private static IDataView ApplyKeyToVec(List <KeyToVectorTransform.ColumnInfo> ktv, IDataView viewTrain, IHost host)
{
Contracts.AssertValueOrNull(ktv);
Contracts.AssertValue(viewTrain);
Contracts.AssertValue(host);
if (Utils.Size(ktv) > 0)
{
// Instead of simply using KeyToVector, we are jumping to some hoops here to do the right thing in a very common case
// when the user has slightly different key values between the training and testing set.
// The solution is to apply KeyToValue, then Term using the terms from the key metadata of the original key column
// and finally the KeyToVector transform.
viewTrain = new KeyToValueTransform(host, ktv.Select(x => (x.Input, x.Output)).ToArray())
.Transform(viewTrain);
viewTrain = TermTransform.Create(host,
new TermTransform.Arguments()
{
Column = ktv
.Select(c => new TermTransform.Column()
{
Name = c.Output, Source = c.Output, Terms = GetTerms(viewTrain, c.Input)
})
.ToArray(),
TextKeyValues = true
},
viewTrain);
viewTrain = KeyToVectorTransform.Create(host, viewTrain, ktv.Select(c => new KeyToVectorTransform.ColumnInfo(c.Output, c.Output)).ToArray());
}
return(viewTrain);
}
void DecomposableTrainAndPredict()
{
using (var env = new LocalEnvironment()
.AddStandardComponents()) // ScoreUtils.GetScorer requires scorers to be registered in the ComponentCatalog
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
var term = TermTransform.Create(env, loader, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
var trainer = new SdcaMultiClassTrainer(env, "Features", "Label", advancedSettings: (s) => { s.MaxIterations = 100; s.Shuffle = true; s.NumThreads = 1; });
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Cut out term transform from pipeline.
var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term);
var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(newScorer);
var model = env.CreatePredictionEngine <IrisDataNoLabel, IrisPrediction>(keyToValue);
var testData = loader.AsEnumerable <IrisDataNoLabel>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == "Iris-setosa");
}
}
}
public TransformWrapper Fit(IDataView input)
{
var xf = new TermTransform(_env, input, _column, _srcColumn);
var empty = new EmptyDataView(_env, input.Schema);
var chunk = ApplyTransformUtils.ApplyAllTransformsToData(_env, xf, empty, input);
return(new TransformWrapper(_env, chunk));
}
public static CommonOutputs.TransformOutput TermTransform(IHostEnvironment env, TermTransform.Arguments input)
{
var h = EntryPointUtils.CheckArgsAndCreateHost(env, "TermTransform", input);
var xf = new TermTransform(h, input, input.Data);
return(new CommonOutputs.TransformOutput()
{
Model = new TransformModel(h, xf, input.Data),
OutputData = xf
});
}
void Extensibility()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new LocalEnvironment())
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
Action <IrisData, IrisData> action = (i, j) =>
{
j.Label = i.Label;
j.PetalLength = i.SepalLength > 3 ? i.PetalLength : i.SepalLength;
j.PetalWidth = i.PetalWidth;
j.SepalLength = i.SepalLength;
j.SepalWidth = i.SepalWidth;
};
var lambda = LambdaTransform.CreateMap(env, loader, action);
var term = TermTransform.Create(env, lambda, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Transform(term);
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(scorer);
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
public static CommonOutputs.TransformOutput PrepareClassificationLabel(IHostEnvironment env, ClassificationLabelInput input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("PrepareClassificationLabel");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
int labelCol;
if (!input.Data.Schema.TryGetColumnIndex(input.LabelColumn, out labelCol))
{
throw host.Except($"Column '{input.LabelColumn}' not found.");
}
var labelType = input.Data.Schema.GetColumnType(labelCol);
if (labelType.IsKey || labelType.IsBool)
{
var nop = NopTransform.CreateIfNeeded(env, input.Data);
return(new CommonOutputs.TransformOutput {
Model = new TransformModel(env, nop, input.Data), OutputData = nop
});
}
var args = new TermTransform.Arguments()
{
Column = new[]
{
new TermTransform.Column()
{
Name = input.LabelColumn,
Source = input.LabelColumn,
TextKeyValues = input.TextKeyValues,
Sort = TermTransform.SortOrder.Value
}
}
};
var xf = TermTransform.Create(host, args, input.Data);
return(new CommonOutputs.TransformOutput {
Model = new TransformModel(env, xf, input.Data), OutputData = xf
});
}
public void Metacomponents()
{
using (var env = new LocalEnvironment())
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
var term = TermTransform.Create(env, loader, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new AveragedPerceptronTrainer(env, new AveragedPerceptronTrainer.Arguments()))
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new TrainContext(trainRoles));
}
}
void Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = new SimpleComponentFactory <ITrainer <IPredictorProducing <float> > >
(
(e) => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments())
)
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
public void Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments()))
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new TrainContext(trainRoles));
}
}
void DecomposableTrainAndPredict()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Cut out term transform from pipeline.
