Class ClassificationProto.ClassificationEvaluationMetrics.Builder (2.13.0)

Version 2.13.0

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public static final class ClassificationProto.ClassificationEvaluationMetrics.Builder extends GeneratedMessageV3.Builder<ClassificationProto.ClassificationEvaluationMetrics.Builder> implements ClassificationProto.ClassificationEvaluationMetricsOrBuilder

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

Protobuf type google.cloud.automl.v1beta1.ClassificationEvaluationMetrics

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ClassificationProto.ClassificationEvaluationMetrics.Builder

Implements

ClassificationProto.ClassificationEvaluationMetricsOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

addAllAnnotationSpecId(Iterable<String> values)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAllAnnotationSpecId(Iterable<String> values)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
Name	Description
values	Iterable<String> The annotationSpecId to add.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

addAllConfidenceMetricsEntry(Iterable<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> values)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAllConfidenceMetricsEntry(Iterable<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> values)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
values	Iterable<? extends com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry>

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

addAnnotationSpecId(String value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAnnotationSpecId(String value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
Name	Description
value	String The annotationSpecId to add.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

addAnnotationSpecIdBytes(ByteString value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAnnotationSpecIdBytes(ByteString value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
Name	Description
value	ByteString The bytes of the annotationSpecId to add.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
value	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
builderForValue	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
Name	Description
index	int
value	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
Name	Description
index	int
builderForValue	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntryBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder addConfidenceMetricsEntryBuilder()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

addConfidenceMetricsEntryBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder addConfidenceMetricsEntryBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
index	int

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ClassificationProto.ClassificationEvaluationMetrics build()

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics

buildPartial()

public ClassificationProto.ClassificationEvaluationMetrics buildPartial()

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics

clear()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clear()

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAnnotationSpecId()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAnnotationSpecId()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

clearAuPrc()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAuPrc()

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

clearAuRoc()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAuRoc()

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

clearBaseAuPrc() (deprecated)

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearBaseAuPrc()

Deprecated. google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

clearConfidenceMetricsEntry()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearConfidenceMetricsEntry()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

clearConfusionMatrix()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

clearField(Descriptors.FieldDescriptor field)

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearLogLoss()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clone()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clone()

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAnnotationSpecId(int index)

public String getAnnotationSpecId(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
Name	Description
index	int The index of the element to return.

Returns
Type	Description
String	The annotationSpecId at the given index.

getAnnotationSpecIdBytes(int index)

public ByteString getAnnotationSpecIdBytes(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
Name	Description
index	int The index of the value to return.

Returns
Type	Description
ByteString	The bytes of the annotationSpecId at the given index.

getAnnotationSpecIdCount()

public int getAnnotationSpecIdCount()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
Type	Description
int	The count of annotationSpecId.

getAnnotationSpecIdList()

public ProtocolStringList getAnnotationSpecIdList()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
Type	Description
ProtocolStringList	A list containing the annotationSpecId.

getAuPrc()

public float getAuPrc()

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Returns
Type	Description
float	The auPrc.

getAuRoc()

public float getAuRoc()

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Returns
Type	Description
float	The auRoc.

getBaseAuPrc() (deprecated)

public float getBaseAuPrc()

Deprecated. google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Returns
Type	Description
float	The baseAuPrc.

getConfidenceMetricsEntry(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry getConfidenceMetricsEntry(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
index	int

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

getConfidenceMetricsEntryBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder getConfidenceMetricsEntryBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
index	int

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

getConfidenceMetricsEntryBuilderList()

public List<ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder> getConfidenceMetricsEntryBuilderList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
List<Builder>

getConfidenceMetricsEntryCount()

public int getConfidenceMetricsEntryCount()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
int

getConfidenceMetricsEntryList()

public List<ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> getConfidenceMetricsEntryList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
List<ConfidenceMetricsEntry>

getConfidenceMetricsEntryOrBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder getConfidenceMetricsEntryOrBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
index	int

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

getConfidenceMetricsEntryOrBuilderList()

public List<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder> getConfidenceMetricsEntryOrBuilderList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
Type	Description
List<? extends com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>

getConfusionMatrix()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix getConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix	The confusionMatrix.

getConfusionMatrixBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder getConfusionMatrixBuilder()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder

getConfusionMatrixOrBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder getConfusionMatrixOrBuilder()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder

getDefaultInstanceForType()

public ClassificationProto.ClassificationEvaluationMetrics getDefaultInstanceForType()

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getLogLoss()

public float getLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns
Type	Description
float	The logLoss.

hasConfusionMatrix()

public boolean hasConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
Type	Description
boolean	Whether the confusionMatrix field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
Name	Description
value	ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

mergeFrom(ClassificationProto.ClassificationEvaluationMetrics other)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(ClassificationProto.ClassificationEvaluationMetrics other)

Parameter
Name	Description
other	ClassificationProto.ClassificationEvaluationMetrics

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

removeConfidenceMetricsEntry(int index)

public ClassificationProto.ClassificationEvaluationMetrics.Builder removeConfidenceMetricsEntry(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
Name	Description
index	int

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

setAnnotationSpecId(int index, String value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAnnotationSpecId(int index, String value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameters
Name	Description
index	int The index to set the value at.
value	String The annotationSpecId to set.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

setAuPrc(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAuPrc(float value)

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Parameter
Name	Description
value	float The auPrc to set.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

setAuRoc(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAuRoc(float value)

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Parameter
Name	Description
value	float The auRoc to set.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

setBaseAuPrc(float value) (deprecated)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setBaseAuPrc(float value)

Deprecated. google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Parameter
Name	Description
value	float The baseAuPrc to set.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
Name	Description
index	int
value	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
Name	Description
index	int
builderForValue	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
Name	Description
value	ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder builderForValue)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
Name	Description
builderForValue	ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setLogLoss(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setLogLoss(float value)

Output only. The Log Loss metric.

float log_loss = 7;

Parameter
Name	Description
value	float The logLoss to set.

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
ClassificationProto.ClassificationEvaluationMetrics.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)