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media / learning / impl / extra_trees_trainer.h [blame]
// Copyright 2018 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#ifndef MEDIA_LEARNING_IMPL_EXTRA_TREES_TRAINER_H_
#define MEDIA_LEARNING_IMPL_EXTRA_TREES_TRAINER_H_
#include <memory>
#include <vector>
#include "base/component_export.h"
#include "base/memory/weak_ptr.h"
#include "media/learning/common/learning_task.h"
#include "media/learning/impl/one_hot.h"
#include "media/learning/impl/random_number_generator.h"
#include "media/learning/impl/random_tree_trainer.h"
#include "media/learning/impl/training_algorithm.h"
namespace media {
namespace learning {
// Bagged forest of extremely randomized trees.
//
// These are an ensemble of trees. Each tree is constructed from the full
// training set. The trees are constructed by selecting a random subset of
// features at each node. For each feature, a uniformly random split point is
// chosen. The feature with the best randomly chosen split point is used.
//
// These will automatically convert nominal values to one-hot vectors.
class COMPONENT_EXPORT(LEARNING_IMPL) ExtraTreesTrainer final
: public TrainingAlgorithm,
public HasRandomNumberGenerator {
public:
ExtraTreesTrainer();
ExtraTreesTrainer(const ExtraTreesTrainer&) = delete;
ExtraTreesTrainer& operator=(const ExtraTreesTrainer&) = delete;
~ExtraTreesTrainer() override;
// TrainingAlgorithm
void Train(const LearningTask& task,
const TrainingData& training_data,
TrainedModelCB model_cb) override;
private:
void OnRandomTreeModel(TrainedModelCB model_cb, std::unique_ptr<Model> model);
std::unique_ptr<TrainingAlgorithm> tree_trainer_;
// In-flight training.
LearningTask task_;
std::vector<std::unique_ptr<Model>> trees_;
std::unique_ptr<OneHotConverter> converter_;
TrainingData converted_training_data_;
base::WeakPtrFactory<ExtraTreesTrainer> weak_ptr_factory_{this};
};
} // namespace learning
} // namespace media
#endif // MEDIA_LEARNING_IMPL_EXTRA_TREES_TRAINER_H_