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Add const feature and className to fit models

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-23 23:15:39 +02:00
parent d82148079d
commit 86ffdfd6f3
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
17 changed files with 33 additions and 36 deletions
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2
src/BayesNet/AODELd.cc
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@ -4,7 +4,7 @@
namespace bayesnet { namespace bayesnet {
using namespace std; using namespace std;
AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {} AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{ {
// This first part should go in a Classifier method called fit_local_discretization o fit_float... // This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_; features = features_;

2
src/BayesNet/AODELd.h
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@ -12,7 +12,7 @@ namespace bayesnet {
void buildModel(const torch::Tensor& weights) override; void buildModel(const torch::Tensor& weights) override;
public: public:
AODELd(); AODELd();
AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override; AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_) override;
virtual ~AODELd() = default; virtual ~AODELd() = default;
vector<string> graph(const string& name = "AODE") const override; vector<string> graph(const string& name = "AODE") const override;
static inline string version() { return "0.0.1"; }; static inline string version() { return "0.0.1"; };

8
src/BayesNet/BaseClassifier.h
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@ -10,11 +10,11 @@ namespace bayesnet {
virtual void trainModel(const torch::Tensor& weights) = 0; virtual void trainModel(const torch::Tensor& weights) = 0;
public: public:
// X is nxm vector, y is nx1 vector // X is nxm vector, y is nx1 vector
virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0; virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
// X is nxm tensor, y is nx1 tensor // X is nxm tensor, y is nx1 tensor
virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0; virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0; virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0; virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
virtual ~BaseClassifier() = default; virtual ~BaseClassifier() = default;
torch::Tensor virtual predict(torch::Tensor& X) = 0; torch::Tensor virtual predict(torch::Tensor& X) = 0;
vector<int> virtual predict(vector<vector<int>>& X) = 0; vector<int> virtual predict(vector<vector<int>>& X) = 0;

1
src/BayesNet/BayesMetrics.cc
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@ -77,7 +77,6 @@ namespace bayesnet {
auto source = vector<string>(features); auto source = vector<string>(features);
source.push_back(className); source.push_back(className);
auto combinations = doCombinations(source); auto combinations = doCombinations(source);
double totalWeight = weights.sum().item<double>();
// Compute class prior // Compute class prior
auto margin = torch::zeros({ classNumStates }, torch::kFloat); auto margin = torch::zeros({ classNumStates }, torch::kFloat);
for (int value = 0; value < classNumStates; ++value) { for (int value = 0; value < classNumStates; ++value) {

1
src/BayesNet/BoostAODE.cc
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@ -37,7 +37,6 @@ namespace bayesnet {
// Step 0: Set the finish condition // Step 0: Set the finish condition
// if not repeatSparent a finish condition is run out of features // if not repeatSparent a finish condition is run out of features
// n_models == maxModels // n_models == maxModels
int numClasses = states[className].size();
while (!exitCondition) { while (!exitCondition) {
// Step 1: Build ranking with mutual information // Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted

10
src/BayesNet/Classifier.cc
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@ -5,7 +5,7 @@ namespace bayesnet {
using namespace torch; using namespace torch;
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {} Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) Classifier& Classifier::build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
{ {
this->features = features; this->features = features;
this->className = className; this->className = className;
@ -39,7 +39,7 @@ namespace bayesnet {
model.fit(dataset, weights, features, className, states); model.fit(dataset, weights, features, className, states);
} }
// X is nxm where n is the number of features and m the number of samples // 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) Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
{ {
dataset = X; dataset = X;
buildDataset(y); buildDataset(y);
@ -47,7 +47,7 @@ namespace bayesnet {
return build(features, className, states, weights); return build(features, className, states, weights);
} }
// X is nxm where n is the number of features and m the number of samples // X is nxm where n is the number of features and m the number of samples
Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
{ {
dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32); dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
for (int i = 0; i < X.size(); ++i) { for (int i = 0; i < X.size(); ++i) {
@ -58,13 +58,13 @@ namespace bayesnet {
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble); const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
return build(features, className, states, weights); return build(features, className, states, weights);
} }
Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states)
{ {
this->dataset = dataset; this->dataset = dataset;
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble); const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
return build(features, className, states, weights); return build(features, className, states, weights);
} }
Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
{ {
this->dataset = dataset; this->dataset = dataset;
return build(features, className, states, weights); return build(features, className, states, weights);

10
src/BayesNet/Classifier.h
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@ -11,7 +11,7 @@ namespace bayesnet {
class Classifier : public BaseClassifier { class Classifier : public BaseClassifier {
private: private:
void buildDataset(torch::Tensor& y); void buildDataset(torch::Tensor& y);
Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights); Classifier& build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights);
protected: protected:
bool fitted; bool fitted;
int m, n; // m: number of samples, n: number of features int m, n; // m: number of samples, n: number of features
@ -28,10 +28,10 @@ namespace bayesnet {
public: public:
Classifier(Network model); Classifier(Network model);
virtual ~Classifier() = default; virtual ~Classifier() = default;
Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override; Classifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override; Classifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override; Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) override; Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
void addNodes(); void addNodes();
int getNumberOfNodes() const override; int getNumberOfNodes() const override;
int getNumberOfEdges() const override; int getNumberOfEdges() const override;

