class TestNDCG(RetrievalMetricTester):
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ['skip', 'neg', 'pos'])
@pytest.mark.parametrize("k", [None, 1, 4, 10])
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_class_metric(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
k: int,
):
metric_args = {'empty_target_action': empty_target_action, 'k': k}
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalNormalizedDCG,
sk_metric=_ndcg_at_k,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize(
**_default_metric_functional_input_arguments_with_non_binary_target)
@pytest.mark.parametrize("k", [None, 1, 4, 10])
def test_functional_metric(self, preds: Tensor, target: Tensor, k: int):
self.run_functional_metric_test(
preds=preds,
target=target,
metric_functional=retrieval_normalized_dcg,
sk_metric=_ndcg_at_k,
metric_args={},
k=k,
)
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_precision_cpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_cpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalNormalizedDCG,
metric_functional=retrieval_normalized_dcg,
)
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_precision_gpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_gpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalNormalizedDCG,
metric_functional=retrieval_normalized_dcg,
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_class_metric_parameters_default,
_errors_test_class_metric_parameters_no_pos_target,
_errors_test_class_metric_parameters_k,
))
def test_arguments_class_metric(self, indexes: Tensor, preds: Tensor,
target: Tensor, message: str,
metric_args: dict):
self.run_metric_class_arguments_test(
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalNormalizedDCG,
message=message,
metric_args=metric_args,
exception_type=ValueError,
kwargs_update={},
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_functional_metric_parameters_default,
_errors_test_functional_metric_parameters_k,
))
def test_arguments_functional_metric(self, preds: Tensor, target: Tensor,
message: str, metric_args: dict):
self.run_functional_metric_arguments_test(
preds=preds,
target=target,
metric_functional=retrieval_normalized_dcg,
message=message,
exception_type=ValueError,
kwargs_update=metric_args,
)
class TestMRR(RetrievalMetricTester):
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ['skip', 'neg', 'pos'])
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_class_metric(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
):
metric_args = {'empty_target_action': empty_target_action}
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalMRR,
sk_metric=_reciprocal_rank,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize(**_default_metric_functional_input_arguments)
def test_functional_metric(self, preds: Tensor, target: Tensor):
self.run_functional_metric_test(
preds=preds,
target=target,
metric_functional=retrieval_reciprocal_rank,
sk_metric=_reciprocal_rank,
metric_args={},
)
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_precision_cpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_cpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalMRR,
metric_functional=retrieval_reciprocal_rank,
)
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_precision_gpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_gpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalMRR,
metric_functional=retrieval_reciprocal_rank,
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_class_metric_parameters_default,
_errors_test_class_metric_parameters_no_pos_target,
))
def test_arguments_class_metric(self, indexes: Tensor, preds: Tensor,
target: Tensor, message: str,
metric_args: dict):
self.run_metric_class_arguments_test(
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalMRR,
message=message,
metric_args=metric_args,
exception_type=ValueError,
kwargs_update={},
)
@pytest.mark.parametrize(**
_errors_test_functional_metric_parameters_default)
def test_arguments_functional_metric(self, preds: Tensor, target: Tensor,
message: str, metric_args: dict):
self.run_functional_metric_arguments_test(
preds=preds,
target=target,
metric_functional=retrieval_reciprocal_rank,
message=message,
exception_type=ValueError,
kwargs_update=metric_args,
)
class TestRPrecision(RetrievalMetricTester):
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ["skip", "neg", "pos"])
@pytest.mark.parametrize(
"ignore_index", [None, 1]
) # avoid setting 0, otherwise test with all 0 targets will fail
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_class_metric(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
ignore_index: int,
):
metric_args = dict(empty_target_action=empty_target_action,
ignore_index=ignore_index)
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalRPrecision,
sk_metric=_r_precision,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ["skip", "neg", "pos"])
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_ignore_index)
def test_class_metric_ignore_index(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
):
