├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── RcppExports.R ├── coordinate_descent.r ├── pirls.r ├── soft_threshold.r └── strong_filter.r ├── README.md ├── man ├── pirls.Rd └── soft_thresh_r.Rd ├── src ├── RcppExports.cpp ├── coordinate_descent.hpp ├── instantiate.cpp └── soft_threshold.hpp └── test └── test.r /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: pirls 2 | Type: Package 3 | Title: Penalized Iteratively Reweighted Least Squares 4 | Version: 0.0.1 5 | Date: 2016-05-04 6 | Author: Taylor Arnold, Michael J. Kane 7 | Maintainer: Michael J. Kane 8 | Description: Fit generalized linear models via penalized maximum likelihood and 9 | iteratively reweighted least squares. 10 | License: GPL (>= 2) 11 | Imports: 12 | Rcpp (>= 0.12.4) 13 | SystemRequirements: C++11 14 | LinkingTo: Rcpp, RcppArmadillo, BH 15 | RoxygenNote: 5.0.1 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU LESSER GENERAL PUBLIC LICENSE 2 | Version 2.1, February 1999 3 | 4 | Copyright (C) 1991, 1999 Free Software Foundation, Inc. 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | (This is the first released version of the Lesser GPL. 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Here is a sample; alter the names: 496 | 497 | Yoyodyne, Inc., hereby disclaims all copyright interest in the 498 | library `Frob' (a library for tweaking knobs) written by James Random 499 | Hacker. 500 | 501 | {signature of Ty Coon}, 1 April 1990 502 | Ty Coon, President of Vice 503 | 504 | That's all there is to it! 505 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(pirls) 4 | export(safe_filter) 5 | export(soft_thresh_r) 6 | export(strong_filter) 7 | importFrom(Rcpp,sourceCpp) 8 | useDynLib(pirls) 9 | -------------------------------------------------------------------------------- /R/RcppExports.R: -------------------------------------------------------------------------------- 1 | # This file was generated by Rcpp::compileAttributes 2 | # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | c_safe_filter <- function(X, z, lambda) { 5 | .Call('pirls_c_safe_filter', PACKAGE = 'pirls', X, z, lambda) 6 | } 7 | 8 | c_update_coordinates <- function(X, W, z, lambda, alpha, beta, parallel = FALSE, grain_size = 100L) { 9 | .Call('pirls_c_update_coordinates', PACKAGE = 'pirls', X, W, z, lambda, alpha, beta, parallel, grain_size) 10 | } 11 | 12 | c_quadratic_loss <- function(X, W, z, lambda, alpha, beta) { 13 | .Call('pirls_c_quadratic_loss', PACKAGE = 'pirls', X, W, z, lambda, alpha, beta) 14 | } 15 | 16 | c_coordinate_descent <- function(X, W, z, lambda, alpha, beta, active_cols, maxit = 1000L) { 17 | .Call('pirls_c_coordinate_descent', PACKAGE = 'pirls', X, W, z, lambda, alpha, beta, active_cols, maxit) 18 | } 19 | 20 | -------------------------------------------------------------------------------- /R/coordinate_descent.r: -------------------------------------------------------------------------------- 1 | update_coordinates = function(X, W, z, lambda, alpha, beta) { 2 | beta_old = beta 3 | for (i in 1:length(beta)) { 4 | beta[i] = soft_thresh_r(sum(W*X[,i]*(z - X[,-i] %*% beta_old[-i])), 5 | sum(W)*lambda*alpha) 6 | } 7 | beta / (colSums(W*X^2) + lambda*(1-alpha)) 8 | } 9 | 10 | quadratic_loss = function(X, W=1, z, lambda, alpha, beta) { 