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StudySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Represents specification of a Study.
Attributes
Name | Description |
metrics |
Sequence[google.cloud.aiplatform_v1.types.StudySpec.MetricSpec]
Required. Metric specs for the Study. |
parameters |
Sequence[google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec]
Required. The set of parameters to tune. |
algorithm |
google.cloud.aiplatform_v1.types.StudySpec.Algorithm
The search algorithm specified for the Study. |
observation_noise |
google.cloud.aiplatform_v1.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_v1.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.
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.
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.