def test_infer_ten_classes():
analyser = output_analysers.ClassificationAnalyser(name="a")
dataset = utils.generate_one_hot_labels(dtype="dataset", num_classes=10)
for data in dataset:
analyser.update(data)
analyser.finalize()
assert analyser.num_classes == 10
def test_multi_label_two_classes_has_two_columns():
analyser = output_analysers.ClassificationAnalyser(name="a", multi_label=True)
dataset = tf.data.Dataset.from_tensor_slices(np.random.rand(10, 2)).batch(32)
for data in dataset:
analyser.update(data)
analyser.finalize()
assert analyser.encoded
def test_specify_two_classes_fit_single_column():
analyser = output_analysers.ClassificationAnalyser(name="a", num_classes=2)
dataset = tf.data.Dataset.from_tensor_slices(np.random.rand(10, 1)).batch(32)
for data in dataset:
analyser.update(data)
analyser.finalize()
assert analyser.num_classes == 2
def test_infer_single_column_two_classes():
analyser = output_analysers.ClassificationAnalyser(name="a")
dataset = tf.data.Dataset.from_tensor_slices(np.random.randint(
0, 2, 10)).batch(32)
for data in dataset:
analyser.update(data)
analyser.finalize()
assert analyser.num_classes == 2
def test_wrong_num_classes_error():
analyser = output_analysers.ClassificationAnalyser(name="a", num_classes=5)
dataset = tf.data.Dataset.from_tensor_slices(np.random.rand(10, 3)).batch(32)
with pytest.raises(ValueError) as info:
for data in dataset:
analyser.update(data)
analyser.finalize()
assert "Expect the target data for a to have shape" in str(info.value)
def test_clf_head_one_hot_shape_error():
analyser = output_analysers.ClassificationAnalyser(name="a", num_classes=9)
dataset = tf.data.Dataset.from_tensor_slices(
utils.generate_one_hot_labels(dtype="np", num_classes=10)).batch(32)
with pytest.raises(ValueError) as info:
for data in dataset:
analyser.update(data)
analyser.finalize()
assert "Expect the target data for a to have shape" in str(info.value)
def test_one_class_error():
analyser = output_analysers.ClassificationAnalyser(name="a")
dataset = tf.data.Dataset.from_tensor_slices(np.array(["a", "a",
"a"])).batch(32)
with pytest.raises(ValueError) as info:
for data in dataset:
analyser.update(data)
analyser.finalize()
assert "Expect the target data for a to have at least 2 classes" in str(
info.value)
