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This PR adds functional UI tests to create a machine learning job using the population wizard.
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243 changes: 243 additions & 0 deletions
243
x-pack/test/functional/apps/machine_learning/create_population_job.ts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,243 @@ | ||
| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License; | ||
| * you may not use this file except in compliance with the Elastic License. | ||
| */ | ||
| import expect from '@kbn/expect'; | ||
|
|
||
| import { FtrProviderContext } from '../../ftr_provider_context'; | ||
|
|
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| // eslint-disable-next-line import/no-default-export | ||
| export default function({ getService }: FtrProviderContext) { | ||
| const esArchiver = getService('esArchiver'); | ||
| const ml = getService('ml'); | ||
| const log = getService('log'); | ||
|
|
||
| const jobId = `ec_population_1_${Date.now()}`; | ||
| const jobDescription = | ||
| 'Create population job based on the ecommerce sample dataset with 2h bucketspan over customer_id' + | ||
| ' - detectors: (Mean(products.base_price) by customer_gender), (Mean(products.quantity) by category.leyword)'; | ||
| const jobGroups = ['automated', 'ecommerce', 'population']; | ||
| const populationField = 'customer_id'; | ||
| const detectors = [ | ||
| { | ||
| identifier: 'Mean(products.base_price)', | ||
| splitField: 'customer_gender', | ||
| frontCardTitle: 'FEMALE', | ||
| numberOfBackCards: 1, | ||
| }, | ||
| { | ||
| identifier: 'Mean(products.quantity)', | ||
| splitField: 'category.keyword', | ||
| frontCardTitle: "Men's Clothing", | ||
| numberOfBackCards: 5, | ||
| }, | ||
| ]; | ||
| const bucketSpan = '2h'; | ||
| const memoryLimit = '8MB'; | ||
|
|
||
| describe('population job creation', function() { | ||
| this.tags(['smoke', 'mlqa']); | ||
| before(async () => { | ||
| await esArchiver.loadIfNeeded('ml/ecommerce'); | ||
| }); | ||
|
|
||
| after(async () => { | ||
| await esArchiver.unload('ml/farequote'); | ||
| await ml.api.cleanMlIndices(); | ||
| await ml.api.cleanDataframeIndices(); | ||
| }); | ||
|
|
||
| it('loads the job management page', async () => { | ||
| await ml.navigation.navigateToMl(); | ||
| await ml.navigation.navigateToJobManagement(); | ||
| }); | ||
|
|
||
| it('loads the new job source selection page', async () => { | ||
| await ml.jobManagement.navigateToNewJobSourceSelection(); | ||
| }); | ||
|
|
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| it('loads the job type selection page', async () => { | ||
| await ml.jobSourceSelection.selectSourceIndexPattern('ecommerce'); | ||
| }); | ||
|
|
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| it('loads the population job wizard page', async () => { | ||
| await ml.jobTypeSelection.selectPopulationJob(); | ||
| }); | ||
|
|
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| it('displays the time range step', async () => { | ||
| await ml.jobWizardCommon.assertTimeRangeSectionExists(); | ||
| }); | ||
|
|
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| it('displays the event rate chart', async () => { | ||
| await ml.jobWizardCommon.clickUseFullDataButton(); | ||
| await ml.jobWizardCommon.assertEventRateChartExists(); | ||
| }); | ||
|
|
||
| it('displays the pick fields step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertPickFieldsSectionExists(); | ||
| }); | ||
|
|
||
| it('selects the population field', async () => { | ||
| await ml.jobWizardPopulation.assertPopulationFieldInputExists(); | ||
| await ml.jobWizardPopulation.selectPopulationField(populationField); | ||
| await ml.jobWizardPopulation.assertPopulationFieldSelection(populationField); | ||
| }); | ||
|
|
||
| it('selects detectors and displays detector previews', async () => { | ||
| for (const [index, detector] of detectors.entries()) { | ||
| await ml.jobWizardCommon.assertAggAndFieldInputExists(); | ||
| await ml.jobWizardCommon.selectAggAndField(detector.identifier); | ||
| await ml.jobWizardCommon.assertDetectorPreviewExists(detector.identifier, index, 'SCATTER'); | ||
| } | ||
| }); | ||
|
|
||
| it('inputs detector split fields and displays split cards', async () => { | ||
| for (const [index, detector] of detectors.entries()) { | ||
| log.debug(detector); | ||
| await ml.jobWizardPopulation.assertDetectorSplitFieldInputExists(index); | ||
| await ml.jobWizardPopulation.selectDetectorSplitField(index, detector.splitField); | ||
| await ml.jobWizardPopulation.assertDetectorSplitFieldSelection(index, detector.splitField); | ||
|
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| await ml.jobWizardPopulation.assertDetectorSplitExists(index); | ||
| await ml.jobWizardPopulation.assertDetectorSplitFrontCardTitle( | ||
| index, | ||
| detector.frontCardTitle | ||
| ); | ||
| await ml.jobWizardPopulation.assertDetectorSplitNumberOfBackCards( | ||
| index, | ||
| detector.numberOfBackCards | ||
| ); | ||
| } | ||
| }); | ||
|
|
||
| it('displays the influencer field', async () => { | ||
