double CAdaBoost::Deviance(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate) {
double loss = 0.0;
double weight = 0.0;
// Switch to validation set if necessary
unsigned long num_of_rows_in_set = kData.get_size_of_set();
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : loss, weight) num_threads(get_num_threads())
for (unsigned long i = 0; i < num_of_rows_in_set; i++) {
loss += kData.weight_ptr()[i] *
std::exp(-(2 * kData.y_ptr()[i] - 1) *
(kData.offset_ptr()[i] + kFuncEstimate[i]));
weight += kData.weight_ptr()[i];
}
// TODO: Check if weights are all zero for validation set
if ((weight == 0.0) && (loss == 0.0)) {
return nan("");
} else if (weight == 0.0) {
return HUGE_VAL;
}
return loss / weight;
}
double CPoisson::InitF(const CDataset& kData) {
double sum = 0.0;
double denom = 0.0;
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : sum, denom) num_threads(get_num_threads())
for (unsigned long i = 0; i < kData.get_trainsize(); i++) {
sum += kData.weight_ptr()[i] * kData.y_ptr()[i];
denom += kData.weight_ptr()[i] * std::exp(kData.offset_ptr()[i]);
}
return std::log(sum / denom);
}
double CGaussian::InitF(const CDataset& kData) {
double sum = 0.0;
double totalweight = 0.0;
// compute the mean
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : sum, totalweight) num_threads(get_num_threads())
for (unsigned long i = 0; i < kData.get_trainsize(); i++) {
sum += kData.weight_ptr()[i] * (kData.y_ptr()[i] - kData.offset_ptr()[i]);
totalweight += kData.weight_ptr()[i];
}
return sum / totalweight;
}
double CAdaBoost::InitF(const CDataset& kData) {
double numerator = 0.0;
double denominator = 0.0;
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : numerator, denominator) num_threads(get_num_threads())
for (unsigned long i = 0; i < kData.get_trainsize(); i++) {
if (kData.y_ptr()[i] == 1.0) {
numerator += kData.weight_ptr()[i] * std::exp(-kData.offset_ptr()[i]);
} else {
denominator += kData.weight_ptr()[i] * std::exp(kData.offset_ptr()[i]);
}
}
return 0.5 * std::log(numerator / denominator);
}
void CPoisson::FitBestConstant(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate,
unsigned long num_terminalnodes,
std::vector<double>& residuals,
CCARTTree& tree) {
unsigned long obs_num = 0;
unsigned long node_num = 0;
vector<double> numerator_vec(num_terminalnodes, 0.0);
vector<double> denominator_vec(num_terminalnodes, 0.0);
vector<double> max_vec(num_terminalnodes, -HUGE_VAL);
vector<double> min_vec(num_terminalnodes, HUGE_VAL);
for (obs_num = 0; obs_num < kData.get_trainsize(); obs_num++) {
if (kBag.get_element(obs_num)) {
numerator_vec[tree.get_node_assignments()[obs_num]] +=
kData.weight_ptr()[obs_num] * kData.y_ptr()[obs_num];
denominator_vec[tree.get_node_assignments()[obs_num]] +=
kData.weight_ptr()[obs_num] *
std::exp(kData.offset_ptr()[obs_num] + kFuncEstimate[obs_num]);
}
}
for (node_num = 0; node_num < num_terminalnodes; node_num++) {
if (tree.has_node(node_num)) {
if (numerator_vec[node_num] == 0.0) {
// DEBUG: if vecdNum==0 then prediction = -Inf
// Not sure what else to do except plug in an arbitrary
// negative number, -1? -10? Let's use -1, then make
// sure |adF| < 19 always.
