func ExamplePGM7_LogisticRegression_HoldoutTesting() {
rand.Seed(98765)
trainFeatures, trainLabels := readTrainData()
for _, testingFraction := range []float64{0.5, 0.25, 0.1, 0.05} {
fmt.Println("---\nfraction: ", testingFraction)
score := ai.HoldoutTestBinaryClassifier(
trainFeatures,
trainLabels,
testingFraction,
ai.NewLogisticRegressionTrainer(0,
&sgrad.NumIterationsCrit{NumIterations: 10},
1e-8))
fmt.Println("Holdout testing:", score)
}
// Output:
// ---
// fraction: 0.5
// Holdout testing: 0.84
// ---
// fraction: 0.25
// Holdout testing: 0.92
// ---
// fraction: 0.1
// Holdout testing: 0.85
// ---
// fraction: 0.05
// Holdout testing: 1
}
func ExamplePGM7_LogisticRegression_OptimizeLambda() {
rand.Seed(98765)
trainFeatures, trainLabels := readTrainData()
benchmarkFeatures, benchmarkLabels := readBenchmarkData()
goalFunc := func(lambda float64) float64 {
score := ai.HoldoutTestBinaryClassifier(
trainFeatures,
trainLabels,
.1,
ai.NewLogisticRegressionTrainer(
lambda,
&sgrad.NumIterationsCrit{NumIterations: 10},
1e-8))
return -score
}
lambda := gssearh.Minimize(0, 10, goalFunc, &gssearh.AbsoluteErrorTermCrit{}, .1)
fmt.Println("Optimal lambda:", lambda)
classifier := ai.TrainLogisticRegressionClassifier(
trainFeatures,
trainLabels,
lambda,
&sgrad.RelativeMeanImprovementCrit{},
1e-2)
fmt.Println("train set: ", ai.EvaluateBinaryClassifier(classifier, trainFeatures, trainLabels))
fmt.Println("benchmark set: ", ai.EvaluateBinaryClassifier(classifier, benchmarkFeatures, benchmarkLabels))
// Output:
// Optimal lambda: 1.4841053312063623
// train set: 0.895
// benchmark set: 0.9
}