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This commit is contained in:
Ricardo Montañana Gómez
2023-11-05 18:43:15 +01:00
parent 8b159f239b
commit f4928386bb
8 changed files with 327 additions and 207 deletions
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2
CMakeLists.txt
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@@ -1,7 +1,7 @@
cmake_minimum_required(VERSION 3.5)
project(PyWrap)
set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
find_package(Python3 3.11...3.11.9 COMPONENTS Interpreter Development REQUIRED)

2
src/CMakeLists.txt
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@@ -3,7 +3,7 @@ include_directories(${Python3_INCLUDE_DIRS})
include_directories(${TORCH_INCLUDE_DIRS})
add_executable(main main.cc STree.cc SVC.cc PyClassifier.cc PyWrap.cc)
add_executable(example example.cpp)
add_executable(example example.cpp PyWrap.cc)
target_link_libraries(main ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)
target_link_libraries(example ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::boost Boost::python Boost::numpy ArffFiles)

71
src/PyClassifier.cc
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@@ -1,6 +1,5 @@
#include "PyClassifier.h"
#include <boost/python/numpy.hpp>
#include <torch/csrc/utils/tensor_numpy.h>
#include <iostream>
namespace pywrap {
@@ -17,6 +16,27 @@ namespace pywrap {
pyWrap->clean(module, className);
std::cout << "Classifier cleaned" << std::endl;
}
void print_array(np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << p::extract<char const*>(p::str(array)) << std::endl;
}
np::ndarray tensor2numpy(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<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), p::object());
Xn = Xn.transpose();
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>(), p::make_tuple(n), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
//std::cout << "Printing from within tensors2numpy" << std::endl;
// print_array(yn);
return { tensor2numpy(X), yn };
}
std::string PyClassifier::version()
{
return pyWrap->version(module, className);
@@ -29,56 +49,35 @@ namespace pywrap {
{
return pyWrap->callMethodString(module, className, method);
}
void print_array(np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << p::extract<char const*>(p::str(array)) << std::endl;
}
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
{
std::cout << "PyClassifier:fit:Converting X to PyObject" << std::endl;
std::cout << "X.defined() = " << X.defined() << std::endl;
int m = X.size(0);
int n = X.size(1);
auto data_numpy = np::from_data(X.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), p::object());
data_numpy = data_numpy.transpose();
print_array(data_numpy);
CPyObject Xp = data_numpy.ptr();
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = Xn.ptr();
std::cout << "PyClassifier:fit:Converting y to PyObject" << std::endl;
auto y_numpy = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(n), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
print_array(y_numpy);
CPyObject yp = y_numpy.ptr();
print_array(yn);
CPyObject yp = yn.ptr();
std::cout << "PyClassifier:fit:Calling fit" << std::endl;
pyWrap->fit(module, this->className, Xp, yp);
return *this;
}
torch::Tensor PyClassifier::predict(torch::Tensor& X)
{
int m = X.size(0);
int n = X.size(1);
auto data_numpy = np::from_data(X.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), p::object());
data_numpy = data_numpy.transpose();
print_array(data_numpy);
CPyObject Xp = data_numpy.ptr();
auto Xn = tensor2numpy(X);
print_array(Xn);
CPyObject Xp = Xn.ptr();
auto PyResult = pyWrap->predict(module, className, Xp);
auto result = torch::tensor({ 1,2,3 });
return result;
}
double PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
{
std::cout << "Converting X to PyObject" << std::endl;
std::cout << "X.defined() = " << X.defined() << std::endl;
//std::cout << "X.pyobj() = " << X.pyobj() << std::endl;
//PyObject* Xp = torch::utils::tensor_to_numpy(X);
auto XX = X.transpose(0, 1);
int m = XX.size(0);
int n = XX.size(1);
auto data_numpy = np::from_data(XX.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(XX.dtype()) * 2 * n, sizeof(XX.dtype()) * 2), p::object());
