Data science toolkit (TK) from Quality-Safety research Institute (QSI).
Project description
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 | description | standalone pypi package | publication | |
qsi.io | File I/O, Dataset loading | |||
qsi.vis | Plotting | |||
qsi.cs | compressed sensing | cs1 | Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks [J]. Talanta, SCI, IF 6.057. JCR Q1, 2020, doi: 10.1016/j.talanta.2019.120681 | |
qsi.fs | feature selection | |||
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, https://doi.org/10.1016/j.heliyon.2021.e06199. |
qsi.dr.mf | matrix-factorization based DR | pyMFDR | ||
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, SCI, IF 5.086. JCR Q1, 2021, doi: 10.1007/s10489-021-02810-8 pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, SCI, IF 1.959, 2022, doi: 10.1016/j.softx.2022.101007 |
qsi.cla.ensemble | homo-stacking, hetero-stacking, FSSE | |||
qsi.cla.nnrw | neural networks with random weights | pyNNRW | Spectroscopic Profiling-based Geographic Herb Identification by Neural Network with Random Weights [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, SCI, IF 4.098. JCR Q1, 2022, doi: 10.1016/j.saa.2022.121348 | |
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