: 0. +reg_weight_2: 0.01 diff --git a/recbole/properties/model/DMF.yaml b/recbole/properties/model/DMF.yaml index 56040f182..d2ca2cfa4 100644 --- a/recbole/properties/model/DMF.yaml +++ b/recbole/properties/model/DMF.yaml @@ -1,6 +1,6 @@ # WARNING: -# 1.if you set inter_matrix_type='rating', you must set drop_filter_field=False in your data config files. -# 2.The dimensions of the last layer of users and items must be the same +# 1. if you set inter_matrix_type='rating', you must set `unused_col: ~` in your data config files. +# 2. The dimensions of the last layer of users and items must be the same inter_matrix_type: '01' user_embedding_size: 64 diff --git a/recbole/properties/model/FOSSIL.yaml b/recbole/properties/model/FOSSIL.yaml new file mode 100644 index 000000000..1eb00d1bb --- /dev/null +++ b/recbole/properties/model/FOSSIL.yaml @@ -0,0 +1,5 @@ +embedding_size: 64 +loss_type: "CE" +reg_weight: 0.00 +order_len: 3 +alpha: 0.6 \ No newline at end of file diff --git a/recbole/properties/model/HGN.yaml b/recbole/properties/model/HGN.yaml new file mode 100644 index 000000000..69b95a440 --- /dev/null +++ b/recbole/properties/model/HGN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +loss_type: 'BPR' +pooling_type: "average" +reg_weight: [0.00,0.00] \ No newline at end of file diff --git a/recbole/properties/model/HRM.yaml b/recbole/properties/model/HRM.yaml new file mode 100644 index 000000000..531e93c3e --- /dev/null +++ b/recbole/properties/model/HRM.yaml @@ -0,0 +1,6 @@ +embedding_size: 64 +high_order: 2 +loss_type: "CE" +dropout_prob: 0.2 +pooling_type_layer_1: "max" +pooling_type_layer_2: "max" \ No newline at end of file diff --git a/recbole/properties/model/LINE.yaml b/recbole/properties/model/LINE.yaml new file mode 100644 index 000000000..eefa06f81 --- /dev/null +++ b/recbole/properties/model/LINE.yaml @@ -0,0 +1,3 @@ +embedding_size: 64 +order: 2 +second_order_loss_weight: 1 \ No newline at end of file diff --git a/recbole/properties/model/MacridVAE.yaml b/recbole/properties/model/MacridVAE.yaml new file mode 100644 index 000000000..db3b426db --- /dev/null +++ b/recbole/properties/model/MacridVAE.yaml @@ -0,0 +1,10 @@ +embedding_size: 64 +drop_out: 0.5 +kfac: 10 +nogb: False +std: 0.01 +encoder_hidden_size: [600] +tau: 0.1 +anneal_cap: 0.2 +total_anneal_steps: 200000 +reg_weights: [0, 0] \ No newline at end of file diff --git a/recbole/properties/model/MultiDAE.yaml b/recbole/properties/model/MultiDAE.yaml new file mode 100644 index 000000000..38e1f44e3 --- /dev/null +++ b/recbole/properties/model/MultiDAE.yaml @@ -0,0 +1,3 @@ +mlp_hidden_size: [600] +latent_dimension: 64 +dropout_prob: 0.5 \ No newline at end of file diff --git a/recbole/properties/model/MultiVAE.yaml b/recbole/properties/model/MultiVAE.yaml new file mode 100644 index 000000000..63021d9b0 --- /dev/null +++ b/recbole/properties/model/MultiVAE.yaml @@ -0,0 +1,5 @@ +mlp_hidden_size: [600] +latent_dimension: 128 +dropout_prob: 0.5 +anneal_cap: 0.2 +total_anneal_steps: 200000 \ No newline at end of file diff --git a/recbole/properties/model/NPE.yaml b/recbole/properties/model/NPE.yaml new file mode 100644 index 000000000..cb93282a7 --- /dev/null +++ b/recbole/properties/model/NPE.yaml @@ -0,0 +1,3 @@ +embedding_size: 64 +loss_type: "CE" +dropout_prob: 0.3 \ No newline at end of file diff --git a/recbole/properties/model/RepeatNet.yaml b/recbole/properties/model/RepeatNet.yaml new file mode 100644 index 000000000..dac46bacc --- /dev/null +++ b/recbole/properties/model/RepeatNet.yaml @@ -0,0 +1,5 @@ +embedding_size: 64 +loss_type: "CE" +hidden_size: 64 +joint_train: False +dropout_prob: 0.5 diff --git a/recbole/properties/model/SHAN.yaml b/recbole/properties/model/SHAN.yaml new file mode 100644 index 000000000..9c83bf5c8 --- /dev/null +++ b/recbole/properties/model/SHAN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +short_item_length: 2 +loss_type: "CE" +reg_weight: [0.01,0.0001] \ No newline at end of file diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml new file mode 100644 index 000000000..6a6620bf1 --- /dev/null +++ b/recbole/properties/model/xgboost.yaml @@ -0,0 +1,36 @@ +# Type of training method +convert_token_to_onehot: False +token_num_threshold: 10000 + +# DMatrix +xgb_weight: ~ +xgb_base_margin: ~ +xgb_missing: ~ +xgb_silent: ~ +xgb_feature_names: ~ +xgb_feature_types: ~ +xgb_nthread: ~ + +xgb_model: ~ +xgb_params: + booster: gbtree + objective: binary:logistic + eval_metric: ['auc','logloss'] + # gamma: 0.1 + max_depth: 3 + # lambda: 1 + # subsample: 0.7 + # colsample_bytree: 0.7 + # min_child_weight: 3 + eta: 1 + seed: 2020 + # nthread: -1 +xgb_num_boost_round: 500 +# xgb_evals: ~ +xgb_obj: ~ +xgb_feval: ~ +xgb_maximize: ~ +xgb_early_stopping_rounds: ~ +# xgb_evals_result: ~ +xgb_verbose_eval: 100 + diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 855804ee3..696e13609 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -6,6 +6,7 @@ state: INFO reproducibility: True data_path: 'dataset/' checkpoint_dir: 'saved' +show_progress: True # training settings epochs: 300 @@ -13,8 +14,11 @@ train_batch_size: 2048 learner: adam learning_rate: 0.001 training_neg_sample_num: 1 +training_neg_sample_distribution: uniform eval_step: 1 stopping_step: 10 +clip_grad_norm: ~ +# clip_grad_norm: {'max_norm': 5, 'norm_type': 2} # evaluation settings eval_setting: RO_RS,full @@ -26,3 +30,5 @@ metrics: ["Recall", "MRR","NDCG","Hit","Precision"] topk: [10] valid_metric: MRR@10 eval_batch_size: 4096 +loss_decimal_place: 4 +metric_decimal_place: 4 diff --git a/recbole/properties/quick_start_config/context-aware.yaml b/recbole/properties/quick_start_config/context-aware.yaml new file mode 100644 index 000000000..cdb71f098 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware.yaml @@ -0,0 +1,5 @@ +eval_setting: RO_RS +group_by_user: False +training_neg_sample_num: 0 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/context-aware_ml-100k.yaml b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml new file mode 100644 index 000000000..150dd4d18 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml @@ -0,0 +1,5 @@ +threshold: {'rating': 4} +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/knowledge_base.yaml b/recbole/properties/quick_start_config/knowledge_base.yaml new file mode 100644 index 000000000..379341326 --- /dev/null +++ b/recbole/properties/quick_start_config/knowledge_base.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + kg: ['head_id', 'relation_id', 'tail_id'] + link: ['item_id', 'entity_id'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential.yaml b/recbole/properties/quick_start_config/sequential.yaml new file mode 100644 index 000000000..87c0fa053 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential.yaml @@ -0,0 +1 @@ +eval_setting: TO_LS,full \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN.yaml b/recbole/properties/quick_start_config/sequential_DIN.yaml new file mode 100644 index 000000000..58b8db955 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN.yaml @@ -0,0 +1,3 @@ +eval_setting: TO_LS, uni100 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml new file mode 100644 index 000000000..702a7a862 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_embedding_model.yaml b/recbole/properties/quick_start_config/sequential_embedding_model.yaml new file mode 100644 index 000000000..59b920994 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_embedding_model.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + ent: ['ent_id', 'ent_emb'] +additional_feat_suffix: ent \ No newline at end of file diff --git a/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml new file mode 100644 index 000000000..1fe509fe6 --- /dev/null +++ b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml @@ -0,0 +1,3 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file diff --git a/recbole/quick_start/quick_start.py b/recbole/quick_start/quick_start.py index 2294ca15e..6fd49ab4a 100644 --- a/recbole/quick_start/quick_start.py +++ b/recbole/quick_start/quick_start.py @@ -8,9 +8,10 @@ """ import logging from logging import getLogger -from recbole.utils import init_logger, get_model, get_trainer, init_seed + from recbole.config import Config from recbole.data import create_dataset, data_preparation +from recbole.utils import init_logger, get_model, get_trainer, init_seed def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True): @@ -49,10 +50,11 @@ def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=Non trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) # model training - best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, saved=saved) + best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, saved=saved, + show_progress=config['show_progress']) # model evaluation - test_result = trainer.evaluate(test_data, load_best_model=saved) + test_result = trainer.evaluate(test_data, load_best_model=saved, show_progress=config['show_progress']) logger.info('best valid result: {}'.format(best_valid_result)) logger.info('test result: {}'.format(test_result)) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 031482be3..dcf261f91 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -13,9 +13,10 @@ ######################## """ -import random import copy + import numpy as np +import torch class AbstractSampler(object): @@ -32,27 +33,40 @@ class AbstractSampler(object): random_list (list or numpy.ndarray): The shuffled result of :meth:`get_random_list`. used_ids (numpy.ndarray): The result of :meth:`get_used_ids`. """ + def __init__(self, distribution): - self.distribution = distribution + self.distribution = '' + self.random_list = [] + self.random_pr = 0 + self.random_list_length = 0 + self.set_distribution(distribution) + self.used_ids = self.get_used_ids() + + def set_distribution(self, distribution): + """Set the distribution of