Class EvaluationJobConfig.Builder (0.168.0) 

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

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

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

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

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

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

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() (deprecated)

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

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() (deprecated)

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

internalGetMapFieldReflection(int number)

protected MapFieldReflectionAccessor internalGetMapFieldReflection(int number)
Parameter
Name Description
number int
Returns
Type Description
com.google.protobuf.MapFieldReflectionAccessor
Overrides
com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

internalGetMutableMapFieldReflection(int number)

protected MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number)
Parameter
Name Description
number int
Returns
Type Description
com.google.protobuf.MapFieldReflectionAccessor
Overrides
com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

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
Exceptions
Type Description
IOException

mergeFrom(Message other)

public EvaluationJobConfig.Builder mergeFrom(Message other)
Parameter
Name Description
other Message
Returns
Type Description
EvaluationJobConfig.Builder
Overrides

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

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

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

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