metric_args = dict(empty_target_action=empty_target_action,
ignore_index=-100)
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalRPrecision,
sk_metric=_r_precision,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize(**_default_metric_functional_input_arguments)
def test_functional_metric(self, preds: Tensor, target: Tensor):
self.run_functional_metric_test(
preds=preds,
target=target,
metric_functional=retrieval_r_precision,
sk_metric=_r_precision,
metric_args={},
)
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_precision_cpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_cpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalRPrecision,
metric_functional=retrieval_r_precision,
)
@pytest.mark.parametrize(**_default_metric_class_input_arguments)
def test_precision_gpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_gpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalRPrecision,
metric_functional=retrieval_r_precision,
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_class_metric_parameters_default,
_errors_test_class_metric_parameters_no_pos_target,
))
def test_arguments_class_metric(self, indexes: Tensor, preds: Tensor,
target: Tensor, message: str,
metric_args: dict):
self.run_metric_class_arguments_test(
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalRPrecision,
message=message,
metric_args=metric_args,
exception_type=ValueError,
kwargs_update={},
)
@pytest.mark.parametrize(**
_errors_test_functional_metric_parameters_default)
def test_arguments_functional_metric(self, preds: Tensor, target: Tensor,
message: str, metric_args: dict):
self.run_functional_metric_arguments_test(
preds=preds,
target=target,
metric_functional=retrieval_r_precision,
message=message,
exception_type=ValueError,
kwargs_update=metric_args,
)
class TestNDCG(RetrievalMetricTester):
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ["skip", "neg", "pos"])
@pytest.mark.parametrize(
"ignore_index", [None, 3]
) # avoid setting 0, otherwise test with all 0 targets will fail
@pytest.mark.parametrize("k", [None, 1, 4, 10])
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_class_metric(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
ignore_index: int,
k: int,
):
metric_args = dict(empty_target_action=empty_target_action,
k=k,
ignore_index=ignore_index)
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalNormalizedDCG,
sk_metric=_ndcg_at_k,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
@pytest.mark.parametrize("empty_target_action", ["skip", "neg", "pos"])
@pytest.mark.parametrize("k", [None, 1, 4, 10])
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_ignore_index)
def test_class_metric_ignore_index(
self,
ddp: bool,
indexes: Tensor,
preds: Tensor,
target: Tensor,
dist_sync_on_step: bool,
empty_target_action: str,
k: int,
):
metric_args = dict(empty_target_action=empty_target_action,
k=k,
ignore_index=-100)
self.run_class_metric_test(
ddp=ddp,
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalNormalizedDCG,
sk_metric=_ndcg_at_k,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
@pytest.mark.parametrize(
**_default_metric_functional_input_arguments_with_non_binary_target)
@pytest.mark.parametrize("k", [None, 1, 4, 10])
def test_functional_metric(self, preds: Tensor, target: Tensor, k: int):
self.run_functional_metric_test(
preds=preds,
target=target,
metric_functional=retrieval_normalized_dcg,
sk_metric=_ndcg_at_k,
metric_args={},
k=k,
)
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_precision_cpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_cpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalNormalizedDCG,
metric_functional=retrieval_normalized_dcg,
)
@pytest.mark.parametrize(
**_default_metric_class_input_arguments_with_non_binary_target)
def test_precision_gpu(self, indexes: Tensor, preds: Tensor,
target: Tensor):
self.run_precision_test_gpu(
indexes=indexes,
preds=preds,
target=target,
metric_module=RetrievalNormalizedDCG,
metric_functional=retrieval_normalized_dcg,
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_class_metric_parameters_with_nonbinary,
_errors_test_class_metric_parameters_k,
))
def test_arguments_class_metric(self, indexes: Tensor, preds: Tensor,
target: Tensor, message: str,
metric_args: dict):
if target.is_floating_point():
pytest.skip("NDCG metric works with float target input")
self.run_metric_class_arguments_test(
indexes=indexes,
preds=preds,
target=target,
metric_class=RetrievalNormalizedDCG,
message=message,
metric_args=metric_args,
exception_type=ValueError,
kwargs_update={},
)
@pytest.mark.parametrize(**_concat_tests(
_errors_test_functional_metric_parameters_with_nonbinary,
_errors_test_functional_metric_parameters_k,
))
def test_arguments_functional_metric(self, preds: Tensor, target: Tensor,
message: str, metric_args: dict):
if target.is_floating_point():
pytest.skip("NDCG metric works with float target input")
self.run_functional_metric_arguments_test(
preds=preds,
target=target,
metric_functional=retrieval_normalized_dcg,
message=message,
exception_type=ValueError,
kwargs_update=metric_args,
)
