// Copyright by Contributors
#include <dmlc/filesystem.h>
#include <gtest/gtest.h>
#include <xgboost/predictor.h>
#include "../helpers.h"

namespace xgboost {
TEST(cpu_predictor, Test) {
  std::unique_ptr<Predictor> cpu_predictor =
      std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));

  std::vector<std::unique_ptr<RegTree>> trees;
  trees.push_back(std::unique_ptr<RegTree>(new RegTree));
  (*trees.back())[0].SetLeaf(1.5f);
  (*trees.back()).Stat(0).sum_hess = 1.0f;
  gbm::GBTreeModel model(0.5);
  model.CommitModel(std::move(trees), 0);
  model.param.num_output_group = 1;
  model.base_margin = 0;

  int n_row = 5;
  int n_col = 5;

  auto dmat = CreateDMatrix(n_row, n_col, 0);

  // Test predict batch
  HostDeviceVector<float> out_predictions;
  cpu_predictor->PredictBatch((*dmat).get(), &out_predictions, model, 0);
  std::vector<float>& out_predictions_h = out_predictions.HostVector();
  for (int i = 0; i < out_predictions.Size(); i++) {
    ASSERT_EQ(out_predictions_h[i], 1.5);
  }

  // Test predict instance
  auto &batch = *(*dmat)->GetRowBatches().begin();
  for (int i = 0; i < batch.Size(); i++) {
    std::vector<float> instance_out_predictions;
    cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
    ASSERT_EQ(instance_out_predictions[0], 1.5);
  }

  // Test predict leaf
  std::vector<float> leaf_out_predictions;
  cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
  for (auto v : leaf_out_predictions) {
    ASSERT_EQ(v, 0);
  }

  // Test predict contribution
  std::vector<float> out_contribution;
  cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
  for (auto const& contri : out_contribution) {
    ASSERT_EQ(contri, 1.5);
  }
  // Test predict contribution (approximate method)
  cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, true);
  for (auto const& contri : out_contribution) {
    ASSERT_EQ(contri, 1.5);
  }

  delete dmat;
}

TEST(cpu_predictor, ExternalMemoryTest) {
  // Create sufficiently large data to make two row pages
  dmlc::TemporaryDirectory tempdir;
  const std::string tmp_file = tempdir.path + "/big.libsvm";
  CreateBigTestData(tmp_file, 12);
  xgboost::DMatrix *dmat = xgboost::DMatrix::Load(
      tmp_file + "#" + tmp_file + ".cache", true, false, "auto", 64UL);
  EXPECT_TRUE(FileExists(tmp_file + ".cache.row.page"));
  int64_t batche_count = 0;
  for (const auto &batch : dmat->GetRowBatches()) {
    batche_count++;
  }
  EXPECT_EQ(batche_count, 2);

  std::unique_ptr<Predictor> cpu_predictor =
      std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));

  std::vector<std::unique_ptr<RegTree>> trees;
  trees.push_back(std::unique_ptr<RegTree>(new RegTree));
  (*trees.back())[0].SetLeaf(1.5f);
  (*trees.back()).Stat(0).sum_hess = 1.0f;
  gbm::GBTreeModel model(0.5);
  model.CommitModel(std::move(trees), 0);
  model.param.num_output_group = 1;
  model.base_margin = 0;

  // Test predict batch
  HostDeviceVector<float> out_predictions;
  cpu_predictor->PredictBatch(dmat, &out_predictions, model, 0);
  std::vector<float> &out_predictions_h = out_predictions.HostVector();
  EXPECT_EQ(out_predictions.Size(), dmat->Info().num_row_);
  for (const auto& v : out_predictions_h) {
    ASSERT_EQ(v, 1.5);
  }

  // Test predict leaf
  std::vector<float> leaf_out_predictions;
  cpu_predictor->PredictLeaf(dmat, &leaf_out_predictions, model);
  EXPECT_EQ(leaf_out_predictions.size(), dmat->Info().num_row_);
  for (const auto& v : leaf_out_predictions) {
    ASSERT_EQ(v, 0);
  }

  // Test predict contribution
  std::vector<float> out_contribution;
  cpu_predictor->PredictContribution(dmat, &out_contribution, model);
  EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_);
  for (const auto& v : out_contribution) {
    ASSERT_EQ(v, 1.5);
  }

  // Test predict contribution (approximate method)
  std::vector<float> out_contribution_approximate;
  cpu_predictor->PredictContribution(dmat, &out_contribution_approximate, model, true);
  EXPECT_EQ(out_contribution_approximate.size(), dmat->Info().num_row_);
  for (const auto& v : out_contribution_approximate) {
    ASSERT_EQ(v, 1.5);
  }

  delete dmat;
}
}  // namespace xgboost