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Ricardo Montañana Gómez
2025-04-30 11:11:49 +02:00
commit e144d65e11
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bayesnet/BaseClassifier.h Normal file
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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#pragma once
#include <vector>
#include <torch/torch.h>
#include <nlohmann/json.hpp>
#include "bayesnet/network/Network.h"
namespace bayesnet {
enum status_t { NORMAL, WARNING, ERROR };
class BaseClassifier {
public:
virtual ~BaseClassifier() = default;
// X is nxm std::vector, y is nx1 std::vector
virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
// X is nxm tensor, y is nx1 tensor
virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
torch::Tensor virtual predict(torch::Tensor& X) = 0;
std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
std::vector<std::vector<double>> virtual predict_proba(std::vector<std::vector<int >>& X) = 0;
status_t virtual getStatus() const = 0;
float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
int virtual getNumberOfNodes() const = 0;
int virtual getNumberOfEdges() const = 0;
int virtual getNumberOfStates() const = 0;
int virtual getClassNumStates() const = 0;
std::vector<std::string> virtual show() const = 0;
std::vector<std::string> virtual graph(const std::string& title = "") const = 0;
virtual std::string getVersion() = 0;
std::vector<std::string> virtual topological_order() = 0;
std::vector<std::string> virtual getNotes() const = 0;
std::string virtual dump_cpt() const = 0;
virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
protected:
virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
std::vector<std::string> validHyperparameters;
std::vector<std::string> notes; // Used to store messages occurred during the fit process
status_t status = NORMAL;
};
}