def __init__(self):
params = ParameterSetup()
pickledDir = params.pickledDir
self.fileIDs_L = []
try:
outlierHandler = open(pickledDir + '/' + 'outliers.txt', 'r')
# print(outlierHandler)
for line in outlierHandler:
elems = line.split(' ')
parts = elems[2].split('-')
fileID = parts[2] + '-' + parts[3] + '-' + parts[4]
# print(fileID)
self.fileIDs_L.append(fileID)
except EnvironmentError:
pass
def __init__(self):
params = ParameterSetup()
pickledDir = params.pickledDir
self.fileIDs_L = []
try:
sd_threshHandler = open(pickledDir + '/' + 'sd_thresh.json', 'r')
d = json.load(sd_threshHandler)
self.W_amount_mean = d["W_amount_mean"]
self.W_amount_2sd = d["W_amount_2sd"]
self.R_amount_mean = d["R_amount_mean"]
self.R_amount_2sd = d["R_amount_2sd"]
self.S_amount_mean = d["S_amount_mean"]
self.S_amount_2sd = d["S_amount_2sd"]
self.W_episode_num_mean = d["W_episode_num_mean"]
self.W_episode_num_2sd = d["W_episode_num_mean"]
self.R_episode_num_mean = d["R_episode_num_mean"]
self.R_episode_num_2sd = d["R_episode_num_mean"]
self.S_episode_num_mean = d["S_episode_num_mean"]
self.S_episode_num_2sd = d["S_episode_num_mean"]
self.W_duration_mean = d["W_duration_mean"]
self.W_duration_2sd = d["W_duration_mean"]
self.R_duration_mean = d["R_duration_mean"]
self.R_duration_2sd = d["R_duration_mean"]
self.S_duration_mean = d["S_duration_mean"]
self.S_duration_2sd = d["S_duration_mean"]
except EnvironmentError:
self.W_amount_mean = 0
self.W_amount_2sd = 0
self.R_amount_mean = 0
self.R_amount_2sd = 0
self.S_amount_mean = 0
self.S_amount_2sd = 0
self.W_episode_num_mean = 0
self.W_episode_num_2sd = 0
self.R_episode_num_mean = 0
self.R_episode_num_2sd = 0
self.S_episode_num_mean = 0
self.S_episode_num_2sd = 0
self.W_duration_mean = 0
self.W_duration_2sd = 0
self.R_duration_mean = 0
self.R_duration_2sd = 0
self.S_duration_mean = 0
self.S_duration_2sd = 0
def getFeatures(self,
eegSegment,
timeStampSegment=0,
time_step=0,
local_mu=0,
local_sigma=0):
params = ParameterSetup()
widths = params.waveletWidths
waveletTransformed = signal.cwt(eegSegment, signal.ricker, widths)
inputTensor = np.array([waveletTransformed])
# print('inputTensor.shape = ' + str(inputTensor.shape))
featureDownSampler = FeatureDownSampler()
# print('self.outputDim = ' + str(self.outputDim))
waveletTransformedDownsampled = featureDownSampler.downSample(
inputTensor, self.outputDim)[0]
# print('waveletTransformedDownsampled.shape = ' + str(waveletTransformedDownsampled.shape))
return waveletTransformedDownsampled
def setStagePredictor(self, classifierID):
paramFileName = 'params.' + str(classifierID) + '.json'
finalClassifierDir = self.params.finalClassifierDir
paramsForNetworkStructure = ParameterSetup(paramDir=finalClassifierDir,
paramFileName=paramFileName)
classifier = DeepClassifier(
self.classLabels,
classifierID=classifierID,
paramsForDirectorySetup=self.params,
paramsForNetworkStructure=paramsForNetworkStructure)
model_path = finalClassifierDir + '/weights.' + str(
classifierID) + '.pkl'
print('model_path = ', model_path)
classifier.load_weights(model_path)
self.stagePredictor = StagePredictor(
paramsForNetworkStructure, self.extractor, classifier,
finalClassifierDir, classifierID,
self.params.markovOrderForPrediction)
def test_by_classifierID(params, datasetType, classifierID):
paramDir = params.pickledDir
testFileDir = params.pickledDir
stageLabels = params.stageLabels4evaluation
labelNum = len(stageLabels)
resultFileDescription = ''
paramID = 0
markovOrder = 0
fileTripletL = readTrainFileIDsUsedForTraining(params, classifierID)
train_fileIDs = [fileID for _, _, fileID in fileTripletL]
# print('# train_fileIDs =', train_fileIDs)
params_test = params
if datasetType == 'test':
params_test.pickledDir = testFileDir
test_fileTripletL = getFilesNotUsedInTrain(params_test, train_fileIDs)
testFileIDandClassifierIDs = [(test_fileID, classifierID) for _, _, test_fileID in test_fileTripletL]
fileNum = len(testFileIDandClassifierIDs)
print('# testFileIDandClassifierIDs =', testFileIDandClassifierIDs)
# totalConfusionMat = np.zeros((labelNum, labelNum))
# for paramID in range(len(classifierParams)):
# print('classifier parameter = ' + str(classifierParams[paramID]))
sensitivityL = [[] for _ in range(labelNum)]
specificityL = [[] for _ in range(labelNum)]
accuracyL = [[] for _ in range(labelNum)]
precisionL = [[] for _ in range(labelNum)]
f1scoreL = [[] for _ in range(labelNum)]
mccL = [[] for _ in range(labelNum)]
mcMCCL = []
mcAccuracyL = []
confusionMatL = []
for testFileIDandClassifierID in testFileIDandClassifierIDs:
print('testFileIDandClassifierID = ' + str(testFileIDandClassifierID))
params_for_classifier = ParameterSetup(paramFileName='params.'+classifierID+'.json')
params_for_classifier.markovOrderForPrediction = markovOrder
(y_test, y_pred) = classifySequentially(params_for_classifier, paramID, paramDir, testFileIDandClassifierID)
print('y_test =', y_test)
print('type(y_test) =', type(y_test))
y_test = np.array(['W' if elem == 'RW' else elem for elem in y_test])
print('after replace: y_test =', y_test)
print('after replace: type(y_test) =', type(y_test))
# ignore ?'s in the beginning produced by
# i = 0
# while y_pred[i] == '?':
# i++
if params.classifierType == 'deep':
i = params.torch_lstm_length - 1 # remove from all clalssifiers because LSTM cannot predict first 9 elements.
