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Add predict_proba with tensors

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Ricardo Montañana Gómez
2024-07-12 12:54:30 +02:00
parent c5ff1a0b2b
commit 37716a57f4
6 changed files with 66 additions and 9 deletions
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28
pyclfs/PyClassifier.cc
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@@ -99,13 +99,37 @@ namespace pywrap {
Py_XDECREF(incoming);
return resultTensor;
}
torch::Tensor PyClassifier::predict_proba(torch::Tensor& X)
{
int dimension = X.size(1);
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_proba(id, Xp);
bp::handle<> handle(incoming);
bp::object object(handle);
np::ndarray prediction = np::from_object(object);
if (PyErr_Occurred()) {
PyErr_Print();
throw std::runtime_error("Error creating object for predict_proba in " + module + " and class " + className);
}
double* data = reinterpret_cast<double*>(prediction.get_data());
std::vector<double> vPrediction(data, data + prediction.shape(0) * prediction.shape(1));
auto resultTensor = torch::tensor(vPrediction, torch::kFloat64).reshape({ prediction.shape(0), prediction.shape(1) });
Py_XDECREF(incoming);
return resultTensor;
}
float PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
{
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
CPyObject yp = bp::incref(bp::object(yn).ptr());
float result = pyWrap->score(id, Xp, yp);
return result;
return pyWrap->score(id, Xp, yp);
}
void PyClassifier::setHyperparameters(const nlohmann::json& hyperparameters)
{