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import pickle
import tempfile
import time
from multiprocessing import Pool
from unittest import TestCase
import pytest
from datasets.features import Features, Sequence, Value
from datasets.metric import Metric, MetricInfo
from .utils import require_tf, require_torch
class DummyMetric(Metric):
def _info(self):
return MetricInfo(
description="dummy metric for tests",
citation="insert citation here",
features=Features({"predictions": Value("int64"), "references": Value("int64")}),
)
def _compute(self, predictions, references):
return (
{
"accuracy": sum(i == j for i, j in zip(predictions, references)) / len(predictions),
"set_equality": set(predictions) == set(references),
}
if predictions
else {}
)
@classmethod
def predictions_and_references(cls):
return ([1, 2, 3, 4], [1, 2, 4, 3])
@classmethod
def expected_results(cls):
return {"accuracy": 0.5, "set_equality": True}
@classmethod
def other_predictions_and_references(cls):
return ([1, 3, 4, 5], [1, 2, 3, 4])
@classmethod
def other_expected_results(cls):
return {"accuracy": 0.25, "set_equality": False}
@classmethod
def distributed_predictions_and_references(cls):
return ([1, 2, 3, 4], [1, 2, 3, 4]), ([1, 2, 4, 5], [1, 2, 3, 4])
@classmethod
def distributed_expected_results(cls):
return {"accuracy": 0.75, "set_equality": False}
@classmethod
def separate_predictions_and_references(cls):
return ([1, 2, 3, 4], [1, 2, 3, 4]), ([1, 2, 4, 5], [1, 2, 3, 4])
@classmethod
def separate_expected_results(cls):
return [{"accuracy": 1.0, "set_equality": True}, {"accuracy": 0.5, "set_equality": False}]
def properly_del_metric(metric):
"""properly delete a metric on windows if the process is killed during multiprocessing"""
if metric is not None:
if metric.filelock is not None:
metric.filelock.release()
if metric.rendez_vous_lock is not None:
metric.rendez_vous_lock.release()
del metric.writer
del metric.data
del metric
def metric_compute(arg):
"""Thread worker function for distributed evaluation testing.
On base level to be pickable.
"""
metric = None
try:
num_process, process_id, preds, refs, exp_id, cache_dir, wait = arg
metric = DummyMetric(
num_process=num_process, process_id=process_id, experiment_id=exp_id, cache_dir=cache_dir, timeout=5
)
time.sleep(wait)
results = metric.compute(predictions=preds, references=refs)
return results
finally:
properly_del_metric(metric)
def metric_add_batch_and_compute(arg):
"""Thread worker function for distributed evaluation testing.
On base level to be pickable.
"""
metric = None
try:
num_process, process_id, preds, refs, exp_id, cache_dir, wait = arg
metric = DummyMetric(
num_process=num_process, process_id=process_id, experiment_id=exp_id, cache_dir=cache_dir, timeout=5
)
metric.add_batch(predictions=preds, references=refs)
time.sleep(wait)
results = metric.compute()
return results
finally:
properly_del_metric(metric)
def metric_add_and_compute(arg):
"""Thread worker function for distributed evaluation testing.
On base level to be pickable.
