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JSON configuration IO. (#5111)

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* Add saving/loading JSON configuration.
* Implement Python pickle interface with new IO routines.
* Basic tests for training continuation.
This commit is contained in:
Jiaming Yuan
2019-12-15 17:31:53 +08:00
committed by GitHub
parent 5aa007d7b2
commit 3136185bc5
24 changed files with 761 additions and 390 deletions
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89
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -13,23 +13,6 @@
#include "../helpers.h"
#include "../../../src/gbm/gbtree_model.h"
namespace {
inline void CheckCAPICall(int ret) {
ASSERT_EQ(ret, 0) << XGBGetLastError();
}
} // namespace anonymous
const std::map<std::string, std::string>&
QueryBoosterConfigurationArguments(BoosterHandle handle) {
CHECK_NE(handle, static_cast<void*>(nullptr));
auto* bst = static_cast<xgboost::Learner*>(handle);
bst->Configure();
return bst->GetConfigurationArguments();
}
namespace xgboost {
namespace predictor {
@@ -110,77 +93,5 @@ TEST(gpu_predictor, ExternalMemoryTest) {
}
}
}
// Test whether pickling preserves predictor parameters
TEST(gpu_predictor, PicklingTest) {
int const gpuid = 0;
dmlc::TemporaryDirectory tempdir;
const std::string tmp_file = tempdir.path + "/simple.libsvm";
CreateBigTestData(tmp_file, 600);
DMatrixHandle dmat[1];
BoosterHandle bst, bst2;
std::vector<bst_float> label;
for (int i = 0; i < 200; ++i) {
label.push_back((i % 2 ? 1 : 0));
}
// Load data matrix
ASSERT_EQ(XGDMatrixCreateFromFile(
tmp_file.c_str(), 0, &dmat[0]), 0) << XGBGetLastError();
ASSERT_EQ(XGDMatrixSetFloatInfo(
dmat[0], "label", label.data(), 200), 0) << XGBGetLastError();
// Create booster
ASSERT_EQ(XGBoosterCreate(dmat, 1, &bst), 0) << XGBGetLastError();
// Set parameters
ASSERT_EQ(XGBoosterSetParam(bst, "seed", "0"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "base_score", "0.5"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "booster", "gbtree"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "learning_rate", "0.01"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "max_depth", "8"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "objective", "binary:logistic"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "seed", "123"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "tree_method", "gpu_hist"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "gpu_id", std::to_string(gpuid).c_str()), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "predictor", "gpu_predictor"), 0) << XGBGetLastError();
// Run boosting iterations
for (int i = 0; i < 10; ++i) {
ASSERT_EQ(XGBoosterUpdateOneIter(bst, i, dmat[0]), 0) << XGBGetLastError();
}
// Delete matrix
CheckCAPICall(XGDMatrixFree(dmat[0]));
// Pickle
const char* dptr;
bst_ulong len;
std::string buf;
CheckCAPICall(XGBoosterGetModelRaw(bst, &len, &dptr));
buf = std::string(dptr, len);
CheckCAPICall(XGBoosterFree(bst));
// Unpickle
CheckCAPICall(XGBoosterCreate(nullptr, 0, &bst2));
CheckCAPICall(XGBoosterLoadModelFromBuffer(bst2, buf.c_str(), len));
{ // Query predictor
const auto& kwargs = QueryBoosterConfigurationArguments(bst2);
ASSERT_EQ(kwargs.at("predictor"), "gpu_predictor");
ASSERT_EQ(kwargs.at("gpu_id"), std::to_string(gpuid).c_str());
}
{ // Change predictor and query again
CheckCAPICall(XGBoosterSetParam(bst2, "predictor", "cpu_predictor"));
const auto& kwargs = QueryBoosterConfigurationArguments(bst2);
ASSERT_EQ(kwargs.at("predictor"), "cpu_predictor");
}
CheckCAPICall(XGBoosterFree(bst2));
}
} // namespace predictor
} // namespace xgboost