Module gapi_grpc::google::cloud::aiplatform::v1beta1::study_spec [−][src]
Modules
| metric_spec | |
| parameter_spec |
Structs
| ConvexStopConfig | Configuration for ConvexStopPolicy. |
| DecayCurveAutomatedStoppingSpec | The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. |
| MedianAutomatedStoppingSpec | The median automated stopping rule stops a pending Trial if the Trial’s best objective_value is strictly below the median ‘performance’ of all completed Trials reported up to the Trial’s last measurement. Currently, ‘performance’ refers to the running average of the objective values reported by the Trial in each measurement. |
| MetricSpec | Represents a metric to optimize. |
| ParameterSpec | Represents a single parameter to optimize. |
Enums
| Algorithm | The available search algorithms for the Study. |
| AutomatedStoppingSpec | |
| MeasurementSelectionType | This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose LAST_MEASUREMENT. On the other hand, if you’re in a situation where your system can “over-train” and you expect the performance to get better for a while but then start declining, choose BEST_MEASUREMENT. B) Are your measurements significantly noisy and/or irreproducible? If so, BEST_MEASUREMENT will tend to be over-optimistic, and it may be better to choose LAST_MEASUREMENT. If both or neither of (A) and (B) apply, it doesn’t matter which selection type is chosen. |
| ObservationNoise | Describes the noise level of the repeated observations. |