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device_ON_bayesian_constant.py
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device_ON_bayesian_constant.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 17 13:12:27 2018
@author: Chandra Sekhar Ravuri
"""
# this for constant prior
import scipy.io as sio
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, f1_score
from prettytable import PrettyTable
import itertools
################ Functions #########################################
def ON_bayesian(device, duration, thresh):
# device = matrix which is output of device_status_matrix.py aka ON OFF Missing data matrix
# duration = time duration in minutes for state to be decided wether ON OFF
# thresh = threshold to PMF to decide device is ON or OFF
################# Time axis #####################
time_min = np.arange(1, device.shape[-1]+1)*duration
mask = device.copy()
mask[device > -1] = 0
mask = np.abs(mask)
#### days were whole day data collected #######
active = np.invert(mask.any(axis=1))
device = device[active,:]
# np.random.shuffle(device)
ind = int(len(device)*0.3)
dev_test_org = device[-ind:,:]#.flatten() # test data
previous_prior = np.sum(device[:ind,:], axis=0, dtype='float32') #1 # constant prior
previous_prior = previous_prior/previous_prior.sum()
dev_test_pred = []
Acc = []
F1Score = []
Preci = []
for itr in range(ind):
likely_hood = np.sum(device[ind:-ind+itr,:], axis=0, dtype='float32')
dev_test_day = likely_hood * previous_prior
dev_test_day[dev_test_day >= thresh] = 1
dev_test_day[dev_test_day < thresh] = 0
dev_test_pred.append(dev_test_day)
Acc.append(round(accuracy_score(dev_test_org[itr,:], dev_test_day), 4))
F1Score.append(round(f1_score(dev_test_org[itr,:], dev_test_day), 4))
Preci.append(round(precision_score(dev_test_org[itr,:], dev_test_day), 4))
dev_test_pred = np.array(dev_test_pred, np.float32).flatten()
conf_mat = confusion_matrix(dev_test_org.flatten(), dev_test_pred)
CM_Acc_F1_Prec = [conf_mat, Acc, F1Score, Preci,]
# this will give day wise what time data collected
"""
observed_data = device.copy()
observed_data[observed_data > -1] = 1
observed_data[observed_data < 1] = 0
plt_obser = np.sum(observed_data,axis=0)
# this will give day wise what time device is ON
on_data = device.copy()
on_data[on_data < 1] = 0
plt_on = np.sum(on_data,axis=0)
"""
return time_min, previous_prior, CM_Acc_F1_Prec
# this below function obtained from
# http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title,size=18)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label',size=18)
plt.xlabel('Predicted label',size=18)
#########################################################################
matrix_data = sio.loadmat('/home/hadoop1/Documents/prml/project/UKDALE/device_status_house2.mat')
#%%
#
#duration =5
#thresh= 0.0005
#device = matrix_data['kettle']
#%%
thresh = 0.0005
# to get bayesian plots and
kettle_time, kettle_plot, kettle_CM_Acc_F1_Prec = ON_bayesian(matrix_data['kettle'], 5, thresh)
rice_cooker_time, rice_cooker_plot, rice_cooker_CM_Acc_F1_Prec = ON_bayesian(matrix_data['rice_cooker'], 5, thresh)
running_machine_time, running_machine_plot, running_machine_CM_Acc_F1_Prec = ON_bayesian(matrix_data['running_machine'], 5, thresh)
washing_machine_time, washing_machine_plot, washing_machine_CM_Acc_F1_Prec = ON_bayesian(matrix_data['washing_machine'], 5, thresh)
dish_washer_time, dish_washer_plot, dish_washer_CM_Acc_F1_Prec = ON_bayesian(matrix_data['dish_washer'], 5, thresh)
microwave_time, microwave_plot, microwave_CM_Acc_F1_Prec = ON_bayesian(matrix_data['microwave'], 5, thresh)
#### Results display ###########################################
print('Threshold = %f'%thresh)
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plot_confusion_matrix(kettle_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='kettle confussion matrix')
plt.show()
plt.figure()
plt.plot(kettle_CM_Acc_F1_Prec[1])
plt.plot(kettle_CM_Acc_F1_Prec[2])
plt.plot(kettle_CM_Acc_F1_Prec[3])
plt.title('Parameters on test days')
plt.xlabel('Days')
plt.ylabel('Parameter')
plt.legend(['Accuracy','F1 Score','Precision'])
plt.show()
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plt.figure()
plot_confusion_matrix(rice_cooker_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='rice_cooker confussion matrix')
plt.show()
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plt.figure()
plot_confusion_matrix(running_machine_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='running_machine confussion matrix')
plt.show()
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plt.figure()
plot_confusion_matrix(washing_machine_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='washing_machine confussion matrix')
plt.show()
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plt.figure()
plot_confusion_matrix(dish_washer_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='dish_washer confussion matrix')
plt.show()
print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++')
plt.figure()
plot_confusion_matrix(microwave_CM_Acc_F1_Prec[0],classes=['OFF','ON'], title='microwave confussion matrix')
plt.show()
plt.close('all')
rc('xtick',labelsize=18)
rc('ytick',labelsize=18)
plt.figure()
plt.bar(kettle_time, kettle_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Kettle',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/kettle_time.jpg')
plt.show()
plt.figure()
plt.bar(rice_cooker_time, rice_cooker_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Rice coocker',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/rice_coocker_time.jpg')
plt.show()
plt.figure()
plt.bar(running_machine_time, running_machine_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Running machine',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/running_machine_time.jpg')
plt.show()
plt.figure()
plt.bar(washing_machine_time, washing_machine_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Washing machine',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/washing_machine_time.jpg')
plt.show()
plt.figure()
plt.bar(dish_washer_time, dish_washer_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Dishwasher',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/dish_washer_time.jpg')
plt.show()
plt.figure()
plt.bar(microwave_time, microwave_plot)
plt.xlabel('Time in Minutes(Whole day)',size=18)
plt.ylabel('Frequency (Number of days)',size =18)
plt.title('final prior Bayesian for Microwave',size=18)
plt.savefig('/home/hadoop1/Documents/prml/project/UKDALE/plots/microwave_time.jpg')
plt.show()