rmontanana/BayesNet
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Extract buildModel method to parent class in Boost

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This commit is contained in:
Ricardo Montañana Gómez 2024-05-15 20:00:44 +02:00
parent 54496c68f1
commit 8784a24898
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
6 changed files with 35 additions and 66 deletions
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32
bayesnet/ensembles/Boost.cc
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@ -3,6 +3,7 @@
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <folding.hpp>
#include "bayesnet/feature_selection/CFS.h"
#include "bayesnet/feature_selection/FCBF.h"
#include "bayesnet/feature_selection/IWSS.h"
@ -67,6 +68,37 @@ namespace bayesnet {
}
Classifier::setHyperparameters(hyperparameters);
}
void Boost::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
models.clear();
significanceModels.clear();
n_models = 0;
// Prepare the validation dataset
auto y_ = dataset.index({ -1, "..." });
if (convergence) {
// Prepare train & validation sets from train data
auto fold = folding::StratifiedKFold(5, y_, 271);
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
y_train = dataset.index({ -1, train_t });
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
y_test = dataset.index({ -1, test_t });
dataset = X_train;
m = X_train.size(1);
auto n_classes = states.at(className).size();
// Build dataset with train data
buildDataset(y_train);
metrics = Metrics(dataset, features, className, n_classes);
} else {
// Use all data to train
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
y_train = y_;
}
}
std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
{
int maxFeatures = 0;

1
bayesnet/ensembles/Boost.h
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@ -31,6 +31,7 @@ namespace bayesnet {
void setHyperparameters(const nlohmann::json& hyperparameters_) override;
protected:
std::vector<int> featureSelection(torch::Tensor& weights_);
void buildModel(const torch::Tensor& weights) override;
std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
torch::Tensor X_train, y_train, X_test, y_test;

32
bayesnet/ensembles/BoostA2DE.cc
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@ -18,38 +18,6 @@ namespace bayesnet {
BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
{
}
void BoostA2DE::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
models.clear();
significanceModels.clear();
n_models = 0;
// Prepare the validation dataset
auto y_ = dataset.index({ -1, "..." });
if (convergence) {
// Prepare train & validation sets from train data
auto fold = folding::StratifiedKFold(5, y_, 271);
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
y_train = dataset.index({ -1, train_t });
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
y_test = dataset.index({ -1, test_t });
dataset = X_train;
m = X_train.size(1);
auto n_classes = states.at(className).size();
// Build dataset with train data
buildDataset(y_train);
metrics = Metrics(dataset, features, className, n_classes);
} else {
// Use all data to train
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
y_train = y_;
}
}
void BoostA2DE::trainModel(const torch::Tensor& weights)
{

1
bayesnet/ensembles/BoostA2DE.h
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@ -17,7 +17,6 @@ namespace bayesnet {
virtual ~BoostA2DE() = default;
std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
protected:
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights) override;
};
}

34
bayesnet/ensembles/BoostAODE.cc
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@ -4,11 +4,11 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <random>
#include <set>
#include <functional>
#include <limits.h>
#include <tuple>
#include <folding.hpp>
#include "BoostAODE.h"
#include "lib/log/loguru.cpp"
@ -17,37 +17,7 @@ namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
{
}
void BoostAODE::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
models.clear();
significanceModels.clear();
n_models = 0;
// Prepare the validation dataset
auto y_ = dataset.index({ -1, "..." });
if (convergence) {
// Prepare train & validation sets from train data
auto fold = folding::StratifiedKFold(5, y_, 271);
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
y_train = dataset.index({ -1, train_t });
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
y_test = dataset.index({ -1, test_t });
dataset = X_train;
m = X_train.size(1);
auto n_classes = states.at(className).size();
// Build dataset with train data
buildDataset(y_train);
metrics = Metrics(dataset, features, className, n_classes);
} else {
// Use all data to train
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
y_train = y_;
}
}
std::vector<int> BoostAODE::initializeModels()
{
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);

1
bayesnet/ensembles/BoostAODE.h
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@ -17,7 +17,6 @@ namespace bayesnet {
virtual ~BoostAODE() = default;
std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
protected:
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights) override;
private:
std::vector<int> initializeModels();