2
src/BayesNet/Ensemble.cc
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@ -3,7 +3,7 @@
namespace bayesnet { namespace bayesnet {
using namespace torch; using namespace torch;
Ensemble::Ensemble() : Classifier(Network()) {} Ensemble::Ensemble() : Classifier(Network()), n_models(0) {}
void Ensemble::trainModel(const torch::Tensor& weights) void Ensemble::trainModel(const torch::Tensor& weights)
{ {

2
src/BayesNet/KDBLd.cc
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@ -3,7 +3,7 @@
namespace bayesnet { namespace bayesnet {
using namespace std; using namespace std;
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {} KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{ {
// This first part should go in a Classifier method called fit_local_discretization o fit_float... // This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_; features = features_;

2
src/BayesNet/KDBLd.h
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@ -10,7 +10,7 @@ namespace bayesnet {
public: public:
explicit KDBLd(int k); explicit KDBLd(int k);
virtual ~KDBLd() = default; virtual ~KDBLd() = default;
KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override; KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "KDB") const override; vector<string> graph(const string& name = "KDB") const override;
Tensor predict(Tensor& X) override; Tensor predict(Tensor& X) override;
void setHyperparameters(nlohmann::json& hyperparameters) override {}; void setHyperparameters(nlohmann::json& hyperparameters) override {};

5
src/BayesNet/Network.cc
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@ -3,8 +3,8 @@
#include "Network.h" #include "Network.h"
#include "bayesnetUtils.h" #include "bayesnetUtils.h"
namespace bayesnet { namespace bayesnet {
Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false) {} Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {} Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}
Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other. Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
getmaxThreads()), fitted(other.fitted) getmaxThreads()), fitted(other.fitted)
{ {
@ -399,7 +399,6 @@ namespace bayesnet {
auto result = features; auto result = features;
result.erase(remove(result.begin(), result.end(), className), result.end()); result.erase(remove(result.begin(), result.end(), className), result.end());
bool ending{ false }; bool ending{ false };
int idx = 0;
while (!ending) { while (!ending) {
ending = true; ending = true;
for (auto feature : features) { for (auto feature : features) {

4
src/BayesNet/SPODELd.cc
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@ -3,7 +3,7 @@
namespace bayesnet { namespace bayesnet {
using namespace std; using namespace std;
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {} SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{ {
// This first part should go in a Classifier method called fit_local_discretization o fit_float... // This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_; features = features_;
@ -18,7 +18,7 @@ namespace bayesnet {
states = localDiscretizationProposal(states, model); states = localDiscretizationProposal(states, model);
return *this; return *this;
} }
SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_) SPODELd& SPODELd::fit(torch::Tensor& dataset, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{ {
Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone(); Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
y = dataset.index({ -1, "..." }).clone(); y = dataset.index({ -1, "..." }).clone();

4
src/BayesNet/SPODELd.h
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@ -9,8 +9,8 @@ namespace bayesnet {
public: public:
explicit SPODELd(int root); explicit SPODELd(int root);
virtual ~SPODELd() = default; virtual ~SPODELd() = default;
SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override; SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
SPODELd& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override; SPODELd& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "SPODE") const override; vector<string> graph(const string& name = "SPODE") const override;
Tensor predict(Tensor& X) override; Tensor predict(Tensor& X) override;
void setHyperparameters(nlohmann::json& hyperparameters) override {}; void setHyperparameters(nlohmann::json& hyperparameters) override {};

2
src/BayesNet/TANLd.cc
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@ -3,7 +3,7 @@
namespace bayesnet { namespace bayesnet {
using namespace std; using namespace std;
TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {} TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
{ {
// This first part should go in a Classifier method called fit_local_discretization o fit_float... // This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_; features = features_;

2
src/BayesNet/TANLd.h
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@ -10,7 +10,7 @@ namespace bayesnet {
public: public:
TANLd(); TANLd();
virtual ~TANLd() = default; virtual ~TANLd() = default;
TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override; TANLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "TAN") const override; vector<string> graph(const string& name = "TAN") const override;
Tensor predict(Tensor& X) override; Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; }; static inline string version() { return "0.0.1"; };

4
src/Platform/ReportExcel.cc
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@ -47,11 +47,11 @@ namespace platform {
void ReportExcel::body() void ReportExcel::body()
{ {
auto header = vector<string>( auto head = vector<string>(
{ "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "Time", { "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "Time",
"Time Std.", "Hyperparameters" }); "Time Std.", "Hyperparameters" });
int col = 1; int col = 1;
for (const auto& item : header) { for (const auto& item : head) {
wks.cell(8, col++).value() = item; wks.cell(8, col++).value() = item;
} }
int row = 9; int row = 9;

8
src/Platform/Results.cc
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@ -100,11 +100,11 @@ namespace platform {
cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl; cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl;
auto data = files.at(index).load(); auto data = files.at(index).load();
if (excelReport) { if (excelReport) {
ReportExcel report(data); ReportExcel reporter(data);
report.show(); reporter.show();
} else { } else {
ReportConsole report(data); ReportConsole reporter(data);
report.show(); reporter.show();
} }
} }
void Results::menu() void Results::menu()