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Ensemble Experiment, Folding, Classifiers and Network together

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-23 14:10:28 +02:00
parent 644b6c9be0
commit 0c226371cc
12 changed files with 116 additions and 31 deletions
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36
src/Platform/platformUtils.cc
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@@ -15,6 +15,22 @@ pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t
}
return { Xd, maxes };
}
pair<Tensor, map<string, int>> discretizeTorch(Tensor& X, Tensor& y, vector<string> features)
{
map<string, int> maxes;
auto fimdlp = mdlp::CPPFImdlp();
auto Xd = torch::zeros_like(X, torch::kInt64);
auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
for (int i = 0; i < X.size(1); i++) {
auto xv = vector<float>(X.select(1, i).data_ptr<float>(), X.select(1, i).data_ptr<float>() + X.size(0));
fimdlp.fit(xv, yv);
auto xdv = fimdlp.transform(xv);
auto xd = torch::tensor(xdv, torch::kInt64);
maxes[features[i]] = xd.max().item<int>() + 1;
Xd.index_put_({ "...", i }, xd);
}
return { Xd, maxes };
}
vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
@@ -38,10 +54,10 @@ bool file_exists(const std::string& name)
}
}
tuple < Tensor, Tensor, vector<string>> loadDataset(string name, bool discretize)
tuple < Tensor, Tensor, vector<string>, string> loadDataset(string name, bool discretize, bool class_last)
{
auto handler = ArffFiles();
handler.load(PATH + static_cast<string>(name) + ".arff");
handler.load(PATH + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
@@ -64,20 +80,20 @@ tuple < Tensor, Tensor, vector<string>> loadDataset(string name, bool discretize
Xd.index_put_({ "...", i }, torch::tensor(X[i], torch::kFloat64));
}
}
return { Xd, torch::tensor(y, torch::kInt64), features };
return { Xd, torch::tensor(y, torch::kInt64), features, className };
}
pair <map<string, int>, map<string, vector<int>>> discretize_info(Tensor& X, Tensor& y, vector<string> features, string className)
map<string, vector<int>> get_states(Tensor& X, Tensor& y, vector<string> features, string className)
{
map<string, int> maxes;
int max;
map<string, vector<int>> states;
for (int i = 0; i < X.size(1); i++) {
maxes[features[i]] = X.select(1, i).max().item<int>() + 1;
states[features[i]] = vector<int>(maxes[features[i]]);
max = X.select(1, i).max().item<int>() + 1;
states[features[i]] = vector<int>(max);
}
maxes[className] = y.max().item<int>() + 1;
states[className] = vector<int>(maxes[className]);
return { maxes, states };
max = y.max().item<int>() + 1;
states[className] = vector<int>(max);
return states;
}
tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name)