Class EvaluationJobConfig.Builder (0.125.0)

Version 0.125.0

• 0.173.0 (latest)
• 0.172.0
• 0.171.0
• 0.169.0
• 0.168.0
• 0.167.0
• 0.166.0
• 0.165.0
• 0.164.0
• 0.163.0
• 0.162.0
• 0.161.0
• 0.160.0
• 0.159.0
• 0.157.0
• 0.156.0
• 0.155.0
• 0.154.0
• 0.153.0
• 0.152.0
• 0.151.0
• 0.150.0
• 0.149.0
• 0.148.0
• 0.147.0
• 0.144.0
• 0.143.0
• 0.142.0
• 0.141.0
• 0.140.0
• 0.139.0
• 0.138.0
• 0.137.0
• 0.136.0
• 0.135.0
• 0.134.0
• 0.133.0
• 0.132.0
• 0.131.0
• 0.129.0
• 0.128.0
• 0.127.0
• 0.126.0
• 0.125.0
• 0.124.0
• 0.123.7
• 0.122.11

public static final class EvaluationJobConfig.Builder extends GeneratedMessageV3.Builder<EvaluationJobConfig.Builder> implements EvaluationJobConfigOrBuilder

Configures specific details of how a continuous evaluation job works. Provide this configuration when you create an EvaluationJob.

Protobuf type google.cloud.datalabeling.v1beta1.EvaluationJobConfig

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > EvaluationJobConfig.Builder

Implements

EvaluationJobConfigOrBuilder

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

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

build()

public EvaluationJobConfig build()

Returns

Type	Description
EvaluationJobConfig

buildPartial()

public EvaluationJobConfig buildPartial()

Returns

Type	Description
EvaluationJobConfig

clear()

public EvaluationJobConfig.Builder clear()

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearBigqueryImportKeys()

public EvaluationJobConfig.Builder clearBigqueryImportKeys()

Returns

Type	Description
EvaluationJobConfig.Builder

clearBoundingPolyConfig()

public EvaluationJobConfig.Builder clearBoundingPolyConfig()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns

Type	Description
EvaluationJobConfig.Builder

clearEvaluationConfig()

public EvaluationJobConfig.Builder clearEvaluationConfig()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns

Type	Description
EvaluationJobConfig.Builder

clearEvaluationJobAlertConfig()

public EvaluationJobConfig.Builder clearEvaluationJobAlertConfig()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns

Type	Description
EvaluationJobConfig.Builder

clearExampleCount()

public EvaluationJobConfig.Builder clearExampleCount()

Required. The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.

int32 example_count = 10;

Returns

Type	Description
EvaluationJobConfig.Builder	This builder for chaining.

clearExampleSamplePercentage()

public EvaluationJobConfig.Builder clearExampleSamplePercentage()

Required. Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.

double example_sample_percentage = 11;

Returns

Type	Description
EvaluationJobConfig.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public EvaluationJobConfig.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

clearHumanAnnotationConfig()

public EvaluationJobConfig.Builder clearHumanAnnotationConfig()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns

Type	Description
EvaluationJobConfig.Builder

clearHumanAnnotationRequestConfig()

public EvaluationJobConfig.Builder clearHumanAnnotationRequestConfig()

Returns

Type	Description
EvaluationJobConfig.Builder

clearImageClassificationConfig()

public EvaluationJobConfig.Builder clearImageClassificationConfig()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns

Type	Description
EvaluationJobConfig.Builder

clearInputConfig()

public EvaluationJobConfig.Builder clearInputConfig()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns

Type	Description
EvaluationJobConfig.Builder

clearOneof(Descriptors.OneofDescriptor oneof)

public EvaluationJobConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

clearTextClassificationConfig()

public EvaluationJobConfig.Builder clearTextClassificationConfig()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns

Type	Description
EvaluationJobConfig.Builder

clone()

public EvaluationJobConfig.Builder clone()

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

containsBigqueryImportKeys(String key)

public boolean containsBigqueryImportKeys(String key)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter

Name	Description
key	String

Returns

Type	Description
boolean

getBigqueryImportKeys()

public Map<String,String> getBigqueryImportKeys()

Use #getBigqueryImportKeysMap() instead.