else:
i = 0
print('for classifier ', testFileIDandClassifierID, ', first ', i, ' elements are removed.', sep='')
y_test, y_pred = y_test[i:], y_pred[i:]
(stageLabels, sensitivity, specificity, accuracy, precision, f1score) = y2sensitivity(y_test, y_pred)
(stageLabels4confusionMat, confusionMat) = y2confusionMat(y_test, y_pred, params.stageLabels4evaluation)
printConfusionMat(stageLabels4confusionMat, confusionMat)
# totalConfusionMat = totalConfusionMat + confusionMat
# print('y_test = ' + str(y_test[:50]))
# print('y_pred = ' + str(y_pred[:50]))
y_length = y_pred.shape[0]
print('stageLabels =', stageLabels)
print('labelNum = ' + str(labelNum))
for labelID in range(labelNum):
targetLabel = stageLabels[labelID]
sensitivityL[labelID].append(sensitivity[labelID])
specificityL[labelID].append(specificity[labelID])
accuracyL[labelID].append(accuracy[labelID])
precisionL[labelID].append(precision[labelID])
f1scoreL[labelID].append(f1score[labelID])
mcc = mathewsCorrelationCoefficient(stageLabels4confusionMat, confusionMat, targetLabel)
mccL[labelID].append(mcc)
print(' targetLabel = ' + targetLabel + ', sensitivity = ' + "{0:.3f}".format(sensitivity[labelID]) + ', specificity = ' + "{0:.3f}".format(specificity[labelID]) + ', accuracy = ' + "{0:.3f}".format(accuracy[labelID])+ ', precision = ' + "{0:.3f}".format(precision[labelID]))
print(' mcc for ' + targetLabel + ' = ' + "{0:.5f}".format(mcc))
mcMCCL.append(multiClassMCC(confusionMat))
print(' multi-class mcc = ' + "{0:.5f}".format(mcMCCL[-1]))
mcAccuracyL.append(sum(y_test == y_pred) / len(y_test))
print(' multi-class accuracy = ' + "{0:.5f}".format(mcAccuracyL[-1]))
confusionMatL.append(confusionMat)
print('')
writePredictionResults(testFileIDandClassifierID, params, y_test, y_pred, resultFileDescription)
if datasetType == 'test':
f = open(paramDir + '/test_result.' + classifierID + '.test.pkl', 'wb')
else:
f = open(paramDir + '/test_result.' + classifierID + '.pkl', 'wb')
pickle.dump((testFileIDandClassifierIDs, sensitivityL, specificityL, accuracyL, precisionL, f1scoreL, mccL, mcMCCL, mcAccuracyL, confusionMatL, stageLabels, fileNum, labelNum), f)
f.close()
#-----
# show the summary (average) of the result
print('Summary for classifierID ' + classifierID + ':')
printMetadata(params)
saveStatistics(params.pickledDir, classifierID, testFileIDandClassifierIDs, sensitivityL, specificityL, accuracyL, precisionL, f1scoreL, mccL, mcMCCL, mcAccuracyL, confusionMatL, stageLabels, fileNum, labelNum, datasetType)
# print('ch2TimeFrameNum = ' + str(params.ch2TimeFrameNum))
# print('binWidth4freqHisto = ' + str(params.binWidth4freqHisto))
sensitivityMeans, specificityMeans, accuracyMeans, precisionMean, f1scoreMean, mccMeans, mcMCCMean, mcAccuracyMean = meanStatistics(sensitivityL, specificityL, accuracyL, precisionL, f1scoreL, mccL, mcMCCL, mcAccuracyL, stageLabels, labelNum, fileNum)
# sensitivity_by_classifier_L.append(sensitivityMeans)
# specificity_by_classifier_L.append(specificityMeans)
# accuracy_by_classifier_L.append(accuracyMeans)
# precision_by_classifier_L.append(precisionMean)
# measures_by_classifier_L.append([sensitivityMeans, specificityMeans, accuracyMeans, precisionMean, f1scoreMean, mccMeans, mcMCCMean, mcAccuracyMean])
return [sensitivityMeans, specificityMeans, accuracyMeans, precisionMean, f1scoreMean, mccMeans, mcMCCMean, mcAccuracyMean]
def __init__(self):
self.params = ParameterSetup()
import sys
import matplotlib.pyplot as plt
import matplotlib
from functools import reduce
from os.path import splitext
import pickle
import numpy as np
from parameterSetup import ParameterSetup
from evaluationCriteria import labels2ID, getEpisodeLengths
from sequentialPrediction import classifySequentially
from writePredictionResults import writePredictionResults
from fileManagement import readTrainFileIDsUsedForTraining, getFilesNotUsedInTrain
from fileManagement import getAllEEGFiles, fileIDsFromEEGFiles
params = ParameterSetup()
lstm_length = params.torch_lstm_length
paramID, markovOrderL = 0, [0]
print('lstm_length =', lstm_length)
fileIDs = fileIDsFromEEGFiles(getAllEEGFiles(params))
# fileIDs = ['D1798', 'D1803', 'D1811', 'D1818', 'D1831', 'D1799', 'D1804', 'D1814',
# 'D1819', 'D1833', 'D1800', 'D1805', 'D1815', 'D1820', 'D1801', 'D1806',
# 'D1816', 'D1826', 'D1802', 'D1810', 'D1817', 'D1827']
classifierIDs = ['Y4HOFA', '5XTKMY']
methodNames = ['UTSN', 'UTSN-L']
allFileIDandClassifierIDsL = []
y_predLL = []
for classifierID, methodName in zip(classifierIDs, methodNames):
train_fileTripletL = readTrainFileIDsUsedForTraining(params, classifierID)
train_fileIDs = [train_fileID for _, _, train_fileID in train_fileTripletL]
import numpy as np
import pickle
from parameterSetup import ParameterSetup
from fileManagement import getFileIDs
from evaluationCriteria import y2sensitivity, y2confusionMat, printConfusionMat
from sequentialPrediction import classifySequentially
from featureExtractor import featureExtraction
from classifierTrainer import trainClassifier
from writeResults import writePredictions
orderMin = 0
orderMax = 8
# orderMax = 1
paramID = 0
params = ParameterSetup()
pickledDir = params.pickledDir
paramDir = params.pickledDir
classifierType = params.classifierType
classifierParams = params.classifierParams
eegFilePrefix = 'eegAndStage'
fileIDs = getFileIDs(pickledDir, eegFilePrefix)
print('classifier type = ' + str(classifierType))
print('useEMG = ' + str(params.useEMG))
print('emgTimeFrameNum = ' + str(params.emgTimeFrameNum))
print('binWidth4freqHisto = ' + str(params.binWidth4freqHisto))
stageLabels = params.stageLabels4evaluation
labelNum = len(stageLabels)
def __init__(self):
self.params = ParameterSetup()
self.eegDir = self.params.eegDir
self.featureDir = self.params.featureDir
self.classifierDir = self.params.classifierDir
self.extractorType = self.params.extractorType
from ctypes import byref
import numpy as np
import socket
import datetime
import threading
import tqdm
import time
import math
import pickle
from parameterSetup import ParameterSetup
params = ParameterSetup()
pickledDir = params.pickledDir
classifierType = params.classifierType
classifierParams = params.classifierParams
samplingFreq = params.samplingFreq
windowSizeInSec = params.windowSizeInSec
wsizeInTimePoints = samplingFreq * windowSizeInSec # window size. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
eegFilePrefix = 'eegAndStage'
fileID = 'DBL-NO-D0846'
# fileID = 'DBL-NO-D0858'
# fileID = 'DBL-NO-D0859'
dataFileHandler = open(
pickledDir + '/' + eegFilePrefix + '.' + fileID + '.pkl', 'rb')
(eeg, emg, stageSeq) = pickle.load(dataFileHandler)
y_test = np.array(stageSeq)
# fileName = '../data/waves/2017-10-05-16-23-02.061887.csv'
fileName = '../data/waves/wave_res.csv'
def __init__(self,
recordWaves,
extractorType,
classifierType,
classifierID,
inputFileID='',
offsetWindowID=0,
chamberID=-1,
samplingFreq=0,
epochTime=0):
self.recordWaves = recordWaves
self.inputFileID = inputFileID
self.chamberID = chamberID
chamberLabel = '_chamber' + str(
self.chamberID +
1) # adds 1 because in Sleep Sign Recorder, chambers start from 1.