"""
metric = None
try:
num_process, process_id, preds, refs, exp_id, cache_dir, wait = arg
metric = DummyMetric(
num_process=num_process, process_id=process_id, experiment_id=exp_id, cache_dir=cache_dir, timeout=5
)
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
time.sleep(wait)
results = metric.compute()
return results
finally:
properly_del_metric(metric)
@pytest.mark.filterwarnings("ignore:Metric is deprecated:FutureWarning")
class TestMetric(TestCase):
def test_dummy_metric(self):
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
metric = DummyMetric(experiment_id="test_dummy_metric")
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
metric = DummyMetric(experiment_id="test_dummy_metric")
metric.add_batch(predictions=preds, references=refs)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(experiment_id="test_dummy_metric")
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
self.assertDictEqual(expected_results, metric.compute())
del metric
# With keep_in_memory
metric = DummyMetric(keep_in_memory=True, experiment_id="test_dummy_metric")
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
metric = DummyMetric(keep_in_memory=True, experiment_id="test_dummy_metric")
metric.add_batch(predictions=preds, references=refs)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(keep_in_memory=True, experiment_id="test_dummy_metric")
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(keep_in_memory=True, experiment_id="test_dummy_metric")
self.assertDictEqual({}, metric.compute(predictions=[], references=[]))
del metric
metric = DummyMetric(keep_in_memory=True, experiment_id="test_dummy_metric")
with self.assertRaisesRegex(ValueError, "Mismatch in the number"):
metric.add_batch(predictions=[1, 2, 3], references=[1, 2, 3, 4])
del metric
def test_metric_with_cache_dir(self):
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
with tempfile.TemporaryDirectory() as tmp_dir:
metric = DummyMetric(experiment_id="test_dummy_metric", cache_dir=tmp_dir)
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
def test_concurrent_metrics(self):
preds, refs = DummyMetric.predictions_and_references()
other_preds, other_refs = DummyMetric.other_predictions_and_references()
expected_results = DummyMetric.expected_results()
other_expected_results = DummyMetric.other_expected_results()
metric = DummyMetric(experiment_id="test_concurrent_metrics")
other_metric = DummyMetric(
experiment_id="test_concurrent_metrics",
)
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
self.assertDictEqual(
other_expected_results, other_metric.compute(predictions=other_preds, references=other_refs)
)
del metric, other_metric
metric = DummyMetric(
experiment_id="test_concurrent_metrics",
)
other_metric = DummyMetric(
experiment_id="test_concurrent_metrics",
)
metric.add_batch(predictions=preds, references=refs)
other_metric.add_batch(predictions=other_preds, references=other_refs)
self.assertDictEqual(expected_results, metric.compute())
self.assertDictEqual(other_expected_results, other_metric.compute())
for pred, ref, other_pred, other_ref in zip(preds, refs, other_preds, other_refs):
metric.add(prediction=pred, reference=ref)
other_metric.add(prediction=other_pred, reference=other_ref)
self.assertDictEqual(expected_results, metric.compute())
self.assertDictEqual(other_expected_results, other_metric.compute())
del metric, other_metric
# With keep_in_memory
metric = DummyMetric(experiment_id="test_concurrent_metrics", keep_in_memory=True)
other_metric = DummyMetric(experiment_id="test_concurrent_metrics", keep_in_memory=True)
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
self.assertDictEqual(
other_expected_results, other_metric.compute(predictions=other_preds, references=other_refs)
)
metric = DummyMetric(experiment_id="test_concurrent_metrics", keep_in_memory=True)
other_metric = DummyMetric(experiment_id="test_concurrent_metrics", keep_in_memory=True)
metric.add_batch(predictions=preds, references=refs)
other_metric.add_batch(predictions=other_preds, references=other_refs)
self.assertDictEqual(expected_results, metric.compute())
self.assertDictEqual(other_expected_results, other_metric.compute())
for pred, ref, other_pred, other_ref in zip(preds, refs, other_preds, other_refs):
metric.add(prediction=pred, reference=ref)
other_metric.add(prediction=other_pred, reference=other_ref)
self.assertDictEqual(expected_results, metric.compute())
self.assertDictEqual(other_expected_results, other_metric.compute())
del metric, other_metric
def test_separate_experiments_in_parallel(self):
with tempfile.TemporaryDirectory() as tmp_dir:
(preds_0, refs_0), (preds_1, refs_1) = DummyMetric.separate_predictions_and_references()