print_array(data_numpy);
CPyObject Xp = data_numpy.ptr();
std::cout << "Converting y to PyObject" << std::endl;
auto y_numpy = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(m), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
CPyObject yp = y_numpy.ptr();
std::cout << "PyClassifier::Score:Converting X to PyObject" << std::endl;
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = Xn.ptr();
CPyObject yp = yn.ptr();
print_array(yn);
auto result = pyWrap->score(module, className, Xp, yp);
return result;
}

3
src/PyClassifier.h
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@@ -3,10 +3,13 @@
#include <string>
#include <map>
#include <vector>
#include <utility>
#include <torch/torch.h>
#include <boost/python/numpy.hpp>
#include "PyWrap.h"
namespace pywrap {
class PyClassifier {
public:
PyClassifier(const std::string& module, const std::string& className);

38
src/PyWrap.cc
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@@ -11,6 +11,7 @@ namespace pywrap {
PyWrap* PyWrap::wrapper = nullptr;
std::mutex PyWrap::mutex;
CPyInstance* PyWrap::pyInstance = nullptr;
auto moduleClassMap = std::map<std::pair<std::string, std::string>, std::tuple<PyObject*, PyObject*, PyObject*>>();
PyWrap* PyWrap::GetInstance()
{
@@ -25,12 +26,21 @@ namespace pywrap {
}
void PyWrap::RemoveInstance()
{
std::lock_guard<std::mutex> lock(mutex);
if (wrapper != nullptr) {
std::cout << "Liberando instancia" << std::endl;
delete pyInstance;
std::cout << "Liberando instancia Python Stack" << std::endl;
if (pyInstance != nullptr) {
std::cout << "-Liberando Python => PyHelper" << std::endl;
delete pyInstance;
} else {
std::cout << "*No había instancia de python para liberar. => PyHelper" << std::endl;
}
pyInstance = nullptr;
delete wrapper;
if (wrapper != nullptr) {
std::cout << "-Liberando PyWrap." << std::endl;
delete wrapper;
} else {
std::cout << "*No había instancia de PyWrap para liberar." << std::endl;
}
wrapper = nullptr;
std::cout << "Instancia liberada" << std::endl;
}
@@ -55,17 +65,25 @@ namespace pywrap {
if (PyErr_Occurred()) {
errorAbort("Couldn't create instance of class " + className);
}
moduleClassMap[{moduleName, className}] = { module, classObject, instance };
std::lock_guard<std::mutex> lock(mutex);
module.AddRef();
classObject.AddRef();
instance.AddRef();
moduleClassMap.insert({ { moduleName, className }, { module.getObject(), classObject.getObject(), instance.getObject() } });
std::cout << "Clase importada" << std::endl;
}
void PyWrap::clean(const std::string& moduleName, const std::string& className)
{
std::lock_guard<std::mutex> lock(mutex);
std::cout << "Limpiando" << std::endl;
auto result = moduleClassMap.find({ moduleName, className });
if (result == moduleClassMap.end()) {
return;
}
std::cout << "--> Limpiando" << std::endl;
Py_DECREF(std::get<0>(result->second));
Py_DECREF(std::get<1>(result->second));
Py_DECREF(std::get<2>(result->second));
moduleClassMap.erase(result);
if (PyErr_Occurred()) {
PyErr_Print();
@@ -122,11 +140,11 @@ namespace pywrap {
void PyWrap::fit(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y)
{
std::cout << "Llamando método fit" << std::endl;
CPyObject instance = getClass(moduleName, className);
PyObject* instance = getClass(moduleName, className);
CPyObject result;
std::string method = "fit";
try {
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, y, NULL)))
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), y.getObject(), NULL)))
errorAbort("Couldn't call method fit");
}
catch (const std::exception& e) {
@@ -142,7 +160,7 @@ namespace pywrap {
CPyObject result;
std::string method = "predict";
try {
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, NULL)))
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), NULL)))
errorAbort("Couldn't call method predict");
}
catch (const std::exception& e) {
@@ -155,11 +173,11 @@ namespace pywrap {
double PyWrap::score(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y)
{
std::cout << "Llamando método score" << std::endl;
CPyObject instance = getClass(moduleName, className);
