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[ML] Fix trained model map associating wrong model to job (#134849)
* [ML] Fix trained model map associating wrong model to job * removing comment * removing throw * changing error message to match test
This commit is contained in:
James Gowdy
GitHub
parent
87e72207bb
commit
4a7d910da5
1 changed files with 81 additions and 152 deletions
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@ -6,18 +6,17 @@
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*/
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import Boom from '@hapi/boom';
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import type * as estypes from '@elastic/elasticsearch/lib/api/typesWithBodyKey';
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import { IScopedClusterClient } from '@kbn/core/server';
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import {
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INDEX_CREATED_BY,
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JOB_MAP_NODE_TYPES,
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JobMapNodeTypes,
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} from '../../../common/constants/data_frame_analytics';
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import { TrainedModelConfigResponse } from '../../../common/types/trained_models';
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import {
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AnalyticsMapEdgeElement,
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AnalyticsMapReturnType,
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AnalyticsMapNodeElement,
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DataFrameAnalyticsStats,
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MapElements,
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} from '../../../common/types/data_frame_analytics';
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import { getAnalysisType } from '../../../common/util/analytics_utils';
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@ -36,53 +35,18 @@ import {
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import type { MlClient } from '../../lib/ml_client';
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export class AnalyticsManager {
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private _client: IScopedClusterClient;
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private _mlClient: MlClient;
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private _inferenceModels: TrainedModelConfigResponse[];
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private _jobStats: DataFrameAnalyticsStats[];
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private _trainedModels: estypes.MlTrainedModelConfig[] = [];
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private _jobs: estypes.MlDataframeAnalyticsSummary[] = [];
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constructor(mlClient: MlClient, client: IScopedClusterClient) {
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this._client = client;
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this._mlClient = mlClient;
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this._inferenceModels = [];
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this._jobStats = [];
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}
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constructor(private _mlClient: MlClient, private _client: IScopedClusterClient) {}
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public set jobStats(stats) {
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this._jobStats = stats;
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}
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public get jobStats() {
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return this._jobStats;
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}
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public set inferenceModels(models) {
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this._inferenceModels = models;
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}
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public get inferenceModels() {
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return this._inferenceModels;
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}
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async setInferenceModels() {
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try {
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const models = await this.getAnalyticsModels();
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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this.inferenceModels = models;
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} catch (error) {
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// eslint-disable-next-line
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console.error('Unable to fetch inference models', error);
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}
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}
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async setJobStats() {
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try {
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const jobStats = await this.getAnalyticsStats();
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this.jobStats = jobStats;
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} catch (error) {
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// eslint-disable-next-line
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console.error('Unable to fetch job stats', error);
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}
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private async initData() {
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const [models, jobs] = await Promise.all([
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this._mlClient.getTrainedModels(),
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this._mlClient.getDataFrameAnalytics({ size: 1000 }),
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]);
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this._trainedModels = models.trained_model_configs;
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this._jobs = jobs.data_frame_analytics;
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}
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private isDuplicateElement(analyticsId: string, elements: MapElements[]): boolean {
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@ -99,50 +63,6 @@ export class AnalyticsManager {
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return isDuplicate;
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}
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private async getAnalyticsModelData(modelId: string) {
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const resp = await this._mlClient.getTrainedModels({
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model_id: modelId,
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});
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const modelData = resp?.trained_model_configs[0];
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return modelData;
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}
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private async getAnalyticsModels() {
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const resp = await this._mlClient.getTrainedModels();
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const models = resp?.trained_model_configs;
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return models;
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}
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private async getAnalyticsStats() {
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const resp = await this._mlClient.getDataFrameAnalyticsStats({ size: 1000 });
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const stats = resp?.data_frame_analytics;
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return stats;
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}
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private async getAnalyticsData(analyticsId?: string) {
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const options = analyticsId
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? {
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id: analyticsId,
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}
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: undefined;
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const resp = await this._mlClient.getDataFrameAnalytics(options);
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let jobData = analyticsId ? resp?.data_frame_analytics[0] : resp?.data_frame_analytics;
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if (analyticsId !== undefined) {
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const jobStats = this.findJobStats(analyticsId);
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobData = { ...jobData, stats: { ...jobStats } };
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} else {
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// @ts-expect-error @elastic-elasticsearch Data frame types incompletes
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jobData = jobData.map((job: any) => {
