From b0068bfbd1f75f9e5c3a6ae8dad5839f60510a9b Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Thu, 15 Oct 2026 23:07:45 -0500 Subject: [PATCH 1/7] Add objective, CV, and latent-space plotting diagnostics --- DESCRIPTION | 8 +- NAMESPACE | 4 + NEWS.md | 2 + R/plot-spaces.R | 189 ++++++++ R/plotting.R | 431 ++++++++++++++++++ README.Rmd | 193 ++++++-- README.md | 234 ++++++---- README_files/figure-gfm/cv-heatmap-1.png | Bin 0 -> 35276 bytes .../figure-gfm/objective-history-1.png | Bin 0 -> 34864 bytes README_files/figure-gfm/unnamed-chunk-6-1.png | Bin 26109 -> 0 bytes README_files/figure-gfm/unnamed-chunk-7-1.png | Bin 26109 -> 0 bytes inst/examples/plot-diagnostics.R | 44 ++ inst/examples/plot-paper-displays.R | 8 + inst/examples/plotting-guide.md | 91 ++++ man/plot_contributions.Rd | 69 +++ man/plot_cv.Rd | 121 +++++ man/plot_objective.Rd | 47 ++ man/plot_source_spaces.Rd | 71 +++ tests/testthat/helper-plotting.R | 15 + tests/testthat/test-cv-heatmap-style.R | 75 +++ tests/testthat/test-cv-plot-errors.R | 48 ++ tests/testthat/test-paper-plots.R | 56 +++ tests/testthat/test-plotting.R | 204 +++++++++ 23 files changed, 1783 insertions(+), 127 deletions(-) create mode 100644 R/plot-spaces.R create mode 100644 R/plotting.R create mode 100644 README_files/figure-gfm/cv-heatmap-1.png create mode 100644 README_files/figure-gfm/objective-history-1.png delete mode 100644 README_files/figure-gfm/unnamed-chunk-6-1.png delete mode 100644 README_files/figure-gfm/unnamed-chunk-7-1.png create mode 100644 inst/examples/plot-diagnostics.R create mode 100644 inst/examples/plot-paper-displays.R create mode 100644 inst/examples/plotting-guide.md create mode 100644 man/plot_contributions.Rd create mode 100644 man/plot_cv.Rd create mode 100644 man/plot_objective.Rd create mode 100644 man/plot_source_spaces.Rd create mode 100644 tests/testthat/helper-plotting.R create mode 100644 tests/testthat/test-cv-heatmap-style.R create mode 100644 tests/testthat/test-cv-plot-errors.R create mode 100644 tests/testthat/test-paper-plots.R create mode 100644 tests/testthat/test-plotting.R diff --git a/DESCRIPTION b/DESCRIPTION index d7b4663..0984cc4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,10 +30,14 @@ URL: https://github.com/stmcg/learner BugReports: https://github.com/stmcg/learner/issues Imports: ScreeNOT, Rcpp (>= 1.0.11), - RcppEigen + RcppEigen, + graphics, + grDevices, + stats LinkingTo: Rcpp, RcppEigen Suggests: - testthat (>= 3.0.0) + testthat (>= 3.0.0), + ggplot2 (>= 3.4.0) Config/testthat/edition: 3 Depends: R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index 1ab3843..02814b0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,6 +3,10 @@ export(cv.learner) export(dlearner) export(learner) +export(plot_contributions) +export(plot_cv) +export(plot_objective) +export(plot_source_spaces) import(RcppEigen) importFrom(Rcpp,sourceCpp) useDynLib(learner, .registration = TRUE) diff --git a/NEWS.md b/NEWS.md index e9ac59c..3631f30 100644 --- a/NEWS.md +++ b/NEWS.md @@ -6,6 +6,8 @@ populations with independently generated noise. * Add separate row- and column-space penalties in `learner()` and cross-validation over these penalties in `cv.learner()`. +* Add plotting functions for objective function values, cross-validation + results, population latent spaces, and variable contributions. ### learner version 1.0.0 (2025-03-02) diff --git a/R/plot-spaces.R b/R/plot-spaces.R new file mode 100644 index 0000000..d4287bc --- /dev/null +++ b/R/plot-spaces.R @@ -0,0 +1,189 @@ +# Internal helpers shared by the space and contribution plots. +check_plot_matrix <- function(x, name) { + if (!is.matrix(x) || !is.numeric(x) || is.complex(x) || any(dim(x)==0) || any(!is.finite(x))) { + stop(name, ' must be a complete finite real numeric matrix.') + } +} + +plot_indices <- function(index, n, max_display, name) { + if(is.null(index)) return(unique(as.integer(round(seq(1,n,length.out=min(n,max_display)))))) + if(!is.numeric(index)||is.complex(index)||length(index)==0||any(!is.finite(index))|| + any(index!=floor(index))||any(index<1|index>n)||anyDuplicated(index)) { + stop(name, ' must contain distinct valid integer indices.') + } + as.integer(index) +} + +plot_svd <- function(x, r) { + s<-svd(x,nu=r,nv=r) + if(s$d[r] <= max(dim(x))*s$d[1]*.Machine$double.eps) { + stop('r includes numerically zero singular directions; use a smaller rank.') + } + s +} + +plot_limits_pair <- function(limits, automatic) { + if(is.null(limits))return(automatic) + if(!is.numeric(limits)||is.complex(limits)||length(limits)!=2L|| + any(!is.finite(limits))||any(limits<=0))stop('limits must contain two finite positive values: row then column.') + stats::setNames(as.numeric(limits),c('row','column')) +} + +# Use a raster to avoid drawing hundreds of thousands of individual rectangles. +draw_matrix_panel <- function(values, x_index, y_index, limits, colors, title, xlab, ylab) { + nx<-nrow(values);ny<-ncol(values) + clipped<-pmax(limits[1],pmin(limits[2],values)) + bins<-1L+floor((clipped-limits[1])/diff(limits)*(length(colors)-1L)) + pixels<-matrix(colors[pmax(1L,pmin(length(colors),bins))],nx,ny) + graphics::plot.new() + # Reserve a constant fraction of the panel width for the color key. + graphics::plot.window(xlim=c(0.5,nx+0.5+nx*0.15),ylim=c(0.5,ny+0.5),xaxs='i',yaxs='i') + graphics::rasterImage(grDevices::as.raster(t(pixels[,ny:1,drop=FALSE])), + 0.5,0.5,nx+0.5,ny+0.5,interpolate=FALSE) + graphics::rect(0.5,0.5,nx+0.5,ny+0.5,border='gray40',lwd=0.7) + ix<-unique(as.integer(round(seq(1,nx,length.out=min(5,nx))))) + iy<-unique(as.integer(round(seq(1,ny,length.out=min(8,ny))))) + graphics::axis(1,at=ix,labels=x_index[ix],cex.axis=0.85) + graphics::axis(2,at=iy,labels=y_index[iy],las=1,cex.axis=0.85) + graphics::title(main=title,xlab=xlab,ylab=ylab,cex.main=1.05,cex.lab=0.9) + draw_color_key(nx+0.5+nx*0.05,nx+0.5+nx*0.075,0.5,ny+0.5,limits,colors) +} + +#' Compare target and source latent spaces +#' +#' Plot column-space and row-space projection submatrices for the target and +#' each source, to compare their retained latent spaces. +#' +#' @param Y_target Complete finite target matrix, or a completed estimate. +#' Missing values are rejected; the function does not impute them. +#' @param Y_source Aligned complete source matrix or nonempty list of matrices. +#' @param r Positive integer giving the retained rank for every matrix. +#' @param row_index,column_index Optional distinct integer indices selecting +#' variables to display, in display order. The same indices are used in all +#' populations. SVD always uses the full matrices before subsetting. +#' @param max_display Maximum number of displayed variables on each side when +#' indices are omitted; default 200. Deterministic evenly spaced indices are +#' used, without changing the random-number state. +#' @param limits Optional two positive color limits, in row-side then column-side +#' order. Each side uses a symmetric scale about zero shared across populations. +#' Values beyond supplied limits are clipped for display and counts are returned. +#' @param row_label,column_label Axis labels. Use Variant index and Phenotype +#' index only when these accurately describe the input rows and columns. +#' @param target_name Panel label describing the supplied target matrix. +#' @return Invisibly, a list with \code{row_projectors}, \code{column_projectors}, +#' \code{row_index}, \code{column_index}, \code{limits}, and \code{clipped_count}. +#' Each projector is the displayed submatrix, computed without allocating the +#' full row-by-row projector. Its population name identifies the input. +#' @details +#' For an SVD basis B, the displayed matrix is a submatrix of B B-transpose. +#' Column-side panels appear on top, row-side panels below. Red denotes negative +#' values and blue positive values. Color scales are shared within each side, +#' including across pages, but can differ between the two sides. At most three +#' populations appear per page. Source names are taken from the input list when +#' supplied. No name-based alignment or matching of factors is performed. +#' These are individual population projectors, not the weighted source operator. +#' A submatrix need not itself be an idempotent projector. +#' Numerically zero retained singular directions are rejected. With repeated +#' singular values at the truncation boundary the retained space is not unique. +#' The input order must already encode any desired phenotype or genomic ordering. +#' @examples +#' plot_source_spaces(dat_multisource$Y_target, dat_multisource$Y_source, +#' r = 3, max_display = 20) +#' @export +plot_source_spaces <- function(Y_target, Y_source, r, row_index=NULL, column_index=NULL, + max_display=200L, limits=NULL, row_label='Row index', + column_label='Column index', target_name='Target population') { + check_plot_matrix(Y_target,'Y_target') + sources<-prepare_sources(Y_source,Y_target,allow_missing_target=FALSE)$matrices + for(s in sources)check_plot_matrix(s,'Y_source') + r<-resolve_rank(r,Y_target);validate_count(max_display,'max_display') + ri<-plot_indices(row_index,nrow(Y_target),max_display,'row_index') + ci<-plot_indices(column_index,ncol(Y_target),max_display,'column_index') + labels<-names(sources) + if(is.null(labels))labels<-rep('',length(sources)) + labels[is.na(labels)]<-'' + labels<-ifelse(nzchar(labels),paste('Source',labels),paste('Source',seq_along(sources))) + populations<-c(list(Y_target),sources);names(populations)<-make.unique(c(target_name,labels)) + bases<-lapply(populations,plot_svd,r=r) + up<-lapply(bases,function(s)tcrossprod(s$u[ri,,drop=FALSE])) + vp<-lapply(bases,function(s)tcrossprod(s$v[ci,,drop=FALSE])) + auto<-c(row=max(vapply(up,function(p)max(abs(p)),numeric(1))), + column=max(vapply(vp,function(p)max(abs(p)),numeric(1)))) + auto[auto==0]<-1e-8 + cap<-plot_limits_pair(limits,auto) + counts<-list(row=vapply(up,function(p)sum(abs(p)>cap[1]),integer(1)), + column=vapply(vp,function(p)sum(abs(p)>cap[2]),integer(1))) + colors<-grDevices::colorRampPalette(c('firebrick3','white','dodgerblue3'))(101) + old<-graphics::par(no.readonly=TRUE);on.exit(graphics::par(old),add=TRUE) + for(page in split(seq_along(populations),ceiling(seq_along(populations)/3))) { + graphics::par(mfrow=c(2,length(page)),mar=c(3.5,3.8,2.5,4),oma=c(2,0,0,0),mgp=c(2.2,0.6,0)) + for(i in page)draw_matrix_panel(vp[[i]],ci,ci,c(-cap[2],cap[2]),colors,names(populations)[i],column_label,column_label) + for(i in page)draw_matrix_panel(up[[i]],ri,ri,c(-cap[1],cap[1]),colors,names(populations)[i],row_label,row_label) + caption<-paste('Red: negative; blue: positive. Shared scales within each row.', + if(sum(unlist(counts))>0)paste(sum(unlist(counts)),'displayed entries clipped across all panels.')else '') + graphics::mtext(caption,outer=TRUE,side=1,line=0.5,cex=0.8) + } + invisible(list(row_projectors=up,column_projectors=vp,row_index=ri,column_index=ci, + limits=cap,clipped_count=counts)) +} + +#' Plot variable contributions to latent components +#' +#' Show which rows and columns contribute to each latent component of a fitted +#' target matrix, using squared singular-vector entries. Higher scores are darker red. +#' +#' @param x A complete estimated matrix, a \code{learner()} result, or a +#' \code{dlearner()} result. No rotation of the singular vectors is performed. +#' @param r Number of components. Required for a matrix; defaults to the stored +#' rank for a fitting result. Numerically zero directions are rejected. +#' @inheritParams plot_source_spaces +#' @param main Title describing the input estimate. +#' @return Invisibly, a list with full \code{row_scores} and \code{column_scores}, +#' displayed \code{row_index} and \code{column_index}, \code{limits}, and +#' \code{clipped_count}. Score matrices have variables in rows and components +#' in columns; all values are returned without display clipping. +#' @details +#' An SVD of the estimated matrix supplies U and V. Row scores are U squared +#' elementwise; column scores are V squared elementwise. Scores sum to one per +#' component over all variables on the corresponding side. They are not squared +#' entries of the raw optimization factors, effect sizes, or correlations. +#' Subsetting is applied only for display and scores are not renormalized. +#' The left panel displays columns and the right panel rows. Color limits are +#' separate for the two sides and start at zero. Optional limits specify row +#' then column upper limits, with clipped-entry counts reported. +#' Signs do not affect squared scores, but rotations within repeated singular +#' subspaces can change component-specific scores. Components from different +#' populations are not automatically aligned or given biological labels. +#' @examples +#' fit <- dlearner(dat_multisource$Y_source, dat_multisource$Y_target, r = 3) +#' plot_contributions(fit,max_display=50) +#' @export +plot_contributions <- function(x, r=NULL, row_index=NULL, column_index=NULL, + max_display=200L, limits=NULL, row_label='Row index', + column_label='Column index', main='Estimated target contributions') { + if(is.list(x)) { + estimate<-if(!is.null(x$learner_estimate))x$learner_estimate else x$dlearner_estimate + if(is.null(r))r<-x$r + } else estimate<-x + check_plot_matrix(estimate,'x') + if(is.null(r))stop('Supply r for a matrix without a stored rank.') + r<-resolve_rank(r,estimate);validate_count(max_display,'max_display') + ri<-plot_indices(row_index,nrow(estimate),max_display,'row_index') + ci<-plot_indices(column_index,ncol(estimate),max_display,'column_index') + s<-plot_svd(estimate,r) + row_scores<-s$u^2;col_scores<-s$v^2 + cap<-plot_limits_pair(limits,c(row=max(row_scores[ri,,drop=FALSE]),column=max(col_scores[ci,,drop=FALSE]))) + cap[cap==0]<-1e-8 + counts<-c(row=sum(row_scores[ri,,drop=FALSE]>cap[1]),column=sum(col_scores[ci,,drop=FALSE]>cap[2])) + colors<-grDevices::colorRampPalette(c('white','firebrick3'))(100) + old<-graphics::par(no.readonly=TRUE);on.exit(graphics::par(old),add=TRUE) + graphics::par(mfrow=c(1,2),mar=c(3.8,3.8,2.2,4),oma=c(2,0,2,0),mgp=c(2.2,0.6,0)) + draw_matrix_panel(col_scores[ci,,drop=FALSE],ci,seq_len(r),c(0,cap[2]),colors,'Column contributions',column_label,'Component') + draw_matrix_panel(row_scores[ri,,drop=FALSE],ri,seq_len(r),c(0,cap[1]),colors,'Row contributions',row_label,'Component') + graphics::mtext(main,outer=TRUE,side=3,line=0.4,font=2,cex=1.1) + caption<-paste('Squared singular-vector entries; separate color scales.', + if(sum(counts)>0)paste(sum(counts),'displayed entries clipped.')else '') + graphics::mtext(caption,outer=TRUE,side=1,line=0.4,cex=0.8) + invisible(list(row_scores=row_scores,column_scores=col_scores,row_index=ri,column_index=ci, + limits=cap,clipped_count=counts)) +} diff --git a/R/plotting.R b/R/plotting.R new file mode 100644 index 0000000..2c3d185 --- /dev/null +++ b/R/plotting.R @@ -0,0 +1,431 @@ +#' Plot the objective function values of a LEARNER fit +#' +#' Plots the values of the objective function at each iteration of the +#' numerical optimization algorithm in \code{learner}. +#' +#' @param x A result returned by \code{learner}. +#' @param iterations Iterations to show: \code{"all"} (the default), +#' \code{"near_optimum"}, or a vector of iteration numbers such as +#' \code{1:1000}. \code{"near_optimum"} shows a window centered on the first +#' smallest finite recorded objective value. The window contains 10\% of the +#' recorded iterations (rounded up), with a minimum of 10, or all iterations +#' for shorter histories. At either boundary the window shifts to stay within +#' the recorded history. Both axes adjust to the selected window. +#' @param main Title of the plot. By default, \code{"Objective function"}, +#' with \code{"(near optimum)"} added when \code{iterations = "near_optimum"}. +#' +#' @return Invisibly, a data frame of the plotted iterations and objective +#' values. +#' +#' @details The red point marks the smallest objective value, which is also +#' given above the plot. The objective value at the initialization is not +#' included. With \code{iterations = "near_optimum"}, the plot shows a shaded +#' band spanning each pair of consecutive objective values, so that values +#' that alternate between iterations remain visible, and a line at their +#' average. +#' +#' @examples +#' fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, +#' r = 3, lambda_1 = 1, lambda_2 = 1, step_size = 0.003, +#' control = list(max_iter = 1000)) +#' plot_objective(fit) +#' plot_objective(fit, iterations = "near_optimum") +#' plot_objective(fit, iterations = 1:100) +#' +#' @export +plot_objective <- function(x, iterations = "all", main = NULL) { + objective <- x$objective_values + if (!is.numeric(objective) || length(objective) == 0L || + !any(is.finite(objective))) { + stop('x must be a result returned by learner().') + } + n <- length(objective) + finite <- is.finite(objective) + minimum <- which.min(replace(objective, !finite, Inf)) + near <- identical(iterations, 'near_optimum') + if (identical(iterations, 'all')) { + shown <- seq_len(n) + } else if (near) { + width <- min(n, max(10L, ceiling(0.1 * n))) + start <- max(1L, min(minimum - floor((width - 1L) / 2), n - width + 1L)) + shown <- seq.int(start, length.out = width) + } else if (is.numeric(iterations) && length(iterations) > 0L && + all(iterations %in% seq_len(n))) { + shown <- sort(unique(as.integer(iterations))) + } else { + stop("iterations must be 'all', 'near_optimum', or iteration numbers between 1 and ", + n, '.') + } + if (!any(finite[shown])) { + stop('The selected iterations contain no finite objective values; choose a different range.') + } + if (is.null(main)) { + main <- if (near) 'Objective function (near optimum)' else 'Objective function' + } + values <- replace(objective[shown], !finite[shown], NA_real_) + + old <- graphics::par(mar = c(5, 4.5, 4, 1.5)) + on.exit(graphics::par(old), add = TRUE) + xlim <- range(shown) + graphics::plot(shown, values, type = 'n', xlim = xlim, xlab = 'Iteration', + ylab = 'Objective value', bty = 'l') + graphics::title(main, line = 2) + graphics::mtext(paste0('Minimum: ', format(objective[minimum], digits = 6), + ' at iteration ', minimum), + side = 3, line = 0.6, cex = 0.8, col = 'gray30') + if (near && length(shown) > 1L) { + # The band spans each pair of consecutive values, so alternating values + # appear as a band rather than as dense vertical lines. + nxt <- c(values[-1], values[length(values)]) # the last value pairs with itself + keep <- !is.na(values) & !is.na(nxt) + graphics::polygon(c(shown[keep], rev(shown[keep])), + c(pmin(values, nxt)[keep], rev(pmax(values, nxt)[keep])), + col = grDevices::adjustcolor('#2166AC', 0.4), border = NA) + graphics::lines(shown[keep], ((values + nxt) / 2)[keep], col = '#2166AC', lwd = 1.5) + } else { + graphics::lines(shown, values, col = '#2166AC', type = if (length(shown) == 1L) 'p' else 'l') + } + if (minimum %in% shown) { + graphics::points(minimum, objective[minimum], pch = 16, cex = 0.9, col = '#B2182B') + } + # Symbol keys distinguish the band, mean line, and minimum without colored text. + band_color <- grDevices::adjustcolor('#2166AC', 0.4) + labels <- if (near && length(shown) > 1L) + c('Consecutive-iteration range', 'Pairwise average') else 'Objective value' + colors <- if (near && length(shown) > 1L) c(band_color, '#2166AC') else '#2166AC' + symbols <- if (near && length(shown) > 1L) c(22, NA) else NA + line_types <- if (near && length(shown) > 1L) c(0, 1) else 1 + if (minimum %in% shown) { + labels <- c(labels, 'Minimum recorded objective') + colors <- c(colors, '#B2182B') + symbols <- c(symbols, 16) + line_types <- c(line_types, 0) + } + graphics::legend('topright', inset = 0.02, legend = labels, + col = colors, text.col = 'gray30', pch = symbols, + pt.bg = band_color, pt.cex = 1.1, lty = line_types, + lwd = 1.5, cex = 0.8, y.intersp = 1.3, bty = 'n') + invisible(data.frame(iteration = shown, objective = objective[shown])) +} + +#' Plot the LEARNER cross-validation scores +#' +#' Draw a common-penalty heatmap or separate-penalty heatmaps, with a shared +#' color scale and a black box at the first minimum in R array order. +#' +#' @param x A result returned by \code{cv.learner()}. +#' @param lambda_1_all Common-penalty candidates in the exact order used for CV. +#' Required for a two-dimensional score array; omit for separate penalties. +#' @param lambda_2_all Balance-penalty candidates in the exact order used for CV. +#' @param lambda_1_row_all,lambda_1_col_all Separate-penalty candidates in the +#' exact order used for CV. Both are required for a three-dimensional array. +#' @param n_observed Optional positive integer giving the number of observed +#' target entries used in CV. If supplied, scores are divided by this count and +#' labeled Validation MSE. Otherwise the original sums are labeled Validation +#' SSE. Missing target entries must not be included in this count. +#' @param lambda_2_index Optional vector of distinct integer indices into +#' \code{lambda_2_all}, selecting panels to draw in separate-penalty mode. +#' \code{NULL} draws all panels. Not used in common-penalty mode. +#' +#' @param log_labels Logical scalar. If TRUE, display base-10 logarithms of +#' the candidates on the heatmap axes. +#' All candidates on those axes must be strictly positive. Log labels are +#' rounded to two decimal places. Defaults to FALSE. +#' +#' @param relative_limit Optional positive relative increase cutoff, default 0.3 +#' (30 percent). Larger finite errors are gray and labeled above the cutoff, +#' never as diverged. NULL keeps all finite values in the color scale. +#' @param show_values Whether to print scores inside cells. Defaults to TRUE. +#' @param main Plot title. +#' +#' @return Invisibly, a list with \code{scores}, \code{scale}, \code{grids}, +#' \code{best_index}, \code{shown_lambda_2_index}, \code{color_limits}, and +#' \code{unavailable_count}, \code{log_labels}, \code{score_range}, \code{minimum}, +#' \code{exact_minima}, \code{boundary}, and \code{lambda_2_slice_range}. +#' Indices refer to the original candidate order. Boundary flags indicate a +#' selected numerical endpoint on a grid with at least two distinct values. +#' Also returns \code{display_data}, \code{display_limits}, \code{relative}, +#' \code{above_limit_count}, \code{plot} (the first ggplot page), and \code{plots} +#' (all ggplot pages). The original absolute score diagnostics are unchanged. +#' +#' @details +#' Existing CV results do not store the full candidate grids, so callers must +#' provide them. Lengths and the selected penalty values are checked, but these +#' checks cannot detect every incorrectly supplied grid. Use the original grids, +#' without sorting or removing duplicates. Axes are equally spaced candidate +#' positions labeled with actual values, or their base-10 logarithms when +#' \code{log_labels=TRUE}. This changes labels only, not scores, candidate order, +#' or cell spacing. The reversed magma palette uses pale colors for lower error +#' and dark colors for higher error. Cell labels show increases over the minimum. +#' +#' Common mode shows \code{lambda_1_all} on the horizontal axis and +#' \code{lambda_2_all} on the vertical axis. Separate mode shows the U-space +#' penalty horizontally and the V-space penalty vertically, with one panel for +#' each balance penalty. All panels share limits computed from the full score +#' array, even when only some panels are selected. Up to six panels are drawn +#' per page. Gray cells have non-finite scores and are excluded from selection. +#' Only the first minimum is marked; a panel need not contain a black box. +#' Captions report the score range, exact ties, and boundary selections. These are +#' descriptive diagnostics, not significance or convergence tests. +#' +#' The stored \code{mse_all} values are sums of held-out squared errors. MSE +#' conversion requires the correct \code{n_observed}; the function cannot infer +#' this count from the scores. Neither this plot nor CV certifies convergence +#' of the individual fits. No fitting is performed and \code{x} is unchanged. +#' Requires optional ggplot2 >= 3.4.0; install it with +#' \code{install.packages("ggplot2")}. Estimation and other plots do not require it. +#' Draws with ggplot2 without modifying base graphics parameters. Each page shares +#' one legend. A sufficiently large device is recommended for multiple panels. +#' Relative increases are undefined when the minimum is zero; the display then +#' uses absolute scores and ignores relative_limit. High relative error alone +#' never establishes optimizer divergence. Non-finite scores are unavailable. +#' Use ggplot2::ggsave(plot = result$plot, ...) to export the first page, or +#' export each member of result$plots for multiple pages. +#' +#' @examples +#' if (requireNamespace("ggplot2", quietly = TRUE) && +#' utils::packageVersion("ggplot2") >= "3.4.0") { +#' row_grid <- c(0.1, 1) +#' col_grid <- c(0.1, 2) +#' balance_grid <- c(0.1, 1) +#' target <- dat_highsim$Y_target[1:12, 1:8] +#' set.seed(42) +#' cv <- cv.learner(dat_highsim$Y_source[1:12, 1:8], target, +#' r = 2, lambda_1_row_all = row_grid, +#' lambda_1_col_all = col_grid, lambda_2_all = balance_grid, +#' step_size = 0.0003, n_folds = 2, +#' control = list(max_iter = 20)) +#' plot_cv(cv, lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, +#' lambda_2_all = balance_grid, n_observed = sum(!is.na(target))) +#' +#' } +#' +#' @export +plot_cv <- function(x, lambda_1_all = NULL, lambda_2_all = NULL, + lambda_1_row_all = NULL, lambda_1_col_all = NULL, + n_observed = NULL, lambda_2_index = NULL, log_labels = FALSE, + relative_limit = 0.3, show_values = TRUE, + main = 'Cross-validation error') { + d <- prepare_cv_plot(x, lambda_1_all, lambda_2_all, lambda_1_row_all, + lambda_1_col_all, n_observed, lambda_2_index, log_labels) + if (!is.null(relative_limit) && (!is.numeric(relative_limit) || + is.complex(relative_limit) || length(relative_limit) != 1L || + !is.finite(relative_limit) || relative_limit <= 0)) { + stop('relative_limit must be NULL or a finite positive number.') + } + if (!is.logical(show_values) || length(show_values) != 1L || is.na(show_values)) { + stop('show_values must be TRUE or FALSE.') + } + if (!is.character(main) || length(main) != 1L || is.na(main)) { + stop('main must be one nonmissing character string.') + } + if (!requireNamespace("ggplot2", quietly = TRUE)) { + stop('plot_cv() requires the optional ggplot2 package. Install it with install.packages("ggplot2").', + call. = FALSE) + } + if (utils::packageVersion("ggplot2") < "3.4.0") { + stop('plot_cv() requires ggplot2 >= 3.4.0. Update it with install.packages("ggplot2").', + call. = FALSE) + } + separate <- length(dim(d$scores)) == 3L + indices <- as.data.frame(arrayInd(seq_along(d$scores), dim(d$scores))) + names(indices) <- if (separate) c('x_index', 'y_index', 'slice') else c('x_index', 'y_index') + if (!separate) indices$slice <- 1L + indices$score <- as.vector(d$scores) + indices$available <- is.finite(indices$score) + # Relative increases are undefined when the best score is zero. + relative <- d$minimum > 0 + indices$display <- if (relative) indices$score / d$minimum - 1 else indices$score + indices$above_limit <- indices$available & relative & !is.null(relative_limit) + if (!is.null(relative_limit)) indices$above_limit <- indices$above_limit & indices$display > relative_limit + 8 * .Machine$double.eps * max(1, relative_limit) + indices$fill_value <- indices$display + indices$fill_value[!indices$available | indices$above_limit] <- NA_real_ + finite_fill <- indices$fill_value[is.finite(indices$fill_value)] + upper <- max(finite_fill) + limits <- c(0, if (upper > 0) upper else if (relative) 0.01 else 1e-8) + # One global scale across all slices, even if only a subset is displayed. + label_digits <- if (limits[2] <= 0.05) 2L else 1L + indices$label <- if (relative) sprintf(paste0('%.', label_digits, 'f%%'), 100 * indices$display) else + formatC(indices$display, digits = 4, format = 'g') + indices$label[!indices$available] <- 'NA' + if (any(indices$above_limit)) indices$label[indices$above_limit] <- + paste0('>', format(100 * relative_limit, trim = TRUE), '%') + indices$text_color <- ifelse(is.finite(indices$fill_value) & + indices$fill_value > limits[2] * 0.55, 'white', '#202020') + best_slice <- if (separate) d$best_index[3] else 1L + indices$selected <- indices$x_index == d$best_index[1] & + indices$y_index == d$best_index[2] & indices$slice == best_slice + panels <- if (separate) d$shown_lambda_2_index else 1L + labels_x <- if (separate) d$grids$row else d$grids$common + labels_y <- if (separate) d$grids$col else d$grids$balance + xlab <- if (separate) expression(lambda[1*','*plain(row)]) else expression(lambda[1]) + ylab <- if (separate) expression(lambda[1*','*plain(col)]) else expression(lambda[2]) + if (log_labels) { + labels_x <- round(log10(labels_x), 2) + labels_y <- round(log10(labels_y), 2) + xlab <- bquote(log[10](.(xlab[[1]]))) + ylab <- bquote(log[10](.(ylab[[1]]))) + } + metric <- if (is.null(n_observed)) 'SSE' else 'MSE' + selected_values <- vapply(seq_along(d$grids), function(i) d$grids[[i]][d$best_index[i]], numeric(1)) + selected_names <- if (separate) c('row', 'col', 'balance') else c('lambda1', 'lambda2') + subtitle <- paste0('Selected ', paste(paste0(selected_names, ' = ', + trimws(formatC(selected_values, digits = 4, format = 'g'))), collapse = ', '), + ' | Minimum ', metric, ' = ', formatC(d$minimum, digits = 6, format = 'g')) + caption <- paste0('Black box: selected parameters. Cell labels rounded.\n', metric, ' range: ', + formatC(d$score_range[1], digits = 6, format = 'g'), ' to ', + formatC(d$score_range[2], digits = 6, format = 'g'), '.') + if (any(d$boundary)) caption <- paste0(caption, ' Selection on grid boundary.') + if (d$exact_minima > 1) caption <- paste0(caption, '\n', d$exact_minima, + ' exact minima; first in CV order selected.') + if (any(indices$above_limit)) caption <- paste0(caption, '\nGray >', + format(100 * relative_limit, trim = TRUE), '%: high error, not a divergence diagnosis.') + if (d$unavailable_count > 0) caption <- paste0(caption, '\nGray NA: unavailable score.') + if (!relative) caption <- paste0(caption, '\nMinimum is zero; colors and labels show absolute ', metric, '.') + breaks <- seq(0, limits[2], length.out = 5L) + key_labels <- if (relative) sprintf(paste0('%.', label_digits, 'f%%'), 100 * breaks) else + formatC(breaks, digits = 4, format = 'g') + # Declare aesthetic bindings for package checks; ggplot evaluates them in data. + x_index <- y_index <- fill_value <- label <- text_color <- NULL + plots <- list() + for (page in split(panels, ceiling(seq_along(panels) / 6))) { + data <- indices[indices$slice %in% page, , drop = FALSE] + facet_labels <- paste0('lambda[2] == ', format(d$grids$balance[page], digits = 4, trim = TRUE)) + if (anyDuplicated(facet_labels)) { + facet_labels <- paste0('lambda[2] == ', format(d$grids$balance[page], digits = 17, trim = TRUE)) + } + # Grid indices identify panels; rounded display labels must not merge them. + data$panel <- factor(data$slice, levels = page) + panel_labels <- stats::setNames(facet_labels, as.character(page)) + plot <- ggplot2::ggplot(data, ggplot2::aes(x = x_index, y = y_index, fill = fill_value)) + + ggplot2::geom_tile(color = 'white', linewidth = 0.35, width = 1, height = 1) + + ggplot2::scale_fill_viridis_c(option = 'magma', direction = -1, + na.value = 'grey85', limits = limits, breaks = breaks, labels = key_labels, + name = if (relative) paste0('CV ', metric, '\nabove minimum') else paste('CV', metric)) + + ggplot2::scale_x_continuous(breaks = seq_along(labels_x), labels = labels_x, + expand = ggplot2::expansion(mult = 0)) + + ggplot2::scale_y_continuous(breaks = seq_along(labels_y), labels = labels_y, + expand = ggplot2::expansion(mult = 0)) + + ggplot2::labs(title = main, subtitle = subtitle, x = xlab, y = ylab, caption = caption) + + ggplot2::theme_minimal(base_size = 11) + + ggplot2::theme(panel.grid = ggplot2::element_blank(), + plot.title = ggplot2::element_text(face = 'bold', size = 14), + plot.subtitle = ggplot2::element_text(size = 10, margin = ggplot2::margin(b = 10)), + plot.caption = ggplot2::element_text(size = 9, hjust = 0, margin = ggplot2::margin(t = 10)), + legend.title = ggplot2::element_text(size = 10), + legend.text = ggplot2::element_text(size = 9), + axis.text = ggplot2::element_text(color = '#303030', size = 10), + strip.text = ggplot2::element_text(size = 11), + plot.margin = ggplot2::margin(12, 18, 10, 12)) + if (show_values) plot <- plot + + ggplot2::geom_text(ggplot2::aes(label = label, color = text_color), size = 2.8) + + ggplot2::scale_color_identity() + plot <- plot + ggplot2::geom_tile(data = data[data$selected, , drop = FALSE], + fill = NA, color = 'black', linewidth = 0.9, width = 0.94, height = 0.94) + if (separate) plot <- plot + ggplot2::facet_wrap(~panel, ncol = min(3L, length(page)), + labeller = ggplot2::as_labeller(panel_labels, default = ggplot2::label_parsed)) + print(plot) + plots[[length(plots) + 1L]] <- plot + } + d$display_data <- indices + d$display_limits <- limits + d$relative <- relative + d$above_limit_count <- sum(indices$above_limit) + d$plots <- plots + d$plot <- plots[[1L]] + invisible(d) +} + +prepare_cv_plot <- function(x, lambda_1_all = NULL, lambda_2_all = NULL, + lambda_1_row_all = NULL, lambda_1_col_all = NULL, + n_observed = NULL, lambda_2_index = NULL, log_labels = FALSE) { + if (!is.list(x) || !is.numeric(x$mse_all) || is.complex(x$mse_all) || + !(length(dim(x$mse_all)) %in% c(2L, 3L)) || any(dim(x$mse_all) < 1L)) { + stop('x must contain a nonempty two- or three-dimensional mse_all array.') + } + if (!is.logical(log_labels) || length(log_labels) != 1L || is.na(log_labels)) { + stop('log_labels must be TRUE or FALSE.') + } + scores <- x$mse_all + finite <- is.finite(scores) + if (!any(finite)) stop('No finite validation scores are available to plot.') + if (any(scores[finite] < 0)) stop('Validation squared-error scores cannot be negative.') + separate <- length(dim(scores)) == 3L + check_grid <- function(values, size, name) { + validate_penalty_grid(values, name) + if (!is.null(dim(values)) || length(values) != size) { + stop(name, ' must match its score-array dimension in the original CV order.') + } + } + if (separate) { + if (!is.null(lambda_1_all)) stop('Do not supply lambda_1_all for separate penalties.') + check_grid(lambda_1_row_all, dim(scores)[1], 'lambda_1_row_all') + check_grid(lambda_1_col_all, dim(scores)[2], 'lambda_1_col_all') + check_grid(lambda_2_all, dim(scores)[3], 'lambda_2_all') + grids <- list(row = lambda_1_row_all, col = lambda_1_col_all, + balance = lambda_2_all) + panels <- if (is.null(lambda_2_index)) seq_along(lambda_2_all) else lambda_2_index + if (!is.numeric(panels) || length(panels) == 0L || any(!is.finite(panels)) || + any(panels != floor(panels)) || any(panels < 1 | panels > length(lambda_2_all)) || + anyDuplicated(panels)) { + stop('lambda_2_index must contain distinct valid balance-grid indices.') + } + } else { + if (!is.null(lambda_1_row_all) || !is.null(lambda_1_col_all) || + !is.null(lambda_2_index)) stop('Separate-penalty arguments require a 3D score array.') + check_grid(lambda_1_all, dim(scores)[1], 'lambda_1_all') + check_grid(lambda_2_all, dim(scores)[2], 'lambda_2_all') + grids <- list(common = lambda_1_all, balance = lambda_2_all) + panels <- 1L + } + axis_grids <- if (separate) c(grids$row, grids$col) else c(grids$common, grids$balance) + if (log_labels && any(axis_grids <= 0)) { + stop('log_labels requires strictly positive candidates on both heatmap axes.') + } + selectable <- scores + selectable[!finite] <- Inf + best <- as.integer(arrayInd(which.min(selectable), dim(scores))) + expected <- if (separate) { + c(lambda_1_row_min = grids$row[best[1]], lambda_1_col_min = grids$col[best[2]], + lambda_2_min = grids$balance[best[3]]) + } else c(lambda_1_min = grids$common[best[1]], lambda_2_min = grids$balance[best[2]]) + for (name in names(expected)) { + if (!is.numeric(x[[name]]) || length(x[[name]]) != 1L || + !isTRUE(all.equal(unname(x[[name]]), unname(expected[[name]]), tolerance = 1e-12))) { + stop('Supplied grids do not match the selected penalties stored in x: ', name) + } + } + scale <- 'Validation SSE' + if (!is.null(n_observed)) { + validate_count(n_observed, 'n_observed', upper = Inf) + scores <- scores / n_observed + scale <- 'Validation MSE' + } + limits <- range(scores[finite]) + if (diff(limits) == 0) { + pad <- max(abs(limits[1]) * 0.01, 1e-8) + limits <- c(max(0, limits[1] - pad), limits[2] + pad) + } + score_range <- range(scores[finite]) + minimum <- min(scores[finite]) + boundary <- vapply(seq_along(grids), function(i) { + g <- grids[[i]] + length(unique(g)) > 1L && g[best[i]] %in% range(g) + }, logical(1)) + names(boundary) <- names(grids) + slice <- if (separate) scores[best[1], best[2], ] else