cece 0.0.2

Semantic Edits as Counterfctual Explanations

Choose Your Data Wisely

This repository contains code for our paper, Choose your Data Wisely: A Framework for Semantic Counterfactuals .

Citation

@article{dervakos2023choose,
  title={Choose your Data Wisely: A Framework for Semantic Counterfactuals},
  author={Dervakos, Edmund and Thomas, Konstantinos and Filandrianos, Giorgos and Stamou, Giorgos},
  journal={arXiv preprint arXiv:2305.17667},
  year={2023}
}

Use Case 1: Calculate GED between 2 objects

from cece.queries import *
from cece.refine import *


q1 = Query(np.array([set(["car", "vehicle"]), set(["tree"])]))
q2 = Query(np.array([set(["car"]), set(["tree"]), set(["person"])]))

r = refine (q1, q2,verbose= True, return_edits = True)

print (r)

Remains the same: {'tree'}
-------------------------------------
Tranform {'car'} from {'vehicle'} -> set()
Add: {'person'}
--------------------------------------
(2, {'additions': [{'person'}], 'removals': [], 'transf': [({'car', 'vehicle'}, {'car'})]})
[{'car', 'vehicle'} {'tree'}]
{}

from cece.wordnet import *

q1 = Query(np.array([set(connect_term_to_wordnet("car")),
                     set(connect_term_to_wordnet("man")),]))

q2 = Query(np.array([set(connect_term_to_wordnet("woman")),
                     set(connect_term_to_wordnet("truck"),)]))

r = refine (q1, q2,verbose= True, return_edits = True)
print ("Cost: ", r[0])

-------------------------------------
Tranform {'object.n.01', 'entity.n.01', 'motor_vehicle.n.01', 'vehicle.n.01', 'instrumentality.n.03', 'physical_entity.n.01', 'conveyance.n.03', 'artifact.n.01', 'whole.n.02', 'self-propelled_vehicle.n.01', 'wheeled_vehicle.n.01'} from {'car.n.01'} -> {'truck.n.01'}
Tranform {'object.n.01', 'entity.n.01', 'living_thing.n.01', 'person.n.01', 'physical_entity.n.01', 'whole.n.02', 'organism.n.01', 'adult.n.01'} from {'man.n.01'} -> {'woman.n.01'}
--------------------------------------
Cost:  4

q1 = Query(np.array([connect_term_to_wordnet("tree"),
                     connect_term_to_wordnet("man"),]))

q2 = Query(np.array([connect_term_to_wordnet("man"),
                     connect_term_to_wordnet("truck"),]))

r = refine (q1, q2,verbose= True, return_edits = True)
print ("Cost: ", r[0])

Remains the same: {'object.n.01', 'entity.n.01', 'living_thing.n.01', 'person.n.01', 'physical_entity.n.01', 'whole.n.02', 'man.n.01', 'organism.n.01', 'adult.n.01'}
-------------------------------------
Tranform {'whole.n.02', 'physical_entity.n.01', 'object.n.01', 'entity.n.01'} from {'living_thing.n.01', 'woody_plant.n.01', 'vascular_plant.n.01', 'plant.n.02', 'organism.n.01', 'tree.n.01'} -> {'motor_vehicle.n.01', 'vehicle.n.01', 'instrumentality.n.03', 'conveyance.n.03', 'truck.n.01', 'artifact.n.01', 'self-propelled_vehicle.n.01', 'wheeled_vehicle.n.01'}
--------------------------------------
Cost:  14

or even simpler

q1 = Query(np.array(connect_list_to_wordnet(["tree", "man"])))
q2 = Query(np.array(connect_list_to_wordnet(["man", "truck"])))
r = refine (q1, q2,verbose= True, return_edits = True)
print ("Cost: ", r[0])

Remains the same: {'object.n.01', 'entity.n.01', 'living_thing.n.01', 'person.n.01', 'physical_entity.n.01', 'whole.n.02', 'man.n.01', 'organism.n.01', 'adult.n.01'}
-------------------------------------
Tranform {'whole.n.02', 'physical_entity.n.01', 'object.n.01', 'entity.n.01'} from {'living_thing.n.01', 'woody_plant.n.01', 'vascular_plant.n.01', 'plant.n.02', 'organism.n.01', 'tree.n.01'} -> {'motor_vehicle.n.01', 'vehicle.n.01', 'instrumentality.n.03', 'conveyance.n.03', 'truck.n.01', 'artifact.n.01', 'self-propelled_vehicle.n.01', 'wheeled_vehicle.n.01'}
--------------------------------------
Cost:  14

