#!usr/bin/python
from __future__ import division
import pandas as pd
from sklearn import model_selection
from sklearn.svm import SVC # support vectors for classification
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import cross_val_score, GridSearchCV
import timeit
from sklearn.preprocessing import MinMaxScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import IncrementalPCA, PCA, KernelPCA
import numpy as np
from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import seaborn as sns
'''this function takes as an input the path of a file with features and labels and returns the content of this file as a csv format in
the form feature1.........feature13,Label'''
def readFile():
#make the format of the csv file. Our format is a vector with 13 features and a label which show the condition of the
#sample hc/pc : helathy case, parkinson case
names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9',
'Feature10','Feature11','Feature12','Feature13','Label']
#path to read the samples, samples consist from healthy subjects and subject suffering from Parkinson's desease.
path = 'PATH_TO_SAMPLES.txt'
#path = '/home/gionanide/Theses_2017-2018_2519/features/parkinson_healthy/mfcc_parkinson_healthy.txt'
#read file in csv format
data = pd.read_csv(path,names=names )
#return an array of the shape (2103, 14), lines are the samples and columns are the features as we mentioned before
return data
'takes the csv file and split the label from the features'
def splitData(data):
# Split-out the set in two different arrayste
array = data.values
#features array contains only the features of the samples
features = array[:,0:13]
#labels array contains only the lables of the samples
labels = array[:,13]
return features,labels
'''
make this class in order to train the model with the same amount of samples of each class, because we have bigger support from class 0
than class1, particularly it is 9 to 1.'''
def equalizeClasses(data):
#take all the samples from the data frame that they have Label value equal to 0 and in the next line equal to 1
class0 = data.loc[data['Label'] == 0]#class0 and class1 are dataFrames
class1 = data.loc[data['Label'] == 1]
#check which class has more samples, by divide them and check if the number is bigger or smaller than 1
weight = len(class0) // len(class1) #take the results as an integer in order to split the class, using prior knowledge that
#class0 has more samples, if it is bigger class0 has more samples and to be exact weight to 1
balance = (len(class0) // weight) #this is the number of samples in order to balance our classes
#the keyword argument frac specifies the fraction of rows to return in the random sample, so fra=1 means, return random all rows
#we kind of a way shuffle our data in order not to take the same samples in every iteration
#class0 = class0.sample(frac=1)
#samples array for training taking the balance number of samples for the shuffled dataFrame
newClass0 = class0.sample(n=balance)
#and now combine the new dataFrame from class0 with the class1 to return the balanced dataFrame
newData = pd.concat([newClass0, class1])
#return the new balanced(number of samples from each class) dataFrame
return newData
'''we made this function in order to make a loop, the equalized data take only a small piece of the existing data, so with this
loop we are going to take iteratably all the data, but from every iteration we are keeping only the samples who were support
vectors, the samples only the class which we are taking a piece of it's samples'''
#def keepSV():
'''we use this function in order to apply greedy search for finding the parameters that best fit our model. We have to mention
that we start this procedure from a very large field and then we tried to focues to the direction where the results
appear better. For example for the C parameter, the first range was [0.0001, 0.001, 0.01, 0.1, 1, 10 ,100 ,1000], the result was that
the best value was 1000 so then we tried [100, 1000, 10000, 100000] and so on in order to focues to the area which give us
the best results. This function is in comments because we found the best parameters and we dont need to run it in every trial.'''
def paramTuning(features_train, labels_train, nfolds):
#using the training data and define the number of folds
#determine the range of the Cs range you want to search
Cs = [0.001, 0.01, 0.1 ,1, 10, 100, 1000, 10000]
#determine the range of the gammas range you want to search
gammas = [0.00000001 ,0.00000001 ,0.0000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1 , 1, 10, 100, 1000]
#make the dictioanry
param_grid = {'C': Cs, 'gamma': gammas}
#start the greedy search using all the matching sets from above
grid_search = GridSearchCV(SVC(kernel='poly'),param_grid,cv=nfolds)
#fit your training data
grid_search.fit(features_train, labels_train)
#visualize the best couple of parameters
print grid_search.best_params_
'''Classify Parkinson and Helathy. Building a model which is going to be trained with of given cases and test according to new ones'''
def classifyPHC():
data = readFile()
#data = equalizeClasses(data)
features,labels = splitData(data)
#determine the training and testing size in the range of 1, 1 = 100%
validation_size = 0.2
#here we are splitting our data based on the validation_size into training and testing data
features_train, features_validation, labels_train, labels_validation = model_selection.train_test_split(features, labels,
test_size=validation_size)
#normalize data in the range [-1,1]
scaler = MinMaxScaler(feature_range=(-1, 1))
#fit only th training data in order to find the margin and then test to data without normalize them
scaler.fit(features_train)
features_train_scalar = scaler.transform(features_train)
#trnasform the validation features without fitting them
