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Merge pull request #139 from forestry-labs/sc/hier-shrinkage
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Sc/hier shrinkage
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@edwardwliu
edwardwliu authored Aug 7, 2023
2 parents 174e54a + 15d502c commit ff265e0
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Showing 25 changed files with 967 additions and 181 deletions.
80 changes: 50 additions & 30 deletions Python/extension/api.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -260,7 +260,9 @@ extern "C" {
size_t num_test_rows,
std::vector<double>& predictions,
std::vector<double>& weight_matrix,
std::vector<double>& coefs
std::vector<double>& coefs,
bool hier_shrinkage,
double lambda_shrinkage
) {


Expand Down Expand Up @@ -311,7 +313,9 @@ extern "C" {
nthread,
exact,
false,
nullptr
nullptr,
hier_shrinkage,
lambda_shrinkage
);

size_t idx = 0;
Expand Down Expand Up @@ -356,7 +360,9 @@ extern "C" {
nthread,
exact,
use_weights,
weights
weights,
hier_shrinkage,
lambda_shrinkage
);

}
Expand All @@ -379,7 +385,9 @@ extern "C" {
bool verbose,
std::vector<double>& predictions,
std::vector<double>& weight_matrix,
std::vector<size_t> training_idx
std::vector<size_t> training_idx,
bool hier_shrinkage,
double lambda_shrinkage
) {
if (verbose)
std::cout << forest_pt << std::endl;
Expand Down Expand Up @@ -416,7 +424,9 @@ extern "C" {
&treeCounts,
doubleOOB,
exact,
training_idx_use
training_idx_use,
hier_shrinkage,
lambda_shrinkage
);

size_t idx = 0;
Expand All @@ -435,7 +445,9 @@ extern "C" {
nullptr,
doubleOOB,
exact,
training_idx_use
training_idx_use,
hier_shrinkage,
lambda_shrinkage
);
}

Expand All @@ -457,21 +469,21 @@ extern "C" {

info_holder = forest->getForest()->at(tree_idx)->getTreeInfo(forest->getTrainingData());
int num_nodes = forest->getForest()->at(tree_idx)->getNodeCount();
int num_leaf_nodes = forest->getForest()->at(tree_idx)->getLeafNodeCount();

for (int i = 0; i < num_nodes + num_leaf_nodes; i++) {
for (int i = 0; i < num_nodes; i++) {
treeInfo[i] = (double)info_holder->var_id.at(i);
}

for (int i = 0; i < num_leaf_nodes; i++) {
treeInfo[num_nodes + num_leaf_nodes + i] = info_holder->values.at(i);
for (int i = 0; i < num_nodes; i++) {
treeInfo[num_nodes + i] = info_holder->values.at(i);
}

for (int i = 0; i < num_nodes; i++) {
treeInfo[num_nodes + num_leaf_nodes*2 + i] = info_holder->split_val.at(i);
treeInfo[num_nodes *2 + num_leaf_nodes*2 + i] = (double)info_holder->naLeftCount.at(i);
treeInfo[num_nodes *3 + num_leaf_nodes*2 + i] = (double)info_holder->naRightCount.at(i);
treeInfo[num_nodes *4 + num_leaf_nodes*2 + i] = (double)info_holder->naDefaultDirection.at(i);
treeInfo[num_nodes *2 + i] = info_holder->split_val.at(i);
treeInfo[num_nodes *3 + i] = (double)info_holder->naLeftCount.at(i);
treeInfo[num_nodes *4 + i] = (double)info_holder->naRightCount.at(i);
treeInfo[num_nodes *5 + i] = (double)info_holder->naDefaultDirection.at(i);
treeInfo[num_nodes *6 + i] = (double)info_holder->average_count.at(i);
treeInfo[num_nodes *7 + i] = (double)info_holder->split_count.at(i);
}

// Populate splitting samples for the tree
Expand All @@ -488,7 +500,7 @@ extern "C" {
av_info[i+1] = info_holder->averagingSampleIndex.at(i);
}

treeInfo[num_nodes *5 + num_leaf_nodes*2] = info_holder->seed;
treeInfo[num_nodes *8] = info_holder->seed;
}


Expand Down Expand Up @@ -578,6 +590,12 @@ extern "C" {
std::unique_ptr< std::vector< std::vector<int> > > var_ids(
new std::vector< std::vector<int> >
);
std::unique_ptr< std::vector< std::vector<int> > > average_counts(
new std::vector< std::vector<int> >
);
std::unique_ptr< std::vector< std::vector<int> > > split_counts(
new std::vector< std::vector<int> >
);
std::unique_ptr< std::vector< std::vector<double> > > split_vals(
new std::vector< std::vector<double> >
);
Expand Down Expand Up @@ -608,6 +626,8 @@ extern "C" {

// Reserve space for each of the vectors equal to ntree
var_ids->reserve(ntree);
average_counts->reserve(ntree);
split_counts->reserve(ntree);
split_vals->reserve(ntree);
averagingSampleIndex->reserve(ntree);
splittingSampleIndex->reserve(ntree);
Expand All @@ -619,36 +639,32 @@ extern "C" {
predictWeights->reserve(ntree);

