[ML] Fixes incorrect feature importance visualization for Data Frame …

…Analytics classification (elastic#150816) Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com> (cherry picked from commit c2476d2)
kibanamachine · Feb 14, 2023 · 6b3e561 · 6b3e561
1 parent 3aaa46a
commit 6b3e561
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Showing 7 changed files with 268 additions and 100 deletions.
diff --git a/x-pack/plugins/ml/public/application/components/data_grid/data_grid.tsx b/x-pack/plugins/ml/public/application/components/data_grid/data_grid.tsx
@@ -124,7 +124,7 @@ export const DataGrid: FC<Props> = memo(
           analysisType === ANALYSIS_CONFIG_TYPE.OUTLIER_DETECTION
         ) {
           if (schema === 'featureImportance') {
-            const row = data[rowIndex];
+            const row = data[rowIndex - pagination.pageIndex * pagination.pageSize];
             if (!row) return <div />;
             // if resultsField for some reason is not available then use ml
             const mlResultsField = resultsField ?? DEFAULT_RESULTS_FIELD;

diff --git a/...ages/analytics_exploration/components/feature_importance/decision_path_classification.tsx b/...ages/analytics_exploration/components/feature_importance/decision_path_classification.tsx
@@ -21,6 +21,7 @@ import type {
 } from '../../../../../../../common/types/feature_importance';
 import { DecisionPathChart } from './decision_path_chart';
 import { MissingDecisionPathCallout } from './missing_decision_path_callout';
+import { TopClass } from '../../../../../../../common/types/feature_importance';
 
 interface ClassificationDecisionPathProps {
   predictedValue: string | boolean;
@@ -42,12 +43,20 @@ export const ClassificationDecisionPath: FC<ClassificationDecisionPathProps> = (
   const [currentClass, setCurrentClass] = useState<string>(
     getStringBasedClassName(topClasses[0].class_name)
   );
+  const selectedClass = topClasses.find(
+    (t) => getStringBasedClassName(t.class_name) === getStringBasedClassName(currentClass)
+  ) as TopClass;
+  const predictedProbabilityForCurrentClass = selectedClass
+    ? selectedClass.class_probability
+    : undefined;
+
   const { decisionPathData } = useDecisionPathData({
     baseline,
     featureImportance,
     predictedValue: currentClass,
-    predictedProbability,
+    predictedProbability: predictedProbabilityForCurrentClass,
   });
+
   const options = useMemo(() => {
     const predictionValueStr = getStringBasedClassName(predictedValue);