Ricardo Montañana Gómez
9f3de4d924
- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU". - *ld_proposed_cuts*: number of cut points to return. - *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition. - *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
51 lines
2.1 KiB
C++
51 lines
2.1 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 PROPOSAL_H
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#define PROPOSAL_H
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#include <string>
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#include <map>
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#include <torch/torch.h>
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#include <fimdlp/CPPFImdlp.h>
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#include <fimdlp/BinDisc.h>
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#include "bayesnet/network/Network.h"
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#include <nlohmann/json.hpp>
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#include "Classifier.h"
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namespace bayesnet {
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class Proposal {
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public:
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Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
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void setHyperparameters(const nlohmann::json& hyperparameters_);
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protected:
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void checkInput(const torch::Tensor& X, const torch::Tensor& y);
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torch::Tensor prepareX(torch::Tensor& X);
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map<std::string, std::vector<int>> localDiscretizationProposal(const map<std::string, std::vector<int>>& states, Network& model);
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map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
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torch::Tensor Xf; // X continuous nxm tensor
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torch::Tensor y; // y discrete nx1 tensor
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map<std::string, std::unique_ptr<mdlp::Discretizer>> discretizers;
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// MDLP parameters
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struct {
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size_t min_length = 3; // Minimum length of the interval to consider it in mdlp
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float proposed_cuts = 0.0; // Proposed cuts for the Discretization algorithm
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int max_depth = std::numeric_limits<int>::max(); // Maximum depth of the MDLP tree
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} ld_params;
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nlohmann::json validHyperparameters_ld = { "ld_algorithm", "ld_proposed_cuts", "mdlp_min_length", "mdlp_max_depth" };
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private:
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std::vector<int> factorize(const std::vector<std::string>& labels_t);
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torch::Tensor& pDataset; // (n+1)xm tensor
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std::vector<std::string>& pFeatures;
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std::string& pClassName;
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enum class discretization_t {
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MDLP,
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BINQ,
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BINU
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} discretizationType = discretization_t::MDLP; // Default discretization type
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};
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}
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#endif |