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Add Linux specific options to compile

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This commit is contained in:
Ricardo Montañana Gómez
2023-08-29 18:20:55 +02:00
parent 284ef6dfd1
commit 7c3e315ae7
10 changed files with 146 additions and 138 deletions
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1
src/Platform/Datasets.cc
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@@ -1,6 +1,7 @@
#include "Datasets.h"
#include "platformUtils.h"
#include "ArffFiles.h"
#include <fstream>
namespace platform {
void Datasets::load()
{

2
src/Platform/Experiment.cc
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@@ -2,7 +2,7 @@
#include "Datasets.h"
#include "Models.h"
#include "ReportConsole.h"
#include <fstream>
namespace platform {
using json = nlohmann::json;
string get_date()

168
src/Platform/Folding.cc
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@@ -1,95 +1,97 @@
#include "Folding.h"
#include <algorithm>
#include <map>
Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
{
random_device rd;
random_seed = default_random_engine(seed == -1 ? rd() : seed);
srand(seed == -1 ? time(0) : seed);
}
KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
{
iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
shuffle(indices.begin(), indices.end(), random_seed);
}
pair<vector<int>, vector<int>> KFold::getFold(int nFold)
{
if (nFold >= k || nFold < 0) {
throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
namespace platform {
Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
{
random_device rd;
random_seed = default_random_engine(seed == -1 ? rd() : seed);
srand(seed == -1 ? time(0) : seed);
}
int nTest = n / k;
auto train = vector<int>();
auto test = vector<int>();
for (int i = 0; i < n; i++) {
if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
test.push_back(indices[i]);
} else {
train.push_back(indices[i]);
}
}
return { train, test };
}
StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
{
n = y.numel();
this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
build();
}
StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
: Fold(k, y.size(), seed)
{
this->y = y;
n = y.size();
build();
}
void StratifiedKFold::build()
{
stratified_indices = vector<vector<int>>(k);
int fold_size = n / k;
// Compute class counts and indices
auto class_indices = map<int, vector<int>>();
vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
for (auto i = 0; i < n; ++i) {
class_counts[y[i]]++;
class_indices[y[i]].push_back(i);
}
// Shuffle class indices
for (auto& [cls, indices] : class_indices) {
KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
{
iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
shuffle(indices.begin(), indices.end(), random_seed);
}
// Assign indices to folds
for (auto label = 0; label < class_counts.size(); ++label) {
auto num_samples_to_take = class_counts[label] / k;
if (num_samples_to_take == 0)
continue;
auto remainder_samples_to_take = class_counts[label] % k;
for (auto fold = 0; fold < k; ++fold) {
auto it = next(class_indices[label].begin(), num_samples_to_take);
move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold])); // ##
class_indices[label].erase(class_indices[label].begin(), it);
pair<vector<int>, vector<int>> KFold::getFold(int nFold)
{
if (nFold >= k || nFold < 0) {
throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
}
while (remainder_samples_to_take > 0) {
int fold = (rand() % static_cast<int>(k));
if (stratified_indices[fold].size() == fold_size + 1) {
continue;
int nTest = n / k;
auto train = vector<int>();
auto test = vector<int>();
for (int i = 0; i < n; i++) {
if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
test.push_back(indices[i]);
} else {
train.push_back(indices[i]);
}
}
return { train, test };
}
StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
{
n = y.numel();
this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
build();
}
StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
: Fold(k, y.size(), seed)
{
this->y = y;
n = y.size();
build();
}
void StratifiedKFold::build()
{
stratified_indices = vector<vector<int>>(k);
int fold_size = n / k;
// Compute class counts and indices
auto class_indices = map<int, vector<int>>();
vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
for (auto i = 0; i < n; ++i) {
class_counts[y[i]]++;
class_indices[y[i]].push_back(i);
}
// Shuffle class indices
for (auto& [cls, indices] : class_indices) {
shuffle(indices.begin(), indices.end(), random_seed);
}
// Assign indices to folds
for (auto label = 0; label < class_counts.size(); ++label) {
auto num_samples_to_take = class_counts[label] / k;
if (num_samples_to_take == 0)
continue;
auto remainder_samples_to_take = class_counts[label] % k;
for (auto fold = 0; fold < k; ++fold) {
auto it = next(class_indices[label].begin(), num_samples_to_take);
move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold])); // ##
class_indices[label].erase(class_indices[label].begin(), it);
}
while (remainder_samples_to_take > 0) {
int fold = (rand() % static_cast<int>(k));
if (stratified_indices[fold].size() == fold_size + 1) {
continue;
}
auto it = next(class_indices[label].begin(), 1);
stratified_indices[fold].push_back(*class_indices[label].begin());
class_indices[label].erase(class_indices[label].begin(), it);
remainder_samples_to_take--;
}
auto it = next(class_indices[label].begin(), 1);
stratified_indices[fold].push_back(*class_indices[label].begin());
class_indices[label].erase(class_indices[label].begin(), it);
remainder_samples_to_take--;
}
}
}
pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
{
if (nFold >= k || nFold < 0) {
throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
{
if (nFold >= k || nFold < 0) {
throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
}
vector<int> test_indices = stratified_indices[nFold];
vector<int> train_indices;
for (int i = 0; i < k; ++i) {
if (i == nFold) continue;
train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
}
return { train_indices, test_indices };
}
vector<int> test_indices = stratified_indices[nFold];
vector<int> train_indices;
for (int i = 0; i < k; ++i) {
if (i == nFold) continue;
train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
}
return { train_indices, test_indices };
}

61
src/Platform/Folding.h
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@@ -4,34 +4,35 @@
#include <vector>
#include <random>
using namespace std;
class Fold {
protected:
int k;
int n;
int seed;
default_random_engine random_seed;
public:
Fold(int k, int n, int seed = -1);
virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
virtual ~Fold() = default;
int getNumberOfFolds() { return k; }
};
class KFold : public Fold {
private:
vector<int> indices;
public:
KFold(int k, int n, int seed = -1);
pair<vector<int>, vector<int>> getFold(int nFold) override;
};
class StratifiedKFold : public Fold {
private:
vector<int> y;
vector<vector<int>> stratified_indices;
void build();
public:
StratifiedKFold(int k, const vector<int>& y, int seed = -1);
StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
pair<vector<int>, vector<int>> getFold(int nFold) override;
};
namespace platform {
class Fold {
protected:
int k;
int n;
int seed;
default_random_engine random_seed;
public:
Fold(int k, int n, int seed = -1);
virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
virtual ~Fold() = default;
int getNumberOfFolds() { return k; }
};
class KFold : public Fold {
private:
vector<int> indices;
public:
KFold(int k, int n, int seed = -1);
pair<vector<int>, vector<int>> getFold(int nFold) override;
};
class StratifiedKFold : public Fold {
private:
vector<int> y;
vector<vector<int>> stratified_indices;
void build();
public:
StratifiedKFold(int k, const vector<int>& y, int seed = -1);
StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
pair<vector<int>, vector<int>> getFold(int nFold) override;
};
}
#endif