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public interface EvaluationJobConfigOrBuilder extends MessageOrBuilder
Implements
MessageOrBuilderMethods
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 orreference_json_key
.reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_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 orreference_json_key
.reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_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 orreference_json_key
.reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_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 orreference_json_key
.reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_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 orreference_json_key
.reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_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 ofIMAGE
,TEXT
, orGENERAL_DATA
.annotationType
must be one ofIMAGE_CLASSIFICATION_ANNOTATION
,TEXT_CLASSIFICATION_ANNOTATION
,GENERAL_CLASSIFICATION_ANNOTATION
, orIMAGE_BOUNDING_BOX_ANNOTATION
(image object detection).- If your machine learning model performs classification, you must specify
classificationMetadata.isMultiLabel
. - You must specify
bigquerySource
(notgcsSource
).
.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 ofIMAGE
,TEXT
, orGENERAL_DATA
.annotationType
must be one ofIMAGE_CLASSIFICATION_ANNOTATION
,TEXT_CLASSIFICATION_ANNOTATION
,GENERAL_CLASSIFICATION_ANNOTATION
, orIMAGE_BOUNDING_BOX_ANNOTATION
(image object detection).- If your machine learning model performs classification, you must specify
classificationMetadata.isMultiLabel
. - You must specify
bigquerySource
(notgcsSource
).
.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 ofIMAGE
,TEXT
, orGENERAL_DATA
.annotationType
must be one ofIMAGE_CLASSIFICATION_ANNOTATION
,TEXT_CLASSIFICATION_ANNOTATION
,GENERAL_CLASSIFICATION_ANNOTATION
, orIMAGE_BOUNDING_BOX_ANNOTATION
(image object detection).- If your machine learning model performs classification, you must specify
classificationMetadata.isMultiLabel
. - You must specify
bigquerySource
(notgcsSource
).
.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. |