From 6a1c99c866970680ce6f8839322122f44f37b013 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Fri, 27 Oct 2017 11:23:10 -0400 Subject: [PATCH 01/15] first pass, transfered the code with few changes from the other repository; also get it to work by changing DESCRIPTION and NAMESPACE files --- DESCRIPTION | 7 +- NAMESPACE | 3 + R/RcppExports.R | 33 +++ R/entropy_feature_selection.R | 20 ++ man/CE_entropy_FS2_new.Rd | 19 ++ man/CE_entropy_SR.Rd | 17 ++ man/entropy_feature_selection.Rd | 14 ++ src/RcppExports.cpp | 54 +++++ src/RcppExports.o | Bin 0 -> 706568 bytes src/cytominer.so | Bin 0 -> 136448 bytes src/entropy_feature_selection.cpp | 328 ++++++++++++++++++++++++++++++ src/entropy_feature_selection.o | Bin 0 -> 762652 bytes 12 files changed, 494 insertions(+), 1 deletion(-) create mode 100644 R/RcppExports.R create mode 100644 R/entropy_feature_selection.R create mode 100644 man/CE_entropy_FS2_new.Rd create mode 100644 man/CE_entropy_SR.Rd create mode 100644 man/entropy_feature_selection.Rd create mode 100644 src/RcppExports.cpp create mode 100644 src/RcppExports.o create mode 100755 src/cytominer.so create mode 100644 src/entropy_feature_selection.cpp create mode 100644 src/entropy_feature_selection.o diff --git a/DESCRIPTION b/DESCRIPTION index dbbb11c..0821204 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,7 +30,9 @@ Imports: purrr (>= 0.2.3), rlang (>= 0.1.2), tibble (>= 1.3.4), - tidyr (>= 0.7.1) + tidyr (>= 0.7.1), + Rcpp (>= 0.12.12), + RcppArmadillo (>= 0.8.100.1.0) Suggests: DBI (>= 0.7), dbplyr (>= 1.1.0), @@ -41,6 +43,9 @@ Suggests: RSQLite (>= 2.0), stringr (>= 1.2.0), testthat (>= 1.0.2) +LinkingTo: + Rcpp (>= 0.12.12), + RcppArmadillo (>= 0.8.100.1.0) VignetteBuilder: knitr URL: https://github.com/cytomining/cytominer BugReports: https://github.com/cytomining/cytominer/issues diff --git a/NAMESPACE b/NAMESPACE index a0946bc..8933bc9 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,6 +6,7 @@ export(count_na_rows) export(covariance) export(drop_na_columns) export(drop_na_rows) +export(entropy_feature_selection) export(extract_subpopulations) export(generalized_log) export(normalize) @@ -27,3 +28,5 @@ importFrom(stats,median) importFrom(stats,sd) importFrom(stats,setNames) importFrom(utils,find) +useDynLib(cytominer) +importFrom(Rcpp, sourceCpp) diff --git a/R/RcppExports.R b/R/RcppExports.R new file mode 100644 index 0000000..08dc61d --- /dev/null +++ b/R/RcppExports.R @@ -0,0 +1,33 @@ +# Generated by using Rcpp::compileAttributes() -> do not edit by hand +# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#' calculate_entropy_new +#' +#' Calculate the entropy of a matrix based on SVD alternative method when more observations than features. A = XtX instead of A = Xt +#' @param A dataset mxm (m features and n observations) +NULL + +#' @return Calculate the entropy contribution of a matrix based on SVD +calculate_entropy_new <- function(A) { + .Call('_cytominer_calculate_entropy_new', PACKAGE = 'cytominer', A) +} + +#' CE_entropy_SR +#' +#' Calculate the entropy contribution of a matrix based on SVD +#' @param A dataset mxn (m features and n observations) +#' @return entropy contribution vector +CE_entropy_SR <- function(A) { + .Call('_cytominer_CE_entropy_SR', PACKAGE = 'cytominer', A) +} + +#' CE_entropy_FS2_new +#' +#' Calculate the entropy of a matrix based on SVD with FS method +#' @param A dataset mxn (m features and n observations) +#' @param mc number of feature to select +#' @return index of features that were selected +CE_entropy_FS2_new <- function(A, mc) { + .Call('_cytominer_CE_entropy_FS2_new', PACKAGE = 'cytominer', A, mc) +} + diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R new file mode 100644 index 0000000..c5da83f --- /dev/null +++ b/R/entropy_feature_selection.R @@ -0,0 +1,20 @@ +#' entropy based feature selection +#' +#' @param X named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names. +#' @param n_features number of features to be extracted +#' +#' @importFrom magrittr %>% +#' @importFrom magrittr %<>% +#' @importFrom foreach %dopar% +#' +#' @return vector containing name of the selected features +#' +#' @export +entropy_feature_selection <- function(X, n_features) { + + A <- tcrossprod(t(X), t(X)) + + feat_index <- CE_entropy_FS2_new(A, n_features) + + return(colnames(X)[feat_index[1:n_features]]) +} diff --git a/man/CE_entropy_FS2_new.Rd b/man/CE_entropy_FS2_new.Rd new file mode 100644 index 0000000..41bd6a4 --- /dev/null +++ b/man/CE_entropy_FS2_new.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{CE_entropy_FS2_new} +\alias{CE_entropy_FS2_new} +\title{CE_entropy_FS2_new} +\usage{ +CE_entropy_FS2_new(A, mc) +} +\arguments{ +\item{A}{dataset mxn (m features and n observations)} + +\item{mc}{number of feature to select} +} +\value{ +index of features that were selected +} +\description{ +Calculate the entropy of a matrix based on SVD with FS method +} diff --git a/man/CE_entropy_SR.Rd b/man/CE_entropy_SR.Rd new file mode 100644 index 0000000..fd1b8bd --- /dev/null +++ b/man/CE_entropy_SR.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{CE_entropy_SR} +\alias{CE_entropy_SR} +\title{CE_entropy_SR} +\usage{ +CE_entropy_SR(A) +} +\arguments{ +\item{A}{dataset mxn (m features and n observations)} +} +\value{ +entropy contribution vector +} +\description{ +Calculate the entropy contribution of a matrix based on SVD +} diff --git a/man/entropy_feature_selection.Rd b/man/entropy_feature_selection.Rd new file mode 100644 index 0000000..e8318a2 --- /dev/null +++ b/man/entropy_feature_selection.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/entropy_feature_selection.R +\name{entropy_feature_selection} +\alias{entropy_feature_selection} +\title{entropy based feature selection} +\usage{ +entropy_feature_selection(x) +} +\arguments{ +\item{x}{data matrix} +} +\description{ +entropy based feature selection +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp new file mode 100644 index 0000000..867414e --- /dev/null +++ b/src/RcppExports.cpp @@ -0,0 +1,54 @@ +// Generated by using Rcpp::compileAttributes() -> do not edit by hand +// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#include +#include + +using namespace Rcpp; + +// calculate_entropy_new +double calculate_entropy_new(arma::mat A); +RcppExport SEXP _cytominer_calculate_entropy_new(SEXP ASEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat >::type A(ASEXP); + rcpp_result_gen = Rcpp::wrap(calculate_entropy_new(A)); + return rcpp_result_gen; +END_RCPP +} +// CE_entropy_SR +NumericVector CE_entropy_SR(NumericMatrix A); +RcppExport SEXP _cytominer_CE_entropy_SR(SEXP ASEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< NumericMatrix >::type A(ASEXP); + rcpp_result_gen = Rcpp::wrap(CE_entropy_SR(A)); + return rcpp_result_gen; +END_RCPP +} +// CE_entropy_FS2_new +NumericVector CE_entropy_FS2_new(NumericMatrix A, unsigned int mc); +RcppExport SEXP _cytominer_CE_entropy_FS2_new(SEXP ASEXP, SEXP mcSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< NumericMatrix >::type A(ASEXP); + Rcpp::traits::input_parameter< unsigned int >::type mc(mcSEXP); + rcpp_result_gen = Rcpp::wrap(CE_entropy_FS2_new(A, mc)); + return rcpp_result_gen; +END_RCPP +} + +static const R_CallMethodDef CallEntries[] = { + {"_cytominer_calculate_entropy_new", (DL_FUNC) &_cytominer_calculate_entropy_new, 1}, + {"_cytominer_CE_entropy_SR", (DL_FUNC) &_cytominer_CE_entropy_SR, 1}, + {"_cytominer_CE_entropy_FS2_new", 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z%>ysARR4|-yzrkqQ}{|1ZdNLM$==*BrH}gWXv0*I!!qOx_S}-B-4di9wpaL%D=G3l zKURlyA5p=A4{783p5IMD@goU@>|24d=_ry$tQn1(M$#rTjc#0;g}mjX{POj!6)SNP zX2G>6CQX#Pe`_N1=%%1|wYAFKA2;3^b}ho9RncrjbEB9*=hs)d6PDx_?nEU018u>} monG+fc?Ou#$~%zY&5d)#H_pAe&GOA{mW5|#_)foYoc{;!mC + +using namespace Rcpp; +using namespace std; +using namespace arma; + + +//' calculate_entropy_new +//' +//' Calculate the entropy of a matrix based on SVD alternative method when more observations than features. A = XtX instead of A = Xt +//' @param A dataset mxm (m features and n observations) +// entropy +//' @return Calculate the entropy contribution of a matrix based on SVD +// [[Rcpp::export]] +double calculate_entropy_new(arma::mat A) { + + // calculate the svd + vec s = svd(A); + + // normalize relative values + vec v = s/sum(s); + + // calculate the entropy for values bigger than 0 (to avoid error) + arma::vec vNoZero = v.elem(find(v > 0)); + double e = -sum(vNoZero % log(vNoZero))/log(v.size()); + + return e; +} + + +/* SR method +Simple Ranking: +select mc features according to the highest ranking order of their CE values +*/ + +//' CE_entropy_SR +//' +//' Calculate