Function InitializeAndCheckMetricData

catboost/libs/train_lib/train_model.cpp:173–227

}

static void InitializeAndCheckMetricData(
    const TTrainModelInternalOptions& internalOptions,
    const TTrainingDataProviders& data,
    const TLearnContext& ctx,
    TMetricsData* metricsData) {

    const int approxDimension = ctx.LearnProgress->ApproxDimension;
    auto& metrics = metricsData->Metrics;
    metrics = CreateMetrics(
        ctx.Params.MetricOptions,
        ctx.EvalMetricDescriptor,
        approxDimension,
        ctx.GetHasWeights()
    );
    CheckMetrics(metrics, ctx.Params.LossFunctionDescription.Get().GetLossFunction());

    CB_ENSURE(!metrics.empty(), "Eval metric is not defined");

    const bool lastTestDatasetHasTargetData = (data.Test.size() > 0) && data.Test.back()->MetaInfo.TargetCount > 0;

    const bool hasTest = data.GetTestSampleCount() > 0;
    if (hasTest && metrics[0]->NeedTarget() && !lastTestDatasetHasTargetData) {
        CATBOOST_WARNING_LOG << "Warning: Eval metric " << metrics[0]->GetDescription() <<
            " needs Target data, but test dataset does not have it so it won't be calculated" << Endl;
    }
    const bool canCalcEvalMetric = hasTest && (!metrics[0]->NeedTarget() || lastTestDatasetHasTargetData);

    if (canCalcEvalMetric) {
        EMetricBestValue bestValueType = {};
        float bestPossibleValue = 0;

        metrics.front()->GetBestValue(&bestValueType, &bestPossibleValue);
        metricsData->ErrorTracker = BuildErrorTracker(bestValueType, bestPossibleValue, hasTest, ctx);
        metricsData->BestModelMinTreesTracker = BuildErrorTracker(bestValueType, bestPossibleValue, hasTest, ctx);
    }

    auto& errorTracker = metricsData->ErrorTracker;
    metricsData->CalcEvalMetricOnEveryIteration
        = canCalcEvalMetric && (internalOptions.ForceCalcEvalMetricOnEveryIteration || errorTracker->IsActive());

    if (ctx.OutputOptions.GetMetricPeriod() > 1 && errorTracker && errorTracker->IsActive() && hasTest) {
        CATBOOST_WARNING_LOG << "Warning: Overfitting detector is active, thus evaluation metric is " <<
            "calculated on every iteration. 'metric_period' is ignored for evaluation metric." << Endl;
    }

    // Use only (last_test, first_metric) for the best iteration and overfitting detection
    // In case of changing the order it should be changed in GPU mode also.
    metricsData->ErrorTrackerMetricIdx = 0;

    if (internalOptions.OffsetMetricPeriodByInitModelSize) {
        metricsData->MetricPeriodOffset = ctx.LearnProgress->GetInitModelTreesSize();
    } else {
        metricsData->MetricPeriodOffset = Nothing();
    }
}

namespace {
struct TLoggingData {