class TestCaltechManager(unittest.TestCase):
def setUp(self):
self.datamanager = CaltechManager()
self.datamanager.change_base_path(os.path.join(BASE_PATH, "testdata"))
def test_invalid_dataset_caltech(self):
self.assertRaises(InvalidDatasetException,
self.datamanager.build_sample_matrix, "rubbish",
"test")
def test_invalid_dataset_caltech2(self):
self.assertRaises(InvalidDatasetException,
self.datamanager.build_class_vector, "rubbish",
"test")
def test_invalid_category_caltech(self):
self.assertRaises(NoSuchCategoryException,
self.datamanager.get_positive_samples, "test",
"rubbish")
def test_invalid_category_caltech2(self):
self.assertRaises(NoSuchCategoryException,
self.datamanager.build_sample_matrix, "test",
"rubbish")
def test_training_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("train", "TestFake")
should_be = np.array([[
0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286,
0.11623112
],
[
0.31588301, 0.05166245, 0.16203263,
0.02196996, 0.96935761, 0.9854272
],
[
0.12442154, 0.57743013, 0.9548108,
0.22592719, 0.10155164, 0.60750473
],
[
0.53320956, 0.18181397, 0.60112703,
0.09004746, 0.31448245, 0.85619318
],
[
0.18139255, 0.83218205, 0.87969971,
0.81630158, 0.57571691, 0.08127511
]],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_test_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("test", "TestFake")
should_be = np.array([[
0.64663881, 0.55629711, 0.11966438, 0.04559849, 0.69156636,
0.4500224
],
[
0.38948518, 0.33885501, 0.567841, 0.36167425,
0.18220702, 0.57701336
],
[
0.08660618, 0.83642531, 0.9239062,
0.53778457, 0.56708116, 0.13766008
],
[
0.31313366, 0.88874122, 0.20000355,
0.56186443, 0.15771926, 0.81349361
]],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_all_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array([[
0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286,
0.11623112
],
[
0.31588301, 0.05166245, 0.16203263,
0.02196996, 0.96935761, 0.9854272
],
[
0.64663881, 0.55629711, 0.11966438,
0.04559849, 0.69156636, 0.4500224
],
[
0.38948518, 0.33885501, 0.567841, 0.36167425,
0.18220702, 0.57701336
],
[
0.12442154, 0.57743013, 0.9548108,
0.22592719, 0.10155164, 0.60750473
],
[
0.53320956, 0.18181397, 0.60112703,
0.09004746, 0.31448245, 0.85619318
],
[
0.18139255, 0.83218205, 0.87969971,
0.81630158, 0.57571691, 0.08127511
],
[
0.08660618, 0.83642531, 0.9239062,
0.53778457, 0.56708116, 0.13766008
],
[
0.31313366, 0.88874122, 0.20000355,
0.56186443, 0.15771926, 0.81349361
]],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_all_sample_matrix_exclude_feature(self):
self.datamanager.exclude_feature = 4
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array(
[[0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.11623112],
[0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.9854272],
[0.64663881, 0.55629711, 0.11966438, 0.04559849, 0.4500224],
[0.38948518, 0.33885501, 0.567841, 0.36167425, 0.57701336],
[0.12442154, 0.57743013, 0.9548108, 0.22592719, 0.60750473],
[0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.85619318],
[0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.08127511],
[0.08660618, 0.83642531, 0.9239062, 0.53778457, 0.13766008],
[0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.81349361]],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
@unittest.expectedFailure # TODO: dependent on file order
def test_complete_sample_matrix(self):
samples = self.datamanager.build_complete_sample_matrix("train")
should_be = np.array([
[
0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.15771926,
0.81349361
],
[
0.12442154, 0.57743013, 0.9548108, 0.22592719, 0.10155164,
0.60750473
],
[
0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.31448245,
0.85619318
],
[
0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.57571691,
0.08127511
],
[
0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286,
0.11623112
],
[
0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.96935761,
0.9854272
],
],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