Returns

Type	Description
Map<String,String>

getBigqueryImportKeysCount()

public int getBigqueryImportKeysCount()

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Returns

Type	Description
int

getBigqueryImportKeysMap()

public Map<String,String> getBigqueryImportKeysMap()

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Returns

Type	Description
Map<String,String>

getBigqueryImportKeysOrDefault(String key, String defaultValue)

public String getBigqueryImportKeysOrDefault(String key, String defaultValue)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameters

Name	Description
key	String
defaultValue	String

Returns

Type	Description
String

getBigqueryImportKeysOrThrow(String key)

public String getBigqueryImportKeysOrThrow(String key)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter

Name	Description
key	String

Returns

Type	Description
String

getBoundingPolyConfig()

public BoundingPolyConfig getBoundingPolyConfig()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns

Type	Description
BoundingPolyConfig	The boundingPolyConfig.

getBoundingPolyConfigBuilder()

public BoundingPolyConfig.Builder getBoundingPolyConfigBuilder()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns

Type	Description
BoundingPolyConfig.Builder

getBoundingPolyConfigOrBuilder()

public BoundingPolyConfigOrBuilder getBoundingPolyConfigOrBuilder()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns

Type	Description
BoundingPolyConfigOrBuilder

getDefaultInstanceForType()

public EvaluationJobConfig getDefaultInstanceForType()

Returns

Type	Description
EvaluationJobConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getEvaluationConfig()

public EvaluationConfig getEvaluationConfig()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns

Type	Description
EvaluationConfig	The evaluationConfig.

getEvaluationConfigBuilder()

public EvaluationConfig.Builder getEvaluationConfigBuilder()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns

Type	Description
EvaluationConfig.Builder

getEvaluationConfigOrBuilder()

public EvaluationConfigOrBuilder getEvaluationConfigOrBuilder()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns

Type	Description
EvaluationConfigOrBuilder

getEvaluationJobAlertConfig()

public EvaluationJobAlertConfig getEvaluationJobAlertConfig()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns

Type	Description
EvaluationJobAlertConfig	The evaluationJobAlertConfig.

getEvaluationJobAlertConfigBuilder()

public EvaluationJobAlertConfig.Builder getEvaluationJobAlertConfigBuilder()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns

Type	Description
EvaluationJobAlertConfig.Builder

getEvaluationJobAlertConfigOrBuilder()

public EvaluationJobAlertConfigOrBuilder getEvaluationJobAlertConfigOrBuilder()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns

Type	Description
EvaluationJobAlertConfigOrBuilder

getExampleCount()

public int getExampleCount()

Required. The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.

int32 example_count = 10;

Returns

Type	Description
int	The exampleCount.

getExampleSamplePercentage()

public double getExampleSamplePercentage()

Required. Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.

double example_sample_percentage = 11;

Returns

Type	Description
double	The exampleSamplePercentage.

getHumanAnnotationConfig()

public HumanAnnotationConfig getHumanAnnotationConfig()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns

Type	Description
HumanAnnotationConfig	The humanAnnotationConfig.

getHumanAnnotationConfigBuilder()

public HumanAnnotationConfig.Builder getHumanAnnotationConfigBuilder()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns

Type	Description
HumanAnnotationConfig.Builder

getHumanAnnotationConfigOrBuilder()

public HumanAnnotationConfigOrBuilder getHumanAnnotationConfigOrBuilder()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns

Type	Description
HumanAnnotationConfigOrBuilder

getHumanAnnotationRequestConfigCase()

public EvaluationJobConfig.HumanAnnotationRequestConfigCase getHumanAnnotationRequestConfigCase()

Returns

Type	Description
EvaluationJobConfig.HumanAnnotationRequestConfigCase

getImageClassificationConfig()

public ImageClassificationConfig getImageClassificationConfig()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns

Type	Description
ImageClassificationConfig	The imageClassificationConfig.

getImageClassificationConfigBuilder()

public ImageClassificationConfig.Builder getImageClassificationConfigBuilder()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns

Type	Description
ImageClassificationConfig.Builder

getImageClassificationConfigOrBuilder()

public ImageClassificationConfigOrBuilder getImageClassificationConfigOrBuilder()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns

Type	Description
ImageClassificationConfigOrBuilder

getInputConfig()

public InputConfig getInputConfig()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns

Type	Description
InputConfig	The inputConfig.

getInputConfigBuilder()

public InputConfig.Builder getInputConfigBuilder()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns

Type	Description
InputConfig.Builder

getInputConfigOrBuilder()

public InputConfigOrBuilder getInputConfigOrBuilder()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns

Type	Description
InputConfigOrBuilder

getMutableBigqueryImportKeys()

public Map<String,String> getMutableBigqueryImportKeys()

Use alternate mutation accessors instead.