sampler. + Args: + distribution (str): Distribution of the negative items. + """ + if self.distribution == distribution: + return + self.distribution = distribution self.random_list = self.get_random_list() - random.shuffle(self.random_list) + np.random.shuffle(self.random_list) self.random_pr = 0 self.random_list_length = len(self.random_list) - self.used_ids = self.get_used_ids() - def get_random_list(self): """ Returns: - np.ndarray or list: Random list of value_id. + numpy.ndarray or list: Random list of value_id. """ raise NotImplementedError('method [get_random_list] should be implemented') def get_used_ids(self): """ Returns: - np.ndarray: Used ids. Index is key_id, and element is a set of value_ids. + numpy.ndarray: Used ids. Index is key_id, and element is a set of value_ids. """ raise NotImplementedError('method [get_used_ids] should be implemented') @@ -65,36 +79,83 @@ def random(self): self.random_pr += 1 return value_id - def sample_by_key_ids(self, key_ids, num, used_ids): + def random_num(self, num): + """ + Args: + num (int): Number of random value_ids. + + Returns: + value_ids (numpy.ndarray): Random value_ids. Generated by :attr:`random_list`. + """ + value_id = [] + self.random_pr %= self.random_list_length + while True: + if self.random_pr + num <= self.random_list_length: + value_id.append(self.random_list[self.random_pr: self.random_pr + num]) + self.random_pr += num + break + else: + value_id.append(self.random_list[self.random_pr:]) + num -= self.random_list_length - self.random_pr + self.random_pr = 0 + return np.concatenate(value_id) + + def sample_by_key_ids(self, key_ids, num): """Sampling by key_ids. Args: - key_ids (np.ndarray or list): Input key_ids. + key_ids (numpy.ndarray or list): Input key_ids. num (int): Number of sampled value_ids for each key_id. - used_ids (np.ndarray): Used ids. index is key_id, and element is a set of value_ids. Returns: - np.ndarray: Sampled value_ids. + torch.tensor: Sampled value_ids. value_ids[0], value_ids[len(key_ids)], value_ids[len(key_ids) * 2], ..., value_id[len(key_ids) * (num - 1)] is sampled for key_ids[0]; value_ids[1], value_ids[len(key_ids) + 1], value_ids[len(key_ids) * 2 + 1], ..., value_id[len(key_ids) * (num - 1) + 1] is sampled for key_ids[1]; ...; and so on. """ + key_ids = np.array(key_ids) key_num = len(key_ids) total_num = key_num * num - value_ids = np.zeros(total_num, dtype=np.int64) - used_id_list = np.tile(used_ids, num) - for i, used_ids in enumerate(used_id_list): - cur = self.random() - while cur in used_ids: - cur = self.random() - value_ids[i] = cur - return value_ids + if (key_ids == key_ids[0]).all(): + key_id = key_ids[0] + used = np.array(list(self.used_ids[key_id])) + value_ids = self.random_num(total_num) + check_list = np.arange(total_num)[np.isin(value_ids, used)] + while len(check_list) > 0: + value_ids[check_list] = value = self.random_num(len(check_list)) + perm = value.argsort(kind='quicksort') + aux = value[perm] + mask = np.empty(aux.shape, dtype=np.bool_) + mask[:1] = True + mask[1:] = aux[1:] != aux[:-1] + value = aux[mask] + rev_idx = np.empty(mask.shape, dtype=np.intp) + rev_idx[perm] = np.cumsum(mask) - 1 + ar = np.concatenate((value, used)) + order = ar.argsort(kind='mergesort') + sar = ar[order] + bool_ar = (sar[1:] == sar[:-1]) + flag = np.concatenate((bool_ar, [False])) + ret = np.empty(ar.shape, dtype=bool) + ret[order] = flag + mask = ret[rev_idx] + check_list = check_list[mask] + else: + value_ids = np.zeros(total_num, dtype=np.int64) + check_list = np.arange(total_num) + key_ids = np.tile(key_ids, num) + while len(check_list) > 0: + value_ids[check_list] = self.random_num(len(check_list)) + check_list = np.array([i for i, used, v in + zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) + if v in used]) + return torch.tensor(value_ids) class Sampler(AbstractSampler): """:class:`Sampler` is used to sample negative items for each input user. In order to avoid positive items - in train-phase to be sampled in vaild-phase, and positive items in train-phase or vaild-phase to be sampled + in train-phase to be sampled in valid-phase, and positive items in train-phase or valid-phase to be sampled in test-phase, we need to input the datasets of all phases for pre-processing. And, before using this sampler, it is needed to call :meth:`set_phase` to get the sampler of corresponding phase. @@ -106,6 +167,7 @@ class Sampler(AbstractSampler): Attributes: phase (str): the phase of sampler. It will not be set until :meth:`set_phase` is called. """ + def __init__(self, phases, datasets, distribution='uniform'): if not isinstance(phases, list): phases = [phases] @@ -128,14 +190,14 @@ def __init__(self, phases, datasets, distribution='uniform'): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of item_id. + numpy.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': - return list(range(1, self.n_items)) + return np.arange(1, self.n_items) elif self.distribution == 'popularity': random_item_list = [] for dataset in self.datasets: - random_item_list.extend(dataset.inter_feat[self.iid_field].values) + random_item_list.extend(dataset.inter_feat[self.iid_field].numpy()) return random_item_list else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) @@ -144,15 +206,21 @@ def get_used_ids(self): """ Returns: dict: Used item_ids is the same as positive item_ids. - Key is phase, and value is a np.ndarray which index is user_id, and element is a set of item_ids. + Key is phase, and value is a numpy.ndarray which index is user_id, and element is a set of item_ids. """ used_item_id = dict() - last = [set() for i in range(self.n_users)] + last = [set() for _ in range(self.n_users)] for phase, dataset in zip(self.phases, self.datasets): cur = np.array([set(s) for s in last]) - for uid, iid in dataset.inter_feat[[self.uid_field, self.iid_field]].values: + for uid, iid in zip(dataset.inter_feat[self.uid_field].numpy(), dataset.inter_feat[self.iid_field].numpy()): cur[uid].add(iid) last = used_item_id[phase] = cur + + for used_item_set in used_item_id[self.phases[-1]]: + if len(used_item_set) + 1 == self.n_items: # [pad] is a item. + raise ValueError('Some users have interacted with all items, ' + 'which we can not sample negative items for them. ' + 'Please set `max_user_inter_num` to filter those users.') return used_item_id def set_phase(self, phase): @@ -176,18 +244,18 @@ def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. Args: - user_ids (np.ndarray or list): Input user_ids. + user_ids (numpy.ndarray or list): Input user_ids. num (int): Number of sampled item_ids for each user_id. Returns: - np.ndarray: Sampled item_ids. + torch.tensor: Sampled item_ids. item_ids[0], item_ids[len(user_ids)], item_ids[len(user_ids) * 2], ..., item_id[len(user_ids) * (num - 1)] is sampled for user_ids[0]; item_ids[1], item_ids[len(user_ids) + 1], item_ids[len(user_ids) * 2 + 1], ..., item_id[len(user_ids) * (num - 1) + 1] is sampled for user_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(user_ids, num, self.used_ids[user_ids]) + return self.sample_by_key_ids(user_ids, num) except IndexError: for user_id in user_ids: if user_id < 0 or user_id >= self.n_users: @@ -201,6 +269,7 @@ class KGSampler(AbstractSampler): dataset (Dataset): The knowledge graph dataset, which contains triplets in a knowledge graph. distribution (str, optional): Distribution of the negative entities. Defaults to 'uniform'. """ + def __init__(self, dataset, distribution='uniform'): self.dataset = dataset @@ -217,10 +286,10 @@ def __init__(self, dataset, distribution='uniform'): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of entity_id. + numpy.ndarray or list: Random list of entity_id. """ if self.distribution == 'uniform': - return list(range(1, self.entity_num)) + return np.arange(1, self.entity_num) elif self.distribution == 'popularity': return list(self.hid_list) + list(self.tid_list) else: @@ -229,30 +298,35 @@ def get_random_list(self): def get_used_ids(self): """ Returns: - np.ndarray: Used entity_ids is the same as tail_entity_ids in knowledge graph. + numpy.ndarray: Used entity_ids is the same as tail_entity_ids in knowledge graph. Index is head_entity_id, and element is a set of tail_entity_ids. """ - used_tail_entity_id = np.array([set() for i in range(self.entity_num)]) + used_tail_entity_id = np.array([set() for _ in range(self.entity_num)]) for hid, tid in zip(self.hid_list, self.tid_list): used_tail_entity_id[hid].add(tid) + + for used_tail_set in used_tail_entity_id: + if len(used_tail_set) + 1 == self.entity_num: # [pad] is a entity. + raise ValueError('Some head entities have relation with all entities, ' + 'which we can not sample negative entities for them.') return used_tail_entity_id def sample_by_entity_ids(self, head_entity_ids, num=1): """Sampling by head_entity_ids. Args: - head_entity_ids (np.ndarray or list): Input head_entity_ids. + head_entity_ids (numpy.ndarray or list): Input head_entity_ids. num (int, optional): Number of sampled entity_ids for each head_entity_id. Defaults to ``1``. Returns: - np.ndarray: Sampled entity_ids. + torch.tensor: Sampled entity_ids. entity_ids[0], entity_ids[len(head_entity_ids)], entity_ids[len(head_entity_ids) * 2], ..., entity_id[len(head_entity_ids) * (num - 1)] is sampled for head_entity_ids[0]; entity_ids[1], entity_ids[len(head_entity_ids) + 1], entity_ids[len(head_entity_ids) * 2 + 1], ..., entity_id[len(head_entity_ids) * (num - 1) + 1] is sampled for head_entity_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(head_entity_ids, num, self.used_ids[head_entity_ids]) + return self.sample_by_key_ids(head_entity_ids, num) except IndexError: for head_entity_id in head_entity_ids: if head_entity_id not in self.head_entities: @@ -271,6 +345,7 @@ class RepeatableSampler(AbstractSampler): Attributes: phase (str): the phase of sampler. It will not be set until :meth:`set_phase` is called. """ + def __init__(self, phases, dataset, distribution='uniform'): if not isinstance(phases, list): phases = [phases] @@ -278,47 +353,47 @@ def __init__(self, phases, dataset, distribution='uniform'): self.dataset = dataset self.iid_field = dataset.iid_field - self.user_num = dataset.user_num - self.item_num = dataset.item_num + self.n_users = dataset.user_num + self.n_items = dataset.item_num super().