@unittest.expectedFailure # TODO: dependent on file order
def test_complete_sample_matrix_exclude_feature(self):
self.datamanager.exclude_feature = 1
samples = self.datamanager.build_complete_sample_matrix("train")
should_be = np.array([
[0.31313366, 0.20000355, 0.56186443, 0.15771926, 0.81349361],
[0.12442154, 0.9548108, 0.22592719, 0.10155164, 0.60750473],
[0.53320956, 0.60112703, 0.09004746, 0.31448245, 0.85619318],
[0.18139255, 0.87969971, 0.81630158, 0.57571691, 0.08127511],
[0.44842428, 0.45302102, 0.54796243, 0.82176286, 0.11623112],
[0.31588301, 0.16203263, 0.02196996, 0.96935761, 0.9854272],
],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_complete_sample_matrix_fail(self):
self.assertRaises(NotImplementedError,
self.datamanager.build_complete_sample_matrix, "all")
def test_training_class_vector(self):
classes = self.datamanager.build_class_vector("train", "TestFake")
should_be = np.array([1, 1, 0, 0, 0])
self.assertTrue((classes == should_be).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_test_class_vector(self):
classes = self.datamanager.build_class_vector("test", "TestFake")
should_be = np.array([1, 1, 0, 0])
self.assertTrue((classes == should_be).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_complete_class_vector(self):
classes = self.datamanager.build_class_vector("all", "TestFake")
should_be = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0])
self.assertTrue((classes == should_be).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_sample_matrix_pca(self):
self.datamanager.use_pca(n_components=1)
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array([[-0.24263228], [0.85717554], [0.29054203],
[0.03857126], [-0.18379566], [0.44021899],
[-0.78841356], [-0.65111911], [-0.08255303]],
dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.000001).all(),
"Should be:\n%s\nbut is:\n%s" % (should_be, samples))
class TestCaltechManager(unittest.TestCase):
def setUp(self):
self.datamanager = CaltechManager()
self.datamanager.change_base_path(os.path.join(BASE_PATH, "testdata"))
def test_invalid_dataset_caltech(self):
self.assertRaises(InvalidDatasetException, self.datamanager.build_sample_matrix, "rubbish", "test")
def test_invalid_dataset_caltech2(self):
self.assertRaises(InvalidDatasetException, self.datamanager.build_class_vector, "rubbish", "test")
def test_invalid_category_caltech(self):
self.assertRaises(NoSuchCategoryException, self.datamanager.get_positive_samples, "test", "rubbish")
def test_invalid_category_caltech2(self):
self.assertRaises(NoSuchCategoryException, self.datamanager.build_sample_matrix, "test", "rubbish")
def test_training_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("train", "TestFake")
should_be = np.array([
[ 0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286, 0.11623112],
[ 0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.96935761, 0.9854272 ],
[ 0.12442154, 0.57743013, 0.9548108 , 0.22592719, 0.10155164, 0.60750473],
[ 0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.31448245, 0.85619318],
[ 0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.57571691, 0.08127511]
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_test_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("test", "TestFake")
should_be = np.array([
[ 0.64663881, 0.55629711, 0.11966438, 0.04559849, 0.69156636, 0.4500224 ],
[ 0.38948518, 0.33885501, 0.567841 , 0.36167425, 0.18220702, 0.57701336],
[ 0.08660618, 0.83642531, 0.9239062 , 0.53778457, 0.56708116, 0.13766008],
[ 0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.15771926, 0.81349361]
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_all_sample_matrix(self):
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array([
[ 0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286, 0.11623112],
[ 0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.96935761, 0.9854272 ],
[ 0.64663881, 0.55629711, 0.11966438, 0.04559849, 0.69156636, 0.4500224 ],
[ 0.38948518, 0.33885501, 0.567841 , 0.36167425, 0.18220702, 0.57701336],
