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lint learner finish

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This commit is contained in:
tqchen 2015-07-03 19:20:45 -07:00
parent 1581de08da
commit aba41d07cd
8 changed files with 127 additions and 112 deletions
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14
src/learner/dmatrix.h
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@ -1,11 +1,13 @@
#ifndef XGBOOST_LEARNER_DMATRIX_H_
#define XGBOOST_LEARNER_DMATRIX_H_
/*!
* Copyright 2014 by Contributors
* \file dmatrix.h
* \brief meta data and template data structure
* used for regression/classification/ranking
* \author Tianqi Chen
*/
#ifndef XGBOOST_LEARNER_DMATRIX_H_
#define XGBOOST_LEARNER_DMATRIX_H_
#include <vector>
#include <cstring>
#include "../data.h"
@ -66,7 +68,7 @@ struct MetaInfo {
return 1.0f;
}
}
inline void SaveBinary(utils::IStream &fo) const {
inline void SaveBinary(utils::IStream &fo) const { // NOLINT(*)
int version = kVersion;
fo.Write(&version, sizeof(version));
fo.Write(&info.num_row, sizeof(info.num_row));
@ -77,7 +79,7 @@ struct MetaInfo {
fo.Write(info.root_index);
fo.Write(base_margin);
}
inline void LoadBinary(utils::IStream &fi) {
inline void LoadBinary(utils::IStream &fi) { // NOLINT(*)
int version;
utils::Check(fi.Read(&version, sizeof(version)) != 0, "MetaInfo: invalid format");
utils::Check(fi.Read(&info.num_row, sizeof(info.num_row)) != 0, "MetaInfo: invalid format");
@ -114,7 +116,7 @@ struct MetaInfo {
return labels;
}
inline const std::vector<float>& GetFloatInfo(const char *field) const {
return ((MetaInfo*)this)->GetFloatInfo(field);
return ((MetaInfo*)this)->GetFloatInfo(field); // NOLINT(*)
}
inline std::vector<unsigned> &GetUIntInfo(const char *field) {
using namespace std;
@ -124,7 +126,7 @@ struct MetaInfo {
return info.root_index;
}
inline const std::vector<unsigned> &GetUIntInfo(const char *field) const {
return ((MetaInfo*)this)->GetUIntInfo(field);
return ((MetaInfo*)this)->GetUIntInfo(field); // NOLINT(*)
}
// try to load weight information from file, if exists
inline bool TryLoadFloatInfo(const char *field, const char* fname, bool silent = false) {

19
src/learner/evaluation-inl.hpp
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@ -1,10 +1,12 @@
/*!
* Copyright 2014 by Contributors
* \file xgboost_evaluation-inl.hpp
* \brief evaluation metrics for regression and classification and rank
* \author Kailong Chen, Tianqi Chen
*/
#ifndef XGBOOST_LEARNER_EVALUATION_INL_HPP_
#define XGBOOST_LEARNER_EVALUATION_INL_HPP_
/*!
* \file xgboost_evaluation-inl.hpp
* \brief evaluation metrics for regression and classification and rank
* \author Kailong Chen, Tianqi Chen
*/
#include <vector>
#include <utility>
#include <string>
@ -344,7 +346,8 @@ struct EvalPrecisionRatio : public IEvaluator{
}
protected:
inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec, const MetaInfo &info) const {
inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec,
const MetaInfo &info) const {
size_t cutoff = static_cast<size_t>(ratio_ * rec.size());
double wt_hit = 0.0, wsum = 0.0, wt_sum = 0.0;
for (size_t j = 0; j < cutoff; ++j) {
@ -489,7 +492,7 @@ struct EvalRankList : public IEvaluator {
}
}
/*! \return evaluation metric, given the pair_sort record, (pred,label) */
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0;
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0; // NOLINT(*)
protected:
unsigned topn_;
@ -530,7 +533,7 @@ struct EvalNDCG : public EvalRankList{
}
return static_cast<float>(sumdcg);
}
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const { // NOLINT(*)
std::stable_sort(rec.begin(), rec.end(), CmpFirst);
float dcg = this->CalcDCG(rec);
std::stable_sort(rec.begin(), rec.end(), CmpSecond);

6
src/learner/evaluation.h
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@ -1,10 +1,12 @@
#ifndef XGBOOST_LEARNER_EVALUATION_H_
#define XGBOOST_LEARNER_EVALUATION_H_
/*!
* Copyright 2014 by Contributors
* \file evaluation.h
* \brief interface of evaluation function supported in xgboost
* \author Tianqi Chen, Kailong Chen
*/
#ifndef XGBOOST_LEARNER_EVALUATION_H_
#define XGBOOST_LEARNER_EVALUATION_H_
#include <string>
#include <vector>
#include <cstdio>

6
src/learner/helper_utils.h
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@ -1,10 +1,12 @@
#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
#define XGBOOST_LEARNER_HELPER_UTILS_H_
/*!
* Copyright 2014 by Contributors
* \file helper_utils.h
* \brief useful helper functions
* \author Tianqi Chen, Kailong Chen
*/
#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
#define XGBOOST_LEARNER_HELPER_UTILS_H_
#include <utility>
#include <vector>
#include <cmath>

