Struct google_api_proto::google::cloud::automl::v1beta1::BatchPredictOutputConfig

pub struct BatchPredictOutputConfig {
    pub destination: Option<Destination>,
}

Output configuration for BatchPredict Action.

As destination the

[gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be “prediction--”, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

• For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,…,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image’s “ID” : “<id_value>” followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same “ID” : “<id_value>” but here followed by exactly one

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields.

• For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,…,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image’s “ID” : “<id_value>” followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same “ID” : “<id_value>” but here followed by exactly one

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields.

• For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created.

  The format of video_classification.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = “OK” if prediction completed successfully, or an error code with message otherwise. If STATUS is not “OK” then the .JSON file for that line may not exist or be empty.

     Each .JSON file, assuming STATUS is "OK", will contain a list of
     AnnotationPayload protos in JSON format, which are the predictions
     for the video time segment the file is assigned to in the
     video_classification.csv. All AnnotationPayload protos will have
     video_classification field set, and will be sorted by
     video_classification.type field (note that the returned types are
     governed by `classifaction_types` parameter in
     [PredictService.BatchPredictRequest.params][]).

• For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,…, video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)).

  The format of video_object_tracking.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = “OK” if prediction completed successfully, or an error code with message otherwise. If STATUS is not “OK” then the .JSON file for that line may not exist or be empty.

     Each .JSON file, assuming STATUS is "OK", will contain a list of
     AnnotationPayload protos in JSON format, which are the predictions
     for each frame of the video time segment the file is assigned to in
     video_object_tracking.csv. All AnnotationPayload protos will have
     video_object_tracking field set.

• For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,…,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

  Each .JSONL file will contain, per line, a JSON representation of a
  proto that wraps input text snippet or input text file and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have classification detail populated. A single text snippet or file
  will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any text snippet or file failed (partially or
  completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
  `errors_N.jsonl` files will be created (N depends on total number of
  failed predictions). These files will have a JSON representation of a
  proto that wraps input text snippet or input text file followed by
  exactly one

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.

• For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,…,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

  Each .JSONL file will contain, per line, a JSON representation of a
  proto that wraps input text snippet or input text file and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have text_sentiment detail populated. A single text snippet or file
  will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any text snippet or file failed (partially or
  completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
  `errors_N.jsonl` files will be created (N depends on total number of
  failed predictions). These files will have a JSON representation of a
  proto that wraps input text snippet or input text file followed by
  exactly one

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.

• For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,…,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet’s “id” (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the “id” : “<id_value>” (in case of inline) or the document proto (in case of document) but here followed by exactly one

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.

• For Tables: Output depends on whether

[gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] or

[bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). GCS case: In the created directory files tables_1.csv, tables_2.csv,…, tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION

[prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns’

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] given on input followed by M target column names in the format of

“<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>__score“ where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction [scores][google.cloud.automl.v1beta1.TablesAnnotation.score]. For REGRESSION and FORECASTING

[prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns’ [display_name-s][google.cloud.automl.v1beta1.display_name] given on input followed by the predicted target column with name in the format of

“predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>“ Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,…, errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a JSON string, and containing only code and message. BigQuery case:

[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name prediction_<model-display-name>_<timestamp-of-prediction-call> where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, predictions, and errors. The predictions table’s column names will be the input columns’

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] followed by the target column with name in the format of

“predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>“ The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of

[AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], represented as STRUCT-s, containing [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation]. The errors table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of

“errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>“, and as a value has

[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing only code and message.

Fields

destination: Option<Destination>

Required. The destination of the output.

Trait Implementations

impl Clone for BatchPredictOutputConfig

fn clone(&self) -> BatchPredictOutputConfig

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for BatchPredictOutputConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for BatchPredictOutputConfig

fn default() -> Self

Returns the “default value” for a type.

impl Message for BatchPredictOutputConfig

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for BatchPredictOutputConfig

fn eq(&self, other: &BatchPredictOutputConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for BatchPredictOutputConfig