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[ML] Add initial docs screenshot generation (#121495)

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This PR utilizes the functional test runner to walk through the UI and take a couple screenshots for use in the documentation.
This commit is contained in:
Robert Oskamp 2021-12-20 15:44:58 +01:00 • committed by GitHub
parent d421ddcf61
commit 1d4dce3ba9
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34 changed files with 1555 additions and 129 deletions
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4
test/functional/services/common/screenshots.ts
View file

@ -74,8 +74,8 @@ export class ScreenshotsService extends FtrService {
}
}
async take(name: string, el?: WebElementWrapper) {
const path = resolve(this.SESSION_DIRECTORY, `${name}.png`);
async take(name: string, el?: WebElementWrapper, subDirectories: string[] = []) {
const path = resolve(this.SESSION_DIRECTORY, ...subDirectories, `${name}.png`);
await this.capture(path, el);
this.failureMetadata.addScreenshot(name, path);
}

2
x-pack/plugins/ml/public/application/components/anomalies_table/anomalies_table_columns.js
View file

@ -91,7 +91,7 @@ export function getColumns(
})
}
data-row-id={item.rowId}
data-test-subj="mlJobListRowDetailsToggle"
data-test-subj="mlAnomaliesListRowDetailsToggle"
/>
),
},

7
x-pack/plugins/ml/public/application/components/anomalies_table/anomaly_details.js
View file

@ -313,7 +313,10 @@ export class AnomalyDetails extends Component {
}),
content: (
<Fragment>
<div className="ml-anomalies-table-details">
<div
className="ml-anomalies-table-details"
data-test-subj="mlAnomaliesListRowDetails"
>
{this.renderDescription()}
<EuiSpacer size="m" />
{this.renderDetails()}
@ -633,7 +636,7 @@ export class AnomalyDetails extends Component {
);
} else {
return (
<div className="ml-anomalies-table-details">
<div className="ml-anomalies-table-details" data-test-subj="mlAnomaliesListRowDetails">
{this.renderDescription()}
<EuiSpacer size="m" />
{this.renderDetails()}

8
x-pack/plugins/ml/public/application/data_frame_analytics/pages/analytics_exploration/components/expandable_section/expandable_section.tsx
View file

@ -84,6 +84,7 @@ export const ExpandableSection: FC<ExpandableSectionProps> = ({
iconType={isExpanded ? 'arrowUp' : 'arrowDown'}
iconSide="right"
flush="left"
data-test-subj={`mlDFExpandableSection-${dataTestId}-toggle-button`}
>
{title}
</EuiButtonEmpty>
@ -126,7 +127,12 @@ export const ExpandableSection: FC<ExpandableSectionProps> = ({
)}
</div>
{isExpanded && (
<div className={contentPadding ? 'mlExpandableSection-contentPadding' : ''}>{content}</div>
<div
className={contentPadding ? 'mlExpandableSection-contentPadding' : ''}
data-test-subj={`mlDFExpandableSection-${dataTestId}-content`}
>
{content}
</div>
)}
</EuiPanel>
);

2
x-pack/plugins/ml/public/application/data_frame_analytics/pages/analytics_exploration/components/exploration_query_bar/exploration_query_bar.tsx
View file

@ -192,7 +192,7 @@ export const ExplorationQueryBar: FC<ExplorationQueryBarProps> = ({
})
}
disableAutoFocus={true}
dataTestSubj="transformQueryInput"
dataTestSubj="mlDFAnalyticsQueryInput"
languageSwitcherPopoverAnchorPosition="rightDown"
/>
</EuiFlexItem>

2
x-pack/plugins/ml/public/application/explorer/anomalies_map.tsx
View file

@ -222,7 +222,7 @@ export const AnomaliesMap: FC<Props> = ({ anomalies, jobIds }) => {
return (
<>
<EuiPanel>
<EuiPanel data-test-subj="mlAnomaliesMapContainer">
<EuiAccordion
id="mlAnomalyExplorerAnomaliesMapAccordionId"
initialIsOpen={true}

14
x-pack/plugins/ml/readme.md
View file

@ -141,6 +141,20 @@ and Kibana instance that the tests will be run against.
ML accessibility tests are located in `x-pack/test/accessibility/apps`.
## Generating docs screenshots
The screenshot generation uses the functional test runner described in the
`Functional tests` section above.
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.
node scripts/functional_tests_server.js --config test/screenshot_creation/config.ts
node scripts/functional_test_runner.js --config test/screenshot_creation/config.ts --include-tag mlqa
The generated screenshots are stored in `x-pack/test/functional/screenshots/session/ml_docs`.
ML screenshot generation tests are located in `x-pack/test/screenshot_creation/apps/ml_docs`.
## Shared functions
You can find the ML shared functions in the following files in GitHub:

31
x-pack/test/functional/apps/ml/anomaly_detection/custom_urls.ts
View file

@ -7,31 +7,12 @@
import { FtrProviderContext } from '../../../ftr_provider_context';
import { Job, Datafeed } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs';
import {
TimeRangeType,
TIME_RANGE_TYPE,
} from '../../../../../plugins/ml/public/application/jobs/components/custom_url_editor/constants';
interface DiscoverUrlConfig {
label: string;
indexPattern: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
}
interface DashboardUrlConfig {
label: string;
dashboardName: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
}
interface OtherUrlConfig {
label: string;
url: string;
}
import { TIME_RANGE_TYPE } from '../../../../../plugins/ml/public/application/jobs/components/custom_url_editor/constants';
import type {
DiscoverUrlConfig,
DashboardUrlConfig,
OtherUrlConfig,
} from '../../../services/ml/job_table';
// @ts-expect-error doesn't implement the full interface
const JOB_CONFIG: Job = {

14
x-pack/test/functional/page_objects/gis_page.ts
View file

@ -294,6 +294,14 @@ export class GisPageObject extends FtrService {
await this.testSubjects.click('layerVisibilityToggleButton');
}
async openLegend() {
const isOpen = await this.testSubjects.exists('mapLayerTOC');
if (isOpen === false) {
await this.testSubjects.click('mapExpandLayerControlButton');
await this.testSubjects.existOrFail('mapLayerTOC');
}
}
async closeLegend() {
const isOpen = await this.testSubjects.exists('mapLayerTOC');
if (isOpen) {
@ -302,6 +310,12 @@ export class GisPageObject extends FtrService {
}
}
async clickFitToData() {
this.log.debug('Fit to data');
await this.testSubjects.click('fitToData');
await this.waitForMapPanAndZoom();
}
async clickFitToBounds(layerName: string) {
this.log.debug(`Fit to bounds, layer: ${layerName}`);
const origView = await this.getView();

24
x-pack/test/functional/services/ml/anomalies_table.ts
View file

@ -149,5 +149,29 @@ export function MachineLearningAnomaliesTableProvider({ getService }: FtrProvide
await testSubjects.click(`tablePagination-${rowsNumber}-rows`);
await this.assertRowsNumberPerPage(rowsNumber);
},
async ensureDetailsOpen(rowIndex: number) {
await retry.tryForTime(10 * 1000, async () => {
const rowSubj = await this.getRowSubjByRowIndex(rowIndex);
if (!(await testSubjects.exists('mlAnomaliesListRowDetails'))) {
await testSubjects.click(`${rowSubj} > mlAnomaliesListRowDetailsToggle`);
await testSubjects.existOrFail('mlAnomaliesListRowDetails', { timeout: 1000 });
}
});
},
async ensureDetailsClosed(rowIndex: number) {
await retry.tryForTime(10 * 1000, async () => {
const rowSubj = await this.getRowSubjByRowIndex(rowIndex);
if (await testSubjects.exists('mlAnomaliesListRowDetails')) {
await testSubjects.click(`${rowSubj} > mlAnomaliesListRowDetailsToggle`);
await testSubjects.missingOrFail('mlAnomaliesListRowDetails', { timeout: 1000 });
}
});
},
async scrollTableIntoView() {
await testSubjects.scrollIntoView('mlAnomaliesTable');
},
};
}

8
x-pack/test/functional/services/ml/anomaly_explorer.ts
View file

@ -174,5 +174,13 @@ export function MachineLearningAnomalyExplorerProvider({
`Expect ${expectedChartsCount} charts to appear, got ${actualChartsCount}`
);
},
async scrollChartsContainerIntoView() {
await testSubjects.scrollIntoView('mlExplorerChartsContainer');
},
async scrollMapContainerIntoView() {
await testSubjects.scrollIntoView('mlAnomaliesMapContainer');
},
};
}