the entropy contribution of a matrix based on SVD +//' @param A dataset mxn (m features and n observations) +//' @return entropy contribution vector +// [[Rcpp::export]] +NumericVector CE_entropy_SR(NumericMatrix A){ + // convert into matrix (armadillo) + mat Amat(A.begin(), A.nrow(), A.ncol(), false); + // total entropy + double E = calculate_entropy_new(Amat); + // Contributio vector to the entropy by a leave-one-out comparison + NumericVector CE(A.nrow()); + // for each feature calculate the contribution to the entropy by a leave-one-out comparison + for(unsigned int i = 0; i < A.nrow(); i++){ + //cout << "i: " << i << endl; + mat Ai = Amat; + // remove the row i + Ai.shed_row(i) ; + //cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; + double Ei = calculate_entropy_new(Ai); + CE[i] = E - Ei; + } + + return CE; +} + +//-------------------------------------------------------------------------------------------------------------- +//-------------------------------------------------------------------------------------------------------------- +//-------------------------------------------------------------------------------------------------------------- + +/* FS1 method +* Forward selection +*/ + +/* +//' Calculate the entropy of a matrix based on SVD with FS method +//' @param A dataset mxn (m features and n observations) +//' @param mc number of feature to select +//' @return index of features that were selected +// [[Rcpp::export]] +NumericVector CE_entropy_FS1(NumericMatrix A, unsigned int mc){ +// convert into matrix (armadillo) +mat Amat(A.begin(), A.nrow(), A.ncol(), false); + +// vector of index of the features +vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); + +// best features index +NumericVector idxBest(mc); + +// total entropy +double E = calculate_entropy(Amat); + +// CE is the contribution vector of each feature to the entropy +vec CE(A.nrow()); + +// for each feature calculate the contribution to the entropy by a leave-one-out comparison +for(unsigned int i = 0; i < A.nrow(); i++){ +mat Ai = Amat; +// remove the row i +Ai.shed_row(i); +//cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; +double Ei = calculate_entropy(Ai); +CE[i] = E - Ei; +} + +// select the best feature +int idx = CE.index_max(); + +// index of the best feature in the R referential +idxBest[0] = idxFeat[idx]; +// remove the selected feature number +idxFeat.shed_row(idx); + +// save the previous entropy contribution +double Eprev = CE[idx]; + +// save selected features in a new matrix newA and remove it from the other matrix +mat newA(mc, Amat.n_cols, fill::zeros); +newA.row(0) = Amat.row(idx); +Amat.shed_row(idx); + +for(unsigned int j = 0; j < mc - 1; j++){ +vec CE(Amat.n_rows); +// for all features +for(unsigned a = 0; a < Amat.n_rows; a++){ +//bind the vector to Atest +mat Atest = join_cols(newA.rows(0, j), Amat.row(a)); +// calculate the entropy on the features selected and an extra one +double Ei = calculate_entropy(Atest); +// gain of entropy +CE[a] = Ei - Eprev; +} +// index of best entropy contribution +int idx = CE.index_max(); +idxBest[j+1] = idxFeat[idx]; + +// update the previous entropy contribution +Eprev = CE[idx]; +newA.row(j+1) = Amat.row(idx); +Amat.shed_row(idx); +idxFeat.shed_row(idx); +} + +cout << "Best features: " << idxBest << endl; + +return idxBest; +}*/ + +/* +//' Calculate the entropy of a matrix based on SVD with FS method and new SV-entropy function +//' @param A dataset mxm (m features and n observations) m << n +//' @param mc number of feature to select +//' @return index of features that were selected +// [[Rcpp::export]] +NumericVector CE_entropy_FS1_new(NumericMatrix A, unsigned int mc){ +// convert into matrix (armadillo) +mat Amat(A.begin(), A.nrow(), A.ncol(), false); + +// vector of index of the features +vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); + +// best features index +NumericVector idxBest(mc); + +// total entropy +double E = calculate_entropy_new(Amat); + +// CE is the contribution vector of each feature to the entropy +vec CE(A.nrow()); + +// for each feature calculate the contribution to the entropy by a leave-one-out comparison +for(unsigned int i = 0; i < A.nrow(); i++){ +mat Ai = Amat; +// remove the row i and column i +Ai.shed_row(i); +Ai.shed_col(i); +//cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; +double Ei = calculate_entropy_new(Ai); +CE[i] = E - Ei; +} + +// select the best feature +int idx = CE.index_max(); + +// index of the best feature in the R referential +idxBest[0] = idxFeat[idx]; +// remove the selected feature number +idxFeat.shed_row(idx); + +// save the previous entropy contribution +double Eprev = CE[idx]; + +// save selected features in a new matrix newA and remove it from the other matrix +// PROBLEM HERE!!!!!! + +mat newA(mc, Amat.n_cols, fill::zeros); +newA.row(0) = Amat.row(idx); +Amat.shed_row(idx); + +for(unsigned int j = 0; j < mc - 1; j++){ +vec CE(Amat.n_rows); +// for all features +for(unsigned a = 0; a < Amat.n_rows; a++){ +//bind the vector to Atest +mat Atest = join_cols(newA.rows(0, j), Amat.row(a)); +// calculate the entropy on the features selected and an extra one +double Ei = calculate_entropy_new(Atest); +// gain of entropy +CE[a] = Ei - Eprev; +} +// index of best entropy contribution +int idx = CE.index_max(); +idxBest[j+1] = idxFeat[idx]; + +// update the previous entropy contribution +Eprev = CE[idx]; +newA.row(j+1) = Amat.row(idx); +Amat.shed_row(idx); +idxFeat.shed_row(idx); +} + +cout << "Best features: " << idxBest << endl; + +return idxBest; +} +*/ + +//-------------------------------------------------------------------------------------------------------------- +//-------------------------------------------------------------------------------------------------------------- +//-------------------------------------------------------------------------------------------------------------- + +/* FS2 method +Forward selection +*/ + +/* +//' Calculate the entropy of a matrix based on SVD with FS method +//' @param A dataset mxn (m features and n observations) +//' @param mc number of feature to select +//' @return index of features that were selected +// [[Rcpp::export]] +NumericVector CE_entropy_FS2(NumericMatrix A, unsigned int mc){ +// convert into matrix (armadillo) +mat Amat(A.begin(), A.nrow(), A.ncol(), false); + +// vector of index of the features +vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); + +// best features index +NumericVector idxBest(mc); + +for(unsigned int j = 0; j < mc; j++){ +// total entropy +double E = calculate_entropy(Amat); + +// Contribution vector to the entropy by a leave-one-out comparison +vec CE(Amat.n_rows); + +// for each feature calculate the contribution to the entropy by a leave-one-out comparison +for(unsigned int i = 0; i < Amat.n_rows; i++){ +mat Ai = Amat; +// remove row i +Ai.shed_row(i); +double Ei = calculate_entropy(Ai); +CE[i] = E - Ei; +} +// find the index of the highest entropy contribution +int idx = CE.index_max(); +idxBest[j] = idxFeat[idx]; + +// remove the best feature +Amat.shed_row(idx); +idxFeat.shed_row(idx); +} +cout << "Best features: " << idxBest << endl; + +return idxBest; +} +*/ + +//' CE_entropy_FS2_new +//' +//' Calculate the entropy of a matrix based on SVD with FS method +//' @param A dataset mxn (m features and n observations) +//' @param mc number of feature to select +//' @return index of features that were selected +// [[Rcpp::export]] +NumericVector CE_entropy_FS2_new(NumericMatrix A, unsigned int mc){ + // convert into matrix (armadillo) + mat Amat(A.begin(), A.nrow(), A.ncol(), false); + + // vector of index of the features + vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); + + // best features index + NumericVector idxBest(mc); + + for(unsigned int j = 0; j < mc; j++){ + //cout << "j: " << j << endl; + // total entropy + double E = calculate_entropy_new(Amat); + + // Contribution vector to the entropy by a leave-one-out comparison + vec CE(Amat.n_rows); + + // for each feature calculate the contribution to the entropy by a leave-one-out comparison + for(unsigned int i = 0; i < Amat.n_rows; i++){ + + mat Ai = Amat; + // remove row i and column i + Ai.shed_row(i); + Ai.shed_col(i); + double Ei = calculate_entropy_new(Ai); + CE[i] = E - Ei; + } + // find the index of the highest entropy contribution + int idx = CE.index_max(); + idxBest[j] = idxFeat[idx]; + + // remove the best feature + Amat.shed_row(idx); + Amat.shed_col(idx); + idxFeat.shed_row(idx); + } + //cout << "Best features: " << idxBest << endl; + + return idxBest; +} diff --git a/src/entropy_feature_selection.o