wrapper/SVMperf_wrapper.R
In aydindemircioglu/SVMBridge: A Bridge to Integrate Different 'SVM' Solvers into R
#
# SVMBridge
# (C) 2015, by Aydin Demircioglu
#
# SVMBridge is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# SVMBridge is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# Please do not use this software to destroy or spy on people, environment or things.
# All negative use is prohibited.
#
createSVMWrapper.SVMperf = function() {
createSVMWrapperInternal(
name = "SVMperf",
par.set = ParamHelpers::makeParamSet(
ParamHelpers::makeDiscreteLearnerParam(id = "kernel", default = "radial", values = c("radial")),
ParamHelpers::makeNumericLearnerParam(id = "budget", default = 128, lower = 1),
ParamHelpers::makeNumericLearnerParam(id = "cost", default = 1, lower = 0),
ParamHelpers::makeNumericLearnerParam(id = "epochs", default = 1, lower = 1),
ParamHelpers::makeNumericLearnerParam(id = "gamma", default = 1, lower = 0, requires = quote(kernel!="linear")),
ParamHelpers::makeNumericLearnerParam(id = "tolerance", default = 0.001, lower = 0)
),
properties = c("twoclass", "multiclass"),
note = ""
)
}
createTrainingArguments.SVMperf = function (x,
trainDataFile = "",
modelFile = "",
kernelCacheSize = 1024,
cost = 1,
gamma = 1,
k = 1000,
epsilon = 0.001,
extraParameter,
verbose = FALSE,
...) {
# count training examples
N = R.utils::countLines(trainDataFile)
# modify COST to C*N/100, as the formulation is C/n instead of C,
# svmperf command tells us:
# NOTE: The '-c' parameters in SVM-light and SVM-perf are related as
# c_light = c_perf*100/n for the 'Errorrate' loss function, where n is the
# number of training examples.
cost = cost*N/100
# even more stuff from the webpage:
# -----
# For training non-linear SVMs with kernels, SVMperf employs the Cutting-Plane Subspace Pursuit (CPSP) algorithm [Joachims & Yu, 2009].
# The algorithm is enabled with the '--i' option. It allows you to limit the number of basis functions (i.e. support vectors) via the '--k'
# option. The larger '--k', the better the approximation quality but the longer training and testing times. Typically, you enable CPSP via
# '--i 2 -w 9 --b 0 --k 500' and set the kernel parameters via '-t' just like in SVMlight. Currently, the full CPSP algorithm is implemented
# only for RBF kernels.
#
# Another option for training non-linear SVMs with kernels is the Nystrom method ('--i 0 --t 1 -w 3') and the incomplete Cholesky
# factorization ('--i 0 --t 2 -w 3'). Again, you can limit the number of basis functions via the --k option. The larger '--k', the better
# the approximation quality but the longer training and testing times. Typically, you enable Nystrom (and incomplete Cholesky respectively)
# via '--i 0 --t 1 -w 3 --b 0 --k 500' and set the kernel parameters via '-t' just like in SVMlight. Normally, the basis functions are sampled
# from the training set, but you can give a file with an explicit set of basis function using the '--f' option. The file has the same format
# as a training/test file.
#
# You can in principle also train non-linear kernels in SVMperf exactly using '--t 0 --i 0 -w 3', and setting the kernel options just like
# in SVMlight. However, this is painfully slow.
# ------
# we will adopt the first paragraph, our default settings (-l 0, --i 0 default, -w 4) were.. painfully slow. here -w 3 seems faster than -w 4!
#
# need to unpack the cost from the ... parameters
eP = list(...)
if (is.null (eP[["submethod"]])) {
method = "CPSP" }
else {
method = eP[["submethod"]]
}
args = list()
if (method == "Nystrom") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 1",
"--i 0",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "Cholesky") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 2",
"--i 0",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "vanilla") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
# FAST, like SVMperf 2.5: --t 1 --i 0
# VERY erratic: --t 0 --i 0
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
# sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "CPSP_w3") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 0", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
# FAST, like SVMperf 2.5: --t 1 --i 0
# VERY erratic: --t 0 --i 0
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 0",
"--i 2",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "CPSP") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 9", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--i 2",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (length(args) < 1) {
stop ("Unknown method selected for SVMperf. Please use something like CPSP, CPSP_w3, etc.")
}
if (verbose == TRUE) {
cat("SVMperf will apply method ", method, "\n")
}
return (args)
}
createTestArguments.SVMperf = function (x, testDataFile = NULL, modelFile = NULL, predictionsFile = NULL, verbose = FALSE, ...) {
args = c(
shQuote (testDataFile),
shQuote (modelFile),
shQuote (predictionsFile)
)
return (args)
}
extractTrainingInfo.SVMperf = function (x, output, verbose) {
pattern <- "Accuracy :\\s*(\\d+\\.?\\d*)"
err = 1 - as.numeric(sub(pattern, '\\1', output[grepl(pattern, output)])) / 100
}
extractTestInfo.SVMperf = function (x, output, verbose) {
pattern <- "Accuracy :\\s*(\\d+\\.?\\d*)"
err = 1 - as.numeric(sub(pattern, '\\1', output[grepl(pattern, output)])) / 100
}
readModel.SVMperf <- function (x, modelFile = "./model", verbose = FALSE)
{
if (verbose == TRUE) {
cat ("Reading SVMperf model from ", modelFile, "\n")
}
# open connection
con <- file(modelFile, open = "r")
# grep needed information step by step, the bias is on the threshold line
while (length(oneLine <- readLines(con, n = 1, warn = FALSE)) > 0) {
if (grepl("threshold", oneLine) == TRUE)
break;
# gamma value
if (grepl("parameter -g", oneLine) == TRUE) {
pattern <- "(.*) # kernel.*"
gamma = as.numeric(sub(pattern, '\\1', oneLine[grepl(pattern, oneLine)]))
}
}
# the last line read should then contain the bias/threshold
if (grepl("threshold", oneLine) == TRUE) {
pattern <- "(.*) # threshold.*"
bias = as.numeric(sub(pattern, '\\1', oneLine[grepl(pattern, oneLine)]))
}
# read and interprete data
# basically all data is sparse data format, but the data around this differs
model = readSparseDataFromConnection(con)
# rename Y to alpha and X to SVs
names(model) = replace(names(model), names(model) == "Y", "alpha")
names(model) = replace(names(model), names(model) == "X", "SV")
model$nSV = nrow(model$SV)
# add header information
model$gamma = gamma
model$bias = bias
model$modelType = "SVMperf"
model$nSV = c(sum(model$alpha[,1] > 0), sum(model$alpha[,1] < 0))
model$label = as.numeric(c(1, -1)) # SVMperf is always binary?
# close connection
close(con)
# this is only true for multiclass, but SVMperf does not support multiclass--
# we must sort the alphas, else computation of optimize values will not work
# as LIBSVMs alphas are sorted and prediction depend on this (because of multiclass
# and the special format LIBSVM saves the alphas)
sortSVs = FALSE
if (sortSVs == TRUE) {
s = sort (model$alpha, index.return = TRUE, decreasing = TRUE)
model$alpha[,1] = s$x
model$SV = model$SV[s$ix,]
}
# invert the alphas...
#model$alpha[1:model$nSV[1],] = - model$alpha[1:model$nSV[1],]
#model$alpha[(1+model$nSV[1]):nrow(model$alpha),] = - model$alpha[(1+model$nSV[1]):nrow(model$alpha),]
# return
return (model)
}
writeModel.SVMperf <- function (x, model = NA, modelFile = "./model", verbose = FALSE) {
if (verbose == TRUE) {
cat ("Writing SVMperf Model to ", modelFile, "\n")
}
# check of S3 object here.
model$nrclass = length(model$label)
posSV = sum(model$alpha > 0)
negSV = sum(model$alpha < 0)
# open connection
if (verbose == TRUE)
cat (" Writing Header\n")
modelFileHandle <- file(modelFile, open = "w+")
writeLines ("SVM-light Version V6.20", modelFileHandle)
writeLines("2 # kernel type", modelFileHandle) #kernel type
writeLines("3 # kernel parameter -d ", modelFileHandle) # degree, not used here
writeLines(paste(as.character(model$gamma), "# kernel parameter -g ", sep = " "), modelFileHandle )
writeLines("1 # kernel parameter -s ", modelFileHandle ) #s?
writeLines("1 # kernel parameter -r ", modelFileHandle ) #r?
writeLines("empty # kernel parameter -u ", modelFileHandle ) #u?