4
x-pack/test/functional/services/ml/api.ts
View file

@ -936,11 +936,11 @@ export function MachineLearningAPIProvider({ getService }: FtrProviderContext) {
log.debug('> DFA job started.');
},
async createAndRunDFAJob(dfaConfig: DataFrameAnalyticsConfig) {
async createAndRunDFAJob(dfaConfig: DataFrameAnalyticsConfig, timeout?: number) {
await this.createDataFrameAnalyticsJob(dfaConfig);
await this.runDFAJob(dfaConfig.id);
await this.waitForDFAJobTrainingRecordCountToBePositive(dfaConfig.id);
await this.waitForAnalyticsState(dfaConfig.id, DATA_FRAME_TASK_STATE.STOPPED);
await this.waitForAnalyticsState(dfaConfig.id, DATA_FRAME_TASK_STATE.STOPPED, timeout);
},
async updateJobSpaces(

32
x-pack/test/functional/services/ml/data_frame_analytics_creation.ts
View file

@ -23,6 +23,8 @@ export function MachineLearningDataFrameAnalyticsCreationProvider(
mlApi: MlApi
) {
const headerPage = getPageObject('header');
const commonPage = getPageObject('common');
const testSubjects = getService('testSubjects');
const comboBox = getService('comboBox');
const retry = getService('retry');
@ -33,6 +35,10 @@ export function MachineLearningDataFrameAnalyticsCreationProvider(
await testSubjects.existOrFail('mlAnalyticsCreateJobWizardJobTypeSelect');
},
async scrollJobTypeSelectionIntoView() {
await testSubjects.scrollIntoView('mlAnalyticsCreateJobWizardJobTypeSelect');
},
async assertJobTypeSelection(jobTypeAttribute: string) {
await retry.tryForTime(5000, async () => {
await testSubjects.existOrFail(`${jobTypeAttribute} selectedJobType`);
@ -324,16 +330,24 @@ export function MachineLearningDataFrameAnalyticsCreationProvider(
await this.assertDependentVariableSelection([dependentVariable]);
},
async assertScatterplotMatrix(expectedValue: CanvasElementColorStats) {
async assertScatterplotMatrixLoaded() {
await testSubjects.existOrFail(
'mlAnalyticsCreateJobWizardScatterplotMatrixPanel > mlScatterplotMatrix loaded',
{
timeout: 5000,
}
);
},
async scrollScatterplotMatrixIntoView() {
await testSubjects.scrollIntoView(
'mlAnalyticsCreateJobWizardScatterplotMatrixPanel > mlScatterplotMatrix loaded'
);
},
async assertScatterplotMatrix(expectedValue: CanvasElementColorStats) {
await this.assertScatterplotMatrixLoaded();
await this.scrollScatterplotMatrixIntoView();
await mlCommonUI.assertColorsInCanvasElement(
'mlAnalyticsCreateJobWizardScatterplotMatrixPanel',
expectedValue,
@ -672,5 +686,21 @@ export function MachineLearningDataFrameAnalyticsCreationProvider(
await testSubjects.click('analyticsWizardCardManagement');
await testSubjects.existOrFail('mlPageDataFrameAnalytics');
},
async assertQueryBarValue(expectedValue: string) {
const actualQuery = await testSubjects.getAttribute('mlDFAnalyticsQueryInput', 'value');
expect(actualQuery).to.eql(
expectedValue,
`Query should be '${expectedValue}' (got '${actualQuery}')`
);
},
async setQueryBarValue(query: string) {
await mlCommonUI.setValueWithChecks('mlDFAnalyticsQueryInput', query, {
clearWithKeyboard: true,
});
await commonPage.pressEnterKey();
await this.assertQueryBarValue(query);
},
};
}

54
x-pack/test/functional/services/ml/data_frame_analytics_results.ts
View file

@ -33,6 +33,10 @@ export function MachineLearningDataFrameAnalyticsResultsProvider(
await testSubjects.existOrFail('mlDFAnalyticsExplorationTablePanel');
},
async scrollRocCurveChartIntoView() {
await testSubjects.scrollIntoView('mlDFAnalyticsClassificationExplorationRocCurveChart');
},
async assertClassificationEvaluatePanelElementsExists() {
await testSubjects.existOrFail('mlDFExpandableSection-ClassificationEvaluation');
await testSubjects.existOrFail('mlDFAnalyticsClassificationExplorationConfusionMatrix');
@ -125,11 +129,15 @@ export function MachineLearningDataFrameAnalyticsResultsProvider(
});
},
async assertScatterplotMatrix(expectedValue: CanvasElementColorStats) {
async assertScatterplotMatrixLoaded() {
await testSubjects.existOrFail('mlDFExpandableSection-splom > mlScatterplotMatrix loaded', {
timeout: 5000,
});
await testSubjects.scrollIntoView('mlDFExpandableSection-splom > mlScatterplotMatrix loaded');
},
async assertScatterplotMatrix(expectedValue: CanvasElementColorStats) {
await this.assertScatterplotMatrixLoaded();
await this.scrollScatterplotMatrixIntoView();
await mlCommonUI.assertColorsInCanvasElement(
'mlDFExpandableSection-splom',
expectedValue,
@ -242,5 +250,47 @@ export function MachineLearningDataFrameAnalyticsResultsProvider(
async scrollResultsIntoView() {
await this.scrollContentSectionIntoView('results');
},
async expandContentSection(sectionId: string, shouldExpand: boolean) {
const contentSubj = `mlDFExpandableSection-${sectionId}-content`;
const expandableContentExists = await testSubjects.exists(contentSubj, { timeout: 1000 });
if (expandableContentExists !== shouldExpand) {
await retry.tryForTime(5 * 1000, async () => {
await testSubjects.clickWhenNotDisabled(
`mlDFExpandableSection-${sectionId}-toggle-button`
);
if (shouldExpand) {
await testSubjects.existOrFail(contentSubj, { timeout: 1000 });
} else {
await testSubjects.missingOrFail(contentSubj, { timeout: 1000 });
}
});
}
},
async expandAnalysisSection(shouldExpand: boolean) {
await this.expandContentSection('analysis', shouldExpand);
},
async expandRegressionEvaluationSection(shouldExpand: boolean) {
await this.expandContentSection('RegressionEvaluation', shouldExpand);
},
async expandClassificationEvaluationSection(shouldExpand: boolean) {
await this.expandContentSection('ClassificationEvaluation', shouldExpand);
},
async expandFeatureImportanceSection(shouldExpand: boolean) {
await this.expandContentSection('FeatureImportanceSummary', shouldExpand);
},
async expandScatterplotMatrixSection(shouldExpand: boolean) {
await this.expandContentSection('splom', shouldExpand);
},
async expandResultsSection(shouldExpand: boolean) {
await this.expandContentSection('results', shouldExpand);
},
};
}

2
x-pack/test/functional/services/ml/data_visualizer_table.ts
View file

@ -295,7 +295,7 @@ export function MachineLearningDataVisualizerTableProvider(
}
public async setSampleSizeInputValue(
sampleSize: number,
sampleSize: number | 'all',
fieldName: string,
docCountFormatted: string
) {

