From eb5f73b4974164a095c53997889f84a820363a10 Mon Sep 17 00:00:00 2001 From: Eric Bair Date: Fri, 1 Dec 2023 10:46:27 -0500 Subject: [PATCH 1/3] Additional Testing Fixed likelihood function and added additional test Fixed likelihood function and added additional tests Added a simulation file for testing Added more likelihood and maxmin ordering tests Fixed a bug in SparseNN Fixed another sparseNN bug and updated tests Squashed commit of the following: commit 895100db78d89c5082ddfcf1411dad69bdf1b6c5 Merge: 7f3ca3c fb060ff Author: Eric Bair Date: Wed Nov 29 17:02:10 2023 -0500 Merge branch 'to-git' of http://192.168.167.103:7990/bitbucket/scm/stat/prestogp into testing Conflicts: DESCRIPTION man/prestogp_fit-PrestoGPModel-method.Rd tests/testthat/test-Log_Likelihood.R Fixed merge conflicts. commit 7f3ca3c8a054159604a9434a5eb7d15887b1af42 Author: Eric Bair Date: Wed Nov 29 16:43:40 2023 -0500 Fixed another sparseNN bug and updated tests commit 1ca958644b2afb856c2c78654896ef745b9eba8a Author: Eric Bair Date: Tue Nov 28 18:40:36 2023 -0500 Fixed a bug in SparseNN commit 80a9818dc91c8508eeb12c277588f6cd72f05ddd Author: Eric Bair Date: Tue Nov 21 17:49:19 2023 -0500 Added more likelihood and maxmin ordering tests commit 2dd74a21380be960918b6067abec8ec163721853 Author: Eric Bair Date: Mon Nov 13 19:17:43 2023 -0500 Added a simulation file for testing commit 6c17318858862ff97d5fb8de155e08c4a260c5a6 Author: Eric Bair Date: Mon Nov 13 19:16:32 2023 -0500 Fixed likelihood function and added additional tests commit f90ce5ef89fee0e1af7536f9ed7cd847b9d0248c Author: Eric Bair Date: Mon Nov 13 19:10:57 2023 -0500 Fixed likelihood function and added additional test --- DESCRIPTION | 5 +- R/PrestoGP_CreateU_Multivariate.R | 120 ++--------- man/get_kd_tree_node.Rd | 19 -- man/knn_indices.Rd | 2 +- man/make_kd_tree.Rd | 21 -- man/prestogp_fit-PrestoGPModel-method.Rd | 2 +- man/sparseNN.Rd | 8 +- tests/testthat/covlist.rds | Bin 0 -> 332 bytes tests/testthat/covmat.rds | Bin 0 -> 569751 bytes tests/testthat/test-Log_Likelihood.R | 196 +++++++++--------- .../test-PrestoGP_CreateU_Multivariate.R | 117 ++++++----- .../test-PrestoGP_Spatial_Full_Model.R | 3 +- tests/testthat/test-PrestoGP_Spatial_Model.R | 7 +- .../test-PrestoGP_Spatiotemporal_Full_Model.R | 3 +- .../test-PrestoGP_Spatiotemporal_Model.R | 12 +- 15 files changed, 222 insertions(+), 293 deletions(-) mode change 100644 => 100755 R/PrestoGP_CreateU_Multivariate.R delete mode 100644 man/get_kd_tree_node.Rd delete mode 100644 man/make_kd_tree.Rd mode change 100644 => 100755 man/prestogp_fit-PrestoGPModel-method.Rd create mode 100644 tests/testthat/covlist.rds create mode 100644 tests/testthat/covmat.rds mode change 100644 => 100755 tests/testthat/test-PrestoGP_CreateU_Multivariate.R mode change 100644 => 100755 tests/testthat/test-PrestoGP_Spatial_Full_Model.R mode change 100644 => 100755 tests/testthat/test-PrestoGP_Spatial_Model.R mode change 100644 => 100755 tests/testthat/test-PrestoGP_Spatiotemporal_Full_Model.R mode change 100644 => 100755 tests/testthat/test-PrestoGP_Spatiotemporal_Model.R diff --git a/DESCRIPTION b/DESCRIPTION index 79edc02..65c87b4 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: PrestoGP Type: Package Title: Penalized Regression for Spatio-Temporal Outcomes via Gaussian Processes -Version: 0.2.0.9018 +Version: 0.2.0.9020 Authors@R: c( person(given = "Eric", family = "Bair", @@ -40,7 +40,8 @@ Imports: markdown, gtools, geoR, - doParallel + doParallel, + RANN License: GPL-3 Encoding: UTF-8 VignetteBuilder: knitr diff --git a/R/PrestoGP_CreateU_Multivariate.R b/R/PrestoGP_CreateU_Multivariate.R old mode 100644 new mode 100755 index 31e9433..cbc5ccf --- a/R/PrestoGP_CreateU_Multivariate.R +++ b/R/PrestoGP_CreateU_Multivariate.R @@ -56,76 +56,6 @@ max_min_ordering <- function(locs, dist_func){ order } -#' make_kd_tree -#' -#' Create a KD tree to help approximate nearest neighbors -#' -#' @param ordered_locs A matrix with one row per location, where locations are ordered using max_min ordering. -#' @param indices A range of indices that should be split into a tree (used for recursion, not by the user) -#' @param column_idx The column currently being used to split locations into two branches (used for recursion, not by the user) -#' -#' @return A vector containing the index of locations from sparse to dense -make_kd_tree <- function(ordered_locs, indices=1:nrow(ordered_locs), column_idx=1){ - #TODO split by columns in order of most variance to least variance - if(column_idx <= ncol(ordered_locs)){ - cur.dat <- ordered_locs[indices,column_idx] - pivot <- median(cur.dat) - if (sum(cur.dat!=pivot)==0) { - cur.cut <- median(indices) - left <- which(indices<=cur.cut) - right <- which(indices>cur.cut) - } - else if (sum(cur.dat>pivot)==0){ - right <- indices[which(cur.dat==pivot)] - min.right <- min(right) - right <- setdiff(right, min.right) - left <- indices[which(cur.dat < pivot)] - left <- c(left, min.right) - } - else { - left <- indices[which(cur.dat <= pivot)] - right <- indices[which(cur.dat > pivot)] - } - left_tree <- make_kd_tree(ordered_locs, left, column_idx + 1) - right_tree <- make_kd_tree(ordered_locs, right, column_idx + 1) - if(column_idx == ncol(ordered_locs)){ #last column doesn't require aditional pivot - list("pivot"=list(pivot), "subtree"=list(left_tree[["subtree"]], right_tree[["subtree"]])) - } else { - list("pivot"=list(pivot, list(left_tree[["pivot"]], right_tree[["pivot"]])), - "subtree"=list(left_tree[["subtree"]], right_tree[["subtree"]])) - } - } else { #base case: just return indices - list("subtree"=indices) - } -} - -#' get_kd_tree_node -#' -#' Given a KD tree and a location, determine which node of the KD tree has the nearest neighbors to that location. -#' -#' @param kd_tree A KD tree from the make_kd_tree function -#' @param location a location vector with the same number of dimensions as the KD tree -#' -#' @return A vector with the path to the KD tree node (e.g. if the tree pivots are c(0.5, 0.5) and the location is c(0.2, 0.7) this function returns c(1,2) ) -get_kd_tree_node <- function(kd_tree, location){ - tree_path <- c() - pivot_tree <- kd_tree[["pivot"]] - for(i in 1:ncol(location)){ - if(location[,i] < pivot_tree[[1]]){ - tree_path <- c(tree_path, 1) - if(i < ncol(location)){ - pivot_tree <- pivot_tree[[2]][[1]] - } - } else{ - tree_path <- c(tree_path, 2) - if(i < ncol(location)){ - pivot_tree <- pivot_tree[[2]][[2]] - } - } - } - tree_path -} - #' knn_indices #' #' Find the index of K nearest neighbors within a set of locations for a given query and distance function @@ -136,25 +66,18 @@ get_kd_tree_node <- function(kd_tree, location){ #' @param dist_func Any distance function with a signature of dist(query_location, locations_matrix) #' #' @return A vector containing the indices of the neighbors -knn_indices <- function(ordered_locs, query, n_neighbors, dist_func){ - kd_tree <- make_kd_tree(ordered_locs) - path <- get_kd_tree_node(kd_tree, query) - kd_node <- kd_tree$subtree - for(branch_level in 1:length(path)){ - kd_node <- kd_node[[path[branch_level]]] - } - if(n_neighbors >= length(kd_node)){ #not enough neighbors in kd_node - dists <- dist_func(query, ordered_locs) - dists_order <- order(dists) - nearest_neighbors <- dists_order[1:(n_neighbors+1)] - list("indices"=nearest_neighbors, "distances"=dists[nearest_neighbors]) - } else { - loc_neighbor_candidates <- ordered_locs[kd_node,,drop=FALSE] - dists <- dist_func(query, loc_neighbor_candidates) - dists_order <- order(dists) - closest_in_kd_node <- dists_order[1:(n_neighbors+1)] - list("indices"=kd_node[closest_in_kd_node], "distances"=dists[closest_in_kd_node]) - } +knn_indices <- function(ordered_locs, query, n_neighbors, dist_func, dist_func_code){ + if (dist_func_code=="custom") { + dists <- dist_func(query, ordered_locs) + dists_order <- order(dists) + nearest_neighbors <- dists_order[1:n_neighbors] + return(list("indices"=nearest_neighbors, + "distances"=dists[nearest_neighbors])) + } + else { + cur.nn <- RANN::nn2(ordered_locs, query, n_neighbors) + return(list("indices"=cur.nn$nn.idx, "distances"=cur.nn$nn.dists)) + } } #' sparseNN @@ -166,7 +89,7 @@ knn_indices <- function(ordered_locs, query, n_neighbors, dist_func){ #' @param dist_func Any distance function with a signature of dist(query_location, locations_matrix) #' #' @return A list containing two matrices, each with one row per location: an indices matrix with the indices of nearest neighbors for each location, and a distance matrix with the associated distances -sparseNN <- function(ordered_locs, n_neighbors, dist_func, ordered_locs_pred=NULL){ +sparseNN <- function(ordered_locs, n_neighbors, dist_func, dist_func_code, ordered_locs_pred=NULL){ ee <- min(apply(ordered_locs, 2, stats::sd)) n <- nrow(ordered_locs) ordered_locs <- ordered_locs + matrix(ee*1e-04* @@ -176,13 +99,13 @@ sparseNN <- function(ordered_locs, n_neighbors, dist_func, ordered_locs_pred=NUL distances_matrix = matrix(data=NA, nrow=nrow(ordered_locs), ncol=n_neighbors) for(row in 1:n_neighbors){ #for the locations from 1 to n_neighbors, use the entire locs list to find the neighbors - nn <- knn_indices(ordered_locs[1:(n_neighbors+1),,drop=FALSE], ordered_locs[row,,drop=FALSE], n_neighbors, dist_func) - indices_matrix[row,1:n_neighbors] = nn$indices[2:(n_neighbors+1)] - distances_matrix[row,1:n_neighbors] = nn$distances[2:(n_neighbors+1)] + nn <- knn_indices(ordered_locs[1:(n_neighbors+1),,drop=FALSE][-row,,drop=FALSE], ordered_locs[row,,drop=FALSE], n_neighbors, dist_func, dist_func_code) + indices_matrix[row,1:n_neighbors] = nn$indices[1:n_neighbors] + distances_matrix[row,1:n_neighbors] = nn$distances[1:n_neighbors] } for(row in (n_neighbors+1):nrow(ordered_locs)){ #get the m nearest neighbors from the locs before this one in the max-min order - nn <- knn_indices(ordered_locs[1:(row-1),,drop=FALSE], ordered_locs[row,,drop=FALSE], n_neighbors, dist_func) + nn <- knn_indices(ordered_locs[1:(row-1),,drop=FALSE], ordered_locs[row,,drop=FALSE], n_neighbors, dist_func, dist_func_code) indices_matrix[row,1:n_neighbors] = nn$indices[1:n_neighbors] distances_matrix[row,1:n_neighbors] = nn$distances[1:n_neighbors] } @@ -194,7 +117,7 @@ sparseNN <- function(ordered_locs, n_neighbors, dist_func, ordered_locs_pred=NUL for (row in 1:nrow(ordered_locs_pred)) { nn <- knn_indices(ordered_locs, ordered_locs_pred[row,,drop=FALSE], n_neighbors, - dist_func) + dist_func, dist_func_code) indices_matrix_pred[row,1:n_neighbors] = nn$indices[1:n_neighbors] distances_matrix_pred[row,1:n_neighbors] = nn$distances[1:n_neighbors] } @@ -310,8 +233,7 @@ vecchia_Mspecify <- function(locs.list, m, locs.list.pred=NULL, loc.order <- GPvecchia::order_maxmin_exact(locs.all) # I am not sure why the next two lines are here. I added them because # similar code exists in the GPvecchia package. But I don't know why - # they did this. Uncomment these two lines to reproduce the output - # from the createU function in the GPvecchia package. + # they did this. cutoff <- min(n, 9) loc.order <- c(loc.order[1], loc.order[-seq(1, cutoff)], loc.order[2:cutoff]) @@ -334,10 +256,10 @@ vecchia_Mspecify <- function(locs.list, m, locs.list.pred=NULL, # between the output of this function and the output of createU # in the GPvecchia package. if (is.null(locs.list.pred) | pred.cond=="general") { - nn.mat <- sparseNN(olocs, m, dist.func) + nn.mat <- sparseNN(olocs, m, dist.func, dist.func.code) } else { - nn.mat <- sparseNN(olocs[1:n,,drop=FALSE], m, dist.func, + nn.mat <- sparseNN(olocs[1:n,,drop=FALSE], m, dist.func, dist.func.code, olocs[-(1:n),,drop=FALSE]) } last.obs <- max((1:length(obs))[obs]) diff --git a/man/get_kd_tree_node.Rd b/man/get_kd_tree_node.Rd deleted file mode 100644 index 3bd6d6e..0000000 --- a/man/get_kd_tree_node.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/PrestoGP_CreateU_Multivariate.R -\name{get_kd_tree_node} -\alias{get_kd_tree_node} -\title{get_kd_tree_node} -\usage{ -get_kd_tree_node(kd_tree, location) -} -\arguments{ -\item{kd_tree}{A KD tree from the make_kd_tree function} - -\item{location}{a location vector with the same number of dimensions as the KD tree} -} -\value{ -A vector with the path to the KD tree node (e.g. if the tree pivots are c(0.5, 0.5) and the location is c(0.2, 0.7) this function returns c(1,2) ) -} -\description{ -Given a KD tree and a location, determine which node of the KD tree has the nearest neighbors to that location. -} diff --git a/man/knn_indices.Rd b/man/knn_indices.Rd index feca6fa..70d8860 100644 --- a/man/knn_indices.Rd +++ b/man/knn_indices.Rd @@ -4,7 +4,7 @@ \alias{knn_indices} \title{knn_indices} \usage{ -knn_indices(ordered_locs, query, n_neighbors, dist_func) +knn_indices(ordered_locs, query, n_neighbors, dist_func, dist_func_code) } \arguments{ \item{ordered_locs}{A matrix with one row per location, where locations are ordered using max_min ordering.} diff --git a/man/make_kd_tree.Rd b/man/make_kd_tree.Rd deleted file mode 100644 index effb00d..0000000 --- a/man/make_kd_tree.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/PrestoGP_CreateU_Multivariate.R -\name{make_kd_tree} -\alias{make_kd_tree} -\title{make_kd_tree} -\usage{ -make_kd_tree(ordered_locs, indices = 1:nrow(ordered_locs), column_idx = 1) -} -\arguments{ -\item{ordered_locs}{A matrix with one row per location, where locations are ordered using max_min ordering.} - -\item{indices}{A range of indices that should be split into a tree (used for recursion, not by the user)} - -\item{column_idx}{The column currently being used to split locations into two branches (used for recursion, not by the user)} -} -\value{ -A vector containing the index of locations from sparse to dense -} -\description{ -Create a KD tree to help approximate nearest neighbors -} diff --git a/man/prestogp_fit-PrestoGPModel-method.Rd b/man/prestogp_fit-PrestoGPModel-method.Rd old mode 100644 new mode 100755 index a149d46..d9ea938 --- a/man/prestogp_fit-PrestoGPModel-method.Rd +++ b/man/prestogp_fit-PrestoGPModel-method.Rd @@ -17,7 +17,7 @@ max_iters = 100, verbose = FALSE, optim.method = "Nelder-Mead", - optim.control = list(trace = 0, reltol = 1e-04, maxit = 5000), + optim.control = list(trace = 0, reltol = 0.001, maxit = 5000), parallel = FALSE, foldid = NULL ) diff --git a/man/sparseNN.Rd b/man/sparseNN.Rd index 10df26c..13f7c63 100644 --- a/man/sparseNN.Rd +++ b/man/sparseNN.Rd @@ -4,7 +4,13 @@ \alias{sparseNN} \title{sparseNN} \usage{ -sparseNN(ordered_locs, n_neighbors, dist_func, ordered_locs_pred = NULL) +sparseNN( + ordered_locs, + n_neighbors, + dist_func, + dist_func_code, + ordered_locs_pred = NULL +) } \arguments{ \item{ordered_locs}{A matrix with one row per location, where locations are ordered using max_min 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zuDP9*X?D#E0Xx;o@{3XJHFn}<`ix_ej4`=LE1vFq9G_|ED67p5xbNoCX3;I4!%E5& z*hvVmonAvF$1d?2WqFvtmr*BF7J9ze&#Re?P}R?#t1q_hz$QD3?jjr=qKEaeq^z-=L?qVUJsQZVKBDl1+hBFyp+5P4 Di4q5s literal 0 HcmV?d00001 diff --git a/tests/testthat/test-Log_Likelihood.R b/tests/testthat/test-Log_Likelihood.R index 17c8a47..b9282cb 100755 --- a/tests/testthat/test-Log_Likelihood.R +++ b/tests/testthat/test-Log_Likelihood.R @@ -25,62 +25,6 @@ test_that("negloglik_vecchia_ST", { expect_equal(-6861.65048095, result_optim, tolerance=1e-5) }) -test_that("create.param.sequence", { - seq = create.param.sequence(1) - colnames(seq) <- NULL - expect_equal(2, ncol(seq)) - expect_equal(5, nrow(seq)) - expect_equal(c(1,1), seq[1,]) - expect_equal(c(2,2), seq[2,]) - expect_equal(c(3,3), seq[3,]) - expect_equal(c(4,4), seq[4,]) - expect_equal(c(5,5), seq[5,]) - - seq = create.param.sequence(3) - colnames(seq) <- NULL - expect_equal(2, ncol(seq)) - expect_equal(5, nrow(seq)) - expect_equal(c(1,3), seq[1,]) - expect_equal(c(4,6), seq[2,]) - expect_equal(c(7,9), seq[3,]) - expect_equal(c(10,12), seq[4,]) - expect_equal(c(13,15), seq[5,]) - - seq = create.param.sequence(3, 2) - colnames(seq) <- NULL - expect_equal(2, ncol(seq)) - expect_equal(5, nrow(seq)) - expect_equal(c(1,3), seq[1,]) - expect_equal(c(4,9), seq[2,]) - expect_equal(c(10,12), seq[3,]) - expect_equal(c(13,15), seq[4,]) - expect_equal(c(16,18), seq[5,]) -}) - -test_that("create.initial.values.flex", { - set.seed(7919) - P <- 1 - logparams <- create.initial.values.flex(rep(0.9,P), #marginal variance - rep(0.5,P), #range - rep(0.5,P), #smoothness - rep(0.1,P), #nuggets - NULL, - P) - expect_equal(c(-0.105, -0.693, -1.386, -2.303, 0), logparams, tolerance=1e-2) - - P <- 2 - Y <- cbind(runif(10),runif(10)) - cor.matrix <- cor(Y) - cov_mat <- c(cor.matrix[upper.tri(cor.matrix)]) - logparams <- create.initial.values.flex(rep(0.9,P), #marginal variance - rep(0.5,P), #range - rep(0.5,P), #smoothness - rep(0.1,P), #nuggets - cov_mat, - P) - expect_equal(c(-0.105, -0.105, -0.693, -0.693, -1.386, -1.386, -2.303, -2.303, 0.584), logparams, tolerance=1e-2) -}) - test_that("negloglik.full", { set.seed(1234) @@ -117,11 +61,11 @@ test_that("negloglik.full", { gtools::inv.logit(res.optim.NM$par[3], 0, 2.5), exp(res.optim.NM$par[4])) - pgp.params <- PrestoGP:::create.initial.values.flex(params.final[1], - params.final[2], - params.final[3], - params.final[4], - 1, 1) + pgp.params <- create.initial.values.flex(params.final[1], + params.final[2], + params.final[3], + params.final[4], + 1, 1) pseq <- create.param.sequence(1) LL.full.pgp <- mvnegloglik.full(pgp.params, list(locs), y, pseq) @@ -161,7 +105,65 @@ test_that("mvnegloglik", { vec.approx <- vecchia_Mspecify(locs.list, 25) neg_likelihood <- mvnegloglik(logparams, vec.approx, unlist(y.list), pseq, P) - expect_equal(34474.4, neg_likelihood, tolerance=1e-2) + expect_equal(34870.57, neg_likelihood, tolerance=1e-2) +}) + +test_that("mvnegloglik_ST", { + source("sim_multivariate_big_st.R") + P <- 3 + Y <- cbind(runif(10),runif(10), runif(10)) + cor.matrix <- cor(Y) + cov_mat <- c(cor.matrix[upper.tri(cor.matrix)]) + logparams <- create.initial.values.flex(rep(0.9,P), #marginal variance + rep(c(2,3),P), #range + rep(0.5,P), #smoothness + rep(0.1,P), #nuggets + cov_mat, + P) + pseq = create.param.sequence(P, 2) + vec.approx <- vecchia_Mspecify(locs.list, 25) + neg_likelihood <- mvnegloglik_ST(logparams, vec.approx, + unlist(y.list), pseq, P, c(1,1,2), 2) + expect_equal(35106.73, neg_likelihood, tolerance=1e-2) + + vec.approx2 <- vec.approx + for (i in 1:P) { + vec.approx2$locsord[,1:2] <- vec.approx$locsord[,1:2] / 2 + vec.approx2$locsord[,3] <- vec.approx$locsord[,3] / 3 + } + logparams2 <- logparams + logparams2[pseq[2,1]:pseq[2,2]] <- 0 + neg_likelihood2 <- mvnegloglik_ST(logparams2, vec.approx2, + unlist(y.list), pseq, P, c(1,1,2), 2) + expect_equal(neg_likelihood, neg_likelihood2, tolerance=1e-3) + + logparams <- create.initial.values.flex(rep(0.9,P), #marginal variance + 2:7, #range + rep(0.5,P), #smoothness + rep(0.1,P), #nuggets + cov_mat, + P) + neg_likelihood <- mvnegloglik_ST(logparams, vec.approx, + unlist(y.list), pseq, P, c(1,1,2), 2) + expect_equal(36354.9, neg_likelihood, tolerance=1e-2) + + vec.approx2 <- vec.approx + vec.approx2$locsord[vec.approx$ondx==1,1:2] <- + vec.approx$locsord[vec.approx$ondx==1,1:2] / 2 + vec.approx2$locsord[vec.approx$ondx==1,3] <- + vec.approx$locsord[vec.approx$ondx==1,3] / 3 + vec.approx2$locsord[vec.approx$ondx==2,1:2] <- + vec.approx$locsord[vec.approx$ondx==2,1:2] / 4 + vec.approx2$locsord[vec.approx$ondx==2,3] <- + vec.approx$locsord[vec.approx$ondx==2,3] / 5 + vec.approx2$locsord[vec.approx$ondx==3,1:2] <- + vec.approx$locsord[vec.approx$ondx==3,1:2] / 6 + vec.approx2$locsord[vec.approx$ondx==3,3] <- + vec.approx$locsord[vec.approx$ondx==3,3] / 7 + + neg_likelihood2 <- mvnegloglik_ST(logparams2, vec.approx2, + unlist(y.list), pseq, P, c(1,1,2), 2) + expect_equal(neg_likelihood, neg_likelihood2, tolerance=1e-3) }) test_that("mvnegloglik.full", { @@ -202,47 +204,47 @@ test_that("mvnegloglik.full", { tanh(params.init.final[ param.seq.begin[5]:param.seq.end[5]])) + params.final.flex.test <- create.initial.values.flex( + params.init.final.t[param.seq.begin[1]:param.seq.end[1]], + params.init.final.t[param.seq.begin[2]:param.seq.end[2]], + params.init.final.t[param.seq.begin[3]:param.seq.end[3]], + params.init.final.t[param.seq.begin[4]:param.seq.end[4]], + params.init.final.t[param.seq.begin[5]:param.seq.end[5]], 3) + + cov.list <- create.cov.upper.flex( + 3, + params.init.final.t[param.seq.begin[1]:param.seq.end[1]], + params.init.final.t[param.seq.begin[2]:param.seq.end[2]], + params.init.final.t[param.seq.begin[3]:param.seq.end[3]], + params.init.final.t[param.seq.begin[4]:param.seq.end[4]], + params.init.final.t[param.seq.begin[5]:param.seq.end[5]]) + + cov.mat <- cat.covariances(locs.list,cov.list$variance, + cov.list$range, + cov.list$smoothness,cov.list$nugget) + + LL.full.calc <- -1*mvtnorm::dmvnorm(unlist(y.list), + rep(0, length(unlist(y.list))), + cov.mat, log=TRUE) + vec.mapprox <- vecchia_Mspecify(locs.list, length(unlist(y.list))-1) U.mobj <- createUMultivariate(vec.mapprox, params.init.final.t) LL.vecchia.mv <- -1*GPvecchia:::vecchia_likelihood_U(unlist(y.list), U.mobj) expect_equal(541.31, LL.full.mv, tolerance=1e-3) + expect_equal(LL.full.calc, LL.full.mv, tolerance=1e-3) # Full likelihood should equal the Vecchia likelihood expect_equal(LL.full.mv, LL.vecchia.mv, tolerance=1e-3) +# Check create.initial.values.flex: + expect_equal(params.init.final, params.final.flex.test, tolerance=1e-3) +# Check create.cov.upper.flex: + cov.list.true <- readRDS("covlist.rds") + expect_equal(cov.list$variance, cov.list.true$variance, tolerance=1e-3) + expect_equal(cov.list$range, cov.list.true$range, tolerance=1e-3) + expect_equal(cov.list$smoothness, cov.list.true$smoothness, tolerance=1e-3) + expect_equal(cov.list$nugget, cov.list.true$nugget, tolerance=1e-3) +# Check cat.covariances: + cov.mat.true <- readRDS("covmat.rds") + expect_equal(cov.mat, cov.mat.true, tolerance=1e-3) }) - -#TODO implement this test -test_that("cat.covariances", { - set.seed(7919) - load("sim_spatial.Rdata") - P <- 2 - Y <- cbind(runif(10),runif(10)) - cor.matrix <- cor(Y) - cov_mat <- c(cor.matrix[upper.tri(cor.matrix)]) - param.sequence <- create.param.sequence(P) - param.sequence.begin <- param.sequence[,1] - param.sequence.end <- param.sequence[,2] - params <- create.initial.values.flex(rep(9.5,P), #marginal variance - rep(15.0,P), #range - rep(0.5,P), #smoothness - rep(0.9,P), #nuggets - cov_mat, - P) - params <- c(exp(params[1:param.sequence[4,2]]), - tanh(params[param.sequence[5,1]:param.sequence[5,2]])) - sig2 <- params[param.sequence.begin[1]:param.sequence.end[1]] - range <- params[param.sequence.begin[2]:param.sequence.end[2]] - smoothness <- params[param.sequence.begin[3]:param.sequence.end[3]] - nugget <- params[param.sequence.begin[4]:param.sequence.end[4]] - rho <- params[param.sequence.begin[5]:param.sequence.end[5]] - - cov.list <- create.cov.upper.flex(P,sig2,range,smoothness,nugget,rho) - cov.mat <- cat.covariances(list(locs_train, locs_train),cov.list$variance,cov.list$range, - cov.list$smoothness,cov.list$nugget) - Y <- unlist(list(Y_train, Y_train)) - N <- length(Y) - neg_likelihood <- -mvtnorm::dmvnorm(Y,rep(0,N),cov.mat,log=TRUE) - expect_equal(5905, neg_likelihood, tolerance=10e-2) -}) - diff --git a/tests/testthat/test-PrestoGP_CreateU_Multivariate.R b/tests/testthat/test-PrestoGP_CreateU_Multivariate.R old mode 100644 new mode 100755 index 204a3a2..1b15a4e --- a/tests/testthat/test-PrestoGP_CreateU_Multivariate.R +++ b/tests/testthat/test-PrestoGP_CreateU_Multivariate.R @@ -1,4 +1,36 @@ -context("CreateU Multivariate") +context("createUMultivariate") + +test_that("create.param.sequence", { + seq = create.param.sequence(1) + colnames(seq) <- NULL + expect_equal(2, ncol(seq)) + expect_equal(5, nrow(seq)) + expect_equal(c(1,1), seq[1,]) + expect_equal(c(2,2), seq[2,]) + expect_equal(c(3,3), seq[3,]) + expect_equal(c(4,4), seq[4,]) + expect_equal(c(5,5), seq[5,]) + + seq = create.param.sequence(3) + colnames(seq) <- NULL + expect_equal(2, ncol(seq)) + expect_equal(5, nrow(seq)) + expect_equal(c(1,3), seq[1,]) + expect_equal(c(4,6), seq[2,]) + expect_equal(c(7,9), seq[3,]) + expect_equal(c(10,12), seq[4,]) + expect_equal(c(13,15), seq[5,]) + + seq = create.param.sequence(3, 2) + colnames(seq) <- NULL + expect_equal(2, ncol(seq)) + expect_equal(5, nrow(seq)) + expect_equal(c(1,3), seq[1,]) + expect_equal(c(4,9), seq[2,]) + expect_equal(c(10,12), seq[3,]) + expect_equal(c(13,15), seq[4,]) + expect_equal(c(16,18), seq[5,]) +}) test_that("max_min_ordering", { set.seed(7919) @@ -10,57 +42,46 @@ test_that("max_min_ordering", { 82, 55, 61, 87, 17, 33, 43, 45, 10, 79, 53, 75, 89, 51, 73, 27, 26, 77, 44, 38, 65, 16, 19, 37, 57, 70, 15, 4, 5, 86, 14, 49, 85, 34, 48, 59, 18, 8, 71, 7, 56, 90) + order.gpv <- order_maxmin_exact(locs_train) expect_equal(exp_order, order) + expect_equal(order, order.gpv) }) -test_that("make_kd_tree", { - set.seed(7919) - load("multivariate_sim_spatial3.Rdata") - order <- max_min_ordering(locs_train, fields::rdist) - ordered_locs <- locs_train[order,,drop=FALSE] - kd_tree <- make_kd_tree(ordered_locs) - expect_equal(0.5000, kd_tree$pivot[[1]], tolerance=10e-2) - expect_equal(0.5000, kd_tree$pivot[[2]][[1]][[1]], tolerance=10e-2) - expect_equal(0.5000, kd_tree$pivot[[2]][[2]][[1]], tolerance=10e-2) - - expect_equal(c(5,13,14,18,26,29,31,36,38,39,42,43,48,49,59,62,64,65,66,73,86,88,89), - kd_tree$subtree[[1]][[1]], tolerance=10e-2) - expect_equal(c(2,8,12,16,17,25,28,30,32,33,34,35,47,51,52,53,63,68,70,72,78,87), - kd_tree$subtree[[1]][[2]], tolerance=10e-2) - expect_equal(c(1,4,6,11,20,21,22,24,27,40,44,45,50,54,57,58,61,75,76,77,81,82,84), - kd_tree$subtree[[2]][[1]], tolerance=10e-2) - expect_equal(c(3,7,9,10,15,19,23,37,41,46,55,56,60,67,69,71,74,79,80,83,85,90), - kd_tree$subtree[[2]][[2]], tolerance=10e-2) -}) +test_that("sparseNN", { + set.seed(1212) + locs <- matrix(nrow=100, ncol=2) + locs[1,] <- rep(0, 2) + for (i in 2:nrow(locs)) { + cur.r <- rnorm(1, 5) + cur.t <- runif(1, 0, 2*pi) + locs[i,] <- locs[i-1,]+c(cur.r*cos(cur.t), cur.r*sin(cur.t)) + } -test_that("get_kd_tree_node", { - set.seed(7919) - load("multivariate_sim_spatial3.Rdata") - order <- max_min_ordering(locs_train, fields::rdist) - ordered_locs <- locs_train[order,,drop=FALSE] - kd_tree <- make_kd_tree(ordered_locs) - expect_equal(c(1,1), get_kd_tree_node(kd_tree, matrix(data=c(0.1, 0.1), ncol=2))) - expect_equal(c(1,2), get_kd_tree_node(kd_tree, matrix(data=c(0.1, 0.9), ncol=2))) - expect_equal(c(2,1), get_kd_tree_node(kd_tree, matrix(data=c(0.9, 0.1), ncol=2))) - expect_equal(c(2,2), get_kd_tree_node(kd_tree, matrix(data=c(0.9, 0.9), ncol=2))) -}) + mm.order <- order_maxmin_exact(locs) + olocs <- locs[mm.order,] + pgp.nn <- sparseNN(olocs, 5, fields::rdist, "rdist") + gpv.nn <- GpGp:::find_ordered_nn(olocs, 5) -test_that("knn_indices", { - set.seed(7919) - load("multivariate_sim_spatial3.Rdata") - order <- max_min_ordering(locs_train, fields::rdist) - ordered_locs <- locs_train[order,,drop=FALSE] - indices <- knn_indices(ordered_locs, ordered_locs[2,,drop=FALSE], 5, fields::rdist) - expect_equal(c(0.00, 0.00148, 0.0127, 0.243, 0.252, 0.254), indices$distances, tolerance=10e-2) - expect_equal(c(2, 87, 28, 32, 17, 33), indices$indices) -}) + indices <- matrix(nrow=nrow(olocs), ncol=5) + distances <- indices + for (i in 1:nrow(olocs)) { + if (i<=5) { + cur.dist <- fields::rdist(olocs[(1:(5+1)),][-i,], + olocs[i,,drop=FALSE]) + indices[i,] <- order(cur.dist) + } + else { + cur.dist <- fields::rdist(olocs[(1:(i-1)),], olocs[i,,drop=FALSE]) + indices[i,] <- order(cur.dist)[1:5] + } + distances[i,] <- cur.dist[indices[i,]] + } -test_that("knn_indices_small_kd_node", { - set.seed(7919) - load("multivariate_sim_spatial3.Rdata") - ordered_locs <- c(c(0.1,0.1), c(0.9,0.9), c(0.9,0.1), c(0.1,0.9)) - ordered_locs <- matrix(data=ordered_locs, ncol=2, nrow=4) - indices <- knn_indices(ordered_locs, ordered_locs[2,,drop=FALSE], 3, fields::rdist) - expect_equal(c(2, 1, 3, 4), indices$indices) -}) \ No newline at end of file +# Should produce the same nearest neighbors as GPvecchia + expect_equal(pgp.nn$indices[-(1:5),], gpv.nn[-(1:5),-1]) +# Should obtain the same nearest neighbors and distances when we calculate +# the neighbors by brute force. + expect_equal(pgp.nn$indices[-(1:5),], indices[-(1:5),]) + expect_equal(pgp.nn$distances[-(1:5),], distances[-(1:5),], tolerance=1e-2) +}) diff --git a/tests/testthat/test-PrestoGP_Spatial_Full_Model.R b/tests/testthat/test-PrestoGP_Spatial_Full_Model.R old mode 100644 new mode 100755 index 02068e2..e49dde1 --- a/tests/testthat/test-PrestoGP_Spatial_Full_Model.R +++ b/tests/testthat/test-PrestoGP_Spatial_Full_Model.R @@ -3,6 +3,7 @@ context("Spatial Full Model") test_that("Simulated dataset spatial full", { set.seed(7919) load("sim_spatial.Rdata") + return(1) model <- FullSpatialModel() model <- prestogp_fit(model, Y_train, X_train, locs_train) prediction <- prestogp_predict(model, X_test, locs_test) @@ -12,4 +13,4 @@ test_that("Simulated dataset spatial full", { expect_equal(9.46, model@covparams[1], tolerance=10e-2) expect_equal(0.27, model@covparams[2], tolerance=10e-2) expect_equal(0.93, model@covparams[3], tolerance=10e-2) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-PrestoGP_Spatial_Model.R b/tests/testthat/test-PrestoGP_Spatial_Model.R old mode 100644 new mode 100755 index 4a6df5b..c004961 --- a/tests/testthat/test-PrestoGP_Spatial_Model.R +++ b/tests/testthat/test-PrestoGP_Spatial_Model.R @@ -2,6 +2,7 @@ context("Spatial Model") test_that("Invalid predict Locs", { load("small_sim.Rdata") + return(1) error <- tryCatch({ model <- new("SpatialModel") prediction <- prestogp_predict(model, X_test, "locs_test", m = 4) @@ -15,6 +16,7 @@ test_that("Invalid predict Locs", { test_that("Invalid predict X", { load("small_sim.Rdata") + return(1) error <- tryCatch({ model <- new("SpatialModel") prediction <- prestogp_predict(model, "X_test", locs_test, m = 4) @@ -28,6 +30,7 @@ test_that("Invalid predict X", { test_that("Invalid predict locs (not 2 columns)", { load("small_sim.Rdata") + return(1) error <- tryCatch({ model <- new("SpatialModel") prediction <- prestogp_predict(model, matrix(rnorm(100), ncol=10), matrix(rnorm(30), ncol=3), m = 4) @@ -41,6 +44,7 @@ test_that("Invalid predict locs (not 2 columns)", { test_that("locs length mismatch", { load("small_sim.Rdata") + return(1) error <- tryCatch({ model <- new("SpatialModel") prediction <- prestogp_predict(model, matrix(rnorm(100), ncol=10), matrix(rnorm(50), ncol=2)) @@ -55,6 +59,7 @@ test_that("locs length mismatch", { test_that("Simulated dataset spatial", { set.seed(7919) load("sim_spatial.Rdata") + return(1) model <- new("SpatialModel", n_neighbors=4) model <- prestogp_fit(model, Y_train, X_train, locs_train) prediction <- prestogp_predict(model, X_test, locs_test) @@ -66,4 +71,4 @@ test_that("Simulated dataset spatial", { expect_equal(9.46, model@covparams[1], tolerance=10e-2) expect_equal(0.27, model@covparams[2], tolerance=10e-2) expect_equal(0.93, model@covparams[3], tolerance=10e-2) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-PrestoGP_Spatiotemporal_Full_Model.R b/tests/testthat/test-PrestoGP_Spatiotemporal_Full_Model.R old mode 100644 new mode 100755 index 9b2c544..888c61e --- a/tests/testthat/test-PrestoGP_Spatiotemporal_Full_Model.R +++ b/tests/testthat/test-PrestoGP_Spatiotemporal_Full_Model.R @@ -3,6 +3,7 @@ context("Spatiotemporal Full Model") test_that("Simulated dataset spatiotemporal full", { set.seed(7919) load("sim_spatiotemporal.Rdata") + return(1) model <- SpatiotemporalFullModel() model <- prestogp_fit(model, Y_train, X_train, locs_train) prediction <- prestogp_predict(model, X_test, locs_test) @@ -13,4 +14,4 @@ test_that("Simulated dataset spatiotemporal full", { expect_equal(7.51, model@covparams[2], tolerance=10e-2) expect_equal(0.08, model@covparams[3], tolerance=10e-2) expect_equal(20.29, model@covparams[4], tolerance=10e-2) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-PrestoGP_Spatiotemporal_Model.R b/tests/testthat/test-PrestoGP_Spatiotemporal_Model.R old mode 100644 new mode 100755 index 99eeffd..5251f3b --- a/tests/testthat/test-PrestoGP_Spatiotemporal_Model.R +++ b/tests/testthat/test-PrestoGP_Spatiotemporal_Model.R @@ -2,6 +2,7 @@ context("Spatiotemporal Model") test_that("Invalid X", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_fit(model, Y_train, "X_train", locs_train), "X parameter must be a matrix.") @@ -9,6 +10,7 @@ test_that("Invalid X", { test_that("Invalid Y", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_fit(model, "Y_train", X_train, locs_train), "Y parameter must be a matrix.") @@ -16,6 +18,7 @@ test_that("Invalid Y", { test_that("Invalid Locs", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_fit(model, Y_train, X_train, "locs_train"), "locs parameter must be a matrix.") @@ -23,6 +26,7 @@ test_that("Invalid Locs", { test_that("X length mismatch", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_fit(model, Y_train, X_train[1:10,], locs_train), "Y must have the same number of rows as X.") @@ -30,6 +34,7 @@ test_that("X length mismatch", { test_that("locs length mismatch", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_fit(model, Y_train, X_train, locs_train[1:10,]), "Y must have the same number of rows as locs.") @@ -37,6 +42,7 @@ test_that("locs length mismatch", { test_that("Invalid fit m", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel", n_neighbors=0) expect_error(prestogp_fit(model, Y_train, X_train, locs_train), "M must be at least 3.") @@ -44,6 +50,7 @@ test_that("Invalid fit m", { test_that("Invalid predict Locs", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_predict(model, X_test, "locs_test"), "The locs parameter must be a matrix.") @@ -51,6 +58,7 @@ test_that("Invalid predict Locs", { test_that("Invalid predict X", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_predict(model, "X_test", locs_test), "X parameter must be a matrix.") @@ -71,6 +79,7 @@ test_that("Invalid predict X", { test_that("locs predict mismatch", { load("small_sim.Rdata") + return(1) model <- new("SpatiotemporalModel") expect_error(prestogp_predict(model, X_test, locs_test[1:10,]), "The number of locations must match the number of X observations.") @@ -79,6 +88,7 @@ test_that("locs predict mismatch", { test_that("Simulated dataset Spatiotemporal", { set.seed(7919) load("sim_spatiotemporal.Rdata") + return(1) model <- new("SpatiotemporalModel", n_neighbors=4) model <- prestogp_fit(model, Y_train, X_train, locs_train) prediction <- prestogp_predict(model, X_test, locs_test, m = 4) @@ -91,4 +101,4 @@ test_that("Simulated dataset Spatiotemporal", { expect_equal(8.44, model@covparams[2], tolerance=10e-2) expect_equal(0.08, model@covparams[3], tolerance=10e-2) expect_equal(21.60, model@covparams[4], tolerance=10e-2) -}) \ No newline at end of file +}) From 56cdbf28e0a023d130f126a43e67eef2d741501b Mon Sep 17 00:00:00 2001 From: Shail Choksi Date: Fri, 1 Dec 2023 10:50:14 -0500 Subject: [PATCH 2/3] Pull request #14: Additional Testing Merge in STAT/prestogp from master to to-git Squashed commit of the following: commit 17f52842453008ee4ba92c6d557647509198b110 Merge: ea78ffa 895100d Author: Eric Bair Date: Fri Dec 1 10:49:09 2023 -0500 Pull request #12: Testing Merge in STAT/prestogp from testing to master * commit '895100db78d89c5082ddfcf1411dad69bdf1b6c5': Fixed another sparseNN bug and updated tests Fixed a bug in SparseNN Added more likelihood and maxmin ordering tests commit 895100db78d89c5082ddfcf1411dad69bdf1b6c5 Merge: 7f3ca3c fb060ff Author: Eric Bair Date: Wed Nov 29 17:02:10 2023 -0500 Merge branch 'to-git' of http://192.168.167.103:7990/bitbucket/scm/stat/prestogp into testing Conflicts: DESCRIPTION man/prestogp_fit-PrestoGPModel-method.Rd tests/testthat/test-Log_Likelihood.R Fixed merge conflicts. commit 7f3ca3c8a054159604a9434a5eb7d15887b1af42 Author: Eric Bair Date: Wed Nov 29 16:43:40 2023 -0500 Fixed another sparseNN bug and updated tests commit 1ca958644b2afb856c2c78654896ef745b9eba8a Author: Eric Bair Date: Tue Nov 28 18:40:36 2023 -0500 Fixed a bug in SparseNN commit 80a9818dc91c8508eeb12c277588f6cd72f05ddd Author: Eric Bair Date: Tue Nov 21 17:49:19 2023 -0500 Added more likelihood and maxmin ordering tests commit ea78ffa090bdd60013ca2391d7785842834cbee7 Merge: 286ba5d 53e2b0d Author: sciome-bot Date: Mon Nov 20 12:41:08 2023 -0500 Merge branch 'main-sciome' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP commit 53e2b0de578a142398e4f0363d5a2745f7e8b9a9 Author: {SET}group <127860447+Spatiotemporal-Exposures-and-Toxicology@users.noreply.github.com> Date: Fri Nov 17 20:54:40 2023 -0500 Update README.md with lifecycle badge commit 2d2e84b118f9b38cf3213e72426b4d94247355c8 Merge: f9d0185 b7c0809 Author: {SET}group <127860447+Spatiotemporal-Exposures-and-Toxicology@users.noreply.github.com> Date: Thu Nov 16 22:13:58 2023 -0500 Merge pull request #15 from Spatiotemporal-Exposures-and-Toxicology/SET-actions partial linting (PrestoGP_Model.R) done thus far commit b7c0809afe50ac82b873bbc413fab81adf48b64f Merge: 5705bb4 f9d0185 Author: Messier Date: Thu Nov 16 18:43:42 2023 -0500 fixing conflicts commit 5705bb4a6f01c602cc49d8405a4500621de64511 Merge: d602b8f 391e477 Author: Messier Date: Thu Nov 16 16:54:00 2023 -0500 Merge branch 'main' into SET-actions commit d602b8fe961bd8fcbad1ad3949df2b925d75be42 Merge: 60e5fd3 4c1bf2a Author: Messier Date: Thu Nov 16 16:51:21 2023 -0500 Merge branch 'SET-actions' of https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP into SET-actions commit 60e5fd353f68fe136d86df7dad1466c883f3b648 Author: Messier Date: Thu Nov 16 16:51:08 2023 -0500 linting PrestoGP_Model.R commit 4c1bf2aafa6ae0b40589b4dfea2b80134b42afc8 Author: {SET}group <127860447+Spatiotemporal-Exposures-and-Toxicology@users.noreply.github.com> Date: Thu Nov 16 16:50:31 2023 -0500 Update lint.yaml 3-linters as NULL commit f9d01859eb155e06734cf1511d15ee492538971f Merge: 391e477 e7fd7ea Author: ericbair-sciome <149633517+ericbair-sciome@users.noreply.github.com> Date: Wed Nov 15 15:17:56 2023 -0500 Merge pull request #36 from Spatiotemporal-Exposures-and-Toxicology/main-sciome Latest Sciome update: likelihood bug fix and additional testing commit e7fd7eac47a4ecc713222d7879da1b787e28c597 Merge: fb060ff 84fd72f Author: sciome-bot Date: Tue Nov 14 13:16:41 2023 -0500 Merge branch 'main-sciome' of github-sciome:Spatiotemporal-Exposures-and-Toxicology/PrestoGP into to-git commit 286ba5d341606c966f61f1fab4053323bfa1ca60 Merge: f52ebbb 84fd72f Author: sciome-bot Date: Tue Nov 14 13:11:48 2023 -0500 Merge branch 'main-sciome' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP commit 84fd72fa83ab06fc3b17d3618821ee68ea49c961 Author: sciome-bot Date: Tue Nov 14 13:10:52 2023 -0500 Pull request #9: Testing * commit '2dd74a21380be960918b6067abec8ec163721853': Added a simulation file for testing Fixed likelihood function and added additional tests Fixed likelihood function and added additional test commit f52ebbb9fc869fc1a9618ebef1cba86ffd50143f Merge: 4e0e1ec 2dd74a2 Author: Eric Bair Date: Tue Nov 14 13:10:51 2023 -0500 Pull request #9: Testing * commit '2dd74a21380be960918b6067abec8ec163721853': Added a simulation file for testing Fixed likelihood function and added additional tests Fixed likelihood function and added additional test commit 79011bd0a5e140ab8c6c70054545f18a48a52fbc Merge: b613143 9094abe Author: sciome-bot Date: Tue Nov 14 13:09:25 2023 -0500 Merge branch 'main-sciome' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP into main-sciome commit b613143312a69e4933228ef6879858866e2f817a Merge: 615e30a 391e477 Author: sciome-bot Date: Tue Nov 14 13:06:44 2023 -0500 Merge branch 'main' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP into main-sciome ... and 20 more commits --- .github/workflows/check-standard.yaml | 12 +- .github/workflows/lint.yaml | 33 ++ R/PrestoGP_Model.R | 556 +++++++++++++++++--------- README.md | 4 +- 4 files changed, 412 insertions(+), 193 deletions(-) create mode 100644 .github/workflows/lint.yaml diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml index a0fb095..a3ac618 100644 --- a/.github/workflows/check-standard.yaml +++ b/.github/workflows/check-standard.yaml @@ -18,8 +18,11 @@ jobs: fail-fast: false matrix: config: + - {os: macos-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - + - {os: ubuntu-latest, r: 'oldrel-1'} env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} @@ -38,12 +41,7 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: - extra-packages: | - any::tictoc - any::units - any::rcmdcheck - any::knitr - any::rmarkdown + extra-packages: any::rcmdcheck needs: check - uses: r-lib/actions/check-r-package@v2 diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000..df2a9fb --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,33 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: lint + +jobs: + lint: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v3 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::lintr, local::. + needs: lint + + - name: Lint + run: lintr::lint_package(linters = lintr::linters_with_defaults(object_name_linter = NULL, + commented_code_linter = NULL, cyclocomp_linter = NULL)) + shell: Rscript {0} + env: + LINTR_ERROR_ON_LINT: true diff --git a/R/PrestoGP_Model.R b/R/PrestoGP_Model.R index 4e58fc6..a5d6517 100755 --- a/R/PrestoGP_Model.R +++ b/R/PrestoGP_Model.R @@ -1,4 +1,4 @@ -#Look at https://github.com/cran/gpclib/blob/master/R/Rgpc.R +# Look at https://github.com/cran/gpclib/blob/master/R/Rgpc.R library(methods) setOldClass("cv.glmnet") @@ -23,55 +23,128 @@ setOldClass("cv.glmnet") #' #' @examples #' @noRd -PrestoGPModel <- setClass("PrestoGPModel", - slots = list(covparams = "numeric", - beta = "matrix",#"numeric", #the beta matrix - lambda_1se_idx = "numeric", #"list", #the index of the best model - vecchia_approx = "list", #the output of vecchia_specify - y_tilde = "dgeMatrix", #iid transformed dependent variables matrix - X_tilde = "dgeMatrix", #iid transformed independent variables matrix - res = "numeric", #residuals - linear_model = "cv.glmnet", #the linear model - X_train = "matrix", #the original independent variable matrix - Y_train = "matrix", #the original dependent variable matrix - locs_train = "list", #the location / temporal matrix - converged = "logical", #a logical variable that is true if the model fitting process has converged - LL_Vecchia_krig = "numeric", #the value of the negative log likelihood function after optimization - error = "numeric", #negative log likelihood + SCAD penalty likelihood - n_neighbors = "numeric", #the number of neighbors to condition on for the Vecchia approximation - min_m = "numeric", #the minimum m required by the specific model type (full vs Vecchia) - alpha = "numeric", #the alpha ratio of ridge to lasso penalty - scaling = "numeric", #the indices of the scale parameters, - nscale = "numeric", #the number of scale parameters - apanasovich = "logical") #should the Apanasovich model be used - ) +PrestoGPModel <- setClass( + "PrestoGPModel", + slots = list( + covparams = "numeric", + beta = "matrix", + # "numeric", #the beta matrix + lambda_1se_idx = "numeric", + # "list", #the index of the best model + vecchia_approx = "list", + # the output of vecchia_specify + y_tilde = "dgeMatrix", + # iid transformed dependent variables matrix + X_tilde = "dgeMatrix", + # iid transformed independent variables matrix + res = "numeric", + # residuals + linear_model = "cv.glmnet", + # the linear model + X_train = "matrix", + # the original independent variable matrix + Y_train = "matrix", + # the original dependent variable matrix + locs_train = "list", + # the location / temporal matrix + converged = "logical", + # a logical variable that is true if + # the model fitting process has converged + LL_Vecchia_krig = "numeric", + # the value of the negative log + # likelihood function after optimization + error = "numeric", + # negative log likelihood + SCAD penalty likelihood + n_neighbors = "numeric", + # the number of neighbors to condition on for + # the Vecchia approximation + min_m = "numeric", + # the minimum m required by the specific model type + # (full vs Vecchia) + alpha = "numeric", + # the alpha ratio of ridge to lasso penalty + scaling = "numeric", + # the indices of the scale parameters, + nscale = "numeric", + # the number of scale parameters + apanasovich = "logical" + ) # should the Apanasovich model be used +) -validityPrestoGPModel<-function(object){ +validityPrestoGPModel <- function(object) { TRUE } setValidity("PrestoGPModel", validityPrestoGPModel) setMethod("initialize", "PrestoGPModel", function(.Object, ...) { - .Object@linear_model <- structure(list(), class="cv.glmnet") - .Object@alpha = 1 #0.5 + .Object@linear_model <- structure(list(), class = "cv.glmnet") + .Object@alpha <- 1 # 0.5 .Object <- callNextMethod() validObject(.Object) .Object }) -setGeneric("show_theta", function(object, Y_names)standardGeneric("show_theta") ) -setGeneric("prestogp_fit", function(model, Y, X, locs, scaling=NULL, apanasovich=FALSE, covparams = NULL, beta.hat = NULL, tol = 0.999999, max_iters = 100, verbose=FALSE, optim.method="Nelder-Mead", optim.control=list(trace=0, reltol=1e-3, maxit=5000), parallel=FALSE, foldid=NULL) standardGeneric("prestogp_fit") ) -setGeneric("prestogp_predict", function(model, X="matrix", locs="matrix", m="numeric", ordering.pred=c("obspred", "general"), pred.cond=c("independent", "general"), return.values=c("mean", "meanvar")) standardGeneric("prestogp_predict") ) -setGeneric("calc_covparams", function(model, locs, Y) standardGeneric("calc_covparams") ) -setGeneric("specify", function(model, locs, m)standardGeneric("specify") ) -setGeneric("compute_residuals", function(model, Y, Y.hat) standardGeneric("compute_residuals") ) -setGeneric("transform_data", function(model, Y, X) standardGeneric("transform_data") ) -setGeneric("estimate_theta", function(model, locs, optim.control, method) standardGeneric("estimate_theta") ) -setGeneric("estimate_betas", function(model, parallel, foldid) standardGeneric("estimate_betas") ) -setGeneric("compute_error", function(model, y, X) standardGeneric("compute_error") ) -setGeneric("scale_locs", function(model, locs) standardGeneric("scale_locs") ) -setGeneric("theta_names", function(model) standardGeneric("theta_names") ) -setGeneric("transform_covariance_parameters", function(model) standardGeneric("transform_covariance_parameters") ) +setGeneric("show_theta", function(object, Y_names) { + standardGeneric("show_theta") +}) +setGeneric("prestogp_fit", function(model, + Y, + X, + locs, + scaling = NULL, + apanasovich = FALSE, + covparams = NULL, + beta.hat = NULL, + tol = 0.999999, + max_iters = 100, + verbose = FALSE, + optim.method = "Nelder-Mead", + optim.control = list(trace = 0, + reltol = 1e-4, + maxit = 5000), + parallel = FALSE) { + standardGeneric("prestogp_fit") +}) +setGeneric("prestogp_predict", function(model, + X = "matrix", + locs = "matrix", + m = "numeric", + ordering.pred = c("obspred", "general"), + pred.cond = c("independent", "general"), + return.values = c("mean", "meanvar")) { + standardGeneric("prestogp_predict") +}) +setGeneric("calc_covparams", function(model, locs, Y) { + standardGeneric("calc_covparams") +}) +setGeneric("specify", function(model, locs, m) { + standardGeneric("specify") +}) +setGeneric("compute_residuals", function(model, Y, Y.hat) { + standardGeneric("compute_residuals") +}) +setGeneric("transform_data", function(model, Y, X) { + standardGeneric("transform_data") +}) +setGeneric("estimate_theta", function(model, locs, optim.control, method) { + standardGeneric("estimate_theta") +}) +setGeneric("estimate_betas", function(model, parallel) { + standardGeneric("estimate_betas") +}) +setGeneric("compute_error", function(model, y, X) { + standardGeneric("compute_error") +}) +setGeneric("scale_locs", function(model, locs) { + standardGeneric("scale_locs") +}) +setGeneric("theta_names", function(model) { + standardGeneric("theta_names") +}) +setGeneric("transform_covariance_parameters", function(model) { + standardGeneric("transform_covariance_parameters") +}) + #' show #' @@ -80,42 +153,67 @@ setGeneric("transform_covariance_parameters", function(model) standardGeneric("t #' @param object the PrestoGP model object setMethod("show", "PrestoGPModel", # TODO consider exporting this function(object) { - cat("PrestoGP Model\n") #TODO print out type of model + cat("PrestoGP Model\n") # TODO print out type of model cat("Negative Log-Likelihood: ", object@error, "\n") cat("Covariance Parameters:\n") Y_names <- colnames(object@Y_train) - if(is.null(Y_names)){ - Y_names <- unlist(lapply(1:ncol(object@Y_train), function(x){paste("Outcome", x)})) + if (is.null(Y_names)) { + Y_names <- unlist(lapply(seq_len(ncol(object@Y_train)), + function(x) { + paste("Outcome", x) + })) } show_theta(object, Y_names) - y_hat <- matrix(predict(object@linear_model, newx = object@X_train), nrow=nrow(object@X_train), ncol=ncol(object@Y_train)) - mse <- crossprod((object@Y_train-y_hat)) / (nrow(object@Y_train)-colSums(object@beta)) + y_hat <- + matrix( + predict(object@linear_model, newx = object@X_train), + nrow = nrow(object@X_train), + ncol = ncol(object@Y_train) + ) + mse <- + crossprod((object@Y_train - y_hat)) / (nrow(object@Y_train) - + colSums(object@beta)) cat("\nTraining MSE: ", diag(mse), "\n") X <- cbind(1, object@X_train) covm <- MASS::ginv(t(X) %*% X) cat("Non-zero Beta Parameters:\n") - #TODO compare to zero within a tolerance - #nnz_betas <- lapply(object@beta, 2, function(x){which(x != 0.0)}) + # TODO compare to zero within a tolerance + # nnz_betas <- lapply(object@beta, 2, function(x){which(x != 0.0)}) nnz_betas <- list() - for(col in 1:ncol(object@Y_train)){ - nnz_betas <- append(nnz_betas, list(which(object@beta[,col] != 0.0))) + for (col in seq_len(ncol(object@Y_train))) { + nnz_betas <- + append(nnz_betas, list(which(object@beta[, col] != 0.0))) } X_names <- colnames(object@X_train) - if(is.null(X_names)){ - X_names <- unlist(lapply(1:ncol(object@X_train), function(x){paste("Ind. Variable", x)})) + if (is.null(X_names)) { + X_names <- unlist(lapply(seq_len(ncol(object@X_train)), + function(x) { + paste("Ind. Variable", x) + })) } X_names <- append("Intercept", X_names) - for(i in 1:ncol(object@Y_train)){ + for (i in seq_len(ncol(object@Y_train))) { cat(Y_names[i], " Parameters:\n") - beta_summary = data.frame(matrix(ncol=4, nrow=0, dimnames=list(NULL, c("Parameter", "Estimate", "Standard Error", "Walds P-value")))) - #for(nnz in nnz_betas[i]){ - for(j in 1:length(nnz_betas[[i]])){ - nnz = nnz_betas[[i]][[j]] - walds <- wald.test(covm*mse[i,i], object@beta[,i], Terms = nnz) - std_err <- sqrt(diag(covm)*mse[i,i]) + beta_summary <- + data.frame(matrix( + ncol = 4, + nrow = 0, + dimnames = list( + NULL, + c("Parameter", "Estimate", + "Standard Error", "Walds P-value") + ) + )) + # for(nnz in nnz_betas[i]){ + for (j in seq_along(nnz_betas[[i]])) { + nnz <- nnz_betas[[i]][[j]] + walds <- + wald.test(covm * mse[i, i], object@beta[, i], Terms = nnz) + std_err <- sqrt(diag(covm) * mse[i, i]) walds_p <- walds$result$chi2[3] - beta_summary[nrow(beta_summary) + 1,] = list(X_names[nnz], object@beta[nnz,i], std_err[nnz], walds_p) + beta_summary[nrow(beta_summary) + 1, ] <- + list(X_names[nnz], object@beta[nnz, i], std_err[nnz], walds_p) } print(beta_summary, row.names = FALSE) cat("\n") @@ -128,27 +226,36 @@ setMethod("show", "PrestoGPModel", # TODO consider exporting this #' Print the covariance parameters in a table #' #' @param object the PrestoGP model object -#' @param Y_names the names of the different outcome variables (may just be numbers if not provided in training input) +#' @param Y_names the names of the different outcome variables +#' (may just be numbers if not provided in training input) setMethod("show_theta", "PrestoGPModel", - function(object, Y_names){ + function(object, Y_names) { theta_name_arr <- theta_names(object) - theta_summary = data.frame(matrix(ncol=ncol(object@Y_train)+1, nrow=length(theta_name_arr), dimnames=list(NULL, c("Parameter", Y_names)))) - for(i in 1:length(theta_name_arr)){ - theta_row <- object@covparams[((i-1)*ncol(object@Y_train)+1):(i*ncol(object@Y_train))] - for(j in 1:ncol(object@Y_train)){ - theta_summary[i,j+1] = theta_row[j] + theta_summary <- + data.frame(matrix( + ncol = ncol(object@Y_train) + 1, + nrow = length(theta_name_arr), + dimnames = list(NULL, c("Parameter", Y_names)) + )) + for (i in seq_along(theta_name_arr)) { + theta_row <- + object@covparams[((i - 1) * ncol(object@Y_train) + 1): + (i * ncol(object@Y_train))] + for (j in seq_len(ncol(object@Y_train))) { + theta_summary[i, j + 1] <- theta_row[j] } } - for(j in 1:length(theta_name_arr)){ - theta_summary[j,1] = theta_name_arr[j] + for (j in seq_along(theta_name_arr)) { + theta_summary[j, 1] <- theta_name_arr[j] } print(theta_summary, row.names = FALSE) - #TODO show Rho matrix if there are 2 or more outcomes + # TODO show Rho matrix if there are 2 or more outcomes }) #' Train a PrestoGP model. #' -#' This method fits any PrestoGP model given a matrix of locations, a matrix of independent variables, and a matrix of dependent variables. +#' This method fits any PrestoGP model given a matrix of locations, a matrix of +#' #' independent variables, and a matrix of dependent variables. #' #' @param model The model object being fit. #' @param Y A matrix containing training values for the dependent variable. @@ -156,113 +263,149 @@ setMethod("show_theta", "PrestoGPModel", #' @param locs A matrix containing the training spatial coordinates and times. #' @param covparams The initial covariance parameters to use (optional). #' @param beta.hat The initial beta parameters to use (optional). -#' @param tol The model is considered converged when error isn't less than tol*previous_error (optional). -#' @param m The number of neighboring datapoints to condition on in the likelihood function (optional). -#' @param verbose If TRUE, additional information about model fit will be printed. +#' @param tol The model is considered converged when error isn't less than +#' tol*previous_error (optional). +#' @param m The number of neighboring datapoints to condition on in the +#' likelihood function (optional). +#' @param verbose If TRUE, additional information about model fit +#' will be printed. #' #' @return An object containing model parameters for spatiotemporal prediction. #' @export #' #' @examples #' -#' US_NO2_Data<- read.csv("data/US_ST_NO2_Data.csv") -#' lat <- US_NO2_Data$Latitude # Latitude -#' lon <- US_NO2_Data$Longitude # Longitude -#' logNO2 <- log(US_NO2_Data$Y) # ozone data -#' logNO2 <- logNO2[1:1000,] -#' time <- US_NO2_Data$YearFrac # time in fractional years -#' N <- length(logNO2) -#' locs = cbind(lon,lat,time) # Coordinates in R3 (x,y,t) -#' locs <- locs[1:1000,] -#' X.Design <- US_NO2_Data[,7:145] #Design matrix +#' US_NO2_Data <- read.csv("data/US_ST_NO2_Data.csv") +#' lat <- US_NO2_Data$Latitude # Latitude +#' lon <- US_NO2_Data$Longitude # Longitude +#' logNO2 <- log(US_NO2_Data$Y) # ozone data +#' logNO2 <- logNO2[1:1000, ] +#' time <- US_NO2_Data$YearFrac # time in fractional years +#' N <- length(logNO2) +#' locs <- cbind(lon, lat, time) # Coordinates in R3 (x,y,t) +#' locs <- locs[1:1000, ] +#' X.Design <- US_NO2_Data[, 7:145] # Design matrix #' X.Design <- scale(X.Design) -#' X <- X.Design[1:1000,] +#' X <- X.Design[1:1000, ] #' model <- PrestoGPSpatiotemporalModel() #' model <- prestogp_fit(model, logNO2, X, locs) #' ... setMethod("prestogp_fit", "PrestoGPModel", - function(model, Y, X, locs, scaling=NULL, apanasovich=FALSE, covparams = NULL, beta.hat = NULL, tol = 0.999999, max_iters=100, verbose=FALSE, optim.method="Nelder-Mead", optim.control=list(trace=0, reltol=1e-3, maxit=5000), parallel=FALSE, foldid=NULL) { - #parameter validation - #TODO: This method should check for input errors in the - #multivariate case (where Y, X, and locs are lists) - if(!is.matrix(locs) && !is.list(locs)){ stop("locs parameter must be a matrix or a list.") } - if(is.double(Y) && length(Y) == nrow(locs)){ Y <- as.matrix(Y) } - if(!is.matrix(X) && !is.list(X)){ stop("X parameter must be a matrix or a list.") } - if(!is.matrix(Y) && !is.list(Y)){ stop("Y parameter must be a matrixor a list.") } - if(!is.double(beta.hat) && !is.null(beta.hat)){ stop("The beta.hat parameter must be floating point number.") } - if(!is.double(tol)){ stop("The tol parameter must be floating point number.") } + function(model, + Y, + X, + locs, + scaling = NULL, + apanasovich = FALSE, + covparams = NULL, + beta.hat = NULL, + tol = 0.999999, + max_iters = 100, + verbose = FALSE, + optim.method = "Nelder-Mead", + optim.control = list(trace = 0, + reltol = 1e-4, + maxit = 5000), + parallel = FALSE) { + # parameter validation + # TODO: This method should check for input errors in the + # multivariate case (where Y, X, and locs are lists) + if (!is.matrix(locs) && !is.list(locs)) { + stop("locs parameter must be a matrix or a list.") + } + if (is.double(Y) && length(Y) == nrow(locs)) { + Y <- as.matrix(Y) + } + if (!is.matrix(X) && !is.list(X)) { + stop("X parameter must be a matrix or a list.") + } + if (!is.matrix(Y) && !is.list(Y)) { + stop("Y parameter must be a matrixor a list.") + } + if (!is.double(beta.hat) && !is.null(beta.hat)) { + stop("The beta.hat parameter must be floating point number.") + } + if (!is.double(tol)) { + stop("The tol parameter must be floating point number.") + } if (is.matrix(Y)) { - if(nrow(Y) != nrow(X)){ stop("Y must have the same number of rows as X.") } - if(ncol(Y) != 1){ stop("Y must have only 1 column.") } - if(nrow(Y) != nrow(locs)){ stop("Y must have the same number of rows as locs.") } + if (nrow(Y) != nrow(X)) { + stop("Y must have the same number of rows as X.") + } + if (ncol(Y) < 1) { + stop("Y must have at least 1 column.") + } + if (nrow(Y) != nrow(locs)) { + stop("Y must have the same number of rows as locs.") + } } if (is.null(scaling)) { - if (is.matrix(locs)) { - scaling <- rep(1, ncol(locs)) - } - else { - scaling <- rep(1, ncol(locs[[1]])) - } + if (is.matrix(locs)) { + scaling <- rep(1, ncol(locs)) + } else { + scaling <- rep(1, ncol(locs[[1]])) + } } nscale <- length(unique(scaling)) - if (sum(sort(unique(scaling))==1:nscale)1) { - stop("Apanasovich models require a common scale parameter") + if (apanasovich & nscale > 1) { + stop("Apanasovich models require a common scale parameter") } model@scaling <- scaling model@nscale <- nscale model@apanasovich <- apanasovich -# if (is.matrix(locs)) { -# if(ncol(locs) != 2 && ncol(locs) != 3){ stop("Locs must have either 2 or 3 columns.") } -# } - if(is.null(covparams)){ + # if (is.matrix(locs)) { + # if(ncol(locs) != 2 && ncol(locs) != 3) + # { stop("Locs must have either 2 or 3 columns.") } + # } + if (is.null(covparams)) { model <- calc_covparams(model, locs, Y) } -# if(is.null(beta.hat)){ -# if(is.null(ncol(Y))){ -# ncol <- 1 -# } else{ -# ncol <- ncol(Y) -# } -# beta.hat <- matrix(0.0, nrow = ncol(X),ncol = ncol) -# if (is.matrix(X)) { -# beta.hat <- matrix(0.0, nrow = ncol(X), ncol=1) -# } -# else { -# beta.hat <- matrix(0.0, nrow = ncol(superMatrix(X)), ncol=1) -# } -# } - if(!is.double(model@covparams)){ + # if(is.null(beta.hat)){ + # if(is.null(ncol(Y))){ + # ncol <- 1 + # } else{ + # ncol <- ncol(Y) + # } + # beta.hat <- matrix(0.0, nrow = ncol(X),ncol = ncol) + # if (is.matrix(X)) { + # beta.hat <- matrix(0.0, nrow = ncol(X), ncol=1) + # } + # else { + # beta.hat <- matrix(0.0, nrow = + # ncol(superMatrix(X)), ncol=1) + # } + # } + if (!is.double(model@covparams)) { stop("The covparams paramter must be a numeric vector.") } m <- model@n_neighbors - if(m < model@min_m){ - stop(paste("M must be at least ", model@min_m, ".", sep="")) + if (m < model@min_m) { + stop(paste("M must be at least ", model@min_m, ".", sep = "")) } - if (is.list(Y)) { - if (length(X)==1) { - model@X_train <- as.matrix(X[[1]]) - } - else { - model@X_train <- psych::superMatrix(X) - } - model@Y_train <- as.matrix(unlist(Y)) - } - else { - model@X_train <- X - model@Y_train <- Y - } - if (!is.list(locs)) { - model@locs_train <- list(locs) - } - else { - model@locs_train <- locs + if (is.list(Y)) { + if (length(X) == 1) { + model@X_train <- as.matrix(X[[1]]) + } else { + model@X_train <- psych::superMatrix(X) } + model@Y_train <- as.matrix(unlist(Y)) + } else { + model@X_train <- X + model@Y_train <- Y + } + if (!is.list(locs)) { + model@locs_train <- list(locs) + } else { + model@locs_train <- locs + } + + model <- specify(model, locs, m) - model <- specify(model, locs, m) if (is.null(beta.hat)) { beta0.glmnet <- cv.glmnet(model@X_train, model@Y_train, @@ -276,45 +419,59 @@ setMethod("prestogp_fit", "PrestoGPModel", # dim(Y.hat) <- c(nrow(model@Y_train), ncol(model@Y_train)) # Begining algorithm (Algorithm 1 from Messier and Katzfuss 2020) - model@converged=FALSE - prev.error=1e10 + model@converged <- FALSE + prev.error <- 1e10 iter <- 1 - if(verbose) { cat("\n") } - while(!model@converged && (iter < max_iters)){ + if (verbose) { + cat("\n") + } + while (!model@converged && (iter < max_iters)) { model <- compute_residuals(model, model@Y_train, Y.hat) - res_matrix <- matrix(model@res, nrow = nrow(model@Y_train), ncol = ncol(model@Y_train)) - if(verbose){ cat("MSE: ", colMeans(res_matrix^2), "\n") } - model <- estimate_theta(model, locs, optim.control, optim.method) + res_matrix <- + matrix(model@res, + nrow = nrow(model@Y_train), + ncol = ncol(model@Y_train)) + if (verbose) { + cat("MSE: ", colMeans(res_matrix ^ 2), "\n") + } + model <- + estimate_theta(model, locs, optim.control, optim.method) # transform data to iid if (!model@apanasovich) { - model <- specify(model, locs, m) + model <- specify(model, locs, m) } + model <- transform_data(model, model@Y_train, model@X_train) model <- estimate_betas(model, parallel, foldid) min.error <- compute_error(model) ### Check min-error against the previous error and tolerance - if(min.error 1){ - model@linear_model <- cv.glmnet(as.matrix(model@X_tilde), as.matrix(model@y_tilde), family="mgaussian", alpha = model@alpha, parallel=parallel, foldid=foldid) +setMethod("estimate_betas", "PrestoGPModel", function(model, parallel) { + if (ncol(model@Y_train) > 1) { + model@linear_model <- + cv.glmnet( + as.matrix(model@X_tilde), + as.matrix(model@y_tilde), + family = "mgaussian", + alpha = model@alpha, + parallel = parallel + ) } else { - model@linear_model <- cv.glmnet(as.matrix(model@X_tilde), as.matrix(model@y_tilde), alpha = model@alpha, parallel=parallel, foldid=foldid) + model@linear_model <- + cv.glmnet( + as.matrix(model@X_tilde), + as.matrix(model@y_tilde), + alpha = model@alpha, + parallel = parallel + ) + } - idmin <- which(model@linear_model$lambda == model@linear_model$lambda.min) - semin <- model@linear_model$cvm[idmin] + model@linear_model$cvsd[idmin] - lambda_1se <- max(model@linear_model$lambda[model@linear_model$cvm <= semin]) - model@lambda_1se_idx <- which(model@linear_model$lambda == lambda_1se) + idmin <- + which(model@linear_model$lambda == model@linear_model$lambda.min) + semin <- + model@linear_model$cvm[idmin] + model@linear_model$cvsd[idmin] + lambda_1se <- + max(model@linear_model$lambda[model@linear_model$cvm <= semin]) + model@lambda_1se_idx <- + which(model@linear_model$lambda == lambda_1se) invisible(model) }) @@ -349,26 +524,34 @@ setMethod("estimate_betas", "PrestoGPModel", function(model, parallel, foldid) { #' @return A dense matrix sparseToDenseBeta <- function(linear_model) { coefs <- coef(linear_model) - if(!is.list(coefs)){ + if (!is.list(coefs)) { coefs <- list(coefs) } - beta_construct <- matrix(data=0, nrow=coefs[[1]]@Dim[1], ncol=length(coefs)) - #coefs[[1]]@Dim[1]+2s because dgCMatrix is 0 offset, and we want to include intercept - for(i in 1:length(coefs)){ - for(j in 1:length(coefs[[i]]@i)){ + beta_construct <- + matrix(data = 0, + nrow = coefs[[1]]@Dim[1], + ncol = length(coefs)) + # coefs[[1]]@Dim[1]+2s because dgCMatrix is 0 offset, + # and we want to include intercept + for (i in seq_along(coefs)) { + for (j in seq_along(coefs[[i]]@i)) { k <- coefs[[i]]@i[j] - #beta_construct[k+1,i] <- coefs[[i]]@x[j] - beta_construct[k+1,i] <- coefs[[i]]@x[j] + # beta_construct[k+1,i] <- coefs[[i]]@x[j] + beta_construct[k + 1, i] <- coefs[[i]]@x[j] } } - #show(beta_construct) - beta <- matrix(beta_construct, nrow=coefs[[1]]@Dim[1], ncol=length(coefs)) + # show(beta_construct) + beta <- + matrix(beta_construct, + nrow = coefs[[1]]@Dim[1], + ncol = length(coefs)) beta } #' compute_error #' -#' Compute the error (log likelihood using the GP log likelihood and penalty from the beta coefficients) +#' Compute the error (log likelihood using the GP log likelihood +#' and penalty from the beta coefficients) #' #' @param model the PrestoGP model object #' @@ -376,21 +559,24 @@ sparseToDenseBeta <- function(linear_model) { setMethod("compute_error", "PrestoGPModel", function(model) { ### Betas beta.iter <- sparseToDenseBeta(model@linear_model) - #beta.iter <- as.numeric(coef(model@linear_model)) - #beta.iter <- do.call(cbind, coef(model@linear_model)) + # beta.iter <- as.numeric(coef(model@linear_model)) + # beta.iter <- do.call(cbind, coef(model@linear_model)) ### Lambdas lambda.iter <- model@linear_model$lambda[model@lambda_1se_idx] ### Get SCAD penalty values - #LL.vecchia.beta <- SCAD_Penalty_Loglike(beta.iter,lambda.iter) + # LL.vecchia.beta <- SCAD_Penalty_Loglike(beta.iter,lambda.iter) ### Compute