Fix XA1DE integration

src/experimental_clfs/ExpClf.cpp

@@ -10,12 +10,7 @@
 namespace platform {
     ExpClf::ExpClf() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)
     {
-    }
+        validHyperparameters = {};
-    void ExpClf::setHyperparameters(const nlohmann::json& hyperparameters)
-    {
-        if (!hyperparameters.empty()) {
-            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
-        }
     }
     //
     // Predict

src/experimental_clfs/ExpClf.h

@@ -6,7 +6,6 @@
 
 #ifndef EXPCLF_H
 #define EXPCLF_H
-#include <iostream>
 #include <vector>
 #include <string>
 #include <cmath>
@@ -18,16 +17,10 @@
 #include "Xaode.hpp"
 
 namespace platform {
-
     class ExpClf : public bayesnet::Boost {
     public:
         ExpClf();
         virtual ~ExpClf() = default;
-        ExpClf& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) { return *this; };
-        // X is nxm tensor, y is nx1 tensor
-        ExpClf& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) { return *this; };
-        ExpClf& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) { return *this; };
-        ExpClf& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) { return *this; };
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict(torch::Tensor& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
@@ -46,8 +39,7 @@ namespace platform {
         bayesnet::status_t getStatus() const override { return status; }
         std::vector<std::string> getNotes() const override { return notes; }
         std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
-        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        void set_active_parents(const std::vector<int>& active_parents) { for (const auto& parent : active_parents) aode_.add_active_parent(parent); }
-        void set_active_parents(const std::vector<int> active_parents) { for (const auto& parent : active_parents) aode_.add_active_parent(parent); }
         void add_active_parent(int parent) { aode_.add_active_parent(parent); }
         void remove_last_parent() { aode_.remove_last_parent(); }
     protected:

src/experimental_clfs/XA1DE.cpp

@@ -8,35 +8,13 @@
 #include "TensorUtils.hpp"
 
 namespace platform {
-    XA1DE& XA1DE::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    void XA1DE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
     {
-        std::vector<std::vector<int>> instances = X;
+        auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1));
-        instances.push_back(y);
+        auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." }));
-        int num_instances = instances[0].size();
+        int num_instances = X[0].size();
-        int num_attributes = instances.size();
+        weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         normalize_weights(num_instances);
         aode_.fit(X, y, features, className, states, weights_, true);
-        fitted = true;
-        return *this;
-    }
-    //
-    // Fit
-    //
-    XA1DE& XA1DE::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
-    {
-        auto X_ = TensorUtils::to_matrix(X);
-        auto y_ = TensorUtils::to_vector<int>(y);
-        return fit(X_, y_, features, className, states, smoothing);
-    }
-    XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
-    {
-        torch::Tensor y = dataset[dataset.size(0) - 1];
-        torch::Tensor X = dataset.slice(0, 0, dataset.size(0) - 1);
-        return fit(X, y, features, className, states, smoothing);
-    }
-    XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
-    {
-        weights_ = weights;
-        return fit(dataset, features, className, states, smoothing);
     }
 }

src/experimental_clfs/XA1DE.h

@@ -6,12 +6,6 @@
 
 #ifndef XA1DE_H
 #define XA1DE_H
-#include <iostream>
-#include <vector>
-#include <cmath>
-#include <algorithm>
-#include <limits>
-#include "common/Timer.hpp"
 #include "Xaode.hpp"
 #include "ExpClf.h"
 
@@ -19,14 +13,11 @@ namespace platform {
     class XA1DE : public ExpClf {
     public:
         XA1DE() = default;
-        virtual ~XA1DE() = default;
+        virtual ~XA1DE() override = default;
-        XA1DE& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
         std::string getVersion() override { return version; };
     protected:
-        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override {};
+        void buildModel(const torch::Tensor& weights) override {};
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
     private:
         std::string version = "1.0.0";
     };

src/experimental_clfs/XBAODE.cpp

@@ -36,7 +36,7 @@ namespace platform {
         // Algorithm based on the adaboost algorithm for classification
         // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         double alpha_t = 0;
-        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         bool finished = false;
         std::vector<int> featuresUsed;
         aode_.fit(X_train_, y_train_, features, className, states, weights_, false);

src/experimental_clfs/XBAODE.h

@@ -18,7 +18,7 @@ namespace platform {
     class XBAODE : public ExpClf {
     public:
         XBAODE();
-        virtual ~XBAODE() = default;
+        virtual ~XBAODE() override = default;
         std::string getVersion() override { return version; };
     protected:
         void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;