Fix bug when given inf objective value

optuna_dashboard/_serializer.py

@@ -1,8 +1,10 @@
 import json
+import math
 from typing import Any
 from typing import Dict
 from typing import List
 from typing import Tuple
+from typing import Union
 
 from optuna.distributions import BaseDistribution
 from optuna.study import StudySummary
@@ -27,7 +29,7 @@ IntermediateValue = TypedDict(
     "IntermediateValue",
     {
         "step": int,
-        "value": float,
+        "value": Union[float, str],
     },
 )
 TrialParam = TypedDict(
@@ -53,7 +55,10 @@ def serialize_attrs(attrs: Dict[str, Any]) -> List[Attribute]:
 
 
 def serialize_intermediate_values(values: Dict[int, float]) -> List[IntermediateValue]:
-    return [{"step": step, "value": value} for step, value in values.items()]
+    return [
+        {"step": step, "value": "inf" if math.isinf(value) else value}
+        for step, value in values.items()
+    ]
 
 
 def serialize_trial_params(params: Dict[str, Any]) -> List[TrialParam]:
@@ -120,7 +125,7 @@ def serialize_frozen_trial(study_id: int, trial: FrozenTrial) -> Dict[str, Any]:
     }
 
     if trial.values is not None:
-        serialized["values"] = trial.values
+        serialized["values"] = ["inf" if math.isinf(v) else v for v in trial.values]
 
     if trial.datetime_start is not None:
         serialized["datetime_start"] = trial.datetime_start.isoformat()

optuna_dashboard/static/apiClient.ts

@@ -7,7 +7,7 @@ interface TrialResponse {
   study_id: number
   number: number
   state: TrialState
-  values?: number[]
+  values?: (number | "inf")[]
   intermediate_values: TrialIntermediateValue[]
   datetime_start?: string
   datetime_complete?: string

optuna_dashboard/static/components/GraphEdf.tsx

@@ -64,15 +64,19 @@ export const Edf: FC<{
   )
 }
 
+const filterFunc = (trial: Trial, objectiveId: number): boolean => {
+  return trial.state !== "Complete" || trial.values![objectiveId] !== "inf"
+}
+
 const plotEdf = (study: StudyDetail, objectiveId: number, mode: string) => {
   if (document.getElementById(plotDomId) === null) {
     return
   }
 
   const trials: Trial[] = study ? study.trials : []
-  const completedTrials = trials.filter((t) => t.state === "Complete")
+  const filteredTrials = trials.filter((t) => filterFunc(t, objectiveId))
 
-  if (completedTrials.length === 0) {
+  if (filteredTrials.length === 0) {
     plotly.react(plotDomId, [])
     return
   }
@@ -80,7 +84,7 @@ const plotEdf = (study: StudyDetail, objectiveId: number, mode: string) => {
   const target_name = "Objective Value"
 
   const target = (t: Trial): number => {
-    return t.values![objectiveId]
+    return t.values![objectiveId] as number
   }
 
   const layout: Partial<plotly.Layout> = {
@@ -99,7 +103,7 @@ const plotEdf = (study: StudyDetail, objectiveId: number, mode: string) => {
     template: mode === "dark" ? plotlyDarkTemplate : {},
   }
 
-  const values = completedTrials.map((t) => target(t))
+  const values = filteredTrials.map((t) => target(t))
   const numValues = values.length
   const minX = Math.min(...values)
   const maxX = Math.max(...values)

optuna_dashboard/static/components/GraphHistory.tsx

@@ -168,6 +168,18 @@ export const GraphHistory: FC<{
   )
 }
 
+const filterFunc = (trial: Trial, objectiveId: number): boolean => {
+  if (trial.state !== "Complete" && trial.state !== "Pruned") {
+    return false
+  }
+  if (trial.values === undefined) {
+    return false
+  }
+  return (
+    trial.values.length > objectiveId && trial.values[objectiveId] !== "inf"
+  )
+}
+
 const plotHistory = (
   study: StudyDetail,
   objectiveId: number,
@@ -198,11 +210,7 @@ const plotHistory = (
     template: mode === "dark" ? plotlyDarkTemplate : {},
   }
 
