func ExamplePGM7_LogisticRegression_Epsilon() {
rand.Seed(98765)
trainFeatures, trainLabels := readTrainData()
benchmarkFeatures, benchmarkLabels := readBenchmarkData()
for _, epsilon := range []float64{1e-1, 1e-2, 1e-3} {
fmt.Println("---\nepsilon: ", epsilon)
classifier := ai.TrainLogisticRegressionClassifier(
trainFeatures,
trainLabels,
0,
&sgrad.RelativeMeanImprovementCrit{},
epsilon)
fmt.Println("train set: ", ai.EvaluateBinaryClassifier(classifier, trainFeatures, trainLabels))
fmt.Println("benchmark set: ", ai.EvaluateBinaryClassifier(classifier, benchmarkFeatures, benchmarkLabels))
}
// Output:
// ---
// epsilon: 0.1
// train set: 0.99
// benchmark set: 0.94
// ---
// epsilon: 0.01
// train set: 0.99
// benchmark set: 0.925
// ---
// epsilon: 0.001
// train set: 1
// benchmark set: 0.93
}
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
}
func ExamplePGM7_LogisticRegression_Iterations() {
rand.Seed(98765)
trainFeatures, trainLabels := readTrainData()
benchmarkFeatures, benchmarkLabels := readBenchmarkData()
for _, iterations := range []int{1, 10, 100, 1000} {
fmt.Println("---\niterations: ", iterations)
classifier := ai.TrainLogisticRegressionClassifier(
trainFeatures,
trainLabels,
0,
&sgrad.NumIterationsCrit{NumIterations: iterations},
1e-8)
fmt.Println("train set: ", ai.EvaluateBinaryClassifier(classifier, trainFeatures, trainLabels))
fmt.Println("benchmark set: ", ai.EvaluateBinaryClassifier(classifier, benchmarkFeatures, benchmarkLabels))
}
// Output:
// ---
// iterations: 1
// train set: 0.955
// benchmark set: 0.94
// ---
// iterations: 10
// train set: 0.985
// benchmark set: 0.925
// ---
// iterations: 100
// train set: 1
// benchmark set: 0.925
// ---
// iterations: 1000
// train set: 1
// benchmark set: 0.93
}
func ExamplePGM7_LogisticRegression_Lambda() {
rand.Seed(98765)
trainFeatures, trainLabels := readTrainData()
benchmarkFeatures, benchmarkLabels := readBenchmarkData()
for _, lambda := range []float64{0, 0.1, 0.2, 0.4, 0.8, 1} {
fmt.Println("---\nlambda: ", 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:
// ---
// lambda: 0
// train set: 1
// benchmark set: 0.93
// ---
// lambda: 0.1
// train set: 0.94
// benchmark set: 0.93
// ---
// lambda: 0.2
// train set: 0.94
// benchmark set: 0.925
// ---
// lambda: 0.4
// train set: 0.925
// benchmark set: 0.915
// ---
// lambda: 0.8
// train set: 0.875
// benchmark set: 0.86
// ---
// lambda: 1
// train set: 0.905
// benchmark set: 0.905
}
