ComputeCPT Optimization

CHANGELOG.md

@@ -14,6 +14,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix the vcpkg configuration in building the library.
 - Fix the sample app to use the vcpkg configuration.
 - Add predict_proba method to all Ld classifiers.
+- Optimize the computeCPT method in the Node class with libtorch vectorized operations and remove the for loop.
 
 ## [1.1.0] - 2025-04-27
 

bayesnet/classifiers/SPODELd.cc

@@ -45,6 +45,7 @@ namespace bayesnet {
     }
     torch::Tensor SPODELd::predict_proba(torch::Tensor& X)
     {
+        std::cout << "Debug: SPODELd::predict_proba" << std::endl;
         auto Xt = prepareX(X);
         return SPODE::predict_proba(Xt);
     }

bayesnet/network/Node.cc

@@ -99,36 +99,55 @@ namespace bayesnet {
         for (const auto& parent : parents) {
             dimensions.push_back(parent->getNumStates());
         }
-        //transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
         // Create a tensor initialized with smoothing
         cpTable = torch::full(dimensions, smoothing, torch::kDouble);
         // Create a map for quick feature index lookup
-        std::unordered_map<std::string, int> featureIndexMap;
-        for (size_t i = 0; i < features.size(); ++i) {
-            featureIndexMap[features[i]] = i;
-        }
-        // Fill table with counts
-        // Get the index of this node's feature
-        int name_index = featureIndexMap[name];
-        // Get parent indices in dataset
-        std::vector<int> parent_indices;
-        parent_indices.reserve(parents.size());
-        for (const auto& parent : parents) {
-            parent_indices.push_back(featureIndexMap[parent->getName()]);
-        }
-        c10::List<c10::optional<at::Tensor>> coordinates;
-        for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            coordinates.clear();
-            auto sample = dataset.index({ "...", n_sample });
-            coordinates.push_back(sample[name_index]);
-            for (size_t i = 0; i < parent_indices.size(); ++i) {
-                coordinates.push_back(sample[parent_indices[i]]);
+        std::unordered_map<std::string, int> cachedFeatureIndexMap;
+        bool featureIndexMapReady = false;
+        // Build featureIndexMap if not ready
+        if (!featureIndexMapReady) {
+            cachedFeatureIndexMap.clear();
+            for (size_t i = 0; i < features.size(); ++i) {
+                cachedFeatureIndexMap[features[i]] = i;
             }
-            // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
+            featureIndexMapReady = true;
         }
+        const auto& featureIndexMap = cachedFeatureIndexMap;
+        // Gather indices for node and parents
+        std::vector<int64_t> all_indices;
+        all_indices.push_back(featureIndexMap.at(name));
+        for (const auto& parent : parents) {
+            all_indices.push_back(featureIndexMap.at(parent->getName()));
+        }
+        // Extract relevant columns: shape (num_features, num_samples)
+        auto indices_tensor = dataset.index_select(0, torch::tensor(all_indices, torch::kLong));
+        // Transpose to (num_samples, num_features)
+        indices_tensor = indices_tensor.transpose(0, 1).to(torch::kLong);
+        // Flatten CPT for easier indexing
+        auto flat_cpt = cpTable.flatten();
+        // Compute strides for flattening multi-dim indices
+        std::vector<int64_t> strides(all_indices.size(), 1);
+        for (int i = strides.size() - 2; i >= 0; --i) {
+            strides[i] = strides[i + 1] * cpTable.size(i + 1);
+        }
+        // Compute flat indices for each sample
+        auto indices_tensor_cpu = indices_tensor.cpu();
+        auto indices_accessor = indices_tensor_cpu.accessor<int64_t, 2>();
+        std::vector<int64_t> flat_indices(indices_tensor.size(0));
+        for (int64_t i = 0; i < indices_tensor.size(0); ++i) {
+            int64_t idx = 0;
+            for (size_t j = 0; j < strides.size(); ++j) {
+                idx += indices_accessor[i][j] * strides[j];
+            }
+            flat_indices[i] = idx;
+        }
+        // Accumulate weights into flat CPT
+        auto flat_indices_tensor = torch::from_blob(flat_indices.data(), { (int64_t)flat_indices.size() }, torch::kLong).clone();
+        flat_cpt.index_add_(0, flat_indices_tensor, weights.cpu());
+        cpTable = flat_cpt.view(cpTable.sizes());
         // Normalize the counts (dividing each row by the sum of the row)
         cpTable /= cpTable.sum(0, true);
+        return;
     }
     double Node::getFactorValue(std::map<std::string, int>& evidence)
     {