cleanlab--cleanlab
Aditya Thyagarajan
142 行
4.3 KiB
Python
142 行
4.3 KiB
Python
# Copyright (C) 2017-2023 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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from cleanlab.internal import latent_algebra
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import numpy as np
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import pytest
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s = [0] * 10 + [1] * 5 + [2] * 15
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nm = np.array([[1.0, 0.0, 0.2], [0.0, 0.7, 0.2], [0.0, 0.3, 0.6]])
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def test_latent_py_ps_inv():
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ps, py, inv = latent_algebra.compute_ps_py_inv_noise_matrix(s, nm)
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assert all(abs(np.dot(inv, ps) - py) < 1e-3)
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assert all(abs(np.dot(nm, py) - ps) < 1e-3)
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def get_latent_py_ps_inv():
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ps, py, inv = latent_algebra.compute_ps_py_inv_noise_matrix(s, nm)
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return ps, py, inv
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def test_latent_inv():
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ps, py, inv = get_latent_py_ps_inv()
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inv2 = latent_algebra.compute_inv_noise_matrix(py, nm)
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assert np.all(abs(inv - inv2) < 1e-3)
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def test_latent_nm():
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ps, py, inv = get_latent_py_ps_inv()
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nm2 = latent_algebra.compute_noise_matrix_from_inverse(ps, inv, py=py)
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assert np.all(abs(nm - nm2) < 1e-3)
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def test_latent_py():
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ps, py, inv = get_latent_py_ps_inv()
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py2 = latent_algebra.compute_py(ps, nm, inv)
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assert np.all(abs(py - py2) < 1e-3)
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def test_latent_py_warning():
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ps, py, inv = get_latent_py_ps_inv()
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with pytest.raises(TypeError) as e:
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with pytest.warns(UserWarning) as w:
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py2 = latent_algebra.compute_py(
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ps=np.array([[[0.1, 0.3, 0.6]]]),
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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)
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py2 = latent_algebra.compute_py(
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ps=np.array([[0.1], [0.2], [0.7]]),
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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)
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def test_compute_py_err():
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ps, py, inv = get_latent_py_ps_inv()
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try:
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py = latent_algebra.compute_py(
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ps=ps,
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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py_method="marginal_ps",
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)
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except ValueError as e:
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assert "true_labels_class_counts" in str(e)
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with pytest.raises(ValueError) as e:
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py = latent_algebra.compute_py(
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ps=ps,
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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py_method="marginal_ps",
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)
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def test_compute_py_marginal_ps():
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ps, py, inv = get_latent_py_ps_inv()
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cj = nm * ps * len(s)
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true_labels_class_counts = cj.sum(axis=0)
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py2 = latent_algebra.compute_py(
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ps=ps,
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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py_method="marginal_ps",
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true_labels_class_counts=true_labels_class_counts,
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)
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assert all(abs(py - py2) < 1e-2)
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def test_pyx():
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pred_probs = np.array(
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[
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[0.1, 0.3, 0.6],
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[0.1, 0.0, 0.9],
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[0.1, 0.0, 0.9],
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[1.0, 0.0, 0.0],
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[0.1, 0.8, 0.1],
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]
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)
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ps, py, inv = get_latent_py_ps_inv()
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pyx = latent_algebra.compute_pyx(pred_probs, nm, inv)
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assert np.all(np.sum(pyx, axis=1) - 1 < 1e-4)
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def test_pyx_error():
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pred_probs = np.array([0.1, 0.3, 0.6])
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ps, py, inv = get_latent_py_ps_inv()
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try:
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pyx = latent_algebra.compute_pyx(pred_probs, nm, inverse_noise_matrix=inv)
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except ValueError as e:
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assert "should be (N, K)" in str(e)
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with pytest.raises(ValueError) as e:
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pyx = latent_algebra.compute_pyx(pred_probs, nm, inverse_noise_matrix=inv)
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def test_compute_py_method_marginal_true_labels_class_counts_none_error():
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ps, py, inv = get_latent_py_ps_inv()
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with pytest.raises(ValueError) as e:
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_ = latent_algebra.compute_py(
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ps=ps,
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noise_matrix=nm,
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inverse_noise_matrix=inv,
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py_method="marginal",
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true_labels_class_counts=None,
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)
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