62 lines
4.0 KiB
C++
62 lines
4.0 KiB
C++
#ifndef PYCLASSIFIER_H
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#define PYCLASSIFIER_H
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#include <string>
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#include <map>
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#include <vector>
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#include <utility>
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#include "boost/python/detail/wrap_python.hpp"
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#include <boost/python/numpy.hpp>
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#include <torch/torch.h>
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#include <nlohmann/json.hpp>
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#include "bayesnet/classifiers/Classifier.h"
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#include "PyWrap.h"
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#include "TypeId.h"
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namespace pywrap {
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class PyClassifier : public bayesnet::BaseClassifier {
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public:
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PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
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virtual ~PyClassifier();
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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, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
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// X is nxm tensor, y is nx1 tensor
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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, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override;
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PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
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PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
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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, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
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torch::Tensor predict(torch::Tensor& X) override;
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std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented
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torch::Tensor predict_proba(torch::Tensor& X) override;
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std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int >>& X) override { return std::vector<std::vector<double>>(); }; // Not implemented
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float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; }; // Not implemented
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float score(torch::Tensor& X, torch::Tensor& y) override;
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int getClassNumStates() const override { return 0; };
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std::string version();
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std::string callMethodString(const std::string& method);
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int callMethodSumOfItems(const std::string& method) const;
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int callMethodInt(const std::string& method) const;
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std::string getVersion() override { return this->version(); };
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int getNumberOfNodes() const override { return 0; };
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int getNumberOfEdges() const override { return 0; };
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int getNumberOfStates() const override { return 0; };
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std::vector<std::string> show() const override { return std::vector<std::string>(); }
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std::vector<std::string> graph(const std::string& title = "") const override { return std::vector<std::string>(); }
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bayesnet::status_t getStatus() const override { return bayesnet::NORMAL; };
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std::vector<std::string> topological_order() override { return std::vector<std::string>(); }
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std::string dump_cpt() const override { return ""; };
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std::vector<std::string> getNotes() const override { return notes; };
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void setHyperparameters(const nlohmann::json& hyperparameters) override;
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protected:
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nlohmann::json hyperparameters;
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void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override {};
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std::vector<std::string> notes;
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bool xgboost = false;
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private:
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PyWrap* pyWrap;
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std::string module;
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std::string className;
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bool sklearn;
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clfId_t id;
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bool fitted;
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};
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} /* namespace pywrap */
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#endif /* PYCLASSIFIER_H */ |