Create Xaode2 and add initializer factor in predict
This commit is contained in:
2
Makefile
2
Makefile
@@ -38,7 +38,7 @@ setup: ## Install dependencies for tests and coverage
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fi
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dest ?= ${HOME}/bin
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main: ## Build the main target
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main: ## Build only the b_main target
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@cmake --build $(f_release) -t b_main --parallel
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@cp $(f_release)/src/b_main $(dest)
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@@ -15,6 +15,7 @@
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#include "common/Timer.hpp"
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#include "CountingSemaphore.hpp"
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#include "Xaode.hpp"
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#include "Xaode2.hpp"
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namespace platform {
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class ExpClf : public bayesnet::Boost {
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@@ -44,7 +45,8 @@ namespace platform {
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void remove_last_parent();
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protected:
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bool debug = false;
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Xaode aode_;
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// Xaode aode;
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Xaode2 aode_;
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torch::Tensor weights_;
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const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
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inline void normalize_weights(int num_instances)
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@@ -18,7 +18,6 @@ namespace platform {
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class XBAODE : public ExpClf {
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public:
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XBAODE();
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virtual ~XBAODE() override = default;
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std::string getVersion() override { return version; };
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protected:
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void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
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464
src/experimental_clfs/Xaode2.hpp
Normal file
464
src/experimental_clfs/Xaode2.hpp
Normal file
@@ -0,0 +1,464 @@
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// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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// Based on the Geoff. I. Webb A1DE java algorithm
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// https://weka.sourceforge.io/packageMetaData/AnDE/Latest.html
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#ifndef XAODE2_H
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#define XAODE2_H
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#include <vector>
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#include <stdexcept>
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#include <algorithm>
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#include <numeric>
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#include <iostream>
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#include <string>
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#include <cmath>
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#include <limits>
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#include <torch/torch.h>
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namespace platform {
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class Xaode2 {
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public:
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// -------------------------------------------------------
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// The Xaode can be EMPTY (just created), in COUNTS mode (accumulating raw counts)
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// or PROBS mode (storing conditional probabilities).
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enum class MatrixState {
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EMPTY,
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COUNTS,
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PROBS
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};
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std::vector<double> significance_models_;
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Xaode2() : nFeatures_{ 0 }, statesClass_{ 0 }, matrixState_{ MatrixState::EMPTY } {}
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// -------------------------------------------------------
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// fit
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// -------------------------------------------------------
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//
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// Classifiers interface
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// all parameter decide if the model is initialized with all the parents active or none of them
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//
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// states.size() = nFeatures + 1,
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// where states.back() = number of class states.
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//
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// We'll store:
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// 1) p(x_i=si | c) in classFeatureProbs_
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// 2) p(x_j=sj | c, x_i=si) in data_, with i<j => i is "superparent," j is "child."
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//
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// Internally, in COUNTS mode, data_ accumulates raw counts, then
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// computeProbabilities(...) normalizes them into conditionals.
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void 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 torch::Tensor& weights, const bool all_parents)
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{
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int num_instances = X[0].size();
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nFeatures_ = X.size();
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significance_models_.resize(nFeatures_, (all_parents ? 1.0 : 0.0));
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for (int i = 0; i < nFeatures_; i++) {
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if (all_parents) active_parents.push_back(i);
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states_.push_back(*max_element(X[i].begin(), X[i].end()) + 1);
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}
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states_.push_back(*max_element(y.begin(), y.end()) + 1);
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//
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statesClass_ = states_.back();
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classCounts_.resize(statesClass_, 0.0);
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classPriors_.resize(statesClass_, 0.0);
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//
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// Initialize data structures
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//
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active_parents.resize(nFeatures_);
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int totalStates = std::accumulate(states_.begin(), states_.end(), 0) - statesClass_;
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// For p(x_i=si | c), we store them in a 1D array classFeatureProbs_ after we compute.
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// We'll need the offsets for each feature i in featureClassOffset_.
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featureClassOffset_.resize(nFeatures_);
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// We'll store p(x_child=sj | c, x_sp=si) for each pair (i<j).
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// So data_(i, si, j, sj, c) indexes into a big 1D array with an offset.
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// For p(x_i=si | c), we store them in a 1D array classFeatureProbs_ after we compute.