scores[best[1], ] + list(scores = scores, scale = scale, grids = grids, best_index = best, + shown_lambda_2_index = if (separate) panels else NULL, + color_limits = limits, unavailable_count = sum(!finite), log_labels = log_labels, + score_range = score_range, minimum = minimum, exact_minima = sum(scores[finite] == minimum), + boundary = boundary, lambda_2_slice_range = range(slice[is.finite(slice)])) +} + +# Five ticks include the numerical endpoints even for a narrow score range. +draw_color_key <- function(left, right, bottom, top, limits, colors) { + edges <- seq(bottom, top, length.out=length(colors)+1L) + graphics::rect(left,edges[-length(edges)],right,edges[-1],col=colors,border=NA) + ticks <- seq(limits[1],limits[2],length.out=5) + graphics::axis(4,pos=right,at=seq(bottom,top,length.out=5), + labels=formatC(ticks,format='g',digits=5),las=1,cex.axis=0.9,tck=-0.015) +} diff --git a/README.Rmd b/README.Rmd index 595e3ff..a145bf8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -4,12 +4,10 @@ output: github_document -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - out.width = "100%" -) +```{r setup, include=FALSE} +knitr::opts_chunk$set(collapse = TRUE, comment = "#>", out.width = "100%", + fig.width = 8, fig.height = 5, + fig.path = "README_files/figure-gfm/") ``` @@ -20,72 +18,171 @@ knitr::opts_chunk$set( # learner -The `learner` package implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by [McGrath et al. (2024)](https://doi.org/10.48550/arXiv.2412.20605). +The `learner` package implements transfer learning methods for low-rank matrix +estimation. These methods use similarity in the latent row and column spaces +of source and target populations to improve estimation in the target population. +The methods include LatEnt spAce-based tRaNsfer lEaRning (LEARNER) and direct +projection LEARNER (D-LEARNER), described by +[McGrath et al. (2024)](https://doi.org/10.48550/arXiv.2412.20605). ## Installation -You can install the released version of `learner` from [CRAN](https://CRAN.R-project.org/package=learner) with: -``` r +Install the released version from CRAN: + +```r install.packages("learner") ``` -You can install the development version of `learner` from [GitHub](https://github.com/stmcg/learner) with: +The examples below use the multiple-source and plotting extensions on the +`plot-diagnostics` development branch: -``` r -# install.packages("devtools") -devtools::install_github("stmcg/learner") +```r +# install.packages("remotes") +remotes::install_github("stmcg/learner", ref = "plot-diagnostics") +# Optional: required for plot_cv(), but not for fitting or the other plots. +install.packages("ggplot2") # version 3.4.0 or later ``` -## Example +## One source population -We illustrate an example of how `learner` can be used. We first load the package. -```{r, results="hide", message=FALSE} +`dat_highsim` contains a simulated source and target with similar latent spaces. +LEARNER fits the target while penalizing departures from the source spaces. +Here `lambda_1` controls both space penalties, and `lambda_2` controls the +balance between the two target factors. + +```{r single-source, message=FALSE} library(learner) +fit_single <- learner( + Y_source = dat_highsim$Y_source, Y_target = dat_highsim$Y_target, + r = 3, lambda_1 = 10, lambda_2 = 1, step_size = 0.003, + control = list(max_iter = 2000) +) +dim(fit_single$learner_estimate) ``` -In this illustration, we will use one of the toy data sets in the package (`dat_highsim`) that has a high degree of similarity between the latent spaces of the source and target populations. The object `dat_highsim` is a list which contains the observed source population data matrix `Y_source` and the target population data matrix `Y_target`. Since the data was simulated, the true values of the matrices are included in `dat_highsim` as `Theta_source` and `Theta_target`. +D-LEARNER projects the target data directly onto the source spaces, without +iterative optimization or penalty selection: -#### LEARNER Method +```{r single-direct} +direct_single <- dlearner(dat_highsim$Y_source, dat_highsim$Y_target, r = 3) +``` + +## Multiple sources and separate penalties + +`dat_multisource` contains one target and two sources, each with 100 rows and +50 columns. The true signals have rank 3 and share their latent spaces. +Independent normal noise is added to each population: the noise variances are +0.10 for the target, 0.01 for source A, and 0.50 for source B. The true signal +matrices are also included, but are not needed for fitting. + +Pass the sources as a list. The weights below are illustrative choices; they +are held fixed during cross-validation. Larger row or column penalties encourage +the corresponding target factors to follow the combined source spaces more closely. + +```{r multiple-data} +sources <- dat_multisource$Y_source +target <- dat_multisource$Y_target +weights <- c(0.7, 0.3) +row_grid <- c(0, 1, 10) +col_grid <- c(0, 1, 3) +balance_grid <- c(0.1, 1) +``` -We can apply the LEARNER method via the `learner` function. +Select penalties by cross-validation. This small grid and iteration budget keep +the example quick; use suitable grids and optimization settings for an analysis. -This method allows for flexible patterns of heterogeneity across the source and target populations. It consequently requires specifying the tuning parameters `lambda_1` and `lambda_2` which control the degree of transfer learning between the populations. These values can be selected based on cross-validation via the `cv.learner` function. For example, we can specify candidate values of 1, 10, and 100 for `lambda_1` and `lambda_2` and select the optimal values based on cross-validation as follows: -```{r} -res_cv.learner <- cv.learner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target, - lambda_1_all = c(1, 10, 100), - lambda_2_all = c(1, 10, 100), - step_size = 0.003) -res_cv.learner$lambda_1_min -res_cv.learner$lambda_2_min +```{r multiple-cv} +set.seed(815) +cv <- cv.learner( + Y_source = sources, Y_target = target, r = 3, source_weights = weights, + lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_folds = 3, step_size = 0.0003, + control = list(max_iter = 2000) +) +c(row = cv$lambda_1_row_min, column = cv$lambda_1_col_min, + balance = cv$lambda_2_min) ``` -Next, we apply the `learner` function with these values of `lambda_1` and `lambda_2`: -```{r} -res_learner <- learner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target, - lambda_1 = 100, lambda_2 = 1, - step_size = 0.003) +For a common space penalty, supply `lambda_1_all` instead of the separate row +and column grids. Cross-validation selects penalties, not the rank or source weights. + +### Cross-validation results + +The heatmap shows how much the CV error increases above its minimum, with a +black box marking the selected combination. Supplying `n_observed` converts +the stored sums of squared errors to mean squared errors (MSE). + +```{r cv-heatmap, fig.height=5.5, fig.cap="Cross-validation error across the candidate penalties."} +plot_cv( + cv, lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_observed = sum(!is.na(target)) +) ``` -The LEARNER estimate is given by the `learner_estimate` component in the output of the `learner` function, e.g., -```{r} -res_learner$learner_estimate[1:5, 1:5] + +A selected value on a grid boundary can be a reason to expand that grid. +CV scores can also come from fits that reached the iteration limit, so inspect +the optimization settings before interpreting the selection. + +### Refit and inspect optimization + +Refit using the selected penalties and all observed target entries: + +```{r multiple-refit} +fit <- learner( + Y_source = sources, Y_target = target, r = cv$r, source_weights = weights, + lambda_1_row = cv$lambda_1_row_min, + lambda_1_col = cv$lambda_1_col_min, lambda_2 = cv$lambda_2_min, + step_size = 0.0003, control = list(max_iter = 5000) +) +fit$convergence_criterion ``` -We can check the convergence of the numerical optimization algorithm by plotting the values of the objective function at each iteration: -```{r, out.width="75%"} -plot(res_learner$objective_values, - xlab = 'Iteration', ylab = 'Objective function', - type = 'l', col = 'blue', lwd = 2) + +```{r objective-history, fig.cap="Objective function values near the optimum for the refitted target model."} +plot_objective(fit, iterations = "near_optimum") ``` +Use `iterations = "all"` (the default) for the full history, or a vector such +as `1:500` for a specific range. -#### D-LEARNER Method +D-LEARNER accepts the same source list and weights: -We can apply the D-LEARNER method via the `dlearner` function. This method makes stronger assumptions on the heterogeneity across the source and target populations. It consequently does not rely on choosing tuning parameters. The `dlearner` function can be applied as follows: -```{r} -res_dlearner <- dlearner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target) -res_dlearner$dlearner_estimate[1:5, 1:5] +```{r multiple-direct} +direct <- dlearner(sources, target, r = 3, source_weights = weights) +dim(direct$dlearner_estimate) ``` +### Compare source choices + +For this simulated dataset, the true target signal is available. Compare +D-LEARNER fits at the same rank to isolate the choice of source spaces: + +```{r source-comparison} +estimates <- list( + A_only = dlearner(sources$A, target, r = 3)$dlearner_estimate, + B_only = dlearner(sources$B, target, r = 3)$dlearner_estimate, + Weighted_A_B = direct$dlearner_estimate +) +source_errors <- vapply(estimates, function(estimate) { + norm(estimate - dat_multisource$Theta_target, "F") +}, numeric(1)) +round(source_errors, 3) +``` + +Lower Frobenius error is better. In this example, source A alone gives the +smallest error, while the weighted combination improves on source B alone. +The weights 0.7/0.3 are illustrative, not optimized. These results describe one +simulated dataset; they are not CV errors or evidence that combining sources +always improves estimation. + +### Latent spaces and contributions + +Compare the target and source spaces, or inspect which variables contribute +to the components of a fitted target matrix: + +```{r additional-plots, eval=FALSE} +plot_source_spaces(target, sources, r = 3) +plot_contributions(fit) +``` +See `?learner`, `?cv.learner`, `?dlearner`, and `?dat_multisource` for details. +More runnable examples are included in `inst/examples/` in the source repository. diff --git a/README.md b/README.md index a059c2c..3258f07 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,3 @@ - @@ -12,121 +11,202 @@ coverage](https://codecov.io/gh/stmcg/learner/graph/badge.svg)](https://app.code # learner The `learner` package implements transfer learning methods for low-rank -matrix estimation. These methods leverage similarity in the latent row -and column spaces between the source and target populations to improve -estimation in the target population. The methods include the LatEnt -spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection -LEARNER (D-LEARNER) method described by [McGrath et -al. (2024)](https://doi.org/10.48550/arXiv.2412.20605). +matrix estimation. These methods use similarity in the latent row and +column spaces of source and target populations to improve estimation in +the target population. The methods include LatEnt spAce-based tRaNsfer +lEaRning (LEARNER) and direct projection LEARNER (D-LEARNER), described +by [McGrath et al. (2024)](https://doi.org/10.48550/arXiv.2412.20605). ## Installation -You can install the released version of `learner` from -[CRAN](https://CRAN.R-project.org/package=learner) with: +Install the released version from CRAN: ``` r install.packages("learner") ``` -You can install the development version of `learner` from -[GitHub](https://github.com/stmcg/learner) with: +The examples below use the multiple-source and plotting extensions on +the `plot-diagnostics` development branch: ``` r -# install.packages("devtools") -devtools::install_github("stmcg/learner") +# install.packages("remotes") +remotes::install_github("stmcg/learner", ref = "plot-diagnostics") +# Optional: required for plot_cv(), but not for fitting or the other plots. +install.packages("ggplot2") # version 3.4.0 or later ``` -## Example +## One source population -We illustrate an example of how `learner` can be used. We first load the -package. +`dat_highsim` contains a simulated source and target with similar latent +spaces. LEARNER fits the target while penalizing departures from the +source spaces. Here `lambda_1` controls both space penalties, and +`lambda_2` controls the balance between the two target factors. ``` r library(learner) +fit_single <- learner( + Y_source = dat_highsim$Y_source, Y_target = dat_highsim$Y_target, + r = 3, lambda_1 = 10, lambda_2 = 1, step_size = 0.003, + control = list(max_iter = 2000) +) +dim(fit_single$learner_estimate) +#> [1] 100 50 ``` -In this illustration, we will use one of the toy data sets in the -package (`dat_highsim`) that has a high degree of similarity between the -latent spaces of the source and target populations. The object -`dat_highsim` is a list which contains the observed source population -data matrix `Y_source` and the target population data matrix `Y_target`. -Since the data was simulated, the true values of the matrices are -included in `dat_highsim` as `Theta_source` and `Theta_target`. +D-LEARNER projects the target data directly onto the source spaces, +without iterative optimization or penalty selection: + +``` r +direct_single <- dlearner(dat_highsim$Y_source, dat_highsim$Y_target, r = 3) +``` -#### LEARNER Method +## Multiple sources and separate penalties -We can apply the LEARNER method via the `learner` function. +`dat_multisource` contains one target and two sources, each with 100 +rows and 50 columns. The true signals have rank 3 and share their latent +spaces. Independent normal noise is added to each population: the noise +variances are 0.10 for the target, 0.01 for source A, and 0.50 for +source B. The true signal matrices are also included, but are not needed +for fitting. -This method allows for flexible patterns of heterogeneity across the -source and target populations. It consequently requires specifying the -tuning parameters `lambda_1` and `lambda_2` which control the degree of -transfer learning between the populations. These values can be selected -based on cross-validation via the `cv.learner` function. For example, we -can specify candidate values of 1, 10, and 100 for `lambda_1` and -`lambda_2` and select the optimal values based on cross-validation as -follows: +Pass the sources as a list. The weights below are illustrative choices; +they are held fixed during cross-validation. Larger row or column +penalties encourage the corresponding target factors to follow the +combined source spaces more closely. ``` r -res_cv.learner <- cv.learner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target, - lambda_1_all = c(1, 10, 100), - lambda_2_all = c(1, 10, 100), - step_size = 0.003) -res_cv.learner$lambda_1_min -#> [1] 100 -res_cv.learner$lambda_2_min -#> [1] 1 +sources <- dat_multisource$Y_source +target <- dat_multisource$Y_target +weights <- c(0.7, 0.3) +row_grid <- c(0, 1, 10) +col_grid <- c(0, 1, 3) +balance_grid <- c(0.1, 1) +``` + +Select penalties by cross-validation. This small grid and iteration +budget keep the example quick; use suitable grids and optimization +settings for an analysis. + +``` r +set.seed(815) +cv <- cv.learner( + Y_source = sources, Y_target = target, r = 3, source_weights = weights, + lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_folds = 3, step_size = 0.0003, + control = list(max_iter = 2000) +) +c(row = cv$lambda_1_row_min, column = cv$lambda_1_col_min, + balance = cv$lambda_2_min) +#> row column balance +#> 10 3 1 +``` + +For a common space penalty, supply `lambda_1_all` instead of the +separate row and column grids. Cross-validation selects penalties, not +the rank or source weights. + +### Cross-validation results + +The heatmap shows how much the CV error increases above its minimum, +with a black box marking the selected combination. Supplying +`n_observed` converts the stored sums of squared errors to mean squared +errors (MSE). + +``` r +plot_cv( + cv, lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_observed = sum(!is.na(target)) +) ``` -Next, we apply the `learner` function with these values of `lambda_1` -and `lambda_2`: +
+ +Cross-validation error across the candidate penalties. +

+Cross-validation error across the candidate penalties. +

+ +
+ +A selected value on a grid boundary can be a reason to expand that grid. +CV scores can also come from fits that reached the iteration limit, so +inspect the optimization settings before interpreting the selection. + +### Refit and inspect optimization + +Refit using the selected penalties and all observed target entries: ``` r -res_learner <- learner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target, - lambda_1 = 100, lambda_2 = 1, - step_size = 0.003) +fit <- learner( + Y_source = sources, Y_target = target, r = cv$r, source_weights = weights, + lambda_1_row = cv$lambda_1_row_min, + lambda_1_col = cv$lambda_1_col_min, lambda_2 = cv$lambda_2_min, + step_size = 0.0003, control = list(max_iter = 5000) +) +fit$convergence_criterion +#> [1] 1 ``` -The LEARNER estimate is given by the `learner_estimate` component in the -output of the `learner` function, e.g., +``` r +plot_objective(fit, iterations = "near_optimum") +``` + +
+ +Objective function values near the optimum for the refitted target model. +

+Objective function values near the optimum for the refitted target +model. +

+ +
