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‎docs/samples/Microsoft.ML.Samples/Dynamic/FastTreeRegression.cs

+1
Original file line numberDiff line numberDiff line change
@@ -6,6 +6,7 @@ namespace Microsoft.ML.Samples.Dynamic
66
{
77
public static class FastTreeRegression
88
{
9+
// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
910
public static void Example()
1011
{
1112
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,

‎docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -73,7 +73,7 @@ public static void Example()
7373
var metrics = ml.BinaryClassification.Evaluate(dataWithPredictions);
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7575
Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.80
76-
Console.WriteLine($"AUC: {metrics.Auc}"); // 0.64
76+
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.64
7777
Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.39
7878

7979
Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.81

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs

+2-2
Original file line numberDiff line numberDiff line change
@@ -32,8 +32,8 @@ public static void Example()
3232
FieldAwareFactorizationMachine(
3333
new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options
3434
{
35-
FeatureColumn = "Features",
36-
LabelColumn = "Sentiment",
35+
FeatureColumnName = "Features",
36+
LabelColumnName = "Sentiment",
3737
LearningRate = 0.1f,
3838
NumberOfIterations = 10
3939
}));

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs

+2-2
Original file line numberDiff line numberDiff line change
@@ -62,8 +62,8 @@ public static void Example()
6262
var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
6363
.Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
6464
new SdcaBinaryTrainer.Options {
65-
LabelColumn = "Sentiment",
66-
FeatureColumn = "Features",
65+
LabelColumnName = "Sentiment",
66+
FeatureColumnName = "Features",
6767
ConvergenceTolerance = 0.01f, // The learning rate for adjusting bias from being regularized
6868
NumThreads = 2, // Degree of lock-free parallelism
6969
}));

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -49,7 +49,7 @@ public static void Example()
4949
var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
5050

5151
// Check if metrics are reasonable.
52-
Console.WriteLine($"Macro accuracy: {metrics.AccuracyMacro:F4}, Micro accuracy: {metrics.AccuracyMicro:F4}.");
52+
Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");
5353
// Console output:
5454
// Macro accuracy: 0.8655, Micro accuracy: 0.8651.
5555

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs

+3-3
Original file line numberDiff line numberDiff line change
@@ -35,8 +35,8 @@ public static void Example()
3535
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label")
3636
.Append(mlContext.MulticlassClassification.Trainers.LightGbm(new Options
3737
{
38-
LabelColumn = "LabelIndex",
39-
FeatureColumn = "Features",
38+
LabelColumnName = "LabelIndex",
39+
FeatureColumnName = "Features",
4040
Booster = new DartBooster.Options
4141
{
4242
DropRate = 0.15,
@@ -60,7 +60,7 @@ public static void Example()
6060
var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
6161

6262
// Check if metrics are reasonable.
63-
Console.WriteLine($"Macro accuracy: {metrics.AccuracyMacro:F4}, Micro accuracy: {metrics.AccuracyMicro:F4}.");
63+
Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");
6464
// Console output:
6565
// Macro accuracy: 0.8619, Micro accuracy: 0.8611.
6666

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbmWithOptions.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -38,7 +38,7 @@ public static void Example()
3838
var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
3939
.Append(mlContext.Regression.Trainers.LightGbm(new Options
4040
{
41-
LabelColumn = labelName,
41+
LabelColumnName = labelName,
4242
NumLeaves = 4,
4343
MinDataPerLeaf = 6,
4444
LearningRate = 0.001,

‎docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -92,7 +92,7 @@ public static void AveragedPerceptronBinaryClassification()
9292
var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
9393

9494
Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.83
95-
Console.WriteLine($"AUC: {metrics.Auc}"); // 0.88
95+
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.88
9696
Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.63
9797

9898
Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.89

‎docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs

+2-1
Original file line numberDiff line numberDiff line change
@@ -6,6 +6,7 @@ namespace Microsoft.ML.Samples.Static
66
{
77
public class FastTreeBinaryClassificationExample
88
{
9+
// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
910
public static void FastTreeBinaryClassification()
1011
{
1112
// Downloading a classification dataset from github.com/dotnet/machinelearning.
@@ -95,7 +96,7 @@ public static void FastTreeBinaryClassification()
9596
var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
9697

