Examples with RankingExpression com.yahoo.searchlib.rankingexpression.RankingExpression used on opensource projects

Example 31 with RankingExpression

use of com.yahoo.searchlib.rankingexpression.RankingExpression in project vespa by vespa-engine.

the class EvaluationTestCase method testProgrammaticBuildingAndPrecedence.

@Test
public void testProgrammaticBuildingAndPrecedence() {
    RankingExpression standardPrecedence = new RankingExpression(new ArithmeticNode(constant(2), ArithmeticOperator.PLUS, new ArithmeticNode(constant(3), ArithmeticOperator.MULTIPLY, constant(4))));
    RankingExpression oppositePrecedence = new RankingExpression(new ArithmeticNode(new ArithmeticNode(constant(2), ArithmeticOperator.PLUS, constant(3)), ArithmeticOperator.MULTIPLY, constant(4)));
    assertEquals(14.0, standardPrecedence.evaluate(null).asDouble(), tolerance);
    assertEquals(20.0, oppositePrecedence.evaluate(null).asDouble(), tolerance);
    assertEquals("2.0 + 3.0 * 4.0", standardPrecedence.toString());
    assertEquals("(2.0 + 3.0) * 4.0", oppositePrecedence.toString());
}

Example 32 with RankingExpression

use of com.yahoo.searchlib.rankingexpression.RankingExpression in project vespa by vespa-engine.

the class SimplifierTestCase method testSimplifyComplexExpression.

// A black box test verifying we are not screwing up real expressions
@Test
public void testSimplifyComplexExpression() throws ParseException {
    RankingExpression initial = new RankingExpression("sqrt(if (if (INFERRED * 0.9 < INFERRED, GMP, (1 + 1.1) * INFERRED) < INFERRED * INFERRED - INFERRED, if (GMP < 85.80799542793133 * GMP, INFERRED, if (GMP < GMP, tanh(INFERRED), log(76.89956221113943))), tanh(tanh(INFERRED))) * sqrt(sqrt(GMP + INFERRED)) * GMP ) + 13.5 * (1 - GMP) * pow(GMP * 0.1, 2 + 1.1 * 0)");
    TransformContext c = new TransformContext(Collections.emptyMap());
    RankingExpression simplified = new Simplifier().transform(initial, c);
    Context context = new MapContext();
    context.put("INFERRED", 0.5);
    context.put("GMP", 80.0);
    context.put("value", 50.0);
    assertEquals(initial.evaluate(context), simplified.evaluate(context));
    context.put("INFERRED", 38.0);
    context.put("GMP", 80.0);
    context.put("value", 50.0);
    assertEquals(initial.evaluate(context), simplified.evaluate(context));
    context.put("INFERRED", 38.0);
    context.put("GMP", 90.0);
    context.put("value", 100.0);
    assertEquals(initial.evaluate(context), simplified.evaluate(context));
    context.put("INFERRED", 500.0);
    context.put("GMP", 90.0);
    context.put("value", 100.0);
    assertEquals(initial.evaluate(context), simplified.evaluate(context));
}

Example 33 with RankingExpression

use of com.yahoo.searchlib.rankingexpression.RankingExpression in project vespa by vespa-engine.

the class GbdtConverterTestCase method assertConvert.

private static void assertConvert(String gbdtModelFile, String expectedExpression) throws ParseException, UnsupportedEncodingException {
    ByteArrayOutputStream out = new ByteArrayOutputStream();
    System.setOut(new PrintStream(out));
    GbdtConverter.main(new String[] { gbdtModelFile });
    String actualExpression = out.toString("UTF-8");
    assertEquals(expectedExpression, actualExpression);
    assertNotNull(new RankingExpression(actualExpression));
}

Example 34 with RankingExpression

use of com.yahoo.searchlib.rankingexpression.RankingExpression in project vespa by vespa-engine.

the class MnistSoftmaxImportTestCase method testMnistSoftmaxImport.

@Test
public void testMnistSoftmaxImport() {
    TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/files/integration/tensorflow/mnist_softmax/saved");
    // Check constants
    assertEquals(2, model.get().largeConstants().size());
    Tensor constant0 = model.get().largeConstants().get("test_Variable_read");
    assertNotNull(constant0);
    assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(), constant0.type());
    assertEquals(7840, constant0.size());
    Tensor constant1 = model.get().largeConstants().get("test_Variable_1_read");
    assertNotNull(constant1);
    assertEquals(new TensorType.Builder().indexed("d1", 10).build(), constant1.type());
    assertEquals(10, constant1.size());
    // Check (provided) macros
    assertEquals(0, model.get().macros().size());
    // Check required macros
    assertEquals(1, model.get().requiredMacros().size());
    assertTrue(model.get().requiredMacros().containsKey("Placeholder"));
    assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), model.get().requiredMacros().get("Placeholder"));
    // Check signatures
    assertEquals(1, model.get().signatures().size());
    TensorFlowModel.Signature signature = model.get().signatures().get("serving_default");
    assertNotNull(signature);
    // ... signature inputs
    assertEquals(1, signature.inputs().size());
    TensorType argument0 = signature.inputArgument("x");
    assertNotNull(argument0);
    assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0);
    // ... signature outputs
    assertEquals(1, signature.outputs().size());
    RankingExpression output = signature.outputExpression("y");
    assertNotNull(output);
    assertEquals("add", output.getName());
    assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable_read), f(a,b)(a * b)), sum, d2), constant(test_Variable_1_read), f(a,b)(a + b))", output.getRoot().toString());
    // Test execution
    model.assertEqualResult("Placeholder", "MatMul");
    model.assertEqualResult("Placeholder", "add");
}