| await ml.jobWizardCommon.assertInfluencerInputExists(); | ||
| await ml.jobWizardCommon.assertInfluencerSelection( | ||
| [populationField].concat(detectors.map(detector => detector.splitField)) | ||
| ); | ||
| }); | ||
|
|
||
| it('inputs the bucket span', async () => { | ||
| await ml.jobWizardCommon.assertBucketSpanInputExists(); | ||
| await ml.jobWizardCommon.setBucketSpan(bucketSpan); | ||
| await ml.jobWizardCommon.assertBucketSpanValue(bucketSpan); | ||
| }); | ||
|
|
||
| it('displays the job details step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertJobDetailsSectionExists(); | ||
| }); | ||
|
|
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| it('inputs the job id', async () => { | ||
| await ml.jobWizardCommon.assertJobIdInputExists(); | ||
| await ml.jobWizardCommon.setJobId(jobId); | ||
| await ml.jobWizardCommon.assertJobIdValue(jobId); | ||
| }); | ||
|
|
||
| it('inputs the job description', async () => { | ||
| await ml.jobWizardCommon.assertJobDescriptionInputExists(); | ||
| await ml.jobWizardCommon.setJobDescription(jobDescription); | ||
| await ml.jobWizardCommon.assertJobDescriptionValue(jobDescription); | ||
| }); | ||
|
|
||
| it('inputs job groups', async () => { | ||
| await ml.jobWizardCommon.assertJobGroupInputExists(); | ||
| for (const jobGroup of jobGroups) { | ||
| await ml.jobWizardCommon.addJobGroup(jobGroup); | ||
| } | ||
| await ml.jobWizardCommon.assertJobGroupSelection(jobGroups); | ||
| }); | ||
|
|
||
| it('opens the advanced section', async () => { | ||
| await ml.jobWizardCommon.ensureAdvancedSectionOpen(); | ||
| }); | ||
|
|
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| it('displays the model plot switch', async () => { | ||
| await ml.jobWizardCommon.assertModelPlotSwitchExists(); | ||
| }); | ||
|
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| it('enables the dedicated index switch', async () => { | ||
| await ml.jobWizardCommon.assertDedicatedIndexSwitchExists(); | ||
| await ml.jobWizardCommon.activateDedicatedIndexSwitch(); | ||
| await ml.jobWizardCommon.assertDedicatedIndexSwitchCheckedState(true); | ||
| }); | ||
|
|
||
| it('inputs the model memory limit', async () => { | ||
| await ml.jobWizardCommon.assertModelMemoryLimitInputExists(); | ||
| await ml.jobWizardCommon.setModelMemoryLimit(memoryLimit); | ||
| await ml.jobWizardCommon.assertModelMemoryLimitValue(memoryLimit); | ||
| }); | ||
|
|
||
| it('displays the validation step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertValidationSectionExists(); | ||
| }); | ||
|
|
||
| it('displays the summary step', async () => { | ||
| await ml.jobWizardCommon.clickNextButton(); | ||
| await ml.jobWizardCommon.assertSummarySectionExists(); | ||
| }); | ||
|
|
||
| it('creates the job and finishes processing', async () => { | ||
| await ml.jobWizardCommon.assertCreateJobButtonExists(); | ||
| await ml.jobWizardCommon.createJobAndWaitForCompletion(); | ||
| }); | ||
|
|
||
| it('displays the created job in the job list', async () => { | ||
| await ml.navigation.navigateToMl(); | ||
| await ml.navigation.navigateToJobManagement(); | ||
|
|
||
| await ml.jobTable.waitForJobsToLoad(); | ||
| await ml.jobTable.filterWithSearchString(jobId); | ||
| const rows = await ml.jobTable.parseJobTable(); | ||
| expect(rows.filter(row => row.id === jobId)).to.have.length(1); | ||
| }); | ||
|
|
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| it('displays details for the created job in the job list', async () => { | ||
| const expectedRow = { | ||
| id: jobId, | ||
| description: jobDescription, | ||
| jobGroups, | ||
| recordCount: '4,675', | ||
| memoryStatus: 'ok', | ||
| jobState: 'closed', | ||
| datafeedState: 'stopped', | ||
| latestTimestamp: '2019-07-12 23:45:36', | ||
| }; | ||
| await ml.jobTable.assertJobRowFields(jobId, expectedRow); | ||
|
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||
| const expectedCounts = { | ||
| job_id: jobId, | ||
| processed_record_count: '4,675', | ||
| processed_field_count: '23,375', | ||
| input_bytes: '867.7 KB', | ||
| input_field_count: '23,375', | ||
| invalid_date_count: '0', | ||
| missing_field_count: '0', | ||
| out_of_order_timestamp_count: '0', | ||
| empty_bucket_count: '0', | ||
| sparse_bucket_count: '0', | ||
| bucket_count: '371', | ||
| earliest_record_timestamp: '2019-06-12 00:04:19', | ||
| latest_record_timestamp: '2019-07-12 23:45:36', | ||
| input_record_count: '4,675', | ||
| latest_bucket_timestamp: '2019-07-12 22:00:00', | ||
| }; | ||
| const expectedModelSizeStats = { | ||
| job_id: jobId, | ||
| result_type: 'model_size_stats', | ||
| model_bytes_exceeded: '0', | ||
| model_bytes_memory_limit: '8388608', | ||
| total_by_field_count: '25', | ||
| total_over_field_count: '92', | ||
| total_partition_field_count: '3', | ||
| bucket_allocation_failures_count: '0', | ||
| memory_status: 'ok', | ||
| timestamp: '2019-07-12 20:00:00', | ||
| }; | ||
| await ml.jobTable.assertJobRowDetailsCounts(jobId, expectedCounts, expectedModelSizeStats); | ||
| }); | ||
| }); | ||
| } |
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