Add status to classifier and Experiment
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sample/sample.cc
386
sample/sample.cc
@ -58,180 +58,226 @@ pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vect
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int main(int argc, char** argv)
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{
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map<string, bool> datasets = {
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{"diabetes", true},
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{"ecoli", true},
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{"glass", true},
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{"iris", true},
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{"kdd_JapaneseVowels", false},
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{"letter", true},
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{"liver-disorders", true},
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{"mfeat-factors", true},
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};
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auto valid_datasets = vector<string>();
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transform(datasets.begin(), datasets.end(), back_inserter(valid_datasets),
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[](const pair<string, bool>& pair) { return pair.first; });
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argparse::ArgumentParser program("BayesNetSample");
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program.add_argument("-d", "--dataset")
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.help("Dataset file name")
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.action([valid_datasets](const std::string& value) {
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if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
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return value;
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}
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throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
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}
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);
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program.add_argument("-p", "--path")
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.help(" folder where the data files are located, default")
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.default_value(string{ PATH }
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);
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program.add_argument("-m", "--model")
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.help("Model to use " + platform::Models::instance()->toString())
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.action([](const std::string& value) {
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static const vector<string> choices = platform::Models::instance()->getNames();
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if (find(choices.begin(), choices.end(), value) != choices.end()) {
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return value;
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}
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throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
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}
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);
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program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
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program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
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program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
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program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
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program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
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try {
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auto k = stoi(value);
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if (k < 2) {
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throw runtime_error("Number of folds must be greater than 1");
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}
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return k;
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}
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catch (const runtime_error& err) {
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throw runtime_error(err.what());
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}
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catch (...) {
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throw runtime_error("Number of folds must be an integer");
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}});
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program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
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bool class_last, stratified, tensors, dump_cpt;
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string model_name, file_name, path, complete_file_name;
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int nFolds, seed;
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try {
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program.parse_args(argc, argv);
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file_name = program.get<string>("dataset");
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path = program.get<string>("path");
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model_name = program.get<string>("model");
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complete_file_name = path + file_name + ".arff";
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stratified = program.get<bool>("stratified");
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tensors = program.get<bool>("tensors");
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nFolds = program.get<int>("folds");
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seed = program.get<int>("seed");
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dump_cpt = program.get<bool>("dumpcpt");
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class_last = datasets[file_name];
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if (!file_exists(complete_file_name)) {
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throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
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}
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torch::Tensor weights_ = torch::full({ 10 }, 1.0 / 10, torch::kFloat64);
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torch::Tensor y_ = torch::tensor({ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, torch::kInt32);
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torch::Tensor ypred = torch::tensor({ 1, 1, 1, 0, 0, 1, 1, 1, 1, 0 }, torch::kInt32);
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cout << "Initial weights_: " << endl;
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for (int i = 0; i < 10; i++) {
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cout << weights_.index({ i }).item<double>() << ", ";
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}
