Package com.google.cloud.aiplatform.v1

Class StudySpec.ConvexAutomatedStoppingSpec.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>
com.google.cloud.aiplatform.v1.StudySpec.ConvexAutomatedStoppingSpec.Builder
All Implemented Interfaces:
StudySpec.ConvexAutomatedStoppingSpecOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
StudySpec.ConvexAutomatedStoppingSpec
public static final class StudySpec.ConvexAutomatedStoppingSpec.Builder extends com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder> implements StudySpec.ConvexAutomatedStoppingSpecOrBuilder
 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.
Protobuf type google.cloud.aiplatform.v1.StudySpec.ConvexAutomatedStoppingSpec

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>

    • clear

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>

    • getDefaultInstanceForType

      public StudySpec.ConvexAutomatedStoppingSpec getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public StudySpec.ConvexAutomatedStoppingSpec build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public StudySpec.ConvexAutomatedStoppingSpec buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public StudySpec.ConvexAutomatedStoppingSpec.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>

    • mergeFrom

      public StudySpec.ConvexAutomatedStoppingSpec.Builder mergeFrom(StudySpec.ConvexAutomatedStoppingSpec other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>

    • mergeFrom

      public StudySpec.ConvexAutomatedStoppingSpec.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<StudySpec.ConvexAutomatedStoppingSpec.Builder>
      Throws:
      IOException

    • getMaxStepCount

      public long getMaxStepCount()
       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. If not defined, it will learn it from the completed trials. When
 use_steps is false, this field is set to the maximum elapsed seconds.
      int64 max_step_count = 1;
      Specified by:
      getMaxStepCount in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The maxStepCount.

    • setMaxStepCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setMaxStepCount(long value)
       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. If not defined, it will learn it from the completed trials. When
 use_steps is false, this field is set to the maximum elapsed seconds.
      int64 max_step_count = 1;
      Parameters:
      value - The maxStepCount to set.
      Returns:
      This builder for chaining.

    • clearMaxStepCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearMaxStepCount()
       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. If not defined, it will learn it from the completed trials. When
 use_steps is false, this field is set to the maximum elapsed seconds.
      int64 max_step_count = 1;
      Returns:
      This builder for chaining.

    • getMinStepCount

      public long getMinStepCount()
       Minimum number of steps for a trial to complete. Trials which do not have
 a measurement with step_count > min_step_count won't be considered for
 early stopping. It's ok to set it to 0, and a trial can be early stopped
 at any stage. By default, min_step_count is set to be one-tenth of the
 max_step_count.
 When use_elapsed_duration is true, this field is set to the minimum
 elapsed seconds.
      int64 min_step_count = 2;
      Specified by:
      getMinStepCount in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The minStepCount.

    • setMinStepCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setMinStepCount(long value)
       Minimum number of steps for a trial to complete. Trials which do not have
 a measurement with step_count > min_step_count won't be considered for
 early stopping. It's ok to set it to 0, and a trial can be early stopped
 at any stage. By default, min_step_count is set to be one-tenth of the
 max_step_count.
 When use_elapsed_duration is true, this field is set to the minimum
 elapsed seconds.
      int64 min_step_count = 2;
      Parameters:
      value - The minStepCount to set.
      Returns:
      This builder for chaining.

    • clearMinStepCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearMinStepCount()
       Minimum number of steps for a trial to complete. Trials which do not have
 a measurement with step_count > min_step_count won't be considered for
 early stopping. It's ok to set it to 0, and a trial can be early stopped
 at any stage. By default, min_step_count is set to be one-tenth of the
 max_step_count.
 When use_elapsed_duration is true, this field is set to the minimum
 elapsed seconds.
      int64 min_step_count = 2;
      Returns:
      This builder for chaining.

    • getMinMeasurementCount

      public long getMinMeasurementCount()
       The minimal number of measurements in a Trial.  Early-stopping checks
 will not trigger if less than min_measurement_count+1 completed trials or
 pending trials with less than min_measurement_count measurements. If not
 defined, the default value is 5.
      int64 min_measurement_count = 3;
      Specified by:
      getMinMeasurementCount in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The minMeasurementCount.

    • setMinMeasurementCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setMinMeasurementCount(long value)
       The minimal number of measurements in a Trial.  Early-stopping checks
 will not trigger if less than min_measurement_count+1 completed trials or
 pending trials with less than min_measurement_count measurements. If not
 defined, the default value is 5.
      int64 min_measurement_count = 3;
      Parameters:
      value - The minMeasurementCount to set.
      Returns:
      This builder for chaining.

    • clearMinMeasurementCount

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearMinMeasurementCount()
       The minimal number of measurements in a Trial.  Early-stopping checks
 will not trigger if less than min_measurement_count+1 completed trials or
 pending trials with less than min_measurement_count measurements. If not
 defined, the default value is 5.
      int64 min_measurement_count = 3;
      Returns:
      This builder for chaining.

    • getLearningRateParameterName

      public String getLearningRateParameterName()
       The hyper-parameter name used in the tuning job that stands for learning
 rate. Leave it blank if learning rate is not in a parameter in tuning.
 The learning_rate is used to estimate the objective value of the ongoing
 trial.
      string learning_rate_parameter_name = 4;
      Specified by:
      getLearningRateParameterName in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The learningRateParameterName.

    • getLearningRateParameterNameBytes

      public com.google.protobuf.ByteString getLearningRateParameterNameBytes()
       The hyper-parameter name used in the tuning job that stands for learning
 rate. Leave it blank if learning rate is not in a parameter in tuning.
 The learning_rate is used to estimate the objective value of the ongoing
 trial.
      string learning_rate_parameter_name = 4;
      Specified by:
      getLearningRateParameterNameBytes in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The bytes for learningRateParameterName.

