LinearSvm(
this SweepableBinaryClassificationTrainers trainer,
string labelColumnName = "Label",
string featureColumnName = "Features",
SweepableOption <LinearSvmTrainer.Options> optionBuilder = null,
LinearSvmTrainer.Options defaultOption = null)
{
var context = trainer.Context;
if (optionBuilder == null)
{
optionBuilder = LinearSvmBinaryTrainerSweepableOptions.Default;
}
optionBuilder.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.BinaryClassification.Trainers.LinearSvm(option);
},
optionBuilder,
new string[] { labelColumnName, featureColumnName },
new string[] { PredictedLabel },
nameof(LinearSvmTrainer)));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="LinearSvmTrainer"/> trainer.
/// </summary>
/// <remarks>
/// <para>
/// The idea behind support vector machines, is to map instances into a high dimensional space
/// in which the two classes are linearly separable, i.e., there exists a hyperplane such that all the positive examples are on one side of it,
/// and all the negative examples are on the other.
/// </para>
/// <para>
/// After this mapping, quadratic programming is used to find the separating hyperplane that maximizes the
/// margin, i.e., the minimal distance between it and the instances.
/// </para>
/// </remarks>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static LinearSvmTrainer LinearSupportVectorMachines(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
LinearSvmTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
return(new LinearSvmTrainer(CatalogUtils.GetEnvironment(catalog), options));
}
static void Main(string[] args)
{
MLContext mLContext = new MLContext();
IDataView dane = mLContext.Data.LoadFromTextFile <onceKom>("C:/Users/Patryk/source/repos/ConsoleApp15/Zeszyt1.csv", separatorChar: ',', hasHeader: true);
var split = mLContext.Data
.TrainTestSplit(dane, testFraction: 0.2);
var trainSet = mLContext.Data
.CreateEnumerable <onceKom>(split.TrainSet, reuseRowObject: false);
var testSet = mLContext.Data
.CreateEnumerable <onceKom>(split.TestSet, reuseRowObject: false);
var trening = mLContext.Data.LoadFromEnumerable(trainSet);
var test = mLContext.Data.LoadFromEnumerable(testSet);
PrintPreviewRows(trainSet, testSet);
//Console.ReadLine();
var ustawieniaSvm = new LinearSvmTrainer.Options
{
BatchSize = 10,
PerformProjection = true,
NumberOfIterations = 10
};
var pipeline = mLContext.BinaryClassification.Trainers.LinearSvm(ustawieniaSvm);
var model = pipeline.Fit(trening);
var transformedTestData = model.Transform(test);
var predictions = mLContext.Data
.CreateEnumerable <onceKom>(transformedTestData,
reuseRowObject: false).ToList();
foreach (var p in predictions.Take(5))
{
Console.WriteLine($"Label: {p.win}, "
+ $"Prediction: {p.Features}");
}
Console.ReadLine();
}
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
// Setting the seed to a fixed number in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// Define trainer options.
var options = new LinearSvmTrainer.Options
{
BatchSize = 10,
PerformProjection = true,
NumberOfIterations = 10
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers.LinearSvm(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable <Prediction>(transformedTestData, reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
{
Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
}
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// 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: 0.85
// AUC: 0.95
// F1 Score: 0.86
// Negative Precision: 0.91
// Negative Recall: 0.80
// Positive Precision: 0.80
// Positive Recall: 0.92
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 218 | 20 | 0.9160
// negative || 53 | 209 | 0.7977
// ||======================
// Precision || 0.8044 | 0.9127 |
}
