/// <summary>
/// Measure quality of the training parameters.
/// </summary>
/// <param name="parameters">The parameters.</param>
/// <returns>The evaluated result.</returns>
public double Evaluate(double[] parameters)
{
var contexts = indexer.GetContexts();
var values = indexer.Values;
var nEventsSeen = indexer.GetNumTimesEventsSeen();
var outcomeList = indexer.GetOutcomeList();
var nOutcomes = outcomeList.Length;
var nPredLabels = indexer.GetPredLabels().Length;
var nCorrect = 0;
var nTotalEvents = 0;
for (var ei = 0; ei < contexts.Length; ei++)
{
var context = contexts[ei];
var value = values == null ? null : values[ei];
var probs = new double[nOutcomes];
QNModel.Eval(context, value, probs, nOutcomes, nPredLabels, parameters);
var outcome = ArrayMath.MaxId(probs);
if (outcome == outcomeList[ei])
{
nCorrect += nEventsSeen[ei];
}
nTotalEvents += nEventsSeen[ei];
}
return(nTotalEvents == 0 ? 0 : (double)nCorrect / nTotalEvents);
}
/// <summary>
/// Gets the negative log-likelihood at the given input vector.
/// </summary>
/// <param name="x">The input vector.</param>
/// <returns>The negative log-likelihood.</returns>
/// <exception cref="ArgumentException">The <paramref name="x"/> is invalid, its dimension is not equal to domain dimension.</exception>
public virtual double ValueAt(double[] x)
{
if (x.Length != dimension)
{
throw new ArgumentException("x is invalid, its dimension is not equal to domain dimension.", nameof(x));
}
int ci;
double negLogLikelihood = 0;
for (ci = 0; ci < numContexts; ci++)
{
int oi;
for (oi = 0; oi < numOutcomes; oi++)
{
tempSums[oi] = 0;
int ai;
for (ai = 0; ai < contexts[ci].Length; ai++)
{
var vectorIndex = IndexOf(oi, contexts[ci][ai]);
var predValue = values != null ? values[ci][ai] : 1.0;
tempSums[oi] += predValue * x[vectorIndex];
}
}
var logSumOfExps = ArrayMath.LogSumOfExps(tempSums);
negLogLikelihood -= (tempSums[outcomeList[ci]] - logSumOfExps) * numTimesEventsSeen[ci];
}
return(negLogLikelihood);
}
private void NegLLCompute(int threadIndex, int startIndex, int length, double[] x)
{
negLogLikelihoodThread[threadIndex] = 0;
// Knuppe: In parallel we can't use the tempSums variable ;)
var temp = new double[numOutcomes];
for (var ci = startIndex; ci < startIndex + length; ci++)
{
for (var oi = 0; oi < numOutcomes; oi++)
{
temp[oi] = 0;
for (var ai = 0; ai < contexts[ci].Length; ai++)
{
var vectorIndex = IndexOf(oi, contexts[ci][ai]);
var predValue = values != null ? values[ci][ai] : 1.0;
temp[oi] += predValue * x[vectorIndex];
}
}
var logSumOfExps = ArrayMath.LogSumOfExps(temp);
var outcome = outcomeList[ci];
negLogLikelihoodThread[threadIndex] -= (temp[outcome] - logSumOfExps) * numTimesEventsSeen[ci];
}
}
/// <summary>
/// L-BFGS two-loop recursion (see Nocedal & Wright 2006, Numerical Optimization, p. 178)
/// </summary>
/// <param name="direction">The direction.</param>
private void ComputeDirection(double[] direction)
{
// Implemented two-loop Hessian update method.
var k = updateInfo.kCounter;
var rho = updateInfo.rho;
var alpha = updateInfo.alpha; // just to avoid recreating alpha
var S = updateInfo.S;
var Y = updateInfo.Y;
// First loop
for (var i = k - 1; i >= 0; i--)
{
alpha[i] = rho[i] * ArrayMath.InnerProduct(S[i], direction);
for (var j = 0; j < dimension; j++)
{
direction[j] = direction[j] - alpha[i] * Y[i][j];
}
}
// Second loop
for (var i = 0; i < k; i++)
{
var beta = rho[i] * ArrayMath.InnerProduct(Y[i], direction);
for (var j = 0; j < dimension; j++)
{
direction[j] = direction[j] + S[i][j] * (alpha[i] - beta);
}
}
for (var i = 0; i < dimension; i++)
{
direction[i] = -direction[i];
}
}
/// <summary>
/// Gets the function value at the given input vector.
