Complete predict & predict_proba with voting & probabilities
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@ -51,32 +51,6 @@ namespace bayesnet {
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result /= sum;
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return result;
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}
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std::vector<std::vector<double>> Ensemble::voting(std::vector<std::vector<int>>& votes)
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{
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// Convert n_models x m matrix to a m x n_class_states matrix
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std::vector<std::vector<double>> y_pred_final;
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int numClasses = states.at(className).size();
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auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
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// y_pred is m x n_models with the prediction of every model for each sample
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std::cout << std::string(80, '*') << std::endl;
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for (int i = 0; i < votes.size(); ++i) {
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// n_votes store in each index (value of class) the significance added by each model
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// i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
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std::vector<double> n_votes(numClasses, 0.0);
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for (int j = 0; j < n_models; ++j) {
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n_votes[votes[i][j]] += significanceModels.at(j);
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}
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for (auto& x : n_votes) {
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std::cout << x << " ";
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}
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std::cout << std::endl;
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// To only do one division per result and gain precision
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std::transform(n_votes.begin(), n_votes.end(), n_votes.begin(), [sum](double x) { return x / sum; });
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y_pred_final.push_back(n_votes);
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}
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std::cout << std::string(80, '*') << std::endl;
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return y_pred_final;
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}
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std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)
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{
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if (!fitted) {
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@ -94,7 +68,6 @@ namespace bayesnet {
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std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
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{
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auto res = predict_proba(X);
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std::cout << "res: " << res.size() << ", " << res[0].size() << std::endl;
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return compute_arg_max(res);
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}
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torch::Tensor Ensemble::predict(torch::Tensor& X)
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@ -151,6 +124,13 @@ namespace bayesnet {
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}
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return y_pred;
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}
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std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
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{
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torch::Tensor Xt = bayesnet::vectorToTensor(X, false);
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auto y_pred = predict_average_voting(Xt);
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std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);
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return result;
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}
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torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)
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{
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// Build a m x n_models tensor with the predictions of each model
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@ -169,21 +149,6 @@ namespace bayesnet {
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}
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return voting(y_pred);
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}
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std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
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{
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auto Xt = vectorToTensor(X);
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auto y_pred = predict_average_voting(Xt);
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auto res = voting(y_pred);
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std::vector<std::vector<double>> result;
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// Iterate over cols
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for (int i = 0; i < res.size(1); ++i) {
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auto col_tensor = res.index({ "...", i });
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auto col = std::vector<double>(col_tensor.data_ptr<double>(), col_tensor.data_ptr<double>() + res.size(0));
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result.push_back(col);
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}
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return result;
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//return tensorToVector<double>(res);
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}
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float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
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{
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auto y_pred = predict(X);
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@ -36,7 +36,6 @@ namespace bayesnet {
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torch::Tensor compute_arg_max(torch::Tensor& X);
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std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
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torch::Tensor voting(torch::Tensor& votes);
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std::vector<std::vector<double>> voting(std::vector<std::vector<int>>& votes);
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unsigned n_models;
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std::vector<std::unique_ptr<Classifier>> models;
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std::vector<double> significanceModels;
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@ -10,28 +10,39 @@ namespace bayesnet {
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sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
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return indices;
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}
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template<typename T>
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std::vector<std::vector<T>> tensorToVector(torch::Tensor& dtensor)
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std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor)
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{
