minor updates and godoc to weighted random picker

pkg/epp/scheduling/framework/plugins/picker/weighted_random_picker.go

@@ -22,6 +22,7 @@ import (
 	"fmt"
 	"math"
 	"math/rand"
+	"slices"
 	"sort"
 	"time"
 
@@ -43,12 +44,12 @@ type weightedScoredPod struct {
 	key float64
 }
 
+// compile-time type validation
 var _ framework.Picker = &WeightedRandomPicker{}
 
+// WeightedRandomPickerFactory defines the factory function for WeightedRandomPicker.
 func WeightedRandomPickerFactory(name string, rawParameters json.RawMessage, _ plugins.Handle) (plugins.Plugin, error) {
-	parameters := pickerParameters{
-		MaxNumOfEndpoints: DefaultMaxNumOfEndpoints,
-	}
+	parameters := pickerParameters{MaxNumOfEndpoints: DefaultMaxNumOfEndpoints}
 	if rawParameters != nil {
 		if err := json.Unmarshal(rawParameters, &parameters); err != nil {
 			return nil, fmt.Errorf("failed to parse the parameters of the '%s' picker - %w", WeightedRandomPickerType, err)
@@ -58,9 +59,10 @@ func WeightedRandomPickerFactory(name string, rawParameters json.RawMessage, _ p
 	return NewWeightedRandomPicker(parameters.MaxNumOfEndpoints).WithName(name), nil
 }
 
+// NewWeightedRandomPicker initializes a new WeightedRandomPicker and returns its pointer.
 func NewWeightedRandomPicker(maxNumOfEndpoints int) *WeightedRandomPicker {
 	if maxNumOfEndpoints <= 0 {
-		maxNumOfEndpoints = DefaultMaxNumOfEndpoints
+		maxNumOfEndpoints = DefaultMaxNumOfEndpoints // on invalid configuration value, fallback to default value
 	}
 
 	return &WeightedRandomPicker{
@@ -70,81 +72,68 @@ func NewWeightedRandomPicker(maxNumOfEndpoints int) *WeightedRandomPicker {
 	}
 }
 
+// WeightedRandomPicker picks pod(s) from the list of candidates based on weighted random sampling using A-Res algorithm.
+// Reference: https://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf.
+//
+// The picker at its core is picking pods randomly, where the probability of the pod to get picked is derived
+// from its weighted score.
+// Algorithm:
+// - Uses A-Res (Algorithm for Reservoir Sampling): keyᵢ = Uᵢ^(1/wᵢ)
+// - Selects k items with largest keys for mathematically correct weighted sampling
+// - More efficient than traditional cumulative probability approach
+//
+// Key characteristics:
+// - Mathematically correct weighted random sampling
+// - Single pass algorithm with O(n + k log k) complexity
 type WeightedRandomPicker struct {
 	typedName         plugins.TypedName
 	maxNumOfEndpoints int
 	randomPicker      *RandomPicker // fallback for zero weights
 }
 
+// WithName sets the name of the picker.
 func (p *WeightedRandomPicker) WithName(name string) *WeightedRandomPicker {
 	p.typedName.Name = name
 	return p
 }
 
+// TypedName returns the type and name tuple of this plugin instance.
 func (p *WeightedRandomPicker) TypedName() plugins.TypedName {
 	return p.typedName
 }
 
