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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/vespalib/testkit/test_kit.h>
#include <vespa/searchlib/attribute/attributefactory.h>
#include <vespa/searchlib/attribute/attributevector.h>
#include <vespa/searchlib/attribute/integerbase.h>
#include <vespa/searchlib/attribute/stringbase.h>
#include <vespa/searchlib/features/setup.h>
#include <vespa/searchlib/features/tensor_from_weighted_set_feature.h>
#include <vespa/searchlib/fef/fef.h>
#include <vespa/searchlib/fef/test/ftlib.h>
#include <vespa/searchlib/fef/test/indexenvironment.h>
#include <vespa/searchcommon/attribute/config.h>
#include <vespa/eval/eval/function.h>
#include <vespa/eval/eval/simple_value.h>
#include <vespa/eval/eval/tensor_spec.h>
#include <vespa/eval/eval/value.h>
#include <vespa/eval/eval/test/value_compare.h>
using search::feature_t;
using namespace search::fef;
using namespace search::fef::test;
using namespace search::features;
using search::AttributeFactory;
using search::IntegerAttribute;
using search::StringAttribute;
using vespalib::eval::Value;
using vespalib::eval::Function;
using vespalib::eval::TensorSpec;
using vespalib::eval::SimpleValue;
using AVC = search::attribute::Config;
using AVBT = search::attribute::BasicType;
using AVCT = search::attribute::CollectionType;
using AttributePtr = search::AttributeVector::SP;
using FTA = FtTestApp;
Value::UP make_tensor(const TensorSpec &spec) {
return SimpleValue::from_spec(spec);
}
Value::UP make_empty(const vespalib::string &type) {
return make_tensor(TensorSpec(type));
}
struct SetupFixture
{
TensorFromWeightedSetBlueprint blueprint;
IndexEnvironment indexEnv;
SetupFixture()
: blueprint(),
indexEnv()
{
}
};
TEST_F("require that blueprint can be created from factory", SetupFixture)
{
EXPECT_TRUE(FTA::assertCreateInstance(f.blueprint, "tensorFromWeightedSet"));
}
TEST_F("require that setup fails if source spec is invalid", SetupFixture)
{
FTA::FT_SETUP_FAIL(f.blueprint, f.indexEnv, StringList().add("source(foo)"));
}
TEST_F("require that setup succeeds with attribute source", SetupFixture)
{
FTA::FT_SETUP_OK(f.blueprint, f.indexEnv, StringList().add("attribute(foo)"),
StringList(), StringList().add("tensor"));
}
TEST_F("require that setup succeeds with query source", SetupFixture)
{
FTA::FT_SETUP_OK(f.blueprint, f.indexEnv, StringList().add("query(foo)"),
StringList(), StringList().add("tensor"));
}
struct ExecFixture
{
BlueprintFactory factory;
FtFeatureTest test;
ExecFixture(const vespalib::string &feature)
: factory(),
test(factory, feature)
{
setup_search_features(factory);
setupAttributeVectors();
setupQueryEnvironment();
ASSERT_TRUE(test.setup());
}
void setupAttributeVectors() {
std::vector<AttributePtr> attrs;
attrs.push_back(AttributeFactory::createAttribute("wsstr", AVC(AVBT::STRING, AVCT::WSET)));
attrs.push_back(AttributeFactory::createAttribute("wsint", AVC(AVBT::INT32, AVCT::WSET)));
attrs.push_back(AttributeFactory::createAttribute("astr", AVC(AVBT::STRING, AVCT::ARRAY)));
for (const auto &attr : attrs) {
attr->addReservedDoc();
attr->addDocs(1);
test.getIndexEnv().getAttributeMap().add(attr);
}
StringAttribute *wsstr = static_cast<StringAttribute *>(attrs[0].get());
wsstr->append(1, "a", 3);
wsstr->append(1, "b", 5);
wsstr->append(1, "c", 7);
IntegerAttribute *wsint = static_cast<IntegerAttribute *>(attrs[1].get());
wsint->append(1, 11, 3);
wsint->append(1, 13, 5);
wsint->append(1, 17, 7);
for (const auto &attr : attrs) {
attr->commit();
}
}
void setupQueryEnvironment() {
test.getQueryEnv().getProperties().add("wsquery", "{d:11,e:13,f:17}");
}
const Value &extractTensor(uint32_t docid) {
return test.resolveObjectFeature(docid);
}
const Value &execute() {
return extractTensor(1);
}
};
TEST_F("require that weighted set string attribute can be converted to tensor (default dimension)",
ExecFixture("tensorFromWeightedSet(attribute(wsstr))"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(wsstr{})")
.add({{"wsstr","b"}}, 5)
.add({{"wsstr","c"}}, 7)
.add({{"wsstr","a"}}, 3)), f.execute());
}
TEST_F("require that weighted set string attribute can be converted to tensor (explicit dimension)",
ExecFixture("tensorFromWeightedSet(attribute(wsstr),dim)"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(dim{})")
.add({{"dim","a"}}, 3)
.add({{"dim","b"}}, 5)
.add({{"dim","c"}}, 7)), f.execute());
}
TEST_F("require that weighted set integer attribute can be converted to tensor (default dimension)",
ExecFixture("tensorFromWeightedSet(attribute(wsint))"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(wsint{})")
.add({{"wsint","13"}}, 5)
.add({{"wsint","17"}}, 7)
.add({{"wsint","11"}}, 3)), f.execute());
}
TEST_F("require that weighted set integer attribute can be converted to tensor (explicit dimension)",
ExecFixture("tensorFromWeightedSet(attribute(wsint),dim)"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(dim{})")
.add({{"dim","17"}}, 7)
.add({{"dim","11"}}, 3)
.add({{"dim","13"}}, 5)), f.execute());
}
TEST_F("require that weighted set from query can be converted to tensor (default dimension)",
ExecFixture("tensorFromWeightedSet(query(wsquery))"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(wsquery{})")
.add({{"wsquery","f"}}, 17)
.add({{"wsquery","d"}}, 11)
.add({{"wsquery","e"}}, 13)), f.execute());
}
TEST_F("require that weighted set from query can be converted to tensor (explicit dimension)",
ExecFixture("tensorFromWeightedSet(query(wsquery),dim)"))
{
EXPECT_EQUAL(*make_tensor(TensorSpec("tensor(dim{})")
.add({{"dim","d"}}, 11)
.add({{"dim","e"}}, 13)
.add({{"dim","f"}}, 17)), f.execute());
}
TEST_F("require that empty tensor is created if attribute does not exists",
ExecFixture("tensorFromWeightedSet(attribute(null))"))
{
EXPECT_EQUAL(*make_empty("tensor(null{})"), f.execute());
}
TEST_F("require that empty tensor is created if attribute type is not supported",
ExecFixture("tensorFromWeightedSet(attribute(astr))"))
{
EXPECT_EQUAL(*make_empty("tensor(astr{})"), f.execute());
}
TEST_F("require that empty tensor is created if query parameter is not found",
ExecFixture("tensorFromWeightedSet(query(null))"))
{
EXPECT_EQUAL(*make_empty("tensor(null{})"), f.execute());
}
TEST_MAIN() { TEST_RUN_ALL(); }
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