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.



23656
23657
23658
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23656

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) 


23589
23590
23591
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23589

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:



23602
23603
23604
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23602

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:



23607
23608
23609
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23607

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:



23616
23617
23618
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23616

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) 


23621
23622
23623
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23621

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:



23630
23631
23632
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23630

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:



23635
23636
23637
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23635

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) 


23642
23643
23644
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23642

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:



23647
23648
23649
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23647

def parameters
  @parameters
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:



23654
23655
23656
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23654

def transfer_learning_config
  @transfer_learning_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



23661
23662
23663
23664
23665
23666
23667
23668
23669
23670
23671
23672
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 23661

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)
  @transfer_learning_config = args[:transfer_learning_config] if args.key?(:transfer_learning_config)
end