/// <summary>
/// Simplify learning of examples with action dependent features.
/// </summary>
/// <typeparam name="TExample">User example type.</typeparam>
/// <typeparam name="TActionDependentFeature">Action dependent feature type.</typeparam>
/// <param name="vw">The VowpalWabbit instances.</param>
/// <param name="serializer">The example serializer.</param>
/// <param name="actionDependentFeatureSerializer">The action dependent feature serializer.</param>
/// <param name="example">The example.</param>
/// <param name="actionDependentFeatures">The action dependent features.</param>
/// <param name="predictOrLearn">An action executed once the set of valid examples is determined. </param>
/// <param name="index">The optional index of the action dependent feature this label belongs too.</param>
/// <param name="label">The optional label to be used for learning or evaluation.</param>
public static void Execute <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IReadOnlyCollection <TActionDependentFeature> actionDependentFeatures,
LearnOrPredictAction <TActionDependentFeature> predictOrLearn,
int?index = null,
ILabel label = null)
{
Contract.Requires(vw != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
var examples = new List <VowpalWabbitExample>(actionDependentFeatures.Count + 1);
var validExamples = new List <VowpalWabbitExample>(actionDependentFeatures.Count + 1);
var validActionDependentFeatures = new List <ActionDependentFeature <TActionDependentFeature> >(actionDependentFeatures.Count + 1);
var emptyActionDependentFeatures = new List <ActionDependentFeature <TActionDependentFeature> >(actionDependentFeatures.Count + 1);
try
{
// contains prediction results
if (serializer != null)
{
var sharedExample = serializer.Serialize(example, SharedLabel.Instance);
// check if we have shared features
if (sharedExample != null)
{
examples.Add(sharedExample);
if (!sharedExample.IsNewLine)
{
validExamples.Add(sharedExample);
}
}
}
var i = 0;
foreach (var actionDependentFeature in actionDependentFeatures)
{
var adfExample = actionDependentFeatureSerializer.Serialize(actionDependentFeature,
index != null && i == index ? label : null);
Contract.Assert(adfExample != null);
examples.Add(adfExample);
if (!adfExample.IsNewLine)
{
validExamples.Add(adfExample);
validActionDependentFeatures.Add(new ActionDependentFeature <TActionDependentFeature>(i, actionDependentFeature));
}
else
{
emptyActionDependentFeatures.Add(new ActionDependentFeature <TActionDependentFeature>(i, actionDependentFeature));
}
i++;
}
if (validActionDependentFeatures.Count == 0)
{
return;
}
// signal we're finished using an empty example
var empty = vw.GetOrCreateEmptyExample();
examples.Add(empty);
validExamples.Add(empty);
predictOrLearn(validExamples, validActionDependentFeatures, emptyActionDependentFeatures);
}
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 prediction 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>
/// <returns>An ranked subset of predicted action indexes.</returns>
public static int[] PredictIndex <TExample, TActionDependentFeature>(
VowpalWabbit vw,
VowpalWabbitSerializer <TExample> serializer,
VowpalWabbitSerializer <TActionDependentFeature> actionDependentFeatureSerializer,
TExample example,
IEnumerable <TActionDependentFeature> actionDependentFeatures)
{
Contract.Requires(vw != null);
Contract.Requires(serializer != null);
Contract.Requires(actionDependentFeatureSerializer != null);
Contract.Requires(example != null);
Contract.Requires(actionDependentFeatures != null);
// shared |userlda :.1 |che a:.1
// `doc1 |lda :.1 :.2 [1]
// `doc2 |lda :.2 :.3 [2]
// <new line>
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.Predict(sharedExample);
}
// leave as loop (vs. linq) so if the serializer throws an exception, anything allocated so far can be free'd
foreach (var actionDependentFeature in actionDependentFeatures)
{
var adfExample = actionDependentFeatureSerializer.Serialize(vw, actionDependentFeature);
Contract.Assert(adfExample != null);
examples.Add(adfExample);
vw.Predict(adfExample);
}
// signal we're finished using an empty example
var empty = vw.GetOrCreateEmptyExample();
examples.Add(empty);
vw.Predict(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();
}
}
}
/// <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();
}
}
}
