[ML] Add probability values in decision path visualization for classi…

…fication data frame analytics (#80229) (#82551) Co-authored-by: Kibana Machine <[email protected]> Co-authored-by: Kibana Machine <[email protected]>
elastic · Nov 4, 2020 · e937618 · e937618
1 parent 7bce2a1
commit e937618
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Showing 16 changed files with 652 additions and 221 deletions.
diff --git a/x-pack/plugins/ml/common/types/feature_importance.ts b/x-pack/plugins/ml/common/types/feature_importance.ts
@@ -4,8 +4,10 @@
  * you may not use this file except in compliance with the Elastic License.
  */
 
+export type FeatureImportanceClassName = string | number | boolean;
+
 export interface ClassFeatureImportance {
-  class_name: string | boolean;
+  class_name: FeatureImportanceClassName;
   importance: number;
 }
 
@@ -18,15 +20,15 @@ export interface FeatureImportance {
 }
 
 export interface TopClass {
-  class_name: string;
+  class_name: FeatureImportanceClassName;
   class_probability: number;
   class_score: number;
 }
 
 export type TopClasses = TopClass[];
 
 export interface ClassFeatureImportanceSummary {
-  class_name: string;
+  class_name: FeatureImportanceClassName;
   importance: {
     max: number;
     min: number;
@@ -52,6 +54,22 @@ export type TotalFeatureImportance =
   | ClassificationTotalFeatureImportance
   | RegressionTotalFeatureImportance;
 
+export interface FeatureImportanceClassBaseline {
+  class_name: FeatureImportanceClassName;
+  baseline: number;
+}
+export interface ClassificationFeatureImportanceBaseline {
+  classes: FeatureImportanceClassBaseline[];
+}
+
+export interface RegressionFeatureImportanceBaseline {
+  baseline: number;
+}
+
+export type FeatureImportanceBaseline =
+  | ClassificationFeatureImportanceBaseline
+  | RegressionFeatureImportanceBaseline;
+
 export function isClassificationTotalFeatureImportance(
   summary: ClassificationTotalFeatureImportance | RegressionTotalFeatureImportance
 ): summary is ClassificationTotalFeatureImportance {
@@ -63,3 +81,19 @@ export function isRegressionTotalFeatureImportance(
 ): summary is RegressionTotalFeatureImportance {
   return (summary as RegressionTotalFeatureImportance).importance !== undefined;
 }
+
+export function isClassificationFeatureImportanceBaseline(
+  baselineData: any
+): baselineData is ClassificationFeatureImportanceBaseline {
+  return (
+    typeof baselineData === 'object' &&
+    baselineData.hasOwnProperty('classes') &&
+    Array.isArray(baselineData.classes)
+  );
+}
+
+export function isRegressionFeatureImportanceBaseline(
+  baselineData: any
+): baselineData is RegressionFeatureImportanceBaseline {
+  return typeof baselineData === 'object' && baselineData.hasOwnProperty('baseline');
+}
diff --git a/x-pack/plugins/ml/common/types/trained_models.ts b/x-pack/plugins/ml/common/types/trained_models.ts
@@ -5,7 +5,7 @@
  */
 
 import { DataFrameAnalyticsConfig } from './data_frame_analytics';
-import { TotalFeatureImportance } from './feature_importance';
+import { FeatureImportanceBaseline, TotalFeatureImportance } from './feature_importance';
 
 export interface IngestStats {
   count: number;
@@ -56,6 +56,7 @@ export interface TrainedModelConfigResponse {
         analytics_config: DataFrameAnalyticsConfig;
         input: any;
         total_feature_importance?: TotalFeatureImportance[];
+        feature_importance_baseline?: FeatureImportanceBaseline;
       }
     | Record<string, any>;
   model_id: string;

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
@@ -35,7 +35,11 @@ import {
 } from './common';
 import { UseIndexDataReturnType } from './types';
 import { DecisionPathPopover } from './feature_importance/decision_path_popover';
-import { FeatureImportance, TopClasses } from '../../../../common/types/feature_importance';
+import {
+  FeatureImportanceBaseline,
+  FeatureImportance,
+  TopClasses,
+} from '../../../../common/types/feature_importance';
 import { DEFAULT_RESULTS_FIELD } from '../../../../common/constants/data_frame_analytics';
 import { DataFrameAnalysisConfigType } from '../../../../common/types/data_frame_analytics';
 
