pyMFDR
publication: Matrix Factorization Based Dimensionality Reduction Algorithms - A Comparative Study on Spectroscopic Profiling Data. Analytical Chemistry, 2022, DOI: 10.1021/acs.analchem.2c01922
This is a python package for MF (matrix factorization) based DR (dimensionality reduction) algorithms.
Content of repo ├── src : source code ├── data : contains the dataset (.csv) used for the study └── notebooks : contains the jupyter notebook for the study
Installation
pip install pyMFDR
How to use
Download the sample dataset from the /data folder Use the following sample code to use the package:
# import the library
from pyMFDR import mfdr
# load the dataset or generate a toy dataset by X,y = mvg(md = 2)
df = pd.read_csv('7047_C02.csv')
X = df.iloc[:,2:cols-1].values # -1 for removing the last column that contains NAN
y = df.iloc[:,1].values.ravel() # first col is index and not used in this study
# get a list of available MFDR algorithms
mfdr.get_algorithms() # it will ouptut ['PCA', 'NMF', 'LAE', 'RP', 'SRP', 'VQ', 'AA', 'ICA']
# Run PCA on X. It will return W, H, Xr and the inner algorithm object.
W,H,Xr,o = mfdr.mf(X, 3, alg = 'PCA', display = False)
# evaluate the dimensionality reduction quality by various metrics
mfdr.evaluate_dr(X,W,Xr)
# visualize H
mfdr.visualize_dictionary(H)
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