[ML] Trained models list: disables 'View training data' action if data frame analytics job no longer exists

x-pack/plugins/ml/common/types/trained_models.ts

@@ -12,6 +12,7 @@ import type {
   TotalFeatureImportance,
 } from '@kbn/ml-data-frame-analytics-utils';
 import { IndexName, IndicesIndexState } from '@elastic/elasticsearch/lib/api/types';
+import { isPopulatedObject } from '@kbn/ml-is-populated-object';
 import type { XOR } from './common';
 import type { MlSavedObjectType } from './saved_objects';
 
@@ -98,6 +99,7 @@ export type TrainedModelConfigResponse = estypes.MlTrainedModelConfig & {
    * Associated pipelines. Extends response from the ES endpoint.
    */
   pipelines?: Record<string, PipelineDefinition> | null;
+  origin_job_exists?: boolean;
 
   metadata?: {
     analytics_config: DataFrameAnalyticsConfig;
@@ -290,3 +292,10 @@ export interface TrainedModelStatsResponse extends estypes.MlTrainedModelStats {
   deployment_stats?: Omit<TrainedModelDeploymentStatsResponse, 'model_id'>;
   model_size_stats?: TrainedModelModelSizeStats;
 }
+
+export function isTrainedModelConfigResponse(arg: unknown): arg is TrainedModelConfigResponse {
+  return (
+    isPopulatedObject(arg, ['metadata']) &&
+    isPopulatedObject(arg.metadata ?? {}, ['analytics_config'])
+  );
+}

x-pack/plugins/ml/public/application/model_management/model_actions.tsx

@@ -7,9 +7,10 @@
 
 import { Action } from '@elastic/eui/src/components/basic_table/action_types';
 import { i18n } from '@kbn/i18n';
+import { FormattedMessage } from '@kbn/i18n-react';
 import { isPopulatedObject } from '@kbn/ml-is-populated-object';
-import { EuiToolTip } from '@elastic/eui';
-import React, { useCallback, useMemo, useEffect, useState } from 'react';
+import { EuiIcon, EuiLink, EuiText, EuiToolTip } from '@elastic/eui';
+import React, { FC, useCallback, useMemo, useEffect, useState } from 'react';
 import {
   BUILT_IN_MODEL_TAG,
   DEPLOYMENT_STATE,
@@ -33,6 +34,80 @@ import { isTestable, isDfaTrainedModel } from './test_models';
 import { ModelItem } from './models_list';
 import { usePermissionCheck } from '../capabilities/check_capabilities';
 
+const ViewTrainingDataAction: FC<{
+  item: ModelItem;
+}> = ({ item }) => {
+  const urlLocator = useMlLocator()!;
+  const {
+    services: {
+      application: { navigateToUrl },
+    },
+  } = useMlKibana();
+
+  const handleClick = async () => {
+    if (item.metadata?.analytics_config === undefined) return;
+
+    const analysisType = getAnalysisType(
+      item.metadata?.analytics_config.analysis
+    ) as DataFrameAnalysisConfigType;
+
+    const url = await urlLocator.getUrl({
+      page: ML_PAGES.DATA_FRAME_ANALYTICS_EXPLORATION,
+      pageState: {
+        jobId: item.metadata?.analytics_config.id as string,
+        analysisType,
+        ...(analysisType === 'classification' || analysisType === 'regression'
+          ? {
+              queryText: `${item.metadata?.analytics_config.dest.results_field}.is_training : true`,
+            }
+          : {}),
+      },
+    });
+
+    await navigateToUrl(url);
+  };
+
+  const buttonContent = (
+    <>
+      <EuiIcon type={'visTable'} css={{ marginRight: '8px' }} />
+      {i18n.translate('xpack.ml.trainedModels.modelsList.viewTrainingDataActionLabel', {
+        defaultMessage: 'View training data',
+      })}
+    </>
+  );
+
+  const isEnabled = item.origin_job_exists;
+  const button = isEnabled ? (
+    <EuiLink onClick={handleClick} color={'text'}>
+      <EuiText size="s" color={'text'}>
+        {buttonContent}
+      </EuiText>
+    </EuiLink>
+  ) : (
+    <EuiText size="s" color={'subdued'} css={{ fontWeight: 300 }}>
+      {buttonContent}
+    </EuiText>
+  );
+
+  if (isEnabled) {
+    return button;
+  } else {
+    return (
+      <EuiToolTip
+        position="top"
+        content={
+          <FormattedMessage
+            id="xpack.ml.trainedModels.modelsList.viewTrainingDataActionTooltip"
+            defaultMessage="Related data frame analytics job was not found."
+          />
+        }
+      >
+        {button}
+      </EuiToolTip>
+    );
+  }
+};
+
 export function useModelActions({
   onDfaTestAction,
   onTestAction,
@@ -54,7 +129,6 @@ export function useModelActions({
 }): Array<Action<ModelItem>> {
   const {
     services: {
-      application: { navigateToUrl },
       overlays,
       theme,
       i18n: i18nStart,
@@ -130,40 +204,11 @@ export function useModelActions({
   return useMemo(
     () => [
       {
-        name: i18n.translate('xpack.ml.trainedModels.modelsList.viewTrainingDataActionLabel', {
-          defaultMessage: 'View training data',
-        }),
-        description: i18n.translate(
-          'xpack.ml.trainedModels.modelsList.viewTrainingDataActionLabel',
-          {
-            defaultMessage: 'View training data',
-          }
-        ),
-        icon: 'visTable',
-        type: 'icon',
-        available: (item) => !!item.metadata?.analytics_config?.id,
-        onClick: async (item) => {
-          if (item.metadata?.analytics_config === undefined) return;
-
-          const analysisType = getAnalysisType(
-            item.metadata?.analytics_config.analysis
-          ) as DataFrameAnalysisConfigType;
-
-          const url = await urlLocator.getUrl({
-            page: ML_PAGES.DATA_FRAME_ANALYTICS_EXPLORATION,
-            pageState: {
-              jobId: item.metadata?.analytics_config.id as string,
-              analysisType,
-              ...(analysisType === 'classification' || analysisType === 'regression'
-                ? {
-                    queryText: `${item.metadata?.analytics_config.dest.results_field}.is_training : true`,
-                  }
-                : {}),
-            },
-          });
-
-          await navigateToUrl(url);
+        render: (item) => {
+          return <ViewTrainingDataAction item={item} />;
         },
+        available: (item) => !!item.metadata?.analytics_config?.id,
+        enabled: (item) => item.origin_job_exists === true,
         isPrimary: true,
       },
       {
@@ -606,7 +651,6 @@ export function useModelActions({
       isLoading,
       modelAndDeploymentIds,
       navigateToPath,
-      navigateToUrl,
       onDfaTestAction,
       onLoading,
       onModelDeployRequest,

