Package com.google.cloud.aiplatform.v1beta1

Class ModelEvaluation.BiasConfig.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder>
com.google.cloud.aiplatform.v1beta1.ModelEvaluation.BiasConfig.Builder
All Implemented Interfaces:
ModelEvaluation.BiasConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
ModelEvaluation.BiasConfig
public static final class ModelEvaluation.BiasConfig.Builder extends com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder> implements ModelEvaluation.BiasConfigOrBuilder
 Configuration for bias detection.
Protobuf type google.cloud.aiplatform.v1beta1.ModelEvaluation.BiasConfig

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder>

    • clear

      public ModelEvaluation.BiasConfig.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder>

    • getDefaultInstanceForType

      public ModelEvaluation.BiasConfig getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public ModelEvaluation.BiasConfig build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public ModelEvaluation.BiasConfig buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public ModelEvaluation.BiasConfig.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ModelEvaluation.BiasConfig.Builder>

    • mergeFrom

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

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<ModelEvaluation.BiasConfig.Builder>

    • mergeFrom

      public ModelEvaluation.BiasConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ModelEvaluation.BiasConfig.Builder>
      Throws:
      IOException

    • 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;
      Specified by:
      hasBiasSlices in interface ModelEvaluation.BiasConfigOrBuilder
      Returns:
      Whether the biasSlices field is set.

    • 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;
      Specified by:
      getBiasSlices in interface ModelEvaluation.BiasConfigOrBuilder
      Returns:
      The biasSlices.

    • setBiasSlices

      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;

    • setBiasSlices

      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;

    • mergeBiasSlices

      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;

    • 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;

    • 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;

    • 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;
      Specified by:
      getBiasSlicesOrBuilder in interface ModelEvaluation.BiasConfigOrBuilder

    • getLabelsList

      public com.google.protobuf.ProtocolStringList getLabelsList()
       Positive labels selection on the target field.
      repeated string labels = 2;
      Specified by:
      getLabelsList in interface ModelEvaluation.BiasConfigOrBuilder
      Returns:
      A list containing the labels.

    • getLabelsCount

      public int getLabelsCount()
       Positive labels selection on the target field.
      repeated string labels = 2;
      Specified by:
      getLabelsCount in interface ModelEvaluation.BiasConfigOrBuilder
      Returns:
      The count of labels.

    • getLabels

      public String getLabels(int index)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Specified by:
      getLabels in interface ModelEvaluation.BiasConfigOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The labels at the given index.

    • getLabelsBytes

      public com.google.protobuf.ByteString getLabelsBytes(int index)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Specified by:
      getLabelsBytes in interface ModelEvaluation.BiasConfigOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the labels at the given index.

    • setLabels

      public ModelEvaluation.BiasConfig.Builder setLabels(int index, String value)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Parameters:
      index - The index to set the value at.
      value - The labels to set.
      Returns:
      This builder for chaining.

    • addLabels

      public ModelEvaluation.BiasConfig.Builder addLabels(String value)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Parameters:
      value - The labels to add.
      Returns:
      This builder for chaining.

    • addAllLabels

      public ModelEvaluation.BiasConfig.Builder addAllLabels(Iterable<String> values)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Parameters:
      values - The labels to add.
      Returns:
      This builder for chaining.

    • clearLabels

      public ModelEvaluation.BiasConfig.Builder clearLabels()
       Positive labels selection on the target field.
      repeated string labels = 2;
      Returns:
      This builder for chaining.

    • addLabelsBytes

      public ModelEvaluation.BiasConfig.Builder addLabelsBytes(com.google.protobuf.ByteString value)
       Positive labels selection on the target field.
      repeated string labels = 2;
      Parameters:
      value - The bytes of the labels to add.
      Returns:
      This builder for chaining.