def test_merged_labels_over_threshold_confusion_matrix():
labels = range(200)
more_labels = range(201, 300)
matrix_1 = ConfusionMatrix(labels)
matrix_2 = ConfusionMatrix(more_labels)
with pytest.raises(ValueError):
matrix_1.merge(matrix_2)
def test_merge_with_none():
targets_1 = ["cat", "dog", "pig"]
predictions_1 = ["cat", "dog", "dog"]
scores_1 = [0.1, 0.2, 0.4]
labels_1 = ["cat", "dog", "pig"]
matrix = ConfusionMatrix(labels_1)
matrix.add(predictions_1, targets_1, scores_1)
res = matrix.merge(other_cm=None)
assert res.target_field is None
assert res.prediction_field is None
assert res.score_field is None
def test_merge_conf_matrix():
"""
tests merging two confusion matrices of different sizes
"""
targets_1 = ["cat", "dog", "pig"]
targets_2 = ["cat", "dog"]
predictions_1 = ["cat", "dog", "dog"]
predictions_2 = ["cat", "cat"]
scores_1 = [0.1, 0.2, 0.4]
scores_2 = [0.4, 0.3]
expected_1 = [[1, 0, 0], [0, 1, 1], [0, 0, 0]]
expected_2 = [[1, 1], [0, 0]]
expected_merge = [[2, 1, 0], [0, 1, 1], [0, 0, 0]]
labels_1 = ["cat", "dog", "pig"]
conf_M_1 = ConfusionMatrix(labels_1)
conf_M_1.add(predictions_1, targets_1, scores_1)
for idx, value in enumerate(conf_M_1.labels):
for jdx, value_2 in enumerate(conf_M_1.labels):
assert conf_M_1.confusion_matrix[
idx, jdx].floats.count == expected_1[idx][jdx]
labels_2 = ["cat", "dog"]
conf_M_2 = ConfusionMatrix(labels_2)
conf_M_2.add(predictions_2, targets_2, scores_2)
for idx, value in enumerate(conf_M_2.labels):
for jdx, value_2 in enumerate(conf_M_2.labels):
assert conf_M_2.confusion_matrix[idx, jdx].floats.count == \
expected_2[idx][jdx]
new_conf = conf_M_1.merge(conf_M_2)
print(new_conf.labels)
for idx, value in enumerate(new_conf.labels):
for jdx, value_2 in enumerate(new_conf.labels):
print(idx, jdx)
assert new_conf.confusion_matrix[
idx, jdx].floats.count == expected_merge[idx][jdx]