11 | 1/nrow(X)/2 * sum(W*(z - X %*% beta)^2) - 12 | lambda * ((1-alpha) * sum(beta^2)/2 + alpha * sum(abs(beta))) 13 | } 14 | 15 | coordinate_descent = function(X, W, z, lambda, alpha, beta, maxit) { 16 | quad_loss = quadratic_loss(X, W, z, lambda, alpha, beta) 17 | for(i in 1:maxit) { 18 | beta_old = beta 19 | quad_loss_old = quad_loss 20 | beta = update_coordinates(X, W, z, lambda, alpha, beta) 21 | quad_loss = quadratic_loss(X, W, z, lambda, alpha, beta) 22 | if(quad_loss >= quad_loss_old) { 23 | beta = beta_old 24 | break 25 | } 26 | } 27 | if (i == maxit && quad_loss <= quad_loss_old) { 28 | warning("Coordinate descent did not converge.") 29 | } 30 | beta 31 | } 32 | -------------------------------------------------------------------------------- /R/pirls.r: -------------------------------------------------------------------------------- 1 | #' @useDynLib pirls 2 | #' @importFrom Rcpp sourceCpp 3 | NULL 4 | 5 | 6 | #' Fit a Penalized Iteratively Reweighted Least Squares (PIRLS) model 7 | #' 8 | #' 'pirls' is used to fit the generalized linear model using penalized 9 | #' maximum likelihood based on the model matrix. 10 | #' @param X the model matrix. 11 | #' @param y the response. 12 | #' @param lambda the penalty 13 | #' @param alpha the elasticnet mixing parameter 0 <= alpha <= 1. Default is 14 | #' 1 (LASSO regression). 15 | #' @param family a description of the error distribution and link function 16 | #' to be used in the model. 17 | #' @param maxit the maximum number of iterations for both the weight updates 18 | #' as well as the beta update. Default is 25. 19 | #' @param tol the numeric tolerance. Default is 1e-8. 20 | #' @param beta initial slope coefficients. Default is zeros. 21 | #' @param beta_update a function for optimizing the slope coefficients 22 | #' for the current weight matrix. Default is coordinate_descent. 23 | #' @export 24 | pirls = function(X, y, lambda, alpha=1, family=binomial, maxit=500, 25 | tol=1e-8, beta=as(spMatrix(nrow=ncol(X), ncol=1), "dgCMatrix"), 26 | beta_update=coordinate_descent) { 27 | converged = FALSE 28 | for(i in 1:maxit) { 29 | # Note that there aren't sparse family functions. 30 | eta = as.vector(X %*% beta) 31 | g = family()$linkinv(eta) 32 | gprime = family()$mu.eta(eta) 33 | z = eta + (y - g) / gprime 34 | W = as.vector(gprime^2 / family()$variance(g)) 35 | beta_old = beta 36 | beta = beta_update(X, W, z, lambda, alpha, beta, maxit) 37 | if(sqrt(as.vector(Matrix::crossprod(beta-beta_old))) < tol) { 38 | converged = TRUE 39 | break 40 | } 41 | } 42 | list(beta=beta, iterations=i, converged=converged) 43 | } 44 | -------------------------------------------------------------------------------- /R/soft_threshold.r: -------------------------------------------------------------------------------- 1 | 2 | #' The R implementation of the soft thresholding function. 3 | #' 4 | #' Soft threshold x with the parameter g in R 5 | #' @param x a vector of values to soft threshold. 6 | #' @param g the threshold paramter 7 | #' @export 8 | #' @examples 9 | #' soft_thresh_r(rnorm(10), 0.5) 10 | soft_thresh_r = function(x, g) { 11 | x = as.vector(x) 12 | x[abs(x) < g] = 0 13 | x[x > 0] = x[x > 0] - g 14 | x[x < 0] = x[x < 0] + g 15 | x 16 | } 17 | 18 | -------------------------------------------------------------------------------- /R/strong_filter.r: -------------------------------------------------------------------------------- 1 | 2 | #' The R implementation of the STRONG filter. 