+ +Use `iterations = "all"` (the default) for the full history, or a vector +such as `1:500` for a specific range. + +D-LEARNER accepts the same source list and weights: ``` r -res_learner$learner_estimate[1:5, 1:5] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 0.1468889 -1.63414688 1.1857308 0.008093962 0.2196070 -#> [2,] 0.1087766 -0.24392770 -1.0061737 -0.058119721 -0.4012974 -#> [3,] -0.6213028 1.04206753 -0.6907776 0.905709331 -0.5324437 -#> [4,] 1.5811588 -3.00001490 0.3978080 -2.124285120 0.6909386 -#> [5,] 0.5046133 0.01845138 -1.4798209 -0.765601712 -0.2230227 +direct <- dlearner(sources, target, r = 3, source_weights = weights) +dim(direct$dlearner_estimate) +#> [1] 100 50 ``` -We can check the convergence of the numerical optimization algorithm by -plotting the values of the objective function at each iteration: +### Compare source choices + +For this simulated dataset, the true target signal is available. Compare +D-LEARNER fits at the same rank to isolate the choice of source spaces: ``` r -plot(res_learner$objective_values, - xlab = 'Iteration', ylab = 'Objective function', - type = 'l', col = 'blue', lwd = 2) +estimates <- list( + A_only = dlearner(sources$A, target, r = 3)$dlearner_estimate, + B_only = dlearner(sources$B, target, r = 3)$dlearner_estimate, + Weighted_A_B = direct$dlearner_estimate +) +source_errors <- vapply(estimates, function(estimate) { + norm(estimate - dat_multisource$Theta_target, "F") +}, numeric(1)) +round(source_errors, 3) +#> A_only B_only Weighted_A_B +#> 2.323 17.214 5.324 ``` - +Lower Frobenius error is better. In this example, source A alone gives +the smallest error, while the weighted combination improves on source B +alone. The weights 0.7/0.3 are illustrative, not optimized. These +results describe one simulated dataset; they are not CV errors or +evidence that combining sources always improves estimation. -#### D-LEARNER Method +### Latent spaces and contributions -We can apply the D-LEARNER method via the `dlearner` function. This -method makes stronger assumptions on the heterogeneity across the source -and target populations. It consequently does not rely on choosing tuning -parameters. The `dlearner` function can be applied as follows: +Compare the target and source spaces, or inspect which variables +contribute to the components of a fitted target matrix: ``` r -res_dlearner <- dlearner(Y_source = dat_highsim$Y_source, - Y_target = dat_highsim$Y_target) -res_dlearner$dlearner_estimate[1:5, 1:5] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 0.0959171 -1.72143637 1.15500149 0.005478273 0.3258085 -#> [2,] 0.1077137 -0.32243480 -1.03557177 -0.108195636 -0.4093049 -#> [3,] -0.7237275 0.86634922 -0.43528206 1.105802194 -0.5961351 -#> [4,] 1.6347696 -2.91985925 0.04876288 -2.357504089 0.8127403 -#> [5,] 0.4757450 0.05665543 -1.50373470 -0.744076401 -0.2876033 +plot_source_spaces(target, sources, r = 3) +plot_contributions(fit) ``` + +See `?learner`, `?cv.learner`, `?dlearner`, and `?dat_multisource` for +details. More runnable examples are included in `inst/examples/` in the +source repository. diff --git a/README_files/figure-gfm/cv-heatmap-1.png b/README_files/figure-gfm/cv-heatmap-1.png new file mode 100644 index 0000000000000000000000000000000000000000..13c0365454bc180b6c33ab3e5a9fb7a36a97bc41 GIT binary patch literal 35276 zcmeFZRZtyWv^5F@f(Ew)cZUGM-QC@TLvVL@cXxujy9M3226wkWaEHJ1*|}BszTKDq z;Z!xLUERB7uhnbLF~%G-Ve+zKAK*U0fq{X2kPsJE1OtQM2Ll6-gn19_VN%C40t16Y zG#3(*w-6E&vaz(WSF+VNG!`?qHnul6R1^~g17nYhQqeKPRYB*?XsxFqod}pKDvC#Z zWl3zXRGbc6K72KB9X?&lGAC!=c`h)g;zbug{!y}-u=%F-gM#Q=`L77N z{08}a$Nkqw2-;6obbGVg z^+Wsp{hW?Jyrw>QP0`UrCs8MngX8SwSDqd2y%|f z?y6&GHNAQL0P>r0t2yii37AfMN02?-1#iEhibn_Tp<`(o+jI|2@m!iI@E z!876KMn=6%I45t#OoV+Te6Y=JYPuTtI}-dF8FT8;(I_=D)}hPL`<8u769}&}I#YAb zg(;3glW+mrvxuEb6xCwheznr<6u}ogx@Po2GM-@FOaw>JXQ$_zM`yLHG??vp4lMXj zkz@?2jTeF-AGJn`0%W}=N1vGORgla|!U%Xych09h`2>Q5U)sF=7^zXi{67NSPBA`z zMH7xCL^Ai(`ue>X?$e=J-eI9x-u=(b(~VxLhgYy>|k`&Iz8tG 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zmqUzkzcMrq;w2&`_X+z$9pPYMO>5D)zhJfRm;YRe$NP=8?a6nNTqz9L+Cg(3GFYeB(F-4+pEWv?Wqb;F(a(ssxf4q2 zeUPlEL6YP{TV!?&`j|Vf?Xm&RQzsxft{@deC&SL>={MHF3N{8>13Cj+=n%l4Y04E# zdz2OAMKK#kmv}lrpxjGI{i-;X!jE=FM?fWoY|?Z$@HQ&|PN_%l&4LKUr)Oyt zL>PzU9!Tm6zYXiuWtp5#*Y}>}QNjD<2T6LtL`4YCfne4gh)d|qk1YFZo&y{;$YD&} zt19LO(mxlw9Xgd6xk^!%6|sc9?M00x$}TBn%%lkIK9!vwDiIxdm$;eWLp29NFK z&VdlsLVmlWIF)VB4eV1<$hZOgl0u*oq!1luibblnxTVe98~(D2>Z&_TH7N(JB%`+w z&1ef|9Dih|+M;={G;X+hnk|I+VPTu1B_Xea?wCK^EH(BD>Mw<-aDSOYUZ;`r9qK}- z6YEDxVG+Lp?YXNoLV6H#B^sCC%P1!v17M~b)l4hDHtG!Be8+HF3EP^+(wKmhNo)OR z6Vk@ZZ)MuN%kV;4sWLN4ZflTF;Th^N$Rg$MchhO@7dWLSd#5((ly`??Y3P725#Io8 z=nwx?&;p@BL~pGplCc*UPmq8bvt43$|4up_T60i@@N%WrnexVb+F7-RKgoPhnIGk9 zug5DgsO;n1#B#uhCNlMbdR7DPX zKsgndK%y(;2#RYLD6GSCgLPSuRoR?QU%R+34!Pw2{a*?c$dEmSZp1KB^iw`~yg9A( z5VWsnaFg-aQZ?Ib_te+kU3C3)2cNG=S|*7xEhRgNaNl<6g(Be_Ra)^2P@}q=6eP5$ zRgo3B@K(B3UgEQQcfDTiH5A3Mbqfc>`= 3.4.0 to run this example: install.packages("ggplot2")') +} +# Run after installing the plotting development version of learner. +# Synthetic data demonstrate the interface, not statistical performance. +# Use a small aligned subset to keep this demonstration quick. +sources <- lapply(learner::dat_multisource$Y_source, + function(x) x[1:20, 1:10]) +target <- learner::dat_multisource$Y_target[1:20, 1:10] +weights <- c(0.7, 0.3) +row_grid <- c(0, 1, 10) +col_grid <- c(0, 1, 3) +balance_grid <- c(0.1, 1) + +set.seed(815) +cv <- learner::cv.learner( + sources, target, r = 3, source_weights = weights, + lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, step_size = 0.0003, n_folds = 3, + control = list(max_iter = 500)) +learner::plot_cv( + cv, lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_observed = sum(!is.na(target))) + +fit <- learner::learner( + sources, target, r = cv$r, source_weights = weights, + lambda_1_row = cv$lambda_1_row_min, lambda_1_col = cv$lambda_1_col_min, + lambda_2 = cv$lambda_2_min, step_size = 0.0003, + control = list(max_iter = 2000)) +learner::plot_objective(fit, iterations = "near_optimum") + +# Common-penalty results are supported as well. +set.seed(815) +common_grid <- c(0, 1, 10) +common_cv <- learner::cv.learner( + sources, target, r = 3, source_weights = weights, + lambda_1_all = common_grid, lambda_2_all = balance_grid, + step_size = 0.0003, n_folds = 3, control = list(max_iter = 500)) +learner::plot_cv(common_cv, lambda_1_all = common_grid, + lambda_2_all = balance_grid, n_observed = sum(!is.na(target))) + +# Inspect stopping codes as well as curves. Plotting does not prove convergence. diff --git a/inst/examples/plot-paper-displays.R b/inst/examples/plot-paper-displays.R new file mode 100644 index 0000000..00539df --- /dev/null +++ b/inst/examples/plot-paper-displays.R @@ -0,0 +1,8 @@ +# Population spaces and contributions using simulated multiple-source data. +# Run after loading the plotting development version of learner. +space_target <- learner::dat_multisource$Y_target +space_sources <- learner::dat_multisource$Y_source +learner::plot_source_spaces(space_target, space_sources, r=3, + target_name='Target (simulated)') +space_fit <- learner::dlearner(space_sources,space_target,r=3,source_weights=c(0.7,0.3)) +learner::plot_contributions(space_fit, main='D-LEARNER contributions (simulated data)') diff --git a/inst/examples/plotting-guide.md b/inst/examples/plotting-guide.md new file mode 100644 index 0000000..1e80e7c --- /dev/null +++ b/inst/examples/plotting-guide.md @@ -0,0 +1,91 @@ +# Plotting guide + +These functions display existing fits or measured results. Plot appearance alone +cannot establish convergence or statistical superiority. + +## Objective history + +```r +plot_objective(fit) # All iterations +plot_objective(fit, iterations = "near_optimum") # Around the smallest recorded value +plot_objective(fit, iterations = 1:500) # A specific range +``` + +`"near_optimum"` centers a window on the first smallest finite recorded +objective value. It shows 10% of the recorded iterations (rounded up), at least +10, or the full history if fewer than 10 were recorded. At either end the window +shifts to stay within the history. Both axes zoom to the selected window. +The shaded band spans consecutive objective values and the line shows their +pairwise average; the band is not an uncertainty interval. + +## CV heatmaps + +Only the heatmap requires the optional package ggplot2 >= 3.4.0. Install it +with `install.packages("ggplot2")`. Estimation and all other plotting functions +work without ggplot2. + +```r +plot_cv(cv, lambda_1_all = common_grid, lambda_2_all = balance_grid, + n_observed = sum(!is.na(target)), log_labels = TRUE) +``` + +Use the exact grids supplied to `cv.learner()`. With independent penalties, +replace `lambda_1_all` with `lambda_1_row_all` and `lambda_1_col_all`. +Heatmaps then have one panel per balance penalty. Zero candidates require +`log_labels = FALSE`. Heatmap positions are equally spaced grid slots. + +The CV object's `mse_all` contains pooled held-out squared-error sums. Supplying +the total number of observed target entries converts these to MSE. Without that +number the plots explicitly show SSE. Source entries are not in the denominator. +Heatmaps use a reversed magma palette: pale cells have lower error. Labels and +colors show percentage increases over the minimum, with a black selection box. +Finite errors more than 30% above the minimum are gray and labeled >30%, not +as optimizer divergence. Use `relative_limit = NULL` to color all finite errors +or `show_values = FALSE` to hide cell text. If the minimum is zero, the heatmap +uses absolute scores instead. Cell labels are rounded, so matching printed +percentages do not establish an exact tie. + +The plots report the score range, minimum, exact ties, and boundary selections. +A boundary minimum deserves investigation; a small change across a parameter +is not an exact tie or evidence that the parameter is irrelevant. + +For a customized export, capture the returned object: + +```r +result <- plot_cv(cv, lambda_1_all = common_grid, lambda_2_all = balance_grid) +ggplot2::ggsave("cv-heatmap.png", plot = result$plot, width = 8, height = 5.5, dpi = 200) +``` + +For multiple pages, `result$plots` contains every ggplot page. Inspect exported +images at the intended size to check text and legend placement. + +## Population latent spaces + +```r +plot_source_spaces(target, list(A = source_A, B = source_B), r = 3) +``` + +For each population, compute an SVD and retain orthonormal bases B_U and B_V. +Plot B_U B_U^T for the row side and B_V B_V^T for the column side. These are +individual population projectors, not raw data matrices or the weighted average +used by the optimizer. Shared color limits within each side support comparisons. +Negative entries are red and positive entries blue. + +SVD uses full matrices before display subsetting. `row_index`, `column_index`, +or `max_display` limit plotting cost without changing the fitted spaces. All +populations must have aligned variables in the same order. Labels default to +Row index and Column index; use biological labels only when appropriate. + +## Variable contributions + +```r +plot_contributions(fit, r = 3) +``` + +Recompute the SVD of the estimated signal. For component j, a row's score is +B_U[i,j]^2 and a column's score is B_V[i,j]^2. The scores sum to one per component +on each side over the full set of variables. They are not squared optimization +factors, which include scaling. The plot uses separate color scales on the two +sides and reports display clipping if explicit limits are supplied. +These component-specific scores depend on basis choice in repeated singular +subspaces; the function does not match components or assign biological meaning. diff --git a/man/plot_contributions.Rd b/man/plot_contributions.Rd new file mode 100644 index 0000000..6f5637f --- /dev/null +++ b/man/plot_contributions.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot-spaces.R +\name{plot_contributions} +\alias{plot_contributions} +\title{Plot variable contributions to latent components} +\usage{ +plot_contributions( + x, + r = NULL, + row_index = NULL, + column_index = NULL, + max_display = 200L, + limits = NULL, + row_label = "Row index", + column_label = "Column index", + main = "Estimated target contributions" +) +} +\arguments{ +\item{x}{A complete estimated matrix, a \code{learner()} result, or a +\code{dlearner()} result. No rotation of the singular vectors is performed.} + +\item{r}{Number of components. Required for a matrix; defaults to the stored +rank for a fitting result. Numerically zero directions are rejected.} + +\item{row_index, column_index}{Optional distinct integer indices selecting +variables to display, in display order. The same indices are used in all +populations. SVD always uses the full matrices before subsetting.} + +\item{max_display}{Maximum number of displayed variables on each side when +indices are omitted; default 200. Deterministic evenly spaced indices are +used, without changing the random-number state.} + +\item{limits}{Optional two positive color limits, in row-side then column-side +order. Each side uses a symmetric scale about zero shared across populations. +Values beyond supplied limits are clipped for display and counts are returned.} + +\item{row_label, column_label}{Axis labels. Use Variant index and Phenotype +index only when these accurately describe the input rows and columns.} + +\item{main}{Title describing the input estimate.} +} +\value{ +Invisibly, a list with full \code{row_scores} and \code{column_scores}, +displayed \code{row_index} and \code{column_index}, \code{limits}, and +\code{clipped_count}. Score matrices have variables in rows and components +in columns; all values are returned without display clipping. +} +\description{ +Show which rows and columns contribute to each latent component of a fitted +target matrix, using squared singular-vector entries. Higher scores are darker red. +} +\details{ +An SVD of the estimated matrix supplies U and V. Row scores are U squared +elementwise; column scores are V squared elementwise. Scores sum to one per +component over all variables on the corresponding side. They are not squared +entries of the raw optimization factors, effect sizes, or correlations. +Subsetting is applied only for display and scores are not renormalized. +The left panel displays columns and the right panel rows. Color limits are +separate for the two sides and start at zero. Optional limits specify row +then column upper limits, with clipped-entry counts reported. +Signs do not affect squared scores, but rotations within repeated singular +subspaces can change component-specific scores. Components from different +populations are not automatically aligned or given biological labels. +} +\examples{ +fit <- dlearner(dat_multisource$Y_source, dat_multisource$Y_target, r = 3) +plot_contributions(fit,max_display=50) +} diff --git a/man/plot_cv.Rd b/man/plot_cv.Rd new file mode 100644 index 0000000..f665d1b --- /dev/null +++ b/man/plot_cv.Rd @@ -0,0 +1,121 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{plot_cv} +\alias{plot_cv} +\title{Plot the LEARNER cross-validation scores} +\usage{ +plot_cv( + x, + lambda_1_all = NULL, + lambda_2_all = NULL, + lambda_1_row_all = NULL, + lambda_1_col_all = NULL, + n_observed = NULL, + lambda_2_index = NULL, + log_labels = FALSE, + relative_limit = 0.3, + show_values = TRUE, + main = "Cross-validation error" +) +} +\arguments{ +\item{x}{A result returned by \code{cv.learner()}.} + +\item{lambda_1_all}{Common-penalty candidates in the exact order used for CV. +Required for a two-dimensional score array; omit for separate penalties.} + +\item{lambda_2_all}{Balance-penalty candidates in the exact order used for CV.} + +\item{lambda_1_row_all, lambda_1_col_all}{Separate-penalty candidates in the +exact order used for CV. Both are required for a three-dimensional array.} + +\item{n_observed}{Optional positive integer giving the number of observed +target entries used in CV. If supplied, scores are divided by this count and +labeled Validation MSE. Otherwise the original sums are labeled Validation +SSE. Missing target entries must not be included in this count.} + +\item{lambda_2_index}{Optional vector of distinct integer indices into +\code{lambda_2_all}, selecting panels to draw in separate-penalty mode. +\code{NULL} draws all panels. Not used in common-penalty mode.} + +\item{log_labels}{Logical scalar. If TRUE, display base-10 logarithms of +the candidates on the heatmap axes. +All candidates on those axes must be strictly positive. Log labels are +rounded to two decimal places. Defaults to FALSE.} + +\item{relative_limit}{Optional positive relative increase cutoff, default 0.3 +(30 percent). Larger finite errors are gray and labeled above the cutoff, +never as diverged. NULL keeps all finite values in the color scale.} + +\item{show_values}{Whether to print scores inside cells. Defaults to TRUE.} + +\item{main}{Plot title.} +} +\value{ +Invisibly, a list with \code{scores}, \code{scale}, \code{grids}, +\code{best_index}, \code{shown_lambda_2_index}, \code{color_limits}, and +\code{unavailable_count}, \code{log_labels}, \code{score_range}, \code{minimum}, +\code{exact_minima}, \code{boundary}, and \code{lambda_2_slice_range}. +Indices refer to the original candidate order. Boundary flags indicate a +selected numerical endpoint on a grid with at least two distinct values. +Also returns \code{display_data}, \code{display_limits}, \code{relative}, +\code{above_limit_count}, \code{plot} (the first ggplot page), and \code{plots} +(all ggplot pages). The original absolute score diagnostics are unchanged. +} +\description{ +Draw a common-penalty heatmap or separate-penalty heatmaps, with a shared +color scale and a black box at the first minimum in R array order. +} +\details{ +Existing CV results do not store the full candidate grids, so callers must +provide them. Lengths and the selected penalty values are checked, but these +checks cannot detect every incorrectly supplied grid. Use the original grids, +without sorting or removing duplicates. Axes are equally spaced candidate +positions labeled with actual values, or their base-10 logarithms when +\code{log_labels=TRUE}. This changes labels only, not scores, candidate order, +or cell spacing. The reversed magma palette uses pale colors for lower error +and dark colors for higher error. Cell labels show increases over the minimum. + +Common mode shows \code{lambda_1_all} on the horizontal axis and +\code{lambda_2_all} on the vertical axis. Separate mode shows the U-space +penalty horizontally and the V-space penalty vertically, with one panel for +each balance penalty. All panels share limits computed from the full score +array, even when only some panels are selected. Up to six panels are drawn +per page. Gray cells have non-finite scores and are excluded from selection. +Only the first minimum is marked; a panel need not contain a black box. +Captions report the score range, exact ties, and boundary selections. These are +descriptive diagnostics, not significance or convergence tests. + +The stored \code{mse_all} values are sums of held-out squared errors. MSE +conversion requires the correct \code{n_observed}; the function cannot infer +this count from the scores. Neither this plot nor CV certifies convergence +of the individual fits. No fitting is performed and \code{x} is unchanged. +Requires optional ggplot2 >= 3.4.0; install it with +\code{install.packages("ggplot2")}. Estimation and other plots do not require it. +Draws with ggplot2 without modifying base graphics parameters. Each page shares +one legend. A sufficiently large device is recommended for multiple panels. +Relative increases are undefined when the minimum is zero; the display then +uses absolute scores and ignores relative_limit. High relative error alone +never establishes optimizer divergence. Non-finite scores are unavailable. +Use ggplot2::ggsave(plot = result$plot, ...) to export the first page, or +export each member of result$plots for multiple pages. +} +\examples{ +if (requireNamespace("ggplot2", quietly = TRUE) && + utils::packageVersion("ggplot2") >= "3.4.0") { +row_grid <- c(0.1, 1) +col_grid <- c(0.1, 2) +balance_grid <- c(0.1, 1) +target <- dat_highsim$Y_target[1:12, 1:8] +set.seed(42) +cv <- cv.learner(dat_highsim$Y_source[1:12, 1:8], target, + r = 2, lambda_1_row_all = row_grid, + lambda_1_col_all = col_grid, lambda_2_all = balance_grid, + step_size = 0.0003, n_folds = 2, + control = list(max_iter = 20)) +plot_cv(cv, lambda_1_row_all = row_grid, lambda_1_col_all = col_grid, + lambda_2_all = balance_grid, n_observed = sum(!is.na(target))) + +} + +} diff --git a/man/plot_objective.Rd b/man/plot_objective.Rd new file mode 100644 index 0000000..7b7ce87 --- /dev/null +++ b/man/plot_objective.