b/src/entropy_feature_selection.o new file mode 100644 index 0000000000000000000000000000000000000000..34fe3156651206c8faaded6e5c62973d1b202967 GIT binary patch literal 762652 zcmeFa3!Ge4l`nicoj?a-s-q~G5v9~PCdi{J=}x*rfE4LUPLWDz6CRmCD5|TgyNmS8 zdZd#d4$XBg_MFoQZZ4Ob1j5=qSpF@r7jCM?h}~py}_w z*53QP>eQ>NgMPp7>tCNb`?2=gYp?xUYwvaTe(JTKojzOBnwmBI;F*Ug15lik&BuBHcY`iBHbK+BY+7<}-Z3#^yH0AzcvB6X}nT`(z zRx*ei-)O%`*(MR}8xiy=d<&IBzD<0cBEXIB0f|qS@DspCb)f#J?DoG{Y-={2O(i13 zDdwUZ-wBCt5mh4oT1N$a6JI!o>#&r6J(-LE<=58W*Zyu*xA4hM+}-{1%uKK|VJ 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zL29!9$Uc``%k%%VmP-D#mby2kKfH9^^R#ol|Gs0O+7P-I?TKb=9wP`%PW4SA{KkafiPv-4nVY=(>Vqv=TcD*p&*{O^5o}=x~ z-u1$yvYGqTLlCiLiHlM3ym@Kzy&UySP@HMH-(*Q{CTjAuPZj0KtH-m% Date: Wed, 8 Nov 2017 13:39:16 -0500 Subject: [PATCH 02/15] polished the code + adopted SR-SVD method --- R/RcppExports.R | 18 +- R/entropy_feature_selection.R | 22 ++- src/RcppExports.cpp | 23 +-- src/RcppExports.o | Bin 706568 -> 642736 bytes src/cytominer.so | Bin 136448 -> 136328 bytes src/entropy_feature_selection.cpp | 296 ++---------------------------- src/entropy_feature_selection.o | Bin 762652 -> 779644 bytes 7 files changed, 43 insertions(+), 316 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 08dc61d..d7713cd 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -3,13 +3,13 @@ #' calculate_entropy_new #' -#' Calculate the entropy of a matrix based on SVD alternative method when more observations than features. A = XtX instead of A = Xt -#' @param A dataset mxm (m features and n observations) +#' Calculate the entropy of a matrix singular values, when their summation is normalized to one. +#' @param A matrix of any arbitrary size NULL #' @return Calculate the entropy contribution of a matrix based on SVD -calculate_entropy_new <- function(A) { - .Call('_cytominer_calculate_entropy_new', PACKAGE = 'cytominer', A) +calculate_sv_entropy <- function(A) { + .Call('_cytominer_calculate_sv_entropy', PACKAGE = 'cytominer', A) } #' CE_entropy_SR @@ -21,13 +21,3 @@ CE_entropy_SR <- function(A) { .Call('_cytominer_CE_entropy_SR', PACKAGE = 'cytominer', A) } -#' CE_entropy_FS2_new -#' -#' Calculate the entropy of a matrix based on SVD with FS method -#' @param A dataset mxn (m features and n observations) -#' @param mc number of feature to select -#' @return index of features that were selected -CE_entropy_FS2_new <- function(A, mc) { - .Call('_cytominer_CE_entropy_FS2_new', PACKAGE = 'cytominer', A, mc) -} - diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index c5da83f..663674c 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -1,20 +1,26 @@ #' entropy based feature selection #' -#' @param X named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names. -#' @param n_features number of features to be extracted +#' @param population named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names. +#' @param variables vector containing the names of numerical variables (or features) in the population. +#' @param n_feature integer specifying number of features to be selected #' #' @importFrom magrittr %>% -#' @importFrom magrittr %<>% -#' @importFrom foreach %dopar% #' #' @return vector containing name of the selected features #' #' @export -entropy_feature_selection <- function(X, n_features) { +entropy_feature_selection <- function(population, variables, n_feature) { - A <- tcrossprod(t(X), t(X)) + population_data <- population %>% + dplyr::select(dplyr::one_of(variables)) %>% + as.matrix() - feat_index <- CE_entropy_FS2_new(A, n_features) + feat_inner_prods <- crossprod(population_data, population_data) - return(colnames(X)[feat_index[1:n_features]]) + CE <- CE_entropy_SR(feat_inner_prods) + + feat_rank <- order(CE, decreasing = T) + + return(list(feats = colnames(population_data)[feat_rank[1:n_feature]], + CE = CE[feat_rank[1:n_feature]])) } diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 867414e..2936329 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -6,14 +6,14 @@ using namespace Rcpp; -// calculate_entropy_new -double calculate_entropy_new(arma::mat A); -RcppExport SEXP _cytominer_calculate_entropy_new(SEXP ASEXP) { +// calculate_sv_entropy +double calculate_sv_entropy(arma::mat A); +RcppExport SEXP _cytominer_calculate_sv_entropy(SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< arma::mat >::type A(ASEXP); - rcpp_result_gen = Rcpp::wrap(calculate_entropy_new(A)); + rcpp_result_gen = Rcpp::wrap(calculate_sv_entropy(A)); return rcpp_result_gen; END_RCPP } @@ -28,23 +28,10 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } -// CE_entropy_FS2_new -NumericVector CE_entropy_FS2_new(NumericMatrix A, unsigned int mc); -RcppExport SEXP _cytominer_CE_entropy_FS2_new(SEXP ASEXP, SEXP mcSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< NumericMatrix >::type A(ASEXP); - Rcpp::traits::input_parameter< unsigned int >::type mc(mcSEXP); - rcpp_result_gen = Rcpp::wrap(CE_entropy_FS2_new(A, mc)); - return rcpp_result_gen; -END_RCPP -} static const R_CallMethodDef CallEntries[] = { - {"_cytominer_calculate_entropy_new", (DL_FUNC) &_cytominer_calculate_entropy_new, 1}, + {"_cytominer_calculate_sv_entropy", (DL_FUNC) &_cytominer_calculate_sv_entropy, 1}, {"_cytominer_CE_entropy_SR", (DL_FUNC) &_cytominer_CE_entropy_SR, 1}, - {"_cytominer_CE_entropy_FS2_new", (DL_FUNC) &_cytominer_CE_entropy_FS2_new, 2}, {NULL, NULL, 0} }; diff --git a/src/RcppExports.o b/src/RcppExports.o index 3f5709c896403d079291869228199a18b0ca1b70..e9aa2e152f2c7597eeb2aff48a193ebb2ae61ebc 100644 GIT binary patch literal 642736 zcmcG%2Y3}l7dJk8>xL9UQ9~dQ!rlucA%H*`#;b3<$3nrJLmU1XU?3Not>SX*}Q$~ 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z%>ysARR4|-yzrkqQ}{|1ZdNLM$==*BrH}gWXv0*I!!qOx_S}-B-4di9wpaL%D=G3l zKURlyA5p=A4{783p5IMD@goU@>|24d=_ry$tQn1(M$#rTjc#0;g}mjX{POj!6)SNP zX2G>6CQX#Pe`_N1=%%1|wYAFKA2;3^b}ho9RncrjbEB9*=hs)d6PDx_?nEU018u>} monG+fc?Ou#$~%zY&5d)#H_pAe&GOA{mW5|#_)foYoc{;!mC 0)); - double e = -sum(vNoZero % log(vNoZero))/log(v.size()); + double e = -sum(vNoZero % log(vNoZero))/log(10); return e; } - /* SR method Simple Ranking: select mc features according to the highest ranking order of their CE values @@ -43,286 +42,31 @@ select mc features according to the highest ranking order of their CE values //' @return entropy contribution vector // [[Rcpp::export]] NumericVector CE_entropy_SR(NumericMatrix A){ - // convert into matrix (armadillo) - mat Amat(A.begin(), A.nrow(), A.ncol(), false); - // total entropy - double E = calculate_entropy_new(Amat); - // Contributio vector to the entropy by a leave-one-out comparison - NumericVector CE(A.nrow()); - // for each feature calculate the contribution to the entropy by a leave-one-out comparison - for(unsigned int i = 0; i < A.nrow(); i++){ - //cout << "i: " << i << endl; - mat Ai = Amat; - // remove the row i - Ai.shed_row(i) ; - //cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; - double Ei = calculate_entropy_new(Ai); - CE[i] = E - Ei; - } - - return CE; -} - -//-------------------------------------------------------------------------------------------------------------- -//-------------------------------------------------------------------------------------------------------------- -//-------------------------------------------------------------------------------------------------------------- - -/* FS1 method -* Forward selection -*/ - -/* -//' Calculate the entropy of a matrix based on SVD with FS method -//' @param A dataset mxn (m features and n observations) -//' @param mc number of feature to select -//' @return index of features that were selected -// [[Rcpp::export]] -NumericVector CE_entropy_FS1(NumericMatrix A, unsigned int mc){ -// convert into matrix (armadillo) -mat Amat(A.begin(), A.nrow(), A.ncol(), false); - -// vector of index of the features -vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); - -// best features index -NumericVector idxBest(mc); - -// total entropy -double E = calculate_entropy(Amat); -// CE is the contribution vector of each feature to the entropy -vec CE(A.nrow()); - -// for each feature calculate the contribution to the entropy by a leave-one-out comparison -for(unsigned int i = 0; i < A.nrow(); i++){ -mat Ai = Amat; -// remove the row i -Ai.shed_row(i); -//cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; -double Ei = calculate_entropy(Ai); -CE[i] = E - Ei; -} - -// select the best feature -int idx = CE.index_max(); - -// index of the best feature in the R referential -idxBest[0] = idxFeat[idx]; -// remove the selected feature number -idxFeat.shed_row(idx); - -// save the previous entropy contribution -double Eprev = CE[idx]; - -// save selected features in a new matrix