Qlik AutoML: How to generate predictions via API realtime-predictions with Python

KellyHobson — Mon, 24 Oct 2022 14:46:36 GMT

In a previous article, I outlined the steps to POST an API call to realtime-predictions with Postman. In this article, we will look at how to do this in Python and send a file with records to predict against.

As before, we use iris.csv dataset, a common data science example. Variety is the target variable.

Steps

Upload, train and deploy the model for iris.csv. See this article for steps on how to deploy a model.

XGBoost Classification was the champion model which I choose to deploy.
Generate an API key. Copy this information for later steps.
Open the Deployed model and navigate to the Real-time predictions tab
Copy the Realtime-predictions URL to a safe place
Open a Python instance (in this case a jupyter notebook) and import the required packages.

Set the API key and API URL as string variables.
Read a local data file to a dataframe
Set the proper header, json_data, and parameter values
Get the response and print the result

In our example, we print response.text, but this could also have been saved as a file. This is a powerful option for generating prediction results locally or in a location python has access to.

Additionally, with pandas, we can send a larger amount of records (from CSV to dataframe) in one POST call rather than having manual JSON entries with Postman.

Here is the code used above:

api_url = 'https://earlyaccess.us.qlik-stage.com/api/v1/automl-deployments/xxx/realtime-predictions' api_key = 'xxxx' df = pd.read_csv('C:\\tmp\\iris_pred.csv') rows = df.to_dict('tight')['data'] headers = { 'Authorization': 'Bearer ' + api_key, 'Content-type': 'application/json', 'Accept': 'text/csv', } json_data = { "rows": rows, "schema": [ {"name": "sepal.length"}, {"name": "sepal.width"}, {"name": "petal.length"}, {"name": "petal.width"} ], } params = { 'includeNotPredictedReason': 'false', 'includeShap': 'true', 'includeSource': 'false' } response = requests.post(api_url, headers=headers, json=json_data, params=params) print(response.text)

Environment

The information in this article is provided as-is and to be used at own discretion. Depending on tool(s) used, customization(s), and/or other factors ongoing support on the solution below may not be provided by Qlik Support.

article Qlik AutoML: How to generate predictions via API realtime-predictions with Python in Official Support Articles

Qlik AutoML: How to generate predictions via API realtime-predictions with Python

Steps

Environment