Interface ModelEvaluation.BiasConfigOrBuilder (3.28.0)

Version 3.28.0

• 3.55.0 (latest)
• 3.54.0
• 3.53.0
• 3.52.0
• 3.50.0
• 3.49.0
• 3.48.0
• 3.47.0
• 3.46.0
• 3.45.0
• 3.44.0
• 3.43.0
• 3.42.0
• 3.41.0
• 3.40.0
• 3.38.0
• 3.37.0
• 3.36.0
• 3.35.0
• 3.34.0
• 3.33.0
• 3.32.0
• 3.31.0
• 3.30.0
• 3.29.0
• 3.28.0
• 3.25.0
• 3.24.0
• 3.23.0
• 3.22.0
• 3.21.0
• 3.20.0
• 3.19.0
• 3.18.0
• 3.17.0
• 3.16.0
• 3.15.0
• 3.14.0
• 3.13.0
• 3.12.0
• 3.11.0
• 3.10.0
• 3.9.0
• 3.8.0
• 3.7.0
• 3.6.0
• 3.5.0
• 3.4.2
• 3.3.0
• 3.2.0
• 3.0.0
• 2.9.8
• 2.8.9
• 2.7.4
• 2.5.3
• 2.4.0

public static interface ModelEvaluation.BiasConfigOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getBiasSlices()

public abstract 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.

getBiasSlicesOrBuilder()

public abstract 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

getLabels(int index)

public abstract 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.

getLabelsBytes(int index)

public abstract 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.

getLabelsCount()

public abstract int getLabelsCount()

Positive labels selection on the target field.

repeated string labels = 2;

Returns
Type	Description
int	The count of labels.

getLabelsList()

public abstract List<String> getLabelsList()

Positive labels selection on the target field.

repeated string labels = 2;

Returns
Type	Description
List<String>	A list containing the labels.

hasBiasSlices()

public abstract 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.