One hot encoded output to categorical value from a ML model

Question

In my one .py file I created a model and saved the .pkl file of it to use afterward for analysis. The model is formed using the code from this kaggle emotional data set https://www.kaggle.com/shivamburnwal/speech-emotion-recognition The issue is that when I am using this code's model to detect the emotion of new audio then the output is in one hot encoded format. Is there any way using which I can get the actual emotion('happy','fear' etc.) instead of 1's and 0's.

Answer 1

prediction output is a list like this:

prediction = [0,0,0,0,1,0,0] , [0,1,0,0,0,0,0], [0,0,0,1,0,0,0]

Then you can change them by using this code:

pred = []

for x in prediction:
    if x == [0,0,0,0,1,0,0]:
        pred.append('Sad')
    
    elif x == [0,1,0,0,0,0,0]:
        pred.append('Happy')
        
    elif x == [0,0,0,1,0,0,0]:
        pred.append('disgust')

print(pred)

output =

['Sad', 'Happy', 'disgust']

Please add more elif statement according to your need.