expected_results = DummyMetric.separate_expected_results()
pool = Pool(processes=4)
results = pool.map(
metric_compute,
[
(1, 0, preds_0, refs_0, None, tmp_dir, 0),
(1, 0, preds_1, refs_1, None, tmp_dir, 0),
],
)
self.assertDictEqual(expected_results[0], results[0])
self.assertDictEqual(expected_results[1], results[1])
del results
# more than one sec of waiting so that the second metric has to sample a new hashing name
results = pool.map(
metric_compute,
[
(1, 0, preds_0, refs_0, None, tmp_dir, 2),
(1, 0, preds_1, refs_1, None, tmp_dir, 2),
],
)
self.assertDictEqual(expected_results[0], results[0])
self.assertDictEqual(expected_results[1], results[1])
del results
results = pool.map(
metric_add_and_compute,
[
(1, 0, preds_0, refs_0, None, tmp_dir, 0),
(1, 0, preds_1, refs_1, None, tmp_dir, 0),
],
)
self.assertDictEqual(expected_results[0], results[0])
self.assertDictEqual(expected_results[1], results[1])
del results
results = pool.map(
metric_add_batch_and_compute,
[
(1, 0, preds_0, refs_0, None, tmp_dir, 0),
(1, 0, preds_1, refs_1, None, tmp_dir, 0),
],
)
self.assertDictEqual(expected_results[0], results[0])
self.assertDictEqual(expected_results[1], results[1])
del results
def test_distributed_metrics(self):
with tempfile.TemporaryDirectory() as tmp_dir:
(preds_0, refs_0), (preds_1, refs_1) = DummyMetric.distributed_predictions_and_references()
expected_results = DummyMetric.distributed_expected_results()
pool = Pool(processes=4)
results = pool.map(
metric_compute,
[
(2, 0, preds_0, refs_0, "test_distributed_metrics_0", tmp_dir, 0),
(2, 1, preds_1, refs_1, "test_distributed_metrics_0", tmp_dir, 0.5),
],
)
self.assertDictEqual(expected_results, results[0])
self.assertIsNone(results[1])
del results
results = pool.map(
metric_compute,
[
(2, 0, preds_0, refs_0, "test_distributed_metrics_0", tmp_dir, 0.5),
(2, 1, preds_1, refs_1, "test_distributed_metrics_0", tmp_dir, 0),
],
)
self.assertDictEqual(expected_results, results[0])
self.assertIsNone(results[1])
del results
results = pool.map(
metric_add_and_compute,
[
(2, 0, preds_0, refs_0, "test_distributed_metrics_1", tmp_dir, 0),
(2, 1, preds_1, refs_1, "test_distributed_metrics_1", tmp_dir, 0),
],
)
self.assertDictEqual(expected_results, results[0])
self.assertIsNone(results[1])
del results
results = pool.map(
metric_add_batch_and_compute,
[
(2, 0, preds_0, refs_0, "test_distributed_metrics_2", tmp_dir, 0),
(2, 1, preds_1, refs_1, "test_distributed_metrics_2", tmp_dir, 0),
],
)
self.assertDictEqual(expected_results, results[0])
self.assertIsNone(results[1])
del results
# To use several distributed metrics on the same local file system, need to specify an experiment_id
try:
results = pool.map(
metric_add_and_compute,
[
(2, 0, preds_0, refs_0, "test_distributed_metrics_3", tmp_dir, 0),
(2, 1, preds_1, refs_1, "test_distributed_metrics_3", tmp_dir, 0),
(2, 0, preds_0, refs_0, "test_distributed_metrics_3", tmp_dir, 0),
(2, 1, preds_1, refs_1, "test_distributed_metrics_3", tmp_dir, 0),
],
)
except ValueError:
# We are fine with either raising a ValueError or computing well the metric
# Being sure we raise the error would means making the dummy dataset bigger
# and the test longer...
pass
else:
self.assertDictEqual(expected_results, results[0])
self.assertDictEqual(expected_results, results[2])
self.assertIsNone(results[1])
self.assertIsNone(results[3])
del results
results = pool.map(
metric_add_and_compute,
[
(2, 0, preds_0, refs_0, "exp_0", tmp_dir, 0),
(2, 1, preds_1, refs_1, "exp_0", tmp_dir, 0),
(2, 0, preds_0, refs_0, "exp_1", tmp_dir, 0),
(2, 1, preds_1, refs_1, "exp_1", tmp_dir, 0),
],
)
self.assertDictEqual(expected_results, results[0])
self.assertDictEqual(expected_results, results[2])
self.assertIsNone(results[1])
self.assertIsNone(results[3])
del results
# With keep_in_memory is not allowed
with self.assertRaises(ValueError):
DummyMetric(
experiment_id="test_distributed_metrics_4",
keep_in_memory=True,
num_process=2,
process_id=0,
cache_dir=tmp_dir,
)
def test_dummy_metric_pickle(self):
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_file = os.path.join(tmp_dir, "metric.pt")
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
metric = DummyMetric(experiment_id="test_dummy_metric_pickle")
with open(tmp_file, "wb") as f:
pickle.dump(metric, f)
del metric
with open(tmp_file, "rb") as f:
metric = pickle.load(f)
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
def test_input_numpy(self):
import numpy as np
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
preds, refs = np.array(preds), np.array(refs)
metric = DummyMetric(experiment_id="test_input_numpy")
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
