Class ModelExplanation (3.14.0)

public final class ModelExplanation extends GeneratedMessageV3 implements ModelExplanationOrBuilder

Aggregated explanation metrics for a Model over a set of instances.

Protobuf type google.cloud.aiplatform.v1.ModelExplanation

Static Fields

MEAN_ATTRIBUTIONS_FIELD_NUMBER

public static final int MEAN_ATTRIBUTIONS_FIELD_NUMBER
Field Value
TypeDescription
int

Static Methods

getDefaultInstance()

public static ModelExplanation getDefaultInstance()
Returns
TypeDescription
ModelExplanation

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

newBuilder()

public static ModelExplanation.Builder newBuilder()
Returns
TypeDescription
ModelExplanation.Builder

newBuilder(ModelExplanation prototype)

public static ModelExplanation.Builder newBuilder(ModelExplanation prototype)
Parameter
NameDescription
prototypeModelExplanation
Returns
TypeDescription
ModelExplanation.Builder

parseDelimitedFrom(InputStream input)

public static ModelExplanation parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static ModelExplanation parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static ModelExplanation parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static ModelExplanation parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static ModelExplanation parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static ModelExplanation parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static ModelExplanation parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelExplanation
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<ModelExplanation> parser()
Returns
TypeDescription
Parser<ModelExplanation>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getDefaultInstanceForType()

public ModelExplanation getDefaultInstanceForType()
Returns
TypeDescription
ModelExplanation

getMeanAttributions(int index)

public Attribution getMeanAttributions(int index)

Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

repeated .google.cloud.aiplatform.v1.Attribution mean_attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
Attribution

getMeanAttributionsCount()

public int getMeanAttributionsCount()

Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

repeated .google.cloud.aiplatform.v1.Attribution mean_attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
int

getMeanAttributionsList()

public List<Attribution> getMeanAttributionsList()

Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

repeated .google.cloud.aiplatform.v1.Attribution mean_attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<Attribution>

getMeanAttributionsOrBuilder(int index)

public AttributionOrBuilder getMeanAttributionsOrBuilder(int index)

Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

repeated .google.cloud.aiplatform.v1.Attribution mean_attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
NameDescription
indexint
Returns
TypeDescription
AttributionOrBuilder

getMeanAttributionsOrBuilderList()

public List<? extends AttributionOrBuilder> getMeanAttributionsOrBuilderList()

Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

repeated .google.cloud.aiplatform.v1.Attribution mean_attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
TypeDescription
List<? extends com.google.cloud.aiplatform.v1.AttributionOrBuilder>

getParserForType()

public Parser<ModelExplanation> getParserForType()
Returns
TypeDescription
Parser<ModelExplanation>
Overrides

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getUnknownFields()

public final UnknownFieldSet getUnknownFields()
Returns
TypeDescription
UnknownFieldSet
Overrides

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilderForType()

public ModelExplanation.Builder newBuilderForType()
Returns
TypeDescription
ModelExplanation.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected ModelExplanation.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
ModelExplanation.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

toBuilder()

public ModelExplanation.Builder toBuilder()
Returns
TypeDescription
ModelExplanation.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides
Exceptions
TypeDescription
IOException