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catch (const exception& err) {
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cerr << err.what() << endl;
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cerr << program;
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exit(1);
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cout << "end." << endl;
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cout << "y_: " << endl;
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for (int i = 0; i < 10; i++) {
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cout << y_.index({ i }).item<int>() << ", ";
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}
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cout << "end." << endl;
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cout << "ypred: " << endl;
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for (int i = 0; i < 10; i++) {
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cout << ypred.index({ i }).item<int>() << ", ";
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}
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cout << "end." << endl;
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auto mask_wrong = ypred != y_;
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auto mask_right = ypred == y_;
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auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
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double epsilon_t = masked_weights.sum().item<double>();
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cout << "epsilon_t: " << epsilon_t << endl;
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double wt = (1 - epsilon_t) / epsilon_t;
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cout << "wt: " << wt << endl;
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double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
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cout << "alpha_t: " << alpha_t << endl;
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// Step 3.2: Update weights for next classifier
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// Step 3.2.1: Update weights of wrong samples
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cout << "exp(alpha_t): " << exp(alpha_t) << endl;
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cout << "exp(-alpha_t): " << exp(-alpha_t) << endl;
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weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
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// Step 3.2.2: Update weights of right samples
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weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
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// Step 3.3: Normalise the weights
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double totalWeights = torch::sum(weights_).item<double>();
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cout << "totalWeights: " << totalWeights << endl;
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cout << "Before normalization: " << endl;
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for (int i = 0; i < 10; i++) {
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cout << weights_.index({ i }).item<double>() << endl;
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}
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weights_ = weights_ / totalWeights;
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cout << "After normalization: " << endl;
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for (int i = 0; i < 10; i++) {
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cout << weights_.index({ i }).item<double>() << endl;
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}
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// map<string, bool> datasets = {
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// {"diabetes", true},
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// {"ecoli", true},
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// {"glass", true},
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// {"iris", true},
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// {"kdd_JapaneseVowels", false},
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// {"letter", true},
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// {"liver-disorders", true},
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// {"mfeat-factors", true},
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// };
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// auto valid_datasets = vector<string>();
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// transform(datasets.begin(), datasets.end(), back_inserter(valid_datasets),
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// [](const pair<string, bool>& pair) { return pair.first; });
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// argparse::ArgumentParser program("BayesNetSample");
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// program.add_argument("-d", "--dataset")
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// .help("Dataset file name")
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// .action([valid_datasets](const std::string& value) {
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// if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
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// return value;
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// }
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// throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
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// }
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// );
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// program.add_argument("-p", "--path")
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// .help(" folder where the data files are located, default")
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// .default_value(string{ PATH }
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// );
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// program.add_argument("-m", "--model")
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// .help("Model to use " + platform::Models::instance()->toString())
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// .action([](const std::string& value) {
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// static const vector<string> choices = platform::Models::instance()->getNames();
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// if (find(choices.begin(), choices.end(), value) != choices.end()) {
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// return value;
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// }
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// throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
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// }
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// );
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// program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
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// program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
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// program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
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// program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
