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28 changed files with 365 additions and 157 deletions

4
.vscode/launch.json vendored
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@@ -25,14 +25,14 @@
"program": "${workspaceFolder}/build/src/Platform/main",
"args": [
"-m",
"TANNew",
"AODELd",
"-p",
"/Users/rmontanana/Code/discretizbench/datasets",
"--stratified",
"-d",
"iris"
],
"cwd": "${workspaceFolder}/build/src/Platform",
"cwd": "/Users/rmontanana/Code/discretizbench",
},
{
"name": "Build & debug active file",

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@@ -17,6 +17,11 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
build: ## Build the main and BayesNetSample
cmake --build build -t main -t BayesNetSample -j 32
clean: ## Clean the debug info
@echo ">>> Cleaning Debug BayesNet ...";
find . -name "*.gcda" -print0 | xargs -0 rm
@echo ">>> Done";
debug: ## Build a debug version of the project
@echo ">>> Building Debug BayesNet ...";
@if [ -d ./build ]; then rm -rf ./build; fi

34
src/BayesNet/AODELd.cc Normal file
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@@ -0,0 +1,34 @@
#include "AODELd.h"
namespace bayesnet {
using namespace std;
AODELd::AODELd() : Ensemble(), Proposal(Ensemble::Xv, Ensemble::yv, features, className) {}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
features = features_;
className = className_;
states = states_;
train();
for (const auto& model : models) {
model->fit(X_, y_, features_, className_, states_);
}
n_models = models.size();
fitted = true;
return *this;
}
void AODELd::train()
{
models.clear();
for (int i = 0; i < features.size(); ++i) {
models.push_back(std::make_unique<SPODELd>(i));
}
}
Tensor AODELd::predict(Tensor& X)
{
return Ensemble::predict(X);
}
vector<string> AODELd::graph(const string& name)
{
return Ensemble::graph(name);
}
}

20
src/BayesNet/AODELd.h Normal file
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@@ -0,0 +1,20 @@
#ifndef AODELD_H
#define AODELD_H
#include "Ensemble.h"
#include "Proposal.h"
#include "SPODELd.h"
namespace bayesnet {
using namespace std;
class AODELd : public Ensemble, public Proposal {
public:
AODELd();
virtual ~AODELd() = default;
AODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "AODE") override;
Tensor predict(Tensor& X) override;
void train() override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !AODELD_H

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@@ -1,4 +1,5 @@
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANNew.cc KDBNew.cc Mst.cc Proposal.cc)
add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc
KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc)
target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")

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@@ -3,24 +3,24 @@
namespace bayesnet {
using namespace torch;
Ensemble::Ensemble() : m(0), n(0), n_models(0), metrics(Metrics()), fitted(false) {}
Ensemble::Ensemble() : n_models(0), metrics(Metrics()), fitted(false) {}
Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
{
dataset = cat({ X, y.view({y.size(0), 1}) }, 1);
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
this->features = features;
this->className = className;
this->states = states;
auto n_classes = states[className].size();
metrics = Metrics(dataset, features, className, n_classes);
metrics = Metrics(samples, features, className, n_classes);
// Build models
train();
// Train models
n_models = models.size();
auto Xt = torch::transpose(X, 0, 1);
for (auto i = 0; i < n_models; ++i) {
if (Xv == vector<vector<int>>()) {
if (Xv.empty()) {
// fit with tensors
models[i]->fit(Xt, y, features, className, states);
models[i]->fit(X, y, features, className, states);
} else {
// fit with vectors
models[i]->fit(Xv, yv, features, className, states);
@@ -29,9 +29,16 @@ namespace bayesnet {
fitted = true;
return *this;
}
void Ensemble::generateTensorXFromVector()
{
X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
for (int i = 0; i < Xv.size(); ++i) {
X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
}
}
Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = torch::transpose(X, 0, 1);
this->X = X;
this->y = y;
Xv = vector<vector<int>>();
yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
@@ -39,11 +46,8 @@ namespace bayesnet {
}
Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, kInt32);
Xv = X;
for (int i = 0; i < X.size(); ++i) {
this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt32));
}
generateTensorXFromVector();
this->y = torch::tensor(y, kInt32);
yv = y;
return build(features, className, states);
@@ -53,10 +57,11 @@ namespace bayesnet {
auto y_pred_ = y_pred.accessor<int, 2>();
vector<int> y_pred_final;
for (int i = 0; i < y_pred.size(0); ++i) {
vector<float> votes(states[className].size(), 0);
vector<float> votes(y_pred.size(1), 0);
for (int j = 0; j < y_pred.size(1); ++j) {
votes[y_pred_[i][j]] += 1;
}
// argsort in descending order
auto indices = argsort(votes);
y_pred_final.push_back(indices[0]);
}
@@ -70,13 +75,12 @@ namespace bayesnet {
Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
//Create a threadpool
auto threads{ vector<thread>() };
auto lock = mutex();
mutex mtx;
for (auto i = 0; i < n_models; ++i) {
threads.push_back(thread([&, i]() {
auto ypredict = models[i]->predict(X);
lock.lock();
lock_guard<mutex> lock(mtx);
y_pred.index_put_({ "...", i }, ypredict);
lock.unlock();
}));
}
for (auto& thread : threads) {

