StandardTrainersCatalog.Sdca 方法
定義
重要
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多載
Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)
SdcaRegressionTrainer使用進階選項建立 ，以使用線性回歸模型預測目標。
public static Microsoft.ML.Trainers.SdcaRegressionTrainer Sdca (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.SdcaRegressionTrainer.Options options);static member Sdca : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.SdcaRegressionTrainer.Options -> Microsoft.ML.Trainers.SdcaRegressionTrainer<Extension()>
Public Function Sdca (catalog As RegressionCatalog.RegressionTrainers, options As SdcaRegressionTrainer.Options) As SdcaRegressionTrainer參數
回歸目錄定型器物件。
- options
- SdcaRegressionTrainer.Options
定型器選項。
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.Regression
{
public static class SdcaWithOptions
{
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 SdcaRegressionTrainer.Options
{
LabelColumnName = nameof(DataPoint.Label),
FeatureColumnName = nameof(DataPoint.Features),
// Make the convergence tolerance tighter. It effectively leads to
// more training iterations.
ConvergenceTolerance = 0.02f,
// Increase the maximum number of passes over training data. Similar
// to ConvergenceTolerance, this value specifics the hard iteration
// limit on the training algorithm.
MaximumNumberOfIterations = 30,
// Increase learning rate for bias.
BiasLearningRate = 0.1f
};
// Define the trainer.
var pipeline =
mlContext.Regression.Trainers.Sdca(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(5, 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();
// Look at 5 predictions for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
// Expected output:
// Label: 0.985, Prediction: 0.927
// Label: 0.155, Prediction: 0.062
// Label: 0.515, Prediction: 0.439
// Label: 0.566, Prediction: 0.500
// Label: 0.096, Prediction: 0.078
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.05
// Mean Squared Error: 0.00
// Root Mean Squared Error: 0.06
// RSquared: 0.97 (closer to 1 is better. The worst case is 0)
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
for (int i = 0; i < count; i++)
{
float label = (float)random.NextDouble();
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
Features = Enumerable.Repeat(label, 50).Select(
x => x + (float)random.NextDouble()).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public float Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public float Label { get; set; }
// Predicted score from the trainer.
public float Score { get; set; }
}
// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine(
"Root Mean Squared Error: " + metrics.RootMeanSquaredError);
Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}
適用於
Sdca(RegressionCatalog+RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
建立 SdcaRegressionTrainer ，其會使用線性回歸模型預測目標。
public static Microsoft.ML.Trainers.SdcaRegressionTrainer Sdca (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaRegressionLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member Sdca : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaRegressionLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaRegressionTrainer<Extension()>
Public Function Sdca (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaRegressionLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaRegressionTrainer參數
回歸目錄定型器物件。
- exampleWeightColumnName
- String
範例加權資料行的名稱 (選擇性) 。
- lossFunction
- ISupportSdcaRegressionLoss
定型程式中最小化的 遺失 函式。 例如，其預設值 SquaredLoss 會導致最小平方定型器。
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.Regression
{
public static class Sdca
{
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.Regression.Trainers.Sdca(
labelColumnName: nameof(DataPoint.Label),
featureColumnName: nameof(DataPoint.Features));
// 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(5, 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();
// Look at 5 predictions for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
// Expected output:
// Label: 0.985, Prediction: 0.960
// Label: 0.155, Prediction: 0.072
// Label: 0.515, Prediction: 0.455
// Label: 0.566, Prediction: 0.500
// Label: 0.096, Prediction: 0.079
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.05
// Mean Squared Error: 0.00
// Root Mean Squared Error: 0.06
// RSquared: 0.97 (closer to 1 is better. The worst case is 0)
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
for (int i = 0; i < count; i++)
{
float label = (float)random.NextDouble();
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
Features = Enumerable.Repeat(label, 50).Select(
x => x + (float)random.NextDouble()).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public float Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public float Label { get; set; }
// Predicted score from the trainer.
public float Score { get; set; }
}
// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine(
"Root Mean Squared Error: " + metrics.RootMeanSquaredError);
Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}