Package com.google.cloud.aiplatform.v1beta1

Interface ModelEvaluation.BiasConfigOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ModelEvaluation.BiasConfig, ModelEvaluation.BiasConfig.Builder

Enclosing class:

ModelEvaluation

public static interface ModelEvaluation.BiasConfigOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method	Description
ModelEvaluationSlice.Slice.SliceSpec	getBiasSlices()	Specification for how the data should be sliced for bias.
ModelEvaluationSlice.Slice.SliceSpecOrBuilder	getBiasSlicesOrBuilder()	Specification for how the data should be sliced for bias.
String	getLabels(int index)	Positive labels selection on the target field.
com.google.protobuf.ByteString	getLabelsBytes(int index)	Positive labels selection on the target field.
int	getLabelsCount()	Positive labels selection on the target field.
List<String>	getLabelsList()	Positive labels selection on the target field.
boolean	hasBiasSlices()	Specification for how the data should be sliced for bias.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Detail

hasBiasSlices

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:

Whether the biasSlices field is set.

getBiasSlices

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:

The biasSlices.

getBiasSlicesOrBuilder

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;

getLabelsList

List<String> getLabelsList()

 Positive labels selection on the target field.
 

repeated string labels = 2;

Returns:

A list containing the labels.

getLabelsCount

int getLabelsCount()

 Positive labels selection on the target field.
 

repeated string labels = 2;

Returns:

The count of labels.

getLabels

String getLabels(int index)

 Positive labels selection on the target field.
 

repeated string labels = 2;

Parameters:

index - The index of the element to return.

Returns:

The labels at the given index.

getLabelsBytes

com.google.protobuf.ByteString getLabelsBytes(int index)

 Positive labels selection on the target field.
 

repeated string labels = 2;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the labels at the given index.