支持向量机适用于训练集，但不适用于 R 中的测试集(使用 e1071)

Question

我正在使用支持向量机来完成我的文档分类任务！ 它对训练集中的所有文章进行分类，但无法对测试集中的文章进行分类！trainDTM 是我的训练集的文档术语矩阵。testDTM 是用于测试集的那个。这是我（不太漂亮）的代码：

# create data.frame with labelled sentences
labeled <- as.data.frame(read.xlsx("C:\Users\LABELED.xlsx", 1, header=T))
# create training set and test set
traindata <- as.data.frame(labeled[1:700,c("ARTICLE","CLASS")])
testdata <- as.data.frame(labeled[701:1000, c("ARTICLE","CLASS")])
# Vector, Source Transformation
trainvector <- as.vector(traindata$"ARTICLE")
testvector <- as.vector(testdata$"ARTICLE")
trainsource <- VectorSource(trainvector)
testsource <- VectorSource(testvector)
# CREATE CORPUS FOR DATA
traincorpus <- Corpus(trainsource)
testcorpus <- Corpus(testsource)
# my own stopwords
sw <- c("i", "me", "my")
## CLEAN TEXT
# FUNCTION FOR CLEANING
cleanCorpus <- function(corpus){
  corpus.tmp <- tm_map(corpus, removePunctuation)
  corpus.tmp <- tm_map(corpus.tmp,stripWhitespace)
  corpus.tmp <- tm_map(corpus.tmp,tolower)
  corpus.tmp <- tm_map(corpus.tmp, removeWords, sw)
  corpus.tmp <- tm_map(corpus.tmp, removeNumbers)
  corpus.tmp <- tm_map(corpus.tmp, stemDocument, language="en")
  return(corpus.tmp)}
# CLEAN CORP WITH ABOVE FUNCTION
traincorpus.cln <- cleanCorpus(traincorpus)
testcorpus.cln <- cleanCorpus(testcorpus)
## CREATE N-GRAM DOCUMENT TERM MATRIX 
# CREATE N-GRAM TOKENIZER
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 1, max = 1))
# CREATE DTM
trainmatrix.cln.bi <- DocumentTermMatrix(traincorpus.cln, control = list(tokenize = BigramTokenizer))
testmatrix.cln.bi <- DocumentTermMatrix(testcorpus.cln, control = list(tokenize = BigramTokenizer))
# REMOVE SPARSE TERMS
trainDTM <- removeSparseTerms(trainmatrix.cln.bi, 0.98)
testDTM <- removeSparseTerms(testmatrix.cln.bi, 0.98)
# train the model
SVM <- svm(as.matrix(trainDTM), as.factor(traindata$CLASS))
# get classifications for training-set
results.train <- predict(SVM, as.matrix(trainDTM)) # works fine!
# get classifications for test-set
results <- predict(SVM,as.matrix(testDTM))
Error in scale.default(newdata[, object$scaled, drop = FALSE], center = object$x.scale$"scaled:center",  : 
  length of 'center' must equal the number of columns of 'x'

我不明白这个错误。 什么是"中心"？

谢谢！！

Answer 1

训练和测试数据必须位于相同的特征空间中;以这种方式构建两个单独的DTM是行不通的。

使用 RTextTools 的解决方案：

DocTermMatrix <- create_matrix(labeled, language="english", removeNumbers=TRUE, stemWords=TRUE, ...)
container <- create_container(DocTermMatrix, labels, trainSize=1:700, testSize=701:1000, virgin=FALSE)
models <- train_models(container, "SVM")
results <- classify_models(container, models)

或者，要回答您的问题（使用 e1071），您可以在投影（DocumentTermMatrix）中指定词汇表（"功能"）：

DocTermMatrixTrain <- DocumentTermMatrix(Corpus(VectorSource(trainDoc)));
Features <- DocTermMatrixTrain$dimnames$Terms;
DocTermMatrixTest <- DocumentTermMatrix(Corpus(VectorSource(testDoc)),control=list(dictionary=Features));