3 | #' 4 | #' @param X the model matrix 5 | #' @param z the response 6 | #' @param lambda the penalty parameter. 7 | #' @export 8 | strong_filter = function(X, z, lambda) { 9 | Xty = t(X) %*% y / nrow(X) 10 | lambda_max = max(Xty) 11 | which( abs(Xty) >= 2*lambda - lambda_max) 12 | } 13 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pirls - fit generalized linear models via penalized iteratively reweighted least squares 2 | -------------------------------------------------------------------------------- /man/pirls.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/pirls.r 3 | \name{pirls} 4 | \alias{pirls} 5 | \title{Fit a Penalized Iteratively Reweighted Least Squares (PIRLS) model} 6 | \usage{ 7 | pirls(X, y, lambda, alpha = 1, family = binomial, maxit = 500, 8 | tol = 1e-08, beta = as(spMatrix(nrow = ncol(X), ncol = 1), "dgCMatrix"), 9 | beta_update = coordinate_descent) 10 | } 11 | \arguments{ 12 | \item{X}{the model matrix.} 13 | 14 | \item{y}{the response.} 15 | 16 | \item{lambda}{the penalty} 17 | 18 | \item{alpha}{the elasticnet mixing parameter 0 <= alpha <= 1. Default is 19 | 1 (LASSO regression).} 20 | 21 | \item{family}{a description of the error distribution and link function 22 | to be used in the model.} 23 | 24 | \item{maxit}{the maximum number of iterations for both the weight updates 25 | as well as the beta update. Default is 25.} 26 | 27 | \item{tol}{the numeric tolerance. Default is 1e-8.} 28 | 29 | \item{beta}{initial slope coefficients. Default is zeros.} 30 | 31 | \item{beta_update}{a function for optimizing the slope coefficients 32 | for the current weight matrix. Default is coordinate_descent.} 33 | } 34 | \description{ 35 | 'pirls' is used to fit the generalized linear model using penalized 36 | maximum likelihood based on the model matrix. 37 | } 38 | 39 | -------------------------------------------------------------------------------- /man/soft_thresh_r.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/soft_threshold.r 3 | \name{soft_thresh_r} 4 | \alias{soft_thresh_r} 5 | \title{The R implementation of the soft thresholding function.} 6 | \usage{ 7 | soft_thresh_r(x, g) 8 | } 9 | \arguments{ 10 | \item{x}{a vector of values to soft threshold.} 11 | 12 | \item{g}{the threshold paramter} 13 | } 14 | \description{ 15 | Soft threshold x with the parameter g in R 16 | } 17 | \examples{ 18 | soft_thresh_r(rnorm(10), 0.5) 19 | } 20 | 21 | -------------------------------------------------------------------------------- /src/RcppExports.cpp: -------------------------------------------------------------------------------- 1 | // This file was generated by Rcpp::compileAttributes 2 | // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 3 | 4 | #include 5 | #include 6 | 7 | using namespace Rcpp; 8 | 9 | // c_safe_filter 10 | arma::uvec c_safe_filter(arma::mat X, arma::mat z, double lambda); 11 | RcppExport SEXP pirls_c_safe_filter(SEXP XSEXP, SEXP zSEXP, SEXP lambdaSEXP) { 12 | BEGIN_RCPP 13 | Rcpp::RObject __result; 14 | Rcpp::RNGScope __rngScope; 15 | Rcpp::traits::input_parameter< arma::mat >::type X(XSEXP); 16 | Rcpp::traits::input_parameter< arma::mat >::type z(zSEXP); 17 | Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); 18 | __result = Rcpp::wrap(c_safe_filter(X, z, lambda)); 19 | return __result; 20 | END_RCPP 21 | } 22 | // c_update_coordinates 23 | arma::sp_mat c_update_coordinates(arma::mat X, arma::sp_mat W, arma::mat z, double lambda, double alpha, arma::sp_mat beta, bool parallel, unsigned int grain_size); 24 | RcppExport SEXP pirls_c_update_coordinates(SEXP XSEXP, SEXP WSEXP, SEXP zSEXP, SEXP lambdaSEXP, SEXP alphaSEXP, SEXP betaSEXP, SEXP parallelSEXP, SEXP grain_sizeSEXP) { 25 | BEGIN_RCPP 26 | Rcpp::RObject __result; 27 | Rcpp::RNGScope __rngScope; 28 | Rcpp::traits::input_parameter< arma::mat >::type X(XSEXP); 29 | Rcpp::traits::input_parameter< arma::sp_mat >::type W(WSEXP); 30 | Rcpp::traits::input_parameter< arma::mat >::type z(zSEXP); 31 | Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); 32 | Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); 33 | Rcpp::traits::input_parameter< arma::sp_mat >::type beta(betaSEXP); 34 | Rcpp::traits::input_parameter< bool >::type parallel(parallelSEXP); 35 | Rcpp::traits::input_parameter< unsigned int >::type grain_size(grain_sizeSEXP); 36 | __result = Rcpp::wrap(c_update_coordinates(X, W, z, lambda, alpha, beta, parallel, grain_size)); 37 | return __result; 38 | END_RCPP 39 | } 40 | // c_quadratic_loss 41 | double c_quadratic_loss(arma::mat X, arma::sp_mat W, arma::mat z, double lambda, double alpha, arma::sp_mat beta); 42 | RcppExport SEXP pirls_c_quadratic_loss(SEXP XSEXP, SEXP WSEXP, SEXP zSEXP, SEXP lambdaSEXP, SEXP alphaSEXP, SEXP betaSEXP) { 43 | BEGIN_RCPP 44 | Rcpp::RObject __result; 45 | Rcpp::RNGScope __rngScope; 46 | Rcpp::traits::input_parameter< arma::mat >::type X(XSEXP); 47 | Rcpp::traits::input_parameter< arma::sp_mat >::type W(WSEXP); 48 | Rcpp::traits::input_parameter< arma::mat >::type z(zSEXP); 49 | Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); 50 | Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); 51 | Rcpp::traits::input_parameter< arma::sp_mat >::type beta(betaSEXP); 52 | __result = Rcpp::wrap(c_quadratic_loss(X, W, z, lambda, alpha, beta)); 53 | return __result; 54 | END_RCPP 55 | } 56 | // c_coordinate_descent 57 | arma::sp_mat c_coordinate_descent(arma::mat X, arma::sp_mat W, arma::mat z, double lambda, double alpha, arma::sp_mat beta, arma::uvec active_cols, unsigned int maxit); 58 | RcppExport SEXP pirls_c_coordinate_descent(SEXP XSEXP, SEXP WSEXP, SEXP zSEXP, SEXP lambdaSEXP, SEXP alphaSEXP, SEXP betaSEXP, SEXP active_colsSEXP, SEXP maxitSEXP) { 59 | BEGIN_RCPP 60 | Rcpp::RObject __result; 61 | Rcpp::RNGScope __rngScope; 62 | Rcpp::traits::input_parameter< arma::mat >::type X(XSEXP); 63 | Rcpp::traits::input_parameter< arma::sp_mat >::type W(WSEXP); 64 | Rcpp::traits::input_parameter< arma::mat >::type z(zSEXP); 65 | Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); 66 | Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); 67 | Rcpp::traits::input_parameter< arma::sp_mat >::type beta(betaSEXP); 68 | Rcpp::traits::input_parameter< arma::uvec >::type active_cols(active_colsSEXP); 69 | Rcpp::traits::input_parameter< unsigned int >::type maxit(maxitSEXP); 70 | __result = Rcpp::wrap(c_coordinate_descent(X, W, z, lambda, alpha, beta, active_cols, maxit)); 71 | return __result; 72 | END_RCPP 73 | } 74 | -------------------------------------------------------------------------------- /src/coordinate_descent.hpp: -------------------------------------------------------------------------------- 1 | #define ARMA_NO_DEBUG 2 | #include "soft_threshold.hpp" 3 | 4 | template 6 | double update_coordinate(const unsigned int i, 7 | const ModelMatrixType &X, const WeightMatrixType &W, const ResponseType &z, 8 | const double &lambda, const double &alpha, const BetaMatrixType &beta, 9 | const double thresh) { 10 | ModelMatrixType X_shed(X), X_col(X.col(i)); 11 | BetaMatrixType beta_shed(beta); 12 | X_shed.shed_col(i); 13 | beta_shed.shed_row(i); 14 | double val = arma::mat((W*X_col).t() * (z - X_shed * beta_shed))[0]; 15 | double ret = soft_thresh(val, thresh); 16 | // Assume W is a diagonal matrix. 17 | if (ret != 0) 18 | ret /= accu(W * square(X_col)) + lambda*(1-alpha); 19 | return ret; 20 | } 21 | 22 | template 24 | double quadratic_loss(const ModelMatrixType &X, const WeightMatrixType &W, 25 | const ResponseType &z, const double &lambda, const double &alpha, 26 | const BetaMatrixType &beta) { 27 | return 1./X.n_rows/2. * accu(W*square(z-X*beta)) - 28 | lambda * ((1-alpha) * accu(square(beta))/2 * alpha * accu(abs(beta))); 29 | } 30 | 31 | template 34 | BetaMatrixType coordinate_descent(const ModelMatrixType &X, 35 | const WeightMatrixType &W, const RealVectorType &z, const double &lambda, 36 | const double &alpha, BetaMatrixType beta, 37 | const UIntVectorType &active_cols, unsigned int maxit) 38 | { 39 | double thresh = accu(W) * lambda * alpha; 40 | double quad_loss = quadratic_loss(X, W, z, lambda, alpha, beta); 41 | double quad_loss_old; 42 | BetaMatrixType beta_old(beta); 43 | for (unsigned int i=0; i < maxit; ++i) { 44 | beta_old = beta; 45 | beta = arma::sp_mat(beta_old.n_rows, beta_old.n_cols); 46 | quad_loss_old = quad_loss; 47 | for (auto it=active_cols.begin(); it != active_cols.end(); ++it) 48 | beta(*it, 0) = update_coordinate(*it, X, W, z, lambda, alpha, 49 | beta_old, thresh); 50 | quad_loss = quadratic_loss(X, W, z, lambda, alpha, beta); 51 | if (quad_loss >= quad_loss_old) { 52 | beta = beta_old; 53 | break; 54 | } 55 | } 56 | return beta; 57 | } 58 | 59 | 60 | -------------------------------------------------------------------------------- /src/instantiate.cpp: -------------------------------------------------------------------------------- 1 | // [[Rcpp::depends(RcppArmadillo)]] 2 | // // [[Rcpp::depends(RcppParallel)]] 3 | // [[Rcpp::depends(BH)]] 4 | 5 | #include 6 | #include 7 | //#include 8 | #include 9 | 10 | #include "coordinate_descent.hpp" 11 | 12 | //using namespace RcppParallel; 13 | using namespace boost; 14 | using namespace std::placeholders; 15 | 16 | template 17 | arma::sp_mat vec_to_diag( const VecType &vec ) { 18 | unsigned long i; 19 | arma::umat locs(2, vec.size()); 20 | arma::vec copy_vec(vec.size()); 21 | for (i=0; i < vec.size(); ++i) { 22 | locs(0, i) = i; 23 | locs(0, i) = i; 24 | copy_vec(i) = vec[i]; 25 | } 26 | return arma::sp_mat(locs, copy_vec, i, i); 27 | } 28 | 29 | template 30 | arma::sp_mat vec_to_col_sp_mat( const VecType &vec ) { 31 | unsigned long i,j; 32 | unsigned int nz=0; 33 | for (i=0; i < vec.size(); ++i) { 34 | if (vec[i] != 0) { 35 | ++nz; 36 | } 37 | } 38 | arma::vec vals(nz); 39 | arma::umat locs(2, nz); 40 | j=0; 41 | for (i=0; i < vec.size(); ++i) { 42 | if (vec[i] != 0) { 43 | vals[j] = vec[i]; 44 | locs(0, j) = i; 45 | locs(1, j++) = 0; 46 | } 47 | } 48 | return arma::sp_mat(locs, vals, vec.size(), 1); 49 | } 50 | 51 | /* 52 | template 54 | struct UpdateCoordinate : public Worker { 55 | // members 56 | const ModelMatrixType &_X; 57 | const WeightMatrixType &_W; 58 | const ResponseType &_z; 59 | const BetaMatrixType &_beta; 60 | const double &_lambda; 61 | const double &_alpha; 62 | const double _thresh; 63 | std::vector _output; 64 | 65 | // constructor 66 | UpdateCoordinate(const ModelMatrixType &X, const WeightMatrixType &W, 67 | const ResponseType &z, const BetaMatrixType &beta, 68 | const double &lambda, const double &alpha, 69 | const double thresh) : 70 | _X(X), _W(W), _z(z), _beta(beta), _lambda(lambda), _alpha(alpha), 71 | _thresh(thresh), _output(beta.n_rows) { 72 | } 73 | 74 | void operator()(std::size_t begin, std::size_t end) { 75 | transform(counting_iterator(begin), counting_iterator(end), 76 | _output.begin(), 77 | [&](const unsigned int i)->double 78 | {return update_coordinate(i, _X, _W, _z, _lambda, _alpha, _beta, 79 | _thresh);}); 80 | } 81 | }; 82 | */ 83 | 84 | // [[Rcpp::export]] 85 | arma::uvec c_safe_filter(arma::mat X, arma::mat z, double lambda) { 86 | arma::vec Xty(X.t() * z); 87 | arma::vec cutoffs(X.n_cols); 88 | double zl2 = sqrt(sum(square(z)))[0]; 89 | double lambda_max = Xty.max(); 90 | unsigned long i=0; 91 | for (i=0; i < X.n_cols; ++i) { 92 | cutoffs[i] = lambda * sqrt(sum(square(X.col(i)))) * zl2 * 93 | (lambda_max - lambda) / lambda_max; 94 | } 95 | return find( abs(Xty) > cutoffs ); 96 | } 97 | 98 | // [[Rcpp::export]] 99 | arma::sp_mat c_update_coordinates(arma::mat X, arma::sp_mat W, 100 | arma::mat z, double lambda, double alpha, arma::sp_mat beta, 101 | bool parallel=false, unsigned int grain_size=100) { 102 | double thresh = accu(W)*lambda*alpha; 103 | // if (!parallel) { 104 | arma::sp_mat beta_new(beta); 105 | for (unsigned i=0; i < beta.n_rows; ++i) { 106 | beta(i, 0) = update_coordinate(i, X, W, z, lambda, alpha, beta, thresh); 107 | } 108 | return beta; 109 | // } else { 110 | // UpdateCoordinate 111 | // uc(X, W, z, beta, lambda, alpha, thresh); 112 | // parallelFor(0, beta.n_rows, uc, grain_size); 113 | // uc(0, beta.n_rows); 114 | // return vec_to_col_sp_mat(uc._output); 115 | // } 116 | } 117 | 118 | // [[Rcpp::export]] 119 | double c_quadratic_loss(arma::mat X, arma::sp_mat W, 120 | arma::mat z, double lambda, double alpha, arma::sp_mat beta) { 121 | return quadratic_loss(X, W, z, lambda, alpha, beta); 122 | } 123 | 124 | // [[Rcpp::export]] 125 | arma::sp_mat c_coordinate_descent(arma::mat X, arma::sp_mat W, 126 | arma::mat z, double lambda, double alpha, arma::sp_mat beta, 127 | arma::uvec active_cols, unsigned int maxit=1000) { 128 | return coordinate_descent(X, W, z, lambda, alpha, beta, active_cols, maxit); 129 | } 130 | 131 | -------------------------------------------------------------------------------- /src/soft_threshold.hpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | double soft_thresh(const double &val, const double thresh) { 4 | return std::abs(val) < thresh ? 