Function to save Random Forest model to txt file.

[crimson-luis]

After reading some issues I realized that would be useful to share a function that I made while working with your project.
This function receives a sklearn Random Forest class and read each tree writing a list with all info.

It is possible that it is not 100% correct.

def model_to_txt(self, index, show: bool = True, save: bool = False):
    # https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py
    forest = self.estimators_
    model_info = list()
    model_info.append(
        f"DATASET_NAME: {config['DATASET']['NAME']}.train{index}.csv"
        f"\nENSEMBLE: RF"
        f"\nNB_TREES: {len(forest)}"
        f"\nNB_FEATURES: {forest[0].tree_.n_features}"
        f"\nNB_CLASSES: {forest[0].tree_.n_classes[0]}"
        f"\nMAX_TREE_DEPTH: {forest[0].tree_.max_depth}"
        "\nFormat: node / node type (LN - leave node, IN - internal node) "
        "left child / right child / feature / threshold / node_depth / "
        "majority class (starts with index 0)"
    )
    for tree_idx, est in enumerate(forest):
        tree = est.tree_
        n_nodes = tree.node_count
        children_left = tree.children_left
        children_right = tree.children_right

        node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
        is_leaves = np.zeros(shape=n_nodes, dtype=bool)
        stack = [(0, 0)]  # start with the root node id (0) and its depth (0)
        model_info.append(f"\n\n[TREE {tree_idx}]\nNB_NODES: {n_nodes}")
        while len(stack) > 0:
            node_id, depth = stack.pop()
            node_depth[node_id] = depth

            if children_left[node_id] != children_right[node_id]:
                stack.append((children_left[node_id], depth + 1))
                stack.append((children_right[node_id], depth + 1))
            else:
                is_leaves[node_id] = True
        for i in range(n_nodes):
            class_idx = np.argmax(tree.value[i][0])
            if is_leaves[i]:
                model_info.append(f"\n{i} LN -1 -1 -1 -1 {node_depth[i]} {class_idx}")
            else:
                model_info.append(
                    f"\n{i} IN {children_left[i]} {children_right[i]} "
                    f"{tree.feature[i]} {tree.threshold[i]} {node_depth[i]} -1"
                )
    model_info.append("\n\n")
    if show:
        print(*model_info)
    if save:
        with open(
                f"./data/processed/forests/{config['DATASET']['NAME']}.RF{index}.txt",
                "w"
        ) as f:
            for item in model_info:
                f.write(item)