55 lines
3.1 KiB
C++
55 lines
3.1 KiB
C++
#ifndef PYCLASSIFIER_H
|
|
#define PYCLASSIFIER_H
|
|
#include "boost/python/detail/wrap_python.hpp"
|
|
#include <boost/python/numpy.hpp>
|
|
#include <nlohmann/json.hpp>
|
|
#include <string>
|
|
#include <map>
|
|
#include <vector>
|
|
#include <utility>
|
|
#include <torch/torch.h>
|
|
#include "PyWrap.h"
|
|
#include "Classifier.h"
|
|
#include "TypeId.h"
|
|
|
|
namespace pywrap {
|
|
class PyClassifier : public bayesnet::BaseClassifier {
|
|
public:
|
|
PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
|
|
virtual ~PyClassifier();
|
|
PyClassifier& 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) override { return *this; };
|
|
// X is nxm tensor, y is nx1 tensor
|
|
PyClassifier& 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) override;
|
|
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
|
|
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
|
|
PyClassifier& 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) override { return *this; };
|
|
torch::Tensor predict(torch::Tensor& X) override;
|
|
std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); };
|
|
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; };
|
|
float score(torch::Tensor& X, torch::Tensor& y) override;
|
|
void setHyperparameters(nlohmann::json& hyperparameters) override;
|
|
std::string version();
|
|
std::string callMethodString(const std::string& method);
|
|
std::string getVersion() override { return this->version(); };
|
|
int getNumberOfNodes()const override { return 0; };
|
|
int getNumberOfEdges()const override { return 0; };
|
|
int getNumberOfStates() const override { return 0; };
|
|
std::vector<std::string> show() const override { return std::vector<std::string>(); }
|
|
std::vector<std::string> graph(const std::string& title = "") const override { return std::vector<std::string>(); }
|
|
bayesnet::status_t getStatus() const override { return bayesnet::NORMAL; };
|
|
std::vector<std::string> topological_order() override { return std::vector<std::string>(); }
|
|
void dump_cpt() const override {};
|
|
protected:
|
|
void checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters);
|
|
nlohmann::json hyperparameters;
|
|
void trainModel(const torch::Tensor& weights) override {};
|
|
private:
|
|
PyWrap* pyWrap;
|
|
std::string module;
|
|
std::string className;
|
|
bool sklearn;
|
|
clfId_t id;
|
|
bool fitted;
|
|
};
|
|
} /* namespace pywrap */
|
|
#endif /* PYCLASSIFIER_H */ |