Szymon Maksymiuk, Alicja Gosiewska, Przemysław Biecek · 2019
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <<a href="https://doi.org/10.48550/arXiv.1705.07874" target="_top">doi:10.48550/arXiv.1705.07874</a>> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Cited by 4
Daniel Rohe · 2023
SAND2023-11957O SDynPy software can be used in digital signal processing, modal analysis, and geometry algorithms that are available in open literature, specifically the Synthesize Modes and Correlate and polynomial-based multiple reference modal fitters. This software can be used to perform structural dynamic testing and analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Cited by 2Open access
Liu, Yang, Zhang, Tianyi · Zenodo (CERN European Organization for Nuclear Research) · 2021
Neural network for Emotion detection, in short as NEmo, is a python library designed to implement Bayesian deep learning algorisms to emotion database for human emotion detection. It offers an easy construction of two types of deep neural network to incorporate the eletronic signal from sensor and predict human emotion, like valance and arousal.
Cited by 0Open access
Garg, Rishabh · Figshare · 2025
The present method consists of using impedance.py python library for fitting the circuit directly to the lab data. Accuracy metrics are yet to be improvised by adjusting the circuit model.<br>
Cited by 0Open access
Armel Soubeiga, Violaine Antoine · ArXiv.org · 2025
A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions.
Cited by 0Open access
Little, Christopher C. · Figshare · 2015
Abydos is a Python library of phonetic algorithms, string distance measures & metrics, stemmers, and string fingerprinters such as Soundex, (Double) Metaphone, NYSIIS, Alpha-SIS, Beider-Morse Phonetic Matching, Levenshtein(-Damerau) distance, Jaro(-Winkler) distance, Jaccard similarity, and Pollock & Zomora's skeleton & omission keys.
Cited by 0Open access
Karev, Evgeny, Camilleri, Pierre, Baptista, Vitor et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
Cited by 1Open access
Karev, Evgeny, Camilleri, Pierre, Baptista, Vitor et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
Cited by 0Open access
Darío Graña, Leandro de Figueiredo · Geophysics · 2021
ABSTRACT Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock-physics modeling with mathematical inverse theory to predict reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We have developed a Python library called SeReMpy with state-of-the-art of seismic reservoir modeling for reservoir properties characterization using seismic and rock-physics models and Bayesian inverse theory. The most innovative component of the library is the Bayesian seismic and rock-physics inversion to predict the spatial distribution of petrophysical and elastic properties from seismic data. The inversion algorithms include Bayesian analytical solutions of the linear-Gaussian inverse problem and Markov chain Monte Carlo (MCMC) numerical methods for nonlinear problems. The library includes four modules: geostatistics, rock physics, facies, and inversion, as well as several scripts with illustrative examples and applications. We illustrate the use of the functions of the module and develop codes for practical inversion problems using synthetic and real data. The applications include a rock-physics model for the prediction of elastic properties and facies using well-log data, a geostatistical simulation of continuous and discrete properties using well logs, a geostatistical interpolation and simulation of 2D maps of temperature, an elastic inversion of partial stacked seismograms with Bayesian linearized amplitude-variation-with-offset inversion, a rock-physics inversion of partial stacked seismograms with MCMC methods, and a 2D seismic inversion.
Cited by 6
Chwilczyński, Sebastian, Trębacz, Kacper, Cyganik, Karol et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024
This Zenodo entry contains the code of the official release of ART, a Python library that teaches good practices when training deep neural networks with PyTorch. ART encourages the user to train DNNs through a series of steps that ensure the correctness and robustness of their training pipeline. The steps implemented using ART can be viewed not only as guidance for early adepts of deep learning but also as a project template and checklist for more advanced users.