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// program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
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// try {
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// auto k = stoi(value);
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// if (k < 2) {
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// throw runtime_error("Number of folds must be greater than 1");
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// }
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// return k;
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// }
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// catch (const runtime_error& err) {
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// throw runtime_error(err.what());
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// }
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// catch (...) {
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// throw runtime_error("Number of folds must be an integer");
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// }});
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// program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
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// bool class_last, stratified, tensors, dump_cpt;
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// string model_name, file_name, path, complete_file_name;
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// int nFolds, seed;
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// try {
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// program.parse_args(argc, argv);
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// file_name = program.get<string>("dataset");
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// path = program.get<string>("path");
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// model_name = program.get<string>("model");
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// complete_file_name = path + file_name + ".arff";
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// stratified = program.get<bool>("stratified");
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// tensors = program.get<bool>("tensors");
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// nFolds = program.get<int>("folds");
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// seed = program.get<int>("seed");
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// dump_cpt = program.get<bool>("dumpcpt");
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// class_last = datasets[file_name];
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// if (!file_exists(complete_file_name)) {
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// throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
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// }
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// }
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// catch (const exception& err) {
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// cerr << err.what() << endl;
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// cerr << program;
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// exit(1);
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// }
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/*
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* Begin Processing
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*/
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auto handler = ArffFiles();
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handler.load(complete_file_name, 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),
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[](const pair<string, string>& item) { return item.first; });
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// Discretize Dataset
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auto [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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auto clf = platform::Models::instance()->create(model_name);
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clf->fit(Xd, y, features, className, states);
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if (dump_cpt) {
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cout << "--- CPT Tables ---" << endl;
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clf->dump_cpt();
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}
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auto lines = clf->show();
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for (auto line : lines) {
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cout << line << endl;
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}
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cout << "--- Topological Order ---" << endl;
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auto order = clf->topological_order();
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for (auto name : order) {
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cout << name << ", ";
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}
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cout << "end." << endl;
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auto score = clf->score(Xd, y);
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cout << "Score: " << score << endl;
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auto graph = clf->graph();
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auto dot_file = model_name + "_" + file_name;
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ofstream file(dot_file + ".dot");
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file << graph;
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file.close();
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cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
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cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
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string stratified_string = stratified ? " Stratified" : "";
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cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
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cout << "==========================================" << endl;
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torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
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torch::Tensor yt = torch::tensor(y, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
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}
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float total_score = 0, total_score_train = 0, score_train, score_test;
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platform::Fold* fold;
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if (stratified)
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fold = new platform::StratifiedKFold(nFolds, y, seed);
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else
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fold = new platform::KFold(nFolds, y.size(), seed);
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for (auto i = 0; i < nFolds; ++i) {
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auto [train, test] = fold->getFold(i);
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cout << "Fold: " << i + 1 << endl;
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if (tensors) {
