Reference documentation and code samples for the Cloud AutoML V1beta1 Client class BatchPredictOutputConfig.
Output configuration for BatchPredict Action.
As destination the 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-
- For Image Classification:
In the created directory files
image_classification_1.jsonl,image_classification_2.jsonl,...,image_classification_N.jsonlwill 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 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 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 onlycodeandmessagefields. - For Image Object Detection:
In the created directory files
image_object_detection_1.jsonl,image_object_detection_2.jsonl,...,image_object_detection_N.jsonlwill 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 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 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 onlycodeandmessagefields. - 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_typesparameter 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.jsonlwill 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 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 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 onlycodeandmessage. - For Text Sentiment:
In the created directory files
text_sentiment_1.jsonl,text_sentiment_2.jsonl,...,text_sentiment_N.jsonlwill 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 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 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 onlycodeandmessage.- 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" (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 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" : "<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 onlycodeandmessage.
- For Text Extraction:
In the created directory files
- For Tables:
Output depends on whether
gcs_destination
or
bigquery_destination
is set (either is allowed).
GCS case:
In the created directory files
tables_1.csv,tables_2.csv,...,tables_N.csvwill be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "<target_column_specs display_name><target_column_specs 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 additionalerrors_1.csv,errors_2.csv,...,errors_N.csvwill be created (N depends on total number of failed rows). These files will have analogous format astables_*.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 onlycodeandmessage. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with nameprediction_<model-display-name>_<timestamp-of-prediction-call>where
Generated from protobuf message google.cloud.automl.v1beta1.BatchPredictOutputConfig
Namespace
Google \ Cloud \ AutoMl \ V1beta1Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ gcs_destination |
Google\Cloud\AutoMl\V1beta1\GcsDestination
The Google Cloud Storage location of the directory where the output is to be written to. |
↳ bigquery_destination |
Google\Cloud\AutoMl\V1beta1\BigQueryDestination
The BigQuery location where the output is to be written to. |
getGcsDestination
The Google Cloud Storage location of the directory where the output is to be written to.
| Returns | |
|---|---|
| Type | Description |
Google\Cloud\AutoMl\V1beta1\GcsDestination|null | |
hasGcsDestination
setGcsDestination
The Google Cloud Storage location of the directory where the output is to be written to.
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Cloud\AutoMl\V1beta1\GcsDestination
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getBigqueryDestination
The BigQuery location where the output is to be written to.
| Returns | |
|---|---|
| Type | Description |
Google\Cloud\AutoMl\V1beta1\BigQueryDestination|null | |
hasBigqueryDestination
setBigqueryDestination
The BigQuery location where the output is to be written to.
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Cloud\AutoMl\V1beta1\BigQueryDestination
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getDestination
| Returns | |
|---|---|
| Type | Description |
string | |