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plotting.R +\name{plot_objective} +\alias{plot_objective} +\title{Plot the objective function values of a LEARNER fit} +\usage{ +plot_objective(x, iterations = "all", main = NULL) +} +\arguments{ +\item{x}{A result returned by \code{learner}.} + +\item{iterations}{Iterations to show: \code{"all"} (the default), +\code{"near_optimum"}, or a vector of iteration numbers such as +\code{1:1000}. \code{"near_optimum"} shows a window centered on the first +smallest finite recorded objective value. The window contains 10\% of the +recorded iterations (rounded up), with a minimum of 10, or all iterations +for shorter histories. At either boundary the window shifts to stay within +the recorded history. Both axes adjust to the selected window.} + +\item{main}{Title of the plot. By default, \code{"Objective function"}, +with \code{"(near optimum)"} added when \code{iterations = "near_optimum"}.} +} +\value{ +Invisibly, a data frame of the plotted iterations and objective +values. +} +\description{ +Plots the values of the objective function at each iteration of the +numerical optimization algorithm in \code{learner}. +} +\details{ +The red point marks the smallest objective value, which is also +given above the plot. The objective value at the initialization is not +included. With \code{iterations = "near_optimum"}, the plot shows a shaded +band spanning each pair of consecutive objective values, so that values +that alternate between iterations remain visible, and a line at their +average. +} +\examples{ +fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, + r = 3, lambda_1 = 1, lambda_2 = 1, step_size = 0.003, + control = list(max_iter = 1000)) +plot_objective(fit) +plot_objective(fit, iterations = "near_optimum") +plot_objective(fit, iterations = 1:100) + +} diff --git a/man/plot_source_spaces.Rd b/man/plot_source_spaces.Rd new file mode 100644 index 0000000..7e341fc --- /dev/null +++ b/man/plot_source_spaces.Rd @@ -0,0 +1,71 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot-spaces.R +\name{plot_source_spaces} +\alias{plot_source_spaces} +\title{Compare target and source latent spaces} +\usage{ +plot_source_spaces( + Y_target, + Y_source, + r, + row_index = NULL, + column_index = NULL, + max_display = 200L, + limits = NULL, + row_label = "Row index", + column_label = "Column index", + target_name = "Target population" +) +} +\arguments{ +\item{Y_target}{Complete finite target matrix, or a completed estimate. +Missing values are rejected; the function does not impute them.} + +\item{Y_source}{Aligned complete source matrix or nonempty list of matrices.} + +\item{r}{Positive integer giving the retained rank for every matrix.} + +\item{row_index, column_index}{Optional distinct integer indices selecting +variables to display, in display order. The same indices are used in all +populations. SVD always uses the full matrices before subsetting.} + +\item{max_display}{Maximum number of displayed variables on each side when +indices are omitted; default 200. Deterministic evenly spaced indices are +used, without changing the random-number state.} + +\item{limits}{Optional two positive color limits, in row-side then column-side +order. Each side uses a symmetric scale about zero shared across populations. +Values beyond supplied limits are clipped for display and counts are returned.} + +\item{row_label, column_label}{Axis labels. Use Variant index and Phenotype +index only when these accurately describe the input rows and columns.} + +\item{target_name}{Panel label describing the supplied target matrix.} +} +\value{ +Invisibly, a list with \code{row_projectors}, \code{column_projectors}, +\code{row_index}, \code{column_index}, \code{limits}, and \code{clipped_count}. +Each projector is the displayed submatrix, computed without allocating the +full row-by-row projector. Its population name identifies the input. +} +\description{ +Plot column-space and row-space projection submatrices for the target and +each source, to compare their retained latent spaces. +} +\details{ +For an SVD basis B, the displayed matrix is a submatrix of B B-transpose. +Column-side panels appear on top, row-side panels below. Red denotes negative +values and blue positive values. Color scales are shared within each side, +including across pages, but can differ between the two sides. At most three +populations appear per page. Source names are taken from the input list when +supplied. No name-based alignment or matching of factors is performed. +These are individual population projectors, not the weighted source operator. +A submatrix need not itself be an idempotent projector. +Numerically zero retained singular directions are rejected. With repeated +singular values at the truncation boundary the retained space is not unique. +The input order must already encode any desired phenotype or genomic ordering. +} +\examples{ +plot_source_spaces(dat_multisource$Y_target, dat_multisource$Y_source, + r = 3, max_display = 20) +} diff --git a/tests/testthat/helper-plotting.R b/tests/testthat/helper-plotting.R new file mode 100644 index 0000000..2e9ca4f --- /dev/null +++ b/tests/testthat/helper-plotting.R @@ -0,0 +1,15 @@ +# Devices are temporary and closed even when an assertion fails. +plot_device <- function() { + path <- tempfile(fileext = '.pdf') + grDevices::pdf(path, width = 10, height = 5) + path +} + +cv_plot_example <- function(separate = TRUE) { + if (separate) { + scores <- array(c(8, 7, 6, 5, 4, 3, 2, 1), c(2, 2, 2)) + list(mse_all = scores, lambda_1_min = NA_real_, lambda_1_row_min = 0, + lambda_1_col_min = 3, lambda_2_min = 1, r = 2) + } else list(mse_all = matrix(c(8, 7, 6, 5), 2, 2), + lambda_1_min = 0, lambda_2_min = 1, r = 2) +} diff --git a/tests/testthat/test-cv-heatmap-style.R b/tests/testthat/test-cv-heatmap-style.R new file mode 100644 index 0000000..1c9e0e8 --- /dev/null +++ b/tests/testthat/test-cv-heatmap-style.R @@ -0,0 +1,75 @@ +test_that('relative heatmaps preserve score alignment and distinguish high error from missing', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- list(mse_all = matrix(c(100, 110, 140, NA_real_), 2), lambda_1_min = 1, lambda_2_min = 0.1) + args <- list(x = x, lambda_1_all = c(1, 2), lambda_2_all = c(.1, 1)) + z <- do.call(plot_cv, args) + expect_s3_class(z$plot, 'ggplot') + expect_equal(z$display_data$score, c(100, 110, 140, NA_real_)) + expect_equal(z$display_data$label, c('0.0%', '10.0%', '>30%', 'NA')) + expect_equal(z$display_data$selected, c(TRUE, FALSE, FALSE, FALSE)) + expect_equal(z$above_limit_count, 1) + expect_equal(z$unavailable_count, 1) + expect_equal(z$display_limits, c(0, .1)) + expect_match(z$plot$labels$caption, 'not a divergence diagnosis', fixed = TRUE) + mse <- do.call(plot_cv, c(args, list(n_observed = 10))) + expect_equal(mse$display_data$display, z$display_data$display) + expect_equal(mse$minimum, 10) + full <- do.call(plot_cv, c(args, list(relative_limit = NULL, show_values = FALSE))) + expect_equal(full$above_limit_count, 0) + expect_equal(full$display_limits, c(0, .4)) + expect_equal(length(full$plot$layers), 2) + expect_error(do.call(plot_cv, c(args, list(relative_limit = 0))), 'relative_limit') + expect_error(do.call(plot_cv, c(args, list(show_values = NA))), 'show_values') +}) + +test_that('zero minima use absolute scores and exact ties remain first in CV order', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- list(mse_all = matrix(c(0, 0, 2, 4), 2), lambda_1_min = 1, lambda_2_min = .1) + z <- plot_cv(x, lambda_1_all = c(1, 2), lambda_2_all = c(.1, 1)) + expect_false(z$relative) + expect_equal(z$above_limit_count, 0) + expect_equal(z$display_data$display, c(0, 0, 2, 4)) + expect_equal(z$exact_minima, 2) + expect_equal(which(z$display_data$selected), 1L) + expect_match(z$plot$labels$caption, 'absolute SSE', fixed = TRUE) + x$mse_all[] <- 0 + expect_gt(diff(plot_cv(x, lambda_1_all = c(1, 2), lambda_2_all = c(.1, 1))$display_limits), 0) +}) + +test_that('separate heatmaps share scales and return printable pages', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- list(mse_all = array(7:1, c(1, 1, 7)), lambda_1_row_min = 1, + lambda_1_col_min = 2, lambda_2_min = 7) + args <- list(x = x, lambda_1_row_all = 1, lambda_1_col_all = 2, lambda_2_all = 1:7, + relative_limit = NULL) + full <- do.call(plot_cv, args) + subset <- do.call(plot_cv, c(args, list(lambda_2_index = c(7, 2)))) + expect_length(full$plots, 2) + expect_equal(full$display_limits, subset$display_limits) + expect_equal(as.character(subset$plot$data$panel), c('2', '7')) + expect_equal(levels(subset$plot$data$panel), c('7', '2')) + expect_equal(sum(subset$plot$data$selected), 1) +}) + + +test_that('close or repeated balance candidates remain distinct heatmap panels', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + for (balance in list(c(1, 1.00001), c(1, 1))) { + x <- list(mse_all = array(1:8, c(2, 2, 2)), lambda_1_row_min = 1, + lambda_1_col_min = 1, lambda_2_min = 1) + result <- plot_cv(x, lambda_1_row_all = c(1, 2), + lambda_1_col_all = c(1, 2), lambda_2_all = balance) + built <- ggplot2::ggplot_build(result$plot) + expect_equal(nrow(built$layout$layout), 2L) + expect_equal(as.integer(table(built$data[[1]]$PANEL)), c(4L, 4L)) + expect_equal(as.integer(built$data[[3]]$PANEL), 1L) + } +}) diff --git a/tests/testthat/test-cv-plot-errors.R b/tests/testthat/test-cv-plot-errors.R new file mode 100644 index 0000000..ec0239f --- /dev/null +++ b/tests/testthat/test-cv-plot-errors.R @@ -0,0 +1,48 @@ +test_that('CV heatmaps reject invalid titles and separate arguments in common mode', { + args <- list(x = cv_plot_example(FALSE), lambda_1_all = c(10, 0), + lambda_2_all = c(0, 1)) + for (title in list(NA_character_, character(), c('a', 'b'), 1)) { + expect_error(do.call(plot_cv, c(args, list(main = title))), + 'main must be one nonmissing character string', fixed = TRUE) + } + for (extra in list(list(lambda_1_row_all = c(1, 2)), + list(lambda_1_col_all = c(1, 2)), + list(lambda_2_index = 1))) { + expect_error(do.call(plot_cv, c(args, extra)), + 'Separate-penalty arguments require a 3D score array', fixed = TRUE) + } +}) + +test_that('CV heatmaps explain how to install the optional plotting dependency', { + # Older supported testthat versions do not provide local_mocked_bindings(). + skip_if_not_installed('testthat', minimum_version = '3.2.0') + original_require <- base::requireNamespace + testthat::local_mocked_bindings( + requireNamespace = function(package, ...) { + if (identical(package, 'ggplot2')) return(FALSE) + original_require(package, ...) + }, .package = 'base') + expect_error(plot_cv(cv_plot_example(FALSE), lambda_1_all = c(10, 0), + lambda_2_all = c(0, 1)), + 'Install it with install.packages("ggplot2")', fixed = TRUE) +}) + +test_that('CV heatmaps explain when the plotting dependency needs updating', { + skip_if_not_installed('testthat', minimum_version = '3.2.0') + # Simulate availability/version checks without changing installed packages. + original_require <- base::requireNamespace + original_version <- utils::packageVersion + testthat::local_mocked_bindings( + requireNamespace = function(package, ...) { + if (identical(package, 'ggplot2')) return(TRUE) + original_require(package, ...) + }, .package = 'base') + testthat::local_mocked_bindings( + packageVersion = function(pkg, ...) { + if (identical(pkg, 'ggplot2')) return(package_version('3.3.6')) + original_version(pkg, ...) + }, .package = 'utils') + expect_error(plot_cv(cv_plot_example(FALSE), lambda_1_all = c(10, 0), + lambda_2_all = c(0, 1)), + 'requires ggplot2 >= 3.4.0. Update it', fixed = TRUE) +}) diff --git a/tests/testthat/test-paper-plots.R b/tests/testthat/test-paper-plots.R new file mode 100644 index 0000000..c4c6a53 --- /dev/null +++ b/tests/testthat/test-paper-plots.R @@ -0,0 +1,56 @@ +test_that('CV annotations distinguish small differences from exact ties and numeric boundaries', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path<-plot_device();on.exit({grDevices::dev.off();unlink(path)},add=TRUE) + x<-list(mse_all=matrix(c(1.06,1.051,1.07,1.06,1.052,1.07),3), + lambda_1_min=50,lambda_2_min=0.01) + d<-plot_cv(x,lambda_1_all=c(10,50,100),lambda_2_all=c(0.01,1)) + expect_equal(d$exact_minima,1) + expect_equal(d$score_range,c(1.051,1.07)) + expect_equal(d$lambda_2_slice_range,c(1.051,1.052)) + expect_identical(d$boundary,c(common=FALSE,balance=TRUE)) + x$mse_all[2,2]<-1.051 + expect_equal(plot_cv(x,lambda_1_all=c(10,50,100),lambda_2_all=c(0.01,1))$exact_minima,2) + x$mse_all<-matrix(c(3,1,2),3);x$lambda_1_min<-1;x$lambda_2_min<-1 + expect_identical(plot_cv(x,lambda_1_all=c(3,1,2),lambda_2_all=1)$boundary, + c(common=TRUE,balance=FALSE)) +}) + +test_that('space plots compute full-matrix spaces before taking common submatrices', { + path<-plot_device();on.exit({grDevices::dev.off();unlink(path)},add=TRUE) + # This rank-one matrix has analytically known normalized left/right vectors. + u<-c(1,2,3);v<-c(2,1);y<-outer(u,v) + set.seed(1);before<-.Random.seed + old<-graphics::par(c('mfrow','mar')) + d<-plot_source_spaces(y,list(A=y,B=2*y),r=1,row_index=c(3,1),column_index=2:1) + expect_equal(d$row_projectors[[1]],outer(u,u)[c(3,1),c(3,1)]/sum(u^2)) + expect_equal(d$column_projectors[[1]],outer(v,v)[2:1,2:1]/sum(v^2)) + expect_equal(d$row_projectors[[2]],d$row_projectors[[3]]) + expect_identical(.Random.seed,before) + expect_equal(graphics::par(c('mfrow','mar')),old) + expect_equal(d$limits,c(row=9/14,column=4/5)) + clipped<-plot_source_spaces(y,y,r=1,limits=c(.1,.1)) + expect_gt(sum(unlist(clipped$clipped_count)),0) + expect_equal(clipped$row_projectors[[1]],outer(u,u)/sum(u^2)) + expect_error(plot_source_spaces(y,y,r=2),'zero singular') + expect_error(plot_source_spaces(y,y,r=1,row_index=c(1,1)),'distinct') + expect_error(plot_source_spaces(y,y,r=1,limits=c(0,.1)),'positive') + y[1]<-NA + expect_error(plot_source_spaces(y,y,r=1),'complete finite') +}) + +test_that('contributions use squared normalized singular vectors, not raw factors', { + path<-plot_device();on.exit({grDevices::dev.off();unlink(path)},add=TRUE) + u<-c(1,2,3);v<-c(2,1);y<-outer(u,v) + d<-plot_contributions(y,r=1,row_index=c(3,1),column_index=2) + expect_equal(drop(d$row_scores),u^2/sum(u^2)) + expect_equal(drop(d$column_scores),v^2/sum(v^2)) + expect_equal(colSums(d$row_scores),1) + expect_equal(colSums(d$column_scores),1) + expect_equal(d$row_index,c(3L,1L)) + expect_equal(plot_contributions(-y,r=1)$row_scores,d$row_scores) + expect_equal(plot_contributions(list(learner_estimate=y,r=1))$column_scores,d$column_scores) + expect_equal(plot_contributions(list(dlearner_estimate=y,r=1))$row_scores,d$row_scores) + expect_error(plot_contributions(y),'Supply r') + expect_error(plot_contributions(y,r=2),'zero singular') + expect_error(plot_contributions(y,r=1,column_index=3),'valid integer') +}) diff --git a/tests/testthat/test-plotting.R b/tests/testthat/test-plotting.R new file mode 100644 index 0000000..7101c73 --- /dev/null +++ b/tests/testthat/test-plotting.R @@ -0,0 +1,204 @@ +test_that('objective plots show the requested iterations', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + # Decrease to the minimum at iteration 31, then values that stay above it. + fit <- list(objective_values = c(seq(100, 10, length.out = 30), rep(c(9, 9.5), 50)), + convergence_criterion = 2L) + original <- fit + result <- withVisible(plot_objective(fit)) + expect_false(result$visible) + expect_equal(result$value$iteration, 1:130) + expect_equal(result$value$objective, fit$objective_values) + expect_equal(plot_objective(fit, iterations = 2:4)$iteration, 2:4) + expect_equal(plot_objective(fit, iterations = c(5, 2, 2))$iteration, c(2, 5)) + # A 13-iteration window centered on the first minimum at iteration 31. + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 25:37) + expect_identical(fit, original) +}) + +test_that('near-optimum windows include both sides and zoom the horizontal axis', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + # Interior minimum followed by a long increase. + fit <- list(objective_values = (seq_len(101) - 51)^2) + plot_objective(fit) + full <- graphics::par('usr')[1:2] + result <- plot_objective(fit, iterations = 'near_optimum') + expect_equal(result$iteration, 46:56) + expect_gt(graphics::par('usr')[1], full[1]) + expect_lt(graphics::par('usr')[2], full[2]) + # At the beginning, shift the window right; exact ties use the first minimum. + fit$objective_values <- seq_len(100) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:10) + fit$objective_values <- rep(1, 100) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:10) +}) + +test_that('near-optimum plots show the final stretch when the minimum is at the end', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + # A history that is still decreasing at the last iteration. + fit <- list(objective_values = seq(100, 10, length.out = 100), convergence_criterion = 1L) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 91:100) + # Short histories are shown in full. + fit <- list(objective_values = c(9, 5, 4), convergence_criterion = 1L) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:3) +}) + +test_that('near-optimum plots handle alternating and missing values and keep graphics settings', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + # Alternating values after a decrease; near_optimum draws them as a band. + values <- c(seq(50, 11, length.out = 40), 10 + rep(c(0, 0.5), 200)) + values[100] <- NA + fit <- list(objective_values = values, convergence_criterion = 2L) + old <- graphics::par('mar') + result <- plot_objective(fit) + expect_equal(result$iteration, seq_along(values)) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 20:63) + expect_equal(graphics::par('mar'), old) +}) + +test_that('objective plots handle flat, singleton, and non-finite histories', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + for (values in list(4, c(4, 4, 4), c(4, Inf, NaN), c(Inf, 3, 1, NA))) { + fit <- list(objective_values = values, convergence_criterion = 2L) + expect_equal(plot_objective(fit)$objective, values) + expect_true(length(plot_objective(fit, iterations = 'near_optimum')$iteration) >= 1L) + } + fit <- list(objective_values = c(2, 1, 3), convergence_criterion = 2L) + for (bad in list(0, 4, 1.5, NA, numeric(), 'near', TRUE)) { + expect_error(plot_objective(fit, iterations = bad), 'iterations') + } + expect_error(plot_objective(list(objective_values = c(Inf, NA))), 'learner') + expect_error(plot_objective(list()), 'learner') +}) + +test_that('CV plots retain candidate order, mark the global minimum, and scale SSE', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- cv_plot_example() + original <- x + old <- graphics::par(c('mfrow','mar')) + args <- list(x = x, lambda_1_row_all = c(10, 0), + lambda_1_col_all = c(0.2, 3), lambda_2_all = c(0, 1)) + sse <- do.call(plot_cv, args) + mse <- do.call(plot_cv, c(args, list(n_observed = 40))) + expect_identical(sse$grids$row, c(10, 0)) + expect_identical(sse$best_index, c(2L, 2L, 2L)) + expect_equal(sse$scores, x$mse_all) + expect_equal(mse$scores, x$mse_all / 40) + expect_equal(sse$scale, 'Validation SSE') + expect_equal(mse$scale, 'Validation MSE') + expect_equal(mse$color_limits, range(x$mse_all) / 40) + expect_identical(x, original) + expect_equal(graphics::par(c('mfrow','mar')), old) + subset <- do.call(plot_cv, c(args, list(lambda_2_index = 1))) + expect_equal(subset$color_limits, sse$color_limits) + expect_equal(subset$shown_lambda_2_index, 1) + expect_equal(subset$best_index, sse$best_index) + common <- plot_cv(cv_plot_example(FALSE), lambda_1_all = c(10, 0), lambda_2_all = c(0, 1)) + expect_identical(common$best_index, c(2L, 2L)) +}) + +test_that('CV plots handle singleton axes, duplicate candidates, ties and unavailable scores', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- list(mse_all = array(c(5, 5, Inf, NA), c(1, 2, 2)), + lambda_1_row_min = 0, lambda_1_col_min = 2, lambda_2_min = 1) + z <- plot_cv(x, lambda_1_row_all = 0, lambda_1_col_all = c(2, 2), + lambda_2_all = c(1, 10)) + expect_identical(z$best_index, c(1L, 1L, 1L)) + expect_equal(z$unavailable_count, 2) + expect_gt(diff(z$color_limits), 0) + x <- list(mse_all = matrix(0, 1, 1), lambda_1_min = 0, lambda_2_min = 0) + z <- plot_cv(x, lambda_1_all = 0, lambda_2_all = 0) + expect_identical(z$best_index, c(1L, 1L)) + expect_gt(diff(z$color_limits), 0) + # Seven balance slices exercise pagination without inventing new candidates. + x <- list(mse_all = array(7:1, c(1, 1, 7)), lambda_1_row_min = 0, + lambda_1_col_min = 2, lambda_2_min = 7) + z <- plot_cv(x, lambda_1_row_all = 0, lambda_1_col_all = 2, lambda_2_all = 1:7) + expect_equal(z$shown_lambda_2_index, 1:7) + expect_identical(z$best_index, c(1L, 1L, 7L)) +}) + +test_that('CV plots reject missing, mismatched, and ambiguous grid information', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + x <- cv_plot_example() + args <- list(x = x, lambda_1_row_all = c(10, 0), + lambda_1_col_all = c(0.2, 3), lambda_2_all = c(0, 1)) + expect_error(plot_cv(x), 'lambda_1_row_all') + bad <- args; bad$lambda_1_row_all <- c(0,10) + expect_error(do.call(plot_cv,bad), 'selected penalties') + bad <- args; bad$lambda_1_row_all <- 0 + expect_error(do.call(plot_cv,bad), 'score-array dimension') + expect_error(do.call(plot_cv,c(args,list(lambda_1_all=1))), 'Do not supply') + expect_error(do.call(plot_cv,c(args,list(n_observed=0))), 'n_observed') + expect_error(do.call(plot_cv,c(args,list(lambda_2_index=c(1,1)))), 'distinct') + expect_error(do.call(plot_cv,c(args,list(lambda_2_index=3))), 'distinct') + expect_error(do.call(plot_cv,c(args,list(lambda_2_index=NA_real_))), 'distinct') + x$mse_all[] <- Inf + expect_error(plot_cv(x), 'No finite') + x$mse_all[] <- -1 + expect_error(plot_cv(x), 'cannot be negative') + expect_error(plot_cv(list(mse_all=1:3)), 'two- or three-dimensional') +}) + +test_that('plot functions accept actual fits and both CV modes without changing outputs', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + set.seed(623) + s <- matrix(rnorm(48), 8, 6) + t <- matrix(rnorm(48), 8, 6); t[c(1,3)] <- NA_real_ + fit <- learner(s,t,r=2,lambda_1=1,lambda_2=1,step_size=0.001, + control=list(max_iter=6)) + expect_equal(plot_objective(fit)$objective,fit$objective_values) + for (separate in c(FALSE,TRUE)) { + args <- list(Y_source=list(s,s*0.9),Y_target=t,r=2,step_size=0.001, + lambda_2_all=c(0,1),n_folds=2,control=list(max_iter=6)) + grid <- if(separate) list(lambda_1_row_all=c(0,1),lambda_1_col_all=c(0.5,2)) else list(lambda_1_all=c(0,1)) + cv <- do.call(cv.learner,c(args,grid)) + original <- cv + result <- do.call(plot_cv,c(list(x=cv,lambda_2_all=c(0,1),n_observed=sum(!is.na(t))),grid)) + expect_equal(result$scores,cv$mse_all/46) + expect_identical(cv,original) + } +}) + + +test_that('paper-style log labels preserve scores and candidate selection', { + testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + x <- list(mse_all = matrix(c(4, 3, 2, 1), 2, 2), + lambda_1_min = 100, lambda_2_min = 1) + raw <- plot_cv(x, lambda_1_all = c(10, 100), lambda_2_all = c(0.01, 1)) + log <- plot_cv(x, lambda_1_all = c(10, 100), lambda_2_all = c(0.01, 1), log_labels = TRUE) + expect_equal(log$scores, raw$scores) + expect_equal(log$best_index, raw$best_index) + expect_equal(log$grids, raw$grids) + expect_true(log$log_labels) + expect_error(plot_cv(x, lambda_1_all = c(0, 100), lambda_2_all = c(0.01, 1), + log_labels = TRUE), 'strictly positive') + expect_error(plot_cv(x, log_labels = NA), 'TRUE or FALSE') + expect_error(plot_cv(x, log_labels = 'yes'), 'TRUE or FALSE') +}) + +test_that('objective plots reject ranges with no finite values before drawing', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + fit <- list(objective_values = c(3, 1, NA_real_, Inf, NaN)) + old <- graphics::par(c('mar', 'usr')) + expect_error( + withCallingHandlers(plot_objective(fit, iterations = 3:5), + warning = function(w) stop('Unexpected warning: ', conditionMessage(w))), + 'selected iterations contain no finite objective values') + expect_equal(graphics::par(c('mar', 'usr')), old) + expect_equal(plot_objective(fit, iterations = 2:5)$objective, + fit$objective_values[2:5]) +}) From 3e076b6dc7089212ba526f08768e08882c9c1ca2 Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Fri, 16 Oct 2026 00:06:38 -0500 Subject: [PATCH 2/7] Fix objective pair endpoints and clarify plotting usage --- R/plotting.R | 73 +++++++++++++----- README.Rmd | 7 +- README.md | 7 +- README_files/figure-gfm/cv-heatmap-1.png | Bin 35276 -> 53310 bytes .../figure-gfm/objective-history-1.png | Bin 34864 -> 50924 bytes inst/examples/plot-diagnostics.R | 1 - inst/examples/plot-paper-displays.R | 1 - inst/examples/plotting-guide.md | 7 +- man/plot_cv.Rd | 5 +- man/plot_objective.Rd | 7 +- tests/testthat/test-plotting.R | 24 ++++++ 11 files changed, 96 insertions(+), 36 deletions(-) diff --git a/R/plotting.R b/R/plotting.R index 2c3d185..29fda28 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -6,7 +6,9 @@ #' @param x A result returned by \code{learner}. #' @param iterations Iterations to show: \code{"all"} (the default), #' \code{"near_optimum"}, or a vector of iteration numbers such as -#' \code{1:1000}. \code{"near_optimum"} shows a window centered on the first +#' \code{1:1000}. Positive integer indices beyond the recorded history are +#' ignored; at least one requested iteration must exist. +#' \code{"near_optimum"} shows a window centered on the first #' smallest finite recorded objective value. The window contains 10\% of the #' recorded iterations (rounded up), with a minimum of 10, or all iterations #' for shorter histories. At either boundary the window shifts to stay within @@ -22,7 +24,8 @@ #' included. With \code{iterations = "near_optimum"}, the plot shows a shaded #' band spanning each pair of consecutive objective values, so that values #' that alternate between iterations remain visible, and a line at their -#' average. +#' average. Each pair is placed halfway between its two iteration numbers. +#' Only actual adjacent pairs are used; missing values interrupt the display. #' #' @examples #' fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, @@ -49,12 +52,15 @@ plot_objective <- function(x, iterations = "all", main = NULL) { width <- min(n, max(10L, ceiling(0.1 * n))) start <- max(1L, min(minimum - floor((width - 1L) / 2), n - width + 1L)) shown <- seq.int(start, length.out = width) - } else if (is.numeric(iterations) && length(iterations) > 0L && - all(iterations %in% seq_len(n))) { - shown <- sort(unique(as.integer(iterations))) + } else if (is.numeric(iterations) && !is.complex(iterations) && + length(iterations) > 0L && all(is.finite(iterations)) && + all(iterations >= 1 & iterations == floor(iterations))) { + shown <- sort(unique(as.integer(iterations[iterations <= n]))) + if (length(shown) == 0L) { + stop('None of the requested iterations were recorded; the history contains ', n, ' iterations.') + } } else { - stop("iterations must be 'all', 'near_optimum', or iteration numbers between 1 and ", - n, '.') + stop("iterations must be 'all', 'near_optimum', or positive integer iteration numbers.") } if (!any(finite[shown])) { stop('The selected iterations contain no finite objective values; choose a different range.') @@ -63,6 +69,8 @@ plot_objective <- function(x, iterations = "all", main = NULL) { main <- if (near) 'Objective function (near optimum)' else 'Objective function' } values <- replace(objective[shown], !finite[shown], NA_real_) + pairs <- if (near) objective_pairs(shown, values) else NULL + draw_pairs <- near && any(is.finite(pairs$average)) old <- graphics::par(mar = c(5, 4.5, 4, 1.5)) on.exit(graphics::par(old), add = TRUE) @@ -73,15 +81,23 @@ plot_objective <- function(x, iterations = "all", main = NULL) { graphics::mtext(paste0('Minimum: ', format(objective[minimum], digits = 6), ' at iteration ', minimum), side = 3, line = 0.6, cex = 0.8, col = 'gray30') - if (near && length(shown) > 1L) { - # The band spans each pair of consecutive values, so alternating values - # appear as a band rather than as dense vertical lines. - nxt <- c(values[-1], values[length(values)]) # the last value pairs with itself - keep <- !is.na(values) & !is.na(nxt) - graphics::polygon(c(shown[keep], rev(shown[keep])), - c(pmin(values, nxt)[keep], rev(pmax(values, nxt)[keep])), - col = grDevices::adjustcolor('#2166AC', 0.4), border = NA) - graphics::lines(shown[keep], ((values + nxt) / 2)[keep], col = '#2166AC', lwd = 1.5) + if (draw_pairs) { + # Separate runs prevent the band from bridging missing iterations. + keep <- which(is.finite(pairs$average)) + runs <- split(keep, cumsum(c(TRUE, diff(keep) > 1L))) + for (indices in runs) { + part <- pairs[indices, , drop = FALSE] + if (nrow(part) == 1L) { + graphics::segments(part$iteration, part$lower, part$iteration, part$upper, + col = grDevices::adjustcolor('#2166AC', 0.4), lwd = 4) + } else { + graphics::polygon(c(part$iteration, rev(part$iteration)), + c(part$lower, rev(part$upper)), + col = grDevices::adjustcolor('#2166AC', 0.4), border = NA) + } + graphics::lines(part$iteration, part$average, col = '#2166AC', lwd = 1.5, + type = if (nrow(part) == 1L) 'p' else 'l') + } } else { graphics::lines(shown, values, col = '#2166AC', type = if (length(shown) == 1L) 'p' else 'l') } @@ -90,11 +106,11 @@ plot_objective <- function(x, iterations = "all", main = NULL) { } # Symbol keys distinguish the band, mean line, and minimum without colored text. band_color <- grDevices::adjustcolor('#2166AC', 0.4) - labels <- if (near && length(shown) > 1L) + labels <- if (draw_pairs) c('Consecutive-iteration range', 'Pairwise average') else 'Objective value' - colors <- if (near && length(shown) > 1L) c(band_color, '#2166AC') else '#2166AC' - symbols <- if (near && length(shown) > 1L) c(22, NA) else NA - line_types <- if (near && length(shown) > 1L) c(0, 1) else 1 + colors <- if (draw_pairs) c(band_color, '#2166AC') else '#2166AC' + symbols <- if (draw_pairs) c(22, NA) else NA + line_types <- if (draw_pairs) c(0, 1) else 1 if (minimum %in% shown) { labels <- c(labels, 'Minimum recorded objective') colors <- c(colors, '#B2182B') @@ -108,6 +124,18 @@ plot_objective <- function(x, iterations = "all", main = NULL) { invisible(data.frame(iteration = shown, objective = objective[shown])) } +# Summarize actual adjacent records, retaining gaps for non-finite pairs. +objective_pairs <- function(iteration, values) { + left <- seq_len(max(0L, length(values) - 1L)) + right <- left + 1L + valid <- is.finite(values[left]) & is.finite(values[right]) & + iteration[right] == iteration[left] + 1 + data.frame(iteration = iteration[left] / 2 + iteration[right] / 2, + lower = ifelse(valid, pmin(values[left], values[right]), NA_real_), + upper = ifelse(valid, pmax(values[left], values[right]), NA_real_), + average = ifelse(valid, values[left] / 2 + values[right] / 2, NA_real_)) +} + #' Plot the LEARNER cross-validation scores #' #' Draw a common-penalty heatmap or separate-penalty heatmaps, with a shared @@ -163,7 +191,10 @@ plot_objective <- function(x, iterations = "all", main = NULL) { #' penalty horizontally and the V-space penalty vertically, with one panel for #' each balance penalty. All panels share limits computed from the full score #' array, even when only some panels are selected. Up to six panels are drawn -#' per page. Gray cells have non-finite scores and are excluded from selection. +#' per page. Gray cells represent either non-finite scores (labeled NA) or +#' finite scores above \code{relative_limit} (labeled with the cutoff). +#' Non-finite scores are excluded from selection; the display cutoff does not +#' affect parameter selection. Labels are omitted when \code{show_values = FALSE}. #' Only the first minimum is marked; a panel need not contain a black box. #' Captions report the score range, exact ties, and boundary selections. These are #' descriptive diagnostics, not significance or convergence tests. diff --git a/README.Rmd b/README.Rmd index a145bf8..8359de6 100644 --- a/README.Rmd +++ b/README.Rmd @@ -33,12 +33,11 @@ Install the released version from CRAN: install.packages("learner") ``` -The examples below use the multiple-source and plotting extensions on the -`plot-diagnostics` development branch: +Install the latest version from GitHub: ```r -# install.packages("remotes") -remotes::install_github("stmcg/learner", ref = "plot-diagnostics") +# install.packages("devtools") +devtools::install_github("stmcg/learner") # Optional: required for plot_cv(), but not for fitting or the other plots. install.packages("ggplot2") # version 3.4.0 or later ``` diff --git a/README.md b/README.md index 3258f07..adc10f1 100644 --- a/README.md +++ b/README.md @@ -25,12 +25,11 @@ Install the released version from CRAN: install.packages("learner") ``` -The examples below use the multiple-source and plotting extensions on -the `plot-diagnostics` development branch: +Install the latest version from GitHub: ``` r -# install.packages("remotes") -remotes::install_github("stmcg/learner", ref = "plot-diagnostics") +# install.packages("devtools") +devtools::install_github("stmcg/learner") # Optional: required for plot_cv(), but not for fitting or the other plots. install.packages("ggplot2") # version 3.4.0 or later ``` diff --git a/README_files/figure-gfm/cv-heatmap-1.png b/README_files/figure-gfm/cv-heatmap-1.png index 13c0365454bc180b6c33ab3e5a9fb7a36a97bc41..3721f9a8e2e52b230f4bfa770f353bff2c837575 100644 GIT binary patch literal 53310 zcmeFZRZtyU+bz7%V8Mf1aCdhL?m>b(1b26rKyV8lEVu`E2pU|1yOV{x{muT~cki95 z|K{ABbMsX-&@~sUXZMsx#u!gWC@Dz3M!-V=fk3aNrNmW0ASmDz5)}>>_`^v8p9=y( zBU^}xDOrh0irHD)Ili|yG&YqqwKa9LFjkQi1%bGuqt$dj5~yJcrZ?5nk&gyV7Z%1N zKXD}1S*uJ0FYG;;*4ix5Cs@~s#10-UXIfCQZ9e2%&?xK2YS^E1E z!