def _test_roc_auc_evaluator_with_labels(data1):
"""test `pos_labels` and `ignore_labels` behavior"""
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data1)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(
iterator, predictor, name='val',
pos_labels=[1, 2], ignore_labels=-1,
)
# --- test evaluate ---
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
expected_roc_auc = 0.75
# print('observation ', observation)
assert observation['target/roc_auc'] == expected_roc_auc
# --- test __call__ ---
result = evaluator()
# print('result ', result)
assert result['val/main/roc_auc'] == expected_roc_auc
def _test_roc_auc_evaluator_with_labels(data1):
"""test `pos_labels` and `ignore_labels` behavior"""
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data1)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(
iterator, predictor, name='val',
pos_labels=[1, 2], ignore_labels=-1,
)
# --- test evaluate ---
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
expected_roc_auc = 0.75
# print('observation ', observation)
assert observation['target/roc_auc'] == expected_roc_auc
# --- test __call__ ---
result = evaluator()
# print('result ', result)
assert result['val/main/roc_auc'] == expected_roc_auc
def _test_roc_auc_evaluator_raise_error(data, raise_value_error=True):
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(
iterator, predictor, name='train',
pos_labels=1, ignore_labels=None,
raise_value_error=raise_value_error
)
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
return observation['target/roc_auc']
def _test_roc_auc_evaluator_raise_error(data, raise_value_error=True):
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(iterator,
predictor,
name='train',
pos_labels=1,
ignore_labels=None,
raise_value_error=raise_value_error)
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
return observation['target/roc_auc']
def _test_roc_auc_evaluator_default_args(data0):
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data0)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(
iterator, predictor, name='train',
pos_labels=1, ignore_labels=None
)
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
expected_roc_auc = 0.75
# print('observation ', observation)
assert observation['target/roc_auc'] == expected_roc_auc
# --- test __call__ ---
result = evaluator()
# print('result ', result)
assert result['train/main/roc_auc'] == expected_roc_auc
def _test_roc_auc_evaluator_default_args(data0):
predictor = DummyPredictor()
dataset = NumpyTupleDataset(*data0)
iterator = SerialIterator(dataset, 2, repeat=False, shuffle=False)
evaluator = ROCAUCEvaluator(
iterator, predictor, name='train',
pos_labels=1, ignore_labels=None
)
repo = chainer.Reporter()
repo.add_observer('target', predictor)
with repo:
observation = evaluator.evaluate()
expected_roc_auc = 0.75
# print('observation ', observation)
assert observation['target/roc_auc'] == expected_roc_auc
# --- test __call__ ---
result = evaluator()
# print('result ', result)
assert result['train/main/roc_auc'] == expected_roc_auc