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Merge pull request 'Optimize BoostAODE -> XBAODE' (#33) from WA2DE into main

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Reviewed-on: #33
This commit is contained in:
Ricardo Montañana Gómez
2025-03-16 17:58:10 +00:00
parent b90e558238 9ee388561f
commit 7076efc2a1
48 changed files with 3049 additions and 470 deletions
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4
.clang-format Normal file
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@@ -0,0 +1,4 @@
# .clang-format
BasedOnStyle: LLVM
IndentWidth: 4
ColumnLimit: 120

1
.gitignore vendored
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@@ -44,4 +44,5 @@ docs/manual
docs/man3
docs/man
docs/Doxyfile
.cache

4
.vscode/launch.json vendored
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@@ -5,7 +5,7 @@
"type": "lldb",
"request": "launch",
"name": "sample",
"program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
"program": "${workspaceFolder}/sample/build/bayesnet_sample",
"args": [
"${workspaceFolder}/tests/data/glass.arff"
]
@@ -16,7 +16,7 @@
"name": "test",
"program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
"args": [
"No features selected"
"[XBAODE]"
],
"cwd": "${workspaceFolder}/build_Debug/tests"
},

18
CHANGELOG.md
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@@ -7,11 +7,23 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## [Unreleased]
## [1.0.7] 2025-03-16
### Added
- Add a new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
- Add a new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
- Add a new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
- A new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
- A new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
- A new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
- A new model named XSPODE, an optimized for speed averaged one dependence estimator.
- A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
- A new model named XBAODE, an optimized for speed BoostAODE model.
- A new model named XBA2DE, an optimized for speed BoostA2DE model.
### Internal
- Optimize ComputeCPT method in the Node class.
- Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.
### Changed

2
CMakeLists.txt
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@@ -1,7 +1,7 @@
cmake_minimum_required(VERSION 3.20)
project(BayesNet
VERSION 1.0.6
VERSION 1.0.7
DESCRIPTION "Bayesian Network and basic classifiers Library."
HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
LANGUAGES CXX

12
Makefile
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@@ -97,9 +97,17 @@ fname = "tests/data/iris.arff"
sample: ## Build sample
@echo ">>> Building Sample...";
@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Debug && cmake --build build -t bayesnet_sample
sample/build/bayesnet_sample $(fname)
@echo ">>> Done";
@echo ">>> Done";
fname = "tests/data/iris.arff"
sample2: ## Build sample2
@echo ">>> Building Sample...";
@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Debug && cmake --build build -t bayesnet_sample_xspode
sample/build/bayesnet_sample_xspode $(fname)
@echo ">>> Done";
opt = ""
test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section

10
bayesnet/BaseClassifier.h
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@@ -14,13 +14,13 @@ namespace bayesnet {
enum status_t { NORMAL, WARNING, ERROR };
class BaseClassifier {
public:
virtual ~BaseClassifier() = default;
// X is nxm std::vector, y is nx1 std::vector
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
// X is nxm tensor, y is nx1 tensor
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
virtual ~BaseClassifier() = default;
torch::Tensor virtual predict(torch::Tensor& X) = 0;
std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
@@ -28,8 +28,8 @@ namespace bayesnet {
status_t virtual getStatus() const = 0;
float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
int virtual getNumberOfNodes()const = 0;
int virtual getNumberOfEdges()const = 0;
int virtual getNumberOfNodes() const = 0;
int virtual getNumberOfEdges() const = 0;
int virtual getNumberOfStates() const = 0;
int virtual getClassNumStates() const = 0;
std::vector<std::string> virtual show() const = 0;
@@ -37,11 +37,13 @@ namespace bayesnet {
virtual std::string getVersion() = 0;
std::vector<std::string> virtual topological_order() = 0;
std::vector<std::string> virtual getNotes() const = 0;
std::string virtual dump_cpt()const = 0;
std::string virtual dump_cpt() const = 0;
virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
protected:
virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
std::vector<std::string> validHyperparameters;
std::vector<std::string> notes; // Used to store messages occurred during the fit process
status_t status = NORMAL;
};
}

1
bayesnet/CMakeLists.txt
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@@ -1,4 +1,5 @@
include_directories(
${BayesNet_SOURCE_DIR}/lib/log
${BayesNet_SOURCE_DIR}/lib/mdlp/src
${BayesNet_SOURCE_DIR}/lib/folding
${BayesNet_SOURCE_DIR}/lib/json/include

3
bayesnet/classifiers/Classifier.cc
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@@ -10,7 +10,6 @@
namespace bayesnet {
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
{
this->features = features;
@@ -191,4 +190,4 @@ namespace bayesnet {
throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
}
}
}
}

3
bayesnet/classifiers/Classifier.h
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@@ -46,12 +46,11 @@ namespace bayesnet {
std::string className;
std::map<std::string, std::vector<int>> states;
torch::Tensor dataset; // (n+1)xm tensor
status_t status = NORMAL;
std::vector<std::string> notes; // Used to store messages occurred during the fit process
void checkFitParameters();
virtual void buildModel(const torch::Tensor& weights) = 0;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
void buildDataset(torch::Tensor& y);
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
private:
Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
};

2
bayesnet/classifiers/KDB.cc
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@@ -3,7 +3,7 @@
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "bayesnet/utils/bayesnetUtils.h"
#include "KDB.h"
namespace bayesnet {

5
bayesnet/classifiers/KDB.h
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@@ -7,15 +7,14 @@
#ifndef KDB_H
#define KDB_H
#include <torch/torch.h>
#include "bayesnet/utils/bayesnetUtils.h"
#include "Classifier.h"
namespace bayesnet {
class KDB : public Classifier {
private:
int k;
float theta;
void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
protected:
void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
void buildModel(const torch::Tensor& weights) override;
public:
explicit KDB(int k, float theta = 0.03);
@@ -24,4 +23,4 @@ namespace bayesnet {
std::vector<std::string> graph(const std::string& name = "KDB") const override;
};
}
#endif
#endif

4
bayesnet/classifiers/Proposal.cc
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@@ -11,7 +11,7 @@ namespace bayesnet {
Proposal::~Proposal()
{
for (auto& [key, value] : discretizers) {
delete value;
delete value;
}
}
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
@@ -126,4 +126,4 @@ namespace bayesnet {
}
return yy;
}
}
}

2
bayesnet/classifiers/SPnDE.cc
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@@ -35,4 +35,4 @@ namespace bayesnet {
return model.graph(name);
}
}
}

575
bayesnet/classifiers/XSP2DE.cc Normal file
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@@ -0,0 +1,575 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "XSP2DE.h"
#include <pthread.h> // for pthread_setname_np on linux
#include <cassert>
#include <cmath>
#include <limits>
#include <stdexcept>
#include <iostream>
#include "bayesnet/utils/TensorUtils.h"
namespace bayesnet {
// --------------------------------------
// Constructor
// --------------------------------------
XSp2de::XSp2de(int spIndex1, int spIndex2)
: superParent1_{ spIndex1 }
, superParent2_{ spIndex2 }
, nFeatures_{0}
, statesClass_{0}
, alpha_{1.0}
, initializer_{1.0}
, semaphore_{ CountingSemaphore::getInstance() }
, Classifier(Network())
{
validHyperparameters = { "parent1", "parent2" };
}
// --------------------------------------
// setHyperparameters
// --------------------------------------
void XSp2de::setHyperparameters(const nlohmann::json &hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("parent1")) {
superParent1_ = hyperparameters["parent1"];
hyperparameters.erase("parent1");
}
if (hyperparameters.contains("parent2")) {
superParent2_ = hyperparameters["parent2"];
hyperparameters.erase("parent2");
}
// Hand off anything else to base Classifier
Classifier::setHyperparameters(hyperparameters);
}
// --------------------------------------
// fitx
// --------------------------------------
void XSp2de::fitx(torch::Tensor & X, torch::Tensor & y,
torch::Tensor & weights_, const Smoothing_t smoothing)
{
m = X.size(1); // number of samples
n = X.size(0); // number of features
dataset = X;
// Build the dataset in your environment if needed:
buildDataset(y);
// Construct the data structures needed for counting
buildModel(weights_);
// Accumulate counts & convert to probabilities
trainModel(weights_, smoothing);
fitted = true;
}
// --------------------------------------
// buildModel
// --------------------------------------
void XSp2de::buildModel(const torch::Tensor &weights)
{
nFeatures_ = n;
// Derive the number of states for each feature from the dataset
// states_[f] = max value in dataset[f] + 1.
states_.resize(nFeatures_);
for (int f = 0; f < nFeatures_; f++) {
// This is naive: we take max in feature f. You might adapt for real data.
states_[f] = dataset[f].max().item<int>() + 1;
}
// Class states:
statesClass_ = dataset[-1].max().item<int>() + 1;
// Initialize the class counts
classCounts_.resize(statesClass_, 0.0);
// For sp1 -> p(sp1Val| c)
sp1FeatureCounts_.resize(states_[superParent1_] * statesClass_, 0.0);
// For sp2 -> p(sp2Val| c)
sp2FeatureCounts_.resize(states_[superParent2_] * statesClass_, 0.0);
// For child features, we store p(childVal | c, sp1Val, sp2Val).
// childCounts_ will hold raw counts. We’ll gather them in one big vector.
// We need an offset for each feature.
childOffsets_.resize(nFeatures_, -1);
int totalSize = 0;
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent1_ || f == superParent2_) {
// skip the superparents
childOffsets_[f] = -1;
continue;
}
childOffsets_[f] = totalSize;
// block size for a single child f: states_[f] * statesClass_
// * states_[superParent1_]
// * states_[superParent2_].
totalSize += (states_[f] * statesClass_
* states_[superParent1_]
* states_[superParent2_]);
}
childCounts_.resize(totalSize, 0.0);
}
// --------------------------------------
// trainModel
// --------------------------------------
void XSp2de::trainModel(const torch::Tensor &weights,
const bayesnet::Smoothing_t smoothing)
{
// Accumulate raw counts
for (int i = 0; i < m; i++) {
std::vector<int> instance(nFeatures_ + 1);
for (int f = 0; f < nFeatures_; f++) {
instance[f] = dataset[f][i].item<int>();
}
instance[nFeatures_] = dataset[-1][i].item<int>(); // class
double w = weights[i].item<double>();
addSample(instance, w);
}
// Choose alpha based on smoothing:
switch (smoothing) {
case bayesnet::Smoothing_t::ORIGINAL:
alpha_ = 1.0 / m;
break;
case bayesnet::Smoothing_t::LAPLACE:
alpha_ = 1.0;
break;
default:
alpha_ = 0.0; // no smoothing
}
// Large initializer factor for numerical stability
initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
// Convert raw counts to probabilities
computeProbabilities();
}
// --------------------------------------
// addSample
// --------------------------------------
void XSp2de::addSample(const std::vector<int> &instance, double weight)
{
if (weight <= 0.0)
return;
int c = instance.back();
// increment classCounts
classCounts_[c] += weight;
int sp1Val = instance[superParent1_];
int sp2Val = instance[superParent2_];
// p(sp1|c)
sp1FeatureCounts_[sp1Val * statesClass_ + c] += weight;
// p(sp2|c)
sp2FeatureCounts_[sp2Val * statesClass_ + c] += weight;
// p(childVal| c, sp1Val, sp2Val)
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent1_ || f == superParent2_)
continue;
int childVal = instance[f];
int offset = childOffsets_[f];
// block layout:
// offset + (sp1Val*(states_[sp2_]* states_[f]* statesClass_))
// + (sp2Val*(states_[f]* statesClass_))
// + childVal*(statesClass_)
// + c
int blockSizeSp2 = states_[superParent2_]
* states_[f]
* statesClass_;
int blockSizeChild = states_[f] * statesClass_;
int idx = offset
+ sp1Val*blockSizeSp2
+ sp2Val*blockSizeChild
+ childVal*statesClass_
+ c;
childCounts_[idx] += weight;
}
}
// --------------------------------------
// computeProbabilities
// --------------------------------------
void XSp2de::computeProbabilities()
{
double totalCount = std::accumulate(classCounts_.begin(),
classCounts_.end(), 0.0);
// classPriors_
classPriors_.resize(statesClass_, 0.0);
if (totalCount <= 0.0) {
// fallback => uniform
double unif = 1.0 / static_cast<double>(statesClass_);
for (int c = 0; c < statesClass_; c++) {
classPriors_[c] = unif;
}
} else {
for (int c = 0; c < statesClass_; c++) {
classPriors_[c] =
(classCounts_[c] + alpha_)
/ (totalCount + alpha_ * statesClass_);
}
}
// p(sp1Val| c)
sp1FeatureProbs_.resize(sp1FeatureCounts_.size());
int sp1Card = states_[superParent1_];
for (int spVal = 0; spVal < sp1Card; spVal++) {
for (int c = 0; c < statesClass_; c++) {
double denom = classCounts_[c] + alpha_ * sp1Card;
double num = sp1FeatureCounts_[spVal * statesClass_ + c] + alpha_;
sp1FeatureProbs_[spVal * statesClass_ + c] =
(denom <= 0.0 ? 0.0 : num / denom);
}
}
// p(sp2Val| c)
sp2FeatureProbs_.resize(sp2FeatureCounts_.size());
int sp2Card = states_[superParent2_];
for (int spVal = 0; spVal < sp2Card; spVal++) {
for (int c = 0; c < statesClass_; c++) {
double denom = classCounts_[c] + alpha_ * sp2Card;
double num = sp2FeatureCounts_[spVal * statesClass_ + c] + alpha_;
sp2FeatureProbs_[spVal * statesClass_ + c] =
(denom <= 0.0 ? 0.0 : num / denom);
}
}
// p(childVal| c, sp1Val, sp2Val)
childProbs_.resize(childCounts_.size());
int offset = 0;
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent1_ || f == superParent2_)
continue;
int fCard = states_[f];
int sp1Card_ = states_[superParent1_];
int sp2Card_ = states_[superParent2_];
int childBlockSizeSp2 = sp2Card_ * fCard * statesClass_;
int childBlockSizeF = fCard * statesClass_;
int blockSize = fCard * sp1Card_ * sp2Card_ * statesClass_;
for (int sp1Val = 0; sp1Val < sp1Card_; sp1Val++) {
for (int sp2Val = 0; sp2Val < sp2Card_; sp2Val++) {
for (int childVal = 0; childVal < fCard; childVal++) {
for (int c = 0; c < statesClass_; c++) {
// index in childCounts_
int idx = offset
+ sp1Val*childBlockSizeSp2
+ sp2Val*childBlockSizeF
+ childVal*statesClass_
+ c;
double num = childCounts_[idx] + alpha_;
// denominator is the count of (sp1Val,sp2Val,c) plus alpha * fCard
// We can find that by summing childVal dimension, but we already
// have it in childCounts_[...] or we can re-check the superparent
// counts if your approach is purely hierarchical.
// Here we'll do it like the XSpode approach: sp1&sp2 are
// conditionally independent given c, so denominators come from
// summing the relevant block or we treat sp1,sp2 as "parents."
// A simpler approach:
double sumSp1Sp2C = 0.0;
// sum over all childVal:
for (int cv = 0; cv < fCard; cv++) {
int idx2 = offset
+ sp1Val*childBlockSizeSp2
+ sp2Val*childBlockSizeF
+ cv*statesClass_ + c;
sumSp1Sp2C += childCounts_[idx2];
}
double denom = sumSp1Sp2C + alpha_ * fCard;
childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
}
}
}
}
offset += blockSize;
}
}
// --------------------------------------
// predict_proba (single instance)
// --------------------------------------
std::vector<double> XSp2de::predict_proba(const std::vector<int> &instance) const
{
if (!fitted) {
throw std::logic_error(CLASSIFIER_NOT_FITTED);
}
std::vector<double> probs(statesClass_, 0.0);
int sp1Val = instance[superParent1_];
int sp2Val = instance[superParent2_];
// Start with p(c) * p(sp1Val| c) * p(sp2Val| c)
for (int c = 0; c < statesClass_; c++) {
double pC = classPriors_[c];
double pSp1C = sp1FeatureProbs_[sp1Val * statesClass_ + c];
double pSp2C = sp2FeatureProbs_[sp2Val * statesClass_ + c];
probs[c] = pC * pSp1C * pSp2C * initializer_;
}
// Multiply by each child feature f
int offset = 0;
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent1_ || f == superParent2_)
continue;
int valF = instance[f];
int fCard = states_[f];
int sp1Card = states_[superParent1_];
int sp2Card = states_[superParent2_];
int blockSizeSp2 = sp2Card * fCard * statesClass_;
int blockSizeF = fCard * statesClass_;
// base index for childProbs_ for this child and sp1Val, sp2Val
int base = offset
+ sp1Val*blockSizeSp2
+ sp2Val*blockSizeF
+ valF*statesClass_;
for (int c = 0; c < statesClass_; c++) {
probs[c] *= childProbs_[base + c];
}
offset += (fCard * sp1Card * sp2Card * statesClass_);
}
// Normalize
normalize(probs);
return probs;
}
// --------------------------------------
// predict_proba (batch)
// --------------------------------------
std::vector<std::vector<double>> XSp2de::predict_proba(std::vector<std::vector<int>> &test_data)
{
int test_size = test_data[0].size(); // each feature is test_data[f], size = #samples
int sample_size = test_data.size(); // = nFeatures_
std::vector<std::vector<double>> probabilities(
test_size, std::vector<double>(statesClass_, 0.0));
// same concurrency approach
int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
std::vector<std::thread> threads;
auto worker = [&](const std::vector<std::vector<int>> &samples,
int begin,
int chunk,
int sample_size,
std::vector<std::vector<double>> &predictions) {
std::string threadName =
"XSp2de-" + std::to_string(begin) + "-" + std::to_string(chunk);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
std::vector<int> instance(sample_size);
for (int sample = begin; sample < begin + chunk; ++sample) {
for (int feature = 0; feature < sample_size; ++feature) {
instance[feature] = samples[feature][sample];
}
predictions[sample] = predict_proba(instance);
}
semaphore_.release();
};
for (int begin = 0; begin < test_size; begin += chunk_size) {
int chunk = std::min(chunk_size, test_size - begin);
semaphore_.acquire();
threads.emplace_back(worker, test_data, begin, chunk, sample_size,
std::ref(probabilities));
}
for (auto &th : threads) {
th.join();
}
return probabilities;
}
// --------------------------------------
// predict (single instance)
// --------------------------------------
int XSp2de::predict(const std::vector<int> &instance) const
{
auto p = predict_proba(instance);
return static_cast<int>(
std::distance(p.begin(), std::max_element(p.begin(), p.end()))
);
}
// --------------------------------------
// predict (batch of data)
// --------------------------------------
std::vector<int> XSp2de::predict(std::vector<std::vector<int>> &test_data)
{
auto probabilities = predict_proba(test_data);
std::vector<int> predictions(probabilities.size(), 0);
for (size_t i = 0; i < probabilities.size(); i++) {
predictions[i] = static_cast<int>(
std::distance(probabilities[i].begin(),
std::max_element(probabilities[i].begin(),
probabilities[i].end()))
);
}
return predictions;
}
// --------------------------------------
// predict (torch::Tensor version)
// --------------------------------------
torch::Tensor XSp2de::predict(torch::Tensor &X)
{
auto X_ = TensorUtils::to_matrix(X);
auto result_v = predict(X_);
return torch::tensor(result_v, torch::kInt32);
}
// --------------------------------------
// predict_proba (torch::Tensor version)
// --------------------------------------
torch::Tensor XSp2de::predict_proba(torch::Tensor &X)
{
auto X_ = TensorUtils::to_matrix(X);
auto result_v = predict_proba(X_);
int n_samples = X.size(1);
torch::Tensor result =
torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
for (int i = 0; i < (int)result_v.size(); ++i) {
result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
}
return result;
}
// --------------------------------------
// score (torch::Tensor version)
// --------------------------------------
float XSp2de::score(torch::Tensor &X, torch::Tensor &y)
{
torch::Tensor y_pred = predict(X);
return (y_pred == y).sum().item<float>() / y.size(0);
}
// --------------------------------------
// score (vector version)
// --------------------------------------
float XSp2de::score(std::vector<std::vector<int>> &X, std::vector<int> &y)
{
auto y_pred = predict(X);
int correct = 0;
for (size_t i = 0; i < y_pred.size(); ++i) {
if (y_pred[i] == y[i]) {
correct++;
}
}
return static_cast<float>(correct) / static_cast<float>(y_pred.size());
}
// --------------------------------------
// Utility: normalize
// --------------------------------------
void XSp2de::normalize(std::vector<double> &v) const
{
double sum = 0.0;
for (auto &val : v) {
sum += val;
}
if (sum > 0.0) {
for (auto &val : v) {
val /= sum;
}
}
}
// --------------------------------------
// to_string
// --------------------------------------
std::string XSp2de::to_string() const
{
std::ostringstream oss;
oss << "----- XSp2de Model -----\n"
<< "nFeatures_ = " << nFeatures_ << "\n"
<< "superParent1_ = " << superParent1_ << "\n"
<< "superParent2_ = " << superParent2_ << "\n"
<< "statesClass_ = " << statesClass_ << "\n\n";
oss << "States: [";
for (auto s : states_) oss << s << " ";
oss << "]\n";
oss << "classCounts_:\n";
for (auto v : classCounts_) oss << v << " ";
oss << "\nclassPriors_:\n";
for (auto v : classPriors_) oss << v << " ";
oss << "\nsp1FeatureCounts_ (size=" << sp1FeatureCounts_.size() << ")\n";
for (auto v : sp1FeatureCounts_) oss << v << " ";
oss << "\nsp2FeatureCounts_ (size=" << sp2FeatureCounts_.size() << ")\n";
for (auto v : sp2FeatureCounts_) oss << v << " ";
oss << "\nchildCounts_ (size=" << childCounts_.size() << ")\n";
for (auto v : childCounts_) oss << v << " ";
oss << "\nchildOffsets_:\n";
for (auto c : childOffsets_) oss << c << " ";
oss << "\n----------------------------------------\n";
return oss.str();
}
// --------------------------------------
// Some introspection about the graph
// --------------------------------------
int XSp2de::getNumberOfNodes() const
{
// nFeatures + 1 class node
return nFeatures_ + 1;
}
int XSp2de::getClassNumStates() const
{
return statesClass_;
}
int XSp2de::getNFeatures() const
{
return nFeatures_;
}
int XSp2de::getNumberOfStates() const
{
// purely an example. Possibly you want to sum up actual
// cardinalities or something else.
return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
}
int XSp2de::getNumberOfEdges() const
{
// In an SPNDE with n=2, for each feature we have edges from class, sp1, sp2.
// So that’s 3*(nFeatures_) edges, minus the ones for the superparents themselves,
// plus the edges from class->superparent1, class->superparent2.
// For a quick approximation:
// - class->sp1, class->sp2 => 2 edges
// - class->child => (nFeatures -2) edges
// - sp1->child, sp2->child => 2*(nFeatures -2) edges
// total = 2 + (nFeatures-2) + 2*(nFeatures-2) = 2 + 3*(nFeatures-2)
// = 3nFeatures - 4 (just an example).
// You can adapt to your liking:
return 3 * nFeatures_ - 4;
}
} // namespace bayesnet

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#ifndef XSP2DE_H
#define XSP2DE_H
#include "Classifier.h"
#include "bayesnet/utils/CountingSemaphore.h"
#include <torch/torch.h>
#include <vector>
namespace bayesnet {
class XSp2de : public Classifier {
public:
XSp2de(int spIndex1, int spIndex2);
void setHyperparameters(const nlohmann::json &hyperparameters_) override;
void fitx(torch::Tensor &X, torch::Tensor &y, torch::Tensor &weights_, const Smoothing_t smoothing);
std::vector<double> predict_proba(const std::vector<int> &instance) const;
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>> &test_data) override;
int predict(const std::vector<int> &instance) const;
std::vector<int> predict(std::vector<std::vector<int>> &test_data) override;
torch::Tensor predict(torch::Tensor &X) override;
torch::Tensor predict_proba(torch::Tensor &X) override;
float score(torch::Tensor &X, torch::Tensor &y) override;
float score(std::vector<std::vector<int>> &X, std::vector<int> &y) override;
std::string to_string() const;
std::vector<std::string> graph(const std::string &title) const override {
return std::vector<std::string>({title});
}
int getNumberOfNodes() const override;
int getNumberOfEdges() const override;
int getNFeatures() const;
int getClassNumStates() const override;
int getNumberOfStates() const override;
protected:
void buildModel(const torch::Tensor &weights) override;
void trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) override;
private:
void addSample(const std::vector<int> &instance, double weight);
void normalize(std::vector<double> &v) const;
void computeProbabilities();
int superParent1_;
int superParent2_;
int nFeatures_;
int statesClass_;
double alpha_;
double initializer_;
std::vector<int> states_;
std::vector<double> classCounts_;
std::vector<double> classPriors_;
std::vector<double> sp1FeatureCounts_, sp1FeatureProbs_;
std::vector<double> sp2FeatureCounts_, sp2FeatureProbs_;
// childOffsets_[f] will be the offset into childCounts_ for feature f.
// If f is either superParent1 or superParent2, childOffsets_[f] = -1
std::vector<int> childOffsets_;
// For each child f, we store p(x_f | c, sp1Val, sp2Val). We'll store the raw
// counts in childCounts_, and the probabilities in childProbs_, with a
// dimension block of size: states_[f]* statesClass_* states_[sp1]* states_[sp2].
std::vector<double> childCounts_;
std::vector<double> childProbs_;
CountingSemaphore &semaphore_;
};
} // namespace bayesnet
#endif // XSP2DE_H

