BatchPredictOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)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--", 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 filesimage_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 message\ fields.
- 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 message\ fields.
- 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 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 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 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 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 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 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
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>__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](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
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](https:
//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
represented as a STRUCT, and containing only code and
message.
Attribute
| Name | Description |
gcs_destination |
Required. The Google Cloud Storage location of the directory where the output is to be written to. |