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add: runtime_test demo

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Daniel Kapla 2019-08-14 20:05:42 +02:00
parent b071a689d9
commit 16e03fad3c
7 changed files with 179 additions and 17 deletions

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@ -6,7 +6,7 @@ Date: 2019-07-29
Author: Loki Author: Loki
Maintainer: Loki <loki@no.mail> Maintainer: Loki <loki@no.mail>
Description: More about what it does (maybe more than one line) Description: More about what it does (maybe more than one line)
License: What license is it under? License: GPL-3
Imports: Rcpp (>= 1.0.2) Imports: Rcpp (>= 1.0.2)
LinkingTo: Rcpp, RcppArmadillo LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8 Encoding: UTF-8

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@ -20,12 +20,22 @@
#' TODO: See ... #' TODO: See ...
#' @examples #' @examples
#' library(CVE) #' library(CVE)
#' ds <- dataset("M5")
#' X <- ds$X
#' Y <- ds$Y
#' dr <- cve(Y ~ X, k = 1)
#' #'
#' @references Fertl L, Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019 #' # sample dataset
#' ds <- dataset("M5")
#'
#' # call ´cve´ with default method (aka "simple")
#' dr.simple <- cve(ds$Y ~ ds$X, k = ncol(ds$B))
#' # plot optimization history (loss via iteration)
#' plot(dr.simple, main = "CVE M5 simple")
#'
#' # call ´cve´ with method "linesearch" using ´data.frame´ as data.
#' data <- data.frame(Y = ds$Y, X = ds$X)
#' # Note: ´Y, X´ are NOT defined, they are extracted from ´data´.
#' dr.linesearch <- cve(Y ~ ., data, method = "linesearch", k = ncol(ds$B))
#' plot(dr.linesearch, main = "CVE M5 linesearch")
#'
#' @references Fertl L., Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
#' #'
#' @import stats #' @import stats
#' @importFrom stats model.frame #' @importFrom stats model.frame
@ -51,6 +61,7 @@ cve <- function(formula, data, method = "simple", ...) {
return(dr) return(dr)
} }
#' @param nObs as describet in the Paper.
#' @rdname cve #' @rdname cve
#' @export #' @export
cve.call <- function(X, Y, method = "simple", nObs = nrow(X)^.5, k, ...) { cve.call <- function(X, Y, method = "simple", nObs = nrow(X)^.5, k, ...) {
@ -101,7 +112,10 @@ cve.call <- function(X, Y, method = "simple", nObs = nrow(X)^.5, k, ...) {
#' } #' }
#' @param ... Pass through parameters to [plot()] and [lines()] #' @param ... Pass through parameters to [plot()] and [lines()]
#' #'
#' @seealso see \code{\link{par}} for graphical parameters to pass through. #' @usage ## S3 method for class 'cve'
#' plot(x, content = "history", ...)
#' @seealso see \code{\link{par}} for graphical parameters to pass through
#' as well as \code{\link{plot}} for standard plot utility.
#' @importFrom graphics plot lines points #' @importFrom graphics plot lines points
#' @method plot cve #' @method plot cve
#' @export #' @export

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CVE/demo/00Index Normal file
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@ -0,0 +1 @@
runtime_test Runtime comparison of CVE against MAVE for M1 - M5 datasets.

106
CVE/demo/runtime_test.R Normal file
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@ -0,0 +1,106 @@
# -----------------------------------------------------------------------------
# Program: runtime_test.R
# Author: Loki
# Date: 2019.08.12
#
# Purpose:
# Comparing runtime of "MAVE" with "CVE".
#
# RevisionHistory:
# Loki -- 2019.08.12 initial creation
# -----------------------------------------------------------------------------
# load CVE package
library(CVE)
# load MAVE package for comparison
library(MAVE)
# set nr of simulations per dataset
nr.sim <- 10
# set names of datasets to run tests on
dataset.names <- c("M1", "M2", "M3", "M4", "M5")
#' Orthogonal projection to sub-space spanned by `B`
#'
#' @param B Matrix
#' @return Orthogonal Projection Matrix
proj <- function(B) {
B %*% solve(t(B) %*% B) %*% t(B)
}
#' Compute nObs given dataset dimension \code{n}.
#'
#' @param n Number of samples
#' @return Numeric estimate of \code{nObs}
nObs <- function (n) { n^0.5 }
## prepare "logging"
# result error, time, ... data.frame's
error <- matrix(nrow = nr.sim, ncol = 2 * length(dataset.names))
time <- matrix(nrow = nr.sim, ncol = 2 * length(dataset.names))
# convert to data.frames
error <- as.data.frame(error)
time <- as.data.frame(time)
# set names
names(error) <- kronecker(c("CVE.", "MAVE."), dataset.names, paste0)
names(time) <- kronecker(c("CVE.", "MAVE."), dataset.names, paste0)
# get current time
start.time <- Sys.time()
## main comparison loop (iterate `nr.sim` times for each dataset)
for (i in seq_along(dataset.names)) {
for (j in 1:nr.sim) {
name <- dataset.names[i]
# reporting progress
cat("\rRunning Test (", name, j , "):",
(i - 1) * nr.sim + j, "/", length(dataset.names) * nr.sim,
" - Time since start:", format(Sys.time() - start.time), "\033[K")
# sample new dataset
ds <- dataset(name)
k <- ncol(ds$B) # real dim
data <- data.frame(X = ds$X, Y = ds$Y)
# call CVE
cve.time <- system.time(
cve.res <- cve(Y ~ .,
data = data,
method = "simple",
k = k)
)
# call MAVE
mave.time <- system.time(
mave.res <- mave(Y ~ .,
data = data,
method = "meanMAVE")
)
# compute real and approximated sub-space projections
P <- proj(ds$B) # real
P.cve <- proj(cve.res$B)
P.mave <- proj(mave.res$dir[[k]])
# compute (and store) errors
error[j, paste0("CVE.", name)] <- norm(P - P.cve, 'F') / sqrt(2 * k)
error[j, paste0("MAVE.", name)] <- norm(P - P.mave, 'F') / sqrt(2 * k)
# store run-times
time[j, paste0("CVE.", name)] <- cve.time["elapsed"]
time[j, paste0("MAVE.", name)] <- mave.time["elapsed"]
}
}
cat("\n\n## Time [sec] Means:\n")
print(colMeans(time))
cat("\n## Error Means:\n")
print(colMeans(error))
len <- length(dataset.names)
boxplot(as.matrix(error),
main = paste0("Error (nr.sim = ", nr.sim, ")"),
ylab = expression(error == group("||", P[B] - P[hat(B)], "||")[F] / sqrt(2*k)),
las = 2,
at = c(1:len, 1:len + len + 1)
)
boxplot(as.matrix(time),
main = paste0("Time (nr.sim = ", nr.sim, ")"),
ylab = "time [sec]",
las = 2,
at = c(1:len, 1:len + len + 1)
)