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@@ -10,23 +10,23 @@ using namespace torch;
namespace bayesnet {
class Ensemble : public BaseClassifier {
private:
bool fitted;
long n_models;
Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
protected:
unsigned n_models;
bool fitted;
vector<unique_ptr<Classifier>> models;
int m, n; // m: number of samples, n: number of features
Tensor X;
vector<vector<int>> Xv;
Tensor y;
vector<int> yv;
Tensor dataset;
Tensor samples;
Metrics metrics;
vector<string> features;
string className;
map<string, vector<int>> states;
void virtual train() = 0;
vector<int> voting(Tensor& y_pred);
void generateTensorXFromVector();
public:
Ensemble();
virtual ~Ensemble() = default;

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@@ -35,7 +35,6 @@ namespace bayesnet {
}
// 2. Compute class conditional mutual information I(Xi;XjIC), f or each
auto conditionalEdgeWeights = metrics.conditionalEdge();
cout << "Conditional edge weights: " << conditionalEdgeWeights << endl;
// 3. Let the used variable list, S, be empty.
vector<int> S;
// 4. Let the DAG network being constructed, BN, begin with a single

35
src/BayesNet/KDBLd.cc Normal file
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@@ -0,0 +1,35 @@
#include "KDBLd.h"
namespace bayesnet {
using namespace std;
KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
className = className_;
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
KDB::fit(KDB::Xv, KDB::yv, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(KDB::Xv, KDB::yv, features, className);
return *this;
}
Tensor KDBLd::predict(Tensor& X)
{
auto Xt = prepareX(X);
return KDB::predict(Xt);
}
vector<string> KDBLd::graph(const string& name)
{
return KDB::graph(name);
}
}

19
src/BayesNet/KDBLd.h Normal file
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@@ -0,0 +1,19 @@
#ifndef KDBLD_H
#define KDBLD_H
#include "KDB.h"
#include "Proposal.h"
namespace bayesnet {
using namespace std;
class KDBLd : public KDB, public Proposal {
private:
public:
explicit KDBLd(int k);
virtual ~KDBLd() = default;
KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "KDB") override;
Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !KDBLD_H

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@@ -1,48 +0,0 @@
#include "KDBNew.h"
namespace bayesnet {
using namespace std;
KDBNew::KDBNew(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
KDBNew& KDBNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
className = className_;
Xf = X_;
y = y_;
model.initialize();
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
cout << "KDBNew: Fitting model" << endl;
KDB::fit(KDB::Xv, KDB::yv, features, className, states);
cout << "KDBNew: Model fitted" << endl;
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(KDB::Xv, KDB::yv, features, className);
return *this;
}
void KDBNew::train()
{
KDB::train();
}
Tensor KDBNew::predict(Tensor& X)
{
auto Xtd = torch::zeros_like(X, torch::kInt32);
for (int i = 0; i < X.size(0); ++i) {
auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
auto Xd = discretizers[features[i]]->transform(Xt);
Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
}
cout << "KDBNew Xtd: " << Xtd.sizes() << endl;
return KDB::predict(Xtd);
}
vector<string> KDBNew::graph(const string& name)
{
return KDB::graph(name);
}
}

