Google Cloud Datalabeling V1beta1 Client - Class EvaluationJobConfig (0.4.2)

Reference documentation and code samples for the Google Cloud Datalabeling V1beta1 Client class EvaluationJobConfig.

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

Generated from protobuf message google.cloud.datalabeling.v1beta1.EvaluationJobConfig

Namespace

Google \ Cloud \ DataLabeling \ V1beta1

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ image_classification_config`	`Google\Cloud\DataLabeling\V1beta1\ImageClassificationConfig` 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.
`↳ bounding_poly_config`	`Google\Cloud\DataLabeling\V1beta1\BoundingPolyConfig` Specify this field if your model version performs image object detection (bounding box detection). `annotationSpecSet` in this configuration must match EvaluationJob.annotationSpecSet.
`↳ text_classification_config`	`Google\Cloud\DataLabeling\V1beta1\TextClassificationConfig` 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.
`↳ input_config`	`Google\Cloud\DataLabeling\V1beta1\InputConfig` 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`).
`↳ evaluation_config`	`Google\Cloud\DataLabeling\V1beta1\EvaluationConfig` 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.
`↳ human_annotation_config`	`Google\Cloud\DataLabeling\V1beta1\HumanAnnotationConfig` 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.
`↳ bigquery_import_keys`	`array\|Google\Protobuf\Internal\MapField` 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.
`↳ example_count`	`int` 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.
`↳ example_sample_percentage`	`float` 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.
`↳ evaluation_job_alert_config`	`Google\Cloud\DataLabeling\V1beta1\EvaluationJobAlertConfig` 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.

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.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\ImageClassificationConfig\|null`

hasImageClassificationConfig

setImageClassificationConfig

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.

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\ImageClassificationConfig`

Returns
Type	Description
`$this`

getBoundingPolyConfig

Specify this field if your model version performs image object detection (bounding box detection).

annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\BoundingPolyConfig\|null`

hasBoundingPolyConfig

setBoundingPolyConfig

Specify this field if your model version performs image object detection (bounding box detection).

annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\BoundingPolyConfig`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\TextClassificationConfig\|null`

hasTextClassificationConfig

setTextClassificationConfig

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.

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\TextClassificationConfig`

Returns
Type	Description
`$this`

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).

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\InputConfig\|null`

hasInputConfig

clearInputConfig

setInputConfig

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).

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\InputConfig`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\EvaluationConfig\|null`

hasEvaluationConfig

clearEvaluationConfig

setEvaluationConfig

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\EvaluationConfig`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\HumanAnnotationConfig\|null`

hasHumanAnnotationConfig

clearHumanAnnotationConfig

setHumanAnnotationConfig

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.

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\HumanAnnotationConfig`

Returns
Type	Description
`$this`

getBigqueryImportKeys

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.

Returns
Type	Description
`Google\Protobuf\Internal\MapField`

setBigqueryImportKeys

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.

Parameter
Name	Description
`var`	`array\|Google\Protobuf\Internal\MapField`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`int`

setExampleCount

Parameter
Name	Description
`var`	`int`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`float`

setExampleSamplePercentage

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.

Parameter
Name	Description
`var`	`float`

Returns
Type	Description
`$this`

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.

Returns
Type	Description
`Google\Cloud\DataLabeling\V1beta1\EvaluationJobAlertConfig\|null`

hasEvaluationJobAlertConfig

clearEvaluationJobAlertConfig

setEvaluationJobAlertConfig

Parameter
Name	Description
`var`	`Google\Cloud\DataLabeling\V1beta1\EvaluationJobAlertConfig`

Returns
Type	Description
`$this`

getHumanAnnotationRequestConfig

Returns
Type	Description
`string`