In [1]:

    

import numpy as np
import pandas as pd
from os.path import join

from pylab import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate import CombinatorialOptimisation, fhmm_exact
from nilmtk.utils import print_dict
from nilmtk.metrics import f1_score

import warnings
warnings.filterwarnings("ignore")


    

    




Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 26 days

In [2]:

    

# download_wikienergy(database_username, database_password, hdf_filename)


    

In [3]:

    

data_dir = '/Users/nipunbatra/Dropbox/nilmtk_datasets/'
we = DataSet(join(data_dir, 'redd.h5'))
print('loaded ' + str(len(we.buildings)) + ' buildings')


    

    




---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-3-3ab466afbfc0> in <module>()
      1 data_dir = '/Users/nipunbatra/Dropbox/nilmtk_datasets/'
----> 2 we = DataSet(join(data_dir, 'redd.h5'))
      3 print('loaded ' + str(len(we.buildings)) + ' buildings')

/Users/nipunbatra/git/nilmtk/nilmtk/dataset.pyc in __init__(self, filename, format)
     42         self.metadata = {}
     43         if filename is not None:
---> 44             self.import_metadata(get_datastore(filename, format))
     45 
     46     def import_metadata(self, store):

/Users/nipunbatra/git/nilmtk/nilmtk/utils.pyc in get_datastore(filename, format, mode)
    310     if filename is not None:
    311         if format == 'HDF':
--> 312             return HDFDataStore(filename, mode)
    313         elif format == 'CSV':
    314             return CSVDataStore(filename)

/Users/nipunbatra/git/nilmtk/nilmtk/docinherit.pyc in f(*args, **kwargs)
     44         @wraps(self.mthd, assigned=('__name__','__module__'))
     45         def f(*args, **kwargs):
---> 46             return self.mthd(obj, *args, **kwargs)
     47 
     48         return self.use_parent_doc(f, overridden)

/Users/nipunbatra/git/nilmtk/nilmtk/datastore/hdfdatastore.pyc in __init__(self, filename, mode)
     27     def __init__(self, filename, mode='a'):
     28         if mode == 'a' and not isfile(filename):
---> 29             raise IOError("No such file as " + filename)
     30         self.store = pd.HDFStore(filename, mode, complevel=9, complib='blosc')
     31         super(HDFDataStore, self).__init__()

IOError: No such file as /Users/nipunbatra/Dropbox/nilmtk_datasets/redd.h5

In [4]:

    

building_number = 1
print_dict(we.buildings[building_number].metadata)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-fe1eb293ce97> in <module>()
      1 building_number = 1
----> 2 print_dict(we.buildings[building_number].metadata)

NameError: name 'we' is not defined

In [5]:

    

elec = we.buildings[building_number].elec
elec.appliances


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-e4ec6f193a95> in <module>()
----> 1 elec = we.buildings[building_number].elec
      2 elec.appliances

NameError: name 'we' is not defined

In [6]:

    

train = DataSet("/Users/nipunbatra/Dropbox/nilmtk_datasets/redd.h5")
test = DataSet("/Users/nipunbatra/Dropbox/nilmtk_datasets/redd.h5")


    

In [7]:

    

train.buildings[1].elec.mains().plot()


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x10a8a3d50>






    

In [8]:

    

train.set_window(end="30-4-2011")
test.set_window(start="30-4-2011")


    

In [9]:

    

train_elec = train.buildings[1].elec
test_elec = test.buildings[1].elec


    

In [10]:

    

train_elec.mains().plot()


    

    
Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x10ca3a850>






    

In [11]:

    

test_elec.mains().plot()


    

    
Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x10b7b2e90>






    

In [12]:

    

m = train_elec['fridge']


    

In [13]:

    

df_raw = m.load().next()


    

In [14]:

    

df_raw.head(10)


    

    
Out[14]:






  

    

      
physical_quantity
      
power
    
    

      
type
      
active
    
  
  

    

      
2011-04-18 09:22:13-04:00
      
 6
    
    

      
2011-04-18 09:22:16-04:00
      
 6
    
    

      
2011-04-18 09:22:20-04:00
      
 6
    
    

      
2011-04-18 09:22:23-04:00
      
 6
    
    

      
2011-04-18 09:22:26-04:00
      
 6
    
    

      
2011-04-18 09:22:30-04:00
      
 6
    
    

      
2011-04-18 09:22:33-04:00
      
 6
    
    

      
2011-04-18 09:22:37-04:00
      
 6
    
    

      
2011-04-18 09:22:40-04:00
      
 6
    
    

      
2011-04-18 09:22:44-04:00
      
 6
    
  

In [15]:

    

df_1_min = m.power_series(sample_period=60).next()


    

In [16]:

    

df_1_min.head()


    

    
Out[16]:





2011-04-18 09:22:00-04:00    6.0000
2011-04-18 09:23:00-04:00    6.0000
2011-04-18 09:24:00-04:00    6.0000
2011-04-18 09:25:00-04:00    5.9375
2011-04-18 09:26:00-04:00    5.9375
Freq: 60S, Name: (power, active), dtype: float64

In [17]:

    

top_5_train_elec = train_elec.submeters().select_top_k(k=5)


    

    




15/16 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)])
16/16 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)])
Calculating total_energy for ElecMeterID(instance=20, building=1, dataset='REDD') ...   

In [18]:

    

top_5_train_elec


    

    
Out[18]:





MeterGroup(meters=
  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])
  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])
  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])
  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])
  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])
)

In [19]:

    

import time


    

In [20]:

    

"""start=time.time()
fhmm = fhmm_exact.FHMM()
fhmm.train(top_5_train_elec)
end=time.time()
print end-start
"""


    

    
Out[20]:





'start=time.time()\nfhmm = fhmm_exact.FHMM()\nfhmm.train(top_5_train_elec)\nend=time.time()\nprint end-start\n'

In [21]:

    

start=time.time()
fhmm = fhmm_exact.FHMM()
fhmm.train(top_5_train_elec, sample_period=60)
end=time.time()
print end-start


    

    




Training model for submeter 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'
Training model for submeter 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'
Training model for submeter 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'
Training model for submeter 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'
Training model for submeter 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'
13.5836830139

In [22]:

    

disag_filename = join(data_dir, 'redd-disag-fhmm-new.h5')
output = HDFDataStore(disag_filename, 'w')
fhmm.disaggregate(test_elec.mains(), output, sample_period=60)
output.close()


    

    




Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.






    




Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in Exception tables.exceptions.HDF5ExtError: HDF5ExtError('Problems closing the Group None',) in 

In [23]:

    

disag_filename = join(data_dir, 'redd-disag-fhmm-new.h5')
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec


    

In [24]:

    

disag_elec.plot()


    

    
Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cbaebd0>






    

In [25]:

    

f1 = f1_score(disag_elec, test_elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance');
plt.xlabel('f-score');
plt.title("FHMM");


    

In [17]:

    

# CSS styling
from IPython.core.display import display, HTML
display(HTML(open('static/styles.css', 'r').read()));