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const jobStats = this.findJobStats(job.id);
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return { ...job, stats: { ...jobStats } };
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});
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}
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return jobData;
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}
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private async getIndexData(index: string) {
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const indexData = await this._client.asInternalUser.indices.get({
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index,
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@ -158,14 +78,30 @@ export class AnalyticsManager {
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return transformData;
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}
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private findJobModel(analyticsId: string): any {
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return this.inferenceModels.find(
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(model) => model.metadata?.analytics_config?.id === analyticsId
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private findJobModel(analyticsId: string, analyticsCreateTime: number): any {
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return this._trainedModels.find(
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(model) =>
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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model.metadata?.analytics_config?.id === analyticsId &&
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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model.metadata?.analytics_config.create_time === analyticsCreateTime
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);
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}
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private findJobStats(analyticsId: string): DataFrameAnalyticsStats | undefined {
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return this.jobStats.find((js) => js.id === analyticsId);
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private findJob(id: string): estypes.MlDataframeAnalyticsSummary {
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const job = this._jobs.find((js) => js.id === id);
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if (job === undefined) {
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throw Error(`No known job with id '${id}'`);
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}
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return job;
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}
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private findTrainedModel(id: string): estypes.MlTrainedModelConfig {
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const trainedModel = this._trainedModels.find((js) => js.model_id === id);
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if (trainedModel === undefined) {
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throw Error(`No known trained model with id '${id}'`);
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}
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return trainedModel;
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}
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private async getNextLink({
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@ -188,7 +124,7 @@ export class AnalyticsManager {
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return { isWildcardIndexPattern, isIndexPattern: true, indexData, meta };
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} else if (type.includes(JOB_MAP_NODE_TYPES.ANALYTICS)) {
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// fetch job associated with this index
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const jobData = await this.getAnalyticsData(id);
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const jobData = this.findJob(id);
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return { jobData, isJob: true };
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} else if (type === JOB_MAP_NODE_TYPES.TRANSFORM) {
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// fetch transform so we can get original index pattern
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@ -200,13 +136,16 @@ export class AnalyticsManager {
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}
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}
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private getAnalyticsModelElements(analyticsId: string): {
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private getAnalyticsModelElements(
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analyticsId: string,
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analyticsCreateTime: number
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): {
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modelElement?: AnalyticsMapNodeElement;
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modelDetails?: any;
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edgeElement?: AnalyticsMapEdgeElement;
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} {
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// Get inference model for analytics job and create model node
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const analyticsModel = this.findJobModel(analyticsId);
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// Get trained model for analytics job and create model node
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const analyticsModel = this.findJobModel(analyticsId, analyticsCreateTime);
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let modelElement;
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let edgeElement;
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@ -260,12 +199,13 @@ export class AnalyticsManager {
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* Prepares the initial elements for incoming modelId
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* @param modelId
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*/
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async getInitialElementsModelRoot(modelId: string): Promise<InitialElementsReturnType> {
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private async getInitialElementsModelRoot(modelId: string): Promise<InitialElementsReturnType> {
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const resultElements = [];
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const modelElements = [];
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const details: any = {};
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let data: estypes.MlTrainedModelConfig | estypes.MlDataframeAnalyticsSummary;
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// fetch model data and create model elements
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let data = await this.getAnalyticsModelData(modelId);
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data = this.findTrainedModel(modelId);
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const modelNodeId = `${data.model_id}-${JOB_MAP_NODE_TYPES.TRAINED_MODEL}`;
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const sourceJobId = data?.metadata?.analytics_config?.id;
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@ -286,22 +226,18 @@ export class AnalyticsManager {
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// fetch source job data and create elements
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if (sourceJobId !== undefined) {
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try {
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// @ts-expect-error @elastic-elasticsearch Data frame types incompletes
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data = await this.getAnalyticsData(sourceJobId);
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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data = this.findJob(sourceJobId);
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nextLinkId = data?.source?.index[0];
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nextType = JOB_MAP_NODE_TYPES.INDEX;
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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previousNodeId = `${data.id}-${JOB_MAP_NODE_TYPES.ANALYTICS}`;
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resultElements.push({
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data: {
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id: previousNodeId,
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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label: data.id,
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type: JOB_MAP_NODE_TYPES.ANALYTICS,
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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analysisType: getAnalysisType(data?.analysis),
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},
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});
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@ -330,25 +266,25 @@ export class AnalyticsManager {
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* Prepares the initial elements for incoming jobId
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* @param jobId
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*/