self.params = ParameterSetup()
if samplingFreq == 0:
self.samplingFreq = self.params.samplingFreq
else:
self.samplingFreq = samplingFreq
if epochTime == 0:
self.samplePointNum = self.params.windowSizeInSec * self.samplingFreq # the number of sample points received at once
else:
self.samplePointNum = epochTime * self.samplingFreq # the number of sample points received at once
self.graphUpdateFreqInHz = self.params.graphUpdateFreqInHz # frequency of updating the graph (if set to 1, redraws graph every second)
assert self.samplingFreq / self.graphUpdateFreqInHz == np.floor(
self.samplingFreq /
self.graphUpdateFreqInHz) # should be an integer
self.updateGraph_samplePointNum = np.int(self.samplingFreq /
self.graphUpdateFreqInHz)
# print('self.updateGraph_samplePointNum =', self.updateGraph_samplePointNum)
self.hasGUI = True
self.graphColors = ['b', 'g']
self.ylim_max_eeg, self.ylim_max_ch2 = 2.0, 2.0
self.graph_ylim = [[-self.ylim_max_eeg, self.ylim_max_eeg],
[-self.ylim_max_ch2, self.ylim_max_ch2]]
self.lightPeriodStartTime = self.params.lightPeriodStartTime
self.sampleID = 0
self.segmentID = offsetWindowID
# makes a classifier class
label4EMG = self.params.label4withoutEMG
self.showCh2 = self.params.showCh2
self.useCh2ForReplace = self.params.useCh2ForReplace
self.minimumCh2Intensity = 0
self.maximumCh2Intensity = 0
self.past_eegSegment, self.past_ch2Segment = np.array([]), np.array([])
# self.previous_eeg, self.previous_ch2 = np.array([]), np.array([])
classifierFilePrefix = self.params.classifierFilePrefix
factory = AlgorithmFactory(extractorType)
print('generating extractor: ')
self.extractor = factory.generateExtractor()
# self.classLabels = list(self.params.labelCorrectionDict.keys())[:self.params.maximumStageNum]
self.classLabels = self.params.sampleClassLabels[:self.params.
maximumStageNum]
self.setStagePredictor(classifierID)
presentTime = timeFormatting.presentTimeEscaped()
logFileID = 'classifier.' + presentTime
if self.chamberID != -1:
logFileID += chamberLabel
logFileName = logFileID + '.csv'
self.logFile = open(self.params.logDir + '/' + logFileName, 'a')
# connect to an output device
self.connected2serialClient = False
# print('in __init__ of classifierClient, self.connected2serialClient = False')
self.serialClient, self.connected2serialClient = connect_laser_device()
self.eeg_till_now = np.zeros((0, ))
'''
# prepare for computing Kolmogorov-Smirnov test
standardMice_L, files_L = readStandardMice(self.params)
self.statisticalTester = StatisticalTester(standardMice_L)
self.dSeries_L = []
self.dMat = readdMat(self.params)
self.dTensor = readdTensor(self.params)
self.dTensorSegmentNum = self.dTensor.shape[1]
self.segmentMax4computingKS = self.dTensorSegmentNum * 4
self.segmentMax4computingKS = self.dTensorSegmentNum
print('Computes KS (Kolmogorov-Smirnov) till the input reaches segment', self.segmentMax4computingKS)
'''
# opens a file for recording waves and prediction results
if self.inputFileID == '':
outputFileID = timeFormatting.presentTimeEscaped()
else:
outputFileID = self.inputFileID
if self.chamberID != -1:
outputFileID += chamberLabel
waveFileName = outputFileID + '_wave.csv'
self.ch2_mode = "Video"
self.ch2_thresh_value = self.params.ch2_thresh_default
self.eeg_normalize_for_prediction = 1
self.ch2_normalize_for_prediction = 0
self.eeg_graph_normalize = 0
self.ch2_graph_normalize = 0
self.currentCh2Intensity = 0
if self.recordWaves:
self.waveOutputFile = open(
self.params.waveOutputDir + '/' + waveFileName, 'a')
self.waveOutputFile_standardized = open(
self.params.waveOutputDir + '/standardized_' + waveFileName,
'a')
self.predictionState = 0
self.one_record = np.zeros((self.samplePointNum, 2))
self.raw_one_record = np.zeros((self.samplePointNum, 2))
self.one_record_for_graph = np.zeros((self.samplePointNum, 2))
self.windowStartTime = ''
self.y_pred_L = []
'''
ksFileID = outputFileID
if self.chamberID != -1:
outputFileID += chamberLabel
ksFileName = ksFileID + '_ks.csv'
try:
self.ksOutputFile = open(self.params.ksDir + '/' + ksFileName, 'a')
outLine = 'segmentID, d, chi^2\n'
self.writeKS2file(outLine)
# self.windowStartTime = presentTime
except EnvironmentError:
pass
'''
# makes a file in params.predDir
if self.inputFileID == '':
self.predFileID = timeFormatting.presentTimeEscaped()
else:
self.predFileID = self.inputFileID
if self.chamberID != -1:
self.predFileID += chamberLabel
print('writes prediction results to ' + self.params.predDir + '/' +
self.predFileID + '_pred.txt')
self.predFile = open(
self.params.predDir + '/' + self.predFileID + '_pred.txt', 'w')
self.predFileBeforeOverwrite = open(
self.params.predDir + '/' + self.predFileID +
'_pred_before_overwrite.txt', 'w')
self.predFileWithTimeStamps = open(
self.params.predDir + '/' + self.predFileID +
'_pred_with_timestamps.txt', 'w')
self.max_storage_for_standardization = self.samplePointNum * self.params.standardization_max_storage_window_num
self.standardizer_eeg = standardizer(
self.max_storage_for_standardization)
self.standardizer_ch2 = standardizer(
self.max_storage_for_standardization)
from __future__ import print_function
import sys
import json
from os import listdir
from parameterSetup import ParameterSetup
from classifierTrainer import trainClassifier
args = sys.argv
optionType = args[1] if len(args) > 2 else ''
optionVals = args[2:] if len(args) > 2 else [0]
print('optionType =', optionType, ', optionVals = ', optionVals)
pathFilePath = open('path.json')
p = json.load(pathFilePath)
paramDir = p['pathPrefix'] + '/' + p['paramsDir']
outputDir = ''
for paramFileName in listdir(paramDir):
if paramFileName.startswith('params.') and not paramFileName.endswith('~'):
print('paramFileName =', paramFileName)
params = ParameterSetup(paramDir, paramFileName, outputDir)
trainClassifier(params, outputDir, optionType, optionVals)
import sys
from parameterSetup import ParameterSetup
if __name__ == '__main__':
args = sys.argv
classifierIDs = args[1:]
res = []
for classifierID in classifierIDs:
paramFileName = 'params.' + classifierID + '.json'
params = ParameterSetup(paramFileName=paramFileName)
stageLabels = params.stageLabels4evaluation
print('stageLabels =', stageLabels)
res.append(classifierID + ', ' + str(params.useRawData) + ', ' + str(params.useFreqHisto) + ', ' + str(params.useTime))
for line in res:
print(line)
def readAll(self, sys):
#---------------
# set up parameters
# get params shared by programs
params = ParameterSetup()
# oFilter = OutlierMouseFilter()
sdFilter = SDFilter()
# for signal processing
windowSizeInSec = params.windowSizeInSec # size of window in time for estimating the state
samplingFreq = params.samplingFreq # sampling frequency of data
# parameters for using history
preContextSize = params.preContextSize
# parameters for making a histogram
wholeBand = params.wholeBand
binWidth4freqHisto = params.binWidth4freqHisto # bin width in the frequency domain for visualizing spectrum as a histogram
# for reading data
classifierDir = params.classifierDir
classifierName = params.classifierName
samplePointNum = samplingFreq * windowSizeInSec # window size. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
time_step = 1 / samplingFreq
binNum4spectrum = round(wholeBand.getBandWidth() / binWidth4freqHisto)
# print('samplePointNum = ' + str(samplePointNum))
# past_eeg = np.empty((samplePointNum, 0), dtype = np.float)
# past_freqHisto = np.empty((binNum4spectrum, 0), dtype = np.float)
# print('in __init__, past_eeg.shape = ' + str(past_eeg.shape))
pickledDir = params.pickledDir
#----------------
# compute parameters
wsizeInTimePoints = samplingFreq * windowSizeInSec # window size. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
#---------------
# read files
outFiles = listdir(pickledDir)
self.dirName = sys.argv[1]
print('self.dirName =', self.dirName)
if len(sys.argv) > 2:
pickledDir = self.dataDir + '/' + sys.argv[2]
dir_stem, dir_extension = splitext(self.dirName)
if dir_extension != '.rar':
eegPath = self.dataDir + '/' + self.dirName + '/' + self.eegDir
print('path =', eegPath)
files = listdir(eegPath) if isdir(eegPath) else []
print('files =', files)
stagePath = self.dataDir + '/' + self.dirName + '/' + self.stageDir
files4stage = listdir(stagePath) if isdir(stagePath) else []
for fileFullName in files:
# print('fileFullName = ' + fileFullName)
fileID, file_extension = splitext(fileFullName)
if file_extension == '.txt' or file_extension == '.csv':
# if oFilter.isOutlier(fileID):
# print('file ' + fileID + ' is an outlier.')
# else:
pklExistsFlag = 0
for outFileName in outFiles:
# print("fileID = " + fileID + ", outFileName = " + outFileName)
if outFileName == fileID + '.pkl':
pklExistsFlag = 1
break
if pklExistsFlag == 0:
print('fileFullName = ' + fileFullName)
# if fileID.endswith('OPT') and file_extension == '.txt':
fileName4eeg = fileFullName
fileName4stage = ''
# if True:
for fileFullName4stage in files4stage:
# print('fileFullName4stage = ' + fileFullName4stage)
fileID4stage, _ = splitext(fileFullName4stage)
# print('fileName4eeg = ' + fileName4eeg + ', fileID2 = ' + fileID2)
# if fileID2.startswith(fileID) and fileID2 != fileID:
if fileID4stage.startswith(fileID):
fileName4stage = fileFullName4stage
dirFullName = self.dataDir + '/' + self.dirName
eegFilePath = dirFullName + '/' + self.eegDir + '/' + fileName4eeg
eeg, emg, timeStamps = self.readEEG(eegFilePath)
# if fileName4stage == '' and len(sys.argv) == 2:
if fileName4stage == '':
print(
'file ' + fileName4eeg +
' does not have a corresponding stage file, so labels all stages by ?.'
