google.cloud.aiplatform.v1.InputDataConfig */ class InputDataConfig extends \Google\Protobuf\Internal\Message { /** * Required. The ID of the Dataset in the same Project and Location which data * will be used to train the Model. The Dataset must use schema compatible * with Model being trained, and what is compatible should be described in the * used TrainingPipeline's [training_task_definition] * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. * For tabular Datasets, all their data is exported to training, to pick * and choose from. * * Generated from protobuf field string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED]; */ private $dataset_id = ''; /** * Applicable only to Datasets that have DataItems and Annotations. * A filter on Annotations of the Dataset. Only Annotations that both * match this filter and belong to DataItems not ignored by the split method * are used in respectively training, validation or test role, depending on * the role of the DataItem they are on (for the auto-assigned that role is * decided by Vertex AI). A filter with same syntax as the one used in * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] * may be used, but note here it filters across all Annotations of the * Dataset, and not just within a single DataItem. * * Generated from protobuf field string annotations_filter = 6; */ private $annotations_filter = ''; /** * Applicable only to custom training with Datasets that have DataItems and * Annotations. * Cloud Storage URI that points to a YAML file describing the annotation * schema. The schema is defined as an OpenAPI 3.0.2 [Schema * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). * The schema files that can be used here are found in * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the * chosen schema must be consistent with * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the * Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. * Only Annotations that both match this schema and belong to DataItems not * ignored by the split method are used in respectively training, validation * or test role, depending on the role of the DataItem they are on. * When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. * * Generated from protobuf field string annotation_schema_uri = 9; */ private $annotation_schema_uri = ''; /** * Only applicable to Datasets that have SavedQueries. * The ID of a SavedQuery (annotation set) under the Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used * for filtering Annotations for training. * Only Annotations that are associated with this SavedQuery are used in * respectively training. When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. * Only one of * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] * should be specified as both of them represent the same thing: problem type. * * Generated from protobuf field string saved_query_id = 7; */ private $saved_query_id = ''; /** * Whether to persist the ML use assignment to data item system labels. * * Generated from protobuf field bool persist_ml_use_assignment = 11; */ private $persist_ml_use_assignment = false; protected $split; protected $destination; /** * Constructor. * * @param array $data { * Optional. Data for populating the Message object. * * @type \Google\Cloud\AIPlatform\V1\FractionSplit $fraction_split * Split based on fractions defining the size of each set. * @type \Google\Cloud\AIPlatform\V1\FilterSplit $filter_split * Split based on the provided filters for each set. * @type \Google\Cloud\AIPlatform\V1\PredefinedSplit $predefined_split * Supported only for tabular Datasets. * Split based on a predefined key. * @type \Google\Cloud\AIPlatform\V1\TimestampSplit $timestamp_split * Supported only for tabular Datasets. * Split based on the timestamp of the input data pieces. * @type \Google\Cloud\AIPlatform\V1\StratifiedSplit $stratified_split * Supported only for tabular Datasets. * Split based on the distribution of the specified column. * @type \Google\Cloud\AIPlatform\V1\GcsDestination $gcs_destination * The Cloud Storage location where the training data is to be * written to. In the given directory a new directory is created with * name: * `dataset---` * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. * All training input data is written into that directory. * The Vertex AI environment variables representing Cloud Storage * data URIs are represented in the Cloud Storage wildcard * format to support sharded data. e.g.: "gs://.../training-*.jsonl" * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * * AIP_TRAINING_DATA_URI = * "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" * * AIP_VALIDATION_DATA_URI = * "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" * * AIP_TEST_DATA_URI = * "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}" * @type \Google\Cloud\AIPlatform\V1\BigQueryDestination $bigquery_destination * Only applicable to custom training with tabular Dataset with BigQuery * source. * The BigQuery project location where the training data is to be written * to. In the given project a new dataset is created with name * `dataset___` * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training * input data is written into that dataset. In the dataset three * tables are created, `training`, `validation` and `test`. * * AIP_DATA_FORMAT = "bigquery". * * AIP_TRAINING_DATA_URI = * "bigquery_destination.dataset___.training" * * AIP_VALIDATION_DATA_URI = * "bigquery_destination.dataset___.validation" * * AIP_TEST_DATA_URI = * "bigquery_destination.dataset___.test" * @type string $dataset_id * Required. The ID of the Dataset in the same Project and Location which data * will be used to train the Model. The Dataset must use schema compatible * with Model being trained, and what is compatible should be described in the * used TrainingPipeline's [training_task_definition] * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. * For