    • setLearningRateParameterName

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setLearningRateParameterName(String value)
       The hyper-parameter name used in the tuning job that stands for learning
 rate. Leave it blank if learning rate is not in a parameter in tuning.
 The learning_rate is used to estimate the objective value of the ongoing
 trial.
      string learning_rate_parameter_name = 4;
      Parameters:
      value - The learningRateParameterName to set.
      Returns:
      This builder for chaining.

    • clearLearningRateParameterName

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearLearningRateParameterName()
       The hyper-parameter name used in the tuning job that stands for learning
 rate. Leave it blank if learning rate is not in a parameter in tuning.
 The learning_rate is used to estimate the objective value of the ongoing
 trial.
      string learning_rate_parameter_name = 4;
      Returns:
      This builder for chaining.

    • setLearningRateParameterNameBytes

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setLearningRateParameterNameBytes(com.google.protobuf.ByteString value)
       The hyper-parameter name used in the tuning job that stands for learning
 rate. Leave it blank if learning rate is not in a parameter in tuning.
 The learning_rate is used to estimate the objective value of the ongoing
 trial.
      string learning_rate_parameter_name = 4;
      Parameters:
      value - The bytes for learningRateParameterName to set.
      Returns:
      This builder for chaining.

    • getUseElapsedDuration

      public boolean getUseElapsedDuration()
       This bool determines whether or not the rule is applied based on
 elapsed_secs or steps. If use_elapsed_duration==false, the early stopping
 decision is made according to the predicted objective values according to
 the target steps. If use_elapsed_duration==true, elapsed_secs is used
 instead of steps. Also, in this case, the parameters max_num_steps and
 min_num_steps are overloaded to contain max_elapsed_seconds and
 min_elapsed_seconds.
      bool use_elapsed_duration = 5;
      Specified by:
      getUseElapsedDuration in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The useElapsedDuration.

    • setUseElapsedDuration

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setUseElapsedDuration(boolean value)
       This bool determines whether or not the rule is applied based on
 elapsed_secs or steps. If use_elapsed_duration==false, the early stopping
 decision is made according to the predicted objective values according to
 the target steps. If use_elapsed_duration==true, elapsed_secs is used
 instead of steps. Also, in this case, the parameters max_num_steps and
 min_num_steps are overloaded to contain max_elapsed_seconds and
 min_elapsed_seconds.
      bool use_elapsed_duration = 5;
      Parameters:
      value - The useElapsedDuration to set.
      Returns:
      This builder for chaining.

    • clearUseElapsedDuration

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearUseElapsedDuration()
       This bool determines whether or not the rule is applied based on
 elapsed_secs or steps. If use_elapsed_duration==false, the early stopping
 decision is made according to the predicted objective values according to
 the target steps. If use_elapsed_duration==true, elapsed_secs is used
 instead of steps. Also, in this case, the parameters max_num_steps and
 min_num_steps are overloaded to contain max_elapsed_seconds and
 min_elapsed_seconds.
      bool use_elapsed_duration = 5;
      Returns:
      This builder for chaining.

    • hasUpdateAllStoppedTrials

      public boolean hasUpdateAllStoppedTrials()
       ConvexAutomatedStoppingSpec by default only updates the trials that needs
 to be early stopped using a newly trained auto-regressive model. When
 this flag is set to True, all stopped trials from the beginning are
 potentially updated in terms of their `final_measurement`. Also, note
 that the training logic of autoregressive models is different in this
 case. Enabling this option has shown better results and this may be the
 default option in the future.
      optional bool update_all_stopped_trials = 6;
      Specified by:
      hasUpdateAllStoppedTrials in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      Whether the updateAllStoppedTrials field is set.

    • getUpdateAllStoppedTrials

      public boolean getUpdateAllStoppedTrials()
       ConvexAutomatedStoppingSpec by default only updates the trials that needs
 to be early stopped using a newly trained auto-regressive model. When
 this flag is set to True, all stopped trials from the beginning are
 potentially updated in terms of their `final_measurement`. Also, note
 that the training logic of autoregressive models is different in this
 case. Enabling this option has shown better results and this may be the
 default option in the future.
      optional bool update_all_stopped_trials = 6;
      Specified by:
      getUpdateAllStoppedTrials in interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
      Returns:
      The updateAllStoppedTrials.

    • setUpdateAllStoppedTrials

      public StudySpec.ConvexAutomatedStoppingSpec.Builder setUpdateAllStoppedTrials(boolean value)
       ConvexAutomatedStoppingSpec by default only updates the trials that needs
 to be early stopped using a newly trained auto-regressive model. When
 this flag is set to True, all stopped trials from the beginning are
 potentially updated in terms of their `final_measurement`. Also, note
 that the training logic of autoregressive models is different in this
 case. Enabling this option has shown better results and this may be the
 default option in the future.
      optional bool update_all_stopped_trials = 6;
      Parameters:
      value - The updateAllStoppedTrials to set.
      Returns:
      This builder for chaining.

    • clearUpdateAllStoppedTrials

      public StudySpec.ConvexAutomatedStoppingSpec.Builder clearUpdateAllStoppedTrials()
       ConvexAutomatedStoppingSpec by default only updates the trials that needs
 to be early stopped using a newly trained auto-regressive model. When
 this flag is set to True, all stopped trials from the beginning are
 potentially updated in terms of their `final_measurement`. Also, note
 that the training logic of autoregressive models is different in this
 case. Enabling this option has shown better results and this may be the
 default option in the future.
      optional bool update_all_stopped_trials = 6;
      Returns:
      This builder for chaining.