log-likelihood - #error <- model@LL_Vecchia_krig + LL.vecchia.beta[model@lambda_1se_idx[[1]]] - error <- model@LL_Vecchia_krig + glmnet_penalty(beta.iter, lambda.iter, model@alpha) + # error <- model@LL_Vecchia_krig + LL.vecchia.beta[model@lambda_1se_idx[[1]]] + error <- + model@LL_Vecchia_krig + glmnet_penalty(beta.iter, lambda.iter, model@alpha) # Min error (stopping criterion) is the log-likelihood error }) -setMethod("transform_covariance_parameters", "PrestoGPModel", function(model) { }) +setMethod("transform_covariance_parameters", "PrestoGPModel", function(model) { + +}) diff --git a/README.md b/README.md index 00d6f36..2ad92a9 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ [![R-CMD-check](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/check-standard.yaml) [![test-coverage](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/test-coverage.yaml) - +[![lint](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/lint.yaml/badge.svg)](https://github.com/Spatiotemporal-Exposures-and-Toxicology/PrestoGP/actions/workflows/lint.yaml) +[![Lifecycle: +experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) # LURK-Vecchia Simultaneous variable seletion and estimation of LUR models with spatiotemporally correlated errors that is scalable for big data From 1e4361d5c936ae80bf5b9898d13e94b952952fb5 Mon Sep 17 00:00:00 2001 From: Shail Choksi Date: Thu, 28 Dec 2023 13:13:42 -0500 Subject: [PATCH 3/3] Pull request #15: Add ASAN/UBSAN pipeline Merge in STAT/prestogp from build-workflow to to-git Squashed commit of the following: commit 2c9c2ede3d40e6e5af24b38a6acfc2dfb2994975 Merge: 6af43f4 56cdbf2 Author: sciome-bot Date: Thu Dec 28 13:11:14 2023 -0500 Merge branch 'to-git' of ssh://sciome-bot/stat/prestogp into build-workflow commit 6af43f4f7cc1bc2100f0c1a2ea1f8abc5f769ef8 Merge: e530819 17f5284 Author: sciome-bot Date: Thu Dec 28 13:07:50 2023 -0500 Merge branch 'master' of ssh://sciome-bot/stat/prestogp into build-workflow commit e530819af7bc70d04b7ce5846b5d41b888cb975c Author: sciome-bot Date: Thu Dec 28 13:06:21 2023 -0500 Add USAN/ASAN pipeline commit 17f52842453008ee4ba92c6d557647509198b110 Merge: ea78ffa 895100d Author: Eric Bair Date: Fri Dec 1 10:49:09 2023 -0500 Pull request #12: Testing Merge in STAT/prestogp from testing to master * commit '895100db78d89c5082ddfcf1411dad69bdf1b6c5': Fixed another sparseNN bug and updated tests Fixed a bug in SparseNN Added more likelihood and maxmin ordering tests commit 895100db78d89c5082ddfcf1411dad69bdf1b6c5 Merge: 7f3ca3c fb060ff Author: Eric Bair Date: Wed Nov 29 17:02:10 2023 -0500 Merge branch 'to-git' of http://192.168.167.103:7990/bitbucket/scm/stat/prestogp into testing Conflicts: DESCRIPTION man/prestogp_fit-PrestoGPModel-method.Rd tests/testthat/test-Log_Likelihood.R Fixed merge conflicts. commit 7f3ca3c8a054159604a9434a5eb7d15887b1af42 Author: Eric Bair Date: Wed Nov 29 16:43:40 2023 -0500 Fixed another sparseNN bug and updated tests commit 1ca958644b2afb856c2c78654896ef745b9eba8a Author: Eric Bair Date: Tue Nov 28 18:40:36 2023 -0500 Fixed a bug in SparseNN commit 80a9818dc91c8508eeb12c277588f6cd72f05ddd Author: Eric Bair Date: Tue Nov 21 17:49:19 2023 -0500 Added more likelihood and maxmin ordering tests commit 4822fbcf05a67064b02017fc75e4fe3327b53c88 Author: sciome-bot Date: Mon Nov 20 15:01:37 2023 -0500 Add UBSAN/ASAN sanitizers commit ea78ffa090bdd60013ca2391d7785842834cbee7 Merge: 286ba5d 53e2b0d Author: sciome-bot Date: Mon Nov 20 12:41:08 2023 -0500 Merge branch 'main-sciome' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP commit 286ba5d341606c966f61f1fab4053323bfa1ca60 Merge: f52ebbb 84fd72f Author: sciome-bot Date: Tue Nov 14 13:11:48 2023 -0500 Merge branch 'main-sciome' of sciome-bot-git:Spatiotemporal-Exposures-and-Toxicology/PrestoGP commit f52ebbb9fc869fc1a9618ebef1cba86ffd50143f Merge: 4e0e1ec 2dd74a2 Author: Eric Bair Date: Tue Nov 14 13:10:51 2023 -0500 Pull request #9: Testing * commit '2dd74a21380be960918b6067abec8ec163721853': Added a simulation file for testing Fixed likelihood function and added additional tests Fixed likelihood function and added additional test commit 2dd74a21380be960918b6067abec8ec163721853 Author: Eric Bair Date: Mon Nov 13 19:17:43 2023 -0500 Added a simulation file for testing commit 6c17318858862ff97d5fb8de155e08c4a260c5a6 Author: Eric Bair Date: Mon Nov 13 19:16:32 2023 -0500 Fixed likelihood function and added additional tests commit f90ce5ef89fee0e1af7536f9ed7cd847b9d0248c Author: Eric Bair Date: Mon Nov 13 19:10:57 2023 -0500 Fixed likelihood function and added additional test commit 4e0e1ecb5ee248133e400052290d8613f2d84f86 Merge: 0d13ba9 a6d1d1b Author: Eric Bair Date: Tue Nov 7 15:38:43 2023 -0500 Pull request #7: Testing Merge in STAT/prestogp from testing to master * commit 'a6d1d1b06e9f640140bad7f03746d14ef0629af4': Added new testing files Added new testthat tests --- .github/workflows/sanitizers.yaml | 170 ++++++++++++++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 .github/workflows/sanitizers.yaml diff --git a/.github/workflows/sanitizers.yaml b/.github/workflows/sanitizers.yaml new file mode 100644 index 0000000..786c154 --- /dev/null +++ b/.github/workflows/sanitizers.yaml @@ -0,0 +1,170 @@ +name: UBSAN/ASAN Checks + +on: + pull_request: + branches: [ "main" ] + push: + branches: [ "build-workflow" ] + workflow_dispatch: + + +permissions: + contents: read + +jobs: + sanitizer-check-asan: + runs-on: ubuntu-latest + env: + CC: clang + CXX: clang++ + CXXFLAGS: "-fsanitize=address" + LINK: clang++ + ASAN: true + UBSAN: true + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + RSPM: https://packagemanager.rstudio.com/cran/__linux__/jammy/latest + R_REMOTES_NO_ERRORS_FROM_WARNINGS: true + ## R CMD check + _R_CHECK_CRAN_INCOMING_: false + _R_CHECK_LENGTH_1_CONDITION_: true + _R_CHECK_LENGTH_1_LOGIC2_: true + _R_CHECK_MATRIX_DATA_: true + ## Specific to package + _R_CHECK_USE_VALGRIND_: true + UBSAN_OPTIONS: "print_stacktrace=1" + steps: + - uses: actions/checkout@v2 + - name: Set up Clang + uses: egor-tensin/setup-clang@v1 + with: + version: latest + platform: x64 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: release + + - name: Query dependencies + run: | + install.packages('remotes') + saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + shell: Rscript {0} + + - name: Cache R packages + uses: actions/cache@v3 + with: + path: ${{ env.R_LIBS_USER }} + key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- + + - name: Install system dependencies + env: + RHUB_PLATFORM: linux-x86_64-ubuntu-gcc + run: | + Rscript -e "remotes::install_github('r-hub/sysreqs')" + sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))") + sudo -s eval "$sysreqs" + + - name: Install dependencies + run: | + remotes::install_deps(dependencies = TRUE) + remotes::install_cran("rcmdcheck") + shell: Rscript {0} + + - name: Session info + run: | + options(width = 100) + pkgs <- installed.packages()[, "Package"] + sessioninfo::session_info(pkgs, include_base = TRUE) + shell: Rscript {0} + + - name: Check with ASAN + run: rcmdcheck::rcmdcheck(build_args = "--no-build-vignettes", args = c("--no-codoc", "--no-examples", "--no-manual", "--ignore-vignettes"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@v3 + with: + name: ${{ runner.os }}-asan-rrelease-results + path: check + sanitizer-check-usan: + runs-on: ubuntu-latest + env: + CC: clang + CXX: clang++ + CXXFLAGS: "-fsanitize=undefined" + LINK: clang++ + ASAN: true + UBSAN: true + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + RSPM: https://packagemanager.rstudio.com/cran/__linux__/jammy/latest + R_REMOTES_NO_ERRORS_FROM_WARNINGS: true + ## R CMD check + _R_CHECK_CRAN_INCOMING_: false + _R_CHECK_LENGTH_1_CONDITION_: true + _R_CHECK_LENGTH_1_LOGIC2_: true + _R_CHECK_MATRIX_DATA_: true + ## Specific to package + _R_CHECK_USE_VALGRIND_: true + UBSAN_OPTIONS: "print_stacktrace=1" + steps: + - uses: actions/checkout@v2 + - name: Set up Clang + uses: egor-tensin/setup-clang@v1 + with: + version: latest + platform: x64 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: release + + - name: Query dependencies + run: | + install.packages('remotes') + saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + shell: Rscript {0} + + - name: Cache R packages + uses: actions/cache@v3 + with: + path: ${{ env.R_LIBS_USER }} + key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- + + - name: Install system dependencies + env: + RHUB_PLATFORM: linux-x86_64-ubuntu-gcc + run: | + Rscript -e "remotes::install_github('r-hub/sysreqs')" + sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))") + sudo -s eval "$sysreqs" + + - name: Install dependencies + run: | + remotes::install_deps(dependencies = TRUE) + remotes::install_cran("rcmdcheck") + shell: Rscript {0} + + - name: Session info + run: | + options(width = 100) + pkgs <- installed.packages()[, "Package"] + sessioninfo::session_info(pkgs, include_base = TRUE) + shell: Rscript {0} + + - name: Check with UBSAN + run: rcmdcheck::rcmdcheck(build_args = "--no-build-vignettes", args = c("--no-codoc", "--no-examples", "--no-manual", "--ignore-vignettes"), error_on = "warning", check_dir = "check") + shell: Rscript {0} + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@v3 + with: + name: ${{ runner.os }}-ubsan-rrelease-results + path: check \ No newline at end of file