Quick Start: General Recommendation¶
For general recommendation, we choose BPR model to show you how to train and test it on the ml-100k dataset from both API and source code.
Quick-start From API¶
1. Prepare your data:¶
Before running a model, firstly you need to prepare and load data. To help users quickly get start, RecBole has a build-in dataset ml-100k and you can directly use it. However, if you want to use other datasets, you can read Running New Dataset for more information.
Then, you need to set data config for data loading. You can create a yaml file called test.yaml and write the following settings:
# dataset config : General Recommendation
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
load_col:
inter: [user_id, item_id]
General recommendation models utilize the historical interactions between users and items to make recommendations, so it needs to specify and load the user and item columns of the dataset.
2. Choose a model:¶
You can choose a model from our Model Introduction. Here we choose BPR model to demonstrate how to train and test the knowledge-based Recommendation,model.
Then, you need to set the parameter for BPR model. You can check the BPR and add the model settings into the test.yaml, like:
# model config
embedding_size: 64
If you want to run different models, you can read Running Different Models for more information.
3. Set training and evaluation config:¶
In RecBole, we support multiple training and evaluation methods. You can choose how to train and test model by simply setting the config.
Here we want to train and test the BPR model in training-validation-test method (optimize model parameters on the training set, do parameter selection according to the results on the validation set, and finally report the results on the test set) and evaluate the model performance by full ranking with all item candidates, so we can add the following settings into the test.yaml.
# Training and evaluation config
epochs: 500
train_batch_size: 4096
eval_batch_size: 4096
train_neg_sample_args:
distribution: uniform
sample_num: 1
alpha: 1.0
dynamic: False
candidate_num: 0
eval_args:
group_by: user
order: RO
split: {'RS': [0.8,0.1,0.1]}
mode: full
metrics: ['Recall', 'MRR', 'NDCG', 'Hit', 'Precision']
topk: 10
valid_metric: MRR@10
metric_decimal_place: 4
For more details of training and evaluation config, please refer to Training Settings and Evaluation Settings.
4. Run the model and collect the result¶
Now you have finished all the preparations, it’s time to run the model!
You can create a new python file (e.g., run.py), and write the following code:
from recbole.quick_start import run_recbole
run_recbole(model='BPR', dataset='ml-100k', config_file_list=['test.yaml'])
Then run the following command:
python run.py
And you will obtain the output like:
24 Aug 01:46 INFO ml-100k
The number of users: 944
Average actions of users: 106.04453870625663
The number of items: 1683
Average actions of items: 59.45303210463734
The number of inters: 100000
The sparsity of the dataset: 93.70575143257098%
Remain Fields: ['user_id', 'item_id']
24 Aug 01:46 INFO [Training]: train_batch_size = [4096] negative sampling: [{'uniform': 1}]
24 Aug 01:46 INFO [Evaluation]: eval_batch_size = [4096] eval_args: [{'split': {'RS': [0.8, 0.1, 0.1]}, 'group_by': 'user', 'order': 'RO', 'mode': 'full'}]
24 Aug 01:46 INFO BPR(
(user_embedding): Embedding(944, 64)
(item_embedding): Embedding(1683, 64)
(loss): BPRLoss()
)
Trainable parameters: 168128
Train 0: 100%|████████████████████████| 40/40 [00:00<00:00, 200.47it/s, GPU RAM: 0.01 G/11.91 G]
24 Aug 01:46 INFO epoch 0 training [time: 0.21s, train loss: 27.7228]
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 518.65it/s, GPU RAM: 0.01 G/11.91 G]
24 Aug 01:46 INFO epoch 0 evaluating [time: 0.92s, valid_score: 0.020500]
......
Train 96: 100%|████████████████████████| 40/40 [00:00<00:00, 229.26it/s, GPU RAM: 0.01 G/11.91 G]
24 Aug 01:47 INFO epoch 96 training [time: 0.18s, train loss: 3.7170]
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 857.00it/s, GPU RAM: 0.01 G/11.91 G]
24 Aug 01:47 INFO epoch 96 evaluating [time: 0.56s, valid_score: 0.375200]
24 Aug 01:47 INFO valid result:
recall@10 : 0.2162 mrr@10 : 0.3752 ndcg@10 : 0.2284 hit@10 : 0.7508 precision@10 : 0.1602
24 Aug 01:47 INFO Finished training, best eval result in epoch 85
24 Aug 01:47 INFO Loading model structure and parameters from saved/BPR-Aug-24-2021_01-46-43.pth
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 866.53it/s, GPU RAM: 0.01 G/11.91 G]
24 Aug 01:47 INFO best valid : {'recall@10': 0.2195, 'mrr@10': 0.3871, 'ndcg@10': 0.2344, 'hit@10': 0.7582, 'precision@10': 0.1627}
24 Aug 01:47 INFO test result: {'recall@10': 0.2523, 'mrr@10': 0.4855, 'ndcg@10': 0.292, 'hit@10': 0.7953, 'precision@10': 0.1962}
Finally you will get the model’s performance on the test set and the model file will be saved under the /saved. Besides, RecBole allows tracking and visualizing train loss and valid score with TensorBoard, please read the Use Tensorboard for more details.
The above is the whole process of running a model in RecBole, and you can read other docs for depth usage.
Quick-start From Source¶
Besides using API, you can also directly run the source code of RecBole. The whole process is similar to Quick-start From API. You can create a yaml file called test.yaml and set all the config as follow:
# dataset config : General Recommendation
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
load_col:
inter: [user_id, item_id]
# model config
embedding_size: 64
# Training and evaluation config
epochs: 500
train_batch_size: 4096
eval_batch_size: 4096
train_neg_sample_args:
distribution: uniform
sample_num: 1
alpha: 1.0
dynamic: False
candidate_num: 0
eval_args:
group_by: user
order: RO
split: {'RS': [0.8,0.1,0.1]}
mode: full
metrics: ['Recall', 'MRR', 'NDCG', 'Hit', 'Precision']
topk: 10
valid_metric: MRR@10
metric_decimal_place: 4
Then run the following command:
python run_recbole.py --model=BPR --dataset=ml-100k --config_files=test.yaml
And you will get the output of running the BPR model on the ml-100k dataset.
If you want to change the parameters, such as embedding_size
,
just set the additional command parameters as you need:
python run_recbole.py --model=BPR --dataset=ml-100k --config_files=test.yaml --embedding_size=100