# highest feature index
maxFeatIndex = ncol(model$SV)
writeLines(paste(as.character(maxFeatIndex), "# highest feature index "), modelFileHandle ) #r?
# FIXME: numer of training data-- we do not have this information..
nTraining = ncol(model$SV)
writeLines(paste(as.character(nTraining), " # number of training documents "), modelFileHandle ) #r?
nr_svValues = nrow(model$SV)+1
writeLines(paste(as.character(nr_svValues), " # number of support vectors plus 1 "), modelFileHandle )
biasvalues = paste(model$bias, collapse = " ")
writeLines(paste(as.character(biasvalues), "# threshold b, each following line is a SV (starting with alpha*y)"), modelFileHandle )
# basically all data is sparse data format, but the data around this differs
#svmatrix = dumpSparseFormat(model$alpha, model$X)
#writeLines(svmatrix, modelFileHandle, sep = "" )
# close connection
close(modelFileHandle)
if (verbose == TRUE)
cat (" Writing SV\n")
appendSparseDataToFile (modelFile, model$SV, model$alpha)
}
detectModel.SVMperf = function (x, modelFile = NULL, verbose = FALSE) {
checkmate::checkFlag (verbose)
if (verbose == TRUE) {
cat ("Checking for SVMperf model.\n")
}
if (is.null (modelFile) == TRUE)
return (FALSE)
# read first lines and detect magic marker
if (file.exists (modelFile) == FALSE)
return (FALSE)
line = readLines(modelFile, n = 12)
if (sum(grepl("SVM-light", line)) > 0) {
return (TRUE)
}
return (FALSE)
}
readPredictions.SVMperf <- function (x, predictionsFile = "", verbose = FALSE) {
ret = readPredictions.LIBSVM (predictionsFile = predictionsFile, verbose = verbose)
return (ret)
}
findSoftware.SVMperf = function (x, searchPath = "./", execute = FALSE, verbose = FALSE) {
if (verbose == TRUE) {
cat (" SVMperf Object: Executing search for software for ", x$method)
}
# can do now OS specific stuff here
if(.Platform$OS.type == "unix") {
if (verbose == TRUE) {
cat (" Unix binaries.\n")
}
trainBinaryPattern = "svm_perf_learn"
testBinaryPattern = "svm_perf_classify"
} else {
if (verbose == TRUE) {
cat (" Windows binaries.\n")
}
trainBinaryPattern = "svm_perf_learn.exe"
testBinaryPattern = "svm_perf_classify.exe"
}
# can do now OS specific stuff here
x$trainBinaryPath = findBinaryInDirectory (trainBinaryPattern,
dir = searchPath, patterns = list ('ROCArea: Percentage of swapped pos/neg pairs', 'usage: svm_struct_learn .options. example_file model_file'), verbose = verbose)
x$testBinaryPath = findBinaryInDirectory (testBinaryPattern, dir = searchPath, patterns = list ('usage: svm_struct_classify .options. example_file model_file output_file'), verbose = verbose)
return(x)
}
print.SVMperf = function(x) {
cat("--- Object: ", x$method, "\n")
cat(" Training Binary at ", x$trainBinaryPath, "\n")
cat(" Test Binary at ", x$testBinaryPath, "\n")
}
aydindemircioglu/SVMBridge documentation built on May 11, 2019, 4:13 p.m.
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#
# SVMBridge
# (C) 2015, by Aydin Demircioglu
#
# SVMBridge is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# SVMBridge is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# Please do not use this software to destroy or spy on people, environment or things.
