Package com.google.cloud.aiplatform.v1beta1

Class ModelEvaluation.BiasConfig.Builder

    • Method Detail

      • getDescriptor

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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.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.GeneratedMessageV3.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

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.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.

      • 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.

      • setUnknownFields

        public final ModelEvaluation.BiasConfig.Builder setUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        setUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ModelEvaluation.BiasConfig.Builder>

      • mergeUnknownFields

        public final ModelEvaluation.BiasConfig.Builder mergeUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        mergeUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ModelEvaluation.BiasConfig.Builder>