Function Train

catboost/libs/train_lib/cross_validation.cpp:240–303

}

void Train(
    const NCatboostOptions::TCatBoostOptions& catboostOption,
    const TString& trainDir,
    const TMaybe<TCustomObjectiveDescriptor>& objectiveDescriptor,
    const TMaybe<TCustomMetricDescriptor>& evalMetricDescriptor,
    const TLabelConverter& labelConverter,
    const TVector<THolder<IMetric>>& metrics,
    bool isErrorTrackerActive,
    ITrainingCallbacks* trainingCallbacks,
    TFoldContext* foldContext,
    IModelTrainer* modelTrainer,
    NPar::ILocalExecutor* localExecutor
) {
    TTrainModelInternalOptions internalOptions;
    internalOptions.CalcMetricsOnly = !foldContext->FullModel.Defined();
    internalOptions.ForceCalcEvalMetricOnEveryIteration = isErrorTrackerActive;

    auto foldOutputOptions = foldContext->OutputOptions;
    foldOutputOptions.SetTrainDir(trainDir);
    if (foldContext->FullModel.Defined()) {
        // TrainModel saves model either to memory pointed by dstModel, or to ResultModelPath
        foldOutputOptions.ResultModelPath = NCatboostOptions::TOption<TString>("result_model_file", "model");
    }
    TMetricsAndTimeLeftHistory metricsAndTimeHistory;
    const auto defaultCustomCallbacks = MakeHolder<TCustomCallbacks>(Nothing());
    modelTrainer->TrainModel(
        internalOptions,
        catboostOption,
        foldOutputOptions,
        objectiveDescriptor,
        evalMetricDescriptor,
        foldContext->TrainingData,
        /*precomputedSingleOnlineCtrDataForSingleFold*/ Nothing(),
        labelConverter,
        trainingCallbacks,
        defaultCustomCallbacks.Get(),
        /*initModel*/ Nothing(),
        THolder<TLearnProgress>(),
        /*initModelApplyCompatiblePools*/ TDataProviders(),
        localExecutor,
        /*rand*/ Nothing(),
        foldContext->FullModel.Defined() ? foldContext->FullModel.Get() : nullptr,
        TVector<TEvalResult*>{&foldContext->LastUpdateEvalResult},
        &metricsAndTimeHistory,
        (foldContext->TaskType == ETaskType::CPU) ? &foldContext->LearnProgress : nullptr
    );
    if (foldContext->FullModel.Defined()) {
        TFileOutput modelFile(JoinFsPaths(trainDir, foldContext->OutputOptions.ResultModelPath.Get()));
        foldContext->FullModel->Save(&modelFile);
    }
    const auto skipMetricOnTrain = GetSkipMetricOnTrain(metrics);
    for (const auto& trainMetrics : metricsAndTimeHistory.LearnMetricsHistory) {
            foldContext->MetricValuesOnTrain.emplace_back(GetMetricValues(metrics, skipMetricOnTrain, trainMetrics));
    }
    for (const auto& testMetrics : metricsAndTimeHistory.TestMetricsHistory) {
        CB_ENSURE(testMetrics.size() <= 1, "Expect only one test dataset");
        if (!testMetrics.empty()) {
            foldContext->MetricValuesOnTest.emplace_back(GetMetricValues(metrics, /*skipMetric*/{}, testMetrics[0]));