# All negative use is prohibited.
#
createSVMWrapper.SVMperf = function() {
createSVMWrapperInternal(
name = "SVMperf",
par.set = ParamHelpers::makeParamSet(
ParamHelpers::makeDiscreteLearnerParam(id = "kernel", default = "radial", values = c("radial")),
ParamHelpers::makeNumericLearnerParam(id = "budget", default = 128, lower = 1),
ParamHelpers::makeNumericLearnerParam(id = "cost", default = 1, lower = 0),
ParamHelpers::makeNumericLearnerParam(id = "epochs", default = 1, lower = 1),
ParamHelpers::makeNumericLearnerParam(id = "gamma", default = 1, lower = 0, requires = quote(kernel!="linear")),
ParamHelpers::makeNumericLearnerParam(id = "tolerance", default = 0.001, lower = 0)
),
properties = c("twoclass", "multiclass"),
note = ""
)
}
createTrainingArguments.SVMperf = function (x,
trainDataFile = "",
modelFile = "",
kernelCacheSize = 1024,
cost = 1,
gamma = 1,
k = 1000,
epsilon = 0.001,
extraParameter,
verbose = FALSE,
...) {
# count training examples
N = R.utils::countLines(trainDataFile)
# modify COST to C*N/100, as the formulation is C/n instead of C,
# svmperf command tells us:
# NOTE: The '-c' parameters in SVM-light and SVM-perf are related as
# c_light = c_perf*100/n for the 'Errorrate' loss function, where n is the
# number of training examples.
cost = cost*N/100
# even more stuff from the webpage:
# -----
# For training non-linear SVMs with kernels, SVMperf employs the Cutting-Plane Subspace Pursuit (CPSP) algorithm [Joachims & Yu, 2009].
# The algorithm is enabled with the '--i' option. It allows you to limit the number of basis functions (i.e. support vectors) via the '--k'
# option. The larger '--k', the better the approximation quality but the longer training and testing times. Typically, you enable CPSP via
# '--i 2 -w 9 --b 0 --k 500' and set the kernel parameters via '-t' just like in SVMlight. Currently, the full CPSP algorithm is implemented
# only for RBF kernels.
#
# Another option for training non-linear SVMs with kernels is the Nystrom method ('--i 0 --t 1 -w 3') and the incomplete Cholesky
# factorization ('--i 0 --t 2 -w 3'). Again, you can limit the number of basis functions via the --k option. The larger '--k', the better
# the approximation quality but the longer training and testing times. Typically, you enable Nystrom (and incomplete Cholesky respectively)
# via '--i 0 --t 1 -w 3 --b 0 --k 500' and set the kernel parameters via '-t' just like in SVMlight. Normally, the basis functions are sampled
# from the training set, but you can give a file with an explicit set of basis function using the '--f' option. The file has the same format
# as a training/test file.
#
# You can in principle also train non-linear kernels in SVMperf exactly using '--t 0 --i 0 -w 3', and setting the kernel options just like
# in SVMlight. However, this is painfully slow.
# ------
# we will adopt the first paragraph, our default settings (-l 0, --i 0 default, -w 4) were.. painfully slow. here -w 3 seems faster than -w 4!
#
# need to unpack the cost from the ... parameters
eP = list(...)
if (is.null (eP[["submethod"]])) {
method = "CPSP" }
else {
method = eP[["submethod"]]
}
args = list()
if (method == "Nystrom") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 1",
"--i 0",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "Cholesky") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 2",
"--i 0",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "vanilla") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
# FAST, like SVMperf 2.5: --t 1 --i 0
# VERY erratic: --t 0 --i 0
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
# sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "CPSP_w3") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 0", # 0/1 loss
"-w 3", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
# FAST, like SVMperf 2.5: --t 1 --i 0
# VERY erratic: --t 0 --i 0
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--t 0",
"--i 2",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (method == "CPSP") {
# DEFAULT values are commented out!
args = c(
sprintf("-c %.16f", cost),
sprintf("-m %d", kernelCacheSize), # in MB
#"-p 1", # use 1-norm for xi
#"-o 2", # in 0/1 case, which we are interested in, both formulations are the same. o 2=margin rescaling is default.
"-l 2", # 0/1 loss
"-w 9", # 1-slack algorithm is superior, one of the main points of the paper. use cache to make it faster
sprintf("-e %.16f", epsilon), # epsilon
#"-k 100", # default cache size before recomputing QP problem
#"-b 100", # unsure of its meaning, default value.