var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term);
var keyToValue = new KeyToValueTransform(env, newScorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisDataNoLabel, IrisPrediction>(keyToValue);
var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisDataNoLabel>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == "Iris-setosa");
}
}
}
public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input,
TermLoaderArguments termLoaderArgs = null)
{
Contracts.CheckValue(env, nameof(env));
var h = env.Register(LoaderSignature);
h.CheckValue(args, nameof(args));
h.CheckValue(input, nameof(input));
h.CheckUserArg(Utils.Size(args.Column) > 0, nameof(args.Column), "Columns must be specified");
// To each input column to the NgramHashExtractorArguments, a HashTransform using 31
// bits (to minimize collisions) is applied first, followed by an NgramHashTransform.
IDataView view = input;
List <TermTransform.Column> termCols = null;
if (termLoaderArgs != null)
{
termCols = new List <TermTransform.Column>();
}
var hashColumns = new List <HashTransformer.Column>();
var ngramHashColumns = new NgramHashTransform.Column[args.Column.Length];
var colCount = args.Column.Length;
// The NGramHashExtractor has a ManyToOne column type. To avoid stepping over the source
// column name when a 'name' destination column name was specified, we use temporary column names.
string[][] tmpColNames = new string[colCount][];
for (int iinfo = 0; iinfo < colCount; iinfo++)
{
var column = args.Column[iinfo];
h.CheckUserArg(!string.IsNullOrWhiteSpace(column.Name), nameof(column.Name));
h.CheckUserArg(Utils.Size(column.Source) > 0 &&
column.Source.All(src => !string.IsNullOrWhiteSpace(src)), nameof(column.Source));
int srcCount = column.Source.Length;
tmpColNames[iinfo] = new string[srcCount];
for (int isrc = 0; isrc < srcCount; isrc++)
{
var tmpName = input.Schema.GetTempColumnName(column.Source[isrc]);
tmpColNames[iinfo][isrc] = tmpName;
if (termLoaderArgs != null)
{
termCols.Add(
new TermTransform.Column
{
Name = tmpName,
Source = column.Source[isrc]
});
}
hashColumns.Add(
new HashTransformer.Column
{
Name = tmpName,
Source = termLoaderArgs == null ? column.Source[isrc] : tmpName,
HashBits = 30,
Seed = column.Seed,
Ordered = false,
InvertHash = column.InvertHash
});
}
ngramHashColumns[iinfo] =
new NgramHashTransform.Column
{
Name = column.Name,
Source = tmpColNames[iinfo],
AllLengths = column.AllLengths,
HashBits = column.HashBits,
NgramLength = column.NgramLength,
RehashUnigrams = false,
Seed = column.Seed,
SkipLength = column.SkipLength,
Ordered = column.Ordered,
InvertHash = column.InvertHash,
// REVIEW: This is an ugly internal hack to get around
// the problem that we want the *original* source names surfacing
// in the descriptions where appropriate, rather than _tmp000 and
// what have you. The alternative is we do something elaborate
// with metadata or something but I'm not sure that's better.
