Class v1.PredictionServiceClient (4.0.1)

Package

Constructors

(constructor)(opts, gaxInstance)

constructor(opts?: ClientOptions, gaxInstance?: typeof gax | typeof gax.fallback);

Properties

apiEndpoint

static get apiEndpoint(): string;

auth

auth: gax.GoogleAuth;

descriptors

descriptors: Descriptors;

innerApiCalls

innerApiCalls: {
        [name: string]: Function;
    };

operationsClient

operationsClient: gax.OperationsClient;

pathTemplates

pathTemplates: {
        [name: string]: gax.PathTemplate;
    };

port

static get port(): number;

predictionServiceStub

predictionServiceStub?: Promise<{
        [name: string]: Function;
    }>;

scopes

static get scopes(): string[];

servicePath

static get servicePath(): string;

warn

warn: (code: string, message: string, warnType?: string) => void;

Methods

annotationSpecPath(project, location, dataset, annotationSpec)

annotationSpecPath(project: string, location: string, dataset: string, annotationSpec: string): string;

batchPredict(request, options)

batchPredict(request?: protos.google.cloud.automl.v1.IBatchPredictRequest, options?: CallOptions): Promise<[
        LROperation<protos.google.cloud.automl.v1.IBatchPredictResult, protos.google.cloud.automl.v1.IOperationMetadata>,
        protos.google.longrunning.IOperation | undefined,
        {} | undefined
    ]>;

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Name of the model requested to serve the batch prediction.
   */
  // const name = 'abc123'
  /**
   *  Required. The input configuration for batch prediction.
   */
  // const inputConfig = {}
  /**
   *  Required. The Configuration specifying where output predictions should
   *  be written.
   */
  // const outputConfig = {}
  /**
   *  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.
   */
  // const params = [1,2,3,4]

  // Imports the Automl library
  const {PredictionServiceClient} = require('@google-cloud/automl').v1;

  // Instantiates a client
  const automlClient = new PredictionServiceClient();

  async function callBatchPredict() {
    // Construct request
    const request = {
      name,
      inputConfig,
      outputConfig,
    };

    // Run request
    const [operation] = await automlClient.batchPredict(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callBatchPredict();

batchPredict(request, options, callback)

batchPredict(request: protos.google.cloud.automl.v1.IBatchPredictRequest, options: CallOptions, callback: Callback<LROperation<protos.google.cloud.automl.v1.IBatchPredictResult, protos.google.cloud.automl.v1.IOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;

batchPredict(request, callback)

batchPredict(request: protos.google.cloud.automl.v1.IBatchPredictRequest, callback: Callback<LROperation<protos.google.cloud.automl.v1.IBatchPredictResult, protos.google.cloud.automl.v1.IOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;

checkBatchPredictProgress(name)

checkBatchPredictProgress(name: string): Promise<LROperation<protos.google.cloud.automl.v1.BatchPredictResult, protos.google.cloud.automl.v1.OperationMetadata>>;

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Name of the model requested to serve the batch prediction.
   */
  // const name = 'abc123'
  /**
   *  Required. The input configuration for batch prediction.
   */
  // const inputConfig = {}
  /**
   *  Required. The Configuration specifying where output predictions should
   *  be written.
   */
  // const outputConfig = {}
  /**
   *  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.
   */
  // const params = [1,2,3,4]

  // Imports the Automl library
  const {PredictionServiceClient} = require('@google-cloud/automl').v1;

  // Instantiates a client
  const automlClient = new PredictionServiceClient();

  async function callBatchPredict() {
    // Construct request
    const request = {
      name,
      inputConfig,
      outputConfig,
    };