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// We'll need the offsets for each feature i in featureClassOffset_.
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featureClassOffset_.resize(nFeatures_);
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pairOffset_.resize(totalStates);
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int feature_offset = 0;
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int runningOffset = 0;
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int feature = 0, index = 0;
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for (int i = 0; i < nFeatures_; ++i) {
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featureClassOffset_[i] = feature_offset;
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feature_offset += states_[i];
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for (int j = 0; j < states_[i]; ++j) {
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pairOffset_[feature++] = index;
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index += runningOffset;
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}
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runningOffset += states_[i];
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}
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int totalSize = index * statesClass_;
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data_.resize(totalSize);
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dataOpp_.resize(totalSize);
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classFeatureCounts_.resize(feature_offset * statesClass_);
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classFeatureProbs_.resize(feature_offset * statesClass_);
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matrixState_ = MatrixState::COUNTS;
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//
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// Add samples
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//
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std::vector<int> instance(nFeatures_ + 1);
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for (int n_instance = 0; n_instance < num_instances; n_instance++) {
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for (int feature = 0; feature < nFeatures_; feature++) {
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instance[feature] = X[feature][n_instance];
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}
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instance[nFeatures_] = y[n_instance];
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addSample(instance, weights[n_instance].item<double>());
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}
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//alpha_ = 1 / num_instances;
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initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
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computeProbabilities();
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}
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// Optional: print a quick summary
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void show() const
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{
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std::cout << "-------- Xaode.show() --------" << std::endl
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<< "- nFeatures = " << nFeatures_ << std::endl
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<< "- statesClass = " << statesClass_ << std::endl
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<< "- matrixState = " << (matrixState_ == MatrixState::COUNTS ? "COUNTS" : "PROBS") << std::endl;
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std::cout << "- states: size: " << states_.size() << std::endl;
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for (int s : states_) std::cout << s << " "; std::cout << std::endl;
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std::cout << "- classCounts: size: " << classCounts_.size() << std::endl;
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for (double cc : classCounts_) std::cout << cc << " "; std::cout << std::endl;
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std::cout << "- classFeatureCounts: size: " << classFeatureCounts_.size() << std::endl;
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for (double cfc : classFeatureCounts_) std::cout << cfc << " "; std::cout << std::endl;
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std::cout << "- classFeatureProbs: size: " << classFeatureProbs_.size() << std::endl;
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for (double cfp : classFeatureProbs_) std::cout << cfp << " "; std::cout << std::endl;
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std::cout << "- featureClassOffset: size: " << featureClassOffset_.size() << std::endl;
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for (int f : featureClassOffset_) std::cout << f << " "; std::cout << std::endl;
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std::cout << "- pairOffset_: size: " << pairOffset_.size() << std::endl;
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for (int p : pairOffset_) std::cout << p << " "; std::cout << std::endl;
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std::cout << "- data: size: " << data_.size() << std::endl;
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for (double d : data_) std::cout << d << " "; std::cout << std::endl;
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std::cout << "--------------------------------" << std::endl;
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}
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// -------------------------------------------------------
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// addSample (only in COUNTS mode)
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// -------------------------------------------------------
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//
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// instance should have the class at the end.
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//
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void addSample(const std::vector<int>& instance, double weight)
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{
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//
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// (A) increment classCounts_
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// (B) increment feature–class counts => for p(x_i|c)
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// (C) increment pair (superparent= i, child= j) counts => data_
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//
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// if (matrixState_ != MatrixState::COUNTS) {
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// throw std::logic_error("addSample: not in COUNTS mode.");
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// }
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// if (static_cast<int>(instance.size()) != nFeatures_ + 1) {
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// throw std::invalid_argument("addSample: instance.size() must be nFeatures_ + 1.");
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// }
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int c = instance.back();
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// if (c < 0 || c >= statesClass_) {
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// throw std::out_of_range("addSample: class index out of range.");
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// }
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if (weight <= 0.0) {
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return;
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}
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// (A) increment classCounts_
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classCounts_[c] += weight;
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// (B,C)
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// We'll store raw counts now and turn them into p(child| c, superparent) later.