#"-f 10", # default constraint cache size for every example is 5, but paper states: 10 in section 5.1
# not changing any svm light parameters
"-t 2", # RBF kernel
sprintf("-g %.16f", gamma),
"--b 0.0", # for non-linear kernels bias must be zero here
"--i 2",
sprintf("--k %d", k), # number of kernel functions, 500 by default
# --r
# --s
extraParameter,
shQuote(trainDataFile),
shQuote(modelFile)
)
}
if (length(args) < 1) {
stop ("Unknown method selected for SVMperf. Please use something like CPSP, CPSP_w3, etc.")
}
if (verbose == TRUE) {
cat("SVMperf will apply method ", method, "\n")
}
return (args)
}
createTestArguments.SVMperf = function (x, testDataFile = NULL, modelFile = NULL, predictionsFile = NULL, verbose = FALSE, ...) {
args = c(
shQuote (testDataFile),
shQuote (modelFile),
shQuote (predictionsFile)
)
return (args)
}
extractTrainingInfo.SVMperf = function (x, output, verbose) {
pattern <- "Accuracy :\\s*(\\d+\\.?\\d*)"
err = 1 - as.numeric(sub(pattern, '\\1', output[grepl(pattern, output)])) / 100
}
extractTestInfo.SVMperf = function (x, output, verbose) {
pattern <- "Accuracy :\\s*(\\d+\\.?\\d*)"
err = 1 - as.numeric(sub(pattern, '\\1', output[grepl(pattern, output)])) / 100
}
readModel.SVMperf <- function (x, modelFile = "./model", verbose = FALSE)
{
if (verbose == TRUE) {
cat ("Reading SVMperf model from ", modelFile, "\n")
}
# open connection
con <- file(modelFile, open = "r")
# grep needed information step by step, the bias is on the threshold line
while (length(oneLine <- readLines(con, n = 1, warn = FALSE)) > 0) {
if (grepl("threshold", oneLine) == TRUE)
break;
# gamma value
if (grepl("parameter -g", oneLine) == TRUE) {
pattern <- "(.*) # kernel.*"
gamma = as.numeric(sub(pattern, '\\1', oneLine[grepl(pattern, oneLine)]))
}
}
# the last line read should then contain the bias/threshold
if (grepl("threshold", oneLine) == TRUE) {
pattern <- "(.*) # threshold.*"
bias = as.numeric(sub(pattern, '\\1', oneLine[grepl(pattern, oneLine)]))
}
# read and interprete data
# basically all data is sparse data format, but the data around this differs
model = readSparseDataFromConnection(con)
# rename Y to alpha and X to SVs
names(model) = replace(names(model), names(model) == "Y", "alpha")
names(model) = replace(names(model), names(model) == "X", "SV")
model$nSV = nrow(model$SV)
# add header information
model$gamma = gamma
model$bias = bias
model$modelType = "SVMperf"
model$nSV = c(sum(model$alpha[,1] > 0), sum(model$alpha[,1] < 0))
model$label = as.numeric(c(1, -1)) # SVMperf is always binary?
# close connection
close(con)
# this is only true for multiclass, but SVMperf does not support multiclass--
# we must sort the alphas, else computation of optimize values will not work
# as LIBSVMs alphas are sorted and prediction depend on this (because of multiclass
# and the special format LIBSVM saves the alphas)
sortSVs = FALSE
if (sortSVs == TRUE) {
s = sort (model$alpha, index.return = TRUE, decreasing = TRUE)
model$alpha[,1] = s$x
model$SV = model$SV[s$ix,]