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <algorithm>
#include <cmath>
#include <limits>
#include <numeric>
#include <sstream>
#include <stdexcept>
#include "XSPODE.h"
#include "bayesnet/utils/TensorUtils.h"
namespace bayesnet {
// --------------------------------------
// Constructor
// --------------------------------------
XSpode::XSpode(int spIndex)
: superParent_{ spIndex }, nFeatures_{ 0 }, statesClass_{ 0 }, alpha_{ 1.0 },
initializer_{ 1.0 }, semaphore_{ CountingSemaphore::getInstance() },
Classifier(Network())
{
validHyperparameters = { "parent" };
}
void XSpode::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("parent")) {
superParent_ = hyperparameters["parent"];
hyperparameters.erase("parent");
}
Classifier::setHyperparameters(hyperparameters);
}
void XSpode::fitx(torch::Tensor & X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing)
{
m = X.size(1);
n = X.size(0);
dataset = X;
buildDataset(y);
buildModel(weights_);
trainModel(weights_, smoothing);
fitted = true;
}
// --------------------------------------
// trainModel
// --------------------------------------
// Initialize storage needed for the super-parent and child features counts and
// probs.
// --------------------------------------
void XSpode::buildModel(const torch::Tensor& weights)
{
int numInstances = m;
nFeatures_ = n;
// Derive the number of states for each feature and for the class.
// (This is just one approach; adapt to match your environment.)
// Here, we assume the user also gave us the total #states per feature in e.g.
// statesMap. We'll simply reconstruct the integer states_ array. The last
// entry is statesClass_.
states_.resize(nFeatures_);
for (int f = 0; f < nFeatures_; f++) {
// Suppose you look up in “statesMap” by the feature name, or read directly
// from X. We'll assume states_[f] = max value in X[f] + 1.
states_[f] = dataset[f].max().item<int>() + 1;
}
// For the class: states_.back() = max(y)+1
statesClass_ = dataset[-1].max().item<int>() + 1;
// Initialize counts
classCounts_.resize(statesClass_, 0.0);
// p(x_sp = spVal | c)
// We'll store these counts in spFeatureCounts_[spVal * statesClass_ + c].
spFeatureCounts_.resize(states_[superParent_] * statesClass_, 0.0);
// For each child ≠ sp, we store p(childVal| c, spVal) in a separate block of
// childCounts_. childCounts_ will be sized as sum_{child≠sp} (states_[child]
// * statesClass_ * states_[sp]). We also need an offset for each child to
// index into childCounts_.
childOffsets_.resize(nFeatures_, -1);
int totalSize = 0;
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent_)
continue; // skip sp
childOffsets_[f] = totalSize;
// block size for this child's counts: states_[f] * statesClass_ *
// states_[superParent_]
totalSize += (states_[f] * statesClass_ * states_[superParent_]);
}
childCounts_.resize(totalSize, 0.0);
}
// --------------------------------------
// buildModel
// --------------------------------------
//
// We only store conditional probabilities for:
// p(x_sp| c) (the super-parent feature)
// p(x_child| c, x_sp) for all child ≠ sp
//
// --------------------------------------
void XSpode::trainModel(const torch::Tensor& weights,
const bayesnet::Smoothing_t smoothing)
{
// Accumulate raw counts
for (int i = 0; i < m; i++) {
std::vector<int> instance(nFeatures_ + 1);
for (int f = 0; f < nFeatures_; f++) {
instance[f] = dataset[f][i].item<int>();
}
instance[nFeatures_] = dataset[-1][i].item<int>();
addSample(instance, weights[i].item<double>());
}
switch (smoothing) {
case bayesnet::Smoothing_t::ORIGINAL:
alpha_ = 1.0 / m;
break;
case bayesnet::Smoothing_t::LAPLACE:
alpha_ = 1.0;
break;
default:
alpha_ = 0.0; // No smoothing
}
initializer_ = std::numeric_limits<double>::max() /
(nFeatures_ * nFeatures_); // for numerical stability
// Convert raw counts to probabilities
computeProbabilities();
}
// --------------------------------------
// addSample
// --------------------------------------
//
// instance has size nFeatures_ + 1, with the class at the end.
// We add 1 to the appropriate counters for each (c, superParentVal, childVal).
//
void XSpode::addSample(const std::vector<int>& instance, double weight)
{
if (weight <= 0.0)
return;
int c = instance.back();
// (A) increment classCounts
classCounts_[c] += weight;
// (B) increment super-parent counts => p(x_sp | c)
int spVal = instance[superParent_];
spFeatureCounts_[spVal * statesClass_ + c] += weight;
// (C) increment child counts => p(childVal | c, x_sp)
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent_)
continue;
int childVal = instance[f];
int offset = childOffsets_[f];
// Compute index in childCounts_.
// Layout: [ offset + (spVal * states_[f] + childVal) * statesClass_ + c ]
int blockSize = states_[f] * statesClass_;
int idx = offset + spVal * blockSize + childVal * statesClass_ + c;
childCounts_[idx] += weight;
}
}
// --------------------------------------
// computeProbabilities
// --------------------------------------
//
// Once all samples are added in COUNTS mode, call this to:
// p(c)
// p(x_sp = spVal | c)
// p(x_child = v | c, x_sp = s_sp)
//
// --------------------------------------
void XSpode::computeProbabilities()
{
double totalCount =
std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
// p(c) => classPriors_
classPriors_.resize(statesClass_, 0.0);
if (totalCount <= 0.0) {
// fallback => uniform
double unif = 1.0 / static_cast<double>(statesClass_);
for (int c = 0; c < statesClass_; c++) {
classPriors_[c] = unif;
}
} else {
for (int c = 0; c < statesClass_; c++) {
classPriors_[c] =
(classCounts_[c] + alpha_) / (totalCount + alpha_ * statesClass_);
}
}
// p(x_sp | c)
spFeatureProbs_.resize(spFeatureCounts_.size());
// denominator for spVal * statesClass_ + c is just classCounts_[c] + alpha_ *
// (#states of sp)
int spCard = states_[superParent_];
for (int spVal = 0; spVal < spCard; spVal++) {
for (int c = 0; c < statesClass_; c++) {
double denom = classCounts_[c] + alpha_ * spCard;
double num = spFeatureCounts_[spVal * statesClass_ + c] + alpha_;
spFeatureProbs_[spVal * statesClass_ + c] = (denom <= 0.0 ? 0.0 : num / denom);
}
}
// p(x_child | c, x_sp)
childProbs_.resize(childCounts_.size());
for (int f = 0; f < nFeatures_; f++) {
if (f == superParent_)
continue;
int offset = childOffsets_[f];
int childCard = states_[f];
// For each spVal, c, childVal in childCounts_:
for (int spVal = 0; spVal < spCard; spVal++) {
for (int childVal = 0; childVal < childCard; childVal++) {
for (int c = 0; c < statesClass_; c++) {
int idx = offset + spVal * (childCard * statesClass_) +
childVal * statesClass_ + c;
double num = childCounts_[idx] + alpha_;
// denominator = spFeatureCounts_[spVal * statesClass_ + c] + alpha_ *
// (#states of child)
double denom =
spFeatureCounts_[spVal * statesClass_ + c] + alpha_ * childCard;
childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
}
}
}
}
}
// --------------------------------------
// predict_proba
// --------------------------------------
//
// For a single instance x of dimension nFeatures_:
// P(c | x) ∝ p(c) × p(x_sp | c) × ∏(child ≠ sp) p(x_child | c, x_sp).
//
// --------------------------------------
std::vector<double> XSpode::predict_proba(const std::vector<int>& instance) const
{
if (!fitted) {
throw std::logic_error(CLASSIFIER_NOT_FITTED);
}
std::vector<double> probs(statesClass_, 0.0);
// Multiply p(c) × p(x_sp | c)
int spVal = instance[superParent_];
for (int c = 0; c < statesClass_; c++) {
double pc = classPriors_[c];
double pSpC = spFeatureProbs_[spVal * statesClass_ + c];
probs[c] = pc * pSpC * initializer_;
}
// Multiply by each child’s probability p(x_child | c, x_sp)
for (int feature = 0; feature < nFeatures_; feature++) {
if (feature == superParent_)
continue; // skip sp
int sf = instance[feature];
int offset = childOffsets_[feature];
int childCard = states_[feature]; // not used directly, but for clarity
// Index into childProbs_ = offset + spVal*(childCard*statesClass_) +
// childVal*statesClass_ + c
int base = offset + spVal * (childCard * statesClass_) + sf * statesClass_;
for (int c = 0; c < statesClass_; c++) {
probs[c] *= childProbs_[base + c];
}
}
// Normalize
normalize(probs);
return probs;
}
std::vector<std::vector<double>> XSpode::predict_proba(std::vector<std::vector<int>>& test_data)
{
int test_size = test_data[0].size();
int sample_size = test_data.size();
auto probabilities = std::vector<std::vector<double>>(
test_size, std::vector<double>(statesClass_));
int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
std::vector<std::thread> threads;
auto worker = [&](const std::vector<std::vector<int>>& samples, int begin,
int chunk, int sample_size,
std::vector<std::vector<double>>& predictions) {
std::string threadName =
"(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
std::vector<int> instance(sample_size);
for (int sample = begin; sample < begin + chunk; ++sample) {
for (int feature = 0; feature < sample_size; ++feature) {
instance[feature] = samples[feature][sample];
}
predictions[sample] = predict_proba(instance);
}
semaphore_.release();
};
for (int begin = 0; begin < test_size; begin += chunk_size) {
int chunk = std::min(chunk_size, test_size - begin);
semaphore_.acquire();
threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
}
for (auto& thread : threads) {
thread.join();
}
return probabilities;
}
// --------------------------------------
// Utility: normalize
// --------------------------------------
void XSpode::normalize(std::vector<double>& v) const
{
double sum = 0.0;
for (auto val : v) {
sum += val;
}
if (sum <= 0.0) {
return;
}
for (auto& val : v) {
val /= sum;
}
}
// --------------------------------------
// representation of the model
// --------------------------------------
std::string XSpode::to_string() const
{
std::ostringstream oss;
oss << "----- XSpode Model -----" << std::endl
<< "nFeatures_ = " << nFeatures_ << std::endl
<< "superParent_ = " << superParent_ << std::endl
<< "statesClass_ = " << statesClass_ << std::endl
<< std::endl;
oss << "States: [";
for (int s : states_)
oss << s << " ";
oss << "]" << std::endl;
oss << "classCounts_: [";
for (double c : classCounts_)
oss << c << " ";
oss << "]" << std::endl;
oss << "classPriors_: [";
for (double c : classPriors_)
oss << c << " ";
oss << "]" << std::endl;
oss << "spFeatureCounts_: size = " << spFeatureCounts_.size() << std::endl
<< "[";
for (double c : spFeatureCounts_)
oss << c << " ";
oss << "]" << std::endl;
oss << "spFeatureProbs_: size = " << spFeatureProbs_.size() << std::endl
<< "[";
for (double c : spFeatureProbs_)
oss << c << " ";
oss << "]" << std::endl;
oss << "childCounts_: size = " << childCounts_.size() << std::endl << "[";
for (double cc : childCounts_)
oss << cc << " ";
oss << "]" << std::endl;
for (double cp : childProbs_)
oss << cp << " ";
oss << "]" << std::endl;
oss << "childOffsets_: [";
for (int co : childOffsets_)
oss << co << " ";
oss << "]" << std::endl;
oss << std::string(40,'-') << std::endl;
return oss.str();
}
int XSpode::getNumberOfNodes() const { return nFeatures_ + 1; }
int XSpode::getClassNumStates() const { return statesClass_; }
int XSpode::getNFeatures() const { return nFeatures_; }
int XSpode::getNumberOfStates() const
{
return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
}
int XSpode::getNumberOfEdges() const
{
return 2 * nFeatures_ + 1;
}
// ------------------------------------------------------
// Predict overrides (classifier interface)
// ------------------------------------------------------
int XSpode::predict(const std::vector<int>& instance) const
{
auto p = predict_proba(instance);
return static_cast<int>(std::distance(p.begin(), std::max_element(p.begin(), p.end())));
}
std::vector<int> XSpode::predict(std::vector<std::vector<int>>& test_data)
{
auto probabilities = predict_proba(test_data);
std::vector<int> predictions(probabilities.size(), 0);
for (size_t i = 0; i < probabilities.size(); i++) {
predictions[i] = std::distance(
probabilities[i].begin(),
std::max_element(probabilities[i].begin(), probabilities[i].end()));
}
return predictions;
}
torch::Tensor XSpode::predict(torch::Tensor& X)
{
auto X_ = TensorUtils::to_matrix(X);
auto result_v = predict(X_);
return torch::tensor(result_v, torch::kInt32);
}
torch::Tensor XSpode::predict_proba(torch::Tensor& X)
{
auto X_ = TensorUtils::to_matrix(X);
auto result_v = predict_proba(X_);
int n_samples = X.size(1);
torch::Tensor result =
torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
for (int i = 0; i < result_v.size(); ++i) {
result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
}
return result;
}
float XSpode::score(torch::Tensor& X, torch::Tensor& y)
{
torch::Tensor y_pred = predict(X);
return (y_pred == y).sum().item<float>() / y.size(0);
}
float XSpode::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
{
auto y_pred = this->predict(X);
int correct = 0;
for (int i = 0; i < y_pred.size(); ++i) {
if (y_pred[i] == y[i]) {
correct++;
}
}
return (double)correct / y_pred.size();
}
} // namespace bayesnet

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#ifndef XSPODE_H
#define XSPODE_H
#include <vector>
#include <torch/torch.h>
#include "Classifier.h"
#include "bayesnet/utils/CountingSemaphore.h"
namespace bayesnet {
class XSpode : public Classifier {
public:
explicit XSpode(int spIndex);
std::vector<double> predict_proba(const std::vector<int>& instance) const;
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
int predict(const std::vector<int>& instance) const;
void normalize(std::vector<double>& v) const;
std::string to_string() const;
int getNFeatures() const;
int getNumberOfNodes() const override;
int getNumberOfEdges() const override;
int getNumberOfStates() const override;
int getClassNumStates() const override;
std::vector<int>& getStates();
std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({ title }); }
void fitx(torch::Tensor& X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing);
void setHyperparameters(const nlohmann::json& hyperparameters_) override;
//
// Classifier interface
//
torch::Tensor predict(torch::Tensor& X) override;
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
float score(torch::Tensor& X, torch::Tensor& y) override;
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
protected:
void buildModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
private:
void addSample(const std::vector<int>& instance, double weight);
void computeProbabilities();
int superParent_;
int nFeatures_;
int statesClass_;
std::vector<int> states_; // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
// Class counts
std::vector<double> classCounts_; // [c], accumulative
std::vector<double> classPriors_; // [c], after normalization
// For p(x_sp = spVal | c)
std::vector<double> spFeatureCounts_; // [spVal * statesClass_ + c]
std::vector<double> spFeatureProbs_; // same shape, after normalization
// For p(x_child = childVal | x_sp = spVal, c)
// childCounts_ is big enough to hold all child features except sp:
// For each child f, we store childOffsets_[f] as the start index, then
// childVal, spVal, c => the data.
std::vector<double> childCounts_;
std::vector<double> childProbs_;
std::vector<int> childOffsets_;
double alpha_ = 1.0;
double initializer_; // for numerical stability
CountingSemaphore& semaphore_;
};
}
#endif // XSPODE_H

