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How to use _node_to_string method in localstack

Best Python code snippet using localstack_python

__init__.py

Source:__init__.py

...109def test_tree_build(DecisionTree, entropy_func):110    tester = Tester()111    correct_tree = np.load(os.path.join(current_folder, "tree_depth3_min2_entropy.npy"), allow_pickle=True)[0]112    topic = "Testing DecisionTree build with depth 3 and min_samples_split 2"113    expected_tree = "\n".join(_node_to_string(correct_tree.root, features_names)[0])114    ins = ", using Problem1 features, and labels"115    def test_build():116        tree = DecisionTree(max_depth=3, min_samples_split=2, impurity_measure=entropy_func).fit(features, labels)117        obtained_tree = "\n".join(_node_to_string(tree.root, features_names)[0])118        comment = topic + ins + "\n expected output: \n" + expected_tree + "\n obtained: " + obtained_tree119        if compare_trees(correct_tree, tree):120            return True, comment121        return False, comment122    tester.add_test("1.6", test_build)123    tester.run()124def _node_to_string(root_node, features_names):125    if root_node is None:126        return "None"127    if 'leaf' in str(type(root_node)).lower():128        return [VERTICAL + "label: %i" % root_node.label], 0129    else:130        string = ["%s" % features_names[root_node.feature_id], "%.2f" % root_node.threshold]131        max_len = max([len(s) for s in string])132        string[0] = "|" + string[0] + " " * (max_len - len(string[0])) + VERTICAL + OUT_UP133        string[1] = "|" + string[1] + " " * (max_len - len(string[1])) + VERTICAL + OUT_DOWN134        left, left_pos = _node_to_string(root_node.left_child, features_names)135        right, right_pos = _node_to_string(root_node.right_child, features_names)136        for i in range(0, left_pos):137            left[i] = " " + left[i]138        left[left_pos] = UP_IN + left[left_pos]139        for i in range(left_pos + 1, len(left)):140            left[i] = VERTICAL + left[i]141        for i in range(0, right_pos):142            right[i] = VERTICAL + right[i]143        right[right_pos] = DOWN_IN + right[right_pos]144        for i in range(right_pos + 1, len(right)):145            right[i] = " " + right[i]146        left = [" " * (max_len + 2) + l for l in left]147        right = [" " * (max_len + 2) + r for r in right]148        return left + string + right, len(left)149def _compare_node(node1, node2):150    # Not equal if one is leaf and another is parent151    if type(node1) != type(node2):152        return False153    # leaf nodes are equal if they have the same label154    if 'leaf' in str(type(node1)).lower():155        return node1.label == node2.label156    # parent nodes are equal if they have the same feature_id, threshold, and equal children157    compare_values = (node1.feature_id == node2.feature_id) and np.isclose(node1.threshold, node2.threshold, atol=1e-5)158    if not compare_values:159        return False160    return _compare_node(node1.left_child, node2.left_child) and _compare_node(node1.right_child, node2.right_child)161def compare_trees(tree1, tree2):162    return _compare_node(tree1.root, tree2.root)163def print_tree(decision_tree, features_names=None):164    if features_names is None:165        features_names= ["feat_%i" % i for i in range(decision_tree.num_features)]...

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