9798
Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.84
98-
Console.WriteLine($"AUC: {metrics.Auc}"); // 0.89
99+
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.89
99100
Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.64
100101

101102
Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.88

‎docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs

+5-4
Original file line numberDiff line numberDiff line change
@@ -8,6 +8,7 @@ namespace Microsoft.ML.Samples.Static
88
{
99
public class FastTreeRegressionExample
1010
{
11+
// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
1112
public static void FastTreeRegression()
1213
{
1314
// Downloading a regression dataset from github.com/dotnet/machinelearning
@@ -47,10 +48,10 @@ public static void FastTreeRegression()
4748

4849
var cvResults = mlContext.Regression.CrossValidate(data, learningPipeline, r => r.label, numFolds: 5);
4950
var averagedMetrics = (
50-
L1: cvResults.Select(r => r.metrics.L1).Average(),
51-
L2: cvResults.Select(r => r.metrics.L2).Average(),
52-
LossFn: cvResults.Select(r => r.metrics.LossFn).Average(),
53-
Rms: cvResults.Select(r => r.metrics.Rms).Average(),
51+
L1: cvResults.Select(r => r.metrics.MeanAbsoluteError).Average(),
52+
L2: cvResults.Select(r => r.metrics.MeanSquaredError).Average(),
53+
LossFn: cvResults.Select(r => r.metrics.LossFunction).Average(),
54+
Rms: cvResults.Select(r => r.metrics.RootMeanSquaredError).Average(),
5455
RSquared: cvResults.Select(r => r.metrics.RSquared).Average()
5556
);
5657
Console.WriteLine($"L1 - {averagedMetrics.L1}"); // 3.091095

‎docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -95,7 +95,7 @@ public static void LightGbmBinaryClassification()
9595
var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
9696

9797
Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.84
98-
Console.WriteLine($"AUC: {metrics.Auc}"); // 0.89
98+
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.89
9999
Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.64
100100

101101
Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.88

‎docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs

+4-4
Original file line numberDiff line numberDiff line change
@@ -60,10 +60,10 @@ public static void LightGbmRegression()
6060
var dataWithPredictions = model.Transform(testData);
6161
var metrics = mlContext.Regression.Evaluate(dataWithPredictions, r => r.label, r => r.score);
6262

63-
Console.WriteLine($"L1 - {metrics.L1}"); // 4.9669731
64-
Console.WriteLine($"L2 - {metrics.L2}"); // 51.37296
65-
Console.WriteLine($"LossFunction - {metrics.LossFn}"); // 51.37296
66-
Console.WriteLine($"RMS - {metrics.Rms}"); // 7.167493
63+
Console.WriteLine($"L1 - {metrics.MeanAbsoluteError}"); // 4.9669731
64+
Console.WriteLine($"L2 - {metrics.MeanSquaredError}"); // 51.37296
65+
Console.WriteLine($"LossFunction - {metrics.LossFunction}"); // 51.37296
66+
Console.WriteLine($"RMS - {metrics.RootMeanSquaredError}"); // 7.167493
6767
Console.WriteLine($"RSquared - {metrics.RSquared}"); // 0.079478
6868
}
6969
}

‎docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs

+1-1
Original file line numberDiff line numberDiff line change
@@ -92,7 +92,7 @@ public static void SdcaBinaryClassification()
9292
var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
9393

9494
Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.83
95-
Console.WriteLine($"AUC: {metrics.Auc}"); // 0.88
95+
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.88
9696
Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.59
9797

9898
Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.87

‎docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs

+4-4
Original file line numberDiff line numberDiff line change
@@ -57,10 +57,10 @@ public static void SdcaRegression()
5757
var dataWithPredictions = model.Transform(testData);
5858
var metrics = mlContext.Regression.Evaluate(dataWithPredictions, r => r.label, r => r.score);
5959