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@ -26,6 +26,8 @@ See: \code{\link{formula}}.}
\item{...}{Further parameters depending on the used method. \item{...}{Further parameters depending on the used method.
TODO: See ...} TODO: See ...}
\item{nObs}{as describet in the Paper.}
} }
\description{ \description{
Conditional Variance Estimator (CVE) is a novel sufficient dimension Conditional Variance Estimator (CVE) is a novel sufficient dimension
@ -35,12 +37,22 @@ where B'X is a lower dimensional projection of the predictors.
} }
\examples{ \examples{
library(CVE) library(CVE)
# sample dataset
ds <- dataset("M5") ds <- dataset("M5")
X <- ds$X
Y <- ds$Y # call ´cve´ with default method (aka "simple")
dr <- cve(Y ~ X, k = 1) dr.simple <- cve(ds$Y ~ ds$X, k = ncol(ds$B))
# plot optimization history (loss via iteration)
plot(dr.simple, main = "CVE M5 simple")
# call ´cve´ with method "linesearch" using ´data.frame´ as data.
data <- data.frame(Y = ds$Y, X = ds$X)
# Note: ´Y, X´ are NOT defined, they are extracted from ´data´.
dr.linesearch <- cve(Y ~ ., data, method = "linesearch", k = ncol(ds$B))
plot(dr.linesearch, main = "CVE M5 linesearch")
} }
\references{ \references{
Fertl L, Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019 Fertl L., Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
} }

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@ -2,24 +2,27 @@
% Please edit documentation in R/CVE.R % Please edit documentation in R/CVE.R
\name{plot.cve} \name{plot.cve}
\alias{plot.cve} \alias{plot.cve}
\title{Ploting helper for objects of class \code{"cve"}.} \title{Ploting helper for objects of class \code{cve}.}
\usage{ \usage{
\method{plot}{cve}(x, ...) ## S3 method for class 'cve'
plot(x, content = "history", ...)
} }
\arguments{ \arguments{
\item{x}{Object of class \code{"cve"} (result of [cve()]).} \item{x}{Object of class \code{cve} (result of [cve()]).}
\item{...}{Pass through parameters to [plot()] and [lines()]} \item{...}{Pass through parameters to [plot()] and [lines()]}
\item{content}{Specifies what to plot: \item{content}{Specifies what to plot:
\itemize{ \itemize{
\item "history" Plots the loss history from stiefel optimization. \item "history" Plots the loss history from stiefel optimization
(default).
\item ... TODO: add (if there are any) \item ... TODO: add (if there are any)
}} }}
} }
\description{ \description{
Ploting helper for objects of class \code{"cve"}. Ploting helper for objects of class \code{cve}.
} }
\seealso{ \seealso{
see \code{\link{par}} for graphical parameters to pass through. see \code{\link{par}} for graphical parameters to pass through
as well as \code{\link{plot}} for standard plot utility.
} }

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@ -5,3 +5,29 @@
The `*.R` and `*.cpp` files in the root directory are _development_ and _test_ files. The `*.R` and `*.cpp` files in the root directory are _development_ and _test_ files.
## TODO: README.md ## TODO: README.md
# Package Structure
## Demos
A demo is an `.R` file that lives in `demo/`. Demos are like examples but tend to
be longer. Instead of focussing on a single function, they show how to weave
together multiple functions to solve a problem.
You list and access demos with `demo()`:
* Show all available demos: `demo()`.
* Show all demos in a package: `demo(package = "CVE")`.
* Run a specific demo: `demo("runtime_test", package = "CVE")`.
* Find a demo: `system.file("demo", "runtime_test.R", package = "CVE")`.
Each demo must be listed in `demo/00Index` in the following form:
`demo-name Demo description`.
The demo name is the name of the file without the extension,
e.g. `demo/runtime_test.R` becomes `runtime_test`.
By default the demo ask for human input for each plot: "Hit to see next plot".
This behaviour can be overridden by adding `devAskNewPage(ask = FALSE)` to
the demo file. You can add pauses by adding:
`readline("press any key to continue")`.
**Note**: Demos are not automatically tested by `R CMD check`. This means that they
can easily break without your knowledge.