/// </summary>
/// <param name="x">The input vector.</param>
/// <returns>The function value.</returns>
public double ValueAt(double[] x)
{
CheckDimension(x);
var value = func.ValueAt(x);
if (l2Cost > 0)
{
value += l2Cost * ArrayMath.InnerProduct(x, x);
}
return(value);
}
/// <summary>
/// Gets the gradient at the given input vector.
/// </summary>
/// <param name="x">The input vector.</param>
/// <returns>The gradient value.</returns>
/// <exception cref="System.ArgumentException">x is invalid, its dimension is not equal to domain dimension.;x</exception>
/// <exception cref="ArgumentException">The <paramref name="x" /> is invalid, its dimension is not equal to domain dimension.</exception>
public virtual double[] GradientAt(double[] x)
{
if (x.Length != dimension)
{
throw new ArgumentException("x is invalid, its dimension is not equal to domain dimension.", nameof(x));
}
int ci;
// Reset gradient
for (var i = 0; i < gradient.Length; i++)
{
gradient[i] = 0;
}
for (ci = 0; ci < numContexts; ci++)
{
int oi;
double predValue;
int vectorIndex;
int ai;
for (oi = 0; oi < numOutcomes; oi++)
{
expectation[oi] = 0;
for (ai = 0; ai < contexts[ci].Length; ai++)
{
vectorIndex = IndexOf(oi, contexts[ci][ai]);
predValue = values != null ? values[ci][ai] : 1.0;
expectation[oi] += predValue * x[vectorIndex];
}
}
var logSumOfExps = ArrayMath.LogSumOfExps(expectation);
for (oi = 0; oi < numOutcomes; oi++)
{
expectation[oi] = Math.Exp(expectation[oi] - logSumOfExps);
}
for (oi = 0; oi < numOutcomes; oi++)
{
var empirical = outcomeList[ci] == oi ? 1 : 0;
for (ai = 0; ai < contexts[ci].Length; ai++)
{
vectorIndex = IndexOf(oi, contexts[ci][ai]);
predValue = values != null ? values[ci][ai] : 1.0;
gradient[vectorIndex] +=
predValue * (expectation[oi] - empirical) * numTimesEventsSeen[ci];
}
}
}
return(gradient);
}
private static readonly double RHO = 0.5; // decrease of step size (must be from 0 to 1)
/// <summary>
/// Backtracking line search. (see Nocedal & Wright 2006, Numerical Optimization, p. 37)
/// </summary>
/// <param name="function">The function.</param>
/// <param name="direction">The direction.</param>
/// <param name="lsr">The result.</param>
/// <param name="initialStepSize">Initial step size.</param>
public static void DoLineSearch(
IFunction function,
double[] direction,
LineSearchResult lsr,
double initialStepSize)
{
var stepSize = initialStepSize;
var currFctEvalCount = lsr.FctEvalCount;
var x = lsr.NextPoint;
var gradAtX = lsr.GradAtNext;
var valueAtX = lsr.ValueAtNext;
var dimension = x.Length;
// Retrieve current points and gradient for array reuse purpose
var nextPoint = lsr.CurrPoint;
var gradAtNextPoint = lsr.GradAtCurr;
double valueAtNextPoint;
var dirGradientAtX = ArrayMath.InnerProduct(direction, gradAtX);
// To avoid recomputing in the loop
var cachedProd = C * dirGradientAtX;
while (true)
{
// Get next point
for (var i = 0; i < dimension; i++)
{
nextPoint[i] = x[i] + direction[i] * stepSize;
}
// New value
valueAtNextPoint = function.ValueAt(nextPoint);
currFctEvalCount++;
// Check Armijo condition
if (valueAtNextPoint <= valueAtX + cachedProd * stepSize)
{
break;
}
// Shrink step size
stepSize *= RHO;
}
// Compute and save gradient at the new point
Array.Copy(function.GradientAt(nextPoint), 0, gradAtNextPoint, 0, gradAtNextPoint.Length);
// Update line search result
lsr.SetAll(stepSize, valueAtX, valueAtNextPoint, gradAtX, gradAtNextPoint, x, nextPoint, currFctEvalCount);
}
private bool IsConverged(LineSearchResult lsr)