PyObject* instance = getClass(moduleName, className);
CPyObject result;
std::string method = "score";
try {
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, y, NULL)))
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), y.getObject(), NULL)))
errorAbort("Couldn't call method score");
}
catch (const std::exception& e) {

14
src/PyWrap.h
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@@ -15,27 +15,29 @@ namespace pywrap {
*/
class PyWrap {
public:
PyWrap() = default;
PyWrap(PyWrap& other) = delete;
static PyWrap* GetInstance();
static void RemoveInstance();
void operator=(const PyWrap&) = delete;
~PyWrap() = default;
void fit(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y);
CPyObject predict(const std::string& moduleName, const std::string& className, CPyObject& X);
std::string callMethodString(const std::string& moduleName, const std::string& className, const std::string& method);
std::string version(const std::string& moduleName, const std::string& className);
std::string graph(const std::string& moduleName, const std::string& className);
void fit(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y);
CPyObject predict(const std::string& moduleName, const std::string& className, CPyObject& X);
double score(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y);
void clean(const std::string& moduleName, const std::string& className);
void importClass(const std::string& moduleName, const std::string& className);
private:
PyWrap() = default;
PyObject* getClass(const std::string& moduleName, const std::string& className);
private:
// Only call RemoveInstance from clean method
static void RemoveInstance();
void errorAbort(const std::string& message);
// No need to use static map here, since this class is a singleton
std::map<std::pair<std::string, std::string>, std::tuple<PyObject*, PyObject*, PyObject*>> moduleClassMap;
static CPyInstance* pyInstance;
static PyWrap* wrapper;
static std::mutex mutex;
std::map<std::pair<std::string, std::string>, std::tuple<CPyObject, CPyObject, CPyObject>> moduleClassMap;
};
} /* namespace pywrap */
#endif /* PYWRAP_H */

343
src/example.cpp
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@@ -1,151 +1,220 @@
#include <torch/torch.h>
#include <string>
#include <iostream>
#include "ArffFiles.h"
#include <torch/torch.h>
#include "PyHelper.hpp"
#include <boost/python/numpy.hpp>
#include "PyWrap.h"
namespace pywrap {
using namespace std;
void errorAbort(const std::string& message)
{
std::cerr << message << std::endl;
PyErr_Print();
exit(1);
}
void print_array(np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << p::extract<char const*>(p::str(array)) << std::endl;
}
np::ndarray to_numpy_matrix(torch::Tensor& input_data, np::dtype numpy_dtype)
{
p::tuple shape = p::make_tuple(input_data.size(0), input_data.size(1));
auto tensor_dtype = input_data.dtype();
p::tuple stride = p::make_tuple(sizeof(tensor_dtype) * input_data.size(1), sizeof(tensor_dtype));
auto dito = input_data.transpose(1, 0);
np::ndarray result = np::from_data(dito.data_ptr(), numpy_dtype, shape, stride, p::object());
return result;
}
np::ndarray to_numpy_vector(torch::Tensor& input_data, np::dtype numpy_dtype)
{
p::tuple shape = p::make_tuple(input_data.size(0));
auto tensor_dtype = input_data.dtype();
p::tuple stride = p::make_tuple(sizeof(tensor_dtype), sizeof(tensor_dtype));
np::ndarray result = np::from_data(input_data.data_ptr(), numpy_dtype, shape, stride, p::object());
return result;
}
void flat()
{
double data[][4] = { {0.1, 0.2, 0.3, 0.4} , { 0.5, 0.6, 0.7, 0.8 }, { 0.9, 0.11, 0.12, 0.13 }, { 0.14, 0.15, 0.16, 0.17 }, { 0.18, 0.19, 0.21, 0.22 }, { 0.23, 0.24, 0.25, 0.26 }, { 0.27, 0.28, 0.29, 0.31 } };
int labels[] = { 0, 1, 0, 1 , 0, 0, 1 };
// cout << "Array data: (" << m << ", " << n << ") " << endl;
// for (int i = 0; i < m; ++i) {
// cout << "[ ";
// for (int j = 0; j < n; ++j) {
// cout << setw(4) << std::setprecision(2) << fixed << data[i][j] << " ";
// }
// cout << "]" << endl;
// }
// cout << "Array labels: " << endl;
// for (int i = 0; i < m; ++i) {
// cout << labels[i] << " ";
// }
// cout << endl;
