qsi-tk 0.2.0

Data science toolkit (TK) from Quality-Safety research Institute (QSI).

qsi-tk

Data science toolkit (TK) from Quality-Safety research Institute (QSI)

Installation

pip install qsi-tk

Contents

This package is a master library containing various previous packages published by our team.

module	sub-module	description	standalone pypi package	publication
qsi.io		File I/O, Dataset loading		TODO qsi-tk open datasets with algorithms
	qsi.io.aug	Data augmentation, e.g., generative models		TODO Data aug with deep generative models. e.g., " variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, energy-based models, and score-based models. "
qsi.vis		Plotting
qsi.cs		compressed sensing	cs1	Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks [J]. Talanta, 2020, doi: 10.1016/j.talanta.2019.120681 Task-adaptive eigenvector-based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor [J]. Analytical Science Advances. Chemistry Europe, 2021, doi: 10.1002/ansa.202100018
qsi.fs		feature selection
qsi.ks		kernels	ackl	TODO
qsi.dr	qsi.dr.metrics	Dimensionality Reduction (DR) quality metrics	pyDRMetrics, wDRMetrics	pyDRMetrics - A Python toolkit for dimensionality reduction quality assessment, Heliyon, Volume 7, Issue 2, 2021, e06199, ISSN 2405-8440, doi: 10.1016/j.heliyon.2021.e06199.
	qsi.dr.mf	matrix-factorization based DR	pyMFDR	Matrix Factorization Based Dimensionality Reduction Algorithms - A Comparative Study on Spectroscopic Profiling Data [J], Analytical Chemistry, 2022. doi: 10.1021/acs.analchem.2c01922
qsi.cla	qsi.cla.metrics	classifiability analysis	pyCLAMs, wCLAMs	A unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8 pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, 2022, doi: 10.1016/j.softx.2022.101007
	qsi.cla.ensemble	homo-stacking, hetero-stacking, FSSE	pyNNRW	Spectroscopic Profiling-based Geographic Herb Identification by Neural Network with Random Weights [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, doi: 10.1016/j.saa.2022.121348
	qsi.cla.kernel	kernel-NNRW
	qsi.cla.nnrw	neural networks with random weights