Quick Start¶

Here is a quick-start example for using RecBole. We will show you how to train and test BPR model 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
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
load_col:
    inter: [user_id, item_id]

For more details of data config, please refer to Data settings.

2. Choose a model:¶

In RecBole, we implement 73 recommendation models covering general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. You can choose a model from our Model Introduction. Here we choose BPR model to train and test.

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
neg_sampling:
    uniform: 1
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')

Then run the following command:

python run.py --config_files=test.yaml

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', 'rating', 'timestamp']
24 Aug 01:46    INFO  [Training]: train_batch_size = [2048] 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
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
neg_sampling:
    uniform: 1
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=0.0001

In-depth Usage¶

For a more in-depth usage about RecBole, take a look at