// auto data_numpy = np::from_data(data, np::dtype::get_builtin<double>(), p::make_tuple(m, n), p::make_tuple(sizeof(double) * n, sizeof(double)), p::object());
// auto y_numpy = np::from_data(labels, np::dtype::get_builtin<int>(), p::make_tuple(m), p::make_tuple(sizeof(int)), p::object());
}
class Paths {
public:
static string datasets()
{
return "/home/rmontanana/Code/discretizbench/datasets/";
}
};
void errorAbort(const std::string& message)
{
std::cerr << message << std::endl;
PyErr_Print();
exit(1);
}
void print_array(pywrap::np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << pywrap::p::extract<char const*>(pywrap::p::str(array)) << std::endl;
}
// np::ndarray to_numpy_matrix(torch::Tensor& input_data, np::dtype numpy_dtype)
// {
// p::tuple shape = p::make_tuple(input_data.size(0), input_data.size(1));
// auto tensor_dtype = input_data.dtype();
// p::tuple stride = p::make_tuple(sizeof(tensor_dtype) * input_data.size(1), sizeof(tensor_dtype));
// auto dito = input_data.transpose(1, 0);
// np::ndarray result = np::from_data(dito.data_ptr(), numpy_dtype, shape, stride, p::object());
// return result;
// }
// np::ndarray to_numpy_vector(torch::Tensor& input_data, np::dtype numpy_dtype)
// {
// p::tuple shape = p::make_tuple(input_data.size(0));
// auto tensor_dtype = input_data.dtype();
// p::tuple stride = p::make_tuple(sizeof(tensor_dtype), sizeof(tensor_dtype));
// np::ndarray result = np::from_data(input_data.data_ptr(), numpy_dtype, shape, stride, p::object());
// return result;
// }
tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
class Paths {
public:
static string datasets()
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<string, vector<int>>();
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
return "../discretizbench/datasets/";
}
};
tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<string, vector<int>>();
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
} /* namespace pywrap */
using namespace pywrap;
np::ndarray tensor2numpy(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<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), p::object());
Xn = Xn.transpose();
return Xn;
}
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>(), p::make_tuple(n), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
return { tensor2numpy(X), yn };
}
pair<np::ndarray, np::ndarray> getData(const string& dataset)
{
auto [X, y, featuresx, classNamex, statesx] = loadDataset(dataset, true);
auto [Xn, yn] = tensors2numpy(X, y);
auto Xn_shapes = Xn.get_shape();
auto yn_shapes = yn.get_shape();
cout << "Xn_shapes: " << Xn_shapes[0] << ", " << Xn_shapes[1] << endl;
cout << "yn_shapes: " << yn_shapes[0] << endl;
cout << "X shapes: " << X.sizes() << endl;
cout << "y shapes: " << y.sizes() << endl;
assert(Xn_shapes[0] == X.sizes()[0]);
assert(Xn_shapes[1] == X.sizes()[1]);
assert(yn_shapes[0] == y.sizes()[0]);
return { Xn, yn };
}
int main(int argc, char** argv)
{
auto [data_tensor, y_label, featuresx, classNamex, statesx] = loadDataset("iris", true);
//data_tensor = data_tensor.transpose(0, 1);
CPyInstance pInstance;
int m = data_tensor.size(0);
int n = data_tensor.size(1);
// int m = 7;
// int n = 4;
// torch::Tensor data_tensor = torch::rand({ m, n }, torch::kFloat64);
//torch::Tensor data_tensor = torch::tensor({ {0.1, 0.2, 0.3, 0.4} , { 0.5, 0.6, 0.7, 0.8 }, { 0.9, 0.11, 0.12, 0.13 }, { 0.14, 0.15, 0.16, 0.17 }, { 0.18, 0.19, 0.21, 0.22 }, { 0.23, 0.24, 0.25, 0.26 }, { 0.27, 0.28, 0.29, 0.31 } }, torch::kFloat32);
// torch::Tensor y_label = torch::randint(0, 2, { m }, torch::kInt16);
//torch::Tensor y_label = torch::tensor({ 17, 18, 19, 20 , 21, 22, 23 }, torch::kInt32);
cout << "Tensor data: (" << data_tensor.size(0) << ", " << data_tensor.size(1) << ") " << endl << data_tensor << endl;
cout << "Tensor data sizes: " << data_tensor.sizes() << endl;
// cout << "Tensor labels: " << y_label << endl;
cout << "Tensor labels sizes: " << y_label.sizes() << endl;
auto data_numpy = np::from_data(data_tensor.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(data_tensor.dtype()) * 2 * n, sizeof(data_tensor.dtype()) * 2), p::object());
data_numpy = data_numpy.transpose();
auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(n), p::make_tuple(sizeof(y_label.dtype()) * 2), p::object());
//auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int64_t>(), p::make_tuple(m), p::make_tuple(sizeof(y_label.dtype()) * 4), p::object());
cout << "Numpy array data: " << endl;
print_array(data_numpy);
cout << "Numpy array labels: " << endl;
print_array(y_numpy);
cout << "primero" << endl;
CPyObject p = data_numpy.ptr();
CPyObject yp = y_numpy.ptr();
cout << "segundo" << endl;
string moduleName = "stree";
string className = "Stree";
string method = "version";
CPyObject module = PyImport_ImportModule(moduleName.c_str());
if (PyErr_Occurred()) {
errorAbort("Could't import module " + moduleName);
cout << "* Begin." << endl;
{
PyWrap* wrapper = PyWrap::GetInstance();
string dataset = "iris";
// Convert Tensor to numpy array
// auto [Xn, yn] = tensors2numpy(X, y);
// cout << "Numpy array data: " << endl;
// print_array(Xn);
// cout << "Numpy array labels: " << endl;
// print_array(yn);
// Import module
string moduleName = "stree";
string className = "Stree";
// Import
{
cout << "--Import Phase--" << endl;
wrapper->importClass(moduleName, className);
cout << "--Import Phase end--" << endl;
}
// Version
{
cout << "--Version Phase--" << endl;
auto version = wrapper->version(moduleName, className);
cout << "Version: " << version << endl;
cout << "--Version Phase end--" << endl;
}
// Fit
{
cout << "--Fit Phase--" << endl;
auto [Xn, yn] = getData(dataset);
CPyObject Xp = Xn.ptr();
CPyObject yp = yn.ptr();
// Call fit
cout << "Calling fit" << endl;
wrapper->fit(moduleName, className, Xp, yp);
cout << "--Fit Phase end--" << endl;
}
// Call score
{
cout << "--Score Phase--" << endl;
auto [Xn, yn] = getData(dataset);
CPyObject Xp = Xn.ptr();
CPyObject yp = yn.ptr();
cout << "Calling score" << endl;
auto score = wrapper->score(moduleName, className, Xp, yp);
cout << "Score: " << score << endl;
cout << "--Score Phase end--" << endl;
}
// Clean module
{
cout << "--Clean Phase--" << endl;
wrapper->clean(moduleName, className);
cout << "--Clean Phase end--" << endl;
}
}
CPyObject classObject = PyObject_GetAttrString(module, className.c_str());
if (PyErr_Occurred()) {
errorAbort("Couldn't find class " + className);
}
CPyObject instance = PyObject_CallObject(classObject, NULL);
if (PyErr_Occurred()) {
errorAbort("Couldn't create instance of class " + className);
}
CPyObject result;
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
std::string value = PyUnicode_AsUTF8(result);
cout << "Version: " << value << endl;
cout << "Calling fit" << endl;
p.AddRef();
yp.AddRef();
method = "fit";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
errorAbort("Couldn't call method fit");
cout << "Calling score" << endl;
method = "score";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
errorAbort("Couldn't call method score");
float score = PyFloat_AsDouble(result);
cout << "Score: " << score << endl;
return 0;
}
cout << "* End." << endl;
}
// int main(int argc, char** argv)
// {
// auto [data_tensor, y_label, featuresx, classNamex, statesx] = loadDataset("iris", true);
// // CPyInstance pInstance;
// // auto wrapper = PyWrap();
// PyWrap* wrapper = PyWrap::GetInstance();
// // PyWrap* wrapper = PyWrap::GetInstance();
// int m = data_tensor.size(0);
// int n = data_tensor.size(1);
// auto data_numpy = np::from_data(data_tensor.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(data_tensor.dtype()) * 2 * n, sizeof(data_tensor.dtype()) * 2), p::object());
// data_numpy = data_numpy.transpose();
// auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(n), p::make_tuple(sizeof(y_label.dtype()) * 2), p::object());
// cout << "Numpy array data: " << endl;
// print_array(data_numpy);
// cout << "Numpy array labels: " << endl;
// print_array(y_numpy);
// cout << "primero" << endl;
// CPyObject p = data_numpy.ptr();
// CPyObject yp = y_numpy.ptr();
// string moduleName = "sklearn.svm";
// string className = "SVC";
// string method = "_repr_html_";
// // CPyObject module = PyImport_ImportModule(moduleName.c_str());
