svc.hpp

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/*
 * Copyright (c) 2019-2025, NVIDIA CORPORATION.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
 
#pragma once
 
#include "svm_model.h"
#include "svm_parameter.h"
 
#include <cuml/common/logger.hpp>
#include <cuml/matrix/kernel_params.hpp>
 
#include <raft/core/handle.hpp>
 
namespace ML {
namespace SVM {
 
// Forward declarations of the stateless API
template <typename math_t>
void svcFit(const raft::handle_t& handle,
            math_t* input,
            int n_rows,
            int n_cols,
            math_t* labels,
            const SvmParameter& param,
            ML::matrix::KernelParams& kernel_params,
            SvmModel<math_t>& model,
            const math_t* sample_weight);
 
template <typename math_t>
void svcFitSparse(const raft::handle_t& handle,
                  int* indptr,
                  int* indices,
                  math_t* data,
                  int n_rows,
                  int n_cols,
                  int nnz,
                  math_t* labels,
                  const SvmParameter& param,
                  ML::matrix::KernelParams& kernel_params,
                  SvmModel<math_t>& model,
                  const math_t* sample_weight);
 
template <typename math_t>
void svcPredict(const raft::handle_t& handle,
                math_t* input,
                int n_rows,
                int n_cols,
                ML::matrix::KernelParams& kernel_params,
                const SvmModel<math_t>& model,
                math_t* preds,
                math_t buffer_size,
                bool predict_class);
 
template <typename math_t>
void svcPredictSparse(const raft::handle_t& handle,
                      int* indptr,
                      int* indices,
                      math_t* data,
                      int n_rows,
                      int n_cols,
                      int nnz,
                      ML::matrix::KernelParams& kernel_params,
                      const SvmModel<math_t>& model,
                      math_t* preds,
                      math_t buffer_size,
                      bool predict_class);
 
template <typename math_t>
void svmFreeBuffers(const raft::handle_t& handle, SvmModel<math_t>& m);
 
template <typename math_t>
class SVC {
 public:
  // Public members for easier access during testing from Python.
 
  ML::matrix::KernelParams kernel_params;
  SvmParameter param;
  SvmModel<math_t> model;
  SVC(raft::handle_t& handle,
      math_t C   = 1,
      math_t tol = 1.0e-3,
      ML::matrix::KernelParams kernel_params =
        ML::matrix::KernelParams{ML::matrix::KernelType::LINEAR, 3, 1, 0},
      math_t cache_size                   = 200,
      int max_iter                        = -1,
      int nochange_steps                  = 1000,
      rapids_logger::level_enum verbosity = rapids_logger::level_enum::info);
 
  ~SVC();
 
  void fit(
    math_t* input, int n_rows, int n_cols, math_t* labels, const math_t* sample_weight = nullptr);
 
  void predict(math_t* input, int n_rows, int n_cols, math_t* preds);
 
  void decisionFunction(math_t* input, int n_rows, int n_cols, math_t* preds);
 
 private:
  const raft::handle_t& handle;
};
 
};  // end namespace SVM
};  // end namespace ML