def malisPreviewSlice(self, batch, name='A'):
pred = self.cnn.class_probabilities(batch[0])[0] # (6, z, x,y)
malis = self.cnn.malis_stats(
*batch[:3]
) # nll, n_pos, n_neg, n_tot, false_splits, false_merges, rand_index, pos_count, neg_count, labels
nll, n_pos, n_neg, n_tot, false_splits, false_merges, rand_index, pos_count, neg_count = malis
data, aff_gt, seg_gt = batch[:3]
print "NLL : ", nll
print "N total: ", n_tot
print "N pos : ", n_pos
print "N neg : ", n_neg
print "Splits : ", false_splits
print "Mergers: ", false_merges
print "Rand-Index: ", rand_index
pred_slices = np.transpose(pred[1::2], (1, 2, 3, 0))
pos_slices = np.transpose(pos_count, (1, 2, 3, 0))
neg_slices = np.transpose(neg_count, (1, 2, 3, 0))
neg_slices = np.log(neg_slices + 1)
data = data[0, :, 0]
aff_gt = np.transpose(aff_gt[0], (1, 2, 3, 0))
seg_gt = seg_gt[0]
trainutils.pickleSave(
[pred_slices, aff_gt, pos_slices, neg_slices, seg_gt, data],
'MALIS-%s.pkl' % (name, ))
def malisPreviewSlice(self, batch, name='A'):
pred = self.cnn.class_probabilities(batch[0])[0] # (6, z, x,y)
malis = self.cnn.malis_stats(*batch[:3]) # nll, n_pos, n_neg, n_tot, false_splits, false_merges, rand_index, pos_count, neg_count, labels
nll, n_pos, n_neg, n_tot, false_splits, false_merges, rand_index, pos_count, neg_count = malis
data, aff_gt, seg_gt = batch[:3]
print "NLL : ", nll
print "N total: ", n_tot
print "N pos : ", n_pos
print "N neg : ", n_neg
print "Splits : ", false_splits
print "Mergers: ", false_merges
print "Rand-Index: ", rand_index
pred_slices = np.transpose(pred[1::2], (1, 2, 3, 0))
pos_slices = np.transpose(pos_count, (1, 2, 3, 0))
neg_slices = np.transpose(neg_count, (1, 2, 3, 0))
neg_slices = np.log(neg_slices + 1)
data = data[0, :, 0]
aff_gt = np.transpose(aff_gt[0], (1, 2, 3, 0))
seg_gt = seg_gt[0]
trainutils.pickleSave([pred_slices, aff_gt, pos_slices, neg_slices, seg_gt, data], 'MALIS-%s.pkl' % (name, ))
def __init__(self, path='~/devel/data/adult.pkl', create=False):
path = os.path.expanduser(path)
if create:
self._fields = 'age,workclass,fnlwgt,education,educationnum,maritalstatus,occupation,relationship,race,sex,capitalgain,capitalloss,hoursperweek,nativecountry,target'.split(',')
self._kinds = 'cont,cat,cat,cat,cont,cat,cat,cat,cat,cat,cont,cont,cont,cat,cat'.split(',')
data_socket = urllib2.urlopen('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data')
train_d = np.genfromtxt(data_socket, skip_header=1, delimiter=',', names=self._fields, dtype=None)
train_d = self._normalise_adult(train_d)
self.train_l = train_d[:, -1].astype('int16') # np.expand_dims(train_d[:,-1].astype('int16'), 1)
self.train_d = train_d[:, :-1]
test_socket = urllib2.urlopen('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test')
valid_d = np.genfromtxt(test_socket, skip_header=1, delimiter=',', names=self._fields, dtype=None)
valid_d = self._normalise_adult(valid_d)
self.valid_l = valid_d[:, -1].astype('int16') #np.expand_dims(valid_d[:,-1].astype('int16'), 1)
self.valid_d = valid_d[:, :-1]
ut.pickleSave((self.train_d, self.train_l, self.valid_d, self.valid_l), path)
else:
self.train_d, self.train_l, self.valid_d, self.valid_l = ut.pickleLoad(path)
super(AdultData, self).__init__()
