MatchPy: A Pattern Matching Library

Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0007

Enhancing R with Advanced Compilation Tools and Methods

I describe an approach to compiling common idioms in R code directly to native machine code and illustrate it with several examples. Not only can this yield significant performance gains, but it allows us to use new approaches to computing in R. Importantly, the compilation requires no changes to R itself, but is done entirely via R packages. This allows others to experiment with different compilation strategies and even to define new domain-specific languages within R. We use the Low-Level Virtual Machine (LLVM) compiler toolkit to create the native code and perform sophisticated optimizations on the code. By adopting this widely used software within R, we leverage its ability to generate code for different platforms such as CPUs and GPUs, and will continue to benefit from its ongoing development. This approach potentially allows us to develop high-level R code that is also fast, that can be compiled to work with different data representations and sources, and that could even be run outside of R. The approach aims to both provide a compiler for a limited subset of the R language and also to enable R programmers to write other compilers. This is another approach to help us write high-level descriptions of what we want to compute, not how.Comment: Published in at http://dx.doi.org/10.1214/13-STS462 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

PonyGE2: Grammatical Evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.Comment: 8 pages, 4 figures, submitted to the 2017 GECCO Workshop on Evolutionary Computation Software Systems (EvoSoft

Gower as Data: Exploring the Application of Machine Learning to Gower’s Middle English Corpus

Distant reading, a digital humanities method in wide use, involves processing and analyzing a large amount of text through computer programs. In treating texts as data, these methods can highlight trends in diction, themes, and linguistic patterns that individual readers may miss or critical traditions may obscure. Though several scholars have undertaken projects using topic models and text mining on Middle English texts, the nonstandard orthography of Middle English makes this process more challenging than for our counterparts in later literature. This collaborative project uses Gower’s Confessio Amantis as a small, fixed corpus for analysis. We employ natural language processing to reexamine the Confessio’s themes, adding data analysis to the more traditional close reading strategies of Gower scholarship. We use Gower’s work as a case study both to help reduce the potential variants across textual versions and to more deeply investigate the corpus than distant reading normally allows. Here, we share our initial findings as well as our methodologies. We hope to share resources that will allow other scholars to engage in similar types of projects

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software