Test

Introduction

How It Works

Prerequisites

Administrator Account and Organisation Creation Application Creation Data Set Requirements

Step 1: Data Cleanup Step 2: Data Transformation Step 3: Upload to S3 Bucket Step 4: Sharing S3 Bucket Access to Xineoh’s API

Applications, Datasets and Schemas

Applications

Application Types

Datasets and Schemas

Minimum Requirements for Datasets and Schemas Customising Datasets and Schemas

Steps in Building a Recommender with the API

Step 1: Batch Upload Step 2: Build Data and Structures for Training and Testing Step 3: Train Model Step 4: Test Model Step 5: Optimise Meta Parameters Step 6: User-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 8: Get Recommendations

Sample Implementation - Movielens 25M Dataset

S3 Bucket Creation Login as Organisation First Application Download MovieLens Preprocessed Data Upload Data to S3 Bucket Building the Recommender

Uploading S3 Bucket data to Xineoh Platform Build Data Train Test Optimization User Clustering and Trade-Off Item Clustering and Trade-Off

First Recommendations

Open Navigation

Your first Xineoh model has been trained, and the next step will be to test the data.

As mentioned with the Build Data API call, the contextual parameter used for that request will affect what you can use for this request.

Test Model API Call

POST - /builder/test
Params: { version: v3.0 }
Body: { contextual: true }

Because contextual was set as true in the Build Data and Structures API call it can be set to either true or false here. If you would like to know more please see the official documentation here.

Successful Response:

{
    "status_code": 200,
    "success": "We will notify you when it's finished",
    "process_id": PROCESS_ID_HERE
}

Using the process_id from the response we can get updates on the progress with the View Process API Request.

Here you will need to record the process_id to get the model's accuracy test results.

Please be aware that testing is quite a bit quicker than training; however, it can still take a few minutes.

Email Notification:

Once you get notified that the test has been completed, you need to call the 'View Process' endpoint with the process_id from the Test API call.

When you call the endpoint, it will also return the test_result parameter. This is so that you can see the test result accuracy percentage.

View Process API Call Response with a testing process_id:

{
    "status_code": 200,
    "response": {
        "process_id": "PROCESS_ID",
        "status": "completed",
        "created_dt": "YEAR-MONTH-DAY HOUR:MINUTE:SECOND",
        "test_result": "TEST_RESULT_ACCURACY_PERCENTAGE"
    }
}

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