@@ -50,7 +54,7 @@ export const DataGridTitle: FC<{ title: string }> = ({ title }) => (
 );
 
 interface PropsWithoutHeader extends UseIndexDataReturnType {
-  baseline?: number;
+  baseline?: FeatureImportanceBaseline;
   analysisType?: DataFrameAnalysisConfigType | 'unknown';
   resultsField?: string;
   dataTestSubj: string;
@@ -124,6 +128,7 @@ export const DataGrid: FC<Props> = memo(
               // if resultsField for some reason is not available then use ml
               const mlResultsField = resultsField ?? DEFAULT_RESULTS_FIELD;
               let predictedValue: string | number | undefined;
+              let predictedProbability: number | undefined;
               let topClasses: TopClasses = [];
               if (
                 predictionFieldName !== undefined &&
@@ -132,6 +137,7 @@ export const DataGrid: FC<Props> = memo(
               ) {
                 predictedValue = row[`${mlResultsField}.${predictionFieldName}`];
                 topClasses = getTopClasses(row, mlResultsField);
+                predictedProbability = row[`${mlResultsField}.prediction_probability`];
               }
 
               const isClassTypeBoolean = topClasses.reduce(
@@ -149,6 +155,7 @@ export const DataGrid: FC<Props> = memo(
                 <DecisionPathPopover
                   analysisType={analysisType}
                   predictedValue={predictedValue}
+                  predictedProbability={predictedProbability}
                   baseline={baseline}
                   featureImportance={parsedFIArray}
                   topClasses={topClasses}

diff --git a/...ins/ml/public/application/components/data_grid/feature_importance/decision_path_chart.tsx b/...ins/ml/public/application/components/data_grid/feature_importance/decision_path_chart.tsx
@@ -26,7 +26,10 @@ import { i18n } from '@kbn/i18n';
 import euiVars from '@elastic/eui/dist/eui_theme_light.json';
 import { DecisionPathPlotData } from './use_classification_path_data';
 import { formatSingleValue } from '../../../formatters/format_value';
-
+import {
+  FeatureImportanceBaseline,
+  isRegressionFeatureImportanceBaseline,
+} from '../../../../../common/types/feature_importance';
 const { euiColorFullShade, euiColorMediumShade } = euiVars;
 const axisColor = euiColorMediumShade;
 
@@ -72,10 +75,9 @@ const theme: PartialTheme = {
 interface DecisionPathChartProps {
   decisionPathData: DecisionPathPlotData;
   predictionFieldName?: string;
-  baseline?: number;
+  baseline?: FeatureImportanceBaseline;
   minDomain: number | undefined;
   maxDomain: number | undefined;
-  showValues?: boolean;
 }
 
 const DECISION_PATH_MARGIN = 125;
@@ -88,38 +90,37 @@ export const DecisionPathChart = ({
   minDomain,
   maxDomain,
   baseline,
-  showValues,
 }: DecisionPathChartProps) => {
   // adjust the height so it's compact for items with more features
-  const baselineData: LineAnnotationDatum[] = useMemo(
-    () => [
-      {
-        dataValue: baseline,
-        header: baseline ? formatSingleValue(baseline).toString() : '',
-        details: i18n.translate(
-          'xpack.ml.dataframe.analytics.explorationResults.decisionPathBaselineText',
-          {
-            defaultMessage:
-              'baseline (average of predictions for all data points in the training data set)',
-          }
-        ),
-      },
-    ],
+  const baselineData: LineAnnotationDatum[] | undefined = useMemo(
+    () =>
+      baseline && isRegressionFeatureImportanceBaseline(baseline)
+        ? [
+            {
+              dataValue: baseline.baseline,
+              header: formatSingleValue(baseline.baseline, '').toString(),
+              details: i18n.translate(
+                'xpack.ml.dataframe.analytics.explorationResults.decisionPathBaselineText',
+                {
+                  defaultMessage:
+                    'baseline (average of predictions for all data points in the training data set)',
+                }
+              ),
+            },
+          ]
+        : undefined,
     [baseline]
   );
   // if regression, guarantee up to num_precision significant digits without having it in scientific notation
   // if classification, hide the numeric values since we only want to show the path
-  const tickFormatter = useCallback(
-    (d) => (showValues === false ? '' : formatSingleValue(d).toString()),
-    []
-  );
+  const tickFormatter = useCallback((d) => formatSingleValue(d, '').toString(), []);
 
