public void TestSimpleExperiment()
{
var dataPath = GetDataPath("adult.tiny.with-schema.txt");
var env = new MLContext();
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var normalizeInput = new Legacy.Transforms.MinMaxNormalizer
{
Data = importOutput.Data
};
normalizeInput.AddColumn("NumericFeatures");
var normalizeOutput = experiment.Add(normalizeInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(normalizeOutput.OutputData);
var schema = data.Schema;
Assert.Equal(5, schema.Count);
var expected = new[] { "Label", "Workclass", "Categories", "NumericFeatures", "NumericFeatures" };
for (int i = 0; i < schema.Count; i++)
{
Assert.Equal(expected[i], schema[i].Name);
}
}
/// <summary>
/// Performs cross validation on a pipeline.
/// </summary>
/// <typeparam name="TInput">Class type that represents input schema.</typeparam>
/// <typeparam name="TOutput">Class type that represents prediction schema.</typeparam>
/// <param name="pipeline">Machine learning pipeline may contain loader, transforms and at least one trainer.</param>
/// <returns>List containing metrics and predictor model for each fold</returns>
public CrossValidationOutput <TInput, TOutput> CrossValidate <TInput, TOutput>(LearningPipeline pipeline)
where TInput : class
where TOutput : class, new()
{
var environment = new MLContext();
{
Experiment subGraph = environment.CreateExperiment();
ILearningPipelineStep step = null;
List <ILearningPipelineLoader> loaders = new List <ILearningPipelineLoader>();
List <Var <ITransformModel> > transformModels = new List <Var <ITransformModel> >();
Var <ITransformModel> lastTransformModel = null;
Var <IDataView> firstPipelineDataStep = null;
Var <IPredictorModel> firstModel = null;
ILearningPipelineItem firstTransform = null;
foreach (ILearningPipelineItem currentItem in pipeline)
{
if (currentItem is ILearningPipelineLoader loader)
{
loaders.Add(loader);
continue;
}
step = currentItem.ApplyStep(step, subGraph);
if (step is ILearningPipelineDataStep dataStep && dataStep.Model != null)
{
transformModels.Add(dataStep.Model);
if (firstPipelineDataStep == null)
{
firstPipelineDataStep = dataStep.Data;
firstTransform = currentItem;
}
}
/// <summary>
/// Computes the quality metrics for the multi-class classification PredictionModel
/// using the specified data set.
/// </summary>
/// <param name="model">
/// The trained multi-class classification PredictionModel to be evaluated.
/// </param>
/// <param name="testData">
/// The test data that will be predicted and used to evaluate the model.
/// </param>
/// <returns>
/// A ClassificationMetrics instance that describes how well the model performed against the test data.
/// </returns>
public ClassificationMetrics Evaluate(PredictionModel model, ILearningPipelineLoader testData)
{
var environment = new MLContext();
environment.CheckValue(model, nameof(model));
environment.CheckValue(testData, nameof(testData));
Experiment experiment = environment.CreateExperiment();
ILearningPipelineStep testDataStep = testData.ApplyStep(previousStep: null, experiment);
if (!(testDataStep is ILearningPipelineDataStep testDataOutput))
{
throw environment.Except($"The {nameof(ILearningPipelineLoader)} did not return a {nameof(ILearningPipelineDataStep)} from ApplyStep.");
}
var datasetScorer = new DatasetTransformScorer
{
Data = testDataOutput.Data,
};
DatasetTransformScorer.Output scoreOutput = experiment.Add(datasetScorer);
Data = scoreOutput.ScoredData;
Output evaluteOutput = experiment.Add(this);
experiment.Compile();
experiment.SetInput(datasetScorer.TransformModel, model.PredictorModel);
testData.SetInput(environment, experiment);
experiment.Run();
IDataView overallMetrics = experiment.GetOutput(evaluteOutput.OverallMetrics);
if (overallMetrics == null)
{
throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(ClassificationEvaluator)} Evaluate.");
}
IDataView confusionMatrix = experiment.GetOutput(evaluteOutput.ConfusionMatrix);
if (confusionMatrix == null)
{
throw environment.Except($"Could not find ConfusionMatrix in the results returned in {nameof(ClassificationEvaluator)} Evaluate.");
