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Fit PyWrap into BayesNet

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-13 11:13:32 +01:00
parent 6a23e2cc26
commit 431b3a3aa5
15 changed files with 48 additions and 40 deletions
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30
src/PyClassifiers/PyClassifier.h
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@@ -13,21 +13,37 @@
#include "TypeId.h"
namespace pywrap {
class PyClassifier : public Classifier {
class PyClassifier : public bayesnet::BaseClassifier {
public:
PyClassifier(const std::string& module, const std::string& className);
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) 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) { return *this; };
torch::Tensor predict(torch::Tensor& X) override;
double score(torch::Tensor& X, torch::Tensor& y) override;
std::string version() override;
std::string sklearnVersion() 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 sklearnVersion();
std::string callMethodString(const std::string& method);
void setHyperparameters(const nlohmann::json& hyperparameters) override;
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) override;
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;