__init__(distribution=distribution) def get_random_list(self): """ Returns: - np.ndarray or list: Random list of item_id. + numpy.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': - return list(range(1, self.item_num)) + return np.arange(1, self.n_items) elif self.distribution == 'popularity': - return self.dataset.inter_feat[self.iid_field].values + return self.dataset.inter_feat[self.iid_field].numpy() else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) def get_used_ids(self): """ Returns: - np.ndarray: Used item_ids is the same as positive item_ids. + numpy.ndarray: Used item_ids is the same as positive item_ids. Index is user_id, and element is a set of item_ids. """ - return np.array([set() for i in range(self.user_num)]) + return np.array([set() for _ in range(self.n_users)]) def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. Args: - user_ids (np.ndarray or list): Input user_ids. + user_ids (numpy.ndarray or list): Input user_ids. num (int): Number of sampled item_ids for each user_id. Returns: - np.ndarray: Sampled item_ids. + torch.tensor: Sampled item_ids. item_ids[0], item_ids[len(user_ids)], item_ids[len(user_ids) * 2], ..., item_id[len(user_ids) * (num - 1)] is sampled for user_ids[0]; item_ids[1], item_ids[len(user_ids) + 1], item_ids[len(user_ids) * 2 + 1], ..., item_id[len(user_ids) * (num - 1) + 1] is sampled for user_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(user_ids, num, self.used_ids[user_ids]) + return self.sample_by_key_ids(user_ids, num) except IndexError: for user_id in user_ids: if user_id < 0 or user_id >= self.n_users: diff --git a/recbole/trainer/hyper_tuning.py b/recbole/trainer/hyper_tuning.py index 1d7f55dc0..a6788fbf1 100644 --- a/recbole/trainer/hyper_tuning.py +++ b/recbole/trainer/hyper_tuning.py @@ -9,9 +9,10 @@ ############################ """ -import numpy as np from functools import partial +import numpy as np + from recbole.utils.utils import dict2str @@ -43,7 +44,6 @@ def _parameters(space): if isinstance(space, dict): space = list(space.values()) for node in _recursiveFindNodes(space, 'switch'): - # Find the name of this parameter paramNode = node.pos_args[0] assert paramNode.name == 'hyperopt_param' @@ -136,7 +136,7 @@ class HyperTuning(object): https://github.com/hyperopt/hyperopt/issues/200 """ - def __init__(self, objective_function, space=None, params_file=None, fixed_config_file_list=None, + def __init__(self, objective_function, space=None, params_file=None, params_dict=None, fixed_config_file_list=None, algo='exhaustive', max_evals=100): self.best_score = None self.best_params = None @@ -150,8 +150,10 @@ def __init__(self, objective_function, space=None, params_file=None, fixed_confi self.space = space elif params_file: self.space = self._build_space_from_file(params_file) + elif params_dict: + self.space = self._build_space_from_dict(params_dict) else: - raise ValueError('at least one of `space` and `params_file` is provided') + raise ValueError('at least one of `space`, `params_file` and `params_dict` is provided') if isinstance(algo, str): if algo == 'exhaustive': self.algo = partial(exhaustive_search, nbMaxSucessiveFailures=1000) @@ -187,6 +189,38 @@ def _build_space_from_file(file): raise ValueError('Illegal param type [{}]'.format(para_type)) return space + @staticmethod + def _build_space_from_dict(config_dict): + from hyperopt import hp + space = {} + for para_type in config_dict: + if para_type == 'choice': + for para_name in config_dict['choice']: + para_value = config_dict['choice'][para_name] + space[para_name] = hp.choice(para_name, para_value) + elif para_type == 'uniform': + for para_name in config_dict['uniform']: + para_value = config_dict['uniform'][para_name] + low = para_value[0] + high = para_value[1] + space[para_name] = hp.uniform(para_name, float(low), float(high)) + elif para_type == 'quniform': + for para_name in config_dict['quniform']: + para_value = config_dict['quniform'][para_name] + low = para_value[0] + high = para_value[1] + q = para_value[2] + space[para_name] = hp.quniform(para_name, float(low), float(high), float(q)) + elif para_type == 'loguniform': + for para_name in config_dict['loguniform']: + para_value = config_dict['loguniform'][para_name] + low = para_value[0] + high = para_value[1] + space[para_name] = hp.loguniform(para_name, float(low), float(high)) + else: + raise ValueError('Illegal param type [{}]'.format(para_type)) + return space + @staticmethod def params2str(params): r""" convert dict to str diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index f855cf6cc..646f85f5e 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -3,9 +3,9 @@ # @Email : slmu@ruc.edu.cn # UPDATE: -# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15 -# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan -# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn +# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15, 2020/11/20 +# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan, Chen Yang +# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn, 254170321@qq.com r""" recbole.trainer.trainer @@ -13,20 +13,20 @@ """ import os -import itertools +from logging import getLogger +from time import time + +import matplotlib.pyplot as plt +import numpy as np import torch import torch.optim as optim from torch.nn.utils.clip_grad import clip_grad_norm_ -import numpy as np -import matplotlib.pyplot as plt - -from time import time -from logging import getLogger +from tqdm import tqdm -from recbole.evaluator import TopKEvaluator, LossEvaluator from recbole.data.interaction import Interaction +from recbole.evaluator import ProxyEvaluator from recbole.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \ - DataLoaderType, KGDataLoaderState, EvaluatorType + DataLoaderType, KGDataLoaderState class AbstractTrainer(object): @@ -94,11 +94,7 @@ def __init__(self, config, model): self.train_loss_dict = dict() self.optimizer = self._build_optimizer() self.eval_type = config['eval_type'] - if self.eval_type == EvaluatorType.INDIVIDUAL: - self.evaluator = LossEvaluator(config) - else: - self.evaluator = TopKEvaluator(config) - + self.evaluator = ProxyEvaluator(config) self.item_tensor = None self.tot_item_num = None @@ -117,13 +113,13 @@ def _build_optimizer(self): elif self.learner.lower() == 'rmsprop': optimizer = optim.RMSprop(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'sparse_adam': - optimizer =optim.SparseAdam(self.model.parameters(), lr=self.learning_rate) + optimizer = optim.SparseAdam(self.model.parameters(), lr=self.learning_rate) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) return optimizer - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): r"""Train the model in an epoch Args: @@ -131,16 +127,26 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): epoch_idx (int): The current epoch id. loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be :attr:`self.model.calculate_loss`. Defaults to ``None``. + show_progress (bool): Show progress of epoch training. Defaults to ``False``. Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains - multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a + multiple parts and the model return these multiple parts loss instead of the sum of loss, it will return a tuple which includes the sum of loss in each part. """ self.model.train() loss_func = loss_func or self.model.calculate_loss total_loss = None - for batch_idx, interaction in enumerate(train_data): + iter_data = ( + tqdm( + enumerate(train_data), + total=len(train_data), + desc=f"Train {epoch_idx:>5}", + ) + if show_progress + else enumerate(train_data) + ) + for batch_idx, interaction in iter_data: interaction = interaction.to(self.device) self.optimizer.zero_grad() losses = loss_func(interaction) @@ -158,17 +164,18 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): self.optimizer.step() return total_loss - def _valid_epoch(self, valid_data): + def _valid_epoch(self, valid_data, show_progress=False): r"""Valid the model with valid data Args: - valid_data (DataLoader): the valid data + valid_data (DataLoader): the valid data. + show_progress (bool): Show progress of epoch evaluate. Defaults to ``False``. Returns: float: valid score dict: valid result """ - valid_result = self.evaluate(valid_data, load_best_model=False) + valid_result = self.evaluate(valid_data, load_best_model=False, show_progress=show_progress) valid_score = calculate_valid_score(valid_result, self.valid_metric) return valid_score, valid_result @@ -218,14 +225,17 @@ def _check_nan(self, loss): raise ValueError('Training loss is nan') def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): + des = self.config['loss_decimal_place'] or 4 train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): - train_loss_output = ', '.join('train_loss%d: %.4f' % (idx + 1, loss) for idx, loss in enumerate(losses)) + des = 'train_loss%d: %.' + str(des) + 'f' + train_loss_output += ', '.join(des % (idx + 1, loss) for idx, loss in enumerate(losses)) else: - train_loss_output += 'train loss: %.4f' % losses + des = '%.' + str(des) + 'f' + train_loss_output += 'train loss:' + des % losses return train_loss_output + ']' - def fit(self, train_data, valid_data=None, verbose=True, saved=True): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): r"""Train the model based on the train data and the valid data. Args: @@ -234,6 +244,9 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): If it's None, the early_stopping is invalid. verbose (bool, optional): whether to write training and evaluation information to logger, default: True saved (bool, optional): whether to save the model parameters, default: True + show_progress (bool): Show progress of epoch training and evaluate. Defaults to ``False``. + callback_fn (callable): Optional callback function executed at end of epoch. + Includes (epoch_idx, valid_score) input arguments. Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) @@ -244,7 +257,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx) + train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ @@ -262,7 +275,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): continue if (epoch_idx + 1) % self.eval_step == 0: valid_start_time = time() - valid_score, valid_result = self._valid_epoch(valid_data) + valid_score, valid_result = self._valid_epoch(valid_data, show_progress=show_progress) self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping( valid_score, self.best_valid_score, self.cur_step, max_step=self.stopping_step, bigger=self.valid_metric_bigger) @@ -281,6 +294,9 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): self.logger.info(update_output) self.best_valid_result = valid_result + if callback_fn: + callback_fn(epoch_idx, valid_score) + if stop_flag: stop_output = 'Finished training, best eval result in epoch %d' % \ (epoch_idx - self.cur_step * self.eval_step) @@ -290,50 +306,33 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): return self.best_valid_score, self.best_valid_result def _full_sort_batch_eval(self, batched_data): - # Note: interaction without item ids - interaction, pos_idx, used_idx, pos_len_list, neg_len_list = batched_data - - batch_size = interaction.length * self.tot_item_num - used_idx = torch.cat([used_idx, torch.arange(interaction.length) * self.tot_item_num]) # remove [pad] item - neg_len_list = list(np.subtract(neg_len_list, 1)) + interaction, history_index, swap_row, swap_col_after, swap_col_before = batched_data try: # Note: interaction without item ids - scores = self.model.full_sort_predict(interaction.to(self.device)).flatten() + scores = self.model.full_sort_predict(interaction.to(self.device)) except NotImplementedError: - interaction = interaction.to(self.device).repeat_interleave(self.tot_item_num) - interaction.update(self.item_tensor[:batch_size]) + new_inter = interaction.to(self.device).repeat_interleave(self.tot_item_num) + batch_size = len(new_inter) + new_inter.update(self.item_tensor[:batch_size]) if batch_size <= self.test_batch_size: - scores = self.model.predict(interaction) + scores = self.model.predict(new_inter) else: - scores = self._spilt_predict(interaction, batch_size) - pos_idx = pos_idx.to(self.device) - used_idx = used_idx.to(self.device) + scores = self._spilt_predict(new_inter, batch_size) - pos_scores = scores.index_select(dim=0, index=pos_idx) - pos_scores = torch.split(pos_scores, pos_len_list, dim=0) + scores = scores.view(-1, self.tot_item_num) + scores[:, 0] = -np.inf + if history_index is not None: + scores[history_index] = -np.inf - ones_tensor = torch.ones(batch_size, dtype=torch.bool, device=self.device) - used_mask = ones_tensor.index_fill(dim=0, index=used_idx, value=0) - neg_scores = scores.masked_select(used_mask) - neg_scores = torch.split(neg_scores, neg_len_list, dim=0) + swap_row = swap_row.to(self.device) + swap_col_after = swap_col_after.to(self.device) + swap_col_before = swap_col_before.to(self.device) + scores[swap_row, swap_col_after] = scores[swap_row, swap_col_before] - tmp_len_list = np.add(pos_len_list, neg_len_list).tolist() - final_scores_width = max(self.tot_item_num, max(tmp_len_list)) - extra_len_list = np.subtract(final_scores_width, tmp_len_list).tolist() - padding_nums = final_scores_width * len(tmp_len_list) - np.sum(tmp_len_list) - padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums) - padding_scores = torch.split(padding_tensor, extra_len_list) - - final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores))) - final_scores = torch.cat(final_scores) - - setattr(interaction, 'pos_len_list', pos_len_list) - setattr(interaction, 'user_len_list', len(tmp_len_list) * [final_scores_width]) - - return interaction, final_scores + return interaction, scores @torch.no_grad() - def evaluate(self, eval_data, load_best_model=True, model_file=None): + def evaluate(self, eval_data, load_best_model=True, model_file=None, show_progress=False): r"""Evaluate the model based on the eval data. Args: @@ -342,6 +341,7 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): It should be set True, if users want to test the model after training. model_file (str, optional): the saved model file, default: None. If users want to test the previously trained model file, they can set this parameter. + show_progress (bool): Show progress of epoch evaluate. Defaults to ``False``. Returns: dict: eval result, key is the eval metric and value in the corresponding metric value @@ -367,22 +367,27 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): self.tot_item_num = eval_data.dataset.item_num batch_matrix_list = [] - for batch_idx, batched_data in enumerate(eval_data): + iter_data = ( + tqdm( + enumerate(eval_data), + total=len(eval_data), + desc=f"Evaluate ", + ) + if show_progress + else enumerate(eval_data) + ) + for batch_idx, batched_data in iter_data: if eval_data.dl_type == DataLoaderType.FULL: - if self.eval_type == EvaluatorType.INDIVIDUAL: - raise ValueError('full sort can\'t use LossEvaluator') interaction, scores = self._full_sort_batch_eval(batched_data) - batch_matrix = self.evaluator.collect(interaction, scores, full=True) else: interaction = batched_data batch_size = interaction.length - if batch_size <= self.test_batch_size: scores = self.model.predict(interaction.to(self.device)) else: scores = self._spilt_predict(interaction, batch_size) - batch_matrix = self.evaluator.collect(interaction, scores) + batch_matrix = self.evaluator.collect(interaction, scores) batch_matrix_list.append(batch_matrix) result = self.evaluator.evaluate(batch_matrix_list, eval_data) @@ -437,7 +442,7 @@ def __init__(self, config, model): self.train_rec_step = config['train_rec_step'] self.train_kg_step = config['train_kg_step'] - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): if self.train_rec_step is None or self.train_kg_step is None: interaction_state = KGDataLoaderState.RSKG elif epoch_idx % (self.train_rec_step + self.train_kg_step) < self.train_rec_step: @@ -446,9 +451,11 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): interaction_state = KGDataLoaderState.KG train_data.set_mode(interaction_state) if interaction_state in [KGDataLoaderState.RSKG, KGDataLoaderState.RS]: - return super()._train_epoch(train_data, epoch_idx) + return super()._train_epoch(train_data, epoch_idx, show_progress=show_progress) elif interaction_state in [KGDataLoaderState.KG]: - return super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + return super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) return None @@ -460,17 +467,21 @@ class KGATTrainer(Trainer): def __init__(self, config, model): super(KGATTrainer, self).__init__(config, model) - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): # train rs train_data.set_mode(KGDataLoaderState.RS) - rs_total_loss = super()._train_epoch(train_data, epoch_idx) + rs_total_loss = super()._train_epoch(train_data, epoch_idx, show_progress=show_progress) # train kg train_data.set_mode(KGDataLoaderState.KG) - kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + kg_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) # update A - self.model.update_attentive_A() + self.model.eval() + with torch.no_grad(): + self.model.update_attentive_A() return rs_total_loss, kg_total_loss @@ -500,12 +511,12 @@ def save_pretrained_model(self, epoch, saved_model_file): } torch.save(state, saved_model_file) - def pretrain(self, train_data, verbose=True): + def pretrain(self, train_data, verbose=True, show_progress=False): for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx) + train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ @@ -524,11 +535,11 @@ def pretrain(self, train_data, verbose=True): return self.best_valid_score, self.best_valid_result - def fit(self, train_data, valid_data=None, verbose=True, saved=True): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): if self.model.train_stage == 'pretrain': - return self.pretrain(train_data, verbose) + return self.pretrain(train_data, verbose, show_progress) elif self.model.train_stage == 'finetune': - return super().fit(train_data, valid_data, verbose, saved) + return super().fit(train_data, valid_data, verbose, saved, show_progress, callback_fn) else: raise ValueError("Please make sure that the 'train_stage' is 'pretrain' or 'finetune' ") @@ -542,19 +553,23 @@ def __init__(self, config, model): super(MKRTrainer, self).__init__(config, model) self.kge_interval = config['kge_interval'] - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): rs_total_loss, kg_total_loss = 0., 0. # train rs self.logger.info('Train RS') train_data.set_mode(KGDataLoaderState.RS) - rs_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_rs_loss) + rs_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_rs_loss, + show_progress=show_progress) # train kg if epoch_idx % self.kge_interval == 0: self.logger.info('Train KG') train_data.set_mode(KGDataLoaderState.KG) - kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + kg_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) return rs_total_loss, kg_total_loss @@ -567,3 +582,180 @@ class TraditionalTrainer(Trainer): def __init__(self, config, model): super(TraditionalTrainer, self).__init__(config, model) self.epochs = 1 # Set the epoch to 1 when running memory based model + + +class xgboostTrainer(AbstractTrainer): + """xgboostTrainer is designed for XGBOOST. + + """ + + def __init__(self, config, model): + super(xgboostTrainer, self).__init__(config, model) + + self.xgb = __import__('xgboost') + + self.logger = getLogger() + self.label_field = config['LABEL_FIELD'] + self.xgb_model = config['xgb_model'] + self.convert_token_to_onehot = self.config['convert_token_to_onehot'] + + # DMatrix params + self.weight = config['xgb_weight'] + self.base_margin = config['xgb_base_margin'] + self.missing = config['xgb_missing'] + self.silent = config['xgb_silent'] + self.feature_names = config['xgb_feature_names'] + self.feature_types = config['xgb_feature_types'] + self.nthread = config['xgb_nthread'] + + # train params + self.params = config['xgb_params'] + self.num_boost_round = config['xgb_num_boost_round'] + self.evals = () + self.obj = config['xgb_obj'] + self.feval = config['xgb_feval'] + self.maximize = config['xgb_maximize'] + self.early_stopping_rounds = config['xgb_early_stopping_rounds'] + self.evals_result = {} + self.verbose_eval = config['xgb_verbose_eval'] + self.callbacks = None + + # evaluator + self.eval_type = config['eval_type'] + self.epochs = config['epochs'] + self.eval_step = min(config['eval_step'], self.epochs) + self.valid_metric = config['valid_metric'].lower() + + self.evaluator = ProxyEvaluator(config) + + # model saved + self.checkpoint_dir = config['checkpoint_dir'] + ensure_dir(self.checkpoint_dir) + saved_model_file = '{}-{}.pth'.format(self.config['model'], get_local_time()) + self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) + + def _interaction_to_DMatrix(self, dataloader): + r"""Convert data format from interaction to DMatrix + + Args: + dataloader (XgboostDataLoader): xgboost dataloader. + Returns: + DMatrix: Data in the form of 'DMatrix'. + """ + interaction = dataloader.dataset[:] + interaction_np = interaction.numpy() + cur_data = np.array([]) + columns = [] + for key, value in interaction_np.items(): + value = np.resize(value, (value.shape[0], 1)) + if key != self.label_field: + columns.append(key) + if cur_data.shape[0] == 0: + cur_data = value + else: + cur_data = np.hstack((cur_data, value)) + + if self.convert_token_to_onehot == True: + from scipy import sparse + from scipy.sparse import dok_matrix + convert_col_list = dataloader.dataset.convert_col_list + hash_count = dataloader.dataset.hash_count + + new_col = cur_data.shape[1] - len(convert_col_list) + for key, values in hash_count.items(): + new_col = new_col + values + onehot_data = dok_matrix((cur_data.shape[0], new_col)) + + cur_j = 0 + new_j = 0 + + for key in columns: + if key in convert_col_list: + for i in range(cur_data.shape[0]): + onehot_data[i, int(new_j + cur_data[i, cur_j])] = 1 + new_j = new_j + hash_count[key] - 1 + else: + for i in range(cur_data.shape[0]): + onehot_data[i, new_j] = cur_data[i, cur_j] + cur_j = cur_j + 1 + new_j = new_j + 1 + + cur_data = sparse.csc_matrix(onehot_data) + + return self.xgb.DMatrix(data=cur_data, + label=interaction_np[self.label_field], + weight=self.weight, + base_margin=self.base_margin, + missing=self.missing, + silent=self.silent, + feature_names=self.feature_names, + feature_types=self.feature_types, + nthread=self.nthread) + + def _train_at_once(self, train_data, valid_data): + r""" + + Args: + train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + self.dtrain = self._interaction_to_DMatrix(train_data) + self.dvalid = self._interaction_to_DMatrix(valid_data) + self.evals = [(self.dtrain, 'train'), (self.dvalid, 'valid')] + self.model = self.xgb.train(self.params, self.dtrain, self.num_boost_round, + self.evals, self.obj, self.feval, self.maximize, + self.early_stopping_rounds, self.evals_result, + self.verbose_eval, self.xgb_model, self.callbacks) + + self.model.save_model(self.saved_model_file) + self.xgb_model = self.saved_model_file + + def _valid_epoch(self, valid_data): + r""" + + Args: + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + valid_result = self.evaluate(valid_data) + valid_score = calculate_valid_score(valid_result, self.valid_metric) + return valid_result, valid_score + + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): + # load model + if self.xgb_model is not None: + self.model.load_model(self.xgb_model) + + self.best_valid_score = 0. + self.best_valid_result = 0. + + for epoch_idx in range(self.epochs): + self._train_at_once(train_data, valid_data) + + if (epoch_idx + 1) % self.eval_step == 0: + # evaluate + valid_start_time = time() + valid_result, valid_score = self._valid_epoch(valid_data) + valid_end_time = time() + valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ + (epoch_idx, valid_end_time - valid_start_time, valid_score) + valid_result_output = 'valid result: \n' + dict2str(valid_result) + if verbose: + self.logger.info(valid_score_output) + self.logger.info(valid_result_output) + + self.best_valid_score = valid_score + self.best_valid_result = valid_result + + return self.best_valid_score, self.best_valid_result + + def evaluate(self, eval_data, load_best_model=True, model_file=None, show_progress=False): + self.eval_pred = torch.Tensor() + self.eval_true = torch.Tensor() + + self.deval = self._interaction_to_DMatrix(eval_data) + self.eval_true = torch.Tensor(self.deval.get_label()) + self.eval_pred = torch.Tensor(self.model.predict(self.deval)) + + batch_matrix_list = [[torch.stack((self.eval_true, self.eval_pred), 1)]] + result = self.evaluator.evaluate(batch_matrix_list, eval_data) + return result diff --git a/recbole/utils/argument_list.py b/recbole/utils/argument_list.py index a1af91f4a..383549e84 100644 --- a/recbole/utils/argument_list.py +++ b/recbole/utils/argument_list.py @@ -7,11 +7,13 @@ 'seed', 'reproducibility', 'state', - 'data_path'] + 'data_path', + 'show_progress'] training_arguments = ['epochs', 'train_batch_size', 'learner', 'learning_rate', 'training_neg_sample_num', + 'training_neg_sample_distribution', 'eval_step', 'stopping_step', 'checkpoint_dir'] @@ -23,15 +25,15 @@ 'eval_batch_size'] dataset_arguments = ['field_separator', 'seq_separator', - 'USER_ID_FIELD', 'ITEM_ID_FIELD', 'RATING_FIELD', 'TIME_FIELD' + 'USER_ID_FIELD', 'ITEM_ID_FIELD', 'RATING_FIELD', 'TIME_FIELD', 'seq_len', 'LABEL_FIELD', 'threshold', 'NEG_PREFIX', 'ITEM_LIST_LENGTH_FIELD', 'LIST_SUFFIX', 'MAX_ITEM_LIST_LENGTH', 'POSITION_FIELD', 'HEAD_ENTITY_ID_FIELD', 'TAIL_ENTITY_ID_FIELD', 'RELATION_ID_FIELD', 'ENTITY_ID_FIELD', - 'load_col', 'unload_col', 'additional_feat_suffix', + 'load_col', 'unload_col', 'unused_col', 'additional_feat_suffix', 'max_user_inter_num', 'min_user_inter_num', 'max_item_inter_num', 'min_item_inter_num', - 'lowest_val', 'highest_val', 'equal_val', 'not_equal_val', 'drop_filter_field', - 'fields_in_same_space', 'fill_nan', - 'preload_weight', 'drop_preload_weight', + 'lowest_val', 'highest_val', 'equal_val', 'not_equal_val', + 'fields_in_same_space', + 'preload_weight', 'normalize_field', 'normalize_all'] diff --git a/recbole/utils/case_study.py b/recbole/utils/case_study.py new file mode 100644 index 000000000..45d39f6a8 --- /dev/null +++ b/recbole/utils/case_study.py @@ -0,0 +1,83 @@ +# @Time : 2020/12/25 +# @Author : Yushuo Chen +# @Email : chenyushuo@ruc.edu.cn + +# UPDATE +# @Time : 2020/12/25 +# @Author : Yushuo Chen +# @email : chenyushuo@ruc.edu.cn + + +import numpy as np +import torch + +from recbole.data.dataloader.general_dataloader import GeneralFullDataLoader +from recbole.data.dataloader.sequential_dataloader import SequentialFullDataLoader + + +@torch.no_grad() +def get_scores(uid_series, model, test_data): + """Calculate the scores of all items for each user in uid_series. + + Note: + The score of [pad] and history items will be set into -inf. + + Args: + uid_series (numpy.ndarray): User id series + model (AbstractRecommender): Model to predict + test_data (AbstractDataLoader): The test_data of model + + Returns: + torch.Tensor: the scores of all items for each user in uid_series. + """ + uid_field = test_data.dataset.uid_field + dataset = test_data.dataset + model.eval() + + if isinstance(test_data, GeneralFullDataLoader): + index = np.isin(test_data.user_df[uid_field].numpy(), uid_series) + input_interaction = test_data.user_df[index] + history_item = test_data.uid2history_item[input_interaction[uid_field]] + history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) + history_col = torch.cat(list(history_item)) + history_index = history_row, history_col + elif isinstance(test_data, SequentialFullDataLoader): + index = np.isin(test_data.uid_list, uid_series) + input_interaction = test_data.augmentation(test_data.item_list_index[index], + test_data.target_index[index], test_data.item_list_length[index]) + history_index = None + else: + raise NotImplementedError + + # Get scores of all items + try: + scores = model.full_sort_predict(input_interaction) + except NotImplementedError: + input_interaction = input_interaction.repeat(dataset.item_num) + input_interaction.update(test_data.get_item_feature().repeat(len(uid_series))) + scores = model.predict(input_interaction) + + scores = scores.view(-1, dataset.item_num) + scores[:, 0] = -np.inf # set scores of [pad] to -inf + if history_index is not None: + scores[history_index] = -np.inf # set scores of history items to -inf + + return scores + + +def get_topk(uid_series, model, test_data, k): + """Calculate the top-k items' scores and ids for each user in uid_series. + + Args: + uid_series (numpy.ndarray): User id series + model (AbstractRecommender): Model to predict + test_data (AbstractDataLoader): The test_data of model + k (int): The top-k items. + + Returns: + tuple: + - topk_scores (torch.Tensor): The scores of topk items. + - topk_index (torch.Tensor): The index of topk items, which is also the internal ids of items. + """ + scores = get_scores(uid_series, model, test_data) + return torch.topk(scores, k) diff --git a/recbole/utils/enum_type.py b/recbole/utils/enum_type.py index 62a11ee7c..84e15b812 100644 --- a/recbole/utils/enum_type.py +++ b/recbole/utils/enum_type.py @@ -26,6 +26,7 @@ class ModelType(Enum): KNOWLEDGE = 4 SOCIAL = 5 TRADITIONAL = 6 + XGBOOST = 7 class DataLoaderType(Enum): diff --git a/recbole/utils/logger.py b/recbole/utils/logger.py index 15e67e47a..e9808fc68 100644 --- a/recbole/utils/logger.py +++ b/recbole/utils/logger.py @@ -68,4 +68,3 @@ def init_logger(config): level=level, handlers=[fh, sh] ) - diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index c2df49040..faa98c938 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -8,12 +8,14 @@ ################################ """ -import os import datetime import importlib +import os import random -import torch + import numpy as np +import torch + from recbole.utils.enum_type import ModelType @@ -53,15 +55,20 @@ def get_model(model_name): 'general_recommender', 'context_aware_recommender', 'sequential_recommender', - 'knowledge_aware_recommender' + 'knowledge_aware_recommender', + 'exlib_recommender' ] model_file_name = model_name.lower() + model_module = None for submodule in model_submodule: - module_path = '.'