}
var metric = ClassificationMetrics.FromMetrics(environment, overallMetrics, confusionMatrix);
if (metric.Count != 1)
{
throw environment.Except($"Exactly one metric set was expected but found {metric.Count} metrics");
}
return(metric[0]);
}
public void CanSuccessfullyRetrieveQuotedData()
{
string dataPath = GetDataPath("QuotingData.csv");
var loader = new Legacy.Data.TextLoader(dataPath).CreateFrom <QuoteInput>(useHeader: true, separator: ',', allowQuotedStrings: true, supportSparse: false);
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = loader.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
experiment.Compile();
loader.SetInput(environment, experiment);
experiment.Run();
IDataView data = experiment.GetOutput(output.Data);
Assert.NotNull(data);
using (var cursor = data.GetRowCursor((a => true)))
{
var IDGetter = cursor.GetGetter <float>(0);
var TextGetter = cursor.GetGetter <ReadOnlyMemory <char> >(1);
Assert.True(cursor.MoveNext());
float ID = 0;
IDGetter(ref ID);
Assert.Equal(1, ID);
ReadOnlyMemory <char> Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("This text contains comma, within quotes.", Text.ToString());
Assert.True(cursor.MoveNext());
ID = 0;
IDGetter(ref ID);
Assert.Equal(2, ID);
Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("This text contains extra punctuations and special characters.;*<>?!@#$%^&*()_+=-{}|[]:;'", Text.ToString());
Assert.True(cursor.MoveNext());
ID = 0;
IDGetter(ref ID);
Assert.Equal(3, ID);
Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("This text has no quotes", Text.ToString());
Assert.False(cursor.MoveNext());
}
}
public void CanSuccessfullyApplyATransform()
{
var loader = new Legacy.Data.TextLoader("fakeFile.txt").CreateFrom <Input>();
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = loader.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
Assert.NotNull(output.Data);
Assert.NotNull(output.Data.VarName);
Assert.Null(output.Model);
}
/// <summary>
/// <a href="https://onnx.ai/">ONNX</a> is an intermediate representation format
/// for machine learning models.
/// </summary>
/// <remarks>
/// <para>It is used to make models portable such that you can
/// train a model using a toolkit and run it in another tookit's runtime, for example,
/// you can create a model using ML.NET, export it to an ONNX-ML model file,
/// then load and run that ONNX-ML model in Windows ML, on an UWP Windows 10 app.</para>
///
/// <para>This API converts an ML.NET model to ONNX-ML format by inspecting the transform pipeline
/// from the end, checking for components that know how to save themselves as ONNX.
/// The first item in the transform pipeline that does not know how to save itself
/// as ONNX, is considered the "input" to the ONNX pipeline. (Ideally this would be the
/// original loader itself, but this may not be possible if the user used unsavable
/// transforms in defining the pipe.) All the columns in the source that are a type the
/// ONNX knows how to deal with will be tracked. Intermediate transformations of the
/// data appearing as new columns will appear in the output block of the ONNX, with names
/// derived from the corresponding column names. The ONNX JSON will be serialized to a
/// path defined through the Json option.</para>
///
/// <para>This API supports the following arguments:</para>
/// <list type="bullet">
/// <item><description><see cref="Onnx"/> indicates the file to write the ONNX protocol buffer file to. This is required.</description></item>
/// <item><description><see cref="Json"/> indicates the file to write the JSON representation of the ONNX model. This is optional.</description></item>
/// <item><description><see cref="Name"/> indicates the name property in the ONNX model. If left unspecified, it will
/// be the extension-less name of the file specified in the onnx indicates the protocol buffer file
/// to write the ONNX representation to.</description></item>
/// <item><description><see cref="Domain"/> indicates the domain name of the model. ONNX uses reverse domain name space indicators.
