[ML] Add decision path charts to exploration results table

x-pack/plugins/ml/common/constants/data_frame_analytics.ts

@@ -0,0 +1,7 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License;
+ * you may not use this file except in compliance with the Elastic License.
+ */
+
+export const DEFAULT_RESULTS_FIELD = 'ml';

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

@@ -79,3 +79,9 @@ export interface DataFrameAnalyticsConfig {
   version: string;
   allow_lazy_start?: boolean;
 }
+
+export enum ANALYSIS_CONFIG_TYPE {
+  OUTLIER_DETECTION = 'outlier_detection',
+  REGRESSION = 'regression',
+  CLASSIFICATION = 'classification',
+}

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

@@ -0,0 +1,23 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License;
+ * you may not use this file except in compliance with the Elastic License.
+ */
+
+export interface ClassFeatureImportance {
+  class_name: string | boolean;
+  importance: number;
+}
+export interface FeatureImportance {
+  feature_name: string;
+  importance?: number;
+  classes?: ClassFeatureImportance[];
+}
+
+export interface TopClass {
+  class_name: string;
+  class_probability: number;
+  class_score: number;
+}
+
+export type TopClasses = TopClass[];

x-pack/plugins/ml/common/util/analytics_utils.ts

@@ -0,0 +1,79 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License;
+ * you may not use this file except in compliance with the Elastic License.
+ */
+
+import {
+  AnalysisConfig,
+  ClassificationAnalysis,
+  OutlierAnalysis,
+  RegressionAnalysis,
+  ANALYSIS_CONFIG_TYPE,
+} from '../types/data_frame_analytics';
+
+export const isOutlierAnalysis = (arg: any): arg is OutlierAnalysis => {
+  const keys = Object.keys(arg);
+  return keys.length === 1 && keys[0] === ANALYSIS_CONFIG_TYPE.OUTLIER_DETECTION;
+};
+
+export const isRegressionAnalysis = (arg: any): arg is RegressionAnalysis => {
+  const keys = Object.keys(arg);
+  return keys.length === 1 && keys[0] === ANALYSIS_CONFIG_TYPE.REGRESSION;
+};
+
+export const isClassificationAnalysis = (arg: any): arg is ClassificationAnalysis => {
+  const keys = Object.keys(arg);
+  return keys.length === 1 && keys[0] === ANALYSIS_CONFIG_TYPE.CLASSIFICATION;
+};
+
+export const getDependentVar = (
+  analysis: AnalysisConfig
+):
+  | RegressionAnalysis['regression']['dependent_variable']
+  | ClassificationAnalysis['classification']['dependent_variable'] => {
+  let depVar = '';
+
+  if (isRegressionAnalysis(analysis)) {
+    depVar = analysis.regression.dependent_variable;
+  }
+
+  if (isClassificationAnalysis(analysis)) {
+    depVar = analysis.classification.dependent_variable;
+  }
+  return depVar;
+};
+
+export const getPredictionFieldName = (
+  analysis: AnalysisConfig
+):
+  | RegressionAnalysis['regression']['prediction_field_name']
+  | ClassificationAnalysis['classification']['prediction_field_name'] => {
+  // If undefined will be defaulted to dependent_variable when config is created
+  let predictionFieldName;
+  if (isRegressionAnalysis(analysis) && analysis.regression.prediction_field_name !== undefined) {
+    predictionFieldName = analysis.regression.prediction_field_name;
+  } else if (
+    isClassificationAnalysis(analysis) &&
+    analysis.classification.prediction_field_name !== undefined
+  ) {
+    predictionFieldName = analysis.classification.prediction_field_name;
+  }
+  return predictionFieldName;
+};
+
+export const getDefaultPredictionFieldName = (analysis: AnalysisConfig) => {
+  return `${getDependentVar(analysis)}_prediction`;
+};
+export const getPredictedFieldName = (
+  resultsField: string,
+  analysis: AnalysisConfig,
+  forSort?: boolean
+) => {
+  // default is 'ml'
+  const predictionFieldName = getPredictionFieldName(analysis);
+  const predictedField = `${resultsField}.${
+    predictionFieldName ? predictionFieldName : getDefaultPredictionFieldName(analysis)
+  }`;
+  return predictedField;
+};

x-pack/plugins/ml/public/application/components/data_grid/common.ts

@@ -119,13 +119,14 @@ export const getDataGridSchemasFromFieldTypes = (fieldTypes: FieldTypes, results
       schema = 'numeric';
     }
 
-    if (
-      field.includes(`${resultsField}.${FEATURE_IMPORTANCE}`) ||
-      field.includes(`${resultsField}.${TOP_CLASSES}`)
-    ) {
+    if (field.includes(`${resultsField}.${TOP_CLASSES}`)) {
       schema = 'json';
     }
 