`C{cuh2o%H{*Abxs%xNhfQp?EeVZQZNp2qI~m8myDLuM6~x#^dhO&D3P^gq3Wn{e zjoSX5-dP?CfQUk>?I#yv#3 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install.packages("ggplot2")') } -# Run after installing the plotting development version of learner. # Synthetic data demonstrate the interface, not statistical performance. # Use a small aligned subset to keep this demonstration quick. sources <- lapply(learner::dat_multisource$Y_source, diff --git a/inst/examples/plot-paper-displays.R b/inst/examples/plot-paper-displays.R index 00539df..59b9b9d 100644 --- a/inst/examples/plot-paper-displays.R +++ b/inst/examples/plot-paper-displays.R @@ -1,5 +1,4 @@ # Population spaces and contributions using simulated multiple-source data. -# Run after loading the plotting development version of learner. space_target <- learner::dat_multisource$Y_target space_sources <- learner::dat_multisource$Y_source learner::plot_source_spaces(space_target, space_sources, r=3, diff --git a/inst/examples/plotting-guide.md b/inst/examples/plotting-guide.md index 1e80e7c..3cfd3e7 100644 --- a/inst/examples/plotting-guide.md +++ b/inst/examples/plotting-guide.md @@ -15,8 +15,11 @@ plot_objective(fit, iterations = 1:500) # A specific range objective value. It shows 10% of the recorded iterations (rounded up), at least 10, or the full history if fewer than 10 were recorded. At either end the window shifts to stay within the history. Both axes zoom to the selected window. -The shaded band spans consecutive objective values and the line shows their -pairwise average; the band is not an uncertainty interval. +The shaded band spans actual adjacent objective values and the line shows their +pairwise average, positioned halfway between the two iterations. Missing values +interrupt the band and line; the band is not an uncertainty interval. +Numeric selections such as `1:1000` keep only recorded iterations. A selection +with no recorded iterations produces an error. ## CV heatmaps diff --git a/man/plot_cv.Rd b/man/plot_cv.Rd index f665d1b..833cf76 100644 --- a/man/plot_cv.Rd +++ b/man/plot_cv.Rd @@ -81,7 +81,10 @@ Common mode shows \code{lambda_1_all} on the horizontal axis and penalty horizontally and the V-space penalty vertically, with one panel for each balance penalty. All panels share limits computed from the full score array, even when only some panels are selected. Up to six panels are drawn -per page. Gray cells have non-finite scores and are excluded from selection. +per page. Gray cells represent either non-finite scores (labeled NA) or +finite scores above \code{relative_limit} (labeled with the cutoff). +Non-finite scores are excluded from selection; the display cutoff does not +affect parameter selection. Labels are omitted when \code{show_values = FALSE}. Only the first minimum is marked; a panel need not contain a black box. Captions report the score range, exact ties, and boundary selections. These are descriptive diagnostics, not significance or convergence tests. diff --git a/man/plot_objective.Rd b/man/plot_objective.Rd index 7b7ce87..5458706 100644 --- a/man/plot_objective.Rd +++ b/man/plot_objective.Rd @@ -11,7 +11,9 @@ plot_objective(x, iterations = "all", main = NULL) \item{iterations}{Iterations to show: \code{"all"} (the default), \code{"near_optimum"}, or a vector of iteration numbers such as -\code{1:1000}. \code{"near_optimum"} shows a window centered on the first +\code{1:1000}. Positive integer indices beyond the recorded history are +ignored; at least one requested iteration must exist. +\code{"near_optimum"} shows a window centered on the first smallest finite recorded objective value. The window contains 10\% of the recorded iterations (rounded up), with a minimum of 10, or all iterations for shorter histories. At either boundary the window shifts to stay within @@ -34,7 +36,8 @@ given above the plot. The objective value at the initialization is not included. With \code{iterations = "near_optimum"}, the plot shows a shaded band spanning each pair of consecutive objective values, so that values that alternate between iterations remain visible, and a line at their -average. +average. Each pair is placed halfway between its two iteration numbers. +Only actual adjacent pairs are used; missing values interrupt the display. } \examples{ fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, diff --git a/tests/testthat/test-plotting.R b/tests/testthat/test-plotting.R index 7101c73..ee36967 100644 --- a/tests/testthat/test-plotting.R +++ b/tests/testthat/test-plotting.R @@ -11,6 +11,8 @@ test_that('objective plots show the requested iterations', { expect_equal(result$value$objective, fit$objective_values) expect_equal(plot_objective(fit, iterations = 2:4)$iteration, 2:4) expect_equal(plot_objective(fit, iterations = c(5, 2, 2))$iteration, c(2, 5)) + expect_equal(plot_objective(fit, iterations = 1:1000)$iteration, 1:130) + expect_equal(plot_objective(fit, iterations = c(1000, 2, 1))$iteration, 1:2) # A 13-iteration window centered on the first minimum at iteration 31. expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 25:37) expect_identical(fit, original) @@ -75,6 +77,28 @@ test_that('objective plots handle flat, singleton, and non-finite histories', { expect_error(plot_objective(list()), 'learner') }) +test_that('adjacent-pair summaries do not invent a final update or bridge gaps', { + values <- rep(c(10, 12), 5) + pairs <- objective_pairs(seq_along(values), values) + expect_equal(nrow(pairs), 9L) + expect_equal(pairs$iteration, seq(1.5, 9.5)) + expect_equal(pairs$average, rep(11, 9)) + expect_equal(pairs$lower, rep(10, 9)) + expect_equal(pairs$upper, rep(12, 9)) + gaps <- objective_pairs(1:5, c(3, NA, 1, 2, 4)) + expect_equal(gaps$average, c(NA, NA, 1.5, 3)) + skipped <- objective_pairs(c(1, 2, 4, 5), c(10, 12, 10, 12)) + expect_equal(skipped$average, c(11, NA, 11)) + expect_equal(nrow(objective_pairs(1, 4)), 0L) + + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + for (history in list(values, c(1, 3), c(1, 3, NA, 2, 4), c(1, NA, 3))) { + expect_equal(plot_objective(list(objective_values = history), + iterations = 'near_optimum')$objective, history) + } +}) + test_that('CV plots retain candidate order, mark the global minimum, and scale SSE', { testthat::skip_if_not_installed("ggplot2", minimum_version = "3.4.0") path <- plot_device() From e743ffad2de15cd39003722efbcf88fe4be72dbf Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Fri, 16 Oct 2026 12:31:01 -0500 Subject: [PATCH 3/7] Place objective-plot window around the minimum and keep the legend off the data --- R/plotting.R | 45 +++++++++---- .../figure-gfm/objective-history-1.png | Bin 50924 -> 49856 bytes inst/examples/plotting-guide.md | 9 +-- man/plot_objective.Rd | 11 ++-- tests/testthat/test-plotting.R | 62 +++++++++++++----- 5 files changed, 92 insertions(+), 35 deletions(-) diff --git a/R/plotting.R b/R/plotting.R index 29fda28..6dfb186 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -1,3 +1,17 @@ +# Corner of the current plot where the legend (drawn with 'args') would cover +# the fewest data points. Ties go to the first corner listed. +legend_corner <- function(x, y, args) { + keep <- is.finite(x) & is.finite(y) + x <- x[keep] + y <- y[keep] + corners <- c('topright', 'topleft', 'bottomright', 'bottomleft') + count <- vapply(corners, function(corner) { + box <- do.call(graphics::legend, c(list(corner, plot = FALSE), args))$rect + sum(x >= box$left & x <= box$left + box$w & y <= box$top & y >= box$top - box$h) + }, numeric(1)) + corners[which.min(count)] +} + #' Plot the objective function values of a LEARNER fit #' #' Plots the values of the objective function at each iteration of the @@ -8,11 +22,11 @@ #' \code{"near_optimum"}, or a vector of iteration numbers such as #' \code{1:1000}. Positive integer indices beyond the recorded history are #' ignored; at least one requested iteration must exist. -#' \code{"near_optimum"} shows a window centered on the first -#' smallest finite recorded objective value. The window contains 10\% of the -#' recorded iterations (rounded up), with a minimum of 10, or all iterations -#' for shorter histories. At either boundary the window shifts to stay within -#' the recorded history. Both axes adjust to the selected window. +#' \code{"near_optimum"} shows up to 41 consecutive records centered on the +#' first smallest finite recorded objective value (20 before and 20 after). +#' At either boundary the window shifts to stay within the recorded history; +#' histories shorter than 41 records are shown in full. Both axes adjust to +#' the selected window. Use numeric iteration indices for a different width. #' @param main Title of the plot. By default, \code{"Objective function"}, #' with \code{"(near optimum)"} added when \code{iterations = "near_optimum"}. #' @@ -26,6 +40,7 @@ #' that alternate between iterations remain visible, and a line at their #' average. Each pair is placed halfway between its two iteration numbers. #' Only actual adjacent pairs are used; missing values interrupt the display. +#' The legend is placed in the corner of the plot with the fewest data points. #' #' @examples #' fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, @@ -49,8 +64,8 @@ plot_objective <- function(x, iterations = "all", main = NULL) { if (identical(iterations, 'all')) { shown <- seq_len(n) } else if (near) { - width <- min(n, max(10L, ceiling(0.1 * n))) - start <- max(1L, min(minimum - floor((width - 1L) / 2), n - width + 1L)) + width <- min(n, 41L) + start <- max(1L, min(minimum - 20L, n - width + 1L)) shown <- seq.int(start, length.out = width) } else if (is.numeric(iterations) && !is.complex(iterations) && length(iterations) > 0L && all(is.finite(iterations)) && @@ -117,10 +132,18 @@ plot_objective <- function(x, iterations = "all", main = NULL) { symbols <- c(symbols, 16) line_types <- c(line_types, 0) } - graphics::legend('topright', inset = 0.02, legend = labels, - col = colors, text.col = 'gray30', pch = symbols, - pt.bg = band_color, pt.cex = 1.1, lty = line_types, - lwd = 1.5, cex = 0.8, y.intersp = 1.3, bty = 'n') + x_data <- shown + y_data <- values + if (draw_pairs) { + x_data <- c(x_data, pairs$iteration, pairs$iteration) + y_data <- c(y_data, pairs$lower, pairs$upper) + } + legend_args <- list(inset = 0.02, legend = labels, col = colors, + text.col = 'gray30', pch = symbols, pt.bg = band_color, + pt.cex = 1.1, lty = line_types, lwd = 1.5, cex = 0.8, + y.intersp = 1.3, bty = 'n') + do.call(graphics::legend, + c(list(legend_corner(x_data, y_data, legend_args)), legend_args)) invisible(data.frame(iteration = shown, objective = objective[shown])) } diff --git a/README_files/figure-gfm/objective-history-1.png b/README_files/figure-gfm/objective-history-1.png index 1475e93f1487227d6b7481eee0ea4d7aca71667b..214a3812d9466c286284f2a695651e5fafb728c1 100644 GIT binary patch literal 49856 zcmdSBg$c8@4(sH3R~C 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zq2kPn5*Eu_r1%f``&a`YVJp$`{{rhN5G`cYu_*@s1N*+qrbMV1TE&@=ZnG> zRz_j4F7jby{|Q;JuaSXfeec}ig7D{imIRzy3WKie!Q=n?DF55rh8lPulOzA1L;Pp5 l{C(CoQNZ^3KYEMzS!^RUITi+!=uz;cs;H$KLGv1Z~g!P diff --git a/inst/examples/plotting-guide.md b/inst/examples/plotting-guide.md index 3cfd3e7..9db1879 100644 --- a/inst/examples/plotting-guide.md +++ b/inst/examples/plotting-guide.md @@ -11,10 +11,11 @@ plot_objective(fit, iterations = "near_optimum") # Around the smallest recorde plot_objective(fit, iterations = 1:500) # A specific range ``` -`"near_optimum"` centers a window on the first smallest finite recorded -objective value. It shows 10% of the recorded iterations (rounded up), at least -10, or the full history if fewer than 10 were recorded. At either end the window -shifts to stay within the history. Both axes zoom to the selected window. +`"near_optimum"` shows up to 41 consecutive records centered on the first +smallest finite recorded objective value: 20 before and 20 after. At either +boundary the window shifts to stay within the history; histories shorter than +41 records are shown in full. Both axes adjust to this window. To choose a +different width, supply numeric iteration indices, such as `79:119`. The shaded band spans actual adjacent objective values and the line shows their pairwise average, positioned halfway between the two iterations. Missing values interrupt the band and line; the band is not an uncertainty interval. diff --git a/man/plot_objective.Rd b/man/plot_objective.Rd index 5458706..b0fb5d5 100644 --- a/man/plot_objective.Rd +++ b/man/plot_objective.Rd @@ -13,11 +13,11 @@ plot_objective(x, iterations = "all", main = NULL) \code{"near_optimum"}, or a vector of iteration numbers such as \code{1:1000}. Positive integer indices beyond the recorded history are ignored; at least one requested iteration must exist. -\code{"near_optimum"} shows a window centered on the first -smallest finite recorded objective value. The window contains 10\% of the -recorded iterations (rounded up), with a minimum of 10, or all iterations -for shorter histories. At either boundary the window shifts to stay within -the recorded history. Both axes adjust to the selected window.} +\code{"near_optimum"} shows up to 41 consecutive records centered on the +first smallest finite recorded objective value (20 before and 20 after). +At either boundary the window shifts to stay within the recorded history; +histories shorter than 41 records are shown in full. Both axes adjust to +the selected window. Use numeric iteration indices for a different width.} \item{main}{Title of the plot. By default, \code{"Objective function"}, with \code{"(near optimum)"} added when \code{iterations = "near_optimum"}.} @@ -38,6 +38,7 @@ band spanning each pair of consecutive objective values, so that values that alternate between iterations remain visible, and a line at their average. Each pair is placed halfway between its two iteration numbers. Only actual adjacent pairs are used; missing values interrupt the display. +The legend is placed in the corner of the plot with the fewest data points. } \examples{ fit <- learner(dat_highsim$Y_source, dat_highsim$Y_target, diff --git a/tests/testthat/test-plotting.R b/tests/testthat/test-plotting.R index ee36967..e3e6bfc 100644 --- a/tests/testthat/test-plotting.R +++ b/tests/testthat/test-plotting.R @@ -13,27 +13,36 @@ test_that('objective plots show the requested iterations', { expect_equal(plot_objective(fit, iterations = c(5, 2, 2))$iteration, c(2, 5)) expect_equal(plot_objective(fit, iterations = 1:1000)$iteration, 1:130) expect_equal(plot_objective(fit, iterations = c(1000, 2, 1))$iteration, 1:2) - # A 13-iteration window centered on the first minimum at iteration 31. - expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 25:37) + # Twenty records on each side of the first minimum (iteration 31). + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 11:51) expect_identical(fit, original) }) -test_that('near-optimum windows include both sides and zoom the horizontal axis', { +test_that('near-optimum windows center on the minimum and zoom both axes', { path <- plot_device() on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) - # Interior minimum followed by a long increase. - fit <- list(objective_values = (seq_len(101) - 51)^2) + # Steep decrease to an interior minimum, then a slow rise. + fit <- list(objective_values = 10 + 100 * exp(-0.05 * (1:2000)) + 0.0005 * (1:2000)) + minimum <- which.min(fit$objective_values) plot_objective(fit) - full <- graphics::par('usr')[1:2] + full_x <- graphics::par('usr')[1:2] + full_y <- graphics::par('usr')[3:4] result <- plot_objective(fit, iterations = 'near_optimum') - expect_equal(result$iteration, 46:56) - expect_gt(graphics::par('usr')[1], full[1]) - expect_lt(graphics::par('usr')[2], full[2]) - # At the beginning, shift the window right; exact ties use the first minimum. + expect_equal(result$iteration, (minimum - 20L):(minimum + 20L)) + expect_lt(diff(graphics::par('usr')[1:2]), 0.05 * diff(full_x)) + expect_lt(diff(graphics::par('usr')[3:4]), 0.01 * diff(full_y)) + # Appending worse records does not change the neighborhood of the minimum. + extended <- list(objective_values = c(fit$objective_values, rep(200, 3000))) + expect_equal(plot_objective(extended, iterations = 'near_optimum')$iteration, + result$iteration) + # Reproduce the early minimum in a 2000-record history. + early <- list(objective_values = (seq_len(2000) - 99)^2) + expect_equal(plot_objective(early, iterations = 'near_optimum')$iteration, 79:119) + # At the start, shift the window right; ties use the first minimum. fit$objective_values <- seq_len(100) - expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:10) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:41) fit$objective_values <- rep(1, 100) - expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:10) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:41) }) test_that('near-optimum plots show the final stretch when the minimum is at the end', { @@ -41,7 +50,11 @@ test_that('near-optimum plots show the final stretch when the minimum is at the on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) # A history that is still decreasing at the last iteration. fit <- list(objective_values = seq(100, 10, length.out = 100), convergence_criterion = 1L) - expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 91:100) + plot_objective(fit) + full_x <- graphics::par('usr')[1:2] + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 60:100) + # The final stretch has its own horizontal axis. + expect_gt(graphics::par('usr')[1], full_x[1] + 50) # Short histories are shown in full. fit <- list(objective_values = c(9, 5, 4), convergence_criterion = 1L) expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 1:3) @@ -52,15 +65,34 @@ test_that('near-optimum plots handle alternating and missing values and keep gra on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) # Alternating values after a decrease; near_optimum draws them as a band. values <- c(seq(50, 11, length.out = 40), 10 + rep(c(0, 0.5), 200)) - values[100] <- NA + values[50] <- NA fit <- list(objective_values = values, convergence_criterion = 2L) old <- graphics::par('mar') result <- plot_objective(fit) expect_equal(result$iteration, seq_along(values)) - expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 20:63) + expect_equal(plot_objective(fit, iterations = 'near_optimum')$iteration, 21:61) expect_equal(graphics::par('mar'), old) }) +test_that('the objective legend is placed in an empty corner', { + path <- plot_device() + on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) + args <- list(legend = c('a long legend label', 'second label', 'third label'), + lty = 1, cex = 0.8, y.intersp = 1.3, bty = 'n', inset = 0.02) + x <- 1:100 + # A falling curve leaves the top right empty; a rising curve leaves the top left empty. + graphics::plot(x, rev(x), type = 'n') + expect_equal(legend_corner(x, rev(x), args), 'topright') + graphics::plot(x, x, type = 'n') + expect_equal(legend_corner(x, x, args), 'topleft') + # Non-finite values are ignored. + expect_equal(legend_corner(c(x, NA, 50), c(x, 5, Inf), args), 'topleft') + # Steep decrease followed by a flat stretch (typical objective history). + y <- c(seq(100, 10, length.out = 20), rep(10, 80)) + graphics::plot(x, y, type = 'n') + expect_equal(legend_corner(x, y, args), 'topright') +}) + test_that('objective plots handle flat, singleton, and non-finite histories', { path <- plot_device() on.exit({grDevices::dev.off(); unlink(path)}, add = TRUE) From 65553ccdf6c061956f1ebcd376110bf655f74374 Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Fri, 16 Oct 2026 12:38:04 -0500 Subject: [PATCH 4/7] Show the objective plot for the single-source example in the README --- README.Rmd | 18 +++++---- README.md | 37 +++++++++--------- .../figure-gfm/objective-history-1.png | Bin 49856 -> 44286 bytes 3 files changed, 29 insertions(+), 26 deletions(-) diff --git a/README.Rmd b/README.Rmd index 8359de6..89bd9af 100644 --- a/README.Rmd +++ b/README.Rmd @@ -59,6 +59,15 @@ fit_single <- learner( dim(fit_single$learner_estimate) ``` +`plot_objective()` shows the recorded objective values. With +`iterations = "near_optimum"` it zooms in on the iterations around the smallest +value; the default `iterations = "all"` shows the full history, and a vector +such as `1:500` shows a specific range. + +```{r objective-history, fig.cap="Objective function values near the optimum for the single-source fit."} +plot_objective(fit_single, iterations = "near_optimum") +``` + D-LEARNER projects the target data directly onto the source spaces, without iterative optimization or penalty selection: @@ -122,7 +131,7 @@ A selected value on a grid boundary can be a reason to expand that grid. CV scores can also come from fits that reached the iteration limit, so inspect the optimization settings before interpreting the selection. -### Refit and inspect optimization +### Refit with the selected penalties Refit using the selected penalties and all observed target entries: @@ -136,13 +145,6 @@ fit <- learner( fit$convergence_criterion ``` -```{r objective-history, fig.cap="Objective function values near the optimum for the refitted target model."} -plot_objective(fit, iterations = "near_optimum") -``` - -Use `iterations = "all"` (the default) for the full history, or a vector such -as `1:500` for a specific range. - D-LEARNER accepts the same source list and weights: ```{r multiple-direct} diff --git a/README.md b/README.md index adc10f1..bfd8e93 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,24 @@ dim(fit_single$learner_estimate) #> [1] 100 50 ``` +`plot_objective()` shows the recorded objective values. With +`iterations = "near_optimum"` it zooms in on the iterations around the +smallest value; the default `iterations = "all"` shows the full history, +and a vector such as `1:500` shows a specific range. + +``` r +plot_objective(fit_single, iterations = "near_optimum") +``` + +

