nilmtk.exceptions.MeasurementError: AC type 'apparent' not available. Available columns = [('power', 'active')]

[mmeism]

I have converted the Refit dataset with the convert_refit function resulting in a refit.h5 file.
Now I have a problem when setting up an experiment with the refit.h5 file.

error code:
Traceback (most recent call last):
File "C:/Users/mime02/PycharmProjects/EnergyPredictionLSTM/Evaluation/NILMTK/NILMTK_con.py", line 168, in
api_res = API(refit)
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py", line 46, in init
self.experiment()
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py", line 105, in experiment
self.test_jointly(d)
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py", line 250, in test_jointly
test_mains=next(test.buildings[building].elec.mains().load(physical_quantity='power', ac_type='apparent', sample_period=self.sample_period))
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\elecmeter.py", line 451, in load
last_node = self.get_source_node(**kwargs)
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\elecmeter.py", line 576, in get_source_node
loader_kwargs = self._convert_physical_quantity_and_ac_type_to_cols(**loader_kwargs)
File "C:\Users\mime02\Anaconda3\envs\nilm\lib\site-packages\nilmtk\elecmeter.py", line 560, in _convert_physical_quantity_and_ac_type_to_cols
raise MeasurementError(msg)
nilmtk.exceptions.MeasurementError: AC type 'apparent' not available. Available columns = [('power', 'active')].
Closing remaining open files:C:\Users\refit.h5

How can I fix this issue?
Hope you can help

My code is:

refit = {
'power': {
'mains': ['apparent', 'active'],
'appliance': ['apparent', 'active']
},
'sample_rate': 100,

'appliances': ['fridge'],
'methods': {
    "CombinatorialOptimisation": CO({}),
    "FHMM_EXACT": FHMMExact({'num_of_states': 2}),

    'WindowGRU': WindowGRU({'n_epochs': 1, 'batch_size': 32}),   
    'RNN': RNN({'n_epochs': 1, 'batch_size': 32}),       
    'DAE': DAE({'n_epochs': 1, 'batch_size': 32}),        
    'Seq2Point': Seq2Point({'n_epochs': 1, 'batch_size': 32}),      
    'Seq2Seq': Seq2Seq({'n_epochs': 1, 'batch_size': 32}),     
},
'train': {
    'datasets': {
        'Dataport': {
            'path': r'C:\Users\refit.h5',
            'buildings': {
                2: {
                    'start_time': '2013-10-10',
                    'end_time': '2013-10-20'
                },
            }

        }
    }
},
'test': {
    'datasets': {
        'Dataport': {
            'path': r'C:\Users\refit.h5',
            'buildings': {
                2: {
                    'start_time': '2013-11-01',
                    'end_time': '2013-11-11'
                },
            }
        }
    },
    'metrics': ['mae', 'rmse']
}

}

api_res = API(refit)

[HYuTing]

Can I apply for the REFIT.h5 dataset? Thank you very much

[mrshekari]

Hi, @mmeism did you manage to solve the problem?