BayesNet/bayesnet/ensembles/Ensemble.cc

// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "Ensemble.h"
#include "bayesnet/utils/CountingSemaphore.h"

namespace bayesnet {

    Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
    {
    };
    const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
    {
        n_models = models.size();
        for (auto i = 0; i < n_models; ++i) {
            // fit with std::vectors
            models[i]->fit(dataset, features, className, states, smoothing);
        }
    }
    std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
    {
        std::vector<int> y_pred;
        for (auto i = 0; i < X.size(); ++i) {
            auto max = std::max_element(X[i].begin(), X[i].end());
            y_pred.push_back(std::distance(X[i].begin(), max));
        }
        return y_pred;
    }
    torch::Tensor Ensemble::compute_arg_max(torch::Tensor& X)
    {
        auto y_pred = torch::argmax(X, 1);
        return y_pred;
    }
    torch::Tensor Ensemble::voting(torch::Tensor& votes)
    {
        // Convert m x n_models tensor to a m x n_class_states with voting probabilities
        auto y_pred_ = votes.accessor<int, 2>();
        std::vector<int> y_pred_final;
        int numClasses = states.at(className).size();
        // votes is m x n_models with the prediction of every model for each sample
        auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);
        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
        for (int i = 0; i < votes.size(0); ++i) {
            // n_votes store in each index (value of class) the significance added by each model
            // i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
            std::vector<double> n_votes(numClasses, 0.0);
            for (int j = 0; j < n_models; ++j) {
                n_votes[y_pred_[i][j]] += significanceModels.at(j);
            }
            result[i] = torch::tensor(n_votes);
        }
        // To only do one division and gain precision
        result /= sum;
        return result;
    }
    std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)
    {
        if (!fitted) {
            throw std::logic_error(ENSEMBLE_NOT_FITTED);
        }
        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
    }
    torch::Tensor Ensemble::predict_proba(torch::Tensor& X)
    {
        if (!fitted) {
            throw std::logic_error(ENSEMBLE_NOT_FITTED);
        }
        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
    }
    std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
    {
        auto res = predict_proba(X);
        return compute_arg_max(res);
    }
    torch::Tensor Ensemble::predict(torch::Tensor& X)
    {
        auto res = predict_proba(X);
        return compute_arg_max(res);
    }
    torch::Tensor Ensemble::predict_average_proba(torch::Tensor& X)
    {
        auto n_states = models[0]->getClassNumStates();
        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);
            y_pred += ypredict * significanceModels[i];
        }
        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
        y_pred /= sum;
        return y_pred;
    }
    std::vector<std::vector<double>> Ensemble::predict_average_proba(std::vector<std::vector<int>>& X)
    {
        auto n_states = models[0]->getClassNumStates();
        std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
        for (auto i = 0; i < n_models; ++i) {
            auto ypredict = models[i]->predict_proba(X);
            assert(ypredict.size() == y_pred.size());
            assert(ypredict[0].size() == y_pred[0].size());
            // Multiply each prediction by the significance of the model and then add it to the final prediction
            for (auto j = 0; j < ypredict.size(); ++j) {
                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
            }
        }
        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
        //Divide each element of the prediction by the sum of the significances
        for (auto j = 0; j < y_pred.size(); ++j) {
            std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });
        }
        return y_pred;
    }
    std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
    {
        torch::Tensor Xt = bayesnet::vectorToTensor(X, false);
        auto y_pred = predict_average_voting(Xt);
        std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);
        return result;
    }
    torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)
    {
        // Build a m x n_models tensor with the predictions of each model
        torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
        for (auto i = 0; i < n_models; ++i) {
            auto ypredict = models[i]->predict(X);
            y_pred.index_put_({ "...", i }, ypredict);
        }
        return voting(y_pred);
    }
    float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
    {
        auto y_pred = predict(X);
        int correct = 0;
        for (int i = 0; i < y_pred.size(0); ++i) {
            if (y_pred[i].item<int>() == y[i].item<int>()) {
                correct++;
            }
        }
        return (double)correct / y_pred.size(0);
    }
    float Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
    {
        auto y_pred = 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();
    }
    std::vector<std::string> Ensemble::show() const
    {
        auto result = std::vector<std::string>();
        for (auto i = 0; i < n_models; ++i) {
            auto res = models[i]->show();
            result.insert(result.end(), res.begin(), res.end());
        }
        return result;
    }
    std::vector<std::string> Ensemble::graph(const std::string& title) const
    {
        auto result = std::vector<std::string>();
        for (auto i = 0; i < n_models; ++i) {
            auto res = models[i]->graph(title + "_" + std::to_string(i));
            result.insert(result.end(), res.begin(), res.end());
        }
        return result;
    }
    int Ensemble::getNumberOfNodes() const
    {
        int nodes = 0;
        for (auto i = 0; i < n_models; ++i) {
            nodes += models[i]->getNumberOfNodes();
        }
        return nodes;
    }
    int Ensemble::getNumberOfEdges() const
    {
        int edges = 0;
        for (auto i = 0; i < n_models; ++i) {
            edges += models[i]->getNumberOfEdges();
        }
        return edges;
    }
    int Ensemble::getNumberOfStates() const
    {
        int nstates = 0;
        for (auto i = 0; i < n_models; ++i) {
            nstates += models[i]->getNumberOfStates();
        }
        return nstates;
    }
}