{
// Check function's change rate
if (lsr.FuncChangeRate < ConvergeTolerance)
{
if (monitor != null)
{
Display("Function change rate is smaller than the threshold " + ConvergeTolerance + ".\nTraining will stop.\n\n");
}
return(true);
}
// Check gradient's norm using the criteria: ||g(x)|| / max(1, ||x||) < threshold
var xNorm = Math.Max(1, ArrayMath.L2Norm(lsr.NextPoint));
var gradNorm = l1Cost > 0 ? ArrayMath.L2Norm(lsr.PseudoGradAtNext) : ArrayMath.L2Norm(lsr.GradAtNext);
if (gradNorm / xNorm < RelGradNormTol)
{
if (monitor != null)
{
Display("Relative L2-norm of the gradient is smaller than the threshold "
+ RelGradNormTol + ".\nTraining will stop.\n\n");
}
return(true);
}
// Check step size
if (lsr.StepSize < MinStepSize)
{
if (monitor != null)
{
Display("Step size is smaller than the minimum step size "
+ MinStepSize + ".\nTraining will stop.\n\n");
}
return(true);
}
// Check number of function evaluations
if (lsr.FctEvalCount > maxFctEval)
{
if (monitor != null)
{
Display("Maximum number of function evaluations has exceeded the threshold "
+ maxFctEval + ".\nTraining will stop.\n\n");
}
return(true);
}
return(false);
}
private void GradientCompute(int threadIndex, int startIndex, int length, double[] x)
{
var exp = new double[numOutcomes];
// Reset gradientThread
Array.Clear(gradientThread[threadIndex], 0, gradientThread[threadIndex].Length);
for (var ci = startIndex; ci < startIndex + length; ci++)
{
double predValue;
int vectorIndex;
for (var oi = 0; oi < numOutcomes; oi++)
{
exp[oi] = 0;
for (var ai = 0; ai < contexts[ci].Length; ai++)
{
vectorIndex = IndexOf(oi, contexts[ci][ai]);
predValue = values != null ? values[ci][ai] : 1.0;
exp[oi] += predValue * x[vectorIndex];
}
}
var logSumOfExps = ArrayMath.LogSumOfExps(exp);
for (var oi = 0; oi < numOutcomes; oi++)
{
exp[oi] = Math.Exp(exp[oi] - logSumOfExps);
}
for (var oi = 0; oi < numOutcomes; oi++)
{
var empirical = outcomeList[ci] == oi ? 1 : 0;
for (var ai = 0; ai < contexts[ci].Length; ai++)
{
vectorIndex = IndexOf(oi, contexts[ci][ai]);
predValue = values != null ? values[ci][ai] : 1.0;
gradientThread[threadIndex][vectorIndex] += predValue * (exp[oi] - empirical) *
numTimesEventsSeen[ci];
}
}
}
}
/// <summary>
/// Model evaluation which should be used during training to report model accuracy.
/// </summary>
/// <param name="context">Indices of the predicates which have been observed at the present decision point.</param>
/// <param name="values">The weights of the predicates which have been observed at the present decision point.</param>
/// <param name="probs">The probability for outcomes.</param>
/// <param name="nOutcomes">The number of outcomes.</param>
/// <param name="nPredLabels">The number of unique predicates.</param>
/// <param name="parameters">The model parameters.</param>
/// <returns>The normalized probabilities for the outcomes given the context.</returns>
public static double[] Eval(int[] context, float[] values, double[] probs, int nOutcomes, int nPredLabels, double[] parameters)
{
for (var i = 0; i < context.Length; i++)
{
var predIdx = context[i];
var predValue = values != null ? values[i] : 1d;
for (var oi = 0; oi < nOutcomes; oi++)
{
probs[oi] += predValue * parameters[oi * nPredLabels + predIdx];
}
}
var logSumExp = ArrayMath.LogSumOfExps(probs);
for (var oi = 0; oi < nOutcomes; oi++)
{
probs[oi] = Math.Exp(probs[oi] - logSumExp);
}
return(probs);
}
/// <summary>
/// Model evaluation which should be used during inference.
