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Add some tests and code quality badge

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-07 02:08:37 +02:00
parent df45fddd45
commit cb26ef2562
7 changed files with 57 additions and 26 deletions
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19
bayesnet/classifiers/SPODELd.cc
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@@ -5,25 +5,23 @@ namespace bayesnet {
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_)
{
checkInput(X_, y_);
features = features_;
className = className_;
Xf = X_;
y = y_;
// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
states = fit_local_discretization(y);
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(dataset, features, className, states);
states = localDiscretizationProposal(states, model);
return *this;
return commonFit(features_, className_, states_);
}
SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
{
if (!torch::is_floating_point(dataset)) {
throw std::runtime_error("Dataset must be a floating point tensor");
}
Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
y = dataset.index({ -1, "..." }).clone();
y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
return commonFit(features_, className_, states_);
}
SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
{
features = features_;
className = className_;
// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
@@ -34,7 +32,6 @@ namespace bayesnet {
states = localDiscretizationProposal(states, model);
return *this;
}
torch::Tensor SPODELd::predict(torch::Tensor& X)
{
auto Xt = prepareX(X);

1
bayesnet/classifiers/SPODELd.h
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@@ -10,6 +10,7 @@ namespace bayesnet {
virtual ~SPODELd() = default;
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) override;
SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
std::vector<std::string> graph(const std::string& name = "SPODE") const override;
torch::Tensor predict(torch::Tensor& X) override;
static inline std::string version() { return "0.0.1"; };

12
bayesnet/utils/bayesnetUtils.cc
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@@ -10,18 +10,6 @@ namespace bayesnet {
sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
return indices;
}
std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor)
{
// convert mxn tensor to nxm std::vector
std::vector<std::vector<int>> result;
// Iterate over cols
for (int i = 0; i < dtensor.size(1); ++i) {
auto col_tensor = dtensor.index({ "...", i });
auto col = std::vector<int>(col_tensor.data_ptr<int>(), col_tensor.data_ptr<int>() + dtensor.size(0));
result.push_back(col);
}
return result;
}
std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor)
{
// convert mxn tensor to mxn std::vector

1
bayesnet/utils/bayesnetUtils.h
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@@ -4,7 +4,6 @@
#include <torch/torch.h>
namespace bayesnet {
std::vector<int> argsort(std::vector<double>& nums);
std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor);
std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor);
torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose = true);
}