I am trying to understand and implement classifiers A class in R is based on several UCIs and one of them (http://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease).
When trying to print a confusion matrix you are giving the error “all arguments must have the same length”.
What am I doing wrong?
library(caret)
library(dplyr)
library(e1071)
library(NLP)
library(tm)
ds = read.csv('kidney_disease.csv',
header = TRUE)
#Remover colunas inutiliz?veis
ds <- subset(ds, select = -c(age), classification =='ckd' )
x <- subset(ds, select = -classification) #make x variables
y <- ds$classification #make y variable(dependent)
# test on the whole set
#pred <- predict(model, subset(ds, select=-classification))
trainPositive<-x
testnegative<-y
inTrain<-createDataPartition(1:nrow(trainPositive),p=0.6,list=FALSE)
trainpredictors<-trainPositive[inTrain,1:4]
trainLabels<-trainPositive[inTrain,6]
testPositive<-trainPositive[-inTrain,]
testPosNeg<-rbind(testPositive,testnegative)
testpredictors<-testPosNeg[,1:4]
testLabels<-testPosNeg[,6]
svm.model<-svm(trainpredictors,y=NULL,
type='one-classification',
nu=0.10,
scale=TRUE,
kernel="radial")
svm.predtrain<-predict(svm.model,trainpredictors)
svm.predtest<-predict(svm.model,testpredictors)
# confusionMatrixTable<-table(Predicted=svm.pred,Reference=testLabels)
# confusionMatrix(confusionMatrixTable,positive='TRUE')
confTrain <- table(Predicted=svm.predtrain,Reference=trainLabels)
confTest <- table(Predicted=svm.predtest,Reference=testLabels)
confusionMatrix(confTest,positive='TRUE')
print(confTrain)
print(confTest)
#grid
Here are some of the first lines of the dataset I'm using:
id bp sg al su rbc pc pcc ba bgr bu sc sod pot hemo pcv wc
1 0 80 1.020 1 0 normal notpresent notpresent 121 36 1.2 NA NA 15.4 44 7800
2 1 50 1.020 4 0 normal notpresent notpresent NA 18 0.8 NA NA 11.3 38 6000
3 2 80 1.010 2 3 normal normal notpresent notpresent 423 53 1.8 NA NA 9.6 31 7500
4 3 70 1.005 4 0 normal abnormal present notpresent 117 56 3.8 111 2.5 11.2 32 6700
5 4 80 1.010 2 0 normal normal notpresent notpresent 106 26 1.4 NA NA 11.6 35 7300
6 5 90 1.015 3 0 notpresent notpresent 74 25 1.1 142 3.2 12.2 39 7800
rc htn dm cad appet pe ane classification
1 5.2 yes yes no good no no ckd
2 no no no good no no ckd
3 no yes no poor no yes ckd
4 3.9 yes no no poor yes yes ckd
5 4.6 no no no good no no ckd
6 4.4 yes yes no good yes no ckd
The error log:
> confTrain <- table (Predicted = svm.predtrain, Reference = trainLabels)
Table error (Predicted = svm.predtrain, Reference = trainLabels):
all arguments must be the same length
> confTest <- table (Predicted = svm.predtest, Reference = testLabels)
Table error (expected = svm.predtest, reference = testLabels):
all arguments must be the same length
>
> confusionMatrix (confTest, positive = 'TRUE')
ConfusionMatrix error (confTest, positive = "TRUE"):
'confTest' object not found
>
>
> print (confTrain)
Printing error (confTrain): object 'confTrain' not found
> print (confTest)
Printing error (confTest): object 'confTest' not found
I see a number of issues. First it seems that a lot of your data is of class character rather than numeric, which is required by the classifier. Let's pick some columns and convert to numeric. I will use data.table because fread is very convenient.
library(caret)
library(e1071)
library(data.table)
setDT(ds)
#Choose columns
mycols <- c("id","bp","sg","al","su")
#Convert to numeric
ds[,(mycols) := lapply(.SD, as.numeric),.SDcols = mycols]
#Convert classification to logical
data <- ds[,.(bp,sg,al,su,classification = ds$classification == "ckd")]
data
bp sg al su classification
1: 80 1.020 1 0 TRUE
2: 50 1.020 4 0 TRUE
3: 80 1.010 2 3 TRUE
4: 70 1.005 4 0 TRUE
5: 80 1.010 2 0 TRUE
---
396: 80 1.020 0 0 FALSE
397: 70 1.025 0 0 FALSE
398: 80 1.020 0 0 FALSE
399: 60 1.025 0 0 FALSE
400: 80 1.025 0 0 FALSE
Once the data is cleaned up, you can sample a training and test set with createDataPartition as in your original code.
#Sample data for training and test set
inTrain<-createDataPartition(1:nrow(data),p=0.6,list=FALSE)
train<- data[inTrain,]
test <- data[-inTrain,]
Then we can create the model and make the predictions.
svm.model<-svm(classification ~ bp + sg + al + su, data = train,
type='one-classification',
nu=0.10,
scale=TRUE,
kernel="radial")
#Perform predictions
svm.predtrain<-predict(svm.model,train)
svm.predtest<-predict(svm.model,test)
Your main issue with the cross table was that the model can only predict for cases that don't have any NAs, so you have to subset the classification levels to those with predictions. Then you can evaluate confusionMatrix:
confTrain <- table(Predicted=svm.predtrain,
Reference=train$classification[as.integer(names(svm.predtrain))])
confTest <- table(Predicted=svm.predtest,
Reference=test$classification[as.integer(names(svm.predtest))])
confusionMatrix(confTest,positive='TRUE')
Confusion Matrix and Statistics
Reference
Predicted FALSE TRUE
FALSE 0 17
TRUE 55 64
Accuracy : 0.4706
95% CI : (0.3845, 0.558)
No Information Rate : 0.5956
P-Value [Acc > NIR] : 0.9988
Kappa : -0.2361
Mcnemar's Test P-Value : 1.298e-05
Sensitivity : 0.7901
Specificity : 0.0000
Pos Pred Value : 0.5378
Neg Pred Value : 0.0000
Prevalence : 0.5956
Detection Rate : 0.4706
Detection Prevalence : 0.8750
Balanced Accuracy : 0.3951
'Positive' Class : TRUE
Data
library(archive)
library(data.table)
tf1 <- tempfile(fileext = ".rar")
#Download data file
download.file("http://archive.ics.uci.edu/ml/machine-learning-databases/00336/Chronic_Kidney_Disease.rar", tf1)
tf2 <- tempfile()
#Un-rar file
archive_extract(tf1, tf2)
#Read in data
ds <- fread(paste0(tf2,"/Chronic_Kidney_Disease/chronic_kidney_disease.arff"), fill = TRUE, skip = "48")
#Remove erroneous last column
ds[,V26:= NULL]
#Set column names (from header)
setnames(ds,c("id","bp","sg","al","su","rbc","pc","pcc","ba","bgr","bu","sc","sod","pot","hemo","pcv","wc","rc","htn","dm","cad","appet","pe","ane","classification"))
#Replace "?" with NA
ds[ds == "?"] <- NA