Score a model using the Sensei Machine Learning API
This tutorial will show you how to leverage the APIs to create an Experiment and an Experiment Run. For a list of all the endpoints in the Sensei Machine Learning API, please refer to .
Create a scheduled Experiment for scoring
Similar to scheduled Experiments for training, creating a scheduled Experiment for scoring is also done by including a template section to the body parameter. Additionally, the name field under tasks in the body is set as score.
The following is an example of creating an Experiment that will run every 20 minutes starting from startTime and will run until endTime.
Request
curl -X POST \
https://platform.adobe.io/data/sensei/experiments \
-H 'Authorization: Bearer {ACCESS_TOKEN}' \
-H 'Content-Type: application/vnd.adobe.platform.sensei+json;profile=experiment.v1.json' \
-H 'x-gw-ims-org-id: {ORG_ID}' \
-H 'x-api-key: {API_KEY}' \
-d '{JSON_PAYLOAD}'
{ORG_ID}: Your organization credentials found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.{ACCESS_TOKEN}: Your specific bearer token value provided after authentication.{API_KEY}: Your specific API key value found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.{JSON_PAYLOAD}: Experiment Run object to be sent. The example we use in our tutorial is shown here:
{
"name": "Experiment for Retail",
"mlInstanceId": "{INSTANCE_ID}",
"template": {
"tasks": [{
"name": "score",
"parameters": [
{
"key": "modelId",
"value": "{MODEL_ID}"
}
],
"specification": {
"type": "SparkTaskSpec",
"executorCores": 5,
"numExecutors": 5
}
}],
"schedule": {
"cron": "*/20 * * * *",
"startTime": "2018-07-04",
"endTime": "2018-07-06"
}
}
}
{INSTANCE_ID}: The ID that represents the MLInstance.{MODEL_ID}: The ID that represents the trained Model.
The following is the response after creating the scheduled Experiment.
Response
{
"id": "{EXPERIMENT_ID}",
"name": "Experiment for Retail",
"mlInstanceId": "{INSTANCE_ID}",
"created": "2018-11-11T11:11:11.111Z",
"updated": "2018-11-11T11:11:11.111Z",
"template": {
"tasks": [
{
"name": "score",
"parameters": [...],
"specification": {
"type": "SparkTaskSpec",
"executorCores": 5,
"numExecutors": 5
}
}
],
"schedule": {
"cron": "*\/20 * * * *",
"startTime": "2018-07-04",
"endTime": "2018-07-06"
}
}
}
{EXPERIMENT_ID}: The ID that represents the Experiment.{INSTANCE_ID}: The ID that represents the MLInstance.
Create an Experiment Run for scoring
Now with the trained model, we can create an Experiment Run for scoring. The value of the modelId parameter is the id parameter returned in the GET Model request above.
Request
curl -X POST \
https://platform.adobe.io/data/sensei/experiments/{EXPERIMENT_ID}/runs \
-H 'Authorization: Bearer {ACCESS_TOKEN}' \
-H 'Content-Type: application/vnd.adobe.platform.sensei+json;profile=experimentRun.v1.json' \
-H 'x-gw-ims-org-id: {ORG_ID}' \
-H 'x-api-key: {API_KEY}' \
-d '{JSON_PAYLOAD}'
{ORG_ID}: Your organization credentials found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.{ACCESS_TOKEN}: Your specific bearer token value provided after authentication.{API_KEY}: Your specific API key value found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.{EXPERIMENT_ID}: The ID corresponding to the Experiment you want to target. This can be found in the response when creating your Experiment.{JSON_PAYLOAD}: Data to be posted. The example we use in our tutorial is here:
{
"mode":"score",
"tasks": [
{
"name": "score",
"parameters": [
{
"key": "modelId",
"value": "{MODEL_ID}"
}
]
}
]
}
{MODEL_ID}: The ID corresponding to the Model.
The response from the Experiment Run creation is shown below:
Response
{
"id": "{EXPERIMENT_RUN_ID}",
"mode": "score",
"experimentId": "{EXPERIMENT_ID}",
"created": "2018-01-01T11:11:11.011Z",
"updated": "2018-01-01T11:11:11.011Z",
"deleted": false,
"tasks": [
{
"name": "score",
"parameters": [...]
}
]
}
{EXPERIMENT_ID}: The ID corresponding to the Experiment the Run is under.{EXPERIMENT_RUN_ID}: The ID corresponding to the Experiment Run you just created.
Retrieve an Experiment Run status for scheduled Experiment Run
To get Experiment Runs for scheduled Experiments, the query is shown below:
Request
curl -X GET \
'https://platform.adobe.io/data/sensei/experiments/{EXPERIMENT_ID}/runs' \
-H 'Authorization: Bearer {ACCESS_TOKEN}' \
-H 'x-gw-ims-org-id: {ORG_ID}'
{EXPERIMENT_ID}: The ID corresponding to the Experiment the Run is under.{ACCESS_TOKEN}: Your specific bearer token value provided after authentication.{ORG_ID}: Your organization credentials found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.
Since there are multiple Experiment Runs for a specific Experiment, the response returned will have an array of Run IDs.
Response
{
"children": [
{
"id": "{EXPERIMENT_RUN_ID}",
"experimentId": "{EXPERIMENT_ID}",
"created": "2018-01-01T11:11:11.011Z",
"updated": "2018-01-01T11:11:11.011Z"
},
{
"id": "{EXPERIMENT_RUN_ID}",
"experimentId": "{EXPERIMENT_ID}",
"created": "2018-01-01T11:11:11.011Z",
"updated": "2018-01-01T11:11:11.011Z"
}
]
}
{EXPERIMENT_RUN_ID}: The ID corresponding to the Experiment Run.{EXPERIMENT_ID}: The ID corresponding to the Experiment the Run is under.
Stop and delete a scheduled Experiment
If you want to stop execution of a scheduled Experiment before its endTime, this can be done by querying a DELETE request to the {EXPERIMENT_ID}
Request
curl -X DELETE \
'https://platform.adobe.io/data/sensei/experiments/{EXPERIMENT_ID}' \
-H 'Authorization: Bearer {ACCESS_TOKEN}' \
-H 'x-gw-ims-org-id: {ORG_ID}'
{EXPERIMENT_ID}: The ID corresponding to the Experiment.{ACCESS_TOKEN}: Your specific bearer token value provided after authentication.{ORG_ID}: Your organization credentials found in your unique ÃÛ¶¹ÊÓƵ Experience Platform integration.
The following is the Response notifying that the Experiment is successfully deleted.
Response
{
"title": "Success",
"status": 200,
"detail": "Experiment successfully deleted"
}