)
stageSeq = ['?' for _ in range(len(timeStamps))]
else:
print('self.dirName = ' + self.dirName +
', fileName4eeg = ' + fileName4eeg +
', fileName4stage = ' + fileName4stage)
#---------------
# read eeg and stages
stageFilePath = dirFullName + '/' + self.stageDir + '/' + fileName4stage
stageSeq = self.readStageSeq(stageFilePath)
#---------------
# write data
if sdFilter.isOutlier(eeg):
print('file' + fileID +
' is an outlier in terms of mean or std')
else:
# eeg = (eeg - np.mean(eeg)) / np.std(eeg)
# emg = (emg - np.mean(emg)) / np.std(emg)
# if fileName4stage == '':
# saveData = (eeg, emg, timeStamps)
# outpath = open(pickledDir + '/eegOnly.' + fileID + '.pkl', 'wb')
#else:
# saveData = (eeg, emg, stageSeq, timeStamps)
# outpath = open(pickledDir + '/eegAndStage.' + fileID + '.pkl', 'wb')
saveData = (eeg, emg, stageSeq, timeStamps)
outpath = open(
pickledDir + '/eegAndStage.' + fileID + '.pkl',
'wb')
pickle.dump(saveData, outpath)
outpath.close()
def __init__(self):
self.extractorType = 'wavelet-downsampled'
params = ParameterSetup()
self.outputDim = params.downsample_outputDim
def __init__(self):
self.extractorType = 'realFourier-downsampled'
params = ParameterSetup()
self.outputDim = params.downsample_outputDim
def __init__(self, extractorType):
self.extractorType = extractorType
params = ParameterSetup()
self.outputDim = params.downsample_outputDim
def __init__(self,
client,
fileID,
recordWaves,
channelNum,
offsetWindowID,
sleepTime,
sampRate=128,
numEpoch=600000,
eeg_std=None,
ch2_std=None,
channelOpt=2):
"""
# Params
- sampRate (int): サンプリングレート
- numEpoch (int): 予測を行うエポック数
- eeg_std (float?): EEGについて決め打ちstdがある場合はこれを指定する
- ch2_std (float?): ch2について決め打ちstdがある場合はこれを指定する
float? はオプショナル型的 (= float or None)．
"""
self.client = client
self.recordWaves = recordWaves
self.channelNum = channelNum
self.offsetWindowID = offsetWindowID
self.sleepTime = sleepTime
self.sampRate = sampRate
self.numEpoch = numEpoch
# あらかじめ定められた標準偏差がある場合、その値を保存する
self.eeg_std = eeg_std
self.ch2_std = ch2_std
self.params = ParameterSetup()
pickledDir = self.params.pickledDir
classifierType = self.params.classifierType
classifierParams = self.params.classifierParams
samplingFreq = self.params.samplingFreq
windowSizeInSec = self.params.windowSizeInSec
# self.wSizeInTimePoints = samplingFreq * windowSizeInSec # window size. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
self.segmentSizeInTimePoints = self.client.updateGraph_samplePointNum
eegFilePrefix = 'eegAndStage'
if fileID.startswith('m'):
files_L = listdir(pickledDir)
shuffle(files_L)
for fileFullName in files_L:
if fileFullName.startswith(eegFilePrefix):
break
else:
fileFullName = eegFilePrefix + '.' + fileID + '.pkl'
print('reading file ' + fileFullName)
dataFileHandler = open(pickledDir + '/' + fileFullName, 'rb')
(eeg, ch2, stageSeq, timeStamps) = pickle.load(dataFileHandler)
self.eeg = eeg
self.ch2 = ch2
self.timeStamps = timeStamps
self.stageSeq = stageSeq
if self.offsetWindowID > 0:
offsetSampleNum = self.offsetWindowID * self.segmentSizeInTimePoints
self.eeg = self.eeg[offsetSampleNum:]
self.ch2 = self.ch2[offsetSampleNum:]
self.timeStamps = self.timeStamps[offsetSampleNum:]
self.stageSeq = self.stageSeq[self.offsetWindowID:]
self.sLen = len(stageSeq)
self.eLen = min(eeg.shape[0],
self.sLen * samplingFreq * windowSizeInSec)
presentTime = timeFormatting.presentTimeEscaped()
fileName = 'daq.' + presentTime + '.csv'
self.logFile = open(self.params.logDir + '/' + fileName, 'a')
def getFeatures(self, eegSegment, timeStampSegment, time_step):
#---------------
# compute power spectrum and sort it
params = ParameterSetup()
wholeBand = params.wholeBand
binWidth4freqHisto = params.binWidth4freqHisto
binNum4spectrum = round(wholeBand.getBandWidth() / binWidth4freqHisto)
powerSpect = np.abs(np.fft.fft(eegSegment))**2
freqs = np.fft.fftfreq(len(powerSpect), d=time_step)
idx = np.argsort(freqs)
sortedFreqs = freqs[idx]
sortedPowerSpect = powerSpect[idx]
# print(' ')
# print('in getFeatures():')
# print('time_step = ' + str(time_step))
# print('eegSegment = ' + str(eegSegment))
# print('powerSpect = ' + str(powerSpect))
# print('idx = ' + str(idx))
# print('freqs = ' + str(freqs))
# print('sortedFreqs = ' + str(sortedFreqs))
# print('sortedPowerSpect = ' + str(sortedPowerSpect))
#---------------
# bin spectrum
binArray4spectrum = np.linspace(wholeBand.bottom, wholeBand.top,
binNum4spectrum + 1)
######
freqs4wholeBand = wholeBand.extractPowerSpectrum(
sortedFreqs, sortedFreqs)
### freqs4wholeBand = wholeBand.extractPowerSpectrum(sortedFreqs, sortedPowerSpect)
######
binnedFreqs = np.digitize(freqs4wholeBand,
binArray4spectrum,
right=False)
#----------------
# make a feature vector that contains context windows
extractedPowerSpect = wholeBand.extractPowerSpectrum(
sortedFreqs, sortedPowerSpect)
# print('binnedFreqs = ' + str(binnedFreqs))
# print('extractedPowerSpect = ' + str(extractedPowerSpect))
#----------------
# extract freqHistoWithContext
freqHisto = np.array([], dtype=np.float)
for key, items in groupby(zip(binnedFreqs, extractedPowerSpect),
lambda i: i[0]):
itemsA = np.array(list(items))
powerSum = np.sum(np.array([x for x in itemsA[:, 1]]))
freqHisto = np.r_[freqHisto, powerSum]
#----------------
# add time after light period started as a features
# print('timeStampSegment[0] = ' + str(timeStampSegment[0]))
# print('timeStampSegment[-1] = ' + str(timeStampSegment[-1]))
timeSinceLight = getTimeDiffInSeconds(self.lightPeriodStartTime,
timeStampSegment[0])
freqHistoWithTime = np.r_[freqHisto, timeSinceLight]
return freqHistoWithTime
from functools import reduce
from os.path import splitext
import pickle
import numpy as np