.join(['...model', submodule, model_file_name]) + module_path = '.'.join(['recbole.model', submodule, model_file_name]) if importlib.util.find_spec(module_path, __name__): model_module = importlib.import_module(module_path, __name__) + break + if model_module is None: + raise ValueError('`model_name` [{}] is not the name of an existing model.'.format(model_name)) model_class = getattr(model_module, model_name) return model_class @@ -159,7 +166,7 @@ def dict2str(result_dict): result_str = '' for metric, value in result_dict.items(): - result_str += str(metric) + ' : ' + '%.04f' % value + ' ' + result_str += str(metric) + ' : ' + str(value) + ' ' return result_str diff --git a/requirements.txt b/requirements.txt index 94a327fc4..7f4bff1bf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,4 @@ hyperopt>=0.2.4 pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 -pyyaml>=5.1.0 +pyyaml>=5.1.0 \ No newline at end of file diff --git a/run_test.sh b/run_test.sh new file mode 100644 index 000000000..190b78540 --- /dev/null +++ b/run_test.sh @@ -0,0 +1,16 @@ +#!/bin/bash + + +python -m pytest -v tests/metrics +printf "metrics tests finished\n" +python -m pytest -v tests/config/test_config.py +python -m pytest -v tests/config/test_overall.py +export PYTHONPATH=. +python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 +printf "config tests finished\n" +python -m pytest -v tests/evaluation_setting +printf "evaluation_setting tests finished\n" +python -m pytest -v tests/model/test_model_auto.py +python -m pytest -v tests/model/test_model_manual.py +printf "model tests finished\n" + diff --git a/run_test_example.py b/run_test_example.py index 4f7b68cc0..82aacfd92 100644 --- a/run_test_example.py +++ b/run_test_example.py @@ -130,6 +130,22 @@ 'model': 'ConvNCF', 'dataset': 'ml-100k', }, + 'Test LINE': { + 'model': 'LINE', + 'dataset': 'ml-100k', + }, + 'Test MultiDAE': { + 'model': 'MultiDAE', + 'dataset': 'ml-100k', + }, + 'Test MultiVAE': { + 'model': 'LINE', + 'dataset': 'ml-100k', + }, + 'Test MacridVAE': { + 'model': 'MacridVAE', + 'dataset': 'ml-100k', + }, # Context-aware Recommendation 'Test FM': { diff --git a/setup.py b/setup.py index a8c0a358a..46c74cab9 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ setup( name='recbole', version= - '0.1.1', # please remember to edit recbole/__init__.py in response, once updating the version + '0.1.2', # please remember to edit recbole/__init__.py in response, once updating the version description='A unified, comprehensive and efficient recommendation library', long_description=long_description, long_description_content_type="text/markdown", diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 0ba2e719b..059f6f010 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -45,6 +45,7 @@ def test_rols_full(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) ''' + def test_tols_full(self): config_dict = { 'eval_setting': 'TO_LS,full', @@ -72,6 +73,7 @@ def test_tols_full(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) ''' + def test_tors_full(self): config_dict = { 'eval_setting': 'TO_RS,full', @@ -182,7 +184,7 @@ def test_tors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) # config_dict = { # 'eval_setting': 'TO_RS,uni100', # 'model': 'NeuMF', @@ -208,10 +210,11 @@ class TestContextRecommender(unittest.TestCase): def test_tors(self): config_dict = { 'eval_setting': 'TO_RS', + 'threshold': {'rating': 4}, 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) # config_dict = { # 'eval_setting': 'TO_RS', # 'model': 'DeepFM', diff --git a/tests/metrics/test_rank_metrics.py b/tests/metrics/test_rank_metrics.py new file mode 100644 index 000000000..c60a2c485 --- /dev/null +++ b/tests/metrics/test_rank_metrics.py @@ -0,0 +1,74 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/12/21 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + + +import os +import sys +import unittest + +sys.path.append(os.getcwd()) +import numpy as np +import torch +from recbole.config import Config +from recbole.data.interaction import Interaction +from recbole.evaluator.metrics import metrics_dict +from recbole.evaluator.evaluators import RankEvaluator + +parameters_dict = { + 'model': 'BPR', + 'eval_setting': 'RO_RS,uni100', +} + + +class MetricsTestCases(object): + user_len_list0 = np.array([2, 3, 5]) + pos_len_list0 = np.array([1, 2, 3]) + pos_rank_sum0 = np.array([1, 4, 9]) + + user_len_list1 = np.array([3, 6, 4]) + pos_len_list1 = np.array([1, 0, 4]) + pos_rank_sum1 = np.array([3, 0, 6]) + + +class CollectTestCases(object): + interaction0 = Interaction({}, [0, 2, 3, 4], [2, 3, 4, 5]) + scores_tensor0 = torch.Tensor([0.1, 0.2, + 0.1, 0.1, 0.2, + 0.2, 0.2, 0.2, 0.2, + 0.3, 0.2, 0.1, 0.4, 0.3]) + + +def get_metric_result(name, case=0): + func = metrics_dict[name] + return func(getattr(MetricsTestCases, f'user_len_list{case}'), + getattr(MetricsTestCases, f'pos_len_list{case}'), + getattr(MetricsTestCases, f'pos_rank_sum{case}')) + + +def get_collect_result(evaluator, case=0): + func = evaluator.collect + return func(getattr(CollectTestCases, f'interaction{case}'), + getattr(CollectTestCases, f'scores_tensor{case}')) + + +class TestRankMetrics(unittest.TestCase): + def test_gauc(self): + name = 'gauc' + self.assertEqual(get_metric_result(name, case=0), (1 * ((2 - (1 - 1) / 2 - 1 / 1) / (2 - 1)) + + 2 * ((3 - (2 - 1) / 2 - 4 / 2) / (3 - 2)) + + 3 * ((5 - (3 - 1) / 2 - 9 / 3) / (5 - 3))) + / (1 + 2 + 3)) + self.assertEqual(get_metric_result(name, case=1), (3 - 0 - 3 / 1) / (3 - 1)) + + def test_collect(self): + config = Config('BPR', 'ml-100k', config_dict=parameters_dict) + metrics = ['GAUC'] + rank_evaluator = RankEvaluator(config, metrics) + self.assertEqual(get_collect_result(rank_evaluator, case=0).squeeze().cpu().numpy().tolist(), + np.array([0, (2 + 3) / 2 * 2, (1 + 2 + 3 + 4) / 4 * 3, 1 + (2 + 3) / 2 + 4 + 5]).tolist()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 46ffeb24a..4604e8a65 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -30,7 +30,7 @@ ENTITY_ID_FIELD: entity_id # Selectively Loading load_col: - inter: [user_id, item_id, rating, timestamp, label] + inter: [user_id, item_id, rating, timestamp] user: [user_id, age, gender, occupation] item: [item_id, movie_title, release_year, class] link: [item_id, entity_id] @@ -47,12 +47,9 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : False # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ -drop_preload_weight: True normalize_field: ~ normalize_all: True diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 74e4c8e79..d8e61e551 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -16,99 +16,157 @@ current_path = os.path.dirname(os.path.realpath(__file__)) config_file_list = [os.path.join(current_path, 'test_model.yaml')] + +def quick_test(config_dict): + objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + + class TestGeneralRecommender(unittest.TestCase): def test_pop(self): config_dict = { 'model': 'Pop', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_itemknn(self): config_dict = { 'model': 'ItemKNN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_bpr(self): config_dict = { 'model': 'BPR', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_neumf(self): config_dict = { 'model': 'NeuMF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_convncf(self): config_dict = { 'model': 'ConvNCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_dmf(self): config_dict = { 'model': 'DMF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_dmf_with_rating(self): + config_dict = { + 'model': 'DMF', + 'inter_matrix_type': 'rating', + } + quick_test(config_dict) def test_fism(self): config_dict = { 'model': 'FISM', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_fism_with_split_to_and_alpha(self): + config_dict = { + 'model': 'FISM', + 'split_to': 10, + 'alpha': 0.5, + } + quick_test(config_dict) def test_nais(self): config_dict = { 'model': 'NAIS', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_nais_with_concat(self): + config_dict = { + 'model': 'NAIS', + 'algorithm': 'concat', + 'split_to': 10, + 'alpha': 0.5, + 'beta': 0.1, + } + quick_test(config_dict) def test_spectralcf(self): config_dict = { 'model': 'SpectralCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_gcmc(self): config_dict = { 'model': 'GCMC', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gcmc_with_stack(self): + config_dict = { + 'model': 'GCMC', + 'accum': 'stack', + 'sparse_feature': False, + } + quick_test(config_dict) def test_ngcf(self): config_dict = { 'model': 'NGCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_lightgcn(self): config_dict = { 'model': 'LightGCN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_dgcf(self): config_dict = { 'model': 'DGCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_line(self): + config_dict = { + 'model': 'LINE', + } + quick_test(config_dict) + + def test_MultiDAE(self): + config_dict = { + 'model': 'MultiDAE', + 'training_neg_sample_num': 0 + } + quick_test(config_dict) + def test_MultiVAE(self): + config_dict = { + 'model': 'MultiVAE', + 'training_neg_sample_num': 0 + } + quick_test(config_dict) + + def test_MacridVAE(self): + config_dict = { + 'model': 'MacridVAE', + 'training_neg_sample_num': 0 + } + quick_test(config_dict) + + def test_CDAE(self): + config_dict = { + 'model': 'CDAE', + 'training_neg_sample_num': 0 + } + quick_test(config_dict) class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset @@ -116,138 +174,193 @@ class TestContextRecommender(unittest.TestCase): def test_lr(self): config_dict = { 'model': 'LR', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fm(self): config_dict = { 'model': 'FM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_nfm(self): config_dict = { 'model': 'NFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_deepfm(self): config_dict = { 'model': 'DeepFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_xdeepfm(self): config_dict = { 'model': 'xDeepFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_xdeepfm_with_direct(self): + config_dict = { + 'model': 'xDeepFM', + 'threshold': {'rating': 4}, + 'direct': True, + } + quick_test(config_dict) def test_afm(self): config_dict = { 'model': 'AFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fnn(self): config_dict = { 'model': 'FNN', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_pnn(self): config_dict = { 'model': 'PNN', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_pnn_with_use_inner_and_use_outer(self): + config_dict = { + 'model': 'PNN', + 'threshold': {'rating': 4}, + 'use_inner': True, + 'use_outer': True, + } + quick_test(config_dict) + + def test_pnn_without_use_inner_and_use_outer(self): + config_dict = { + 'model': 'PNN', + 'threshold': {'rating': 4}, + 'use_inner': False, + 'use_outer': False, + } + quick_test(config_dict) def test_dssm(self): config_dict = { 'model': 'DSSM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_widedeep(self): config_dict = { 'model': 'WideDeep', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - # def test_dcn(self): - # config_dict = { - # 'model': 'DCN', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_autoint(self): config_dict = { 'model': 'AutoInt', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_ffm(self): config_dict = { 'model': 'FFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fwfm(self): config_dict = { 'model': 'FwFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_din(self): - # config_dict = { - # 'model': 'DIN', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_dcn(self): + config_dict = { + 'model': 'DCN', + 'threshold': {'rating': 4}, + } + quick_test(config_dict) + + def test_xgboost(self): + config_dict = { + 'model': 'xgboost', + 'threshold': {'rating': 4}, + 'xgb_params': { + 'booster': 'gbtree', + 'objective': 'binary:logistic', + 'eval_metric': ['auc', 'logloss'] + }, + 'xgb_num_boost_round': 1, + } + quick_test(config_dict) class TestSequentialRecommender(unittest.TestCase): + def test_din(self): + config_dict = { + 'model': 'DIN', + } + quick_test(config_dict) + def test_fpmc(self): config_dict = { 'model': 'FPMC', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_gru4rec(self): config_dict = { 'model': 'GRU4Rec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gru4rec_with_BPR_loss(self): + config_dict = { + 'model': 'GRU4Rec', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_narm(self): config_dict = { 'model': 'NARM', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_narm_with_BPR_loss(self): + config_dict = { + 'model': 'NARM', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_stamp(self): config_dict = { 'model': 'STAMP', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_stamp_with_BPR_loss(self): + config_dict = { + 'model': 'STAMP', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_caser(self): config_dict = { @@ -255,429 +368,370 @@ def test_caser(self): 'MAX_ITEM_LIST_LENGTH': 10, 'reproducibility': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_caser_with_BPR_loss(self): + config_dict = { + 'model': 'Caser', + 'loss_type': 'BPR', + 'MAX_ITEM_LIST_LENGTH': 10, + 'reproducibility': False, + } + quick_test(config_dict) def test_nextitnet(self): config_dict = { 'model': 'NextItNet', 'reproducibility': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_nextitnet_with_BPR_loss(self): + config_dict = { + 'model': 'NextItNet', + 'loss_type': 'BPR', + 'reproducibility': False, + } + quick_test(config_dict) def test_transrec(self): config_dict = { 'model': 'TransRec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_sasrec(self): config_dict = { 'model': 'SASRec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_bert4rec(self): - # config_dict = { - # 'model': 'BERT4Rec', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_sasrec_with_BPR_loss_and_relu(self): + config_dict = { + 'model': 'SASRec', + 'loss_type': 'BPR', + 'hidden_act': 'relu' + } + quick_test(config_dict) + + def test_sasrec_with_BPR_loss_and_sigmoid(self): + config_dict = { + 'model': 'SASRec', + 'loss_type': 'BPR', + 'hidden_act': 'sigmoid' + } + quick_test(config_dict) def test_srgnn(self): config_dict = { 'model': 'SRGNN', 'MAX_ITEM_LIST_LENGTH': 3, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_srgnn_with_BPR_loss(self): + config_dict = { + 'model': 'SRGNN', + 'loss_type': 'BPR', + 'MAX_ITEM_LIST_LENGTH': 3, + } + quick_test(config_dict) def test_gcsan(self): config_dict = { 'model': 'GCSAN', 'MAX_ITEM_LIST_LENGTH': 3, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gcsan_with_BPR_loss_and_tanh(self): + config_dict = { + 'model': 'GCSAN', + 'loss_type': 'BPR', + 'hidden_act': 'tanh', + 'MAX_ITEM_LIST_LENGTH': 3, + } + quick_test(config_dict) def test_gru4recf(self): config_dict = { 'model': 'GRU4RecF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gru4recf_with_max_pooling(self): + config_dict = { + 'model': 'GRU4RecF', + 'pooling_mode': 'max', + } + quick_test(config_dict) + + def test_gru4recf_with_sum_pooling(self): + config_dict = { + 'model': 'GRU4RecF', + 'pooling_mode': 'sum', + } + quick_test(config_dict) def test_sasrecf(self): config_dict = { 'model': 'SASRecF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_fdsa(self): - # config_dict = { - # 'model': 'FDSA', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_sasrecf_with_max_pooling(self): + config_dict = { + 'model': 'SASRecF', + 'pooling_mode': 'max', + } + quick_test(config_dict) - # def test_gru4reckg(self): - # config_dict = { - # 'model': 'GRU4RecKG', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_sasrecf_with_sum_pooling(self): + config_dict = { + 'model': 'SASRecF', + 'pooling_mode': 'sum', + } + quick_test(config_dict) - # def test_s3rec(self): - # config_dict = { - # 'model': 'S3Rec', - # 'train_stage': 'pretrain', - # 'save_step': 1, - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_hrm(self): + config_dict = { + 'model': 'HRM', + } + quick_test(config_dict) - # config_dict = { - # 'model': 'S3Rec', - # 'train_stage': 'finetune', - # 'pre_model_path': './saved/S3Rec-test-1.pth', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_hrm_with_BPR_loss(self): + config_dict = { + 'model': 'HRM', + 'loss_type': 'BPR', + } + quick_test(config_dict) + def test_npe(self): + config_dict = { + 'model': 'NPE', + } + quick_test(config_dict) -class TestKnowledgeRecommender(unittest.TestCase): + def test_npe_with_BPR_loss(self): + config_dict = { + 'model': 'NPE', + 'loss_type': 'BPR', + } + quick_test(config_dict) - def test_cke(self): + def test_shan(self): config_dict = { - 'model': 'CKE', + 'model': 'SHAN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_cfkg(self): + def test_shan_with_BPR_loss(self): config_dict = { - 'model': 'CFKG', + 'model': 'SHAN', + 'loss_type': 'BPR', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_ktup(self): + def test_hgn(self): config_dict = { - 'model': 'KTUP', - 'train_rec_step': 1, - 'train_kg_step': 1, - 'epochs': 2, + 'model': 'HGN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgat(self): + def test_hgn_with_CE_loss(self): config_dict = { - 'model': 'KGAT', + 'model': 'HGN', + 'loss_type': 'CE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_ripplenet(self): + def test_fossil(self): config_dict = { - 'model': 'RippleNet', + 'model': 'FOSSIL', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_mkr(self): + def test_repeat_net(self): config_dict = { - 'model': 'MKR', + 'model': 'RepeatNet', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgcn(self): + def test_fdsa(self): config_dict = { - 'model': 'KGCN', + 'model': 'FDSA', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgnnls(self): + def test_fdsa_with_max_pooling(self): config_dict = { - 'model': 'KGNNLS', + 'model': 'FDSA', + 'pooling_mode': 'max', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - + quick_test(config_dict) -class TestGeneralRecommender2(unittest.TestCase): - - def test_dmf(self): + def test_fdsa_with_sum_pooling(self): config_dict = { - 'model': 'DMF', - 'inter_matrix_type': 'rating', + 'model': 'FDSA', + 'pooling_mode': 'sum', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_fism(self): + def test_bert4rec(self): config_dict = { - 'model': 'FISM', - 'split_to': 10, - 'alpha': 0.5, + 'model': 'BERT4Rec', } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_nais(self): + def test_bert4rec_with_BPR_loss_and_swish(self): config_dict = { - 'model': 'NAIS', - 'algorithm': 'concat', - 