forked from purzelrakete/bandit
/
bandit_test.go
271 lines (228 loc) · 6.95 KB
/
bandit_test.go
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// Copyright 2013 SoundCloud, Rany Keddo. All rights reserved. Use of this
// source code is governed by a license that can be found in the LICENSE file.
package bandit
import (
bmath "github.com/purzelrakete/bandit/math"
"github.com/purzelrakete/bandit/sim"
"math"
"testing"
)
func TestEpsilonGreedy(t *testing.T) {
ε := 0.1
sims := 5000
trials := 300
bestArmIndex := 4 // Bernoulli(bestArm)
bestArm := 0.8
arms := []sim.Arm{
bmath.BernRand(0.1),
bmath.BernRand(0.3),
bmath.BernRand(0.2),
bmath.BernRand(bestArm),
}
strategy, err := NewEpsilonGreedy(len(arms), ε)
if err != nil {
t.Fatalf(err.Error())
}
s, err := sim.MonteCarlo(sims, trials, arms, strategy)
if err != nil {
t.Fatalf(err.Error())
}
expected := sims * trials
if got := len(s.Selected); got != expected {
t.Fatalf("incorrect number of trials: %d", got)
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got < 0.9 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 200.0
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}
func TestSoftmax(t *testing.T) {
τ := 0.1
sims := 5000
trials := 300
bestArmIndex := 4 // Bernoulli(bestArm)
bestArm := 0.8
arms := []sim.Arm{
bmath.BernRand(0.1),
bmath.BernRand(0.3),
bmath.BernRand(0.2),
bmath.BernRand(0.8),
}
strategy, err := NewSoftmax(len(arms), τ)
if err != nil {
t.Fatalf(err.Error())
}
s, err := sim.MonteCarlo(sims, trials, arms, strategy)
if err != nil {
t.Fatalf(err.Error())
}
expected := sims * trials
if got := len(s.Selected); got != expected {
t.Fatalf("incorrect number of trials: %d", got)
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got < 0.9 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 200.0
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}
func TestSoftmaxGaussian(t *testing.T) {
τ := 0.1
sims := 5000
trials := 300
bestArmIndex := 1 // Gaussian(bestArm)
bestArm := 5000.0
arms := []sim.Arm{
bmath.NormRand(5000, 1), // is five times better
bmath.NormRand(0, 1),
}
strategy, err := NewSoftmax(len(arms), τ)
if err != nil {
t.Fatalf(err.Error())
}
s, err := sim.MonteCarlo(sims, trials, arms, strategy)
if err != nil {
t.Fatalf(err.Error())
}
expected := sims * trials
if got := len(s.Selected); got != expected {
t.Fatalf("incorrect number of trials: %d", got)
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got != 1.0 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 4500.0 * float64(trials) // (mean(bestArm)-tolerance) * num trials
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}
func TestUCB1(t *testing.T) {
sims := 5000
trials := 300
bestArmIndex := 4 // Bernoulli(bestArm)
bestArm := 0.8
arms := []sim.Arm{
bmath.BernRand(0.1),
bmath.BernRand(0.3),
bmath.BernRand(0.2),
bmath.BernRand(0.8),
}
s, err := sim.MonteCarlo(sims, trials, arms, NewUCB1(len(arms)))
if err != nil {
t.Fatalf(err.Error())
}
expected := sims * trials
if got := len(s.Selected); got != expected {
t.Fatalf("incorrect number of trials: %d", got)
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got < 0.9 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 200.0
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}
func TestDelayedStrategy(t *testing.T) {
τ := 0.1
sims := 5000
trials := 300
flushAfter := 100
bestArmIndex := 4 // Bernoulli(bestArm)
bestArm := 0.8
arms := []sim.Arm{
bmath.BernRand(0.1),
bmath.BernRand(0.3),
bmath.BernRand(0.2),
bmath.BernRand(0.8),
}
b, err := NewSoftmax(len(arms), τ)
if err != nil {
t.Fatalf(err.Error())
}
d := NewSimulatedDelayedStrategy(b, len(arms), flushAfter)
s, err := sim.MonteCarlo(sims, trials, arms, d)
if err != nil {
t.Fatalf(err.Error())
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got < 0.9 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 200.0
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}
func TestThompson(t *testing.T) {
α := 10.0
sims := 5000
trials := 300
bestArmIndex := 4 // Bernoulli(bestArm)
bestArm := 0.8
arms := []sim.Arm{
bmath.BernRand(0.1),
bmath.BernRand(0.3),
bmath.BernRand(0.2),
bmath.BernRand(0.8),
}
strategy, err := NewThompson(len(arms), α)
if err != nil {
t.Fatalf(err.Error())
}
s, err := sim.MonteCarlo(sims, trials, arms, strategy)
if err != nil {
t.Fatalf(err.Error())
}
expected := sims * trials
if got := len(s.Selected); got != expected {
t.Fatalf("incorrect number of trials: %d", got)
}
accuracies := sim.Accuracy([]int{bestArmIndex})(&s)
if got := accuracies[len(accuracies)-1]; got < 0.9 {
t.Fatalf("accuracy is only %f. %d sims, %d trials", got, sims, trials)
}
performances := sim.Performance(&s)
if got := performances[len(performances)-1]; math.Abs(bestArm-got) > 0.1 {
t.Fatalf("performance converge to %f. is %f", bestArm, got)
}
expectedCumulative := 200.0
cumulatives := sim.Cumulative(&s)
if got := cumulatives[len(cumulatives)-1]; got < expectedCumulative {
t.Fatalf("cumulative performance should be > %f. is %f", expectedCumulative, got)
}
}