/// <summary>
/// Learns an XGBoost classification model.
/// </summary>
/// <param name="observations"></param>
/// <param name="targets"></param>
/// <param name="indices"></param>
/// <returns></returns>
public ClassificationXGBoostModel Learn(F64Matrix observations, double[] targets, int[] indices)
{
Checks.VerifyObservationsAndTargets(observations, targets);
Checks.VerifyIndices(indices, observations, targets);
var floatObservations = observations.ToFloatJaggedArray(indices);
var floatTargets = targets.ToFloat(indices);
// Only specify XGBoost number of classes if the objective is multi-class.
var objective = (string)m_parameters[ParameterNames.objective];
if (objective == ClassificationObjective.Softmax.ToXGBoostString() || objective == ClassificationObjective.SoftProb.ToXGBoostString())
{
var numberOfClasses = floatTargets.Distinct().Count();
m_parameters[ParameterNames.NumberOfClasses] = numberOfClasses;
}
using (var train = new DMatrix(floatObservations, floatTargets))
{
var booster = new Booster(m_parameters.ToDictionary(v => v.Key, v => v.Value), train);
var iterations = (int)m_parameters[ParameterNames.Estimators];
for (int iteration = 0; iteration < iterations; iteration++)
{
booster.Update(train, iteration);
}
return(new ClassificationXGBoostModel(booster));
}
}
/// <summary>
///
/// </summary>
/// <param name="observations"></param>
/// <returns></returns>
public double[] Predict(F64Matrix observations)
{
var floatObservations = observations.ToFloatJaggedArray();
using (var data = new DMatrix(floatObservations))
{
return(m_model.Predict(data).ToDouble());
}
}
public void Conversions_ToFloatJaggedArray_Indexed()
{
var data = new double[]
{
10, 11,
12, 13,
14, 15
};
var matrix = new F64Matrix(data, 3, 2);
var actual = matrix.ToFloatJaggedArray(new int[] { 0, 2 });
var expected = new float[][]
{
new float[] { 10, 11 },
new float[] { 14, 15 },
};
AssertArrays(actual, expected);
}
/// <summary>
/// Learns an XGBoost regression model.
/// </summary>
/// <param name="observations"></param>
/// <param name="targets"></param>
/// <param name="indices"></param>
/// <returns></returns>
public RegressionXGBoostModel Learn(F64Matrix observations, double[] targets, int[] indices)
{
Checks.VerifyObservationsAndTargets(observations, targets);
Checks.VerifyIndices(indices, observations, targets);
var floatObservations = observations.ToFloatJaggedArray(indices);
var floatTargets = targets.ToFloat(indices);
using (var train = new DMatrix(floatObservations, floatTargets))
{
var booster = new Booster(m_parameters.ToDictionary(v => v.Key, v => v.Value), train);
var iterations = (int)m_parameters[ParameterNames.Estimators];
for (int iteration = 0; iteration < iterations; iteration++)
{
booster.Update(train, iteration);
}
return(new RegressionXGBoostModel(booster));
}
}
