rmontanana/BayesNet
rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests Packages Projects Releases 3 Wiki Activity

Solve voting vector error

Browse Source
This commit is contained in:
Ricardo Montañana Gómez 2023-09-02 13:58:12 +02:00
parent 7806f961e2
commit 182b07ed90
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
11 changed files with 45 additions and 60 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
.vscode/launch.json vendored
View File

@ -27,15 +27,15 @@
"-m",
"AODE",
"-p",
"/Users/rmontanana/Code/discretizbench/datasets",
"/home/rmontanana/Code/discretizbench/datasets",
"--stratified",
"-d",
"letter",
"mfeat-morphological",
"--discretize"
// "--hyperparameters",
// "{\"repeatSparent\": true, \"maxModels\": 12}"
],
"cwd": "/Users/rmontanana/Code/discretizbench",
"cwd": "/home/rmontanana/Code/discretizbench",
},
{
"type": "lldb",

5
CMakeLists.txt
View File

@ -1,7 +1,7 @@
cmake_minimum_required(VERSION 3.20)
project(BayesNet
VERSION 0.1.0
VERSION 0.2.0
DESCRIPTION "Bayesian Network and basic classifiers Library."
HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
LANGUAGES CXX
@ -40,8 +40,7 @@ if (CODE_COVERAGE)
enable_testing()
include(CodeCoverage)
MESSAGE("Code coverage enabled")
set(CMAKE_C_FLAGS " ${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0")
SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
endif (CODE_COVERAGE)

1
data/_TAN_cpp_accuracy__.json
View File

@ -1 +0,0 @@
null

2
src/BayesNet/Classifier.cc
View File

@ -13,7 +13,7 @@ namespace bayesnet {
m = dataset.size(1);
n = dataset.size(0) - 1;
checkFitParameters();
auto n_classes = states[className].size();
auto n_classes = states.at(className).size();
metrics = Metrics(dataset, features, className, n_classes);
model.initialize();
buildModel(weights);

33
src/BayesNet/Ensemble.cc
View File

@ -17,9 +17,13 @@ namespace bayesnet {
{
auto y_pred_ = y_pred.accessor<int, 2>();
vector<int> y_pred_final;
int numClasses = states.at(className).size();
// y_pred is m x n_models with the prediction of every model for each sample
for (int i = 0; i < y_pred.size(0); ++i) {
vector<double> votes(y_pred.size(1), 0);
for (int j = 0; j < y_pred.size(1); ++j) {
// votes store in each index (value of class) the significance added by each model
// i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
vector<double> votes(numClasses, 0.0);
for (int j = 0; j < n_models; ++j) {
votes[y_pred_[i][j]] += significanceModels[j];
}
// argsort in descending order
@ -34,22 +38,17 @@ namespace bayesnet {
throw logic_error("Ensemble has not been fitted");
}
Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
// //Create a threadpool
// auto threads{ vector<thread>() };
// mutex mtx;
// for (auto i = 0; i < n_models; ++i) {
// threads.push_back(thread([&, i]() {
// auto ypredict = models[i]->predict(X);
// lock_guard<mutex> lock(mtx);
// y_pred.index_put_({ "...", i }, ypredict);
// }));
// Hacer voting aquí ? ? ?
// }
// for (auto& thread : threads) {
// thread.join();
// }
auto threads{ vector<thread>() };
mutex mtx;
for (auto i = 0; i < n_models; ++i) {
y_pred.index_put_({ "...", i }, models[i]->predict(X));
threads.push_back(thread([&, i]() {
auto ypredict = models[i]->predict(X);
lock_guard<mutex> lock(mtx);
y_pred.index_put_({ "...", i }, ypredict);
}));
}
for (auto& thread : threads) {
thread.join();
}
return torch::tensor(voting(y_pred));
}