features_validation_scalar = scaler.transform(features_validation)
#determine the pca, and determine the dimension you want to end up
pca = KernelPCA(n_components=6,kernel='rbf',fit_inverse_transform=True)
#fit only the features train
pca.fit(features_train_scalar)
#dimensionality reduction of features train
features_train_pca = pca.transform(features_train_scalar)
#dimensionality reduction of fatures validation
features_validation_pca = pca.transform(features_validation_scalar)
#reconstruct data training error
reconstruct_data = pca.inverse_transform(features_train_pca)
error_percentage = (sum(sum(error_matrix))/(len(features_train_scalar)*len(features_train_scalar[0])))*100
#len(features_train_scalar) = len(reconstruct_data) = 89
#len(features_train_scalar[0]) = len(reconstruct_data[0]) = 13
#len(error_matrix) = 89, which means for all the samples
#len(error_matrix[0]) = 13, for every feature of every sample
#we take the sum and we conlcude in an array which has the sum for every feature (error)
#so we take the sum again and we divide it with the 89 samples * 13 features
print 'Information loss of KernelPCA:',error_percentage,'% \n'
lda = LinearDiscriminantAnalysis()
lda.fit(features_train_pca,labels_train)
features_train_pca = lda.transform(features_train_pca)
features_validation_pca = lda.transform(features_validation_pca)
#we can see the shapes of the array just to check
print 'feature training array: ',features_train.shape,'and label training array: ',labels_train.shape
print 'feature testing array: ',features_validation.shape,'and label testing array: ',labels_validation.shape,'\n'
#take the best couple of parameters from the procedure of greedy search
#paramTuning(features_train, labels_train, 5)
#we initialize our model
#svm = SVC(kernel='poly',C=0.001,gamma=10,degree=3,decision_function_shape='ovr')
svm = KNeighborsClassifier(n_neighbors=3)
#train our model with the data that we previously precessed
svm.fit(features_train_pca,labels_train)
#now test our model with the test data
predicted_labels = svm.predict(features_validation_pca)
accuracy = accuracy_score(labels_validation, predicted_labels)
print 'Classification accuracy: ',accuracy*100,'\n'
#see the accuracy in training procedure
predicted_labels_train = svm.predict(features_train_pca)
accuracy_train = accuracy_score(labels_train, predicted_labels_train)
print 'Training accuracy: ',accuracy_train*100,'\n'
#confusion matrix to illustrate the faulty classification of each class
conf_matrix = confusion_matrix(labels_validation, predicted_labels)
print 'Confusion matrix: \n',conf_matrix,'\n'
print 'Support class 0 class 1:'
#calculate the support of each class
print ' ',conf_matrix[0][0]+conf_matrix[0][1],' ',conf_matrix[1][0]+conf_matrix[1][1],'\n'
#calculate the accuracy of each class
hC = (conf_matrix[0][0]/(conf_matrix[0][0]+conf_matrix[0][1]))*100
pC = (conf_matrix[1][1]/(conf_matrix[1][0]+conf_matrix[1][1]))*100
#see the inside details of the classification
print 'For class 0 man cases:',conf_matrix[0][0],'classified correctly and',conf_matrix[0][1],'missclassified,',hC,'accuracy \n'
print 'For class 1 woman cases:',conf_matrix[1][1],'classified correctly and',conf_matrix[1][0],'missclassified,',pC,'accuracy\n'
#try 5-fold cross validation
scores = cross_val_score(svm, features_train_pca, labels_train, cv=5)
print 'cross validation scores for 5-fold',scores,'\n'
print 'parameters of the model: \n',svm.get_params(),'\n'
#print 'number of samples used as support vectors',len(svm.support_vectors_),'\n'
#return svm.support_vectors_
'''#plot the training features before the kpca and the lda procedure
kpca_lda = pd.DataFrame({'Feature1': features_train[: ,0], 'Feature2': features_train[: ,1],'Feature3': features_train[: ,2], 'Feature4': features_train[: ,3],'Feature5': features_train[: ,4],'Feature6': features_train[: ,5],'Feature7': features_train[: ,6],'Feature8': features_train[: ,7],'Feature9': features_train[: ,8],'Feature10': features_train[: ,9],'Feature11': features_train[: ,10],'Feature12': features_train[: ,11],'Feature13': features_train[: ,12],'Label': labels_train})
#'Feature10','Feature11','Feature12','Feature13','Label'])
sns.pairplot(kpca_lda, hue='Label')
plt.savefig('training_features_female_male.png')
#plt.show()
#plot the validation features before the kpca and the lda procedure
kpca_lda = pd.DataFrame({'Feature1': features_validation[: ,0], 'Feature2': features_validation[: ,1],'Feature3': features_validation[: ,2], 'Feature4': features_validation[: ,3],'Feature5': features_validation[: ,4],'Feature6': features_validation[: ,5],'Feature7': features_validation[: ,6],'Feature8': features_validation[: ,7],'Feature9': features_validation[: ,8],'Feature10': features_validation[: ,9],'Feature11': features_validation[: ,10],'Feature12': features_validation[: ,11],'Feature13': features_validation[: ,12],'Label': labels_validation})
#'Feature10','Feature11','Feature12','Feature13','Label'])
sns.pairplot(kpca_lda, hue='Label')
plt.savefig('validation_features_female_male.png')
#plt.show()
#plot the training features after the kpca and the lda procedure
kpca_lda = pd.DataFrame({'Feature1': features_train_pca[:, 0],'Label': labels_train})
sns.pairplot(kpca_lda, hue='Label')
plt.savefig('kpca_lda_knn_trainingset_female_male.png')
#plt.show()
#plot the validation features after the kpca and the lda procedure
kpca_lda = pd.DataFrame({'Feature1': features_validation_pca[:, 0],'Label': labels_validation})
sns.pairplot(kpca_lda, hue='Label')
plt.savefig('kpca_lda_knn_validationset_female_male.png')
plt.show()'''
def main():
#calculate the time
import time
start_time = time.time()
#we are making an array in order to keep the support vectors and feed the function with them for the next iteration
#support_vectors =
classifyPHC()
time = time.time()-start_time
print 'time: ',time
main()