newA and remove it from the other matrix -mat newA(mc, Amat.n_cols, fill::zeros); -newA.row(0) = Amat.row(idx); -Amat.shed_row(idx); - -for(unsigned int j = 0; j < mc - 1; j++){ -vec CE(Amat.n_rows); -// for all features -for(unsigned a = 0; a < Amat.n_rows; a++){ -//bind the vector to Atest -mat Atest = join_cols(newA.rows(0, j), Amat.row(a)); -// calculate the entropy on the features selected and an extra one -double Ei = calculate_entropy(Atest); -// gain of entropy -CE[a] = Ei - Eprev; -} -// index of best entropy contribution -int idx = CE.index_max(); -idxBest[j+1] = idxFeat[idx]; - -// update the previous entropy contribution -Eprev = CE[idx]; -newA.row(j+1) = Amat.row(idx); -Amat.shed_row(idx); -idxFeat.shed_row(idx); -} - -cout << "Best features: " << idxBest << endl; - -return idxBest; -}*/ - -/* -//' Calculate the entropy of a matrix based on SVD with FS method and new SV-entropy function -//' @param A dataset mxm (m features and n observations) m << n -//' @param mc number of feature to select -//' @return index of features that were selected -// [[Rcpp::export]] -NumericVector CE_entropy_FS1_new(NumericMatrix A, unsigned int mc){ -// convert into matrix (armadillo) -mat Amat(A.begin(), A.nrow(), A.ncol(), false); - -// vector of index of the features -vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); - -// best features index -NumericVector idxBest(mc); - -// total entropy -double E = calculate_entropy_new(Amat); - -// CE is the contribution vector of each feature to the entropy -vec CE(A.nrow()); - -// for each feature calculate the contribution to the entropy by a leave-one-out comparison -for(unsigned int i = 0; i < A.nrow(); i++){ -mat Ai = Amat; -// remove the row i and column i -Ai.shed_row(i); -Ai.shed_col(i); -//cout << "Ai size: " << Ai.n_cols << "columns and " << Ai.n_rows << "rows" << endl; -double Ei = calculate_entropy_new(Ai); -CE[i] = E - Ei; -} - -// select the best feature -int idx = CE.index_max(); - -// index of the best feature in the R referential -idxBest[0] = idxFeat[idx]; -// remove the selected feature number -idxFeat.shed_row(idx); - -// save the previous entropy contribution -double Eprev = CE[idx]; - -// save selected features in a new matrix newA and remove it from the other matrix -// PROBLEM HERE!!!!!! - -mat newA(mc, Amat.n_cols, fill::zeros); -newA.row(0) = Amat.row(idx); -Amat.shed_row(idx); - -for(unsigned int j = 0; j < mc - 1; j++){ -vec CE(Amat.n_rows); -// for all features -for(unsigned a = 0; a < Amat.n_rows; a++){ -//bind the vector to Atest -mat Atest = join_cols(newA.rows(0, j), Amat.row(a)); -// calculate the entropy on the features selected and an extra one -double Ei = calculate_entropy_new(Atest); -// gain of entropy -CE[a] = Ei - Eprev; -} -// index of best entropy contribution -int idx = CE.index_max(); -idxBest[j+1] = idxFeat[idx]; - -// update the previous entropy contribution -Eprev = CE[idx]; -newA.row(j+1) = Amat.row(idx); -Amat.shed_row(idx); -idxFeat.shed_row(idx); -} - -cout << "Best features: " << idxBest << endl; - -return idxBest; -} -*/ - -//-------------------------------------------------------------------------------------------------------------- -//-------------------------------------------------------------------------------------------------------------- -//-------------------------------------------------------------------------------------------------------------- - -/* FS2 method -Forward selection -*/ - -/* -//' Calculate the entropy of a matrix based on SVD with FS method -//' @param A dataset mxn (m features and n observations) -//' @param mc number of feature to select -//' @return index of features that were selected -// [[Rcpp::export]] -NumericVector CE_entropy_FS2(NumericMatrix A, unsigned int mc){ -// convert into matrix (armadillo) -mat Amat(A.begin(), A.nrow(), A.ncol(), false); - -// vector of index of the features -vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); - -// best features index -NumericVector idxBest(mc); - -for(unsigned int j = 0; j < mc; j++){ -// total entropy -double E = calculate_entropy(Amat); - -// Contribution vector to the entropy by a leave-one-out comparison -vec CE(Amat.n_rows); - -// for each feature calculate the contribution to the entropy by a leave-one-out comparison -for(unsigned int i = 0; i < Amat.n_rows; i++){ -mat Ai = Amat; -// remove row i -Ai.shed_row(i); -double Ei = calculate_entropy(Ai); -CE[i] = E - Ei; -} -// find the index of the highest entropy contribution -int idx = CE.index_max(); -idxBest[j] = idxFeat[idx]; - -// remove the best feature -Amat.shed_row(idx); -idxFeat.shed_row(idx); -} -cout << "Best features: " << idxBest << endl; - -return idxBest; -} -*/ - -//' CE_entropy_FS2_new -//' -//' Calculate the entropy of a matrix based on SVD with FS method -//' @param A dataset mxn (m features and n observations) -//' @param mc number of feature to select -//' @return index of features that were selected -// [[Rcpp::export]] -NumericVector CE_entropy_FS2_new(NumericMatrix A, unsigned int mc){ // convert into matrix (armadillo) mat Amat(A.begin(), A.nrow(), A.ncol(), false); - // vector of index of the features - vec idxFeat(linspace(1, Amat.n_rows, Amat.n_rows)); + // total entropy + double E = calculate_sv_entropy(Amat); - // best features index - NumericVector idxBest(mc); + // vector of contribution to the entropy by on a leave-a-feature-out basis + NumericVector CE(A.nrow()); - for(unsigned int j = 0; j < mc; j++){ - //cout << "j: " << j << endl; - // total entropy - double E = calculate_entropy_new(Amat); + // vector of selected feature indices + NumericVector selected_indx; - // Contribution vector to the entropy by a leave-one-out comparison - vec CE(Amat.n_rows); + // for each feature calculate the contribution to the entropy by leaving that feature out + for(unsigned int i = 0; i < A.nrow(); i++){ - // for each feature calculate the contribution to the entropy by a leave-one-out comparison - for(unsigned int i = 0; i < Amat.n_rows; i++){ + mat Ai = Amat; - mat Ai = Amat; - // remove row i and column i - Ai.shed_row(i); - Ai.shed_col(i); - double Ei = calculate_entropy_new(Ai); - CE[i] = E - Ei; - } - // find the index of the highest entropy contribution - int idx = CE.index_max(); - idxBest[j] = idxFeat[idx]; + // remove the row and column i + Ai.shed_row(i); + Ai.shed_col(i); - // remove the best feature - Amat.shed_row(idx); - Amat.shed_col(idx); - idxFeat.shed_row(idx); + double Ei = calculate_sv_entropy(Ai); + CE[i] = E - Ei; } - //cout << "Best features: " << idxBest << endl; - return idxBest; + return(CE); } diff --git a/src/entropy_feature_selection.o b/src/entropy_feature_selection.o index 34fe3156651206c8faaded6e5c62973d1b202967..57f7940d492705df6375496ed75407e872d871dd 100644 GIT binary patch literal 779644 zcmc$Hd3+Sb)^_!*9Z1MXf-F4)VE`cs5J-aTkU)S51Ox?TkBA^B05n5CqgKf+FCKxPd4tuDJ0%=Ty~9cQf33-#^~(&F?oeUFUhusZ*!c?&|Km 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zL29!9$Uc``%k%%VmP-D#mby2kKfH9^^R#ol|Gs0O+7P-I?TKb=9wP`%PW4SA{KkafiPv-4nVY=(>Vqv=TcD*p&*{O^5o}=x~ z-u1$yvYGqTLlCiLiHlM3ym@Kzy&UySP@HMH-(*Q{CTjAuPZj0KtH-m% Date: Wed, 8 Nov 2017 14:56:25 -0500 Subject: [PATCH 03/15] updated the documentation --- R/entropy_feature_selection.R | 2 +- man/CE_entropy_FS2_new.Rd | 19 ------------------- man/entropy_feature_selection.Rd | 11 +++++++++-- 3 files changed, 10 insertions(+), 22 deletions(-) delete mode 100644 man/CE_entropy_FS2_new.Rd diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index 663674c..5453b1e 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -6,7 +6,7 @@ #' #' @importFrom magrittr %>% #' -#' @return vector containing name of the selected features +#' @return vector containing name of the features sorted based on their SVD score. Higher score means better. #' #' @export entropy_feature_selection <- function(population, variables, n_feature) { diff --git a/man/CE_entropy_FS2_new.Rd b/man/CE_entropy_FS2_new.Rd deleted file mode 100644 index 41bd6a4..0000000 --- a/man/CE_entropy_FS2_new.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{CE_entropy_FS2_new} -\alias{CE_entropy_FS2_new} -\title{CE_entropy_FS2_new} -\usage{ -CE_entropy_FS2_new(A, mc) -} -\arguments{ -\item{A}{dataset mxn (m features and n observations)} - -\item{mc}{number of feature to select} -} -\value{ -index of features that were selected -} -\description{ -Calculate the entropy of a matrix based on SVD with FS method -} diff --git a/man/entropy_feature_selection.Rd b/man/entropy_feature_selection.Rd index e8318a2..184ae50 100644 --- a/man/entropy_feature_selection.Rd +++ b/man/entropy_feature_selection.Rd @@ -4,10 +4,17 @@ \alias{entropy_feature_selection} \title{entropy based feature selection} \usage{ -entropy_feature_selection(x) +entropy_feature_selection(population, variables, n_feature) } \arguments{ -\item{x}{data matrix} +\item{population}{named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names.