x-pack/plugins/ml/public/application/model_management/models_list.tsx

@@ -79,6 +79,7 @@ export type ModelItem = TrainedModelConfigResponse & {
   type?: string[];
   stats?: Stats & { deployment_stats: TrainedModelDeploymentStatsResponse[] };
   pipelines?: ModelPipelines['pipelines'] | null;
+  origin_job_exists?: boolean;
   deployment_ids: string[];
   putModelConfig?: object;
   state: ModelState;

x-pack/plugins/ml/server/routes/trained_models.ts

@@ -29,9 +29,10 @@ import {
   createIngestPipelineSchema,
   modelDownloadsQuery,
 } from './schemas/inference_schema';
-import type {
+import {
+  isTrainedModelConfigResponse,
   PipelineDefinition,
-  TrainedModelConfigResponse,
+  type TrainedModelConfigResponse,
 } from '../../common/types/trained_models';
 import { mlLog } from '../lib/log';
 import { forceQuerySchema } from './schemas/anomaly_detectors_schema';
@@ -191,10 +192,50 @@ export function trainedModelsRoutes(
             mlLog.debug(e);
           }
 
-          const body = filterForEnabledFeatureModels(result, getEnabledFeatures());
+          const filteredModels = filterForEnabledFeatureModels(result, getEnabledFeatures());
+
+          try {
+            // @ts-ignore
+            const jobIdsString = filteredModels.reduce(
+              (
+                jobIdsStr: string,
+                currentModel: TrainedModelConfigResponse | estypes.MlTrainedModelConfig,
+                idx: number
+              ) => {
+                if (isTrainedModelConfigResponse(currentModel)) {
+                  let id = currentModel.metadata?.analytics_config?.id ?? '';
+                  if (id !== '') {
+                    id = `${idx > 0 ? ',' : ''}${id}*`;
+                  }
+                  return `${jobIdsStr}${id}`;
+                }
+                return jobIdsStr;
+              },
+              ''
+            );
+
+            if (jobIdsString !== '') {
+              const { data_frame_analytics: jobs } = await mlClient.getDataFrameAnalytics({
+                id: jobIdsString,
+                allow_no_match: true,
+              });
+
+              filteredModels.forEach((model) => {
+                if (isTrainedModelConfigResponse(model)) {
+                  const dfaId = model?.metadata?.analytics_config?.id;
+                  if (dfaId !== undefined) {
+                    // if this is a dfa model, set origin_job_exists
+                    model.origin_job_exists = jobs.find((job) => job.id === dfaId) !== undefined;
+                  }
+                }
+              });
+            }
+          } catch (e) {
+            // Swallow error to prevent blocking trained models result
+          }
 
           return response.ok({
-            body,
+            body: filteredModels,
           });
         } catch (e) {
           return response.customError(wrapError(e));