SpyderZ: An Efficient Support Vector Machine Library for Photometric Redshift Estimation and Redshift Probability Information

Abstract

We present SpyderZ, a Python-based library for photometric redshift estimation using support vector machines (implemented with scikit-learn). Our approach discretizes redshift values into uniformly-sized bins and uses one-vs-one support vector classifiers with voting strategies to produce effective probability density functions (ePDFs) over redshift for each galaxy. These ePDFs, which are not constrained to be Gaussian or any other shape, allow for our model's predictions to be used quantitatively with uncertainty analysis methods, and have been shown to enable reliable catastrophic outlier detection. Adapted from the previous IDL package SpiderZ, SpyderZ offers training and evaluation speed optimizations on the order of 102, along with support for parallelization across CPU cores. Our library also offers in-built data sanity checks, result visualizations, metric calculations, cross validation, batch evaluations, and parallelized hyperparameter search (grid search and random search).