def greedy_solve(items, max_cost, key_function):
    """
    Finds local maximal value from a combination of Items,
    given a cost constraint and metric (e.g. value, cost, density)
    Parameters:
        items, a list of Items to consider
        max_cost, the cost/weight constraint
        key_function, the metric to determine most favorable items
    Returns:
        the list of Items to take and the total value attained
    """

    # Make a copy of items, sorted by key (e.g. cost, density, value)
    sorted_items = sorted(items, key=key_function, reverse=True)

    # Make an empty list to hold results
    result = []

    # Initialize total value and total cost at 0
    total_value, total_cost = 0, 0

    # Iterate through sorted items
    for item in sorted_items:
        # If we can "afford" the next item, add it to the list
        if (total_cost + item.cost) <= max_cost:
            result.append(item)
            total_cost += item.cost
            total_value += item.value

    # Return the result and total value of it
    return result, total_value