/// For example com.microsoft.cognitiveservices. This is a required field.</description></item>
/// <item><description><see cref="InputsToDrop"/> is a string array of input column names to omit from the input mapping.
/// A common scenario might be to drop the label column, for instance, since it may not be practically
/// useful for the pipeline. Note that any columns depending on these naturally cannot be saved.</description></item>
/// <item><description><see cref="OutputsToDrop"/> is similar, except for the output schema. Note that the pipeline handler
/// is currently not intelligent enough to drop intermediate calculations that produce this value: this will
/// merely omit that value from the actual output.</description></item>
/// </list>
///
/// <para>Transforms that can be exported to ONNX</para>
/// <list type="number">
/// <item><description>Concat</description></item>
/// <item><description>KeyToVector</description></item>
/// <item><description>NAReplace</description></item>
/// <item><description>Normalize</description></item>
/// <item><description>Term</description></item>
/// <item><description>Categorical</description></item>
/// </list>
///
/// <para>Learners that can be exported to ONNX</para>
/// <list type="number">
/// <item><description>FastTree</description></item>
/// <item><description>LightGBM</description></item>
/// <item><description>Logistic Regression</description></item>
/// </list>
///
/// <para>See <a href="https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/OnnxTests.cs">OnnxTests.cs</a>
/// for an example on how to train a model and then convert that model to ONNX.</para>
/// </remarks>
/// <param name="model">Model that needs to be converted to ONNX format.</param>
public void Convert(PredictionModel model)
{
var environment = new MLContext();
environment.CheckValue(model, nameof(model));
Experiment experiment = environment.CreateExperiment();
experiment.Add(this);
experiment.Compile();
experiment.SetInput(Model, model.PredictorModel);
experiment.Run();
}
public void CanSuccessfullyApplyATransform()
{
var collection = CollectionDataSource.Create(new List <Input>()
{
new Input {
Number1 = 1, String1 = "1"
}
});
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = (Legacy.ILearningPipelineDataStep)collection.ApplyStep(null, experiment);
Assert.NotNull(output.Data);
Assert.NotNull(output.Data.VarName);
Assert.Null(output.Model);
}
public void CanSuccessfullyTrimSpaces()
{
string dataPath = GetDataPath("TrimData.csv");
var loader = new Legacy.Data.TextLoader(dataPath).CreateFrom <QuoteInput>(useHeader: true, separator: ',', allowQuotedStrings: false, supportSparse: false, trimWhitespace: true);
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = loader.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
experiment.Compile();
loader.SetInput(environment, experiment);
experiment.Run();
IDataView data = experiment.GetOutput(output.Data);
Assert.NotNull(data);
using (var cursor = data.GetRowCursor((a => true)))
{
var IDGetter = cursor.GetGetter <float>(0);
var TextGetter = cursor.GetGetter <ReadOnlyMemory <char> >(1);
Assert.True(cursor.MoveNext());
float ID = 0;
IDGetter(ref ID);
Assert.Equal(1, ID);
ReadOnlyMemory <char> Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("There is a space at the end", Text.ToString());
Assert.True(cursor.MoveNext());
ID = 0;
IDGetter(ref ID);
Assert.Equal(2, ID);
Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("There is no space at the end", Text.ToString());
Assert.False(cursor.MoveNext());
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TestTensorFlowEntryPoint()
{
var dataPath = GetDataPath("Train-Tiny-28x28.txt");
var env = new MLContext(42);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
importInput.Arguments.Column = new TextLoaderColumn[]
{
new TextLoaderColumn {
Name = "Label", Source = new[] { new TextLoaderRange(0) }
},
new TextLoaderColumn {
Name = "Placeholder", Source = new[] { new TextLoaderRange(1, 784) }
}
};
var importOutput = experiment.Add(importInput);
var tfTransformInput = new Legacy.Transforms.TensorFlowScorer
{
Data = importOutput.Data,
ModelLocation = "mnist_model/frozen_saved_model.pb",
InputColumns = new[] { "Placeholder" },
OutputColumns = new[] { "Softmax" },
};
var tfTransformOutput = experiment.Add(tfTransformInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(tfTransformOutput.OutputData);
var schema = data.Schema;
Assert.Equal(3, schema.Count);
Assert.Equal("Softmax", schema[2].Name);
Assert.Equal(10, (schema[2].Type as VectorType)?.Size);
}
/// <summary>
/// Train the model using the ML components in the pipeline.