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int idx, fcIndex, si, sj, i_offset;
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for (int i = 0; i < nFeatures_; ++i) {
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si = instance[i];
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// (B) increment feature–class counts => for p(x_i|c)
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fcIndex = (featureClassOffset_[i] + si) * statesClass_ + c;
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classFeatureCounts_[fcIndex] += weight;
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// (C) increment pair (superparent= i, child= j) counts => data_
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i_offset = pairOffset_[featureClassOffset_[i] + si];
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for (int j = 0; j < i; ++j) {
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sj = instance[j];
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idx = (i_offset + featureClassOffset_[j] + sj) * statesClass_ + c;
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data_[idx] += weight;
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}
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}
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}
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// -------------------------------------------------------
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// computeProbabilities
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// -------------------------------------------------------
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//
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// Once all samples are added in COUNTS mode, call this to:
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// 1) compute p(c) => classPriors_
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// 2) compute p(x_i=si | c) => classFeatureProbs_
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// 3) compute p(x_j=sj | c, x_i=si) => data_ (for i<j) dataOpp_ (for i>j)
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//
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void computeProbabilities()
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{
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if (matrixState_ != MatrixState::COUNTS) {
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throw std::logic_error("computeProbabilities: must be in COUNTS mode.");
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}
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double totalCount = std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
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// (1) p(c)
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if (totalCount <= 0.0) {
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// fallback => uniform
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double unif = 1.0 / statesClass_;
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for (int c = 0; c < statesClass_; ++c) {
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classPriors_[c] = unif;
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}
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} else {
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for (int c = 0; c < statesClass_; ++c) {
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classPriors_[c] = classCounts_[c] / totalCount;
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}
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}
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// (2) p(x_i=si | c) => classFeatureProbs_
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int idx, sf;
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double denom, countVal, p;
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for (int feature = 0; feature < nFeatures_; ++feature) {
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sf = states_[feature];
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for (int c = 0; c < statesClass_; ++c) {
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denom = classCounts_[c] * sf;
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if (denom <= 0.0) {
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// fallback => uniform
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for (int sf_value = 0; sf_value < sf; ++sf_value) {
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idx = (featureClassOffset_[feature] + sf_value) * statesClass_ + c;
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classFeatureProbs_[idx] = 1.0 / sf;
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}
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} else {
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for (int sf_value = 0; sf_value < sf; ++sf_value) {
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idx = (featureClassOffset_[feature] + sf_value) * statesClass_ + c;
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countVal = classFeatureCounts_[idx];
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p = ((countVal + alpha_ / (statesClass_ * states_[feature])) / (totalCount + alpha_));
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classFeatureProbs_[idx] = p;
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}
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}
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}
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}
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// getCountFromTable(int classVal, int pIndex, int childIndex)
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// (3) p(x_c=sc | c, x_p=sp) => data_(parent,sp,child,sc,c)
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// (3) p(x_p=sp | c, x_c=sc) => dataOpp_(child,sc,parent,sp,c)
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// C(x_c, x_p, c) + alpha_/Card(xp)
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// P(x_p | x_c, c) = -----------------------------------
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// C(x_c, c) + alpha_
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double pcc_count, pc_count, cc_count;
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double conditionalProb, oppositeCondProb;
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int part1, part2, p1, part2_class, p1_class;
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for (int parent = 1; parent < nFeatures_; ++parent) {
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for (int sp = 0; sp < states_[parent]; ++sp) {
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p1 = featureClassOffset_[parent] + sp;
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part1 = pairOffset_[p1];
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p1_class = p1 * statesClass_;
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for (int child = 0; child < parent; ++child) {
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for (int sc = 0; sc < states_[child]; ++sc) {
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part2 = featureClassOffset_[child] + sc;
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part2_class = part2 * statesClass_;
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for (int c = 0; c < statesClass_; c++) {
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idx = (part1 + part2) * statesClass_ + c;
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// Parent, Child, Class Count
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pcc_count = data_[idx];
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// Parent, Class count
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pc_count = classFeatureCounts_[p1_class + c];
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// Child, Class count
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cc_count = classFeatureCounts_[part2_class + c];
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// p(x_c=sc | c, x_p=sp)
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conditionalProb = (pcc_count + alpha_ / states_[parent]) / (cc_count + alpha_);
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data_[idx] = conditionalProb;
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// p(x_p=sp | c, x_c=sc)
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oppositeCondProb = (pcc_count + alpha_ / states_[child]) / (pc_count + alpha_);
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dataOpp_[idx] = oppositeCondProb;
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}
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}
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}
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}
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}
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matrixState_ = MatrixState::PROBS;
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}
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// -------------------------------------------------------
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// predict_proba_spode
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// -------------------------------------------------------
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//
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// Single-superparent approach:
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// P(c | x) ∝ p(c) * p(x_sp| c) * ∏_{i≠sp} p(x_i | c, x_sp)
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//
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// 'instance' should have size == nFeatures_ (no class).
