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Complete SPODE & AODE

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-15 01:59:30 +02:00
parent db6908acd0
commit e311c27d43
7 changed files with 94 additions and 31 deletions
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68
src/Ensemble.cc
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@@ -4,10 +4,10 @@ namespace bayesnet {
using namespace std;
using namespace torch;
Ensemble::Ensemble() : m(0), n(0), n_models(0), metrics(Metrics()) {}
Ensemble::Ensemble() : m(0), n(0), n_models(0), metrics(Metrics()), fitted(false) {}
Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
{
dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
dataset = cat({ X, y.view({y.size(0), 1}) }, 1);
this->features = features;
this->className = className;
this->states = states;
@@ -18,34 +18,35 @@ namespace bayesnet {
// Train models
n_models = models.size();
for (auto i = 0; i < n_models; ++i) {
models[i].fit(X, y, features, className, states);
models[i]->fit(Xv, yv, features, className, states);
}
fitted = true;
return *this;
}
Ensemble& Ensemble::fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = X;
this->y = y;
auto sizes = X.sizes();
m = sizes[0];
n = sizes[1];
return build(features, className, states);
}
Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = torch::zeros({ static_cast<int64_t>(X[0].size()), static_cast<int64_t>(X.size()) }, kInt64);
Xv = X;
for (int i = 0; i < X.size(); ++i) {
this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt64));
}
this->y = torch::tensor(y, kInt64);
yv = y;
return build(features, className, states);
}
Tensor Ensemble::predict(Tensor& X)
{
Tensor y_pred = torch::zeros({ X.size(0), n_models }, torch::kInt64);
for (auto i = 0; i < n_models; ++i) {
y_pred.index_put_({ "...", i }, models[i].predict(X));
if (!fitted) {
throw logic_error("Ensemble has not been fitted");
}
Tensor y_pred = torch::zeros({ X.size(0), n_models }, kInt64);
for (auto i = 0; i < n_models; ++i) {
y_pred.index_put_({ "...", i }, models[i]->predict(X));
}
return torch::tensor(voting(y_pred));
}
vector<int> Ensemble::voting(Tensor& y_pred)
{
auto y_pred_ = y_pred.accessor<int64_t, 2>();
vector<int> y_pred_final;
for (int i = 0; i < y_pred.size(0); ++i) {
@@ -56,18 +57,45 @@ namespace bayesnet {
auto indices = argsort(votes);
y_pred_final.push_back(indices[0]);
}
return torch::tensor(y_pred_final, torch::kInt64);
return y_pred_final;
}
float Ensemble::score(Tensor& X, Tensor& y)
vector<int> Ensemble::predict(vector<vector<int>>& X)
{
Tensor y_pred = predict(X);
return (y_pred == y).sum().item<float>() / y.size(0);
if (!fitted) {
throw logic_error("Ensemble has not been fitted");
}
long m_ = X[0].size();
long n_ = X.size();
vector<vector<int>> Xd(n_, vector<int>(m_, 0));
for (auto i = 0; i < n_; i++) {
Xd[i] = vector<int>(X[i].begin(), X[i].end());
}
Tensor y_pred = torch::zeros({ m_, n_models }, kInt64);
for (auto i = 0; i < n_models; ++i) {
y_pred.index_put_({ "...", i }, torch::tensor(models[i]->predict(Xd), kInt64));
}
return voting(y_pred);
}
float Ensemble::score(vector<vector<int>>& X, vector<int>& y)
{
if (!fitted) {
throw logic_error("Ensemble has not been fitted");
}
auto y_pred = predict(X);
int correct = 0;
for (int i = 0; i < y_pred.size(); ++i) {
if (y_pred[i] == y[i]) {
correct++;
}
}
return (double)correct / y_pred.size();
}
vector<string> Ensemble::show()
{
vector<string> result;
for (auto i = 0; i < n_models; ++i) {
auto res = models[i].show();
auto res = models[i]->show();
result.insert(result.end(), res.begin(), res.end());
}
return result;