183
x-pack/test/functional/services/ml/job_table.ts
View file

@ -20,6 +20,27 @@ import {
export type MlADJobTable = ProvidedType<typeof MachineLearningJobTableProvider>;
export interface DiscoverUrlConfig {
label: string;
indexPattern: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
}
export interface DashboardUrlConfig {
label: string;
dashboardName: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
}
export interface OtherUrlConfig {
label: string;
url: string;
}
export function MachineLearningJobTableProvider(
{ getService }: FtrProviderContext,
mlCommonUI: MlCommonUI,
@ -564,121 +585,103 @@ export function MachineLearningJobTableProvider(
await testSubjects.existOrFail('mlJobCustomUrlForm');
}
public async addDiscoverCustomUrl(
jobId: string,
customUrl: {
label: string;
indexPattern: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
public async getExistingCustomUrlCount(): Promise<number> {
const existingCustomUrls = await testSubjects.findAll('mlJobEditCustomUrlItemLabel');
return existingCustomUrls.length;
}
public async saveCustomUrl(expectedLabel: string, expectedIndex: number) {
await retry.tryForTime(5000, async () => {
await testSubjects.click('mlJobAddCustomUrl');
await customUrls.assertCustomUrlLabel(expectedIndex, expectedLabel);
});
}
public async fillInDiscoverUrlForm(customUrl: DiscoverUrlConfig) {
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(
`mlJobCustomUrlLinkToTypeInput`,
URL_TYPE.KIBANA_DISCOVER
);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlDiscoverIndexPatternInput',
customUrl.indexPattern
);
await customUrls.setCustomUrlQueryEntityFieldNames(customUrl.queryEntityFieldNames);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlTimeRangeInput',
customUrl.timeRange
);
if (customUrl.timeRange === TIME_RANGE_TYPE.INTERVAL) {
await customUrls.setCustomUrlTimeRangeInterval(customUrl.timeRangeInterval!);
}
) {
}
public async fillInDashboardUrlForm(customUrl: DashboardUrlConfig) {
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(
`mlJobCustomUrlLinkToTypeInput`,
URL_TYPE.KIBANA_DASHBOARD
);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlDashboardNameInput',
customUrl.dashboardName
);
await customUrls.setCustomUrlQueryEntityFieldNames(customUrl.queryEntityFieldNames);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlTimeRangeInput',
customUrl.timeRange
);
if (customUrl.timeRange === TIME_RANGE_TYPE.INTERVAL) {
await customUrls.setCustomUrlTimeRangeInterval(customUrl.timeRangeInterval!);
}
}
public async fillInOtherUrlForm(customUrl: OtherUrlConfig) {
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(`mlJobCustomUrlLinkToTypeInput`, URL_TYPE.OTHER);
await customUrls.setCustomUrlOtherTypeUrl(customUrl.url);
}
public async addDiscoverCustomUrl(jobId: string, customUrl: DiscoverUrlConfig) {
await retry.tryForTime(30 * 1000, async () => {
await this.closeEditJobFlyout();
await this.openEditCustomUrlsForJobTab(jobId);
const existingCustomUrlCount = await this.getExistingCustomUrlCount();
const existingCustomUrls = await testSubjects.findAll('mlJobEditCustomUrlItemLabel');
// Fill-in the form
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(
`mlJobCustomUrlLinkToTypeInput`,
URL_TYPE.KIBANA_DISCOVER
);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlDiscoverIndexPatternInput',
customUrl.indexPattern
);
await customUrls.setCustomUrlQueryEntityFieldNames(customUrl.queryEntityFieldNames);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlTimeRangeInput',
customUrl.timeRange
);
if (customUrl.timeRange === TIME_RANGE_TYPE.INTERVAL) {
await customUrls.setCustomUrlTimeRangeInterval(customUrl.timeRangeInterval!);
}
// Save custom URL
await retry.tryForTime(5000, async () => {
await testSubjects.click('mlJobAddCustomUrl');
const expectedIndex = existingCustomUrls.length;
await customUrls.assertCustomUrlLabel(expectedIndex, customUrl.label);
});
await this.fillInDiscoverUrlForm(customUrl);
await this.saveCustomUrl(customUrl.label, existingCustomUrlCount);
});
// Save the job
await this.saveEditJobFlyoutChanges();
}
public async addDashboardCustomUrl(
jobId: string,
customUrl: {
label: string;
dashboardName: string;
queryEntityFieldNames: string[];
timeRange: TimeRangeType;
timeRangeInterval?: string;
}
) {
public async addDashboardCustomUrl(jobId: string, customUrl: DashboardUrlConfig) {
await retry.tryForTime(30 * 1000, async () => {
await this.closeEditJobFlyout();
await this.openEditCustomUrlsForJobTab(jobId);
const existingCustomUrlCount = await this.getExistingCustomUrlCount();
const existingCustomUrls = await testSubjects.findAll('mlJobEditCustomUrlItemLabel');
// Fill-in the form
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(
`mlJobCustomUrlLinkToTypeInput`,
URL_TYPE.KIBANA_DASHBOARD
);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlDashboardNameInput',
customUrl.dashboardName
);
await customUrls.setCustomUrlQueryEntityFieldNames(customUrl.queryEntityFieldNames);
await mlCommonUI.selectSelectValueByVisibleText(
'mlJobCustomUrlTimeRangeInput',
customUrl.timeRange
);
if (customUrl.timeRange === TIME_RANGE_TYPE.INTERVAL) {
await customUrls.setCustomUrlTimeRangeInterval(customUrl.timeRangeInterval!);
}
// Save custom URL
await retry.tryForTime(5000, async () => {
await testSubjects.click('mlJobAddCustomUrl');
const expectedIndex = existingCustomUrls.length;
await customUrls.assertCustomUrlLabel(expectedIndex, customUrl.label);
});
await this.fillInDashboardUrlForm(customUrl);
await this.saveCustomUrl(customUrl.label, existingCustomUrlCount);
});
// Save the job
await this.saveEditJobFlyoutChanges();
}
public async addOtherTypeCustomUrl(jobId: string, customUrl: { label: string; url: string }) {
public async addOtherTypeCustomUrl(jobId: string, customUrl: OtherUrlConfig) {
await retry.tryForTime(30 * 1000, async () => {
await this.closeEditJobFlyout();
await this.openEditCustomUrlsForJobTab(jobId);
const existingCustomUrlCount = await this.getExistingCustomUrlCount();
const existingCustomUrls = await testSubjects.findAll('mlJobEditCustomUrlItemLabel');
// Fill-in the form
await this.clickOpenCustomUrlEditor();
await customUrls.setCustomUrlLabel(customUrl.label);
await mlCommonUI.selectRadioGroupValue(`mlJobCustomUrlLinkToTypeInput`, URL_TYPE.OTHER);
await customUrls.setCustomUrlOtherTypeUrl(customUrl.url);
// Save custom URL
await retry.tryForTime(5000, async () => {
await testSubjects.click('mlJobAddCustomUrl');
const expectedIndex = existingCustomUrls.length;
await customUrls.assertCustomUrlLabel(expectedIndex, customUrl.label);
});
await this.fillInOtherUrlForm(customUrl);
await this.saveCustomUrl(customUrl.label, existingCustomUrlCount);
});
// Save the job

4
x-pack/test/functional/services/ml/job_wizard_multi_metric.ts
View file

@ -33,6 +33,10 @@ export function MachineLearningJobWizardMultiMetricProvider({ getService }: FtrP
await this.assertSplitFieldSelection([identifier]);
},
async scrollSplitFieldIntoView() {
await testSubjects.scrollIntoView('mlMultiMetricSplitFieldSelect');
},
async assertDetectorSplitExists(splitField: string) {
await testSubjects.existOrFail(`mlDataSplit > mlDataSplitTitle ${splitField}`);
await testSubjects.existOrFail(`mlDataSplit > mlSplitCard front`);

7
x-pack/test/functional/services/ml/navigation.ts
View file

@ -223,6 +223,13 @@ export function MachineLearningNavigationProvider({
await testSubjects.existOrFail('collapsibleNav');
},
async closeKibanaNav() {
if (await testSubjects.exists('collapsibleNav')) {
await testSubjects.click('toggleNavButton');
}
await testSubjects.missingOrFail('collapsibleNav');
},
async assertKibanaNavMLEntryExists() {
const navArea = await testSubjects.find('collapsibleNav');
const mlNavLink = await navArea.findAllByCssSelector('[title="Machine Learning"]');

34
x-pack/test/functional/services/ml/test_resources.ts
View file

@ -537,5 +537,39 @@ export function MachineLearningTestResourcesProvider({ getService }: FtrProvider
async clearAdvancedSettingProperty(propertyName: string) {
await kibanaServer.uiSettings.unset(propertyName);
},
async installKibanaSampleData(sampleDataId: 'ecommerce' | 'flights' | 'logs') {
log.debug(`Installing Kibana sample data '${sampleDataId}'`);
await supertest
.post(`/api/sample_data/${sampleDataId}`)
.set(COMMON_REQUEST_HEADERS)
.expect(200);
log.debug(` > Installed`);
},
async removeKibanaSampleData(sampleDataId: 'ecommerce' | 'flights' | 'logs') {
log.debug(`Removing Kibana sample data '${sampleDataId}'`);
await supertest
.delete(`/api/sample_data/${sampleDataId}`)
.set(COMMON_REQUEST_HEADERS)
.expect(204); // No Content
log.debug(` > Removed`);
},
async installAllKibanaSampleData() {
await this.installKibanaSampleData('ecommerce');
await this.installKibanaSampleData('flights');
await this.installKibanaSampleData('logs');
},
async removeAllKibanaSampleData() {
await this.removeKibanaSampleData('ecommerce');
await this.removeKibanaSampleData('flights');
await this.removeKibanaSampleData('logs');
},
};
}

