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

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License: LICENSE.txt

Author: Yinsheng Zhang (Ph.D.)

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 sub-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 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
qsi.cla.kernel kernel-NNRW
qsi.cla.nnrw neural networks with random weights

Project details

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View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

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License: LICENSE.txt

Author: Yinsheng Zhang (Ph.D.)


Release history Release notifications | RSS feed

1.3.0

1.2.0

1.1.1

1.1.0

1.0.9

1.0.8

1.0.6

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

0.6.0

0.5.4

0.5.3

0.5.2

0.5.0

0.4.8

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0.4.1

0.4.0

0.3.9

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0.3.3

0.3.2

0.3.1

0.3.0

0.2.9

0.2.8

0.2.7

0.2.6

0.2.5

0.2.4

0.2.3

0.2.2

0.2.1

0.2.0

0.1.9

0.1.8

This version

0.1.7

0.1.6

0.1.5

0.1.4

0.1.3

0.1.2

0.1.1

0.1.0

0.0.9

0.0.7

0.0.6

0.0.5

0.0.4

0.0.3

0.0.2

0.0.1

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