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Upgrade clang-tidy on CI. (#5469)

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* Correct all clang-tidy errors.
* Upgrade clang-tidy to 10 on CI.

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2020-04-05 04:42:29 +08:00
committed by GitHub
parent 30e94ddd04
commit 0012f2ef93
107 changed files with 932 additions and 903 deletions
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30
src/common/column_matrix.h
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@@ -141,7 +141,7 @@ class ColumnMatrix {
feature_offsets_[fid] = accum_index_;
}
SetTypeSize(gmat.max_num_bins_);
SetTypeSize(gmat.max_num_bins);
index_.resize(feature_offsets_[nfeature] * bins_type_size_, 0);
if (!all_dense) {
@@ -161,24 +161,24 @@ class ColumnMatrix {
// pre-fill index_ for dense columns
if (all_dense) {
BinTypeSize gmat_bin_size = gmat.index.getBinTypeSize();
if (gmat_bin_size == UINT8_BINS_TYPE_SIZE) {
BinTypeSize gmat_bin_size = gmat.index.GetBinTypeSize();
if (gmat_bin_size == kUint8BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues);
} else if (gmat_bin_size == UINT16_BINS_TYPE_SIZE) {
} else if (gmat_bin_size == kUint16BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues);
} else {
CHECK_EQ(gmat_bin_size, UINT32_BINS_TYPE_SIZE);
CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues);
}
/* For sparse DMatrix gmat.index.getBinTypeSize() returns always UINT32_BINS_TYPE_SIZE
/* For sparse DMatrix gmat.index.getBinTypeSize() returns always kUint32BinsTypeSize
but for ColumnMatrix we still have a chance to reduce the memory consumption */
} else {
if (bins_type_size_ == UINT8_BINS_TYPE_SIZE) {
if (bins_type_size_ == kUint8BinsTypeSize) {
SetIndex<uint8_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
} else if (bins_type_size_ == UINT16_BINS_TYPE_SIZE) {
} else if (bins_type_size_ == kUint16BinsTypeSize) {
SetIndex<uint16_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
} else {
CHECK_EQ(bins_type_size_, UINT32_BINS_TYPE_SIZE);
CHECK_EQ(bins_type_size_, kUint32BinsTypeSize);
SetIndex<uint32_t>(gmat.index.data<uint32_t>(), gmat, nrow, nfeature);
}
}
@@ -187,11 +187,11 @@ class ColumnMatrix {
/* Set the number of bytes based on numeric limit of maximum number of bins provided by user */
void SetTypeSize(size_t max_num_bins) {
if ( (max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint8_t>::max()) ) {
bins_type_size_ = UINT8_BINS_TYPE_SIZE;
bins_type_size_ = kUint8BinsTypeSize;
} else if ((max_num_bins - 1) <= static_cast<int>(std::numeric_limits<uint16_t>::max())) {
bins_type_size_ = UINT16_BINS_TYPE_SIZE;
bins_type_size_ = kUint16BinsTypeSize;
} else {
bins_type_size_ = UINT32_BINS_TYPE_SIZE;
bins_type_size_ = kUint32BinsTypeSize;
}
}
@@ -227,7 +227,7 @@ class ColumnMatrix {
/* missing values make sense only for column with type kDenseColumn,
and if no missing values were observed it could be handled much faster. */
if (noMissingValues) {
const int32_t nthread = omp_get_max_threads();
const int32_t nthread = omp_get_max_threads(); // NOLINT
#pragma omp parallel for num_threads(nthread)
for (omp_ulong rid = 0; rid < nrow; ++rid) {
const size_t ibegin = rid*nfeature;
@@ -241,7 +241,7 @@ class ColumnMatrix {
} else {
/* to handle rows in all batches, sum of all batch sizes equal to gmat.row_ptr.size() - 1 */
size_t rbegin = 0;
for (const auto &batch : gmat.p_fmat_->GetBatches<SparsePage>()) {
for (const auto &batch : gmat.p_fmat->GetBatches<SparsePage>()) {
const xgboost::Entry* data_ptr = batch.data.HostVector().data();
const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
const size_t batch_size = batch.Size();
@@ -276,7 +276,7 @@ class ColumnMatrix {
T* local_index = reinterpret_cast<T*>(&index_[0]);
size_t rbegin = 0;
for (const auto &batch : gmat.p_fmat_->GetBatches<SparsePage>()) {
for (const auto &batch : gmat.p_fmat->GetBatches<SparsePage>()) {
const xgboost::Entry* data_ptr = batch.data.HostVector().data();
const std::vector<bst_row_t>& offset_vec = batch.offset.HostVector();
const size_t batch_size = batch.Size();