class TestOAOSVM(TestCase):
training_file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/satimage/sat-train-s.csv'
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
class_cnt = len(training_classes.keys())
gamma = 0.1
svm = OAOSVM(gamma=gamma)
def test_train(self):
self.svm.train(self.training_classes)
def test_predict(self):
errors = 0
total = 0
for class_name, class_samples in self.training_classes.items():
for sample in class_samples:
total += 1
if self.svm.predict(sample) != class_name:
# wrong prediction
errors += 1
# just to see the idea
print('errors:', errors, ' total:', total)
assert errors == 0
def test_cross_validate(self):
# 10 folds validation
res = self.svm.cross_validate(10, self.training_classes)
# this just to get the idea
assert res == 0
class TestGroup(TestCase):
training_file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/satimage/sat-train-s.csv'
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
class_cnt = len(training_classes.keys())
def test___init__(self):
pass
class TestSimMultiSVM(TestCase):
training_file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/satimage/sat-train-s.csv'
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
class_cnt = len(training_classes.keys())
gamma = 0.1
svm = SimMultiSVM(gamma=gamma)
def test__find_separability(self):
# svm = SimBinarySVM(Kernel)
(self.svm.separability, self.svm.label_to_int,
self.svm.int_to_label) = self.svm._find_separability(
self.training_classes)
# print('similarity', similarity)
assert self.svm.separability.size == self.class_cnt * self.class_cnt
assert self.svm.separability[0].size == self.class_cnt
# print('labelToINt:', labelToInt)
assert len(self.svm.label_to_int.keys()) == 6
# print('int_to_label', int_to_label)
for idx, val in enumerate(self.svm.int_to_label):
assert self.svm.label_to_int[val] == idx
@pytest.mark.run(after='test__find_similarity')
def test_Train(self):
self.svm.train(self.training_classes)
def runner(current):
if current.children == None:
return
assert len(current.svms) == len(current.children)
for child in current.children:
runner(child)
runner(self.svm.tree.root)
@pytest.mark.run(after='test_train')
def test_predict(self):
errors = 0
total = 0
for class_name, class_samples in self.training_classes.items():
for sample in class_samples:
total += 1
if self.svm.predict(sample) != class_name:
# wrong prediction
errors += 1
# just to see the idea
print('errors:', errors, ' total:', total)
assert errors == 0
@pytest.mark.run(after='test_predict')
def test_cross_validate(self):
# 10 folds validation
res = self.svm.cross_validate(10, self.training_classes)
# this just to get the idea
assert res == 0
class TestDataset(TestCase):
file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/iris.csv'
dataset = Dataset.load(file)
splitted = Dataset.split(dataset)
def test_Load(self):
assert len(self.dataset.features[0]) == 4
def test_Split(self):
assert len(self.splitted.keys()) == 3
sum_splitted = 0
for name, members in self.splitted.items():
sum_splitted += len(members)
for each in members:
assert len(each) == 4
assert sum_splitted == len(self.dataset.features)
num_workers = multiprocessing.cpu_count()
print('workers: ', num_workers)
training_files = [
('satimage', 'satimage/sat-train-s.csv', 'satimage/sat-test.csv'),
]
for training in training_files:
project_name = training[0]
print('working on project: ', project_name)
# load dataset
training_file = training[1]
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
testing_file = training[2]
testing_set = Dataset.load(testing_file)
testing_classes = Dataset.split(testing_set)
best = {}
for each in (
('OAO', OAOSVM),
('SimBinarySVM', SimBinarySVM),
('SimMultiSVM', SimMultiSVM),
):
svm_type = each[0]
SVM = each[1]
class TestSimBinarySVM(TestCase):
training_file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/satimage/sat-train-s.csv'
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
class_cnt = len(training_classes.keys())
gamma = 1e-6
C = 0.01
svm = SimBinarySVM(gamma=gamma, C=C)
# def test_MakeRBFKernel(self):
# self.fail()
def test_find_separability(self):
# svm = SimBinarySVM(Kernel)
(self.svm.separability, self.svm.label_to_int,
self.svm.int_to_label) = self.svm._find_separability(
self.training_classes)
# print('similarity', similarity)
assert self.svm.separability.size == self.class_cnt * self.class_cnt
assert self.svm.separability[0].size == self.class_cnt
# print('labelToINt:', labelToInt)
assert len(self.svm.label_to_int.keys()) == 6
# print('intToLabel', intToLabel)
for idx, val in enumerate(self.svm.int_to_label):
assert self.svm.label_to_int[val] == idx
@pytest.mark.run(after='test_find_separability')
def test_construct_mst_graph(self):
(self.svm.mst_graph,
self.svm.mst_list) = self.svm._construct_mst_graph(
self.training_classes, self.svm.separability)
assert len(self.svm.mst_list) == self.class_cnt - 1
assert len(self.svm.mst_graph.connected_with(0)) == self.class_cnt
cnt = 0
for i, row in enumerate(self.svm.mst_graph.connection):
