prediction_service.explain.js

// Copyright 2022 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// ** This file is automatically generated by gapic-generator-typescript. **
// ** https://github.com/googleapis/gapic-generator-typescript **
// ** All changes to this file may be overwritten. **



'use strict';

function main(endpoint, instances) {
  // [START aiplatform_v1_generated_PredictionService_Explain_async]
  /**
   * 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. The name of the Endpoint requested to serve the explanation.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const endpoint = 'abc123'
  /**
   *  Required. The instances that are the input to the explanation call.
   *  A DeployedModel may have an upper limit on the number of instances it
   *  supports per request, and when it is exceeded the explanation call errors
   *  in case of AutoML Models, or, in case of customer created Models, the
   *  behaviour is as documented by that Model.
   *  The schema of any single instance may be specified via Endpoint's
   *  DeployedModels' Model's google.cloud.aiplatform.v1.DeployedModel.model 
   *  PredictSchemata's google.cloud.aiplatform.v1.Model.predict_schemata 
   *  instance_schema_uri google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri.
   */
  // const instances = 1234
  /**
   *  The parameters that govern the prediction. The schema of the parameters may
   *  be specified via Endpoint's DeployedModels' Model's  google.cloud.aiplatform.v1.DeployedModel.model 
   *  PredictSchemata's google.cloud.aiplatform.v1.Model.predict_schemata 
   *  parameters_schema_uri google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri.
   */
  // const parameters = {}
  /**
   *  If specified, overrides the
   *  explanation_spec google.cloud.aiplatform.v1.DeployedModel.explanation_spec  of the DeployedModel.
   *  Can be used for explaining prediction results with different
   *  configurations, such as:
   *   - Explaining top-5 predictions results as opposed to top-1;
   *   - Increasing path count or step count of the attribution methods to reduce
   *     approximate errors;
   *   - Using different baselines for explaining the prediction results.
   */
  // const explanationSpecOverride = {}
  /**
   *  If specified, this ExplainRequest will be served by the chosen
   *  DeployedModel, overriding Endpoint.traffic_split google.cloud.aiplatform.v1.Endpoint.traffic_split.
   */
  // const deployedModelId = 'abc123'

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

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

  async function callExplain() {
    // Construct request
    const request = {
      endpoint,
      instances,
    };

    // Run request
    const response = await aiplatformClient.explain(request);
    console.log(response);
  }

  callExplain();
  // [END aiplatform_v1_generated_PredictionService_Explain_async]
}

process.on('unhandledRejection', err => {
  console.error(err.message);
  process.exitCode = 1;
});
main(...process.argv.slice(2));