from parameterSetup import ParameterSetup
from evaluationCriteria import labels2ID, getEpisodeLengths
from sequentialPrediction import classifySequentially
from writePredictionResults import writePredictionResults
from fileManagement import readTrainFileIDsUsedForTraining, getFileIDsFromRemainingBlocks
# crossValidationID = 'ARS0Q'
# crossValidationID = '6S3BL'
args = sys.argv
crossValidationID = args[1]
params = ParameterSetup()
lstm_length = params.torch_lstm_length
paramID, markovOrder = 0, 0
# cross-validate, i.e. test using files not used for training
outputDir = '../data/pickled'
with open(outputDir + '/crossvalidation_metadata.' + crossValidationID + '.pkl', 'rb') as f:
splitID, classifierIDsByMethod = pickle.load(f)
print('splitID =', splitID)
fileIDsByBlocks = []
with open(outputDir + '/blocks_of_records.' + splitID + '.csv') as f:
for line in f:
fileIDsByBlocks.append(line.rstrip().split(','))
# print(fieIDs)
def classifySequentially(params, paramID, paramDir, fileIDpair):
# print('classifySequentially by ' + str(fileID))
pickledDir = params.pickledDir
extractorType = params.extractorType
classifierType = params.classifierType
classifierParams = params.classifierParams
samplingFreq = params.samplingFreq
windowSizeInSec = params.windowSizeInSec
wsizeInSamplePointNum = windowSizeInSec * samplingFreq # window size in sample points. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
timeWindowStrideInSec = params.timeWindowStrideInSec
timeWindowStrideInSamplePointNum = timeWindowStrideInSec * samplingFreq
markovOrderForPrediction = params.markovOrderForPrediction
strideNumInTimeWindow = np.ceil(windowSizeInSec / timeWindowStrideInSec)
classifierType = params.classifierType
eegFilePrefix = params.eegFilePrefix
classifierFilePrefix = params.classifierFilePrefix
# replacesWWWRtoWWWW = params.replacesWWWRtoWWWW
numOfConsecutiveWsThatProhibitsR = params.numOfConsecutiveWsThatProhibitsR
predict_by_batch = params.predict_by_batch
testFileID = fileIDpair[0]
classifierID = fileIDpair[1]
print('$#$#$# in sequentialPrediction, classifierID =', classifierID)
predictionTargetDataFilePath = pickledDir + '/' + eegFilePrefix + '.' + testFileID + '.pkl'
print('predictionTargetDataFilePath =', predictionTargetDataFilePath)
dataFileHandler = open(predictionTargetDataFilePath, 'rb')
(eeg, ch2, stageSeq, timeStamps) = pickle.load(dataFileHandler)
dataFileHandler.close()
print('eeg.shape = ' + str(eeg.shape))
print('len(stageSeq) = ' + str(len(stageSeq)))
if params.useEMG:
label4EMG = params.label4withEMG
else:
label4EMG = params.label4withoutEMG
params.classifierName = classifierFilePrefix + '.' + classifierType + '.' + label4EMG + '.excludedFileID.' + testFileID + '.classifierID.' + classifierID
factory = AlgorithmFactory(extractorType)
extractor = factory.generateExtractor()
# if params.classifierType == 'deep':
# classifier = DeepClassifier(classifierID=testFileID)
# else:
classifierDir = params.classifierDir
pwd = dirname(abspath(__file__))
print('# classifierID =', classifierID)
# classLabels = list(params.labelCorrectionDict.keys())[:params.maximumStageNum]
classLabels = self.params.sampleClassLabels[:self.params.maximumStageNum]
paramFileName = 'params.' + classifierID + '.json'
params_for_network_structure = ParameterSetup(paramDir=paramDir,
paramFileName=paramFileName)
if params.classifierType == 'deep':
classifier = DeepClassifier(
classLabels,
classifierID=classifierID,
paramsForDirectorySetup=params,
paramsForNetworkStructure=params_for_network_structure)
# if classifierID == '':
# model_path = pwd + '/' + paramDir + '/model.pkl'
#else:
# model_path = pwd + '/' + params.deepParamsDir + '/' + testFileID + '/' + classifierID + '/model.pkl'
model_path = pwd + '/' + paramDir + '/weights.' + classifierID + '.pkl'
print('model_path = ', model_path)
classifier.load_weights(model_path)
else:
classifierFileName = params.classifierDir + '/' + params.classifierPrefix + '.' + classifierID + '.pkl'
classifierFileHandler = open(classifierFileName, 'rb')
classifier = pickle.load(classifierFileHandler)
classifierFileHandler.close()
stagePredictor = StagePredictor(params_for_network_structure, extractor,
classifier, classifierDir, classifierID,
markovOrderForPrediction)
### stagePredictor = StagePredictor(params, extractor, classifier, classifierDir, classifierID)
# stagePredictor = StagePredictor(params, extractor, classifier)
sLen = len(stageSeq)
samplePointNum = min(eeg.shape[0], sLen * wsizeInSamplePointNum)
print('%$%$%$ sLen =', sLen)
print('eeg.shape[0] =', eeg.shape[0])
print('sLen * wsizeInSamplePointNum =', sLen * wsizeInSamplePointNum)
print('%$%$%$ samplePointNum =', samplePointNum)
print('%$%$%$ timeWindowStrideInSamplePointNum =',
timeWindowStrideInSamplePointNum)
# reset eeg and emg statistical values
# eeg_mean = 0
# eeg_variance = 0
# emg_mean = 0
# emg_variance= 0
# oldSampleNum = 0
y_test_L = []
y_pred_L = []
wID = 0
# startSamplePoint = 0
# stagePredictions_L = []
replacedR = False
# while startSamplePoint + wsizeInSamplePointNum <= samplePointNum:
records_L = []
timeStampSegments_L = []
### all_past_eeg, all_past_ch2 = np.array([]), np.array([])
standardizer_eeg = standardizer(samplePointNum)
standardizer_ch2 = standardizer(samplePointNum)
# standardized_all_past_eeg = np.array([])
for startSamplePoint in range(0, samplePointNum,
timeWindowStrideInSamplePointNum):
endSamplePoint = startSamplePoint + wsizeInSamplePointNum
if endSamplePoint > samplePointNum:
break
timeStampSegment = timeStamps[startSamplePoint:endSamplePoint]
eegSegment = eeg[startSamplePoint:endSamplePoint]
### standardized_eegSegment, all_past_eeg = standardize(eegSegment, all_past_eeg)
standardized_eegSegment = standardizer_eeg.standardize(eegSegment)
