| Name | ml4xcube JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | ML package for data cubes |
| upload_time | 2024-09-06 16:42:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2022 by ScaDS.AI, the xcube development team and contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
machine learning
tools
data cube utilities
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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:
```bash
pip install ml4xcube
```
or Conda:
```bash
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:
```python
from ml4xcube.preprocessing import normalize, standardize
from ml4xcube.training.pytorch import Trainer
# Other imports...
```
You can then call the functions directly:
```python
# 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](https://github.com/deepesdl/ML-Toolkits/blob/master/LICENSE) file for more details.
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"description": "# ml4xcube: Machine Learning Toolkits for Data Cubes\n\nWelcome 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`. \n\n## Installation\n\nGet started with `ml4xcube` effortlessly by installing it directly through pip:\n```bash\npip install ml4xcube\n```\nor Conda:\n```bash\nconda install -c conda-forge ml4xcube\n```\n\nMake sure you have Python version 3.8 or higher.\n\nIf you're planning to use `ml4xcube` with TensorFlow or PyTorch, set up these frameworks properly in your Conda environment. \n\n## Features\n\n- Data preprocessing and normalization/standardization functions\n- Gap filling features\n- Dataset creation and train-/ test split sampling techniques\n- Trainer classes for `sklearn`, `TensorFlow` and `PyTorch`\n- Distributed training framework compatible with `PyTorch`\n- chunk utilities for working with data cubes\n\n## Usage\n\nTo use `ml4xcube` in your project, simply import the necessary module:\n\n```python\nfrom ml4xcube.preprocessing import normalize, standardize\nfrom ml4xcube.training.pytorch import Trainer\n# Other imports...\n```\n\nYou can then call the functions directly:\n\n```python\n# Normalizing data\nnormalized_data = normalize(your_data, data_min, data_max)\n\n# Trainer instance\ntrainer = Trainer(\n model = reg_model,\n train_data = train_loader,\n test_data = test_loader,\n optimizer = optimizer,\n best_model_path = best_model_path,\n early_stopping = True,\n patience = 3,\n epochs = epochs\n)\n\n# Start training\nreg_model = trainer.train()\n```\n\n## License\n\n`ml4xcube` is released under the MIT License. See the [LICENSE](https://github.com/deepesdl/ML-Toolkits/blob/master/LICENSE) file for more details.\n",
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