14
x-pack/test/screenshot_creation/apps/index.ts Normal file
View file

@ -0,0 +1,14 @@
/*
* 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 { FtrProviderContext } from '../ftr_provider_context';
export default function ({ loadTestFile }: FtrProviderContext) {
describe('apps', function () {
loadTestFile(require.resolve('./ml_docs'));
});
}

101
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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { Job, Datafeed } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs';
import { TIME_RANGE_TYPE } from '../../../../../plugins/ml/public/application/jobs/components/custom_url_editor/constants';
import { ECOMMERCE_INDEX_PATTERN } from '../index';
export default function ({ getService }: FtrProviderContext) {
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const screenshotDirectories = ['ml_docs', 'anomaly_detection'];
const ecommerceJobConfig = {
job_id: `ecommerce-custom-url`,
analysis_config: {
bucket_span: '2h',
influencers: ['geoip.country_iso_code', 'day_of_week', 'category.keyword', 'user'],
detectors: [
{
detector_description: 'mean("products.base_price") over "customer_full_name.keyword"',
function: 'mean',
field_name: 'products.base_price',
over_field_name: 'customer_full_name.keyword',
},
],
},
data_description: { time_field: 'order_date' },
};
const ecommerceDatafeedConfig = {
datafeed_id: 'datafeed-ecommerce-custom-url',
indices: [ECOMMERCE_INDEX_PATTERN],
job_id: 'ecommerce-custom-url',
query: { bool: { must: [{ match_all: {} }] } },
};
const testDashboardCustomUrl = {
label: 'Data dashboard',
dashboardName: '[eCommerce] Revenue Dashboard',
queryEntityFieldNames: ['customer_full_name.keyword'],
timeRange: TIME_RANGE_TYPE.AUTO,
};
describe('custom urls', function () {
before(async () => {
await ml.api.createAndRunAnomalyDetectionLookbackJob(
ecommerceJobConfig as Job,
ecommerceDatafeedConfig as Datafeed
);
});
after(async () => {
await ml.api.deleteAnomalyDetectionJobES(ecommerceJobConfig.job_id);
await ml.api.cleanMlIndices();
});
it('custom url config screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep(
'fill in the dashboard custom url form and take screenshot'
);
await ml.jobTable.closeEditJobFlyout();
await ml.jobTable.openEditCustomUrlsForJobTab(ecommerceJobConfig.job_id);
const existingCustomUrlCount = await ml.jobTable.getExistingCustomUrlCount();
await ml.jobTable.fillInDashboardUrlForm(testDashboardCustomUrl);
await mlScreenshots.takeScreenshot('ml-customurl-edit', screenshotDirectories);
await ml.testExecution.logTestStep('add the custom url and save the job');
await ml.jobTable.saveCustomUrl(testDashboardCustomUrl.label, existingCustomUrlCount);
await ml.jobTable.saveEditJobFlyoutChanges();
});
it('anomaly list screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('open job in anomaly explorer');
await ml.jobTable.waitForJobsToLoad();
await ml.jobTable.filterWithSearchString(ecommerceJobConfig.job_id, 1);
await ml.jobTable.clickOpenJobInAnomalyExplorerButton(ecommerceJobConfig.job_id);
await ml.commonUI.waitForMlLoadingIndicatorToDisappear();
await ml.testExecution.logTestStep('open anomaly list actions and take screenshot');
await ml.anomaliesTable.scrollTableIntoView();
await ml.anomaliesTable.ensureAnomalyActionsMenuOpen(0);
await mlScreenshots.takeScreenshot('ml-population-results', screenshotDirectories);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { Job, Datafeed } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs';
import { ML_JOB_FIELD_TYPES } from '../../../../../plugins/ml/common/constants/field_types';
import { ECOMMERCE_INDEX_PATTERN, LOGS_INDEX_PATTERN } from '../index';
export default function ({ getPageObject, getService }: FtrProviderContext) {
const elasticChart = getService('elasticChart');
const maps = getPageObject('maps');
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const renderable = getService('renderable');
const screenshotDirectories = ['ml_docs', 'anomaly_detection'];
const ecommerceGeoJobConfig = {
job_id: `ecommerce-geo`,
analysis_config: {
bucket_span: '15m',
influencers: ['geoip.country_iso_code', 'day_of_week', 'category.keyword', 'user'],
detectors: [
{
detector_description: 'Unusual coordinates by user',
function: 'lat_long',
field_name: 'geoip.location',
by_field_name: 'user',
},
],
},
data_description: { time_field: 'order_date' },
};
const ecommerceGeoDatafeedConfig = {
datafeed_id: 'datafeed-ecommerce-geo',
indices: [ECOMMERCE_INDEX_PATTERN],
job_id: 'ecommerce-geo',
query: { bool: { must: [{ match_all: {} }] } },
};
const weblogGeoJobConfig = {
job_id: `weblogs-geo`,
analysis_config: {
bucket_span: '15m',
influencers: ['geo.src', 'extension.keyword', 'geo.dest'],
detectors: [
{
detector_description: 'Unusual coordinates',
function: 'lat_long',
field_name: 'geo.coordinates',
},
{
function: 'high_sum',
field_name: 'bytes',
},
],
},
data_description: { time_field: 'timestamp', time_format: 'epoch_ms' },
};
const weblogGeoDatafeedConfig = {
datafeed_id: 'datafeed-weblogs-geo',
indices: [LOGS_INDEX_PATTERN],
job_id: 'weblogs-geo',
query: { bool: { must: [{ match_all: {} }] } },
};
const cellSize = 15;
const overallSwimLaneTestSubj = 'mlAnomalyExplorerSwimlaneOverall';
describe('geographic data', function () {
before(async () => {
await ml.api.createAndRunAnomalyDetectionLookbackJob(
ecommerceGeoJobConfig as Job,
ecommerceGeoDatafeedConfig as Datafeed
);
await ml.api.createAndRunAnomalyDetectionLookbackJob(
weblogGeoJobConfig as Job,
weblogGeoDatafeedConfig as Datafeed
);
});
after(async () => {
await elasticChart.setNewChartUiDebugFlag(false);
await ml.api.deleteAnomalyDetectionJobES(ecommerceGeoJobConfig.job_id);
await ml.api.deleteAnomalyDetectionJobES(weblogGeoJobConfig.job_id);
await ml.api.cleanMlIndices();
});
it('data visualizer screenshot', async () => {
await ml.testExecution.logTestStep('open index in data visualizer');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataVisualizer();
await ml.dataVisualizer.navigateToIndexPatternSelection();
await ml.jobSourceSelection.selectSourceForIndexBasedDataVisualizer(LOGS_INDEX_PATTERN);
await ml.testExecution.logTestStep('set data visualizer options');
await ml.dataVisualizerIndexBased.assertTimeRangeSelectorSectionExists();
await ml.dataVisualizerIndexBased.clickUseFullDataButton('14,074');
await ml.dataVisualizerTable.setSampleSizeInputValue(
'all',
'geo.coordinates',
'14074 (100%)'
);
await ml.dataVisualizerTable.setFieldTypeFilter([ML_JOB_FIELD_TYPES.GEO_POINT]);
await ml.testExecution.logTestStep('set maps options and take screenshot');
await ml.dataVisualizerTable.ensureDetailsOpen('geo.coordinates');
await renderable.waitForRender();
// setView only works with displayed legend
await maps.openLegend();
await maps.setView(44.1, -68.9, 4.5);
await maps.closeLegend();
await mlScreenshots.takeScreenshot('weblogs-data-visualizer-geopoint', screenshotDirectories);
});
it('ecommerce wizard screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('load the advanced wizard');
await ml.jobManagement.navigateToNewJobSourceSelection();
await ml.jobSourceSelection.selectSourceForAnomalyDetectionJob(ECOMMERCE_INDEX_PATTERN);
await ml.jobTypeSelection.selectAdvancedJob();
await ml.testExecution.logTestStep('continue to the pick fields step');
await ml.jobWizardCommon.assertConfigureDatafeedSectionExists();
await ml.jobWizardCommon.advanceToPickFieldsSection();
await ml.testExecution.logTestStep('add detector');
await ml.jobWizardAdvanced.openCreateDetectorModal();
await ml.jobWizardAdvanced.selectDetectorFunction('lat_long');
await ml.jobWizardAdvanced.selectDetectorField('geoip.location');
await ml.jobWizardAdvanced.selectDetectorByField('user');
await ml.jobWizardAdvanced.confirmAddDetectorModal();
await ml.testExecution.logTestStep('set the bucket span');
await ml.jobWizardCommon.assertBucketSpanInputExists();
await ml.jobWizardCommon.setBucketSpan('15m');
await ml.testExecution.logTestStep('set influencers');
await ml.jobWizardCommon.assertInfluencerInputExists();
await ml.jobWizardCommon.assertInfluencerSelection([]);
for (const influencer of ['geoip.country_iso_code', 'day_of_week', 'category.keyword']) {
await ml.jobWizardCommon.addInfluencer(influencer);
}
await ml.testExecution.logTestStep('set the model memory limit');
await ml.jobWizardCommon.assertModelMemoryLimitInputExists({
withAdvancedSection: false,
});
await ml.jobWizardCommon.setModelMemoryLimit('12MB', {
withAdvancedSection: false,
});
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot(
'ecommerce-advanced-wizard-geopoint',
screenshotDirectories
);
});
it('weblogs wizard screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('load the advanced wizard');
await ml.jobManagement.navigateToNewJobSourceSelection();
await ml.jobSourceSelection.selectSourceForAnomalyDetectionJob(LOGS_INDEX_PATTERN);
await ml.jobTypeSelection.selectAdvancedJob();
await ml.testExecution.logTestStep('continue to the pick fields step');
await ml.jobWizardCommon.assertConfigureDatafeedSectionExists();
await ml.jobWizardCommon.advanceToPickFieldsSection();
await ml.testExecution.logTestStep('add detectors');
await ml.jobWizardAdvanced.openCreateDetectorModal();
await ml.jobWizardAdvanced.selectDetectorFunction('lat_long');
await ml.jobWizardAdvanced.selectDetectorField('geo.coordinates');
await ml.jobWizardAdvanced.setDetectorDescription('lat_long("geo.coordinates")');
await ml.jobWizardAdvanced.confirmAddDetectorModal();
await ml.jobWizardAdvanced.openCreateDetectorModal();
