def evaluate_res_to_proto(
res: typing.EvaluateRes) -> ClientMessage.EvaluateRes:
"""Serialize flower.EvaluateIns to ProtoBuf message."""
num_examples, loss, acc = res
return ClientMessage.EvaluateRes(num_examples=num_examples,
loss=loss,
accuracy=acc)
def evaluate_res_to_proto(res: typing.EvaluateRes) -> ClientMessage.EvaluateRes:
"""Serialize flower.EvaluateIns to ProtoBuf message."""
metrics_msg = None if res.metrics is None else metrics_to_proto(res.metrics)
# Legacy case, will be removed in a future release
if res.accuracy is not None:
return ClientMessage.EvaluateRes(
loss=res.loss,
num_examples=res.num_examples,
accuracy=res.accuracy, # Deprecated
metrics=metrics_msg,
)
# Forward-compatible case
return ClientMessage.EvaluateRes(
loss=res.loss,
num_examples=res.num_examples,
metrics=metrics_msg,
)
def fpe_res_to_proto(
res: Tuple[typing.EvaluateRes, typing.EvaluateRes]
) -> ClientMessage.FederatedPersonalizedEvaluateRes:
"""Serialize flower.EvaluateRes to ProtoBuf message."""
baseline, personalized = res[0], res[1]
baseline_metrics_msg = None if baseline.metrics is None else metrics_to_proto(
baseline.metrics)
personalized_metrics_msg = None if personalized.metrics is None else metrics_to_proto(
personalized.metrics)
# Legacy case, will be removed in a future release
if baseline.accuracy is not None:
baseline_fpe_res_proto = ClientMessage.EvaluateRes(
loss=baseline.loss,
num_examples=baseline.num_examples,
accuracy=baseline.accuracy, # Deprecated
metrics=baseline_metrics_msg,
)
personalized_fpe_res_proto = ClientMessage.EvaluateRes(
loss=personalized.loss,
num_examples=personalized.num_examples,
accuracy=personalized.accuracy, # Deprecated
metrics=personalized_metrics_msg,
)
return ClientMessage.FederatedPersonalizedEvaluateRes(
baseline=baseline_fpe_res_proto,
personalized=personalized_fpe_res_proto)
# Forward-compatible case
baseline_fpe_res_proto = ClientMessage.EvaluateRes(
loss=baseline.loss,
num_examples=baseline.num_examples,
metrics=baseline_metrics_msg,
)
personalized_fpe_res_proto = ClientMessage.EvaluateRes(
loss=personalized.loss,
num_examples=personalized.num_examples,
metrics=personalized_metrics_msg,
)
return ClientMessage.FederatedPersonalizedEvaluateRes(
baseline=baseline_fpe_res_proto,
personalized=personalized_fpe_res_proto)
def evaluate_res_to_proto(
res: typing.EvaluateRes) -> ClientMessage.EvaluateRes:
"""Serialize flower.EvaluateIns to ProtoBuf message."""
metrics_msg = None if res.metrics is None else metrics_to_proto(
res.metrics)
return ClientMessage.EvaluateRes(
loss=res.loss,
num_examples=res.num_examples,
accuracy=res.accuracy, # Deprecated
metrics=metrics_msg,
)