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// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <folding.hpp>
#include "Boost.h"
#include "bayesnet/feature_selection/CFS.h"
#include "bayesnet/feature_selection/FCBF.h"
#include "bayesnet/feature_selection/IWSS.h"
#include "Boost.h"
#include <folding.hpp>
namespace bayesnet {
Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
{
validHyperparameters = { "alpha_block", "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
"predict_voting", "select_features", "block_update" };
Boost::Boost(bool predict_voting) : Ensemble(predict_voting) {
validHyperparameters = {"alpha_block", "order", "convergence", "convergence_best", "bisection",
"threshold", "maxTolerance", "predict_voting", "select_features", "block_update"};
}
void Boost::setHyperparameters(const nlohmann::json &hyperparameters_) {
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("order")) {
std::vector<std::string> algos = {Orders.ASC, Orders.DESC, Orders.RAND};
order_algorithm = hyperparameters["order"];
if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC +
", " + Orders.RAND + "]");
}
hyperparameters.erase("order");
}
void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("order")) {
std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
order_algorithm = hyperparameters["order"];
if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
}
hyperparameters.erase("order");
}
if (hyperparameters.contains("alpha_block")) {
alpha_block = hyperparameters["alpha_block"];
hyperparameters.erase("alpha_block");
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
hyperparameters.erase("convergence");
}
if (hyperparameters.contains("convergence_best")) {
convergence_best = hyperparameters["convergence_best"];
hyperparameters.erase("convergence_best");
}
if (hyperparameters.contains("bisection")) {
bisection = hyperparameters["bisection"];
hyperparameters.erase("bisection");
}
if (hyperparameters.contains("threshold")) {
threshold = hyperparameters["threshold"];
hyperparameters.erase("threshold");
}
if (hyperparameters.contains("maxTolerance")) {
maxTolerance = hyperparameters["maxTolerance"];
if (maxTolerance < 1 || maxTolerance > 6)
throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 6]");
hyperparameters.erase("maxTolerance");
}
if (hyperparameters.contains("predict_voting")) {
predict_voting = hyperparameters["predict_voting"];
hyperparameters.erase("predict_voting");
}
if (hyperparameters.contains("select_features")) {
auto selectedAlgorithm = hyperparameters["select_features"];
std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
selectFeatures = true;
select_features_algorithm = selectedAlgorithm;
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
}
hyperparameters.erase("select_features");
}
if (hyperparameters.contains("block_update")) {
block_update = hyperparameters["block_update"];
hyperparameters.erase("block_update");
}
if (block_update && alpha_block) {
throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
}
if (block_update && !bisection) {
throw std::invalid_argument("block_update needs bisection to be true");
}
Classifier::setHyperparameters(hyperparameters);
if (hyperparameters.contains("alpha_block")) {
alpha_block = hyperparameters["alpha_block"];
hyperparameters.erase("alpha_block");
}
void Boost::buildModel(const torch::Tensor& weights)
{
// Models shall be built in trainModel
models.clear();
significanceModels.clear();
n_models = 0;
// Prepare the validation dataset
auto y_ = dataset.index({ -1, "..." });
if (convergence) {
// Prepare train & validation sets from train data
auto fold = folding::StratifiedKFold(5, y_, 271);
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
y_train = dataset.index({ -1, train_t });
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
y_test = dataset.index({ -1, test_t });
dataset = X_train;
m = X_train.size(1);
auto n_classes = states.at(className).size();
// Build dataset with train data
buildDataset(y_train);
metrics = Metrics(dataset, features, className, n_classes);
} else {
// Use all data to train
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
y_train = y_;
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
hyperparameters.erase("convergence");
}
std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
{
int maxFeatures = 0;
if (select_features_algorithm == SelectFeatures.CFS) {
featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
} else if (select_features_algorithm == SelectFeatures.IWSS) {
if (threshold < 0 || threshold >0.5) {
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
}
featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
} else if (select_features_algorithm == SelectFeatures.FCBF) {
if (threshold < 1e-7 || threshold > 1) {
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
}
featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
}
featureSelector->fit();
auto featuresUsed = featureSelector->getFeatures();
delete featureSelector;
return featuresUsed;
if (hyperparameters.contains("convergence_best")) {
convergence_best = hyperparameters["convergence_best"];
hyperparameters.erase("convergence_best");
}
std::tuple<torch::Tensor&, double, bool> Boost::update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
{
bool terminate = false;
double alpha_t = 0;
auto mask_wrong = ypred != ytrain;
auto mask_right = ypred == ytrain;
auto masked_weights = weights * mask_wrong.to(weights.dtype());
double epsilon_t = masked_weights.sum().item<double>();
if (epsilon_t > 0.5) {
// Inverse the weights policy (plot ln(wt))
// "In each round of AdaBoost, there is a sanity check to ensure that the current base
// learner is better than random guess" (Zhi-Hua Zhou, 2012)
terminate = true;
} else {
double wt = (1 - epsilon_t) / epsilon_t;
alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
// Step 3.2: Update weights for next classifier
// Step 3.2.1: Update weights of wrong samples
weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
// Step 3.2.2: Update weights of right samples
weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
// Step 3.3: Normalise the weights
double totalWeights = torch::sum(weights).item<double>();
weights = weights / totalWeights;
}
return { weights, alpha_t, terminate };
if (hyperparameters.contains("bisection")) {
bisection = hyperparameters["bisection"];
hyperparameters.erase("bisection");
}
std::tuple<torch::Tensor&, double, bool> Boost::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
{
/* Update Block algorithm
k = # of models in block
n_models = # of models in ensemble to make predictions
n_models_bak = # models saved
models = vector of models to make predictions
models_bak = models not used to make predictions
significances_bak = backup of significances vector
if (hyperparameters.contains("threshold")) {
threshold = hyperparameters["threshold"];
hyperparameters.erase("threshold");
}
if (hyperparameters.contains("maxTolerance")) {
maxTolerance = hyperparameters["maxTolerance"];
if (maxTolerance < 1 || maxTolerance > 6)
throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 6]");
hyperparameters.erase("maxTolerance");
}
if (hyperparameters.contains("predict_voting")) {
predict_voting = hyperparameters["predict_voting"];
hyperparameters.erase("predict_voting");
}
if (hyperparameters.contains("select_features")) {
auto selectedAlgorithm = hyperparameters["select_features"];
std::vector<std::string> algos = {SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF};
selectFeatures = true;
select_features_algorithm = selectedAlgorithm;
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " +
SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
}
hyperparameters.erase("select_features");
}
if (hyperparameters.contains("block_update")) {
block_update = hyperparameters["block_update"];
hyperparameters.erase("block_update");
}
if (block_update && alpha_block) {
throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
}
if (block_update && !bisection) {
throw std::invalid_argument("block_update needs bisection to be true");
}
Classifier::setHyperparameters(hyperparameters);
}
void Boost::add_model(std::unique_ptr<Classifier> model, double significance) {
models.push_back(std::move(model));
n_models++;
significanceModels.push_back(significance);
}
void Boost::remove_last_model() {
models.pop_back();
significanceModels.pop_back();
n_models--;
}
void Boost::buildModel(const torch::Tensor &weights) {
// Models shall be built in trainModel
models.clear();
significanceModels.clear();
n_models = 0;
// Prepare the validation dataset
auto y_ = dataset.index({-1, "..."});
if (convergence) {
// Prepare train & validation sets from train data
auto fold = folding::StratifiedKFold(5, y_, 271);
auto [train, test] = fold.getFold(0);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
// Get train and validation sets
X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), train_t});
y_train = dataset.index({-1, train_t});
X_test = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), test_t});
y_test = dataset.index({-1, test_t});
dataset = X_train;
m = X_train.size(1);
auto n_classes = states.at(className).size();
// Build dataset with train data
buildDataset(y_train);
metrics = Metrics(dataset, features, className, n_classes);
} else {
// Use all data to train
X_train = dataset.index({torch::indexing::Slice(0, dataset.size(0) - 1), "..."});
y_train = y_;
}
}
std::vector<int> Boost::featureSelection(torch::Tensor &weights_) {
int maxFeatures = 0;
if (select_features_algorithm == SelectFeatures.CFS) {
featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
} else if (select_features_algorithm == SelectFeatures.IWSS) {
if (threshold < 0 || threshold > 0.5) {
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
}
featureSelector =
new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
} else if (select_features_algorithm == SelectFeatures.FCBF) {
if (threshold < 1e-7 || threshold > 1) {
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
}
featureSelector =
new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
}
featureSelector->fit();
auto featuresUsed = featureSelector->getFeatures();
delete featureSelector;
return featuresUsed;
}
std::tuple<torch::Tensor &, double, bool> Boost::update_weights(torch::Tensor &ytrain, torch::Tensor &ypred,
torch::Tensor &weights) {
bool terminate = false;
double alpha_t = 0;
auto mask_wrong = ypred != ytrain;
auto mask_right = ypred == ytrain;
auto masked_weights = weights * mask_wrong.to(weights.dtype());
double epsilon_t = masked_weights.sum().item<double>();
// std::cout << "epsilon_t: " << epsilon_t << " count wrong: " << mask_wrong.sum().item<int>() << " count right: "
// << mask_right.sum().item<int>() << std::endl;
if (epsilon_t > 0.5) {
// Inverse the weights policy (plot ln(wt))
// "In each round of AdaBoost, there is a sanity check to ensure that the current base
// learner is better than random guess" (Zhi-Hua Zhou, 2012)
terminate = true;
} else {
double wt = (1 - epsilon_t) / epsilon_t;
alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
// Step 3.2: Update weights for next classifier
// Step 3.2.1: Update weights of wrong samples
weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
// Step 3.2.2: Update weights of right samples
weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
// Step 3.3: Normalise the weights
double totalWeights = torch::sum(weights).item<double>();
weights = weights / totalWeights;
}
return {weights, alpha_t, terminate};
}
std::tuple<torch::Tensor &, double, bool> Boost::update_weights_block(int k, torch::Tensor &ytrain,
torch::Tensor &weights) {
/* Update Block algorithm
k = # of models in block
n_models = # of models in ensemble to make predictions
n_models_bak = # models saved
models = vector of models to make predictions
models_bak = models not used to make predictions
significances_bak = backup of significances vector
Case list
A) k = 1, n_models = 1 => n = 0 , n_models = n + k
B) k = 1, n_models = n + 1 => n_models = n + k
C) k > 1, n_models = k + 1 => n= 1, n_models = n + k
D) k > 1, n_models = k => n = 0, n_models = n + k
E) k > 1, n_models = k + n => n_models = n + k
Case list
A) k = 1, n_models = 1 => n = 0 , n_models = n + k
B) k = 1, n_models = n + 1 => n_models = n + k
C) k > 1, n_models = k + 1 => n= 1, n_models = n + k
D) k > 1, n_models = k => n = 0, n_models = n + k
E) k > 1, n_models = k + n => n_models = n + k
A, D) n=0, k > 0, n_models == k
1. n_models_bak <- n_models
2. significances_bak <- significances
3. significances = vector(k, 1)
4. Don’t move any classifiers out of models
5. n_models <- k
6. Make prediction, compute alpha, update weights
7. Don’t restore any classifiers to models
8. significances <- significances_bak
9. Update last k significances
10. n_models <- n_models_bak
A, D) n=0, k > 0, n_models == k
1. n_models_bak <- n_models
2. significances_bak <- significances
3. significances = vector(k, 1)
4. Don’t move any classifiers out of models
5. n_models <- k
6. Make prediction, compute alpha, update weights
7. Don’t restore any classifiers to models
8. significances <- significances_bak
9. Update last k significances
10. n_models <- n_models_bak
B, C, E) n > 0, k > 0, n_models == n + k
1. n_models_bak <- n_models
2. significances_bak <- significances
3. significances = vector(k, 1)
4. Move first n classifiers to models_bak
5. n_models <- k
6. Make prediction, compute alpha, update weights
7. Insert classifiers in models_bak to be the first n models
8. significances <- significances_bak
9. Update last k significances
10. n_models <- n_models_bak
*/
//
// Make predict with only the last k models
//
std::unique_ptr<Classifier> model;
std::vector<std::unique_ptr<Classifier>> models_bak;
// 1. n_models_bak <- n_models 2. significances_bak <- significances
auto significance_bak = significanceModels;
auto n_models_bak = n_models;
// 3. significances = vector(k, 1)
significanceModels = std::vector<double>(k, 1.0);
// 4. Move first n classifiers to models_bak
// backup the first n_models - k models (if n_models == k, don't backup any)
for (int i = 0; i < n_models - k; ++i) {
model = std::move(models[0]);
models.erase(models.begin());
models_bak.push_back(std::move(model));
}
assert(models.size() == k);
// 5. n_models <- k
n_models = k;
// 6. Make prediction, compute alpha, update weights
auto ypred = predict(X_train);
//
// Update weights
//
double alpha_t;
bool terminate;
std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
//
// Restore the models if needed
//
// 7. Insert classifiers in models_bak to be the first n models
// if n_models_bak == k, don't restore any, because none of them were moved
if (k != n_models_bak) {
// Insert in the same order as they were extracted
int bak_size = models_bak.size();
for (int i = 0; i < bak_size; ++i) {
model = std::move(models_bak[bak_size - 1 - i]);
models_bak.erase(models_bak.end() - 1);
models.insert(models.begin(), std::move(model));
}
}
// 8. significances <- significances_bak
significanceModels = significance_bak;
//
// Update the significance of the last k models
//
// 9. Update last k significances
for (int i = 0; i < k; ++i) {
significanceModels[n_models_bak - k + i] = alpha_t;
}
// 10. n_models <- n_models_bak
n_models = n_models_bak;
return { weights, alpha_t, terminate };
B, C, E) n > 0, k > 0, n_models == n + k
1. n_models_bak <- n_models
2. significances_bak <- significances
3. significances = vector(k, 1)
4. Move first n classifiers to models_bak
5. n_models <- k
6. Make prediction, compute alpha, update weights
7. Insert classifiers in models_bak to be the first n models
8. significances <- significances_bak
9. Update last k significances
10. n_models <- n_models_bak
*/
//
// Make predict with only the last k models
//
std::unique_ptr<Classifier> model;
std::vector<std::unique_ptr<Classifier>> models_bak;
// 1. n_models_bak <- n_models 2. significances_bak <- significances
auto significance_bak = significanceModels;
auto n_models_bak = n_models;
// 3. significances = vector(k, 1)
significanceModels = std::vector<double>(k, 1.0);
// 4. Move first n classifiers to models_bak
// backup the first n_models - k models (if n_models == k, don't backup any)
for (int i = 0; i < n_models - k; ++i) {
model = std::move(models[0]);
models.erase(models.begin());
models_bak.push_back(std::move(model));
}
}
assert(models.size() == k);
// 5. n_models <- k
n_models = k;
// 6. Make prediction, compute alpha, update weights
auto ypred = predict(X_train);
//
// Update weights
//
double alpha_t;
bool terminate;
std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
//
// Restore the models if needed
//
// 7. Insert classifiers in models_bak to be the first n models
// if n_models_bak == k, don't restore any, because none of them were moved
if (k != n_models_bak) {
// Insert in the same order as they were extracted
int bak_size = models_bak.size();
for (int i = 0; i < bak_size; ++i) {
model = std::move(models_bak[bak_size - 1 - i]);
models_bak.erase(models_bak.end() - 1);
models.insert(models.begin(), std::move(model));
}
}
// 8. significances <- significances_bak
significanceModels = significance_bak;
//
// Update the significance of the last k models
//
// 9. Update last k significances
for (int i = 0; i < k; ++i) {
significanceModels[n_models_bak - k + i] = alpha_t;
}
// 10. n_models <- n_models_bak
n_models = n_models_bak;
return {weights, alpha_t, terminate};
}
} // namespace bayesnet

13
bayesnet/ensembles/Boost.h
View File

@@ -27,26 +27,31 @@ namespace bayesnet {
class Boost : public Ensemble {
public:
explicit Boost(bool predict_voting = false);
virtual ~Boost() = default;
virtual ~Boost() override = default;
void setHyperparameters(const nlohmann::json& hyperparameters_) override;
protected:
std::vector<int> featureSelection(torch::Tensor& weights_);
void buildModel(const torch::Tensor& weights) override;
std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
void add_model(std::unique_ptr<Classifier> model, double significance);
void remove_last_model();
//
// Attributes
//
torch::Tensor X_train, y_train, X_test, y_test;
// Hyperparameters
bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
int maxTolerance = 3;
std::string order_algorithm; // order to process the KBest features asc, desc, rand
std::string order_algorithm = Orders.DESC; // order to process the KBest features asc, desc, rand
bool convergence = true; //if true, stop when the model does not improve
bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
bool selectFeatures = false; // if true, use feature selection
std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
std::string select_features_algorithm; // Selected feature selection algorithm
FeatureSelect* featureSelector = nullptr;
double threshold = -1;
bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
};
}
#endif
#endif

7
bayesnet/ensembles/BoostA2DE.cc
View File

@@ -4,14 +4,9 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <set>
#include <functional>
#include <limits.h>
#include <tuple>
#include <folding.hpp>
#include "bayesnet/feature_selection/CFS.h"
#include "bayesnet/feature_selection/FCBF.h"
#include "bayesnet/feature_selection/IWSS.h"
#include "BoostA2DE.h"
namespace bayesnet {
@@ -167,4 +162,4 @@ namespace bayesnet {
{
return Ensemble::graph(title);
}
}
}

14
bayesnet/ensembles/BoostAODE.cc
View File

@@ -6,10 +6,12 @@
#include <random>
#include <set>
#include <functional>
#include <limits.h>
#include <tuple>
#include "BoostAODE.h"
#include "bayesnet/classifiers/SPODE.h"
#include <loguru.hpp>
#include <loguru.cpp>
namespace bayesnet {
@@ -46,14 +48,16 @@ namespace bayesnet {
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
n_models = 0;
if (selectFeatures) {
featuresUsed = initializeModels(smoothing);
auto ypred = predict(X_train);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
// Update significance of the models
for (int i = 0; i < n_models; ++i) {
significanceModels[i] = alpha_t;
significanceModels.push_back(alpha_t);
}
// VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
if (finished) {
return;
}
@@ -120,7 +124,7 @@ namespace bayesnet {
models.push_back(std::move(model));
significanceModels.push_back(alpha_t);
n_models++;
// VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
// VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
}
if (block_update) {
std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -163,7 +167,7 @@ namespace bayesnet {
}
} else {
notes.push_back("Convergence threshold reached & 0 models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
// VLG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
}
}
if (featuresUsed.size() != features.size()) {
@@ -176,4 +180,4 @@ namespace bayesnet {
{
return Ensemble::graph(title);
}
}
}

