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Fix xgboost error in predict/predict_proba

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This commit is contained in:
Ricardo Montañana Gómez
2025-04-12 17:48:23 +02:00
parent 761f57be6c
commit 830265d91b
4 changed files with 55 additions and 30 deletions
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38
pyclfs/PyClassifier.cc
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@@ -93,11 +93,19 @@ namespace pywrap {
PyErr_Print();
throw std::runtime_error("Error creating object for predict in " + module + " and class " + className);
}
int* data = reinterpret_cast<int*>(prediction.get_data());
std::vector<int> vPrediction(data, data + prediction.shape(0));
auto resultTensor = torch::tensor(vPrediction, torch::kInt32);
Py_XDECREF(incoming);
return resultTensor;
if (xgboost) {
long* data = reinterpret_cast<long*>(prediction.get_data());
std::vector<int> vPrediction(data, data + prediction.shape(0));
auto resultTensor = torch::tensor(vPrediction, torch::kInt32);
Py_XDECREF(incoming);
return resultTensor;
} else {
int* data = reinterpret_cast<int*>(prediction.get_data());
std::vector<int> vPrediction(data, data + prediction.shape(0));
auto resultTensor = torch::tensor(vPrediction, torch::kInt32);
Py_XDECREF(incoming);
return resultTensor;
}
}
torch::Tensor PyClassifier::predict_proba(torch::Tensor& X)
{
@@ -118,11 +126,19 @@ namespace pywrap {
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;
if (xgboost) {
float* data = reinterpret_cast<float*>(prediction.get_data());
std::vector<float> 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;
} else {
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)
{
@@ -135,4 +151,4 @@ namespace pywrap {
{
this->hyperparameters = hyperparameters;
}
} /* namespace pywrap */
} /* namespace pywrap */

1
pyclfs/PyClassifier.h
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@@ -49,6 +49,7 @@ namespace pywrap {
nlohmann::json hyperparameters;
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override {};
std::vector<std::string> notes;
bool xgboost = false;
private:
PyWrap* pyWrap;
std::string module;

1
pyclfs/XGBoost.cc
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@@ -5,5 +5,6 @@ namespace pywrap {
XGBoost::XGBoost() : PyClassifier("xgboost", "XGBClassifier", true)
{
validHyperparameters = { "tree_method", "early_stopping_rounds", "n_jobs" };
xgboost = true;
}
} /* namespace pywrap */