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 will be 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 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 Extraction: In the created directory files
text_extraction_1.jsonl,text_extraction_2.jsonl,...,\text_extraction_N.jsonlwill 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" : "" followed by 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 additionalerrors_1.jsonl,errors_2.jsonl,...,errors_N.jsonlfiles 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" : "" (in case of inline) or the document proto (in case of document) but here followed by exactly one
`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__
containing onlycodeandmessage`.
- 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. 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:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__
represented as a JSON string, and containing onlycodeandmessage`. 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,
represented as STRUCT-s, containing
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:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__
represented as a STRUCT, and containing onlycodeandmessage`.
The Google Cloud Storage location of the directory where the output is to be written to.