cleanlab--cleanlab
Elías Snorrason
18dfb0db7c
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
112 行
3.7 KiB
Python
112 行
3.7 KiB
Python
# Copyright (C) 2017-2024 Cleanlab Inc.
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# This file is part of cleanlab.
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#
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# cleanlab is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# cleanlab is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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import pytest
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from hypothesis import given, settings, strategies as st
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from hypothesis.strategies import composite
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from hypothesis.extra.numpy import arrays
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from sklearn.neighbors import NearestNeighbors
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from cleanlab.data_valuation import _knn_shapley_score, data_shapley_knn
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from cleanlab.internal.neighbor.knn_graph import create_knn_graph_and_index
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class TestDataValuation:
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K = 3
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N = 100
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num_features = 10
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@pytest.fixture
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def features(self):
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return np.random.rand(self.N, self.num_features)
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@pytest.fixture
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def labels(self):
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return np.random.randint(0, 2, self.N)
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@pytest.fixture
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def knn_graph(self, features):
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knn = NearestNeighbors(n_neighbors=self.K).fit(features)
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knn_graph = knn.kneighbors_graph(mode="distance")
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return knn_graph
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def test_data_shapley_knn(self, labels, features):
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shapley = data_shapley_knn(labels, features=features, k=self.K)
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assert shapley.shape == (100,)
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assert np.all(shapley >= 0)
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assert np.all(shapley <= 1)
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def test_data_shapley_knn_with_knn_graph(self, labels, knn_graph):
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shapley = data_shapley_knn(labels, knn_graph=knn_graph, k=self.K)
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assert shapley.shape == (100,)
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assert np.all(shapley >= 0)
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assert np.all(shapley <= 1)
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@composite
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def valid_data(draw):
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"""
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A custom strategy to generate valid labels, features, and k such that:
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- labels and features have the same length
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- k is less than the length of labels and features
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"""
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# Generate a valid length for labels and features
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length = draw(st.integers(min_value=11, max_value=1000))
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# Generate labels and features of the same length
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labels = draw(
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arrays(
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dtype=np.int32,
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shape=length,
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elements=st.integers(min_value=0, max_value=length - 1),
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)
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)
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features = draw(
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arrays(
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dtype=np.float64,
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shape=(length, draw(st.integers(min_value=2, max_value=50))),
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elements=st.floats(min_value=-1.0, max_value=1.0),
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)
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)
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# Generate k such that it is less than the length of labels and features
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k = draw(st.integers(min_value=1, max_value=length - 1))
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return labels, features, k
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class TestDataShapleyKNNScore:
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"""This test class prioritizes testing the raw/untransformed outputs of the _knn_shapley_score function."""
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@settings(
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max_examples=1000, deadline=None
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) # Increase the number of examples to test more cases
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@given(valid_data())
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def test_knn_shapley_score_property(self, data):
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labels, features, k = data
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knn_graph, _ = create_knn_graph_and_index(features, n_neighbors=k)
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neighbor_indices = knn_graph.indices.reshape(-1, k)
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scores = _knn_shapley_score(neighbor_indices, labels, k)
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# Shapley scores should be between -1 and 1
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assert scores.shape == (len(labels),)
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assert np.all(scores >= -1)
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assert np.all(scores <= 1)
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