+ +Objective function values near the optimum for the single-source fit. +

+Objective function values near the optimum for the single-source fit. +

+ +
+ D-LEARNER projects the target data directly onto the source spaces, without iterative optimization or penalty selection: @@ -131,7 +149,7 @@ A selected value on a grid boundary can be a reason to expand that grid. CV scores can also come from fits that reached the iteration limit, so inspect the optimization settings before interpreting the selection. -### Refit and inspect optimization +### Refit with the selected penalties Refit using the selected penalties and all observed target entries: @@ -146,23 +164,6 @@ fit$convergence_criterion #> [1] 1 ``` -``` r -plot_objective(fit, iterations = "near_optimum") -``` - -
- -Objective function values near the optimum for the refitted target model. -

-Objective function values near the optimum for the refitted target -model. -

- -
- -Use `iterations = "all"` (the default) for the full history, or a vector -such as `1:500` for a specific range. - D-LEARNER accepts the same source list and weights: ``` r diff --git a/README_files/figure-gfm/objective-history-1.png b/README_files/figure-gfm/objective-history-1.png index 214a3812d9466c286284f2a695651e5fafb728c1..620a1b236587a3d75e8f7fb1e35d43c419b61477 100644 GIT binary patch literal 44286 zcmeEu^;=Y1AMeaCbSoW#ATWR+5>nD23J8dF3j&fNN_Qh5ASp_UfYL|}Aq**9(w)*R z(!*VI&U@bXcz?Km!F`_Lah@%+XYaMw`hGvPc&)CgNK8OS0D(Y=AKaJMfIzUoTj(4f z4*2JaJ^>p90w=MSlT){qQ+zc27)G5(H3R)Q#a%6%4K3YHm6gq*##EQKUs51Qpk`sex8e) zyEJIzxNeib6~XhdT>T?F5OLgps+c)QjlWSXP*xj}2w#9lRe_Y-^f^KeHca3!!q=@*+CCbo8%+h#_8R~=niZ4{|1 zLc^wm#|lI`Sr#lHXv)^FMt*Ravv&MZvt#@gak~`eg)7Y!5FMH{(%r9p$#40?OcZV+ z@*>eKlVP|ktiSc)xG#4nqwWfQF63nCQs}so|6Y~#$G5;U-%5pP+m^{dwdgWFZ;Dwg z*2ANyn;wkYsV!c-K(3&f(>y6H{JKy)i zj*&m9EMeZ;JXY3Xqy3`Umfu}Op&rx3kr=W{+k zJay4YO~zY@6{RMmi)6c{U9l(I?yFP5@rlf&@|AD&!c_ai$+%8vdP6$hvU(g-+?Jta 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zF!(cYtpEJ|`_UIKUR?U%{^ZT4-%E;%l?Y}=NOSzsm9fw&R!FX@s$%7kt%JvM{Wef} zCVye!k_wo*=DvFeHLjnK03C&Pt_PljemgcXF~01VEz)OAfdqO0F)Y_{@d9A5DN%+Ux^Tomc{s znzkF>T6Lo{Gwz6iiCNNgOvev{XId%HIW+P2_dmZo281|#RnFgoYE z+ho%Emtuyf?FB=(YD^w?>wswlxj!-S(;9RO9Oo@jDY`m;YQDmZvT#I99OnXV9%o|y zj2c;4?VRZ=y-8^z>WBB6Kz;1kPT*U*efu^sk)=igM{0HT zz(C06tkxZe^?v$z>gEAzuSGmJ(Aai(db&W*$ER8=X2APvM8bIJQGa@x$}e1esNz3A z7Z-c4xD`uQ{2np2x1sP2_2igxz8*a?q&w zlazYl{z}5+Bzaffd1rgCpvKEmQc}_?&ePK~R?YuFFqaw$@JJQU(aN8bO zsZrmsxFBt)uiv9+3>l+%#g^Qy#4HFMaCkxs>9i0E9iM{(8h?HU_-Auir~{pRFTNAuy|BmEXLi zjBs=@xLTY&cWARR0S-V!lr^{1!iH()A-2x8i>lE7RORX%K}S9{H5Grk0UX1xMbi^m zE9Svh)aaqwi2#0n)jt1tLUDo<$KC9M(PI{E*CH3@?` zbHZX_<2i61oM^|IJ$Y1fv9@BYKWi&0?vB2!F3^fV5!$Q4sbFF)A(T-@IoioVA`WoA z@>l(RPcnYHYTD6+f36_Vnh6m@_GcvE{&S0!!M&a8tni-tKUaLn9=Hde znucTlb3+4;2Rf%yfd~J&8{sInSAsSE|GyX+lmGKWG4zM}IYHvu!ce{k3jWj4GSqym HVIS~6GTk~u From 42aca12e8d186a7e5d6adeecedc95cad9d3d04fb Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Fri, 16 Oct 2026 12:43:05 -0500 Subject: [PATCH 5/7] Clarify README installation, grid boundaries, stopping code, and source comparison --- README.Rmd | 33 ++++++++++++++++++++++++--------- README.md | 41 +++++++++++++++++++++++++++++------------ 2 files changed, 53 insertions(+), 21 deletions(-) diff --git a/README.Rmd b/README.Rmd index 89bd9af..62600e7 100644 --- a/README.Rmd +++ b/README.Rmd @@ -38,8 +38,15 @@ Install the latest version from GitHub: ```r # install.packages("devtools") devtools::install_github("stmcg/learner") -# Optional: required for plot_cv(), but not for fitting or the other plots. -install.packages("ggplot2") # version 3.4.0 or later +``` + +The multiple-source features and plotting functions below are not in the CRAN +release yet; install the GitHub version to run these examples. `plot_cv()` +also requires ggplot2 (version 3.4.0 or later), which is optional for fitting +and for the other plots: + +```r +install.packages("ggplot2") ``` ## One source population @@ -127,7 +134,9 @@ plot_cv( ) ``` -A selected value on a grid boundary can be a reason to expand that grid. +A selected value on a grid boundary can be a reason to expand that grid. Here +all three selected values are at the upper end of their grids; the grids are +kept small to keep the example quick, but an analysis would expand them. CV scores can also come from fits that reached the iteration limit, so inspect the optimization settings before interpreting the selection. @@ -140,11 +149,15 @@ fit <- learner( Y_source = sources, Y_target = target, r = cv$r, source_weights = weights, lambda_1_row = cv$lambda_1_row_min, lambda_1_col = cv$lambda_1_col_min, lambda_2 = cv$lambda_2_min, - step_size = 0.0003, control = list(max_iter = 5000) + step_size = 0.0003, control = list(max_iter = 2000) ) fit$convergence_criterion ``` +The stopping code `1` means that the objective-change threshold was met (`2`: +the iteration limit was reached; `3`: excessive objective growth or a +non-finite update; see `?learner`). + D-LEARNER accepts the same source list and weights: ```{r multiple-direct} @@ -155,10 +168,12 @@ dim(direct$dlearner_estimate) ### Compare source choices For this simulated dataset, the true target signal is available. Compare -D-LEARNER fits at the same rank to isolate the choice of source spaces: +D-LEARNER fits at the same rank to isolate the choice of source spaces, together +with the truncated SVD of the target alone as a baseline: ```{r source-comparison} estimates <- list( + Target_only = dlearner(target, target, r = 3)$dlearner_estimate, A_only = dlearner(sources$A, target, r = 3)$dlearner_estimate, B_only = dlearner(sources$B, target, r = 3)$dlearner_estimate, Weighted_A_B = direct$dlearner_estimate @@ -170,10 +185,10 @@ round(source_errors, 3) ``` Lower Frobenius error is better. In this example, source A alone gives the -smallest error, while the weighted combination improves on source B alone. -The weights 0.7/0.3 are illustrative, not optimized. These results describe one -simulated dataset; they are not CV errors or evidence that combining sources -always improves estimation. +smallest error. The weighted combination improves on the target alone, while +source B alone is worse than ignoring the sources. The weights 0.7/0.3 are +illustrative, not optimized. These results describe one simulated dataset; they +are not CV errors or evidence that combining sources always improves estimation. ### Latent spaces and contributions diff --git a/README.md b/README.md index bfd8e93..721cd42 100644 --- a/README.md +++ b/README.md @@ -30,8 +30,15 @@ Install the latest version from GitHub: ``` r # install.packages("devtools") devtools::install_github("stmcg/learner") -# Optional: required for plot_cv(), but not for fitting or the other plots. -install.packages("ggplot2") # version 3.4.0 or later +``` + +The multiple-source features and plotting functions below are not in the +CRAN release yet; install the GitHub version to run these examples. +`plot_cv()` also requires ggplot2 (version 3.4.0 or later), which is +optional for fitting and for the other plots: + +``` r +install.packages("ggplot2") ``` ## One source population @@ -146,8 +153,11 @@ Cross-validation error across the candidate penalties. A selected value on a grid boundary can be a reason to expand that grid. -CV scores can also come from fits that reached the iteration limit, so -inspect the optimization settings before interpreting the selection. +Here all three selected values are at the upper end of their grids; the +grids are kept small to keep the example quick, but an analysis would +expand them. CV scores can also come from fits that reached the +iteration limit, so inspect the optimization settings before +interpreting the selection. ### Refit with the selected penalties @@ -158,12 +168,16 @@ fit <- learner( Y_source = sources, Y_target = target, r = cv$r, source_weights = weights, lambda_1_row = cv$lambda_1_row_min, lambda_1_col = cv$lambda_1_col_min, lambda_2 = cv$lambda_2_min, - step_size = 0.0003, control = list(max_iter = 5000) + step_size = 0.0003, control = list(max_iter = 2000) ) fit$convergence_criterion #> [1] 1 ``` +The stopping code `1` means that the objective-change threshold was met +(`2`: the iteration limit was reached; `3`: excessive objective growth +or a non-finite update; see `?learner`). + D-LEARNER accepts the same source list and weights: ``` r @@ -175,10 +189,12 @@ dim(direct$dlearner_estimate) ### Compare source choices For this simulated dataset, the true target signal is available. Compare -D-LEARNER fits at the same rank to isolate the choice of source spaces: +D-LEARNER fits at the same rank to isolate the choice of source spaces, +together with the truncated SVD of the target alone as a baseline: ``` r estimates <- list( + Target_only = dlearner(target, target, r = 3)$dlearner_estimate, A_only = dlearner(sources$A, target, r = 3)$dlearner_estimate, B_only = dlearner(sources$B, target, r = 3)$dlearner_estimate, Weighted_A_B = direct$dlearner_estimate @@ -187,15 +203,16 @@ source_errors <- vapply(estimates, function(estimate) { norm(estimate - dat_multisource$Theta_target, "F") }, numeric(1)) round(source_errors, 3) -#> A_only B_only Weighted_A_B -#> 2.323 17.214 5.324 +#> Target_only A_only B_only Weighted_A_B +#> 6.238 2.323 17.214 5.324 ``` Lower Frobenius error is better. In this example, source A alone gives -the smallest error, while the weighted combination improves on source B -alone. The weights 0.7/0.3 are illustrative, not optimized. These -results describe one simulated dataset; they are not CV errors or -evidence that combining sources always improves estimation. +the smallest error. The weighted combination improves on the target +alone, while source B alone is worse than ignoring the sources. The +weights 0.7/0.3 are illustrative, not optimized. These results describe +one simulated dataset; they are not CV errors or evidence that combining +sources always improves estimation. ### Latent spaces and contributions From 5cae4ca7eafe3996abde2067609ef69884893a69 Mon Sep 17 00:00:00 2001 From: Jodie Li Date: Fri, 16 Oct 2026 12:55:47 -0500 Subject: [PATCH 6/7] Add a second source comparison with noisier populations --- README.Rmd | 37 +++++++++++++++++++++++++++++ README.md | 40 ++++++++++++++++++++++++++++++++ inst/examples/multiple-sources.R | 28 ++++++++++++++++++++++ 3 files changed, 105 insertions(+) diff --git a/README.Rmd b/README.Rmd index 62600e7..be524a3 100644 --- a/README.Rmd +++ b/README.Rmd @@ -190,6 +190,43 @@ source B alone is worse than ignoring the sources. The weights 0.7/0.3 are illustrative, not optimized. These results describe one simulated dataset; they are not CV errors or evidence that combining sources always improves estimation. +### When both sources are noisy + +A second setting keeps the same true signals but generates new independent +noise with variances 0.10 for the target, 0.25 for A, and 0.50 for B. The built-in +`dat_multisource` data are unchanged. Rank and weights remain fixed at 3 and +0.7/0.3, respectively. + +```{r noisy-source-comparison} +# Reuse the true signals, with new independent noise for every population. +set.seed(20261016) +add_noise <- function(signal, variance) { + signal + matrix(rnorm(length(signal), sd = sqrt(variance)), + nrow(signal), ncol(signal)) +} +target_noisy <- add_noise(dat_multisource$Theta_target, variance = 0.10) +sources_noisy <- list( + A = add_noise(dat_multisource$Theta_source$A, variance = 0.25), + B = add_noise(dat_multisource$Theta_source$B, variance = 0.50) +) +estimates_noisy <- list( + Target_only = dlearner(target_noisy, target_noisy, r = 3)$dlearner_estimate, + A_only = dlearner(sources_noisy$A, target_noisy, r = 3)$dlearner_estimate, + B_only = dlearner(sources_noisy$B, target_noisy, r = 3)$dlearner_estimate, + Weighted_A_B = dlearner(sources_noisy, target_noisy, r = 3, + source_weights = c(0.7, 0.3))$dlearner_estimate +) +errors_noisy <- vapply(estimates_noisy, function(estimate) { + norm(estimate - dat_multisource$Theta_target, "F") +}, numeric(1)) +round(errors_noisy, 3) +``` + +Here combining the sources gives a lower error than using either source alone. +However, the target-only SVD still has the smallest error. Together, the two +examples illustrate that the benefit depends on source quality and weights; +neither setting establishes that combining sources is always preferable. + ### Latent spaces and contributions Compare the target and source spaces, or inspect which variables contribute diff --git a/README.md b/README.md index 721cd42..fa05164 100644 --- a/README.md +++ b/README.md @@ -214,6 +214,46 @@ weights 0.7/0.3 are illustrative, not optimized. These results describe one simulated dataset; they are not CV errors or evidence that combining sources always improves estimation. +### When both sources are noisy + +A second setting keeps the same true signals but generates new +independent noise with variances 0.10 for the target, 0.25 for A, and +0.50 for B. The built-in `dat_multisource` data are unchanged. Rank and +weights remain fixed at 3 and 0.7/0.3, respectively. + +``` r +# Reuse the true signals, with new independent noise for every population. +set.seed(20261016) +add_noise <- function(signal, variance) { + signal + matrix(rnorm(length(signal), sd = sqrt(variance)), + nrow(signal), ncol(signal)) +} +target_noisy <- add_noise(dat_multisource$Theta_target, variance = 0.10) +sources_noisy <- list( + A = add_noise(dat_multisource$Theta_source$A, variance = 0.25), + B = add_noise(dat_multisource$Theta_source$B, variance = 0.50) +) +estimates_noisy <- list( + Target_only = dlearner(target_noisy, target_noisy, r = 3)$dlearner_estimate, + A_only = dlearner(sources_noisy$A, target_noisy, r = 3)$dlearner_estimate, + B_only = dlearner(sources_noisy$B, target_noisy, r = 3)$dlearner_estimate, + Weighted_A_B = dlearner(sources_noisy, target_noisy, r = 3, + source_weights = c(0.7, 0.3))$dlearner_estimate +) +errors_noisy <- vapply(estimates_noisy, function(estimate) { + norm(estimate - dat_multisource$Theta_target, "F") +}, numeric(1)) +round(errors_noisy, 3) +#> Target_only A_only B_only Weighted_A_B +#> 6.436 13.377 16.549 10.566 +``` + +Here combining the sources gives a lower error than using either source +alone. However, the target-only SVD still has the smallest error. +Together, the two examples illustrate that the benefit depends on source +quality and weights; neither setting establishes that combining sources +is always preferable. + ### Latent spaces and contributions Compare the target and source spaces, or inspect which variables diff --git a/inst/examples/multiple-sources.R b/inst/examples/multiple-sources.R index 61ea953..fd72a87 100644 --- a/inst/examples/multiple-sources.R +++ b/inst/examples/multiple-sources.R @@ -58,3 +58,31 @@ comparison <- do.call(rbind, lapply(names(source_choices), function(label) { print(comparison, row.names = FALSE) # Lower Frobenius error is better. These are single-dataset results, not CV MSE. # The weights 0.7/0.3 are illustrative; combining sources need not beat source A. + +# A second setting: both sources have appreciable noise. +# Keep the built-in data unchanged; variances are target 0.10, A 0.25, B 0.50. +# Reuse the true signals, with new independent noise for every population. +set.seed(20261016) +add_noise <- function(signal, variance) { + signal + matrix(rnorm(length(signal), sd = sqrt(variance)), + nrow(signal), ncol(signal)) +} +target_noisy <- add_noise(learner::dat_multisource$Theta_target, variance = 0.10) +sources_noisy <- list( + A = add_noise(learner::dat_multisource$Theta_source$A, variance = 0.25), + B = add_noise(learner::dat_multisource$Theta_source$B, variance = 0.50) +) +estimates_noisy <- list( + Target_only = learner::dlearner(target_noisy, target_noisy, r = 3)$dlearner_estimate, + A_only = learner::dlearner(sources_noisy$A, target_noisy, r = 3)$dlearner_estimate, + B_only = learner::dlearner(sources_noisy$B, target_noisy, r = 3)$dlearner_estimate, + Weighted_A_B = learner::dlearner(sources_noisy, target_noisy, r = 3, + source_weights = c(0.7, 0.3))$dlearner_estimate +) +errors_noisy <- vapply(estimates_noisy, function(estimate) { + norm(estimate - learner::dat_multisource$Theta_target, "F") +}, numeric(1)) +print(round(errors_noisy, 3)) +# Here the weighted combination improves on either source alone, but the +# target-only SVD has the smallest error. Fixed weights and one noise draw +# illustrate a possible outcome, not a general performance guarantee. 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