Pycaret does't well manage multicollinearity

Question

I have a Panda Dataframe df in input to Pycaret library. So the df has :

3 categoricals variables:
    LIB_SOURCE  : values: 'arome_001', 'gfs_025' and 'arpege_01'
    MonthNumber : values from 1 to 12
    origine     : 'Sencrop' and 'Visiogreen' values

3 continuous variables : 

    TEMPERATURE_PREDITE  DIFF_HOURS  TEMPERATURE_OBSERVEE

I let Pycaret encoding categorical features to 0/1 and manage multicollinearity:

regression = setup(data = dataset_predictions_meteo, 
                   target = 'TEMPERATURE_PREDITE', 
                   categorical_features = ['MonthNumber' , 'origine' , 'LIB_SOURCE'],
                   numeric_features = ['DIFF_HOURS' , 'TEMPERATURE_OBSERVEE'],  
                   session_id=123,
                   train_size=0.8, 
                   normalize=True, 
                   #transform_target=True,
                   remove_perfect_collinearity = True
                  )

But as you can see in the screen above, Pycaret doesn't well manage multicollinearity : PyCaret should remove by itself 1 of 3 columns 'arome_001', 'gfs_025' and 'arpege_01' (get_config('X')). But PyCaret keeps all 3 columns.

Why PyCaret doesn't remove one of 3 columns? Thanks.