PredictionServiceClient

PredictionServiceClient

AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

Constructor

new PredictionServiceClient(optionsopt)

Construct an instance of PredictionServiceClient.

Parameters:
Name Type Attributes Description
options object <optional>

The configuration object. See the subsequent parameters for more details.

Properties
Name Type Attributes Description
credentials object <optional>

Credentials object.

Properties
Name Type Attributes Description
client_email string <optional>
private_key string <optional>
email string <optional>

Account email address. Required when using a .pem or .p12 keyFilename.

keyFilename string <optional>

Full path to the a .json, .pem, or .p12 key downloaded from the Google Developers Console. If you provide a path to a JSON file, the projectId option below is not necessary. NOTE: .pem and .p12 require you to specify options.email as well.

port number <optional>

The port on which to connect to the remote host.

projectId string <optional>

The project ID from the Google Developer's Console, e.g. 'grape-spaceship-123'. We will also check the environment variable GCLOUD_PROJECT for your project ID. If your app is running in an environment which supports Application Default Credentials, your project ID will be detected automatically.

apiEndpoint string <optional>

The domain name of the API remote host.

Members

(static) apiEndpoint

The DNS address for this API service - same as servicePath(), exists for compatibility reasons.

(static) port

The port for this API service.

(static) scopes

The scopes needed to make gRPC calls for every method defined in this service.

(static) servicePath

The DNS address for this API service.

Methods

annotationSpecPath(project, location, dataset, annotation_spec) → {string}

Return a fully-qualified annotationSpec resource name string.

Parameters:
Name Type Description
project string
location string
dataset string
annotation_spec string

batchPredict(request, optionsopt) → {Promise}

Perform a batch prediction. Unlike the online Predict, batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via GetOperation method. Once the operation is done, BatchPredictResult is returned in the response field. Available for following ML scenarios:

  • AutoML Vision Classification
  • AutoML Vision Object Detection
  • AutoML Video Intelligence Classification
  • AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
  • AutoML Natural Language Entity Extraction
  • AutoML Natural Language Sentiment Analysis
  • AutoML Tables
Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. Name of the model requested to serve the batch prediction.

inputConfig google.cloud.automl.v1.BatchPredictInputConfig

Required. The input configuration for batch prediction.

outputConfig google.cloud.automl.v1.BatchPredictOutputConfig

Required. The Configuration specifying where output predictions should be written.

params Array.<number>

Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long.

AutoML Natural Language Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Object Detection

score_threshold : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server.

AutoML Video Intelligence Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5.

segment_classification : (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is true.

shot_classification : (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. The default is false.

WARNING: Model evaluation is not done for this classification type,
the quality of it depends on training data, but there are no metrics
provided to describe that quality.

1s_interval_classification : (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. The default is false.

WARNING: Model evaluation is not done for this classification
type, the quality of it depends on training data, but there are no
metrics provided to describe that quality.

AutoML Video Intelligence Object Tracking

score_threshold : (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server.

min_bounding_box_size : (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Value in 0 to 1 range. Default is 0.

options object <optional>

Call options. See CallOptions for more details.

close()

Terminate the GRPC channel and close the client.

The client will no longer be usable and all future behavior is undefined.

datasetPath(project, location, dataset) → {string}

Return a fully-qualified dataset resource name string.

Parameters:
Name Type Description
project string
location string
dataset string

getProjectId(callback)

Return the project ID used by this class.

Parameters:
Name Type Description
callback function

the callback to be called with the current project Id.

initialize() → {Promise}

Initialize the client. Performs asynchronous operations (such as authentication) and prepares the client. This function will be called automatically when any class method is called for the first time, but if you need to initialize it before calling an actual method, feel free to call initialize() directly.

You can await on this method if you want to make sure the client is initialized.

matchAnnotationSpecFromAnnotationSpecName(annotationSpecName) → {string}

Parse the annotation_spec from AnnotationSpec resource.

Parameters:
Name Type Description
annotationSpecName string

A fully-qualified path representing AnnotationSpec resource.

matchDatasetFromAnnotationSpecName(annotationSpecName) → {string}

Parse the dataset from AnnotationSpec resource.

