Model IO in JSON. (#5110)

tests/cpp/gbm/test_gblinear.cc

@@ -0,0 +1,57 @@
+/*!
+ * Copyright 2019 by Contributors
+ */
+#include <gtest/gtest.h>
+
+#include <memory>
+#include <sstream>
+
+#include "../helpers.h"
+#include "xgboost/json.h"
+#include "xgboost/logging.h"
+#include "xgboost/gbm.h"
+#include "xgboost/generic_parameters.h"
+#include "xgboost/learner.h"
+
+namespace xgboost {
+namespace gbm {
+
+TEST(GBLinear, Json_IO) {
+  size_t constexpr kRows = 16, kCols = 16;
+
+  LearnerModelParam param;
+  param.num_feature = kCols;
+  param.num_output_group = 1;
+
+  GenericParameter gparam;
+  gparam.Init(Args{});
+
+  std::unique_ptr<GradientBooster> gbm {
+    CreateTrainedGBM("gblinear", Args{}, kRows, kCols, &param, &gparam) };
+  Json model { Object() };
+  gbm->SaveModel(&model);
+  ASSERT_TRUE(IsA<Object>(model));
+
+  std::string model_str;
+  Json::Dump(model, &model_str);
+
+  model = Json::Load({model_str.c_str(), model_str.size()});
+  ASSERT_TRUE(IsA<Object>(model));
+  model = model["model"];
+
+  {
+    auto weights = get<Array>(model["weights"]);
+    ASSERT_EQ(weights.size(), 17);
+  }
+
+  {
+    model = Json::Load({model_str.c_str(), model_str.size()});
+    model = model["model"];
+    auto weights = get<Array>(model["weights"]);
+    ASSERT_EQ(weights.size(), 17);  // 16 + 1 (bias)
+  }
+
+}
+
+}  // namespace gbm
+}  // namespace xgboost

tests/cpp/gbm/test_gbtree.cc

@@ -96,6 +96,71 @@ TEST(GBTree, ChoosePredictor) {
   }
   // data is not pulled back into host
   ASSERT_FALSE(data.HostCanWrite());
+
+  delete pp_dmat;
+}
+#endif  // XGBOOST_USE_CUDA
+
+// Some other parts of test are in `Tree.Json_IO'.
+TEST(GBTree, Json_IO) {
+  size_t constexpr kRows = 16, kCols = 16;
+
+  LearnerModelParam mparam;
+  mparam.num_feature = kCols;
+  mparam.num_output_group = 1;
+  mparam.base_score = 0.5;
+
+  GenericParameter gparam;
+  gparam.Init(Args{});
+
+  std::unique_ptr<GradientBooster> gbm {
+    CreateTrainedGBM("gbtree", Args{}, kRows, kCols, &mparam, &gparam) };
+
+  Json model {Object()};
+  model["model"] = Object();
+  auto& j_model = model["model"];
+
+  gbm->SaveModel(&j_model);
+
+  std::stringstream ss;
+  Json::Dump(model, &ss);
+
+  auto model_str = ss.str();
+  model = Json::Load({model_str.c_str(), model_str.size()});
+  ASSERT_EQ(get<String>(model["model"]["name"]), "gbtree");
+}
+
+TEST(Dart, Json_IO) {
+  size_t constexpr kRows = 16, kCols = 16;
+
+  LearnerModelParam mparam;
+  mparam.num_feature = kCols;
+  mparam.base_score = 0.5;
+  mparam.num_output_group = 1;
+
+  GenericParameter gparam;
+  gparam.Init(Args{});
+
+  std::unique_ptr<GradientBooster> gbm {
+    CreateTrainedGBM("dart", Args{}, kRows, kCols, &mparam, &gparam) };
+
+  Json model {Object()};
+  model["model"] = Object();
+  auto& j_model = model["model"];
+  model["parameters"] = Object();
+
+  gbm->SaveModel(&j_model);
+
+  std::string model_str;
+  Json::Dump(model, &model_str);
+
+  model = Json::Load({model_str.c_str(), model_str.size()});
+
+  {
+    auto const& gbtree = model["model"]["gbtree"];
+    ASSERT_TRUE(IsA<Object>(gbtree));
+    ASSERT_EQ(get<String>(model["model"]["name"]), "dart");
+    ASSERT_NE(get<Array>(model["model"]["weight_drop"]).size(), 0);
+  }
 }
-#endif
 }  // namespace xgboost