3
bayesnet/ensembles/BoostAODE.h
View File

@@ -8,7 +8,6 @@
#define BOOSTAODE_H
#include <string>
#include <vector>
#include "bayesnet/classifiers/SPODE.h"
#include "Boost.h"
namespace bayesnet {
@@ -23,4 +22,4 @@ namespace bayesnet {
std::vector<int> initializeModels(const Smoothing_t smoothing);
};
}
#endif
#endif

4
bayesnet/ensembles/Ensemble.cc
View File

@@ -4,7 +4,6 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "Ensemble.h"
#include "bayesnet/utils/CountingSemaphore.h"
namespace bayesnet {
@@ -86,6 +85,7 @@ namespace bayesnet {
torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
for (auto i = 0; i < n_models; ++i) {
auto ypredict = models[i]->predict_proba(X);
/*std::cout << "model " << i << " prediction: " << ypredict << " significance " << significanceModels[i] << std::endl;*/
y_pred += ypredict * significanceModels[i];
}
auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
@@ -194,4 +194,4 @@ namespace bayesnet {
}
return nstates;
}
}
}

10
bayesnet/ensembles/Ensemble.h
View File

@@ -33,9 +33,15 @@ namespace bayesnet {
}
std::string dump_cpt() const override
{
return "";
std::string output;
for (auto& model : models) {
output += model->dump_cpt();
output += std::string(80, '-') + "\n";
}
return output;
}
protected:
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
torch::Tensor predict_average_voting(torch::Tensor& X);
std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
torch::Tensor predict_average_proba(torch::Tensor& X);
@@ -43,10 +49,10 @@ namespace bayesnet {
torch::Tensor compute_arg_max(torch::Tensor& X);
std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
torch::Tensor voting(torch::Tensor& votes);
// Attributes
unsigned n_models;
std::vector<std::unique_ptr<Classifier>> models;
std::vector<double> significanceModels;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
bool predict_voting;
};
}

168
bayesnet/ensembles/XBA2DE.cc Normal file
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@@ -0,0 +1,168 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <folding.hpp>
#include <limits.h>
#include "XBA2DE.h"
#include "bayesnet/classifiers/XSP2DE.h"
#include "bayesnet/utils/TensorUtils.h"
namespace bayesnet {
XBA2DE::XBA2DE(bool predict_voting) : Boost(predict_voting) {}
std::vector<int> XBA2DE::initializeModels(const Smoothing_t smoothing) {
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
if (featuresSelected.size() < 2) {
notes.push_back("No features selected in initialization");
status = ERROR;
return std::vector<int>();
}
for (int i = 0; i < featuresSelected.size() - 1; i++) {
for (int j = i + 1; j < featuresSelected.size(); j++) {
std::unique_ptr<Classifier> model = std::make_unique<XSp2de>(featuresSelected[i], featuresSelected[j]);
model->fit(dataset, features, className, states, weights_, smoothing);
add_model(std::move(model), 1.0);
}
}
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void XBA2DE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing) {
//
// Logging setup
//
// loguru::set_thread_name("XBA2DE");
// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
// loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
// Algorithm based on the adaboost algorithm for classification
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
X_train_ = TensorUtils::to_matrix(X_train);
y_train_ = TensorUtils::to_vector<int>(y_train);
if (convergence) {
X_test_ = TensorUtils::to_matrix(X_test);
y_test_ = TensorUtils::to_vector<int>(y_test);
}
fitted = true;
double alpha_t = 0;
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
if (selectFeatures) {
featuresUsed = initializeModels(smoothing);
if (featuresUsed.size() == 0) {
return;
}
auto ypred = predict(X_train);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
// Update significance of the models
for (int i = 0; i < n_models; ++i) {
significanceModels[i] = alpha_t;
}
if (finished) {
return;
}
}
int numItemsPack = 0; // The counter of the models inserted in the current pack
// Variables to control the accuracy finish condition
double priorAccuracy = 0.0;
double improvement = 1.0;
double convergence_threshold = 1e-4;
int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
// Step 0: Set the finish condition
// epsilon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
// run out of features
bool ascending = order_algorithm == Orders.ASC;
std::mt19937 g{173};
std::vector<std::pair<int, int>> pairSelection;
while (!finished) {
// Step 1: Build ranking with mutual information
pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
if (order_algorithm == Orders.RAND) {
std::shuffle(pairSelection.begin(), pairSelection.end(), g);
}
int k = bisection ? pow(2, tolerance) : 1;
int counter = 0; // The model counter of the current pack
// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
while (counter++ < k && pairSelection.size() > 0) {
auto feature_pair = pairSelection[0];
pairSelection.erase(pairSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<XSp2de>(feature_pair.first, feature_pair.second);
model->fit(dataset, features, className, states, weights_, smoothing);
alpha_t = 0.0;
if (!block_update) {
auto ypred = model->predict(X_train);
// Step 3.1: Compute the classifier amout of say
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
}
// Step 3.4: Store classifier and its accuracy to weigh its future vote
numItemsPack++;
models.push_back(std::move(model));
significanceModels.push_back(alpha_t);
n_models++;
// VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models,
// featuresUsed.size());
}
if (block_update) {
std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
}
if (convergence && !finished) {
auto y_val_predict = predict(X_test);
double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
if (priorAccuracy == 0) {
priorAccuracy = accuracy;
} else {
improvement = accuracy - priorAccuracy;
}
if (improvement < convergence_threshold) {
// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f
// current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
tolerance++;
} else {
// VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f
// prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
tolerance = 0; // Reset the counter if the model performs better
numItemsPack = 0;
}
if (convergence_best) {
// Keep the best accuracy until now as the prior accuracy
priorAccuracy = std::max(accuracy, priorAccuracy);
} else {
// Keep the last accuray obtained as the prior accuracy
priorAccuracy = accuracy;
}
}
// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(),
// features.size());
finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
}
if (tolerance > maxTolerance) {
if (numItemsPack < n_models) {
notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
for (int i = 0; i < numItemsPack; ++i) {
significanceModels.pop_back();
models.pop_back();
n_models--;
}
} else {
notes.push_back("Convergence threshold reached & 0 models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d",
// n_models, numItemsPack);
}
}
if (pairSelection.size() > 0) {
notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
status = WARNING;
}
notes.push_back("Number of models: " + std::to_string(n_models));
}
std::vector<std::string> XBA2DE::graph(const std::string &title) const { return Ensemble::graph(title); }
} // namespace bayesnet

28
bayesnet/ensembles/XBA2DE.h Normal file
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@@ -0,0 +1,28 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#ifndef XBA2DE_H
#define XBA2DE_H
#include <string>
#include <vector>
#include "Boost.h"
namespace bayesnet {
class XBA2DE : public Boost {
public:
explicit XBA2DE(bool predict_voting = false);
virtual ~XBA2DE() = default;
std::vector<std::string> graph(const std::string& title = "XBA2DE") const override;
std::string getVersion() override { return version; };
protected:
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
private:
std::vector<int> initializeModels(const Smoothing_t smoothing);
std::vector<std::vector<int>> X_train_, X_test_;
std::vector<int> y_train_, y_test_;
std::string version = "0.9.7";
};
}
#endif

184
bayesnet/ensembles/XBAODE.cc Normal file
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@@ -0,0 +1,184 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "XBAODE.h"
#include "bayesnet/classifiers/XSPODE.h"
#include "bayesnet/utils/TensorUtils.h"
#include <limits.h>
#include <random>
#include <tuple>
namespace bayesnet {
XBAODE::XBAODE() : Boost(false) {
validHyperparameters = {"alpha_block", "order", "convergence", "convergence_best", "bisection",
"threshold", "maxTolerance", "predict_voting", "select_features"};
}
std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int &feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
model->fit(dataset, features, className, states, weights_, smoothing);
add_model(std::move(model), 1.0);
}
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) {
X_train_ = TensorUtils::to_matrix(X_train);
y_train_ = TensorUtils::to_vector<int>(y_train);
if (convergence) {
X_test_ = TensorUtils::to_matrix(X_test);
y_test_ = TensorUtils::to_vector<int>(y_test);
}
fitted = true;
double alpha_t;
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
n_models = 0;
if (selectFeatures) {
featuresUsed = initializeModels(smoothing);
auto ypred = predict(X_train_);
auto ypred_t = torch::tensor(ypred);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
// Update significance of the models
for (const int &feature : featuresUsed) {
significanceModels.pop_back();
}
for (const int &feature : featuresUsed) {
significanceModels.push_back(alpha_t);
}
// VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
// n_models);
if (finished) {
return;
}
}
int numItemsPack = 0; // The counter of the models inserted in the current pack
// Variables to control the accuracy finish condition
double priorAccuracy = 0.0;
double improvement = 1.0;
double convergence_threshold = 1e-4;
int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
// Step 0: Set the finish condition
// epsilon sub t > 0.5 => inverse the weights_ policy
// validation error is not decreasing
// run out of features
bool ascending = order_algorithm == bayesnet::Orders.ASC;
std::mt19937 g{173};
while (!finished) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
if (order_algorithm == bayesnet::Orders.RAND) {
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
}
// Remove used features
featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
[&](auto x) {
return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
featuresUsed.end();
}),
featureSelection.end());
int k = bisection ? pow(2, tolerance) : 1;
int counter = 0; // The model counter of the current pack
// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
// featureSelection.size());
while (counter++ < k && featureSelection.size() > 0) {
auto feature = featureSelection[0];
featureSelection.erase(featureSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<XSpode>(feature);
model->fit(dataset, features, className, states, weights_, smoothing);
/*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
* smoothing); // using exclusive XSpode fit method*/
// DEBUG
/*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
* std::endl;*/
// DEBUG
std::vector<int> ypred;
if (alpha_block) {
//
// Compute the prediction with the current ensemble + model
//
// Add the model to the ensemble
add_model(std::move(model), 1.0);
// Compute the prediction
ypred = predict(X_train_);
model = std::move(models.back());
// Remove the model from the ensemble
remove_last_model();
} else {
ypred = model->predict(X_train_);
}
// Step 3.1: Compute the classifier amout of say
auto ypred_t = torch::tensor(ypred);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
// Step 3.4: Store classifier and its accuracy to weigh its future vote
numItemsPack++;
featuresUsed.push_back(feature);
add_model(std::move(model), alpha_t);
// VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
// featuresUsed: %zu", finished, numItemsPack, n_models,
// featuresUsed.size());
} // End of the pack
if (convergence && !finished) {
auto y_val_predict = predict(X_test);
double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
if (priorAccuracy == 0) {
priorAccuracy = accuracy;
} else {
improvement = accuracy - priorAccuracy;
}
if (improvement < convergence_threshold) {
// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d
// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
// numItemsPack, improvement, priorAccuracy, accuracy);
tolerance++;
} else {
// VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
// numItemsPack, improvement, priorAccuracy, accuracy);
tolerance = 0; // Reset the counter if the model performs better
numItemsPack = 0;
}
if (convergence_best) {
// Keep the best accuracy until now as the prior accuracy
priorAccuracy = std::max(accuracy, priorAccuracy);
} else {
// Keep the last accuray obtained as the prior accuracy
priorAccuracy = accuracy;
}
}
// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
// %zu", tolerance, featuresUsed.size(), features.size());
finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
}
if (tolerance > maxTolerance) {
if (numItemsPack < n_models) {
notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
// of %d", numItemsPack, n_models);
for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
remove_last_model();
}
// VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
// used.", n_models, featuresUsed.size());
} else {
notes.push_back("Convergence threshold reached & 0 models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
// n_models=%d numItemsPack=%d", n_models, numItemsPack);
}
}
if (featuresUsed.size() != features.size()) {
notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
std::to_string(features.size()));
status = bayesnet::WARNING;
}
notes.push_back("Number of models: " + std::to_string(n_models));
return;
}
} // namespace bayesnet

27
bayesnet/ensembles/XBAODE.h Normal file
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@@ -0,0 +1,27 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#ifndef XBAODE_H
#define XBAODE_H
#include <vector>
#include <cmath>
#include "Boost.h"
namespace bayesnet {
class XBAODE : public Boost {
public:
XBAODE();
std::string getVersion() override { return version; };
protected:
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
private:
std::vector<int> initializeModels(const Smoothing_t smoothing);
std::vector<std::vector<int>> X_train_, X_test_;
std::vector<int> y_train_, y_test_;
std::string version = "0.9.7";
};
}
#endif // XBAODE_H

8
bayesnet/network/Network.h
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@@ -10,14 +10,10 @@
#include <vector>
#include "bayesnet/config.h"
#include "Node.h"
#include "Smoothing.h"
namespace bayesnet {
enum class Smoothing_t {
NONE = -1,
ORIGINAL = 0,
LAPLACE,
CESTNIK
};
class Network {
public:
Network();

42
bayesnet/network/Node.cc
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@@ -93,36 +93,42 @@ namespace bayesnet {
void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
{
dimensions.clear();
dimensions.reserve(parents.size() + 1);
// Get dimensions of the CPT
dimensions.push_back(numStates);
transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
// Create a tensor of zeros with the dimensions of the CPT
cpTable = torch::zeros(dimensions, torch::kDouble) + smoothing;
// Fill table with counts
auto pos = find(features.begin(), features.end(), name);
if (pos == features.end()) {
throw std::logic_error("Feature " + name + " not found in dataset");
for (const auto& parent : parents) {
dimensions.push_back(parent->getNumStates());
}
//transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
// Create a tensor initialized with smoothing
cpTable = torch::full(dimensions, smoothing, torch::kDouble);
// Create a map for quick feature index lookup
std::unordered_map<std::string, int> featureIndexMap;
for (size_t i = 0; i < features.size(); ++i) {
featureIndexMap[features[i]] = i;
}
// Fill table with counts
// Get the index of this node's feature
int name_index = featureIndexMap[name];
// Get parent indices in dataset
std::vector<int> parent_indices;
parent_indices.reserve(parents.size());
for (const auto& parent : parents) {
parent_indices.push_back(featureIndexMap[parent->getName()]);
}
int name_index = pos - features.begin();
c10::List<c10::optional<at::Tensor>> coordinates;
for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
coordinates.clear();
auto sample = dataset.index({ "...", n_sample });
coordinates.push_back(sample[name_index]);
for (auto parent : parents) {
pos = find(features.begin(), features.end(), parent->getName());
if (pos == features.end()) {
throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
}
int parent_index = pos - features.begin();
coordinates.push_back(sample[parent_index]);
for (size_t i = 0; i < parent_indices.size(); ++i) {
coordinates.push_back(sample[parent_indices[i]]);
}
// Increment the count of the corresponding coordinate
cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
}
// Normalize the counts
// Divide each row by the sum of the row
cpTable = cpTable / cpTable.sum(0);
// Normalize the counts (dividing each row by the sum of the row)
cpTable /= cpTable.sum(0, true);
}
double Node::getFactorValue(std::map<std::string, int>& evidence)
{