} + +\item{variables}{vector containing the names of numerical variables (or features) in the population.} + +\item{n_feature}{integer specifying number of features to be selected} +} +\value{ +vector containing name of the features sorted based on their SVD score. Higher score means better. } \description{ entropy based feature selection From 2c9f704f36a8eef884345dbb0b54d5dc134a87e0 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Wed, 13 Dec 2017 16:32:01 -0500 Subject: [PATCH 04/15] standardize the namings --- NAMESPACE | 2 + R/RcppExports.R | 28 ++++---- R/entropy_feature_selection.R | 6 +- ...opy_SR.Rd => score_features_sv_entropy.Rd} | 10 +-- man/singular_value_entropy.Rd | 17 +++++ src/RcppExports.cpp | 22 +++--- src/RcppExports.o | Bin 642736 -> 642796 bytes src/cytominer.so | Bin 136328 -> 136160 bytes src/entropy_feature_selection.cpp | 63 +++++++++--------- src/entropy_feature_selection.o | Bin 779644 -> 776160 bytes 10 files changed, 84 insertions(+), 64 deletions(-) rename man/{CE_entropy_SR.Rd => score_features_sv_entropy.Rd} (54%) create mode 100644 man/singular_value_entropy.Rd diff --git a/NAMESPACE b/NAMESPACE index 8933bc9..83366c5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,6 +11,8 @@ export(extract_subpopulations) export(generalized_log) export(normalize) export(replicate_correlation) +export(score_features_sv_entropy) +export(singular_value_entropy) export(transform) export(variable_importance) export(variable_select) diff --git a/R/RcppExports.R b/R/RcppExports.R index d7713cd..d2df9ac 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,23 +1,27 @@ # Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 -#' calculate_entropy_new +#' singular_value_entropy #' -#' Calculate the entropy of a matrix singular values, when their summation is normalized to one. -#' @param A matrix of any arbitrary size -NULL - -#' @return Calculate the entropy contribution of a matrix based on SVD -calculate_sv_entropy <- function(A) { - .Call('_cytominer_calculate_sv_entropy', PACKAGE = 'cytominer', A) +#' Calculate the entropy of a matrix singular values in the SVD decomposition, when their summation is normalized to one. +#' @param A a matrix of any arbitrary size +#' @return entropy of the normalized singular values using log base 10 +#' +#' @export +#' +singular_value_entropy <- function(A) { + .Call('_cytominer_singular_value_entropy', PACKAGE = 'cytominer', A) } -#' CE_entropy_SR +#' score_features_sv_entropy #' #' Calculate the entropy contribution of a matrix based on SVD -#' @param A dataset mxn (m features and n observations) +#' @param data a dataset mxn (m features and n observations) #' @return entropy contribution vector -CE_entropy_SR <- function(A) { - .Call('_cytominer_CE_entropy_SR', PACKAGE = 'cytominer', A) +#' +#' @export +#' +score_features_sv_entropy <- function(data) { + .Call('_cytominer_score_features_sv_entropy', PACKAGE = 'cytominer', data) } diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index 5453b1e..0caf881 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -17,10 +17,10 @@ entropy_feature_selection <- function(population, variables, n_feature) { feat_inner_prods <- crossprod(population_data, population_data) - CE <- CE_entropy_SR(feat_inner_prods) + entropy_score <- score_features_sv_entropy(feat_inner_prods) - feat_rank <- order(CE, decreasing = T) + feat_rank <- order(entropy_score, decreasing = T) return(list(feats = colnames(population_data)[feat_rank[1:n_feature]], - CE = CE[feat_rank[1:n_feature]])) + entropy_score = entropy_score[feat_rank[1:n_feature]])) } diff --git a/man/CE_entropy_SR.Rd b/man/score_features_sv_entropy.Rd similarity index 54% rename from man/CE_entropy_SR.Rd rename to man/score_features_sv_entropy.Rd index fd1b8bd..fdafc42 100644 --- a/man/CE_entropy_SR.Rd +++ b/man/score_features_sv_entropy.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R -\name{CE_entropy_SR} -\alias{CE_entropy_SR} -\title{CE_entropy_SR} +\name{score_features_sv_entropy} +\alias{score_features_sv_entropy} +\title{score_features_sv_entropy} \usage{ -CE_entropy_SR(A) +score_features_sv_entropy(data) } \arguments{ -\item{A}{dataset mxn (m features and n observations)} +\item{data}{a dataset mxn (m features and n observations)} } \value{ entropy contribution vector diff --git a/man/singular_value_entropy.Rd b/man/singular_value_entropy.Rd new file mode 100644 index 0000000..7839ce7 --- /dev/null +++ b/man/singular_value_entropy.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{singular_value_entropy} +\alias{singular_value_entropy} +\title{singular_value_entropy} +\usage{ +singular_value_entropy(A) +} +\arguments{ +\item{A}{a matrix of any arbitrary size} +} +\value{ +entropy of the normalized singular values using log base 10 +} +\description{ +Calculate the entropy of a matrix singular values in the SVD decomposition, when their summation is normalized to one. +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 2936329..414419c 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -6,32 +6,32 @@ using namespace Rcpp; -// calculate_sv_entropy -double calculate_sv_entropy(arma::mat A); -RcppExport SEXP _cytominer_calculate_sv_entropy(SEXP ASEXP) { +// singular_value_entropy +double singular_value_entropy(arma::mat A); +RcppExport SEXP _cytominer_singular_value_entropy(SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< arma::mat >::type A(ASEXP); - rcpp_result_gen = Rcpp::wrap(calculate_sv_entropy(A)); + rcpp_result_gen = Rcpp::wrap(singular_value_entropy(A)); return rcpp_result_gen; END_RCPP } -// CE_entropy_SR -NumericVector CE_entropy_SR(NumericMatrix A); -RcppExport SEXP _cytominer_CE_entropy_SR(SEXP ASEXP) { +// score_features_sv_entropy +NumericVector score_features_sv_entropy(NumericMatrix data); +RcppExport SEXP _cytominer_score_features_sv_entropy(SEXP dataSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< NumericMatrix >::type A(ASEXP); - rcpp_result_gen = Rcpp::wrap(CE_entropy_SR(A)); + Rcpp::traits::input_parameter< NumericMatrix >::type data(dataSEXP); + rcpp_result_gen = Rcpp::wrap(score_features_sv_entropy(data)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { - {"_cytominer_calculate_sv_entropy", (DL_FUNC) &_cytominer_calculate_sv_entropy, 1}, - {"_cytominer_CE_entropy_SR", (DL_FUNC) &_cytominer_CE_entropy_SR, 1}, + {"_cytominer_singular_value_entropy", (DL_FUNC) &_cytominer_singular_value_entropy, 1}, + {"_cytominer_score_features_sv_entropy", (DL_FUNC) &_cytominer_score_features_sv_entropy, 1}, {NULL, NULL, 0} }; diff --git a/src/RcppExports.o b/src/RcppExports.o index e9aa2e152f2c7597eeb2aff48a193ebb2ae61ebc..6bf9de6c67a2439946606aebc1e821edcbf106a8 100644 GIT binary patch delta 145771 zcmaHU2Y6J)_x8+gHoK{`gk*QKo3@*t5K0Opp%YppAcWqLUX@}&42TrzFv0+WfJ*sM zq$#4HfKmhmK~%6&6l@g1hE%`z%)K|+ME_r&=gvKI-ZN)+_F}sib|~f>7OpAgnG0{N;(v>Ud=*a;wN1smv6y0N#AC$)Q%n2GAgcC~goYNqZe=Er3&a6)PWZMR8d_BY z+3GzAE2q3~b&4dvm}>iVSWhn7Cc61GvY*nqm-ImUDG=!WV!K}s-z|RjE3USGr>6KI z*Mzo-)|N*01#fBGTz`!_`F$;1mkAgH`?ju^L-C>%)y*Le&b<=$3LN)YeKbuNWjEJ@6 z*v}2t(2D^Y`q3B-O%jjWqC@si3t*EF=Yo|1tmn>^wk1J)gQyo)H*vRKK0%Minb?|f zjM>|<;ORFR7k?7SuRGst0klSo~_h4@_F<2PO?Z0r*i76CRPEl;7eSe2Z5>=xTFOV>d=e)*X&e;p#goaTNjnAq-0H>DN6z;M-+m^G>#EI~T zQo@_MmBx_9?KxY52YJyIg-(J(@g!qa^t|Sd^v&y567GN}^#2GDYSsc6xo-*Kbrj%% zNsFPBSBm0+Nef{V$RS84Jc;iQOH;XJW7Et>(TMhL4Lj&x?Et2 z8AUm|Y`U&$?*gQ&cSMLQWzkg(afMR=7Lp91sUkWe!l_E;gko29p5T8fJ*xrxbrKyS zYI3_65)rM+*}029k%mSPaCE4d?ZA^$l7srDJkguVS=Cog2^CIwPRSMMs_8cqf?d9* z))P@Q+|1_T>ABn|jLOqx^fnc^=W+@h_AEm6T>dwzi$%OM+}wyIi~wWm{~c^6j`5t-43&xVSTtom0I%`D(qGs}DDL#C2%qp&Q%30^+&caR_13UeTY z<_jC3NNSkO{>skxB71SZcBj*k!JDpq(#*K2Dh1#jkq!Sfs3X!BkFGWZPn{@TUuYKk zv??Yl^g2=0Ihda{K+JaL=Pdh(F-JIKX(^_78@Eh24zfN}`ahLvf;x@Sq>N!xfvuCo zL#Hc43AZlCRDR+V_fc?l9kxl(1Vy5Cl-t(n2=v#5XLM9}x{@4TF8B+jUyE^aId`ER ze0x-Q>YA&JeSs(KZWRnrkgGGw2Y*7$aC!JTvDszk=fq)`#}slyTysTYp0l{)Y~Q^A<*A-Aq82>w-hd~~ zb69k9yD=v|1vgblw>z-+uZ+!(1=w3mafkCo;&XRAKO*kHeIQ)Xakdp04Aas*>qN_F zH;)j5qTPWn-eN2m<9^y$F*-WD!994J{RZ!oe4RsO&i{+rR0p29_c4RvNt?&tMctJb z4P}{m4vBA}^NzR=H$ymM;`sp4C?-DY9DLTB4!t7azwsoJT=jq{h(CbX+w{lb#yy1Hog%Etz005zbhrWwWC4Kvg$$ zwomkkjj?>DsL8Q0fm6^bf5MaYg@}o*8Fs#jVG;_A2oSo+Or7W z!^9fsr}qY

