def _deserialize_trees(cls, tree_list):
previous = Tree.from_dict(tree_list[0])
trees = [previous]
for tree_dict in tree_list[1:]:
tree = Tree.from_dict(tree_dict, previous)
trees.append(tree)
previous = tree
return trees
def test_serialization_fit_model(self):
# Setup
instance = get_tree(TreeTypes.REGULAR)
X = pd.DataFrame(data=[
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]
])
index = 0
n_nodes = X.shape[1]
tau_matrix = X.corr(method='kendall').values
univariates_matrix = np.empty(X.shape)
for i, column in enumerate(X):
distribution = GaussianKDE()
distribution.fit(X[column])
univariates_matrix[:, i] = distribution.cumulative_distribution(X[column])
instance.fit(index, n_nodes, tau_matrix, univariates_matrix)
# Run
result = Tree.from_dict(instance.to_dict())
# Check
assert result.to_dict() == instance.to_dict()
def test_serialization_unfitted_model(self):
# Setup
instance = get_tree(TreeTypes.REGULAR)
# Run
result = Tree.from_dict(instance.to_dict())
# Check
assert instance.to_dict() == result.to_dict()
def test_from_dict_unfitted_model(self):
# Setup
params = {'tree_type': TreeTypes.REGULAR, 'fitted': False}
# Run
result = Tree.from_dict(params)
# Check
assert result.tree_type == TreeTypes.REGULAR
assert result.fitted is False
