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// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.vespa.model.ml;
import com.yahoo.config.application.api.ApplicationPackage;
import com.yahoo.io.IOUtils;
import com.yahoo.path.Path;
import com.yahoo.vespa.config.search.RankProfilesConfig;
import com.yahoo.vespa.model.VespaModel;
import org.junit.Test;
import java.io.IOException;
import static org.junit.Assert.assertEquals;
/**
* Tests rank profile imported model evaluation
*
* @author bratseth
*/
public class MlModelsTest {
@Test
public void testMl_serving() throws IOException {
Path appDir = Path.fromString("src/test/cfg/application/ml_models");
Path storedAppDir = appDir.append("copy");
try {
ImportedModelTester tester = new ImportedModelTester("ml_models", appDir);
verify(tester.createVespaModel());
// At this point the expression is stored - copy application to another location which do not have a models dir
storedAppDir.toFile().mkdirs();
IOUtils.copy(appDir.append("services.xml").toString(), storedAppDir.append("services.xml").toString());
IOUtils.copyDirectory(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(),
storedAppDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
IOUtils.copyDirectory(appDir.append(ApplicationPackage.SEARCH_DEFINITIONS_DIR).toFile(),
storedAppDir.append(ApplicationPackage.SEARCH_DEFINITIONS_DIR).toFile());
ImportedModelTester storedTester = new ImportedModelTester("ml_models", storedAppDir);
verify(storedTester.createVespaModel());
}
finally {
IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
IOUtils.recursiveDeleteDir(storedAppDir.toFile());
}
}
private void verify(VespaModel model) {
assertEquals("Global models are created (although not used directly here",
4, model.rankProfileList().getRankProfiles().size());
RankProfilesConfig.Builder builder = new RankProfilesConfig.Builder();
model.getSearchClusters().get(0).getConfig(builder);
RankProfilesConfig config = new RankProfilesConfig(builder);
assertEquals(3, config.rankprofile().size());
assertEquals("test", config.rankprofile(2).name());
RankProfilesConfig.Rankprofile.Fef test = config.rankprofile(2).fef();
// Compare string content in a denser for that config:
StringBuilder b = new StringBuilder();
for (RankProfilesConfig.Rankprofile.Fef.Property p : test.property())
b.append(p.name()).append(": ").append(p.value()).append("\n");
assertEquals(testProfile, b.toString());
}
private final String testProfile =
"rankingExpression(input).rankingScript: attribute(argument)\n" +
"rankingExpression(input).type: tensor(d0[],d1[784])\n" +
"rankingExpression(Placeholder).rankingScript: attribute(argument)\n" +
"rankingExpression(Placeholder).type: tensor(d0[],d1[784])\n" +
"rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(rankingExpression(input), (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))\n" +
"rankingExpression(mnist_tensorflow).rankingScript: join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.050701 * if (a >= 0, a, 1.673263 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))\n" +
"rankingExpression(mnist_softmax_tensorflow).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))\n" +
"rankingExpression(mnist_softmax_onnx).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))\n" +
"rankingExpression(my_xgboost).rankingScript: if (f29 < -0.1234567, if (f56 < -0.242398, 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (f60 < -0.482947, if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)\n" +
"vespa.rank.firstphase: rankingExpression(firstphase)\n" +
"rankingExpression(firstphase).rankingScript: rankingExpression(mnist_tensorflow) + rankingExpression(mnist_softmax_tensorflow) + rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost)\n" +
"vespa.type.attribute.argument: tensor(d0[],d1[784])\n";
}
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