--- parser: v2 auto_validation: true time: 20 tags: [tutorial>beginner, topic>machine-learning, topic>artificial-intelligence, topic>cloud, software-product>sap-business-technology-platform, software-product>sap-ai-services, software-product>data-attribute-recommendation, tutorial>free-tier] primary_tag: topic>machine-learning author_name: Juliana Morais author_profile: https://github.com/Juliana-Morais slug: cp-aibus-dar-swagger-ior-predict canonical_url: https://developers.sap.com/tutorials/cp-aibus-dar-swagger-ior-predict --- # Use the Invoice Object Recommendation (IOR) Business Blueprint to Predict Financial Objects Get financial objects recommendations using your machine learning model. ## You will learn - How to predict financial objects using your machine learning model - How to `undeploy` and delete your model - How to delete datasets and dataset schemas --- ### Authorize Swagger UI In the service key you created for Data Attribute Recommendation in the previous tutorial: [Use Free Tier to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-free-key) or [Use Trial to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-key), you find a section called `swagger` (as highlighted in the image below) with three entries, called `dm` (data manager), `mm` (model manager) and `inference`. ![Service Key](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/service-key-details.png) For the following step, copy the URL of the Swagger UI for `inference` and open it in a browser tab. The Swagger UI for inference allows you to classify new data using your machine learning model that you have created in [Use the Invoice Object Recommendation (IOR) Business Blueprint to Train a Machine Learning Model](cp-aibus-dar-swagger-ior-model). 1. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click **Authorize**. ![Authorize](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/swagger-authorize.png) 2. Get the `access_token` value created in the previous tutorial: [Get OAuth Access Token for Data Attribute Recommendation Using Any Web Browser](cp-aibus-dar-web-oauth-token), then add **Bearer** (with capitalized "B") in front of it, and enter in the **Value** field. ``` Bearer ``` 3. Click **Authorize** and then click **Close**. ![Authorize](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/swagger-token.png) ### Predict financial objects To get the machine learning model predictions, proceed as follows: 1. Expand the endpoint `POST /models/{modelName}/versions/1` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/inference-endpoint.png) 2. Fill the parameter `modelName` with the name of your machine learning model (`ior_tutorial_model`). 3. In the parameter `body`, you have to provide the data that needs to be predicted. According to the dataset schema that you have created in [Use the Invoice Object Recommendation (IOR) Business Blueprint to Upload Data to Data Attribute Recommendation with Swagger UI](cp-aibus-dar-swagger-ior-upload), the machine learning model takes the training fields such as BUKRS for company code as input and predicts G/L accounts (HKONT). Replace the text in the parameter `body` with the following: ```JSON { "topN":3, "objects":[ { "objectId":"optional-identifier-1", "features":[ { "name":"BUKRS", "value":"ZN02" }, { "name":"BELNR", "value":"1500022169" }, { "name":"GJAHR", "value":"2021" }, { "name":"BUZEI", "value":"3" }, { "name":"KOART", "value":"S" }, { "name":"WRBTR", "value":"162709.54" }, { "name":"LIFNR", "value":"68046473" }, { "name":"BLART", "value":"KN" }, { "name":"BUDAT", "value":"20210331" }, { "name":"MWSKZ", "value":"IF" } ] }, { "objectId":"optional-identifier-2", "features":[ { "name":"BUKRS", "value":"ZC04" }, { "name":"BELNR", "value":"1510043834" }, { "name":"GJAHR", "value":"2022" }, { "name":"BUZEI", "value":"176" }, { "name":"KOART", "value":"S" }, { "name":"WRBTR", "value":"19554" }, { "name":"LIFNR", "value":"69089950" }, { "name":"BLART", "value":"KN" }, { "name":"BUDAT", "value":"20220326" }, { "name":"MWSKZ", "value":"Q1" } ] } ] } ``` 4. Click **Execute** to send the above input to the service to get financial object predictions. ![Inference Execute](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/inference-execute.png) In the response of the service, you find the probability and the values for G/L account numbers (HKONT). The probability represents how certain the model is about its prediction. The higher the probability the more confident the model is that the prediction is actually correct. If the probability is close to 1, the model is very certain. The service provides one main prediction and two alternative predictions for each G/L account. ![Inference Execute](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/inference-response.png) ```JSON { "id": "a4860abd-6a14-40ea-4203-5a3cabb6371a", "predictions": [ { "labels": [ { "name": "HKONT", "results": [ { "probability": 0.9826196432, "value": "7325581" }, { "probability": 0.0098451525, "value": "8015506" }, { "probability": 0.0063656443, "value": "8044756" } ] } ], "objectId": "optional-identifier-1" }, { "labels": [ { "name": "HKONT", "results": [ { "probability": 0.9783372283, "value": "7515556" }, { "probability": 0.018033715, "value": "7325581" }, { "probability": 0.0034737068, "value": "8044756" } ] } ], "objectId": "optional-identifier-2" } ], "processedTime": "2022-05-10T09:51:12.253115", "status": "DONE" } ``` You have successfully used a machine learning model to predict financial objects. Feel free to adapt the examples above and retry the prediction. ### Undeploy your model Now that you have learned the whole process about how to use the Invoice Object Recommendation (IOR) Business Blueprint from the Data Attribute Recommendation service, it's time to clean up. This way, the technical limits won't get in your way when trying out other Data Attribute Recommendation tutorials. See [Technical Constraints](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/686d2ae094014c8085cebecdb1d37e37.html) and [Free Tier Option Technical Constraints](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/c03b561eea1744c9b9892b416037b99a.html). First, `undeploy` your model. For that, go back to the Swagger UI for `mm` and: 1. Expand the endpoint `DELETE /deployments/{deploymentId}` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/deploy-endpoint.png) 2. Fill the parameter `deploymentId` with the ID of your deployment. Use the `GET /deployments` endpoint in case you no longer have the deployment `id` in hand. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/deploy-execute.png) If the response code is `204`, the model has been successfully `undeployed`. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/deploy-response.png) You have successfully `undeployed` your model, but the model is not yet deleted. Instead it isn't in production which means that you cannot make inference requests. You can deploy it again at any time using the `POST /deployments` endpoint. ### Delete your model Once `undeployed`, you can delete your model. 1. Expand the endpoint `DELETE /models/{modelName}` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/model-endpoint.png) 2. Fill the parameter `modelName` with the name of your machine learning model (`ior_tutorial_model`). Use the `GET /models` endpoint in case you no longer have the model `name` in hand. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/model-execute.png) If the response code is `204`, the model has been successfully deleted. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/model-response.png) ### Delete your training job Now that the model is deleted, you can delete the training job that created the model. 1. Expand the endpoint `DELETE /jobs/{jobId}` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/job-endpoint.png) 2. Fill the parameter `jobId` with the ID of your training job. Use the `GET /jobs` endpoint in case you no longer have the job `id` in hand. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/job-execute.png) If the response code is `204`, the training job has been successfully deleted. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/job-response.png) ### Delete your dataset To clear the uploaded data, you can now delete the dataset as the associated training job is already deleted. For that, go back to the Swagger UI for `dm` and: 1. Expand the endpoint `DELETE /datasets/{id}` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/data-endpoint.png) 2. Fill the parameter `id` with the ID of your dataset. Use the `GET /datasets` endpoint in case you no longer have the dataset `id` in hand. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/data-execute.png) If the response code is `204`, the dataset has been successfully deleted. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/data-response.png) ### Delete your dataset schema If you do not need your dataset schema anymore, you can delete it as well. 1. Expand the endpoint `DELETE /datasetSchemas/{id}` by clicking on it. Then click **Try it out**. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/schema-endpoint.png) 2. Fill the parameter `id` with the ID of your dataset schema. Use the `GET /datasetSchemas` endpoint in case you no longer have the dataset schema `id` in hand. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/schema-execute.png) If the response code is `204`, the dataset schema has been successfully deleted. ![Inference Endpoint](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-predict/schema-response.png) Congratulations, you have completed this tutorial.