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This commit is contained in:
Ricardo Montañana Gómez
2025-02-18 11:04:24 +01:00
parent bd5ba14f04
commit 14dd8ebb66
2 changed files with 19 additions and 19 deletions
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10
src/experimental_clfs/XA1DE.cpp
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@@ -22,22 +22,21 @@ namespace platform {
throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump()); throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
} }
} }
void XA1DE::fit(std::vector<std::vector<int>> X, std::vector<int> y, std::vector<double> weights) 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)
{ {
Timer timer, timert; Timer timer, timert;
timer.start(); timer.start();
timert.start(); timert.start();
weights_ = weights;
std::vector<std::vector<int>> instances = X; std::vector<std::vector<int>> instances = X;
instances.push_back(y); instances.push_back(y);
int num_instances = instances[0].size(); int num_instances = instances[0].size();
int num_attributes = instances.size(); int num_attributes = instances.size();
normalize_weights(num_instances); normalize_weights(num_instances);
std::vector<int> states; std::vector<int> statesv;
for (int i = 0; i < num_attributes; i++) { for (int i = 0; i < num_attributes; i++) {
states.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1); statesv.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1);
} }
aode_.init(states); aode_.init(statesv);
aode_.duration_first += timer.getDuration(); timer.start(); aode_.duration_first += timer.getDuration(); timer.start();
std::vector<int> instance; std::vector<int> instance;
for (int n_instance = 0; n_instance < num_instances; n_instance++) { for (int n_instance = 0; n_instance < num_instances; n_instance++) {
@@ -62,6 +61,7 @@ namespace platform {
std::cout << "* Time to build the model: " << timert.getDuration() << " seconds" << std::endl; std::cout << "* Time to build the model: " << timert.getDuration() << " seconds" << std::endl;
// exit(1); // exit(1);
} }
return *this;
} }
std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data) std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data)
{ {

28
src/experimental_clfs/XA1DE.h
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@@ -21,34 +21,34 @@ namespace platform {
public: public:
XA1DE(); XA1DE();
virtual ~XA1DE() = default; virtual ~XA1DE() = default;
void setDebug(bool debug) { this->debug = debug; }
std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
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(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& 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 { return *this; };
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 bayesnet::Smoothing_t smoothing) override { return *this; };
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; 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 { return *this; };
torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
int getNumberOfNodes() const override { return 0; }; int getNumberOfNodes() const override { return 0; };
int getNumberOfEdges() const override { return 0; }; int getNumberOfEdges() const override { return 0; };
int getNumberOfStates() const override { return 0; }; int getNumberOfStates() const override { return 0; };
int getClassNumStates() const override { return 0; }; int getClassNumStates() const override { return 0; };
torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
bayesnet::status_t getStatus() const override { return status; } bayesnet::status_t getStatus() const override { return status; }
std::string getVersion() override { return { project_version.begin(), project_version.end() }; }; std::string getVersion() override { return version; };
float score(torch::Tensor& X, torch::Tensor& y) override { return 0; }; float score(torch::Tensor& X, torch::Tensor& y) override { return 0; };
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
std::vector<std::string> show() const override { return {}; } std::vector<std::string> show() const override { return {}; }
std::vector<std::string> topological_order() override { return {}; } std::vector<std::string> topological_order() override { return {}; }
std::vector<std::string> getNotes() const override { return notes; } std::vector<std::string> getNotes() const override { return notes; }
std::string dump_cpt() const override { return ""; } std::string dump_cpt() const override { return ""; }
void setHyperparameters(const nlohmann::json& hyperparameters) override; void setHyperparameters(const nlohmann::json& hyperparameters) override;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; } std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
void setDebug(bool debug) { this->debug = debug; }
protected: protected:
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override; void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override {};
private: private:
inline void normalize_weights(int num_instances) inline void normalize_weights(int num_instances)
@@ -61,7 +61,6 @@ namespace platform {
w = w * num_instances / sum; w = w * num_instances / sum;
} }
} }
// The instances of the dataset
Xaode aode_; Xaode aode_;
std::vector<double> weights_; std::vector<double> weights_;
CountingSemaphore& semaphore_; CountingSemaphore& semaphore_;
@@ -69,6 +68,7 @@ namespace platform {
bayesnet::status_t status = bayesnet::NORMAL; bayesnet::status_t status = bayesnet::NORMAL;
std::vector<std::string> notes; std::vector<std::string> notes;
bool use_threads = false; bool use_threads = false;
std::string version = "0.9.7";
}; };
} }
#endif // XA1DE_H #endif // XA1DE_H