66 lines
3.6 KiB
C++
66 lines
3.6 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#ifndef CLASSIFIER_H
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#define CLASSIFIER_H
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#include <torch/torch.h>
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#include "bayesnet/utils/BayesMetrics.h"
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#include "bayesnet/BaseClassifier.h"
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namespace bayesnet {
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class Classifier : public BaseClassifier {
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public:
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Classifier(Network model);
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virtual ~Classifier() = default;
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Classifier& 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) override;
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Classifier& 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) override;
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Classifier& 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) override;
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Classifier& 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) override;
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void addNodes();
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int getNumberOfNodes() const override;
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int getNumberOfEdges() const override;
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int getNumberOfStates() const override;
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int getClassNumStates() const override;
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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;
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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;
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status_t getStatus() const override { return status; }
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std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
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float score(torch::Tensor& X, torch::Tensor& y) override;
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float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
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std::vector<std::string> show() const override;
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std::vector<std::string> topological_order() override;
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std::vector<std::string> getNotes() const override { return notes; }
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std::string dump_cpt() const override;
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void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
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protected:
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torch::Device device;
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bool fitted;
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unsigned int m, n; // m: number of samples, n: number of features
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Network model;
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Metrics metrics;
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std::vector<std::string> features;
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std::string className;
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std::map<std::string, std::vector<int>> states;
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torch::Tensor dataset; // (n+1)xm tensor
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status_t status = NORMAL;
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std::vector<std::string> notes; // Used to store messages occurred during the fit process
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void checkFitParameters();
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virtual void buildModel(const torch::Tensor& weights) = 0;
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void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
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void buildDataset(torch::Tensor& y);
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private:
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Classifier& build(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);
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};
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}
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#endif
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