17
bayesnet/network/Smoothing.h Normal file
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@@ -0,0 +1,17 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#ifndef SMOOTHING_H
#define SMOOTHING_H
namespace bayesnet {
enum class Smoothing_t {
NONE = -1,
ORIGINAL = 0,
LAPLACE,
CESTNIK
};
}
#endif // SMOOTHING_H

8
bayesnet/utils/CountingSemaphore.h
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@@ -32,6 +32,14 @@ public:
cv_.notify_one();
}
}
uint getCount() const
{
return count_;
}
uint getMaxCount() const
{
return max_count_;
}
private:
CountingSemaphore()
: max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),

51
bayesnet/utils/TensorUtils.h Normal file
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@@ -0,0 +1,51 @@
#ifndef TENSORUTILS_H
#define TENSORUTILS_H
#include <torch/torch.h>
#include <vector>
namespace bayesnet {
class TensorUtils {
public:
static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
{
// Ensure tensor is contiguous in memory
auto X_contig = X.contiguous();
// Access tensor data pointer directly
auto data_ptr = X_contig.data_ptr<int>();
// IF you are using int64_t as the data type, use the following line
//auto data_ptr = X_contig.data_ptr<int64_t>();
//std::vector<std::vector<int64_t>> data(X.size(0), std::vector<int64_t>(X.size(1)));
// Prepare output container
std::vector<std::vector<int>> data(X.size(0), std::vector<int>(X.size(1)));
// Fill the 2D vector in a single loop using pointer arithmetic
int rows = X.size(0);
int cols = X.size(1);
for (int i = 0; i < rows; ++i) {
std::copy(data_ptr + i * cols, data_ptr + (i + 1) * cols, data[i].begin());
}
return data;
}
template <typename T>
static std::vector<T> to_vector(const torch::Tensor& y)
{
// Ensure the tensor is contiguous in memory
auto y_contig = y.contiguous();
// Access data pointer
auto data_ptr = y_contig.data_ptr<T>();
// Prepare output container
std::vector<T> data(y.size(0));
// Copy data efficiently
std::copy(data_ptr, data_ptr + y.size(0), data.begin());
return data;
}
};
}
#endif // TENSORUTILS_H

1
lib/catch2 Submodule

Submodule lib/catch2 added at 029fe3b460

6
sample/CMakeLists.txt
View File

@@ -18,8 +18,10 @@ include_directories(
../tests/lib/Files
lib/json/include
/usr/local/include
${FImdlp_INCLUDE_DIRS}
/usr/local/include/fimdlp/
)
add_executable(bayesnet_sample sample.cc)
target_link_libraries(bayesnet_sample fimdlp "${TORCH_LIBRARIES}" "${BayesNet}")
target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
add_executable(bayesnet_sample_xspode sample_xspode.cc)
target_link_libraries(bayesnet_sample_xspode ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")

19
sample/sample.cc
View File

@@ -6,7 +6,7 @@
#include <ArffFiles.hpp>
#include <CPPFImdlp.h>
#include <bayesnet/ensembles/BoostAODE.h>
#include <bayesnet/ensembles/XBAODE.h>
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
@@ -57,10 +57,23 @@ int main(int argc, char* argv[])
std::vector<std::string> features;
std::string className;
map<std::string, std::vector<int>> states;
auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
auto clf = bayesnet::XBAODE(); // false for not using voting in predict
std::cout << "Library version: " << clf.getVersion() << std::endl;
tie(X, y, features, className, states) = loadDataset(file_name, true);
clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
torch::Tensor dataset;
try {
auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
dataset = torch::cat({ X, yresized }, 0);
}
catch (const std::exception& e) {
std::stringstream oss;
oss << "* Error in X and y dimensions *\n";
oss << "X dimensions: " << dataset.sizes() << "\n";
oss << "y dimensions: " << y.sizes();
throw std::runtime_error(oss.str());
}
clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
auto score = clf.score(X, y);
std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
return 0;

65
sample/sample_xspode.cc Normal file
View File

@@ -0,0 +1,65 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <ArffFiles.hpp>
#include <CPPFImdlp.h>
#include <bayesnet/ensembles/BoostAODE.h>
#include <bayesnet/classifiers/XSPODE.h>
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
std::vector<mdlp::labels_t> Xd;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
mdlp::labels_t& xd = fimdlp.transform(X[i]);
Xd.push_back(xd);
}
return Xd;
}
tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(name, class_last);
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
auto Xr = discretizeDataset(X, y);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
}
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
return { Xr, y, features, className, states };
}
int main(int argc, char* argv[])
{
if (argc < 2) {
std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
return 1;
}
std::string file_name = argv[1];
bayesnet::BaseClassifier* clf = new bayesnet::XSpode(0);
std::cout << "Library version: " << clf->getVersion() << std::endl;
auto [X, y, features, className, states] = loadDataset(file_name, true);
torch::Tensor weights = torch::full({ static_cast<long>(X[0].size()) }, 1.0 / X[0].size(), torch::kDouble);
clf->fit(X, y, features, className, states, bayesnet::Smoothing_t::ORIGINAL);
auto score = clf->score(X, y);
std::cout << "File: " << file_name << " Model: XSpode(0) score: " << score << std::endl;
delete clf;
return 0;
}

11
tests/CMakeLists.txt
View File

@@ -3,19 +3,24 @@ if(ENABLE_TESTING)
${BayesNet_SOURCE_DIR}/tests/lib/Files
${BayesNet_SOURCE_DIR}/lib/folding
${BayesNet_SOURCE_DIR}/lib/mdlp/src
${BayesNet_SOURCE_DIR}/lib/log
${BayesNet_SOURCE_DIR}/lib/json/include
${BayesNet_SOURCE_DIR}
${CMAKE_BINARY_DIR}/configured_files/include
)
file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc
TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestA2DE.cc
TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc ${BayesNet_SOURCES})
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc TestXSPnDE.cc TestXBA2DE.cc
TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestXBAODE.cc TestA2DE.cc
TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc TestXSPODE.cc ${BayesNet_SOURCES})
target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" fimdlp PRIVATE Catch2::Catch2WithMain)
add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
add_test(NAME A2DE COMMAND TestBayesNet "[A2DE]")
add_test(NAME BoostA2DE COMMAND TestBayesNet "[BoostA2DE]")
add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
add_test(NAME XSPODE COMMAND TestBayesNet "[XSPODE]")
add_test(NAME XSPnDE COMMAND TestBayesNet "[XSPnDE]")
add_test(NAME XBAODE COMMAND TestBayesNet "[XBAODE]")
add_test(NAME XBA2DE COMMAND TestBayesNet "[XBA2DE]")
add_test(NAME Classifier COMMAND TestBayesNet "[Classifier]")
add_test(NAME Ensemble COMMAND TestBayesNet "[Ensemble]")
add_test(NAME FeatureSelection COMMAND TestBayesNet "[FeatureSelection]")

2
tests/TestBayesEnsemble.cc
View File

@@ -28,7 +28,7 @@ TEST_CASE("Dump CPT", "[Ensemble]")
auto clf = bayesnet::BoostAODE();
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto dump = clf.dump_cpt();
REQUIRE(dump == "");
REQUIRE(dump.size() == 39916);
}
TEST_CASE("Number of States", "[Ensemble]")
{

257
tests/TestBayesModels.cc
View File

@@ -4,48 +4,81 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <type_traits>
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "TestUtils.h"
#include "bayesnet/classifiers/KDB.h"
#include "bayesnet/classifiers/TAN.h"
#include "bayesnet/classifiers/SPODE.h"
#include "bayesnet/classifiers/TANLd.h"
#include "bayesnet/classifiers/KDBLd.h"
#include "bayesnet/classifiers/SPODE.h"
#include "bayesnet/classifiers/SPODELd.h"
#include "bayesnet/classifiers/TAN.h"
#include "bayesnet/classifiers/TANLd.h"
#include "bayesnet/classifiers/XSPODE.h"
#include "bayesnet/ensembles/AODE.h"
#include "bayesnet/ensembles/AODELd.h"
#include "bayesnet/ensembles/BoostAODE.h"
#include "TestUtils.h"
const std::string ACTUAL_VERSION = "1.0.6";
const std::string ACTUAL_VERSION = "1.0.7";
TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{
map <pair<std::string, std::string>, float> scores{
// Diabetes
{{"diabetes", "AODE"}, 0.82161}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8125f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.7890625f}, {{"diabetes", "TANLd"}, 0.803385437f}, {{"diabetes", "BoostAODE"}, 0.83984f},
// Ecoli
{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
{{"ecoli", "AODELd"}, 0.875f}, {{"ecoli", "KDBLd"}, 0.880952358f}, {{"ecoli", "SPODELd"}, 0.839285731f}, {{"ecoli", "TANLd"}, 0.848214269f}, {{"ecoli", "BoostAODE"}, 0.89583f},
// Glass
{{"glass", "AODE"}, 0.79439}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
{{"glass", "AODELd"}, 0.799065411f}, {{"glass", "KDBLd"}, 0.82710278f}, {{"glass", "SPODELd"}, 0.780373812f}, {{"glass", "TANLd"}, 0.869158864f}, {{"glass", "BoostAODE"}, 0.84579f},
// Iris
{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
{{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
};
std::map<std::string, bayesnet::BaseClassifier*> models{
{"AODE", new bayesnet::AODE()}, {"AODELd", new bayesnet::AODELd()},
{"BoostAODE", new bayesnet::BoostAODE()},
{"KDB", new bayesnet::KDB(2)}, {"KDBLd", new bayesnet::KDBLd(2)},
{"SPODE", new bayesnet::SPODE(1)}, {"SPODELd", new bayesnet::SPODELd(1)},
{"TAN", new bayesnet::TAN()}, {"TANLd", new bayesnet::TANLd()}
};
std::string name = GENERATE("AODE", "AODELd", "KDB", "KDBLd", "SPODE", "SPODELd", "TAN", "TANLd");
map<pair<std::string, std::string>, float> scores{// Diabetes
{{"diabetes", "AODE"}, 0.82161},
{{"diabetes", "KDB"}, 0.852865},
{{"diabetes", "XSPODE"}, 0.631510437f},
{{"diabetes", "SPODE"}, 0.802083},
{{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8125f},
{{"diabetes", "KDBLd"}, 0.80208f},
{{"diabetes", "SPODELd"}, 0.7890625f},
{{"diabetes", "TANLd"}, 0.803385437f},
{{"diabetes", "BoostAODE"}, 0.83984f},
// Ecoli
{{"ecoli", "AODE"}, 0.889881},
{{"ecoli", "KDB"}, 0.889881},
{{"ecoli", "XSPODE"}, 0.696428597f},
{{"ecoli", "SPODE"}, 0.880952},
{{"ecoli", "TAN"}, 0.892857},
{{"ecoli", "AODELd"}, 0.875f},
{{"ecoli", "KDBLd"}, 0.880952358f},
{{"ecoli", "SPODELd"}, 0.839285731f},
{{"ecoli", "TANLd"}, 0.848214269f},
{{"ecoli", "BoostAODE"}, 0.89583f},
// Glass
{{"glass", "AODE"}, 0.79439},
{{"glass", "KDB"}, 0.827103},
{{"glass", "XSPODE"}, 0.775701},
{{"glass", "SPODE"}, 0.775701},
{{"glass", "TAN"}, 0.827103},
{{"glass", "AODELd"}, 0.799065411f},
{{"glass", "KDBLd"}, 0.82710278f},
{{"glass", "SPODELd"}, 0.780373812f},
{{"glass", "TANLd"}, 0.869158864f},
{{"glass", "BoostAODE"}, 0.84579f},
// Iris
{{"iris", "AODE"}, 0.973333},
{{"iris", "KDB"}, 0.973333},
{{"iris", "XSPODE"}, 0.853333354f},
{{"iris", "SPODE"}, 0.973333},
{{"iris", "TAN"}, 0.973333},
{{"iris", "AODELd"}, 0.973333},
{{"iris", "KDBLd"}, 0.973333},
{{"iris", "SPODELd"}, 0.96f},
{{"iris", "TANLd"}, 0.97333f},
{{"iris", "BoostAODE"}, 0.98f} };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"AODE", new bayesnet::AODE()},
{"AODELd", new bayesnet::AODELd()},
{"BoostAODE", new bayesnet::BoostAODE()},
{"KDB", new bayesnet::KDB(2)},
{"KDBLd", new bayesnet::KDBLd(2)},
{"XSPODE", new bayesnet::XSpode(1)},
{"SPODE", new bayesnet::SPODE(1)},
{"SPODELd", new bayesnet::SPODELd(1)},
{"TAN", new bayesnet::TAN()},
{"TANLd", new bayesnet::TANLd()} };
std::string name = GENERATE("AODE", "AODELd", "KDB", "KDBLd", "SPODE", "XSPODE", "SPODELd", "TAN", "TANLd");
auto clf = models[name];
SECTION("Test " + name + " classifier")
@@ -56,6 +89,8 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
auto raw = RawDatasets(file_name, discretize);
clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf->score(raw.Xt, raw.yt);
// std::cout << "Classifier: " << name << " File: " << file_name << " Score: " << score << " expected = " <<
// scores[{file_name, name}] << std::endl;
INFO("Classifier: " << name << " File: " << file_name);
REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
REQUIRE(clf->getStatus() == bayesnet::NORMAL);
@@ -70,13 +105,13 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
}
TEST_CASE("Models features & Graph", "[Models]")
{
auto graph = std::vector<std::string>({ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
"\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
"\"class\" -> \"sepallength\"", "\"class\" -> \"sepalwidth\"", "\"class\" -> \"petallength\"", "\"class\" -> \"petalwidth\"", "\"petallength\" [shape=circle] \n",
"\"petallength\" -> \"sepallength\"", "\"petalwidth\" [shape=circle] \n", "\"sepallength\" [shape=circle] \n",
"\"sepallength\" -> \"sepalwidth\"", "\"sepalwidth\" [shape=circle] \n", "\"sepalwidth\" -> \"petalwidth\"", "}\n"
}
);
auto graph = std::vector<std::string>(
{ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
"\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
"\"class\" -> \"sepallength\"", "\"class\" -> \"sepalwidth\"", "\"class\" -> \"petallength\"",
"\"class\" -> \"petalwidth\"", "\"petallength\" [shape=circle] \n", "\"petallength\" -> \"sepallength\"",
"\"petalwidth\" [shape=circle] \n", "\"sepallength\" [shape=circle] \n", "\"sepallength\" -> \"sepalwidth\"",
"\"sepalwidth\" [shape=circle] \n", "\"sepalwidth\" -> \"petalwidth\"", "}\n" });
SECTION("Test TAN")
{
auto raw = RawDatasets("iris", true);
@@ -86,7 +121,9 @@ TEST_CASE("Models features & Graph", "[Models]")
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.getNumberOfStates() == 19);
REQUIRE(clf.getClassNumStates() == 3);
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ",
"petallength -> sepallength, ", "petalwidth -> ",
"sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.graph("Test") == graph);
}
SECTION("Test TANLd")
@@ -98,7 +135,9 @@ TEST_CASE("Models features & Graph", "[Models]")
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.getNumberOfStates() == 27);
REQUIRE(clf.getClassNumStates() == 3);
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ",
"petallength -> sepallength, ", "petalwidth -> ",
"sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.graph("Test") == graph);
}
}
@@ -114,52 +153,43 @@ TEST_CASE("Get num features & num edges", "[Models]")
TEST_CASE("Model predict_proba", "[Models]")
{
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");
auto res_prob_tan = std::vector<std::vector<double>>({
{ 0.00375671, 0.994457, 0.00178621 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00137462, 0.992734, 0.00589123 },
{ 0.00218225, 0.992877, 0.00494094 },
{ 0.00494209, 0.0978534, 0.897205 },
{ 0.0054192, 0.974275, 0.0203054 },
{ 0.00433012, 0.985054, 0.0106159 },
{ 0.000860806, 0.996922, 0.00221698 }
});
auto res_prob_spode = std::vector<std::vector<double>>({
{0.00419032, 0.994247, 0.00156265},
{0.00172808, 0.993433, 0.00483862},
{0.00172808, 0.993433, 0.00483862},
{0.00172808, 0.993433, 0.00483862},
{0.00279211, 0.993737, 0.00347077},
{0.0120674, 0.357909, 0.630024},
{0.00386239, 0.913919, 0.0822185},
{0.0244389, 0.966447, 0.00911374},
{0.003135, 0.991799, 0.0050661}
});
auto res_prob_baode = std::vector<std::vector<double>>({
{0.0112349, 0.962274, 0.0264907},
{0.00371025, 0.950592, 0.0456973},
{0.00371025, 0.950592, 0.0456973},
{0.00371025, 0.950592, 0.0456973},
{0.00369275, 0.84967, 0.146637},
{0.0252205, 0.113564, 0.861215},
{0.0284828, 0.770524, 0.200993},
{0.0213182, 0.857189, 0.121493},
{0.00868436, 0.949494, 0.0418215}
});
auto res_prob_voting = std::vector<std::vector<double>>({
{0, 1, 0},
{0, 1, 0},
{0, 1, 0},
{0, 1, 0},
{0, 1, 0},
{0, 0, 1},
{0, 1, 0},
{0, 1, 0},
{0, 1, 0}
});
std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan}, {"SPODE", res_prob_spode} , {"BoostAODEproba", res_prob_baode }, {"BoostAODEvoting", res_prob_voting } };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()}, {"SPODE", new bayesnet::SPODE(0)}, {"BoostAODEproba", new bayesnet::BoostAODE(false)}, {"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
auto res_prob_tan = std::vector<std::vector<double>>({ {0.00375671, 0.994457, 0.00178621},
{0.00137462, 0.992734, 0.00589123},
{0.00137462, 0.992734, 0.00589123},
{0.00137462, 0.992734, 0.00589123},
{0.00218225, 0.992877, 0.00494094},
{0.00494209, 0.0978534, 0.897205},
{0.0054192, 0.974275, 0.0203054},
{0.00433012, 0.985054, 0.0106159},
{0.000860806, 0.996922, 0.00221698} });
auto res_prob_spode = std::vector<std::vector<double>>({ {0.00419032, 0.994247, 0.00156265},
{0.00172808, 0.993433, 0.00483862},
{0.00172808, 0.993433, 0.00483862},
{0.00172808, 0.993433, 0.00483862},
{0.00279211, 0.993737, 0.00347077},
{0.0120674, 0.357909, 0.630024},
{0.00386239, 0.913919, 0.0822185},
{0.0244389, 0.966447, 0.00911374},
{0.003135, 0.991799, 0.0050661} });
auto res_prob_baode = std::vector<std::vector<double>>({ {0.0112349, 0.962274, 0.0264907},
{0.00371025, 0.950592, 0.0456973},
{0.00371025, 0.950592, 0.0456973},
{0.00371025, 0.950592, 0.0456973},
{0.00369275, 0.84967, 0.146637},
{0.0252205, 0.113564, 0.861215},
{0.0284828, 0.770524, 0.200993},
{0.0213182, 0.857189, 0.121493},
{0.00868436, 0.949494, 0.0418215} });
auto res_prob_voting = std::vector<std::vector<double>>(
{ {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 0, 1}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0} });
std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan},
{"SPODE", res_prob_spode},
{"BoostAODEproba", res_prob_baode},
{"BoostAODEvoting", res_prob_voting} };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()},
{"SPODE", new bayesnet::SPODE(0)},
{"BoostAODEproba", new bayesnet::BoostAODE(false)},
{"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
int init_index = 78;
auto raw = RawDatasets("iris", true);
@@ -192,7 +222,8 @@ TEST_CASE("Model predict_proba", "[Models]")
REQUIRE(y_pred[i] == yt_pred[i].item<int>());
for (int j = 0; j < 3; j++) {
REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
REQUIRE(res_prob[model][i][j] == Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
REQUIRE(res_prob[model][i][j] ==
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
}
}
delete clf;
@@ -207,7 +238,7 @@ TEST_CASE("AODE voting-proba", "[Models]")
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
{"predict_voting",true},
{"predict_voting", true},
});
auto score_voting = clf.score(raw.Xv, raw.yv);
auto pred_voting = clf.predict_proba(raw.Xv);
@@ -293,10 +324,58 @@ TEST_CASE("TAN & SPODE with invalid hyperparameters", "[Models]")
clf.setHyperparameters({
{"parent", 5},
});
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
auto clf2 = bayesnet::SPODE(0);
clf2.setHyperparameters({
{"parent", 5},
});
REQUIRE_THROWS_AS(clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
}
TEST_CASE("Check proposal checkInput", "[Models]")
{
class testProposal : public bayesnet::Proposal {
public:
testProposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_)
: Proposal(dataset_, features_, className_)
{
}
void test_X_y(const torch::Tensor& X, const torch::Tensor& y) { checkInput(X, y); }
};
auto raw = RawDatasets("iris", true);
auto clf = testProposal(raw.dataset, raw.features, raw.className);
torch::Tensor X = torch::randint(0, 3, { 10, 4 });
torch::Tensor y = torch::rand({ 10 });
INFO("Check X is not float");
REQUIRE_THROWS_AS(clf.test_X_y(X, y), std::invalid_argument);
X = torch::rand({ 10, 4 });
INFO("Check y is not integer");
REQUIRE_THROWS_AS(clf.test_X_y(X, y), std::invalid_argument);
y = torch::randint(0, 3, { 10 });
INFO("X and y are correct");
REQUIRE_NOTHROW(clf.test_X_y(X, y));
}
TEST_CASE("Check KDB loop detection", "[Models]")
{
class testKDB : public bayesnet::KDB {
public:
testKDB() : KDB(2, 0) {}
void test_add_m_edges(std::vector<std::string> features_, int idx, std::vector<int>& S, torch::Tensor& weights)
{
features = features_;
add_m_edges(idx, S, weights);
}
};
auto clf = testKDB();
auto features = std::vector<std::string>{ "A", "B", "C" };
int idx = 0;
std::vector<int> S = { 0 };
torch::Tensor weights = torch::tensor({
{ 1.0, 10.0, 0.0 }, // row0 -> picks col1
{ 0.0, 1.0, 10.0 }, // row1 -> picks col2
{ 10.0, 0.0, 1.0 }, // row2 -> picks col0
});
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 0, S, weights));
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 1, S, weights));
}

