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

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ErrorGeneratingOutput
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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namespace Samples.Dynamic.Trainers.MulticlassClassification
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{
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public static class LbfgsMaximumEntropy
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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// Setting the seed to a fixed number in this example to make outputs deterministic.
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var mlContext = new MLContext(seed: 0);
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// Create a list of training data points.
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var dataPoints = GenerateRandomDataPoints(1000);
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// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
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var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
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// Define the trainer.
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var pipeline =
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// Convert the string labels into key types.
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mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
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// Apply LbfgsMaximumEntropy multiclass trainer.
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.Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy());
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// Train the model.
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var model = pipeline.Fit(trainingData);
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// Create testing data. Use different random seed to make it different from training data.
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var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
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// Run the model on test data set.
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var transformedTestData = model.Transform(testData);
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// Convert IDataView object to a list.
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var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
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// Look at 5 predictions
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foreach (var p in predictions.Take(5))
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Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
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// Expected output:
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// Label: 1, Prediction: 1
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 2
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 3
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// Evaluate the overall metrics
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var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
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PrintMetrics(metrics);
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// Expected output:
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// Micro Accuracy: 0.91
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// Macro Accuracy: 0.91
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// Log Loss: 0.24
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// Log Loss Reduction: 0.78
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}
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private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
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{
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var random = new Random(seed);
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float randomFloat() => (float)random.NextDouble();
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for (int i = 0; i < count; i++)
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{
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// Generate Labels that are integers 1, 2 or 3
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var label = random.Next(1, 4);
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yield return new DataPoint
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{
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Label = (uint)label,
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// Create random features that are correlated with the label.
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// The feature values are slightly increased by adding a constant multiple of label.
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Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
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};
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}
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}
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// Example with label and 20 feature values. A data set is a collection of such examples.
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private class DataPoint
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{
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public uint Label { get; set; }
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[VectorType(20)]
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public float[] Features { get; set; }
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}
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// Class used to capture predictions.
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private class Prediction
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{
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// Original label.
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public uint Label { get; set; }
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// Predicted label from the trainer.
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public uint PredictedLabel { get; set; }
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}
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// Pretty-print MulticlassClassificationMetrics objects.
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public static void PrintMetrics(MulticlassClassificationMetrics metrics)
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{
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Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
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Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
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Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
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Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}");
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}
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}
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}

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

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ErrorGeneratingOutput
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.Trainers;
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namespace Samples.Dynamic.Trainers.MulticlassClassification
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{
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public static class LbfgsMaximumEntropyWithOptions
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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// Setting the seed to a fixed number in this example to make outputs deterministic.
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var mlContext = new MLContext(seed: 0);
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// Create a list of training data points.
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var dataPoints = GenerateRandomDataPoints(1000);
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// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
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var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
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// Define trainer options.
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var options = new LbfgsMaximumEntropyMulticlassTrainer.Options
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{
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HistorySize = 50,
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L1Regularization = 0.1f,
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NumberOfThreads = 1
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};
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// Define the trainer.
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var pipeline =
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// Convert the string labels into key types.
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mlContext.Transforms.Conversion.MapValueToKey("Label")
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// Apply LbfgsMaximumEntropy multiclass trainer.
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.Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(options));
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// Train the model.
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var model = pipeline.Fit(trainingData);
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// Create testing data. Use different random seed to make it different from training data.
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var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
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// Run the model on test data set.
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var transformedTestData = model.Transform(testData);
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// Convert IDataView object to a list.
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var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
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// Look at 5 predictions
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foreach (var p in predictions.Take(5))
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Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
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// Expected output:
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// Label: 1, Prediction: 1
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 2
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 3
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// Evaluate the overall metrics
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var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
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PrintMetrics(metrics);
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// Expected output:
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// Micro Accuracy: 0.91
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// Macro Accuracy: 0.91
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// Log Loss: 0.22
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// Log Loss Reduction: 0.80
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}
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private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
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{
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var random = new Random(seed);
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float randomFloat() => (float)random.NextDouble();
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for (int i = 0; i < count; i++)
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{
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// Generate Labels that are integers 1, 2 or 3
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var label = random.Next(1, 4);
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yield return new DataPoint
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{
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Label = (uint)label,
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// Create random features that are correlated with the label.
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// The feature values are slightly increased by adding a constant multiple of label.
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Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
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};
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}
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}
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// Example with label and 20 feature values. A data set is a collection of such examples.
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private class DataPoint
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{
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public uint Label { get; set; }
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[VectorType(20)]
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public float[] Features { get; set; }
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}
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// Class used to capture predictions.
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private class Prediction
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{
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// Original label.
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public uint Label { get; set; }
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// Predicted label from the trainer.
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public uint PredictedLabel { get; set; }
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}
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// Pretty-print MulticlassClassificationMetrics objects.
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public static void PrintMetrics(MulticlassClassificationMetrics metrics)
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{
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Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
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Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
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Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
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Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}");
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}
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}
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}

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

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ErrorGeneratingOutput
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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namespace Samples.Dynamic.Trainers.MulticlassClassification
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{
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public static class LightGbm
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{
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// This example requires installation of additional NuGet package
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// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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// Setting the seed to a fixed number in this example to make outputs deterministic.
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var mlContext = new MLContext(seed: 0);
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// Create a list of training data points.
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var dataPoints = GenerateRandomDataPoints(1000);
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// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
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var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
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// Define the trainer.
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var pipeline =
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// Convert the string labels into key types.
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mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
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// Apply LightGbm multiclass trainer.
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.Append(mlContext.MulticlassClassification.Trainers.LightGbm());
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// Train the model.
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var model = pipeline.Fit(trainingData);
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// Create testing data. Use different random seed to make it different from training data.
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var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
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// Run the model on test data set.
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var transformedTestData = model.Transform(testData);
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// Convert IDataView object to a list.
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var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
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// Look at 5 predictions
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foreach (var p in predictions.Take(5))
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Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
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// Expected output:
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// Label: 1, Prediction: 1
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 3
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// Label: 2, Prediction: 2
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// Label: 3, Prediction: 3
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// Evaluate the overall metrics
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var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
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PrintMetrics(metrics);
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// Expected output:
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// Micro Accuracy: 0.99
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// Macro Accuracy: 0.99
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// Log Loss: 0.05
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// Log Loss Reduction: 0.96
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}
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private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
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{
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var random = new Random(seed);
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float randomFloat() => (float)random.NextDouble();
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for (int i = 0; i < count; i++)
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{
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// Generate Labels that are integers 1, 2 or 3
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var label = random.Next(1, 4);
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yield return new DataPoint
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{
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Label = (uint)label,
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// Create random features that are correlated with the label.
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// The feature values are slightly increased by adding a constant multiple of label.
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Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
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};
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}
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}
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// Example with label and 20 feature values. A data set is a collection of such examples.
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private class DataPoint
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{
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public uint Label { get; set; }
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[VectorType(20)]
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public float[] Features { get; set; }
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}
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// Class used to capture predictions.
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private class Prediction
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{
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// Original label.
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public uint Label { get; set; }
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// Predicted label from the trainer.
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public uint PredictedLabel { get; set; }
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}
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// Pretty-print MulticlassClassificationMetrics objects.
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public static void PrintMetrics(MulticlassClassificationMetrics metrics)
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{
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Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
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Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
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Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
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Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}");
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}
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}
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}

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