FriendlyNames = column.FriendlyNames
};
}
if (termLoaderArgs != null)
{
h.Assert(Utils.Size(termCols) == hashColumns.Count);
var termArgs =
new TermTransform.Arguments()
{
MaxNumTerms = int.MaxValue,
Terms = termLoaderArgs.Terms,
Term = termLoaderArgs.Term,
DataFile = termLoaderArgs.DataFile,
Loader = termLoaderArgs.Loader,
TermsColumn = termLoaderArgs.TermsColumn,
Sort = termLoaderArgs.Sort,
Column = termCols.ToArray()
};
view = TermTransform.Create(h, termArgs, view);
if (termLoaderArgs.DropUnknowns)
{
var naDropArgs = new NADropTransform.Arguments {
Column = new NADropTransform.Column[termCols.Count]
};
for (int iinfo = 0; iinfo < termCols.Count; iinfo++)
{
naDropArgs.Column[iinfo] =
new NADropTransform.Column {
Name = termCols[iinfo].Name, Source = termCols[iinfo].Name
};
}
view = new NADropTransform(h, naDropArgs, view);
}
}
// Args for the Hash function with multiple columns
var hashArgs =
new HashTransformer.Arguments
{
HashBits = 31,
Seed = args.Seed,
Ordered = false,
Column = hashColumns.ToArray(),
InvertHash = args.InvertHash
};
view = HashTransformer.Create(h, hashArgs, view);
// creating the NgramHash function
var ngramHashArgs =
new NgramHashTransform.Arguments
{
AllLengths = args.AllLengths,
HashBits = args.HashBits,
NgramLength = args.NgramLength,
SkipLength = args.SkipLength,
RehashUnigrams = false,
Ordered = args.Ordered,
Seed = args.Seed,
Column = ngramHashColumns,
InvertHash = args.InvertHash
};
view = new NgramHashTransform(h, ngramHashArgs, view);
return(SelectColumnsTransform.CreateDrop(h, view, tmpColNames.SelectMany(cols => cols).ToArray()));
}
public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input,
TermLoaderArguments termLoaderArgs = null)
{
Contracts.CheckValue(env, nameof(env));
var h = env.Register(LoaderSignature);
h.CheckValue(args, nameof(args));
h.CheckValue(input, nameof(input));
h.CheckUserArg(Utils.Size(args.Column) > 0, nameof(args.Column), "Columns must be specified");
IDataView view = input;
var termCols = new List <Column>();
var isTermCol = new bool[args.Column.Length];
for (int i = 0; i < args.Column.Length; i++)
{
var col = args.Column[i];
h.CheckNonWhiteSpace(col.Name, nameof(col.Name));
h.CheckNonWhiteSpace(col.Source, nameof(col.Source));
int colId;
if (input.Schema.TryGetColumnIndex(col.Source, out colId) &&
input.Schema.GetColumnType(colId).ItemType.IsText)
{
termCols.Add(col);
isTermCol[i] = true;
}
}
// If the column types of args.column are text, apply term transform to convert them to keys.
// Otherwise, skip term transform and apply ngram transform directly.
// This logic allows NgramExtractorTransform to handle both text and key input columns.
// Note: ngram transform handles the validation of the types natively (in case the types
// of args.column are not text nor keys).
if (termCols.Count > 0)
{
TermTransform.Arguments termArgs = null;
MissingValueDroppingTransformer.Arguments naDropArgs = null;
if (termLoaderArgs != null)
{
termArgs =
new TermTransform.Arguments()
{
MaxNumTerms = int.MaxValue,
Terms = termLoaderArgs.Terms,
Term = termLoaderArgs.Term,
DataFile = termLoaderArgs.DataFile,
Loader = termLoaderArgs.Loader,
TermsColumn = termLoaderArgs.TermsColumn,
Sort = termLoaderArgs.Sort,
Column = new TermTransform.Column[termCols.Count]
};
if (termLoaderArgs.DropUnknowns)
{
naDropArgs = new MissingValueDroppingTransformer.Arguments {
Column = new MissingValueDroppingTransformer.Column[termCols.Count]
}
}
;
}
else
{
termArgs =
new TermTransform.Arguments()
{
MaxNumTerms = Utils.Size(args.MaxNumTerms) > 0 ? args.MaxNumTerms[0] : NgramTransform.Arguments.DefaultMaxTerms,
Column = new TermTransform.Column[termCols.Count]
};
}
for (int iinfo = 0; iinfo < termCols.Count; iinfo++)
{
var column = termCols[iinfo];
termArgs.Column[iinfo] =
new TermTransform.Column()
{
Name = column.Name,
Source = column.Source,
MaxNumTerms = Utils.Size(column.MaxNumTerms) > 0 ? column.MaxNumTerms[0] : default(int?)
};
if (naDropArgs != null)
{
naDropArgs.Column[iinfo] = new MissingValueDroppingTransformer.Column {
Name = column.Name, Source = column.Name
}
}
;
}
view = TermTransform.Create(h, termArgs, view);
if (naDropArgs != null)
{
view = new MissingValueDroppingTransformer(h, naDropArgs, view);
}
}
var ngramArgs =
new NgramTransform.Arguments()
{
MaxNumTerms = args.MaxNumTerms,
NgramLength = args.NgramLength,
SkipLength = args.SkipLength,
AllLengths = args.AllLengths,
Weighting = args.Weighting,
Column = new NgramTransform.Column[args.Column.Length]
};
for (int iinfo = 0; iinfo < args.Column.Length; iinfo++)
{
var column = args.Column[iinfo];
ngramArgs.Column[iinfo] =
new NgramTransform.Column()
{
Name = column.Name,
Source = isTermCol[iinfo] ? column.Name : column.Source,
AllLengths = column.AllLengths,
MaxNumTerms = column.MaxNumTerms,
NgramLength = column.NgramLength,
SkipLength = column.SkipLength,
Weighting = column.Weighting
};
}
return(new NgramTransform(h, ngramArgs, view));
}
