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this is attempt at iregnet pred #2

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this is attempt at iregnet pred #2

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7751244
this is attempt at iregnet pred
andruuhurst Oct 9, 2019
8a42042
added initial loops to vary scale and family for each fit
andruuhurst Oct 23, 2019
7917e43
modifying OneFold function to format testFold
andruuhurst Oct 30, 2019
be67bc8
put various predictions into pred.list
andruuhurst Nov 1, 2019
f293b00
prediction and accuracy of various iregnet distributions added
andruuhurst Nov 12, 2019
d50ac57
prediction.csv files added for each distribution for firt testFold of…
andruuhurst Nov 12, 2019
f2c8cd5
Accuracy.csv and prediction.csv
andruuhurst Nov 24, 2019
6dfc4c4
iregenet distro: gaus/log with fixed/estimated scale pred files for a…
andruuhurst May 5, 2020
833f86a
updated accuracy.csv files for irgenet models
andruuhurst May 7, 2020
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76 changes: 31 additions & 45 deletions iregnet.predictions.R
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Original file line number Diff line number Diff line change
@@ -1,24 +1,27 @@
source("packages.R")

testFold.csv.vec <- Sys.glob(file.path(
testFold.dir.vec <- Sys.glob(file.path(
"data", "*", "cv", "*", "testFolds",
"*"))

n.pred.vec <- sapply(testFold.csv.vec, function(testFold.csv){
path <- file.path(dirname(testFold.csv), "models", "*", "predictions.csv")
n.pred.vec <- sapply(testFold.dir.vec, function(testFold.dir){
path <- file.path(dirname(testFold.dir), "models", "*", "predictions.csv")
length(Sys.glob(path))
})
table(n.pred.vec)

OneFold <- function(testFold.csv){
OneFold <- function(testFold.dir){
library(data.table)
test.fold <- as.integer(basename(testFold.csv))
cv.type.dir <- dirname(dirname(testFold.csv))
test.fold <- as.integer(basename(testFold.dir))
cv.type.dir <- dirname(dirname(testFold.dir))
data.dir <- dirname(dirname(cv.type.dir))
folds.csv <- file.path(cv.type.dir, "folds.csv")
folds.dt <- fread(folds.csv)
data.list <- list()
for(data.type in c("inputs", "outputs")){
csv.xz <- file.path(data.dir, paste0(data.type, ".csv.xz"))
dt <- fread(cmd=paste("xzcat", csv.xz))
stopifnot(nrow(dt) == nrow(folds.dt))
m <- as.matrix(dt[, -1, with=FALSE])
rownames(m) <- dt$sequenceID
data.list[[data.type]] <- m
Expand All @@ -34,14 +37,9 @@ OneFold <- function(testFold.csv){
}
keep.inputs <- apply(is.finite(data.list$inputs), 2, all)
data.list$inputs <- data.list$inputs[, keep.inputs, drop=FALSE]
#order.dt <- fread(testFold.csv)
testFold.dt <- fread(testFold.csv)
all.id.vec <- rownames(data.list$inputs)
id.list <- list(
#train=order.dt$sequenceID,
train=testFold.dt$sequenceID,
#test=all.id.vec[!all.id.vec %in% order.dt$sequenceID])
test=all.id.vec[!all.id.vec %in% testFold.dt$sequenceID])
train=folds.dt[fold != test.fold, sequenceID],
test=folds.dt[fold == test.fold, sequenceID])
set.list <- list()
for(set.name in names(id.list)){
set.id.vec <- id.list[[set.name]]
Expand All @@ -50,13 +48,28 @@ OneFold <- function(testFold.csv){
})
}

#### Stopped HERE


result.list <- list()
pred.mat.list <- list()
size.i.vec <- seq_along(train.size.vec)
##size.i.vec <- length(train.size.vec)

####

fit.list <- list()
scale.i.list <- list( estimated = list( init= NA, estimate = TRUE),
fixed = list( init= 1, estimate = FALSE) )

