def _cross_validate(
self,
search_space: SearchSpace,
obs_feats: List[ObservationFeatures],
obs_data: List[ObservationData],
cv_test_points: List[ObservationFeatures],
) -> List[ObservationData]:
"""Make predictions at cv_test_points using only the data in obs_feats
and obs_data.
"""
Xs_train, Ys_train, Yvars_train, candidate_metadata = _convert_observations(
observation_data=obs_data,
observation_features=obs_feats,
outcomes=self.outcomes,
parameters=self.parameters,
)
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
X_test = np.array(
[[obsf.parameters[p] for p in self.parameters] for obsf in cv_test_points]
)
# Use the model to do the cross validation
f_test, cov_test = self._model_cross_validate(
Xs_train=Xs_train,
Ys_train=Ys_train,
Yvars_train=Yvars_train,
X_test=X_test,
search_space_digest=search_space_digest,
metric_names=self.outcomes,
)
# Convert array back to ObservationData
return array_to_observation_data(f=f_test, cov=cov_test, outcomes=self.outcomes)
def _cross_validate(
self,
search_space: SearchSpace,
obs_feats: List[ObservationFeatures],
obs_data: List[ObservationData],
cv_test_points: List[ObservationFeatures],
) -> List[ObservationData]:
"""Make predictions at cv_test_points using only the data in obs_feats
and obs_data.
"""
Xs_train, Ys_train, Yvars_train = _convert_observations(
observation_data=obs_data,
observation_features=obs_feats,
outcomes=self.outcomes,
parameters=self.parameters,
)
X_test = [[obsf.parameters[param] for param in self.parameters]
for obsf in cv_test_points]
# Use the model to do the cross validation
f_test, cov_test = self.model.cross_validate(Xs_train=Xs_train,
Ys_train=Ys_train,
Yvars_train=Yvars_train,
X_test=X_test)
# Convert array back to ObservationData
return array_to_observation_data(f=f_test,
cov=cov_test,
outcomes=self.outcomes)
def _predict(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationData]:
X = observation_features_to_array(self.parameters, observation_features)
f, cov = self._model_predict(X=X)
# Convert arrays to observations
return array_to_observation_data(f=f, cov=cov, outcomes=self.outcomes)
def _predict(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationData]:
# Convert observations to array
X = [[of.parameters[param] for param in self.parameters]
for of in observation_features]
f, cov = self.model.predict(X=X)
# Convert arrays to observations
return array_to_observation_data(f=f, cov=cov, outcomes=self.outcomes)
