使用预测方法获取表格回归预测。
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代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
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import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.TabularRegressionPredictionResult;
import com.google.protobuf.ListValue;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.List;
public class PredictTabularRegressionSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String instance = "[{ “feature_column_a”: “value”, “feature_column_b”: “value”}]";
String endpointId = "YOUR_ENDPOINT_ID";
predictTabularRegression(instance, project, endpointId);
}
static void predictTabularRegression(String instance, String project, String endpointId)
throws IOException {
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings)) {
String location = "us-central1";
EndpointName endpointName = EndpointName.of(project, location, endpointId);
ListValue.Builder listValue = ListValue.newBuilder();
JsonFormat.parser().merge(instance, listValue);
List<Value> instanceList = listValue.getValuesList();
Value parameters = Value.newBuilder().setListValue(listValue).build();
PredictResponse predictResponse =
predictionServiceClient.predict(endpointName, instanceList, parameters);
System.out.println("Predict Tabular Regression Response");
System.out.format("\tDisplay Model Id: %s\n", predictResponse.getDeployedModelId());
System.out.println("Predictions");
for (Value prediction : predictResponse.getPredictionsList()) {
TabularRegressionPredictionResult.Builder resultBuilder =
TabularRegressionPredictionResult.newBuilder();
TabularRegressionPredictionResult result =
(TabularRegressionPredictionResult) ValueConverter.fromValue(resultBuilder, prediction);
System.out.printf("\tUpper bound: %f\n", result.getUpperBound());
System.out.printf("\tLower bound: %f\n", result.getLowerBound());
System.out.printf("\tValue: %f\n", result.getValue());
}
}
}
}Node.js
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const endpointId = 'YOUR_ENDPOINT_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {prediction} =
aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;
// Imports the Google Cloud Prediction service client
const {PredictionServiceClient} = aiplatform.v1;
// Import the helper module for converting arbitrary protobuf.Value objects.
const {helpers} = aiplatform;
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);
async function predictTablesRegression() {
// Configure the endpoint resource
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
const parameters = helpers.toValue({});
// TODO (erschmid): Make this less painful
const instance = helpers.toValue({
BOOLEAN_2unique_NULLABLE: false,
DATETIME_1unique_NULLABLE: '2019-01-01 00:00:00',
DATE_1unique_NULLABLE: '2019-01-01',
FLOAT_5000unique_NULLABLE: 1611,
FLOAT_5000unique_REPEATED: [2320, 1192],
INTEGER_5000unique_NULLABLE: '8',
NUMERIC_5000unique_NULLABLE: 16,
STRING_5000unique_NULLABLE: 'str-2',
STRUCT_NULLABLE: {
BOOLEAN_2unique_NULLABLE: false,
DATE_1unique_NULLABLE: '2019-01-01',
DATETIME_1unique_NULLABLE: '2019-01-01 00:00:00',
FLOAT_5000unique_NULLABLE: 1308,
FLOAT_5000unique_REPEATED: [2323, 1178],
FLOAT_5000unique_REQUIRED: 3089,
INTEGER_5000unique_NULLABLE: '1777',
NUMERIC_5000unique_NULLABLE: 3323,
TIME_1unique_NULLABLE: '23:59:59.999999',
STRING_5000unique_NULLABLE: 'str-49',
TIMESTAMP_1unique_NULLABLE: '1546387199999999',
},
TIMESTAMP_1unique_NULLABLE: '1546387199999999',
TIME_1unique_NULLABLE: '23:59:59.999999',
});
const instances = [instance];
const request = {
endpoint,
instances,
parameters,
};
// Predict request
const [response] = await predictionServiceClient.predict(request);
console.log('Predict tabular regression response');
console.log(`\tDeployed model id : ${response.deployedModelId}`);
const predictions = response.predictions;
console.log('\tPredictions :');
for (const predictionResultVal of predictions) {
const predictionResultObj =
prediction.TabularRegressionPredictionResult.fromValue(
predictionResultVal
);
console.log(`\tUpper bound: ${predictionResultObj.upper_bound}`);
console.log(`\tLower bound: ${predictionResultObj.lower_bound}`);
console.log(`\tLower bound: ${predictionResultObj.value}`);
}
}
predictTablesRegression();Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
from typing import Dict
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def predict_tabular_regression_sample(
project: str,
endpoint_id: str,
instance_dict: Dict,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
# for more info on the instance schema, please use get_model_sample.py
# and look at the yaml found in instance_schema_uri
instance = json_format.ParseDict(instance_dict, Value())
instances = [instance]
parameters_dict = {}
parameters = json_format.ParseDict(parameters_dict, Value())
endpoint = client.endpoint_path(
project=project, location=location, endpoint=endpoint_id
)
response = client.predict(
endpoint=endpoint, instances=instances, parameters=parameters
)
print("response")
print(" deployed_model_id:", response.deployed_model_id)
# See gs://google-cloud-aiplatform/schema/predict/prediction/tabular_regression_1.0.0.yaml for the format of the predictions.
predictions = response.predictions
for prediction in predictions:
print(" prediction:", dict(prediction))
后续步骤
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