}
# invert the alphas...
#model$alpha[1:model$nSV[1],] = - model$alpha[1:model$nSV[1],]
#model$alpha[(1+model$nSV[1]):nrow(model$alpha),] = - model$alpha[(1+model$nSV[1]):nrow(model$alpha),]
# return
return (model)
}
writeModel.SVMperf <- function (x, model = NA, modelFile = "./model", verbose = FALSE) {
if (verbose == TRUE) {
cat ("Writing SVMperf Model to ", modelFile, "\n")
}
# check of S3 object here.
model$nrclass = length(model$label)
posSV = sum(model$alpha > 0)
negSV = sum(model$alpha < 0)
# open connection
if (verbose == TRUE)
cat (" Writing Header\n")
modelFileHandle <- file(modelFile, open = "w+")
writeLines ("SVM-light Version V6.20", modelFileHandle)
writeLines("2 # kernel type", modelFileHandle) #kernel type
writeLines("3 # kernel parameter -d ", modelFileHandle) # degree, not used here
writeLines(paste(as.character(model$gamma), "# kernel parameter -g ", sep = " "), modelFileHandle )
writeLines("1 # kernel parameter -s ", modelFileHandle ) #s?
writeLines("1 # kernel parameter -r ", modelFileHandle ) #r?
writeLines("empty # kernel parameter -u ", modelFileHandle ) #u?
# highest feature index
maxFeatIndex = ncol(model$SV)
writeLines(paste(as.character(maxFeatIndex), "# highest feature index "), modelFileHandle ) #r?
# FIXME: numer of training data-- we do not have this information..
nTraining = ncol(model$SV)
writeLines(paste(as.character(nTraining), " # number of training documents "), modelFileHandle ) #r?
nr_svValues = nrow(model$SV)+1
writeLines(paste(as.character(nr_svValues), " # number of support vectors plus 1 "), modelFileHandle )
biasvalues = paste(model$bias, collapse = " ")
writeLines(paste(as.character(biasvalues), "# threshold b, each following line is a SV (starting with alpha*y)"), modelFileHandle )
# basically all data is sparse data format, but the data around this differs
#svmatrix = dumpSparseFormat(model$alpha, model$X)
#writeLines(svmatrix, modelFileHandle, sep = "" )
# close connection
close(modelFileHandle)
if (verbose == TRUE)
cat (" Writing SV\n")
appendSparseDataToFile (modelFile, model$SV, model$alpha)
}
detectModel.SVMperf = function (x, modelFile = NULL, verbose = FALSE) {
checkmate::checkFlag (verbose)
if (verbose == TRUE) {
cat ("Checking for SVMperf model.\n")
}
if (is.null (modelFile) == TRUE)
return (FALSE)
# read first lines and detect magic marker
if (file.exists (modelFile) == FALSE)
return (FALSE)
line = readLines(modelFile, n = 12)
if (sum(grepl("SVM-light", line)) > 0) {
return (TRUE)
}
return (FALSE)
}
readPredictions.SVMperf <- function (x, predictionsFile = "", verbose = FALSE) {
ret = readPredictions.LIBSVM (predictionsFile = predictionsFile, verbose = verbose)
return (ret)
}
findSoftware.SVMperf = function (x, searchPath = "./", execute = FALSE, verbose = FALSE) {
if (verbose == TRUE) {
cat (" SVMperf Object: Executing search for software for ", x$method)
}
# can do now OS specific stuff here
if(.Platform$OS.type == "unix") {
if (verbose == TRUE) {
cat (" Unix binaries.\n")
}
trainBinaryPattern = "svm_perf_learn"
testBinaryPattern = "svm_perf_classify"
} else {
if (verbose == TRUE) {
cat (" Windows binaries.\n")
}
trainBinaryPattern = "svm_perf_learn.exe"
testBinaryPattern = "svm_perf_classify.exe"
}
# can do now OS specific stuff here
x$trainBinaryPath = findBinaryInDirectory (trainBinaryPattern,
dir = searchPath, patterns = list ('ROCArea: Percentage of swapped pos/neg pairs', 'usage: svm_struct_learn .options. example_file model_file'), verbose = verbose)
x$testBinaryPath = findBinaryInDirectory (testBinaryPattern, dir = searchPath, patterns = list ('usage: svm_struct_classify .options. example_file model_file output_file'), verbose = verbose)
return(x)
}
print.SVMperf = function(x) {
cat("--- Object: ", x$method, "\n")
cat(" Training Binary at ", x$trainBinaryPath, "\n")
cat(" Test Binary at ", x$testBinaryPath, "\n")
}
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