torch-pca 0.0.5

PyTorch implementation of PCA (similar to sklearn PCA).

Pytorch Principal Component Analysis (PCA)

Principal Component Anlaysis (PCA) in PyTorch. The intention is to provide a simple and easy to use implementation of PCA in PyTorch, the most similar to the sklearn's PCA as possible (in terms of API and, of course, output).

Links

Github repository: https://github.com/valentingol/torch_pca

Pypi project: https://pypi.org/project/torch_pca/

Documentation: https://torch-pca.readthedocs.io/en/latest/

Installation

Simply install it with pip:

pip install torch-pca

How to use

Exactly like sklearn.decomposition.PCA but it uses PyTorch tensors as input and output!

from torch_cpa import PCA

# Create like sklearn.decomposition.PCA, e.g.:
pca_model = PCA(n_components=None, svd_solver='full')

# Use like sklearn.decomposition.PCA, e.g.:
>>> new_train_data = pca_model.fit_transform(train_data)
>>> new_test_data = pca_model.transform(test_data)
>>> print(pca.explained_variance_ratio_)
[0.756, 0.142, 0.062, ...]

More details and features in the API documentation.

Implemented features

• fit, transform, fit_transform methods.
• All attributes from sklean's PCA are available: explained_variance_(ratio_), singular_values_, components_, mean_, noise_variance_, ...
• Full SVD solver
• SVD by covariance matrix solver
• (absent from sklearn) Decide how to center the input data in transform method (default is like sklearn's PCA)
• Find number of components with explained variance proportion
• Automatically find number of components with MLE
• inverse_transform method

To be implemented

• Whitening option
• Randomized SVD solver
• ARPACK solver
• get_covariance method
• get_precision method and score method
• Support sparse matrices

Contributing

Feel free to contribute to this project! Just fork it and make an issue or a pull request.

See the CONTRIBUTING.md file for more information.