Package com.google.cloud.datalabeling.v1beta1

Interface EvaluationJobConfigOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
EvaluationJobConfig, EvaluationJobConfig.Builder
@Generated public interface EvaluationJobConfigOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • hasImageClassificationConfig

      boolean hasImageClassificationConfig()
       Specify this field if your model version performs image classification or
 general classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;
      Returns:
      Whether the imageClassificationConfig field is set.

    • getImageClassificationConfig

      ImageClassificationConfig getImageClassificationConfig()
       Specify this field if your model version performs image classification or
 general classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;
      Returns:
      The imageClassificationConfig.

    • getImageClassificationConfigOrBuilder

      ImageClassificationConfigOrBuilder getImageClassificationConfigOrBuilder()
       Specify this field if your model version performs image classification or
 general classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

    • hasBoundingPolyConfig

      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.EvaluationJob.annotation_spec_set].
      .google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;
      Returns:
      Whether the boundingPolyConfig field is set.

    • getBoundingPolyConfig

      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.EvaluationJob.annotation_spec_set].
      .google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;
      Returns:
      The boundingPolyConfig.

    • getBoundingPolyConfigOrBuilder

      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.EvaluationJob.annotation_spec_set].
      .google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

    • hasTextClassificationConfig

      boolean hasTextClassificationConfig()
       Specify this field if your model version performs text classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;
      Returns:
      Whether the textClassificationConfig field is set.

    • getTextClassificationConfig

      TextClassificationConfig getTextClassificationConfig()
       Specify this field if your model version performs text classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;
      Returns:
      The textClassificationConfig.

    • getTextClassificationConfigOrBuilder

      TextClassificationConfigOrBuilder getTextClassificationConfigOrBuilder()
       Specify this field if your model version performs text classification.

 `annotationSpecSet` in this configuration must match
 [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
 `allowMultiLabel` in this configuration must match
 `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
      .google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

    • hasInputConfig

      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:
      Whether the inputConfig field is set.

    • getInputConfig

      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:
      The inputConfig.

    • getInputConfigOrBuilder

      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;

    • hasEvaluationConfig

      boolean hasEvaluationConfig()
       Required. Details for calculating evaluation metrics and creating
 [Evaulations][google.cloud.datalabeling.v1beta1.Evaluation]. 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:
      Whether the evaluationConfig field is set.

    • getEvaluationConfig

      EvaluationConfig getEvaluationConfig()
       Required. Details for calculating evaluation metrics and creating
 [Evaulations][google.cloud.datalabeling.v1beta1.Evaluation]. 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:
      The evaluationConfig.

    • getEvaluationConfigOrBuilder

      EvaluationConfigOrBuilder getEvaluationConfigOrBuilder()
       Required. Details for calculating evaluation metrics and creating
 [Evaulations][google.cloud.datalabeling.v1beta1.Evaluation]. 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;

    • hasHumanAnnotationConfig

      boolean hasHumanAnnotationConfig()
       Optional. Details for human annotation of your data. If you set
 [labelMissingGroundTruth][google.cloud.datalabeling.v1beta1.EvaluationJob.label_missing_ground_truth] 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][google.cloud.datalabeling.v1beta1.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:
      Whether the humanAnnotationConfig field is set.

    • getHumanAnnotationConfig

      HumanAnnotationConfig getHumanAnnotationConfig()
       Optional. Details for human annotation of your data. If you set
 [labelMissingGroundTruth][google.cloud.datalabeling.v1beta1.EvaluationJob.label_missing_ground_truth] 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][google.cloud.datalabeling.v1beta1.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:
      The humanAnnotationConfig.

    • getHumanAnnotationConfigOrBuilder

      HumanAnnotationConfigOrBuilder getHumanAnnotationConfigOrBuilder()
       Optional. Details for human annotation of your data. If you set
 [labelMissingGroundTruth][google.cloud.datalabeling.v1beta1.EvaluationJob.label_missing_ground_truth] 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][google.cloud.datalabeling.v1beta1.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;

    • getBigqueryImportKeysCount

      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](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
      map<string, string> bigquery_import_keys = 9;

    • containsBigqueryImportKeys

      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](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
      map<string, string> bigquery_import_keys = 9;

    • getBigqueryImportKeys

      @Deprecated Map<String,String> getBigqueryImportKeys()
      Deprecated.
      Use getBigqueryImportKeysMap() instead.

    • getBigqueryImportKeysMap

      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](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
      map<string, string> bigquery_import_keys = 9;

    • getBigqueryImportKeysOrDefault

      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](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
      map<string, string> bigquery_import_keys = 9;

    • getBigqueryImportKeysOrThrow

      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](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
      map<string, string> bigquery_import_keys = 9;

    • getExampleCount

      int getExampleCount()
       Required. The maximum number of predictions to sample and save to BigQuery
 during each [evaluation interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. 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:
      The exampleCount.

    • getExampleSamplePercentage

      double getExampleSamplePercentage()
       Required. Fraction of predictions to sample and save to BigQuery during
 each [evaluation interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. For example, 0.1 means
 10% of predictions served by your model version get saved to BigQuery.
      double example_sample_percentage = 11;
      Returns:
      The exampleSamplePercentage.

    • hasEvaluationJobAlertConfig

      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:
      Whether the evaluationJobAlertConfig field is set.

    • getEvaluationJobAlertConfig

      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:
      The evaluationJobAlertConfig.

    • getEvaluationJobAlertConfigOrBuilder

      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;

    • getHumanAnnotationRequestConfigCase

      EvaluationJobConfig.HumanAnnotationRequestConfigCase getHumanAnnotationRequestConfigCase()