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@@ -1,20 +0,0 @@
#ifndef KDBNEW_H
#define KDBNEW_H
#include "KDB.h"
#include "Proposal.h"
namespace bayesnet {
using namespace std;
class KDBNew : public KDB, public Proposal {
private:
public:
KDBNew(int k);
virtual ~KDBNew() = default;
KDBNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "KDB") override;
Tensor predict(Tensor& X) override;
void train() override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !KDBNew_H

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@@ -47,25 +47,25 @@ namespace bayesnet {
//
//
//
auto tmp = discretizers[feature]->transform(xvf);
Xv[index] = tmp;
auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
//Update new states of the feature/node
states[feature] = xStates;
// auto tmp = discretizers[feature]->transform(xvf);
// Xv[index] = tmp;
// auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
// iota(xStates.begin(), xStates.end(), 0);
// //Update new states of the feature/node
// states[feature] = xStates;
}
if (upgrade) {
// Discretize again X (only the affected indices) with the new fitted discretizers
for (auto index : indicesToReDiscretize) {
auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
//Update new states of the feature/node
states[pFeatures[index]] = xStates;
}
}
// if (upgrade) {
// // Discretize again X (only the affected indices) with the new fitted discretizers
// for (auto index : indicesToReDiscretize) {
// auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
// auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
// Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
// auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
// iota(xStates.begin(), xStates.end(), 0);
// //Update new states of the feature/node
// states[pFeatures[index]] = xStates;
// }
// }
}
void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
{
@@ -89,4 +89,14 @@ namespace bayesnet {
iota(yStates.begin(), yStates.end(), 0);
states[pClassName] = yStates;
}
torch::Tensor Proposal::prepareX(torch::Tensor& X)
{
auto Xtd = torch::zeros_like(X, torch::kInt32);
for (int i = 0; i < X.size(0); ++i) {
auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
auto Xd = discretizers[pFeatures[i]]->transform(Xt);
Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
}
return Xtd;
}
}

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@@ -5,6 +5,7 @@
#include <torch/torch.h>
#include "Network.h"
#include "CPPFImdlp.h"
#include "Classifier.h"
namespace bayesnet {
class Proposal {
@@ -12,6 +13,7 @@ namespace bayesnet {
Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
virtual ~Proposal();
protected:
torch::Tensor prepareX(torch::Tensor& X);
void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
torch::Tensor Xf; // X continuous nxm tensor

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src/BayesNet/SPODELd.cc Normal file
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@@ -0,0 +1,35 @@
#include "SPODELd.h"
namespace bayesnet {
using namespace std;
SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
className = className_;
Xf = X_;
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(SPODE::Xv, SPODE::yv, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(SPODE::Xv, SPODE::yv, features, className);
return *this;
}
Tensor SPODELd::predict(Tensor& X)
{
auto Xt = prepareX(X);
return SPODE::predict(Xt);
}
vector<string> SPODELd::graph(const string& name)
{
return SPODE::graph(name);
}
}

19
src/BayesNet/SPODELd.h Normal file
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@@ -0,0 +1,19 @@
#ifndef SPODELD_H
#define SPODELD_H
#include "SPODE.h"
#include "Proposal.h"
namespace bayesnet {
using namespace std;
class SPODELd : public SPODE, public Proposal {
private:
public:
explicit SPODELd(int root);
virtual ~SPODELd() = default;
SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "SPODE") override;
Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !SPODELD_H