[ 0.12442154, 0.57743013, 0.9548108 , 0.22592719, 0.10155164, 0.60750473],
[ 0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.31448245, 0.85619318],
[ 0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.57571691, 0.08127511],
[ 0.08660618, 0.83642531, 0.9239062 , 0.53778457, 0.56708116, 0.13766008],
[ 0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.15771926, 0.81349361]
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_all_sample_matrix_exclude_feature(self):
self.datamanager.exclude_feature = 4
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array([
[ 0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.11623112],
[ 0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.9854272 ],
[ 0.64663881, 0.55629711, 0.11966438, 0.04559849, 0.4500224 ],
[ 0.38948518, 0.33885501, 0.567841 , 0.36167425, 0.57701336],
[ 0.12442154, 0.57743013, 0.9548108 , 0.22592719, 0.60750473],
[ 0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.85619318],
[ 0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.08127511],
[ 0.08660618, 0.83642531, 0.9239062 , 0.53778457, 0.13766008],
[ 0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.81349361]
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
@unittest.expectedFailure # TODO: dependent on file order
def test_complete_sample_matrix(self):
samples = self.datamanager.build_complete_sample_matrix("train")
should_be = np.array([
[ 0.31313366, 0.88874122, 0.20000355, 0.56186443, 0.15771926, 0.81349361],
[ 0.12442154, 0.57743013, 0.9548108 , 0.22592719, 0.10155164, 0.60750473],
[ 0.53320956, 0.18181397, 0.60112703, 0.09004746, 0.31448245, 0.85619318],
[ 0.18139255, 0.83218205, 0.87969971, 0.81630158, 0.57571691, 0.08127511],
[ 0.44842428, 0.50402522, 0.45302102, 0.54796243, 0.82176286, 0.11623112],
[ 0.31588301, 0.05166245, 0.16203263, 0.02196996, 0.96935761, 0.9854272 ],
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
@unittest.expectedFailure # TODO: dependent on file order
def test_complete_sample_matrix_exclude_feature(self):
self.datamanager.exclude_feature = 1
samples = self.datamanager.build_complete_sample_matrix("train")
should_be = np.array([
[ 0.31313366, 0.20000355, 0.56186443, 0.15771926, 0.81349361],
[ 0.12442154, 0.9548108 , 0.22592719, 0.10155164, 0.60750473],
[ 0.53320956, 0.60112703, 0.09004746, 0.31448245, 0.85619318],
[ 0.18139255, 0.87969971, 0.81630158, 0.57571691, 0.08127511],
[ 0.44842428, 0.45302102, 0.54796243, 0.82176286, 0.11623112],
[ 0.31588301, 0.16203263, 0.02196996, 0.96935761, 0.9854272 ],
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.00000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
def test_complete_sample_matrix_fail(self):
self.assertRaises(NotImplementedError, self.datamanager.build_complete_sample_matrix, "all")
def test_training_class_vector(self):
classes = self.datamanager.build_class_vector("train", "TestFake")
should_be = np.array([1, 1, 0, 0, 0])
self.assertTrue((classes==should_be).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_test_class_vector(self):
classes = self.datamanager.build_class_vector("test", "TestFake")
should_be = np.array([1, 1, 0, 0])
self.assertTrue((classes==should_be).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_complete_class_vector(self):
classes = self.datamanager.build_class_vector("all", "TestFake")
should_be = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0])
self.assertTrue((classes==should_be).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, classes))
def test_sample_matrix_pca(self):
self.datamanager.use_pca(n_components = 1)
samples = self.datamanager.build_sample_matrix("all", "TestFake")
should_be = np.array([
[-0.24263228],
[0.85717554],
[0.29054203],
[0.03857126],
[-0.18379566],
[0.44021899],
[-0.78841356],
[-0.65111911],
[-0.08255303]
], dtype=np.float32)
difference_matrix = np.abs(samples - should_be)
self.assertTrue((difference_matrix < 0.000001).all(), "Should be:\n%s\nbut is:\n%s" % (should_be, samples))