Or even simpler {#or-even-simpler}

from cece.xDataset import createMSQ

q1 = createMSQ(["tree", "man"], connect_to_wordnet = True)
q2 = createMSQ(["man", "truck"], connect_to_wordnet = True)
r = refine (q1, q2,verbose= True, return_edits = True)
print ("Cost: ", r[0])

Remains the same: {'object.n.01', 'entity.n.01', 'living_thing.n.01', 'person.n.01', 'physical_entity.n.01', 'whole.n.02', 'man.n.01', 'organism.n.01', 'adult.n.01'}
-------------------------------------
Tranform {'whole.n.02', 'physical_entity.n.01', 'object.n.01', 'entity.n.01'} from {'living_thing.n.01', 'woody_plant.n.01', 'vascular_plant.n.01', 'plant.n.02', 'organism.n.01', 'tree.n.01'} -> {'motor_vehicle.n.01', 'vehicle.n.01', 'instrumentality.n.03', 'conveyance.n.03', 'truck.n.01', 'artifact.n.01', 'self-propelled_vehicle.n.01', 'wheeled_vehicle.n.01'}
--------------------------------------
Cost:  14

Use Case 2: Use it for a Datatset {#-use-case-2-use-it-for-a-datatset}

from cece.xDataset import *


# initialize an instance of the Dataset
ds = xDataset(dataset = [["tree", "man"],
                         ["man", "truck"],
                         ["kitchen", "oven", "refrigerator"], 
                         ["bed", "pillow", "blanket", "woman"],
                         ["sofa", "cushion", "pillow"],],

              labels = ["outdoor", "outdoor", "indoor", "indoor", "indoor"],
              connect_to_wordnet = True)


ds.retrieve(ds.dataset[1])

for idx, cost in ds.retrieve(ds.dataset[1]).items():
    print (f"Cost: {cost} for '{ds.labels[idx]}' with id: {idx}")

Cost: 0 for 'outdoor' with id: 1
Cost: 14 for 'outdoor' with id: 0
Cost: 28 for 'indoor' with id: 4
Cost: 29 for 'indoor' with id: 3
Cost: 36 for 'indoor' with id: 2

results = ds.retrieve([ "car", "woman"])

for idx, cost in results.items():
    print (f"Cost: {cost} for '{ds.labels[idx]}' with id: {idx}")

Cost: 4 for 'outdoor' with id: 1
Cost: 16 for 'outdoor' with id: 0
Cost: 27 for 'indoor' with id: 3
Cost: 28 for 'indoor' with id: 4
Cost: 36 for 'indoor' with id: 2

Explain Method - Get a Semantic Counterfatual

results = ds.explain([ "car", "woman"], "outdoor")

print (f"Cost: {results[1]} for '{ds.labels[results[0]]}' with id: {results[0]}")

Cost: 27 for 'indoor' with id: 3

from cece.xDataset import *
ds.find_edits(["car", "man"], ["woman", "truck"])

(4,
 {'additions': [],
  'removals': [],
  'transf': [({'artifact.n.01',
     'car.n.01',
     'conveyance.n.03',
     'entity.n.01',
     'instrumentality.n.03',
     'motor_vehicle.n.01',
     'object.n.01',
     'physical_entity.n.01',
     'self-propelled_vehicle.n.01',
     'vehicle.n.01',
     'wheeled_vehicle.n.01',
     'whole.n.02'},
    {'artifact.n.01',
     'conveyance.n.03',
     'entity.n.01',
     'instrumentality.n.03',
     'motor_vehicle.n.01',
     'object.n.01',
     'physical_entity.n.01',
     'self-propelled_vehicle.n.01',
     'truck.n.01',
     'vehicle.n.01',
     'wheeled_vehicle.n.01',
     'whole.n.02'}),
   ({'adult.n.01',
     'entity.n.01',
     'living_thing.n.01',
     'man.n.01',
     'object.n.01',
     'organism.n.01',
     'person.n.01',
     'physical_entity.n.01',
     'whole.n.02'},
    {'adult.n.01',
     'entity.n.01',
     'living_thing.n.01',
     'object.n.01',
     'organism.n.01',
     'person.n.01',
     'physical_entity.n.01',
     'whole.n.02',
     'woman.n.01'})]})