0 : (val > 0 ? val - thresh : val + thresh); 5 | } 6 | -------------------------------------------------------------------------------- /test/test.r: -------------------------------------------------------------------------------- 1 | library(glmnet) 2 | library(foreach) 3 | library(doMC) 4 | registerDoMC(cores=6) 5 | 6 | document() 7 | 8 | # lmnet sanity check 9 | x=matrix(rnorm(100*20),100,ncol=20) 10 | y=rnorm(100) 11 | fit1 = glmnet(x, y, standardize=FALSE, intercept=FALSE) 12 | lambdas = fit1$lambda 13 | fit2_betas = foreach(lambda=lambdas, .combine=cbind) %do% { 14 | pirls(x, y, lambda=lambda, family=gaussian)$beta 15 | } 16 | beta_diff = sqrt(crossprod(fit1$beta,fit2_betas)) 17 | if (any(sqrt(colSums(beta_diff)) > 1e-3)) stop("Precision problem.") 18 | 19 | cbind(fit1$beta, fit2$beta) 20 | 21 | mycd = function(X, W, z, lambda, alpha, beta, active_cols, maxit = 1000L) { 22 | W = as(diag(length(W)), "dgCMatrix") 23 | beta = as(beta, "dgCMatrix") 24 | z = matrix(z, nrow=length(z)) 25 | c_coordinate_descent(X, W, z, lambda, alpha, beta, 26 | as.integer(0:(ncol(X)-1)), maxit) 27 | } 28 | 29 | # Timing 30 | glmnet_times = c() 31 | glmnet_fit_times=c() 32 | glmnet_c_fit_times=c() 33 | num_cols = round(seq(20, 1000, length.out=20)) 34 | for (j in num_cols) { 35 | x=matrix(rnorm(1000*j), 1000, ncol=j) 36 | y=rnorm(1000) 37 | 38 | lambdas=glmnet(x, y, standardize=FALSE, intercept=FALSE)$lambda 39 | lambda = lambdas[round(length(lambdas)/2)] 40 | glmnet_times = c(glmnet_times, 41 | system.time({ 42 | a = glmnet(x, y, standardize=FALSE, lambda=lambda, intercept=FALSE) 43 | })[3]) 44 | 45 | glmnet_fit_times = c(glmnet_fit_times, 46 | system.time({ 47 | b = pirls(x, y, lambda=lambda, family=gaussian) 48 | })[3]) 49 | 50 | glmnet_c_fit_times = c(glmnet_c_fit_times, 51 | system.time({ 52 | b = pirls(x, y, lambda=lambda, family=gaussian, 53 | beta_update=mycd) 54 | })[3]) 55 | cat(paste(j, "cols.\n")) 56 | } 57 | library(ggplot2) 58 | library(tidyr) 59 | 60 | df = data.frame(list(glmnet=glmnet_times, pirls=glmnet_fit_times, 61 | Columns=num_cols)) 62 | dfg = gather(df, key=Regression, value=Time, glmnet:pirls) 63 | p=ggplot(aes(y=Time, x=Columns, color=Regression), data=dfg) + 64 | geom_line(size=1.5) 65 | ggsave("pirls1.jpg", p, width=8, height=6) 66 | 67 | df2 = data.frame(list(glmnet=glmnet_times, pirls=glmnet_fit_times, 68 | pirls_c=glmnet_c_fit_times, Columns=num_cols)) 69 | dfg2 = gather(df2, key=Regression, value=Time, glmnet:pirls_c) 70 | p=ggplot(aes(y=Time, x=Columns, color=Regression), data=dfg2) + 71 | geom_line(size=1.5) 72 | ggsave("pirls2.jpg", p, width=8, height=6) 73 | 74 | x[,1] = x[,1] 75 | # Timing 2 76 | glmnet_times = c() 77 | glmnet_fit_times=c() 78 | glmnet_c_fit_times=c() 79 | num_cols = round(seq(20, 1000, length.out=20)) 80 | for (j in num_cols) { 81 | x=matrix(rnorm(1000*j), 1000, ncol=j) 82 | y=rnorm(1000) 83 | 84 | lambdas=glmnet(x, y, standardize=FALSE, intercept=FALSE)$lambda 85 | lambda = lambdas[round(length(lambdas)/10)] 86 | 87 | glmnet_times = c(glmnet_times, 88 | system.time({ 89 | a = glmnet(x, y, standardize=FALSE, lambda=lambda, intercept=FALSE) 90 | })[3]) 91 | 92 | glmnet_fit_times = c(glmnet_fit_times, 93 | system.time({ 94 | inds = strong_filter(x, y, lambda) 95 | if (length(inds) > 0) 96 | xs = x[,inds] 97 | else 98 | xs = x 99 | b = pirls(x, y, lambda=lambda, family=gaussian) 100 | })[3]) 101 | 102 | glmnet_c_fit_times = c(glmnet_c_fit_times, 103 | system.time({ 104 | inds = strong_filter(x, y, lambda) 105 | if (length(inds) > 0) 106 | xs = x[,inds] 107 | else 108 | xs = x 109 | b = pirls(xs, y, lambda=lambda, family=gaussian, 110 | beta_update=mycd) 111 | })[3]) 112 | cat(paste(j, "cols.\n")) 113 | } 114 | library(ggplot2) 115 | library(tidyr) 116 | 117 | #df = data.frame(list(glmnet=glmnet_times, pirls=glmnet_fit_times, 118 | # Columns=num_cols)) 119 | #dfg = gather(df, key=Regression, value=Time, glmnet:pirls) 120 | #p=ggplot(aes(y=Time, x=Columns, color=Regression), data=dfg) + 121 | # geom_line(size=1.5) 122 | #ggsave("pirls1.jpg", p, width=8, height=6) 123 | 124 | df2 = data.frame(list(glmnet=glmnet_times, pirls=glmnet_fit_times, 125 | pirls_c=glmnet_c_fit_times, Columns=num_cols)) 126 | dfg2 = gather(df2, key=Regression, value=Time, glmnet:pirls_c) 127 | p=ggplot(aes(y=Time, x=Columns, color=Regression), data=dfg2) + 128 | geom_line(size=1.5) 129 | ggsave("pirls3.jpg", p, width=8, height=6) 130 | 131 | 132 | 133 | Rprof(append=FALSE, interval=0.02, memory.profiling=TRUE, gc.profiling=TRUE, 134 | line.profiling = TRUE) 135 | Rprof(NULL) 136 | 137 | system.time({b = pirls(x, y, lambda=lambda, family=gaussian)}) 138 | 139 | #binomial 140 | x=matrix(rnorm(100*20),100,ncol=20) 141 | g2=sample(1:2,100,replace=TRUE) 142 | fit1 = glmnet(x,g2,family="binomial", standardize=FALSE, intercept=FALSE) 143 | lambdas = fit1$lambda 144 | lambda = lambdas[length(round(lambdas))/2] 145 | fit2 = pirls(x, g2-1, family=binomial, lambda=lambda) 146 | cbind(fit1$beta[,length(round(lambdas))/2], fit2$beta) 147 | 148 | 149 | # Soft threshold testing 150 | RcppParallel::setThreadOptions(numThreads = 4) 151 | library(Matrix) 152 | library(devtools) 153 | document("..") 154 | nrows = 1000 155 | ncols = 1000 156 | sp_mat = spMatrix(ncol=1, nrow=nrows) 157 | 158 | X = matrix(rnorm(nrows*ncols), nrow=nrows, ncol=ncols) 159 | W = as(diag(nrows), "dgCMatrix") 160 | z = matrix(rnorm(nrows), ncol=1) 161 | lambda = 0.3 162 | alpha = 1 163 | beta = as(matrix(0, nrow=ncols, ncol=1), "dgCMatrix") 164 | 165 | mycd = function(X, W, z, lambda, alpha, beta, active_cols, maxit = 1000L) { 166 | W = as(diag(length(W)), "dgCMatrix") 167 | beta = as(beta, "dgCMatrix") 168 | z = matrix(z, nrow=length(z)) 169 | c_coordinate_descent(X, W, z, lambda, alpha, beta, 170 | as.integer(0:(ncol(X)-1)), maxit) 171 | } 172 | 173 | system.time({b1 = pirls(X, z, lambda=lambda, family=gaussian, 174 | beta_update=mycd)}) 175 | system.time({b2 = pirls(X, z, lambda=lambda, family=gaussian)}) 176 | 177 | 178 | system.time({ 179 | cuc = c_update_coordinates(X, W, z, lambda, alpha, beta, parallel=FALSE, 180 | grain_size=100)}) 181 | system.time({ 182 | uc = update_coordinates(X, diag(W), z, lambda, alpha, beta)}) 183 | 184 | c_quadratic_loss(X, W, z, lambda, alpha, beta) 185 | quadratic_loss(X, diag(W), z, lambda, alpha, beta) 186 | 187 | c_safe_filter(X, z, lambda) 188 | --------------------------------------------------------------------------------