Joel Therrien
a5fe856857
Changed the covariates to be more clever with how they produce the different splits. In the future (not yet implemented) a clever GroupDifferentiator could update the current score calculation based just on how many rows moved from one hand to the other. There were a few other changes as well; TreeTrainer#growTree now accepts a Random as a parameter which is used throughout the entire growing process. This means it's now theoretically possible to grow trees using a seed, so that results can be fully reproducible.
103 lines
3.8 KiB
Java
103 lines
3.8 KiB
Java
package ca.joeltherrien.randomforest.csv;
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import ca.joeltherrien.randomforest.DataLoader;
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import ca.joeltherrien.randomforest.Row;
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import ca.joeltherrien.randomforest.Settings;
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import ca.joeltherrien.randomforest.covariates.settings.BooleanCovariateSettings;
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import ca.joeltherrien.randomforest.covariates.Covariate;
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import ca.joeltherrien.randomforest.covariates.settings.FactorCovariateSettings;
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import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
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import ca.joeltherrien.randomforest.utils.Utils;
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import com.fasterxml.jackson.databind.node.JsonNodeFactory;
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import com.fasterxml.jackson.databind.node.ObjectNode;
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import com.fasterxml.jackson.databind.node.TextNode;
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import org.junit.jupiter.api.Test;
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import java.io.IOException;
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import java.util.List;
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import static org.junit.jupiter.api.Assertions.assertEquals;
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import static org.junit.jupiter.api.Assertions.assertTrue;
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public class TestLoadingCSV {
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/*
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y,x1,x2,x3
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5,3.0,"mouse",true
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2,1.0,"dog",false
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9,1.5,"cat",true
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-3,NA,NA,NA
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*/
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public List<Row<Double>> loadData(String filename) throws IOException {
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final ObjectNode yVarSettings = new ObjectNode(JsonNodeFactory.instance);
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yVarSettings.set("type", new TextNode("Double"));
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yVarSettings.set("name", new TextNode("y"));
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final Settings settings = Settings.builder()
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.trainingDataLocation(filename)
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.covariateSettings(
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Utils.easyList(new NumericCovariateSettings("x1"),
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new FactorCovariateSettings("x2", Utils.easyList("dog", "cat", "mouse")),
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new BooleanCovariateSettings("x3"))
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)
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.yVarSettings(yVarSettings)
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.build();
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final List<Covariate> covariates = settings.getCovariates();
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final DataLoader.ResponseLoader loader = settings.getResponseLoader();
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return DataLoader.loadData(covariates, loader, settings.getTrainingDataLocation());
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}
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@Test
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public void verifyLoadingNormal(final List<Covariate> covariates) throws IOException {
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final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv");
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assertData(data, covariates);
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}
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@Test
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public void verifyLoadingGz(final List<Covariate> covariates) throws IOException {
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final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv.gz");
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assertData(data, covariates);
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}
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private void assertData(final List<Row<Double>> data, final List<Covariate> covariates){
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final Covariate x1 = covariates.get(0);
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final Covariate x2 = covariates.get(0);
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final Covariate x3 = covariates.get(0);
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assertEquals(4, data.size());
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Row<Double> row = data.get(0);
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assertEquals(5.0, (double)row.getResponse());
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assertEquals(3.0, row.getCovariateValue(x1).getValue());
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assertEquals("mouse", row.getCovariateValue(x2).getValue());
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assertEquals(true, row.getCovariateValue(x3).getValue());
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row = data.get(1);
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assertEquals(2.0, (double)row.getResponse());
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assertEquals(1.0, row.getCovariateValue(x1).getValue());
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assertEquals("dog", row.getCovariateValue(x2).getValue());
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assertEquals(false, row.getCovariateValue(x3).getValue());
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row = data.get(2);
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assertEquals(9.0, (double)row.getResponse());
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assertEquals(1.5, row.getCovariateValue(x1).getValue());
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assertEquals("cat", row.getCovariateValue(x2).getValue());
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assertEquals(true, row.getCovariateValue(x3).getValue());
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row = data.get(3);
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assertEquals(-3.0, (double)row.getResponse());
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assertTrue(row.getCovariateValue(x1).isNA());
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assertTrue(row.getCovariateValue(x2).isNA());
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assertTrue(row.getCovariateValue(x3).isNA());
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}
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}
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