await ml.jobWizardAdvanced.selectDetectorFunction('high_sum');
await ml.jobWizardAdvanced.selectDetectorField('bytes');
await ml.jobWizardAdvanced.setDetectorDescription('sum(bytes)');
await ml.jobWizardAdvanced.confirmAddDetectorModal();
await ml.testExecution.logTestStep('set the bucket span');
await ml.jobWizardCommon.assertBucketSpanInputExists();
await ml.jobWizardCommon.setBucketSpan('15m');
await ml.testExecution.logTestStep('set influencers');
await ml.jobWizardCommon.assertInfluencerInputExists();
await ml.jobWizardCommon.assertInfluencerSelection([]);
for (const influencer of ['geo.src', 'geo.dest', 'extension.keyword']) {
await ml.jobWizardCommon.addInfluencer(influencer);
}
await ml.testExecution.logTestStep('set the model memory limit');
await ml.jobWizardCommon.assertModelMemoryLimitInputExists({
withAdvancedSection: false,
});
await ml.jobWizardCommon.setModelMemoryLimit('11MB', {
withAdvancedSection: false,
});
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('weblogs-advanced-wizard-geopoint', screenshotDirectories);
});
// the job stopped to produce an anomaly, needs investigation
it.skip('ecommerce anomaly explorer screenshots', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await elasticChart.setNewChartUiDebugFlag(true);
await ml.testExecution.logTestStep('open job in anomaly explorer');
await ml.jobTable.waitForJobsToLoad();
await ml.jobTable.filterWithSearchString(ecommerceGeoJobConfig.job_id, 1);
await ml.jobTable.clickOpenJobInAnomalyExplorerButton(ecommerceGeoJobConfig.job_id);
await ml.commonUI.waitForMlLoadingIndicatorToDisappear();
await ml.testExecution.logTestStep('select swim lane tile');
const cells = await ml.swimLane.getCells(overallSwimLaneTestSubj);
const sampleCell = cells[0];
await ml.swimLane.selectSingleCell(overallSwimLaneTestSubj, {
x: sampleCell.x + cellSize,
y: sampleCell.y + cellSize,
});
await ml.swimLane.waitForSwimLanesToLoad();
await ml.testExecution.logTestStep('take screenshot');
await ml.anomaliesTable.ensureDetailsOpen(0);
await ml.anomalyExplorer.scrollChartsContainerIntoView();
await mlScreenshots.takeScreenshot(
'ecommerce-anomaly-explorer-geopoint',
screenshotDirectories
);
});
it('weblogs anomaly explorer screenshots', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await elasticChart.setNewChartUiDebugFlag(true);
await ml.testExecution.logTestStep('open job in anomaly explorer');
await ml.jobTable.waitForJobsToLoad();
await ml.jobTable.filterWithSearchString(weblogGeoJobConfig.job_id, 1);
await ml.jobTable.clickOpenJobInAnomalyExplorerButton(weblogGeoJobConfig.job_id);
await ml.commonUI.waitForMlLoadingIndicatorToDisappear();
await ml.testExecution.logTestStep('select swim lane tile');
const cells = await ml.swimLane.getCells(overallSwimLaneTestSubj);
const sampleCell1 = cells[11];
const sampleCell2 = cells[12];
await ml.swimLane.selectCells(overallSwimLaneTestSubj, {
x1: sampleCell1.x + cellSize,
y1: sampleCell1.y + cellSize,
x2: sampleCell2!.x + cellSize,
y2: sampleCell2!.y + cellSize,
});
await ml.swimLane.waitForSwimLanesToLoad();
await ml.testExecution.logTestStep('set map options and take screenshot');
await ml.anomalyExplorer.scrollChartsContainerIntoView();
// clickFitToData only works with displayed legend
await maps.openLegend();
await maps.clickFitToData();
await maps.closeLegend();
await mlScreenshots.takeScreenshot(
'weblogs-anomaly-explorer-geopoint',
screenshotDirectories
);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
export default function ({ loadTestFile }: FtrProviderContext) {
describe('anomaly detection', function () {
loadTestFile(require.resolve('./geographic_data'));
loadTestFile(require.resolve('./population_analysis'));
loadTestFile(require.resolve('./custom_urls'));
loadTestFile(require.resolve('./mapping_anomalies'));
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { Job, Datafeed } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs';
import { LOGS_INDEX_PATTERN } from '../index';
export default function ({ getPageObject, getService }: FtrProviderContext) {
const header = getPageObject('header');
const maps = getPageObject('maps');
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const renderable = getService('renderable');
const screenshotDirectories = ['ml_docs', 'anomaly_detection'];
const weblogVectorJobConfig = {
job_id: `weblogs-vectors`,
analysis_config: {
bucket_span: '15m',
influencers: ['geo.src', 'agent.keyword', 'geo.dest'],
detectors: [
{
detector_description: 'Sum of bytes',
function: 'sum',
field_name: 'bytes',
partition_field_name: 'geo.dest',
},
],
},
data_description: { time_field: 'timestamp', time_format: 'epoch_ms' },
custom_settings: { created_by: 'multi-metric-wizard' },
};
const weblogVectorDatafeedConfig = {
datafeed_id: 'datafeed-weblogs-vectors',
indices: [LOGS_INDEX_PATTERN],
job_id: 'weblogs-vectors',
query: { bool: { must: [{ match_all: {} }] } },
};
describe('mapping anomalies', function () {
before(async () => {
await ml.api.createAndRunAnomalyDetectionLookbackJob(
weblogVectorJobConfig as Job,
weblogVectorDatafeedConfig as Datafeed
);
});
after(async () => {
await ml.api.deleteAnomalyDetectionJobES(weblogVectorJobConfig.job_id);
await ml.api.cleanMlIndices();
});
it('data visualizer screenshot', async () => {
await ml.testExecution.logTestStep('open index in data visualizer');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataVisualizer();
await ml.dataVisualizer.navigateToIndexPatternSelection();
await ml.jobSourceSelection.selectSourceForIndexBasedDataVisualizer(LOGS_INDEX_PATTERN);
await ml.testExecution.logTestStep('set data visualizer options');
await ml.dataVisualizerIndexBased.assertTimeRangeSelectorSectionExists();
await ml.dataVisualizerIndexBased.clickUseFullDataButton('14,074');
await ml.dataVisualizerTable.setSampleSizeInputValue(
'all',
'geo.coordinates',
'14074 (100%)'
);
await ml.dataVisualizerTable.setFieldNameFilter(['geo.dest']);
await ml.testExecution.logTestStep('set maps options and take screenshot');
await ml.dataVisualizerTable.ensureDetailsOpen('geo.dest');
await renderable.waitForRender();
await maps.openLegend();
await mlScreenshots.takeScreenshot(
'weblogs-data-visualizer-choropleth',
screenshotDirectories
);
});
it('wizard screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('load job in the multi-metric wizard');
await ml.navigation.navigateToJobManagement();
await ml.jobTable.filterWithSearchString(weblogVectorJobConfig.job_id, 1);
await ml.jobTable.clickCloneJobAction(weblogVectorJobConfig.job_id);
await ml.jobTypeSelection.assertMultiMetricJobWizardOpen();
await ml.testExecution.logTestStep('navigate to pick fields step');
await ml.jobWizardCommon.advanceToPickFieldsSection();
await header.awaitGlobalLoadingIndicatorHidden();
await ml.jobWizardMultiMetric.scrollSplitFieldIntoView();
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.takeScreenshot(
'weblogs-multimetric-wizard-vector',
screenshotDirectories
);
});
it('anomaly explorer screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('open job in anomaly explorer');
await ml.jobTable.waitForJobsToLoad();
await ml.jobTable.filterWithSearchString(weblogVectorJobConfig.job_id, 1);
await ml.jobTable.clickOpenJobInAnomalyExplorerButton(weblogVectorJobConfig.job_id);
await ml.commonUI.waitForMlLoadingIndicatorToDisappear();
await ml.testExecution.logTestStep('scroll map into view and take screenshot');
await ml.anomalyExplorer.scrollMapContainerIntoView();
await renderable.waitForRender();
await maps.openLegend();
await mlScreenshots.takeScreenshot('weblogs-anomaly-explorer-vectors', screenshotDirectories);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { Job, Datafeed } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs';
import { LOGS_INDEX_PATTERN } from '../index';
export default function ({ getService }: FtrProviderContext) {
const elasticChart = getService('elasticChart');
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const testSubjects = getService('testSubjects');
const screenshotDirectories = ['ml_docs', 'anomaly_detection'];
const populationJobConfig = {
job_id: `population`,
analysis_config: {
bucket_span: '15m',
influencers: ['clientip'],
detectors: [
{
function: 'mean',
field_name: 'bytes',
over_field_name: 'clientip',
},
],
},
data_description: { time_field: 'timestamp', time_format: 'epoch_ms' },
custom_settings: { created_by: 'population-wizard' },
};
const populationDatafeedConfig = {
datafeed_id: 'datafeed-population',
indices: [LOGS_INDEX_PATTERN],
job_id: 'population',
query: { bool: { must: [{ match_all: {} }] } },
};
const cellSize = 15;
const viewBySwimLaneTestSubj = 'mlAnomalyExplorerSwimlaneViewBy';
describe('population analysis', function () {
before(async () => {
await ml.api.createAndRunAnomalyDetectionLookbackJob(
populationJobConfig as Job,
populationDatafeedConfig as Datafeed
);
});
after(async () => {
await elasticChart.setNewChartUiDebugFlag(false);
await ml.api.deleteAnomalyDetectionJobES(populationJobConfig.job_id);
await ml.api.cleanMlIndices();
});
it('wizard screenshot', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await ml.testExecution.logTestStep('open job in wizard');
await ml.jobTable.filterWithSearchString(populationJobConfig.job_id, 1);
await ml.jobTable.clickCloneJobAction(populationJobConfig.job_id);
await ml.jobTypeSelection.assertPopulationJobWizardOpen();
await ml.testExecution.logTestStep('continue to the pick fields step and take screenshot');
