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Add torch methods to discretize

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Add fit_transform methods
This commit is contained in:
Ricardo Montañana Gómez
2024-06-07 23:54:42 +02:00
parent 633aa52849
commit e205668906
14 changed files with 183 additions and 87 deletions
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109
sample/sample.cpp
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@@ -5,12 +5,12 @@
#include <algorithm>
#include <cstring>
#include <getopt.h>
#include <torch/torch.h>
#include "../Discretizer.h"
#include "../CPPFImdlp.h"
#include "../BinDisc.h"
#include "../tests/ArffFiles.h"
using namespace std;
using namespace mdlp;
const string PATH = "tests/datasets/";
/* print a description of all supported options */
@@ -20,17 +20,17 @@ void usage(const char* path)
const char* basename = strrchr(path, '/');
basename = basename ? basename + 1 : path;
cout << "usage: " << basename << "[OPTION]" << endl;
cout << " -h, --help\t\t Print this help and exit." << endl;
cout
std::cout << "usage: " << basename << "[OPTION]" << std::endl;
std::cout << " -h, --help\t\t Print this help and exit." << std::endl;
std::cout
<< " -f, --file[=FILENAME]\t {all, diabetes, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
<< endl;
cout << " -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << endl;
cout << " -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << endl;
cout
<< std::endl;
std::cout << " -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << std::endl;
std::cout << " -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << std::endl;
std::cout
<< " -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 -> any"
<< endl;
cout << " -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << endl;
<< std::endl;
std::cout << " -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << std::endl;
}
tuple<string, string, int, int, float> parse_arguments(int argc, char** argv)
@@ -96,62 +96,79 @@ void process_file(const string& path, const string& file_name, bool class_last,
file.load(path + file_name + ".arff", class_last);
const auto attributes = file.getAttributes();
const auto items = file.getSize();
cout << "Number of lines: " << items << endl;
cout << "Attributes: " << endl;
std::cout << "Number of lines: " << items << std::endl;
std::cout << "Attributes: " << std::endl;
for (auto attribute : attributes) {
cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
std::cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << std::endl;
}
cout << "Class name: " << file.getClassName() << endl;
cout << "Class type: " << file.getClassType() << endl;
cout << "Data: " << endl;
vector<samples_t>& X = file.getX();
labels_t& y = file.getY();
std::cout << "Class name: " << file.getClassName() << std::endl;
std::cout << "Class type: " << file.getClassType() << std::endl;
std::cout << "Data: " << std::endl;
std::vector<mdlp::samples_t>& X = file.getX();
mdlp::labels_t& y = file.getY();
for (int i = 0; i < 5; i++) {
for (auto feature : X) {
cout << fixed << setprecision(1) << feature[i] << " ";
std::cout << fixed << setprecision(1) << feature[i] << " ";
}
cout << y[i] << endl;
std::cout << y[i] << std::endl;
}
auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
size_t total = 0;
for (auto i = 0; i < attributes.size(); i++) {
auto min_max = minmax_element(X[i].begin(), X[i].end());
cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
std::cout << "Cut points for feature " << get<0>(attributes[i]) << ": [" << setprecision(3);
test.fit(X[i], y);
auto cut_points = test.getCutPoints();
for (auto item : cut_points) {
cout << item;
std::cout << item;
if (item != cut_points.back())
cout << ", ";
std::cout << ", ";
}
total += test.getCutPoints().size();
cout << "]" << endl;
cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
cout << "--------------------------" << endl;
std::cout << "]" << std::endl;
std::cout << "Min: " << *min_max.first << " Max: " << *min_max.second << std::endl;