/// </summary>
/// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
/// <para/>
/// Please see https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet/get-started/windows for more details on input type.
/// </typeparam>
/// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
/// Please see https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet/get-started/windows for more details on output type.
/// </typeparam>
/// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
public PredictionModel <TInput, TOutput> Train <TInput, TOutput>()
where TInput : class
where TOutput : class, new()
{
var environment = new MLContext(seed: _seed, conc: _conc);
Experiment experiment = environment.CreateExperiment();
ILearningPipelineStep step = null;
List <ILearningPipelineLoader> loaders = new List <ILearningPipelineLoader>();
List <Var <ITransformModel> > transformModels = new List <Var <ITransformModel> >();
Var <ITransformModel> lastTransformModel = null;
foreach (ILearningPipelineItem currentItem in this)
{
if (currentItem is ILearningPipelineLoader loader)
{
loaders.Add(loader);
}
step = currentItem.ApplyStep(step, experiment);
if (step is ILearningPipelineDataStep dataStep && dataStep.Model != null)
{
transformModels.Add(dataStep.Model);
}
public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment)
{
var env = new MLContext();
var subgraph = env.CreateExperiment();
subgraph.Add(_trainer);
var ova = new OneVersusAll();
if (previousStep != null)
{
if (!(previousStep is ILearningPipelineDataStep dataStep))
{
throw new InvalidOperationException($"{ nameof(OneVersusAll)} only supports an { nameof(ILearningPipelineDataStep)} as an input.");
}
_data = dataStep.Data;
ova.TrainingData = dataStep.Data;
ova.UseProbabilities = _useProbabilities;
ova.Nodes = subgraph;
}
Output output = experiment.Add(ova);
return(new OvaPipelineStep(output));
}
public void TestOvaMacroWithUncalibratedLearner()
{
var dataPath = GetDataPath(@"iris.txt");
var env = new MLContext(42);
// Specify subgraph for OVA
var subGraph = env.CreateExperiment();
var learnerInput = new Legacy.Trainers.AveragedPerceptronBinaryClassifier {
Shuffle = false
};
var learnerOutput = subGraph.Add(learnerInput);
// Create pipeline with OVA and multiclass scoring.
