Interface EvaluationJobConfigOrBuilder (0.138.0)

Version 0.138.0

• 0.174.0 (latest)
• 0.173.0
• 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 interface EvaluationJobConfigOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

containsBigqueryImportKeys(String key)

public abstract 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 abstract Map<String,String> getBigqueryImportKeys()

Use #getBigqueryImportKeysMap() instead.

Returns
Type	Description
Map<String,String>

getBigqueryImportKeysCount()

public abstract 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 abstract 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 abstract 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 abstract 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 abstract 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.

getBoundingPolyConfigOrBuilder()

public abstract 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

getEvaluationConfig()

public abstract 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.

getEvaluationConfigOrBuilder()

public abstract 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 abstract 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.

getEvaluationJobAlertConfigOrBuilder()

public abstract 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 abstract 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 abstract 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 abstract 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.

getHumanAnnotationConfigOrBuilder()

public abstract 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 abstract EvaluationJobConfig.HumanAnnotationRequestConfigCase getHumanAnnotationRequestConfigCase()

Returns
Type	Description
EvaluationJobConfig.HumanAnnotationRequestConfigCase

getImageClassificationConfig()

public abstract 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.

getImageClassificationConfigOrBuilder()

public abstract 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 abstract 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.

getInputConfigOrBuilder()

public abstract 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

getTextClassificationConfig()

public abstract 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.

getTextClassificationConfigOrBuilder()

public abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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.