Question

嗨，我正在研究在LIME模型上使用R进行解释。当我运行此部分时，一切都很好。

# Library
library(tm)
library(SnowballC)
library(caTools)
library(RWeka)
library(caret)
library(text2vec)
library(lime)

# Importing the dataset
dataset_original = read.delim('Restaurant_Reviews.tsv', quote = '', stringsAsFactors = FALSE)
dataset_original$Liked = as.factor(dataset_original$Liked)

# Splitting the dataset into the Training set and Test set
set.seed(123)
split = sample.split(dataset_original$Liked, SplitRatio = 0.8)
training_set = subset(dataset_original, split == TRUE)
test_set = subset(dataset_original, split == FALSE)

#Create & clean corpus
#clean corpus function
clean_text <- function(text) {
  corpus = VCorpus(VectorSource(text))
  corpus = tm_map(corpus, content_transformer(tolower))
  corpus = tm_map(corpus, removeNumbers)
  corpus = tm_map(corpus, removePunctuation)
  corpus = tm_map(corpus, removeWords, stopwords())
  corpus = tm_map(corpus, stemDocument)
  corpus = tm_map(corpus, stripWhitespace)
  return(corpus)
}

#ngram function
BigramTokenizer <- function(x){NGramTokenizer(x, Weka_control(min=1,max=2))}

#create dtm
dtm <- function(text){
  corpus = VCorpus(VectorSource(text))
  dtm = DocumentTermMatrix(corpus, control = list(weighting=weightTfIdf, tokenize=BigramTokenizer))
  dataset = as.data.frame(as.matrix(dtm))
  dataset = dataset[,order(names(dataset))] 
  return(dataset)
}

#cleaning train & test text
for (i in seq(nrow(training_set))) {
  training_set$clean_text[i] = as.character(clean_text(training_set$Review)[[i]])
  print(i)
}

for (i in seq(nrow(test_set))) {
  test_set$clean_text[i] = as.character(clean_text(test_set$Review)[[i]])
  print(i)
}

#Create document term matrix
dataset_train <- dtm(training_set$clean_text)
dataset_test <- dtm(test_set$clean_text)

#Drop new words in test set & ensure same number of columns as train set
test_colname <- colnames(dataset_test)[colnames(dataset_test) %in% colnames(dataset_train)]
test_colname <- test_colname[!is.na(test_colname)] #Remove NA
new_test_colname <- colnames(dataset_train)[!(colnames(dataset_train) %in% test_colname)] #Columns in train not in test
dataset_test <- dataset_test[,test_colname]
dataset_test[new_test_colname] <- 0
dataset_test = dataset_test[,order(names(dataset_test))] 

dataset_train = as.matrix(dataset_train)
dataset_test = as.matrix(dataset_test)

#xgboost caret model
set.seed(123)
model <- train(dataset_train, training_set$Liked, method="xgbTree")
predict(model, newdata=dataset_test)

但是，当我运行此部分时：

######
#LIME#
######
explainer <- lime(training_set$Review, model, preprocess = dtm)
explanation <- explain(training_set$Review[1], explainer, n_labels = 1, n_features = 5)
plot_features(explanation)

它说：

 Error in predict.xgb.Booster(modelFit, newdata) : 
Feature names stored in `object` and `newdata` are different!

在运行此程序之前，我确保我的火车和测试数据具有相同的列名和编号。我也环顾四周，发现我的问题与这篇文章相似，但是我仍然缺乏对此链接的理解。 R: LIME returns error on different feature numbers when it's not the case

我花了数周时间进行这项工作并在线搜索，但无济于事，因此，非常感谢您对我应该做的任何帮助或指导！

我的数据：

数据集：https://drive.google.com/file/d/1-pzY7IQVyB_GmT5dT0yRx3hYzOFGrZSr/view?usp=sharing

Answer 1

将xgboost软件包从v0.6.xxx更新到v0.7.xxx时，我遇到了同样的问题。

我解决了这一问题，不仅要确保训练和测试集中的列名相同，而且要确保列的顺序相同。

希望这对您有用。

Answer 2

以下是对我来说适用于同一问题的代码。您的clean_text和dtm函数中有一个小问题。您需要将语料库传递给dtm而不是原始文本；我将它们合并在一起。

dataset_original$Liked = as.factor(dataset_original$Liked)

