Cleanup data generator. (#8094)

- Avoid duplicated definition of data shape.
- Explicitly define numpy iterator for CPU data.

tests/cpp/data/test_iterative_dmatrix.cu

@@ -27,8 +27,8 @@ void TestEquivalent(float sparsity) {
     offset += num_elements;
   }
   auto from_iter = page_concatenated->GetDeviceAccessor(0);
-  ASSERT_EQ(m.Info().num_col_, CudaArrayIterForTest::kCols);
-  ASSERT_EQ(m.Info().num_row_, CudaArrayIterForTest::kRows);
+  ASSERT_EQ(m.Info().num_col_, CudaArrayIterForTest::Cols());
+  ASSERT_EQ(m.Info().num_row_, CudaArrayIterForTest::Rows());
 
   std::string interface_str = iter.AsArray();
   auto adapter = CupyAdapter(interface_str);
@@ -98,8 +98,8 @@ TEST(IterativeDeviceDMatrix, RowMajor) {
     auto impl = ellpack.Impl();
     common::CompressedIterator<uint32_t> iterator(
         impl->gidx_buffer.HostVector().data(), impl->NumSymbols());
-    auto cols = CudaArrayIterForTest::kCols;
-    auto rows = CudaArrayIterForTest::kRows;
+    auto cols = CudaArrayIterForTest::Cols();
+    auto rows = CudaArrayIterForTest::Rows();
 
     auto j_interface =
         Json::Load({interface_str.c_str(), interface_str.size()});

tests/cpp/helpers.cc

@@ -1,25 +1,27 @@
 /*!
  * Copyright 2016-2022 by XGBoost contributors
  */
+#include "helpers.h"
+
 #include <dmlc/filesystem.h>
-#include <xgboost/logging.h>
-#include <xgboost/objective.h>
-#include <xgboost/metric.h>
-#include <xgboost/learner.h>
+#include <gtest/gtest.h>
 #include <xgboost/gbm.h>
 #include <xgboost/json.h>
-#include <gtest/gtest.h>
+#include <xgboost/learner.h>
+#include <xgboost/logging.h>
+#include <xgboost/metric.h>
+#include <xgboost/objective.h>
 
 #include <algorithm>
-#include <random>
 #include <cinttypes>
+#include <random>
 
-#include "helpers.h"
-#include "xgboost/c_api.h"
 #include "../../src/data/adapter.h"
+#include "../../src/data/iterative_dmatrix.h"
 #include "../../src/data/simple_dmatrix.h"
 #include "../../src/data/sparse_page_dmatrix.h"
 #include "../../src/gbm/gbtree_model.h"
+#include "xgboost/c_api.h"
 #include "xgboost/predictor.h"
 
 #if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
@@ -379,6 +381,30 @@ RandomDataGenerator::GenerateDMatrix(bool with_label, bool float_label,
   return out;
 }
 
+std::shared_ptr<DMatrix> RandomDataGenerator::GenerateQuantileDMatrix() {
+  NumpyArrayIterForTest iter{this->sparsity_, this->rows_, this->cols_, 1};
+  auto m = std::make_shared<data::IterativeDMatrix>(
+      &iter, iter.Proxy(), Reset, Next, std::numeric_limits<float>::quiet_NaN(), 0, bins_);
+  return m;
+}
+
+NumpyArrayIterForTest::NumpyArrayIterForTest(float sparsity, size_t rows, size_t cols,
+                                             size_t batches)
+    : ArrayIterForTest{sparsity, rows, cols, batches} {
+  rng_->Device(Context::kCpuId);
+  std::tie(batches_, interface_) = rng_->GenerateArrayInterfaceBatch(&data_, n_batches_);
+  this->Reset();
+}
+
+int NumpyArrayIterForTest::Next() {
+  if (iter_ == n_batches_) {
+    return 0;
+  }
+  XGProxyDMatrixSetDataDense(proxy_, batches_[iter_].c_str());
+  iter_++;
+  return 1;
+}
+
 std::shared_ptr<DMatrix>
 GetDMatrixFromData(const std::vector<float> &x, int num_rows, int num_columns){
   data::DenseAdapter adapter(x.data(), num_rows, num_columns);
@@ -389,7 +415,7 @@ GetDMatrixFromData(const std::vector<float> &x, int num_rows, int num_columns){
 std::unique_ptr<DMatrix> CreateSparsePageDMatrix(bst_row_t n_samples, bst_feature_t n_features,
                                                  size_t n_batches, std::string prefix) {
   CHECK_GE(n_samples, n_batches);
-  ArrayIterForTest iter(0, n_samples, n_features, n_batches);
+  NumpyArrayIterForTest iter(0, n_samples, n_features, n_batches);
 
