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- // Copyright 2017 The Chromium Authors. All rights reserved.
- // Use of this source code is governed by a BSD-style license that can be
- // found in the LICENSE file.
- #include "components/assist_ranker/generic_logistic_regression_inference.h"
- #include "components/assist_ranker/example_preprocessing.h"
- #include "testing/gtest/include/gtest/gtest.h"
- #include "third_party/protobuf/src/google/protobuf/map.h"
- namespace assist_ranker {
- using ::google::protobuf::Map;
- class GenericLogisticRegressionInferenceTest : public testing::Test {
- protected:
- GenericLogisticRegressionModel GetProto() {
- GenericLogisticRegressionModel proto;
- proto.set_bias(bias_);
- proto.set_threshold(threshold_);
- auto& weights = *proto.mutable_weights();
- weights[scalar1_name_].set_scalar(scalar1_weight_);
- weights[scalar2_name_].set_scalar(scalar2_weight_);
- weights[scalar3_name_].set_scalar(scalar3_weight_);
- auto* one_hot_feat = weights[one_hot_name_].mutable_one_hot();
- one_hot_feat->set_default_weight(one_hot_default_weight_);
- (*one_hot_feat->mutable_weights())[one_hot_elem1_name_] =
- one_hot_elem1_weight_;
- (*one_hot_feat->mutable_weights())[one_hot_elem2_name_] =
- one_hot_elem2_weight_;
- (*one_hot_feat->mutable_weights())[one_hot_elem3_name_] =
- one_hot_elem3_weight_;
- SparseWeights* sparse_feat = weights[sparse_name_].mutable_sparse();
- sparse_feat->set_default_weight(sparse_default_weight_);
- (*sparse_feat->mutable_weights())[sparse_elem1_name_] =
- sparse_elem1_weight_;
- (*sparse_feat->mutable_weights())[sparse_elem2_name_] =
- sparse_elem2_weight_;
- BucketizedWeights* bucketized_feat =
- weights[bucketized_name_].mutable_bucketized();
- bucketized_feat->set_default_weight(bucketization_default_weight_);
- for (const float boundary : bucketization_boundaries_) {
- bucketized_feat->add_boundaries(boundary);
- }
- for (const float weight : bucketization_weights_) {
- bucketized_feat->add_weights(weight);
- }
- return proto;
- }
- const std::string scalar1_name_ = "scalar_feature1";
- const std::string scalar2_name_ = "scalar_feature2";
- const std::string scalar3_name_ = "scalar_feature3";
- const std::string one_hot_name_ = "one_hot_feature";
- const std::string one_hot_elem1_name_ = "one_hot_elem1";
- const std::string one_hot_elem2_name_ = "one_hot_elem2";
- const std::string one_hot_elem3_name_ = "one_hot_elem3";
- const float bias_ = 1.5f;
- const float threshold_ = 0.6f;
- const float scalar1_weight_ = 0.8f;
- const float scalar2_weight_ = -2.4f;
- const float scalar3_weight_ = 0.01f;
- const float one_hot_elem1_weight_ = -1.0f;
- const float one_hot_elem2_weight_ = 5.0f;
- const float one_hot_elem3_weight_ = -1.5f;
- const float one_hot_default_weight_ = 10.0f;
- const float epsilon_ = 0.001f;
- const std::string sparse_name_ = "sparse_feature";
- const std::string sparse_elem1_name_ = "sparse_elem1";
- const std::string sparse_elem2_name_ = "sparse_elem2";
- const float sparse_elem1_weight_ = -2.2f;
- const float sparse_elem2_weight_ = 3.1f;
- const float sparse_default_weight_ = 4.4f;
- const std::string bucketized_name_ = "bucketized_feature";
- const float bucketization_boundaries_[2] = {0.3f, 0.7f};
- const float bucketization_weights_[3] = {-1.0f, 1.0f, 3.0f};
- const float bucketization_default_weight_ = -3.3f;
- };
- TEST_F(GenericLogisticRegressionInferenceTest, BaseTest) {
- auto predictor = GenericLogisticRegressionInference(GetProto());
- RankerExample example;
- auto& features = *example.mutable_features();
- features[scalar1_name_].set_bool_value(true);
- features[scalar2_name_].set_int32_value(42);
- features[scalar3_name_].set_float_value(0.666f);
- features[one_hot_name_].set_string_value(one_hot_elem1_name_);
- float score = predictor.PredictScore(example);
- float expected_score =
- Sigmoid(bias_ + 1.0f * scalar1_weight_ + 42.0f * scalar2_weight_ +
- 0.666f * scalar3_weight_ + one_hot_elem1_weight_);
- EXPECT_NEAR(expected_score, score, epsilon_);
- EXPECT_EQ(expected_score >= threshold_, predictor.Predict(example));
- }
- TEST_F(GenericLogisticRegressionInferenceTest, UnknownElement) {
- RankerExample example;
- auto& features = *example.mutable_features();
- features[one_hot_name_].set_string_value("Unknown element");
- auto predictor = GenericLogisticRegressionInference(GetProto());
- float score = predictor.PredictScore(example);
- float expected_score = Sigmoid(bias_ + one_hot_default_weight_);
- EXPECT_NEAR(expected_score, score, epsilon_);
- }
- TEST_F(GenericLogisticRegressionInferenceTest, MissingFeatures) {
- RankerExample example;
- auto predictor = GenericLogisticRegressionInference(GetProto());
- float score = predictor.PredictScore(example);
- // Missing features will use default weights for one_hot features and drop
- // scalar features.
- float expected_score = Sigmoid(bias_ + one_hot_default_weight_);
- EXPECT_NEAR(expected_score, score, epsilon_);
- }
- TEST_F(GenericLogisticRegressionInferenceTest, UnknownFeatures) {
- RankerExample example;
- auto& features = *example.mutable_features();
- features["foo1"].set_bool_value(true);
- features["foo2"].set_int32_value(42);
- features["foo3"].set_float_value(0.666f);
- features["foo4"].set_string_value(one_hot_elem1_name_);
- // All features except this one will be ignored.
- features[one_hot_name_].set_string_value(one_hot_elem2_name_);
- auto predictor = GenericLogisticRegressionInference(GetProto());
- float score = predictor.PredictScore(example);
- // Unknown features will be ignored.
- float expected_score = Sigmoid(bias_ + one_hot_elem2_weight_);
- EXPECT_NEAR(expected_score, score, epsilon_);
- }
- TEST_F(GenericLogisticRegressionInferenceTest, Threshold) {
- // In this test, we calculate the score for a given example and set the model
- // threshold to this value. We then add a feature to the example that should
- // tip the score slightly on either side of the treshold and verify that the
- // decision is as expected.
- auto proto = GetProto();
- auto threshold_calculator = GenericLogisticRegressionInference(proto);
- RankerExample example;
- auto& features = *example.mutable_features();
- features[scalar1_name_].set_bool_value(true);
- features[scalar2_name_].set_int32_value(2);
- features[one_hot_name_].set_string_value(one_hot_elem1_name_);
- float threshold = threshold_calculator.PredictScore(example);
- proto.set_threshold(threshold);
- // Setting the model with the calculated threshold.
- auto predictor = GenericLogisticRegressionInference(proto);
- // Adding small positive contribution from scalar3 to tip the decision the
- // positive side of the threshold.
- features[scalar3_name_].set_float_value(0.01f);
- float score = predictor.PredictScore(example);
- // The score is now greater than, but still near the threshold. The
- // decision should be positive.
- EXPECT_LT(threshold, score);
- EXPECT_NEAR(threshold, score, epsilon_);
- EXPECT_TRUE(predictor.Predict(example));
- // A small negative contribution from scalar3 should tip the decision the
- // other way.
- features[scalar3_name_].set_float_value(-0.01f);
- score = predictor.PredictScore(example);
- EXPECT_GT(threshold, score);
- EXPECT_NEAR(threshold, score, epsilon_);
- EXPECT_FALSE(predictor.Predict(example));
- }
- TEST_F(GenericLogisticRegressionInferenceTest, NoThreshold) {
- auto proto = GetProto();
- // When no threshold is specified, we use the default of 0.5.
- proto.clear_threshold();
- auto predictor = GenericLogisticRegressionInference(proto);
- RankerExample example;
- auto& features = *example.mutable_features();
- // one_hot_elem3 exactly balances the bias, so we expect the pre-sigmoid score
- // to be zero, and the post-sigmoid score to be 0.5 if this is the only active
- // feature.
- features[one_hot_name_].set_string_value(one_hot_elem3_name_);
- float score = predictor.PredictScore(example);
- EXPECT_NEAR(0.5f, score, epsilon_);
- // Adding small contribution from scalar3 to tip the decision on one side or
- // the other of the threshold.
