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%matplotlib inline
import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
#import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate.hart_85 import Hart85
from nilmtk.disaggregate import CombinatorialOptimisation
from nilmtk.utils import print_dict, show_versions
from nilmtk.metrics import f1_score
#import seaborn as sns
#sns.set_palette("Set3", n_colors=12)
import warnings
warnings.filterwarnings("ignore") #suppress warnings, comment out if warnings required
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#uncomment if required
#show_versions()
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data_dir = '/Users/GJWood/nilm_gjw_data/HDF5/'
gjw = DataSet(join(data_dir, 'nilm_gjw_data.hdf5'))
print('loaded ' + str(len(gjw.buildings)) + ' buildings')
building_number=1
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gjw.set_window('2015-06-01 00:00:00', '2015-06-05 00:00:00')
elec = gjw.buildings[building_number].elec
mains = elec.mains()
mains.plot()
#plt.show()
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house = elec['fridge'] #only one meter so any selection will do
df = house.load().next() #load the first chunk of data into a dataframe
#df.info() #check that the data is what we want (optional)
#note the data has two columns and a time index
We'll now do the training from the aggregate data. The algorithm segments the time series data into steady and transient states. Thus, we'll first figure out the transient and the steady states. Next, we'll try and pair the on and the off transitions based on their proximity in time and value.
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df.ix['2015-06-01 10:00:00+01:00':'2015-06-05 12:00:00+01:00'].plot()# select a time range and plot it
#plt.show()
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h = Hart85()
h.train(mains,cols=[('power','active')])
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h.steady_states.head()
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h.steady_states.tail()
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h.centroids
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h.model
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ax = mains.plot()
h.steady_states['active average'].plot(style='o', ax = ax);
plt.ylabel("Power (W)")
plt.xlabel("Time");
#plt.show()
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h.pair_df.head()
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pair_shape_df = pd.DataFrame(columns=['Height','Duration'])
pair_shape_df['Height']= (h.pair_df['T1 Active'].abs()+h.pair_df['T2 Active'].abs())/2
pair_shape_df['Duration']= pd.to_timedelta(h.pair_df['T2 Time']-h.pair_df['T1 Time'],unit='s').dt.seconds
pair_shape_df.head()
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fig = plt.figure(figsize=(13,6))
ax = fig.add_subplot(1, 1, 1)
ax.set_yscale('log')
ax.scatter(pair_shape_df['Height'],pair_shape_df['Duration'])
plt.title("Paired event - Signature Space")
plt.ylabel("Log Duration (sec)")
plt.xlabel("Transition (W)");
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gjw.set_window('2015-06-08 00:00:00','2015-06-10 00:00:00')
elec = gjw.buildings[building_number].elec
mains = elec.mains()
mains.plot()
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ax = mains.plot()
h.steady_states['active average'].plot(style='o', ax = ax);
plt.ylabel("Power (W)")
plt.xlabel("Time");
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plt.hist(h.steady_states['active average'],250)
plt.ylabel("Frequency")
plt.xlabel("Power (w)")
plt.title("Active Average distribution");
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disag_filename = join(data_dir, 'disag_gjw_hart.hdf5')
output = HDFDataStore(disag_filename, 'w')
h.disaggregate(mains,output,sample_period=1)
output.close()
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ax = mains.plot()
h.steady_states['active average'].plot(style='o', ax = ax);
plt.ylabel("Power (W)")
plt.xlabel("Time");
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disag_hart = DataSet(disag_filename)
disag_hart
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disag_hart_elec = disag_hart.buildings[building_number].elec
disag_hart_elec
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disag_hart_elec.mains()
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h.centroids
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h.model
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h.steady_states
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from nilmtk.metrics import f1_score
f1_hart= f1_score(disag_hart_elec, test_elec)
f1_hart.index = disag_hart_elec.get_labels(f1_hart.index)
f1_hart.plot(kind='barh')
plt.ylabel('appliance');
plt.xlabel('f-score');
plt.title("Hart");
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