Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpec

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Overview

Represents specification of a Study.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1StudySpec

Returns a new instance of GoogleCloudAiplatformV1beta1StudySpec.



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27621

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#algorithm ⇒ String

The search algorithm specified for the Study. Corresponds to the JSON property algorithm

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27548

def algorithm
  @algorithm
end

#convex_automated_stopping_spec ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec

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. Corresponds to the JSON property convexAutomatedStoppingSpec

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27561

def convex_automated_stopping_spec
  @convex_automated_stopping_spec
end

#convex_stop_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig

Configuration for ConvexStopPolicy. Corresponds to the JSON property convexStopConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27566

def convex_stop_config
  @convex_stop_config
end

#decay_curve_stopping_spec ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec

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. Corresponds to the JSON property decayCurveStoppingSpec

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27575

def decay_curve_stopping_spec
  @decay_curve_stopping_spec
end

#measurement_selection_type ⇒ String

Describe which measurement selection type will be used Corresponds to the JSON property measurementSelectionType

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27580

def measurement_selection_type
  @measurement_selection_type
end

#median_automated_stopping_spec ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec

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. Corresponds to the JSON property medianAutomatedStoppingSpec

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27589

def median_automated_stopping_spec
  @median_automated_stopping_spec
end

#metrics ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecMetricSpec>

Required. Metric specs for the Study. Corresponds to the JSON property metrics

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27594

def metrics
  @metrics
end

#observation_noise ⇒ String

The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. Corresponds to the JSON property observationNoise

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27601

def observation_noise
  @observation_noise
end

#parameters ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecParameterSpec>

Required. The set of parameters to tune. Corresponds to the JSON property parameters

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27606

def parameters
  @parameters
end

#study_stopping_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. Corresponds to the JSON property studyStoppingConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27612

def study_stopping_config
  @study_stopping_config
end

#transfer_learning_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. Corresponds to the JSON property transferLearningConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27619

def transfer_learning_config
  @transfer_learning_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 27626

def update!(**args)
  @algorithm = args[:algorithm] if args.key?(:algorithm)
  @convex_automated_stopping_spec = args[:convex_automated_stopping_spec] if args.key?(:convex_automated_stopping_spec)
  @convex_stop_config = args[:convex_stop_config] if args.key?(:convex_stop_config)
  @decay_curve_stopping_spec = args[:decay_curve_stopping_spec] if args.key?(:decay_curve_stopping_spec)
  @measurement_selection_type = args[:measurement_selection_type] if args.key?(:measurement_selection_type)
  @median_automated_stopping_spec = args[:median_automated_stopping_spec] if args.key?(:median_automated_stopping_spec)
  @metrics = args[:metrics] if args.key?(:metrics)
  @observation_noise = args[:observation_noise] if args.key?(:observation_noise)
  @parameters = args[:parameters] if args.key?(:parameters)
  @study_stopping_config = args[:study_stopping_config] if args.key?(:study_stopping_config)
  @transfer_learning_config = args[:transfer_learning_config] if args.key?(:transfer_learning_config)
end