Redo pass states to Network Fit needed in crossval

fix mistake in headerline (report)

.vscode/launch.json

@@ -25,12 +25,12 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "AODELd",
+                "TANLd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
                 "-d",
-                "iris"
+                "vehicle"
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },

src/BayesNet/Classifier.cc

@@ -37,7 +37,7 @@ namespace bayesnet {
     }
     void Classifier::trainModel()
     {
-        model.fit(dataset, features, className);
+        model.fit(dataset, features, className, states);
     }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)

src/BayesNet/Network.cc

@@ -104,7 +104,7 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className)
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");
@@ -122,39 +122,42 @@ namespace bayesnet {
             if (find(features.begin(), features.end(), feature) == features.end()) {
                 throw invalid_argument("Feature " + feature + " not found in Network::features");
             }
+            if (states.find(feature) == states.end()) {
+                throw invalid_argument("Feature " + feature + " not found in states");
             }
         }
-    void Network::setStates()
+    }
+    void Network::setStates(const map<string, vector<int>>& states)
     {
         // Set states to every Node in the network
         for (int i = 0; i < features.size(); ++i) {
-            nodes[features[i]]->setNumStates(static_cast<int>(torch::max(samples.index({ i, "..." })).item<int>()) + 1);
+            nodes[features[i]]->setNumStates(states.at(features[i]).size());
         }
         classNumStates = nodes[className]->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states);
         this->className = className;
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
         samples = torch::cat({ X , ytmp }, 0);
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit();
+        completeFit(states);
     }
-    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className);
+        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states);
         this->className = className;
         this->samples = samples;
-        completeFit();
+        completeFit(states);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className)
+    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
+        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states);
         this->className = className;
         // Build tensor of samples (nxm) (n+1 because of the class)
         samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
@@ -162,11 +165,11 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit();
+        completeFit(states);
     }
-    void Network::completeFit()
+    void Network::completeFit(const map<string, vector<int>>& states)
     {
-        setStates();
+        setStates(states);
         int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
         if (maxThreadsRunning < 1) {
             maxThreadsRunning = 1;
@@ -212,7 +215,7 @@ namespace bayesnet {
         torch::Tensor result;
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         for (int i = 0; i < samples.size(1); ++i) {
-            auto sample = samples.index({ "...", i });
+            const Tensor sample = samples.index({ "...", i });
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
             //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));

src/BayesNet/Network.h

@@ -20,13 +20,9 @@ namespace bayesnet {
         vector<double> predict_sample(const torch::Tensor&);
         vector<double> exactInference(map<string, int>&);
         double computeFactor(map<string, int>&);
-        double mutual_info(torch::Tensor&, torch::Tensor&);
+        void completeFit(const map<string, vector<int>>&);
-        double entropy(torch::Tensor&);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>&);
-        double conditionalEntropy(torch::Tensor&, torch::Tensor&);
+        void setStates(const map<string, vector<int>>&);
-        double mutualInformation(torch::Tensor&, torch::Tensor&);
-        void completeFit();
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className);
-        void setStates();
     public:
         Network();
         explicit Network(float, int);
@@ -43,13 +39,11 @@ namespace bayesnet {
         int getNumEdges() const;
         int getClassNumStates() const;
         string getClassName() const;
-        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
+        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const map<string, vector<int>>&);
-        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&);
+        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
-        void fit(const torch::Tensor&, const vector<string>&, const string&);
+        void fit(const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
         vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
-        //Computes the conditional edge weight of variable index u and v conditioned on class_node
-        torch::Tensor conditionalEdgeWeight();
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
         vector<vector<double>> predict_proba(const vector<vector<int>>&); // Return mxn vector of probabilities
         torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities

src/BayesNet/Proposal.cc

@@ -64,7 +64,7 @@ namespace bayesnet {
                 //Update new states of the feature/node
                 states[pFeatures[index]] = xStates;
             }
-            model.fit(pDataset, pFeatures, pClassName);
+            model.fit(pDataset, pFeatures, pClassName, states);
         }
     }
     void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)

src/Platform/Report.cc

@@ -4,6 +4,7 @@ namespace platform {
     string headerLine(const string& text)
     {
         int n = MAXL - text.length() - 3;
+        n = n < 0 ? 0 : n;
         return "* " + text + string(n, ' ') + "*\n";
     }
     string Report::fromVector(const string& key)
@@ -13,7 +14,7 @@ namespace platform {
         for (auto& item : data[key]) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     string fVector(const json& data)
     {
@@ -21,7 +22,7 @@ namespace platform {
         for (const auto& item : data) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     void Report::show()
     {