for j, dist in enumerate(row):
if dist != float('inf'):
cnt += 1
# the graph bidirectional
assert cnt == (self.class_cnt - 1) * 2
@pytest.mark.run(after='test_construct_mst_graph')
def test_construct_tree(self):
self.svm.tree = self.svm._construct_tree(self.svm.mst_graph,
self.svm.mst_list)
def runner(current):
if current.left is None and current.right is None:
return
assert len(
current.val) == len(current.left.val) + len(current.right.val)
assert set(current.val) == set(current.left.val +
current.right.val)
runner(current.left)
runner(current.right)
runner(self.svm.tree.root)
@pytest.mark.run(after='test_construct_tree')
def test_train(self):
self.svm.train(self.training_classes)
def runner(current):
if current.left is None and current.right is None:
return
assert current.svm
runner(current.left)
runner(current.right)
runner(self.svm.tree.root)
@pytest.mark.run(after='test_train')
def test_predict(self):
errors = 0
total = 0
for class_name, class_samples in self.training_classes.items():
for sample in class_samples:
total += 1
if self.svm.predict(sample) != class_name:
# wrong prediction
errors += 1
# just to see the idea
print('errors:', errors, ' total:', total)
assert errors == 0
@pytest.mark.run(after='test_predict')
def test_cross_validate(self):
# 10 folds validation
res = self.svm.cross_validate(10, self.training_classes)
# this just to get the idea
assert res == 0
def test_make_gram_matrix(self):
gamma = 0.1
vectors = []
training_classes_with_idx = {}
idx = 0
for name, points in self.training_classes.items():
this_class = training_classes_with_idx[name] = []
for point in points:
# give it an index
vector = point.tolist()
vector_with_idx = [idx] + vector
idx += 1
vectors.append(vector)
this_class.append(vector_with_idx)
training_classes_with_idx[name] = numpy.array(this_class)
vectors = numpy.array(vectors)
kernel = self.svm.make_gram_matrix(vectors, gamma)
def original_kernel(a, b):
import numpy
return numpy.exp(-gamma * numpy.linalg.norm(a - b)**2)
for class_name, samples in training_classes_with_idx.items():
a = samples
b = a[:].tolist()
random.shuffle(b)
b = numpy.array(b)
for i in range(a.shape[0]):
assert abs(
kernel(a[i], b[i]) -
original_kernel(a[i][1:], b[i][1:])) < 1e-5
class TestSimBinarySVMORI(TestCase):
training_file = '/Users/phizaz/Dropbox/waseda-internship/svm-implementations/simbinarysvm/satimage/sat-train-s.csv'
training_set = Dataset.load(training_file)
training_classes = Dataset.split(training_set)
class_cnt = len(training_classes.keys())
gamma = 1e-6
C = 0.01
svm = SimBinarySVMORI(gamma=gamma, C=C)
def test_create_mapping(self):
self.label_to_int, self.int_to_label = self.svm._create_mapping(
self.training_classes)
@pytest.mark.run(after='test_create_mapping')
def test_create_tree(self):
self.label_to_int, self.int_to_label = self.svm._create_mapping(
self.training_classes)
self.group_mgr = self.svm._create_tree(self.training_classes,
self.label_to_int)
def runner(current):
if current.children == None:
return
child_universe = []
for child in current.children:
child_universe += list(child.universe.keys())
assert set(current.universe.keys()) == set(child_universe)
for child in current.children:
runner(child)
runner(next(iter(self.group_mgr.groups.values())))
@pytest.mark.run(after='test_construct_tree')
def test_train(self):
group_mgr = self.svm.train(self.training_classes)
def runner(current):
if current.children == None:
return
assert current.svm
for child in current.children:
runner(child)
runner(next(iter(group_mgr.groups.values())))
@pytest.mark.run(after='test_train')
def test_predict(self):
group_mgr = self.svm.train(self.training_classes)
errors = 0
total = 0
for class_name, class_samples in self.training_classes.items():
for sample in class_samples:
total += 1
if self.svm.predict(sample) != class_name:
# wrong prediction
errors += 1
# just to see the idea
print('errors:', errors, ' total:', total)
assert errors == 0
@pytest.mark.run(after='test_predict')
def test_cross_validate(self):
group_mgr = self.svm.train(self.training_classes)
# 10 folds validation
res = self.svm.cross_validate(10, self.training_classes)
# this just to get the idea
assert res == 0
def test_make_gram_matrix(self):
gamma = 0.1
vectors = []
training_classes_with_idx = {}
idx = 0
for name, points in self.training_classes.items():
this_class = training_classes_with_idx[name] = []
for point in points:
# give it an index
vector = point.tolist()
vector_with_idx = [idx] + vector
idx += 1
vectors.append(vector)
this_class.append(vector_with_idx)
training_classes_with_idx[name] = numpy.array(this_class)
vectors = numpy.array(vectors)
kernel = self.svm.make_gram_matrix(vectors, gamma)
def original_kernel(a, b):
import numpy
return numpy.exp(-gamma * numpy.linalg.norm(a - b)**2)
for class_name, samples in training_classes_with_idx.items():
a = samples
b = a[:].tolist()
random.shuffle(b)
b = numpy.array(b)
for i in range(a.shape[0]):
assert abs(
kernel(a[i], b[i]) -
original_kernel(a[i][1:], b[i][1:])) < 1e-5