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pas mock, need to fix rabit lib for not initialization

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This commit is contained in:
tqchen
2014-12-21 00:14:00 -08:00
parent b078663982
commit 31eedfea59
7 changed files with 69 additions and 32 deletions
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46
src/learner/learner-inl.hpp
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@@ -48,11 +48,9 @@ class BoostLearner : public rabit::ISerializable {
* \param mats array of pointers to matrix whose prediction result need to be cached
*/
inline void SetCacheData(const std::vector<DMatrix*>& mats) {
// estimate feature bound
unsigned num_feature = 0;
utils::Assert(cache_.size() == 0, "can only call cache data once");
// assign buffer index
size_t buffer_size = 0;
utils::Assert(cache_.size() == 0, "can only call cache data once");
for (size_t i = 0; i < mats.size(); ++i) {
bool dupilicate = false;
for (size_t j = 0; j < i; ++j) {
@@ -63,16 +61,10 @@ class BoostLearner : public rabit::ISerializable {
mats[i]->cache_learner_ptr_ = this;
cache_.push_back(CacheEntry(mats[i], buffer_size, mats[i]->info.num_row()));
buffer_size += mats[i]->info.num_row();
num_feature = std::max(num_feature, static_cast<unsigned>(mats[i]->info.num_col()));
}
rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
char str_temp[25];
if (num_feature > mparam.num_feature) {
utils::SPrintf(str_temp, sizeof(str_temp), "%u", num_feature);
this->SetParam("bst:num_feature", str_temp);
}
utils::SPrintf(str_temp, sizeof(str_temp), "%lu",
static_cast<unsigned long>(buffer_size));
utils::SPrintf(str_temp, sizeof(str_temp), "%lu",
static_cast<unsigned long>(buffer_size));
this->SetParam("num_pbuffer", str_temp);
if (!silent) {
utils::Printf("buffer_size=%ld\n", static_cast<long>(buffer_size));
@@ -126,10 +118,29 @@ class BoostLearner : public rabit::ISerializable {
cfg_.push_back(std::make_pair(std::string(name), std::string(val)));
}
}
// this is an internal function
// initialize the trainer, called at InitModel and LoadModel
inline void InitTrainer(bool calc_num_feature = true) {
if (calc_num_feature) {
// estimate feature bound
unsigned num_feature = 0;
for (size_t i = 0; i < cache_.size(); ++i) {
num_feature = std::max(num_feature,
static_cast<unsigned>(cache_[i].mat_->info.num_col()));
}
// run allreduce on num_feature to find the maximum value
rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
if (num_feature > mparam.num_feature) mparam.num_feature = num_feature;
}
char str_temp[25];
utils::SPrintf(str_temp, sizeof(str_temp), "%d", mparam.num_feature);
this->SetParam("bst:num_feature", str_temp);
}
/*!
* \brief initialize the model
*/
inline void InitModel(void) {
this->InitTrainer();
// initialize model
this->InitObjGBM();
// reset the base score
@@ -141,8 +152,9 @@ class BoostLearner : public rabit::ISerializable {
* \brief load model from stream
* \param fi input stream
* \param with_pbuffer whether to load with predict buffer
* \param calc_num_feature whether call InitTrainer with calc_num_feature
*/
inline void LoadModel(utils::IStream &fi, bool with_pbuffer = true) {
inline void LoadModel(utils::IStream &fi, bool with_pbuffer = true, bool calc_num_feature = true) {
utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
"BoostLearner: wrong model format");
utils::Check(fi.Read(&name_obj_), "BoostLearner: wrong model format");
@@ -150,9 +162,10 @@ class BoostLearner : public rabit::ISerializable {
// delete existing gbm if any
if (obj_ != NULL) delete obj_;
if (gbm_ != NULL) delete gbm_;
this->InitTrainer(calc_num_feature);
this->InitObjGBM();
gbm_->LoadModel(fi, with_pbuffer);
if (with_pbuffer && distributed_mode == 2 && rabit::GetRank() != 0) {
if (!with_pbuffer || distributed_mode == 2) {
gbm_->ResetPredBuffer(pred_buffer_size);
}
}
@@ -160,7 +173,7 @@ class BoostLearner : public rabit::ISerializable {
virtual void Load(rabit::IStream &fi) {
RabitStreamAdapter fs(fi);
// for row split, we should not keep pbuffer
this->LoadModel(fs, distributed_mode != 2);
this->LoadModel(fs, distributed_mode != 2, false);
}
// rabit save model to rabit checkpoint
virtual void Save(rabit::IStream &fo) const {
@@ -209,9 +222,12 @@ class BoostLearner : public rabit::ISerializable {
* \param p_train pointer to the data matrix
*/
inline void UpdateOneIter(int iter, const DMatrix &train) {
printf("!!UpdateOneIter\n");
this->PredictRaw(train, &preds_);
obj_->GetGradient(preds_, train.info, iter, &gpair_);
printf("!!UpdateOneDoboost\n");
gbm_->DoBoost(train.fmat(), this->FindBufferOffset(train), train.info.info, &gpair_);
printf("!!UpdateOneIter finish\n");
}
/*!
* \brief evaluate the model for specific iteration
@@ -335,7 +351,7 @@ class BoostLearner : public rabit::ISerializable {
/* \brief number of class, if it is multi-class classification */
int num_class;
/*! \brief reserved field */
int reserved[32];
int reserved[31];
/*! \brief constructor */
ModelParam(void) {
base_score = 0.5f;