Class StudySpec (0.8.0)

Version 0.8.0

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

Represents specification of a Study. .. attribute:: metrics

Required. Metric specs for the Study.

:type: Sequence[google.cloud.aiplatform_v1.types.StudySpec.MetricSpec]

Attributes

Name	Description
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 > StudySpec

Classes

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. .. 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_v1.types.StudySpec.ParameterSpec.DoubleValueSpec