- features[scalar3_name_].set_float_value(0.01f);
- score = predictor.PredictScore(example);
- // The score is now greater than, but still near 0.5. The decision should be
- // positive.
- EXPECT_LT(0.5f, score);
- EXPECT_NEAR(0.5f, score, epsilon_);
- EXPECT_TRUE(predictor.Predict(example));
- features[scalar3_name_].set_float_value(-0.01f);
- score = predictor.PredictScore(example);
- // The score is now lower than, but near 0.5. The decision should be
- // negative.
- EXPECT_GT(0.5f, score);
- EXPECT_NEAR(0.5f, score, epsilon_);
- EXPECT_FALSE(predictor.Predict(example));
- }
- TEST_F(GenericLogisticRegressionInferenceTest, PreprossessedModel) {
- GenericLogisticRegressionModel proto = GetProto();
- proto.set_is_preprocessed_model(true);
- // Clear the weights to make sure the inference is done by fullname_weights.
- proto.clear_weights();
- // Build fullname weights.
- Map<std::string, float>& weights = *proto.mutable_fullname_weights();
- weights[scalar1_name_] = scalar1_weight_;
- weights[scalar2_name_] = scalar2_weight_;
- weights[scalar3_name_] = scalar3_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- one_hot_name_, one_hot_elem1_name_)] = one_hot_elem1_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- one_hot_name_, one_hot_elem2_name_)] = one_hot_elem2_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- one_hot_name_, one_hot_elem3_name_)] = one_hot_elem3_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- sparse_name_, sparse_elem1_name_)] = sparse_elem1_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- sparse_name_, sparse_elem2_name_)] = sparse_elem2_weight_;
- weights[ExamplePreprocessor::FeatureFullname(bucketized_name_, "0")] =
- bucketization_weights_[0];
- weights[ExamplePreprocessor::FeatureFullname(bucketized_name_, "1")] =
- bucketization_weights_[1];
- weights[ExamplePreprocessor::FeatureFullname(bucketized_name_, "2")] =
- bucketization_weights_[2];
- weights[ExamplePreprocessor::FeatureFullname(
- ExamplePreprocessor::kMissingFeatureDefaultName, one_hot_name_)] =
- one_hot_default_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- ExamplePreprocessor::kMissingFeatureDefaultName, sparse_name_)] =
- sparse_default_weight_;
- weights[ExamplePreprocessor::FeatureFullname(
- ExamplePreprocessor::kMissingFeatureDefaultName, bucketized_name_)] =
- bucketization_default_weight_;
- // Build preprocessor_config.
- ExamplePreprocessorConfig& config = *proto.mutable_preprocessor_config();
- config.add_missing_features(one_hot_name_);
- config.add_missing_features(sparse_name_);
- config.add_missing_features(bucketized_name_);
- (*config.mutable_bucketizers())[bucketized_name_].add_boundaries(
- bucketization_boundaries_[0]);
- (*config.mutable_bucketizers())[bucketized_name_].add_boundaries(
- bucketization_boundaries_[1]);
- auto predictor = GenericLogisticRegressionInference(proto);
- // Build example.
- RankerExample example;
- Map<std::string, Feature>& features = *example.mutable_features();
- features[scalar1_name_].set_bool_value(true);
- features[scalar2_name_].set_int32_value(42);
- features[scalar3_name_].set_float_value(0.666f);
- features[one_hot_name_].set_string_value(one_hot_elem1_name_);
- features[sparse_name_].mutable_string_list()->add_string_value(
- sparse_elem1_name_);
- features[sparse_name_].mutable_string_list()->add_string_value(
- sparse_elem2_name_);
- features[bucketized_name_].set_float_value(0.98f);
- // Inference.
- float score = predictor.PredictScore(example);
- float expected_score = Sigmoid(
- bias_ + 1.0f * scalar1_weight_ + 42.0f * scalar2_weight_ +
- 0.666f * scalar3_weight_ + one_hot_elem1_weight_ + sparse_elem1_weight_ +
- sparse_elem2_weight_ + bucketization_weights_[2]);
- EXPECT_NEAR(expected_score, score, epsilon_);
- EXPECT_EQ(expected_score >= threshold_, predictor.Predict(example));
- }
- } // namespace assist_ranker
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