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In [1]:



    


from nilmtk import DataSet, MeterGroup
from nilmtk import metrics

predictions_dataset = DataSet('output.h5')
ground_truth_dataset = DataSet('redd.h5')



    


















    






/home/jack/workspace/python/nilmtk/nilmtk/metergroup.py:45: RuntimeWarning: Building 1 has an empty 'appliances' list.
  .format(building_id.instance), RuntimeWarning)

















In [2]:



    


predictions_metergroup = predictions_dataset.buildings[1].elec
ground_truth_metergroup = MeterGroup.from_other_metergroup(predictions_metergroup, 'REDD')



    











In [3]:



    


predictions_metergroup



    


















    
Out[3]:








MeterGroup(meters=
  ElecMeter(instance=(1, 2), building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', site_meter, appliances=[])
  ElecMeter(instance=5, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=6, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=7, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=8, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=9, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=11, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=12, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=13, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=14, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=15, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=16, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=17, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=18, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=19, building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=(10, 20), building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
  ElecMeter(instance=(3, 4), building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', appliances=[])
)

















In [4]:



    


ground_truth_metergroup



    


















    
Out[4]:








MeterGroup(meters=
  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])
  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])
  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])
  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])
  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])
  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])
  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])
  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])
  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])
  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])
  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])
  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])
  MeterGroup(meters=
    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
  )
  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])
  MeterGroup(meters=
    ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])
    ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])
  )
  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])
  MeterGroup(meters=
    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
  )
)

















In [5]:



    


ground_truth_metergroup.mains()



    


















    
Out[5]:








MeterGroup(meters=
  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])
  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])
)

















In [6]:



    


predictions_metergroup.mains()



    


















    
Out[6]:








ElecMeter(instance=(1, 2), building=1, dataset='NILMTK_CO_2014-07-10T16:30:56', site_meter, appliances=[])

















In [7]:



    


errors = metrics.error_in_assigned_energy(predictions_metergroup, ground_truth_metergroup)



    


















    






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TimeFrame(start='2011-05-23 10:32:00-04:00', end='2011-05-23 10:33:00-04:00', empty=False)

















In [8]:



    


errors



    


















    
Out[8]:








5            7.250376
6            1.743792
7            4.037591
8            6.418041
9            2.352815
11           0.661055
12          13.695720
13           3.031163
14           8.804081
15           5.888153
16           7.379035
17           7.006063
18           5.055786
19           0.000033
(3, 4)       2.689063
(10, 20)     5.880746
dtype: float64

















In [9]:



    


fraction = metrics.fraction_energy_assigned_correctly(predictions_metergroup, ground_truth_metergroup)
fraction



    


















    






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TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)
TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 09:22:16-04:00', empty=False)










    
Out[9]:








0.74121929523700858

















In [3]:



    


mne = metrics.mean_normalized_error_power(predictions_metergroup, ground_truth_metergroup)
mne



    


















    
Out[3]:








5             0.962096
6             1.619474
7             0.447724
8             0.695128
9             0.741031
11            1.571353
12            6.226984
13           55.373225
14          176.195009
15            4.049207
16            9.995547
17            2.096691
18            2.040126
19            1.000000
(3, 4)        2.105052
(10, 20)      0.584508
dtype: float64

















In [4]:



    


rms_error = metrics.rms_error_power(predictions_metergroup, ground_truth_metergroup)
rms_error



    


















    
Out[4]:








5            98.328120
6           176.584842
7            13.935285
8            26.067940
9            50.690155
11          190.312771
12          146.574397
13            9.188827
14          136.211012
15          149.817818
16          174.649488
17           49.406151
18           37.284800
19            0.007764
(3, 4)      227.366786
(10, 20)    176.503204
dtype: float64

















In [5]:



    


from nilmtk import metrics
f1 = metrics.f1_score(predictions_metergroup, ground_truth_metergroup)
f1



    


















    






/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py:1249: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [1]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [1]. 
  average=average)
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py:1249: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [0]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [0]. 
  average=average)
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py:1249: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [0]. 
  average=average)
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py:1249: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [1]. 
  average=average)
/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py:1249: UserWarning: The sum of true positives and false negatives are equal to zero for some labels. Recall is ill defined for those labels [1]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [1]. 
  average=average)










    
Out[5]:








5           0.616173
6           0.081353
7           0.723777
8           0.647695
9           0.643429
11          0.185846
12          0.061215
13          0.001644
14          0.000000
15          0.124658
16          0.029418
17          0.269020
18          0.276048
19          0.000000
(3, 4)      0.059182
(10, 20)    0.284728
dtype: float64

















In [ ]:

Content source: josemao/nilmtk

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