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// sp in [0..nFeatures_).
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// We multiply p(c) * p(x_sp| c) * p(x_i| c, x_sp).
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// Then normalize the distribution.
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//
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std::vector<double> predict_proba_spode(const std::vector<int>& instance, int parent)
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{
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// accumulates posterior probabilities for each class
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auto probs = std::vector<double>(statesClass_);
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auto spodeProbs = std::vector<double>(statesClass_);
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// Initialize the probabilities with the feature|class probabilities x class priors
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int localOffset;
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int sp = instance[parent];
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localOffset = (featureClassOffset_[parent] + sp) * statesClass_;
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for (int c = 0; c < statesClass_; ++c) {
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spodeProbs[c] = classFeatureProbs_[localOffset + c] * classPriors_[c] * initializer_;
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}
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int idx, base, sc, parent_offset;
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sp = instance[parent];
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parent_offset = pairOffset_[featureClassOffset_[parent] + sp];
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for (int child = 0; child < nFeatures_; ++child) {
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if (child == parent) {
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continue;
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}
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sc = instance[child];
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base = (parent_offset + featureClassOffset_[child] + sc) * statesClass_;
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for (int c = 0; c < statesClass_; ++c) {
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/*
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* The probability P(xc|xp,c) is stored in dataOpp_, and
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* the probability P(xp|xc,c) is stored in data_
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*/
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idx = base + c;
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spodeProbs[c] *= child < parent ? dataOpp_[idx] : data_[idx];
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}
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}
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// Normalize the probabilities
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normalize(spodeProbs);
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return spodeProbs;
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}
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int predict_spode(const std::vector<int>& instance, int parent)
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{
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auto probs = predict_proba_spode(instance, parent);
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return (int)std::distance(probs.begin(), std::max_element(probs.begin(), probs.end()));
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}
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// -------------------------------------------------------
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// predict_proba
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// -------------------------------------------------------
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//
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// P(c | x) ∝ p(c) * ∏_{i} p(x_i | c) * ∏_{i<j} p(x_j | c, x_i) * p(x_i | c, x_j)
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//
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// 'instance' should have size == nFeatures_ (no class).
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// We multiply p(c) * p(x_i| c) * p(x_j| c, x_i) for all i, j.
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// Then normalize the distribution.