if params.useEMG:
ch2Segment = ch2[startSamplePoint:endSamplePoint]
### standardized_ch2Segment, all_past_ch2 = standardize(ch2Segment, all_past_ch2)
standardized_ch2Segment = standardizer_ch2.standardize(ch2Segment)
one_record = np.r_[standardized_eegSegment,
standardized_ch2Segment]
else:
one_record = standardized_eegSegment
# standardized_all_past_eeg = np.r_[standardized_all_past_eeg, standardized_eegSegment]
# print('standardized_all_past_eeg.mean() =', standardized_all_past_eeg.mean(), ', standardized_all_past_eeg.std() =', standardized_all_past_eeg.std())
'''
eegSegment_orig_mean = np.mean(eegSegment)
eegSegment_orig_std = np.std(eegSegment)
eeg_old_mean = eeg_mean
eeg_mean = recompMean(eegSegment, eeg_mean, oldSampleNum)
eeg_variance = recompVariance(eegSegment, eeg_variance, eeg_old_mean, eeg_mean, oldSampleNum)
standardized_eegSegment = (eegSegment - eeg_mean) / np.sqrt(eeg_variance)
if params.usech2:
ch2Segment = ch2[startSamplePoint:endSamplePoint]
ch2_old_mean = ch2_mean
ch2_mean = recompMean(ch2Segment, ch2_mean, oldSampleNum)
ch2_variance = recompVariance(ch2Segment, ch2_variance, ch2_old_mean, ch2_mean, oldSampleNum)
standardized_ch2Segment = (ch2Segment - ch2_mean) / np.sqrt(ch2_variance)
one_record = np.r_[standardized_eegSegment, standardized_ch2Segment]
else:
one_record = standardized_eegSegment
oldSampleNum += eegSegment.shape[0]
'''
# local_mu = np.mean(standardized_eegSegment)
# local_sigma = np.std(standardized_eegSegment)
# print('local_mu =', local_mu)
# print(' ')
# print('in sequentialPredictionDecisionTree.classifySequentially():')
### print('one_record.shape = ' + str(one_record.shape))
### print('one_record[:] = ' + str(one_record[:]))
# print('one_record[:10] = ' + str(one_record[:10]))
# print('one_record[:,1] = ' + str(one_record[:,1]))
# stageEstimate is one of ['w', 'n', 'r']
# stagePrediction = params.reverseLabel(stagePredictor.predict(one_record, local_mu=eegSegment_orig_mean, local_sigma=eegSegment_orig_std))
# print('np.mean(one_record[:,0]) = ' + str(np.mean(one_record[:])))
# print('np.std(one_record[:,0]) = ' + str(np.std(one_record[:])))
# print('before stagePredictor.predict')
# stagePrediction = params.reverseLabel(stagePredictor.predict(one_record, timeStampSegment, local_mu=local_mu, local_sigma=local_sigma, wID=wID))
if predict_by_batch:
records_L.append(one_record)
timeStampSegments_L.append(timeStampSegment)
else:
orig_prediction = stagePredictor.predict(
one_record, timeStampSegment, params.stageLabels4evaluation,
params.stageLabel2stageID)
# print(orig_prediction, end='')
stagePrediction = params.reverseLabel(orig_prediction)
# print('after stagePredictor.predict')
# replaces R to W when W appears consecutively
'''
Ws = 'W' * numOfConsecutiveWsThatProhibitsR
for wCnt in range(1,numOfConsecutiveWsThatProhibitsR+1):
if len(y_pred_L) >= wCnt:
if y_pred_L[len(y_pred_L)-wCnt] != 'W':
break
if stagePrediction == 'R':
print(Ws + '->R changed to ' + Ws + '->W at wID = ' + str(wID) + ', startSamplePoint = ' + str(startSamplePoint))
stagePrediction = 'W'
replacedR = True
'''
#----
# if the prediction is P, then use the previous one
if stagePrediction == 'P':
# print('stagePrediction == P for wID = ' + str(wID))
if len(y_pred_L) > 0:
stagePrediction = y_pred_L[len(y_pred_L) - 1]
else:
stagePrediction = 'M'
#-----
# append to the lists of results
y_pred_L.append(stagePrediction)
if wID >= len(stageSeq):
print('len(stageSeq) =', len(stageSeq), ', wID =', wID)
print('startSamplePoint =', startSamplePoint,
', samplePointNum =', samplePointNum,
', timeWindowStrideInSamplePointNum =',
timeWindowStrideInSamplePointNum)
trueLabel = stageSeq[wID]
if replacedR:
print(' -> for wID = ' + str(wID) + ', trueLabel = ' +
trueLabel)
replacedR = False
y_test_L.append(trueLabel)
wID += 1
#-----
# vote for choosing the label for 10 second window
'''
stagePredictions_L.append(stagePrediction)
if len(stagePredictions_L) == strideNumInTimeWindow:
finalStagePrediction = voteForStagePrediction(stagePredictions_L[-(markovOrderForPrediction+1):])
#-----
# append to the lists of results
y_pred_L.append(finalStagePrediction)
stagePredictions_L = []
# print('stagePredictions_L[' + stagePrediction + '] = ' + )
# startSamplePoint = endSamplePoint
trueLabel = stageSeq[wID]
if replacedR:
print(' -> for wID = ' + str(wID) + ', trueLabel = ' + trueLabel)
replacedR = False
y_test_L.append(trueLabel)
### print('wID = ' + str(wID) + ', trueLabel = ' + trueLabel + ', stagePrediction = ' + stagePrediction)
wID += 1
'''
if predict_by_batch:
# print('predicting by batch:')
y_pred = np.array([
params.reverseLabel(y_pred_orig)
for y_pred_orig in stagePredictor.batch_predict(
np.array(records_L),
np.array(timeStampSegments_L),
local_mu=local_mu,
local_sigma=local_sigma,
stageLabels4evaluation=params.stageLabels4evaluation,
stageLabel2stageID=params.stageLabel2stageID)
])
y_test = np.array(stageSeq)
else:
y_test = np.array(y_test_L)
y_pred = np.array(y_pred_L)
return (y_test, y_pred)
np.array(records_L),
np.array(timeStampSegments_L),
local_mu=local_mu,
local_sigma=local_sigma)
])
'''
else:
y_pred = np.array(y_pred_L)
'''
return y_pred
#--------------------
# main
params = ParameterSetup()
# print('params.finalClassifierDir = ' + str(params.finalClassifierDir))
extractorType = params.extractorType
classifierType = params.classifierType
classifierParams = params.classifierParams
samplingFreq = params.samplingFreq
windowSizeInSec = params.windowSizeInSec
wsizeInSamplePointNum = windowSizeInSec * samplingFreq # window size in sample points. data is sampled at 128 Hz, so 1280 sample points = 10 sec.
timeWindowStrideInSec = params.timeWindowStrideInSec
timeWindowStrideInSamplePointNum = timeWindowStrideInSec * samplingFreq
lookBackTimeWindowNum = params.lookBackTimeWindowNum
strideNumInTimeWindow = np.ceil(windowSizeInSec / timeWindowStrideInSec)
classifierFilePrefix = params.classifierFilePrefix
# replacesWWWRtoWWWW = params.replacesWWWRtoWWWW
numOfConsecutiveWsThatProhibitsR = params.numOfConsecutiveWsThatProhibitsR
def supersample(x, y):
params = ParameterSetup()
classLabels = params.sampleClassLabels
ratios = np.array(params.subsampleRatios)
do_supersample = np.array(params.supersample)
do_subsample = 1
for ratio in ratios:
if ratio == -1:
do_subsample = 0
break
if do_supersample or do_subsample:
minimumRatio = min(ratios)
ratios = ratios / minimumRatio
print('sampling ratios = ' + str(ratios))
featureDim = x.shape[1]
sorted_y = sorted(y)
grouped = [[key, len(list(g))] for key, g in groupby(sorted_y)]
for (key, elem_num) in grouped:
print(' ' + key + ':' + str(elem_num))
if do_supersample:
max_class, max_elem_num = max(grouped)
print('max_elem_num = ' + str(max_elem_num) + ' for max_class = ' +
max_class)
target_nums = [np.int(x) for x in np.floor(max_elem_num * ratios)]
else:
min_class, min_elem_num = max(grouped)
print('min_elem_num = ' + str(min_elem_num) + ' for min_class = ' +
min_class)
target_nums = [np.int(x) for x in np.floor(min_elem_num * ratios)]
print('target_nums = ' + str(target_nums))
classID = 0
sampled_x = np.zeros((0, featureDim), dtype=float)
sampled_y = np.zeros((0))
for targetClass in classLabels:
print('sampling for classID = ' + str(classID) + ', class = ' +
targetClass)
isTarget = judgeIfTarget(y, targetClass)
orderedIndices = np.arange(isTarget.shape[0])
targetIDs = orderedIndices[isTarget == True]
print(' target_nums[' + str(classID) + '] = ' +
str(target_nums[classID]))
sampledIDs = targetIDs
print(' sampledIDs.shape = ' + str(sampledIDs.shape))
if do_supersample:
while sampledIDs.shape[0] <= target_nums[classID]:
orderedIndices = np.arange(isTarget.shape[0])
targetIDs = orderedIndices[isTarget == True]
sampledIDs = np.r_[sampledIDs, targetIDs]
print(' sampledIDs.shape = ' + str(sampledIDs.shape))
sampledIDs = sampledIDs[:target_nums[classID]]
print(' finally, sampledIDs.shape is reduced to: ' +
str(sampledIDs.shape))
print(' x[sampledIDs].shape = ' + str(x[sampledIDs].shape))
# print('x[sampled_IDs] = ' + str(x[sampled_IDs]))
sampled_x = np.r_[sampled_x, x[sampledIDs]]
sampled_y = np.r_[sampled_y, y[sampledIDs]]
# print('sampled_y = ' + str(sampled_y) + ', y[sampled_IDs] = ' + str(y[sampled_IDs]))
# if sampled_y.shape[0] > 0:
# sampled_y = np.r_[sampled_y, y[sampled_IDs]]
# else:
# sampled_y = y[sampled_IDs]
# sampled_isTarget = isTarget[sampled_IDs]
print('sampled_x.shape = ' + str(sampled_x.shape))
print('sampled_y.shape = ' + str(sampled_y.shape))
classID = classID + 1
return (sampled_x, sampled_y)
else:
print('no supersampling nor subsampling')
return (x, y)
def __init__(self):
self.extractorType = 'realFourier'
params = ParameterSetup()
from parameterSetup import ParameterSetup
from tester import printMetadata, test_by_classifierID
args = sys.argv
if args[1] == 'test':
datasetType = 'test'
classifierIDs = args[2:]
elif args[1] == 'validation':
datasetType = 'validation'
classifierIDs = args[2:]
else:
datasetType = 'validation'
classifierIDs = args[1:]
params = ParameterSetup()
printMetadata(params)
pickledDir = params.pickledDir
# sensitivity_by_classifier_L, specificity_by_classifier_L, accuracy_by_classifier_L, precision_by_classifier_L, measures_by_classifier_L = [], [], [], [], []
measures_by_classifier_L = []
for classifierID in classifierIDs:
measures_by_classifier_L.append(
test_by_classifierID(params, datasetType, classifierID))
if datasetType == 'test':
f = open(
pickledDir + '/test_result.measures.' + classifierIDs[0] +
'.etc.test.pkl', 'wb')
else:
f = open(
pickledDir + '/test_result.measures.' + classifierIDs[0] + '.etc.pkl',
def start(self):
channelOpt = 1
params = ParameterSetup()
self.recordWaves = params.writeWholeWaves
self.extractorType = params.extractorType
self.classifierType = params.classifierType
self.postDir = params.postDir
self.predDir = params.predDir
self.finalClassifierDir = params.finalClassifierDir
self.samplingFreq = params.samplingFreq
# eegFilePath = args[1]
# inputFileID = splitext(split(eegFilePath)[1])[0]
postFiles = listdir(self.postDir)
fileCnt = 0
for inputFileName in postFiles:
if not inputFileName.startswith('.'):
print('inputFileName = ' + inputFileName)
inputFileID = splitext(inputFileName)[0]
print('inputFileID = ' + inputFileID)
predFileFullPath = self.predDir + '/' + inputFileID + '_pred.txt'
print('predFileFullPath = ' + predFileFullPath)
if not isfile(predFileFullPath):
fileCnt += 1
print(' processing ' + inputFileID)
try:
classifierID = selectClassifierID(
self.finalClassifierDir, self.classifier_type)
if len(self.args) > 1:
if self.args[1] == '--output_the_same_fileID':
self.client = ClassifierClient(
self.recordWaves,
self.extractorType,
self.classifierType,
classifierID,
inputFileID=inputFileID)
else:
self.client = ClassifierClient(
self.recordWaves, self.extractorType,
self.classifierType, classifierID)
else:
self.client = ClassifierClient(
self.recordWaves, self.extractorType,
self.classifierType, classifierID)
self.client.predictionStateOn()
self.client.hasGUI = False
# sys.stdout.write('classifierClient started by ' + str(channelOpt) + ' channel.')
except Exception as e:
print(str(e))
raise e
try:
eegFilePath = self.postDir + '/' + inputFileName
self.server = EEGFileReaderServer(
self.client,
eegFilePath,
samplingFreq=self.samplingFreq)
except Exception as e:
print(str(e))
raise e
else:
print(' skipping ' + inputFileID + ' because ' +
predFileFullPath + ' exists.')