tabular Datasets, all their data is exported to training, to pick * and choose from. * @type string $annotations_filter * Applicable only to Datasets that have DataItems and Annotations. * A filter on Annotations of the Dataset. Only Annotations that both * match this filter and belong to DataItems not ignored by the split method * are used in respectively training, validation or test role, depending on * the role of the DataItem they are on (for the auto-assigned that role is * decided by Vertex AI). A filter with same syntax as the one used in * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] * may be used, but note here it filters across all Annotations of the * Dataset, and not just within a single DataItem. * @type string $annotation_schema_uri * Applicable only to custom training with Datasets that have DataItems and * Annotations. * Cloud Storage URI that points to a YAML file describing the annotation * schema. The schema is defined as an OpenAPI 3.0.2 [Schema * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). * The schema files that can be used here are found in * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the * chosen schema must be consistent with * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the * Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. * Only Annotations that both match this schema and belong to DataItems not * ignored by the split method are used in respectively training, validation * or test role, depending on the role of the DataItem they are on. * When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. * @type string $saved_query_id * Only applicable to Datasets that have SavedQueries. * The ID of a SavedQuery (annotation set) under the Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used * for filtering Annotations for training. * Only Annotations that are associated with this SavedQuery are used in * respectively training. When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. * Only one of * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] * should be specified as both of them represent the same thing: problem type. * @type bool $persist_ml_use_assignment * Whether to persist the ML use assignment to data item system labels. * } */ public function __construct($data = NULL) { \GPBMetadata\Google\Cloud\Aiplatform\V1\TrainingPipeline::initOnce(); parent::__construct($data); } /** * Split based on fractions defining the size of each set. * * Generated from protobuf field .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2; * @return \Google\Cloud\AIPlatform\V1\FractionSplit|null */ public function getFractionSplit() { return $this->readOneof(2); } public function hasFractionSplit() { return $this->hasOneof(2); } /** * Split based on fractions defining the size of each set. * * Generated from protobuf field .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2; * @param \Google\Cloud\AIPlatform\V1\FractionSplit $var * @return $this */ public function setFractionSplit($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\FractionSplit::class); $this->writeOneof(2, $var); return $this; } /** * Split based on the provided filters for each set. * * Generated from protobuf field .google.cloud.aiplatform.v1.FilterSplit filter_split = 3; * @return \Google\Cloud\AIPlatform\V1\FilterSplit|null */ public function getFilterSplit() { return $this->readOneof(3); } public function hasFilterSplit() { return $this->hasOneof(3); } /** * Split based on the provided filters for each set. * * Generated from protobuf field .google.cloud.aiplatform.v1.FilterSplit filter_split = 3; * @param \Google\Cloud\AIPlatform\V1\FilterSplit $var * @return $this */ public function setFilterSplit($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\FilterSplit::class); $this->writeOneof(3, $var); return $this; } /** * Supported only for tabular Datasets. * Split based on a predefined key. * * Generated from protobuf field .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4; * @return \Google\Cloud\AIPlatform\V1\PredefinedSplit|null */ public function getPredefinedSplit() { return $this->readOneof(4); } public function hasPredefinedSplit() { return $this->hasOneof(4); } /** * Supported only for tabular Datasets. * Split based on a predefined key. * * Generated from protobuf field .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4; * @param \Google\Cloud\AIPlatform\V1\PredefinedSplit $var * @return $this */ public function setPredefinedSplit($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\PredefinedSplit::class); $this->writeOneof(4, $var); return $this; } /** * Supported only for tabular Datasets. * Split based on the timestamp of the input data pieces. * * Generated from protobuf field .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5; * @return \Google\Cloud\AIPlatform\V1\TimestampSplit|null */ public function getTimestampSplit() { return $this->readOneof(5); } public function hasTimestampSplit() { return $this->hasOneof(5); } /** * Supported only for tabular Datasets. * Split based on the timestamp of the input data pieces. * * Generated from protobuf field .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5; * @param \Google\Cloud\AIPlatform\V1\TimestampSplit $var * @return $this */ public function setTimestampSplit($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\TimestampSplit::class); $this->writeOneof(5, $var); return $this; } /** * Supported only for tabular Datasets. * Split based on the distribution of the specified column. * * Generated from protobuf field .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12; * @return \Google\Cloud\AIPlatform\V1\StratifiedSplit|null */ public function getStratifiedSplit() { return $this->readOneof(12); } public function hasStratifiedSplit() { return $this->hasOneof(12); } /** * Supported only for tabular Datasets. * Split based on the distribution of the specified column. * * Generated from protobuf field .