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values using log base 10 +//' +//' @export //' -//' Calculate the entropy of a matrix singular values, when their summation is normalized to one. -//' @param A matrix of any arbitrary size -// entropy -//' @return Calculate the entropy contribution of a matrix based on SVD // [[Rcpp::export]] -double calculate_sv_entropy(arma::mat A) { +double singular_value_entropy(arma::mat A) { // calculate the svd - vec s = svd(A); + vec singular_values = svd(A); // normalize relative values - vec v = s/sum(s); + vec singular_values_nrm = singular_values/sum(singular_values); - // calculate the entropy for values greater than 0 (to avoid error) - arma::vec vNoZero = v.elem(find(v > 0)); - double e = -sum(vNoZero % log(vNoZero))/log(10); + // calculate the entropy for values greater than 0 (to avoid log(0)) + arma::vec sv_nonzero = singular_values_nrm.elem(find(singular_values_nrm > 0)); + double sv_entropy = -sum(sv_nonzero % log(sv_nonzero))/log(10); - return e; + return sv_entropy; } -/* SR method -Simple Ranking: -select mc features according to the highest ranking order of their CE values -*/ - -//' CE_entropy_SR +//' score_features_sv_entropy //' //' Calculate the entropy contribution of a matrix based on SVD -//' @param A dataset mxn (m features and n observations) +//' @param data a dataset mxn (m features and n observations) //' @return entropy contribution vector +//' +//' @export +//' // [[Rcpp::export]] -NumericVector CE_entropy_SR(NumericMatrix A){ +NumericVector score_features_sv_entropy(NumericMatrix data){ // convert into matrix (armadillo) - mat Amat(A.begin(), A.nrow(), A.ncol(), false); + mat data_mat(data.begin(), data.nrow(), data.ncol(), false); // total entropy - double E = calculate_sv_entropy(Amat); + double sv_entropy_orig = singular_value_entropy(data_mat); // vector of contribution to the entropy by on a leave-a-feature-out basis - NumericVector CE(A.nrow()); - - // vector of selected feature indices - NumericVector selected_indx; + NumericVector sv_entropy(data.nrow()); // for each feature calculate the contribution to the entropy by leaving that feature out - for(unsigned int i = 0; i < A.nrow(); i++){ + for(unsigned int i = 0; i < data.nrow(); i++){ - mat Ai = Amat; + mat data_mat_i = data_mat; // remove the row and column i - Ai.shed_row(i); - Ai.shed_col(i); + data_mat_i.shed_row(i); + data_mat_i.shed_col(i); - double Ei = calculate_sv_entropy(Ai); - CE[i] = E - Ei; + double sv_entropy_i = singular_value_entropy(data_mat_i); + sv_entropy[i] = sv_entropy_orig - sv_entropy_i; } - return(CE); + return(sv_entropy); } diff --git a/src/entropy_feature_selection.o b/src/entropy_feature_selection.o index 57f7940d492705df6375496ed75407e872d871dd..fe4c929ae55a15815557e853f951520c81ac3f15 100644 GIT binary patch literal 776160 zcmc${d3Y2>*FIc5YX=fCKoGEJfC(Zb0g@1q9TG@j0%227_8k;Ob_7=x2oghF5D*m< z_Z7u`K@?=yM+8Jralu_gQPD?HM85l+s+#F;hUa+0#QbKmFGsZ(pOdH1g$ z{|O3Vcm;m2(LT?_Pa|yjtB-952xt1c?M23a%NQwN__Qs4IJ7dBXr`wxwzqB(=7EiqJxJ=t@B)2zX 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z^ev$B!?W(pygxuk-*Gsb+T#gZuT{ErQ{$!FnBKxZ``hT<%Vr=VJ%-oK;WOFE;oy5t3Rd!DqeRX)$M2!CKw~+N6!QDMX-eNXi-b3LIs+m7Id12fi>wANj?BV#N zKCBhO1987DTc#)VS#><6x9xTkq>2-I?2YUkinS{dx#Xnx9Ws53e)k4N-oGzz&7+s_ zs7sm)G44;e`ucHD;rv);{KD=}1fH~GeRQN6@kKE$jugm&cS~=i$HvH{9P3?OOJ#sz5x%&3HrDlQEci_D0?{#~) zYeCgFOXUxXE1#;>-1+&e<@0T4^B=k1PxqAm_(eX&L-`{Y`uJz?N6#L4;O(qp_Dy zp5$Hspkz;)@tT%IO-o}l00FJH1D@KUrR2 zYk7gk`A5x0xA?f~XrIX!1~d8KoPI;dK5S|%k497ZAC2XuHnr3&rP5FBs3!K<8MS*O zvB!)}RfEvj?1PXS*EdLtnUcQVy`kR^(T~^1&*&%mXU~>DWnri7GxX&PLe0A-dwk`{ zzOWJb94`EPNB4Ggyc5aVrsnd{SB9;*cB0ymOdPMBskTwDv#4+Y4bnWQxU!LinYb@Wa?$i%IG!Keb;UCi7LYyDd94 zln;jW!E9ISSY|X++p66T*0d&?Ue9KW`r@L?uxriE)^*!EeVLMe<~=zzsCTM+tD=5K zhVPGrmnMD3P2c(`jO{sBSw-hcj^y(Bn!yiSo9(M;lU2PlvF@na)~4)*>cM9T)@}8m XRyFI_-BO+Ub-2u!zNnV}m5BX+)Frxu From dc26c7c64c40e522a375f47a59fe185f8b93a2fd Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 10:10:24 -0500 Subject: [PATCH 05/15] made edits to documentation in the cpp file --- R/RcppExports.R | 10 ++++++---- man/score_features_sv_entropy.Rd | 8 +++++--- man/singular_value_entropy.Rd | 2 +- src/cytominer.so | Bin 136160 -> 136160 bytes src/entropy_feature_selection.cpp | 10 ++++++---- src/entropy_feature_selection.o | Bin 776160 -> 776160 bytes 6 files changed, 18 insertions(+), 12 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index d2df9ac..77f6a61 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -3,7 +3,7 @@ #' singular_value_entropy #' -#' Calculate the entropy of a matrix singular values in the SVD decomposition, when their summation is normalized to one. +#' Calculate the entropy of a matrix singular values (SV) in the SVD decomposition, when the SVs summation is normalized to one. #' @param A a matrix of any arbitrary size #' @return entropy of the normalized singular values using log base 10 #' @@ -15,9 +15,11 @@ singular_value_entropy <- function(A) { #' score_features_sv_entropy #' -#' Calculate the entropy contribution of a matrix based on SVD -#' @param data a dataset mxn (m features and n observations) -#' @return entropy contribution vector +#' Scores each feature based on the difference of normalized SVs entropy of the data +#' with and without the feature; the higher the difference, the more informative the +#' feature would be. +#' @param data a matrix which represents the dataset; columns and rows correspond to features and observations, respectively. +#' @return vector containing scores for all the features, in the same order as the columns of data are arranged #' #' @export #' diff --git a/man/score_features_sv_entropy.Rd b/man/score_features_sv_entropy.Rd index fdafc42..797694b 100644 --- a/man/score_features_sv_entropy.Rd +++ b/man/score_features_sv_entropy.Rd @@ -7,11 +7,13 @@ score_features_sv_entropy(data) } \arguments{ -\item{data}{a dataset mxn (m features and n observations)} +\item{data}{a matrix which represents the dataset; columns and rows correspond to features and observations, respectively.} } \value{ -entropy contribution vector +vector containing scores for all the features, in the same order as the columns of data are arranged } \description{ -Calculate the entropy contribution of a matrix based on SVD +Scores each feature based on the difference of normalized SVs entropy of the data +with and without the feature; the higher the difference, the more informative the +feature would be. } diff --git a/man/singular_value_entropy.Rd b/man/singular_value_entropy.Rd index 7839ce7..48b85c7 100644 --- a/man/singular_value_entropy.Rd +++ b/man/singular_value_entropy.Rd @@ -13,5 +13,5 @@ singular_value_entropy(A) entropy of the normalized singular values using log base 10 } \description{ -Calculate the entropy of a matrix singular values in the SVD decomposition, when their summation is normalized to one. +Calculate the entropy of a matrix singular values (SV) in the SVD decomposition, when the SVs summation is normalized to one. } diff --git a/src/cytominer.so b/src/cytominer.so index e5cd5d8db347c6153f5364565ae45c688660fd92..e64f8c0d8a5dd17410e949bfcd6212334850e603 100755 GIT binary patch delta 21 dcmaE`oa4cAj)pCa4(ZHKCL3*cPG_9E1^{b#2~q$6 delta 21 dcmaE`oa4cAj)pCa4(ZH$XBlpHPG_9E1^{YG2{8Zw diff --git a/src/entropy_feature_selection.cpp b/src/entropy_feature_selection.cpp index 7f1ed2c..c65e11b 100644 --- a/src/entropy_feature_selection.cpp +++ b/src/entropy_feature_selection.cpp @@ -10,7 +10,7 @@ using namespace arma; //' singular_value_entropy //' -//' Calculate the entropy of a matrix singular values in the SVD decomposition, when their summation is normalized to one. +//' Calculate the entropy of a matrix singular values (SV) in the SVD decomposition, when the SVs summation is normalized to one. //' @param A a matrix of any arbitrary size //' @return entropy of the normalized singular values using log base 10 //' @@ -34,9 +34,11 @@ double singular_value_entropy(arma::mat A) { //' score_features_sv_entropy //' -//' Calculate the entropy contribution of a matrix based on SVD -//' @param data a dataset mxn (m features and n observations) -//' @return entropy contribution vector +//' Scores each feature based on the difference of normalized SVs entropy of the data +//' with and without the feature; the higher the difference, the more informative the +//' feature would be. +//' @param data a matrix which represents the dataset; columns and rows correspond to features and observations, respectively. +//' @return vector containing scores for all the features, in the same order as the columns of data are arranged //' //' @export //' diff --git a/src/entropy_feature_selection.o b/src/entropy_feature_selection.o index fe4c929ae55a15815557e853f951520c81ac3f15..5a165db53f0f7cd7c1daeb4f5c7df6fad0192c77 100644 GIT binary patch delta 412 zcmXAiPbhikoH!If=a)?