def Do13TestClassifier_ShuffledLabels_permutation(subject=['Subj01'],
epoch=['study'],
NOF_PERMUTATIONS=5,
size=[20, 20]):
for j, sub in enumerate(subject):
for e in epoch:
print(sub)
cdata = loadmat(
f'./Visual/{sub}/6-ClassificationData/{sub}_test_data_visual',
chars_as_strings=True,
simplify_cells=True)[
f'{sub}_test_data_visual'] #Skip first key
directory = f'./data/Visual/{sub}/7-ClassifierTraining/'
#Create new dir if not exist
os.makedirs(os.path.dirname(directory), exist_ok=True)
with open(directory + f'{sub}_{e}_crossvalclass.pkl',
'rb') as file:
crossdata = load(file)[
f'{sub}_{e}_crossvalclass'] #give imput data
#Create a permutation of already existing data
for permutation in range(
NOF_PERMUTATIONS): #Number of Permutations
R = np.random.permutation(len(cdata['category_name']))
per = {
'category_name': cdata['category_name'][R],
'category': cdata['category'][R],
'feature_name': cdata['feature_name'],
'trial_info': cdata['trialinfo'],
'feature': cdata['feature'],
'numclassifiers': cdata['numclassifiers']
}
#Apply calssifier into the shuffled category trials
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': ['Face', 'Landmark', 'Object'],
'trials': 'all',
'category_model': ['Face', 'Landmark', 'Object']
}
per_predtest = mvpa_applycrossvalclassifier(
cfg, crossdata, per) #PER%d_predtest
#Construct and save the Permutation Map
sub_P = np.zeros((size[0], size[1]))
for col in range(size[0]):
#Easier to read, and doesn't have to do three seperate lookups, saving some performance
tbCol = per_predtest['timebin'][col]['confmatfinal']
for row in range(size[1]):
confRow = tbCol[row]
trials = np.sum(confRow, axis=1)
sub_P[row, col] = ((confRow[0, 0] / trials[0] * 100) +
(confRow[1, 1] / trials[1] * 100) +
(confRow[2, 2] / trials[2] * 100))
directory = f'./data/Visual/{sub}/8-ClassifierTesting/PermutationStudyDecodeTestVisual/'
os.makedirs(os.path.dirname(directory), exist_ok=True)
with open(directory + f'{sub}_P{permutation+1}.pkl',
'wb') as file:
dump({f'{sub}_P{permutation+1}': sub_P}, file)
def analysis(job, sliceno, slices):
#Divide the subject list so that each sliceno processes their own subjects.
sublist = array_split(options.subjects, slices)[sliceno]
size = options.size
NOF_PERMUTATIONS = options.NOF_PERMUTATIONS
jobbig = jobs.do11Job
for epo in options.epoch:
for sub in sublist:
print(sub)
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']
directory = f'./data/Visual/{sub}/7-ClassifierTraining/' #directory of the data
crossdata = jobbig.load(
directory + f'{sub}_{epo}_crossvalclass.pickle')[
f'{sub}_{epo}_crossvalclass'] #give imput data
for permutation in range(
NOF_PERMUTATIONS): #Number of Permutations
R = np.random.permutation(len(cdata['category_name']))
per = {
'category_name': cdata['category_name'][R],
'category': cdata['category'][R],
'feature_name': cdata['feature_name'],
'trial_info': cdata['trialinfo'],
'feature': cdata['feature'],
'numclassifiers': cdata['numclassifiers']
}
#Apply calssifier into the shuffled category trials
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': ['Face', 'Landmark', 'Object'],
'trials': 'all',
'category_model': ['Face', 'Landmark', 'Object']
}
per_predtest = acc.mvpa_applycrossvalclassifier(
cfg, crossdata, per) #PER%d_predtest
#Construct and save the Permutation Map
sub_P = np.zeros((size[0], size[1]))
for col in range(size[0]):
for row in range(size[1]):
trials = np.sum(
per_predtest['timebin'][col]['confmatfinal'][row],
axis=1)
sub_P[row, col] = (
(per_predtest['timebin'][col]['confmatfinal'][row][
0, 0] / trials[0] * 100) +
(per_predtest['timebin'][col]['confmatfinal'][row][
1, 1] / trials[1] * 100) +
(per_predtest['timebin'][col]['confmatfinal'][row][
2, 2] / trials[2] * 100))
directory = f'./data/Visual/{sub}/8-ClassifierTesting/PermutationStudyDecodeTestVisual/'
os.makedirs(os.path.dirname(directory), exist_ok=True)
job.save({f'{sub}_P{permutation+1}': sub_P},
directory + f'{sub}_P{permutation+1}.pickle')
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
def Do13TestClassifier_ShuffledLabels_permutation(sub, epo, perm, size, cdata,
crossdata):
"""Do13 shuffling labels, create a permutation from old data
sub -> current subject to process
epo -> current epoch
perm -> current permutation number
size -> list with dimension of size ie. [20,20]
cdata -> subject test data, classificationdata from 6-ClassificationData
crossdata -> classifier from Do11
"""
#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')
#Openstack container name
ContainerName = 'DroneContainer'
#Create permutation by reshuffling old data randomly
R = np.random.permutation(len(cdata['category_name']))
per = {
'category_name': cdata['category_name'][R],
'category': cdata['category'][R],
'feature_name': cdata['feature_name'],
'trial_info': cdata['trialinfo'],
'feature': cdata['feature'],
'numclassifiers': cdata['numclassifiers']
}
#Apply classifier into the shuffled category trials
cfg = {
'fold': 10,
'classifiernumber': 20,
'timebinsnumber': 20,
'category_predict': ['Face', 'Landmark', 'Object'],
'trials': 'all',
'category_model': ['Face', 'Landmark', 'Object']
}
per_predtest = mvpa_applycrossvalclassifier(cfg, crossdata,
per) #PER%d_predtest
#Construct and save the Permutation Map
sub_P = np.zeros((size[0], size[1]))
for col in range(size[0]):
for row in range(size[1]):
trials = np.sum(per_predtest['timebin'][col]['confmatfinal'][row],
axis=1)
sub_P[row, col] = (
(per_predtest['timebin'][col]['confmatfinal'][row][0, 0] /
trials[0] * 100) +
(per_predtest['timebin'][col]['confmatfinal'][row][1, 1] /
trials[1] * 100) +
(per_predtest['timebin'][col]['confmatfinal'][row][2, 2] /
trials[2] * 100))
#Create mock name and save to objectstore in openstack
filename = f'data/Visual/{sub}/8-ClassifierTesting/PermutationStudyDecodeTestVisual/{sub}_P{perm+1}'
conn.create_object(ContainerName, filename, data=pickle.dumps(sub_P))
#Clean upp connectionobject, not necessary but good practice and potentially less problems
conn.close()
#Apply gaussian filter before returning sub_P
return filter.gaussian(sub_P, sigma=1, truncate=2, preserve_range=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]
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()
def Do12TestClassifier(subject=['Subj01'], epoch=['study']):
for j, sub in enumerate(subject):
for epo in epoch:
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')