Class BatchPredictOutputConfig (2.46.0)

public final class BatchPredictOutputConfig extends GeneratedMessageV3 implements BatchPredictOutputConfigOrBuilder

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-<model-display-name>-<timestamp-of-prediction-call>", 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 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 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 <code>classifaction_types</code> 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 file (or document) in
    the text snippet (or document) proto and a list of
    zero or more AnnotationPayload protos (called annotations), which
    have classification detail populated. A single text file (or
    document) will be listed only once with all its annotations, and its
    annotations will never be split across files.
    
    If prediction for any input file (or document) failed (partially or
    completely), then additional <code>errors_1.jsonl</code>, <code>errors_2.jsonl</code>,...,
    <code>errors_N.jsonl</code> 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 file followed by exactly one
    <a href="https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto"><code>google.rpc.Status</code></a>
    containing only <code>code</code> and <code>message</code>.
    
  • 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 file (or document) in
    the text snippet (or document) proto and a list of
    zero or more AnnotationPayload protos (called annotations), which
    have text_sentiment detail populated. A single text file (or
    document) will be listed only once with all its annotations, and its
    annotations will never be split across files.
    
    If prediction for any input file (or document) failed (partially or
    completely), then additional <code>errors_1.jsonl</code>, <code>errors_2.jsonl</code>,...,
    <code>errors_N.jsonl</code> 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 file followed by exactly one
    <a href="https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto"><code>google.rpc.Status</code></a>
    containing only <code>code</code> and <code>message</code>.
    
    • 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 containing only code and message.
  • For Tables: Output depends on whether gcs_destination or bigquery_destination is set (either is allowed). Google Cloud Storage 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: 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 value>_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: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted<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 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 represented as a JSON string, and containing only code and message. BigQuery case: 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 <model-display-name> 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 followed by the target column with name in the format of "predicted<target_column_specs 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 display_name>", and as a value has google.rpc.Status represented as a STRUCT, and containing only code and message.

Protobuf type google.cloud.automl.v1.BatchPredictOutputConfig

Static Fields

GCS_DESTINATION_FIELD_NUMBER

public static final int GCS_DESTINATION_FIELD_NUMBER
Field Value
Type Description
int

Static Methods

getDefaultInstance()

public static BatchPredictOutputConfig getDefaultInstance()
Returns
Type Description
BatchPredictOutputConfig

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

newBuilder()

public static BatchPredictOutputConfig.Builder newBuilder()
Returns
Type Description
BatchPredictOutputConfig.Builder

newBuilder(BatchPredictOutputConfig prototype)

public static BatchPredictOutputConfig.Builder newBuilder(BatchPredictOutputConfig prototype)
Parameter
Name Description
prototype BatchPredictOutputConfig
Returns
Type Description
BatchPredictOutputConfig.Builder

parseDelimitedFrom(InputStream input)

public static BatchPredictOutputConfig parseDelimitedFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseFrom(byte[] data)

public static BatchPredictOutputConfig parseFrom(byte[] data)
Parameter
Name Description
data byte[]
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data byte[]
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data)

public static BatchPredictOutputConfig parseFrom(ByteString data)
Parameter
Name Description
data ByteString
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteString
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static BatchPredictOutputConfig parseFrom(CodedInputStream input)
Parameter
Name Description
input CodedInputStream
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseFrom(InputStream input)

public static BatchPredictOutputConfig parseFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
IOException

parseFrom(ByteBuffer data)

public static BatchPredictOutputConfig parseFrom(ByteBuffer data)
Parameter
Name Description
data ByteBuffer
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static BatchPredictOutputConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteBuffer
extensionRegistry ExtensionRegistryLite
Returns
Type Description
BatchPredictOutputConfig
Exceptions
Type Description
InvalidProtocolBufferException

parser()

public static Parser<BatchPredictOutputConfig> parser()
Returns
Type Description
Parser<BatchPredictOutputConfig>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
Name Description
obj Object
Returns
Type Description
boolean
Overrides

getDefaultInstanceForType()

public BatchPredictOutputConfig getDefaultInstanceForType()
Returns
Type Description
BatchPredictOutputConfig

getDestinationCase()

public BatchPredictOutputConfig.DestinationCase getDestinationCase()
Returns
Type Description
BatchPredictOutputConfig.DestinationCase

getGcsDestination()

public GcsDestination getGcsDestination()

Required. The Google Cloud Storage location of the directory where the output is to be written to.

.google.cloud.automl.v1.GcsDestination gcs_destination = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type Description
GcsDestination

The gcsDestination.

getGcsDestinationOrBuilder()

public GcsDestinationOrBuilder getGcsDestinationOrBuilder()

Required. The Google Cloud Storage location of the directory where the output is to be written to.

.google.cloud.automl.v1.GcsDestination gcs_destination = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type Description
GcsDestinationOrBuilder

getParserForType()

public Parser<BatchPredictOutputConfig> getParserForType()
Returns
Type Description
Parser<BatchPredictOutputConfig>
Overrides

getSerializedSize()

public int getSerializedSize()
Returns
Type Description
int
Overrides

hasGcsDestination()

public boolean hasGcsDestination()

Required. The Google Cloud Storage location of the directory where the output is to be written to.

.google.cloud.automl.v1.GcsDestination gcs_destination = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type Description
boolean

Whether the gcsDestination field is set.

hashCode()

public int hashCode()
Returns
Type Description
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

newBuilderForType()

public BatchPredictOutputConfig.Builder newBuilderForType()
Returns
Type Description
BatchPredictOutputConfig.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected BatchPredictOutputConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
Name Description
parent BuilderParent
Returns
Type Description
BatchPredictOutputConfig.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
Name Description
unused UnusedPrivateParameter
Returns
Type Description
Object
Overrides

toBuilder()

public BatchPredictOutputConfig.Builder toBuilder()
Returns
Type Description
BatchPredictOutputConfig.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
Name Description
output CodedOutputStream
Overrides
Exceptions
Type Description
IOException