// // if (PyErr_Occurred()) {
// // errorAbort("Could't import module " + moduleName);
// // }
// // CPyObject classObject = PyObject_GetAttrString(module, className.c_str());
// // if (PyErr_Occurred()) {
// // errorAbort("Couldn't find class " + className);
// // }
// // CPyObject instance = PyObject_CallObject(classObject, NULL);
// // if (PyErr_Occurred()) {
// // errorAbort("Couldn't create instance of class " + className);
// // }
// // wrapper.moduleClassMap.insert({ { moduleName, className }, { module, classObject, instance } });
// wrapper->importClass(moduleName, className);
// PyObject* instance = wrapper->getClass(moduleName, className);
// CPyObject result;
// if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
// errorAbort("Couldn't call method " + method);
// std::string value = PyUnicode_AsUTF8(result);
// cout << "Version: " << value << endl;
// cout << "Calling fit" << endl;
// p.AddRef();
// yp.AddRef();
// method = "fit";
// wrapper->fit(moduleName, className, p, yp);
// // PyObject* instance2 = wrapper->getClass(moduleName, className);
// // if (!(result = PyObject_CallMethodObjArgs(instance2, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
// // errorAbort("Couldn't call method fit");
// // method = "fit";
// // if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
// // errorAbort("Couldn't call method fit");
// cout << "Calling score" << endl;
// // method = "score";
// // if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
// // errorAbort("Couldn't call method score");
// // float score = PyFloat_AsDouble(result);
// auto score = wrapper->score(moduleName, className, p, yp);
// cout << "Score: " << score << endl;
// wrapper->clean(moduleName, className);
// return 0;
// }

61
src/main.cc
View File

@@ -15,7 +15,7 @@ class Paths {
public:
static string datasets()
{
return "/home/rmontanana/Code/discretizbench/datasets/";
return "../discretizbench/datasets/";
}
};
@@ -40,21 +40,50 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
// int main(int argc, char* argv[])
// {
// auto [X, y, features, className, states] = loadDataset("iris", true);
// auto stree = pywrap::STree();
// stree.version();
// auto svc = pywrap::SVC();
// svc.version();
// cout << "Graph: " << stree.graph() << endl;
// stree.version();
// cout << string(80, '-') << endl;
// cout << "X: " << X.sizes() << endl;
// cout << "y: " << y.sizes() << endl;
// auto result = stree.fit(X, y, features, className, states);
// result = svc.fit(X, y, features, className, states);
// cout << "Now calling score" << endl;
// // auto result1 = stree.score(X, y);
// // auto result2 = svc.score(X, y);
// // cout << "STree score " << result1 << endl;
// // cout << "SVC score " << result2 << endl;
// return 0;
// }
int main(int argc, char* argv[])
{
auto [X, y, features, className, states] = loadDataset("iris", true);
auto stree = pywrap::STree();
stree.version();
auto svc = pywrap::SVC();
//svc.version();
cout << "Graph: " << stree.graph() << endl;
stree.version();
cout << string(80, '-') << endl;
cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl;
// auto result = stree.fit(X, y, features, className, states);
// cout << "Now calling score" << endl;
// auto result2 = stree.score(X, y);
// cout << "SVC score " << result2 << endl;
return 0;
cout << "* Begin." << endl;
{
auto [X, y, features, className, states] = loadDataset("iris", true);
cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl;
cout << "y: " << y << endl;
auto clf = pywrap::PyClassifier("stree", "Stree");
cout << "STree Version: " << clf.version() << endl;
// if (true) {
// auto svc = pywrap::PyClassifier("sklearn.svm", "SVC");
// cout << "SVC Version: " << svc.callMethodString("_repr_html_") << endl;
// cout << "Calling fit" << endl;
// svc.fit(X, y, features, className, states);
// cout << "Calling score" << endl;
// cout << "SVC Score: " << svc.score(X, y) << endl;
// }
cout << "Graph: " << clf.graph() << endl;
cout << "Calling fit" << endl;
clf.fit(X, y, features, className, states);
cout << "Calling score" << endl;
cout << "STree Score: " << clf.score(X, y) << endl;
}
cout << "* End." << endl;
}