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// Copyright 2018 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "components/assist_ranker/classifier_predictor.h"
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "base/feature_list.h"
#include "base/functional/bind.h"
#include "base/functional/callback_helpers.h"
#include "base/metrics/field_trial_params.h"
#include "base/test/scoped_feature_list.h"
#include "components/assist_ranker/example_preprocessing.h"
#include "components/assist_ranker/fake_ranker_model_loader.h"
#include "components/assist_ranker/nn_classifier_test_util.h"
#include "components/assist_ranker/proto/ranker_model.pb.h"
#include "components/assist_ranker/ranker_model.h"
#include "testing/gmock/include/gmock/gmock-matchers.h"
#include "testing/gtest/include/gtest/gtest.h"
namespace assist_ranker {
using ::assist_ranker::testing::FakeRankerModelLoader;
using ::testing::FloatEq;
// Preprocessor feature names.
const char kFeatureName0[] = "feature_0";
const char kFeatureName1[] = "feature_1";
const char kFeatureExtra[] = "feature_extra";
class ClassifierPredictorTest : public ::testing::Test {
public:
void SetUp() override;
std::unique_ptr<ClassifierPredictor> InitPredictor(
std::unique_ptr<RankerModel> ranker_model,
const PredictorConfig& config);
PredictorConfig GetConfig();
protected:
base::test::ScopedFeatureList scoped_feature_list_;
};
void ClassifierPredictorTest::SetUp() {
::testing::Test::SetUp();
scoped_feature_list_.Init();
}
std::unique_ptr<ClassifierPredictor> ClassifierPredictorTest::InitPredictor(
std::unique_ptr<RankerModel> ranker_model,
const PredictorConfig& config) {
std::unique_ptr<ClassifierPredictor> predictor(
new ClassifierPredictor(config));
auto fake_model_loader = std::make_unique<FakeRankerModelLoader>(
base::BindRepeating(&ClassifierPredictor::ValidateModel),
base::BindRepeating(&ClassifierPredictor::OnModelAvailable,
base::Unretained(predictor.get())),
std::move(ranker_model));
predictor->LoadModel(std::move(fake_model_loader));
return predictor;
}
BASE_FEATURE(kTestRankerQuery,
"TestRankerQuery",
base::FEATURE_ENABLED_BY_DEFAULT);
const base::FeatureParam<std::string> kTestRankerUrl{
&kTestRankerQuery, "url-param-name", "https://default.model.url"};
PredictorConfig ClassifierPredictorTest::GetConfig() {
return PredictorConfig("model_name", "logging_name", "uma_prefix", LOG_NONE,
GetEmptyAllowlist(), &kTestRankerQuery,
&kTestRankerUrl, 0);
}
TEST_F(ClassifierPredictorTest, EmptyRankerModel) {
auto ranker_model = std::make_unique<RankerModel>();
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_FALSE(predictor->IsReady());
RankerExample ranker_example;
auto& features = *ranker_example.mutable_features();
features[kFeatureName0].set_bool_value(true);
std::vector<float> response;
EXPECT_FALSE(predictor->Predict(ranker_example, &response));
}
TEST_F(ClassifierPredictorTest, NoInferenceModuleForModel) {
auto ranker_model = std::make_unique<RankerModel>();
// TranslateRankerModel does not have an inference module. Validation will
// fail.
ranker_model->mutable_proto()
->mutable_translate()
->mutable_translate_logistic_regression_model()
->set_bias(1);
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_FALSE(predictor->IsReady());
RankerExample ranker_example;
auto& features = *ranker_example.mutable_features();
features[kFeatureName0].set_bool_value(true);
std::vector<float> response;
EXPECT_FALSE(predictor->Predict(ranker_example, &response));
EXPECT_FALSE(predictor->Predict({{0, 0}}, &response));
}
TEST_F(ClassifierPredictorTest, PredictFeatureVector) {
auto ranker_model = std::make_unique<RankerModel>();
*ranker_model->mutable_proto()->mutable_nn_classifier() =
nn_classifier::CreateXorClassifierModel();
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_TRUE(predictor->IsReady());
std::vector<float> response;
// True responses.