await ml.jobWizardCommon.advanceToPickFieldsSection();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('ml-population-job', screenshotDirectories);
});
it('anomaly explorer screenshots', async () => {
await ml.testExecution.logTestStep('navigate to job list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToJobManagement();
await elasticChart.setNewChartUiDebugFlag(true);
await ml.testExecution.logTestStep('open job in anomaly explorer');
await ml.jobTable.waitForJobsToLoad();
await ml.jobTable.filterWithSearchString(populationJobConfig.job_id, 1);
await ml.jobTable.clickOpenJobInAnomalyExplorerButton(populationJobConfig.job_id);
await ml.commonUI.waitForMlLoadingIndicatorToDisappear();
await ml.testExecution.logTestStep('open tooltip and take screenshot');
const viewBySwimLanes = await testSubjects.find(viewBySwimLaneTestSubj);
const cells = await ml.swimLane.getCells(viewBySwimLaneTestSubj);
const sampleCell = cells[0];
await viewBySwimLanes.moveMouseTo({
xOffset: Math.floor(cellSize / 2.0),
yOffset: Math.floor(cellSize / 2.0),
});
await mlScreenshots.takeScreenshot('ml-population-results', screenshotDirectories);
await ml.testExecution.logTestStep(
'select swim lane tile, expand anomaly row and take screenshot'
);
await ml.swimLane.selectSingleCell(viewBySwimLaneTestSubj, {
x: sampleCell.x + cellSize,
y: sampleCell.y + cellSize,
});
await ml.swimLane.waitForSwimLanesToLoad();
await ml.anomalyExplorer.scrollChartsContainerIntoView();
await ml.anomaliesTable.ensureDetailsOpen(0);
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.takeScreenshot('ml-population-anomaly', screenshotDirectories);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { FLIGHTS_INDEX_PATTERN } from '../index';
import { DataFrameAnalyticsConfig } from '../../../../../plugins/ml/public/application/data_frame_analytics/common';
import { DeepPartial } from '../../../../../plugins/ml/common/types/common';
export default function ({ getService }: FtrProviderContext) {
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const screenshotDirectories = ['ml_docs', 'data_frame_analytics'];
const classificationJobConfig: DeepPartial<DataFrameAnalyticsConfig> = {
id: 'model-flight-delays-classification',
source: {
index: FLIGHTS_INDEX_PATTERN,
},
dest: { index: 'model-flight-delays-classification', results_field: 'ml' },
analysis: {
classification: {
dependent_variable: 'FlightDelay',
training_percent: 10,
num_top_feature_importance_values: 10,
},
},
analyzed_fields: {
includes: [],
excludes: ['Cancelled', 'FlightDelayMin', 'FlightDelayType'],
},
model_memory_limit: '1gb',
};
describe('classification job', function () {
before(async () => {
await ml.api.createAndRunDFAJob(classificationJobConfig as DataFrameAnalyticsConfig);
await ml.testResources.createIndexPatternIfNeeded(classificationJobConfig.dest!.index!);
});
after(async () => {
await ml.api.deleteDataFrameAnalyticsJobES(classificationJobConfig.id as string);
await ml.testResources.deleteIndexPatternByTitle(classificationJobConfig.dest!.index!);
await ml.api.deleteIndices(classificationJobConfig.dest!.index!);
await ml.api.cleanMlIndices();
});
it('wizard screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('start new classification job creation in wizard');
await ml.dataFrameAnalytics.startAnalyticsCreation();
await ml.jobSourceSelection.selectSourceForAnalyticsJob(FLIGHTS_INDEX_PATTERN);
await ml.dataFrameAnalyticsCreation.assertConfigurationStepActive();
await ml.testExecution.logTestStep('select job type and set options');
await ml.dataFrameAnalyticsCreation.assertJobTypeSelectExists();
await ml.dataFrameAnalyticsCreation.selectJobType('classification');
await ml.dataFrameAnalyticsCreation.assertDependentVariableInputExists();
await ml.dataFrameAnalyticsCreation.selectDependentVariable('FlightDelay');
await ml.dataFrameAnalyticsCreation.assertSourceDataPreviewExists();
await ml.dataFrameAnalyticsCreation.assertIncludeFieldsSelectionExists();
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await ml.dataFrameAnalyticsCreation.scrollJobTypeSelectionIntoView();
await mlScreenshots.takeScreenshot('flights-classification-job-1', screenshotDirectories);
await ml.testExecution.logTestStep('scroll to scatterplot matrix and take screenshot');
await ml.dataFrameAnalyticsCreation.assertScatterplotMatrixLoaded();
await ml.dataFrameAnalyticsCreation.scrollScatterplotMatrixIntoView();
await mlScreenshots.takeScreenshot(
'flights-classification-scatterplot',
screenshotDirectories
);
});
it('list row screenshot', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open job row details');
await ml.dataFrameAnalyticsTable.refreshAnalyticsTable();
await ml.dataFrameAnalyticsTable.ensureDetailsOpen(classificationJobConfig.id as string);
await ml.dataFrameAnalyticsTable.ensureDetailsTabOpen(
classificationJobConfig.id as string,
'job-details'
);
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('flights-classification-details', screenshotDirectories);
});
it('results view screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open job results view');
await ml.dataFrameAnalyticsTable.refreshAnalyticsTable();
await ml.dataFrameAnalyticsTable.filterWithSearchString(
classificationJobConfig.id as string,
1
);
await ml.dataFrameAnalyticsTable.openResultsView(classificationJobConfig.id as string);
await ml.dataFrameAnalyticsResults.assertClassificationEvaluatePanelElementsExists();
await ml.dataFrameAnalyticsResults.assertClassificationTablePanelExists();
await ml.dataFrameAnalyticsResults.assertResultsTableExists();
await ml.dataFrameAnalyticsResults.assertResultsTableNotEmpty();
await ml.testExecution.logTestStep('fold sections and take screenshot');
await ml.dataFrameAnalyticsResults.expandAnalysisSection(false);
await ml.dataFrameAnalyticsResults.expandClassificationEvaluationSection(false);
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(false);
await ml.dataFrameAnalyticsResults.expandScatterplotMatrixSection(false);
await ml.dataFrameAnalyticsResults.scrollAnalysisIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('flights-classification-results', screenshotDirectories);
await ml.testExecution.logTestStep('expand feature importance section and take screenshot');
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(true);
await ml.dataFrameAnalyticsResults.scrollFeatureImportanceIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot(
'flights-classification-total-importance',
screenshotDirectories
);
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(false);
await ml.testExecution.logTestStep('expand evaluation section and take screenshot');
await ml.dataFrameAnalyticsResults.expandClassificationEvaluationSection(true);
await ml.dataFrameAnalyticsResults.scrollClassificationEvaluationIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot(
'flights-classification-evaluation',
screenshotDirectories
);
await mlScreenshots.takeScreenshot('confusion-matrix-binary', screenshotDirectories);
await mlScreenshots.takeScreenshot('confusion-matrix-binary-accuracy', screenshotDirectories);
await ml.dataFrameAnalyticsResults.scrollRocCurveChartIntoView();
await mlScreenshots.takeScreenshot('flights-classification-roc-curve', screenshotDirectories);
await ml.dataFrameAnalyticsResults.expandClassificationEvaluationSection(false);
await ml.testExecution.logTestStep('open decision path popover and take screenshot');
await ml.dataFrameAnalyticsResults.scrollResultsIntoView();
await ml.dataFrameAnalyticsResults.openFeatureImportancePopover();
await mlScreenshots.takeScreenshot(
'flights-classification-importance',
screenshotDirectories
);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
export default function ({ loadTestFile }: FtrProviderContext) {
describe('data frame analytics', function () {
loadTestFile(require.resolve('./outlier_detection'));
loadTestFile(require.resolve('./regression'));
loadTestFile(require.resolve('./classification'));
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { LOGS_INDEX_PATTERN } from '../index';
import { DataFrameAnalyticsConfig } from '../../../../../plugins/ml/public/application/data_frame_analytics/common';
import { DeepPartial } from '../../../../../plugins/ml/common/types/common';
export default function ({ getService }: FtrProviderContext) {
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const transform = getService('transform');
const screenshotDirectories = ['ml_docs', 'data_frame_analytics'];
const transformConfig = {
id: `logs-by-clientip`,
source: { index: LOGS_INDEX_PATTERN },
pivot: {
group_by: { clientip: { terms: { field: 'clientip' } } },
aggregations: {
'@timestamp.value_count': { value_count: { field: '@timestamp' } },
'bytes.max': { max: { field: 'bytes' } },
'bytes.sum': { sum: { field: 'bytes' } },
'request.value_count': { value_count: { field: 'request.keyword' } },
},
},
description: 'Web logs by client IP',
dest: { index: 'weblog-clientip' },
};
const outlierJobConfig: DeepPartial<DataFrameAnalyticsConfig> = {
id: 'weblog-outliers',
source: { index: 'weblog-clientip' },
dest: { index: 'weblog-outliers', results_field: 'ml' },
analysis: { outlier_detection: {} },
analyzed_fields: {
includes: ['@timestamp.value_count', 'bytes.max', 'bytes.sum', 'request.value_count'],
excludes: [],
},
model_memory_limit: '20mb',
};
describe('outlier detection job', function () {
before(async () => {
await transform.api.createAndRunTransform(transformConfig.id, transformConfig);
await ml.testResources.createIndexPatternIfNeeded(transformConfig.dest.index);
