2023-07-27 03:16:41 +02:00
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from sklearn.linear_model import LogisticRegression
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from quacc.estimator import BinaryQuantifierAccuracyEstimator
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2023-07-26 00:38:23 +02:00
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2023-07-27 03:16:41 +02:00
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class TestBinaryQuantifierAccuracyEstimator:
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@pytest.mark.parametrize(
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"instances,preds0,preds1,result",
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[
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(
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np.asarray(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.15, 0.2, 0.35, 0.3]),
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),
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(
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sp.csr_matrix(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.15, 0.2, 0.35, 0.3]),
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),
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(
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.0, 0.4, 0.0, 0.6]),
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),
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(
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.0, 0.4, 0.0, 0.6]),
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),
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(
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.3, 0.0, 0.7, 0.0]),
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),
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(
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0.3, 0.7]),
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np.asarray([0.4, 0.6]),
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np.asarray([0.3, 0.0, 0.7, 0.0]),
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),
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],
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)
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def test_estimate_ndarray(self, mocker, instances, preds0, preds1, result):
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estimator = BinaryQuantifierAccuracyEstimator(LogisticRegression())
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estimator.n_classes = 4
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with mocker.patch.object(estimator.q_model_0, "quantify"), mocker.patch.object(
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estimator.q_model_1, "quantify"
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):
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estimator.q_model_0.quantify.return_value = preds0
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estimator.q_model_1.quantify.return_value = preds1
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assert np.array_equal(
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estimator.estimate(instances, ext=True),
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result,
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)
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