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// convert mxn tensor to nxm std::vector
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std::vector<std::vector<T>> result;
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std::vector<std::vector<int>> result;
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// Iterate over cols
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for (int i = 0; i < dtensor.size(1); ++i) {
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auto col_tensor = dtensor.index({ "...", i });
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auto col = std::vector<T>(col_tensor.data_ptr<T>(), col_tensor.data_ptr<T>() + dtensor.size(0));
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auto col = std::vector<int>(col_tensor.data_ptr<int>(), col_tensor.data_ptr<int>() + dtensor.size(0));
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result.push_back(col);
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}
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return result;
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}
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torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector)
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std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor)
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{
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// convert nxm std::vector to mxn tensor
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long int m = vector[0].size();
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long int n = vector.size();
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// convert mxn tensor to mxn std::vector
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std::vector<std::vector<double>> result;
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// Iterate over cols
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for (int i = 0; i < dtensor.size(0); ++i) {
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auto col_tensor = dtensor.index({ i, "..." });
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auto col = std::vector<double>(col_tensor.data_ptr<float>(), col_tensor.data_ptr<float>() + dtensor.size(1));
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result.push_back(col);
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}
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return result;
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}
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torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose)
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{
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// convert nxm std::vector to mxn tensor if transpose
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long int m = transpose ? vector[0].size() : vector.size();
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long int n = transpose ? vector.size() : vector[0].size();
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auto tensor = torch::zeros({ m, n }, torch::kInt32);
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for (int i = 0; i < m; ++i) {
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for (int j = 0; j < n; ++j) {
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tensor[i][j] = vector[j][i];
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tensor[i][j] = transpose ? vector[j][i] : vector[i][j];
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}
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}
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return tensor;
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@ -4,8 +4,8 @@
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#include <vector>
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namespace bayesnet {
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std::vector<int> argsort(std::vector<double>& nums);
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template<typename T>
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std::vector<std::vector<T>> tensorToVector(torch::Tensor& dtensor);
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torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector);
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std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor);
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std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor);
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torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose = true);
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}
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#endif //BAYESNET_UTILS_H
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@ -21,104 +21,104 @@ TEST_CASE("Library check version", "[BayesNet]")
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auto clf = bayesnet::KDB(2);
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REQUIRE(clf.getVersion() == "1.0.2");
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}
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// TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
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// {
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// map <pair<std::string, std::string>, float> scores = {
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// // Diabetes
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// {{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
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// {{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
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// // Ecoli
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// {{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
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// {{"ecoli", "AODELd"}, 0.8869f}, {{"ecoli", "KDBLd"}, 0.875f}, {{"ecoli", "SPODELd"}, 0.84226f}, {{"ecoli", "TANLd"}, 0.86905f}, {{"ecoli", "BoostAODE"}, 0.89583f},
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// // Glass
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// {{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
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// {{"glass", "AODELd"}, 0.79439f}, {{"glass", "KDBLd"}, 0.85047f}, {{"glass", "SPODELd"}, 0.79439f}, {{"glass", "TANLd"}, 0.86449f}, {{"glass", "BoostAODE"}, 0.84579f},
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// // Iris
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// {{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
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// {{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
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// };
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TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
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{
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map <pair<std::string, std::string>, float> scores = {
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// Diabetes
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{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
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{{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f}, {{"diabetes", "BoostAODE"}, 0.83984f},
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// Ecoli
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{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
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{{"ecoli", "AODELd"}, 0.8869f}, {{"ecoli", "KDBLd"}, 0.875f}, {{"ecoli", "SPODELd"}, 0.84226f}, {{"ecoli", "TANLd"}, 0.86905f}, {{"ecoli", "BoostAODE"}, 0.89583f},
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// Glass
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{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
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{{"glass", "AODELd"}, 0.79439f}, {{"glass", "KDBLd"}, 0.85047f}, {{"glass", "SPODELd"}, 0.79439f}, {{"glass", "TANLd"}, 0.86449f}, {{"glass", "BoostAODE"}, 0.84579f},
