Explain for tabular

  from 
  
 typing 
  
 import 
 Dict 
 from 
  
 google.cloud 
  
 import 
 aiplatform_v1beta1 
 from 
  
 google.protobuf 
  
 import 
 json_format 
 from 
  
 google.protobuf.struct_pb2 
  
 import 
 Value 
 def 
  
 explain_tabular_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_v1beta1 
 . 
  PredictionServiceClient 
 
 ( 
 client_options 
 = 
 client_options 
 ) 
 # The format of each instance should conform to the deployed model's prediction input schema. 
 instance 
 = 
 json_format 
 . 
 ParseDict 
 ( 
 instance_dict 
 , 
 Value 
 ()) 
 instances 
 = 
 [ 
 instance 
 ] 
 # tabular models do not have additional parameters 
 parameters_dict 
 = 
 {} 
 parameters 
 = 
 json_format 
 . 
 ParseDict 
 ( 
 parameters_dict 
 , 
 Value 
 ()) 
 endpoint 
 = 
 client 
 . 
  endpoint_path 
 
 ( 
 project 
 = 
 project 
 , 
 location 
 = 
 location 
 , 
 endpoint 
 = 
 endpoint_id 
 ) 
 response 
 = 
 client 
 . 
  explain 
 
 ( 
 endpoint 
 = 
 endpoint 
 , 
 instances 
 = 
 instances 
 , 
 parameters 
 = 
 parameters 
 ) 
 print 
 ( 
 "response" 
 ) 
 print 
 ( 
 " deployed_model_id:" 
 , 
 response 
 . 
 deployed_model_id 
 ) 
 explanations 
 = 
 response 
 . 
 explanations 
 for 
 explanation 
 in 
 explanations 
 : 
 print 
 ( 
 " explanation" 
 ) 
 # Feature attributions. 
 attributions 
 = 
 explanation 
 . 
 attributions 
 for 
 attribution 
 in 
 attributions 
 : 
 print 
 ( 
 "  attribution" 
 ) 
 print 
 ( 
 "   baseline_output_value:" 
 , 
 attribution 
 . 
 baseline_output_value 
 ) 
 print 
 ( 
 "   instance_output_value:" 
 , 
 attribution 
 . 
 instance_output_value 
 ) 
 print 
 ( 
 "   output_display_name:" 
 , 
 attribution 
 . 
 output_display_name 
 ) 
 print 
 ( 
 "   approximation_error:" 
 , 
 attribution 
 . 
 approximation_error 
 ) 
 print 
 ( 
 "   output_name:" 
 , 
 attribution 
 . 
 output_name 
 ) 
 output_index 
 = 
 attribution 
 . 
 output_index 
 for 
 output_index 
 in 
 output_index 
 : 
 print 
 ( 
 "   output_index:" 
 , 
 output_index 
 ) 
 predictions 
 = 
 response 
 . 
 predictions 
 for 
 prediction 
 in 
 predictions 
 : 
 print 
 ( 
 " prediction:" 
 , 
 dict 
 ( 
 prediction 
 ))