   return (
     <Chart
       size={{ height: DECISION_PATH_MARGIN + decisionPathData.length * DECISION_PATH_ROW_HEIGHT }}
     >
       <Settings theme={theme} rotation={90} />
-      {baseline && (
+      {baselineData && (
         <LineAnnotation
           id="xpack.ml.dataframe.analytics.explorationResults.decisionPathBaseline"
           domainType={AnnotationDomainTypes.YDomain}
@@ -132,7 +133,6 @@ export const DecisionPathChart = ({
       <Axis
         id={'xpack.ml.dataframe.analytics.explorationResults.decisionPathXAxis'}
         tickFormat={tickFormatter}
-        ticks={showValues === false ? 0 : undefined}
         title={i18n.translate(
           'xpack.ml.dataframe.analytics.explorationResults.decisionPathXAxisTitle',
           {

diff --git a/...blic/application/components/data_grid/feature_importance/decision_path_classification.tsx b/...blic/application/components/data_grid/feature_importance/decision_path_classification.tsx
@@ -13,29 +13,39 @@ import {
   useDecisionPathData,
   getStringBasedClassName,
 } from './use_classification_path_data';
-import { FeatureImportance, TopClasses } from '../../../../../common/types/feature_importance';
+import {
+  FeatureImportance,
+  FeatureImportanceBaseline,
+  TopClasses,
+} from '../../../../../common/types/feature_importance';
 import { DecisionPathChart } from './decision_path_chart';
 import { MissingDecisionPathCallout } from './missing_decision_path_callout';
 
 interface ClassificationDecisionPathProps {
   predictedValue: string | boolean;
+  predictedProbability: number | undefined;
   predictionFieldName?: string;
   featureImportance: FeatureImportance[];
   topClasses: TopClasses;
+  baseline?: FeatureImportanceBaseline;
 }
 
 export const ClassificationDecisionPath: FC<ClassificationDecisionPathProps> = ({
   featureImportance,
   predictedValue,
   topClasses,
   predictionFieldName,
+  predictedProbability,
+  baseline,
 }) => {
   const [currentClass, setCurrentClass] = useState<string>(
     getStringBasedClassName(topClasses[0].class_name)
   );
   const { decisionPathData } = useDecisionPathData({
+    baseline,
     featureImportance,
     predictedValue: currentClass,
+    predictedProbability,
   });
   const options = useMemo(() => {
     const predictionValueStr = getStringBasedClassName(predictedValue);
@@ -99,7 +109,6 @@ export const ClassificationDecisionPath: FC<ClassificationDecisionPathProps> = (
         predictionFieldName={predictionFieldName}
         minDomain={domain.minDomain}
         maxDomain={domain.maxDomain}
-        showValues={false}
       />
     </>
   );

diff --git a/...s/ml/public/application/components/data_grid/feature_importance/decision_path_popover.tsx b/...s/ml/public/application/components/data_grid/feature_importance/decision_path_popover.tsx
@@ -9,18 +9,26 @@ import { EuiLink, EuiTab, EuiTabs, EuiText } from '@elastic/eui';
 import { FormattedMessage } from '@kbn/i18n/react';
 import { RegressionDecisionPath } from './decision_path_regression';
 import { DecisionPathJSONViewer } from './decision_path_json_viewer';
-import { FeatureImportance, TopClasses } from '../../../../../common/types/feature_importance';
+import {
+  FeatureImportance,
+  FeatureImportanceBaseline,
+  isClassificationFeatureImportanceBaseline,
+  isRegressionFeatureImportanceBaseline,
+  TopClasses,
+} from '../../../../../common/types/feature_importance';
 import { ANALYSIS_CONFIG_TYPE } from '../../../data_frame_analytics/common';
 import { ClassificationDecisionPath } from './decision_path_classification';
 import { useMlKibana } from '../../../contexts/kibana';
 import { DataFrameAnalysisConfigType } from '../../../../../common/types/data_frame_analytics';
+import { getStringBasedClassName } from './use_classification_path_data';
 
 interface DecisionPathPopoverProps {
   featureImportance: FeatureImportance[];
   analysisType: DataFrameAnalysisConfigType;
   predictionFieldName?: string;
-  baseline?: number;
+  baseline?: FeatureImportanceBaseline;
   predictedValue?: number | string | undefined;
+  predictedProbability?: number; // for classification
   topClasses?: TopClasses;
 }
 
