def predict(self,
parameter_values_pandas_frame,
t=None,
context_values_pandas_frame=None): # pylint: disable=unused-argument
# TODO: make this streaming and/or using arrow.
#
if context_values_pandas_frame is not None:
raise NotImplementedError(
"Context not currently supported on remote optimizers")
feature_values_dict = parameter_values_pandas_frame.to_dict(
orient='list')
prediction_request = OptimizerService_pb2.PredictRequest(
OptimizerHandle=self.optimizer_handle,
Features=OptimizerService_pb2.Features(
FeaturesJsonString=json.dumps(feature_values_dict)))
prediction_response = self._optimizer_stub.Predict(prediction_request)
# To be compliant with the OptimizerBase, we need to recover a single Prediction object and return it.
#
objective_predictions_pb2 = prediction_response.ObjectivePredictions
assert len(objective_predictions_pb2) == 1
only_prediction_pb2 = objective_predictions_pb2[0]
objective_name = only_prediction_pb2.ObjectiveName
valid_predictions_df = Prediction.dataframe_from_json(
only_prediction_pb2.PredictionDataFrameJsonString)
prediction = Prediction.create_prediction_from_dataframe(
objective_name=objective_name, dataframe=valid_predictions_df)
return prediction
def register(self, feature_values_pandas_frame, target_values_pandas_frame):
register_request = OptimizerService_pb2.RegisterObservationsRequest(
OptimizerHandle=self.optimizer_handle,
Observations=OptimizerService_pb2.Observations(
Features=OptimizerService_pb2.Features(FeaturesJsonString=feature_values_pandas_frame.to_json(orient='index', double_precision=15)),
ObjectiveValues=OptimizerService_pb2.ObjectiveValues(
ObjectiveValuesJsonString=target_values_pandas_frame.to_json(orient='index', double_precision=15)
)
)
)
self._optimizer_stub.RegisterObservations(register_request)
def register(self, parameter_values_pandas_frame, target_values_pandas_frame, context_values_pandas_frame=None):
if context_values_pandas_frame is not None:
raise NotImplementedError("Context not currently supported on remote optimizers")
feature_values_pandas_frame = parameter_values_pandas_frame
register_request = OptimizerService_pb2.RegisterObservationsRequest(
OptimizerHandle=self.optimizer_handle_for_optimizer_service,
Observations=OptimizerService_pb2.Observations(
Features=OptimizerService_pb2.Features(FeaturesJsonString=feature_values_pandas_frame.to_json(orient='index', double_precision=15)),
ObjectiveValues=OptimizerService_pb2.ObjectiveValues(
ObjectiveValuesJsonString=target_values_pandas_frame.to_json(orient='index', double_precision=15)
)
)
)
self._optimizer_stub.RegisterObservations(register_request) # TODO: we should be using the optimizer_stub for this.
def register(self, feature_values_pandas_frame,
target_values_pandas_frame): # pylint: disable=unused-argument
# TODO: implement RegisterObservations <- plural
#
features_dicts_per_record = feature_values_pandas_frame.to_dict(
orient='records')
objectives_dicts_per_record = target_values_pandas_frame.to_dict(
orient='records')
# TODO: Either implement streaming or arrow flight or batch.
#
for feature_dict, objective_dict in zip(features_dicts_per_record,
objectives_dicts_per_record):
register_request = OptimizerService_pb2.RegisterObservationRequest(
OptimizerHandle=self.optimizer_handle,
Observation=OptimizerService_pb2.Observation(
Features=OptimizerService_pb2.Features(
FeaturesJsonString=json.dumps(feature_dict)),
ObjectiveValues=OptimizerService_pb2.ObjectiveValues(
ObjectiveValuesJsonString=json.dumps(objective_dict))))
self._optimizer_stub.RegisterObservation(register_request)
def predict(self, feature_values_pandas_frame, t=None): # pylint: disable=unused-argument
# TODO: make this streaming and/or using arrow.
#
feature_values_dict = feature_values_pandas_frame.to_dict(
orient='list')
prediction_request = OptimizerService_pb2.PredictRequest(
OptimizerHandle=self.optimizer_handle,
Features=OptimizerService_pb2.Features(
FeaturesJsonString=json.dumps(feature_values_dict)))
prediction_response = self._optimizer_stub.Predict(prediction_request)
# To be compliant with the OptimizerInterface, we need to recover a single Prediction object and return it.
#
objective_predictions_pb2 = prediction_response.ObjectivePredictions
assert len(objective_predictions_pb2) == 1
only_prediction_pb2 = objective_predictions_pb2[0]
objective_name = only_prediction_pb2.ObjectiveName
valid_predictions_df = Prediction.dataframe_from_json(
only_prediction_pb2.PredictionDataFrameJsonString)
prediction = Prediction.create_prediction_from_dataframe(
objective_name=objective_name, dataframe=valid_predictions_df)
prediction.add_invalid_rows_at_missing_indices(
desired_index=feature_values_pandas_frame.index)
return prediction