Co-op

Co-op is a python library consists of a set of measures (./src/measures) and operators (./src/operators) that can compute counterfactual subsets of input dataset for designing and implementing visualization systems.

See the paper for more details about the subset computation theory, operator-based model, measures, and operators.

Import packages directly if you want to use it. Import operators package only if you've implemented your own measures.

A JavaScript implementation of Co-op is attached in the folder ./utils/JavaScript as well.

Usage

Usage demo for filtering operators is as follows:

def filtering_usage_demo():
    data = pandas.DataFrame([
        [800,    1000],
        [1200,    12000],
        [3600,    36000],
        [500,    15000],
        [100,    12000]
    ], columns=['profit', 'sales'])

    cstr1 = ['sales', geq_constraint]
    cstr2 = ['sales', geq_constraint_10000]
    cstr = [cstr1, cstr2]

    in_set, ex_set = filtering.filtering([cstr1], data)
    in_groups = filtering.groupby(cstr, data)

    return in_set, ex_set, in_groups

Some examples for the filtering constriants are described as follows:

def less_constraint(point, number=1):
    return point < number


def leq_constraint(point, number=1):
    return point <= number


def greater_constraint(point, number=1):
    return point > number


def geq_constraint(point, number=1):
    return point >= number


def geq_constraint_10000(point, number=10000):
    return point >= number


def geq_constraint_30000(point, number=30000):
    return point >= number

Usage demo for counterfactual operators is as follows:

def cf_usage_demo():
    data = pandas.DataFrame([
        [800,    1000],
        [1200,    12000],
        [3600,    36000],
        [500,    15000],
        [100,    12000]
    ], columns=['profit', 'sales'])

    cstr = ['sales', geq_constraint_30000]

    in_set, ex_set = filtering.filtering([cstr], data)

    Measure = [[1, customized_measure]]
    cf_set, ex_set = counterfactual.counterfactual(in_set, ex_set, Measure)

    return in_set, cf_set, ex_set

An example of customized measure computation is as follows:

def customized_measure(p, S, weights=[1, 0.5, 3, 8, 21], measure='mahalanobis', dims=['a', 'b', 'c']):
    res = 0
    target_data = S.loc[:, dims]
    for index, row in target_data.iterrows():
        res += p2p.pd_w(p, row.tolist(), weights)
    return res