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StudySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Represents specification of a Study.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
Attributes
Name | Description |
decay_curve_stopping_spec |
google.cloud.aiplatform_v1beta1.types.StudySpec.DecayCurveAutomatedStoppingSpec
The automated early stopping spec using decay curve rule. This field is a member of `oneof`_ ``automated_stopping_spec``. |
median_automated_stopping_spec |
google.cloud.aiplatform_v1beta1.types.StudySpec.MedianAutomatedStoppingSpec
The automated early stopping spec using median rule. This field is a member of `oneof`_ ``automated_stopping_spec``. |
convex_stop_config |
google.cloud.aiplatform_v1beta1.types.StudySpec.ConvexStopConfig
Deprecated. The automated early stopping using convex stopping rule. This field is a member of `oneof`_ ``automated_stopping_spec``. |
convex_automated_stopping_spec |
google.cloud.aiplatform_v1beta1.types.StudySpec.ConvexAutomatedStoppingSpec
The automated early stopping spec using convex stopping rule. This field is a member of `oneof`_ ``automated_stopping_spec``. |
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 Vertex AI 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 > StudySpecClasses
Algorithm
Algorithm(value)
The available search algorithms for the Study.
ConvexAutomatedStoppingSpec
ConvexAutomatedStoppingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.
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.
This message has oneof
_ fields (mutually exclusive fields).
For each oneof, at most one member field can be set at the same time.
Setting any member of the oneof automatically clears all other
members.
.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields