Method EvalSingleQuery

catboost/libs/metrics/metric.cpp:3227–3287

}

TMetricHolder TQuerySoftMaxMetric::EvalSingleQuery(
    int start,
    int count,
    TConstArrayRef<double> approxesRef,
    TConstArrayRef<TConstArrayRef<double>> approxDeltaRef,
    bool isExpApprox,
    TConstArrayRef<float> targets,
    TConstArrayRef<float> weights,
    TArrayRef<double> softmax
) const {
    Y_ASSERT(!isExpApprox);
    const auto impl = [=, this] (auto hasDelta, auto hasWeight) {
        TConstArrayRef<double> approx = approxesRef;
        TConstArrayRef<double> approxDelta = GetRowRef(approxDeltaRef, /*rowIdx*/0);
        double sumWeightedTargets = 0;
        for (int dim : xrange(count)) {
            if (targets[start + dim] > 0) {
                const double weight = hasWeight ? weights[start + dim] : 1;
                sumWeightedTargets += weight * targets[start + dim];
            }
        }
        TMetricHolder error(2);
        if (sumWeightedTargets <= 0) {
            return error;
        }
        error.Stats[1] = sumWeightedTargets;

        for (int dim : xrange(count)) {
            const double delta = hasDelta ? approxDelta[start + dim] : 0;
            softmax[dim] = Beta * (approx[start + dim] + delta);
        }
        double maxApprox = -std::numeric_limits<double>::max();
        for (int dim : xrange(count)) {
            if (!hasWeight || weights[start + dim] > 0) {
                maxApprox = Max(maxApprox, softmax[dim]);
            }
        }
        for (int dim : xrange(count)) {
            softmax[dim] -= maxApprox;
        }
        FastExpWithInfInplace(softmax.data(), count);
        double sumExpApprox = 0;
        for (int dim : xrange(count)) {
            const double weight = hasWeight ? weights[start + dim] : 1;
            if (weight > 0) {
                softmax[dim] *= weight;
                sumExpApprox += softmax[dim];
            }
        }
        for (int dim : xrange(count)) {
            if (targets[start + dim] > 0) {
                const double weight = hasWeight ? weights[start + dim] : 1;
                if (weight > 0) {
                    error.Stats[0] -= weight * targets[start + dim] * log(softmax[dim] / sumExpApprox);
                }
            }
        }
        return error;