-// WeightedRandomPicker performs weighted random sampling using A-Res algorithm.
-// Reference: https://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
-// Algorithm:
-// - Uses A-Res (Algorithm for Reservoir Sampling): keyᵢ = Uᵢ^(1/wᵢ)
-// - Selects k items with largest keys for mathematically correct weighted sampling
-// - More efficient than traditional cumulative probability approach
-//
-// Key characteristics:
-// - Mathematically correct weighted random sampling
-// - Single pass algorithm with O(n + k log k) complexity
+// Pick selects the pod(s) randomly from the list of candidates, where the probability of the pod to get picked is derived
+// from its weighted score.
 func (p *WeightedRandomPicker) Pick(ctx context.Context, cycleState *types.CycleState, scoredPods []*types.ScoredPod) *types.ProfileRunResult {
-	log.FromContext(ctx).V(logutil.DEBUG).Info(fmt.Sprintf("Selecting maximum '%d' pods from %d candidates using weighted random sampling: %+v",
-		p.maxNumOfEndpoints, len(scoredPods), scoredPods))
-
-	// Check if all weights are zero or negative
-	allZeroWeights := true
-	for _, scoredPod := range scoredPods {
-		if scoredPod.Score > 0 {
-			allZeroWeights = false
-			break
-		}
-	}
-
-	// Delegate to RandomPicker for uniform selection when all weights are zero
-	if allZeroWeights {
-		log.FromContext(ctx).V(logutil.DEBUG).Info("All weights are zero, delegating to RandomPicker for uniform selection")
+	// Check if there is at least one pod with Score > 0, if not let random picker run
+	if slices.IndexFunc(scoredPods, func(scoredPod *types.ScoredPod) bool { return scoredPod.Score > 0 }) == -1 {
+		log.FromContext(ctx).V(logutil.DEBUG).Info("All scores are zero, delegating to RandomPicker for uniform selection")
 		return p.randomPicker.Pick(ctx, cycleState, scoredPods)
 	}
 
+	log.FromContext(ctx).V(logutil.DEBUG).Info("Selecting pods from candidates by random weighted picker", "max-num-of-endpoints", p.maxNumOfEndpoints,
+		"num-of-candidates", len(scoredPods), "scored-pods", scoredPods)
+
 	randomGenerator := rand.New(rand.NewSource(time.Now().UnixNano()))
 
 	// A-Res algorithm: keyᵢ = Uᵢ^(1/wᵢ)
-	weightedPods := make([]weightedScoredPod, 0, len(scoredPods))
-
-	for _, scoredPod := range scoredPods {
-		weight := float64(scoredPod.Score)
-
-		// Handle zero or negative weights
-		if weight <= 0 {
-			// Assign very small key for zero-weight pods (effectively excludes them)
-			weightedPods = append(weightedPods, weightedScoredPod{
-				ScoredPod: scoredPod,
-				key:       0,
-			})
+	weightedPods := make([]weightedScoredPod, len(scoredPods))
+
+	for i, scoredPod := range scoredPods {
+		// Handle zero score
+		if scoredPod.Score <= 0 {
+			// Assign key=0 for zero-score pods (effectively excludes them from selection)
+			weightedPods[i] = weightedScoredPod{ScoredPod: scoredPod, key: 0}
 			continue
 		}
 
-		// Generate random number U in (0,1)
+		// If we're here the scoredPod.Score > 0. Generate a random number U in (0,1)
 		u := randomGenerator.Float64()
 		if u == 0 {
 			u = 1e-10 // Avoid log(0)
 		}
 
-		// Calculate key = U^(1/weight)
-		key := math.Pow(u, 1.0/weight)
-
-		weightedPods = append(weightedPods, weightedScoredPod{
-			ScoredPod: scoredPod,
-			key:       key,
-		})
+		weightedPods[i] = weightedScoredPod{ScoredPod: scoredPod, key: math.Pow(u, 1.0/scoredPod.Score)} // key = U^(1/weight)
 	}
 
 	// Sort by key in descending order (largest keys first)
@@ -155,14 +144,9 @@ func (p *WeightedRandomPicker) Pick(ctx context.Context, cycleState *types.Cycle
 	// Select top k pods
 	selectedCount := min(p.maxNumOfEndpoints, len(weightedPods))
 
-	scoredPods = make([]*types.ScoredPod, selectedCount)
+	targetPods := make([]types.Pod, selectedCount)
 	for i := range selectedCount {
-		scoredPods[i] = weightedPods[i].ScoredPod
-	}
-
-	targetPods := make([]types.Pod, len(scoredPods))
-	for i, scoredPod := range scoredPods {
-		targetPods[i] = scoredPod
+		targetPods[i] = weightedPods[i].ScoredPod
 	}
 
 	return &types.ProfileRunResult{TargetPods: targetPods}