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public static final class ModelEvaluation.BiasConfig.Builder extends GeneratedMessageV3.Builder<ModelEvaluation.BiasConfig.Builder> implements ModelEvaluation.BiasConfigOrBuilderConfiguration for bias detection.
Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.BiasConfig
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ModelEvaluation.BiasConfig.BuilderImplements
ModelEvaluation.BiasConfigOrBuilderStatic 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 |
StringThe 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 |
ByteStringThe 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 |
|
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 |
|
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 |
|
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 |
|
clone()
public ModelEvaluation.BiasConfig.Builder clone()| Returns | |
|---|---|
| Type | Description |
ModelEvaluation.BiasConfig.Builder |
|
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 |
|
getLabels(int index)
public String getLabels(int index)Positive labels selection on the target field.
repeated string labels = 2;
| Parameter | |
|---|---|
| Name | Description |
index |
intThe 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 |
intThe 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 |
|
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean |
|
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 |
|
| 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 |
|
mergeUnknownFields(UnknownFieldSet unknownFields)
public final ModelEvaluation.BiasConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields |
UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
ModelEvaluation.BiasConfig.Builder |
|
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 |
|
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 |
intThe index to set the value at. |
value |
StringThe 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 |
|
setUnknownFields(UnknownFieldSet unknownFields)
public final ModelEvaluation.BiasConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields |
UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
ModelEvaluation.BiasConfig.Builder |
|