public static final class ModelEvaluation.BiasConfig extends GeneratedMessageV3 implements ModelEvaluation.BiasConfigOrBuilder
Configuration for bias detection.
Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.BiasConfig
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int BIAS_SLICES_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int LABELS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
public static ModelEvaluation.BiasConfig getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static ModelEvaluation.BiasConfig.Builder newBuilder()
public static ModelEvaluation.BiasConfig.Builder newBuilder(ModelEvaluation.BiasConfig prototype)
public static ModelEvaluation.BiasConfig parseDelimitedFrom(InputStream input)
public static ModelEvaluation.BiasConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelEvaluation.BiasConfig parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
public static ModelEvaluation.BiasConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static ModelEvaluation.BiasConfig parseFrom(ByteString data)
public static ModelEvaluation.BiasConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static ModelEvaluation.BiasConfig parseFrom(CodedInputStream input)
public static ModelEvaluation.BiasConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelEvaluation.BiasConfig parseFrom(InputStream input)
public static ModelEvaluation.BiasConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static ModelEvaluation.BiasConfig parseFrom(ByteBuffer data)
public static ModelEvaluation.BiasConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<ModelEvaluation.BiasConfig> parser()
Methods
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
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;
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;
public ModelEvaluation.BiasConfig getDefaultInstanceForType()
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.
|
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.
|
public int getLabelsCount()
Positive labels selection on the target field.
repeated string labels = 2;
| Returns |
| Type |
Description |
int |
The count of labels.
|
public ProtocolStringList getLabelsList()
Positive labels selection on the target field.
repeated string labels = 2;
public Parser<ModelEvaluation.BiasConfig> getParserForType()
Overrides
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
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.
|
| Returns |
| Type |
Description |
int |
|
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public ModelEvaluation.BiasConfig.Builder newBuilderForType()
protected ModelEvaluation.BiasConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
public ModelEvaluation.BiasConfig.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