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async getInitialElementsJobRoot(jobId: string): Promise<InitialElementsReturnType> {
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private async getInitialElementsJobRoot(
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jobId: string,
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jobCreateTime: number
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): Promise<InitialElementsReturnType> {
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const resultElements = [];
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const modelElements = [];
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const details: any = {};
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const data = await this.getAnalyticsData(jobId);
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const data = this.findJob(jobId);
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const nextLinkId = data?.source?.index[0];
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const nextType: JobMapNodeTypes = JOB_MAP_NODE_TYPES.INDEX;
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const previousNodeId = `${data.id}-${JOB_MAP_NODE_TYPES.ANALYTICS}`;
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resultElements.push({
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data: {
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id: previousNodeId,
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// @ts-expect-error @elastic-elasticsearch Data frame types incompletes
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label: data.id,
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type: JOB_MAP_NODE_TYPES.ANALYTICS,
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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analysisType: getAnalysisType(data?.analysis),
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isRoot: true,
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},
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@ -356,7 +292,10 @@ export class AnalyticsManager {
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details[previousNodeId] = data;
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const { modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(jobId);
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const { modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(
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jobId,
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jobCreateTime
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);
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if (isAnalyticsMapNodeElement(modelElement)) {
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modelElements.push(modelElement);
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details[modelElement.data.id] = modelDetails;
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@ -373,7 +312,7 @@ export class AnalyticsManager {
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* @param jobId (optional)
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* @param modelId (optional)
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*/
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async getAnalyticsMap({
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public async getAnalyticsMap({
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analyticsId,
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modelId,
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}: GetAnalyticsMapArgs): Promise<AnalyticsMapReturnType> {
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@ -382,11 +321,13 @@ export class AnalyticsManager {
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const indexPatternElements: MapElements[] = [];
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try {
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await Promise.all([this.setInferenceModels(), this.setJobStats()]);
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await this.initData();
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// Create first node for incoming analyticsId or modelId
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let initialData: InitialElementsReturnType = {} as InitialElementsReturnType;
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if (analyticsId !== undefined) {
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initialData = await this.getInitialElementsJobRoot(analyticsId);
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const job = analyticsId === undefined ? undefined : this.findJob(analyticsId);
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if (analyticsId !== undefined && job !== undefined) {
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const jobCreateTime = job.create_time!;
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initialData = await this.getInitialElementsJobRoot(analyticsId, jobCreateTime);
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} else if (modelId !== undefined) {
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initialData = await this.getInitialElementsModelRoot(modelId);
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}
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@ -486,8 +427,11 @@ export class AnalyticsManager {
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nextLinkId = data?.source?.index[0];
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nextType = JOB_MAP_NODE_TYPES.INDEX;
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// Get inference model for analytics job and create model node
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({ modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(data.id));
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// Get trained model for analytics job and create model node
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({ modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(
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data.id,
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data.create_time
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));
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if (isAnalyticsMapNodeElement(modelElement)) {
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modelElements.push(modelElement);
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result.details[modelElement.data.id] = modelDetails;
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@ -534,30 +478,23 @@ export class AnalyticsManager {
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// fetch all jobs associated with root transform if defined, otherwise check root index
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if (rootTransform !== undefined || rootIndexPattern !== undefined) {
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const jobs = await this.getAnalyticsData();
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const jobs = this._jobs;
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const comparator = rootTransform !== undefined ? rootTransform : rootIndexPattern;
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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for (let i = 0; i < jobs.length; i++) {
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if (
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobs[i]?.source?.index[0] === comparator &&
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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this.isDuplicateElement(jobs[i].id, result.elements) === false
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) {
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const nodeId = `${jobs[i].id}-${JOB_MAP_NODE_TYPES.ANALYTICS}`;
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result.elements.push({
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data: {
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id: nodeId,
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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label: jobs[i].id,
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type: JOB_MAP_NODE_TYPES.ANALYTICS,
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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analysisType: getAnalysisType(jobs[i]?.analysis),
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},
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});
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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result.details[nodeId] = jobs[i];
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const source = `${comparator}-${JOB_MAP_NODE_TYPES.INDEX}`;
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result.elements.push({
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@ -567,10 +504,10 @@ export class AnalyticsManager {
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target: nodeId,
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},
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});
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// Get inference model for analytics job and create model node
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// Get trained model for analytics job and create model node
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({ modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobs[i].id
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jobs[i].id,
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jobs[i].create_time!