await ml.api.createAndRunDFAJob(outlierJobConfig as DataFrameAnalyticsConfig);
await ml.testResources.createIndexPatternIfNeeded(outlierJobConfig.dest!.index!);
});
after(async () => {
await ml.testResources.deleteIndexPatternByTitle(transformConfig.dest.index);
await transform.api.deleteIndices(transformConfig.dest.index);
await transform.api.cleanTransformIndices();
await ml.api.deleteDataFrameAnalyticsJobES(outlierJobConfig.id as string);
await ml.testResources.deleteIndexPatternByTitle(outlierJobConfig.dest!.index!);
await ml.api.deleteIndices(outlierJobConfig.dest!.index!);
await ml.api.cleanMlIndices();
});
it('transform screenshot', async () => {
await ml.testExecution.logTestStep('navigate to transform list');
await transform.navigation.navigateTo();
await transform.testExecution.logTestStep('open transform in wizard');
await transform.management.assertTransformListPageExists();
await transform.table.refreshTransformList();
await transform.table.filterWithSearchString(transformConfig.id, 1);
await transform.table.assertTransformRowActions(transformConfig.id, false);
await transform.table.clickTransformRowAction(transformConfig.id, 'Clone');
await transform.wizard.assertDefineStepActive();
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.takeScreenshot('logs-transform-preview', screenshotDirectories);
});
it('wizard screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open outlier detection job in wizard');
await ml.dataFrameAnalyticsTable.waitForAnalyticsToLoad();
await ml.dataFrameAnalyticsTable.filterWithSearchString(outlierJobConfig.id as string, 1);
await ml.dataFrameAnalyticsTable.cloneJob(outlierJobConfig.id as string);
await ml.dataFrameAnalyticsCreation.assertSourceDataPreviewExists();
await ml.dataFrameAnalyticsCreation.assertIncludeFieldsSelectionExists();
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.takeScreenshot('weblog-outlier-job-1', screenshotDirectories);
await ml.testExecution.logTestStep('scroll to scatterplot matrix and take screenshot');
await ml.dataFrameAnalyticsCreation.assertScatterplotMatrixLoaded();
await ml.dataFrameAnalyticsCreation.scrollScatterplotMatrixIntoView();
await mlScreenshots.takeScreenshot('weblog-outlier-scatterplot', screenshotDirectories);
});
it('results view screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open job results view');
await ml.dataFrameAnalyticsTable.refreshAnalyticsTable();
await ml.dataFrameAnalyticsTable.filterWithSearchString(outlierJobConfig.id as string, 1);
await ml.dataFrameAnalyticsTable.openResultsView(outlierJobConfig.id as string);
await ml.dataFrameAnalyticsResults.assertOutlierTablePanelExists();
await ml.dataFrameAnalyticsResults.assertResultsTableExists();
await ml.dataFrameAnalyticsResults.assertResultsTableNotEmpty();
await ml.testExecution.logTestStep('fold scatterplot section and take screenshot');
await ml.dataFrameAnalyticsResults.expandScatterplotMatrixSection(false);
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('outliers', screenshotDirectories);
await ml.testExecution.logTestStep('scroll to scatterplot matrix and take screenshot');
await ml.dataFrameAnalyticsResults.expandScatterplotMatrixSection(true);
await mlScreenshots.removeFocusFromElement();
await ml.dataFrameAnalyticsResults.assertScatterplotMatrixLoaded();
await ml.dataFrameAnalyticsResults.scrollScatterplotMatrixIntoView();
await mlScreenshots.takeScreenshot('outliers-scatterplot', screenshotDirectories);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../../ftr_provider_context';
import { FLIGHTS_INDEX_PATTERN } from '../index';
import { DataFrameAnalyticsConfig } from '../../../../../plugins/ml/public/application/data_frame_analytics/common';
import { DeepPartial } from '../../../../../plugins/ml/common/types/common';
export default function ({ getService }: FtrProviderContext) {
const ml = getService('ml');
const mlScreenshots = getService('mlScreenshots');
const screenshotDirectories = ['ml_docs', 'data_frame_analytics'];
const regressionJobConfig: DeepPartial<DataFrameAnalyticsConfig> = {
id: 'model-flight-delays-regression',
source: {
index: FLIGHTS_INDEX_PATTERN,
query: { range: { DistanceKilometers: { gt: 0 } } },
},
dest: { index: 'model-flight-delays-regression', results_field: 'ml' },
analysis: {
regression: {
dependent_variable: 'FlightDelayMin',
training_percent: 10,
num_top_feature_importance_values: 5,
},
},
analyzed_fields: {
includes: [],
excludes: ['Cancelled', 'FlightDelay', 'FlightDelayType'],
},
model_memory_limit: '1gb',
};
describe('regression job', function () {
before(async () => {
await ml.api.createAndRunDFAJob(regressionJobConfig as DataFrameAnalyticsConfig);
await ml.testResources.createIndexPatternIfNeeded(regressionJobConfig.dest!.index!);
});
after(async () => {
await ml.api.deleteDataFrameAnalyticsJobES(regressionJobConfig.id as string);
await ml.testResources.deleteIndexPatternByTitle(regressionJobConfig.dest!.index!);
await ml.api.deleteIndices(regressionJobConfig.dest!.index!);
await ml.api.cleanMlIndices();
});
it('wizard screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('start new regression job creation in wizard');
await ml.dataFrameAnalytics.startAnalyticsCreation();
await ml.jobSourceSelection.selectSourceForAnalyticsJob(FLIGHTS_INDEX_PATTERN);
await ml.dataFrameAnalyticsCreation.assertConfigurationStepActive();
await ml.testExecution.logTestStep('select job type and set options');
await ml.dataFrameAnalyticsCreation.assertJobTypeSelectExists();
await ml.dataFrameAnalyticsCreation.selectJobType('regression');
await ml.dataFrameAnalyticsCreation.setQueryBarValue('DistanceKilometers > 0');
await ml.dataFrameAnalyticsCreation.assertDependentVariableInputExists();
await ml.dataFrameAnalyticsCreation.selectDependentVariable('FlightDelayMin');
await ml.dataFrameAnalyticsCreation.assertSourceDataPreviewExists();
await ml.dataFrameAnalyticsCreation.assertIncludeFieldsSelectionExists();
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await ml.dataFrameAnalyticsCreation.scrollJobTypeSelectionIntoView();
await mlScreenshots.takeScreenshot('flights-regression-job-1', screenshotDirectories);
await ml.testExecution.logTestStep('scroll to scatterplot matrix and take screenshot');
await ml.dataFrameAnalyticsCreation.assertScatterplotMatrixLoaded();
await ml.dataFrameAnalyticsCreation.scrollScatterplotMatrixIntoView();
await mlScreenshots.takeScreenshot(
'flightdata-regression-scatterplot',
screenshotDirectories
);
});
it('list row screenshot', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open job row details');
await ml.dataFrameAnalyticsTable.refreshAnalyticsTable();
await ml.dataFrameAnalyticsTable.ensureDetailsOpen(regressionJobConfig.id as string);
await ml.dataFrameAnalyticsTable.ensureDetailsTabOpen(
regressionJobConfig.id as string,
'job-details'
);
await ml.testExecution.logTestStep('take screenshot');
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('flights-regression-details', screenshotDirectories);
});
it('results view screenshots', async () => {
await ml.testExecution.logTestStep('navigate to data frame analytics list');
await ml.navigation.navigateToMl();
await ml.navigation.navigateToDataFrameAnalytics();
await ml.testExecution.logTestStep('open job results view');
await ml.dataFrameAnalyticsTable.refreshAnalyticsTable();
await ml.dataFrameAnalyticsTable.filterWithSearchString(regressionJobConfig.id as string, 1);
await ml.dataFrameAnalyticsTable.openResultsView(regressionJobConfig.id as string);
await ml.dataFrameAnalyticsResults.assertRegressionEvaluatePanelElementsExists();
await ml.dataFrameAnalyticsResults.assertRegressionTablePanelExists();
await ml.dataFrameAnalyticsResults.assertResultsTableExists();
await ml.dataFrameAnalyticsResults.assertResultsTableNotEmpty();
await ml.testExecution.logTestStep('fold sections and take screenshot');
await ml.dataFrameAnalyticsResults.expandAnalysisSection(false);
await ml.dataFrameAnalyticsResults.expandRegressionEvaluationSection(false);
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(false);
await ml.dataFrameAnalyticsResults.expandScatterplotMatrixSection(false);
await ml.dataFrameAnalyticsResults.scrollAnalysisIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('flights-regression-results', screenshotDirectories);
await ml.testExecution.logTestStep('expand feature importance section and take screenshot');
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(true);
await ml.dataFrameAnalyticsResults.scrollFeatureImportanceIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot(
'flights-regression-total-importance',
screenshotDirectories
);
await ml.dataFrameAnalyticsResults.expandFeatureImportanceSection(false);
await ml.testExecution.logTestStep('expand evaluation section and take screenshot');
await ml.dataFrameAnalyticsResults.expandRegressionEvaluationSection(true);
await ml.dataFrameAnalyticsResults.scrollRegressionEvaluationIntoView();
await mlScreenshots.removeFocusFromElement();
await mlScreenshots.takeScreenshot('flights-regression-evaluation', screenshotDirectories);
await ml.dataFrameAnalyticsResults.expandRegressionEvaluationSection(false);
await ml.testExecution.logTestStep('open decision path popover and take screenshot');
await ml.dataFrameAnalyticsResults.scrollResultsIntoView();
await ml.dataFrameAnalyticsResults.openFeatureImportancePopover();
await mlScreenshots.takeScreenshot('flights-regression-importance', screenshotDirectories);
});
});
}

