Processing Text Content with SAP Leonardo Machine Learning Foundation services on the API Business Hub

In order to consume the Topic Detection SAP Leonardo Machine Learning Functional Services, you will first need to get the service URI, request and response parameters.

Go to https://api.sap.com/ and click on the Browse tile.

Then you will be able to search for the SAP Leonardo Machine Learning - Functional Services, then click on the package found.

Click on Artifacts, then click on the Topic Detection API API.

As you can notice the Topic Detection API has only one resource (or service): /inference_sync.

Now click on the /inference_sync link to expand the section.

Note: the term inference refers to the application phase (scoring) an existing model (as opposed to the training or inception phase) and sync for synchronous.

As stated in the description, the service accepts either an archive file with a zip/tar extensions containing multiple text files, a single text file or a list of text files as input and returns a detected topic list with the associated keywords and scores (confidence).

The supported text file formats is plain text only.

The input file, files or archive file will be sent as a FormData query parameter in the service request.

A series of options are also required for the following parameters:

• numTopics: Total number of topics to be detected
• numTopicsPerDoc: Number of most relevant topics to be listed per document
• numKeywordsPerTopic: Number of keywords to be listed per topic
• numFeatures: Maximum number of keywords to be extracted from documents in total

And the response model schema is the following:

{
  "_id": "string",
  "error": "string",
  "request": {},
  "status": "QUEUED",
  "tenantName": "string",
  "topicDetection": [
    {
      "docName": "string",
      "keywords": [
        [
          "string"
        ]
      ],
      "scores": [
        0
      ],
      "topics": [
        0
      ]
    }
  ],
  "error_description": "string"
}

Now, scroll down a bit further and click on . You will be prompted for your SAP Cloud Platform credentials.

Scroll up a little for the Query Parameters section.

In the Options field enter the following:

{"numTopics":3, "numTopicsPerDoc":3, "numKeywordsPerTopic":5}

Now let’s build an archive with at least more than 2 text files.

If you are missing some inspiration, use the following articles content to create your text files:

• Wikipedia article on Machine Learning
• Wikipedia article on Deep Learning
• Wikipedia article on Cheesecake

Create a zip out of these text files.

Click on the Browse button next to files parameter and pick your zip file.

Now scroll down a bit further and click on .

Please take a note of the Request URL which we will use later:

https://sandbox.api.sap.com/ml/topicdetection/inference_sync

In the Response Body, you will get for each file, the associated keywords, topics and score.

The output will be sorted by descending topic score.

For example here, for the cheesecake.txt document, the first list of keywords represent to top topic (with id 1) and it’s score is 1.0059799604699027.

{
  "_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  "processed_time": "Mon, 31 Jul 2017 14:52:20 GMT",
    "request": {
      "files": [
        "Desktop.zip"
      ],
      "options": {
        "numKeywordsPerTopic": 5,
        "numTopics": 3,
        "numTopicsPerDoc": 3
      },
      "tenantName": "imgclassif-tech-user",
      "texts": []
    },
    "status": "DONE",
    "tenantName": "imgclassif-tech-user",
    "topicDetection": [
      {
        "docName": "cheesecake.txt",
        "keywords": [
          [ "cheesecake", "cheese", "cream", "bake", "make" ],
          [ "deep", "learn", "neural", "jump", "network" ],
          [ "learn", "machine", "data", "jump", "algorithm" ]
        ],
        "scores": [
          1.0059799604699027,
          0,
          0
        ],
        "topics": [
          1,
          2,
          0
        ]
      },
      {
        "docName": "deep_learning.txt",
        "keywords": [
          [ "deep", "learn", "neural", "jump", "network" ],
          [ "learn", "machine", "data", "jump", "algorithm" ],
          [ "cheesecake", "cheese", "cream", "bake", "make" ]
        ],
        "scores": [
          1.0683225059034496,
          0.0008786062197741417,
          0
        ],
        "topics": [
          2,
          0,
          1
        ]
      },
      {
        "docName": "machine_learning.txt",
        "keywords": [
		  [ "learn", "machine", "data", "jump", "algorithm" ],
		  [ "cheesecake", "cheese", "cream", "bake", "make" ],
          [ "deep", "learn", "neural", "jump", "network" ]
        ],
        "scores": [
          0.890515209507582,
          2.1184791916652968e-9,
          0
        ],
        "topics": [
          0,
          1,
          2
        ]
      }
    ]
  }

Provide an answer to the question below then click on Validate.

You can also try the following Machine Learning Functional Services consuming text content:

• the Document Feature Extraction API
• the Product Text Classification API

For more information, you can also check the online SAP Leonardo Machine Learning Foundation documentation