Error in unique.default(x, nmax = nmax) : What should I do?-CodePudding

I am building a Deep Learning model for intrusion detection using UNSW18 dataset but at the confusion matrix stage, I kept having this error, Error in unique.default(x, nmax = nmax) : unique() applies only to vectors. Everything seems ok up until the confusion matrix. Can someone help me with what I can do. The dataset has 22 columns and over 1 million rows. The 22nd column is the target variable. Below is code I used:

#convert datafarame to matrix and remove dimensions
DF <- as.matrix(new_UNSW2A)
dimnames(DF) <-NULL
set.seed(222)

#data partition
part <- sample(1:1999934, 0.97*nrow(DF), replace = F)
training<- DF[part,1:21]
testing <- DF[-part,1:21]
trainingtarget <- DF[part,22]
testingtarget <- DF[-part,22]
trainLabels <- to_categorical(trainingtarget)
testLabels <- to_categorical(testingtarget)
testLabels[1:10,]

#build model
model <- keras_model_sequential()
model%>%layer_dense(units = 8, activation = "relu", input_shape = c(21)) %>%
  layer_dense(units = 2, activation = "sigmoid")
summary(model)

# compile the loss function
model %>% compile(loss= "binary_crossentropy", 
                  optimizer="adam",
                  metrics=c("accuracy")
)
#fit model
history <- model %>% fit(training, trainLabels, epochs=20, batch_size=32, 
                         validation_split=0.03,)
plot(history)


# model evaluation
model %>% evaluate(testing, testLabels)
pred <- model %>% predict(testing) %>% `>`(0.5) %>% k_cast("int32")
#table(Predicted=pred, Actual=testingtarget)
confusionMatrix(pred, as.factor(testingtarget))

The error I get is and even when I checked the levels, they're the same.

> confusionMatrix(pred, as.factor(testingtarget))
Error: `data` and `reference` should be factors with the same levels.

A dput output is here below

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0.441558442, 0.441558441558442, 0.051948052, 0.246753247, 0.831168831, 
0.779220779, 1, 0.779220779, 0.779220779220779, 0.454545454545455
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CodePudding user response：

You could convert your pred to a vector like this:

# model evaluation
model %>% evaluate(testing, testLabels)
#pred <- model %>% predict(testing) %>% `>`(0.5) %>% k_cast("int32")
pred <- model %>% predict(testing) %>% as.data.frame() %>%
  mutate(target = ifelse(V1 > 0.5, 1, 0)) %>% select(target) %>% as.vector()
#table(Predicted=pred, Actual=testingtarget)
confusionMatrix(table(pred$target, as.factor(testingtarget)))

Output:

Confusion Matrix and Statistics

   
     0  1
  0  0  1
  1 20  9
                                         
               Accuracy : 0.3            
                 95% CI : (0.1473, 0.494)
    No Information Rate : 0.6667         
    P-Value [Acc > NIR] : 1              
                                         
                  Kappa : -0.0678        
                                         
 Mcnemar's Test P-Value : 8.568e-05      
                                         
            Sensitivity : 0.00000        
            Specificity : 0.90000        
         Pos Pred Value : 0.00000        
         Neg Pred Value : 0.31034        
             Prevalence : 0.66667        
         Detection Rate : 0.00000        
   Detection Prevalence : 0.03333        
      Balanced Accuracy : 0.45000        
                                         
       'Positive' Class : 0