14
tests/TestBayesNode.cc
View File

@@ -110,14 +110,14 @@ TEST_CASE("Test Node computeCPT", "[Node]")
// Oddities
auto features_back = features;
// Remove a parent from features
features.pop_back();
REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature parent Class not found in dataset");
// features.pop_back();
// REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
// REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature parent Class not found in dataset");
// Remove a feature from features
features = features_back;
features.erase(features.begin());
REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature F1 not found in dataset");
// features = features_back;
// features.erase(features.begin());
// REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
// REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature F1 not found in dataset");
}
TEST_CASE("TEST MinFill method", "[Node]")
{

2
tests/TestBoostA2DE.cc
View File

@@ -90,7 +90,7 @@ TEST_CASE("Voting vs proba", "[BoostA2DE]")
REQUIRE(score_voting == Catch::Approx(0.946667).epsilon(raw.epsilon));
REQUIRE(pred_voting[83][2] == Catch::Approx(0.53508).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.48394).epsilon(raw.epsilon));
REQUIRE(clf.dump_cpt() == "");
REQUIRE(clf.dump_cpt().size() == 7742);
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("Order asc, desc & random", "[BoostA2DE]")

105
tests/TestBoostAODE.cc
View File

@@ -4,20 +4,17 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <type_traits>
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "bayesnet/ensembles/BoostAODE.h"
#include "TestUtils.h"
#include "bayesnet/ensembles/BoostAODE.h"
TEST_CASE("Feature_select CFS", "[BoostAODE]")
{
TEST_CASE("Feature_select CFS", "[BoostAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "CFS"} });
clf.setHyperparameters({{"select_features", "CFS"}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
@@ -25,11 +22,10 @@ TEST_CASE("Feature_select CFS", "[BoostAODE]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("Feature_select IWSS", "[BoostAODE]")
{
TEST_CASE("Feature_select IWSS", "[BoostAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
@@ -37,11 +33,10 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("Feature_select FCBF", "[BoostAODE]")
{
TEST_CASE("Feature_select FCBF", "[BoostAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
@@ -49,15 +44,14 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("Test used features in train note and score", "[BoostAODE]")
{
TEST_CASE("Test used features in train note and score", "[BoostAODE]") {
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::BoostAODE(true);
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"select_features","CFS"},
});
{"select_features", "CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 72);
REQUIRE(clf.getNumberOfEdges() == 120);
@@ -69,39 +63,35 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
}
TEST_CASE("Voting vs proba", "[BoostAODE]")
{
TEST_CASE("Voting vs proba", "[BoostAODE]") {
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(false);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
{"predict_voting",true},
});
{"predict_voting", true},
});
auto score_voting = clf.score(raw.Xv, raw.yv);
auto pred_voting = clf.predict_proba(raw.Xv);
REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
REQUIRE(clf.dump_cpt() == "");
REQUIRE(clf.dump_cpt().size() == 7004);
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("Order asc, desc & random", "[BoostAODE]")
{
TEST_CASE("Order asc, desc & random", "[BoostAODE]") {
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
};
for (const std::string& order : { "asc", "desc", "rand" }) {
std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
for (const std::string &order : {"asc", "desc", "rand"}) {
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({
{"order", order},
{"bisection", false},
{"maxTolerance", 1},
{"convergence", false},
});
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
@@ -110,44 +100,43 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("Oddities", "[BoostAODE]")
{
TEST_CASE("Oddities", "[BoostAODE]") {
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("iris", true);
auto bad_hyper = nlohmann::json{
{ { "order", "duck" } },
{ { "select_features", "duck" } },
{ { "maxTolerance", 0 } },
{ { "maxTolerance", 7 } },
{{"order", "duck"}},
{{"select_features", "duck"}},
{{"maxTolerance", 0}},
{{"maxTolerance", 7}},
};
for (const auto& hyper : bad_hyper.items()) {
for (const auto &hyper : bad_hyper.items()) {
INFO("BoostAODE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
auto bad_hyper_fit = nlohmann::json{
{ { "select_features","IWSS" }, { "threshold", -0.01 } },
{ { "select_features","IWSS" }, { "threshold", 0.51 } },
{ { "select_features","FCBF" }, { "threshold", 1e-8 } },
{ { "select_features","FCBF" }, { "threshold", 1.01 } },
{{"select_features", "IWSS"}, {"threshold", -0.01}},
{{"select_features", "IWSS"}, {"threshold", 0.51}},
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
{{"select_features", "FCBF"}, {"threshold", 1.01}},
};
for (const auto& hyper : bad_hyper_fit.items()) {
for (const auto &hyper : bad_hyper_fit.items()) {
INFO("BoostAODE hyper: " << hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
}
auto bad_hyper_fit2 = nlohmann::json{
{ { "alpha_block", true }, { "block_update", true } },
{ { "bisection", false }, { "block_update", true } },
{{"alpha_block", true}, {"block_update", true}},
{{"bisection", false}, {"block_update", true}},
};
for (const auto& hyper : bad_hyper_fit2.items()) {
for (const auto &hyper : bad_hyper_fit2.items()) {
INFO("BoostAODE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
}
TEST_CASE("Bisection Best", "[BoostAODE]")
{
TEST_CASE("Bisection Best", "[BoostAODE]") {
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
@@ -156,7 +145,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
{"convergence", true},
{"block_update", false},
{"convergence_best", false},
});
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 210);
REQUIRE(clf.getNumberOfEdges() == 378);
@@ -167,8 +156,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
}
TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{
TEST_CASE("Bisection Best vs Last", "[BoostAODE]") {
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
auto clf = bayesnet::BoostAODE(true);
auto hyperparameters = nlohmann::json{
@@ -188,8 +176,7 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[BoostAODE]")
{
TEST_CASE("Block Update", "[BoostAODE]") {
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("mfeat-factors", true, 500);
clf.setHyperparameters({
@@ -197,7 +184,7 @@ TEST_CASE("Block Update", "[BoostAODE]")
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 868);
REQUIRE(clf.getNumberOfEdges() == 1724);
@@ -218,18 +205,18 @@ TEST_CASE("Block Update", "[BoostAODE]")
// }
// std::cout << "Score " << score << std::endl;
}
TEST_CASE("Alphablock", "[BoostAODE]")
{
TEST_CASE("Alphablock", "[BoostAODE]") {
auto clf_alpha = bayesnet::BoostAODE();
auto clf_no_alpha = bayesnet::BoostAODE();
auto raw = RawDatasets("diabetes", true);
clf_alpha.setHyperparameters({
{"alpha_block", true},
});
});
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
}
}

