Complete proposal with only discretizing numeric features
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@@ -37,6 +37,7 @@ namespace bayesnet {
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className = className_;
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states = iterativeLocalDiscretization(y, static_cast<KDB*>(this), dataset, features, className, states_, smoothing);
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KDB::fit(dataset, features, className, states, smoothing);
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fitted = true;
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return *this;
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}
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torch::Tensor KDBLd::predict(torch::Tensor& X)
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@@ -118,17 +118,20 @@ namespace bayesnet {
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}
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return states;
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}
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map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y, map<std::string, std::vector<int>> states_)
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map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y, map<std::string, std::vector<int>> states)
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{
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// Discretize the continuous input data and build pDataset (Classifier::dataset)
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// We expect to have in states for numeric features an empty vector and for discretized features a vector of states
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int m = Xf.size(1);
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int n = Xf.size(0);
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map<std::string, std::vector<int>> states;
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pDataset = torch::zeros({ n + 1, m }, torch::kInt32);
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auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
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// discretize input data by feature(row)
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std::unique_ptr<mdlp::Discretizer> discretizer;
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wasNumeric.resize(pFeatures.size());
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for (auto i = 0; i < pFeatures.size(); ++i) {
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auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
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auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
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if (discretizationType == discretization_t::BINQ) {
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discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::QUANTILE);
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} else if (discretizationType == discretization_t::BINU) {
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@@ -136,13 +139,19 @@ namespace bayesnet {
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} else { // Default is MDLP
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discretizer = std::make_unique<mdlp::CPPFImdlp>(ld_params.min_length, ld_params.max_depth, ld_params.proposed_cuts);
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}
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auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
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auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
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discretizer->fit(Xt, yv);
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pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
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auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
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iota(xStates.begin(), xStates.end(), 0);
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states[pFeatures[i]] = xStates;
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if (states[pFeatures[i]].empty()) {
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// If the feature is numeric, we discretize it
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pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->fit_transform(Xt, yv)));
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int n_states = discretizer->getCutPoints().size() + 1;
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auto xStates = std::vector<int>(n_states);
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iota(xStates.begin(), xStates.end(), 0);
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states[pFeatures[i]] = xStates;
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wasNumeric[i] = true;
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} else {
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wasNumeric[i] = false;
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// If the feature is categorical, we just copy it
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pDataset.index_put_({ i, "..." }, Xf[i].to(torch::kInt32));
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}
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discretizers[pFeatures[i]] = std::move(discretizer);
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}
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int n_classes = torch::max(y).item<int>() + 1;
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@@ -157,8 +166,13 @@ namespace bayesnet {
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auto Xtd = torch::zeros_like(X, torch::kInt32);
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for (int i = 0; i < X.size(0); ++i) {
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auto Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
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auto Xd = discretizers[pFeatures[i]]->transform(Xt);
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Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
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std::vector<int> Xd;
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if (wasNumeric[i]) {
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auto Xd = discretizers[pFeatures[i]]->transform(Xt);
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Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
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} else {
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Xtd.index_put_({ i }, Xf[i].to(torch::kInt32));
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}
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}
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return Xtd;
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}
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@@ -61,6 +61,7 @@ namespace bayesnet {
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std::vector<std::string>& notes; // Notes during fit from BaseClassifier
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torch::Tensor& pDataset; // (n+1)xm tensor
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std::vector<std::string>& pFeatures;
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std::vector<bool> wasNumeric;
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std::string& pClassName;
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enum class discretization_t {
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MDLP,
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@@ -36,6 +36,7 @@ namespace bayesnet {
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className = className_;
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states = iterativeLocalDiscretization(y, static_cast<SPODE*>(this), dataset, features, className, states_, smoothing);
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SPODE::fit(dataset, features, className, states, smoothing);
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fitted = true;
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return *this;
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}
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torch::Tensor SPODELd::predict(torch::Tensor& X)
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@@ -35,6 +35,7 @@ namespace bayesnet {
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className = className_;
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states = iterativeLocalDiscretization(y, static_cast<TAN*>(this), dataset, features, className, states_, smoothing);
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TAN::fit(dataset, features, className, states, smoothing);
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fitted = true;
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return *this;
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}
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torch::Tensor TANLd::predict(torch::Tensor& X)
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