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