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Assign algorithm

Assign the algorithm to a resource once it is ready.

Check if the algorithm is ready

An algorithm model must train on a minimum amount of data before it is ready.

Algorithms train a model for each advertiser that can use them. Models train on existing impressions data. It can take 1-3 days to train a model after the data requirements are met by the advertiser.

Retrieve the readiness of each model from the algorithm. The ReadinessState determines next steps:

ReadinessState

READINESS_STATE_NO_VALID_SCRIPT

There is no valid script. Upload a new script or rules file .

READINESS_STATE_EVALUATION_FAILURE

There is no script that could be evaluated in the allotted time. Upload a new script or rules file .

READINESS_STATE_INSUFFICIENT_DATA

There is not a sufficient amount of data available under the advertiser to train the model. Continue to run campaigns under the advertiser to meet the minimum data requirements.

READINESS_STATE_TRAINING

The model is training on existing data and not ready for serving. Wait 12-24 hours before checking to see if the model is ready for serving.

READINESS_STATE_ACTIVE

The model is fully trained and ready to be assigned to campaigns under the advertiser.

Assign algorithm to line item

An algorithm is used to measure and adjust bidding performance. It can be used with bid strategies that optimize towards spending the whole budget or meeting a goal . In these cases, the strategy performance goal type would be BIDDING_STRATEGY_PERFORMANCE_GOAL_TYPE_CUSTOM_ALGO .

Here's how to update a line item to use an algorithm with a bid strategy that optimizes for spending the whole budget:

Java

 // Provide the ID of the advertiser that owns the parent algorithm. 
 long 
  
 advertiserId 
  
 = 
  
  advertiser 
 - 
 id 
 
 ; 
 // Provide the ID of the parent algorithm. 
 long 
  
 algorithmId 
  
 = 
  
  algorithm 
 - 
 id 
 
 ; 
 // Provide the ID of the line item to assign the algorithm to. 
 long 
  
 lineItemId 
  
 = 
  
  line 
 - 
 item 
 - 
 id 
 
 ; 
 // Create the line item structure. 
 LineItem 
  
 lineItem 
  
 = 
  
 new 
  
 LineItem 
 () 
  
 . 
 setBidStrategy 
 ( 
  
 new 
  
 BiddingStrategy 
 () 
  
 . 
 setMaximizeSpendAutoBid 
 ( 
  
 new 
  
 MaximizeSpendBidStrategy 
 () 
  
 . 
 setPerformanceGoalType 
 ( 
  
 "BIDDING_STRATEGY_PERFORMANCE_GOAL_TYPE_CUSTOM_ALGO" 
 ) 
  
 . 
 setCustomBiddingAlgorithmId 
 ( 
 algorithmId 
 ))); 
 // Configure the patch request and set update mask to only update the bid 
 // strategy. 
 LineItems 
 . 
 Patch 
  
 request 
  
 = 
  
 service 
  
 . 
 advertisers 
 () 
  
 . 
 lineItems 
 () 
  
 . 
 patch 
 ( 
 advertiserId 
 , 
  
 lineItemId 
 , 
  
 lineItem 
 ) 
  
 . 
 setUpdateMask 
 ( 
 "bidStrategy" 
 ); 
 // Update the line item. 
 LineItem 
  
 response 
  
 = 
  
 request 
 . 
 execute 
 (); 
 // Display the new algorithm ID used by the line item. 
 System 
 . 
 out 
 . 
 printf 
 ( 
  
 "Line item %s now uses algorithm ID %s in its bidding strategy." 
 , 
  
 response 
 . 
 getName 
 (), 
  
 response 
 . 
 getBidStrategy 
 (). 
 getMaximizeSpendAutoBid 
 (). 
 getCustomBiddingAlgorithmId 
 ());

Python

 # Provide the parent advertiser ID of the line item and creative. 
 advertiser_id 
 = 
  advertiser 
 - 
 id 
 
 # Provide the ID of the creative to assign. 
 algorithm_id 
 = 
  algorithm 
 - 
 id 
 
 # Provide the ID of the line item to assign the creative to. 
 line_item_id 
 = 
  line 
 - 
 item 
 - 
 id 
 
 # Build bidding strategy object. 
 bidding_strategy_obj 
 = 
 { 
 "maximizeSpendAutoBid" 
 : 
 { 
 "performanceGoalType" 
 : 
 ( 
 "BIDDING_STRATEGY_PERFORMANCE_GOAL_TYPE_CUSTOM_ALGO" 
 ), 
 "customBiddingAlgorithmId" 
 : 
 algorithm_id 
 , 
 } 
 } 
 # Build line item object. 
 line_item_obj 
 = 
 { 
 "bidStrategy" 
 : 
 bidding_strategy_obj 
 } 
 # Build and execute request. 
 line_item_resp 
 = 
 ( 
 service 
 . 
 advertisers 
 () 
 . 
 lineItems 
 () 
 . 
 patch 
 ( 
 advertiserId 
 = 
 advertiser_id 
 , 
 lineItemId 
 = 
 line_item_id 
 , 
 updateMask 
 = 
 "bidStrategy" 
 , 
 body 
 = 
 line_item_obj 
 , 
 ) 
 . 
 execute 
 () 
 ) 
 # Print the algorithm ID now assigned to the line item. 
 print 
 ( 
 f 
 'Line Item 
 { 
 line_item_resp 
 [ 
 "name" 
 ] 
 } 
 now uses algorithm ID' 
 f 
 ' 
 { 
 line_item_resp 
 [ 
 "bidStrategy" 
 ][ 
 "maximizeSpendAutoBid" 
 ][ 
 "customBiddingAlgorithmId" 
 ] 
 } 
 ' 
 " in its bidding strategy." 
 )

PHP

 // Provide the ID of the advertiser that owns the parent algorithm. 
 $advertiserId = advertiser-id 
; 
 // Provide the ID of the parent algorithm. 
 $algorithmId = algorithm-id 
; 
 // Provide the ID of the line item to assign the algorithm to. 
 $lineItemId = line-item-id 
; 
 // Create the bidding strategy structure. 
 $maxSpendBidStrategy = new Google_ServiceDisplayVideo_MaximizeSpendBidStrategy(); 
 $maxSpendBidStrategy->setPerformanceGoalType('BIDDING_STRATEGY_PERFORMANCE_GOAL_TYPE_CUSTOM_ALGO'); 
 $maxSpendBidStrategy->setCustomBiddingAlgorithmId($algorithmId); 
 $biddingStrategy = new Google_ServiceDisplayVideo_BiddingStrategy(); 
 $biddingStrategy->setMaximizeSpendAutoBid($maxSpendBidStrategy); 
 // Create the line item structure. 
 $lineItem = new Google_Service_DisplayVideo_LineItem(); 
 $lineItem->setBidStrategy($biddingStrategy); 
 $optParams = array('updateMask' => 'bidStrategy'); 
 // Call the API, updating the bid strategy for the identified line 
 // item. 
 try { 
 $result = $this->service->advertisers_lineItems->patch( 
 $advertiserId, 
 $lineItemId, 
 $lineItem, 
 $optParams 
 ); 
 } catch (\Exception $e) { 
 $this->renderError($e); 
 return; 
 } 
 printf( 
 '<p>Line Item %s now uses algorithm ID %s in its bid strategy.</p>', 
 $result['name'], 
 $result['bidStrategy']['maximizeSpendAutoBid']['customBiddingAlgorithmId'] 
 );

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