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
importInput.Arguments.Column = new TextLoaderColumn[]
{
new TextLoaderColumn {
Name = "Label", Source = new[] { new TextLoaderRange(0) }
},
new TextLoaderColumn {
Name = "Features", Source = new[] { new TextLoaderRange(1, 4) }
}
};
var importOutput = experiment.Add(importInput);
var oneVersusAll = new Legacy.Models.OneVersusAll
{
TrainingData = importOutput.Data,
Nodes = subGraph,
UseProbabilities = true,
};
var ovaOutput = experiment.Add(oneVersusAll);
var scoreInput = new Legacy.Transforms.DatasetScorer
{
Data = importOutput.Data,
PredictorModel = ovaOutput.PredictorModel
};
var scoreOutput = experiment.Add(scoreInput);
var evalInput = new Legacy.Models.ClassificationEvaluator
{
Data = scoreOutput.ScoredData
};
var evalOutput = experiment.Add(evalInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(evalOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == accCol))
{
var getter = cursor.GetGetter <double>(accCol);
b = cursor.MoveNext();
Assert.True(b);
double acc = 0;
getter(ref acc);
Assert.Equal(0.71, acc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
public void CanSuccessfullyEnumerated()
{
var collection = CollectionDataSource.Create(new List <Input>()
{
new Input {
Number1 = 1, String1 = "1"
},
new Input {
Number1 = 2, String1 = "2"
},
new Input {
Number1 = 3, String1 = "3"
}
});
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = collection.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
experiment.Compile();
collection.SetInput(environment, experiment);
experiment.Run();
IDataView data = experiment.GetOutput(output.Data);
Assert.NotNull(data);
using (var cursor = data.GetRowCursor((a => true)))
{
var IDGetter = cursor.GetGetter <float>(0);
var TextGetter = cursor.GetGetter <ReadOnlyMemory <char> >(1);
Assert.True(cursor.MoveNext());
float ID = 0;
IDGetter(ref ID);
Assert.Equal(1, ID);
ReadOnlyMemory <char> Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("1", Text.ToString());
Assert.True(cursor.MoveNext());
ID = 0;
IDGetter(ref ID);
Assert.Equal(2, ID);
Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("2", Text.ToString());
Assert.True(cursor.MoveNext());
ID = 0;
IDGetter(ref ID);
Assert.Equal(3, ID);
Text = new ReadOnlyMemory <char>();
TextGetter(ref Text);
Assert.Equal("3", Text.ToString());
Assert.False(cursor.MoveNext());
}
}
public void TestSimpleTrainExperiment()
{
var dataPath = GetDataPath("adult.tiny.with-schema.txt");
var env = new MLContext();
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var catInput = new Legacy.Transforms.CategoricalOneHotVectorizer
{
Data = importOutput.Data
};
catInput.AddColumn("Categories");
var catOutput = experiment.Add(catInput);
var concatInput = new Legacy.Transforms.ColumnConcatenator
{
Data = catOutput.OutputData
};
concatInput.AddColumn("Features", "Categories", "NumericFeatures");
var concatOutput = experiment.Add(concatInput);
var sdcaInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier
{
TrainingData = concatOutput.OutputData,
LossFunction = new HingeLossSDCAClassificationLossFunction()
{
Margin = 1.1f
},
NumThreads = 1,
Shuffle = false
};
var sdcaOutput = experiment.Add(sdcaInput);
var scoreInput = new Legacy.Transforms.DatasetScorer
{
Data = concatOutput.OutputData,
PredictorModel = sdcaOutput.PredictorModel
};
var scoreOutput = experiment.Add(scoreInput);
var evalInput = new Legacy.Models.BinaryClassificationEvaluator
{
Data = scoreOutput.ScoredData
};
var evalOutput = experiment.Add(evalInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(evalOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int aucCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == aucCol))
{
var getter = cursor.GetGetter <double>(aucCol);
b = cursor.MoveNext();
Assert.True(b);
double auc = 0;
getter(ref auc);
Assert.Equal(0.93, auc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
public void TestCrossValidationMacroWithNonDefaultNames()
{
string dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
var env = new MLContext(42);
var subGraph = env.CreateExperiment();
var textToKey = new Legacy.Transforms.TextToKeyConverter();
