precalculation(TunePareto)
precalculation()所属R语言包：TunePareto
Predefined precalculation functions for objectives
预定义的预先计算功能为目标
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These predefined precalculation functions can be employed to create own objectives using createObjective. They perform a reclassification or a cross-validation and return the true labels and the predictions.
这些预定义的预先计算功能，可以采用，使用createObjective创建自己的目标。他们进行重新分类或交叉验证，并返回真实的标签和预测。
用法----------Usage----------
reclassification(data, labels,
classifier, classifierParams, predictorParams)
crossValidation(data, labels,
classifier, classifierParams, predictorParams,
ntimes = 10, nfold = 10,
leaveOneOut = FALSE, stratified = FALSE,
foldList = NULL)
参数----------Arguments----------
参数：data
The data set to be used for the precalculation. This is usually a matrix or data frame with the samples in the rows and the features in the columns.
预先计算的数据设置为不可用。这通常是与样品中的行和列中的功能的一个矩阵或数据框。
参数：labels
A vector of class labels for the samples in data.
一个向量的样品中data类的标签。
参数：classifier
A TuneParetoClassifier wrapper object containing the classifier to tune. A number of state-of-the-art classifiers are included in TunePareto (see predefinedClassifiers). Custom classifiers can be employed using tuneParetoClassifier.
ATuneParetoClassifier包装对象，它包含的分类调整。许多国家的艺术分类都包含在TunePareto（见predefinedClassifiers“）。自定义分类，可以采用使用tuneParetoClassifier。
参数：classifierParams
A named list of parameter assignments for the training routine of the classifier.
命名的例行训练的分类列表中的参数分配。
参数：predictorParams
If the classifier consists of separate training and prediction functions, a named list of parameter assignments for the predictor function.
如果分类由独立的训练和预测功能，命名列表的预测函数的参数分配。
参数：nfold
The number of groups of the cross-validation. Ignored if leaveOneOut=TRUE.
交叉验证的基团的数目。如果忽略leaveOneOut=TRUE。
参数：ntimes
The number of repeated runs of the cross-validation.
交叉验证的反复运行的数目。
参数：leaveOneOut
If this is true, a leave-one-out cross-validation is performed, i.e. each sample is left out once in the training phase and used as a test sample
如果这是真的，离开一个进行交叉验证，即每个样品离开了曾经在训练阶段和使用作为测试样本
参数：stratified
If set to true, a stratified cross-validation is carried out. That is, the percentage of samples from different classes in the cross-validation folds corresponds to the class sizes in the complete data set. If set to false, the folds may be unbalanced.
如果设置为true，进行分层交叉验证。即是，从不同的类中的交叉验证褶皱的样品的百分比对应于在完整的数据集的类的大小。如果设置为false，则可能是不平衡的褶皱。
参数：foldList
If this parameter is set, the other cross-validation parameters (ntimes, nfold, leaveOneOut, stratified) are ignored. Instead, the precalculated cross-validation partition supplied in foldList is used. This allows for using the same cross-validation experiment in multiple tunePareto calls. Partitions can be generated using generateCVRuns.
如果此参数设置，其他交叉验证参数（ntimes，nfold，leaveOneOut，stratified）将被忽略。相反，使用预先计算的交叉验证分区提供foldList。这允许在多个tunePareto呼叫使用相同的交叉验证实验。分区可以生成使用generateCVRuns。
Details
详细信息----------Details----------
reclassification trains the classifier with the full data set. Afterwards, the classifier is applied to the same data set.
reclassification完整的数据集训练分类。之后，分类器被施加到相同的数据集。
crossValidate partitions the samples in the data set into a number of groups (depending on nfold and leaveOneOut). Each of these groups is left out once in the training phase and used for prediction. The whole procedure is repeated several times (as specified in ntimes).
crossValidate分区的样本中的数据设置成若干组（根据nfold和leaveOneOut）。这些基团中的每一个离开一次在训练阶段，用于预测。整个过程重复几次（ntimes）。
值----------Value----------
reclassification returns a list with the following components:
reclassification返回一个列表，有以下组件：
trueLabels The original labels of the dataset as supplied in labels
labels trueLabels原标签的数据集提供
predictedLabels A vector of predicted labels of the data set
predictedLabels预测标签的数据集的向量
crossValidation returns a nested list structure. At the top level, there is one list element for each run of the cross-validation. Each of these elements consists of a list of sub-structures for each fold. The sub-structures have the following components:
crossValidation返回一个嵌套列表结构。在顶层，有一个列表中的元素为每个运行的交叉验证。这些元素中的每一个都包括子结构的每个褶皱的列表。子结构有以下组件：
trueLabels The original labels of the test samples in the fold
trueLabels的试验样品中的倍的原始标签
predictedLabels A vector of predicted labels of the test samples in the fold
predictedLabels预测的试验样品的标签的向量中折
That is, for a cross-validation with n runs and m folds, there are n top-level lists, each having m sub-lists comprising the true labels and the predicted labels.
也就是说，对于交叉验证用n运行m褶皱，有n顶层的列表，每一个具有m子列表包括真正的标签和预测的标签。
参见----------See Also----------
createObjective, generateCVRuns.
createObjective，generateCVRuns。
实例----------Examples----------
# create new objective minimizing the[创建新的目标，最大限度地减少]
# false positives of a reclassification[误报重新分类]
cvFalsePositives <- function(nfold=10, ntimes=10, leaveOneOut=FALSE, foldList=NULL, caseClass)
{
return(createObjective(
precalculationFunction = "crossValidation",
precalculationParams = list(nfold=nfold,
ntimes=ntimes,
leaveOneOut=leaveOneOut,
foldList=foldList),
objectiveFunction =
function(result, caseClass)
{
# take mean value over the cv runs[取平均值的CV运行]
return(mean(sapply(result,
function(run)
# iterate over runs of cross-validation[迭代运行的交叉验证]
{
# extract all predicted labels in the folds[提取的褶皱所有预测的标签]
predictedLabels <-
unlist(lapply(run,
function(fold)fold$predictedLabels))
# extract all true labels in the folds[提取的褶皱所有真正的标签]
trueLabels <-
unlist(lapply(run,
function(fold)fold$trueLabels))
# calculate number of false positives in the run[计算在运行误报数量]
return(sum(predictedLabels == caseClass &
trueLabels != caseClass))
})))
},
objectiveFunctionParams = list(caseClass=caseClass),
direction = "minimize",
name = "CV.FalsePositives"))
}
# use the objective in an SVM cost parameter tuning on the 'iris' data set[IRIS数据集，使用成本的SVM参数整定的目标]
r <- tunePareto(data = iris[, -ncol(iris)],
labels = iris[, ncol(iris)],
classifier = tunePareto.svm(),
cost = c(0.001,0.005,0.01,0.05,0.1,0.5,1,5,10,50),
objectiveFunctions=list(cvFalsePositives(10, 10, caseClass="setosa")))
print(r)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 12:58:48
