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Add hyperparameters management in experiments

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Ricardo Montañana Gómez
2023-08-20 17:57:38 +02:00
parent 7a6ec73d63
commit 4964aab722
17 changed files with 141 additions and 117 deletions
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1
sample/CMakeLists.txt
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@@ -3,5 +3,6 @@ include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
target_link_libraries(BayesNetSample BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

201
sample/sample.cc
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@@ -3,6 +3,7 @@
#include <string>
#include <map>
#include <argparse/argparse.hpp>
#include <nlohmann/json.hpp>
#include "ArffFiles.h"
#include "BayesMetrics.h"
#include "CPPFImdlp.h"
@@ -141,111 +142,97 @@ int main(int argc, char** argv)
/*
* Begin Processing
*/
auto ypred = torch::tensor({ 1,2,3,2,2,3,4,5,2,1 });
auto y = torch::tensor({ 0,0,0,0,2,3,4,0,0,0 });
auto weights = torch::ones({ 10 }, kDouble);
auto mask = ypred == y;
cout << "ypred:" << ypred << endl;
cout << "y:" << y << endl;
cout << "weights:" << weights << endl;
cout << "mask:" << mask << endl;
double value_to_add = 0.5;
weights += mask.to(torch::kDouble) * value_to_add;
cout << "New weights:" << weights << endl;
auto masked_weights = weights * mask.to(weights.dtype());
double sum_of_weights = masked_weights.sum().item<double>();
cout << "Sum of weights: " << sum_of_weights << endl;
//weights.index_put_({ mask }, weights + 10);
// auto handler = ArffFiles();
// handler.load(complete_file_name, class_last);
// // Get Dataset X, y
// vector<mdlp::samples_t>& X = handler.getX();
// mdlp::labels_t& y = handler.getY();
// // Get className & Features
// auto className = handler.getClassName();
// vector<string> features;
// auto attributes = handler.getAttributes();
// transform(attributes.begin(), attributes.end(), back_inserter(features),
// [](const pair<string, string>& item) { return item.first; });
// // Discretize Dataset
// auto [Xd, maxes] = discretize(X, y, features);
// maxes[className] = *max_element(y.begin(), y.end()) + 1;
// map<string, vector<int>> states;
// for (auto feature : features) {
// states[feature] = vector<int>(maxes[feature]);
// }
// states[className] = vector<int>(maxes[className]);
// auto clf = platform::Models::instance()->create(model_name);
// clf->fit(Xd, y, features, className, states);
// if (dump_cpt) {
// cout << "--- CPT Tables ---" << endl;
// clf->dump_cpt();
// }
// auto lines = clf->show();
// for (auto line : lines) {
// cout << line << endl;
// }
// cout << "--- Topological Order ---" << endl;
// auto order = clf->topological_order();
// for (auto name : order) {
// cout << name << ", ";
// }
// cout << "end." << endl;
// auto score = clf->score(Xd, y);
// cout << "Score: " << score << endl;
// auto graph = clf->graph();
// auto dot_file = model_name + "_" + file_name;
// ofstream file(dot_file + ".dot");
// file << graph;
// file.close();
// cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
// cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
// string stratified_string = stratified ? " Stratified" : "";
// cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
// cout << "==========================================" << endl;
// torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
// torch::Tensor yt = torch::tensor(y, torch::kInt32);
// for (int i = 0; i < features.size(); ++i) {
// Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
// }
// float total_score = 0, total_score_train = 0, score_train, score_test;
// Fold* fold;
// if (stratified)
// fold = new StratifiedKFold(nFolds, y, seed);
// else
// fold = new KFold(nFolds, y.size(), seed);
// for (auto i = 0; i < nFolds; ++i) {
// auto [train, test] = fold->getFold(i);
// cout << "Fold: " << i + 1 << endl;
// if (tensors) {
// auto ttrain = torch::tensor(train, torch::kInt64);
// auto ttest = torch::tensor(test, torch::kInt64);
// torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
// torch::Tensor ytraint = yt.index({ ttrain });
// torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
// torch::Tensor ytestt = yt.index({ ttest });
// clf->fit(Xtraint, ytraint, features, className, states);
// auto temp = clf->predict(Xtraint);
// score_train = clf->score(Xtraint, ytraint);
// score_test = clf->score(Xtestt, ytestt);
// } else {
// auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
// auto [Xtest, ytest] = extract_indices(test, Xd, y);
// clf->fit(Xtrain, ytrain, features, className, states);
// score_train = clf->score(Xtrain, ytrain);
// score_test = clf->score(Xtest, ytest);
// }
// if (dump_cpt) {
// cout << "--- CPT Tables ---" << endl;
// clf->dump_cpt();
// }
// total_score_train += score_train;
// total_score += score_test;
// cout << "Score Train: " << score_train << endl;
// cout << "Score Test : " << score_test << endl;
// cout << "-------------------------------------------------------------------------------" << endl;
// }
// cout << "**********************************************************************************" << endl;
// cout << "Average Score Train: " << total_score_train / nFolds << endl;
// cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
weights.index_put_({ mask }, weights + 10);
auto handler = ArffFiles();
handler.load(complete_file_name, class_last);
// Get Dataset X, y
vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features),
[](const pair<string, string>& item) { return item.first; });
// Discretize Dataset
auto [Xd, maxes] = discretize(X, y, features);
maxes[className] = *max_element(y.begin(), y.end()) + 1;
map<string, vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
}
states[className] = vector<int>(maxes[className]);
auto clf = platform::Models::instance()->create(model_name);
clf->fit(Xd, y, features, className, states);
if (dump_cpt) {
cout << "--- CPT Tables ---" << endl;
clf->dump_cpt();
}
auto lines = clf->show();
for (auto line : lines) {
cout << line << endl;
}
cout << "--- Topological Order ---" << endl;
auto order = clf->topological_order();
for (auto name : order) {
cout << name << ", ";
}
cout << "end." << endl;
auto score = clf->score(Xd, y);
cout << "Score: " << score << endl;
auto graph = clf->graph();
auto dot_file = model_name + "_" + file_name;
ofstream file(dot_file + ".dot");
file << graph;
file.close();
cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
string stratified_string = stratified ? " Stratified" : "";
cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
cout << "==========================================" << endl;
torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
torch::Tensor yt = torch::tensor(y, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
}
float total_score = 0, total_score_train = 0, score_train, score_test;
Fold* fold;
if (stratified)
fold = new StratifiedKFold(nFolds, y, seed);
else
fold = new KFold(nFolds, y.size(), seed);
for (auto i = 0; i < nFolds; ++i) {
auto [train, test] = fold->getFold(i);
cout << "Fold: " << i + 1 << endl;
if (tensors) {
auto ttrain = torch::tensor(train, torch::kInt64);
auto ttest = torch::tensor(test, torch::kInt64);
torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
torch::Tensor ytraint = yt.index({ ttrain });
torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
torch::Tensor ytestt = yt.index({ ttest });
clf->fit(Xtraint, ytraint, features, className, states);
auto temp = clf->predict(Xtraint);
score_train = clf->score(Xtraint, ytraint);
score_test = clf->score(Xtestt, ytestt);
} else {
auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
auto [Xtest, ytest] = extract_indices(test, Xd, y);
clf->fit(Xtrain, ytrain, features, className, states);
score_train = clf->score(Xtrain, ytrain);
score_test = clf->score(Xtest, ytest);
}
if (dump_cpt) {
cout << "--- CPT Tables ---" << endl;
clf->dump_cpt();
}
total_score_train += score_train;
total_score += score_test;
cout << "Score Train: " << score_train << endl;
cout << "Score Test : " << score_test << endl;
cout << "-------------------------------------------------------------------------------" << endl;
}
cout << "**********************************************************************************" << endl;
cout << "Average Score Train: " << total_score_train / nFolds << endl;
cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
}