Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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Scikit-learn: Machine Learning in Python

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.

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Array programming with NumPy

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

/pdf/scipy-1-0-fundamental-algorithms-for-scientific-computing-in-45othhotbu.pdf

SciPy 1.0: fundamental algorithms for scientific computing in Python.

Most iterative techniques for solving the symmetric positive-definite systemAx=b involve approximating the matrixA by another symmetric positive-definite matrixM and then solving a system of the formMz=d at each iteration. On a vector machine such as the CDC-STAR-100, the solution of this new system can be very time consuming. If, however, an approximationM−1 can be given toA−1, the solutionz=M−1d can be computed rapidly by matrix multiplication, a fast operation on the STAR. Approximations using the Neumann expansion of the inverse ofA give reasonable forms forM−1 and are presented. Computational results using the conjugate gradient method for the “5-point” matrixA are given.

Approximating the inverse of a matrix for use in iterative algorithms on vector processors

Our special theme this issue is the computer programming language Python and the increasing role it plays in scientific projects. Free and universally available, Python comes with a vast standard library containing support for nearly every area of computer science. An even more extensive set of third-party tools and modules covers additional tasks, from managing a Web site to doing a fast Fourier transform to distributed or parallel programming. The Python motto "batteries included" is meant to convey the idea that Python comes with everything you need.

Guest Editor's Introduction: Python: Batteries Included

We describe a MIMD multiprocessor simulator and application of that simulator to a multiprocessor of current interest, the S-1 MkIIa. The simulator runs on the CRAY-1 and is designed so that computational physics benchmarks are actually run and produce results. Simulator output from this run is fed into a second level (hardware) simulator which calculates the behavior of the multiprocessor. The simulator can simulate multiprocessors whose basic architecture is that of a few, large processors with or without data caches, sharing global memory through an interconnection switch. The simulator is applied to the investigation of the behavior of four problems on the S-1: The benchmark physics code SIMPLE, a conjugate gradient linear algebra problem, a simple Monte-Carlo problem, and a new method for neutron transport calculations.

A simulator for MIMD performance prediction: application to the S-1 MkIIa multiprocessor

The title of this article refers to Rudolph Flesch's famous 1955 book, "Why Johnny Can't Read", which called attention to a nationwide decline in reading ability. Here, the author wants to talk about another situation in which an important ability is lacking: the ability to create significant, portable scientific software. The author discusses some of the reasons this problem exists and suggests some approaches to solving it that seem promising.

Why Johnny can't build [portable scientific software]

Combine a high rate of change (which makes correctness hard to maintain) with an increased sensitivity to failure to maintain correctness and you have a big problem. Solving this problem must be the focus of our methodology. In this paper, the author describes the layered approach that he found to be the most successful in maintaining correctness in the face of rapid change.

Paul F. Dubois

Papers

Approximating the inverse of a matrix for use in iterative algorithms on vector processors

Guest Editor's Introduction: Python: Batteries Included

A simulator for MIMD performance prediction: application to the S-1 MkIIa multiprocessor

Why Johnny can't build [portable scientific software]

Maintaining correctness in scientific programs