Class: Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig

Inherits:
Object
  • Object
show all
Defined in:
lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/io.rb

Overview

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 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.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" : "" 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 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" : "" (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). 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: 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>__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 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.

Instance Attribute Summary collapse

Instance Attribute Details

#bigquery_destination ⇒ Google::Cloud::AutoML::V1beta1::BigQueryDestination

Returns The BigQuery location where the output is to be written to.

Returns:



831
 
# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/io.rb', line 831

class BatchPredictOutputConfig; end

#gcs_destination ⇒ Google::Cloud::AutoML::V1beta1::GcsDestination

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

Returns:



831
 
# File 'lib/google/cloud/automl/v1beta1/doc/google/cloud/automl/v1beta1/io.rb', line 831

class BatchPredictOutputConfig; end