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//
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std::vector<double> predict_proba(const std::vector<int>& instance)
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{
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// accumulates posterior probabilities for each class
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auto probs = std::vector<double>(statesClass_);
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auto spodeProbs = std::vector<std::vector<double>>(nFeatures_, std::vector<double>(statesClass_));
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// Initialize the probabilities with the feature|class probabilities
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int localOffset;
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for (int feature = 0; feature < nFeatures_; ++feature) {
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// if feature is not in the active_parents, skip it
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if (std::find(active_parents.begin(), active_parents.end(), feature) == active_parents.end()) {
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continue;
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}
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localOffset = (featureClassOffset_[feature] + instance[feature]) * statesClass_;
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for (int c = 0; c < statesClass_; ++c) {
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spodeProbs[feature][c] = classFeatureProbs_[localOffset + c] * classPriors_[c];
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}
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}
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int idx, base, sp, sc, parent_offset;
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for (int parent = 1; parent < nFeatures_; ++parent) {
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// if parent is not in the active_parents, skip it
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if (std::find(active_parents.begin(), active_parents.end(), parent) == active_parents.end()) {
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continue;
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}
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sp = instance[parent];
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parent_offset = pairOffset_[featureClassOffset_[parent] + sp];
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for (int child = 0; child < parent; ++child) {
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sc = instance[child];
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base = (parent_offset + featureClassOffset_[child] + sc) * statesClass_;
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for (int c = 0; c < statesClass_; ++c) {
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/*
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* The probability P(xc|xp,c) is stored in dataOpp_, and
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* the probability P(xp|xc,c) is stored in data_
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*/
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idx = base + c;
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spodeProbs[child][c] *= data_[idx];
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||||
spodeProbs[parent][c] *= dataOpp_[idx];
|
||||
}
|
||||
}
|
||||
}
|
||||
/* add all the probabilities for each class */
|
||||
for (int c = 0; c < statesClass_; ++c) {
|
||||
for (int i = 0; i < nFeatures_; ++i) {
|
||||
probs[c] += spodeProbs[i][c] * significance_models_[i];
|
||||
}
|
||||
}
|
||||
// Normalize the probabilities
|
||||
normalize(probs);
|
||||
return probs;
|
||||
}
|
||||
void normalize(std::vector<double>& probs) const
|
||||
{
|
||||
double sum = std::accumulate(probs.begin(), probs.end(), 0.0);
|
||||
if (std::isnan(sum)) {
|
||||
throw std::runtime_error("Can't normalize array. Sum is NaN.");
|
||||
}
|
||||
if (sum == 0) {
|
||||
return;
|
||||
}
|
||||
for (int i = 0; i < (int)probs.size(); i++) {
|
||||
probs[i] /= sum;
|
||||
}
|
||||
}
|
||||
// Returns current mode: INIT, COUNTS or PROBS
|
||||
MatrixState state() const
|
||||
{
|
||||
return matrixState_;
|
||||
}
|
||||
int statesClass() const
|
||||
{
|
||||
return statesClass_;
|
||||
}
|
||||
int nFeatures() const
|
||||
{
|
||||
return nFeatures_;
|
||||
}
|
||||
int getNumberOfStates() const
|
||||
{
|
||||
return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
|
||||
}
|
||||
int getNumberOfEdges() const
|
||||
{
|
||||
return nFeatures_ * (2 * nFeatures_ - 1);
|
||||
}
|
||||
int getNumberOfNodes() const
|
||||
{
|
||||
return (nFeatures_ + 1) * nFeatures_;
|
||||
}
|
||||
void add_active_parent(int active_parent)
|
||||
{
|
||||
active_parents.push_back(active_parent);
|
||||
}
|
||||
void remove_last_parent()
|
||||
{
|
||||
active_parents.pop_back();
|
||||
}
|
||||
|
||||
private:
|
||||
// -----------
|
||||
// MEMBER DATA
|
||||
// -----------
|
||||
std::vector<int> states_; // [states_feat0, ..., states_feat(n-1), statesClass_]
|
||||
int nFeatures_;
|
||||
int statesClass_;
|
||||
|
||||
// data_ means p(child=sj | c, superparent= si) after normalization.
|
||||
// But in COUNTS mode, it accumulates raw counts.
|
||||
std::vector<int> pairOffset_;
|
||||
// data_ stores p(child=sj | c, superparent=si) for each pair (i<j).
|
||||
std::vector<double> data_;
|
||||
// dataOpp_ stores p(superparent=si | c, child=sj) for each pair (i<j).
|
||||
std::vector<double> dataOpp_;
|
||||
|
||||
// classCounts_[c]
|
||||
std::vector<double> classCounts_;
|
||||
std::vector<double> classPriors_; // => p(c)
|
||||
|
||||
// For p(x_i=si| c), we store counts in classFeatureCounts_ => offset by featureClassOffset_[i]
|
||||
std::vector<int> featureClassOffset_;
|
||||
std::vector<double> classFeatureCounts_;
|
||||
std::vector<double> classFeatureProbs_; // => p(x_i=si | c) after normalization
|
||||
|
||||
MatrixState matrixState_;
|
||||
|
||||
double alpha_ = 1.0;
|
||||
double initializer_ = std::numeric_limits<double>::max();
|
||||
std::vector<int> active_parents;
|
||||
};
|
||||
}
|
||||
#endif // XAODE2_H
|
||||
Reference in New Issue
Block a user