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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/binary_classifier_predictor.h"
#include <memory>
#include "base/bind.h"
#include "base/bind_helpers.h"
#include "base/files/file_path.h"
#include "components/assist_ranker/generic_logistic_regression_inference.h"
#include "components/assist_ranker/proto/ranker_model.pb.h"
#include "components/assist_ranker/ranker_model.h"
#include "components/assist_ranker/ranker_model_loader_impl.h"
#include "services/network/public/cpp/shared_url_loader_factory.h"
namespace assist_ranker {
BinaryClassifierPredictor::BinaryClassifierPredictor(
const PredictorConfig& config)
: BasePredictor(config){};
BinaryClassifierPredictor::~BinaryClassifierPredictor(){};
// static
std::unique_ptr<BinaryClassifierPredictor> BinaryClassifierPredictor::Create(
const PredictorConfig& config,
const base::FilePath& model_path,
scoped_refptr<network::SharedURLLoaderFactory> url_loader_factory) {
std::unique_ptr<BinaryClassifierPredictor> predictor(
new BinaryClassifierPredictor(config));
if (!predictor->is_query_enabled()) {
DVLOG(1) << "Query disabled, bypassing model loading.";
return predictor;
}
const GURL& model_url = predictor->GetModelUrl();
DVLOG(1) << "Creating predictor instance for " << predictor->GetModelName();
DVLOG(1) << "Model URL: " << model_url;
DVLOG(1) << "Using predict threshold replacement: "
<< predictor->GetPredictThresholdReplacement();
auto model_loader = std::make_unique<RankerModelLoaderImpl>(
base::BindRepeating(&BinaryClassifierPredictor::ValidateModel),
base::BindRepeating(&BinaryClassifierPredictor::OnModelAvailable,
base::Unretained(predictor.get())),
url_loader_factory, model_path, model_url, config.uma_prefix);
predictor->LoadModel(std::move(model_loader));
return predictor;
}
bool BinaryClassifierPredictor::Predict(const RankerExample& example,
bool* prediction) {
if (!IsReady()) {
DVLOG(1) << "Predictor " << GetModelName() << " not ready for prediction.";
return false;
}
float predict_threshold_replacement = GetPredictThresholdReplacement();
if (predict_threshold_replacement != kNoPredictThresholdReplacement) {
*prediction = inference_module_->PredictScore(PreprocessExample(example)) >=
predict_threshold_replacement;
} else {
*prediction = inference_module_->Predict(PreprocessExample(example));
}
DVLOG(1) << "Predictor " << GetModelName() << " predicted: " << *prediction;
return true;
}
bool BinaryClassifierPredictor::PredictScore(const RankerExample& example,
float* prediction) {
if (!IsReady()) {
DVLOG(1) << "Predictor " << GetModelName() << " not ready for prediction.";
return false;
}
*prediction = inference_module_->PredictScore(PreprocessExample(example));
DVLOG(1) << "Predictor " << GetModelName() << " predicted: " << prediction;
return true;
}
// static
RankerModelStatus BinaryClassifierPredictor::ValidateModel(
const RankerModel& model) {
if (model.proto().model_case() != RankerModelProto::kLogisticRegression) {
DVLOG(0) << "Model is incompatible.";
return RankerModelStatus::INCOMPATIBLE;
}
const GenericLogisticRegressionModel& glr =
model.proto().logistic_regression();
if (glr.is_preprocessed_model()) {
if (glr.fullname_weights().empty() || !glr.weights().empty()) {
DVLOG(0) << "Model is incompatible. Preprocessed model should use "
"fullname_weights.";
return RankerModelStatus::INCOMPATIBLE;
}
if (!glr.preprocessor_config().feature_indices().empty()) {
DVLOG(0) << "Preprocessed model doesn't need feature indices.";
return RankerModelStatus::INCOMPATIBLE;
}
} else {
if (!glr.fullname_weights().empty() || glr.weights().empty()) {
DVLOG(0) << "Model is incompatible. Non-preprocessed model should use "
"weights.";
return RankerModelStatus::INCOMPATIBLE;
}
}
return RankerModelStatus::OK;
}
bool BinaryClassifierPredictor::Initialize() {
if (ranker_model_->proto().model_case() ==
RankerModelProto::kLogisticRegression) {
inference_module_ = std::make_unique<GenericLogisticRegressionInference>(
ranker_model_->proto().logistic_regression());
return true;
}
DVLOG(0) << "Could not initialize inference module.";
return false;
}
} // namespace assist_ranker
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