int(settingsDict['Feature DownSample Factor']),
int(settingsDict['CRP Time Delay']),
int(settingsDict['CRP Dimension']),
settingsDict['CRP Method'],
settingsDict['CRP Neighbourhood Size'],
None,
None,
int(settingsDict['NCD Sequence Length']),
featureFileDict=featureFileDict,
pieceIds=pieceIds)
# Convert NCD files into a dataframe
runTime = str(datetime.now()).replace(':', '-')
MAPresult = None
if NCDlist is None: # there were errors e.g. in CRP calculation after downsampling
MAPresult = 0 # need to use something that is not None for the optimiser to find its best result
else:
dfNCDs = convertNCDs(NCDlist, dataFrameFileName=None)
# Get the overall MAP of the run and add to the setting
print 'Calculating MAP Result...'
MAPresult = getDataFrameMAPresult(dfNCDs)
if MAPresult is not None and MAPresult != 0:
print 'Mean Average Precision: %0.3f\n' % MAPresult
else:
print 'No MAP result found!'
settingsDict['Mean Average Precision'] = MAPresult
settingsDict['Run Duration'] = datetime.now() - startDateTime
# Save run settings and result
pickle.dump(settingsDict, open(resultsFn, 'wb'))
setting['DownSample Factor'],
setting['Time Delay'],
setting['Dimension'], CRPmethod,
setting['Neighbourhood Size'],
numFolders, numFilesPerFolder,
setting['Sequence Length'],
weightMatrix, biases, featureOffset,
featureScaling, timeStacking)
# Convert NCD files into a dataframe
runTime = str(datetime.now()).replace(':', '-')
MAPresult = None
if NCDlist is None: # there were errors e.g. in CRP calculation after downsampling
MAPresult = 0 # need to use something that is not None for the optimiser to find its best result
else:
dfNCDs = convertNCDs(NCDlist, dataFrameFileName=runTime)
# Get the overall MAP of the run and add to the setting
MAPresult = getDataFrameMAPresult(dfNCDs)
if MAPresult is not None and MAPresult != 0:
print 'Mean Average Precision: %0.3f\n' % MAPresult
else:
print 'No MAP result found!'
setting['Mean Average Precision'] = MAPresult
# Create and save a runDict of the settings and result
# assign single (non-optimised) settings to the runDict
runDict = {
'featureName': featureName,
'method': CRPmethod,
'numFilesPerFolder': numFilesPerFolder
}