// Now actually populate the vectors
size_t ind = 0, ind_s = 0, ind_a = 0, ind_var = 0, ind_weights = 0;
size_t ind = 0, ind_s = 0, ind_a = 0;
for(size_t i = 0; i < ntree; i++){
// Should be num total nodes + num leaf nodes
std::vector<int> cur_var_ids((tree_counts[4*i]+tree_counts[4*i+3]), 0);
// Should be num total nodes
std::vector<int> cur_var_ids((tree_counts[4*i]), 0);
std::vector<int> cur_average_counts((tree_counts[4*i]), 0);
std::vector<int> cur_split_counts((tree_counts[4*i]), 0);
std::vector<double> cur_split_vals(tree_counts[4*i], 0);
std::vector<int> curNaLeftCounts(tree_counts[4*i], 0);
std::vector<int> curNaRightCounts(tree_counts[4*i], 0);
std::vector<int> curNaDefaultDirections(tree_counts[4*i], 0);
std::vector<size_t> curSplittingSampleIndex(tree_counts[4*i+1], 0);
std::vector<size_t> curAveragingSampleIndex(tree_counts[4*i+2], 0);
std::vector<double> cur_predict_weights(tree_counts[4*i+3], 0);
std::vector<double> cur_predict_weights(tree_counts[4*i], 0);

for(size_t j = 0; j < tree_counts[4*i]; j++){
cur_split_vals.at(j) = thresholds[ind];
curNaLeftCounts.at(j) = na_left_count[ind];
curNaRightCounts.at(j) = na_right_count[ind];
curNaDefaultDirections.at(j) = na_default_directions[ind];
cur_predict_weights.at(j) = predict_weights[ind];
cur_var_ids.at(j) = features[ind];
cur_average_counts.at(j) = features[ind];
cur_split_counts.at(j) = features[ind];

ind++;
}

for (size_t j = 0; j < tree_counts[4*i+3]; j++){
cur_predict_weights.at(j) = predict_weights[ind_weights];
ind_weights++;
}

for (size_t j = 0; j < (tree_counts[4*i]+tree_counts[4*i+3]); j++) {
cur_var_ids.at(j) = features[ind_var];
ind_var++;
}

for(size_t j = 0; j < tree_counts[4*i+1]; j++){
curSplittingSampleIndex.at(j) = split_idx[ind_s];
Expand All @@ -661,6 +677,8 @@ extern "C" {
}

var_ids->push_back(cur_var_ids);
average_counts->push_back(cur_average_counts);
split_counts->push_back(cur_split_counts);
split_vals->push_back(cur_split_vals);
naLeftCounts->push_back(curNaLeftCounts);
naRightCounts->push_back(curNaRightCounts);
Expand All @@ -677,6 +695,8 @@ extern "C" {
categoricalFeatureCols_copy,
treeSeeds,
var_ids,
average_counts,
split_counts,
split_vals,
naLeftCounts,
naRightCounts,
Expand Down
8 changes: 6 additions & 2 deletions Python/extension/api.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,9 @@ extern "C" {
bool verbose,
std::vector<double>& predictions,
std::vector<double>& weight_matrix,
std::vector<size_t> training_idx
std::vector<size_t> training_idx,
bool hier_shrinkage,
double lambda_shrinkage
);
void predict_forest(
void* forest_pt,
Expand All @@ -126,7 +128,9 @@ extern "C" {
size_t num_test_rows,
std::vector<double>& predictions,
std::vector<double>& weight_matrix,
std::vector<double>& coefs
std::vector<double>& coefs,
bool hier_shrinkage = false,
double lambda_shrinkage = 0
);
void fill_tree_info(
void* forest_ptr,
Expand Down
16 changes: 12 additions & 4 deletions Python/extension/main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -176,7 +176,9 @@ py::tuple predictOOB_forest_wrapper(
bool use_training_idx,
unsigned int n_preds,
unsigned int n_weight_matrix,
py::array_t<size_t> training_idx
py::array_t<size_t> training_idx,
bool hier_shrinkage,
double lambda_shrinkage
) {
py::array_t<double> predictions = create_numpy_array(n_preds);
std::vector<double> predictions_vector(n_preds);
Expand All @@ -199,7 +201,9 @@ py::tuple predictOOB_forest_wrapper(
verbose,
predictions_vector,
weight_matrix_vector,
training_idx_vector
training_idx_vector,
hier_shrinkage,
lambda_shrinkage
);

copy_vector_to_numpy_array(predictions_vector, predictions);
Expand Down Expand Up @@ -241,7 +245,9 @@ py::tuple predict_forest_wrapper(
size_t num_test_rows,
unsigned int n_preds,
unsigned int n_weight_matrix,
unsigned int n_coefficients
unsigned int n_coefficients,
bool hier_shrinkage,
double lambda_shrinkage
) {
py::array_t<double> predictions = create_numpy_array(n_preds);
std::vector<double> predictions_vector(n_preds);
Expand All @@ -266,7 +272,9 @@ py::tuple predict_forest_wrapper(
num_test_rows,
predictions_vector,
weight_matrix_vector,
coefficients_vector
coefficients_vector,
hier_shrinkage,
lambda_shrinkage
);

copy_vector_to_numpy_array(predictions_vector, predictions);
Expand Down
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