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));
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if (isAnalyticsMapNodeElement(modelElement)) {
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modelElements.push(modelElement);
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@ -592,32 +529,31 @@ export class AnalyticsManager {
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}
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}
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async extendAnalyticsMapForAnalyticsJob({
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public async extendAnalyticsMapForAnalyticsJob({
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analyticsId,
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index,
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}: ExtendAnalyticsMapArgs): Promise<AnalyticsMapReturnType> {
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const result: AnalyticsMapReturnType = { elements: [], details: {}, error: null };
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try {
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await Promise.all([this.setInferenceModels(), this.setJobStats()]);
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const jobs = await this.getAnalyticsData();
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await this.initData();
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const jobs = this._jobs;
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let rootIndex;
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let rootIndexNodeId;
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if (analyticsId !== undefined) {
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const jobData = await this.getAnalyticsData(analyticsId);
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const jobData = this.findJob(analyticsId);
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const currentJobNodeId = `${jobData.id}-${JOB_MAP_NODE_TYPES.ANALYTICS}`;
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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rootIndex = Array.isArray(jobData?.dest?.index)
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? // @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobData?.dest?.index[0]
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: // @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobData?.dest?.index;
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? jobData?.dest?.index[0]
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: jobData?.dest?.index;
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rootIndexNodeId = `${rootIndex}-${JOB_MAP_NODE_TYPES.INDEX}`;
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// Fetch inference model for incoming job id and add node and edge
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const { modelElement, modelDetails, edgeElement } =
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this.getAnalyticsModelElements(analyticsId);
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// Fetch trained model for incoming job id and add node and edge
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const { modelElement, modelDetails, edgeElement } = this.getAnalyticsModelElements(
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analyticsId,
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jobData.create_time!
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);
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if (isAnalyticsMapNodeElement(modelElement)) {
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result.elements.push(modelElement);
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result.details[modelElement.data.id] = modelDetails;
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@ -650,28 +586,21 @@ export class AnalyticsManager {
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rootIndexNodeId = `${rootIndex}-${JOB_MAP_NODE_TYPES.INDEX}`;
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}
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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for (let i = 0; i < jobs.length; i++) {
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if (
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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jobs[i]?.source?.index[0] === rootIndex &&
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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this.isDuplicateElement(jobs[i].id, result.elements) === false
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) {
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// Create node for associated job
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// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
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const nodeId = `${jobs[i].id}-${JOB_MAP_NODE_TYPES.ANALYTICS}`;
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result.elements.push({
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data: {
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id: nodeId,
|
||||
// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
|
||||
label: jobs[i].id,
|
||||
type: JOB_MAP_NODE_TYPES.ANALYTICS,
|
||||
// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
|
||||
analysisType: getAnalysisType(jobs[i]?.analysis),
|
||||
},
|
||||
});
|
||||
// @ts-expect-error @elastic-elasticsearch Data frame types incomplete
|
||||
result.details[nodeId] = jobs[i];
|
||||
|
||||
result.elements.push({
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue