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import numpy as np
import pandas
from math import ceil, floor
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.model_selection import KFold, StratifiedKFold, permutation_test_score
from sklearn import linear_model
from sklearn.svm import SVC
import pickle
import matplotlib.pyplot as plt
import numpy.fft as fft
from sklearn import datasets
class MyOVBox(OVBox):
def __init__(self):
OVBox.__init__(self)
# Names of CSV files
self.signalFileName = ""
self.stimFileName = ""
# Name of Save File
self.saveFileName = ""
# other variables
self.windowSize = 0 # window size in ms
self.numOfPreviousWindowsAsOne = 0 # number of windows before actual stimulation to be marked as 1
self.numOfWindowsBefore = 0 # number of windows before those marked as 1, to be marked as 0
self.numOfWindowsAfter = 0 # number of windows after those marked as 1, to be marked as 0
def filter_signal(self, sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal):
""" returns tuple of filtered (signal chunks, classes) """
splittedSignal_filtrd = []
classes_filtrd = []
temp_classes = np.zeros(len(splittedSignal))
for stim in stimulationTimes:
index = int(floor(stim*sampleRate/numberOfSamplesWindow))
temp_classes[index] = 1
for i in range(1, self.numOfPreviousWindowsAsOne):
temp_classes[index-i] = 1
tmp_cls_winds = temp_classes[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter]
tmp_sig_winds = np.concatenate(splittedSignal[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter])
if len(tmp_sig_winds)/len(tmp_cls_winds)!=numberOfSamplesWindow: # if np.array_split does not split in equal windows
tmp_sig_winds = np.lib.pad(tmp_sig_winds, ((0, int(len(tmp_cls_winds)*numberOfSamplesWindow-len(tmp_sig_winds))),(0, 0)), 'edge') # pad with same values on end of array
classes_filtrd.extend(tmp_cls_winds)
splittedSignal_filtrd.extend(np.array_split(tmp_sig_winds, len(tmp_cls_winds)))
return (splittedSignal_filtrd, classes_filtrd)
def avg_k_fold(self, data, classes, k=4):
""" return average CA of k-fold cross validation """
avg_val = 0
kf = KFold(n_splits=k)
for train, test in kf.split(data):
clf = svm.SVC(kernel='linear', C=1).fit(data[train], classes[train])
cur_score = clf.score(data[test], classes[test])
avg_val += cur_score
# print cur_score
return avg_val/k
def permutation_significance_classification_score(self, X, y, k_folds=4):
n_classes = np.unique(y).size
svm = SVC(kernel='linear')
cv = StratifiedKFold(k_folds)
score, permutation_scores, pvalue = permutation_test_score(svm, X, y, scoring="accuracy", cv=cv, n_permutations=200, n_jobs=1)
print("Classification score %s (pvalue : %s)" % (score, pvalue))
plt.hist(permutation_scores, 20, label='Permutation scores')
ylim = plt.ylim()
plt.plot(2 * [score], ylim, '--g', linewidth=3,
label='Classification Score'
' (pvalue %s)' % pvalue)
plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
plt.ylim(ylim)
plt.legend()
plt.xlabel('Score')
plt.show()
def initialize(self):
# Names of CSV files
self.signalFileName = self.setting['InputCSVSignal']
self.stimFileName = self.setting['InputCSVStimulations']
# Name of Save File
self.saveFileName = self.setting['SaveFile']
# other variables
self.windowSize = int(self.setting['WindowSize (ms)']) # in ms
self.numOfPreviousWindowsAsOne = int(self.setting['NumOfPrevWindows'])
self.numOfWindowsBefore = int(self.setting['NumOfWindowsBefore'])-1
self.numOfWindowsAfter = int(self.setting['NumOfWindowsAfter'])+1
self.k_folds = int(self.setting['K-folds'])
print "Reading files..."
# read CSV files
signalArray = pandas.read_csv(self.signalFileName, delimiter=";", encoding="utf-8-sig")
stimsArray = pandas.read_csv(self.stimFileName, delimiter=";", encoding="utf-8-sig")
print "Files read!"
# sort information from tables (pandas dataframe)
time = signalArray['Time (s)']
electrodes = signalArray.iloc[:, 1:signalArray.shape[1]-1]
sampleRate = signalArray['Sampling Rate'][0]
stimulationTimes = stimsArray['Time (s)']
numberOfSamplesWindow = floor(self.windowSize / ((1/sampleRate)*1000)) # number of samples for approximately self.windowSize ms
splittedSignal = np.array_split(electrodes, ceil(len(time)/numberOfSamplesWindow)) # split signal into chunks of specified length
s_c = self.filter_signal(sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal) # filter signal and return sparsed version of chunks and assign classes
splittedSignal_filtrd = s_c[0]
classes_filtrd = s_c[1]
splittedSignal_filtrd_means = np.array(np.mean(splittedSignal_filtrd, axis=1)) # for each window calculate mean value
# additional attributes could be added besides splittedSignal_filtrd_means
classes_filtrd = np.array(classes_filtrd)
# print average k-fold CA
print "Average " + str(self.k_folds) + "-folds value: " + str(self.avg_k_fold(splittedSignal_filtrd_means, classes_filtrd, k=self.k_folds))
#self.permutation_significance_classification_score(splittedSignal_filtrd_means, classes_filtrd, k_folds=2) # k_folds=2, last long if k is bigger
if self.saveFileName:
clf = svm.SVC(kernel='linear', C=1).fit(splittedSignal_filtrd_means, classes_filtrd)
clf.fit(splittedSignal_filtrd_means, classes_filtrd)
print "Saving to pickle file: " + self.saveFileName
pickle.dump(clf, open(self.saveFileName, 'wb'))
print "Learned score: " + str(clf.score(splittedSignal_filtrd_means, classes_filtrd))
# send finish stimulation output (for OpenViBE)
self.finishBySendingStimulation(32774) # OVTK_StimulationId_TrialStop code
def finishBySendingStimulation(self, stimulationCode):
stimSetFinish = OVStimulationSet(self.getCurrentTime(), self.getCurrentTime()+1./self.getClock())
stimSetFinish.append(OVStimulation(stimulationCode, self.getCurrentTime(), 0.))
self.output[0].append(stimSetFinish)
def process(self):
return
def uninitialize(self):
# nop
return
box = MyOVBox()
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