def test_protobuf_roundtrip():
x0 = NumberTracker()
for v in [10, 11, 13]:
x0.track(v)
msg = x0.to_protobuf()
roundtrip = NumberTracker.from_protobuf(msg)
assert x0.ints.count == roundtrip.ints.count
assert x0.floats.count == roundtrip.floats.count
assert x0.histogram.get_n() == roundtrip.histogram.get_n()
assert x0.histogram.get_min_value() == roundtrip.histogram.get_min_value()
assert x0.histogram.get_max_value() == roundtrip.histogram.get_max_value()
def to_protobuf(self, ):
"""
Convert to protobuf
Returns:
TYPE: Description
"""
return ScoreMatrixMessage(
labels=self.labels,
prediction_field=self.prediction_field,
target_field=self.target_field,
score_field=self.score_field,
scores=[
nt.to_protobuf()
if nt else NumberTracker.to_protobuf(NumberTracker())
for nt in np.ravel(self.confusion_matrix)
])
class NLPMetrics:
def __init__(self, prediction_field: str = None, target_field: str = None):
self.prediction_field = prediction_field
self.target_field = target_field
self.mer = NumberTracker()
self.wer = NumberTracker()
self.wil = NumberTracker()
def update(self, predictions: Union[List[str], str], targets: Union[List[str]], transform=None) -> None:
"""
Function adds predictions and targets computation of nlp metrics.
Args:
predictions (Union[str,List[str]]):
targets (Union[List[str],str]):
"""
if transform:
mes = jiwer.compute_measures(truth=targets, hypothesis=predictions, truth_transform=transform, hypothesis_transform=transform)
else:
mes = jiwer.compute_measures(truth=targets, hypothesis=predictions)
self.mer.track(mes["mer"])
self.wer.track(mes["wer"])
self.wil.track(mes["wil"])
def merge(self, other: "NLPMetrics") -> "NLPMetrics":
"""
Merge two seperate nlp metrics
Args:
other : nlp metrics to merge with self
Returns:
NLPMetrics: merged nlp metrics
"""
if other is None:
return self
merged_nlp_metrics = NLPMetrics()
merged_nlp_metrics.mer = self.mer.merge(other.mer)
merged_nlp_metrics.wer = self.wer.merge(other.wer)
merged_nlp_metrics.wil = self.wil.merge(other.wil)
return merged_nlp_metrics
return merged_nlp_metrics
def to_protobuf(
self,
) -> NLPMetricsMessage:
"""
Convert to protobuf
Returns:
TYPE: Protobuf Message
"""
return NLPMetricsMessage(
mer=self.mer.to_protobuf(),
wer=self.wer.to_protobuf(),
wil=self.wil.to_protobuf(),
)
@classmethod
def from_protobuf(
cls: "NLPMetrics",
message: NLPMetricsMessage,
):
nlp_met = NLPMetrics()
nlp_met.wer = NumberTracker.from_protobuf(message.wer)
nlp_met.wil = NumberTracker.from_protobuf(message.wil)
nlp_met.mer = NumberTracker.from_protobuf(message.mer)
return nlp_met