public final class EvaluationJobConfig extends GeneratedMessageV3 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
Static Fields
BIGQUERY_IMPORT_KEYS_FIELD_NUMBER
public static final int BIGQUERY_IMPORT_KEYS_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
BOUNDING_POLY_CONFIG_FIELD_NUMBER
public static final int BOUNDING_POLY_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
EVALUATION_CONFIG_FIELD_NUMBER
public static final int EVALUATION_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
EVALUATION_JOB_ALERT_CONFIG_FIELD_NUMBER
public static final int EVALUATION_JOB_ALERT_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
EXAMPLE_COUNT_FIELD_NUMBER
public static final int EXAMPLE_COUNT_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
EXAMPLE_SAMPLE_PERCENTAGE_FIELD_NUMBER
public static final int EXAMPLE_SAMPLE_PERCENTAGE_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
HUMAN_ANNOTATION_CONFIG_FIELD_NUMBER
public static final int HUMAN_ANNOTATION_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
IMAGE_CLASSIFICATION_CONFIG_FIELD_NUMBER
public static final int IMAGE_CLASSIFICATION_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int INPUT_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
TEXT_CLASSIFICATION_CONFIG_FIELD_NUMBER
public static final int TEXT_CLASSIFICATION_CONFIG_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
Static Methods
getDefaultInstance()
public static EvaluationJobConfig getDefaultInstance()
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()
newBuilder()
public static EvaluationJobConfig.Builder newBuilder()
newBuilder(EvaluationJobConfig prototype)
public static EvaluationJobConfig.Builder newBuilder(EvaluationJobConfig prototype)
public static EvaluationJobConfig parseDelimitedFrom(InputStream input)
public static EvaluationJobConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(byte[] data)
public static EvaluationJobConfig parseFrom(byte[] data)
| Parameter |
|---|
| Name | Description |
data | byte[]
|
parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static EvaluationJobConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteString data)
public static EvaluationJobConfig parseFrom(ByteString data)
parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static EvaluationJobConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static EvaluationJobConfig parseFrom(CodedInputStream input)
public static EvaluationJobConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static EvaluationJobConfig parseFrom(InputStream input)
public static EvaluationJobConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteBuffer data)
public static EvaluationJobConfig parseFrom(ByteBuffer data)
parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static EvaluationJobConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
parser()
public static Parser<EvaluationJobConfig> parser()
Methods
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
|
equals(Object obj)
public boolean equals(Object obj)
| Parameter |
|---|
| Name | Description |
obj | Object
|
Overrides
getBigqueryImportKeys()
public Map<String,String> getBigqueryImportKeys()
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;
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;
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
|
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;
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;
getDefaultInstanceForType()
public EvaluationJobConfig getDefaultInstanceForType()
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;
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;
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;
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;
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;
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;
getHumanAnnotationRequestConfigCase()
public EvaluationJobConfig.HumanAnnotationRequestConfigCase getHumanAnnotationRequestConfigCase()
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;
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;
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;
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;
getParserForType()
public Parser<EvaluationJobConfig> getParserForType()
Overrides
getSerializedSize()
public int getSerializedSize()
| Returns |
|---|
| Type | Description |
int | |
Overrides
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;
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;
getUnknownFields()
public final UnknownFieldSet getUnknownFields()
Overrides
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.
|
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.
|
hashCode()
| Returns |
|---|
| Type | Description |
int | |
Overrides
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
internalGetMapField(int number)
protected MapField internalGetMapField(int number)
| Parameter |
|---|
| Name | Description |
number | int
|
Overrides
isInitialized()
public final boolean isInitialized()
Overrides
newBuilderForType()
public EvaluationJobConfig.Builder newBuilderForType()
newBuilderForType(GeneratedMessageV3.BuilderParent parent)
protected EvaluationJobConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Overrides
toBuilder()
public EvaluationJobConfig.Builder toBuilder()
writeTo(CodedOutputStream output)
public void writeTo(CodedOutputStream output)
Overrides
