Class ModelEvaluation.BiasConfig.Builder (3.52.0)

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public static final class ModelEvaluation.BiasConfig.Builder extends GeneratedMessageV3.Builder<ModelEvaluation.BiasConfig.Builder> implements ModelEvaluation.BiasConfigOrBuilder

Configuration for bias detection.

Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.BiasConfig

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ModelEvaluation.BiasConfig.Builder

Implements

ModelEvaluation.BiasConfigOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

addAllLabels(Iterable<String> values)

public ModelEvaluation.BiasConfig.Builder addAllLabels(Iterable<String> values)

Positive labels selection on the target field.

repeated string labels = 2;

Parameter
Name	Description
values	Iterable<String> The labels to add.

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder	This builder for chaining.

addLabels(String value)

public ModelEvaluation.BiasConfig.Builder addLabels(String value)

Positive labels selection on the target field.

repeated string labels = 2;

Parameter
Name	Description
value	String The labels to add.

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder	This builder for chaining.

addLabelsBytes(ByteString value)

public ModelEvaluation.BiasConfig.Builder addLabelsBytes(ByteString value)

Positive labels selection on the target field.

repeated string labels = 2;

Parameter
Name	Description
value	ByteString The bytes of the labels to add.

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder	This builder for chaining.

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ModelEvaluation.BiasConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ModelEvaluation.BiasConfig build()

Returns
Type	Description
ModelEvaluation.BiasConfig

buildPartial()

public ModelEvaluation.BiasConfig buildPartial()

Returns
Type	Description
ModelEvaluation.BiasConfig

clear()

public ModelEvaluation.BiasConfig.Builder clear()

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearBiasSlices()

public ModelEvaluation.BiasConfig.Builder clearBiasSlices()

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

clearField(Descriptors.FieldDescriptor field)

public ModelEvaluation.BiasConfig.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearLabels()

public ModelEvaluation.BiasConfig.Builder clearLabels()

Positive labels selection on the target field.

repeated string labels = 2;

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ModelEvaluation.BiasConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clone()

public ModelEvaluation.BiasConfig.Builder clone()

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getBiasSlices()

public ModelEvaluationSlice.Slice.SliceSpec getBiasSlices()

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Returns
Type	Description
ModelEvaluationSlice.Slice.SliceSpec	The biasSlices.

getBiasSlicesBuilder()

public ModelEvaluationSlice.Slice.SliceSpec.Builder getBiasSlicesBuilder()

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Returns
Type	Description
ModelEvaluationSlice.Slice.SliceSpec.Builder

getBiasSlicesOrBuilder()

public ModelEvaluationSlice.Slice.SliceSpecOrBuilder getBiasSlicesOrBuilder()

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Returns
Type	Description
ModelEvaluationSlice.Slice.SliceSpecOrBuilder

getDefaultInstanceForType()

public ModelEvaluation.BiasConfig getDefaultInstanceForType()

Returns
Type	Description
ModelEvaluation.BiasConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getLabels(int index)

public String getLabels(int index)

Positive labels selection on the target field.

repeated string labels = 2;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The labels at the given index.

getLabelsBytes(int index)

public ByteString getLabelsBytes(int index)

Positive labels selection on the target field.

repeated string labels = 2;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the labels at the given index.

getLabelsCount()

public int getLabelsCount()

Positive labels selection on the target field.

repeated string labels = 2;

Returns
Type	Description
int	The count of labels.

getLabelsList()

public ProtocolStringList getLabelsList()

Positive labels selection on the target field.

repeated string labels = 2;

Returns
Type	Description
ProtocolStringList	A list containing the labels.

hasBiasSlices()

public boolean hasBiasSlices()

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Returns
Type	Description
boolean	Whether the biasSlices field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeBiasSlices(ModelEvaluationSlice.Slice.SliceSpec value)

public ModelEvaluation.BiasConfig.Builder mergeBiasSlices(ModelEvaluationSlice.Slice.SliceSpec value)

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Parameter
Name	Description
value	ModelEvaluationSlice.Slice.SliceSpec

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

mergeFrom(ModelEvaluation.BiasConfig other)

public ModelEvaluation.BiasConfig.Builder mergeFrom(ModelEvaluation.BiasConfig other)

Parameter
Name	Description
other	ModelEvaluation.BiasConfig

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ModelEvaluation.BiasConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public ModelEvaluation.BiasConfig.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ModelEvaluation.BiasConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setBiasSlices(ModelEvaluationSlice.Slice.SliceSpec value)

public ModelEvaluation.BiasConfig.Builder setBiasSlices(ModelEvaluationSlice.Slice.SliceSpec value)

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Parameter
Name	Description
value	ModelEvaluationSlice.Slice.SliceSpec

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

setBiasSlices(ModelEvaluationSlice.Slice.SliceSpec.Builder builderForValue)

public ModelEvaluation.BiasConfig.Builder setBiasSlices(ModelEvaluationSlice.Slice.SliceSpec.Builder builderForValue)

Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset:

Example 1:

 bias_slices = [{'education': 'low'}]

A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'.

Example 2:

 bias_slices = [{'education': 'low'},
                {'education': 'high'}]

Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.

.google.cloud.aiplatform.v1beta1.ModelEvaluationSlice.Slice.SliceSpec bias_slices = 1;

Parameter
Name	Description
builderForValue	ModelEvaluationSlice.Slice.SliceSpec.Builder

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ModelEvaluation.BiasConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setLabels(int index, String value)

public ModelEvaluation.BiasConfig.Builder setLabels(int index, String value)

Positive labels selection on the target field.

repeated string labels = 2;

Parameters
Name	Description
index	int The index to set the value at.
value	String The labels to set.

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder	This builder for chaining.

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

public ModelEvaluation.BiasConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setUnknownFields(UnknownFieldSet unknownFields)

public final ModelEvaluation.BiasConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
ModelEvaluation.BiasConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)