Add XBAODE & XSpode classifiers
This commit is contained in:
379
bayesnet/classifiers/XSPODE.cc
Normal file
379
bayesnet/classifiers/XSPODE.cc
Normal file
@@ -0,0 +1,379 @@
|
||||
// ***************************************************************
|
||||
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
|
||||
// SPDX-FileType: SOURCE
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include "XSPODE.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())
|
||||
{
|
||||
}
|
||||
|
||||
void XSpode::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, torch::Tensor& weights_, const Smoothing_t smoothing)
|
||||
{
|
||||
m = X[0].size();
|
||||
n = X.size();
|
||||
buildModel(weights_);
|
||||
trainModel(weights_, smoothing);
|
||||
}
|
||||
|
||||
// --------------------------------------
|
||||
// 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].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
|
||||
{
|
||||
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(const 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;
|
||||
}
|
||||
|
||||
// --------------------------------------
|
||||
// predict
|
||||
// --------------------------------------
|
||||
//
|
||||
// Return the class argmax( P(c|x) ).
|
||||
// --------------------------------------
|
||||
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)
|
||||
{
|
||||
if (!fitted) {
|
||||
throw std::logic_error(CLASSIFIER_NOT_FITTED);
|
||||
}
|
||||
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;
|
||||
}
|
||||
|
||||
// --------------------------------------
|
||||
// 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 << "---- SPODE 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::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 nFeatures_ * (2 * nFeatures_ - 1);
|
||||
}
|
||||
std::vector<int>& XSpode::getStates() { return states_; }
|
||||
|
||||
}
|
||||
|
||||
79
bayesnet/classifiers/XSPODE.h
Normal file
79
bayesnet/classifiers/XSPODE.h
Normal file
@@ -0,0 +1,79 @@
|
||||
// ***************************************************************
|
||||
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
|
||||
// SPDX-FileType: SOURCE
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#ifndef XSPODE_H
|
||||
#define XSPODE_H
|
||||
|
||||
#include <vector>
|
||||
#include <map>
|
||||
#include <stdexcept>
|
||||
#include <algorithm>
|
||||
#include <numeric>
|
||||
#include <string>
|
||||
#include <cmath>
|
||||
#include <limits>
|
||||
#include <sstream>
|
||||
#include <iostream>
|
||||
#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(const std::vector<std::vector<int>>& test_data);
|
||||
int predict(const std::vector<int>& instance) const;
|
||||
std::vector<int> predict(std::vector<std::vector<int>>& test_data);
|
||||
void normalize(std::vector<double>& v) const;
|
||||
std::string to_string() const;
|
||||
int statesClass() 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 fit(std::vector<std::vector<int>>& X, std::vector<int>& y, torch::Tensor& weights_, const Smoothing_t smoothing);
|
||||
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)
|
||||
|
||||
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
|
||||
|
||||
// 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
|
||||
Reference in New Issue
Block a user