237
tests/TestXBA2DE.cc Normal file
View File

@@ -0,0 +1,237 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <catch2/catch_approx.hpp>
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "TestUtils.h"
#include "bayesnet/ensembles/XBA2DE.h"
TEST_CASE("Normal test", "[XBA2DE]") {
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XBA2DE();
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getVersion() == "0.9.7");
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 13 models eliminated");
REQUIRE(clf.getNotes()[1] == "Number of models: 1");
REQUIRE(clf.getNumberOfStates() == 64);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(1.0f));
REQUIRE(clf.graph().size() == 1);
}
TEST_CASE("Feature_select CFS", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "CFS"}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 220);
REQUIRE(clf.getNumberOfEdges() == 506);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 22");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
}
TEST_CASE("Feature_select IWSS", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 220);
REQUIRE(clf.getNumberOfEdges() == 506);
REQUIRE(clf.getNotes().size() == 4);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[3] == "Number of models: 22");
REQUIRE(clf.getNumberOfStates() == 5346);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.72093));
}
TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 290);
REQUIRE(clf.getNumberOfEdges() == 667);
REQUIRE(clf.getNumberOfStates() == 7047);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[2] == "Number of models: 29");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.744186));
}
TEST_CASE("Test used features in train note and score", "[XBA2DE]") {
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"select_features", "CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 144);
REQUIRE(clf.getNumberOfEdges() == 320);
REQUIRE(clf.getNumberOfStates() == 5504);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 16");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
}
TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{{"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103}};
for (const std::string &order : {"asc", "desc", "rand"}) {
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({
{"order", order},
{"bisection", false},
{"maxTolerance", 1},
{"convergence", true},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("XBA2DE order: " << order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("Oddities", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("iris", true);
auto bad_hyper = nlohmann::json{
{{"order", "duck"}},
{{"select_features", "duck"}},
{{"maxTolerance", 0}},
{{"maxTolerance", 7}},
};
for (const auto &hyper : bad_hyper.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
auto bad_hyper_fit = nlohmann::json{
{{"select_features", "IWSS"}, {"threshold", -0.01}},
{{"select_features", "IWSS"}, {"threshold", 0.51}},
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
{{"select_features", "FCBF"}, {"threshold", 1.01}},
};
for (const auto &hyper : bad_hyper_fit.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
}
auto bad_hyper_fit2 = nlohmann::json{
{{"alpha_block", true}, {"block_update", true}},
{{"bisection", false}, {"block_update", true}},
};
for (const auto &hyper : bad_hyper_fit2.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
// Check not enough selected features
raw.Xv.pop_back();
raw.Xv.pop_back();
raw.Xv.pop_back();
raw.features.pop_back();
raw.features.pop_back();
raw.features.pop_back();
clf.setHyperparameters({{"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes()[0] == "No features selected in initialization");
}
TEST_CASE("Bisection Best", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 330);
REQUIRE(clf.getNumberOfEdges() == 836);
REQUIRE(clf.getNumberOfStates() == 31108);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes().at(1) == "Pairs not used in train: 83");
REQUIRE(clf.getNotes().at(2) == "Number of models: 22");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.975).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.975).epsilon(raw.epsilon));
}
TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
auto clf = bayesnet::XBA2DE();
auto hyperparameters = nlohmann::json{
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", true},
};
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(0.983333).epsilon(raw.epsilon));
// Now we will set the hyperparameter to use the last accuracy
hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.99).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
clf.setHyperparameters({
{"bisection", true},
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 120);
REQUIRE(clf.getNumberOfEdges() == 304);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 83");
REQUIRE(clf.getNotes()[2] == "Number of models: 8");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.963333).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.963333).epsilon(raw.epsilon));
/*std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;*/
/*std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;*/
/*std::cout << "Notes size " << clf.getNotes().size() << std::endl;*/
/*for (auto note : clf.getNotes()) {*/
/* std::cout << note << std::endl;*/
/*}*/
/*std::cout << "Score " << score << std::endl;*/
}
TEST_CASE("Alphablock", "[XBA2DE]") {
auto clf_alpha = bayesnet::XBA2DE();
auto clf_no_alpha = bayesnet::XBA2DE();
auto raw = RawDatasets("diabetes", true);
clf_alpha.setHyperparameters({
{"alpha_block", true},
});
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
REQUIRE(score_alpha == Catch::Approx(0.714286).epsilon(raw.epsilon));
REQUIRE(score_no_alpha == Catch::Approx(0.714286).epsilon(raw.epsilon));
}

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <catch2/catch_approx.hpp>
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "TestUtils.h"
#include "bayesnet/ensembles/XBAODE.h"
TEST_CASE("Normal test", "[XBAODE]") {
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XBAODE();
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 20);
REQUIRE(clf.getNumberOfEdges() == 36);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getVersion() == "0.9.7");
REQUIRE(clf.getNotes()[0] == "Number of models: 4");
REQUIRE(clf.getNumberOfStates() == 256);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.933333));
}
TEST_CASE("Feature_select CFS", "[XBAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBAODE();
clf.setHyperparameters({{"select_features", "CFS"}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 171);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
}
TEST_CASE("Feature_select IWSS", "[XBAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBAODE();
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 171);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.697674394));
}
TEST_CASE("Feature_select FCBF", "[XBAODE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBAODE();
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 171);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
}
TEST_CASE("Test used features in train note and score", "[XBAODE]") {
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::XBAODE();
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"select_features", "CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 72);
REQUIRE(clf.getNumberOfEdges() == 136);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 8");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
}
TEST_CASE("Order asc, desc & random", "[XBAODE]") {
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
for (const std::string &order : {"asc", "desc", "rand"}) {
auto clf = bayesnet::XBAODE();
clf.setHyperparameters({
{"order", order},
{"bisection", false},
{"maxTolerance", 1},
{"convergence", false},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("XBAODE order: " << order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("Oddities", "[XBAODE]") {
auto clf = bayesnet::XBAODE();
auto raw = RawDatasets("iris", true);
auto bad_hyper = nlohmann::json{
{{"order", "duck"}},
{{"select_features", "duck"}},
{{"maxTolerance", 0}},
{{"maxTolerance", 7}},
};
for (const auto &hyper : bad_hyper.items()) {
INFO("XBAODE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
auto bad_hyper_fit = nlohmann::json{
{{"select_features", "IWSS"}, {"threshold", -0.01}},
{{"select_features", "IWSS"}, {"threshold", 0.51}},
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
{{"select_features", "FCBF"}, {"threshold", 1.01}},
};
for (const auto &hyper : bad_hyper_fit.items()) {
INFO("XBAODE hyper: " << hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
}
auto bad_hyper_fit2 = nlohmann::json{
{{"alpha_block", true}, {"block_update", true}},
{{"bisection", false}, {"block_update", true}},
};
for (const auto &hyper : bad_hyper_fit2.items()) {
INFO("XBAODE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
}
TEST_CASE("Bisection Best", "[XBAODE]") {
auto clf = bayesnet::XBAODE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 210);
REQUIRE(clf.getNumberOfEdges() == 406);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
}
TEST_CASE("Bisection Best vs Last", "[XBAODE]") {
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
auto clf = bayesnet::XBAODE();
auto hyperparameters = nlohmann::json{
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", true},
};
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(0.973333359f).epsilon(raw.epsilon));
// Now we will set the hyperparameter to use the last accuracy
hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[XBAODE]") {
auto clf = bayesnet::XBAODE();
auto raw = RawDatasets("mfeat-factors", true, 500);
clf.setHyperparameters({
{"bisection", true},
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 1085);
REQUIRE(clf.getNumberOfEdges() == 2165);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Used features in train: 20 of 216");
REQUIRE(clf.getNotes()[2] == "Number of models: 5");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
//
// std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
// std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
// std::cout << "Notes size " << clf.getNotes().size() << std::endl;
// for (auto note : clf.getNotes()) {
// std::cout << note << std::endl;
// }
// std::cout << "Score " << score << std::endl;
}
TEST_CASE("Alphablock", "[XBAODE]") {
auto clf_alpha = bayesnet::XBAODE();
auto clf_no_alpha = bayesnet::XBAODE();
auto raw = RawDatasets("diabetes", true);
clf_alpha.setHyperparameters({
{"alpha_block", true},
});
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
}

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include <stdexcept>
#include "bayesnet/classifiers/XSPODE.h"
#include "TestUtils.h"
TEST_CASE("fit vector test", "[XSPODE]") {
auto raw = RawDatasets("iris", true);
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
for (int i = 0; i < 4; ++i) {
auto clf = bayesnet::XSpode(i);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 9);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
}
}
TEST_CASE("fit dataset test", "[XSPODE]") {
auto raw = RawDatasets("iris", true);
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
for (int i = 0; i < 4; ++i) {
auto clf = bayesnet::XSpode(i);
clf.fit(raw.dataset, raw.features, raw.className, raw.states,
raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 9);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
}
}
TEST_CASE("tensors dataset predict & predict_proba", "[XSPODE]") {
auto raw = RawDatasets("iris", true);
auto scores = std::vector<float>({0.966667, 0.9333333, 0.966667, 0.966667});
auto probs_expected = std::vector<std::vector<float>>({
{0.999017, 0.000306908, 0.000676449},
{0.99831, 0.00119304, 0.000497099},
{0.998432, 0.00078416, 0.00078416},
{0.998801, 0.000599438, 0.000599438}
});
for (int i = 0; i < 4; ++i) {
auto clf = bayesnet::XSpode(i);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states,
raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 9);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
// Get the first 4 lines of X_test to do predict_proba
auto X_reduced = raw.X_test.slice(1, 0, 4);
auto proba = clf.predict_proba(X_reduced);
for (int p = 0; p < 3; ++p) {
REQUIRE(proba[0][p].item<double>() == Catch::Approx(probs_expected.at(i).at(p)));
}
}
}
TEST_CASE("mfeat-factors dataset test", "[XSPODE]") {
auto raw = RawDatasets("mfeat-factors", true);
auto scores = std::vector<float>({0.9825, 0.9775, 0.9775, 0.99});
for (int i = 0; i < 4; ++i) {
auto clf = bayesnet::XSpode(i);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 217);
REQUIRE(clf.getNumberOfEdges() == 433);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.getNumberOfStates() == 652320);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
}
}
TEST_CASE("Laplace predict", "[XSPODE]") {
auto raw = RawDatasets("iris", true);
auto scores = std::vector<float>({0.966666639, 1.0f, 0.933333337, 1.0f});
for (int i = 0; i < 4; ++i) {
auto clf = bayesnet::XSpode(0);
clf.setHyperparameters({ {"parent", i} });
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 9);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.getNumberOfStates() == 64);
REQUIRE(clf.getNFeatures() == 4);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(scores.at(i)));
}
}
TEST_CASE("Not fitted model predict", "[XSPODE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpode(0);
REQUIRE_THROWS_AS(clf.predict(std::vector<int>({1,2,3})), std::logic_error);
}
TEST_CASE("Test instance predict", "[XSPODE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpode(0);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 1);
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
// Cestnik is not defined in the classifier so it should imply alpha_ = 0
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::CESTNIK);
REQUIRE(clf.predict(std::vector<int>({1,2,3,4})) == 0);
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f));
}
TEST_CASE("Test to_string and fitx", "[XSPODE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSpode(0);
auto weights = torch::full({raw.Xt.size(1)}, 1.0 / raw.Xt.size(1), torch::kFloat64);
clf.fitx(raw.Xt, raw.yt, weights, bayesnet::Smoothing_t::ORIGINAL);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 9);
REQUIRE(clf.getNotes().size() == 0);
REQUIRE(clf.getNumberOfStates() == 64);
REQUIRE(clf.getNFeatures() == 4);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.966666639f));
REQUIRE(clf.to_string().size() == 1966);
REQUIRE(clf.graph("Not yet implemented") == std::vector<std::string>({"Not yet implemented"}));
}

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "bayesnet/classifiers/XSP2DE.h" // <-- your new 2-superparent classifier
#include "TestUtils.h" // for RawDatasets, etc.
// Helper function to handle each (sp1, sp2) pair in tests
static void check_spnde_pair(
int sp1,
int sp2,
RawDatasets &raw,
bool fitVector,
bool fitTensor)
{
// Create our classifier
bayesnet::XSp2de clf(sp1, sp2);
// Option A: fit with vector-based data
if (fitVector) {
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
}
// Option B: fit with the whole dataset in torch::Tensor form
else if (fitTensor) {
// your “tensor” version of fit
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
}
// Option C: or you might do the “dataset” version:
else {
clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing);
}
// Basic checks
REQUIRE(clf.getNumberOfNodes() == 5); // for iris: 4 features + 1 class
REQUIRE(clf.getNumberOfEdges() == 8);
REQUIRE(clf.getNotes().size() == 0);
// Evaluate on test set
float sc = clf.score(raw.X_test, raw.y_test);
REQUIRE(sc >= 0.93f);
}
// ------------------------------------------------------------
// 1) Fit vector test
// ------------------------------------------------------------
TEST_CASE("fit vector test (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
std::vector<std::pair<int,int>> parentPairs = {
{0,1}, {2,3}
};
for (auto &p : parentPairs) {
check_spnde_pair(p.first, p.second, raw, /*fitVector=*/true, /*fitTensor=*/false);
}
}
// ------------------------------------------------------------
// 2) Fit dataset test
// ------------------------------------------------------------
TEST_CASE("fit dataset test (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
// Again test multiple pairs:
std::vector<std::pair<int,int>> parentPairs = {
{0,2}, {1,3}
};
for (auto &p : parentPairs) {
check_spnde_pair(p.first, p.second, raw, /*fitVector=*/false, /*fitTensor=*/false);
}
}
// ------------------------------------------------------------
// 3) Tensors dataset predict & predict_proba
// ------------------------------------------------------------
TEST_CASE("tensors dataset predict & predict_proba (XSP2DE)", "[XSP2DE]") {
auto raw = RawDatasets("iris", true);
std::vector<std::pair<int,int>> parentPairs = {
{0,3}, {1,2}
};
for (auto &p : parentPairs) {
bayesnet::XSp2de clf(p.first, p.second);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
REQUIRE(clf.getNotes().size() == 0);
// Check the score
float sc = clf.score(raw.X_test, raw.y_test);
REQUIRE(sc >= 0.90f);
auto X_reduced = raw.X_test.slice(1, 0, 3);
auto proba = clf.predict_proba(X_reduced);
}
}
TEST_CASE("Check hyperparameters", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSp2de(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto clf2 = bayesnet::XSp2de(2, 3);
clf2.setHyperparameters({{"parent1", 0}, {"parent2", 1}});
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.to_string() == clf2.to_string());
}
TEST_CASE("Check different smoothing", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSp2de(0, 1);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::ORIGINAL);
auto clf2 = bayesnet::XSp2de(0, 1);
clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::LAPLACE);
auto clf3 = bayesnet::XSp2de(0, 1);
clf3.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, bayesnet::Smoothing_t::NONE);
auto score = clf.score(raw.X_test, raw.y_test);
auto score2 = clf2.score(raw.X_test, raw.y_test);
auto score3 = clf3.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0).epsilon(raw.epsilon));
REQUIRE(score2 == Catch::Approx(0.7333333).epsilon(raw.epsilon));
REQUIRE(score3 == Catch::Approx(0.966667).epsilon(raw.epsilon));
}
TEST_CASE("Check rest", "[XSP2DE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XSp2de(0, 1);
REQUIRE_THROWS_AS(clf.predict_proba(std::vector<int>({1,2,3,4})), std::logic_error);
clf.fitx(raw.Xt, raw.yt, raw.weights, bayesnet::Smoothing_t::ORIGINAL);
REQUIRE(clf.getNFeatures() == 4);
REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.973333359f).epsilon(raw.epsilon));
REQUIRE(clf.predict({1,2,3,4}) == 1);
}