Returns

Type	Description
Map<String,String>

getTextClassificationConfig()

public TextClassificationConfig getTextClassificationConfig()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns

Type	Description
TextClassificationConfig	The textClassificationConfig.

getTextClassificationConfigBuilder()

public TextClassificationConfig.Builder getTextClassificationConfigBuilder()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns

Type	Description
TextClassificationConfig.Builder

getTextClassificationConfigOrBuilder()

public TextClassificationConfigOrBuilder getTextClassificationConfigOrBuilder()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns

Type	Description
TextClassificationConfigOrBuilder

hasBoundingPolyConfig()

public boolean hasBoundingPolyConfig()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns

Type	Description
boolean	Whether the boundingPolyConfig field is set.

hasEvaluationConfig()

public boolean hasEvaluationConfig()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns

Type	Description
boolean	Whether the evaluationConfig field is set.

hasEvaluationJobAlertConfig()

public boolean hasEvaluationJobAlertConfig()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns

Type	Description
boolean	Whether the evaluationJobAlertConfig field is set.

hasHumanAnnotationConfig()

public boolean hasHumanAnnotationConfig()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns

Type	Description
boolean	Whether the humanAnnotationConfig field is set.

hasImageClassificationConfig()

public boolean hasImageClassificationConfig()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns

Type	Description
boolean	Whether the imageClassificationConfig field is set.

hasInputConfig()

public boolean hasInputConfig()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns

Type	Description
boolean	Whether the inputConfig field is set.

hasTextClassificationConfig()

public boolean hasTextClassificationConfig()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns

Type	Description
boolean	Whether the textClassificationConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

internalGetMapField(int number)

protected MapField internalGetMapField(int number)

Parameter

Name	Description
number	int

Returns

Type	Description
MapField

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetMapField(int fieldNumber)

internalGetMutableMapField(int number)

protected MapField internalGetMutableMapField(int number)

Parameter

Name	Description
number	int

Returns

Type	Description
MapField

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetMutableMapField(int fieldNumber)

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeBoundingPolyConfig(BoundingPolyConfig value)

public EvaluationJobConfig.Builder mergeBoundingPolyConfig(BoundingPolyConfig value)

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Parameter

Name	Description
value	BoundingPolyConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeEvaluationConfig(EvaluationConfig value)

public EvaluationJobConfig.Builder mergeEvaluationConfig(EvaluationConfig value)

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Parameter

Name	Description
value	EvaluationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeEvaluationJobAlertConfig(EvaluationJobAlertConfig value)

public EvaluationJobConfig.Builder mergeEvaluationJobAlertConfig(EvaluationJobAlertConfig value)

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Parameter

Name	Description
value	EvaluationJobAlertConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeFrom(EvaluationJobConfig other)

public EvaluationJobConfig.Builder mergeFrom(EvaluationJobConfig other)

Parameter

Name	Description
other	EvaluationJobConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public EvaluationJobConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public EvaluationJobConfig.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

mergeHumanAnnotationConfig(HumanAnnotationConfig value)

public EvaluationJobConfig.Builder mergeHumanAnnotationConfig(HumanAnnotationConfig value)

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Parameter

Name	Description
value	HumanAnnotationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeImageClassificationConfig(ImageClassificationConfig value)

public EvaluationJobConfig.Builder mergeImageClassificationConfig(ImageClassificationConfig value)

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Parameter

Name	Description
value	ImageClassificationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeInputConfig(InputConfig value)

public EvaluationJobConfig.Builder mergeInputConfig(InputConfig value)

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Parameter

Name	Description
value	InputConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeTextClassificationConfig(TextClassificationConfig value)

public EvaluationJobConfig.Builder mergeTextClassificationConfig(TextClassificationConfig value)

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Parameter

Name	Description
value	TextClassificationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final EvaluationJobConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

putAllBigqueryImportKeys(Map<String,String> values)

public EvaluationJobConfig.Builder putAllBigqueryImportKeys(Map<String,String> values)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter

Name	Description
values	Map<String,String>

Returns

Type	Description
EvaluationJobConfig.Builder

putBigqueryImportKeys(String key, String value)

public EvaluationJobConfig.Builder putBigqueryImportKeys(String key, String value)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameters

Name	Description
key	String
value	String

Returns

Type	Description
EvaluationJobConfig.Builder

removeBigqueryImportKeys(String key)

public EvaluationJobConfig.Builder removeBigqueryImportKeys(String key)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

• data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
• reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
• label_json_key: the label key for prediction output. Required.
• label_score_json_key: the score key for prediction output. Required.
• bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter

Name	Description
key	String

Returns

Type	Description
EvaluationJobConfig.Builder

setBoundingPolyConfig(BoundingPolyConfig value)

public EvaluationJobConfig.Builder setBoundingPolyConfig(BoundingPolyConfig value)

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Parameter

Name	Description
value	BoundingPolyConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setBoundingPolyConfig(BoundingPolyConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setBoundingPolyConfig(BoundingPolyConfig.Builder builderForValue)

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Parameter

Name	Description
builderForValue	BoundingPolyConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setEvaluationConfig(EvaluationConfig value)

public EvaluationJobConfig.Builder setEvaluationConfig(EvaluationConfig value)

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Parameter

Name	Description
value	EvaluationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setEvaluationConfig(EvaluationConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setEvaluationConfig(EvaluationConfig.Builder builderForValue)

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Parameter

Name	Description
builderForValue	EvaluationConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setEvaluationJobAlertConfig(EvaluationJobAlertConfig value)

public EvaluationJobConfig.Builder setEvaluationJobAlertConfig(EvaluationJobAlertConfig value)

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Parameter

Name	Description
value	EvaluationJobAlertConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setEvaluationJobAlertConfig(EvaluationJobAlertConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setEvaluationJobAlertConfig(EvaluationJobAlertConfig.Builder builderForValue)

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Parameter

Name	Description
builderForValue	EvaluationJobAlertConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setExampleCount(int value)

public EvaluationJobConfig.Builder setExampleCount(int value)

Required. The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.

int32 example_count = 10;

Parameter

Name	Description
value	int The exampleCount to set.

Returns

Type	Description
EvaluationJobConfig.Builder	This builder for chaining.

setExampleSamplePercentage(double value)

public EvaluationJobConfig.Builder setExampleSamplePercentage(double value)

Required. Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.

double example_sample_percentage = 11;

Parameter

Name	Description
value	double The exampleSamplePercentage to set.

Returns

Type	Description
EvaluationJobConfig.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

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

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

setHumanAnnotationConfig(HumanAnnotationConfig value)

public EvaluationJobConfig.Builder setHumanAnnotationConfig(HumanAnnotationConfig value)

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Parameter

Name	Description
value	HumanAnnotationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setHumanAnnotationConfig(HumanAnnotationConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setHumanAnnotationConfig(HumanAnnotationConfig.Builder builderForValue)

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Parameter

Name	Description
builderForValue	HumanAnnotationConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setImageClassificationConfig(ImageClassificationConfig value)

public EvaluationJobConfig.Builder setImageClassificationConfig(ImageClassificationConfig value)

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Parameter

Name	Description
value	ImageClassificationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setImageClassificationConfig(ImageClassificationConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setImageClassificationConfig(ImageClassificationConfig.Builder builderForValue)

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Parameter

Name	Description
builderForValue	ImageClassificationConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setInputConfig(InputConfig value)

public EvaluationJobConfig.Builder setInputConfig(InputConfig value)

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Parameter

Name	Description
value	InputConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setInputConfig(InputConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setInputConfig(InputConfig.Builder builderForValue)

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

• dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
• annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
• If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
• You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Parameter

Name	Description
builderForValue	InputConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

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

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

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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

setTextClassificationConfig(TextClassificationConfig value)

public EvaluationJobConfig.Builder setTextClassificationConfig(TextClassificationConfig value)

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Parameter

Name	Description
value	TextClassificationConfig

Returns

Type	Description
EvaluationJobConfig.Builder

setTextClassificationConfig(TextClassificationConfig.Builder builderForValue)

public EvaluationJobConfig.Builder setTextClassificationConfig(TextClassificationConfig.Builder builderForValue)

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Parameter

Name	Description
builderForValue	TextClassificationConfig.Builder

Returns

Type	Description
EvaluationJobConfig.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final EvaluationJobConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
EvaluationJobConfig.Builder

Overrides

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