Class StudySpec.ConvexStopConfig.Builder (3.45.0)

public static final class StudySpec.ConvexStopConfig.Builder extends GeneratedMessageV3.Builder<StudySpec.ConvexStopConfig.Builder> implements StudySpec.ConvexStopConfigOrBuilder

Configuration for ConvexStopPolicy.

Protobuf type google.cloud.aiplatform.v1beta1.StudySpec.ConvexStopConfig

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public StudySpec.ConvexStopConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

build()

public StudySpec.ConvexStopConfig build()
Returns
Type Description
StudySpec.ConvexStopConfig

buildPartial()

public StudySpec.ConvexStopConfig buildPartial()
Returns
Type Description
StudySpec.ConvexStopConfig

clear()

public StudySpec.ConvexStopConfig.Builder clear()
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

clearAutoregressiveOrder()

public StudySpec.ConvexStopConfig.Builder clearAutoregressiveOrder()

The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.

int64 autoregressive_order = 3;

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public StudySpec.ConvexStopConfig.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Name Description
field FieldDescriptor
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

clearLearningRateParameterName()

public StudySpec.ConvexStopConfig.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
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

clearMaxNumSteps()

public StudySpec.ConvexStopConfig.Builder clearMaxNumSteps()

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. When use_steps is false, this field is set to the maximum elapsed seconds.

int64 max_num_steps = 1;

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

clearMinNumSteps()

public StudySpec.ConvexStopConfig.Builder clearMinNumSteps()

Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps 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_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.

int64 min_num_steps = 2;

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public StudySpec.ConvexStopConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Name Description
oneof OneofDescriptor
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

clearUseSeconds()

public StudySpec.ConvexStopConfig.Builder clearUseSeconds()

This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==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_seconds = 5;

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

clone()

public StudySpec.ConvexStopConfig.Builder clone()
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

getAutoregressiveOrder()

public long getAutoregressiveOrder()

The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.

int64 autoregressive_order = 3;

Returns
Type Description
long

The autoregressiveOrder.

getDefaultInstanceForType()

public StudySpec.ConvexStopConfig getDefaultInstanceForType()
Returns
Type Description
StudySpec.ConvexStopConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
Type Description
Descriptor
Overrides

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;

Returns
Type Description
String

The learningRateParameterName.

getLearningRateParameterNameBytes()

public 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;

Returns
Type Description
ByteString

The bytes for learningRateParameterName.

getMaxNumSteps()

public long getMaxNumSteps()

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. When use_steps is false, this field is set to the maximum elapsed seconds.

int64 max_num_steps = 1;

Returns
Type Description
long

The maxNumSteps.

getMinNumSteps()

public long getMinNumSteps()

Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps 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_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.

int64 min_num_steps = 2;

Returns
Type Description
long

The minNumSteps.

getUseSeconds()

public boolean getUseSeconds()

This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==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_seconds = 5;

Returns
Type Description
boolean

The useSeconds.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

mergeFrom(StudySpec.ConvexStopConfig other)

public StudySpec.ConvexStopConfig.Builder mergeFrom(StudySpec.ConvexStopConfig other)
Parameter
Name Description
other StudySpec.ConvexStopConfig
Returns
Type Description
StudySpec.ConvexStopConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public StudySpec.ConvexStopConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides
Exceptions
Type Description
IOException

mergeFrom(Message other)

public StudySpec.ConvexStopConfig.Builder mergeFrom(Message other)
Parameter
Name Description
other Message
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

mergeUnknownFields(UnknownFieldSet unknownFields)

public final StudySpec.ConvexStopConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

setAutoregressiveOrder(long value)

public StudySpec.ConvexStopConfig.Builder setAutoregressiveOrder(long value)

The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.

int64 autoregressive_order = 3;

Parameter
Name Description
value long

The autoregressiveOrder to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public StudySpec.ConvexStopConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Name Description
field FieldDescriptor
value Object
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

setLearningRateParameterName(String value)

public StudySpec.ConvexStopConfig.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;

Parameter
Name Description
value String

The learningRateParameterName to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

setLearningRateParameterNameBytes(ByteString value)

public StudySpec.ConvexStopConfig.Builder setLearningRateParameterNameBytes(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;

Parameter
Name Description
value ByteString

The bytes for learningRateParameterName to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

setMaxNumSteps(long value)

public StudySpec.ConvexStopConfig.Builder setMaxNumSteps(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. When use_steps is false, this field is set to the maximum elapsed seconds.

int64 max_num_steps = 1;

Parameter
Name Description
value long

The maxNumSteps to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

setMinNumSteps(long value)

public StudySpec.ConvexStopConfig.Builder setMinNumSteps(long value)

Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps 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_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.

int64 min_num_steps = 2;

Parameter
Name Description
value long

The minNumSteps to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public StudySpec.ConvexStopConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Name Description
field FieldDescriptor
index int
value Object
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

setUnknownFields(UnknownFieldSet unknownFields)

public final StudySpec.ConvexStopConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
StudySpec.ConvexStopConfig.Builder
Overrides

setUseSeconds(boolean value)

public StudySpec.ConvexStopConfig.Builder setUseSeconds(boolean value)

This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==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_seconds = 5;

Parameter
Name Description
value boolean

The useSeconds to set.

Returns
Type Description
StudySpec.ConvexStopConfig.Builder

This builder for chaining.