   std::unique_ptr<DMatrix> dmat{
       DMatrix::Create(static_cast<DataIterHandle>(&iter), iter.Proxy(), Reset, Next,
@@ -416,7 +442,7 @@ std::unique_ptr<DMatrix> CreateSparsePageDMatrix(size_t n_entries,
                                                  std::string prefix) {
   size_t n_columns = 3;
   size_t n_rows = n_entries / n_columns;
-  ArrayIterForTest iter(0, n_rows, n_columns, 2);
+  NumpyArrayIterForTest iter(0, n_rows, n_columns, 2);
 
   std::unique_ptr<DMatrix> dmat{DMatrix::Create(
       static_cast<DataIterHandle>(&iter), iter.Proxy(), Reset, Next,
@@ -563,18 +589,6 @@ ArrayIterForTest::ArrayIterForTest(float sparsity, size_t rows, size_t cols,
 
 ArrayIterForTest::~ArrayIterForTest() { XGDMatrixFree(proxy_); }
 
-int ArrayIterForTest::Next() {
-  if (iter_ == n_batches_) {
-    return 0;
-  }
-  XGProxyDMatrixSetDataDense(proxy_, batches_[iter_].c_str());
-  iter_++;
-  return 1;
-}
-
-size_t constexpr ArrayIterForTest::kRows;
-size_t constexpr ArrayIterForTest::kCols;
-
 void DMatrixToCSR(DMatrix *dmat, std::vector<float> *p_data,
                   std::vector<size_t> *p_row_ptr,
                   std::vector<bst_feature_t> *p_cids) {

tests/cpp/helpers.cu

@@ -15,10 +15,6 @@ CudaArrayIterForTest::CudaArrayIterForTest(float sparsity, size_t rows,
   this->Reset();
 }
 
-size_t constexpr CudaArrayIterForTest::kRows;
-size_t constexpr CudaArrayIterForTest::kCols;
-size_t constexpr CudaArrayIterForTest::kBatches;
-
 int CudaArrayIterForTest::Next() {
   if (iter_ == n_batches_) {
     return 0;

tests/cpp/helpers.h

@@ -298,6 +298,7 @@ class RandomDataGenerator {
 #if defined(XGBOOST_USE_CUDA)
   std::shared_ptr<DMatrix> GenerateDeviceDMatrix();
 #endif
+  std::shared_ptr<DMatrix> GenerateQuantileDMatrix();
 };
 
 inline std::vector<float>
@@ -401,38 +402,38 @@ class ArrayIterForTest {
   size_t n_batches_;
 
  public:
-  size_t static constexpr kRows { 1000 };
-  size_t static constexpr kBatches { 100 };
-  size_t static constexpr kCols { 13 };
+  size_t static constexpr Rows() { return 1024; }
+  size_t static constexpr Batches() { return 100; }
+  size_t static constexpr Cols() { return 13; }
 
-  std::string AsArray() const {
-    return interface_;
-  }
+ public:
+  std::string AsArray() const { return interface_; }
 
-  virtual int Next();
-  virtual void Reset() {
-    iter_ = 0;
-  }
+  virtual int Next() = 0;
+  virtual void Reset() { iter_ = 0; }
   size_t Iter() const { return iter_; }
   auto Proxy() -> decltype(proxy_) { return proxy_; }
 
-  explicit ArrayIterForTest(float sparsity, size_t rows = kRows,
-                            size_t cols = kCols, size_t batches = kBatches);
+  explicit ArrayIterForTest(float sparsity, size_t rows, size_t cols, size_t batches);
   virtual ~ArrayIterForTest();
 };
 
 class CudaArrayIterForTest : public ArrayIterForTest {
  public:
-  size_t static constexpr kRows{1000};
-  size_t static constexpr kBatches{100};
-  size_t static constexpr kCols{13};
-
-  explicit CudaArrayIterForTest(float sparsity, size_t rows = kRows,
-                                size_t cols = kCols, size_t batches = kBatches);
+  explicit CudaArrayIterForTest(float sparsity, size_t rows = Rows(), size_t cols = Cols(),
+                                size_t batches = Batches());
   int Next() override;
   ~CudaArrayIterForTest() override = default;
 };
 
+class NumpyArrayIterForTest : public ArrayIterForTest {
+ public:
+  explicit NumpyArrayIterForTest(float sparsity, size_t rows = Rows(), size_t cols = Cols(),
+                                 size_t batches = Batches());
+  int Next() override;
+  ~NumpyArrayIterForTest() override = default;
+};
+
 void DMatrixToCSR(DMatrix *dmat, std::vector<float> *p_data,
                   std::vector<size_t> *p_row_ptr,
                   std::vector<bst_feature_t> *p_cids);