ml4xcube: Machine Learning Toolkits for Data Cubes

Welcome to ml4xcube, a comprehensive Python-based toolkit designed for researchers and developers in the field of machine learning with an emphasis on xarray data cubes. Our toolkit is engineered to provide specialized and robust support for data cube management and analysis, operating with the state-of-the-art machine learning libraries (1) scikit-learn, (2) PyTorch and (3) TensorFlow.

Installation

Get started with ml4xcube effortlessly by installing it directly through pip:

pip install ml4xcube

or Conda:

conda install -c conda-forge ml4xcube

Make sure you have Python version 3.8 or higher.

If you're planning to use ml4xcube with TensorFlow or PyTorch, set up these frameworks properly in your Conda environment.

Features

• Data preprocessing and normalization/standardization functions
• Gap filling features
• Dataset creation and train-/ test split sampling techniques
• Trainer classes for sklearn, TensorFlow and PyTorch
• Distributed training framework compatible with PyTorch
• chunk utilities for working with data cubes

Usage

To use ml4xcube in your project, simply import the necessary module:

from ml4xcube.preprocessing import normalize, standardize
from ml4xcube.training.pytorch import Trainer
# Other imports...

You can then call the functions directly:

# Normalizing data
normalized_data = normalize(your_data, data_min, data_max)

# Trainer instance
trainer = Trainer(
    model           = reg_model,
    train_data      = train_loader,
    test_data       = test_loader,
    optimizer       = optimizer,
    best_model_path = best_model_path,
    early_stopping  = True,
    patience        = 3,
    epochs          = epochs
)

# Start training
reg_model = trainer.train()

License

ml4xcube is released under the MIT License. See the LICENSE file for more details.