def test_find(self):
tree = MultiTree()
root = tree.add_root(MultiTreeNode([1,2,3,4]))
a = tree.add_child(root, MultiTreeNode([1]))
b = tree.add_child(root, MultiTreeNode([2]))
c = tree.add_child(root, MultiTreeNode([3,4]))
assert tree.find(3).val == [3,4]
d = tree.add_child(c, MultiTreeNode([3]))
e = tree.add_child(c, MultiTreeNode([4]))
assert tree.find(4).val == [4]
def test_find(self):
tree = MultiTree()
root = tree.add_root(MultiTreeNode([1, 2, 3, 4]))
a = tree.add_child(root, MultiTreeNode([1]))
b = tree.add_child(root, MultiTreeNode([2]))
c = tree.add_child(root, MultiTreeNode([3, 4]))
assert tree.find(3).val == [3, 4]
d = tree.add_child(c, MultiTreeNode([3]))
e = tree.add_child(c, MultiTreeNode([4]))
assert tree.find(4).val == [4]
def train(self, training_classes):
separability, label_to_int, int_to_label = self._find_separability(training_classes)
# create a mesh
class_cnt = len(training_classes.keys())
mesh = Graph(class_cnt)
for i, row in enumerate(separability):
for j, sep in enumerate(row):
mesh.link(i, j, sep)
# create the mst of this mesh
mst_list = mesh.mst()
mst_graph = Graph(class_cnt)
for link in mst_list:
mst_graph.double_link(link[0], link[1], link[2])
# recursively remove links (that are greater than the average of the mst)
# at the same time create the binary tree
tree = MultiTree()
# the root node is the node of all members, assume that all connected with 0
all_classes = mst_graph.connected_with(0)
tree.add_root(MultiTreeNode(all_classes))
def runner(current):
# loop to remove every link that larger than average
sum_weight, edges = mst_graph.sum_weight(current.val[0])
# terminate when there is not edge to go
if len(edges) is 0:
return
# sort weight desc
edges.sort(key=lambda x: -x[2])
avg_weight = sum_weight / len(edges)
btree = BinaryTree()
btree.add_root(BinaryTreeNode(current.val))
left = btree.root
right = None
for edge in edges:
# remove this link
if edge[2] >= avg_weight:
# remove the link
mst_graph.double_unlink(edge[0], edge[1])
# add this link to the binary tree
if edge[0] in left.val:
parent = left
else:
parent = right
left = btree.add_left(parent, BinaryTreeNode(mst_graph.connected_with(edge[0])))
right = btree.add_right(parent, BinaryTreeNode(mst_graph.connected_with(edge[1])))
# else or the last one
if edge[2] < avg_weight or edge == edges[-1]:
# groups are the display output of the btree
groups = btree.leaves()
for group in groups:
new_node = MultiTreeNode(group)
# add new group to the leat of the multi tree
tree.add_child(current, new_node)
# recursively run it
runner(new_node)
# if the link's weight has become smaller than avg_weight,
# there is no need to keep going on
break
runner(tree.root)
# now got the tree
# train svm according to the mulitree
def train(training_classes):
def runner(current, universe):
if current.children == None:
return
child_universes = [{} for each in current.children]
for class_name, samples in universe.items():
for i, child in enumerate(current.children):
# the class belongs to this child
if class_name in child.val:
child_universes[i][class_name] = samples
current.svms = [None for child in current.children]
# one against the rest method
for i, child in enumerate(current.children):
training = []
labels = []
for class_int, samples in universe.items():
# class in this child is marked as 0
if class_int in child.val:
training += samples.tolist()
labels += [0 for each in samples]
else:
# put to one labeled box
training += samples.tolist()
labels += [1 for each in samples]
training = numpy.array(training)
labels = numpy.array(labels)
# train the svms
# using one against the rest method
current.svms[i] = sklearn.svm.SVC(kernel='rbf', gamma=self.gamma, C=self.C) \
.fit(training, labels)
# the recursive part
runner(child, child_universes[i])
# relabel all the classes to int based
universe = {}
