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from __future__ import print_function, division
import time
from matplotlib import rcParams
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from six import iteritems

%matplotlib inline

rcParams['figure.figsize'] = (13, 6)

from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate import CombinatorialOptimisation, FHMM
import nilmtk.utils

import warnings
warnings.filterwarnings("ignore")


    

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train = DataSet('../datasets/REDD/low_freq.h5')
test = DataSet('../datasets/REDD/low_freq.h5')


    

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building = 1


    

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# The dates are interpreted by Pandas, prefer using ISO dates (yyyy-mm-dd)
train.set_window(end="2011-04-30")
test.set_window(start="2011-04-30")

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


    

In [5]:

    

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


    

    




15/16 MeterGroup(meters==19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])e=1)])ce=1)])
  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= for ElecMeterID(instance=4, building=1, dataset='REDD') ...   
  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 [6]:

    

def predict(clf, test_elec, sample_period, timezone):
    pred = {}
    gt= {}

    for i, chunk in enumerate(test_elec.mains().load(sample_period=sample_period)):
        chunk_drop_na = chunk.dropna()
        pred[i] = clf.disaggregate_chunk(chunk_drop_na)
        gt[i]={}

        for meter in test_elec.submeters().meters:
            # Only use the meters that we trained on (this saves time!)    
            gt[i][meter] = next(meter.load(sample_period=sample_period))
        gt[i] = pd.DataFrame({k:v.squeeze() for k,v in iteritems(gt[i]) if len(v)}, index=next(iter(gt[i].values())).index).dropna()
        
    # If everything can fit in memory
    gt_overall = pd.concat(gt)
    gt_overall.index = gt_overall.index.droplevel()
    pred_overall = pd.concat(pred)
    pred_overall.index = pred_overall.index.droplevel()

    # Having the same order of columns
    gt_overall = gt_overall[pred_overall.columns]
    
    #Intersection of index
    gt_index_utc = gt_overall.index.tz_convert("UTC")
    pred_index_utc = pred_overall.index.tz_convert("UTC")
    common_index_utc = gt_index_utc.intersection(pred_index_utc)
    
    
    common_index_local = common_index_utc.tz_convert(timezone)
    gt_overall = gt_overall.loc[common_index_local]
    pred_overall = pred_overall.loc[common_index_local]
    appliance_labels = [m for m in gt_overall.columns.values]
    gt_overall.columns = appliance_labels
    pred_overall.columns = appliance_labels
    return gt_overall, pred_overall


    

In [7]:

    

# Since the methods use randomized initialization, let's fix a seed here
# to make this notebook reproducible
import numpy.random
numpy.random.seed(42)


    

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classifiers = {'CO':CombinatorialOptimisation(), 'FHMM':FHMM()}
predictions = {}
sample_period = 120
for clf_name, clf in classifiers.items():
    print("*"*20)
    print(clf_name)
    print("*" *20)
    clf.train(top_5_train_elec, sample_period=sample_period)
    gt, predictions[clf_name] = predict(clf, test_elec, 120, train.metadata['timezone'])


    

    




********************
CO
********************
Training model for submeter 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'
Training model for submeter 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'
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)])'
Done training!
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     

In [ ]:

    

rmse = {}
for clf_name in classifiers.keys():
    rmse[clf_name] = nilmtk.utils.compute_rmse(gt, predictions[clf_name], pretty=True)

rmse = pd.DataFrame(rmse)


    

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rmse


    

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