/// <summary>
/// Learn <paramref name="k" /> top-ranked Transformation.Text programs for a given set of input-output examples.
/// </summary>
/// <param name="trainingExamples">
/// The set of input-output examples as a Tuple of the input and the output.
/// </param>
/// <param name="additionalInputs">
/// The set of additional inputs that do not have output examples, which helps rank learnt programs.
/// </param>
/// <param name="k">the number of top programs</param>
/// <returns>The top-k ranked programs as <see cref="TextTransformationProgram" />s</returns>
public static IEnumerable <TextTransformationProgram> LearnTopK(IDictionary <string, string> trainingExamples,
IEnumerable <string> additionalInputs = null,
int k = 1)
{
if (trainingExamples == null)
{
throw new ArgumentNullException(nameof(trainingExamples));
}
// Load Transformation.Text grammar
Grammar grammar = Language.Grammar;
DomainLearningLogic learningLogic = new Witnesses(grammar,
// This is currently required as a workaround for a bug.
((RankingScore)Learner.Instance.ScoreFeature).Clone());
// Setup configuration of synthesis process.
var engine = new SynthesisEngine(grammar, new SynthesisEngine.Config
{
// Strategies perform the actual logic of the synthesis process.
Strategies = new[] { new DeductiveSynthesis(learningLogic) },
UseThreads = false,
CacheSize = int.MaxValue
});
// Convert the examples in the format expected by Microsoft.ProgramSynthesis.
// Internally, Transformation.Text represents strings as ValueSubstrings to save on
// allocating new strings for each substring.
// Could also use InputRow.AsState() to construct the input state.
Dictionary <State, object> trainExamples = trainingExamples.ToDictionary(
t => State.Create(grammar.InputSymbol, new[] { ValueSubstring.Create(t.Key) }),
t => (object)ValueSubstring.Create(t.Value));
var spec = new ExampleSpec(trainExamples);
// Learn an entire Transformation.Text program (i.e. start at the grammar's start symbol)
// for the specification consisting of the examples.
// Learn the top-k programs according to the score feature used by Transformation.Text by default.
// You could define your own feature on the Transformation.Text grammar to rank programs differently.
var task = new LearningTask(grammar.StartSymbol, spec, k, Learner.Instance.ScoreFeature);
if (additionalInputs != null)
{
task.AdditionalInputs =
additionalInputs.Select(
input =>
State.Create(grammar.InputSymbol, new[] { ValueSubstring.Create(input) }))
.ToList();
}
IEnumerable <ProgramNode> topk = engine.Learn(task).RealizedPrograms;
// Return the generated programs wraped in a TextTransformationProgram object.
return(topk.Select(prog => new TextTransformationProgram(prog)));
}
public JObject ToJson()
{
JObject json = new JObject();
json["txid"] = Hash.ToString();
json["size"] = Size;
json["version"] = Version;
json["script"] = Script.ToHexString();
json["sender"] = Sender.ToAddress();
json["gas"] = new BigDecimal(Gas, (byte)NativeContract.GAS.Decimals).ToString();
json["net_fee"] = new BigDecimal(NetworkFee, (byte)NativeContract.GAS.Decimals).ToString();
json["attributes"] = Attributes.Select(p => p.ToJson()).ToArray();
json["witnesses"] = Witnesses.Select(p => p.ToJson()).ToArray();
return(json);
}
public virtual JObject ToJson()
{
JObject json = new JObject();
json["txid"] = Hash.ToString();
json["size"] = Size;
json["type"] = Type;
json["version"] = Version;
json["nonce"] = Nonce.ToString();
json["account"] = Account.ToString();
json["attributes"] = Attributes.Select(p => p.ToJson()).ToArray();
json["sys_fee"] = SystemFee.ToString();
json["scripts"] = Witnesses.Select(p => p.ToJson()).ToArray();
return(json);
}
public virtual JObject ToJson()
{
JObject json = new JObject();
json["txid"] = Hash.ToString();
json["size"] = Size;
json["type"] = Type;
json["version"] = Version;
json["attributes"] = Attributes.Select(p => p.ToJson()).ToArray();
json["vin"] = Inputs.Select(p => p.ToJson()).ToArray();
json["vout"] = Outputs.Select((p, i) => p.ToJson((ushort)i)).ToArray();
json["sys_fee"] = SystemFee.ToString();
json["net_fee"] = NetworkFee.ToString();
json["scripts"] = Witnesses.Select(p => p.ToJson()).ToArray();
return(json);
}
public JObject ToJson()
{
JObject json = new JObject();
json["hash"] = Hash.ToString();
json["size"] = Size;
json["version"] = Version;
json["nonce"] = Nonce;
json["sender"] = Sender.ToAddress();
json["sys_fee"] = new BigDecimal(SystemFee, NativeContract.GAS.Decimals).ToString();
json["net_fee"] = new BigDecimal(NetworkFee, NativeContract.GAS.Decimals).ToString();
json["valid_until_block"] = ValidUntilBlock;
json["attributes"] = Attributes.Select(p => p.ToJson()).ToArray();
json["script"] = Script.ToHexString();
json["witnesses"] = Witnesses.Select(p => p.ToJson()).ToArray();
return(json);
}
public JObject ToJson()
{
JObject json = new JObject();
json["hash"] = Hash.ToString();
json["size"] = Size;
json["version"] = Version;
json["nonce"] = Nonce;
json["sender"] = Sender.ToAddress();
json["sys_fee"] = SystemFee.ToString();
json["net_fee"] = NetworkFee.ToString();
json["valid_until_block"] = ValidUntilBlock;
json["attributes"] = Attributes.Select(p => p.ToJson()).ToArray();
json["cosigners"] = Cosigners.Select(p => p.ToJson()).ToArray();
json["script"] = Convert.ToBase64String(Script);
json["witnesses"] = Witnesses.Select(p => p.ToJson()).ToArray();
return(json);
}
public static SimData ScoreStates(bool ground_truth, State A, State B)
{
Stopwatch s = new Stopwatch();
s.Start();
ProgramNode program = Synthesizer.Learn(1, A, B);
s.Stop();
Synthesizer.Engine.ClearLearningCache();
return(new SimData {
A = Synthesizer.StateToString(A),
B = Synthesizer.StateToString(B),
GroundTruth = ground_truth,
SynthesisTime = Convert.ToUInt32(s.ElapsedMilliseconds),
Pattern = (program == null ? "<NULL>" : program.AcceptVisitor(new TokensCollector()).CombinedDescription()),
Cost = Convert.ToSingle(program == null ? -DefaultTokens.Any.Score
: -program.GetFeatureValue(Learner.Instance.ScoreFeature)),
Score = Convert.ToSingle(1.0 / (program == null ? Witnesses.ScoreTransform(-DefaultTokens.Any.Score)
: Witnesses.ScoreTransform(-program.GetFeatureValue(Learner.Instance.ScoreFeature))))
});
}
