BayesNet/SPODELd.cc.gcov.html at 7efd95095ce569915bc3a5f53e81577c9cc774ec

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Add some tests to reach 99%

2024-05-06 17:56:00 +02:00

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LCOV - code coverage report

Current view:	top level - bayesnet/classifiers - SPODELd.cc (source / functions)		Coverage	Total	Hit
Test:	BayesNet Coverage Report	Lines:	100.0 %	26	26
Test Date:	2024-05-06 17:54:04	Functions:	100.0 %	6	6
Legend:	Lines: hit not hit

            Line data    Source code

       1              : // ***************************************************************
       2              : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
       3              : // SPDX-FileType: SOURCE
       4              : // SPDX-License-Identifier: MIT
       5              : // ***************************************************************
       6              : 
       7              : #include "SPODELd.h"
       8              : 
       9              : namespace bayesnet {
      10          220 :     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
      11          168 :     SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
      12              :     {
      13          168 :         checkInput(X_, y_);
      14          168 :         Xf = X_;
      15          168 :         y = y_;
      16          168 :         return commonFit(features_, className_, states_);
      17              :     }
      18              : 
      19            8 :     SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
      20              :     {
      21            8 :         if (!torch::is_floating_point(dataset)) {
      22            4 :             throw std::runtime_error("Dataset must be a floating point tensor");
      23              :         }
      24           16 :         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
      25           12 :         y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
      26            4 :         return commonFit(features_, className_, states_);
      27           12 :     }
      28              : 
      29          172 :     SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
      30              :     {
      31          172 :         features = features_;
      32          172 :         className = className_;
      33              :         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
      34          172 :         states = fit_local_discretization(y);
      35              :         // We have discretized the input data
      36              :         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
      37          172 :         SPODE::fit(dataset, features, className, states);
      38          172 :         states = localDiscretizationProposal(states, model);
      39          172 :         return *this;
      40              :     }
      41          136 :     torch::Tensor SPODELd::predict(torch::Tensor& X)
      42              :     {
      43          136 :         auto Xt = prepareX(X);
      44          272 :         return SPODE::predict(Xt);
      45          136 :     }
      46           36 :     std::vector<std::string> SPODELd::graph(const std::string& name) const
      47              :     {
      48           36 :         return SPODE::graph(name);
      49              :     }
      50              : }

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