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156 lines (128 loc) · 4.87 KB
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import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.fail;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.HashSet;
import java.util.Set;
import org.json.JSONObject;
import org.junit.Test;
public class MMLJuliaTest {
@Test
public void testJulia1() throws Exception {
String string_json = Files.readString(Paths.get("./mml_test/LanguageTest/mml_LanguageTest1.json")); // no error
ReadJsonParameters parameters = new ReadJsonParameters(string_json);
parameters.read();
String filename = parameters.getFilemame();
String target_variable = parameters.getTargetVariable();
Set<Float> set_train_sizes = parameters.getTrainSizes();
Set<String> set_metrics = parameters.getMetrics();
Set<Integer> set_max_depth_values = parameters.getMaxDepthValues();
int repetition = parameters.getRepetition();
// set parameters to ConfigurationML
ConfigurationML configuration = new ConfigurationML();
configuration.setFilePath(filename);
configuration.setTarget(target_variable);
configuration.setMetrics(set_metrics);
for (float train_size : set_train_sizes) {
// Split dataset in train/test with traintestsplit script python
MLExecutor split = null;
configuration.setTrainSize(train_size);
split = new TrainTestSplit(configuration);
split.generateCode();
split.run();
for (int max_depth : set_max_depth_values) {
configuration.setMaxDepth(max_depth);
for(int i=0; i<repetition; i++){
MLExecutor ex = new JuliaMLExecutor(configuration);
ex.generateCode();
MLResult result = ex.run();
try {
assertTrue(result.getStringResult().contains("accuracy"));
}
catch (AssertionError e) {
fail("not the good scoring");
}
}
}
}
}
@Test
public void testJulia2() throws Exception {
String string_json = Files.readString(Paths.get("./mml_test/LanguageTest/mml_LanguageTest2.json")); // colonne nom non valide
ReadJsonParameters parameters = new ReadJsonParameters(string_json);
parameters.read();
String filename = parameters.getFilemame();
String target_variable = parameters.getTargetVariable();
Set<Float> set_train_sizes = parameters.getTrainSizes();
Set<String> set_metrics = parameters.getMetrics();
Set<Integer> set_max_depth_values = parameters.getMaxDepthValues();
int repetition = parameters.getRepetition();
// set parameters to ConfigurationML
ConfigurationML configuration = new ConfigurationML();
configuration.setFilePath(filename);
configuration.setTarget(target_variable);
configuration.setMetrics(set_metrics);
for (float train_size : set_train_sizes) {
// Split dataset in train/test with traintestsplit script python
MLExecutor split = null;
configuration.setTrainSize(train_size);
split = new TrainTestSplit(configuration);
split.generateCode();
split.run();
for (int max_depth : set_max_depth_values) {
configuration.setMaxDepth(max_depth);
for(int i=0; i<repetition; i++){
MLExecutor ex = new JuliaMLExecutor(configuration);
ex.generateCode();
MLResult result = ex.run();
if (result.getStringResult().contains("not found in the data frame")) {
assertTrue(true);
}
else {
fail("Il n'y a pas d'erreur renvoyée par Python");
}
}
}
}
}
@Test
public void testJulia3() throws Exception {
String string_json = Files.readString(Paths.get("./mml_test/LanguageTest/mml_LanguageTest3.json")); // no error & test autre dataset
ReadJsonParameters parameters = new ReadJsonParameters(string_json);
parameters.read();
String filename = parameters.getFilemame();
String target_variable = parameters.getTargetVariable();
Set<Float> set_train_sizes = parameters.getTrainSizes();
Set<String> set_metrics = parameters.getMetrics();
Set<Integer> set_max_depth_values = parameters.getMaxDepthValues();
int repetition = parameters.getRepetition();
// set parameters to ConfigurationML
ConfigurationML configuration = new ConfigurationML();
configuration.setFilePath(filename);
configuration.setTarget(target_variable);
configuration.setMetrics(set_metrics);
for (float train_size : set_train_sizes) {
// Split dataset in train/test with traintestsplit script python
MLExecutor split = null;
configuration.setTrainSize(train_size);
split = new TrainTestSplit(configuration);
split.generateCode();
split.run();
for (int max_depth : set_max_depth_values) {
configuration.setMaxDepth(max_depth);
for(int i=0; i<repetition; i++){
MLExecutor ex = new JuliaMLExecutor(configuration);
ex.generateCode();
MLResult result = ex.run();
try {
assertTrue(result.getStringResult().contains("accuracy"));
}
catch (AssertionError e) {
fail("not the good scoring");
}
}
}
}
}
}