public void TestEvenlyDistributed()
{
int size = DatasetSize;
for (int numLabels = 2; numLabels <= size / 2; numLabels++)
{
var labelCounts = new int[numLabels, 2];
for (int label = 1; label <= numLabels; label++)
{
int segSize = size / numLabels;
if (label <= size % numLabels)
{
segSize++;
}
labelCounts[label - 1, 0] = label;
labelCounts[label - 1, 1] = segSize;
}
double labelDistr = 1.0 / numLabels;
LabeledDataset <int, int> ld = NewData(labelCounts, true);
for (int numFolds = 2; numFolds <= size / numLabels; numFolds++)
{
var aggTestSet = new LabeledDataset <int, int>();
for (int i = 0; i < numFolds; i++)
{
LabeledDataset <int, int> trainSet, testSet;
ld.SplitForStratifiedCrossValidation(numFolds, i + 1, out trainSet, out testSet);
AssertSetEquality(trainSet.Concat(testSet), ld);
aggTestSet.AddRange(testSet);
foreach (double distr in testSet.GroupBy(le => le.Label).Select(g => (double)g.Count() / testSet.Count))
{
Assert.IsTrue(Math.Abs(labelDistr - distr) <= 1.0 / testSet.Count);
}
foreach (double distr in trainSet.GroupBy(le => le.Label).Select(g => (double)g.Count() / trainSet.Count))
{
Assert.IsTrue(Math.Abs(labelDistr - distr) <= 1.0 / trainSet.Count);
}
}
AssertSetEquality(aggTestSet, ld);
}
}
}
public void TestFolding()
{
for (int size = 2; size <= DatasetSize; size++)
{
LabeledDataset <int, int> ld = NewData(new[, ] {
{ 1, size }
}, true);
for (int numFolds = 2; numFolds <= size; numFolds++)
{
var aggTestSet = new LabeledDataset <int, int>();
for (int i = 0; i < numFolds; i++)
{
LabeledDataset <int, int> trainSet, testSet;
ld.SplitForStratifiedCrossValidation(numFolds, i + 1, out trainSet, out testSet);
AssertSetEquality(trainSet.Concat(testSet), ld);
aggTestSet.AddRange(testSet);
}
AssertSetEquality(aggTestSet, ld);
}
}
}
public void TestUnevenlyDistributed()
{
int size = DatasetSize;
double[] labelDistrs = { 0.2, 0.4, 0.1, 0.3 };
var labelCounts = new int[labelDistrs.Length, 2];
int addedCount = 0;
for (int label = 1; label <= labelDistrs.Length; label++)
{
labelCounts[label - 1, 0] = label;
var labelCount = (int)Math.Truncate(labelDistrs[label - 1] * size);
labelCounts[label - 1, 1] = labelCount;
addedCount += labelCount;
}
for (int i = 0; i < size - addedCount; i++)
{
int idx = i % labelCounts.Length;
labelCounts[idx, 1]++;
labelDistrs[idx] = (double)labelCounts[idx, 1] / size;
}
LabeledDataset <int, int> ld = NewData(labelCounts, true);
for (int numFolds = 2; numFolds <= size / labelDistrs.Length; numFolds++)
{
var aggTestSet = new LabeledDataset <int, int>();
for (int i = 0; i < numFolds; i++)
{
LabeledDataset <int, int> trainSet, testSet;
ld.SplitForStratifiedCrossValidation(numFolds, i + 1, out trainSet, out testSet);
AssertSetEquality(trainSet.Concat(testSet), ld);
aggTestSet.AddRange(testSet);
var test = new List <double>();
foreach (IGrouping <int, LabeledExample <int, int> > group in testSet.GroupBy(le => le.Label))
{
double distr = (double)group.Count() / testSet.Count;
int label = group.Key;
int j = 0;
for (; labelCounts[j, 0] != label; j++)
{
}
Assert.IsTrue(Math.Abs(labelDistrs[j] - distr) <= 1.0 / testSet.Count + 0.00001);
test.Add((double)group.Count() / testSet.Count);
}
var train = new List <double>();
foreach (IGrouping <int, LabeledExample <int, int> > group in trainSet.GroupBy(le => le.Label))
{
double distr = (double)group.Count() / trainSet.Count;
int label = group.Key;
int j = 0;
for (; labelCounts[j, 0] != label; j++)
{
}
Assert.IsTrue(Math.Abs(labelDistrs[j] - distr) <= 1.0 / trainSet.Count + 0.00001);
train.Add((double)group.Count() / trainSet.Count);
}
}
AssertSetEquality(aggTestSet, ld);
}
}