'split_to': 10, - 'alpha': 0.5, - 'beta': 0.1, + 'model': 'BERT4Rec', + 'loss_type': 'BPR', + 'hidden_act': 'swish' } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_gcmc(self): - config_dict = { - 'model': 'GCMC', - 'accum': 'stack', - 'sparse_feature': False, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # def test_gru4reckg(self): + # config_dict = { + # 'model': 'GRU4RecKG', + # } + # quick_test(config_dict) + # def test_s3rec(self): + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'pretrain', + # 'save_step': 1, + # } + # quick_test(config_dict) + # + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'finetune', + # 'pre_model_path': './saved/S3Rec-test-1.pth', + # } + # quick_test(config_dict) -class TestKnowledgeRecommender2(unittest.TestCase): - def test_cfkg(self): - config_dict = { - 'model': 'CFKG', - 'loss_function': 'transe', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) +class TestKnowledgeRecommender(unittest.TestCase): - def test_ktup(self): + def test_cke(self): config_dict = { - 'model': 'KTUP', - 'use_st_gumbel': False, - 'L1_flag': True, + 'model': 'CKE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgat(self): - config_dict = { - 'model': 'KGAT', - 'aggregator_type': 'gcn', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_cfkg(self): config_dict = { - 'model': 'KGAT', - 'aggregator_type': 'graphsage', + 'model': 'CFKG', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_mkr(self): + def test_cfkg_with_transe(self): config_dict = { - 'model': 'MKR', - 'use_inner_product': False, + 'model': 'CFKG', + 'loss_function': 'transe', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgcn(self): - config_dict = { - 'model': 'KGCN', - 'aggregator': 'neighbor', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_ktup(self): config_dict = { - 'model': 'KGCN', - 'aggregator': 'concat', + 'model': 'KTUP', + 'train_rec_step': 1, + 'train_kg_step': 1, + 'epochs': 2, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgnnls(self): + def test_ktup_with_L1_flag(self): config_dict = { - 'model': 'KGNNLS', - 'aggregator': 'neighbor', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'KGNNLS', - 'aggregator': 'concat', + 'model': 'KTUP', + 'use_st_gumbel': False, + 'L1_flag': True, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - -class TestContextRecommender2(unittest.TestCase): - - def test_xdeepfm(self): + def test_kgat(self): config_dict = { - 'model': 'xDeepFM', - 'direct': True, + 'model': 'KGAT', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_pnn(self): + def test_kgat_with_gcn(self): config_dict = { - 'model': 'PNN', - 'use_inner': True, - 'use_outer': True, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'PNN', - 'use_inner': False, - 'use_outer': False, + 'model': 'KGAT', + 'aggregator_type': 'gcn', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - + quick_test(config_dict) -class TestSequentialRecommender2(unittest.TestCase): - - def test_gru4rec(self): + def test_kgat_with_graphsage(self): config_dict = { - 'model': 'GRU4Rec', - 'loss_type': 'BPR', + 'model': 'KGAT', + 'aggregator_type': 'graphsage', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_narm(self): + def test_ripplenet(self): config_dict = { - 'model': 'NARM', - 'loss_type': 'BPR', + 'model': 'RippleNet', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_stamp(self): + def test_mkr(self): config_dict = { - 'model': 'STAMP', - 'loss_type': 'BPR', + 'model': 'MKR', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_caser(self): + def test_mkr_without_use_inner_product(self): config_dict = { - 'model': 'Caser', - 'loss_type': 'BPR', - 'MAX_ITEM_LIST_LENGTH': 10, - 'reproducibility': False, + 'model': 'MKR', + 'use_inner_product': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_nextitnet(self): + def test_kgcn(self): config_dict = { - 'model': 'NextItNet', - 'loss_type': 'BPR', - 'reproducibility': False, + 'model': 'KGCN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_srgnn(self): + def test_kgcn_with_neighbor(self): config_dict = { - 'model': 'SRGNN', - 'loss_type': 'BPR', - 'MAX_ITEM_LIST_LENGTH': 3, + 'model': 'KGCN', + 'aggregator': 'neighbor', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_sasrec(self): - config_dict = { - 'model': 'SASRec', - 'loss_type': 'BPR', - 'hidden_act': 'relu' - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_kgcn_with_concat(self): config_dict = { - 'model': 'SASRec', - 'loss_type': 'BPR', - 'hidden_act': 'sigmoid' + 'model': 'KGCN', + 'aggregator': 'concat', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_bert4rec(self): - # config_dict = { - # 'model': 'BERT4Rec', - # 'loss_type': 'BPR', - # 'hidden_act': 'swish' - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) - - def test_gcsan(self): + def test_kgnnls(self): config_dict = { - 'model': 'GCSAN', - 'loss_type': 'BPR', - 'hidden_act': 'tanh', - 'MAX_ITEM_LIST_LENGTH': 3, + 'model': 'KGNNLS', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_gru4recf(self): + def test_kgnnls_with_neighbor(self): config_dict = { - 'model': 'GRU4RecF', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'GRU4RecF', - 'pooling_mode': 'sum', + 'model': 'KGNNLS', + 'aggregator': 'neighbor', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_sasrecf(self): + def test_kgnnls_with_concat(self): config_dict = { - 'model': 'SASRecF', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'SASRecF', - 'pooling_mode': 'sum', + 'model': 'KGNNLS', + 'aggregator': 'concat', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_fdsa(self): - config_dict = { - 'model': 'FDSA', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'FDSA', - 'pooling_mode': 'sum', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) if __name__ == '__main__': unittest.main() diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py index b3ce5a2bf..6f9f2a0f9 100644 --- a/tests/model/test_model_manual.py +++ b/tests/model/test_model_manual.py @@ -13,46 +13,17 @@ config_file_list = [os.path.join(current_path, 'test_model.yaml')] -class TestContextRecommender(unittest.TestCase): - # todo: more complex context information should be test, such as criteo dataset - - def test_dcn(self): - config_dict = { - 'model': 'DCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - # def test_din(self): - # config_dict = { - # 'model': 'DIN', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) +def quick_test(config_dict): + objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_fdsa(self): - config_dict = { - 'model': 'FDSA', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - # def test_gru4reckg(self): # config_dict = { # 'model': 'GRU4RecKG', # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) def test_s3rec(self): config_dict = { @@ -60,29 +31,15 @@ def test_s3rec(self): 'train_stage': 'pretrain', 'save_step': 1, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) config_dict = { 'model': 'S3Rec', 'train_stage': 'finetune', 'pre_model_path': './saved/S3Rec-test-1.pth', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - + quick_test(config_dict) -class TestSequentialRecommender2(unittest.TestCase): - - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - 'loss_type': 'BPR', - 'hidden_act': 'swish' - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - if __name__ == '__main__': unittest.main() diff --git a/tests/test_data/test/test.inter b/tests/test_data/test/test.inter index d46a0f745..278c83243 100644 --- a/tests/test_data/test/test.inter +++ b/tests/test_data/test/test.inter @@ -1,6018 +1,6018 @@ -user_id:token item_id:token rating:float timestamp:float label:float -6 86 3 883603013 1 -38 95 5 892430094 0 -97 194 3 884238860 0 -7 32 4 891350932 1 -10 16 4 877888877 0 -99 4 5 886519097 0 -25 181 5 885853415 1 -59 196 5 888205088 0 -115 20 3 881171009 0 -138 26 5 879024232 1 -194 165 4 879546723 0 -11 111 4 891903862 0 -162 25 4 877635573 1 -135 23 4 879857765 1 -160 174 5 876860807 0 -42 96 5 881107178 1 -168 151 5 884288058 0 -58 144 4 884304936 0 -62 21 3 879373460 1 -44 195 5 878347874 1 -72 195 5 880037702 0 -82 135 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889739243 +43 123 1 875975520 +25 176 4 885852862 +98 194 5 880498898 +10 198 3 877889005 +5 174 5 875636130 +102 7 2 888801407 +102 172 3 888801232 +130 93 5 874953665 130 121 5 876250746 1 \ No newline at end of file diff --git a/time_test_result/General_recommendation.md b/time_test_result/General_recommendation.md new file mode 100644 index 000000000..7e6c40b75 --- /dev/null +++ b/time_test_result/General_recommendation.md @@ -0,0 +1,71 @@ +## Training and testing time of general recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | +| ------- | ------- | ------ | ------------ | -------- | +| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | +| Netflix | 80,476 | 16,821 | 1,977,844 | 0.9985 | +| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (s) | Evaluate Time (s) | Memory (MB) | +| ---------- | ----------------- | ----------------- | ----------- | +| Popularity | 2.11 | 8.08 | 843 | +| ItemKNN | 2 | 11.76 | 843 | +| BPRMF | 1.93 | 7.43 | 931 | +| NeuMF | 4.94 | 13.12 | 965 | +| DMF | 4.47 | 12.63 | 1555 | +| NAIS | 59.27 | 24.41 | 22351 | +| NGCF | 12.09 | 7.12 | 1231 | +| GCMC | 9.04 | 54.15 | 1353 | +| LightGCN | 7.83 | 7.47 | 1177 | +| DGCF | 181.66 | 8.06 | 6745 | +| ConvNCF | 8.46 | 19.6 | 1341 | +| FISM | 19.3 | 10.92 | 7109 | +| SpectralCF | 13.87 | 6.97 | 1219 | + +#### Config file of ml-1m: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +TIME_FIELD: timestamp +LABEL_FIELD: label +NEG_PREFIX: neg_ +load_col: + inter: [user_id, item_id, rating, timestamp] +min_user_inter_num: 0 +min_item_inter_num: 0 + + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + + + +### Time and memory cost on Netflix dataset: + + + +### Time and memory cost on Yelp dataset: + + + + + + + + +