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 void ConcatWithAliases()
{
string dataPath = GetDataPath("adult.test");
var source = new MultiFileSource(dataPath);
var loader = new TextLoader(Env, new TextLoader.Arguments
{
Column = new[] {
new TextLoader.Column("float1", DataKind.R4, 0),
new TextLoader.Column("float4", DataKind.R4, new[] { new TextLoader.Range(0), new TextLoader.Range(2), new TextLoader.Range(4), new TextLoader.Range(10) }),
new TextLoader.Column("vfloat", DataKind.R4, new[] { new TextLoader.Range(0), new TextLoader.Range(2), new TextLoader.Range(4), new TextLoader.Range(10, null)
{
AutoEnd = false, VariableEnd = true
} })
},
Separator = ",",
HasHeader = true
}, new MultiFileSource(dataPath));
var data = loader.Read(source);
ColumnType GetType(Schema schema, string name)
{
Assert.True(schema.TryGetColumnIndex(name, out int cIdx), $"Could not find '{name}'");
return(schema.GetColumnType(cIdx));
}
data = TakeFilter.Create(Env, data, 10);
var concater = new ConcatTransform(Env,
new ConcatTransform.ColumnInfo("f2", new[] { ("float1", "FLOAT1"), ("float1", "FLOAT2") }),
public ITransformer Fit(IDataView input)
{
var h = _host;
h.CheckValue(input, nameof(input));
var tparams = new TransformApplierParams(this);
string[] textCols = _inputColumns;
string[] wordTokCols = null;
string[] charTokCols = null;
string wordFeatureCol = null;
string charFeatureCol = null;
List <string> tempCols = new List <string>();
IDataView view = input;
if (tparams.NeedInitialSourceColumnConcatTransform && textCols.Length > 1)
{
var srcCols = textCols;
textCols = new[] { GenerateColumnName(input.Schema, OutputColumn, "InitialConcat") };
tempCols.Add(textCols[0]);
view = new ConcatTransform(h, textCols[0], srcCols).Transform(view);
}
if (tparams.NeedsNormalizeTransform)
{
var xfCols = new (string input, string output)[textCols.Length];
public TransformWrapper Fit(IDataView input)
{
var xf = new ConcatTransform(_env, input, _name, _source);
var empty = new EmptyDataView(_env, input.Schema);
var chunk = ApplyTransformUtils.ApplyAllTransformsToData(_env, xf, empty, input);
return(new TransformWrapper(_env, chunk));
}
public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
{
string dataPath = GetDataPath("iris.txt");
string testDataPath = dataPath;
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env,
new TextLoader.Arguments()
{
HasHeader = false,
Column = new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4)
}
}, new MultiFileSource(dataPath));
IDataTransform trans = new ConcatTransform(env, loader, "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
// Normalizer is not automatically added though the trainer has 'NormalizeFeatures' On/Auto
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "Features");
// Train
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments()
{
NumThreads = 1
});
// Explicity adding CacheDataView since caching is not working though trainer has 'Caching' On/Auto
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var pred = trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = Evaluate(env, testDataScorer);
CompareMatrics(metrics);
// Create prediction engine and test predictions
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(testDataScorer);
ComparePredictions(model);
// Get feature importance i.e. weight vector
var summary = ((MulticlassLogisticRegressionPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
Assert.Equal(7.757864, Convert.ToDouble(summary[0].Value), 5);
}
}
public static CommonOutputs.TransformOutput PrepareFeatures(IHostEnvironment env, FeatureCombinerInput input)
{
const string featureCombiner = "FeatureCombiner";
Contracts.CheckValue(env, nameof(env));
var host = env.Register(featureCombiner);
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
using (var ch = host.Start(featureCombiner))
{
var viewTrain = input.Data;
var rms = new RoleMappedSchema(viewTrain.Schema, input.GetRoles());
var feats = rms.GetColumns(RoleMappedSchema.ColumnRole.Feature);
if (Utils.Size(feats) == 0)
{
throw ch.Except("No feature columns specified");
}
var featNames = new HashSet <string>();
var concatNames = new List <KeyValuePair <string, string> >();
List <ConvertTransform.Column> cvt;
int errCount;
var ktv = ConvertFeatures(feats.ToArray(), featNames, concatNames, ch, out cvt, out errCount);
Contracts.Assert(featNames.Count > 0);
Contracts.Assert(concatNames.Count == featNames.Count);
if (errCount > 0)
{
throw ch.Except("Encountered {0} invalid training column(s)", errCount);
}
viewTrain = ApplyConvert(cvt, viewTrain, host);
viewTrain = ApplyKeyToVec(ktv, viewTrain, host);
// REVIEW: What about column name conflicts? Eg, what if someone uses the group id column
// (a key type) as a feature column. We convert that column to a vector so it is no longer valid
// as a group id. That's just one example - you get the idea.