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auto ttrain = torch::tensor(train, torch::kInt64);
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auto ttest = torch::tensor(test, torch::kInt64);
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torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
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torch::Tensor ytraint = yt.index({ ttrain });
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torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
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torch::Tensor ytestt = yt.index({ ttest });
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clf->fit(Xtraint, ytraint, features, className, states);
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auto temp = clf->predict(Xtraint);
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score_train = clf->score(Xtraint, ytraint);
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score_test = clf->score(Xtestt, ytestt);
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} else {
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auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
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auto [Xtest, ytest] = extract_indices(test, Xd, y);
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clf->fit(Xtrain, ytrain, features, className, states);
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score_train = clf->score(Xtrain, ytrain);
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score_test = clf->score(Xtest, ytest);
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}
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if (dump_cpt) {
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cout << "--- CPT Tables ---" << endl;
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clf->dump_cpt();
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}
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total_score_train += score_train;
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total_score += score_test;
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cout << "Score Train: " << score_train << endl;
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cout << "Score Test : " << score_test << endl;
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cout << "-------------------------------------------------------------------------------" << endl;
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}
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cout << "**********************************************************************************" << endl;
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cout << "Average Score Train: " << total_score_train / nFolds << endl;
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cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
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// auto handler = ArffFiles();
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// handler.load(complete_file_name, 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),
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// [](const pair<string, string>& item) { return item.first; });
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// // Discretize Dataset
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// auto [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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// auto clf = platform::Models::instance()->create(model_name);
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// clf->fit(Xd, y, features, className, states);
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// if (dump_cpt) {
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// cout << "--- CPT Tables ---" << endl;
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// clf->dump_cpt();
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// }
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// auto lines = clf->show();
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// for (auto line : lines) {
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// cout << line << endl;
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// }
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// cout << "--- Topological Order ---" << endl;
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// auto order = clf->topological_order();
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// for (auto name : order) {
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// cout << name << ", ";
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// }
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// cout << "end." << endl;
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// auto score = clf->score(Xd, y);
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// cout << "Score: " << score << endl;
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// auto graph = clf->graph();
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// auto dot_file = model_name + "_" + file_name;
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// ofstream file(dot_file + ".dot");
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// file << graph;
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// file.close();
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// cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
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// cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
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// string stratified_string = stratified ? " Stratified" : "";
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// cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
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// cout << "==========================================" << endl;
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// torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
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// torch::Tensor yt = torch::tensor(y, torch::kInt32);
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// for (int i = 0; i < features.size(); ++i) {
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// Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
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// }
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// float total_score = 0, total_score_train = 0, score_train, score_test;
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// platform::Fold* fold;
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// if (stratified)
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// fold = new platform::StratifiedKFold(nFolds, y, seed);
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// else
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// fold = new platform::KFold(nFolds, y.size(), seed);
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// for (auto i = 0; i < nFolds; ++i) {
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// auto [train, test] = fold->getFold(i);
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// cout << "Fold: " << i + 1 << endl;
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// if (tensors) {
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// auto ttrain = torch::tensor(train, torch::kInt64);
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// auto ttest = torch::tensor(test, torch::kInt64);
