private protected LightGbmTrainerBase(IHostEnvironment env, string name, LightGbmArguments args, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(args.FeatureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn))
{
Host.CheckValue(args, nameof(args));
Args = args;
InitParallelTraining();
}
private protected LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, string name)
: base(env, name)
{
Host.CheckValue(args, nameof(args));
Args = args;
Options = Args.ToDictionary(Host);
ParallelTraining = Args.ParallelTrainer != null?Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer();
InitParallelTraining();
}
public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, LightGbmArguments input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("TrainLightGBM");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
return(LearnerEntryPointsUtils.Train <LightGbmArguments, CommonOutputs.RankingOutput>(host, input,
() => new LightGbmRankingTrainer(host, input),
getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn),
getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn),
getGroup: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.GroupIdColumn)));
}
protected LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, PredictionKind predictionKind, string name)
{
Contracts.CheckValue(env, nameof(env));
env.CheckNonWhiteSpace(name, nameof(name));
Host = env.Register(name);
Host.CheckValue(args, nameof(args));
Args = args;
Options = Args.ToDictionary(Host);
_predictionKind = predictionKind;
_env = env;
ParallelTraining = Args.ParallelTrainer != null?Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer();
InitParallelTraining();
}
/// <summary>
/// If, after applying the advancedSettings delegate, the args are different that the default value
/// and are also different than the value supplied directly to the xtension method, warn the user
/// about which value is being used.
/// The parameters that appear here, numTrees, minDocumentsInLeafs, numLeaves, learningRate are the ones the users are most likely to tune.
/// This list should follow the one in the constructor, and the extension methods on the <see cref="TrainContextBase"/>.
/// REVIEW: we should somehow mark the arguments that are set apart in those two places. Currently they stand out by their sort order annotation.
/// </summary>
protected void CheckArgsAndAdvancedSettingMismatch(int?numLeaves,
int?minDataPerLeaf,
double?learningRate,
int numBoostRound,
LightGbmArguments snapshot,
LightGbmArguments currentArgs)
{
using (var ch = Host.Start("Comparing advanced settings with the directly provided values."))
{
// Check that the user didn't supply different parameters in the args, from what it specified directly.
TrainerUtils.CheckArgsAndAdvancedSettingMismatch(ch, numLeaves, snapshot.NumLeaves, currentArgs.NumLeaves, nameof(numLeaves));
TrainerUtils.CheckArgsAndAdvancedSettingMismatch(ch, numBoostRound, snapshot.NumBoostRound, currentArgs.NumBoostRound, nameof(numBoostRound));
TrainerUtils.CheckArgsAndAdvancedSettingMismatch(ch, minDataPerLeaf, snapshot.MinDataPerLeaf, currentArgs.MinDataPerLeaf, nameof(minDataPerLeaf));
TrainerUtils.CheckArgsAndAdvancedSettingMismatch(ch, learningRate, snapshot.LearningRate, currentArgs.LearningRate, nameof(learningRate));
}
}
private protected LightGbmTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumn,
string weightColumn,
string groupIdColumn,
int?numLeaves,
int?minDataPerLeaf,
double?learningRate,
int numBoostRound,
Action <LightGbmArguments> advancedSettings)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), TrainerUtils.MakeU4ScalarColumn(groupIdColumn))
{
Args = new LightGbmArguments();
Args.NumLeaves = numLeaves;
Args.MinDataPerLeaf = minDataPerLeaf;
Args.LearningRate = learningRate;
Args.NumBoostRound = numBoostRound;
//apply the advanced args, if the user supplied any
advancedSettings?.Invoke(Args);
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = Optional <string> .Explicit(weightColumn);
}
if (groupIdColumn != null)
{
Args.GroupIdColumn = Optional <string> .Explicit(groupIdColumn);
}
InitParallelTraining();
}
private protected LightGbmTrainerBase(IHostEnvironment env, string name, SchemaShape.Column label, string featureColumn,
string weightColumn = null, string groupIdColumn = null, Action <LightGbmArguments> advancedSettings = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Args = new LightGbmArguments();
//apply the advanced args, if the user supplied any
advancedSettings?.Invoke(Args);
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = weightColumn;
}
if (groupIdColumn != null)
{
Args.GroupIdColumn = groupIdColumn;
}
InitParallelTraining();
}
internal LightGbmRegressorTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, LoadNameValue, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
}
internal LightGbmMulticlassTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, LoadNameValue, args, TrainerUtils.MakeBoolScalarLabel(args.LabelColumn))
{
_numClass = -1;
}
public LightGbmMulticlassTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, PredictionKind.MultiClassClassification, "LightGBMMulticlass")
{
_numClass = -1;
}
internal LightGbmBinaryTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, LoadNameValue, args, TrainerUtils.MakeBoolScalarLabel(args.LabelColumn))
{
}
public LightGbmRegressorTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, PredictionKind.Regression, "LightGBMRegressor")
{
}
public LightGbmRankingTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, LoadNameValue)
{
}
public LightGbmRankingTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, PredictionKind.Ranking, "LightGBMRanking")
{
}
public LightGbmBinaryTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, PredictionKind.BinaryClassification, "LGBBINCL")
{
}
public LightGbmMulticlassTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, args, LoadNameValue)
{
_numClass = -1;
}