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// Iris
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{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
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{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
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};
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// std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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// auto raw = RawDatasets(file_name, false);
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std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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auto raw = RawDatasets(file_name, false);
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// SECTION("Test TAN classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::TAN();
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// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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// auto score = clf.score(raw.Xv, raw.yv);
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// //scores[{file_name, "TAN"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test TANLd classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::TANLd();
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// clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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// auto score = clf.score(raw.Xt, raw.yt);
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// //scores[{file_name, "TANLd"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test KDB classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::KDB(2);
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// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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// auto score = clf.score(raw.Xv, raw.yv);
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// //scores[{file_name, "KDB"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
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// }]).epsilon(raw.epsilon));
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// }
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// SECTION("Test KDBLd classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::KDBLd(2);
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// clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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// auto score = clf.score(raw.Xt, raw.yt);
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// //scores[{file_name, "KDBLd"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "KDBLd"
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// }]).epsilon(raw.epsilon));
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// }
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// SECTION("Test SPODE classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::SPODE(1);
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// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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// auto score = clf.score(raw.Xv, raw.yv);
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// // scores[{file_name, "SPODE"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test SPODELd classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::SPODELd(1);
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// clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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// auto score = clf.score(raw.Xt, raw.yt);
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// // scores[{file_name, "SPODELd"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test AODE classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::AODE();
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// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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// auto score = clf.score(raw.Xv, raw.yv);
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// // scores[{file_name, "AODE"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test AODELd classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::AODELd();
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// clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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// auto score = clf.score(raw.Xt, raw.yt);
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// // scores[{file_name, "AODELd"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(raw.epsilon));
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// }
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// SECTION("Test BoostAODE classifier (" + file_name + ")")
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// {
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// auto clf = bayesnet::BoostAODE();
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// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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// auto score = clf.score(raw.Xv, raw.yv);
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// // scores[{file_name, "BoostAODE"}] = score;
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// REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(raw.epsilon));
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// }
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// // for (auto scores : scores) {
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// // std::cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
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// // }
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// }
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SECTION("Test TAN classifier (" + file_name + ")")
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{
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auto clf = bayesnet::TAN();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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//scores[{file_name, "TAN"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(raw.epsilon));
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}
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SECTION("Test TANLd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::TANLd();
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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//scores[{file_name, "TANLd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "TANLd"}]).epsilon(raw.epsilon));
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}
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SECTION("Test KDB classifier (" + file_name + ")")
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{
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auto clf = bayesnet::KDB(2);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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//scores[{file_name, "KDB"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
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}]).epsilon(raw.epsilon));
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}
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SECTION("Test KDBLd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::KDBLd(2);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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//scores[{file_name, "KDBLd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "KDBLd"
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}]).epsilon(raw.epsilon));
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}
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SECTION("Test SPODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::SPODE(1);
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
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// scores[{file_name, "SPODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(raw.epsilon));
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}
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SECTION("Test SPODELd classifier (" + file_name + ")")
