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from __future__ import print_function, division
import matplotlib
matplotlib.use('nbagg') # interactive plots in iPython. New in matplotlib v1.4
# %matplotlib inline
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import matplotlib.pyplot as plt
from nilmtk import DataSet, MeterGroup
import pandas as pd
import numpy as np
from time import time
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from pybrain.supervised import RPropMinusTrainer
from pybrain.datasets import SequentialDataSet
from pybrain.structure import RecurrentNetwork, FullConnection
from pybrain.structure.modules import LSTMLayer, BiasUnit, LinearLayer, TanhLayer, SigmoidLayer
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CONFIG = dict(
EPOCHS_PER_CYCLE = 5,
CYCLES = 6,
HIDDEN_LAYERS = [15],
PEEPHOLES = True,
TRAINERCLASS = RPropMinusTrainer,
# instead, you may also try
# TRAINERCLASS = BackpropTrainer(net, dataset=trndata, verbose=True,
# momentum=0.9, learningrate=0.00001)
INPUTS = [], #, 'hour of day (int)', 'outside temperature', 'is business day (-1, 1)'
EXPERIMENT_NUMBER = 16
)
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# Load dataset
dataset = DataSet('/data/mine/vadeec/merged/ukdale.h5')
dataset.set_window("2014-01-01", "2014-01-07")
elec = dataset.buildings[1].elec
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# Select top-5 meters identified in UK-DALE paper
# APPLIANCES = ['kettle', 'dish washer', 'HTPC', 'washer dryer', 'fridge freezer']
APPLIANCES = ['kettle', 'toaster']
selected_meters = [elec[appliance] for appliance in APPLIANCES]
selected_meters.append(elec.mains())
selected = MeterGroup(selected_meters)
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df = selected.dataframe_of_meters()
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# Use human-readable column names
df.columns = selected.get_labels(df.columns)
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mains = (df['Toaster'] + df['Kettle']).fillna(0).diff().dropna()
appliances = df['Toaster'].fillna(0).diff().dropna()
del df
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# Constrain outputs to [-1,1] because we're using TanH
maximum = appliances.abs().max()
appliances /= maximum
mains_same_scale_as_appliances = mains / maximum
# standardise input
mains = (mains - mains.mean()) / mains.std()
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ax = mains.plot()
ax = appliances.plot(ax=ax)
plt.show()
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# Build PyBrain dataset
N_OUTPUTS = 1
N_INPUTS = 1
N = len(mains)
ds = SequentialDataSet(N_INPUTS, N_OUTPUTS)
ds.newSequence()
ds.setField('input', pd.DataFrame(mains).values)
ds.setField('target', pd.DataFrame(appliances).values)
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ds.getSequence(0)
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# Build network
net = RecurrentNetwork()
def lstm_layer_name(i):
return 'LSTM{:d}'.format(i)
# Add modules
net.addInputModule(LinearLayer(ds.indim, name='in'))
net.addOutputModule(TanhLayer(dim=ds.outdim, name='out'))
for i, n_cells in enumerate(CONFIG['HIDDEN_LAYERS']):
net.addModule(LSTMLayer(n_cells, name=lstm_layer_name(i+1), peepholes=CONFIG['PEEPHOLES']))
# Bias
bias = BiasUnit()
net.addModule(bias)
c_output_bias = FullConnection(bias, net['out'], name='c_output_bias')
c_output_bias._setParameters(np.zeros(1))
net.addConnection(c_output_bias)
# Add other connections
n_hidden_layers = len(CONFIG['HIDDEN_LAYERS'])
prev_layer_name = 'in'
for i in range(n_hidden_layers):
hidden_layer_i = i + 1
layer_name = lstm_layer_name(hidden_layer_i)
recurrent_connection = FullConnection(net[layer_name], net[layer_name], name='c_' + layer_name + '_to_' + layer_name)
recurrent_connection._params = np.random.uniform(-0.05, 0.05, size=recurrent_connection.paramdim)
net.addRecurrentConnection(recurrent_connection)
#bias_connection = FullConnection(bias, net[layer_name], name='c_' + layer_name + '_bias')
#bias_connection._params = np.zeros(bias_connection.paramdim)
#net.addConnection(bias_connection)
forwards_connection = FullConnection(net[prev_layer_name], net[layer_name], name='c_' + prev_layer_name + '_to_' + layer_name)
forwards_connection._params = np.random.uniform(-0.2, 0.2, size=forwards_connection.paramdim)
net.addConnection(forwards_connection)
prev_layer_name = layer_name
layer_name = lstm_layer_name(n_hidden_layers)
connect_to_out = FullConnection(net[layer_name], net['out'], name='c_' + layer_name + '_to_out')
connect_to_out._params = np.random.uniform(-0.2, 0.2, size=connect_to_out.paramdim)
net.addConnection(connect_to_out)
net.sortModules()
print(net)
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# define a training method
trainer = CONFIG['TRAINERCLASS'](net, dataset=ds, verbose=True)
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# carry out the training
net.reset()
# train_errors = []
t0 = time()
EPOCHS = CONFIG['EPOCHS_PER_CYCLE'] * CONFIG['CYCLES']
# trainer.trainUntilConvergence(maxEpochs=EPOCHS, verbose=True)
# start_time = time()
print("Starting training with", EPOCHS, "epochs...")
for i in xrange(CONFIG['CYCLES']):
trainer.trainEpochs(CONFIG['EPOCHS_PER_CYCLE'])
# train_errors.append(trainer.testOnData())
# epoch = (i+1) * CONFIG['EPOCHS_PER_CYCLE']
# seconds_elapsed = time() - start_time
# seconds_per_epoch = seconds_elapsed / epoch
# seconds_remaining = (EPOCHS - epoch) * seconds_per_epoch
# td_elapsed = timedelta(seconds=seconds_elapsed)
# td_elapsed_str = str(td_elapsed).split('.')[0]
# eta = (datetime.now() + timedelta(seconds=seconds_remaining)).time()
# eta = eta.strftime("%H:%M:%S")
# print("\r epoch = {}/{} error = {} elapsed = {} ETA = {}"
# .format(epoch, EPOCHS, train_errors[-1], td_elapsed_str, eta),
# end="")
# stdout.flush()
print("Finished training. total seconds =", time() - t0)
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# Disaggregate!
START = "2014-01-01"
END = "2014-01-03"
print("Starting disaggregation...")
net.reset()
estimates = pd.Series(index=appliances[START:END].index)
for date, mains_value in mains[START:END].iteritems():
estimates[date] = net.activate(mains_value)
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estimates.plot()
plt.show()
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mains[START:END].plot()
plt.show()
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appliances[START:END].plot()
plt.show()
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ax = estimates[START:END].cumsum().plot(label='estimates')
ax = mains_same_scale_as_appliances[START:END].cumsum().plot(ax=ax, label='aggregate')
ax = appliances[START:END].cumsum().plot(ax=ax)
plt.legend()
plt.show()
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estimates.cumsum().to_hdf('neuronilm_estimates_{:03d}.hdf'.format(CONFIG['EXPERIMENT_NUMBER']), 'df')
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