EXPECT_TRUE(predictor->Predict({{0.0, 1}}, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(2.8271765));
EXPECT_TRUE(predictor->Predict({{1, 0}}, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(2.6790769));
// False responses.
EXPECT_TRUE(predictor->Predict({{0, 0}}, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(-2.7154054));
EXPECT_TRUE(predictor->Predict({{1, 1}}, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(-3.1652793));
}
TEST_F(ClassifierPredictorTest, PredictRankerExampleNoPreprocessor) {
auto ranker_model = std::make_unique<RankerModel>();
*ranker_model->mutable_proto()->mutable_nn_classifier() =
nn_classifier::CreateXorClassifierModel();
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_TRUE(predictor->IsReady());
// Prediction of RankerExample without preprocessor config should fail.
std::vector<float> response;
RankerExample example;
EXPECT_FALSE(predictor->Predict(RankerExample(), &response));
}
TEST_F(ClassifierPredictorTest, PredictRankerExampleWithPreprocessor) {
auto ranker_model = std::make_unique<RankerModel>();
auto& model = *ranker_model->mutable_proto()->mutable_nn_classifier();
model = nn_classifier::CreateXorClassifierModel();
// Set up the preprocessor config with two features at feature vector
// indices 0 and 1.
auto& indices =
*model.mutable_preprocessor_config()->mutable_feature_indices();
indices[kFeatureName0] = 0;
indices[kFeatureName1] = 1;
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_TRUE(predictor->IsReady());
// Prediction of RankerExample with preprocessor config should work.
RankerExample example;
auto& feature_map = *example.mutable_features();
std::vector<float> response;
// True responses.
feature_map[kFeatureName0].set_float_value(0);
feature_map[kFeatureName1].set_float_value(1);
EXPECT_TRUE(predictor->Predict(example, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(2.8271765));
feature_map[kFeatureName0].set_float_value(1);
feature_map[kFeatureName1].set_float_value(0);
EXPECT_TRUE(predictor->Predict(example, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(2.6790769));
// False responses.
feature_map[kFeatureName0].set_float_value(0);
feature_map[kFeatureName1].set_float_value(0);
EXPECT_TRUE(predictor->Predict(example, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(-2.7154054));
feature_map[kFeatureName0].set_float_value(1);
feature_map[kFeatureName1].set_float_value(1);
EXPECT_TRUE(predictor->Predict(example, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(-3.1652793));
// Check that extra features do not cause an error.
feature_map[kFeatureName0].set_float_value(0);
feature_map[kFeatureName1].set_float_value(1);
feature_map[kFeatureExtra].set_float_value(1);
EXPECT_TRUE(predictor->Predict(example, &response));
EXPECT_EQ(response.size(), 1u);
EXPECT_THAT(response[0], FloatEq(2.8271765));
}
TEST_F(ClassifierPredictorTest, PredictRankerExamplePreprocessorError) {
auto ranker_model = std::make_unique<RankerModel>();
auto& model = *ranker_model->mutable_proto()->mutable_nn_classifier();
model = nn_classifier::CreateXorClassifierModel();
// Set up the preprocessor config with two features at feature vector
// indices 0 and 1.
auto& config = *model.mutable_preprocessor_config();
auto& indices = *config.mutable_feature_indices();
indices[kFeatureName0] = 0;
indices[kFeatureName1] = 1;
// Zero normalizer will generate a preprocessing error.
(*config.mutable_normalizers())[kFeatureName0] = 0;
auto predictor = InitPredictor(std::move(ranker_model), GetConfig());
EXPECT_TRUE(predictor->IsReady());
// Prediction of RankerExample should fail due to preprocessing error.
RankerExample example;
auto& feature_map = *example.mutable_features();
std::vector<float> response;
feature_map[kFeatureName0].set_float_value(0);
feature_map[kFeatureName1].set_float_value(1);
EXPECT_FALSE(predictor->Predict(example, &response));
}
} // namespace assist_ranker
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