/// </summary>
/// <param name="context">The predicates which have been observed at the present decision point.</param>
/// <param name="values">The weights of the predicates which have been observed at the present decision point.</param>
/// <param name="probs">The probability for outcomes.</param>
/// <returns>The normalized probabilities for the outcomes given the context.</returns>
public double[] Eval(string[] context, float[] values, double[] probs)
{
var ep = evalParameters.Parameters;
for (var ci = 0; ci < context.Length; ci++)
{
var predIdx = GetPredIndex(context[ci]);
if (predIdx < 0)
{
continue;
}
var predValue = 1d;
if (values != null)
{
predValue = values[ci];
}
var outcomes = ep[predIdx].Outcomes;
var parameters = ep[predIdx].Parameters;
for (var i = 0; i < outcomes.Length; i++)
{
probs[outcomes[i]] += predValue * parameters[i];
}
}
var logSumExp = ArrayMath.LogSumOfExps(probs);
for (var oi = 0; oi < outcomeNames.Length; oi++)
{
probs[oi] = Math.Exp(probs[oi] - logSumExp);
}
return(probs);
}
/// <summary>
/// Constrained line search (see section 3.2 in the paper "Scalable Training of L1-Regularized Log-Linear Models", Andrew et al. 2007)
/// </summary>
/// <param name="function">The function.</param>
/// <param name="direction">The direction.</param>
/// <param name="lsr">The line search result.</param>
/// <param name="l1Cost">The l1 cost.</param>
/// <param name="initialStepSize">Initial size of the step.</param>
public static void DoConstrainedLineSearch(
IFunction function,
double[] direction,
LineSearchResult lsr,
double l1Cost,
double initialStepSize)
{
var stepSize = initialStepSize;
var currFctEvalCount = lsr.FctEvalCount;
var x = lsr.NextPoint;
var signX = lsr.SignVector; // existing sign vector
var gradAtX = lsr.GradAtNext;
var pseudoGradAtX = lsr.PseudoGradAtNext;
var valueAtX = lsr.ValueAtNext;
var dimension = x.Length;
// Retrieve current points and gradient for array reuse purpose
var nextPoint = lsr.CurrPoint;
var gradAtNextPoint = lsr.GradAtCurr;
double valueAtNextPoint;
// New sign vector
for (var i = 0; i < dimension; i++)
{
signX[i] = x[i].Equals(0d) ? -pseudoGradAtX[i] : x[i];
}
while (true)
{
// Get next point
for (var i = 0; i < dimension; i++)
{
nextPoint[i] = x[i] + direction[i] * stepSize;
}
// Projection
for (var i = 0; i < dimension; i++)
{
if (nextPoint[i] * signX[i] <= 0)
{
nextPoint[i] = 0;
}
}
// New value
valueAtNextPoint = function.ValueAt(nextPoint) + l1Cost * ArrayMath.L1Norm(nextPoint);
currFctEvalCount++;
double dirGradientAtX = 0;
for (var i = 0; i < dimension; i++)
{
dirGradientAtX += (nextPoint[i] - x[i]) * pseudoGradAtX[i];
}
// Check the sufficient decrease condition
if (valueAtNextPoint <= valueAtX + C * dirGradientAtX)
{
break;
}
// Shrink step size
stepSize *= RHO;
}
// Compute and save gradient at the new point
Array.Copy(function.GradientAt(nextPoint), 0, gradAtNextPoint, 0, gradAtNextPoint.Length);
// Update line search result
lsr.SetAll(stepSize, valueAtX, valueAtNextPoint, gradAtX, gradAtNextPoint, pseudoGradAtX, x, nextPoint,
signX, currFctEvalCount);
}
/// <summary>
/// Find the parameters that minimize the objective function.