-  let filteredTrials = study.trials.filter(
-    (t) =>
-      t.state === "Complete" ||
-      (t.state === "Pruned" && t.values && t.values.length > 0)
-  )
+  let filteredTrials = study.trials.filter((t) => filterFunc(t, objectiveId))
   if (filterCompleteTrial) {
     filteredTrials = filteredTrials.filter((t) => t.state !== "Complete")
   }
@@ -215,22 +223,22 @@ const plotHistory = (
   }
   const trialsForLinePlot: Trial[] = []
   let currentBest: number | null = null
-  filteredTrials.forEach((item) => {
+  filteredTrials.forEach((t) => {
     if (currentBest === null) {
-      currentBest = item.values![objectiveId]
-      trialsForLinePlot.push(item)
+      currentBest = t.values![objectiveId] as number
+      trialsForLinePlot.push(t)
     } else if (
       study.directions[objectiveId] === "maximize" &&
-      item.values![objectiveId] > currentBest
+      t.values![objectiveId] > currentBest
     ) {
-      currentBest = item.values![objectiveId]
-      trialsForLinePlot.push(item)
+      currentBest = t.values![objectiveId] as number
+      trialsForLinePlot.push(t)
     } else if (
       study.directions[objectiveId] === "minimize" &&
-      item.values![objectiveId] < currentBest
+      t.values![objectiveId] < currentBest
     ) {
-      currentBest = item.values![objectiveId]
-      trialsForLinePlot.push(item)
+      currentBest = t.values![objectiveId] as number
+      trialsForLinePlot.push(t)
     }
   })
 
@@ -245,14 +253,16 @@ const plotHistory = (
   const xForLinePlot = trialsForLinePlot.map(getAxisX)
   xForLinePlot.push(getAxisX(filteredTrials[filteredTrials.length - 1]))
   const yForLinePlot = trialsForLinePlot.map(
-    (t: Trial): number => t.values![objectiveId]
+    (t: Trial): number => t.values![objectiveId] as number
   )
   yForLinePlot.push(yForLinePlot[yForLinePlot.length - 1])
 
   const plotData: Partial<plotly.PlotData>[] = [
     {
       x: filteredTrials.map(getAxisX),
-      y: filteredTrials.map((t: Trial): number => t.values![objectiveId]),
+      y: filteredTrials.map(
+        (t: Trial): number => t.values![objectiveId] as number
+      ),
       mode: "markers",
       type: "scatter",
     },

optuna_dashboard/static/components/GraphIntermediateValues.tsx

@@ -54,9 +54,10 @@ const plotIntermediateValue = (trials: Trial[], mode: string) => {
       (t.state === "Pruned" && t.values && t.values.length > 0)
   )
   const plotData: Partial<plotly.PlotData>[] = filteredTrials.map((trial) => {
+    const values = trial.intermediate_values.filter((iv) => iv.value !== "inf")
     return {
-      x: trial.intermediate_values.map((iv) => iv.step),
-      y: trial.intermediate_values.map((iv) => iv.value),
+      x: values.map((iv) => iv.step),
+      y: values.map((iv) => iv.value),
       mode: "lines+markers",
       type: "scatter",
       name: `trial #${trial.number}`,

optuna_dashboard/static/components/GraphParallelCoordinate.tsx

@@ -65,6 +65,18 @@ export const GraphParallelCoordinate: FC<{
   )
 }
 
+const filterFunc = (trial: Trial, objectiveId: number): boolean => {
+  if (trial.state !== "Complete" && trial.state !== "Pruned") {
+    return false
+  }
+  if (trial.values === undefined) {
+    return false
+  }
+  return (
+    trial.values.length > objectiveId && trial.values[objectiveId] !== "inf"
+  )
+}
+
 const plotCoordinate = (
   study: StudyDetail,
   objectiveId: number,
@@ -89,11 +101,7 @@ const plotCoordinate = (
     return
   }
 
-  const filteredTrials = study.trials.filter(
-    (t) =>
-      t.state === "Complete" ||
-      (t.state === "Pruned" && t.values && t.values.length > 0)
-  )
+  const filteredTrials = study.trials.filter((t) => filterFunc(t, objectiveId))
 
   const maxLabelLength = 40
   const breakLength = maxLabelLength / 2
@@ -115,7 +123,7 @@ const plotCoordinate = (
 
   // Intersection param names
   const objectiveValues: number[] = filteredTrials.map(
-    (t) => t.values![objectiveId]
+    (t) => t.values![objectiveId] as number
   )
   const dimensions = [
     {

optuna_dashboard/static/components/GraphParetoFront.tsx

@@ -85,6 +85,10 @@ export const GraphParetoFront: FC<{
   )
 }
 
+const filterFunc = (trial: Trial): boolean => {
+  return trial.state !== "Complete" || trial.values!.every((v) => v !== "inf")
+}
+
 const plotParetoFront = (
   study: StudyDetail,
   objectiveXId: number,
@@ -106,18 +110,20 @@ const plotParetoFront = (
   }
 
   const trials: Trial[] = study ? study.trials : []
-  const completedTrials = trials.filter((t) => t.state === "Complete")
+  const filteredTrials = trials.filter(filterFunc)
 
