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// Copyright 2024 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/segmentation_platform/embedder/default_model/metrics_clustering.h"
#include <memory>
#include "base/task/sequenced_task_runner.h"
#include "components/segmentation_platform/internal/metadata/metadata_writer.h"
#include "components/segmentation_platform/public/config.h"
#include "components/segmentation_platform/public/features.h"
#include "components/segmentation_platform/public/proto/aggregation.pb.h"
#include "components/segmentation_platform/public/proto/model_metadata.pb.h"
namespace segmentation_platform {
namespace {
using proto::SegmentId;
// Default parameters for MetricsClustering model.
constexpr SegmentId kSegmentId =
SegmentId::OPTIMIZATION_TARGET_SEGMENTATION_METRICS_CLUSTERING;
constexpr int64_t kModelVersion = 2;
constexpr int64_t kSignalStorageLength = 28;
constexpr int64_t kMinSignalCollectionLength = 15;
constexpr int64_t kResultTTLHours = 1;
// InputFeatures.
// Set UMA metrics to use as input.
constexpr std::array<MetadataWriter::UMAFeature, 1> kUMAFeatures = {
MetadataWriter::UMAFeature{
.signal_type = proto::SignalType::HISTOGRAM_VALUE,
.name = "Session.TotalDuration",
.bucket_count = 14,
.tensor_length = 14,
.aggregation = proto::Aggregation::BUCKETED_COUNT,
.enum_ids_size = 0},
};
} // namespace
// static
std::unique_ptr<Config> MetricsClustering::GetConfig() {
if (!base::FeatureList::IsEnabled(features::kSegmentationSurveyPage)) {
return nullptr;
}
auto config = std::make_unique<Config>();
config->segmentation_key = kMetricsClusteringKey;
config->segmentation_uma_name = kMetricsClusteringUmaName;
config->AddSegmentId(kSegmentId, std::make_unique<MetricsClustering>());
config->auto_execute_and_cache = false;
return config;
}
MetricsClustering::MetricsClustering() : DefaultModelProvider(kSegmentId) {}
std::unique_ptr<DefaultModelProvider::ModelConfig>
MetricsClustering::GetModelConfig() {
proto::SegmentationModelMetadata metadata;
MetadataWriter writer(&metadata);
writer.SetDefaultSegmentationMetadataConfig(kMinSignalCollectionLength,
kSignalStorageLength);
writer.AddOutputConfigForGenericPredictor({"active_count"});
writer.AddPredictedResultTTLInOutputConfig(
/*top_label_to_ttl_list=*/{},
/*default_ttl=*/kResultTTLHours, proto::TimeUnit::HOUR);
// Set features.
writer.AddUmaFeatures(kUMAFeatures.data(), kUMAFeatures.size());
// Use a time from last day, since metrics related to the survey opening
// should not be included in the data.
base::Time prediction_time = base::Time::Now() - base::Days(1);
metadata.set_fixed_prediction_timestamp(
prediction_time.ToDeltaSinceWindowsEpoch().InMicroseconds());
return std::make_unique<ModelConfig>(std::move(metadata), kModelVersion);
}
void MetricsClustering::ExecuteModelWithInput(
const ModelProvider::Request& inputs,
ExecutionCallback callback) {
// Invalid inputs.
if (inputs.size() != 14) {
base::SequencedTaskRunner::GetCurrentDefault()->PostTask(
FROM_HERE, base::BindOnce(std::move(callback), std::nullopt));
return;
}
int day_count = 0;
for (unsigned i = 0; i < 14; ++i) {
day_count += inputs[i] > 0 ? 1 : 0;
}
if (day_count == 0) {
base::SequencedTaskRunner::GetCurrentDefault()->PostTask(
FROM_HERE, base::BindOnce(std::move(callback), std::nullopt));
return;
}
std::vector<float> result;
result.push_back(day_count);
base::SequencedTaskRunner::GetCurrentDefault()->PostTask(
FROM_HERE, base::BindOnce(std::move(callback), std::move(result)));
}
} // namespace segmentation_platform
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