recipes | Pipeable steps for feature engineering | Development Tools library

There are basically three approaches to this.

1. Easy approach

If you use only few icons. https://fonts.google.com/icons allows you to download each icon as svg or png file. So you can easily use those in Image component of Vaadin.

1. Light approach, use it as a font

There is training video at Vaadin's site which describes various things related to theming and styling of the app, at 19:30 timestamp there is a chapter about how to configure custom font:

https://vaadin.com/learn/training/v14-theming

Material icons are just a font, which you can include in your project.

Place the webfont files under e.g. "src/main/webapp/fonts/MaterialIcons" (note location is different if you have Spring Boot jar packaged project) and import the generated css

the data model , seems reasonable,you can use exists in:

in JSON your recipes would be like

If you want to use dependency on the host during compile time you must always depend to native version of a recipe. Modify your recipe as following:

You are getting this error because of recipes::step_string2factor(churn)

This step works fine when you are training the data. But when it is time to apply the same transformation to the training set, then step_string2factor() complains because it is asked to turn churn from a string to a factor but the dataset doesn't include the churn variable. You can deal with this in two ways.

By setting skip = FALSE in step_string2factor() you are telling the step o only be applied to when prepping/training the recipe. This is not favorable as this approach can produce errors in certain resampling scenarios using {tune} when the response is expected to be a factor instead of a string.

You configured your client as a typed client and not a named client. No need for the factory.

You should explicitly inject the http client in constructor instead, not the http client factory.

Change your code to this:

Based on the comments and the code, you've got a bit of a mess.

First off, your service should not have an @NgModule declaration: it's a service, not a module. Further, since your service isn't providedIn: "root", you need to actually provide it in your module.

Assuming AppModule is where this component and this service both live, you should have an app.module.ts something like this:

The entity extraction is using machine learning, where a model is 'trained' to do the extraction by looking at example sentences.

You do not need to provide a very long list of example sentences. As long as you provide sufficient examples, typically between 5-20, the model will learn to extract the entity value, even if it was not provided in a training example.

When creating the example sentences, it is recommended to use a wide variety of ways that users talk to your bot, like in your example:

The solution I found is to set individual timeout value for the test.

In RTL for waitFor async method according to docs :

"The default timeout is 1000ms which will keep you under Jest's default timeout of 5000ms."

I had to set timeout option for RTL async function, but it was not enough as I was getting an error:

"Exceeded timeout of 5000 ms for a test.Use jest.setTimeout(newTimeout) to increase the timeout value, if this is a long-running test." I had to change timeout value set by Jest for the test. It can be done in two ways as described in this comment:

" 1. Add a third parameter to the it. I.e., it('runs slow', () => {...}, 10000) 2. Write jest.setTimeout(10000); in a file named src/setupTests.js, as specified here."

Finally my test looks like this: