public void TestWiki()
{
using (var vw = new VW.VowpalWabbit("-f rcv1.model"))
{
// 1 |f 13:3.9656971e-02 24:3.4781646e-02 69:4.6296168e-02
using (var exampleBuilder = new VW.VowpalWabbitExampleBuilder(vw))
{
// important to dispose the namespace builder at the end, as data is only added to the example
// if there is any feature added to the namespace
using (var ns = exampleBuilder.AddNamespace('f'))
{
var namespaceHash = vw.HashSpace("f");
var featureHash = vw.HashFeature("13", namespaceHash);
ns.AddFeature(featureHash, 8.5609287e-02f);
featureHash = vw.HashFeature("24", namespaceHash);
ns.AddFeature(featureHash, 3.4781646e-02f);
featureHash = vw.HashFeature("69", namespaceHash);
ns.AddFeature(featureHash, 4.6296168e-02f);
}
exampleBuilder.ApplyLabel(new SimpleLabel() { Label = 1 });
// hand over of memory management
using (var example = exampleBuilder.CreateExample())
{
VowpalWabbitExampleValidator.Validate("1 |f 13:8.5609287e-02 24:3.4781646e-02 69:4.6296168e-02", example, VowpalWabbitLabelComparator.Simple);
vw.Learn(example);
}
}
}
}
/// <summary>
/// Simplify learning of examples with action dependent features.
/// </summary>
public static void Learn <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IReadOnlyCollection <TActionDependentFeature> actionDependentFeatures,
int index,
ILabel label)
{
Contract.Requires(vw != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
Contract.Requires(index >= 0);
Contract.Requires(label != null);
Execute(
vw,
serializer,
actionDependentFeatureSerializer,
example,
actionDependentFeatures,
(examples, _, __) =>
{
foreach (var ex in examples)
{
vw.Learn(ex);
}
},
index,
label);
}
/// <summary>
/// Simplify learning of examples with action dependent features.
/// </summary>
/// <typeparam name="TExample">The type of the user example.</typeparam>
/// <typeparam name="TActionDependentFeature">The type of the user action dependent features.</typeparam>
/// <param name="vw">The vw instance.</param>
/// <param name="serializer">The serializer for <typeparamref name="TExample"/>.</param>
/// <param name="actionDependentFeatureSerializer">The serializer for <typeparamref name="TActionDependentFeature"/>.</param>
/// <param name="example">The user example.</param>
/// <param name="actionDependentFeatures">The action dependent features.</param>
/// <param name="index">The index of action dependent feature to label.</param>
/// <param name="label">The label for the selected action dependent feature.</param>
/// <returns>An ranked subset of predicted actions.</returns>
public static ActionDependentFeature <TActionDependentFeature>[] LearnAndPredict <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IReadOnlyCollection <TActionDependentFeature> actionDependentFeatures,
int index,
ILabel label)
{
Contract.Requires(vw != null);
Contract.Requires(serializer != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
Contract.Requires(index >= 0);
Contract.Requires(label != null);
ActionDependentFeature <TActionDependentFeature>[] predictions = null;
Execute(
vw,
serializer,
actionDependentFeatureSerializer,
example,
actionDependentFeatures,
(examples, validActionDependentFeatures, emptyActionDependentFeatures) =>
{
foreach (var ex in examples)
{
vw.Learn(ex);
}
predictions = VowpalWabbitMultiLine.GetPrediction(vw, examples, validActionDependentFeatures, emptyActionDependentFeatures);
},
index,
label);
// default to the input list
return(predictions ?? actionDependentFeatures.Select((o, i) => new ActionDependentFeature <TActionDependentFeature>(i, o)).ToArray());
}
public void TestWiki()
{
using (var vw = new VW.VowpalWabbit("-f rcv1.model"))
{
// 1 |f 13:3.9656971e-02 24:3.4781646e-02 69:4.6296168e-02
using (var exampleBuilder = new VW.VowpalWabbitExampleBuilder(vw))
{
// important to dispose the namespace builder at the end, as data is only added to the example
// if there is any feature added to the namespace
using (var ns = exampleBuilder.AddNamespace('f'))
{
var namespaceHash = vw.HashSpace("f");
var featureHash = vw.HashFeature("13", namespaceHash);
ns.AddFeature(featureHash, 8.5609287e-02f);
featureHash = vw.HashFeature("24", namespaceHash);
ns.AddFeature(featureHash, 3.4781646e-02f);
featureHash = vw.HashFeature("69", namespaceHash);
ns.AddFeature(featureHash, 4.6296168e-02f);
}
exampleBuilder.ApplyLabel(new SimpleLabel()
{
Label = 1
});
// hand over of memory management
using (var example = exampleBuilder.CreateExample())
{
VowpalWabbitExampleValidator.Validate("1 |f 13:8.5609287e-02 24:3.4781646e-02 69:4.6296168e-02", example, VowpalWabbitLabelComparator.Simple);
vw.Learn(example);
}
}
}
}
/// <summary>
/// Learns from these examples.
/// </summary>
protected override void LearnInternal(VowpalWabbit vw)
{
// unfortunately can't specify <void>
this.Execute <int>(vw, ex => vw.Learn(ex));
}
/// <summary>
/// Learn from these examples and returns the current prediction for it.
