def test_xvalidation(self):
img_per_cat = {}
[img_per_cat.update({cat: list(range(1,50))}) for cat in range(1,11)]
fm = fixmat.TestFixmatFactory(categories = list(range(1,11)), subjectindices = list(range(1,11)),
filenumbers = list(range(1,11)))
l = loader.TestLoader(img_per_cat,size = (10,10))
stim = stimuli.FixmatStimuliFactory(fm,l)
img_ratio = 0.3
sub_ratio = 0.3
data_slices = SimpleXValidation(fm, stim,img_ratio,sub_ratio,10)
for i,(fm_train, cat_train, fm_test, cat_test) in enumerate(data_slices.generate()):
self.assertEqual (len(np.unique(fm_train.SUBJECTINDEX)),
round(len(np.unique(fm.SUBJECTINDEX))*(1-sub_ratio)))
self.assertEqual(len(np.unique(fm_test.SUBJECTINDEX)),
round(len(np.unique(fm.SUBJECTINDEX))*(sub_ratio)))
for (test,train,reference) in zip(cat_test,
cat_train,
stim):
self.assertEqual(test.category, reference.category)
self.assertEqual(len(test.images()),
len(reference.images())*img_ratio)
self.assertEqual(len(train.images()),
len(reference.images())*(1-img_ratio))
self.assertTrue((np.sort(np.unique(fm_train[fm_train.category==test.category].filenumber)) ==
np.sort( train.images())).all())
def setUp(self):
self.features = range(1, 11)
self.inputs = {
'a': {
1: self.features,
2: self.features,
3: self.features
},
'b': {
10: self.features
}
}
self.test_loader = loader.TestLoader(self.inputs)
self.inp = stimuli.Categories(self.test_loader, self.inputs)
def test_testloader(self):
img_per_cat = {1: range(1, 10), 2: range(1, 10)}
l = loader.TestLoader(img_per_cat=img_per_cat, features=['a', 'b'])
for cat in range(1, 10):
for img in range(1, 100):
if cat in img_per_cat.keys() and img in img_per_cat[cat]:
self.assertEquals(l.test_for_category(cat), True)
self.assertEquals(l.test_for_image(cat, img), True)
self.assertEquals(l.test_for_feature(cat, img, 'a'), True)
l.get_image(cat, img)
l.get_feature(cat, img, 'a')
l.get_feature(cat, img, 'b')
self.assertTrue(l.test_for_feature(cat, img, 'a'))
else:
self.assertEquals(l.test_for_image(cat, img), False)
self.assertRaises(IndexError,
lambda: l.get_image(cat, img))
def test_fixmat_input_factory(self):
# Another way to create an input object is with the
# FixmatInputFactory
features = ['a', 'b', 'c', 'd']
img_per_cat = {7: range(1, 65), 8: range(1, 65)}
l = loader.TestLoader(img_per_cat, features, size=(10, 10))
with NamedTemporaryFile(mode='w', prefix='fix_occ_test',
suffix='.mat') as ntf:
ntf.write(get_data('ocupy.tests', 'fixmat_demo.mat'))
ntf.seek(0)
fm = fixmat.FixmatFactory(ntf.name)
fm = fm[fm.category > 0]
inp = stimuli.FixmatStimuliFactory(fm, l)
# Now we can iterate over the input object and get fixations on each image
for cat in inp:
for img in cat:
self.assertEquals(img.fixations.filenumber[0], img.image)
self.assertEquals(img.fixations.category[0], img.category)
def test_fixations(self):
img_per_cat = {7: range(1, 65), 8: range(1, 65)}
l = loader.TestLoader(img_per_cat, size=(10, 10))
with NamedTemporaryFile(mode='w', prefix='fix_occ_test',
suffix='.mat') as ntf:
ntf.write(get_data('ocupy.tests', 'fixmat_demo.mat'))
ntf.seek(0)
fm = fixmat.FixmatFactory(ntf.name)
fm = fm[fm.category > 0]
inp = stimuli.FixmatStimuliFactory(fm, l)
# Now we can iterate over the input object and get fixations on each image
for cat in inp:
for img in cat:
self.assertEqual(img.fixations.filenumber[0], img.image)
self.assertEqual(img.fixations.category[0], img.category)
inp = stimuli.Categories(l, img_per_cat)
self.assertRaises(RuntimeError, lambda: inp.fixations)
self.assertRaises(RuntimeError, lambda: inp[7].fixations)
self.assertRaises(RuntimeError, lambda: inp[7][1].fixations)
def test_general(self):
#Create an input object: There are two ways, one is the
features = ['a', 'b', 'c', 'd']
img_per_cat = {7: range(1, 65), 8: range(1, 65)}
l = loader.TestLoader(img_per_cat, features, size=(10, 10))
inp = stimuli.Categories(l, img_per_cat, features)
# In this case it should have two categories (2,9) with 10 and 50 images
# Let's check this
self.assert_(7 in inp.categories())
self.assert_(8 in inp.categories())
self.assertEquals(len(inp[7].images()), 64)
self.assertEquals(len(inp[8].images()), 64)
# Good, now we can access some data
img = inp[7][16].data
self.assertTrue(img.shape[0] == 10 and img.shape[1] == 10)
# In general we can use [] to access elements in the object
# But we can also iterate trough these objects
# This should take a long time because it loads all images
# from disk
for cat in inp:
for img in cat:
img.image
img.category
img.data