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12; * @param \Google\Cloud\AIPlatform\V1\StratifiedSplit $var * @return $this */ public function setStratifiedSplit($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\StratifiedSplit::class); $this->writeOneof(12, $var); return $this; } /** * The Cloud Storage location where the training data is to be * written to. In the given directory a new directory is created with * name: * `dataset---` * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. * All training input data is written into that directory. * The Vertex AI environment variables representing Cloud Storage * data URIs are represented in the Cloud Storage wildcard * format to support sharded data. e.g.: "gs://.../training-*.jsonl" * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * * AIP_TRAINING_DATA_URI = * "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" * * AIP_VALIDATION_DATA_URI = * "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" * * AIP_TEST_DATA_URI = * "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}" * * Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8; * @return \Google\Cloud\AIPlatform\V1\GcsDestination|null */ public function getGcsDestination() { return $this->readOneof(8); } public function hasGcsDestination() { return $this->hasOneof(8); } /** * The Cloud Storage location where the training data is to be * written to. In the given directory a new directory is created with * name: * `dataset---` * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. * All training input data is written into that directory. * The Vertex AI environment variables representing Cloud Storage * data URIs are represented in the Cloud Storage wildcard * format to support sharded data. e.g.: "gs://.../training-*.jsonl" * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * * AIP_TRAINING_DATA_URI = * "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" * * AIP_VALIDATION_DATA_URI = * "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" * * AIP_TEST_DATA_URI = * "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}" * * Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8; * @param \Google\Cloud\AIPlatform\V1\GcsDestination $var * @return $this */ public function setGcsDestination($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\GcsDestination::class); $this->writeOneof(8, $var); return $this; } /** * Only applicable to custom training with tabular Dataset with BigQuery * source. * The BigQuery project location where the training data is to be written * to. In the given project a new dataset is created with name * `dataset___` * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training * input data is written into that dataset. In the dataset three * tables are created, `training`, `validation` and `test`. * * AIP_DATA_FORMAT = "bigquery". * * AIP_TRAINING_DATA_URI = * "bigquery_destination.dataset___.training" * * AIP_VALIDATION_DATA_URI = * "bigquery_destination.dataset___.validation" * * AIP_TEST_DATA_URI = * "bigquery_destination.dataset___.test" * * Generated from protobuf field .google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10; * @return \Google\Cloud\AIPlatform\V1\BigQueryDestination|null */ public function getBigqueryDestination() { return $this->readOneof(10); } public function hasBigqueryDestination() { return $this->hasOneof(10); } /** * Only applicable to custom training with tabular Dataset with BigQuery * source. * The BigQuery project location where the training data is to be written * to. In the given project a new dataset is created with name * `dataset___` * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training * input data is written into that dataset. In the dataset three * tables are created, `training`, `validation` and `test`. * * AIP_DATA_FORMAT = "bigquery". * * AIP_TRAINING_DATA_URI = * "bigquery_destination.dataset___.training" * * AIP_VALIDATION_DATA_URI = * "bigquery_destination.dataset___.validation" * * AIP_TEST_DATA_URI = * "bigquery_destination.dataset___.test" * * Generated from protobuf field .google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10; * @param \Google\Cloud\AIPlatform\V1\BigQueryDestination $var * @return $this */ public function setBigqueryDestination($var) { GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\BigQueryDestination::class); $this->writeOneof(10, $var); return $this; } /** * Required. The ID of the Dataset in the same Project and Location which data * will be used to train the Model. The Dataset must use schema compatible * with Model being trained, and what is compatible should be described in the * used TrainingPipeline's [training_task_definition] * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. * For tabular Datasets, all their data is exported to training, to pick * and choose from. * * Generated from protobuf field string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED]; * @return string */ public function getDatasetId() { return $this->dataset_id; } /** * Required. The ID of the Dataset in the same Project and Location which data * will be used to train the Model. The Dataset must use schema compatible * with Model being trained, and what is compatible should be described in the * used TrainingPipeline's [training_task_definition] * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. * For tabular Datasets, all their data is exported to training, to pick * and choose from. * * Generated from protobuf field string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED]; * @param string $var * @return $this */ public function setDatasetId($var) { GPBUtil::checkString($var, True); $this->dataset_id = $var; return $this; } /** * Applicable only to Datasets that have DataItems and Annotations. * A filter on Annotations of the Dataset. Only Annotations that both * match this filter and