-
Notifications
You must be signed in to change notification settings - Fork 8.3k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[ML] API integration tests for
partition_fields_values endpoint (#1…
…33519) * api integration tests, improve type definitions * refactor * more tests * tests for disabled model plot * describe section * runRequest function * change maxRecordScore assertion
- Loading branch information
Showing
3 changed files
with
269 additions
and
14 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
233 changes: 233 additions & 0 deletions
233
x-pack/test/api_integration/apis/ml/results/get_partition_fields_values.ts
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,233 @@ | ||
| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License | ||
| * 2.0; you may not use this file except in compliance with the Elastic License | ||
| * 2.0. | ||
| */ | ||
|
|
||
| import expect from '@kbn/expect'; | ||
| import { Datafeed, Job } from '@kbn/ml-plugin/common/types/anomaly_detection_jobs'; | ||
| import type { PartitionFieldValueResponse } from '@kbn/ml-plugin/server/models/results_service/get_partition_fields_values'; | ||
| import { USER } from '../../../../functional/services/ml/security_common'; | ||
| import { FtrProviderContext } from '../../../ftr_provider_context'; | ||
| import { COMMON_REQUEST_HEADERS } from '../../../../functional/services/ml/common_api'; | ||
|
|
||
| export default ({ getService }: FtrProviderContext) => { | ||
| const esArchiver = getService('esArchiver'); | ||
| const supertest = getService('supertestWithoutAuth'); | ||
| const ml = getService('ml'); | ||
|
|
||
| function getJobConfig(jobId: string, enableModelPlot = true) { | ||
| return { | ||
| job_id: jobId, | ||
| description: | ||
| 'mean/min/max(responsetime) partition=airline on farequote dataset with 1h bucket span', | ||
| groups: ['farequote', 'automated', 'multi-metric'], | ||
| analysis_config: { | ||
| bucket_span: '1h', | ||
| influencers: ['airline'], | ||
| detectors: [ | ||
| { function: 'mean', field_name: 'responsetime', partition_field_name: 'airline' }, | ||
| { function: 'min', field_name: 'responsetime', partition_field_name: 'airline' }, | ||
| { function: 'max', field_name: 'responsetime', partition_field_name: 'airline' }, | ||
| ], | ||
| }, | ||
| data_description: { time_field: '@timestamp' }, | ||
| analysis_limits: { model_memory_limit: '20mb' }, | ||
| model_plot_config: { enabled: enableModelPlot }, | ||
| } as Job; | ||
| } | ||
|
|
||
| function getDatafeedConfig(jobId: string) { | ||
| return { | ||
| datafeed_id: `datafeed-${jobId}`, | ||
| indices: ['ft_farequote'], | ||
| job_id: jobId, | ||
| query: { bool: { must: [{ match_all: {} }] } }, | ||
| } as Datafeed; | ||
| } | ||
|
|
||
| async function createMockJobs() { | ||
| await ml.api.createAndRunAnomalyDetectionLookbackJob( | ||
| getJobConfig('fq_multi_1_ae'), | ||
| getDatafeedConfig('fq_multi_1_ae') | ||
| ); | ||
|
|
||
| await ml.api.createAndRunAnomalyDetectionLookbackJob( | ||
| getJobConfig('fq_multi_2_ae', false), | ||
| getDatafeedConfig('fq_multi_2_ae') | ||
| ); | ||
| } | ||
|
|
||
| async function runRequest(requestBody: object): Promise<PartitionFieldValueResponse> { | ||
| const { body, status } = await supertest | ||
| .post(`/api/ml/results/partition_fields_values`) | ||
| .auth(USER.ML_VIEWER, ml.securityCommon.getPasswordForUser(USER.ML_VIEWER)) | ||
| .set(COMMON_REQUEST_HEADERS) | ||
| .send(requestBody); | ||
| ml.api.assertResponseStatusCode(200, status, body); | ||
| return body; | ||
| } | ||
|
|
||
| describe('GetAnomaliesTableData', function () { | ||
| before(async () => { | ||
| await esArchiver.loadIfNeeded('x-pack/test/functional/es_archives/ml/farequote'); | ||
| await ml.testResources.setKibanaTimeZoneToUTC(); | ||
| await createMockJobs(); | ||
| }); | ||
|
|
||
| after(async () => { | ||
| await ml.api.cleanMlIndices(); | ||
| }); | ||
|
|
||
| describe('when model plot is enabled', () => { | ||
| it('should fetch anomalous only field values within the time range with an empty search term sorting by anomaly score', async () => { | ||
| const requestBody = { | ||
| jobId: 'fq_multi_1_ae', | ||
| criteriaFields: [{ fieldName: 'detector_index', fieldValue: 0 }], | ||
| earliestMs: 1454889600000, // February 8, 2016 12:00:00 AM GMT | ||
| latestMs: 1454976000000, // February 9, 2016 12:00:00 AM GMT, | ||
| searchTerm: {}, | ||
| fieldsConfig: { | ||
| partition_field: { | ||
| applyTimeRange: true, | ||
| anomalousOnly: true, | ||
| sort: { by: 'anomaly_score', order: 'desc' }, | ||
| }, | ||
| }, | ||
| }; | ||
|
|
||
| const body = await runRequest(requestBody); | ||
|
|
||
| expect(body.partition_field.name).to.eql('airline'); | ||
| expect(body.partition_field.values.length).to.eql(6); | ||
| expect(body.partition_field.values[0].value).to.eql('ACA'); | ||
| expect(body.partition_field.values[0].maxRecordScore).to.be.above(0); | ||
| expect(body.partition_field.values[1].value).to.eql('JBU'); | ||
| expect(body.partition_field.values[1].maxRecordScore).to.be.above(0); | ||
| expect(body.partition_field.values[2].value).to.eql('SWR'); | ||
| expect(body.partition_field.values[2].maxRecordScore).to.be.above(0); | ||
| expect(body.partition_field.values[3].value).to.eql('BAW'); | ||
| expect(body.partition_field.values[3].maxRecordScore).to.be.above(0); | ||
| expect(body.partition_field.values[4].value).to.eql('TRS'); | ||
| expect(body.partition_field.values[4].maxRecordScore).to.be.above(0); | ||
| expect(body.partition_field.values[5].value).to.eql('EGF'); | ||
| expect(body.partition_field.values[5].maxRecordScore).to.be.above(0); | ||
| }); | ||
|
|
||
| it('should fetch all values withing the time range sorting by name', async () => { | ||
| const requestBody = { | ||
| jobId: 'fq_multi_1_ae', | ||
| criteriaFields: [{ fieldName: 'detector_index', fieldValue: 0 }], | ||
| earliestMs: 1454889600000, // February 8, 2016 12:00:00 AM GMT | ||
| latestMs: 1454976000000, // February 9, 2016 12:00:00 AM GMT, | ||
| searchTerm: {}, | ||
| fieldsConfig: { | ||
| partition_field: { | ||
| applyTimeRange: true, | ||
| anomalousOnly: false, | ||
| sort: { by: 'name', order: 'asc' }, | ||
| }, | ||
| }, | ||
| }; | ||
|
|
||
| const body = await runRequest(requestBody); | ||
|
|
||
| expect(body).to.eql({ | ||
| partition_field: { | ||
| name: 'airline', | ||
| values: [ | ||
| { value: 'AAL' }, | ||
| { value: 'ACA' }, | ||
| { value: 'AMX' }, | ||
| { value: 'ASA' }, | ||
| { value: 'AWE' }, | ||
| { value: 'BAW' }, | ||
| { value: 'DAL' }, | ||
| { value: 'EGF' }, | ||
| { value: 'FFT' }, | ||
| { value: 'JAL' }, | ||
| { value: 'JBU' }, | ||
| { value: 'JZA' }, | ||
| { value: 'KLM' }, | ||
| { value: 'NKS' }, | ||
| { value: 'SWA' }, | ||
| { value: 'SWR' }, | ||
| { value: 'TRS' }, | ||
| { value: 'UAL' }, | ||
| { value: 'VRD' }, | ||
| ], | ||
| }, | ||
| }); | ||
| }); | ||
|
|
||
| it('should fetch anomalous only field value applying the search term', async () => { | ||
| const requestBody = { | ||
| jobId: 'fq_multi_1_ae', | ||
| criteriaFields: [{ fieldName: 'detector_index', fieldValue: 0 }], | ||
| earliestMs: 1454889600000, // February 8, 2016 12:00:00 AM GMT | ||
| latestMs: 1454976000000, // February 9, 2016 12:00:00 AM GMT, | ||
| searchTerm: { | ||
| partition_field: 'JB', | ||
| }, | ||
| fieldsConfig: { | ||
| partition_field: { | ||
| applyTimeRange: true, | ||
| anomalousOnly: true, | ||
| sort: { by: 'anomaly_score', order: 'asc' }, | ||
| }, | ||
| }, | ||
| }; | ||
|
|
||
| const body = await runRequest(requestBody); | ||
|
|
||
| expect(body.partition_field.name).to.eql('airline'); | ||
| expect(body.partition_field.values.length).to.eql(1); | ||
| expect(body.partition_field.values[0].value).to.eql('JBU'); | ||
| expect(body.partition_field.values[0].maxRecordScore).to.be.above(0); | ||
| }); | ||
| }); | ||
|
|
||
| describe('when model plot is disabled', () => { | ||
| it('should fetch results within the time range', async () => { | ||
| const requestBody = { | ||
| jobId: 'fq_multi_2_ae', | ||
| criteriaFields: [{ fieldName: 'detector_index', fieldValue: 0 }], | ||
| earliestMs: 1454889600000, // February 8, 2016 12:00:00 AM GMT | ||
| latestMs: 1454976000000, // February 9, 2016 12:00:00 AM GMT, | ||
| searchTerm: {}, | ||
| fieldsConfig: { | ||
| partition_field: { | ||
| applyTimeRange: true, | ||
| anomalousOnly: false, | ||
| sort: { by: 'name', order: 'asc' }, | ||
| }, | ||
| }, | ||
| }; | ||
|
|
||
| const body = await runRequest(requestBody); | ||
| expect(body.partition_field.values.length).to.eql(6); | ||
| }); | ||
|
|
||
| it('should fetch results outside the time range', async () => { | ||
| const requestBody = { | ||
| jobId: 'fq_multi_2_ae', | ||
| criteriaFields: [{ fieldName: 'detector_index', fieldValue: 0 }], | ||
| earliestMs: 1454889600000, // February 8, 2016 12:00:00 AM GMT | ||
| latestMs: 1454976000000, // February 9, 2016 12:00:00 AM GMT, | ||
| searchTerm: {}, | ||
| fieldsConfig: { | ||
| partition_field: { | ||
| applyTimeRange: false, | ||
| anomalousOnly: false, | ||
| sort: { by: 'name', order: 'asc' }, | ||
| }, | ||
| }, | ||
| }; | ||
|
|
||
| const body = await runRequest(requestBody); | ||
| expect(body.partition_field.values.length).to.eql(19); | ||
| }); | ||
| }); | ||
| }); | ||
| }; |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters