from pycaret.datasets import get_data
data = get_data('juice')

from pycaret.classification import *
s = setup(data, target = 'Purchase', session_id = 123, silent = True)

# create a custom function
def calculate_specificity(y, y_pred):
    
    tp, tn, fn, fp = 0.0,0.0,0.0,0.0
    for l,m in enumerate(y):        
        if m==y_pred[l] and m==1:
            tp+=1
        if m==y_pred[l] and m==0:
            tn+=1
        if m!=y_pred[l] and m==1:
            fn+=1
        if m!=y_pred[l] and m==0:
            fp+=1
            
    return tn/(tn+fp)

# add metric to PyCaret
add_metric('specificity', 'specificity', calculate_specificity)

lr = create_model('lr')