for key, val in training_classes.items():
universe[label_to_int[key]] = val
runner(tree.root, universe)
train(training_classes)
# make these vars visible to the outsiders
self.tree = tree
self.int_to_label = int_to_label
self.label_to_int = label_to_int
return tree
def train(self, training_classes):
separability, label_to_int, int_to_label = self._find_separability(training_classes)
# create a mesh
class_cnt = len(training_classes.keys())
mesh = Graph(class_cnt)
for i, row in enumerate(separability):
for j, sep in enumerate(row):
mesh.link(i, j, sep)
# create the mst of this mesh
mst_list = mesh.mst()
mst_graph = Graph(class_cnt)
for link in mst_list:
mst_graph.double_link(link[0], link[1], link[2])
# recursively remove links (that are greater than the average of the mst)
# at the same time create the binary tree
tree = MultiTree()
# the root node is the node of all members, assume that all connected with 0
all_classes = mst_graph.connected_with(0)
tree.add_root(MultiTreeNode(all_classes))
def runner(current):
# loop to remove every link that larger than average
sum_weight, edges = mst_graph.sum_weight(current.val[0])
# terminate when there is not edge to go
if len(edges) is 0:
return
# sort weight desc
edges.sort(key=lambda x: -x[2])
avg_weight = sum_weight / len(edges)
btree = BinaryTree()
btree.add_root(BinaryTreeNode(current.val))
for edge in edges:
# remove this link
if edge[2] >= avg_weight:
# remove the link
mst_graph.double_unlink(edge[0], edge[1])
# add this link to the binary tree
parent = btree.find(edge[0])
# if edge[0] in left.val:
# parent = left
# else:
# parent = right
left = btree.add_left(parent, BinaryTreeNode(mst_graph.connected_with(edge[0])))
right = btree.add_right(parent, BinaryTreeNode(mst_graph.connected_with(edge[1])))
# else or the last one
if edge[2] < avg_weight or edge == edges[-1]:
# groups are the display output of the btree
groups = btree.leaves()
for group in groups:
new_node = MultiTreeNode(group)
# add new group to the leat of the multi tree
tree.add_child(current, new_node)
# recursively run it
runner(new_node)
# if the link's weight has become smaller than avg_weight,
# there is no need to keep going on
break
if self.verbose:
start_time = time.process_time()
runner(tree.root)
if self.verbose:
print('train: %.4f' % (time.process_time() - start_time))
# now got the tree
# train svm according to the mulitree
svm_cnt = 0
def train(training_classes):
def runner(current, universe):
if current.children == None:
return
child_universes = [{} for each in current.children]
for class_name, samples in universe.items():
for i, child in enumerate(current.children):
# the class belongs to this child
if class_name in child.val:
child_universes[i][class_name] = samples
current.svms = [None for child in current.children]
# one against the rest method
for i, child in enumerate(current.children):
training = []
labels = []
for class_int, samples in universe.items():
# class in this child is marked as 0
if class_int in child.val:
training += samples.tolist()
labels += [0 for each in samples]
else:
# put to one labeled box
training += samples.tolist()
labels += [1 for each in samples]
training = numpy.array(training)
labels = numpy.array(labels)
# train the svms
# using one against the rest method
current.svms[i] = sklearn.svm.SVC(kernel='rbf', gamma=self.gamma, C=self.C) \
.fit(training, labels)
nonlocal svm_cnt
svm_cnt += 1
# the recursive part
runner(child, child_universes[i])
# relabel all the classes to int based
universe = {}
for key, val in training_classes.items():
universe[label_to_int[key]] = val
runner(tree.root, universe)
train(training_classes)
# make these vars visible to the outsiders
self.tree = tree
self.int_to_label = int_to_label
self.label_to_int = label_to_int
return svm_cnt