60-
Console.WriteLine($"L1 - {metrics.L1}"); // 3.7226085
61-
Console.WriteLine($"L2 - {metrics.L2}"); // 24.250636
62-
Console.WriteLine($"LossFunction - {metrics.LossFn}"); // 24.25063
63-
Console.WriteLine($"RMS - {metrics.Rms}"); // 4.924493
60+
Console.WriteLine($"L1 - {metrics.MeanAbsoluteError}"); // 3.7226085
61+
Console.WriteLine($"L2 - {metrics.MeanSquaredError}"); // 24.250636
62+
Console.WriteLine($"LossFunction - {metrics.LossFunction}"); // 24.25063
63+
Console.WriteLine($"RMS - {metrics.RootMeanSquaredError}"); // 4.924493
6464
Console.WriteLine($"RSquared - {metrics.RSquared}"); // 0.565467
6565
}
6666
}

‎pkg/Microsoft.ML.FastTree/Microsoft.ML.FastTree.nupkgproj

+12
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,12 @@
1+
<Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack">
2+
3+
<PropertyGroup>
4+
<TargetFramework>netstandard2.0</TargetFramework>
5+
<PackageDescription>ML.NET component for FastTree</PackageDescription>
6+
</PropertyGroup>
7+
8+
<ItemGroup>
9+
<ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
10+
</ItemGroup>
11+
12+
</Project>

‎pkg/Microsoft.ML.FastTree/Microsoft.ML.FastTree.symbols.nupkgproj

+5
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,5 @@
1+
<Project DefaultTargets="Pack">
2+
3+
<Import Project="Microsoft.ML.FastTree.nupkgproj" />
4+
5+
</Project>

‎pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.nupkgproj

+1
Original file line numberDiff line numberDiff line change
@@ -7,6 +7,7 @@
77

88
<ItemGroup>
99
<ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
10+
<ProjectReference Include="../Microsoft.ML.FastTree/Microsoft.ML.FastTree.nupkgproj" />
1011
<PackageReference Include="LightGBM" Version="$(LightGBMPackageVersion)" />
1112
</ItemGroup>
1213

‎pkg/Microsoft.ML.StaticPipe/Microsoft.ML.StaticPipe.nupkgproj

+1
Original file line numberDiff line numberDiff line change
@@ -9,6 +9,7 @@
99
<ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
1010
<ProjectReference Include="../Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.nupkgproj" />
1111
<ProjectReference Include="../Microsoft.ML.Recommender/Microsoft.ML.Recommender.nupkgproj" />
12+
<ProjectReference Include="../Microsoft.ML.FastTree/Microsoft.ML.FastTree.nupkgproj" />
1213
</ItemGroup>
1314

1415
</Project>

‎src/Microsoft.ML.Core/Utilities/BinFinder.cs

+2-3
Original file line numberDiff line numberDiff line change
@@ -4,7 +4,6 @@
44

55
using System;
66
using System.Collections.Generic;
7-
using Float = System.Single;
87

98
namespace Microsoft.ML.Internal.Utilities
109
{
@@ -318,7 +317,7 @@ public Peg(int index, int split)
318317
private HeapNode.Heap<Peg> _pegHeap; // heap used for selecting the largest energy decrease
319318
private int[] _accum; // integral of counts
320319
private int[] _path; // current set of pegs
321-
private Float _meanBinSize;
320+
private float _meanBinSize;
322321

323322
public GreedyBinFinder()
324323
{
@@ -338,7 +337,7 @@ protected override void FindBinsCore(List<int> counts, int[] path)
338337
_accum = new int[CountValues + 1];
339338
for (int i = 0; i < CountValues; i++)
340339
_accum[i + 1] = _accum[i] + counts[i];
341-
_meanBinSize = (Float)_accum[CountValues] / CountBins;
340+
_meanBinSize = (float)_accum[CountValues] / CountBins;
342341

343342
PlacePegs();
344343

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