std::cout << "--------------------------" << std::endl;
}
cout << "Total cut points ...: " << total << endl;
cout << "Total feature states: " << total + attributes.size() << endl;
cout << "Version ............: " << test.version() << endl;
cout << "Transformed data ...: " << endl;
std::cout << "Total cut points ...: " << total << std::endl;
std::cout << "Total feature states: " << total + attributes.size() << std::endl;
std::cout << "Version ............: " << test.version() << std::endl;
std::cout << "Transformed data (vector)..: " << std::endl;
test.fit(X[0], y);
auto data = test.transform(X[0]);
for (int i = 0; i < 5; i++) {
cout << fixed << setprecision(1) << X[0][i] << " " << data[i] << endl;
for (int i = 130; i < 135; i++) {
std::cout << std::fixed << std::setprecision(1) << X[0][i] << " " << data[i] << std::endl;
}
auto Xt = torch::tensor(X[0], torch::kFloat32);
auto yt = torch::tensor(y, torch::kInt64);
//test.fit_t(Xt, yt);
auto result = test.fit_transform_t(Xt, yt);
std::cout << "Transformed data (torch)...: " << std::endl;
for (int i = 130; i < 135; i++) {
std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << result[i].item<int64_t>() << std::endl;
}
auto disc = mdlp::BinDisc(3);
auto res_v = disc.fit_transform(X[0], y);
disc.fit_t(Xt, yt);
auto res_t = disc.transform_t(Xt);
std::cout << "Transformed data (BinDisc)...: " << std::endl;
for (int i = 130; i < 135; i++) {
std::cout << std::fixed << std::setprecision(1) << Xt[i].item<float>() << " " << res_v[i] << " " << res_t[i].item<int64_t>() << std::endl;
}
}
void process_all_files(const map<string, bool>& datasets, const string& path, int max_depth, int min_length,
float max_cutpoints)
{
cout << "Results: " << "Max_depth: " << max_depth << " Min_length: " << min_length << " Max_cutpoints: "
<< max_cutpoints << endl << endl;
std::cout << "Results: " << "Max_depth: " << max_depth << " Min_length: " << min_length << " Max_cutpoints: "
<< max_cutpoints << std::endl << std::endl;
printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
printf("==================== ==== ==== ========\n");
for (const auto& dataset : datasets) {
ArffFiles file;
file.load(path + dataset.first + ".arff", dataset.second);
auto attributes = file.getAttributes();
vector<samples_t>& X = file.getX();
labels_t& y = file.getY();
std::vector<mdlp::samples_t>& X = file.getX();
mdlp::labels_t& y = file.getY();
size_t timing = 0;
size_t cut_points = 0;
for (auto i = 0; i < attributes.size(); i++) {
@@ -169,7 +186,7 @@ void process_all_files(const map<string, bool>& datasets, const string& path, in
int main(int argc, char** argv)
{
map<string, bool> datasets = {
std::map<std::string, bool> datasets = {
{"diabetes", true},
{"glass", true},
{"iris", true},
@@ -179,14 +196,14 @@ int main(int argc, char** argv)
{"mfeat-factors", true},
{"test", true}
};
string file_name;
string path;
std::string file_name;
std::string path;
int max_depth;
int min_length;
float max_cutpoints;
tie(file_name, path, max_depth, min_length, max_cutpoints) = parse_arguments(argc, argv);
if (datasets.find(file_name) == datasets.end() && file_name != "all") {
cout << "Invalid file name: " << file_name << endl;
std::cout << "Invalid file name: " << file_name << std::endl;
usage(argv[0]);
exit(1);
}
@@ -194,10 +211,10 @@ int main(int argc, char** argv)
process_all_files(datasets, path, max_depth, min_length, max_cutpoints);
else {
process_file(path, file_name, datasets[file_name], max_depth, min_length, max_cutpoints);
cout << "File name ....: " << file_name << endl;
cout << "Max depth ....: " << max_depth << endl;
cout << "Min length ...: " << min_length << endl;
cout << "Max cutpoints : " << max_cutpoints << endl;
std::cout << "File name ....: " << file_name << std::endl;
std::cout << "Max depth ....: " << max_depth << std::endl;
std::cout << "Min length ...: " << min_length << std::endl;
std::cout << "Max cutpoints : " << max_cutpoints << std::endl;
}
return 0;
}