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SOFTWARE IMPACTS 2025年 23卷

作者： Wang, Ruijin Du, Yuchen Dai, Chunchun Deng, Yang Leng, Jiantao Chang, Tienchong Shanghai Univ Shanghai Inst Appl Math & Mech Shanghai Frontier Sci Ctr Mechanoinformat Sch Mech & Engn SciShanghai Key Lab Mech Energy E Shanghai 200072 Peoples R China Tongji Univ Shanghai Inst Aircraft Mech & Control Shanghai 200092 Peoples R China Joint Res Ctr Computat Mat Zhejiang Lab Hangzhou 311100 Peoples R China

Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.

关键词： Solution-guided machine learning (SGML) Data insufficiency Extrapolation python library

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ChemTS: an efficient python library for de novo molecular generation

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SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2017年第1期18卷 972-976页

作者： Yang, Xiufeng Zhang, Jinzhe Yoshizoe, Kazuki Terayama, Kei Tsuda, Koji Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba Japan INSA Lyon Dept Biosci Villeurbanne France RIKEN Ctr Adv Intelligence Project Tokyo Japan Natl Inst Mat Sci Tsukuba Ibaraki Japan

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://***/tsudalab/ChemTS. [GRAPHICS] .

关键词： Molecular design Monte Carlo tree search recurrent neural network python library

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Simpful: A User-Friendly python library for Fuzzy Logic

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 1687-1698页

作者： Spolaor, Simone Fuchs, Caro Cazzaniga, Paolo Kaymak, Uzay Besozzi, Daniela Nobile, Marco S. Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Eindhoven Univ Technol Sch Ind Engn Eindhoven Netherlands Univ Bergamo Dept Human & Social Sci Bergamo Italy Ctr Syst Biol SYSBIO ISBE IT Milan Italy Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： Decision support Fuzzy logic Fuzzy networks Modeling and control Open- source software python library

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PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

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JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 1-11页

作者： Dong, Jie Yao, Zhi-Jiang Zhang, Lin Luo, Feijun Lin, Qinlu Lu, Ai-Ping Chen, Alex F. Cao, Dong-Sheng Cent S Univ Xiangya Sch Pharmaceut Sci 172 Tongzipo Rd Changsha Hunan Peoples R China Cent South Univ Forestry & Technol Coll Food Sci & Engn Natl Engn Lab Deep Proc Rice & Byprod Changsha Hunan Peoples R China Hong Kong Baptist Univ Sch Chinese Med Inst Adv Translat Med Bone & Joint Dis Hong Kong Hong Kong Peoples R China Cent S Univ Xiangya Hosp 3 Ctr Vasc Dis & Translat Med Changsha Hunan Peoples R China

Background: With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Results: Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. Co

关键词： Molecular representation Molecular descriptors python library Chemoinformatics Data integration Bioinformatics

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PaDuA: A python library for High-Throughput (Phospho)proteomics Data Analysis

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JOURNAL OF PROTEOME RESEARCH 2019年第2期18卷 576-584页

作者： Ressa, Anna Fitzpatrick, Martin van den Toorn, Henk Heck, Albert J. R. Altelaar, Maarten Univ Utrecht Biomol Mass Spectrometry & Prote Grp Utrecht Inst Pharmaceut Sci Padualaan 8 NL-3584 CH Utrecht Netherlands Univ Utrecht Bijvoet Ctr Biomol Res Padualaan 8 NL-3584 CH Utrecht Netherlands

The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers.

关键词： proteomics high-throughput data analysis python library

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MANGO: A python library FOR PARALLEL HYPERPARAMETER TUNING

MANGO: A PYTHON LIBRARY FOR PARALLEL HYPERPARAMETER TUNING

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sandha, Sandeep Singh Aggarwal, Mohit Fedorov, Igor Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90024 USA Arm Research Cambridge MA USA

ISBN: (纸本)9781509066315

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps remain in existing libraries, including lack of abstractions, fault tolerance, and flexibility to support scheduling on any distributed computing framework. To address these challenges, we present Mango, a python library for parallel hyperparameter tuning. Mango enables the use of any distributed scheduling framework, implements intelligent parallel search strategies, and provides rich abstractions for defining complex hyperparameter search spaces that are compatible with scikit-learn. Mango is comparable in performance to Hyperopt [1], another widely used library. Mango is available open-source [2] and is currently used in production at Arm Research to provide state-of-art hyperparameter tuning capabilities.

