Rename utilities sources to match final names
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2c2159f192
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46064262FD9A7ADE
33
mac_mst.txt
33
mac_mst.txt
@ -1,33 +0,0 @@
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Weights matrix:
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0.0000000, 0.0384968, 0.0795434, 0.1546867, -0.0000000, 0.1788104, 0.2214721, 0.0323837, 0.0366549,
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0.0384968, 0.0000000, 0.0200662, 0.0200937, -0.0000000, 0.0637224, 0.0183005, 0.0127657, 0.0136054,
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0.0795434, 0.0200662, 0.0000000, 0.0605489, -0.0000000, 0.0894469, 0.1689408, 0.0321602, 0.0223184,
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0.1546867, 0.0200937, 0.0605489, 0.0000000, -0.0000000, 0.1150757, 0.1332292, 0.0422865, 0.0191138,
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-0.0000000, -0.0000000, -0.0000000, -0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000,
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0.1788104, 0.0637224, 0.0894469, 0.1150757, 0.0000000, 0.0000000, 0.1407102, 0.0406590, 0.0366986,
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0.2214721, 0.0183005, 0.1689408, 0.1332292, 0.0000000, 0.1407102, 0.0000000, 0.0427515, 0.0349965,
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0.0323837, 0.0127657, 0.0321602, 0.0422865, 0.0000000, 0.0406590, 0.0427515, 0.0000000, 0.0343376,
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0.0366549, 0.0136054, 0.0223184, 0.0191138, 0.0000000, 0.0366986, 0.0349965, 0.0343376, 0.0000000,
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Edge : Weight
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0 - 6 : 0.2214721
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0 - 5 : 0.1788104
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2 - 6 : 0.1689408
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0 - 3 : 0.1546867
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1 - 5 : 0.0637224
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6 - 7 : 0.0427515
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5 - 8 : 0.0366986
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4 - 5 : 0.0000000
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-------------------------------------------------------------------------------
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Metrics Test
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Test Maximum Spanning Tree
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-------------------------------------------------------------------------------
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/Users/rmontanana/Code/BayesNet/tests/TestBayesMetrics.cc:58
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...............................................................................
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/Users/rmontanana/Code/BayesNet/tests/TestBayesMetrics.cc:69: PASSED:
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REQUIRE( result == resultsMST.at(file_name) )
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with expansion:
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(0, 6) (0, 5) (0, 3) (5, 1) (5, 8) (5, 4) (6, 2) (6, 7)
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==
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(0, 6) (0, 5) (0, 3) (5, 1) (5, 8) (5, 4) (6, 2) (6, 7)
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@ -5,13 +5,11 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
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include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
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include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
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include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
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add_executable(b_main main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc ReportConsole.cc ReportBase.cc)
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add_executable(b_manage manage.cc Results.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
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add_executable(b_list list.cc Datasets.cc Dataset.cc)
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add_executable(b_best best.cc BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ExcelFile.cc)
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add_executable(testx testx.cpp Datasets.cc Dataset.cc Folding.cc )
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add_executable(b_main b_main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc ReportConsole.cc ReportBase.cc)
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add_executable(b_manage b_manage.cc Results.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
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add_executable(b_list b_list.cc Datasets.cc Dataset.cc)
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add_executable(b_best b_best.cc BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ExcelFile.cc)
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target_link_libraries(b_main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
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target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
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target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}")
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target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
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target_link_libraries(testx ArffFiles BayesNet "${TORCH_LIBRARIES}")
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target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
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@ -1,248 +0,0 @@
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#include "Folding.h"
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#include <torch/torch.h>
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#include "nlohmann/json.hpp"
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#include "map"
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#include <iostream>
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#include <sstream>
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#include "Datasets.h"
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#include "Network.h"
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#include "ArffFiles.h"
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#include "CPPFImdlp.h"
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#include "CFS.h"
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#include "IWSS.h"
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#include "FCBF.h"
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using namespace std;
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using namespace platform;
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using namespace torch;
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string counts(vector<int> y, vector<int> indices)
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{
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auto result = map<int, int>();
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stringstream oss;
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for (auto i = 0; i < indices.size(); ++i) {
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result[y[indices[i]]]++;
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}
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string final_result = "";
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for (auto i = 0; i < result.size(); ++i)
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oss << i << " -> " << setprecision(2) << fixed
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<< (double)result[i] * 100 / indices.size() << "% (" << result[i] << ") //";
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oss << endl;
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return oss.str();
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}
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class Paths {
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public:
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static string datasets()
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{
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return "datasets/";
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}
