Trainer.h

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#ifndef TRAINER_H
#define TRAINER_H
#include "txeo/Logger.h"
#include "txeo/LoggerConsole.h"
#pragma once
 
#include "txeo/DataTable.h"
#include "txeo/DataTableNorm.h"
#include "txeo/Tensor.h"
#include "txeo/types.h"
 
#include <cstddef>
#include <stdexcept>
 
namespace txeo {
enum class LossFunc;
 
template <typename T>
class Trainer {
  public:
    Trainer(const Trainer &) = delete;
    Trainer(Trainer &&) = delete;
    Trainer &operator=(const Trainer &) = delete;
    Trainer &operator=(Trainer &&) = delete;
    virtual ~Trainer() = default;
 
    Trainer(txeo::DataTable<T> &&data, txeo::Logger &logger = txeo::LoggerConsole::instance())
        : _data_table{std::make_unique<txeo::DataTable<T>>(std::move(data))}, _logger{&logger} {};
 
    Trainer(const txeo::DataTable<T> &data) : Trainer{data.clone()} {};
 
    virtual void fit(size_t epochs, txeo::LossFunc metric);
 
    virtual void fit(size_t epochs, txeo::LossFunc metric, size_t patience);
 
    virtual void fit(size_t epochs, txeo::LossFunc metric, size_t patience,
                     txeo::NormalizationType type);
 
    T compute_test_loss(txeo::LossFunc metric) const;
 
    virtual txeo::Tensor<T> predict(const txeo::Tensor<T> &input) const = 0;
 
    [[nodiscard]] bool is_trained() const { return _is_trained; }
 
    const txeo::DataTable<T> &data_table() const { return *_data_table; }
 
    void enable_feature_norm(txeo::NormalizationType type);
 
    void disable_feature_norm() { _is_norm_enabled = false; };
 
  protected:
    Trainer() = default;
 
    bool _is_trained{false};
    bool _is_early_stop{false};
    size_t _patience{0};
 
    std::unique_ptr<txeo::DataTable<T>> _data_table;
    txeo::Logger *_logger{nullptr};
 
    txeo::DataTableNorm<T> _data_table_norm;
    bool _is_norm_enabled{false};
 
    virtual void train(size_t epochs, txeo::LossFunc loss_func) = 0;
};
 
class TrainerError : public std::runtime_error {
  public:
    using std::runtime_error::runtime_error;
};
 
} // namespace txeo
 
#endif