* Adding Security Authentication jobs in 7.14 * Renamed some jobs * Changing memory limits and linting change * Linting fix * Changed the order * Adding module to ml_modules.tsx * Update recognize_module.ts this test modules uses older Auditbeat data which predates the event.category field so the test has to be skipped per https://elastic.zoom.us/j/93000943632?pwd=TmpvNWhtYUNzMUc0c0N6Tlc2QlVPZz09 * Update recognize_module.ts needs to be a single line * Update recognize_module.ts Some linters want spaces and some linters want no spaces. This linter wants spaces. * descriptions added description text * Update auth_rare_hour_for_a_user.json removed a wayward newline char * Minor nitpicking * memory limits raised memory limits to 128mb which is larger than the highest observed peak model bytes for the most memory hungry jobs in this event class. Co-authored-by: Craig <mailredirector36@gmail.com> Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com> |
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.. | ||
__mocks__ | ||
common | ||
public | ||
server | ||
.gitignore | ||
jest.config.js | ||
kibana.json | ||
package.json | ||
readme.md | ||
shared_imports.ts | ||
tsconfig.json |
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
-
Fork and clone the Kibana repo.
-
Install
nvm
,node
,yarn
(for example, by using Homebrew). See Install dependencies. -
Make sure that Elasticsearch is deployed and running on localhost:9200.
-
Navigate to the directory of the
kibana
repository on your machine. -
Fetch the latest changes from the repository.
-
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
. -
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. -
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. -
Make a copy of
kibana.yml
and save askibana.dev.yml
. (Git will not track the changes inkibana.dev.yml
but yarn will use it.) -
Provide the appropriate password and user name in
kibana.dev.yml
. -
Run
yarn start
to start Kibana. -
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.
-
Click the Elastic logo in the upper left hand corner of your browser to navigate to the Kibana home page.
-
Click Load a data set and a Kibana dashboard.
-
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
Documentation: https://www.elastic.co/guide/en/kibana/current/development-tests.html#_unit_testing
Run the test following jest tests from kibana/x-pack/plugins/ml
.
New snapshots, all plugins:
yarn test:jest
Update snapshots for the ML plugin:
yarn test:jest -u
Update snapshots for a specific directory only:
yarn test:jest public/application/settings/filter_lists
Run tests with verbose output:
yarn test:jest --verbose
Functional tests
Before running the test server, make sure to quit all other instances of Elasticsearch.
Run the following commands from the x-pack
directory and use separate terminals
for test server and test runner. The test server command starts an Elasticsearch
and Kibana instance that the tests will be run against.
-
Functional UI tests with
Trial
license (default config):node scripts/functional_tests_server.js node scripts/functional_test_runner.js --include-tag mlqa
ML functional
Trial
license tests are located inx-pack/test/functional/apps/ml
. -
Functional UI tests with
Basic
license:node scripts/functional_tests_server.js --config test/functional_basic/config.ts node scripts/functional_test_runner.js --config test/functional_basic/config.ts --include-tag mlqa
ML functional
Basic
license tests are located inx-pack/test/functional_basic/apps/ml
. -
API integration tests with
Trial
license:node scripts/functional_tests_server.js node scripts/functional_test_runner.js --config test/api_integration/config.ts --include-tag mlqa
ML API integration
Trial
license tests are located inx-pack/test/api_integration/apis/ml
. -
API integration tests with
Basic
license:node scripts/functional_tests_server.js --config test/api_integration_basic/config.ts node scripts/functional_test_runner.js --config test/api_integration_basic/config.ts --include-tag mlqa
ML API integration
Basic
license tests are located inx-pack/test/api_integration_basic/apis/ml
. -
Accessibility tests:
We maintain a suite of accessibility tests (you may see them referred to elsewhere as
a11y
tests). These tests render each of our pages and ensure that the inputs and other elements contain the attributes necessary to ensure all users are able to make use of ML (for example, users relying on screen readers).node scripts/functional_tests_server --config test/accessibility/config.ts node scripts/functional_test_runner.js --config test/accessibility/config.ts --grep=ml
ML accessibility tests are located in
x-pack/test/accessibility/apps
.
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