Learning Benchmark Workshop (lbw)

Welcome to the Learning Benchmark Workshop (lbw) repository! This project aims to provide a unified data interface for machine learning (ML) and deep learning (DL) models.

Features

• Unified Data Interface: Simplify data preprocessing and handling with a consistent and modular interface.

Installation

You can install the lbw package via pip:

pip install git+https://github.com/Saltsmart/lbw

Getting Started

Here is a quick example to help you get started:

from lbw.samples import TimeSeries
from lbw.datasets import TimeSeriesDataset

# Load your dataset
dataset = TimeSeriesDataset()
df_lst = [
    pd.DataFrame(
        {
            "time": pd.date_range(start=f"2023-0{i}-01", periods=10, freq="D"),
            "value": np.random.randn(10)
        }
    ) for i in range(4)
]
covar_lst = [i for i in range(4)]


for df, covariate in zip(df_lst, covar_lst):
    series = TimeSeries(
        df,
        {
            "covariate": covariate
        },
    )
    dataset.append(series)

Contributing

We welcome contributions from the community! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with a clear description of your changes.

License

This project is licensed under the BSD License. See the LICENSE file for details.

Contact

For questions or feedback, please reach out to us via the issue tracker.