    // Run request
    const [operation] = await automlClient.batchPredict(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callBatchPredict();

close()

close(): Promise<void>;

datasetPath(project, location, dataset)

datasetPath(project: string, location: string, dataset: string): string;

getProjectId()

getProjectId(): Promise<string>;

getProjectId(callback)

getProjectId(callback: Callback<string, undefined, undefined>): void;

initialize()

initialize(): Promise<{
        [name: string]: Function;
    }>;

matchAnnotationSpecFromAnnotationSpecName(annotationSpecName)

matchAnnotationSpecFromAnnotationSpecName(annotationSpecName: string): string | number;

matchDatasetFromAnnotationSpecName(annotationSpecName)

matchDatasetFromAnnotationSpecName(annotationSpecName: string): string | number;

matchDatasetFromDatasetName(datasetName)

matchDatasetFromDatasetName(datasetName: string): string | number;

matchLocationFromAnnotationSpecName(annotationSpecName)

matchLocationFromAnnotationSpecName(annotationSpecName: string): string | number;

matchLocationFromDatasetName(datasetName)

matchLocationFromDatasetName(datasetName: string): string | number;

matchLocationFromModelEvaluationName(modelEvaluationName)

matchLocationFromModelEvaluationName(modelEvaluationName: string): string | number;

matchLocationFromModelName(modelName)

matchLocationFromModelName(modelName: string): string | number;

matchModelEvaluationFromModelEvaluationName(modelEvaluationName)

matchModelEvaluationFromModelEvaluationName(modelEvaluationName: string): string | number;

matchModelFromModelEvaluationName(modelEvaluationName)

matchModelFromModelEvaluationName(modelEvaluationName: string): string | number;

matchModelFromModelName(modelName)

matchModelFromModelName(modelName: string): string | number;

matchProjectFromAnnotationSpecName(annotationSpecName)

matchProjectFromAnnotationSpecName(annotationSpecName: string): string | number;

matchProjectFromDatasetName(datasetName)

matchProjectFromDatasetName(datasetName: string): string | number;

matchProjectFromModelEvaluationName(modelEvaluationName)

matchProjectFromModelEvaluationName(modelEvaluationName: string): string | number;

matchProjectFromModelName(modelName)

matchProjectFromModelName(modelName: string): string | number;

modelEvaluationPath(project, location, model, modelEvaluation)

modelEvaluationPath(project: string, location: string, model: string, modelEvaluation: string): string;

modelPath(project, location, model)

modelPath(project: string, location: string, model: string): string;

predict(request, options)

predict(request?: protos.google.cloud.automl.v1.IPredictRequest, options?: CallOptions): Promise<[
        protos.google.cloud.automl.v1.IPredictResponse,
        protos.google.cloud.automl.v1.IPredictRequest | undefined,
        {} | undefined
    ]>;

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Name of the model requested to serve the prediction.
   */
  // const name = 'abc123'
  /**
   *  Required. Payload to perform a prediction on. The payload must match the
   *  problem type that the model was trained to solve.
   */
  // const payload = {}
  /**
   *  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 google.cloud.automl.v1.TablesModelColumnInfo.feature_importance 
   *    is populated in the returned list of
   *    TablesAnnotation google.cloud.automl.v1.TablesAnnotation 
   *    objects. The default is false.
   */
  // const params = [1,2,3,4]

  // Imports the Automl library
  const {PredictionServiceClient} = require('@google-cloud/automl').v1;

  // Instantiates a client
  const automlClient = new PredictionServiceClient();

  async function callPredict() {
    // Construct request
    const request = {
      name,
      payload,
    };

    // Run request
    const response = await automlClient.predict(request);
    console.log(response);
  }

  callPredict();

predict(request, options, callback)

predict(request: protos.google.cloud.automl.v1.IPredictRequest, options: CallOptions, callback: Callback<protos.google.cloud.automl.v1.IPredictResponse, protos.google.cloud.automl.v1.IPredictRequest | null | undefined, {} | null | undefined>): void;

predict(request, callback)

predict(request: protos.google.cloud.automl.v1.IPredictRequest, callback: Callback<protos.google.cloud.automl.v1.IPredictResponse, protos.google.cloud.automl.v1.IPredictRequest | null | undefined, {} | null | undefined>): void;