Practical Machine Learning with Python
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A Dataset Simulation Package for Strong Gravitational Lensing
deeplenstronomy: A dataset simulation package for strong gravitational lensing Robert Morgan∗1, 2, Brian Nord3, 4, Simon Birrer5, Joshua Yao-Yu Lin6, and Jason Poh4 1 University of Wisconsin-Madison 2 Legacy Survey of Space and Time Data Science Fellowship Program 3 Fermi National Accelerator Laboratory 4 University of Chicago 5 Stanford University 6 DOI: 10.21105/joss.02854 University of Illinois Urbana-Champaign Software • Review Background • Repository • Archive Astronomical observations and statistical modeling permit the high-fidelity analysis of strong gravitational lensing (SL) systems, which display an astronomical phenomenon in Submitted: 10 November 2020 which light from a distant object is deflected by the gravitational field of another object Published: 04 February 2021 along its path to the observer. These systems are of great scientific interest because they License provide information about multiple astrophysical and cosmological phenomena, including Authors of papers retain copyright the nature of dark matter, the expansion rate of the Universe, and characteristics of galaxy and release the work under a Cre- populations. They also serve as standing tests of the theory of General Relativity and ative Commons Attribution 4.0 In- modified theories of gravity. ternational License (CC BY 4.0). Traditional searches for SL systems have involved time- and effort-intensive visual or manual inspection of images by humans to identify characteristic features — like arcs, particular color combinations, and object orientations. However, a comprehensive search using the traditional approach is prohibitively expensive for large numbers of images, like those in cosmological surveys — e.g., the Sloan Digital Sky Survey (York et al. 2000), the Dark Energy Survey (Abbott et al. -
A Python Toolbox for Ensemble Learning and Visualisation Benjamin Guedj, Bhargav Srinivasa Desikan
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation Benjamin Guedj, Bhargav Srinivasa Desikan To cite this version: Benjamin Guedj, Bhargav Srinivasa Desikan. Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. Journal of Machine Learning Research, Microtome Publishing, 2018, 18, pp.1 - 5. hal-01514059v3 HAL Id: hal-01514059 https://hal.inria.fr/hal-01514059v3 Submitted on 20 Jun 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - ShareAlike| 4.0 International License Journal of Machine Learning Research 18 (2018) 1-5 Submitted 4/17; Revised 3/18; Published 4/18 Pycobra: A Python Toolbox for Ensemble Learning and Visualisation Benjamin Guedj [email protected] Modal project-team, Lille - Nord Europe research center Inria, France Bhargav Srinivasa Desikan [email protected] Modal project-team, Lille - Nord Europe research center Inria, France Editor: Geoff Holmes Abstract We introduce pycobra, a Python library devoted to ensemble learning (regression and clas- sification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a predict method is given), and visualisation tools such as Voronoi tessellations. -
Python Data Science Essentials Table of Contents
Python Data Science Essentials Table of Contents Python Data Science Essentials Credits About the Authors About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Errata Piracy Questions 1. First Steps Introducing data science and Python Installing Python Python 2 or Python 3? Step-by-step installation A glance at the essential Python packages NumPy SciPy pandas Scikit-learn IPython Matplotlib Statsmodels Beautiful Soup NetworkX NLTK Gensim PyPy The installation of packages Package upgrades Scientific distributions Anaconda Enthought Canopy PythonXY WinPython Introducing IPython The IPython Notebook Datasets and code used in the book Scikit-learn toy datasets The MLdata.org public repository LIBSVM data examples Loading data directly from CSV or text files Scikit-learn sample generators Summary 2. Data Munging The data science process Data loading and preprocessing with pandas Fast and easy data loading Dealing with problematic data Dealing with big datasets Accessing other data formats Data preprocessing Data selection Working with categorical and textual data A special type of data – text Data processing with NumPy NumPy's n-dimensional array The basics of NumPy ndarray objects Creating NumPy arrays From lists to unidimensional arrays Controlling the memory size Heterogeneous lists From lists to multidimensional arrays Resizing arrays Arrays derived from NumPy functions Getting an array directly from a file Extracting data from pandas NumPy fast operation and computations Matrix operations Slicing and indexing with NumPy arrays Stacking NumPy arrays Summary 3. -
A Python Toolbox for Ensemble Learning and Visualisation
Journal of Machine Learning Research 18 (2018) 1-5 Submitted 4/17; Revised 3/18; Published 4/18 Pycobra: A Python Toolbox for Ensemble Learning and Visualisation Benjamin Guedj [email protected] Modal project-team, Lille - Nord Europe research center Inria, France Bhargav Srinivasa Desikan [email protected] Modal project-team, Lille - Nord Europe research center Inria, France Editor: Geoff Holmes Abstract We introduce pycobra, a Python library devoted to ensemble learning (regression and clas- sification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a predict method is given), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learn compatible and is released under the MIT open-source license. pycobra can be down- loaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at https://github.com/bhargavvader/pycobra and official documentation website is https://modal.lille.inria.fr/pycobra. Keywords: ensemble methods, machine learning, Voronoi tesselation, Python, open source software 1. Introduction Combined statistical procedures, also known as ensemble or aggregation methods, are very popular in Machine Learning competitions { the celebrated Netflix Challenge (as are re- peatedly Kaggle competitions) was won by an ensemble technique (see for example Bell and Koren, 2007, for a discussion). In a nutshell, ensemble methods combine different predictors (each trained on the same dataset) to improve performance.