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clean up warnings from msvc

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This commit is contained in:
Tianqi Chen
2014-08-25 11:01:21 -07:00
parent 4f0b0d2c88
commit ca0b008fb0
20 changed files with 217 additions and 46 deletions
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6
src/learner/dmatrix.h
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@@ -78,9 +78,9 @@ struct MetaInfo {
}
inline void LoadBinary(utils::IStream &fi) {
int version;
utils::Check(fi.Read(&version, sizeof(version)), "MetaInfo: invalid format");
utils::Check(fi.Read(&info.num_row, sizeof(info.num_row)), "MetaInfo: invalid format");
utils::Check(fi.Read(&info.num_col, sizeof(info.num_col)), "MetaInfo: invalid format");
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");
utils::Check(fi.Read(&info.num_col, sizeof(info.num_col)) != 0, "MetaInfo: invalid format");
utils::Check(fi.Read(&labels), "MetaInfo: invalid format");
utils::Check(fi.Read(&group_ptr), "MetaInfo: invalid format");
utils::Check(fi.Read(&weights), "MetaInfo: invalid format");

14
src/learner/evaluation-inl.hpp
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@@ -132,7 +132,7 @@ struct EvalAMS : public IEvaluator {
b_fp += wt;
}
if (rec[i].first != rec[i+1].first) {
double ams = sqrtf(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp));
double ams = sqrt(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp));
if (tams < ams) {
thresindex = i;
tams = ams;
@@ -141,9 +141,9 @@ struct EvalAMS : public IEvaluator {
}
if (ntop == ndata) {
fprintf(stderr, "\tams-ratio=%g", static_cast<float>(thresindex) / ndata);
return tams;
return static_cast<float>(tams);
} else {
return sqrtf(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp));
return static_cast<float>(sqrt(2*((s_tp+b_fp+br) * log(1.0 + s_tp/(b_fp+br)) - s_tp)));
}
}
virtual const char *Name(void) const {
@@ -171,7 +171,7 @@ struct EvalPrecisionRatio : public IEvaluator{
utils::Assert(preds.size() == info.labels.size(), "label size predict size not match");
std::vector< std::pair<float, unsigned> > rec;
for (size_t j = 0; j < preds.size(); ++j) {
rec.push_back(std::make_pair(preds[j], j));
rec.push_back(std::make_pair(preds[j], static_cast<unsigned>(j)));
}
std::sort(rec.begin(), rec.end(), CmpFirst);
double pratio = CalcPRatio(rec, info);
@@ -207,7 +207,7 @@ struct EvalAuc : public IEvaluator {
virtual float Eval(const std::vector<float> &preds,
const MetaInfo &info) const {
utils::Check(preds.size() == info.labels.size(), "label size predict size not match");
std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(preds.size());
const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
utils::Check(gptr.back() == preds.size(),
"EvalAuc: group structure must match number of prediction");
@@ -264,7 +264,7 @@ struct EvalRankList : public IEvaluator {
utils::Check(preds.size() == info.labels.size(),
"label size predict size not match");
// quick consistency when group is not available
std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(preds.size());
const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
utils::Assert(gptr.size() != 0, "must specify group when constructing rank file");
utils::Assert(gptr.back() == preds.size(),
@@ -339,7 +339,7 @@ struct EvalNDCG : public EvalRankList{
for (size_t i = 0; i < rec.size() && i < this->topn_; ++i) {
const unsigned rel = rec[i].second;
if (rel != 0) {
sumdcg += ((1 << rel) - 1) / logf(i + 2);
sumdcg += ((1 << rel) - 1) / log(i + 2.0);
}
}
return static_cast<float>(sumdcg);

1
src/learner/evaluation.h
View File

@@ -7,6 +7,7 @@
*/
#include <string>
#include <vector>
#include <cstdio>
#include "../utils/utils.h"
#include "./dmatrix.h"

2
src/learner/learner-inl.hpp
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@@ -330,7 +330,7 @@ class BoostLearner {
for (size_t i = 0; i < cache_.size(); ++i) {
if (cache_[i].mat_ == &mat && mat.cache_learner_ptr_ == this) {
if (cache_[i].num_row_ == mat.info.num_row()) {
return cache_[i].buffer_offset_;
return static_cast<int64_t>(cache_[i].buffer_offset_);
}
}
}

10
src/learner/objective-inl.hpp
View File

@@ -221,7 +221,7 @@ class SoftmaxMultiClassObj : public IObjFunction {
rec[k] = preds[j * nclass + k];
}
if (prob == 0) {
tmp[j] = FindMaxIndex(rec);
tmp[j] = static_cast<float>(FindMaxIndex(rec));
} else {
Softmax(&rec);
for (int k = 0; k < nclass; ++k) {
@@ -259,7 +259,7 @@ class LambdaRankObj : public IObjFunction {
std::vector<bst_gpair> &gpair = *out_gpair;
gpair.resize(preds.size());
// quick consistency when group is not available
std::vector<unsigned> tgptr(2, 0); tgptr[1] = info.labels.size();
std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(info.labels.size());
const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
utils::Check(gptr.size() != 0 && gptr.back() == info.labels.size(),
"group structure not consistent with #rows");
@@ -290,7 +290,7 @@ class LambdaRankObj : public IObjFunction {
unsigned j = i + 1;
while (j < rec.size() && rec[j].first == rec[i].first) ++j;
// bucket in [i,j), get a sample outside bucket
unsigned nleft = i, nright = rec.size() - j;
unsigned nleft = i, nright = static_cast<unsigned>(rec.size() - j);
if (nleft + nright != 0) {
int nsample = num_pairsample;
while (nsample --) {
@@ -436,9 +436,9 @@ class LambdaRankObjNDCG : public LambdaRankObj {
inline static float CalcDCG(const std::vector<float> &labels) {
double sumdcg = 0.0;
for (size_t i = 0; i < labels.size(); ++i) {
const unsigned rel = labels[i];
const unsigned rel = static_cast<unsigned>(labels[i]);
if (rel != 0) {
sumdcg += ((1 << rel) - 1) / logf(i + 2);
sumdcg += ((1 << rel) - 1) / logf(static_cast<float>(i + 2));
}
}
return static_cast<float>(sumdcg);