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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.lightgbm;
import com.yahoo.searchlib.rankingexpression.RankingExpression;
import com.yahoo.searchlib.rankingexpression.evaluation.ArrayContext;
import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer;
import com.yahoo.searchlib.rankingexpression.evaluation.gbdtoptimization.GBDTForestNode;
import org.junit.Test;
import static org.junit.Assert.assertTrue;
/**
* @author lesters
*/
public class LightGBMImportEvaluationTestCase extends LightGBMTestBase {
@Test
public void testRegression() {
RankingExpression expression = importModel("src/test/models/lightgbm/regression.json");
ArrayContext context = new ArrayContext(expression, true, DoubleValue.NaN);
assertEvaluation(1.91300868, expression, features(context));
assertEvaluation(2.05469776, expression, features(context).add("numerical_1", 0.1).add("numerical_2", 0.2).add("categorical_1", "a").add("categorical_2", "i"));
assertEvaluation(2.0745534, expression, features(context).add("numerical_2", 0.5).add("categorical_1", "b").add("categorical_2", "j"));
assertEvaluation(2.3571838, expression, features(context).add("numerical_1", 0.7).add("numerical_2", 0.8).add("categorical_2", "m"));
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
assertTrue(expression.getRoot() instanceof GBDTForestNode);
assertEvaluation(1.91300868, expression, features(context));
assertEvaluation(2.05469776, expression, features(context).add("numerical_1", 0.1).add("numerical_2", 0.2).add("categorical_1", "a").add("categorical_2", "i"));
assertEvaluation(2.0745534, expression, features(context).add("numerical_2", 0.5).add("categorical_1", "b").add("categorical_2", "j"));
assertEvaluation(2.3571838, expression, features(context).add("numerical_1", 0.7).add("numerical_2", 0.8).add("categorical_2", "m"));
}
@Test
public void testClassification() {
RankingExpression expression = importModel("src/test/models/lightgbm/classification.json");
ArrayContext context = new ArrayContext(expression, DoubleValue.NaN);
assertEvaluation(0.37464997, expression, features(context));
assertEvaluation(0.37464997, expression, features(context).add("numerical_1", 0.1).add("numerical_2", 0.2).add("categorical_1", "a").add("categorical_2", "i"));
assertEvaluation(0.38730827, expression, features(context).add("numerical_2", 0.5).add("categorical_1", "b").add("categorical_2", "j"));
assertEvaluation(0.5647872, expression, features(context).add("numerical_1", 0.7).add("numerical_2", 0.8).add("categorical_2", "m"));
}
}
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