-  if (completedTrials.length === 0) {
+  if (filteredTrials.length === 0) {
     plotly.react(plotDomId, [], layout)
     return
   }
 
   const normalizedValues: number[][] = []
-  completedTrials.forEach((t) => {
+  filteredTrials.forEach((t) => {
     if (t.values && t.values.length === study.directions.length) {
-      const trialValues = t.values.map((v: number, i: number) => {
-        return study.directions[i] === "minimize" ? v : -v
+      const trialValues = t.values.map((v, i) => {
+        return study.directions[i] === "minimize"
+          ? (v as number)
+          : (-v as number)
       })
       normalizedValues.push(trialValues)
     }
@@ -144,11 +150,11 @@ const plotParetoFront = (
   const plotData: Partial<plotly.PlotData>[] = [
     {
       type: "scatter",
-      x: completedTrials.map((t: Trial): number => {
-        return t.values![objectiveXId]
+      x: filteredTrials.map((t: Trial): number => {
+        return t.values![objectiveXId] as number
       }),
-      y: completedTrials.map((t: Trial): number => {
-        return t.values![objectiveYId]
+      y: filteredTrials.map((t: Trial): number => {
+        return t.values![objectiveYId] as number
       }),
       mode: "markers",
       xaxis: "Objective X",
@@ -156,7 +162,7 @@ const plotParetoFront = (
       marker: {
         color: pointColors,
       },
-      text: completedTrials.map(
+      text: filteredTrials.map(
         (t: Trial): string => `Trial (number=${t.number})`
       ),
       hovertemplate: "%{text}<extra></extra>",

optuna_dashboard/static/components/GraphSlice.tsx

@@ -129,6 +129,25 @@ export const GraphSlice: FC<{
   )
 }
 
+const filterFunc = (
+  trial: Trial,
+  objectiveId: number,
+  selected: string | null
+): boolean => {
+  if (trial.state !== "Complete" && trial.state !== "Pruned") {
+    return false
+  }
+  if (trial.params.find((p) => p.name == selected) === undefined) {
+    return false
+  }
+  if (trial.values === undefined) {
+    return false
+  }
+  return (
+    trial.values.length > objectiveId && trial.values[objectiveId] !== "inf"
+  )
+}
+
 const plotSlice = (
   trials: Trial[],
   objectiveId: number,
@@ -164,11 +183,8 @@ const plotSlice = (
     template: mode === "dark" ? plotlyDarkTemplate : {},
   }
 
-  const filteredTrials = trials.filter(
-    (t) =>
-      (t.state === "Complete" ||
-        (t.state === "Pruned" && t.values && t.values.length > 0)) &&
-      t.params.find((p) => p.name == selected) !== undefined
+  const filteredTrials = trials.filter((t) =>
+    filterFunc(t, objectiveId, selected)
   )
 
   if (filteredTrials.length === 0 || selected === null) {
@@ -177,7 +193,7 @@ const plotSlice = (
   }
 
   const objectiveValues: number[] = filteredTrials.map(
-    (t) => t.values![objectiveId]
+    (t) => t.values![objectiveId] as number
   )
   const valueStrings = filteredTrials.map((t) => {
     return t.params.find((p) => p.name == selected)!.value

optuna_dashboard/static/types/index.d.ts

@@ -21,7 +21,7 @@ type Distribution =
 
 declare interface TrialIntermediateValue {
   step: number
-  value: number
+  value: number | "inf"
 }
 
 declare interface TrialParam {
@@ -50,7 +50,7 @@ declare interface Trial {
   study_id: number
   number: number
   state: TrialState
-  values?: number[]
+  values?: (number | "inf")[]
   intermediate_values: TrialIntermediateValue[]
   datetime_start?: Date
   datetime_complete?: Date

visual_regression_test.py

@@ -1,5 +1,6 @@
 import argparse
 import asyncio
+import math
 import os
 import sys
 import threading
@@ -84,6 +85,18 @@ def create_dummy_storage() -> optuna.storages.InMemoryStorage:
 
     study.optimize(objective_single_dynamic, n_trials=50)
 
+    # Single objective study with 'inf' value
+    study = optuna.create_study(study_name="single-inf", storage=storage)
+
+    def objective_single_inf(trial: optuna.Trial) -> float:
+        x = trial.suggest_float("x", -10, 10)
+        if x > 0:
+            return math.inf
+        else:
+            return x ** 2
+
+    study.optimize(objective_single_inf, n_trials=50)
+
     # Multi-objective study
     study = optuna.create_study(
         study_name="multi-objective",