def mli_test_weights(self, weights: Weights) -> ProposedWeights:
current_weights = self.mli_get_current_weights()
self.set_weights(weights)
vote_score = self.test(self.xg_vote)
test_score = self.test(self.xg_test)
vote = self.vote_score >= vote_score
self.set_weights(current_weights)
return ProposedWeights(weights=weights,
vote_score=vote_score,
test_score=test_score,
vote=vote)
def mli_test_weights(self, weights: Weights) -> ProposedWeights:
current_weights = self.mli_get_current_weights()
self.set_weights(weights)
vote_score = self.test(self.train_data, self.train_labels)
test_score = self.test(self.test_data, self.test_labels)
vote = self.vote_score <= vote_score
self.set_weights(current_weights)
return ProposedWeights(weights=weights,
vote_score=vote_score,
test_score=test_score,
vote=vote)
def mli_test_weights(self, weights: Weights = None) -> ProposedWeights:
try:
if weights:
response = self.stub.TestWeights(
weights_to_iterator(weights, encode=False))
else:
raise Exception(
"mli_test_weights(None) is not currently supported")
return ProposedWeights(weights=weights,
vote_score=response.vote_score,
test_score=response.test_score,
vote=response.vote)
except grpc.RpcError as ex:
_logger.exception(f"Failed to test_model: {ex}")
raise ConnectionError(f"GRPC error: {ex}")
def mli_test_weights(self, weights) -> ProposedWeights:
if weights.weights > self.current_value:
test_score = 1.0
vote_score = 1.0
vote = True
elif weights == self.current_value:
test_score = 0.5
vote_score = 0.5
vote = False
else:
test_score = 0.0
vote_score = 0.0
vote = False
result = ProposedWeights(weights=weights,
vote_score=vote_score,
test_score=test_score,
vote=vote)
return result
def mli_test_weights(self, weights: Weights) -> ProposedWeights:
"""
Tests given weights on training and test set and returns weights with score values
:param weights: Weights to be tested
:return: ProposedWeights - Weights with vote and test score
"""
current_weights = self.mli_get_current_weights()
self.set_weights(weights)
vote_score = self.test(self.vote_data, self.vote_labels)
test_score = self.test(self.test_data, self.test_labels)
vote = self.vote_score <= vote_score
self.set_weights(current_weights)
return ProposedWeights(weights=weights,
vote_score=vote_score,
test_score=test_score,
vote=vote)
def mli_test_weights(self, weights: Weights) -> ProposedWeights:
"""
Tests given weights on training and test set and returns weights with score values
:param weights: Weights to be tested
:return: ProposedWeights - Weights with vote and test score
"""
current_weights = self.mli_get_current_weights()
self.set_weights(weights)
vote_score = self.test(self.train_loader)
if self.test_loader:
test_score = self.test(self.test_loader)
else:
test_score = 0
vote = self.vote(vote_score)
self.set_weights(current_weights)
return ProposedWeights(weights=weights,
vote_score=vote_score,
test_score=test_score,
vote=vote)