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{
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auto clf = bayesnet::SPODELd(1);
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clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
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auto score = clf.score(raw.Xt, raw.yt);
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// scores[{file_name, "SPODELd"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "SPODELd"}]).epsilon(raw.epsilon));
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}
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SECTION("Test AODE classifier (" + file_name + ")")
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{
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auto clf = bayesnet::AODE();
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clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
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auto score = clf.score(raw.Xv, raw.yv);
|
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// scores[{file_name, "AODE"}] = score;
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(raw.epsilon));
|
||||
}
|
||||
SECTION("Test AODELd classifier (" + file_name + ")")
|
||||
{
|
||||
auto clf = bayesnet::AODELd();
|
||||
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
|
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auto score = clf.score(raw.Xt, raw.yt);
|
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// scores[{file_name, "AODELd"}] = score;
|
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REQUIRE(score == Catch::Approx(scores[{file_name, "AODELd"}]).epsilon(raw.epsilon));
|
||||
}
|
||||
SECTION("Test BoostAODE classifier (" + file_name + ")")
|
||||
{
|
||||
auto clf = bayesnet::BoostAODE(true);
|
||||
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
// scores[{file_name, "BoostAODE"}] = score;
|
||||
REQUIRE(score == Catch::Approx(scores[{file_name, "BoostAODE"}]).epsilon(raw.epsilon));
|
||||
}
|
||||
// for (auto scores : scores) {
|
||||
// std::cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
|
||||
// }
|
||||
}
|
||||
TEST_CASE("Models features", "[BayesNet]")
|
||||
{
|
||||
auto graph = std::vector<std::string>({ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
|
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@ -158,35 +158,31 @@ 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 and score", "[BayesNet]")
|
||||
// {
|
||||
// auto raw = RawDatasets("diabetes", true);
|
||||
// auto clf = bayesnet::BoostAODE();
|
||||
// clf.setHyperparameters({
|
||||
// {"ascending",true},
|
||||
// {"convergence", true},
|
||||
// {"repeatSparent",true},
|
||||
// {"select_features","CFS"},
|
||||
// });
|
||||
// clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
|
||||
// REQUIRE(clf.getNumberOfNodes() == 72);
|
||||
// REQUIRE(clf.getNumberOfEdges() == 120);
|
||||
// REQUIRE(clf.getNotes().size() == 3);
|
||||
// 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("BoostAODE test used features in train note and score", "[BayesNet]")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
auto clf = bayesnet::BoostAODE(true);
|
||||
clf.setHyperparameters({
|
||||
{"ascending",true},
|
||||
{"convergence", true},
|
||||
{"repeatSparent",true},
|
||||
{"select_features","CFS"},
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
|
||||
REQUIRE(clf.getNumberOfNodes() == 72);
|
||||
REQUIRE(clf.getNumberOfEdges() == 120);
|
||||
REQUIRE(clf.getNotes().size() == 3);
|
||||
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("Model predict_proba", "[BayesNet]")
|
||||
{
|
||||
// std::string model = GENERATE("TAN", "SPODE", "BoostAODEprobabilities", "BoostAODEvoting");
|
||||
std::string model = GENERATE("TAN", "SPODE");
|
||||
std::cout << string(100, '*') << std::endl;
|
||||
std::cout << "************************************* CHANGE MODEL GENERATE ****************************************" << std::endl;
|
||||
std::cout << string(100, '*') << std::endl;
|
||||
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");
|
||||
auto res_prob_tan = std::vector<std::vector<double>>({
|
||||
{ 0.00375671, 0.994457, 0.00178621 },
|
||||
{ 0.00137462, 0.992734, 0.00589123 },
|
||||
@ -220,7 +216,18 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
|
||||
{0.0204803, 0.844276, 0.135244},
|
||||
{0.00576313, 0.961665, 0.0325716},
|
||||
});
|
||||
std::map<std::string, std::vector<std::vector<double>>> res_prob = { {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_baode } };
|
||||
auto res_prob_voting = std::vector<std::vector<double>>({
|
||||
{0, 1, 0},
|
||||
{0, 1, 0},
|
||||
{0, 1, 0},
|
||||
{0, 1, 0},
|
||||
{0, 1, 0},
|
||||
{0, 0.447909, 0.552091},
|
||||
{0, 0.811482, 0.188517},
|
||||
{0, 1, 0},
|
||||
{0, 1, 0}
|
||||
});
|
||||
std::map<std::string, std::vector<std::vector<double>>> res_prob = { {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
|
||||
std::map<std::string, bayesnet::BaseClassifier*> models = { {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
|
||||
int init_index = 78;
|
||||
auto raw = RawDatasets("iris", true);
|
||||
@ -257,107 +264,3 @@ TEST_CASE("Model predict_proba", "[BayesNet]")
|
||||
delete clf;
|
||||
}
|
||||
}
|
||||
TEST_CASE("BoostAODE predict_proba proba", "[BayesNet]")
|
||||
{
|
||||
auto res_prob = std::vector<std::vector<double>>({
|
||||
{0.00803291, 0.9676, 0.0243672},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00189227, 0.859575, 0.138533},
|
||||
{0.0118341, 0.442149, 0.546017},
|
||||
{0.0216135, 0.785781, 0.192605},
|
||||
{0.0204803, 0.844276, 0.135244},
|
||||
{0.00576313, 0.961665, 0.0325716},
|
||||
});
|
||||
int init_index = 78;
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::BoostAODE(false);
|
||||
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 = clf.predict(raw.Xt);
|
||||
auto yt_pred_proba = clf.predict_proba(raw.Xt);
|
||||
std::cout << "yt_pred_proba proba sizes " << yt_pred_proba.sizes() << std::endl;
|
||||
REQUIRE(y_pred.size() == yt_pred.size(0));
|
||||
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) {
|
||||
// Check predict is coherent with predict_proba
|
||||
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]);
|
||||
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++) {
|
||||
REQUIRE(y_pred[i] == yt_pred[i].item<int>());
|
||||
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));
|
||||
}
|
||||
}
|
||||
// for (int i = 0; i < res_prob.size(); i++) {
|
||||
// for (int j = 0; j < 3; j++) {
|
||||
// std::cout << y_pred_proba[i + init_index][j] << " ";
|
||||
// }
|
||||
// std::cout << std::endl;
|
||||
// }
|
||||
}
|
||||
TEST_CASE("BoostAODE predict_proba voting", "[BayesNet]")
|
||||
{
|
||||
auto res_prob = std::vector<std::vector<double>>({
|
||||
{0.00803291, 0.9676, 0.0243672},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00398714, 0.945126, 0.050887},
|
||||
{0.00189227, 0.859575, 0.138533},
|
||||
{0.0118341, 0.442149, 0.546017},
|
||||
{0.0216135, 0.785781, 0.192605},
|
||||
{0.0204803, 0.844276, 0.135244},
|
||||
{0.00576313, 0.961665, 0.0325716},
|
||||
});
|
||||
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 = clf.predict(raw.Xt);
|
||||
auto yt_pred_proba = clf.predict_proba(raw.Xt);
|
||||
std::cout << "yt_pred_proba proba sizes " << yt_pred_proba.sizes() << std::endl;
|
||||
REQUIRE(y_pred.size() == yt_pred.size(0));
|
||||
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
|
||||
for (int k = 0; k < yt_pred_proba[i].size(0); k++) {
|
||||
std::cout << yt_pred_proba[i][k].item<double>() << " ";
|
||||
}
|
||||
std::cout << "-> " << y_pred[i] << std::endl;
|
||||
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++) {
|
||||
REQUIRE(y_pred[i] == yt_pred[i].item<int>());
|
||||
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));
|
||||
}
|
||||
}
|
||||
// for (int i = 0; i < res_prob.size(); i++) {
|
||||
// for (int j = 0; j < 3; j++) {
|
||||
// std::cout << y_pred_proba[i + init_index][j] << " ";
|
||||
// }
|
||||
// std::cout << std::endl;
|
||||
// }
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user