Cross-fold validation

evaluation/cross_fold.go

@@ -0,0 +1,82 @@
+package evaluation
+
+import (
+	"github.com/sjwhitworth/golearn/base"
+	"math/rand"
+)
+
+// GetCrossValidatedMetric returns the mean and variance of the confusion-matrix-derived
+// metric across all folds.
+func GetCrossValidatedMetric(in []ConfusionMatrix, metric func(ConfusionMatrix) float64) (mean, variance float64) {
+	scores := make([]float64, len(in))
+	for i, c := range in {
+		scores[i] = metric(c)
+	}
+
+	// Compute mean, variance
+	sum := 0.0
+	for _, s := range scores {
+		sum += s
+	}
+	sum /= float64(len(scores))
+	mean = sum
+	sum = 0.0
+	for _, s := range scores {
+		sum += (s - mean) * (s - mean)
+	}
+	sum /= float64(len(scores))
+	variance = sum
+	return mean, variance
+}
+
+// GenerateCrossFoldValidationConfusionMatrices divides the data into a number of folds
+// then trains and evaluates the classifier on each fold, producing a new ConfusionMatrix.
+func GenerateCrossFoldValidationConfusionMatrices(data base.FixedDataGrid, cls base.Classifier, folds int) ([]ConfusionMatrix, error) {
+	_, rows := data.Size()
+
+	// Assign each row to a fold
+	foldMap := make([]int, rows)
+	inverseFoldMap := make(map[int][]int)
+	for i := 0; i < rows; i++ {
+		fold := rand.Intn(folds)
+		foldMap[i] = fold
+		if _, ok := inverseFoldMap[fold]; !ok {
+			inverseFoldMap[fold] = make([]int, 0)
+		}
+		inverseFoldMap[fold] = append(inverseFoldMap[fold], i)
+	}
+
+	ret := make([]ConfusionMatrix, folds)
+
+	// Create training/test views for each fold
+	for i := 0; i < folds; i++ {
+		// Fold i is for testing
+		testData := base.NewInstancesViewFromVisible(data, inverseFoldMap[i], data.AllAttributes())
+		otherRows := make([]int, 0)
+		for j := 0; j < folds; j++ {
+			if i == j {
+				continue
+			}
+			otherRows = append(otherRows, inverseFoldMap[j]...)
+		}
+		trainData := base.NewInstancesViewFromVisible(data, otherRows, data.AllAttributes())
+		// Train
+		err := cls.Fit(trainData)
+		if err != nil {
+			return nil, err
+		}
+		// Predict
+		pred, err := cls.Predict(testData)
+		if err != nil {
+			return nil, err
+		}
+		// Evaluate
+		cf, err := GetConfusionMatrix(testData, pred)
+		if err != nil {
+			return nil, err
+		}
+		ret[i] = cf
+	}
+	return ret, nil
+
+}

examples/crossfold/rf.go

@@ -0,0 +1,40 @@
+// Demonstrates decision tree classification
+
+package main
+
+import (
+	"fmt"
+	"github.com/sjwhitworth/golearn/base"
+	"github.com/sjwhitworth/golearn/ensemble"
+	"github.com/sjwhitworth/golearn/evaluation"
+	"math"
+	"math/rand"
+)
+
+func main() {
+
+	var tree base.Classifier
+
+	// Load in the iris dataset
+	iris, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
+	if err != nil {
+		panic(err)
+	}
+
+	for i := 1; i < 60; i += 2 {
+		// Demonstrate the effect of adding more trees to the forest
+		// and also how much better it is without discretisation.
+		rand.Seed(44111342)
+
+		tree = ensemble.NewRandomForest(i, 4)
+		cfs, err := evaluation.GenerateCrossFoldValidationConfusionMatrices(iris, tree, 5)
+		if err != nil {
+			panic(err)
+		}
+
+		mean, variance := evaluation.GetCrossValidatedMetric(cfs, evaluation.GetAccuracy)
+		stdev := math.Sqrt(variance)
+
+		fmt.Printf("%d\t%.2f\t(+/- %.2f)\n", i, mean, stdev*2)
+	}
+}