# Splitting the dataset into the Training set and Test set
set.seed(123)
split = sample.split(dataset_original$Liked, SplitRatio = 0.8)
training_set = subset(dataset_original, split == TRUE)
test_set = subset(dataset_original, split == FALSE)

#ngram function
BigramTokenizer <- function(x){NGramTokenizer(x, Weka_control(min=1,max=2))}

#create dtm    
dtm <- function(text){
  corpus = VCorpus(VectorSource(text))
  corpus = tm_map(corpus, content_transformer(tolower))
  corpus = tm_map(corpus, removeNumbers)
  corpus = tm_map(corpus, removePunctuation)
  corpus = tm_map(corpus, removeWords, stopwords())
  corpus = tm_map(corpus, stemDocument)
  corpus = tm_map(corpus, stripWhitespace)

  # pass corpus to dtm
  dtm = DocumentTermMatrix(corpus, control = list(weighting=weightTfIdf, 
      tokenize=BigramTokenizer))

  return(as.matrix(dtm))
}

#Create document term matrix
dataset_train <- dtm(training_set$Review)
dataset_test <- dtm(test_set$Review)

# same columns and same order for for both data 
matrix_columns_same <- function(a,b) {  
  # a and b: two matrices  
  intersect_cols12 <- intersect(colnames(a),colnames(b))
  result_matrix <- matrix(0, nrow = nrow(b), ncol = ncol(a))
  rownames(result_matrix) <- rownames(b)
  colnames(result_matrix) <- colnames(a)  
  result_matrix[,intersect_cols12] <- b[, intersect_cols12]  
  return(result_matrix)
}
dataset_test <- matrix_columns_same(dataset_train,dataset_test)

# from xgboost package
param <- list(max_depth = 3, 
          eta = 0.1, 
          objective = "binary:logistic", 
          eval_metric = "error", 
          nthread = 1)

model <-xgboost::xgb.train(
 param, 
 xgb.DMatrix(dataset_train, label = as.numeric(training_set$Liked)-1),
 nrounds = 50
)

predictions <- predict(model, dataset_test) 

# text to explain
text_to_explain <- test_set$Review[1:4]
explainer <- lime(text_to_explain, model, preprocess = dtm)
explanation <- explain(text_to_explain, explainer, n_labels = 1, n_features = 3)
plot_features(explanation)

也请参见R Lime package for text data中的类似讨论。

＃＃

这是使用您的数据的代码。它对我有用，如果再次遇到错误，请告诉我。

# 
library(tm)
library(lime)
library(xgboost)

# read data
dataset_original = read.delim('./data/Restaurant_Reviews.tsv', quote = '', 
stringsAsFactors = FALSE)
dataset_original$Liked = as.factor(dataset_original$Liked)

# removing docs with less words
nwords <- 5
docs_split <- lapply(strsplit(dataset_original$Review, " "), function(x){x[!x 
==""]}) #docs to list of tokens
ind_len <- unlist(lapply(docs_split,function(d) length(d)))
ind_len <- which(ind_len>nwords)
dataset_original    <- dataset_original[ind_len,]

groups <- levels(dataset_original$Liked)

# Splitting the dataset into the Training set and Test set
set.seed(123)
split = sample.split(dataset_original$Liked, SplitRatio = 0.8)
training_set = subset(dataset_original, split == TRUE)
test_set = subset(dataset_original, split == FALSE)

########################################
#ngram function
BigramTokenizer <- function(x){NGramTokenizer(x, Weka_control(min=1,max=2))}

#create dtm
dtm <- function(text){
  corpus = VCorpus(VectorSource(text))
  corpus = tm_map(corpus, content_transformer(tolower))
  corpus = tm_map(corpus, removeNumbers)
  corpus = tm_map(corpus, removePunctuation)
  corpus = tm_map(corpus, removeWords, stopwords())
  corpus = tm_map(corpus, stemDocument)
  corpus = tm_map(corpus, stripWhitespace)

 dtm = DocumentTermMatrix(corpus, control = list(weighting=weightTf, 
 tokenize=BigramTokenizer))
 dtm = removeSparseTerms(dtm,0.99)

 dtm <- as.matrix(dtm)
 dtm <- as.data.frame(dtm)
 return(dtm)
}

#Create document term matrix
dataset_train <- dtm(training_set$Review)
dataset_test  <- dtm(test_set$Review)
colnames(dataset_train) <- gsub(" ","_",colnames(dataset_train))
colnames(dataset_test) <- gsub(" ","_",colnames(dataset_test))

