[REVIEW] mdspan-ify make_regression

cpp/include/raft/random/make_regression.cuh

@@ -24,6 +24,8 @@
 #pragma once
 
 #include <algorithm>
+#include <optional>
+#include <raft/mdarray.hpp>
 
 #include "detail/make_regression.cuh"
 
@@ -58,7 +60,7 @@ namespace raft::random {
  * @param[in]   tail_strength   The relative importance of the fat noisy tail
  *                              of the singular values profile if
  *                              effective_rank is not -1
- * @param[in]   noise           Standard deviation of the gaussian noise
+ * @param[in]   noise           Standard deviation of the Gaussian noise
  *                              applied to the output
  * @param[in]   shuffle         Shuffle the samples and the features
  * @param[in]   seed            Seed for the random number generator
@@ -100,6 +102,81 @@ void make_regression(const raft::handle_t& handle,
                                  type);
 }
 
+/**
+ * @brief GPU-equivalent of sklearn.datasets.make_regression as documented at:
+ * https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html
+ *
+ * @tparam  DataT  Scalar type
+ * @tparam  IdxT   Index type
+ *
+ * @param[in]   handle          RAFT handle
+ * @param[out]  out             Row-major (samples, features) matrix to store
+ *                              the problem data
+ * @param[out]  values          Row-major (samples, targets) matrix to store
+ *                              the values for the regression problem
+ * @param[in]   n_informative   Number of informative features (non-zero
+ *                              coefficients)
+ * @param[out]  coef            If present, a row-major (features, targets) matrix
+ *                              to store the coefficients used to generate the values
+ *                              for the regression problem
+ * @param[in]   bias            A scalar that will be added to the values
+ * @param[in]   effective_rank  The approximate rank of the data matrix (used
+ *                              to create correlations in the data). -1 is the
+ *                              code to use well-conditioned data
+ * @param[in]   tail_strength   The relative importance of the fat noisy tail
+ *                              of the singular values profile if
+ *                              effective_rank is not -1
+ * @param[in]   noise           Standard deviation of the Gaussian noise
+ *                              applied to the output
+ * @param[in]   shuffle         Shuffle the samples and the features
+ * @param[in]   seed            Seed for the random number generator
+ * @param[in]   type            Random generator type
+ */
+template <typename DataT, typename IdxT>
+void make_regression(const raft::handle_t& handle,
+                     raft::device_matrix_view<DataT, IdxT, raft::row_major> out,
+                     raft::device_matrix_view<DataT, IdxT, raft::row_major> values,
+                     IdxT n_informative,
+                     std::optional<raft::device_matrix_view<DataT, IdxT, raft::row_major>> coef,
+                     DataT bias          = DataT{},
+                     IdxT effective_rank = static_cast<IdxT>(-1),
+                     DataT tail_strength = DataT{0.5},
+                     DataT noise         = DataT{},
+                     bool shuffle        = true,
+                     uint64_t seed       = 0ULL,
+                     GeneratorType type  = GenPhilox)
+{
+  const auto n_samples = out.extent(0);
+  assert(values.extent(0) == n_samples);
+  const auto n_features = out.extent(1);
+  const auto n_targets  = values.extent(1);
+
+  const bool have_coef = coef.has_value();
+  if (have_coef) {
+    const auto coef_ref = *coef;
+    assert(coef_ref.extent(0) == n_features);
+    assert(coef_ref.extent(1) == n_targets);
+  }
+  DataT* coef_ptr = have_coef ? (*coef).data_handle() : nullptr;
+
+  detail::make_regression_caller(handle,
+                                 out.data_handle(),
+                                 values.data_handle(),
+                                 n_samples,
+                                 n_features,
+                                 n_informative,
+                                 handle.get_stream(),
+                                 coef_ptr,
+                                 n_targets,
+                                 bias,
+                                 effective_rank,
+                                 tail_strength,
+                                 noise,
+                                 shuffle,
+                                 seed,
+                                 type);
+}
+
 }  // namespace raft::random
 
-#endif
+#endif

cpp/test/random/make_regression.cu

@@ -78,6 +78,10 @@ class MakeRegressionTest : public ::testing::TestWithParam<MakeRegressionInputs<
                     params.seed,
                     params.gtype);
 
+    // FIXME (mfh 2022/09/07) This test passes even if it doesn't call
+    // make_regression.  Please see
+    // https://github.com/rapidsai/raft/issues/814.
+
     // Calculate the values from the data and coefficients (column-major)
     T alpha = (T)1.0, beta = (T)0.0;
     RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(handle.get_cublas_handle(),
@@ -161,4 +165,126 @@ TEST_P(MakeRegressionTestD, Result)
 }
 INSTANTIATE_TEST_CASE_P(MakeRegressionTests, MakeRegressionTestD, ::testing::ValuesIn(inputsd_t));
 
