public void TestLabelBinarizerSetLabelEncoding()
{
var lb = new LabelBinarizer<int>(negLabel: -2, posLabel: 2);
// two-class case
var inp = new[] {0, 1, 1, 0};
var expected = new double[,] {{-2, 2, 2, -2}}.ToDenseMatrix().Transpose();
var got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
// multi-class case
inp = new[] {3, 2, 1, 2, 0};
expected = new double[,]
{
{-2, -2, -2, +2},
{-2, -2, +2, -2},
{-2, +2, -2, -2},
{-2, -2, +2, -2},
{+2, -2, -2, -2}
}.ToDenseMatrix();
got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
}
public void TestLabelBinarizerSetLabelEncoding()
{
var lb = new LabelBinarizer <int>(negLabel: -2, posLabel: 2);
// two-class case
var inp = new[] { 0, 1, 1, 0 };
var expected = new double[, ] {
{ -2, 2, 2, -2 }
}.ToDenseMatrix().Transpose();
var got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
// multi-class case
inp = new[] { 3, 2, 1, 2, 0 };
expected = new double[, ]
{
{ -2, -2, -2, +2 },
{ -2, -2, +2, -2 },
{ -2, +2, -2, -2 },
{ -2, -2, +2, -2 },
{ +2, -2, -2, -2 }
}.ToDenseMatrix();
got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
}
public void TestLabelBinarizer()
{
var lb = new LabelBinarizer<string>();
// two-class case
var inp = new[] {"neg", "pos", "pos", "neg"};
var expected = new double[,] {{0, 1, 1, 0}}.ToDenseMatrix().Transpose();
var got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
// multi-class case
inp = new[] {"spam", "ham", "eggs", "ham", "0"};
expected = new double[,]
{
{0, 0, 0, 1},
{0, 0, 1, 0},
{0, 1, 0, 0},
{0, 0, 1, 0},
{1, 0, 0, 0}
}.ToDenseMatrix();
got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
}
public void TestLabelBinarizer()
{
var lb = new LabelBinarizer <string>();
// two-class case
var inp = new[] { "neg", "pos", "pos", "neg" };
var expected = new double[, ] {
{ 0, 1, 1, 0 }
}.ToDenseMatrix().Transpose();
var got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
// multi-class case
inp = new[] { "spam", "ham", "eggs", "ham", "0" };
expected = new double[, ]
{
{ 0, 0, 0, 1 },
{ 0, 0, 1, 0 },
{ 0, 1, 0, 0 },
{ 0, 0, 1, 0 },
{ 1, 0, 0, 0 }
}.ToDenseMatrix();
got = lb.Fit(inp).Transform(inp);
Assert.AreEqual(expected, got);
Assert.IsTrue(lb.InverseTransform(got).SequenceEqual(inp));
}
