StandardTrainersCatalog.SgdNonCalibrated Method
Definition
Important
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Overloads
| SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdNonCalibratedTrainer+Options) |
Create SgdNonCalibratedTrainer with advanced options, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. |
| SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, IClassificationLoss, Int32, Double, Single) |
Create SgdNonCalibratedTrainer, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. |
SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdNonCalibratedTrainer+Options)
Create SgdNonCalibratedTrainer with advanced options, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
public static Microsoft.ML.Trainers.SgdNonCalibratedTrainer SgdNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SgdNonCalibratedTrainer.Options options);static member SgdNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SgdNonCalibratedTrainer.Options -> Microsoft.ML.Trainers.SgdNonCalibratedTrainer<Extension()>
Public Function SgdNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SgdNonCalibratedTrainer.Options) As SgdNonCalibratedTrainerParameters
The binary classification catalog trainer object.
- options
- SgdNonCalibratedTrainer.Options
Trainer options.
Returns
Examples
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SgdNonCalibratedWithOptions
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// Define trainer options.
var options = new SgdNonCalibratedTrainer.Options
{
LearningRate = 0.01,
NumberOfIterations = 10,
L2Regularization = 1e-7f
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SgdNonCalibrated(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.59
// AUC: 0.61
// F1 Score: 0.41
// Negative Precision: 0.57
// Negative Recall: 0.85
// Positive Precision: 0.64
// Positive Recall: 0.30
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 137 | 101 | 0.5756
// negative || 118 | 144 | 0.5496
// ||======================
// Precision || 0.5373 | 0.5878 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Applies to
SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, IClassificationLoss, Int32, Double, Single)
Create SgdNonCalibratedTrainer, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
public static Microsoft.ML.Trainers.SgdNonCalibratedTrainer SgdNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.IClassificationLoss lossFunction = default, int numberOfIterations = 20, double learningRate = 0.01, float l2Regularization = 1E-06);static member SgdNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Microsoft.ML.Trainers.IClassificationLoss * int * double * single -> Microsoft.ML.Trainers.SgdNonCalibratedTrainer<Extension()>
Public Function SgdNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As IClassificationLoss = Nothing, Optional numberOfIterations As Integer = 20, Optional learningRate As Double = 0.01, Optional l2Regularization As Single = 1E-06) As SgdNonCalibratedTrainerParameters
The binary classification catalog trainer object.
- labelColumnName
- String
The name of the label column, or dependent variable. The column data must be Boolean.
- featureColumnName
- String
The features, or independent variables. The column data must be a known-sized vector of Single.
- exampleWeightColumnName
- String
The name of the example weight column (optional).
- lossFunction
- IClassificationLoss
The loss function minimized in the training process. Using, for example, HingeLoss leads to a support vector machine trainer.
- numberOfIterations
- Int32
The maximum number of passes through the training dataset; set to 1 to simulate online learning.
- learningRate
- Double
The initial learning rate used by SGD.
- l2Regularization
- Single
The L2 weight for regularization.
Returns
Examples
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SgdNonCalibrated
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SgdNonCalibrated();
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.60
// AUC: 0.63
// F1 Score: 0.43
// Negative Precision: 0.58
// Negative Recall: 0.85
// Positive Precision: 0.66
// Positive Recall: 0.32
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 76 | 162 | 0.3193
// negative || 42 | 220 | 0.8397
// ||======================
// Precision || 0.6441 | 0.5759 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Applies to
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