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StudySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Represents specification of a Study. .. attribute:: decay_curve_stopping_spec
The automated early stopping spec using decay curve rule.
:type: google.cloud.aiplatform_v1beta1.types.StudySpec.DecayCurveAutomatedStoppingSpec
Attributes
Name | Description |
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 > StudySpecClasses
Algorithm
Algorithm(value)
The available search algorithms for the Study.
ConvexStopConfig
ConvexStopConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration for ConvexStopPolicy. .. attribute:: max_num_steps
Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
:type: int
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. .. attribute:: metric_id
Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
:type: str
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. .. attribute:: double_value_spec
The value spec for a 'DOUBLE' parameter.
:type: google.cloud.aiplatform_v1beta1.types.StudySpec.ParameterSpec.DoubleValueSpec