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[LightGBM] fix bug for empty categorical values #5048

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Merged
merged 1 commit into from
Apr 23, 2020
Merged

[LightGBM] fix bug for empty categorical values #5048

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48 changes: 26 additions & 22 deletions src/Microsoft.ML.LightGbm/LightGbmTrainerBase.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Microsoft.ML.CommandLine;
using Microsoft.ML.Data;
Expand Down Expand Up @@ -124,7 +125,7 @@ private protected OptionsBase() { }
/// </value>
[Argument(ArgumentType.Multiple,
HelpText = "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.",
Name="Booster",
Name = "Booster",
SortOrder = 3)]
internal IBoosterParameterFactory BoosterFactory = new GradientBooster.Options();

Expand Down Expand Up @@ -285,6 +286,7 @@ private sealed class CategoricalMetaData
public int[] OnehotIndices;
public int[] OnehotBias;
public bool[] IsCategoricalFeature;
public int[] CatIndices;
}

// Contains the passed in options when the API is called
Expand Down Expand Up @@ -317,16 +319,16 @@ private protected LightGbmTrainerBase(IHostEnvironment env,
double? learningRate,
int numberOfIterations)
: this(env, name, new TOptions()
{
NumberOfLeaves = numberOfLeaves,
MinimumExampleCountPerLeaf = minimumExampleCountPerLeaf,
LearningRate = learningRate,
NumberOfIterations = numberOfIterations,
LabelColumnName = labelColumn.Name,
FeatureColumnName = featureColumnName,
ExampleWeightColumnName = exampleWeightColumnName,
RowGroupColumnName = rowGroupColumnName
},
{
NumberOfLeaves = numberOfLeaves,
MinimumExampleCountPerLeaf = minimumExampleCountPerLeaf,
LearningRate = learningRate,
NumberOfIterations = numberOfIterations,
LabelColumnName = labelColumn.Name,
FeatureColumnName = featureColumnName,
ExampleWeightColumnName = exampleWeightColumnName,
RowGroupColumnName = rowGroupColumnName
},
labelColumn)
{
}
Expand Down Expand Up @@ -384,7 +386,7 @@ private protected override TModel TrainModelCore(TrainContext context)
return CreatePredictor();
}

private protected virtual void InitializeBeforeTraining(){}
private protected virtual void InitializeBeforeTraining() { }

private void InitParallelTraining()
{
Expand Down Expand Up @@ -522,6 +524,7 @@ private static List<string> ConstructCategoricalFeatureMetaData(int[] categorica
++j;
}
}
catMetaData.CatIndices = catIndices.Select(int.Parse).ToArray();
return catIndices;
}

Expand Down Expand Up @@ -761,7 +764,7 @@ private void GetFeatureValueDense(IChannel ch, FloatLabelCursor cursor, Categori
}
}
// All-Zero is category 0.
fv = hotIdx - catMetaData.CategoricalBoudaries[i] + 1;
fv = hotIdx - catMetaData.CategoricalBoudaries[i];
}
featureValuesTemp[i] = fv;
}
Expand All @@ -781,8 +784,9 @@ private void GetFeatureValueSparse(IChannel ch, FloatLabelCursor cursor,
var cursorFeaturesIndices = cursor.Features.GetIndices();
if (catMetaData.CategoricalBoudaries != null)
{
List<int> featureIndices = new List<int>();
List<float> values = new List<float>();
Dictionary<int, float> ivPair = new Dictionary<int, float>();
foreach (var idx in catMetaData.CatIndices)
ivPair[idx] = -1;
int lastIdx = -1;
int nhot = 0;
for (int i = 0; i < cursorFeaturesValues.Length; ++i)
Expand All @@ -791,11 +795,10 @@ private void GetFeatureValueSparse(IChannel ch, FloatLabelCursor cursor,
int colIdx = cursorFeaturesIndices[i];
int newColIdx = catMetaData.OnehotIndices[colIdx];
if (catMetaData.IsCategoricalFeature[newColIdx])
fv = catMetaData.OnehotBias[colIdx] + 1;
fv = catMetaData.OnehotBias[colIdx];
if (newColIdx != lastIdx)
{
featureIndices.Add(newColIdx);
values.Add(fv);
ivPair[newColIdx] = fv;
nhot = 1;
}
else
Expand All @@ -804,13 +807,14 @@ private void GetFeatureValueSparse(IChannel ch, FloatLabelCursor cursor,
++nhot;
var prob = rand.NextSingle();
if (prob < 1.0f / nhot)
values[values.Count - 1] = fv;
ivPair[newColIdx] = fv;
}
lastIdx = newColIdx;
}
indices = featureIndices.ToArray();
featureValues = values.ToArray();
cnt = featureIndices.Count;
var sortedIVPair = new SortedDictionary<int, float>(ivPair);
indices = sortedIVPair.Keys.ToArray();
featureValues = sortedIVPair.Values.ToArray();
cnt = ivPair.Count;
}
else
{
Expand Down
4 changes: 2 additions & 2 deletions src/Microsoft.ML.LightGbm/WrappedLightGbmBooster.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -179,7 +179,7 @@ private static int[] GetCatThresholds(UInt32[] catThreshold, int lowerBound, int
for (int k = 0; k < 32; ++k)
{
int cat = (j - lowerBound) * 32 + k;
if (FindInBitset(catThreshold, lowerBound, upperBound, cat) && cat > 0)
if (FindInBitset(catThreshold, lowerBound, upperBound, cat))
cats.Add(cat);
}
}
Expand Down Expand Up @@ -239,7 +239,7 @@ public InternalTreeEnsemble GetModel(int[] categoricalFeatureBoudaries)
categoricalSplitFeatures[node] = new int[cats.Length];
// Convert Cat thresholds to feature indices.
for (int j = 0; j < cats.Length; ++j)
categoricalSplitFeatures[node][j] = splitFeature[node] + cats[j] - 1;
categoricalSplitFeatures[node][j] = splitFeature[node] + cats[j];

splitFeature[node] = -1;
categoricalSplit[node] = true;
Expand Down