关键词： Hyperparameter tuning Bayesian optimization python library Parallel Optimizer

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TOWARDS AN OPEN SOURCE python library FOR AUTOMATED EXPLORATORY SPATIAL DATA ANALYSIS 24

TOWARDS AN OPEN SOURCE PYTHON LIBRARY FOR AUTOMATED EXPLORAT...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： de Kock, Nicholas Rautenbach, Victoria Fabris-Rotelli, Inger Univ Pretoria Dept Geog Geoinformat & Meteorol Pretoria South Africa Univ Pretoria Dept Stat Pretoria South Africa

The exploratory spatial data analysis (ESDA) process refers to the use of various functions to gain an initial understanding of a spatial dataset. These include measures of spatial heterogeneity and spatial autocorrelation. Currently, the ESDA process is repetitive and time-consuming. Additionally, while different results arise for different datasets, how these results are generated does not change significantly. Results are also generated individually for each variable which means that they cannot be easily compared or shared. The automation of the ESDA process would therefore have multiple benefits as it would not only save time, but it would also allow the data analyst to keep up with the rapid rate at which we generate data. This paper aims to introduce the first iteration of autoESDA - a python library capable of automating the ESDA process by summarising the results into a single report. In this paper, we present the defined high-level requirements for the implementation of autoESDA. Various dependency libraries are discussed and a high-level overview of the workflow of autoESDA is described. The library is then evaluated against the requirements laid out earlier in the study. Semi-structured interviews were carried out, which yielded a wealth of feedback and suggestions from the participants, describing how the output report could be improved. Finally, a roadmap of proposed further developments and improvements is discussed. The first version demonstrates that the automation of ESDA is possible and lays the foundation for further development in this regard. This is an important contribution to understanding spatial data as it enables the data analyst to keep up with the magnitude of data that is generated on a daily basis.

关键词： Open Source python library Spatial Statistics ESDA

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Exploring How Deprecated python library APIs Are (Not) Handled 2020

Exploring How Deprecated Python Library APIs Are (Not) Handl...

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28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Wang, Jiawei Li, Li Liu, Kui Cai, Haipeng Monash Univ Melbourne Vic Australia Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Washington State Univ Pullman WA 99164 USA

ISBN: (纸本)9781450370431

In this paper, we present the first exploratory study of deprecated python library APIs to understand the status quo of API deprecation in the realm of python libraries. Specifically, we aim to comprehend how deprecated library APIs are declared and documented in practice by their maintainers, and how library users react to them. By thoroughly looking into six reputed python libraries and 1,200 GitHub projects, we experimentally observe that API deprecation is poorly handled by library contributors, which subsequently introduce difficulties for python developers to resolve the usage of deprecated library APIs. This empirical evidence suggests that our community should take immediate actions to appropriately handle the deprecation of python library APIs.

关键词： Deprecated API Deprecation python library Evolution

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Datawindow: python library to Simplify the Data Science Process 5

Datawindow: Python Library to Simplify the Data Science Proc...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lawrence, Daniel Anu Barathi, B.U. Sreeji, S. Sathyabama Institute of Science & Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350383522

This paper is a documentation for datawindow, a python package which has the goal of simplifying the data processing and analysis techniques. Datawindow provides eight methods which allows users to quickly and easily handle activities in the data science process such as model building, data cleaning and summarization using user-friendly interfaces. The datawindow keeps user-friendliness and modularity as the main focus in the library's design. Datawindow provides the user-friendly experience by utilizing Tkinter for graphical user interfaces. Datawindow users Pandas for data processing and Tkinter for the interfaces to help provide clarity to the users. This ensures that the library satisfies user needs and aims to provide the user with a streamlined data science experience. Datawindow seeks to refocus the focus of the data analysis and data science process from coding and programming to allowing data workers to focus more on the creative processes and analytical thinking part of the analysis using interfaces. © 2024 IEEE.

关键词： cluster analysis data analysis workflow data cleaning data processing data summarization deep learning exploratory data analysis machine learning modular design numpy pandas python library tkinter user interface

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Pixyz: a python library for developing deep generative models

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ADVANCED ROBOTICS 2023年第19期37卷 1221-1236页

作者： Suzuki, Masahiro Kaneko, Takaaki Matsuo, Yutaka Univ Tokyo Tokyo Japan Hiroshima Univ Hiroshima Japan

With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of DGMs: (1) deep neural networks are encapsulated by probability distributions, and (2) models are designed and learned based on an objective function. Taking these features into account, we propose a new python library to implement DGMs called Pixyz. This library adopts a step-by-step implementation method with three APIs, which allows us to implement various DGMs more concisely and intuitively. In addition, the library introduces memoization to reduce the cost of duplicate computations in DGMs to speed up the computation. We demonstrate experimentally that this library is faster than existing probabilistic programming languages in training DGMs.

关键词： Deep generative models python library >

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