X.train <- matrix(set.list$train$inputs, nrow(set.list$train$inputs), ncol(set.list$train$inputs))
Y.train <- matrix( set.list$train$outputs , nrow(set.list$train$outputs) , ncol(set.list$train$outputs))

for( model.type in c( "gaussian", "logistic", "extreme_value")){
for( scale.type in scale.i.list){
fit.list[length(fit.list) + 1] <- cv.iregnet(X.train, Y.train , family = model.type,
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@andruuhurst andruuhurst Nov 1, 2019 •
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Hey @tdhock
the for loops iterate through until it gets to the extreme_value. then I get this error

Error in stopifnot_error(paste("family must be one of", paste(names(pfun.list), : family must be one of gaussian, logistic, exponential

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@andruuhurst andruuhurst Nov 1, 2019 •
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I tried using iregnet( ...., extreme_value,..) instead and it returned this error

Error in dimnames(x) <- dn : 
  length of 'dimnames' [1] not equal to array extent

any thoughts?

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first of all you should use cv.iregnet (which automatically performs selection of regularization/penalty parameter) and not iregnet (which does not perform selection).

for a quick fix I would suggest using only logistic and gaussian (remove extreme value).

also please post a minimal reproducible example as an issue on https://github.com/anujkhare/iregnet/issues . Make sure to include what version of the software you are using, via the packageDescription command, this is what it looks like on my system,

> packageDescription("iregnet")
Package: iregnet
Type: Package
Title: Regularized interval regression
Version: 0.1.0.9000
Author: Anuj Khare <khareanuj18@gmail.com>, Toby D Hocking
        <toby.hocking@r-project.org>, Jelle Goeman, Aditya Samantaray
        <aditya.samantaray1@gmail.com>
Maintainer: Anuj Khare <khareanuj18@gmail.com>, Aditya Samantaray
        <aditya.samantaray1@gmail.com>
Description: Interval regression with four types of censoring and
        elastic net regularization.
License: GPL-3
LazyData: TRUE
Suggests: ElemStatLearn, glmnet, testthat, knitr, rmarkdown
LinkingTo: Rcpp
Depends: R (>= 2.10)
Imports: ggplot2, utils, methods, stats, survival, foreach, doParallel,
        data.table, Matrix, namedCapture, penaltyLearning
RoxygenNote: 6.1.1
Remotes: tdhock/penaltyLearning
VignetteBuilder: knitr
RemoteType: github
RemoteHost: api.github.com
RemoteRepo: iregnet
RemoteUsername: anujkhare
RemoteRef: 4d77f047c3a00a5524e1cbe140226417e3aedd92
RemoteSha: 4d77f047c3a00a5524e1cbe140226417e3aedd92
GithubRepo: iregnet
GithubUsername: anujkhare
GithubRef: 4d77f047c3a00a5524e1cbe140226417e3aedd92
GithubSHA1: 4d77f047c3a00a5524e1cbe140226417e3aedd92
NeedsCompilation: yes
Packaged: 2019-07-19 18:40:17 UTC; th798
Built: R 3.6.1; x86_64-w64-mingw32; 2019-07-19 18:40:18 UTC; windows

-- File: C:/Users/th798/R/win-library/3.6/iregnet/Meta/package.rds 
>

scale_init= scale.type$init ,estimate_scale= scale.type$estimate)
}
}


######

for(size.i in size.i.vec){
train.size <- train.size.vec[[size.i]]
maybe.both.inf <- set.list$train$outputs[1:train.size, ]
Expand Down Expand Up @@ -187,31 +200,4 @@ consistent.dt[pred.in.test != test.seqIDs]








folds.csv.vec <- Sys.glob("data/*/cv/*/folds.csv")

fit.list <- list()
scale.i.list <- list( estimated = list( init= "NA", estimate = TRUE), fixed = list( init= 1, estimate = FALSE) )
model.list <- list( "guassian", "logistic", "extreme_value")



table(n.pred.vec)
#take in test fold
#one fold



#for( model.type in model.i.list){
# for( scale.type in scale.i.list){
# fit.list <- c( fit.list , cv.iregnet(X.train, Y.train , family = model.type,
# scale_init= scale.type$init ,estimate_scale = scale.type$estimate))
# }
#}


## fwrite(result, iregnet.csv)