metric = DummyMetric(experiment_id="test_input_numpy")
metric.add_batch(predictions=preds, references=refs)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(experiment_id="test_input_numpy")
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
self.assertDictEqual(expected_results, metric.compute())
del metric
@require_torch
def test_input_torch(self):
import torch
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
preds, refs = torch.tensor(preds), torch.tensor(refs)
metric = DummyMetric(experiment_id="test_input_torch")
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
metric = DummyMetric(experiment_id="test_input_torch")
metric.add_batch(predictions=preds, references=refs)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(experiment_id="test_input_torch")
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
self.assertDictEqual(expected_results, metric.compute())
del metric
@require_tf
def test_input_tf(self):
import tensorflow as tf
preds, refs = DummyMetric.predictions_and_references()
expected_results = DummyMetric.expected_results()
preds, refs = tf.constant(preds), tf.constant(refs)
metric = DummyMetric(experiment_id="test_input_tf")
self.assertDictEqual(expected_results, metric.compute(predictions=preds, references=refs))
del metric
metric = DummyMetric(experiment_id="test_input_tf")
metric.add_batch(predictions=preds, references=refs)
self.assertDictEqual(expected_results, metric.compute())
del metric
metric = DummyMetric(experiment_id="test_input_tf")
for pred, ref in zip(preds, refs):
metric.add(prediction=pred, reference=ref)
self.assertDictEqual(expected_results, metric.compute())
del metric
class MetricWithMultiLabel(Metric):
def _info(self):
return MetricInfo(
description="dummy metric for tests",
citation="insert citation here",
features=Features(
{"predictions": Sequence(Value("int64")), "references": Sequence(Value("int64"))}
if self.config_name == "multilabel"
else {"predictions": Value("int64"), "references": Value("int64")}
),
)
def _compute(self, predictions=None, references=None):
return (
{
"accuracy": sum(i == j for i, j in zip(predictions, references)) / len(predictions),
}
if predictions
else {}
)
@pytest.mark.parametrize(
"config_name, predictions, references, expected",
[
(None, [1, 2, 3, 4], [1, 2, 4, 3], 0.5), # Multiclass: Value("int64")
(
"multilabel",
[[1, 0], [1, 0], [1, 0], [1, 0]],
[[1, 0], [0, 1], [1, 1], [0, 0]],
0.25,
), # Multilabel: Sequence(Value("int64"))
],
)
def test_metric_with_multilabel(config_name, predictions, references, expected, tmp_path):
cache_dir = tmp_path / "cache"
metric = MetricWithMultiLabel(config_name, cache_dir=cache_dir)
results = metric.compute(predictions=predictions, references=references)
assert results["accuracy"] == expected
def test_safety_checks_process_vars():
with pytest.raises(ValueError):
_ = DummyMetric(process_id=-2)
with pytest.raises(ValueError):
_ = DummyMetric(num_process=2, process_id=3)
class AccuracyWithNonStandardFeatureNames(Metric):
def _info(self):
return MetricInfo(
description="dummy metric for tests",
citation="insert citation here",
features=Features({"inputs": Value("int64"), "targets": Value("int64")}),
)
def _compute(self, inputs, targets):
return (
{
"accuracy": sum(i == j for i, j in zip(inputs, targets)) / len(targets),
}
if targets
else {}
)
@classmethod
def inputs_and_targets(cls):
return ([1, 2, 3, 4], [1, 2, 4, 3])
@classmethod
def expected_results(cls):
return {"accuracy": 0.5}
def test_metric_with_non_standard_feature_names_add(tmp_path):
cache_dir = tmp_path / "cache"
inputs, targets = AccuracyWithNonStandardFeatureNames.inputs_and_targets()
metric = AccuracyWithNonStandardFeatureNames(cache_dir=cache_dir)
for input, target in zip(inputs, targets):
metric.add(inputs=input, targets=target)
results = metric.compute()
assert results == AccuracyWithNonStandardFeatureNames.expected_results()
def test_metric_with_non_standard_feature_names_add_batch(tmp_path):
cache_dir = tmp_path / "cache"
inputs, targets = AccuracyWithNonStandardFeatureNames.inputs_and_targets()
metric = AccuracyWithNonStandardFeatureNames(cache_dir=cache_dir)
metric.add_batch(inputs=inputs, targets=targets)
results = metric.compute()
assert results == AccuracyWithNonStandardFeatureNames.expected_results()
def test_metric_with_non_standard_feature_names_compute(tmp_path):
cache_dir = tmp_path / "cache"
inputs, targets = AccuracyWithNonStandardFeatureNames.inputs_and_targets()
metric = AccuracyWithNonStandardFeatureNames(cache_dir=cache_dir)
results = metric.compute(inputs=inputs, targets=targets)
assert results == AccuracyWithNonStandardFeatureNames.expected_results()
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