belong to DataItems not ignored by the split method * are used in respectively training, validation or test role, depending on * the role of the DataItem they are on (for the auto-assigned that role is * decided by Vertex AI). A filter with same syntax as the one used in * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] * may be used, but note here it filters across all Annotations of the * Dataset, and not just within a single DataItem. * * Generated from protobuf field string annotations_filter = 6; * @return string */ public function getAnnotationsFilter() { return $this->annotations_filter; } /** * Applicable only to Datasets that have DataItems and Annotations. * A filter on Annotations of the Dataset. Only Annotations that both * match this filter and belong to DataItems not ignored by the split method * are used in respectively training, validation or test role, depending on * the role of the DataItem they are on (for the auto-assigned that role is * decided by Vertex AI). A filter with same syntax as the one used in * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] * may be used, but note here it filters across all Annotations of the * Dataset, and not just within a single DataItem. * * Generated from protobuf field string annotations_filter = 6; * @param string $var * @return $this */ public function setAnnotationsFilter($var) { GPBUtil::checkString($var, True); $this->annotations_filter = $var; return $this; } /** * Applicable only to custom training with Datasets that have DataItems and * Annotations. * Cloud Storage URI that points to a YAML file describing the annotation * schema. The schema is defined as an OpenAPI 3.0.2 [Schema * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). * The schema files that can be used here are found in * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the * chosen schema must be consistent with * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the * Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. * Only Annotations that both match this schema and belong to DataItems not * ignored by the split method are used in respectively training, validation * or test role, depending on the role of the DataItem they are on. * When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. * * Generated from protobuf field string annotation_schema_uri = 9; * @return string */ public function getAnnotationSchemaUri() { return $this->annotation_schema_uri; } /** * Applicable only to custom training with Datasets that have DataItems and * Annotations. * Cloud Storage URI that points to a YAML file describing the annotation * schema. The schema is defined as an OpenAPI 3.0.2 [Schema * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). * The schema files that can be used here are found in * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the * chosen schema must be consistent with * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the * Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. * Only Annotations that both match this schema and belong to DataItems not * ignored by the split method are used in respectively training, validation * or test role, depending on the role of the DataItem they are on. * When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. * * Generated from protobuf field string annotation_schema_uri = 9; * @param string $var * @return $this */ public function setAnnotationSchemaUri($var) { GPBUtil::checkString($var, True); $this->annotation_schema_uri = $var; return $this; } /** * Only applicable to Datasets that have SavedQueries. * The ID of a SavedQuery (annotation set) under the Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used * for filtering Annotations for training. * Only Annotations that are associated with this SavedQuery are used in * respectively training. When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. * Only one of * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] * should be specified as both of them represent the same thing: problem type. * * Generated from protobuf field string saved_query_id = 7; * @return string */ public function getSavedQueryId() { return $this->saved_query_id; } /** * Only applicable to Datasets that have SavedQueries. * The ID of a SavedQuery (annotation set) under the Dataset specified by * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used * for filtering Annotations for training. * Only Annotations that are associated with this SavedQuery are used in * respectively training. When used in conjunction with * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], * the Annotations used for training are filtered by both * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. * Only one of * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] * and * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] * should be specified as both of them represent the same thing: problem type. * * Generated from protobuf field string saved_query_id = 7; * @param string $var * @return $this */ public function setSavedQueryId($var) { GPBUtil::checkString($var, True); $this->saved_query_id = $var; return $this; } /** * Whether to persist the ML use assignment to data item system labels. * * Generated from protobuf field bool persist_ml_use_assignment = 11; * @return bool */ public function getPersistMlUseAssignment() { return $this->persist_ml_use_assignment; } /** * Whether to persist the ML use assignment to data item system labels. * * Generated from protobuf field bool persist_ml_use_assignment = 11; * @param bool $var * @return $this */ public function setPersistMlUseAssignment($var) { GPBUtil::checkBool($var); $this->persist_ml_use_assignment = $var; return $this; } /** * @return string */ public function getSplit() { return $this->whichOneof("split"); } /** * @return string */ public function getDestination() { return $this->whichOneof("destination"); } }