rmontanana/BayesNet
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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
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
15 changed files with 48 additions and 40 deletions
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2
src/BayesNet/BaseClassifier.h
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@ -26,7 +26,7 @@ namespace bayesnet {
int virtual getNumberOfStates() const = 0;
std::vector<std::string> virtual show() const = 0;
std::vector<std::string> virtual graph(const std::string& title = "") const = 0;
const std::string inline getVersion() const { return "0.2.0"; };
virtual std::string getVersion() = 0;
std::vector<std::string> virtual topological_order() = 0;
void virtual dump_cpt()const = 0;
virtual void setHyperparameters(nlohmann::json& hyperparameters) = 0;

3
src/BayesNet/CMakeLists.txt
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@ -3,6 +3,9 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
include_directories(${BayesNet_SOURCE_DIR}/src/PyClassifiers)
include_directories(${Python3_INCLUDE_DIRS})
add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc
KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc
Mst.cc Proposal.cc CFS.cc FCBF.cc IWSS.cc FeatureSelect.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)

1
src/BayesNet/Classifier.h
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@ -37,6 +37,7 @@ namespace bayesnet {
int getNumberOfStates() const override;
torch::Tensor predict(torch::Tensor& X) override;
status_t getStatus() const override { return status; }
std::string getVersion() override { return "0.2.0"; };
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
float score(torch::Tensor& X, torch::Tensor& y) override;
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;

1
src/Platform/CMakeLists.txt
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@ -6,6 +6,7 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
include_directories(${Python3_INCLUDE_DIRS})
add_executable(b_main b_main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc ReportConsole.cc ReportBase.cc)
add_executable(b_manage b_manage.cc Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)

3
src/Platform/Models.h
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@ -12,6 +12,9 @@
#include "AODELd.h"
#include "BoostAODE.h"
#include "STree.h"
#include "ODTE.h"
#include "SVC.h"
#include "RandomForest.h"
namespace platform {
class Models {
private:

8
src/Platform/modelRegister.h
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@ -18,6 +18,12 @@ static platform::Registrar registrarALD("AODELd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
static platform::Registrar registrarBA("BoostAODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
static pywrap::Registrar registrarSt("STree",
static platform::Registrar registrarSt("STree",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::STree();});
static platform::Registrar registrarOdte("Odte",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::ODTE();});
static platform::Registrar registrarSvc("SVC",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::SVC();});
static platform::Registrar registrarRaF("RandomForest",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
#endif

22
src/PyClassifiers/Classifier.h
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@ -1,22 +0,0 @@
#ifndef CLASSIFIER_H
#define CLASSIFIER_H
#include <torch/torch.h>
#include "BaseClassifier.h"
#include <nlohmann/json.hpp>
#include <string>
#include <map>
#include <vector>
namespace pywrap {
class Classifier : bayesnet::BaseClassifier {
public:
Classifier() = default;
virtual ~Classifier() = default;
virtual Classifier& fit(torch::Tensor& X, torch::Tensor& y) = 0;
virtual std::string version() = 0;
virtual std::string sklearnVersion() = 0;
protected:
virtual void checkHyperparameters(const std::vector<std::string>& validKeys, const nlohmann::json& hyperparameters) = 0;
};
} /* namespace pywrap */
#endif /* CLASSIFIER_H */

2
src/PyClassifiers/ODTE.cc
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@ -5,7 +5,7 @@ namespace pywrap {
{
return callMethodString("graph");
}
void ODTE::setHyperparameters(const nlohmann::json& hyperparameters)
void ODTE::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "n_jobs", "n_estimators", "random_state" };

2
src/PyClassifiers/ODTE.h
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@ -9,7 +9,7 @@ namespace pywrap {
ODTE() : PyClassifier("odte", "Odte") {};
~ODTE() = default;
std::string graph();
void setHyperparameters(const nlohmann::json& hyperparameters) override;
void setHyperparameters(nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* ODTE_H */

6
src/PyClassifiers/PyClassifier.cc
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@ -74,15 +74,15 @@ namespace pywrap {
Py_XDECREF(incoming);
return resultTensor;
}
double PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
float PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
{
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
CPyObject yp = bp::incref(bp::object(yn).ptr());
auto result = pyWrap->score(id, Xp, yp);
float result = pyWrap->score(id, Xp, yp);
return result;
}
void PyClassifier::setHyperparameters(const nlohmann::json& hyperparameters)
void PyClassifier::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid, default is no hyperparameters
const std::vector<std::string> validKeys = { };

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;

2
src/PyClassifiers/STree.cc
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@ -5,7 +5,7 @@ namespace pywrap {
{
return callMethodString("graph");
}
void STree::setHyperparameters(const nlohmann::json& hyperparameters)
void STree::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "C", "n_jobs", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy" };

2
src/PyClassifiers/STree.h
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@ -9,7 +9,7 @@ namespace pywrap {
STree() : PyClassifier("stree", "Stree") {};
~STree() = default;
std::string graph();
void setHyperparameters(const nlohmann::json& hyperparameters) override;
void setHyperparameters(nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */
#endif /* STREE_H */

2
src/PyClassifiers/SVC.cc
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@ -5,7 +5,7 @@ namespace pywrap {
{
return sklearnVersion();
}
void SVC::setHyperparameters(const nlohmann::json& hyperparameters)
void SVC::setHyperparameters(nlohmann::json& hyperparameters)
{
// Check if hyperparameters are valid
const std::vector<std::string> validKeys = { "C", "gamma", "kernel", "random_state" };

2
src/PyClassifiers/SVC.h
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@ -8,7 +8,7 @@ namespace pywrap {
SVC() : PyClassifier("sklearn.svm", "SVC") {};
~SVC() = default;
std::string version();
void setHyperparameters(const nlohmann::json& hyperparameters) override;
void setHyperparameters(nlohmann::json& hyperparameters) override;
};
} /* namespace pywrap */