def custom_tree():
tree = Tree()
tree.root = Node("1", [[0.9, 0.1]])
child1 = Node("2", [[0.15, 0.85], [0.1, 0.9]])
child2 = Node("3", [[0.5, 0.5], [0.1, 0.9]])
child3 = Node("3", [[0.3, 0.7], [0.4, 0.6]])
child1.sample = 0
child2.sample = 0
child3.sample = 0
child1.ancestor = tree.root
child2.ancestor = tree.root
child3.ancestor = tree.root
tree.root.descendants.append(child1)
tree.root.descendants.append(child2)
tree.root.descendants.append(child3)
tree.root.sample = 1
return tree
def sample_given_trees(params, samples):
# choose a set of param
for key in params.keys():
print("#### TREE : " + key)
parameters = params[key]
for j in [1, 2, 3, 4, 5]:
t = Tree()
t.load_params(parameters)
samples_name = key + '_sample_' + str(j)
try:
sample = samples[samples_name]
except:
break
t.load_sample(sample)
compute_posterior(t)
# sample_tree(t)
t.print_tree(True, True, True)
def compute_observation_prob(params, samples):
# choose a set of param
for key in params.keys():
print("#### TREE : " + key)
parameters = params[key]
for j in [1, 2, 3, 4, 5]:
t = Tree()
t.load_params(parameters)
samples_name = key + '_sample_' + str(j)
try:
sample = samples[samples_name]
except:
break
t.load_sample(sample)
compute_posterior(t)
print("\tsample_" + str(j) + " : " +
str(compute_leaves_probability(t)))
child3 = Node("3", [[0.3, 0.7], [0.4, 0.6]])
child1.sample = 0
child2.sample = 0
child3.sample = 0
child1.ancestor = tree.root
child2.ancestor = tree.root
child3.ancestor = tree.root
tree.root.descendants.append(child1)
tree.root.descendants.append(child2)
tree.root.descendants.append(child3)
tree.root.sample = 1
return tree
if __name__ == "__main__":
t = Tree()
my_data_path = ''
# get data to load into the tree
params = np.load(my_data_path + 'tree_params.npy').tolist()
samples = np.load(my_data_path + 'tree_samples.npy').tolist()
# choose a set of param
params_name = params.keys()[0]
parameters = params[params_name]
# choose samples
samples_name = params_name + '_sample_2'
sample = samples[samples_name]
Print the tree (not very sophisticated). Structure: nodename_parentname
"""
print("### printing the tree:")
print_tree(root)
"""
Print the tree with sample (not very sophisticated). Structure: nodename_parentname:sample
"""
print("### printing the tree with samples:")
print_tree(root, print_sample=True)
print(root.cat)
"""
Use tree object:
"""
t = Tree()
my_data_path = ''
with open(my_data_path + 'tree_params.pickle', 'rb') as handle:
params = pickle.load(handle)
key = params.keys()[0]
"""
Load params into tree
"""
t.load_params(params[key])
print("### printing loaded tree")
t.print_tree(False, True)
"""
Generate a random tree
def sample_given_tree(root):
# choose a set of param
tree = Tree()
tree.root = root
sample_tree(tree)
tree.print_tree(True, True, True)