@@ -30,7 +38,7 @@ enum DECISION_PATH_TABS {
 }
 
 export interface ExtendedFeatureImportance extends FeatureImportance {
-  absImportance?: number;
+  absImportance: number;
 }
 
 export const DecisionPathPopover: FC<DecisionPathPopoverProps> = ({
@@ -40,6 +48,7 @@ export const DecisionPathPopover: FC<DecisionPathPopoverProps> = ({
   topClasses,
   analysisType,
   predictionFieldName,
+  predictedProbability,
 }) => {
   const [selectedTabId, setSelectedTabId] = useState(DECISION_PATH_TABS.CHART);
   const {
@@ -109,22 +118,29 @@ export const DecisionPathPopover: FC<DecisionPathPopoverProps> = ({
               }}
             />
           </EuiText>
-          {analysisType === ANALYSIS_CONFIG_TYPE.CLASSIFICATION && (
-            <ClassificationDecisionPath
-              featureImportance={featureImportance}
-              topClasses={topClasses as TopClasses}
-              predictedValue={predictedValue as string}
-              predictionFieldName={predictionFieldName}
-            />
-          )}
-          {analysisType === ANALYSIS_CONFIG_TYPE.REGRESSION && (
-            <RegressionDecisionPath
-              featureImportance={featureImportance}
-              baseline={baseline}
-              predictedValue={predictedValue as number}
-              predictionFieldName={predictionFieldName}
-            />
-          )}
+          {analysisType === ANALYSIS_CONFIG_TYPE.CLASSIFICATION &&
+            isClassificationFeatureImportanceBaseline(baseline) && (
+              <ClassificationDecisionPath
+                featureImportance={featureImportance}
+                topClasses={topClasses as TopClasses}
+                predictedValue={getStringBasedClassName(predictedValue)}
+                predictedProbability={predictedProbability}
+                predictionFieldName={predictionFieldName}
+                baseline={baseline}
+              />
+            )}
+          {analysisType === ANALYSIS_CONFIG_TYPE.REGRESSION &&
+            isRegressionFeatureImportanceBaseline(baseline) &&
+            predictedValue !== undefined && (
+              <RegressionDecisionPath
+                featureImportance={featureImportance}
+                baseline={baseline}
+                predictedValue={
+                  typeof predictedValue === 'string' ? parseFloat(predictedValue) : predictedValue
+                }
+                predictionFieldName={predictionFieldName}
+              />
+            )}
         </>
       )}
       {selectedTabId === DECISION_PATH_TABS.JSON && (

diff --git a/...l/public/application/components/data_grid/feature_importance/decision_path_regression.tsx b/...l/public/application/components/data_grid/feature_importance/decision_path_regression.tsx
@@ -8,14 +8,18 @@ import React, { FC, useMemo } from 'react';
 import { EuiCallOut } from '@elastic/eui';
 import { FormattedMessage } from '@kbn/i18n/react';
 import d3 from 'd3';
-import { FeatureImportance, TopClasses } from '../../../../../common/types/feature_importance';
+import {
+  FeatureImportance,
+  FeatureImportanceBaseline,
+  TopClasses,
+} from '../../../../../common/types/feature_importance';
 import { useDecisionPathData, isDecisionPathData } from './use_classification_path_data';
 import { DecisionPathChart } from './decision_path_chart';
 import { MissingDecisionPathCallout } from './missing_decision_path_callout';
 
 interface RegressionDecisionPathProps {
   predictionFieldName?: string;
-  baseline?: number;
+  baseline?: FeatureImportanceBaseline;
   predictedValue?: number | undefined;
   featureImportance: FeatureImportance[];
   topClasses?: TopClasses;