/// </summary>
/// <param name="function">The objective function.</param>
/// <returns>The minimizing parameters.</returns>
/// <exception cref="OperationCanceledException">Occurs when the evaluation monitor cancels the operation.</exception>
public double[] Minimize(IFunction function)
{
var l2RegFunction = new L2RegFunction(function, l2Cost);
dimension = l2RegFunction.Dimension;
updateInfo = new UpdateInfo(updates, dimension);
// Current point is at the origin
var currPoint = new double[dimension];
var currValue = l2RegFunction.ValueAt(currPoint);
// Gradient at the current point
var currGrad = new double[dimension];
Array.Copy(l2RegFunction.GradientAt(currPoint), 0, currGrad, 0, dimension);
// Pseudo-gradient - only use when L1-regularization is enabled
double[] pseudoGrad = null;
if (l1Cost > 0)
{
currValue += l1Cost * ArrayMath.L1Norm(currPoint);
pseudoGrad = new double[dimension];
ComputePseudoGrad(currPoint, currGrad, pseudoGrad);
}
var lsr = l1Cost > 0
? LineSearchResult.GetInitialObjectForL1(currValue, currGrad, pseudoGrad, currPoint)
: LineSearchResult.GetInitialObject(currValue, currGrad, currPoint);
if (monitor != null)
{
Display("\nSolving convex optimization problem.");
Display("\nObjective function has " + dimension + " variable(s).");
Display("\n\nPerforming " + iterations + " iterations with " + "L1Cost=" + l1Cost + " and L2Cost=" + l2Cost + "\n");
}
var direction = new double[dimension];
var startTime = DateTime.Now;
var token = monitor != null ? monitor.Token : CancellationToken.None;
// Initial step size for the 1st iteration
var initialStepSize = l1Cost > 0
? ArrayMath.InvL2Norm(lsr.PseudoGradAtNext)
: ArrayMath.InvL2Norm(lsr.GradAtNext);
for (var iteration = 1; iteration <= iterations; iteration++)
{
// cancel if requested
token.ThrowIfCancellationRequested();
// Find direction
Array.Copy(l1Cost > 0
? lsr.PseudoGradAtNext
: lsr.GradAtNext, 0, direction, 0, direction.Length);
ComputeDirection(direction);
// Line search
if (l1Cost > 0)
{
// Constrain the search direction
pseudoGrad = lsr.PseudoGradAtNext;
for (var i = 0; i < dimension; i++)
{
if (direction[i] * pseudoGrad[i] >= 0)
{
direction[i] = 0;
}
}
LineSearch.DoConstrainedLineSearch(l2RegFunction, direction, lsr, l1Cost, initialStepSize);
ComputePseudoGrad(lsr.NextPoint, lsr.GradAtNext, pseudoGrad);
lsr.PseudoGradAtNext = pseudoGrad;
}
else
{
LineSearch.DoLineSearch(l2RegFunction, direction, lsr, initialStepSize);
}
// Save Hessian updates
updateInfo.Update(lsr);
if (monitor != null)
{
if (iteration < 10)
{
Display(" " + iteration + ": ");
}
else if (iteration < 100)
{
Display(" " + iteration + ": ");
}
else
{
Display(iteration + ": ");
}
if (Evaluator != null)
{
Display("\t" + lsr.ValueAtNext
+ "\t" + lsr.FuncChangeRate
+ "\t" + Evaluator.Evaluate(lsr.NextPoint) + "\n");
}
else
{
Display("\t " + lsr.ValueAtNext +
"\t" + lsr.FuncChangeRate + "\n");
}
}
if (IsConverged(lsr))
{
break;
}
initialStepSize = InitialStepSize;
}
// Undo L2-shrinkage if Elastic Net is used (since in that case, the shrinkage is done twice)
//
// Knuppe: The original code makes no sense, so I change the NextPoint value!
//
// if (l1Cost > 0 && l2Cost > 0) {
// double[] x = lsr.getNextPoint();
// for (int i = 0; i < dimension; i++) {
// x[i] = Math.sqrt(1 + l2Cost) * x[i];
// }
// }
if (l1Cost > 0 && l2Cost > 0)
{
for (var i = 0; i < dimension; i++)
{
lsr.NextPoint[i] = Math.Sqrt(1 + l2Cost) * lsr.NextPoint[i];
}
}
if (monitor != null)
{
var endTime = DateTime.Now;
var duration = endTime - startTime;
Display("Running time: " + duration.TotalSeconds + "s\n");
}
// Release memory
updateInfo = null;
// Avoid returning the reference to LineSearchResult's member so that GC can
// collect memory occupied by lsr after this function completes (is it necessary?)
// double[] parameters = new double[dimension];
// System.arraycopy(lsr.getNextPoint(), 0, parameters, 0, dimension);
return(lsr.NextPoint);
}