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Fix fit & predict with discretized datasets

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This commit is contained in:
Ricardo Montañana Gómez
2024-05-27 12:00:09 +02:00
parent 99c57d95f3
commit 235c345e87
5 changed files with 38 additions and 12 deletions
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22
pyclfs/PyClassifier.cc
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@@ -21,10 +21,22 @@ namespace pywrap {
Xn = Xn.transpose();
return Xn;
}
np::ndarray tensorInt2numpy(torch::Tensor& X)
{
int m = X.size(0);
int n = X.size(1);
auto Xn = np::from_data(X.data_ptr(), np::dtype::get_builtin<int>(), bp::make_tuple(m, n), bp::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), bp::object());
Xn = Xn.transpose();
//std::cout << "Transposed array:\n" << boost::python::extract<char const*>(boost::python::str(Xn)) << std::endl;
return Xn;
}
std::pair<np::ndarray, np::ndarray> tensors2numpy(torch::Tensor& X, torch::Tensor& y)
{
int n = X.size(1);
auto yn = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), bp::make_tuple(n), bp::make_tuple(sizeof(y.dtype()) * 2), bp::object());
if (X.dtype() == torch::kInt32) {
return { tensorInt2numpy(X), yn };
}
return { tensor2numpy(X), yn };
}
std::string PyClassifier::version()
@@ -65,8 +77,14 @@ namespace pywrap {
torch::Tensor PyClassifier::predict(torch::Tensor& X)
{
int dimension = X.size(1);
auto Xn = tensor2numpy(X);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
CPyObject Xp;
if (X.dtype() == torch::kInt32) {
auto Xn = tensorInt2numpy(X);
Xp = bp::incref(bp::object(Xn).ptr());
} else {
auto Xn = tensor2numpy(X);
Xp = bp::incref(bp::object(Xn).ptr());
}
PyObject* incoming = pyWrap->predict(id, Xp);
bp::handle<> handle(incoming);
bp::object object(handle);