GoogleCloudDatalabelingV1beta1EvaluationJobConfig
import type { GoogleCloudDatalabelingV1beta1EvaluationJobConfig } from "https://googleapis.deno.dev/v1/datalabeling:v1beta1.ts";
Configures specific details of how a continuous evaluation job works. Provide this configuration when you create an EvaluationJob.
§Properties
Required. Prediction keys that tell Data Labeling Service where to find
the data for evaluation in your BigQuery table. When the service samples
prediction input and output from your model version and saves it to
BigQuery, the data gets stored as JSON strings in the BigQuery table. These
keys tell Data Labeling Service how to parse the JSON. You can provide the
following entries in this field: * data_json_key
: the data key for
prediction input. You must provide either this key or reference_json_key
.
reference_json_key
: the data reference key for prediction input. You must provide either this key ordata_json_key
. *label_json_key
: the label key for prediction output. Required. *label_score_json_key
: the score key for prediction output. Required. *bounding_box_json_key
: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
Specify this field if your model version performs image object detection
(bounding box detection). annotationSpecSet
in this configuration must
match EvaluationJob.annotationSpecSet.
Required. Details for calculating evaluation metrics and creating
Evaulations. If your model version performs image object detection, you
must specify the boundingBoxEvaluationOptions
field within this
configuration. Otherwise, provide an empty object for this configuration.
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
Required. The maximum number of predictions to sample and save to BigQuery
during each evaluation interval. This limit overrides
example_sample_percentage
: even if the service has not sampled enough
predictions to fulfill example_sample_perecentage
during an interval, it
stops sampling predictions when it meets this limit.
Required. Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
Optional. Details for human annotation of your data. If you set
labelMissingGroundTruth to true
for this evaluation job, then you must
specify this field. If you plan to provide your own ground truth labels,
then omit this field. Note that you must create an Instruction resource
before you can specify this field. Provide the name of the instruction
resource in the instruction
field within this configuration.
Specify this field if your model version performs image classification or
general classification. annotationSpecSet
in this configuration must
match EvaluationJob.annotationSpecSet. allowMultiLabel
in this
configuration must match classificationMetadata.isMultiLabel
in
input_config.
Rquired. Details for the sampled prediction input. Within this
configuration, there are requirements for several fields: * dataType
must
be one of IMAGE
, TEXT
, or GENERAL_DATA
. * annotationType
must be
one of IMAGE_CLASSIFICATION_ANNOTATION
, TEXT_CLASSIFICATION_ANNOTATION
,
GENERAL_CLASSIFICATION_ANNOTATION
, or IMAGE_BOUNDING_BOX_ANNOTATION
(image object detection). * If your machine learning model performs
classification, you must specify classificationMetadata.isMultiLabel
. *
You must specify bigquerySource
(not gcsSource
).
Specify this field if your model version performs text classification.
annotationSpecSet
in this configuration must match
EvaluationJob.annotationSpecSet. allowMultiLabel
in this configuration
must match classificationMetadata.isMultiLabel
in input_config.