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@@ -1,9 +1,9 @@
#include "TANNew.h"
#include "TANLd.h"
namespace bayesnet {
using namespace std;
TANNew::TANNew() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
TANNew& TANNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
features = features_;
@@ -15,9 +15,7 @@ namespace bayesnet {
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
cout << "TANNew: Fitting model" << endl;
TAN::fit(TAN::Xv, TAN::yv, features, className, states);
cout << "TANNew: Model fitted" << endl;
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
@@ -25,18 +23,12 @@ namespace bayesnet {
model.fit(TAN::Xv, TAN::yv, features, className);
return *this;
}
Tensor TANNew::predict(Tensor& X)
Tensor TANLd::predict(Tensor& X)
{
auto Xtd = torch::zeros_like(X, torch::kInt32);
for (int i = 0; i < X.size(0); ++i) {
auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
auto Xd = discretizers[features[i]]->transform(Xt);
Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
}
cout << "TANNew Xtd: " << Xtd.sizes() << endl;
return TAN::predict(Xtd);
auto Xt = prepareX(X);
return TAN::predict(Xt);
}
vector<string> TANNew::graph(const string& name)
vector<string> TANLd::graph(const string& name)
{
return TAN::graph(name);
}

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src/BayesNet/TANLd.h Normal file
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@@ -0,0 +1,19 @@
#ifndef TANLD_H
#define TANLD_H
#include "TAN.h"
#include "Proposal.h"
namespace bayesnet {
using namespace std;
class TANLd : public TAN, public Proposal {
private:
public:
TANLd();
virtual ~TANLd() = default;
TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "TAN") override;
Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !TANLD_H

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@@ -1,19 +0,0 @@
#ifndef TANNEW_H
#define TANNEW_H
#include "TAN.h"
#include "Proposal.h"
namespace bayesnet {
using namespace std;
class TANNew : public TAN, public Proposal {
private:
public:
TANNew();
virtual ~TANNew() = default;
TANNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "TAN") override;
Tensor predict(Tensor& X) override;
static inline string version() { return "0.0.1"; };
};
}
#endif // !TANNEW_H

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@@ -3,6 +3,7 @@
namespace bayesnet {
using namespace std;
using namespace torch;
// Return the indices in descending order
vector<int> argsort(vector<float>& nums)
{
int n = nums.size();

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@@ -4,5 +4,5 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc)
add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc Report.cc)
target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

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@@ -1,6 +1,7 @@
#include "Experiment.h"
#include "Datasets.h"
#include "Models.h"
#include "Report.h"
namespace platform {
using json = nlohmann::json;
@@ -86,6 +87,13 @@ namespace platform {
file.close();
}
void Experiment::report()
{
json data = build_json();
Report report(data);
report.show();
}
void Experiment::show()
{
json data = build_json();
@@ -146,11 +154,6 @@ namespace platform {
auto y_test = y.index({ test_t });
cout << nfold + 1 << ", " << flush;
clf->fit(X_train, y_train, features, className, states);
cout << endl;
auto lines = clf->show();
for (auto line : lines) {
cout << line << endl;
}
nodes[item] = clf->getNumberOfNodes();
edges[item] = clf->getNumberOfEdges();
num_states[item] = clf->getNumberOfStates();

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@@ -108,6 +108,7 @@ namespace platform {
void cross_validation(const string& path, const string& fileName);
void go(vector<string> filesToProcess, const string& path);
void show();
void report();
};
}
#endif

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@@ -6,8 +6,10 @@
#include "TAN.h"
#include "KDB.h"
#include "SPODE.h"
#include "TANNew.h"
#include "KDBNew.h"
#include "TANLd.h"
#include "KDBLd.h"
#include "SPODELd.h"
#include "AODELd.h"
namespace platform {
class Models {
private:

