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Remove learningRate parameter from RandomForest #2691

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Merged
merged 1 commit into from
Feb 23, 2019
Merged

Remove learningRate parameter from RandomForest #2691

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1 change: 0 additions & 1 deletion src/Microsoft.ML.FastTree/RandomForest.cs
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Expand Up @@ -31,7 +31,6 @@ private protected RandomForestTrainerBase(IHostEnvironment env,
int numLeaves,
int numTrees,
int minDatapointsInLeaves,
double learningRate,
bool quantileEnabled = false)
: base(env, label, featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves)
{
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6 changes: 2 additions & 4 deletions src/Microsoft.ML.FastTree/RandomForestClassification.cs
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Expand Up @@ -143,16 +143,14 @@ public sealed class Options : FastForestOptionsBase
/// <param name="numLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="minDatapointsInLeaves">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
internal FastForestClassification(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weightColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
: base(env, TrainerUtils.MakeBoolScalarLabel(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves, learningRate)
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
: base(env, TrainerUtils.MakeBoolScalarLabel(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
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6 changes: 2 additions & 4 deletions src/Microsoft.ML.FastTree/RandomForestRegression.cs
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Expand Up @@ -271,16 +271,14 @@ public sealed class Options : FastForestOptionsBase
/// <param name="numLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="minDatapointsInLeaves">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
internal FastForestRegression(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weightColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
: base(env, TrainerUtils.MakeR4ScalarColumn(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves, learningRate)
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
: base(env, TrainerUtils.MakeR4ScalarColumn(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
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12 changes: 4 additions & 8 deletions src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
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Expand Up @@ -257,19 +257,17 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastForestRegression FastForest(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new FastForestRegression(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
return new FastForestRegression(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves);
}

/// <summary>
Expand Down Expand Up @@ -297,19 +295,17 @@ public static FastForestRegression FastForest(this RegressionCatalog.RegressionT
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastForestClassification FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new FastForestClassification(env, labelColumn, featureColumn, weights,numLeaves, numTrees, minDatapointsInLeaves, learningRate);
return new FastForestClassification(env, labelColumn, featureColumn, weights,numLeaves, numTrees, minDatapointsInLeaves);
}

/// <summary>
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