sklearn-contrib-py-earth 0.1.0

Description:

sklearncontribpyearth 0.1.0

py-earth [](https://travis-ci.org/scikit-learn-contrib/py-earth?branch=master)========A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm, in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces. For more information about Multivariate Adaptive Regression Splines, see the references below.## Now With Missing Data Support!The py-earth package now supports missingness in its predictors. Just set `allow_missing=True` when constructing an `Earth` object.## Requesting FeedbackIf there are other features or improvements you'd like to see in py-earth, please send me an email or open or comment on an issue. In particular, please let me know if any of the following are important to you:1. Improved speed2. Exporting models to additional formats3. Support for shared memory multiprocessing during fitting4. Support for cyclic predictors (such as time of day)5. Better support for categorical predictors6. Better support for large data sets7. Iterative reweighting during fitting## InstallationMake sure you have numpy and scikit-learn installed. Then do the following:```git clone git://github.com/scikit-learn-contrib/py-earth.gitcd py-earthsudo python setup.py install```## Usage```pythonimport numpyfrom pyearth import Earthfrom matplotlib import pyplot#Create some fake datanumpy.random.seed(0)m = 1000n = 10X = 80*numpy.random.uniform(size=(m,n)) - 40y = numpy.abs(X[:,6] - 4.0) + 1*numpy.random.normal(size=m)#Fit an Earth modelmodel = Earth()model.fit(X,y)#Print the modelprint(model.trace())print(model.summary())#Plot the modely_hat = model.predict(X)pyplot.figure()pyplot.plot(X[:,6],y,'r.')pyplot.plot(X[:,6],y_hat,'b.')pyplot.xlabel('x_6')pyplot.ylabel('y')pyplot.title('Simple Earth Example')pyplot.show() ```## Other ImplementationsI am aware of the following implementations of Multivariate Adaptive Regression Splines:1. The R package earth (coded in C by Stephen Millborrow): http://cran.r-project.org/web/packages/earth/index.html2. The R package mda (coded in Fortran by Trevor Hastie and Robert Tibshirani): http://cran.r-project.org/web/packages/mda/index.html3. The Orange data mining library for Python (uses the C code from 1): http://orange.biolab.si/4. The xtal package (uses Fortran code written in 1991 by Jerome Friedman): http://www.ece.umn.edu/users/cherkass/ee4389/xtalpackage.html5. MARSplines by StatSoft: http://www.statsoft.com/textbook/multivariate-adaptive-regression-splines/6. MARS by Salford Systems (also uses Friedman's code): http://www.salford-systems.com/products/mars7. ARESLab (written in Matlab by Gints Jekabsons): http://www.cs.rtu.lv/jekabsons/regression.htmlThe R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).## References1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1–67. http://www.jstor.org/stable/10.2307/22418372. Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani. (2012). earth: Multivariate Adaptive Regression Spline Models. R package version 3.2-3. http://CRAN.R-project.org/package=earth3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110. https://statistics.stanford.edu/sites/default/files/LCS%20110.pdf4. Friedman, J. (1991). Estimating functions of mixed ordinal and categorical variables using adaptive splines. Stanford University Department of Statistics, Technical Report No 108. http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied Mathematics.6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied Mathematics.7. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning (2nd Edition). (2009). Springer Series in Statistics8. Golub, G., & Van Loan, C. Matrix Computations (3rd Edition). (1996). Johns Hopkins University Press.References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth. References 1, 2, 6, 5, 8, 3, and 4 were useful during the implementation process.