Class StudySpec (0.6.0)

StudySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Represents specification of a Study.

Attributes

NameDescription
decay_curve_stopping_spec google.cloud.aiplatform_v1beta1.types.StudySpec.DecayCurveAutomatedStoppingSpec
The automated early stopping spec using decay curve rule.
median_automated_stopping_spec google.cloud.aiplatform_v1beta1.types.StudySpec.MedianAutomatedStoppingSpec
The automated early stopping spec using median rule.
convex_stop_config google.cloud.aiplatform_v1beta1.types.StudySpec.ConvexStopConfig
The automated early stopping using convex stopping rule.
metrics Sequence[google.cloud.aiplatform_v1beta1.types.StudySpec.MetricSpec]
Required. Metric specs for the Study.
parameters Sequence[google.cloud.aiplatform_v1beta1.types.StudySpec.ParameterSpec]
Required. The set of parameters to tune.
algorithm google.cloud.aiplatform_v1beta1.types.StudySpec.Algorithm
The search algorithm specified for the Study.
observation_noise google.cloud.aiplatform_v1beta1.types.StudySpec.ObservationNoise
The observation noise level of the study. Currently only supported by the Vizier service. Not supported by HyperparamterTuningJob or TrainingPipeline.
measurement_selection_type google.cloud.aiplatform_v1beta1.types.StudySpec.MeasurementSelectionType
Describe which measurement selection type will be used

Inheritance

builtins.object > proto.message.Message > StudySpec

Classes

Algorithm

Algorithm(value)

The available search algorithms for the Study.

ConvexStopConfig

ConvexStopConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Configuration for ConvexStopPolicy.

DecayCurveAutomatedStoppingSpec

DecayCurveAutomatedStoppingSpec(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

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.

MeasurementSelectionType

MeasurementSelectionType(value)

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.

MedianAutomatedStoppingSpec

MedianAutomatedStoppingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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

MetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Represents a metric to optimize.

ObservationNoise

ObservationNoise(value)

Describes the noise level of the repeated observations. "Noisy" means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

ParameterSpec

ParameterSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Represents a single parameter to optimize.