+template <typename T>
+class MakeRegressionMdspanTest : public ::testing::TestWithParam<MakeRegressionInputs<T>> {
+ public:
+  MakeRegressionMdspanTest() = default;
+
+ protected:
+  void SetUp() override
+  {
+    auto stream = handle.get_stream();
+
+    // Noise must be zero to compare the actual and expected values
+    T noise = (T)0.0, tail_strength = (T)0.5;
+
+    rmm::device_uvector<T> data(params.n_samples * params.n_features, stream);
+    rmm::device_uvector<T> values_cm(params.n_samples * params.n_targets, stream);
+    rmm::device_uvector<T> coef(params.n_features * params.n_targets, stream);
+
+    using index_type = typename rmm::device_uvector<T>::index_type;
+    using matrix_view =
+      raft::device_matrix_view<T, raft::matrix_extent<index_type>, raft::row_major>;
+    matrix_view out_mat(data.data(), params.n_samples, params.n_features);
+    matrix_view values_mat(values_ret.data(), params.n_samples, params.n_targets);
+    matrix_view coef_mat(coef.data(), params.n_features, params.n_targets);
+
+    // Create the regression problem
+    make_regression(handle,
+                    out_mat,
+                    values_mat,
+                    params.n_informative,
+                    coef_mat,
+                    params.bias,
+                    params.effective_rank,
+                    tail_strength,
+                    noise,
+                    params.shuffle,
+                    params.seed,
+                    params.gtype);
+
+    // FIXME (mfh 2022/09/07) This test passes even if it doesn't call
+    // make_regression.  Please see
+    // https://github.com/rapidsai/raft/issues/814.
+
+    // Calculate the values from the data and coefficients (column-major)
+    T alpha{};
+    T beta{};
+    RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(handle.get_cublas_handle(),
+                                                     CUBLAS_OP_T,
+                                                     CUBLAS_OP_T,
+                                                     params.n_samples,
+                                                     params.n_targets,
+                                                     params.n_features,
+                                                     &alpha,
+                                                     data.data(),
+                                                     params.n_features,
+                                                     coef.data(),
+                                                     params.n_targets,
+                                                     &beta,
+                                                     values_cm.data(),
+                                                     params.n_samples,
+                                                     stream));
+
+    // Transpose the values to row-major
+    raft::linalg::transpose(
+      handle, values_cm.data(), values_prod.data(), params.n_samples, params.n_targets, stream);
+
+    // Add the bias
+    raft::linalg::addScalar(values_prod.data(),
+                            values_prod.data(),
+                            params.bias,
+                            params.n_samples * params.n_targets,
+                            stream);
+
+    // Count the number of zeroes in the coefficients
+    thrust::device_ptr<T> __coef = thrust::device_pointer_cast(coef.data());
+    constexpr T ZERO{};
+    zero_count = thrust::count(__coef, __coef + params.n_features * params.n_targets, ZERO);
+  }
+
+ private:
+  MakeRegressionInputs<T> params{::testing::TestWithParam<MakeRegressionInputs<T>>::GetParam()};
+  raft::handle_t handle;
+  rmm::device_uvector<T> values_ret{params.n_samples * params.n_targets, handle.get_stream()};
+  rmm::device_uvector<T> values_prod{params.n_samples * params.n_targets, handle.get_stream()};
+  int zero_count = -1;
+};
+
+using MakeRegressionMdspanTestF = MakeRegressionTest<float>;
+
+TEST_P(MakeRegressionMdspanTestF, Result)
+{
+  ASSERT_TRUE(match(params.n_targets * (params.n_features - params.n_informative),
+                    zero_count,
+                    raft::Compare<int>()));
+  ASSERT_TRUE(devArrMatch(values_ret.data(),
+                          values_prod.data(),
+                          params.n_samples,
+                          params.n_targets,
+                          raft::CompareApprox<float>(params.tolerance),
+                          handle.get_stream()));
+}
+INSTANTIATE_TEST_CASE_P(MakeRegressionMdspanTests,
+                        MakeRegressionMdspanTestF,
+                        ::testing::ValuesIn(inputsf_t));
+
+using MakeRegressionMdspanTestD = MakeRegressionTest<double>;
+
+TEST_P(MakeRegressionMdspanTestD, Result)
+{
+  ASSERT_TRUE(match(params.n_targets * (params.n_features - params.n_informative),
+                    zero_count,
+                    raft::Compare<int>()));
+  ASSERT_TRUE(devArrMatch(values_ret.data(),
+                          values_prod.data(),
+                          params.n_samples,
+                          params.n_targets,
+                          raft::CompareApprox<double>(params.tolerance),
+                          handle.get_stream()));
+}
+INSTANTIATE_TEST_CASE_P(MakeRegressionMdspanTests,
+                        MakeRegressionMdspanTestD,
+                        ::testing::ValuesIn(inputsd_t));
+
 }  // end namespace raft::random