30
src/BayesNet/Network.cc
View File

@ -299,25 +299,19 @@ namespace bayesnet {
vector<double> Network::exactInference(map<string, int>& evidence)
{
vector<double> result(classNumStates, 0.0);
// vector<thread> threads;
// mutex mtx;
// for (int i = 0; i < classNumStates; ++i) {
// threads.emplace_back([this, &result, &evidence, i, &mtx]() {
// auto completeEvidence = map<string, int>(evidence);
// completeEvidence[getClassName()] = i;
// double factor = computeFactor(completeEvidence);
// lock_guard<mutex> lock(mtx);
// result[i] = factor;
// });
// }
// for (auto& thread : threads) {
// thread.join();
// }
vector<thread> threads;
mutex mtx;
for (int i = 0; i < classNumStates; ++i) {
auto completeEvidence = map<string, int>(evidence);
completeEvidence[getClassName()] = i;
double factor = computeFactor(completeEvidence);
result[i] = factor;
threads.emplace_back([this, &result, &evidence, i, &mtx]() {
auto completeEvidence = map<string, int>(evidence);
completeEvidence[getClassName()] = i;
double factor = computeFactor(completeEvidence);
lock_guard<mutex> lock(mtx);
result[i] = factor;
});
}
for (auto& thread : threads) {
thread.join();
}
// Normalize result
double sum = accumulate(result.begin(), result.end(), 0.0);

8
src/BayesNet/Node.cc
View File

@ -100,7 +100,7 @@ namespace bayesnet {
}
int name_index = pos - features.begin();
for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
torch::List<c10::optional<torch::Tensor>> coordinates;
c10::List<c10::optional<at::Tensor>> coordinates;
coordinates.push_back(dataset.index({ name_index, n_sample }));
for (auto parent : parents) {
pos = find(features.begin(), features.end(), parent->getName());
@ -118,10 +118,10 @@ namespace bayesnet {
}
float Node::getFactorValue(map<string, int>& evidence)
{
torch::List<c10::optional<torch::Tensor>> coordinates;
c10::List<c10::optional<at::Tensor>> coordinates;
// following predetermined order of indices in the cpTable (see Node.h)
coordinates.push_back(torch::tensor(evidence[name]));
transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return torch::tensor(evidence[parent->getName()]); });
coordinates.push_back(at::tensor(evidence[name]));
transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
return cpTable.index({ coordinates }).item<float>();
}
vector<string> Node::graph(const string& className)

9
src/BayesNet/Proposal.cc
View File

@ -53,15 +53,6 @@ namespace bayesnet {
auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
discretizers[feature]->fit(xvf, yxv);
//
//
//
// auto tmp = discretizers[feature]->transform(xvf);
// Xv[index] = tmp;
// auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
// iota(xStates.begin(), xStates.end(), 0);
// //Update new states of the feature/node
// states[feature] = xStates;
}
if (upgrade) {
// Discretize again X (only the affected indices) with the new fitted discretizers

5
src/Platform/Datasets.cc
View File

@ -213,10 +213,11 @@ namespace platform {
{
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = vector<int>(*max_element(Xd[i].begin(), Xd[i].end()) + 1);
iota(begin(states[features[i]]), end(states[features[i]]), 0);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
}
states[className] = vector<int>(*max_element(yv.begin(), yv.end()) + 1);
iota(begin(states[className]), end(states[className]), 0);
iota(begin(states.at(className)), end(states.at(className)), 0);
}
void Dataset::load_arff()
{

1
src/Platform/Experiment.cc
View File

@ -182,6 +182,7 @@ namespace platform {
clf.reset();
}
cout << "end. " << flush;
delete fold;
}
result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());

5
src/Platform/platformUtils.cc
View File

@ -69,11 +69,12 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
Xd = torch::zeros({ static_cast<int>(Xr[0].size()), static_cast<int>(Xr.size()) }, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
iota(begin(states[features[i]]), end(states[features[i]]), 0);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
Xd.index_put_({ "...", i }, torch::tensor(Xr[i], torch::kInt32));
}
states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states[className]), end(states[className]), 0);
iota(begin(states.at(className)), end(states.at(className)), 0);
} else {
Xd = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {