Predict for tabular regression

  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 
  
 { 
  
 PredictionServic PredictionServiceSettings 
ceSettings 
  
 = 
  
 PredictionServic PredictionServiceSettings 
 
  
 . 
 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 
  
 ( 
 PredictionServic PredictionServiceClient 
ceClient 
  
 = 
  
 PredictionServic PredictionServiceClient 
onServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 EndpointName 
  
 end EndpointName 
EndpointName 
 . 
 of 
 ( 
  EndpointName 
 
ation , 
  
 endpointId 
 ); 
  
 ListValue 
 . 
 Builde ListValue 
ue 
  
 = 
  
 ListValue 
 . 
 newBui ListValue 
 
  
 JsonFormat 
 . 
 parse JsonFormat 
instance 
 , 
  
 listValue 
 ); 
  
 List<Value> 
  
 instan ListValue 
listValue 
 . 
 getValuesList 
 (); 
  
 Value 
  
 parameters Value 
lue 
 . 
 newBuilder Value 
tListValue 
 ( 
 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 
 . 
 predictResponse 
 . 
  getDeployedModelId 
 
 (). 
 out 
 . 
 println 
 ( 
 "Predictions" 
 ); 
  
 for 
  
 ( 
 Value 
  
 prediction Value 
edictResponse 
 . 
 predictResponse 
 . 
  getPredictionsList 
 
 () 
 larRegressio TabularRegressionPredictionResult 
uilder 
  
 = 
  
 TabularRegressio TabularRegressionPredictionResult 
 
  
 TabularRegressio TabularRegressionPredictionResult 
 
  
 ( 
 TabularRegressio TabularRegressionPredictionResult 
 
 . 
 f ValueConverter 
tBuilder 
 , 
  
 prediction 
 ); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "\tUpper bound: %f\n" 
 , 
  
 result 
 . 
 getUpperBresult 
 . 
  getUpperBound 
 
 () 
 m 
 . 
 out 
 . 
 printf 
 ( 
 "\tLower bound: %f\n" 
 , 
  
 result 
 . 
 getLowerBresult 
 . 
  getLowerBound 
 
 () 
 m 
 . 
 out 
 . 
 printf 
 ( 
 "\tValue: %f\n" 
 , 
  
 result 
 . 
 getValue 
 ( 
 result 
 . 
  getValue 
 
 () 
  
 } 
 } 
 

  /** 
 * 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 
 (); 
 

  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 
 ))