19
src/learner/learner-inl.hpp
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@ -1,10 +1,12 @@
#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
#define XGBOOST_LEARNER_LEARNER_INL_HPP_
/*!
* Copyright 2014 by Contributors
* \file learner-inl.hpp
* \brief learning algorithm
* \author Tianqi Chen
*/
#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
#define XGBOOST_LEARNER_LEARNER_INL_HPP_
#include <algorithm>
#include <vector>
#include <utility>
@ -30,7 +32,7 @@ class BoostLearner : public rabit::Serializable {
gbm_ = NULL;
name_obj_ = "reg:linear";
name_gbm_ = "gbtree";
silent= 0;
silent = 0;
prob_buffer_row = 1.0f;
distributed_mode = 0;
updater_mode = 0;
@ -68,7 +70,7 @@ class BoostLearner : public rabit::Serializable {
}
char str_temp[25];
utils::SPrintf(str_temp, sizeof(str_temp), "%lu",
static_cast<unsigned long>(buffer_size));
static_cast<unsigned long>(buffer_size)); // NOLINT(*)
this->SetParam("num_pbuffer", str_temp);
this->pred_buffer_size = buffer_size;
}
@ -161,7 +163,7 @@ class BoostLearner : public rabit::Serializable {
* \param fi input stream
* \param calc_num_feature whether call InitTrainer with calc_num_feature
*/
inline void LoadModel(utils::IStream &fi,
inline void LoadModel(utils::IStream &fi, // NOLINT(*)
bool calc_num_feature = true) {
utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
"BoostLearner: wrong model format");
@ -228,7 +230,7 @@ class BoostLearner : public rabit::Serializable {
}
delete fi;
}
inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const {
inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const { // NOLINT(*)
ModelParam p = mparam;
p.saved_with_pbuffer = static_cast<int>(with_pbuffer);
fo.Write(&p, sizeof(ModelParam));
@ -345,8 +347,7 @@ class BoostLearner : public rabit::Serializable {
bool output_margin,
std::vector<float> *out_preds,
unsigned ntree_limit = 0,
bool pred_leaf = false
) const {
bool pred_leaf = false) const {
if (pred_leaf) {
gbm_->PredictLeaf(data.fmat(), data.info.info, out_preds, ntree_limit);
} else {
@ -517,7 +518,7 @@ class BoostLearner : public rabit::Serializable {
protected:
// magic number to transform random seed
const static int kRandSeedMagic = 127;
static const int kRandSeedMagic = 127;
// cache entry object that helps handle feature caching
struct CacheEntry {
const DMatrix *mat_;

18
src/learner/objective-inl.hpp
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@ -1,10 +1,12 @@
#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
/*!
* Copyright 2014 by Contributors
* \file objective-inl.hpp
* \brief objective function implementations
* \author Tianqi Chen, Kailong Chen
*/
#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#include <vector>
#include <algorithm>
#include <utility>
@ -176,14 +178,14 @@ class RegLossObj : public IObjFunction {
// poisson regression for count
class PoissonRegression : public IObjFunction {
public:
explicit PoissonRegression(void) {
PoissonRegression(void) {
max_delta_step = 0.0f;
}
virtual ~PoissonRegression(void) {}
virtual void SetParam(const char *name, const char *val) {
using namespace std;
if (!strcmp( "max_delta_step", name )) {
if (!strcmp("max_delta_step", name)) {
max_delta_step = static_cast<float>(atof(val));
}
}
@ -201,9 +203,9 @@ class PoissonRegression : public IObjFunction {
// check if label in range
bool label_correct = true;
// start calculating gradient
const long ndata = static_cast<bst_omp_uint>(preds.size());
const long ndata = static_cast<bst_omp_uint>(preds.size()); // NOLINT(*)
#pragma omp parallel for schedule(static)
for (long i = 0; i < ndata; ++i) {
for (long i = 0; i < ndata; ++i) { // NOLINT(*)
float p = preds[i];
float w = info.GetWeight(i);
float y = info.labels[i];
@ -219,9 +221,9 @@ class PoissonRegression : public IObjFunction {
}
virtual void PredTransform(std::vector<float> *io_preds) {
std::vector<float> &preds = *io_preds;
const long ndata = static_cast<long>(preds.size());
const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
#pragma omp parallel for schedule(static)
for (long j = 0; j < ndata; ++j) {
for (long j = 0; j < ndata; ++j) { // NOLINT(*)
preds[j] = std::exp(preds[j]);
}
}

13
src/learner/objective.h
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@ -1,11 +1,14 @@
#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
#define XGBOOST_LEARNER_OBJECTIVE_H_
/*!
* Copyright 2014 by Contributors
* \file objective.h
* \brief interface of objective function used for gradient boosting
* \author Tianqi Chen, Kailong Chen
*/
#include "dmatrix.h"
#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
#define XGBOOST_LEARNER_OBJECTIVE_H_
#include <vector>
#include "./dmatrix.h"
namespace xgboost {
namespace learner {
@ -13,7 +16,7 @@ namespace learner {
class IObjFunction{
public:
/*! \brief virtual destructor */
virtual ~IObjFunction(void){}
virtual ~IObjFunction(void) {}
/*!
* \brief set parameters from outside
* \param name name of the parameter
@ -38,7 +41,7 @@ class IObjFunction{
* \brief transform prediction values, this is only called when Prediction is called
* \param io_preds prediction values, saves to this vector as well
*/
virtual void PredTransform(std::vector<float> *io_preds){}
virtual void PredTransform(std::vector<float> *io_preds) {}
/*!
* \brief transform prediction values, this is only called when Eval is called,
* usually it redirect to PredTransform

10
src/sync/sync.h
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@ -1,13 +1,13 @@
#ifndef XGBOOST_SYNC_H_
#define XGBOOST_SYNC_H_
/*!
* Copyright 2014 by Contributors
* \file sync.h
* \brief the synchronization module of rabit
* redirects to subtree rabit header
* \author Tianqi Chen
*/
#ifndef XGBOOST_SYNC_SYNC_H_
#define XGBOOST_SYNC_SYNC_H_
#include "../../subtree/rabit/include/rabit.h"
#include "../../subtree/rabit/include/rabit/timer.h"
#endif // XGBOOST_SYNC_H_
#endif // XGBOOST_SYNC_SYNC_H_