Parameters:
Name Type Description
annotationSpecName string

A fully-qualified path representing AnnotationSpec resource.

matchDatasetFromDatasetName(datasetName) → {string}

Parse the dataset from Dataset resource.

Parameters:
Name Type Description
datasetName string

A fully-qualified path representing Dataset resource.

matchLocationFromAnnotationSpecName(annotationSpecName) → {string}

Parse the location from AnnotationSpec resource.

Parameters:
Name Type Description
annotationSpecName string

A fully-qualified path representing AnnotationSpec resource.

matchLocationFromDatasetName(datasetName) → {string}

Parse the location from Dataset resource.

Parameters:
Name Type Description
datasetName string

A fully-qualified path representing Dataset resource.

matchLocationFromModelEvaluationName(modelEvaluationName) → {string}

Parse the location from ModelEvaluation resource.

Parameters:
Name Type Description
modelEvaluationName string

A fully-qualified path representing ModelEvaluation resource.

matchLocationFromModelName(modelName) → {string}

Parse the location from Model resource.

Parameters:
Name Type Description
modelName string

A fully-qualified path representing Model resource.

matchModelEvaluationFromModelEvaluationName(modelEvaluationName) → {string}

Parse the model_evaluation from ModelEvaluation resource.

Parameters:
Name Type Description
modelEvaluationName string

A fully-qualified path representing ModelEvaluation resource.

matchModelFromModelEvaluationName(modelEvaluationName) → {string}

Parse the model from ModelEvaluation resource.

Parameters:
Name Type Description
modelEvaluationName string

A fully-qualified path representing ModelEvaluation resource.

matchModelFromModelName(modelName) → {string}

Parse the model from Model resource.

Parameters:
Name Type Description
modelName string

A fully-qualified path representing Model resource.

matchProjectFromAnnotationSpecName(annotationSpecName) → {string}

Parse the project from AnnotationSpec resource.

Parameters:
Name Type Description
annotationSpecName string

A fully-qualified path representing AnnotationSpec resource.

matchProjectFromDatasetName(datasetName) → {string}

Parse the project from Dataset resource.

Parameters:
Name Type Description
datasetName string

A fully-qualified path representing Dataset resource.

matchProjectFromModelEvaluationName(modelEvaluationName) → {string}

Parse the project from ModelEvaluation resource.

Parameters:
Name Type Description
modelEvaluationName string

A fully-qualified path representing ModelEvaluation resource.

matchProjectFromModelName(modelName) → {string}

Parse the project from Model resource.

Parameters:
Name Type Description
modelName string

A fully-qualified path representing Model resource.

modelEvaluationPath(project, location, model, model_evaluation) → {string}

Return a fully-qualified modelEvaluation resource name string.

Parameters:
Name Type Description
project string
location string
model string
model_evaluation string

modelPath(project, location, model) → {string}

Return a fully-qualified model resource name string.

Parameters:
Name Type Description
project string
location string
model string

predict(request, optionsopt) → {Promise}

Perform an online prediction. The prediction result is directly returned in the response. Available for following ML scenarios, and their expected request payloads:

AutoML Vision Classification An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
AutoML Vision Object Detection An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
AutoML Natural Language Classification A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.
AutoML Natural Language Entity Extraction A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document in .PDF, .TIF or .TIFF format with size upto 20MB.
AutoML Natural Language Sentiment Analysis A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.
AutoML Translation A TextSnippet up to 25,000 characters, UTF-8 encoded.
AutoML Tables A row with column values matching the columns of the model, up to 5MB. Not available for FORECASTING `prediction_type`.
Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. Name of the model requested to serve the prediction.

payload google.cloud.automl.v1.ExamplePayload

Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.

params Array.<number>

Additional domain-specific parameters, any string must be up to 25000 characters long.

AutoML Vision Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Object Detection

score_threshold : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned. The default is 100. The number of returned bounding boxes might be limited by the server.

AutoML Tables

feature_importance : (boolean) Whether

feature_importance is populated in the returned list of TablesAnnotation objects. The default is false.

options object <optional>

Call options. See CallOptions for more details.