QuAcc/tests/test_estimator.py

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