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/*
* 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 { FtrProviderContext } from '../../ftr_provider_context';
export const ECOMMERCE_INDEX_PATTERN = 'kibana_sample_data_ecommerce';
export const FLIGHTS_INDEX_PATTERN = 'kibana_sample_data_flights';
export const LOGS_INDEX_PATTERN = 'kibana_sample_data_logs';
export default function ({ getService, loadTestFile }: FtrProviderContext) {
const browser = getService('browser');
const ml = getService('ml');
describe('machine learning docs', function () {
this.tags(['mlqa']);
before(async () => {
await ml.testResources.installAllKibanaSampleData();
await ml.testResources.setKibanaTimeZoneToUTC();
await browser.setWindowSize(1920, 1080);
});
after(async () => {
await ml.testResources.removeAllKibanaSampleData();
await ml.testResources.resetKibanaTimeZone();
});
loadTestFile(require.resolve('./anomaly_detection'));
loadTestFile(require.resolve('./data_frame_analytics'));
});
}

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/*
* 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 { FtrConfigProviderContext } from '@kbn/test';
import { services } from './services';
export default async function ({ readConfigFile }: FtrConfigProviderContext) {
const xpackFunctionalConfig = await readConfigFile(require.resolve('../functional/config.js'));
return {
// default to the xpack functional config
...xpackFunctionalConfig.getAll(),
services,
testFiles: [require.resolve('./apps')],
junit: {
...xpackFunctionalConfig.get('junit'),
reportName: 'Chrome X-Pack UI Screenshot Creation',
},
};
}

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/*
* 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 { GenericFtrProviderContext } from '@kbn/test';
import { pageObjects } from '../functional/page_objects';
import { services } from './services';
export type FtrProviderContext = GenericFtrProviderContext<typeof services, typeof pageObjects>;

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/*
* 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 { services as kibanaFunctionalServices } from '../../functional/services';
import { MachineLearningScreenshotsProvider } from './ml_screenshots';
export const services = {
...kibanaFunctionalServices,
mlScreenshots: MachineLearningScreenshotsProvider,
};

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/*
* 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 { FtrProviderContext } from '../ftr_provider_context';
export function MachineLearningScreenshotsProvider({ getService }: FtrProviderContext) {
const ml = getService('ml');
const screenshot = getService('screenshots');
return {
async takeScreenshot(name: string, subDirectories: string[]) {
await screenshot.take(`${name}_new`, undefined, subDirectories);
},
async removeFocusFromElement() {
// open and close the Kibana nav to un-focus the last used element
await ml.navigation.openKibanaNav();
await ml.navigation.closeKibanaNav();
},
};
}