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};
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pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
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{
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vector<mdlp::labels_t> Xd;
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map<string, int> maxes;
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auto fimdlp = mdlp::CPPFImdlp();
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for (int i = 0; i < X.size(); i++) {
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fimdlp.fit(X[i], y);
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mdlp::labels_t& xd = fimdlp.transform(X[i]);
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maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
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Xd.push_back(xd);
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}
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return { Xd, maxes };
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}
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vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
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{
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vector<mdlp::labels_t> Xd;
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auto fimdlp = mdlp::CPPFImdlp();
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for (int i = 0; i < X.size(); i++) {
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fimdlp.fit(X[i], y);
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mdlp::labels_t& xd = fimdlp.transform(X[i]);
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Xd.push_back(xd);
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}
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return Xd;
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}
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bool file_exists(const string& name)
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{
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if (FILE* file = fopen(name.c_str(), "r")) {
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fclose(file);
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return true;
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} else {
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return false;
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}
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}
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tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last, bool discretize_dataset)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
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// Get Dataset X, y
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vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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// Get className & Features
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auto className = handler.getClassName();
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vector<string> features;
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auto attributes = handler.getAttributes();
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transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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Tensor Xd;
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auto states = map<string, vector<int>>();
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if (discretize_dataset) {
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auto Xr = discretizeDataset(X, y);
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Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
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auto item = states.at(features[i]);
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iota(begin(item), end(item), 0);
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Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
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}
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states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
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iota(begin(states.at(className)), end(states.at(className)), 0);
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} else {
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Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
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for (int i = 0; i < features.size(); ++i) {
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Xd.index_put_({ i, "..." }, torch::tensor(X[i]));
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}
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}
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return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
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}
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tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<string>(name) + ".arff");
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// Get Dataset X, y
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vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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// Get className & Features
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auto className = handler.getClassName();
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vector<string> features;
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auto attributes = handler.getAttributes();
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transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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// Discretize Dataset
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vector<mdlp::labels_t> Xd;
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map<string, int> maxes;
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tie(Xd, maxes) = discretize(X, y, features);
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maxes[className] = *max_element(y.begin(), y.end()) + 1;
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map<string, vector<int>> states;
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for (auto feature : features) {
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states[feature] = vector<int>(maxes[feature]);
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}
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states[className] = vector<int>(maxes[className]);
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return { Xd, y, features, className, states };
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}
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class RawDatasets {
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public:
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RawDatasets(const string& file_name, bool discretize)
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{
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// Xt can be either discretized or not
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tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
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// Xv is always discretized
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tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
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auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
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dataset = torch::cat({ Xt, yresized }, 0);
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nSamples = dataset.size(1);
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weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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weightsv = vector<double>(nSamples, 1.0 / nSamples);
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classNumStates = discretize ? statest.at(classNamet).size() : 0;
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}
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torch::Tensor Xt, yt, dataset, weights;
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vector<vector<int>> Xv;
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vector<double> weightsv;
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vector<int> yv;
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vector<string> featurest, featuresv;
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map<string, vector<int>> statest, statesv;
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string classNamet, classNamev;