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// torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
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// torch::Tensor ytraint = yt.index({ ttrain });
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// torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
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// torch::Tensor ytestt = yt.index({ ttest });
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// clf->fit(Xtraint, ytraint, features, className, states);
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// auto temp = clf->predict(Xtraint);
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// score_train = clf->score(Xtraint, ytraint);
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// score_test = clf->score(Xtestt, ytestt);
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// } else {
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// auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
|
||||
// auto [Xtest, ytest] = extract_indices(test, Xd, y);
|
||||
// clf->fit(Xtrain, ytrain, features, className, states);
|
||||
// score_train = clf->score(Xtrain, ytrain);
|
||||
// score_test = clf->score(Xtest, ytest);
|
||||
// }
|
||||
// if (dump_cpt) {
|
||||
// cout << "--- CPT Tables ---" << endl;
|
||||
// clf->dump_cpt();
|
||||
// }
|
||||
// total_score_train += score_train;
|
||||
// total_score += score_test;
|
||||
// cout << "Score Train: " << score_train << endl;
|
||||
// cout << "Score Test : " << score_test << endl;
|
||||
// cout << "-------------------------------------------------------------------------------" << endl;
|
||||
// }
|
||||
// cout << "**********************************************************************************" << endl;
|
||||
// cout << "Average Score Train: " << total_score_train / nFolds << endl;
|
||||
// cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
|
||||
}
|
||||
@ -5,6 +5,7 @@
|
||||
#include <vector>
|
||||
namespace bayesnet {
|
||||
using namespace std;
|
||||
enum status_t { NORMAL, WARNING, ERROR };
|
||||
class BaseClassifier {
|
||||
protected:
|
||||
virtual void trainModel(const torch::Tensor& weights) = 0;
|
||||
@ -18,6 +19,7 @@ namespace bayesnet {
|
||||
virtual ~BaseClassifier() = default;
|
||||
torch::Tensor virtual predict(torch::Tensor& X) = 0;
|
||||
vector<int> virtual predict(vector<vector<int>>& X) = 0;
|
||||
status_t virtual getStatus() const = 0;
|
||||
float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
|
||||
float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
|
||||
int virtual getNumberOfNodes()const = 0;
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
#include "BoostAODE.h"
|
||||
#include <set>
|
||||
#include "BayesMetrics.h"
|
||||
#include "Colors.h"
|
||||
|
||||
namespace bayesnet {
|
||||
BoostAODE::BoostAODE() : Ensemble() {}
|
||||
@ -64,22 +65,26 @@ namespace bayesnet {
|
||||
auto ypred = model->predict(X_);
|
||||
// Step 3.1: Compute the classifier amout of say
|
||||
auto mask_wrong = ypred != y_;
|
||||
auto mask_right = ypred == y_;
|
||||
auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
|
||||
double wrongWeights = masked_weights.sum().item<double>();
|
||||
double significance = wrongWeights == 0 ? 1 : 0.5 * log((1 - wrongWeights) / wrongWeights);
|
||||
double epsilon_t = masked_weights.sum().item<double>();
|
||||
double wt = (1 - epsilon_t) / epsilon_t;
|
||||
double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
|
||||
// Step 3.2: Update weights for next classifier
|
||||
// Step 3.2.1: Update weights of wrong samples
|
||||
weights_ += mask_wrong.to(weights_.dtype()) * exp(significance) * weights_;
|
||||
weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
|
||||
// Step 3.2.2: Update weights of right samples
|
||||
weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
|
||||
// Step 3.3: Normalise the weights
|
||||
double totalWeights = torch::sum(weights_).item<double>();
|
||||
weights_ = weights_ / totalWeights;
|
||||
// Step 3.4: Store classifier and its accuracy to weigh its future vote
|
||||
models.push_back(std::move(model));
|
||||
significanceModels.push_back(significance);
|
||||
exitCondition = n_models == maxModels && repeatSparent;
|
||||
significanceModels.push_back(alpha_t);
|
||||
exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5;
|
||||
}
|
||||
if (featuresUsed.size() != features.size()) {
|
||||
cout << "Warning: BoostAODE did not use all the features" << endl;
|
||||
status = WARNING;
|
||||
}
|
||||
weights.copy_(weights_);
|
||||
}
|
||||
|
||||
@ -21,6 +21,7 @@ namespace bayesnet {
|
||||
string className;
|
||||
map<string, vector<int>> states;
|
||||
Tensor dataset; // (n+1)xm tensor
|
||||
status_t status = NORMAL;
|
||||
void checkFitParameters();
|
||||
virtual void buildModel(const torch::Tensor& weights) = 0;
|
||||
void trainModel(const torch::Tensor& weights) override;
|
||||
@ -37,6 +38,7 @@ namespace bayesnet {
|
||||
int getNumberOfEdges() const override;
|
||||
int getNumberOfStates() const override;
|
||||
Tensor predict(Tensor& X) override;
|
||||
status_t getStatus() const override { return status; }
|
||||
vector<int> predict(vector<vector<int>>& X) override;
|
||||
float score(Tensor& X, Tensor& y) override;
|
||||
float score(vector<vector<int>>& X, vector<int>& y) override;
|
||||
|
||||
@ -111,6 +111,26 @@ namespace platform {
|
||||
}
|
||||
}
|
||||
|
||||
string getColor(bayesnet::status_t status)
|
||||
{
|
||||
switch (status) {
|
||||
case bayesnet::NORMAL:
|
||||
return Colors::GREEN();
|
||||
case bayesnet::WARNING:
|
||||
return Colors::YELLOW();
|
||||
case bayesnet::ERROR:
|
||||
return Colors::RED();
|
||||
default:
|
||||
return Colors::RESET();
|
||||
}
|
||||
}
|
||||
|
||||
void showProgress(int fold, const string& color, const string& phase)
|
||||
{
|
||||
string prefix = phase == "a" ? "" : "\b\b\b\b";
|
||||
cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
|
||||
|
||||
}
|
||||
void Experiment::cross_validation(const string& path, const string& fileName)
|
||||
{
|
||||
auto datasets = platform::Datasets(path, discretized, platform::ARFF);
|
||||
@ -159,23 +179,24 @@ namespace platform {
|
||||
auto y_train = y.index({ train_t });
|
||||
auto X_test = X.index({ "...", test_t });
|
||||
auto y_test = y.index({ test_t });
|
||||
cout << nfold + 1 << "(a)" << flush;
|
||||
showProgress(nfold + 1, getColor(clf->getStatus()), "a");
|
||||
// Train model
|
||||
clf->fit(X_train, y_train, features, className, states);
|
||||
cout << "\b\bb)" << flush;
|
||||
showProgress(nfold + 1, getColor(clf->getStatus()), "b");
|
||||
nodes[item] = clf->getNumberOfNodes();
|
||||
edges[item] = clf->getNumberOfEdges();
|
||||
num_states[item] = clf->getNumberOfStates();
|
||||
train_time[item] = train_timer.getDuration();
|
||||
// Score train
|
||||
auto accuracy_train_value = clf->score(X_train, y_train);
|
||||
cout << "\b\bc)" << flush;
|
||||
// Test model
|
||||
showProgress(nfold + 1, getColor(clf->getStatus()), "c");
|
||||
test_timer.start();
|
||||
auto accuracy_test_value = clf->score(X_test, y_test);
|
||||
cout << "\b\b\b, " << flush;
|
||||
test_time[item] = test_timer.getDuration();
|
||||
accuracy_train[item] = accuracy_train_value;
|
||||
accuracy_test[item] = accuracy_test_value;
|
||||
cout << "\b\b\b, " << flush;
|
||||
// Store results and times in vector
|
||||
result.addScoreTrain(accuracy_train_value);
|
||||
result.addScoreTest(accuracy_test_value);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user