def test_inequality():
a = GroupMetricSet()
b = GroupMetricSet()
a.compute(Y_true,
Y_pred,
groups,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
b.compute(Y_true,
Y_pred,
gr_alp,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
assert not (a == b)
assert a != b
def test_groups_not_sequential_int():
target = GroupMetricSet()
regular = GroupMetricSet()
# Make 'target' the same as 'regular' but with different integers for the groups
target.compute(Y_true,
Y_pred,
gr_in2,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
regular.compute(Y_true,
Y_pred,
groups,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
assert np.array_equal(['2', '4'], target.group_names)
assert target.metrics == regular.metrics
def test_compute_binary():
target = GroupMetricSet()
target.compute(Y_true,
Y_pred,
groups,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
sample_expected = group_accuracy_score(Y_true, Y_pred, groups)
assert np.array_equal(Y_true, target.y_true)
assert np.array_equal(Y_pred, target.y_pred)
assert np.array_equal(groups, target.groups)
assert np.array_equal(['0', '1'], target.group_names)
assert len(target.metrics) == 10
assert target.metrics[
GroupMetricSet.GROUP_ACCURACY_SCORE].overall == sample_expected.overall
for g in np.unique(groups):
assert (target.metrics[GroupMetricSet.GROUP_ACCURACY_SCORE].by_group[g]
== sample_expected.by_group[g])
def test_compute_regression():
target = GroupMetricSet()
target.compute(Y_true,
Y_pred,
groups,
model_type=GroupMetricSet.REGRESSION)
sample_expected = group_balanced_root_mean_squared_error(
Y_true, Y_pred, groups)
assert np.array_equal(Y_true, target.y_true)
assert np.array_equal(Y_pred, target.y_pred)
assert np.array_equal(groups, target.groups)
assert np.array_equal(['0', '1'], target.group_names)
assert len(target.metrics) == 12
assert target.metrics[
GroupMetricSet.
GROUP_BALANCED_ROOT_MEAN_SQUARED_ERROR].overall == sample_expected.overall # noqa: E501
for g in np.unique(groups):
assert (target.metrics[
GroupMetricSet.GROUP_BALANCED_ROOT_MEAN_SQUARED_ERROR].by_group[g]
== sample_expected.by_group[g])
def test_groups_alphabetical():
target = GroupMetricSet()
regular = GroupMetricSet()
# Make 'target' the same as 'regular' but with strings for the groups
target.compute(Y_true,
Y_pred,
gr_alp,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
regular.compute(Y_true,
Y_pred,
groups,
model_type=GroupMetricSet.BINARY_CLASSIFICATION)
assert np.array_equal(['a', 'b'], target.group_names)
tm = target.metrics[GroupMetricSet.GROUP_ACCURACY_SCORE]
rm = target.metrics[GroupMetricSet.GROUP_ACCURACY_SCORE]
assert tm.overall == rm.overall
assert tm.by_group == rm.by_group
assert tm == rm
assert target.metrics == regular.metrics