/// </summary>
/// <typeparam name="TPrediction">The prediction type.</typeparam>
/// <param name="predictionFactory">The prediction factory to be used. See <see cref="VowpalWabbitPredictionType"/>.</param>
/// <returns>The prediction for the this example.</returns>
protected override TPrediction LearnInternal <TPrediction>(IVowpalWabbitPredictionFactory <TPrediction> predictionFactory, VowpalWabbit vw)
{
return(this.Execute(vw, ex => vw.Learn(ex), predictionFactory));
}
/// <summary>
/// Learn from this example and returns the current prediction for it.
/// </summary>
/// <typeparam name="TPrediction">The prediction type.</typeparam>
/// <param name="predictionFactory">The prediction factory to be used. See <see cref="VowpalWabbitPredictionType"/>.</param>
/// <param name="vw">The VW native instance.</param>
/// <returns>The prediction for the this example.</returns>
protected override TPrediction LearnInternal <TPrediction>(IVowpalWabbitPredictionFactory <TPrediction> predictionFactory, VowpalWabbit vw)
{
return(vw.Learn <TPrediction>(this.Example, predictionFactory));
}
/// <summary>
/// Learns from this example.
/// </summary>
protected override void LearnInternal(VowpalWabbit vw)
{
vw.Learn(this.Example);
}
/// <summary>
/// Simplify learning of examples with action dependent features.
/// </summary>
public static void Learn <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IEnumerable <TActionDependentFeature> actionDependentFeatures,
int index,
ILabel label)
{
Contract.Requires(vw != null);
Contract.Requires(serializer != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
Contract.Requires(index >= 0);
Contract.Requires(label != null);
#if DEBUG
// only in debug, since it's a hot path
if (actionDependentFeatureSerializer.CachesExamples)
{
throw new NotSupportedException("Cached examples cannot be used for learning");
}
#endif
var examples = new List <VowpalWabbitExample>();
try
{
// contains prediction results
var sharedExample = serializer.Serialize(vw, example, SharedLabel.Instance);
// check if we have shared features
if (sharedExample != null)
{
examples.Add(sharedExample);
vw.Learn(sharedExample);
}
var i = 0;
foreach (var actionDependentFeature in actionDependentFeatures)
{
var adfExample = actionDependentFeatureSerializer.Serialize(vw, actionDependentFeature, i == index ? label : null);
Contract.Assert(adfExample != null);
examples.Add(adfExample);
vw.Learn(adfExample);
i++;
}
// signal we're finished using an empty example
var empty = vw.GetOrCreateEmptyExample();
examples.Add(empty);
vw.Learn(empty);
// Dump input file for command line learning
//File.AppendAllLines(@"c:\temp\msn.txt",
// examples.OfType<VowpalWabbitDebugExample>()
// .Select(e => e.VowpalWabbitString)
// .Union(new[] { "" }));
}
finally
{
// dispose examples
// Note: must not dispose examples before final example
// as the learning algorithm (such as cbf) keeps a reference
// to the example
foreach (var e in examples)
{
e.Dispose();
}
}
}
/// <summary>
/// Simplify learning of examples with action dependent features.
/// </summary>
/// <typeparam name="TExample">The type of the user example.</typeparam>
/// <typeparam name="TActionDependentFeature">The type of the user action dependent features.</typeparam>
/// <param name="vw">The vw instance.</param>
/// <param name="serializer">The serializer for <typeparamref name="TExample"/>.</param>
/// <param name="actionDependentFeatureSerializer">The serializer for <typeparamref name="TActionDependentFeature"/>.</param>
/// <param name="example">The user example.</param>
/// <param name="actionDependentFeatures">The action dependent features.</param>
/// <param name="index">The index of action dependent feature to label.</param>
/// <param name="label">The label for the selected action dependent feature.</param>
/// <returns>An ranked subset of predicted action indexes.</returns>
public static int[] LearnAndPredictIndex <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IEnumerable <TActionDependentFeature> actionDependentFeatures,
int index,
ILabel label)
{
Contract.Requires(vw != null);
Contract.Requires(serializer != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
Contract.Requires(index >= 0);
Contract.Requires(label != null);
#if DEBUG
// only in debug, since it's a hot path
if (actionDependentFeatureSerializer.CachesExamples)
{
throw new NotSupportedException("Cached examples cannot be used for learning");
}
#endif
var examples = new List <VowpalWabbitExample>();
try
{
// contains prediction results
var sharedExample = serializer.Serialize(vw, example);
// check if we have shared features
if (sharedExample != null)
{
examples.Add(sharedExample);
vw.Learn(sharedExample);
}
// leave as loop (vs. linq) so if the serializer throws an exception, anything allocated so far can be free'd
var i = 0;
foreach (var actionDependentFeature in actionDependentFeatures)
{
var adfExample = actionDependentFeatureSerializer.Serialize(vw, actionDependentFeature, i == index ? label : null);
Contract.Assert(adfExample != null);
examples.Add(adfExample);
vw.Learn(adfExample);
i++;
}
// signal we're finished using an empty example
var empty = vw.GetOrCreateEmptyExample();
examples.Add(empty);
vw.Learn(empty);
// Nasty workaround. Since the prediction result is stored in the first example
// and we'll have to get an actual VowpalWabbitExampt
var firstExample = examples.FirstOrDefault();
if (firstExample == null)
{
return(null);
}
return(firstExample.GetPrediction(vw, VowpalWabbitPredictionType.Multilabel));
}
finally
{
// dispose examples
// Note: must not dispose examples before final example
// as the learning algorithm (such as cbf) keeps a reference
// to the example
foreach (var e in examples)
{
e.Dispose();
}
}
}