+    if (field.includes(`${resultsField}.${FEATURE_IMPORTANCE}`)) {
+      schema = 'featureImportance';
+    }
+
     return { id: field, schema, isSortable };
   });
 };
@@ -250,10 +251,6 @@ export const useRenderCellValue = (
         return cellValue ? 'true' : 'false';
       }
 
-      if (typeof cellValue === 'object' && cellValue !== null) {
-        return JSON.stringify(cellValue);
-      }
-
       return cellValue;
     };
   }, [indexPattern?.fields, pagination.pageIndex, pagination.pageSize, tableItems]);

x-pack/plugins/ml/public/application/components/data_grid/data_grid.tsx

@@ -5,8 +5,7 @@
  */
 
 import { isEqual } from 'lodash';
-import React, { memo, useEffect, FC } from 'react';
-
+import React, { memo, useEffect, FC, useMemo } from 'react';
 import { i18n } from '@kbn/i18n';
 
 import {
@@ -24,13 +23,16 @@ import {
 } from '@elastic/eui';
 
 import { CoreSetup } from 'src/core/public';
-
 import { DEFAULT_SAMPLER_SHARD_SIZE } from '../../../../common/constants/field_histograms';
 
-import { INDEX_STATUS } from '../../data_frame_analytics/common';
+import { ANALYSIS_CONFIG_TYPE, INDEX_STATUS } from '../../data_frame_analytics/common';
 
 import { euiDataGridStyle, euiDataGridToolbarSettings } from './common';
 import { UseIndexDataReturnType } from './types';
+import { DecisionPathPopover } from './feature_importance/decision_path_popover';
+import { TopClasses } from '../../../../common/types/feature_importance';
+import { DEFAULT_RESULTS_FIELD } from '../../../../common/constants/data_frame_analytics';
+
 // TODO Fix row hovering + bar highlighting
 // import { hoveredRow$ } from './column_chart';
 
@@ -41,6 +43,9 @@ export const DataGridTitle: FC<{ title: string }> = ({ title }) => (
 );
 
 interface PropsWithoutHeader extends UseIndexDataReturnType {
+  baseline?: number;
+  analysisType?: ANALYSIS_CONFIG_TYPE;
+  resultsField?: string;
   dataTestSubj: string;
   toastNotifications: CoreSetup['notifications']['toasts'];
 }
@@ -60,6 +65,7 @@ type Props = PropsWithHeader | PropsWithoutHeader;
 export const DataGrid: FC<Props> = memo(
   (props) => {
     const {
+      baseline,
       chartsVisible,
       chartsButtonVisible,
       columnsWithCharts,
@@ -80,8 +86,10 @@ export const DataGrid: FC<Props> = memo(
       toastNotifications,
       toggleChartVisibility,
       visibleColumns,
+      predictionFieldName,
+      resultsField,
+      analysisType,
     } = props;
-
     // TODO Fix row hovering + bar highlighting
     // const getRowProps = (item: any) => {
     //   return {
@@ -90,6 +98,45 @@ export const DataGrid: FC<Props> = memo(
     //   };
     // };
 
+    const popOverContent = useMemo(() => {
+      return analysisType === ANALYSIS_CONFIG_TYPE.REGRESSION ||
+        analysisType === ANALYSIS_CONFIG_TYPE.CLASSIFICATION
+        ? {
+            featureImportance: ({ children }: { cellContentsElement: any; children: any }) => {
+              const rowIndex = children?.props?.visibleRowIndex;
+              const row = data[rowIndex];
+              if (!row) return <div />;
+              // if resultsField for some reason is not available then use ml
+              const mlResultsField = resultsField ?? DEFAULT_RESULTS_FIELD;
+              const parsedFIArray = row[mlResultsField].feature_importance;
+              let predictedValue: string | number | undefined;
+              let topClasses: TopClasses = [];
+              if (
+                predictionFieldName !== undefined &&
+                row &&
+                row[mlResultsField][predictionFieldName] !== undefined
+              ) {
+                predictedValue = row[mlResultsField][predictionFieldName];
+                topClasses = row[mlResultsField].top_classes;
+              }
+
+              return (
+                <DecisionPathPopover
+                  analysisType={analysisType}
+                  predictedValue={predictedValue}
+                  baseline={baseline}
+                  featureImportance={parsedFIArray}
+                  topClasses={topClasses}
+                  predictionFieldName={
+                    predictionFieldName ? predictionFieldName.replace('_prediction', '') : undefined
+                  }
+                />
+              );
+            },
+          }
+        : undefined;
+    }, [baseline, data]);
+
     useEffect(() => {
       if (invalidSortingColumnns.length > 0) {
         invalidSortingColumnns.forEach((columnId) => {
@@ -225,6 +272,7 @@ export const DataGrid: FC<Props> = memo(
                   }
                 : {}),
             }}
+            popoverContents={popOverContent}
             pagination={{
               ...pagination,
               pageSizeOptions: [5, 10, 25],