Pseudo code for cost estimation

cost_est.py

def cost_model(**kwargs):
    tc, sc = 15, 5
    #method = kwargs['method']
    in_dictionary = kwargs['included']
    not_in_dictionary = kwargs['excluded']

    if in_dictionary:
        return tc*not_in_dictionary + sc*in_dictionary
    else:
        return tc*not_in_dictionary

def naive(errors):
    #pdb.set_trace()
    cost = cost_model(excluded= errors, included = None)
    return cost

def spell_check(correctable, uncorrectable):
    cost = cost_model(excluded = uncorrectable, included = correctable)
    return cost
  
def cluster_cost(components, **kwargs):
    mode = kwargs['mode']
    cost = 0
    tc, ti = 20, 2 # tc = time to correct one cluster, ti = time to correct each word in a cluster
    if mode == 'Naive':
        
        return components*tc
    if mode == 'Advanced':
        for c in components:
            cost += len(c)*tc
        return cost

Hey, Could you please provide some more insight into cost estimation using clustering. The function cluster_cost is a rough implementation of what I understood. What more to add?

Secondly, what is the mode of correction of for the annotator, what I mean is whether the annotator is only going to select and deselect the word prediction or we also provide an option either to type the correct word or select a suggestion from a drop down menu.

if we provide the above two options then the cost computation will change.