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 | 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.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. | |||
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