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// Copyright 2022 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#ifndef CONTENT_BROWSER_PRELOADING_PRELOADING_PREDICTION_H_
#define CONTENT_BROWSER_PRELOADING_PRELOADING_PREDICTION_H_
#include <optional>
#include <string_view>
#include "base/timer/elapsed_timer.h"
#include "content/browser/preloading/preloading_confidence.h"
#include "content/public/browser/preloading_data.h"
#include "services/metrics/public/cpp/ukm_source_id.h"
#include "url/gurl.h"
namespace content {
// PreloadingPrediction keeps track of every preloading prediction associated
// with various predictors as defined in content/public/preloading.h
// (please see for more details); whether the prediction is accurate or not;
// whether the prediction is confident enough or not.
class PreloadingPrediction {
public:
~PreloadingPrediction();
// Disallow copy and assign.
PreloadingPrediction(const PreloadingPrediction& other) = delete;
PreloadingPrediction& operator=(const PreloadingPrediction& other) = delete;
PreloadingPrediction(PreloadingPrediction&&);
PreloadingPrediction& operator=(PreloadingPrediction&&);
// Records both UKMs Preloading_Prediction and
// Preloading_Prediction_PreviousPrimaryPage. Metrics for both these are same.
// Only difference is that the Preloading_Prediction_PreviousPrimaryPage UKM
// is associated with the WebContents primary page that triggered the
// preloading prediction. This is done to easily analyze the impact of the
// preloading prediction on the primary visible page.
void RecordPreloadingPredictionUKMs(
ukm::SourceId navigated_page_source_id,
std::optional<double> sampling_likelihood);
// Sets `is_accurate_prediction_` to true if `navigated_url` matches the URL
// predicate. It also records `time_to_next_navigation_`.
void SetIsAccuratePrediction(const GURL& navigated_url);
bool IsAccuratePrediction() const { return is_accurate_prediction_; }
PreloadingPrediction(
PreloadingPredictor predictor,
PreloadingConfidence confidence,
ukm::SourceId triggered_primary_page_source_id,
base::RepeatingCallback<bool(const GURL&)> url_match_predicate);
// Called by the `PreloadingDataImpl` that owns this prediction, to check the
// validity of `predictor_type_`.
PreloadingPredictor predictor_type() const { return predictor_type_; }
private:
// Preloading predictor of this preloading prediction.
PreloadingPredictor predictor_type_;
// Holds the triggered primary page of preloading operation ukm::SourceId.
ukm::SourceId triggered_primary_page_source_id_;
// Triggers can specify their own predicate to judge whether two URLs are
// considered as pointing to the same destination as this varies for different
// predictors.
PreloadingURLMatchCallback url_match_predicate_;
// Confidence percentage of predictor's preloading prediction. This value
// should be between 0 - 100.
PreloadingConfidence confidence_;
// Set to true when preloading prediction was correct i.e., when the
// navigation happens to the same predicted URL.
bool is_accurate_prediction_ = false;
// Records when the preloading prediction was first recorded.
base::ElapsedTimer elapsed_timer_;
// The time between the creation of the prediction and the start of the next
// navigation, whether accurate or not. The latency is reported as standard
// buckets, of 1.15 spacing.
std::optional<base::TimeDelta> time_to_next_navigation_;
};
// The output of many predictors is a logit/probability score. To use this score
// for binary classification, we compare it to a threshold. If the score is
// above the threshold, we classify the instance as positive; otherwise, we
// classify it as negative. Threshold choice affects classifier precision and
// recall. There is a trade-off between precision and recall. If we set the
// threshold too low, we will have high precision but low recall. If we set the
// threshold too high, we will have high recall but low precision. To choose the
// best threshold, we can use ROC curves, precision-recall curves, or
// logit-precision and logit-recall curves. `ExperimentalPreloadingPrediction`
// helps us collect the UMA data required to achieve this.
class ExperimentalPreloadingPrediction {
public:
ExperimentalPreloadingPrediction() = delete;
ExperimentalPreloadingPrediction(
std::string_view name,
PreloadingURLMatchCallback url_match_predicate,
float score,
float min_score,
float max_score,
size_t buckets);
~ExperimentalPreloadingPrediction();
ExperimentalPreloadingPrediction(
const ExperimentalPreloadingPrediction& other) = delete;
ExperimentalPreloadingPrediction& operator=(
const ExperimentalPreloadingPrediction& other) = delete;
ExperimentalPreloadingPrediction(ExperimentalPreloadingPrediction&&);
ExperimentalPreloadingPrediction& operator=(
ExperimentalPreloadingPrediction&&);
std::string_view PredictorName() const { return name_; }
bool IsAccuratePrediction() const { return is_accurate_prediction_; }
void SetIsAccuratePrediction(const GURL& navigated_url);
void RecordToUMA() const;
private:
// Experimental predictor's name
std::string_view name_;
// Set to true when preloading prediction was correct i.e., when the
// navigation happens to the same predicted URL.
bool is_accurate_prediction_ = false;
// The number of buckets that will be used for UMA aggregation. It must be
// less than 101.
uint8_t buckets_;
// The logit or probability score output of the predictor model.
// Normalized based on the min and max score values.
float normalized_score_;
// The callback to verify that the navigated URL is a match.
PreloadingURLMatchCallback url_match_predicate_;
};
// Stores data relating to a prediction made by the preloading ML model. Once
// the outcome of whether the prediction is accurate is known, the provided
// callback is invoked.
class ModelPredictionTrainingData {
public:
using OutcomeCallback =
base::OnceCallback<void(std::optional<double> sampling_likelihood,
bool is_accurate_prediction)>;
ModelPredictionTrainingData(OutcomeCallback on_record_outcome,
PreloadingURLMatchCallback url_match_predicate);
~ModelPredictionTrainingData();
ModelPredictionTrainingData(const ModelPredictionTrainingData&) = delete;
ModelPredictionTrainingData& operator=(const ModelPredictionTrainingData&) =
delete;
ModelPredictionTrainingData(ModelPredictionTrainingData&&);
ModelPredictionTrainingData& operator=(ModelPredictionTrainingData&&);
void SetIsAccuratePrediction(const GURL& navigated_url);
void Record(std::optional<double> sampling_likelihood);
private:
OutcomeCallback on_record_outcome_;
PreloadingURLMatchCallback url_match_predicate_;
bool is_accurate_prediction_ = false;
};
} // namespace content
#endif // CONTENT_BROWSER_PRELOADING_PRELOADING_PREDICTION_H_
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