66
src/Platform/Report.cc Normal file
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@@ -0,0 +1,66 @@
#include "Report.h"
namespace platform {
string headerLine(const string& text)
{
int n = MAXL - text.length() - 3;
return "* " + text + string(n, ' ') + "*\n";
}
string Report::fromVector(const string& key)
{
string result = "";
for (auto& item : data[key]) {
result += to_string(item) + ", ";
}
return "[" + result.substr(0, result.length() - 2) + "]";
}
string fVector(const json& data)
{
string result = "";
for (const auto& item : data) {
result += to_string(item) + ", ";
}
return "[" + result.substr(0, result.length() - 2) + "]";
}
void Report::show()
{
header();
body();
}
void Report::header()
{
cout << string(MAXL, '*') << endl;
cout << headerLine("Report " + data["model"].get<string>() + " ver. " + data["version"].get<string>() + " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) + " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>());
cout << headerLine(data["title"].get<string>());
cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
cout << headerLine("Execution took " + to_string(data["duration"].get<float>()) + " seconds, " + to_string(data["duration"].get<float>() / 3600) + " hours, on " + data["platform"].get<string>());
cout << headerLine("Score is " + data["score_name"].get<string>());
cout << string(MAXL, '*') << endl;
cout << endl;
}
void Report::body()
{
cout << "Dataset Sampl. Feat. Cls Nodes Edges States Score Time Hyperparameters" << endl;
cout << "============================== ====== ===== === ======= ======= ======= =============== ================= ===============" << endl;
for (const auto& r : data["results"]) {
cout << setw(30) << left << r["dataset"].get<string>() << " ";
cout << setw(6) << right << r["samples"].get<int>() << " ";
cout << setw(5) << right << r["features"].get<int>() << " ";
cout << setw(3) << right << r["classes"].get<int>() << " ";
cout << setw(7) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
cout << setw(7) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
cout << setw(7) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
cout << setw(8) << right << setprecision(6) << fixed << r["score_test"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_test_std"].get<double>() << " ";
cout << setw(10) << right << setprecision(6) << fixed << r["test_time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["test_time_std"].get<double>() << " ";
cout << " " << r["hyperparameters"].get<string>();
cout << endl;
cout << string(MAXL, '*') << endl;
cout << headerLine("Train scores: " + fVector(r["scores_train"]));
cout << headerLine("Test scores: " + fVector(r["scores_test"]));
cout << headerLine("Train times: " + fVector(r["times_train"]));
cout << headerLine("Test times: " + fVector(r["times_test"]));
cout << string(MAXL, '*') << endl;
}
}
}

23
src/Platform/Report.h Normal file
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@@ -0,0 +1,23 @@
#ifndef REPORT_H
#define REPORT_H
#include <string>
#include <iostream>
#include <nlohmann/json.hpp>
using json = nlohmann::json;
const int MAXL = 121;
namespace platform {
using namespace std;
class Report {
public:
explicit Report(json data_) { data = data_; };
virtual ~Report() = default;
void show();
private:
void header();
void body();
string fromVector(const string& key);
json data;
};
};
#endif

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@@ -102,9 +102,10 @@ int main(int argc, char** argv)
/*
* Begin Processing
*/
auto env = platform::DotEnv();
auto experiment = platform::Experiment();
experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("BayesNet");
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
for (auto seed : seeds) {
experiment.addRandomSeed(seed);
@@ -116,7 +117,7 @@ int main(int argc, char** argv)
if (saveResults)
experiment.save(PATH_RESULTS);
else
experiment.show();
experiment.report();
cout << "Done!" << endl;
return 0;
}

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@@ -2,14 +2,18 @@
#define MODEL_REGISTER_H
static platform::Registrar registrarT("TAN",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
static platform::Registrar registrarTN("TANNew",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANNew();});
static platform::Registrar registrarTLD("TANLd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
static platform::Registrar registrarS("SPODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
static platform::Registrar registrarSLD("SPODELd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
static platform::Registrar registrarK("KDB",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
static platform::Registrar registrarKN("KDBNew",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBNew(2);});
static platform::Registrar registrarKLD("KDBLd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
static platform::Registrar registrarA("AODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
static platform::Registrar registrarALD("AODELd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
#endif