Calculates the node importance in a decision tree

node_importance.py

def node_importance(
    node_main: str, 
    node_left: str, 
    node_right: str, 
    n_entries_weight: dict, 
    i_sq: dict
    ) -> float:
    """
    Calculated the importance of the node_main
    
    Arguments
    ---------
    node_main: str
        The main parent node
    node_left: str
        The left child node
    node_right: str
        The right child node
    n_entries_weight: dict
        The weight of each node, calculated by the samples in node divided by the total number of samples
    i_sq: dict
        The squared error of each node

    Returns
    -------
    float
        The importance of the node_main
    """
    # Calculating the main nodes part of the equation 
    _main_part = n_entries_weight[node_main] * i_sq[node_main]

    # Calculating the left child nodes part of the equation
    _left_part = n_entries_weight[node_left] * i_sq[node_left]

    # Calculating the right child nodes part of the equation
    _right_part = n_entries_weight[node_right] * i_sq[node_right]

    # Returning the gain of the node_main; This gain is the importance of the node_main
    return _main_part - _left_part - _right_part