+wqJdOm%h@AK66n?G;KpSNsmVulJI=-1W<)phwF8UZ#-0k-je zSpfzg8DOpqAo}Fk03t&4%~{*#E(j7GjSQ^@h}FXdIuywjXi}uzq9&0Ui`FopG0D~0 zcZ-@4gSzUIY6D%fKLA!azybP70ODUxX2{9pz1?IKR>bx@ZHdcyhJs5fLVEF&ey~R` z6loNDb*4xm?9W<78b=(^1K%`@gZlKF5*X3dwiHM7zD+TVWPfdZ`cQRyIHpT; zyk*4HIxpy>9M^j$Mhq~w#P8^<9H(8r7ul!v(ldJp8`3JwQNKd2Ov`o3tT4Q0s~R_e zZCI7_S!znv7)^Xl<_$Ge{EtHU-zI4c)exWPyJwEOJIxVQ!;jnHJPS%N1ef3zOrazj I&U=pSKe7URO8@`> delta 412 zcmXAiODIHf6vofvf5$Z}L{a3CBnuhIpb2@+grsI8p~S{xkUWOX#DXkrJo`&ov7ADw z*;yhxMK&6I5;qGv5>iqi7cTRm@;?$Ek^{lSp1tmYQQd=KXby@XtZf+n0kEsws z6!l=UzDQ9cw#2Ly1rb|y`!@|^n?C-gF#2`AEyaGlW78D+W4|`htNUPuT2VE*c}8c% zdEJ;)%RHg?<6P_xb{k->i{I3jagMr*USuECv(KE{Q4&=Fo~}~JIZ?R|nAt|IS*?Nw zunh}xK3VBk1x7hvk$FR<+5e+Z{?`%eMb*hi`u3R@)9TG`RltuMfdmUmFa(#7CYVBc JERb*?+JApqd>sG) From 01a74bbf25a56acff4659b82f0955948dcb06380 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 10:31:19 -0500 Subject: [PATCH 06/15] modified doc. for the R file --- R/entropy_feature_selection.R | 7 ++++--- man/entropy_feature_selection.Rd | 8 ++++---- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index 0caf881..abb8da0 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -1,12 +1,12 @@ -#' entropy based feature selection +#' feature selection based on the entropy of data singular values #' -#' @param population named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names. +#' @param population named tbl containing data, where columns and rows correspond to features (and/or metadata) and samples, respectively. Column names are assumed to be feature or metadata names. #' @param variables vector containing the names of numerical variables (or features) in the population. #' @param n_feature integer specifying number of features to be selected #' #' @importFrom magrittr %>% #' -#' @return vector containing name of the features sorted based on their SVD score. Higher score means better. +#' @return vector containing name of the features sorted based on their score, and the actual score values. Higher score means more informative feature. #' #' @export entropy_feature_selection <- function(population, variables, n_feature) { @@ -15,6 +15,7 @@ entropy_feature_selection <- function(population, variables, n_feature) { dplyr::select(dplyr::one_of(variables)) %>% as.matrix() + # working with the matrix inner product; as it would be computationally more efficient for large number of samples feat_inner_prods <- crossprod(population_data, population_data) entropy_score <- score_features_sv_entropy(feat_inner_prods) diff --git a/man/entropy_feature_selection.Rd b/man/entropy_feature_selection.Rd index 184ae50..f74a438 100644 --- a/man/entropy_feature_selection.Rd +++ b/man/entropy_feature_selection.Rd @@ -2,20 +2,20 @@ % Please edit documentation in R/entropy_feature_selection.R \name{entropy_feature_selection} \alias{entropy_feature_selection} -\title{entropy based feature selection} +\title{feature selection based on the entropy of data singular values} \usage{ entropy_feature_selection(population, variables, n_feature) } \arguments{ -\item{population}{named tbl containing data, where columns and rows correspond to features and samples, respectively. Column names are assumed to be feature names.} +\item{population}{named tbl containing data, where columns and rows correspond to features (and/or metadata) and samples, respectively. Column names are assumed to be feature or metadata names.} \item{variables}{vector containing the names of numerical variables (or features) in the population.} \item{n_feature}{integer specifying number of features to be selected} } \value{ -vector containing name of the features sorted based on their SVD score. Higher score means better. +vector containing name of the features sorted based on their score, and the actual score values. Higher score means more informative feature. } \description{ -entropy based feature selection +feature selection based on the entropy of data singular values } From bc3e6e4740852ce2901c72cce6718e777f17e779 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 10:43:23 -0500 Subject: [PATCH 07/15] accounts for dataset in form of a db; edited output names --- R/entropy_feature_selection.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index abb8da0..3466fec 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -13,8 +13,10 @@ entropy_feature_selection <- function(population, variables, n_feature) { population_data <- population %>% dplyr::select(dplyr::one_of(variables)) %>% + dplyr::collect() %>% as.matrix() + # working with the matrix inner product; as it would be computationally more efficient for large number of samples feat_inner_prods <- crossprod(population_data, population_data) @@ -22,6 +24,6 @@ entropy_feature_selection <- function(population, variables, n_feature) { feat_rank <- order(entropy_score, decreasing = T) - return(list(feats = colnames(population_data)[feat_rank[1:n_feature]], + return(list(features = colnames(population_data)[feat_rank[1:n_feature]], entropy_score = entropy_score[feat_rank[1:n_feature]])) } From fcdfd78d6ed64c1735b0d264a9bf1883c7f6734d Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 10:43:38 -0500 Subject: [PATCH 08/15] a simple test for feature selection --- .../testthat/test-entropy_feature_selection.R | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 tests/testthat/test-entropy_feature_selection.R diff --git a/tests/testthat/test-entropy_feature_selection.R b/tests/testthat/test-entropy_feature_selection.R new file mode 100644 index 0000000..ec5debe --- /dev/null +++ b/tests/testthat/test-entropy_feature_selection.R @@ -0,0 +1,31 @@ +context("entropy_feature_selection") + +test_that("`entropy_feature_selection` selects features based on the singular values entropy", { + + set.seed(24) + xa <- rnorm(5) + xb <- rnorm(5) + ya <- rnorm(5) + yb <- rnorm(5) + + data <- + rbind( + data.frame(g = "a", x = xa, y = ya, z = xa + ya), + data.frame(g = "b", x = xb, y = yb, z = xb + yb) + ) + + db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") + + RSQLite::initExtension(db) + + data <- dplyr::copy_to(db, data) + + expect_equal( + sort(entropy_feature_selection(population = data, variables = c("x", "y", "z"), n_feature = 2)[["features"]], + decreasing = F), + c("x", "y") + ) + + DBI::dbDisconnect(db) + +}) From a5f183b845c7c7552a14f96281b1eadc967e4c57 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 12:15:20 -0500 Subject: [PATCH 09/15] have the Rcpp requirements in the NAMESPACE file --- R/cytominer.R | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 R/cytominer.R diff --git a/R/cytominer.R b/R/cytominer.R new file mode 100644 index 0000000..61c4222 --- /dev/null +++ b/R/cytominer.R @@ -0,0 +1,4 @@ +#' @useDynLib cytominer +#' @importFrom Rcpp evalCpp +#' @exportPattern "^[[:alpha:]]+" +NULL From 7dece71720f05f432b6229820856181b9427b088 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Thu, 14 Dec 2017 12:16:24 -0500 Subject: [PATCH 10/15] made an example for feature selection; added the function in the variable_select file --- NAMESPACE | 3 ++- R/entropy_feature_selection.R | 12 +++++++++++- R/variable_select.R | 27 +++++++++++++++------------ man/entropy_feature_selection.Rd | 16 ++++++++++++++-- man/variable_select.Rd | 4 ++-- 5 files changed, 44 insertions(+), 18 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 83366c5..5729324 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -18,6 +18,8 @@ export(variable_importance) export(variable_select) export(variance_threshold) export(whiten) +exportPattern("^[[:alpha:]]+") +importFrom(Rcpp,evalCpp) importFrom(foreach,"%dopar%") importFrom(magrittr,"%<>%") importFrom(magrittr,"%>%") @@ -31,4 +33,3 @@ importFrom(stats,sd) importFrom(stats,setNames) importFrom(utils,find) useDynLib(cytominer) -importFrom(Rcpp, sourceCpp) diff --git a/R/entropy_feature_selection.R b/R/entropy_feature_selection.R index 3466fec..6c258d1 100644 --- a/R/entropy_feature_selection.R +++ b/R/entropy_feature_selection.R @@ -1,4 +1,5 @@ -#' feature selection based on the entropy of data singular values +#' Feature selection based on redundancy +#' \code{entropy_feature_selection} is a feature selection method based on the entropy of data singular values #' #' @param population named tbl containing data, where columns and rows correspond to features (and/or metadata) and samples, respectively. Column names are assumed to be feature or metadata names. #' @param variables vector containing the names of numerical variables (or features) in the population. @@ -8,6 +9,15 @@ #' #' @return vector containing name of the features sorted based on their score, and the actual score values. Higher score means more informative feature. #' +#' @examples +#' population <- tibble::data_frame( +#' AreaShape_MinorAxisLength = c(10, 12, 15, 16, 8, 8, 7, 7, 13, 18), +#' AreaShape_MajorAxisLength = c(35, 18, 22, 16, 9, 20, 11, 15, 18, 42), +#' AreaShape_Area = c(245, 151, 231, 179, 50, 112, 53, 73, 164, 529) +#' ) +#' variables <- c("AreaShape_MinorAxisLength", "AreaShape_MajorAxisLength", "AreaShape_Area") +#' entropy_feature_selection(population, variables, 2) +#' #' @export entropy_feature_selection <- function(population, variables, n_feature) { diff --git a/R/variable_select.R b/R/variable_select.R index 4ed5666..4db3e37 100644 --- a/R/variable_select.R +++ b/R/variable_select.R @@ -11,32 +11,32 @@ #' @return variable-selected data of the same class as \code{population}. #' #' @examples -#' -#' # In this example, we use `correlation_threshold` as the operation for +#' +#' # In this example, we use `correlation_threshold` as the operation for #' # variable selection. -#' +#' #' suppressMessages(suppressWarnings(library(magrittr))) #' population <- tibble::data_frame( #' x = rnorm(100), #' y = rnorm(100)/1000 #' ) -#' +#' #' population %<>% dplyr::mutate(z = x + rnorm(100) / 10) -#' +#' #' sample <- population %>% dplyr::slice(1:30) -#' +#' #' variables <- c("x", "y", "z") -#' +#' #' operation <- "correlation_threshold" -#' +#' #' cor(sample) -#' +#' #' # `x` and `z` are highly correlated; one of them will be removed -#' +#' #' head(population) -#' +#' #' futile.logger::flog.threshold(futile.logger::ERROR) -#' +#' #' variable_select(population, variables, sample, operation) %>% head() #' #' @importFrom magrittr %>% @@ -50,6 +50,9 @@ variable_select <- function(population, variables, sample = NULL, excluded <- correlation_threshold( variables, sample, ...) } else if (operation == "drop_na_columns") { excluded <- drop_na_columns(population, variables, ...) + } else if (operation == "entropy_based") { + included <- entropy_feature_selection(population, variables, ...)[["features"]] + excluded <- setdiff(variables, included) } else { error <- paste0("undefined operation `", operation, "'") diff --git a/man/entropy_feature_selection.Rd b/man/entropy_feature_selection.Rd index f74a438..ec5bc4c 100644 --- a/man/entropy_feature_selection.Rd +++ b/man/entropy_feature_selection.Rd @@ -2,7 +2,8 @@ % Please edit documentation in R/entropy_feature_selection.R \name{entropy_feature_selection} \alias{entropy_feature_selection} -\title{feature selection based on the entropy of data singular values} +\title{Feature selection based on redundancy +\code{entropy_feature_selection} is a feature selection method based on the entropy of data singular values} \usage{ entropy_feature_selection(population, variables, n_feature) } @@ -17,5 +18,16 @@ entropy_feature_selection(population, variables, n_feature) vector containing name of the features sorted based on their score, and the actual score values. Higher score means more informative feature. } \description{ -feature selection based on the entropy of data singular values +Feature selection based on redundancy +\code{entropy_feature_selection} is a feature selection method based on the entropy of data singular values +} +\examples{ +population <- tibble::data_frame( + AreaShape_MinorAxisLength = c(10, 12, 15, 16, 8, 8, 7, 7, 13, 18), + AreaShape_MajorAxisLength = c(35, 18, 22, 16, 9, 20, 11, 15, 18, 42), + AreaShape_Area = c(245, 151, 231, 179, 50, 112, 53, 73, 164, 529) + ) +variables <- c("AreaShape_MinorAxisLength", "AreaShape_MajorAxisLength", "AreaShape_Area") +entropy_feature_selection(population, variables, 2) + } diff --git a/man/variable_select.Rd b/man/variable_select.Rd index 633121d..456b803 100644 --- a/man/variable_select.Rd +++ b/man/variable_select.Rd @@ -26,7 +26,7 @@ variable-selected data of the same class as \code{population}. } \examples{ -# In this example, we use `correlation_threshold` as the operation for +# In this example, we use `correlation_threshold` as the operation for # variable selection. suppressMessages(suppressWarnings(library(magrittr))) @@ -34,7 +34,7 @@ population <- tibble::data_frame( x = rnorm(100), y = rnorm(100)/1000 ) - + population \%<>\% dplyr::mutate(z = x + rnorm(100) / 10) sample <- population \%>\% dplyr::slice(1:30) From 80d380d0e96d2a071013060cc293bac481b51aa6 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Fri, 15 Dec 2017 08:46:55 -0500 Subject: [PATCH 11/15] removed .o files --- src/RcppExports.o | Bin 642736 -> 0 bytes src/cytominer.so | Bin 136328 -> 0 bytes src/entropy_feature_selection.o | Bin 779644 -> 0 bytes 3 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/RcppExports.o delete mode 100755 src/cytominer.so delete mode 100644 src/entropy_feature_selection.o diff --git a/src/RcppExports.o b/src/RcppExports.o deleted file mode 100644 index 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zGfp*_OEmFReJ6L?S%I%6Jn2a7N$z~CbVh5Ds^31H)kTet%mxeCS-%>c4HmV2Q+L*{ zY-e;TNY!u6uKI~~)h}|Foq}E(c;z!M4g^7ekPSP?zCAP0IW(@H#D`Z#zie8IzFVWk z4zOgKz8^Vb-x{fu2U63;>D-R;WLCe6wAKCF#!M#lD;s(Jf|Y)dzvC2ori($gRGKM< zU!Ms-dEGXdlpf!+JI%vnp+*k$_2b`W+j+Ep4ny`h^(2EE1lF^c9@G=O|7!94k`su~H}Vg+g8DhpnCV vm9lcp+!(|!ONA+uM*(tq(M*m9ccXR(gjPw4z From 73734468b15bbe9062f9648509969fc598ebc49a Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Fri, 15 Dec 2017 09:29:27 -0500 Subject: [PATCH 13/15] added RcppArmadillo req to src/ --- src/Makevars | 6 ++++++ src/Makevars.win | 6 ++++++ 2 files changed, 12 insertions(+) create mode 100644 src/Makevars create mode 100644 src/Makevars.win diff --git a/src/Makevars b/src/Makevars new file mode 100644 index 0000000..d26f652 --- /dev/null +++ b/src/Makevars @@ -0,0 +1,6 @@ + +## optional +#CXX_STD = CXX11 + +PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) +PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/src/Makevars.win b/src/Makevars.win new file mode 100644 index 0000000..d26f652 --- /dev/null +++ b/src/Makevars.win @@ -0,0 +1,6 @@ + +## optional +#CXX_STD = CXX11 + +PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) +PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) From 49402ea65162cb1ef7aeb790ec56cb4bef9f3521 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Fri, 15 Dec 2017 10:05:15 -0500 Subject: [PATCH 14/15] temporarily remove test for entr. feat. sel. --- .../testthat/test-entropy_feature_selection.R | 31 ------------------- 1 file changed, 31 deletions(-) delete mode 100644 tests/testthat/test-entropy_feature_selection.R diff --git a/tests/testthat/test-entropy_feature_selection.R b/tests/testthat/test-entropy_feature_selection.R deleted file mode 100644 index ec5debe..0000000 --- a/tests/testthat/test-entropy_feature_selection.R +++ /dev/null @@ -1,31 +0,0 @@ -context("entropy_feature_selection") - -test_that("`entropy_feature_selection` selects features based on the singular values entropy", { - - set.seed(24) - xa <- rnorm(5) - xb <- rnorm(5) - ya <- rnorm(5) - yb <- rnorm(5) - - data <- - rbind( - data.frame(g = "a", x = xa, y = ya, z = xa + ya), - data.frame(g = "b", x = xb, y = yb, z = xb + yb) - ) - - db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") - - RSQLite::initExtension(db) - - data <- dplyr::copy_to(db, data) - - expect_equal( - sort(entropy_feature_selection(population = data, variables = c("x", "y", "z"), n_feature = 2)[["features"]], - decreasing = F), - c("x", "y") - ) - - DBI::dbDisconnect(db) - -}) From ed890829f95e9a5984997ab77041de6103c8a6c9 Mon Sep 17 00:00:00 2001 From: Mohammad Hossein Rohban Date: Fri, 15 Dec 2017 10:38:14 -0500 Subject: [PATCH 15/15] added the test back --- .../testthat/test-entropy_feature_selection.R | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 tests/testthat/test-entropy_feature_selection.R diff --git a/tests/testthat/test-entropy_feature_selection.R b/tests/testthat/test-entropy_feature_selection.R new file mode 100644 index 0000000..ec5debe --- /dev/null +++ b/tests/testthat/test-entropy_feature_selection.R @@ -0,0 +1,31 @@ +context("entropy_feature_selection") + +test_that("`entropy_feature_selection` selects features based on the singular values entropy", { + + set.seed(24) + xa <- rnorm(5) + xb <- rnorm(5) + ya <- rnorm(5) + yb <- rnorm(5) + + data <- + rbind( + data.frame(g = "a", x = xa, y = ya, z = xa + ya), + data.frame(g = "b", x = xb, y = yb, z = xb + yb) + ) + + db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") + + RSQLite::initExtension(db) + + data <- dplyr::copy_to(db, data) + + expect_equal( + sort(entropy_feature_selection(population = data, variables = c("x", "y", "z"), n_feature = 2)[["features"]], + decreasing = F), + c("x", "y") + ) + + DBI::dbDisconnect(db) + +})

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