tree.get_terminal_nodes()[node_num]->set_prediction(-19.0);
} else if (denominator_vec[node_num] == 0.0) {
tree.get_terminal_nodes()[node_num]->set_prediction(0.0);
} else {
tree.get_terminal_nodes()[node_num]->set_prediction(
std::log(numerator_vec[node_num] / denominator_vec[node_num]));
}
tree.get_terminal_nodes()[node_num]->set_prediction(
R::fmin2(tree.get_terminal_nodes()[node_num]->get_prediction(),
19 - max_vec[node_num]));
tree.get_terminal_nodes()[node_num]->set_prediction(
R::fmax2(tree.get_terminal_nodes()[node_num]->get_prediction(),
-19 - min_vec[node_num]));
}
}
}
double CGaussian::BagImprovement(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate,
const double kShrinkage,
const std::vector<double>& kDeltaEstimate) {
double returnvalue = 0.0;
double weight = 0.0;
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : returnvalue, weight) num_threads(get_num_threads())
for (unsigned long i = 0; i < kData.get_trainsize(); i++) {
if (!kBag.get_element(i)) {
const double deltafunc_est = kFuncEstimate[i] + kData.offset_ptr()[i];
returnvalue += kData.weight_ptr()[i] * kShrinkage * kDeltaEstimate[i] *
(2.0 * (kData.y_ptr()[i] - deltafunc_est) -
kShrinkage * kDeltaEstimate[i]);
weight += kData.weight_ptr()[i];
}
}
return returnvalue / weight;
}
void CGaussian::ComputeWorkingResponse(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate,
std::vector<double>& residuals) {
if (!(kData.y_ptr() && kFuncEstimate &&
kData.weight_ptr())) {
throw gbm_exception::InvalidArgument();
}
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
num_threads(get_num_threads())
for (unsigned long i = 0; i < kData.get_trainsize(); i++) {
residuals[i] = kData.y_ptr()[i] - kData.offset_ptr()[i] - kFuncEstimate[i];
}
}
void CAdaBoost::FitBestConstant(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate,
unsigned long num_terminalnodes,
std::vector<double>& residuals,
CCARTTree& tree) {
unsigned long obs_num = 0;
unsigned long node_num = 0;
numerator_bestconstant_.resize(num_terminalnodes);
numerator_bestconstant_.assign(numerator_bestconstant_.size(), 0.0);
denominator_bestconstant_.resize(num_terminalnodes);
denominator_bestconstant_.assign(denominator_bestconstant_.size(), 0.0);
for (obs_num = 0; obs_num < kData.get_trainsize(); obs_num++) {
if (kBag.get_element(obs_num)) {
const double deltafunc_est =
kFuncEstimate[obs_num] + kData.offset_ptr()[obs_num];
numerator_bestconstant_[tree.get_node_assignments()[obs_num]] +=
kData.weight_ptr()[obs_num] * (2 * kData.y_ptr()[obs_num] - 1) *
std::exp(-(2 * kData.y_ptr()[obs_num] - 1) * deltafunc_est);
denominator_bestconstant_[tree.get_node_assignments()[obs_num]] +=
kData.weight_ptr()[obs_num] *
std::exp(-(2 * kData.y_ptr()[obs_num] - 1) * deltafunc_est);
}
}
for (node_num = 0; node_num < num_terminalnodes; node_num++) {
if (tree.has_node(node_num)) {
if (denominator_bestconstant_[node_num] == 0) {
tree.get_terminal_nodes()[node_num]->set_prediction(0.0);
} else {
tree.get_terminal_nodes()[node_num]->set_prediction(
numerator_bestconstant_[node_num] /
denominator_bestconstant_[node_num]);
}
}
}
}
double CGaussian::Deviance(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate) {
double loss = 0.0;
double weight = 0.0;
unsigned long num_rows_in_set = kData.get_size_of_set();
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : loss, weight) num_threads(get_num_threads())
for (unsigned long i = 0; i < num_rows_in_set; i++) {
const double tmp =
(kData.y_ptr()[i] - kData.offset_ptr()[i] - kFuncEstimate[i]);
loss += kData.weight_ptr()[i] * tmp * tmp;
weight += kData.weight_ptr()[i];
}
// TODO: Check if weights are all zero for validation set
if ((weight == 0.0) && (loss == 0.0)) {
return nan("");
} else if (weight == 0.0) {
return copysign(HUGE_VAL, loss);
}
return loss / weight;
}