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merged 2 commits into from
Jan 11, 2022
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README update #118

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42b6c8a
Add checkpoint links to readme, update code example
SpirinEgor Jan 11, 2022
62c95d0
Add info about dataset miners
SpirinEgor Jan 11, 2022
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40 changes: 30 additions & 10 deletions README.md
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Expand Up @@ -14,9 +14,31 @@ You can easily install model through the PIP:
pip install code2seq
```

## Usage
## Dataset mining

Minimal code example to run the model:
To prepare your own dataset with a storage format supported by this implementation, use on the following:
1. Original dataset preprocessing from vanilla repository
2. [`astminer`](https://github.com/JetBrains-Research/astminer):
the tool for mining path-based representation and more with multiple language support.
3. [`PSIMiner`](https://github.com/JetBrains-Research/psiminer):
the tool for extracting PSI trees from IntelliJ Platform and creating datasets from them.
## Available checkpoints

### Method name prediction
| Dataset (with link) | Checkpoint | # epochs | F1-score | Precision | Recall | ChrF |
|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|----------|----------|-----------|--------|-------|
| [Java-small](https://s3.eu-west-1.amazonaws.com/datasets.ml.labs.aws.intellij.net/java-paths-methods/java-small.tar.gz) | [link](https://s3.eu-west-1.amazonaws.com/datasets.ml.labs.aws.intellij.net/checkpoints/code2seq_java_small.ckpt) | 11 | 41.49 | 54.26 | 33.59 | 30.21 |
| [Java-med](https://s3.eu-west-1.amazonaws.com/datasets.ml.labs.aws.intellij.net/java-paths-methods/java-med.tar.gz) | [link](https://s3.eu-west-1.amazonaws.com/datasets.ml.labs.aws.intellij.net/checkpoints/code2seq_java_med.ckpt) | 10 | 48.17 | 58.87 | 40.76 | 42.32 |

## Configuration

The model is fully configurable by standalone YAML file.
Navigate to [config](config) directory to see examples of configs.

## Examples

Model training may be done via PyTorch Lightning trainer.
See it [documentation](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) for more information.

```python
from argparse import ArgumentParser
Expand All @@ -29,20 +51,21 @@ from code2seq.model import Code2Seq


def train(config: DictConfig):
# Load data module
# Define data module
data_module = PathContextDataModule(config.data_folder, config.data)
data_module.prepare_data()
data_module.setup()

# Load model
# Define model
model = Code2Seq(
config.model,
config.optimizer,
data_module.vocabulary,
config.train.teacher_forcing
)

trainer = Trainer(max_epochs=config.hyper_parameters.n_epochs)
# Define hyper parameters
trainer = Trainer(max_epochs=config.train.n_epochs)

# Train model
trainer.fit(model, datamodule=data_module)


Expand All @@ -54,6 +77,3 @@ if __name__ == "__main__":
__config = OmegaConf.load(__args.config)
train(__config)
```

Navigate to [config](config) directory to see examples of configs.
If you have any questions, then feel free to open the issue.