[8.0] [ML] Functional tests - stabilize and re-enable feature importance tests (#117503)

x-pack/test/functional/apps/ml/data_frame_analytics/feature_importance.ts

@@ -14,8 +14,7 @@ export default function ({ getService }: FtrProviderContext) {
   const esArchiver = getService('esArchiver');
   const ml = getService('ml');
 
-  // FLAKY: https://github.com/elastic/kibana/issues/116078
-  describe.skip('total feature importance panel and decision path popover', function () {
+  describe('total feature importance panel and decision path popover', function () {
     const testDataList: Array<{
       suiteTitle: string;
       archive: string;
@@ -64,6 +63,7 @@ export default function ({ getService }: FtrProviderContext) {
                 training_percent: 35,
                 prediction_field_name: 'CentralAir_prediction',
                 num_top_classes: -1,
+                max_trees: 10,
               },
             },
             model_memory_limit: '60mb',
@@ -109,6 +109,7 @@ export default function ({ getService }: FtrProviderContext) {
                 training_percent: 35,
                 prediction_field_name: 'heatingqc',
                 num_top_classes: -1,
+                max_trees: 10,
               },
             },
             model_memory_limit: '60mb',
@@ -140,6 +141,7 @@ export default function ({ getService }: FtrProviderContext) {
                 dependent_variable: 'stab',
                 num_top_feature_importance_values: 5,
                 training_percent: 35,
+                max_trees: 10,
               },
             },
             analyzed_fields: {

x-pack/test/functional/services/ml/data_frame_analytics_results.ts

@@ -73,6 +73,7 @@ export function MachineLearningDataFrameAnalyticsResultsProvider(
 
     async assertTotalFeatureImportanceEvaluatePanelExists() {
       await testSubjects.existOrFail('mlDFExpandableSection-FeatureImportanceSummary');
+      await this.scrollFeatureImportanceIntoView();
       await testSubjects.existOrFail('mlTotalFeatureImportanceChart', { timeout: 30 * 1000 });
     },
 
@@ -213,5 +214,33 @@ export function MachineLearningDataFrameAnalyticsResultsProvider(
         expect(buttonVisible).to.equal(true, 'Expected data grid cell button to be visible');
       });
     },
+
+    async scrollContentSectionIntoView(sectionId: string) {
+      await testSubjects.scrollIntoView(`mlDFExpandableSection-${sectionId}`);
+    },
+
+    async scrollAnalysisIntoView() {
+      await this.scrollContentSectionIntoView('analysis');
+    },
+
+    async scrollRegressionEvaluationIntoView() {
+      await this.scrollContentSectionIntoView('RegressionEvaluation');
+    },
+
+    async scrollClassificationEvaluationIntoView() {
+      await this.scrollContentSectionIntoView('ClassificationEvaluation');
+    },
+
+    async scrollFeatureImportanceIntoView() {
+      await this.scrollContentSectionIntoView('FeatureImportanceSummary');
+    },
+
+    async scrollScatterplotMatrixIntoView() {
+      await this.scrollContentSectionIntoView('splom');
+    },
+
+    async scrollResultsIntoView() {
+      await this.scrollContentSectionIntoView('results');
+    },
   };
 }