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import pytest | ||
import torch | ||
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from src.explainers.aggregators.aggregators import ( | ||
AbsSumAggregator, | ||
SumAggregator, | ||
) | ||
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@pytest.mark.aggregators | ||
@pytest.mark.parametrize( | ||
"test_id, dataset, explanations", | ||
[ | ||
( | ||
"mnist", | ||
"load_mnist_dataset", | ||
"load_mnist_explanations_1", | ||
), | ||
], | ||
) | ||
def test_sum_aggregator(test_id, dataset, explanations, request): | ||
dataset = request.getfixturevalue(dataset) | ||
explanations = request.getfixturevalue(explanations) | ||
aggregator = SumAggregator(training_size=len(dataset)) | ||
aggregator.update(explanations) | ||
global_rank = aggregator.compute() | ||
assert torch.allclose(global_rank, explanations.sum(dim=0).argsort()) | ||
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@pytest.mark.aggregators | ||
@pytest.mark.parametrize( | ||
"test_id, dataset, explanations", | ||
[ | ||
( | ||
"mnist", | ||
"load_mnist_dataset", | ||
"load_mnist_explanations_1", | ||
), | ||
], | ||
) | ||
def test_abs_aggregator(test_id, dataset, explanations, request): | ||
dataset = request.getfixturevalue(dataset) | ||
explanations = request.getfixturevalue(explanations) | ||
aggregator = AbsSumAggregator(training_size=len(dataset)) | ||
aggregator.update(explanations) | ||
global_rank = aggregator.compute() | ||
assert torch.allclose(global_rank, explanations.abs().mean(dim=0).argsort()) |
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from collections import OrderedDict | ||
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import pytest | ||
import torch | ||
from torch.utils.data import TensorDataset | ||
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from src.explainers.aggregators.self_influence import ( | ||
get_self_influence_ranking, | ||
) | ||
from src.utils.explain_wrapper import explain | ||
from src.utils.functions.similarities import dot_product_similarity | ||
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@pytest.mark.self_influence | ||
@pytest.mark.parametrize( | ||
"test_id, explain_kwargs", | ||
[ | ||
( | ||
"random_data", | ||
{"method": "SimilarityInfluence", "layer": "identity", "similarity_metric": dot_product_similarity}, | ||
), | ||
], | ||
) | ||
def test_self_influence_ranking(test_id, explain_kwargs, request): | ||
model = torch.nn.Sequential(OrderedDict([("identity", torch.nn.Identity())])) | ||
X = torch.randn(100, 200) | ||
rand_dataset = TensorDataset(X, torch.randint(0, 10, (100,))) | ||
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self_influence_rank = get_self_influence_ranking( | ||
model=model, | ||
model_id="0", | ||
cache_dir="temp_captum", | ||
training_data=rand_dataset, | ||
explain_fn=explain, | ||
explain_fn_kwargs=explain_kwargs, | ||
) | ||
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assert torch.allclose(self_influence_rank, torch.linalg.norm(X, dim=-1).argsort()) |