rmontanana/BayesNet
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Add the probabilities aggregation method to compute prediction with ensembles #16

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rmontanana merged 7 commits from baode_proba into main 2024-02-25 11:26:28 +00:00
Conversation 0 Commits 7 Files Changed 56 +67 -23
4 changed files with 67 additions and 23 deletions
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2
src/BayesNet/BoostAODE.cc
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@ -8,7 +8,7 @@
#include "folding.hpp"
namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble(false)
BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
{
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features", "tolerance" };

2
src/BayesNet/BoostAODE.h
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@ -7,7 +7,7 @@
namespace bayesnet {
class BoostAODE : public Ensemble {
public:
BoostAODE();
BoostAODE(bool predict_voting = false);
virtual ~BoostAODE() = default;
std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
void setHyperparameters(const nlohmann::json& hyperparameters) override;

1
src/BayesNet/Ensemble.h
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@ -36,7 +36,6 @@ namespace bayesnet {
std::vector<double> significanceModels;
void trainModel(const torch::Tensor& weights) override;
std::vector<int> voting(torch::Tensor& y_pred);
private:
bool predict_voting;
};
}

85
tests/TestBayesModels.cc
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@ -156,7 +156,7 @@ TEST_CASE("BoostAODE feature_select CFS", "[BayesNet]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE test used features in train note", "[BayesNet]")
TEST_CASE("BoostAODE test used features in train note and score", "[BayesNet]")
{
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::BoostAODE();
@ -173,9 +173,25 @@ TEST_CASE("BoostAODE test used features in train note", "[BayesNet]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Used features in train: 7 of 8");
REQUIRE(clf.getNotes()[2] == "Number of models: 8");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.8138).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.8138).epsilon(raw.epsilon));
}
TEST_CASE("TAN predict_proba", "[BayesNet]")
{
auto res_prob = std::vector<std::vector<double>>({
{ 0.00375671, 0.994457, 0.00178621 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00218225, 0.992877, 0.00494094 },
{ 0.00494209, 0.0978534, 0.897205 },
{ 0.0054192, 0.974275, 0.0203054 },
{ 0.00433012, 0.985054, 0.0106159 },
{ 0.000860806, 0.996922, 0.00221698 }
});
int init_index = 78;
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::TAN();
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
@ -191,25 +207,54 @@ TEST_CASE("TAN predict_proba", "[BayesNet]")
auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
REQUIRE(predictedClass == y_pred[i]);
// Check predict is coherent with predict_proba
REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
}
// Check predict_proba values for vectors and tensors
for (int i = 0; i < res_prob.size(); i++) {
for (int j = 0; j < 3; j++) {
REQUIRE(res_prob[i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
REQUIRE(res_prob[i][j] == Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
}
}
}
TEST_CASE("BoostAODE predict_proba voting", "[BayesNet]")
{
// auto res_prob = std::vector<std::vector<double>>({
// { 0.00375671, 0.994457, 0.00178621 },
// { 0.00137462, 0.992734, 0.00589123 },
// { 0.00137462, 0.992734, 0.00589123 },
// { 0.00137462, 0.992734, 0.00589123 },
// { 0.00218225, 0.992877, 0.00494094 },
// { 0.00494209, 0.0978534, 0.897205 },
// { 0.0054192, 0.974275, 0.0203054 },
// { 0.00433012, 0.985054, 0.0106159 },
// { 0.000860806, 0.996922, 0.00221698 }
// });
// int init_index = 78;
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(true);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto y_pred_proba = clf.predict_proba(raw.Xv);
auto y_pred = clf.predict(raw.Xv);
auto yt_pred_proba = clf.predict_proba(raw.Xt);
// REQUIRE(y_pred.size() == y_pred_proba.size());
// REQUIRE(y_pred.size() == yt_pred_proba.size(0));
// REQUIRE(y_pred.size() == raw.yv.size());
// REQUIRE(y_pred_proba[0].size() == 3);
// REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
// for (int i = 0; i < y_pred_proba.size(); ++i) {
// auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
// int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
// REQUIRE(predictedClass == y_pred[i]);
// // Check predict is coherent with predict_proba
// REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
// }
// // Check predict_proba values for vectors and tensors
// for (int i = 0; i < res_prob.size(); i++) {
// for (int j = 0; j < 3; j++) {
// REQUIRE(res_prob[i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
// REQUIRE(res_prob[i][j] == Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
// }
// }
}
// TEST_CASE("BoostAODE predict_proba", "[BayesNet]")
// {
// auto raw = RawDatasets("iris", true);
// auto clf = bayesnet::BoostAODE();
// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
// auto y_pred = clf.predict_proba(raw.Xv);
// REQUIRE(y_pred.size(0) == raw.yv.size(0));
// REQUIRE(y_pred.size(1) == 3);
// auto y_pred2 = clf.predict_proba(raw.Xv);
// REQUIRE(y_pred2.size(0) == raw.yv.size(0));
// REQUIRE(y_pred2.size(1) == 3);
// REQUIRE(y_pred.equal(y_pred2));
// for (int i = 0; i < y_pred.size(0); ++i) {
// for (int j = 0; j < y_pred.size(1); ++j) {
// REQUIRE(y_pred[i][j].item<float>() == y_pred2[i][j].item<float>());
// }
// }
// }