diff options
Diffstat (limited to 'config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java')
-rw-r--r-- | config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java | 78 |
1 files changed, 78 insertions, 0 deletions
diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java new file mode 100644 index 00000000000..9ed82b9eef5 --- /dev/null +++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java @@ -0,0 +1,78 @@ +// 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"; + +} |