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int nSamples, classNumStates;
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double epsilon = 1e-5;
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};
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int main()
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{
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// map<string, string> balance = {
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// {"iris", "33,33% (50) / 33,33% (50) / 33,33% (50)"},
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// {"diabetes", "34,90% (268) / 65,10% (500)"},
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// {"ecoli", "42,56% (143) / 22,92% (77) / 0,60% (2) / 0,60% (2) / 10,42% (35) / 5,95% (20) / 1,49% (5) / 15,48% (52)"},
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// {"glass", "32,71% (70) / 7,94% (17) / 4,21% (9) / 35,51% (76) / 13,55% (29) / 6,07% (13)"}
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// };
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// for (const auto& file_name : { "iris", "glass", "ecoli", "diabetes" }) {
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// auto dt = Datasets(true, "Arff");
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// auto [X, y] = dt.getVectors(file_name);
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// //auto fold = KFold(5, 150);
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// auto fold = StratifiedKFold(5, y, -1);
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// cout << "***********************************************************************************************" << endl;
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// cout << "Dataset: " << file_name << endl;
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// cout << "Nº Samples: " << dt.getNSamples(file_name) << endl;
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// cout << "Class states: " << dt.getNClasses(file_name) << endl;
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// cout << "Balance: " << balance.at(file_name) << endl;
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// for (int i = 0; i < 5; ++i) {
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// cout << "Fold: " << i << endl;
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// auto [train, test] = fold.getFold(i);
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// cout << "Train: ";
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// cout << "(" << train.size() << "): ";
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// // for (auto j = 0; j < static_cast<int>(train.size()); j++)
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// // cout << train[j] << ", ";
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// cout << endl;
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// cout << "Train Statistics : " << counts(y, train);
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// cout << "-------------------------------------------------------------------------------" << endl;
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// cout << "Test: ";
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// cout << "(" << test.size() << "): ";
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// // for (auto j = 0; j < static_cast<int>(test.size()); j++)
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// // cout << test[j] << ", ";
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// cout << endl;
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// cout << "Test Statistics: " << counts(y, test);
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// cout << "==============================================================================" << endl;
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// }
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// cout << "***********************************************************************************************" << endl;
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// }
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// const string file_name = "iris";
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// auto net = bayesnet::Network();
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// auto dt = Datasets(true, "Arff");
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// auto raw = RawDatasets("iris", true);
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// auto [X, y] = dt.getVectors(file_name);
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// cout << "Dataset dims " << raw.dataset.sizes() << endl;
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// cout << "weights dims " << raw.weights.sizes() << endl;
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// cout << "States dims " << raw.statest.size() << endl;
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// cout << "features: ";
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// for (const auto& feature : raw.featurest) {
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// cout << feature << ", ";
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// net.addNode(feature);
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// }
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// net.addNode(raw.classNamet);
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// cout << endl;
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// net.fit(raw.dataset, raw.weights, raw.featurest, raw.classNamet, raw.statest);
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auto dt = Datasets(true, "Arff");
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nlohmann::json output;
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for (const auto& name : dt.getNames()) {
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// for (const auto& name : { "iris" }) {
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auto [X, y] = dt.getTensors(name);
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auto features = dt.getFeatures(name);
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auto states = dt.getStates(name);
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auto className = dt.getClassName(name);
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int maxFeatures = 0;
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auto classNumStates = states.at(className).size();
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torch::Tensor weights = torch::full({ X.size(1) }, 1.0 / X.size(1), torch::kDouble);
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auto dataset = X;
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auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
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dataset = torch::cat({ dataset, yresized }, 0);
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auto cfs = bayesnet::CFS(dataset, features, className, maxFeatures, classNumStates, weights);
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auto fcbf = bayesnet::FCBF(dataset, features, className, maxFeatures, classNumStates, weights, 1e-7);
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auto iwss = bayesnet::IWSS(dataset, features, className, maxFeatures, classNumStates, weights, 0.5);
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cout << "Dataset: " << setw(20) << name << flush;
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cfs.fit();
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cout << " CFS: " << setw(4) << cfs.getFeatures().size() << flush;
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fcbf.fit();
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cout << " FCBF: " << setw(4) << fcbf.getFeatures().size() << flush;
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iwss.fit();
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cout << " IWSS: " << setw(4) << iwss.getFeatures().size() << flush;
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cout << endl;
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output[name]["CFS"] = cfs.getFeatures();
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output[name]["FCBF"] = fcbf.getFeatures();
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output[name]["IWSS"] = iwss.getFeatures();
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}
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ofstream file("features_cpp.json");
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file << output;
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file.close();
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}
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