kibana/x-pack/plugins/ml

Documentation for ML UI developers

This plugin provides access to the machine learning features provided by Elastic.

Requirements

To use machine learning features, you must have a Platinum or Enterprise license or a free 14-day trial. File Data Visualizer requires a Basic license. For more info, refer to Set up machine learning features.

Setup local environment

Kibana

1. Fork and clone the Kibana repo.

2. Install nvm, node, yarn (for example, by using Homebrew). See Install dependencies.

3. Make sure that Elasticsearch is deployed and running on localhost:9200.

4. Navigate to the directory of the kibana repository on your machine.

5. Fetch the latest changes from the repository.

6. Checkout the branch of the version you want to use. For example, if you want to use a 7.9 version, run git checkout 7.9.

7. Run nvm use. The response shows the Node version that the environment uses. If you need to update your Node version, the response message contains the command you need to run to do it.

8. Run yarn kbn bootstrap. It takes all the dependencies in the code and installs/checks them. It is recommended to use it every time when you switch between branches.

9. Make a copy of kibana.yml and save as kibana.dev.yml. (Git will not track the changes in kibana.dev.yml but yarn will use it.)

10. Provide the appropriate password and user name in kibana.dev.yml.

11. Run yarn start to start Kibana.

12. Go to http://localhost:560x/xxx (check the terminal message for the exact path).

For more details, refer to this getting started page.

Adding sample data to Kibana

Kibana has sample data sets that you can add to your setup so that you can test different configurations on sample data.

1. Click the Elastic logo in the upper left hand corner of your browser to navigate to the Kibana home page.

2. Click Load a data set and a Kibana dashboard.

3. Pick a data set or feel free to click Add on all of the available sample data sets.

These data sets are now ready be analyzed in ML jobs in Kibana.

Running tests

Jest tests

Run the test following jest tests from kibana/x-pack.

New snapshots, all plugins:

node scripts/jest

Update snapshots for the ML plugin:

node scripts/jest plugins/ml -u

Update snapshots for a specific directory only:

node scripts/jest plugins/ml/public/application/settings/filter_lists

Run tests with verbose output:

node scripts/jest plugins/ml --verbose

Functional tests

Before running the test server, make sure to quit all other instances of Elasticsearch.

1. From one terminal, in the x-pack directory, run:

    node scripts/functional_tests_server.js --config test/functional/config.js
   

   This command starts an Elasticsearch and Kibana instance that the tests will be run against.

2. In another tab, run the following command to perform API integration tests (from the x-pack directory):

    node scripts/functional_test_runner.js --include-tag mlqa --config test/api_integration/config
   

   ML API integration tests are located in x-pack/test/api_integration/apis/ml.

3. In another tab, run the following command to perform UI functional tests (from the x-pack directory):

    node scripts/functional_test_runner.js --include-tag mlqa
   

   ML functional tests are located in x-pack/test/functional/apps/ml.

Shared functions

You can find the ML shared functions in the following files in GitHub:

https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/public/shared.ts

https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/shared.ts

These functions are shared from the root of the ML plugin, you can import them with an import statement. For example:

import { MlPluginSetup } from '../../../../ml/server';

or

import { ANOMALY_SEVERITY } from '../../ml/common';

Functions are shared from the following directories:

ml/common
ml/public
ml/server