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classifiers/gmm_healthy_captured.py

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+#!usr/bin/python
+from __future__ import division
+import pickle
+import pandas as pd
+import numpy as np
+from sklearn.mixture import GaussianMixture
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn import metrics
+import matplotlib.patches as mpatches
+
+def readFeaturesFile(gender):
+	names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9',
+'Feature10','Feature11','Feature12','Feature13','Gender']
+
+	#check the gender
+	if(int(gender)==0):
+		data = pd.read_csv("/home/gionanide/Theses_2017-2018_2519/features/gmm_healthy_mfcc.txt",names=names )
+	elif(int(gender)==1):
+		data = pd.read_csv("/home/gionanide/Theses_2017-2018_2519/features/gmm_captured_mfcc.txt",names=names )
+	else:
+		data = pd.read_csv("/home/gionanide/Theses_2017-2018_2519/features/mfcc_featuresNewDatabase.txt",names=names )
+	#the outcome is a list of lists containing the samples with the following format
+	#[charachteristic,feature1,feature2.......,feature13]
+	#characheristic based on what we want for classification , can be (male , female) , also can be (normal-female,edema-female)
+	#in general characheristic is the target value .
+	return data
+
+def preparingData(data):
+	# Split-out validation dataset
+	array = data.values
+	#input
+	X = array[:,0:13]
+	#target 
+	Y = array[:,13]
+	return X,Y
+
+
+def testModels(data,threshold_input):
+
+	gmmFiles = ['/home/gionanide/Theses_2017-2018_2519/models/finalizedModel_0_healthy.gmm','/home/gionanide/Theses_2017-2018_2519/models/finalizedModel_1_parkinson.gmm']
+	models = [pickle.load(open(filename,'r')) for filename in gmmFiles]
+	log_likelihood = np.zeros(len(models))
+	genders = ['healthy','parkinson']
+	X,Y = preparingData(data)
+	assessModel = []
+	prediction = []
+	features = X
+	for i in range(len(models)):
+		gmm = models[i]
+		scores = np.array(gmm.score(features))
+		#first take for the male model all the log likelihoods and then the same procedure for the female model
+		assessModel.append(gmm.score_samples(features))
+		log_likelihood[i] = scores.sum()
+	#higher the value it is, the more likely your model fits the model
+	for x in range(len(assessModel[0])):
+		#the division is gmm(Malemodel) / gmm(Femalemodel) if the result is > 1 then the example is male
+
+
+		#if the prediction for male in negative and the prediction for female positive we dont have to check
+		#because the difference is obvious and we are pretty sure that it is female
+		if(assessModel[0][x] < 0 and assessModel[1][x] > 0):
+			# x / y and x is < 0 , so we have to classify this as female
+			# we have to be sure that the prediction will be above the threshold 
+			prediction.append(float(threshold_input) + 1)
+		#same as above , we need to be sure that the prediction is below the threshold (male) because we are pretty
+		#sure from the model's outcome that this sample is female
+		elif(assessModel[0][x] > 0 and assessModel[1][x] < 0):
+			prediction.append(float(threshold_input) - 1)
+		else:
+			prediction.append( abs(( assessModel[0][x] / assessModel[1][x] )) ) 
+
+
+	#take an array with the predictions and check if they are true(correct classification) or false(wrong classification)
+	assessment=[]
+	condition=[]
+	true_negative=0
+	true_positive=0
+	false_positive=0
+	false_negative=0
+	for x in range(len(prediction)):
+		if(prediction[x]<1.04790 and prediction[x]>0.97890):
+			print prediction[x] , ' can not decide'
+			print '\n'
+			continue
+		if(prediction[x]<float(threshold_input)):#the model predict healthy and we check if it is indeed male
+			#print prediction[x], ( Y[x] == 0 )
+			decision = (Y[x] == 0)
+			condition.append('healthy')
+			assessment.append(decision)
+			if(decision):
+				true_negative+=1
+			else:
+				false_negative+=1
+		else:
+			decision1 = (Y[x] == 1)#the model predict parkinson and we check if it is indeed female
+			#print prediction[x], ( Y[x] == 1 )
+			condition.append('parkinson')
+			assessment.append(decision1)
+			if(decision1):
+				true_positive+=1
+			else:
+				false_positive+=1
+	for x in range(len(assessment)):
+		if(assessment[x]==False):
+			print prediction[x],assessment[x],condition[x]
+			print assessModel[0][x],assessModel[1][x]
+			print '\n'
+	#construct confusion matrix
+	confusion_matrix= [[0 for x in range(2)] for y in range(2)]
+	confusion_matrix[0][0]= true_negative
+	confusion_matrix[0][1]= false_positive
+	confusion_matrix[1][0]= false_negative
+	confusion_matrix[1][1]= true_positive
+
+	#sensitivity/ true positive rate
+	tpr = (true_positive)/(true_positive + false_negative)
+	#fall-out/ false positive rate
+	fpr = (false_positive)/(false_positive + true_negative)
+
+	print 'tpr: ',tpr
+	print 'fpr : ',fpr
+
+	#ROC curve with error and reject option
+
+	winner = np.argmax(log_likelihood)
+	print ''
+	print "\tdetected as - ", genders[winner],"\n\tscores: healthy ",log_likelihood[0],",parkinson ", log_likelihood[1],"\n"
+
+	male_support = confusion_matrix[0][0] + confusion_matrix[0][1]
+	female_support = confusion_matrix[1][0] + confusion_matrix[1][1]
+
+	print 'Confusion matrix:       support: '
+	print '               ',confusion_matrix[0],'    0.0: ',male_support
+	print '               ',confusion_matrix[1],'    1.0: ',female_support
+	print ''
+	print 'Accuracy without reject option: 88.1122206372 \n'
+	print 'Accuracy with reject option: ', ( assessment.count(True) / len(assessment) ) * 100
+
+
+	#with reject option
+	tpr_plot = np.array([0,tpr,1])
+	fpr_plot = np.array([0,fpr,1])
+	#without reject option
+	tpr_plot_wr = np.array([0,0.23381294964,1])
+	fpr_plot_wr = np.array([0,0.0202739726027,1])
+
+	#area under the curve with the reject option
+	area_under_plot =  metrics.auc(fpr_plot,tpr_plot)
+	#area under the curve without the reject option
+	area_under_wr = metrics.auc(fpr_plot_wr,tpr_plot_wr)
+
+
+
+
+	plt.figure(1)
+	green_patch = mpatches.Patch(color='green', label='With reject option (area = %0.2f)' %area_under_plot)
+	blue_patch = mpatches.Patch(color='blue', label='Without reject option (area = %0.2f)' %area_under_wr)
+	plt.legend(handles=[green_patch,blue_patch])
+	plt.plot(fpr_plot,tpr_plot,marker='d',linestyle='--',color='g')
+	plt.plot(fpr_plot_wr,tpr_plot_wr,marker='d',linestyle='--',color='b')
+	plt.plot([0,1],[0,1],'r--')
+	plt.xlabel('False positive rate')
+	plt.ylabel('True positive rate')
+	plt.title('Receiver operating characteristic')
+	plt.legend(loc='lower right')
+	plt.show()
+
+
+
+
+def main():
+	#gender = raw_input('Choose the gender for training press 1(Female) or 0(Male) and any other number for testing: ')
+	data = readFeaturesFile(gender=6)
+
+	#threshold = raw_input('Threshold: ')
+	threshold = 1.05
+	testModels(data,threshold)
+
+
+main()
+