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Additional improvements for gblinear (#3134)

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* fix rebase conflict

* [core] additional gblinear improvements

* [R] callback for gblinear coefficients history

* force eta=1 for gblinear python tests

* add top_k to GreedyFeatureSelector

* set eta=1 in shotgun test

* [core] fix SparsePage processing in gblinear; col-wise multithreading in greedy updater

* set sorted flag within TryInitColData

* gblinear tests: use scale, add external memory test

* fix multiclass for greedy updater

* fix whitespace

* fix typo
This commit is contained in:
Vadim Khotilovich
2018-03-13 01:27:13 -05:00
committed by GitHub
parent a1b48afa41
commit 706be4e5d4
18 changed files with 750 additions and 260 deletions
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59
src/gbm/gblinear.cc
View File

@@ -21,14 +21,12 @@ namespace gbm {
DMLC_REGISTRY_FILE_TAG(gblinear);
// training parameter
// training parameters
struct GBLinearTrainParam : public dmlc::Parameter<GBLinearTrainParam> {
/*! \brief learning_rate */
std::string updater;
// flag to print out detailed breakdown of runtime
int debug_verbose;
float tolerance;
// declare parameters
size_t max_row_perbatch;
int debug_verbose;
DMLC_DECLARE_PARAMETER(GBLinearTrainParam) {
DMLC_DECLARE_FIELD(updater)
.set_default("shotgun")
@@ -37,6 +35,9 @@ struct GBLinearTrainParam : public dmlc::Parameter<GBLinearTrainParam> {
.set_lower_bound(0.0f)
.set_default(0.0f)
.describe("Stop if largest weight update is smaller than this number.");
DMLC_DECLARE_FIELD(max_row_perbatch)
.set_default(std::numeric_limits<size_t>::max())
.describe("Maximum rows per batch.");
DMLC_DECLARE_FIELD(debug_verbose)
.set_lower_bound(0)
.set_default(0)
@@ -84,12 +85,10 @@ class GBLinear : public GradientBooster {
if (!p_fmat->HaveColAccess(false)) {
std::vector<bool> enabled(p_fmat->info().num_col, true);
p_fmat->InitColAccess(enabled, 1.0f, std::numeric_limits<size_t>::max(),
false);
p_fmat->InitColAccess(enabled, 1.0f, param.max_row_perbatch, false);
}
model.LazyInitModel();
this->LazySumWeights(p_fmat);
if (!this->CheckConvergence()) {
@@ -191,40 +190,7 @@ class GBLinear : public GradientBooster {
std::vector<std::string> DumpModel(const FeatureMap& fmap,
bool with_stats,
std::string format) const override {
const int ngroup = model.param.num_output_group;
const unsigned nfeature = model.param.num_feature;
std::stringstream fo("");
if (format == "json") {
fo << " { \"bias\": [" << std::endl;
for (int gid = 0; gid < ngroup; ++gid) {
if (gid != 0) fo << "," << std::endl;
fo << " " << model.bias()[gid];
}
fo << std::endl << " ]," << std::endl
<< " \"weight\": [" << std::endl;
for (unsigned i = 0; i < nfeature; ++i) {
for (int gid = 0; gid < ngroup; ++gid) {
if (i != 0 || gid != 0) fo << "," << std::endl;
fo << " " << model[i][gid];
}
}
fo << std::endl << " ]" << std::endl << " }";
} else {
fo << "bias:\n";
for (int gid = 0; gid < ngroup; ++gid) {
fo << model.bias()[gid] << std::endl;
}
fo << "weight:\n";
for (unsigned i = 0; i < nfeature; ++i) {
for (int gid = 0; gid < ngroup; ++gid) {
fo << model[i][gid] << std::endl;
}
}
}
std::vector<std::string> v;
v.push_back(fo.str());
return v;
return model.DumpModel(fmap, with_stats, format);
}
protected:
@@ -272,9 +238,12 @@ class GBLinear : public GradientBooster {
bool CheckConvergence() {
if (param.tolerance == 0.0f) return false;
if (is_converged) return true;
if (previous_model.weight.size() != model.weight.size()) return false;
if (previous_model.weight.size() != model.weight.size()) {
previous_model = model;
return false;
}
float largest_dw = 0.0;
for (auto i = 0; i < model.weight.size(); i++) {
for (size_t i = 0; i < model.weight.size(); i++) {
largest_dw = std::max(
largest_dw, std::abs(model.weight[i] - previous_model.weight[i]));
}
@@ -287,7 +256,7 @@ class GBLinear : public GradientBooster {
void LazySumWeights(DMatrix *p_fmat) {
if (!sum_weight_complete) {
auto &info = p_fmat->info();
for (int i = 0; i < info.num_row; i++) {
for (size_t i = 0; i < info.num_row; i++) {
sum_instance_weight += info.GetWeight(i);
}
sum_weight_complete = true;

40
src/gbm/gblinear_model.h
View File

@@ -4,7 +4,9 @@
#pragma once
#include <dmlc/io.h>
#include <dmlc/parameter.h>
#include <xgboost/feature_map.h>
#include <vector>
#include <string>
#include <cstring>
namespace xgboost {
@@ -68,6 +70,44 @@ class GBLinearModel {
inline const bst_float* operator[](size_t i) const {
return &weight[i * param.num_output_group];
}
std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
std::string format) const {
const int ngroup = param.num_output_group;
const unsigned nfeature = param.num_feature;
std::stringstream fo("");
if (format == "json") {
fo << " { \"bias\": [" << std::endl;
for (int gid = 0; gid < ngroup; ++gid) {
if (gid != 0) fo << "," << std::endl;
fo << " " << this->bias()[gid];
}
fo << std::endl << " ]," << std::endl
<< " \"weight\": [" << std::endl;
for (unsigned i = 0; i < nfeature; ++i) {
for (int gid = 0; gid < ngroup; ++gid) {
if (i != 0 || gid != 0) fo << "," << std::endl;
fo << " " << (*this)[i][gid];
}
}
fo << std::endl << " ]" << std::endl << " }";
} else {
fo << "bias:\n";
for (int gid = 0; gid < ngroup; ++gid) {
fo << this->bias()[gid] << std::endl;
}
fo << "weight:\n";
for (unsigned i = 0; i < nfeature; ++i) {
for (int gid = 0; gid < ngroup; ++gid) {
fo << (*this)[i][gid] << std::endl;
}
}
}
std::vector<std::string> v;
v.push_back(fo.str());
return v;
}
};
} // namespace gbm
} // namespace xgboost