def test_set_empty_input(self):
"""
Test that calling ``set_classification`` with an empty dictionary
raises a ValueError.
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
with pytest.raises(ValueError,
match=r"No classification labels/values given\."):
ClassificationElement.set_classification(e, {})
def test_set_no_input(self):
"""
Test that calling ``set_classification`` with default arguments raises
a ValueError.
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
with pytest.raises(ValueError,
match="No classification labels/values "
"given\."):
ClassificationElement.set_classification(e)
def test_set_kwargs(self):
"""
Test that passing a keyword arguments to ``set_classification`` returns
the appropriately normalized dictionary.
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
expected_v = {'a': 1, 'b': 0}
actual_v = ClassificationElement.set_classification(e, a=1, b=0)
assert actual_v == expected_v
def test_set_input_dict(self):
"""
Test that passing a dictionary to ``set_classification`` returns the
appropriately normalized dictionary.
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
expected_v = {1: 0, 2: 1}
actual_v = ClassificationElement.set_classification(e, expected_v)
assert actual_v == expected_v
def test_set_mixed(self):
"""
Test that passing mixed dictionary and keyword arguments to
``set_classification`` returns the appropriately normalize dictionary.
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
expected_v = {'a': .25, 1: .25, 'b': .25, 'd': .25}
actual_v = ClassificationElement.set_classification(e, {
'a': .25,
1: .25
},
b=.25,
d=.25)
assert actual_v == expected_v
def test_set_nonstandard(self):
"""
Test that setting a set of label/confidence pairs where the
confidence sums to greater than 1.0 is acceptable and returns the
appropriately normalized dictionary.
Many classifiers output 1-sum confidence values, but not all (e.g.
CNN final layers like AlexNet).
"""
# Mock element instance
#: :type: ClassificationElement
e = mock.MagicMock(spec_set=ClassificationElement)
expected_v = {'a': 1, 1: 1, 'b': 1, 'd': 1}
actual_v = ClassificationElement.set_classification(e, {
'a': 1,
1: 1
},
b=1,
d=1)
assert actual_v == expected_v