StandardTrainersCatalog.SdcaLogisticRegression Method
Definition
Important
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Overloads
| SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer+Options) |
Create SdcaLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear classification model. |
| SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>) |
Create SdcaLogisticRegressionBinaryTrainer, which predicts a target using a linear classification model. |
SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer+Options)
Create SdcaLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear classification model.
public static Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer SdcaLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer.Options options);static member SdcaLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer.Options -> Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer<Extension()>
Public Function SdcaLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SdcaLogisticRegressionBinaryTrainer.Options) As SdcaLogisticRegressionBinaryTrainerParameters
The binary classification catalog trainer object.
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 SdcaLogisticRegressionWithOptions
{
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);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaLogisticRegressionBinaryTrainer.Options()
{
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
MaximumNumberOfIterations = 30,
// Give the instances of the positive class slightly more weight.
PositiveInstanceWeight = 1.2f,
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaLogisticRegression(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}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.60
// AUC: 0.67
// F1 Score: 0.65
// Negative Precision: 0.69
// Negative Recall: 0.45
// Positive Precision: 0.56
// Positive Recall: 0.77
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
//
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 165 | 73 | 0.6933
// negative || 112 | 150 | 0.5725
// ||======================
// Precision || 0.5957 | 0.6726 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"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
SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
Create SdcaLogisticRegressionBinaryTrainer, which predicts a target using a linear classification model.
public static Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer SdcaLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer<Extension()>
Public Function SdcaLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaLogisticRegressionBinaryTrainerParameters
The binary classification catalog trainer object.
- featureColumnName
- String
The name of the feature column. The column data must be a known-sized vector of Single.
- exampleWeightColumnName
- String
The name of the example weight column (optional).
The L2 weight for regularization.
The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.
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 SdcaLogisticRegression
{
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);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaLogisticRegression();
// 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}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.63
// AUC: 0.70
// F1 Score: 0.64
// Negative Precision: 0.67
// Negative Recall: 0.60
// Positive Precision: 0.60
// Positive Recall: 0.68
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 154 | 84 | 0.6471
// negative || 94 | 168 | 0.6412
// ||======================
// Precision || 0.6210 | 0.6667 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"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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