qsi-tk

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

Installation

pip install qsi-tk

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, KDE, normalizing flow models, energy-based models, and score-based models. "
	qsi.io.pre	Data processing, e.g., channel alignment and 1D-laplacian kernel fs for e-nose data; x-binning, baseline removal for TOF MS.		TODO
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 Compressed Sensing library for spectroscopic profiling data [J]. Software Impacts, 2023, doi: 10.1016/j.simpa.2023.100492 Secured telemetry based on time-variant sensing matrix – An empirical study of spectroscopic profiling, Smart Agricultural Technology, Volume 5, 2023, doi: 10.1016/j.atech.2023.100268
qsi.fs
	qsi.fs.nch_time_series_fs	multi-channel enose data fs with 1d-laplacian conv kernel		基于电子鼻和一维拉普拉斯卷积核的奶粉基粉产地鉴别
	qsi.fs.glasso	Structured-fs of Raman data with group lasso		in progress
qsi.kernel		kernels	ackl	Analytical chemistry kernel library for spectroscopic profiling data, Food Chemistry Advances, Volume 3, 2023, 100342, ISSN 2772-753X, https://doi.org/10.1016/j.focha.2023.100342.
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	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
qsi.pipeline		General data analysis pipelines.
qsi.gui		Web-based apps. e.g., `python -m qsi.gui.chaihu` will launch the app for bupleurum origin discrimination.

Release files for qsi-tk 1.4.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for qsi-tk 1.4.1
File	Interpreter	ABI	Platform	Reset
qsi_tk-1.4.1-py3-none-any.whl 91.5 MB Aug 12, 2024	Python 3	none	any	Details

Release files / qsi_tk-1.4.1-py3-none-any.whl

Download URL	qsi_tk-1.4.1-py3-none-any.whl
Size	91.5 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`c710fdb6f1a75158eed89743b13cd37ba6a66d482d8e1c58b709c0c2195067e8`
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Uploaded using Trusted Publishing? What is trusted publishing?	No
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