string nameFeat = DefaultColumnNames.Features;
viewTrain = new ConcatTransform(host,
new ConcatTransform.TaggedArguments()
{
Column =
new[] { new ConcatTransform.TaggedColumn()
{
Name = nameFeat, Source = concatNames.ToArray()
} }
},
viewTrain);
ch.Done();
return(new CommonOutputs.TransformOutput {
Model = new TransformModel(env, viewTrain, input.Data), OutputData = viewTrain
});
}
}
public Mapper(ConcatTransform parent, Schema inputSchema)
{
Contracts.AssertValue(parent);
Contracts.AssertValue(inputSchema);
_host = parent._host.Register(nameof(Mapper));
_parent = parent;
_inputSchema = inputSchema;
_columns = new BoundColumn[_parent._columns.Length];
for (int i = 0; i < _parent._columns.Length; i++)
{
_columns[i] = MakeColumn(inputSchema, i);
}
}
public static CommonOutputs.TransformOutput ConcatColumns(IHostEnvironment env, ConcatTransform.Arguments input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("ConcatColumns");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
var xf = new ConcatTransform(env, input, input.Data);
return(new CommonOutputs.TransformOutput {
Model = new TransformModel(env, 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 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 IDataView ApplyConcatOnSources(IHostEnvironment env, ManyToOneColumn[] columns, IDataView input)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(columns, nameof(columns));
env.CheckValue(input, nameof(input));
IDataView view = input;
var concatCols = new List <ConcatTransform.Column>();
foreach (var col in columns)
{
env.CheckUserArg(col != null, nameof(WordBagTransform.Arguments.Column));
env.CheckUserArg(!string.IsNullOrWhiteSpace(col.Name), nameof(col.Name));
env.CheckUserArg(Utils.Size(col.Source) > 0, nameof(col.Source));
env.CheckUserArg(col.Source.All(src => !string.IsNullOrWhiteSpace(src)), nameof(col.Source));
if (col.Source.Length > 1)
{
concatCols.Add(
new ConcatTransform.Column
{
Source = col.Source,
Name = col.Name
});
}
}
if (concatCols.Count > 0)
{
var concatArgs = new ConcatTransform.Arguments {
Column = concatCols.ToArray()
};
return(ConcatTransform.Create(env, concatArgs, view));
}
return(view);
}
private static IPredictor TrainKMeansAndLRCore()
{
string dataPath = s_dataPath;
using (var env = new TlcEnvironment(seed: 1))
{
// Pipeline
var loader = new TextLoader(env,
new TextLoader.Arguments()
{
HasHeader = true,
Separator = ",",
Column = new[] {
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 14, Max = 14
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "CatFeatures",
Source = new [] {
new TextLoader.Range()
{
Min = 1, Max = 1
},
new TextLoader.Range()
{
Min = 3, Max = 3
},
new TextLoader.Range()
{
Min = 5, Max = 9
},
new TextLoader.Range()
{
Min = 13, Max = 13
}
},
Type = DataKind.TX
},
new TextLoader.Column()
{
Name = "NumFeatures",
Source = new [] {
new TextLoader.Range()
{
Min = 0, Max = 0
},
new TextLoader.Range()
{
Min = 2, Max = 2
},
new TextLoader.Range()
{
Min = 4, Max = 4
},
new TextLoader.Range()
{
Min = 10, Max = 12
}
},
Type = DataKind.R4
}
}
}, new MultiFileSource(dataPath));
IDataTransform trans = CategoricalTransform.Create(env, new CategoricalTransform.Arguments
{
Column = new[]
{
new CategoricalTransform.Column {
Name = "CatFeatures", Source = "CatFeatures"
}
}
}, loader);
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "NumFeatures");
trans = new ConcatTransform(env, trans, "Features", "NumFeatures", "CatFeatures");
trans = TrainAndScoreTransform.Create(env, new TrainAndScoreTransform.Arguments
{
Trainer = new SubComponent <ITrainer, SignatureTrainer>("KMeans", "k=100"),
FeatureColumn = "Features"
}, trans);
trans = new ConcatTransform(env, trans, "Features", "Features", "Score");
// Train
var trainer = new LogisticRegression(env, new LogisticRegression.Arguments()
{
EnforceNonNegativity = true, OptTol = 1e-3f
});
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}
public ParameterMixingCalibratedPredictor TrainKMeansAndLR()
{
using (var env = new ConsoleEnvironment(seed: 1))
{
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
HasHeader = true,
Separator = ",",
Column = new[] {
new TextLoader.Column("Label", DataKind.R4, 14),
new TextLoader.Column("CatFeatures", DataKind.TX,
new [] {
new TextLoader.Range()
{
Min = 1, Max = 1
},
new TextLoader.Range()
{
Min = 3, Max = 3
},
new TextLoader.Range()
{
Min = 5, Max = 9
},
new TextLoader.Range()
{
Min = 13, Max = 13
}
}),
new TextLoader.Column("NumFeatures", DataKind.R4,
new [] {
new TextLoader.Range()
{
Min = 0, Max = 0
},
new TextLoader.Range()
{
Min = 2, Max = 2
},
new TextLoader.Range()
{
Min = 4, Max = 4
},
new TextLoader.Range()
{
Min = 10, Max = 12
}
})
}
}, new MultiFileSource(_dataPath));
IDataView trans = new CategoricalEstimator(env, "CatFeatures").Fit(loader).Transform(loader);
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "NumFeatures");
trans = new ConcatTransform(env, "Features", "NumFeatures", "CatFeatures").Transform(trans);
trans = TrainAndScoreTransform.Create(env, new TrainAndScoreTransform.Arguments
{
Trainer = ComponentFactoryUtils.CreateFromFunction(host =>
new KMeansPlusPlusTrainer(host, "Features", advancedSettings: s =>
{
s.K = 100;
})),
FeatureColumn = "Features"
}, trans);
trans = new ConcatTransform(env, "Features", "Features", "Score").Transform(trans);
// Train
var trainer = new LogisticRegression(env, "Features", "Label", advancedSettings: args => { args.EnforceNonNegativity = true; args.OptTol = 1e-3f; });
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}
public void TensorFlowTransformMNISTConvTest()
{
var model_location = "mnist_model/frozen_saved_model.pb";
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var dataPath = GetDataPath("Train-Tiny-28x28.txt");
var testDataPath = GetDataPath("MNIST.Test.tiny.txt");
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.Num
},
new TextLoader.Column()
{
Name = "Placeholder",
Source = new [] { new TextLoader.Range()
{
Min = 1, Max = 784
} },
Type = DataKind.Num
}
}
}, new MultiFileSource(dataPath));
IDataView trans = TensorFlowTransform.Create(env, loader, model_location, "Softmax", "Placeholder");
trans = new ConcatTransform(env, trans, "reshape_input", "Placeholder");
trans = TensorFlowTransform.Create(env, trans, model_location, "dense/Relu", "reshape_input");
trans = new ConcatTransform(env, trans, "Features", "Softmax", "dense/Relu");
var trainer = new LightGbmMulticlassTrainer(env, new LightGbmArguments());
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var pred = trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = Evaluate(env, testDataScorer);
Assert.Equal(0.99, metrics.AccuracyMicro, 2);
Assert.Equal(0.99, metrics.AccuracyMicro, 2);
// Create prediction engine and test predictions
var model = env.CreatePredictionEngine <MNISTData, MNISTPrediction>(testDataScorer);
var sample1 = new MNISTData()
{
Placeholder = new float[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26,
166, 255, 247, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253,
225, 172, 253, 242, 195, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253,
253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198,
182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0,
43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }
};
var prediction = model.Predict(sample1);
float max = -1;
int maxIndex = -1;
for (int i = 0; i < prediction.PredictedLabels.Length; i++)
{
if (prediction.PredictedLabels[i] > max)
{
max = prediction.PredictedLabels[i];
maxIndex = i;
}
}
Assert.Equal(5, maxIndex);
}
}
public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input)
{
Contracts.CheckValue(env, nameof(env));
var h = env.Register("Categorical");
h.CheckValue(args, nameof(args));
h.CheckValue(input, nameof(input));
h.CheckUserArg(Utils.Size(args.Column) > 0, nameof(args.Column));
var replaceCols = new List <NAReplaceTransform.ColumnInfo>();
var naIndicatorCols = new List <NAIndicatorTransform.Column>();
var naConvCols = new List <ConvertingTransform.ColumnInfo>();
var concatCols = new List <ConcatTransform.TaggedColumn>();
var dropCols = new List <string>();
var tmpIsMissingColNames = input.Schema.GetTempColumnNames(args.Column.Length, "IsMissing");
var tmpReplaceColNames = input.Schema.GetTempColumnNames(args.Column.Length, "Replace");
for (int i = 0; i < args.Column.Length; i++)
{
var column = args.Column[i];
var addInd = column.ConcatIndicator ?? args.Concat;
if (!addInd)
{
replaceCols.Add(new NAReplaceTransform.ColumnInfo(column.Source, column.Name, (NAReplaceTransform.ColumnInfo.ReplacementMode)(column.Kind ?? args.ReplaceWith), column.ImputeBySlot ?? args.ImputeBySlot));
continue;
}
// Check that the indicator column has a type that can be converted to the NAReplaceTransform output type,
// so that they can be concatenated.
if (!input.Schema.TryGetColumnIndex(column.Source, out int inputCol))
{
throw h.Except("Column '{0}' does not exist", column.Source);
}
var replaceType = input.Schema.GetColumnType(inputCol);
if (!Runtime.Data.Conversion.Conversions.Instance.TryGetStandardConversion(BoolType.Instance, replaceType.ItemType, out Delegate conv, out bool identity))
{
throw h.Except("Cannot concatenate indicator column of type '{0}' to input column of type '{1}'",
BoolType.Instance, replaceType.ItemType);
}
// Find a temporary name for the NAReplaceTransform and NAIndicatorTransform output columns.
var tmpIsMissingColName = tmpIsMissingColNames[i];
var tmpReplacementColName = tmpReplaceColNames[i];
// Add an NAHandleTransform column.
naIndicatorCols.Add(new NAIndicatorTransform.Column()
{
Name = tmpIsMissingColName, Source = column.Source
});
// Add a ConvertTransform column if necessary.
if (!identity)
{
naConvCols.Add(new ConvertingTransform.ColumnInfo(tmpIsMissingColName, tmpIsMissingColName, replaceType.ItemType.RawKind));
}
// Add the NAReplaceTransform column.
replaceCols.Add(new NAReplaceTransform.ColumnInfo(column.Source, tmpReplacementColName, (NAReplaceTransform.ColumnInfo.ReplacementMode)(column.Kind ?? args.ReplaceWith), column.ImputeBySlot ?? args.ImputeBySlot));
// Add the ConcatTransform column.
if (replaceType.IsVector)
{
concatCols.Add(new ConcatTransform.TaggedColumn()
{
Name = column.Name,
Source = new[] {
new KeyValuePair <string, string>(tmpReplacementColName, tmpReplacementColName),
new KeyValuePair <string, string>("IsMissing", tmpIsMissingColName)
}
});
}
else
{
concatCols.Add(new ConcatTransform.TaggedColumn()
{
Name = column.Name,
Source = new[]
{
new KeyValuePair <string, string>(column.Source, tmpReplacementColName),
new KeyValuePair <string, string>(string.Format("IsMissing.{0}", column.Source), tmpIsMissingColName),
}
});
}
// Add the temp column to the list of columns to drop at the end.
dropCols.Add(tmpIsMissingColName);
dropCols.Add(tmpReplacementColName);
}
IDataTransform output = null;
// Create the indicator columns.
if (naIndicatorCols.Count > 0)
{
output = NAIndicatorTransform.Create(h, new NAIndicatorTransform.Arguments()
{
Column = naIndicatorCols.ToArray()
}, input);
}
// Convert the indicator columns to the correct type so that they can be concatenated to the NAReplace outputs.
if (naConvCols.Count > 0)
{
h.AssertValue(output);
//REVIEW: all this need to be converted to estimatorChain as soon as we done with dropcolumns.
output = new ConvertingTransform(h, naConvCols.ToArray()).Transform(output) as IDataTransform;
}
// Create the NAReplace transform.
output = NAReplaceTransform.Create(env, output ?? input, replaceCols.ToArray());
// Concat the NAReplaceTransform output and the NAIndicatorTransform output.
if (naIndicatorCols.Count > 0)
{
output = ConcatTransform.Create(h, new ConcatTransform.TaggedArguments()
{
Column = concatCols.ToArray()
}, output);
}
// Finally, drop the temporary indicator columns.
if (dropCols.Count > 0)
{
output = SelectColumnsTransform.CreateDrop(h, output, dropCols.ToArray());
}
return(output);
}