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cp-offline.cpp
195 lines (170 loc) · 5.61 KB
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cp-offline.cpp
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#include "cp.h"
#include <iostream>
#include <fstream>
#include <iomanip>
#include <cmath>
#include <vector>
void ExitWithHelp();
void ParseCommandLine(int argc, char *argv[], char *train_file_name, char *test_file_name, char *output_file_name, char *model_file_name);
struct Parameter param;
int main(int argc, char *argv[]) {
char train_file_name[256];
char test_file_name[256];
char output_file_name[256];
char model_file_name[256];
struct Problem *train, *test;
struct Model *model;
int num_correct = 0, num_empty = 0, num_multi = 0, num_incl = 0;
double avg_conf = 0, avg_cred = 0;
const char *error_message;
ParseCommandLine(argc, argv, train_file_name, test_file_name, output_file_name, model_file_name);
error_message = CheckParameter(¶m);
if (error_message != NULL) {
std::cerr << error_message << std::endl;
exit(EXIT_FAILURE);
}
train = ReadProblem(train_file_name);
test = ReadProblem(test_file_name);
std::ofstream output_file(output_file_name);
if (!output_file.is_open()) {
std::cerr << "Unable to open output file: " << output_file_name << std::endl;
exit(EXIT_FAILURE);
}
std::chrono::time_point<std::chrono::steady_clock> start_time = std::chrono::high_resolution_clock::now();
if (param.load_model == 1) {
model = LoadModel(model_file_name);
if (model == NULL) {
exit(EXIT_FAILURE);
}
} else {
model = TrainCP(train, ¶m);
}
if (param.save_model == 1) {
if (SaveModel(model_file_name, model) != 0) {
std::cerr << "Unable to save model file" << std::endl;
}
}
for (int i = 0; i < test->num_ex; ++i) {
double conf, cred;
std::vector<int> predict_label;
predict_label = PredictCP(train, model, test->x[i], conf, cred);
avg_conf += conf;
avg_cred += cred;
output_file << std::resetiosflags(std::ios::fixed) << test->y[i] << ' ' << predict_label[0] << ' '
<< std::setiosflags(std::ios::fixed) << conf << ' ' << cred;
if (predict_label[0] == test->y[i]) {
++num_correct;
}
if (predict_label.size() == 1) {
++num_empty;
output_file << " Empty\n";
} else {
output_file << " set:";
for (size_t j = 1; j < predict_label.size(); ++j) {
output_file << ' ' << predict_label[j];
if (predict_label[j] == test->y[i]) {
++num_incl;
}
}
if (predict_label.size() > 2) {
++num_multi;
output_file << " Multi\n";
} else {
output_file << " Single\n";
}
}
std::vector<int>().swap(predict_label);
}
avg_conf /= test->num_ex;
avg_cred /= test->num_ex;
std::chrono::time_point<std::chrono::steady_clock> end_time = std::chrono::high_resolution_clock::now();
std::cout << "Simple Accuracy: " << 100.0*num_correct/test->num_ex << '%'
<< " (" << num_correct << '/' << test->num_ex << ") "
<< "Mean Confidence: " << std::fixed << std::setprecision(4) << 100*avg_conf << "%, "
<< "Mean Credibility: " << 100*avg_cred << "%\n";
std::cout << "Accuracy: " << 100.0*num_incl/test->num_ex << '%'
<< " (" << num_incl << '/' << test->num_ex << ") "
<< "Multi Prediction: " << std::fixed << std::setprecision(4) << 100.0*num_multi/test->num_ex << "%, "
<< "Empty Prediction: " << 100.0*num_empty/test->num_ex << "%\n";
output_file.close();
std::cout << "Time cost: " << std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time).count()/1000.0 << " s\n";
FreeProblem(train);
FreeProblem(test);
FreeModel(model);
FreeParam(¶m);
return 0;
}
void ExitWithHelp() {
std::cout << "Usage: cp-offline [options] train_file test_file [output_file]\n"
<< "options:\n"
<< " -t non-conformity measure : set type of NCM (default 0)\n"
<< " 0 -- k-nearest neighbors (KNN)\n"
<< " -k num_neighbors : set number of neighbors in kNN (default 1)\n"
<< " -s model_file_name : save model\n"
<< " -l model_file_name : load model\n"
<< " -e epsilon : set significance level (default 0.05)\n";
exit(EXIT_FAILURE);
}
void ParseCommandLine(int argc, char **argv, char *train_file_name, char *test_file_name, char *output_file_name, char *model_file_name) {
int i;
param.measure_type = KNN;
param.save_model = 0;
param.load_model = 0;
param.epsilon = 0.05;
param.knn_param = new KNNParameter;
InitKNNParam(param.knn_param);
for (i = 1; i < argc; ++i) {
if (argv[i][0] != '-') break;
if ((i+2) >= argc)
ExitWithHelp();
switch (argv[i][1]) {
case 't': {
++i;
param.measure_type = std::atoi(argv[i]);
break;
}
case 'k': {
++i;
param.knn_param->num_neighbors = std::atoi(argv[i]);
break;
}
case 's': {
++i;
param.save_model = 1;
std::strcpy(model_file_name, argv[i]);
break;
}
case 'l': {
++i;
param.load_model = 1;
std::strcpy(model_file_name, argv[i]);
break;
}
case 'e': {
++i;
param.epsilon = std::atof(argv[i]);
break;
}
default: {
std::cerr << "Unknown option: -" << argv[i][1] << std::endl;
ExitWithHelp();
}
}
}
if ((i+1) >= argc)
ExitWithHelp();
std::strcpy(train_file_name, argv[i]);
std::strcpy(test_file_name, argv[i+1]);
if ((i+2) < argc) {
std::strcpy(output_file_name, argv[i+2]);
} else {
char *p = std::strrchr(argv[i+1],'/');
if (p == NULL) {
p = argv[i+1];
} else {
++p;
}
std::sprintf(output_file_name, "%s_output", p);
}
return;
}