decompy is a Python package containing several robust algorithms for matrix decomposition and analysis. The types of algorithms includes
- Robust PCA or SVD based methods
- Matrix completion methods
- Robust matrix or tensor factorization methods.
- Data decomposition using various methods
- Support for sparse decomposition, low-rank approximation, and more
- User-friendly API for easy integration into your projects
- Extensive documentation and examples
You can install decompy using pip:
pip install decompyHere's a simple example demonstrating how to use decompy for data decomposition:
import numpy as np
from decompy.robust_svd import DensityPowerDivergence
# Load your data
data = np.arange(100).reshape(20,5).astype(np.float64)
# Perform data decomposition
algo = DensityPowerDivergence(alpha = 0.5)
result = algo.decompose(data)
# Access the decomposed components
U, V = result.singular_vectors(type = "both")
S = result.singular_values()
low_rank_component = U @ S @ V.T
sparse_component = data - low_rank_component
print(low_rank_component)
print(sparse_component)While the singular values are about 573 and 7.11 for this case (check the S variable), it can get highly affected if you use the simple SVD and change a single entry of the data matrix.
s2 = np.linalg.svd(data, compute_uv = False)
print(np.round(s2, 2)) # estimated by usual SVD
print(np.diag(np.round(S, 2))) # estimated by robust SVD
data[1, 1] = 10000 # just change a single entry
s3 = np.linalg.svd(data, compute_uv = False)
print(np.round(s3, 2)) # usual SVD shoots up
s4 = algo.decompose(data).singular_values()
print(np.diag(np.round(s4, 2)))
You can find more example notebooks in examples folder. For more detailed usage instructions, please refer to the documentation.
Contributions are welcome! If you find any issues or have suggestions for improvements, please create an issue or submit a pull request on the GitHub repository. For contributing developers, please refer to Contributing.md file.
This project is licensed under the BSD 3-Clause License.