########################################
matrix_columns_same <- function(a,b) {
  # a and b: two matrices
  intersect_cols12 <- intersect(colnames(a),colnames(b))
  result_matrix <- matrix(0, nrow = nrow(b), ncol = ncol(a))
  rownames(result_matrix) <- rownames(b)
  colnames(result_matrix) <- colnames(a)
  result_matrix[,intersect_cols12] <- b[, intersect_cols12]
  return(result_matrix)
}

dataset_train <- as.matrix(dataset_train)
dataset_test  <- as.matrix(dataset_test)

dataset_test  <- matrix_columns_same(dataset_train,dataset_test)

# filter docs; make sure documents have at least one word
nword <- 0
ind <- which(rowSums(dataset_train)>nword)
dataset_train <- dataset_train[ind,]
training_set  <- training_set[ind,]

ind <- which(rowSums(dataset_test)>nword)
dataset_test <- dataset_test[ind,]
test_set      <- test_set[ind,]

########################################
# using xgboost package
param <- list(max_depth = 3, 
          eta = 0.1, 
          objective = "binary:logistic", 
          eval_metric = "error", 
          nthread = 1)

model <-xgboost::xgb.train(
  param, 
  xgb.DMatrix(as.matrix(dataset_train), label = 
as.numeric(training_set$Liked)-1),
  nrounds = 50
)

predictions <- predict(model, as.matrix(dataset_test)) > 0.5
test_labels <- test_set$Liked==groups[2]

# Accuracy
caret::confusionMatrix(table(predictions,test_labels))

########################################
# lime
ind_tr <- sample(1:nrow(test_set),4,replace = F)
text_to_explain <- test_set$Review[ind_tr]

explainer   <- lime(text_to_explain, model, preprocess = dtm, 
                bin_continuous=T, n_bins = 4, n_permutations = 5000) 
explanation <- lime::explain(text_to_explain, explainer, n_labels = 1, n_features = 3)
plot_features(explanation, ncol=2)

Answer 3

从xgboost模型进行预测时，我遇到了同样的问题。
就我而言，我在训练之前进行了sparse.model.matrix转换。

varX=c('l8','l21','v8','v21','fa','fb')
f1=as.formula(paste0('rV','~',paste(varX,collapse='+')))
sparse_matrix=sparse.model.matrix(f1, data = rstac)
mod=xgboost(data=sparse_matrix,label=rV,...)

我在

中遇到了错误

y=predict(mod,newdata=as.matrix(rstac[1:10,varX]))
Error in predict.xgb.Booster(mod, newdata = as.matrix(rstac[1:10, varX])) : 
Feature names stored in `object` and `newdata` are different!

我可以在mod[[8]]中看到模型中使用的功能：

mod[[8]]
[1] "(Intercept)"              "l8"                       "l21"                      "v8"                      
[5] "v21"                      "fa"                       "fb"

(Intercept)丢失。在工作前先做sparse.model.matrix。

y=predict(mod,newdata=sparse.model.matrix(~.,rstac[1:10,varX]))
y
[1] 0.3290127 0.3290127 0.6757481 0.6667279 0.6668081 0.6668081 0.3290127 0.2944945 0.2944945 0.2944945

Answer 4

我有完全一样的问题。对我来说，解决方案是确保lime :: lime仅包括预测列和NO RESPONSE COLUMN，而对石灰:: explain函数也是如此。

Answer 5

我使用最新的xgboost版本1.1.1.1遇到的两件事

1. xgboost训练和测试数据中的列顺序应该相同
1. 使用带有'save'/'saveRDS'的旧版xgboost版本保存的模型与新版本不兼容，您需要分别使用xgb.save和xgb.load进行保存和检索

也请通过github源链接获取更多信息 https://github.com/dmlc/xgboost/pull/5940

R在Lime上进行了解释-存储在`object`和`newdata`中的特征名称不同

5 个答案: