def Do11ClassifierPerfNonAmp(sub, epo, crossvalclass):
"""Classifier performance NonAmp part,
sub -> a subject string or similiar to process
epo -> the epoch for the subject
crossvalclass -> data structure with results from a train classifier function
"""
#Connection Object to connect to object store
conn = connection.Connection(cloud='openstack')
#The container's name in the objectstore
ContainerName = 'DroneContainer'
#Mock directory to store the data in the object store
NonAmpDirectory = f'data/Visual/{sub}/7-ClassifierTraining/'#directory of the data
print('Cross Validated model successfully performed')
#Saving the data to the object store
conn.create_object(ContainerName, NonAmpDirectory + f'{sub}_{epo}_crossvalclass.pkl', data = dumps({f'{sub}_{epo}_crossvalclass': crossvalclass}))
#Performance
cfg = {'performance': 1, 'category_model': ['Face', 'Landmark', 'Object'], 'classifiernumber': 20}
crossvalclass_performance = mvpa_classifierperf(cfg, crossvalclass)
print('Performance calculated')
#Saving the data to the object store
conn.create_object(ContainerName, NonAmpDirectory + f'{sub}_{epo}_crossvalclass_performance.pkl', data = dumps({f'{sub}_{epo}_crossvalclass_performance': crossvalclass_performance}))
#Outcommented because falls out of scope, but still good practice
#conn.close()
return True
def analysis(job, sliceno, slices):
#Divide the subject list so that each sliceno processes their own subjects.
sublist = array_split(options.subjects, slices)[sliceno]
for epo in options.epoch:
for sub in sublist:
fname = job.input_filename(
f'./{sub}/6-ClassificationData/{sub}_{epo}_data_amp.mat')
cdata = loadmat(fname, chars_as_strings=True,
simplify_cells=True)[f'{sub}_{epo}_data_amp']
directory = f'./data/Visual/{sub}/7-ClassifierTraining/'
os.makedirs(os.path.dirname(directory), exist_ok=True)
#Partition of the data
cfg = {'classifiernumber': 40, 'fold': 10}
datapart = dp.mvpa_datapartition(cfg, cdata)
#Partition of the data
print(
'Data was partitioned and feature reduction for each partition is complete...'
)
#Commented out because datapartition is by far the largest dataobject and takes ridicolous space, of course no problem if one has more than like 50 Gb
#job.save({f'{sub}_{epo}_datapart_amp': datapart}, directory + f'{sub}_{epo}_datapart_amp.pickle')
#Train the cross validated classifier
cfg = {
'training_algorithm': 1,
'fold': 10,
'classifiernumber': 40,
'category_model': ['Face', 'Landmark', 'Object']
}
crossval = tcc.mvpa_traincrossvalclassifier(cfg, datapart)
print('Cross Validated model successfully performed')
job.save({f'{sub}_{epo}_crossvalclass_amp': crossval},
directory + f'{sub}_{epo}_crossvalclass_amp.pickle')
#Performance
cfg = {
'performance': 1,
'category_model': ['Face', 'Landmark', 'Object'],
'classifiernumber': 40
}
crossval_perf = cp.mvpa_classifierperf(cfg, crossval)
print('Performance calculated')
job.save(
{f'{sub}_{epo}_crossvalclass_performance_amp': crossval_perf},
directory +
f'{sub}_{epo}_crossvalclass_performance_amp.pickle')
#Serial code after analysis to aggregate results, or do something which must be serial.
#def synthesis(analysis_res):
# pass
def iClass(epo):
directory = f'./data/Visual/{sub}/7-ClassifierTraining/' #directory for saving of the data
#Create the new directory in case it doesn't already exist
os.makedirs(os.path.dirname(directory), exist_ok=True)
#Python handles loading .mat files almost better than matlab itself
cdata = loadmat(
f'./Visual/{sub}/6-ClassificationData/{sub}_{epo}_data',
chars_as_strings=True,
simplify_cells=True
)[f'{sub}_{epo}_data'] #Skip first key since it's unecessary.
#Partition of the data
cfg = {'classifiernumber': 20, 'fold': 10}
datapart = mvpa_datapartition(cfg,
cdata,
random_state=random_state)
print(
'Data was partitioned and feature reduction for each partition is complete...'
)
#While it is possible to save the files as .mat files with scipy.io I didn't get it to work without giving severe penalaties on performance
with open(directory + f'{sub}_{epo}_datapart.pkl', 'wb') as file:
dump({f'{sub}_{epo}_datapart': datapart}, file)
#Train the cross validated classifier
cfg = {
'training_algorithm': 1,
'fold': 10,
'classifiernumber': 20,
'category_model': ['Face', 'Landmark', 'Object']
}
crossvalclass = mvpa_traincrossvalclassifier(cfg, datapart)
del datapart #Deleting now redundant variable to free memory
print('Cross Validated model successfully performed')
#Save
with open(directory + f'{sub}_{epo}_crossvalclass.pkl',
'wb') as file:
dump({f'{sub}_{epo}_crossvalclass': crossvalclass}, file)
#Performance
cfg = {
'performance': 1,
'category_model': ['Face', 'Landmark', 'Object'],
'classifiernumber': 20
}
crossvalclass_performance = mvpa_classifierperf(cfg, crossvalclass)
print('Performance calculated')
#Save
with open(directory + f'{sub}_{epo}_crossvalclass_performance.pkl',
'wb') as file:
dump(
{
f'{sub}_{epo}_crossvalclass_performance':
crossvalclass_performance
}, file)
def Do12TestClassifier(sub, epo, crossdata):
"""Do12 Clsasifier testing
sub -> the subject to be tested
epo -> the epoch the subject's in
crossdata -> data structure with classifier of Do11
"""
print(sub)
#Connection object to talk with openstack
conn = connection.Connection(cloud='openstack')
ContainerName = 'DroneContainer'
#Mock Directory to load from object store.
directory = f'data/Visual/{sub}/8-ClassifierTesting/'
#Retrieve data as a raw binary data.
cFile = BytesIO()
#Retrieve data from objectstore
conn.get_object(ContainerName, f'Visual/{sub}/6-ClassificationData/{sub}_test_data_visual.mat', outfile = cFile)
cdata = sio.loadmat(cFile, chars_as_strings = True, simplify_cells = True)[f'{sub}_test_data_visual']
#Clean up after ourselves so we don't destroy stuff and free memory
cFile.close()
cfg = {'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'trials': 'all',
'category_model': np.array(['Face', 'Landmark', 'Object'])}
#Apply calssifier into the correct visual/ correct visual trials
predtest_visual = mvpa_applycrossvalclassifier(cfg = cfg, model = crossdata, data = cdata)
#Store data in object store
conn.create_object(ContainerName, directory + f'{sub}_{epo}_predtest_visual.pkl', data = dumps({f'{sub}_{epo}_predtest_visual': predtest_visual}))
cfg = {'performance': 2,
'category_model': np.array(['Face', 'Landmark', 'Object']),
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'classifiernumber': 20,
'timebinsnumber': 20}
predtest_visual_performance = mvpa_classifierperf(cfg, predtest_visual)#Performance
#Store data in object store
conn.create_object(ContainerName, directory + f'{sub}_{epo}_predtest_visual.pkl', data = dumps({f'{sub}_{epo}_predtest_visual_performance': predtest_visual_performance}))
#Cleanup Crew reporting for duty!
conn.close()
print('Performance calculated')
return cdata
def Do11ClassifierPerfAmp(sub, epo, crossvalclass_amp):
"""Classifier performance NonAmp part,
sub -> a subject string or similiar to process
epo -> the epoch for the subject
crossvalclass -> data structure with results from a train classifier function
"""
#Connection Object to connect to object store
conn = connection.Connection(
region_name='RegionOne',
auth=dict(auth_url='https://xerces.ericsson.net:5000',
username='******',
password='******',
project_id='b5c7a743b2554fd6972b841d3e853ac9',
user_domain_name='xerces',
project_name='bcix'),
compute_api_version='3',
identity_interface='public')
#The container's name in the objectstore
ContainerName = 'DroneContainer'
#Mock directory to store the data in the object store
AmpDirectory = f'data/Visual/{sub}/7-ClassifierTraining/'
print('Cross Validated model successfully performed!')
#Saving the data to the object store
conn.create_object(
ContainerName,
AmpDirectory + f'{sub}_{epo}_crossvalclass_amp.pkl',
data=dumps({f'{sub}_{epo}_crossvalclass_amp': crossvalclass_amp}))
#Performance
cfg = {
'performance': 1,
'category_model': ['Face', 'Landmark', 'Object'],
'classifiernumber': 40
}
crossvalclass_performance_amp = mvpa_classifierperf(cfg, crossvalclass_amp)
print('Performance calculated!')
#Saving the data to the object store
conn.create_object(ContainerName,
AmpDirectory +
f'{sub}_{epo}_crossvalclass_performance_amp.pkl',
data=dumps({
f'{sub}_{epo}_crossvalclass_performance_amp':
crossvalclass_performance_amp
}))
#Falls out of scope, but good practice
#conn.close()
return True
def Do12TestClassifier(subject=['Subj01'], epoch=['study']):
for j, sub in enumerate(subject):
for epo in epoch: #Should rename e, very hard to identify and change etc.
directory = f'./data/Visual/{sub}/7-ClassifierTraining/' #directory of the data, should already exist otherwise can't load data
cdata = loadmat(
f'./Visual/{sub}/6-ClassificationData/{sub}_test_data_visual',
chars_as_strings=True,
simplify_cells=True
)[f'{sub}_test_data_visual'] #give imput data, skip first key since unecessary.
with open(directory + f'{sub}_{epo}_crossvalclass.pkl',
'rb') as file:
crossdata = load(file)[
f'{sub}_{epo}_crossvalclass'] #give classifier
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'trials': 'all',
'category_model': np.array(['Face', 'Landmark', 'Object'])
}
#Apply calssifier into the correct visual/ correct visual trials
predtest_visual = mvpa_applycrossvalclassifier(
cfg, crossdata, cdata)
print(f'{sub} Done')
directory = f'./data/Visual/{sub}/8-ClassifierTesting/'
#Create new dir if not exist
os.makedirs(os.path.dirname(directory), exist_ok=True)
with open(directory + f'{sub}_{epo}_predtest_visual.pkl',
'wb') as file:
dump({f'{sub}_{epo}_predtest_visual': predtest_visual}, file)
cfg = {
'performance': 2,
'category_model': np.array(['Face', 'Landmark', 'Object']),
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'classifiernumber': 20,
'timebinsnumber': 20
}
predtest_visual_performance = mvpa_classifierperf(
cfg, predtest_visual) #Performance
with open(directory + f'{sub}_{epo}_predtest_visual.pkl',
'wb') as file:
dump(
{
f'{sub}_{epo}_predtest_visual_performance':
predtest_visual_performance
}, file)
print('Performance calculated')
def analysis(job, sliceno, slices):
#Divide the subject list so that each sliceno processes their own subjects.
sublist = array_split(options.subjects, slices)[sliceno]
jobbig = jobs.do11Job
for epo in options.epoch:
for sub in sublist:
directory = f'./data/Visual/{sub}/7-ClassifierTraining/' #directory of the data
fname = job.input_filename(
f'./{sub}/6-ClassificationData/{sub}_test_data_visual.mat')
cdata = loadmat(fname, chars_as_strings=True, simplify_cells=True)[
f'{sub}_test_data_visual'] #give imput data
crossdata = jobbig.load(
directory + f'{sub}_{epo}_crossvalclass.pickle')[
f'{sub}_{epo}_crossvalclass'] #give classifier
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'trials': 'all',
'category_model': np.array(['Face', 'Landmark', 'Object'])
}
#Apply calssifier into the correct visual/ correct visual trials
predtest_visual = acc.mvpa_applycrossvalclassifier(
cfg, crossdata, cdata)
print(f'{sub} Done')
directory = f'./data/Visual/{sub}/8-ClassifierTesting/'
os.makedirs(os.path.dirname(directory), exist_ok=True)
job.save({f'{sub}_{epo}_predtest_visual': predtest_visual},
directory + f'{sub}_{epo}_predtest_visual.pickle')
cfg = {
'performance': 2,
'category_model': np.array(['Face', 'Landmark', 'Object']),
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'classifiernumber': 20,
'timebinsnumber': 20
}
predtest_visual_performance = cp.mvpa_classifierperf(
cfg, predtest_visual) #Performance
print('Performance calculated')
job.save(
{
f'{sub}_{epo}_predtest_visual_performance':
predtest_visual_performance
}, directory + f'{sub}_{epo}_predtest_visual.pickle')
#something serial after prepare and analysis
#def synthesis()
datapart = mvpa_datapartition(cfg, testdata, random_state=0)
cfg = {
'training_algorithm': 1,
'fold': 5,
'classifiernumber': 2,
'category_model': ['Face', 'Landmark', 'Object']
}
crossdata = mvpa_traincrossvalclassifier(cfg, datapart)
cfg = {
'performance': 1,
'category_model': ['Face', 'Landmark', 'Object'],
'classifiernumber': 2
}
crossPerf = mvpa_classifierperf(cfg, crossdata)
cdata = loadmat(f'./testData/TestData_test_data_visual.mat',
chars_as_strings=True,
simplify_cells=True)[f'Subj01_test_data_visual']
cfg = {
'fold': 5,
'classifiernumber': 2,
'timebinsnumber': 3,
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'trials': 'all',
'category_model': np.array(['Face', 'Landmark', 'Object'])
}
applyCross = mvpa_applycrossvalclassifier(cfg=cfg, model=crossdata, data=cdata)
def iClass(epo):
directory = f'./data/Visual/{sub}/7-ClassifierTraining/'
#Create dir, if not exist
os.makedirs(os.path.dirname(directory), exist_ok=True)
cdata = loadmat(
f'./Visual/{sub}/6-ClassificationData/{sub}_{epo}_data_amp',
chars_as_strings=True,
simplify_cells=True)[f'{sub}_{epo}_data_amp']
#partition of the data
cfg = {'classifiernumber': 40, 'fold': 10}
datapart_amp = mvpa_datapartition(cfg,
cdata,
random_state=random_state)
print(
'Data was partitioned and feature reduction for each partition is complete'
)
#Again saving data.
with open(directory + f'{sub}_{epo}_datapart_amp.pkl',
'wb') as file:
dump({f'{sub}_{epo}_datapart_amp': datapart_amp}, file)
#Train the cross validated classifier
cfg = {
'training_algorithm': 1,
'fold': 10,
'classifiernumber': 40,
'category_model': ['Face', 'Landmark', 'Object']
}
crossvalclass_amp = mvpa_traincrossvalclassifier(cfg, datapart_amp)
del datapart_amp #Free up memory
print('Cross Validated model successfully performed!')
with open(directory + f'{sub}_{epo}_crossvalclass_amp.pkl',
'wb') as file:
dump({f'{sub}_{epo}_crossvalclass_amp': crossvalclass_amp},
file)
#Performance
cfg = {
'performance': 1,
'category_model': ['Face', 'Landmark', 'Object'],
'classifiernumber': 40
}
crossvalclass_performance_amp = mvpa_classifierperf(
cfg, crossvalclass_amp)
print('Performance calculated!')
#save
with open(
directory +
f'{sub}_{epo}_crossvalclass_performance_amp.pkl',
'wb') as file:
dump(
{
f'{sub}_{epo}_crossvalclass_performance_amp':
crossvalclass_performance_amp
}, file)
def Do12TestClassifier(sub, epo, crossdata):
"""Do12 Clsasifier testing
sub -> the subject to be tested
epo -> the epoch the subject's in
crossdata -> data structure with classifier of Do11
"""
print(sub)
#Connection object to talk with openstack
conn = connection.Connection(
region_name='RegionOne',
auth=dict(auth_url='https://xerces.ericsson.net:5000',
username='******',
password='******',
project_id='b5c7a743b2554fd6972b841d3e853ac9',
user_domain_name='xerces',
project_name='bcix'),
compute_api_version='3',
identity_interface='public')
ContainerName = 'DroneContainer'
#Mock Directory to load from object store.
directory = f'data/Visual/{sub}/8-ClassifierTesting/'
#Retrieve data as a raw binary data.
cFile = BytesIO()
#Retrieve data from objectstore
conn.get_object(
ContainerName,
f'Visual/{sub}/6-ClassificationData/{sub}_test_data_visual.mat',
outfile=cFile)
cdata = sio.loadmat(cFile, chars_as_strings=True,
simplify_cells=True)[f'{sub}_test_data_visual']
#Clean up after ourselves so we don't destroy stuff and free memory
cFile.close()
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'trials': 'all',
'category_model': np.array(['Face', 'Landmark', 'Object'])
}
#Apply calssifier into the correct visual/ correct visual trials
predtest_visual = mvpa_applycrossvalclassifier(cfg=cfg,
model=crossdata,
data=cdata)
#Store data in object store
conn.create_object(ContainerName,
directory + f'{sub}_{epo}_predtest_visual.pkl',
data=dumps(
{f'{sub}_{epo}_predtest_visual': predtest_visual}))
cfg = {
'performance': 2,
'category_model': np.array(['Face', 'Landmark', 'Object']),
'category_predict': np.array(['Face', 'Landmark', 'Object']),
'classifiernumber': 20,
'timebinsnumber': 20
}
predtest_visual_performance = mvpa_classifierperf(
cfg, predtest_visual) #Performance
#Store data in object store
conn.create_object(ContainerName,
directory + f'{sub}_{epo}_predtest_visual.pkl',
data=dumps({
f'{sub}_{epo}_predtest_visual_performance':
predtest_visual_performance
}))
#Cleanup Crew reporting for duty!
conn.close()
print('Performance calculated')
return cdata