Cited by 0Open access
Mierzwinski, W. Gregory · Zenodo (CERN European Organization for Nuclear Research) · 2019
This is a python library for performing trial extraction on data obtained from the Pupil Labs [1] eye trackers in combination with Lab Streaming Layer (LSL) [2] for high-precision event-based research experiments. The extracted trials have millisecond precision as the errors in synchronization between markers and data are based entirely on LSL's precision. See https://github.com/gmierz/pupil-lib-python for more info on usage and development. See this page for the Matlab version: https://github.com/gmierz/pupil-lib-python v1.0.0: Contains various bug fixes, stronger CSV exporting, and a better command-line tool. It also simplifies processing trials with the pre/post-processor functions. [1]: https://pupil-labs.com/ [2]: https://github.com/sccn/labstreaminglayer
Cited by 0Open access
Xiaojing Wang, Jingang Miao, Yunting Sun · arXiv (Cornell University) · 2019
Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to unrepresentative samples. In causal studies with observational data, the treated versus untreated group assignment is often correlated with covariates, i.e., not random. Empirical calibration is a generic weighting method that presents a unified view on correcting or reducing the data biases for the tasks mentioned above. We provide a Python library EC to compute the empirical calibration weights. The problem is formulated as convex optimization and solved efficiently in the dual form. Compared to existing software, EC is both more efficient and robust. EC also accommodates different optimization objectives, supports weight clipping, and allows inexact calibration, which improves usability. We demonstrate its usage across various experiments with both simulated and real-world data.
Cited by 0Open access
Fosso, Olav Bjarte · 2020
Cited by 0Open access
Liu, Yang, Zhang, Tianyi · Figshare · 2021
Neural network for Emotion detection, in short as NEmo, is a python library designed to implement Bayesian deep learning algorisms to emotion database for human emotion detection. It offers an easy construction of two types of deep neural network to incorporate the eletronic signal from sensor and predict human emotion, like valance and arousal.
Cited by 0Open access
Smith Clark, Alexander, Johnson, Eric T., Chen, Zhi et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
This submission contains all the supplemental Jupyter notebooks that generates the figures of the journal article entitled: A Python Library for Nuclear Astrophysics.
Cited by 0Open access
Smith Clark, Alexander, Johnson, Eric T., Chen, Zhi et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
This submission contains all the supplemental Jupyter notebooks that generates the figures of the journal article entitled: A Python Library for Nuclear Astrophysics.
Cited by 0Open access
Vallat, Raphael, Mallett, Remington · Zenodo (CERN European Organization for Nuclear Research) · 2024
YASA (Yet Another Spindle Algorithm) is a Python package to analyze polysomnographic sleep recordings. This Zenodo repository holds data files used in YASA tests, examples, and tutorials from the online documentation and GitHub repository.
Cited by 0Open access
Mierzwinski, W. Gregory · Figshare · 2019
This is a python library for performing trial extraction on data obtained from the Pupil Labs [1] eye trackers in combination with Lab Streaming Layer (LSL) [2] for high-precision event-based research experiments. The extracted trials have millisecond precision as the errors in synchronization between markers and data are based entirely on LSL's precision. See https://github.com/gmierz/pupil-lib-python for more info on usage and development. See this page for the Matlab version (no longer updated): https://github.com/gmierz/pupil-lib-python v1.0.0: Contains various bug fixes, stronger CSV exporting, and a better command-line tool. It also simplifies processing trials with the pre/post-processor functions. v1.1.0: See release information here, main update is the new MATLAB output support: https://github.com/gmierz/pupil-lib/releases/tag/v1.1.0 [1]: https://pupil-labs.com/ [2]: https://github.com/sccn/labstreaminglayer
Cited by 0Open access
Martínez-López Javier · Zenodo (CERN European Organization for Nuclear Research) · 2014
eHabitat Python and GRASS GIS library
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Little, Christopher C. · Zenodo (CERN European Organization for Nuclear Research) · 2018
Abydos is a Python library of phonetic algorithms, string distance measures & metrics, stemmers, and string fingerprinters such as Soundex, (Double) Metaphone, NYSIIS, Alpha-SIS, Beider-Morse Phonetic Matching, Levenshtein(-Damerau) distance, Jaro(-Winkler) distance, Jaccard similarity, and Pollock & Zomora's skeleton & omission keys.
Cited by 0Open access