textToKey.Column = new[] { new Legacy.Transforms.ValueToKeyMappingTransformerColumn()
{
Name = "Label1", Source = "Label"
} };
var textToKeyOutput = subGraph.Add(textToKey);
var hash = new Legacy.Transforms.HashConverter();
hash.Column = new[] { new Legacy.Transforms.HashJoiningTransformColumn()
{
Name = "GroupId1", Source = "Workclass"
} };
hash.Data = textToKeyOutput.OutputData;
var hashOutput = subGraph.Add(hash);
var learnerInput = new Legacy.Trainers.FastTreeRanker
{
TrainingData = hashOutput.OutputData,
NumThreads = 1,
LabelColumn = "Label1",
GroupIdColumn = "GroupId1"
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <TransformModel>(textToKeyOutput.Model, hashOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
importInput.Arguments.HasHeader = true;
importInput.Arguments.Column = new TextLoaderColumn[]
{
new TextLoaderColumn {
Name = "Label", Source = new[] { new TextLoaderRange(0) }
},
new TextLoaderColumn {
Name = "Workclass", Source = new[] { new TextLoaderRange(1) }, Type = Legacy.Data.DataKind.Text
},
new TextLoaderColumn {
Name = "Features", Source = new[] { new TextLoaderRange(9, 14) }
}
};
var importOutput = experiment.Add(importInput);
var crossValidate = new Legacy.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
TransformModel = null,
LabelColumn = "Label1",
GroupColumn = "GroupId1",
NameColumn = "Workclass",
Kind = Legacy.Models.MacroUtilsTrainerKinds.SignatureRankerTrainer
};
crossValidate.Inputs.Data = textToKey.Data;
crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("NDCG", out int metricCol);
Assert.True(b);
b = schema.TryGetColumnIndex("Fold Index", out int foldCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol))
{
var getter = cursor.GetGetter <VBuffer <double> >(metricCol);
var foldGetter = cursor.GetGetter <ReadOnlyMemory <char> >(foldCol);
ReadOnlyMemory <char> fold = default;
// Get the verage.
b = cursor.MoveNext();
Assert.True(b);
var avg = default(VBuffer <double>);
getter(ref avg);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold));
// Get the standard deviation.
b = cursor.MoveNext();
Assert.True(b);
var stdev = default(VBuffer <double>);
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
var stdevValues = stdev.GetValues();
Assert.Equal(2.462, stdevValues[0], 3);
Assert.Equal(2.763, stdevValues[1], 3);
Assert.Equal(3.273, stdevValues[2], 3);
var sumBldr = new BufferBuilder <double>(R8Adder.Instance);
sumBldr.Reset(avg.Length, true);
var val = default(VBuffer <double>);
for (int f = 0; f < 2; f++)
{
b = cursor.MoveNext();
Assert.True(b);
getter(ref val);
foldGetter(ref fold);
sumBldr.AddFeatures(0, in val);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold));
}
var sum = default(VBuffer <double>);
sumBldr.GetResult(ref sum);
var avgValues = avg.GetValues();
var sumValues = sum.GetValues();
for (int i = 0; i < avgValues.Length; i++)
{
Assert.Equal(avgValues[i], sumValues[i] / 2);
}
b = cursor.MoveNext();
Assert.False(b);
}
data = experiment.GetOutput(crossValidateOutput.PerInstanceMetrics);
Assert.True(data.Schema.TryGetColumnIndex("Instance", out int nameCol));
using (var cursor = data.GetRowCursor(col => col == nameCol))
{
var getter = cursor.GetGetter <ReadOnlyMemory <char> >(nameCol);
while (cursor.MoveNext())
{
ReadOnlyMemory <char> name = default;
getter(ref name);
Assert.Subset(new HashSet <string>()
{
"Private", "?", "Federal-gov"
}, new HashSet <string>()
{
name.ToString()
});
if (cursor.Position > 4)
{
break;
}
}
}
}
public void TestCrossValidationMacroMultiClassWithWarnings()
{
var dataPath = GetDataPath(@"Train-Tiny-28x28.txt");
var env = new MLContext(42);
var subGraph = env.CreateExperiment();
var nop = new Legacy.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var learnerInput = new Legacy.Trainers.LogisticRegressionClassifier
{
TrainingData = nopOutput.OutputData,
NumThreads = 1
};
var learnerOutput = subGraph.Add(learnerInput);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var filter = new Legacy.Transforms.RowRangeFilter();
filter.Data = importOutput.Data;
filter.Column = "Label";
filter.Min = 0;
filter.Max = 5;
var filterOutput = experiment.Add(filter);
var term = new Legacy.Transforms.TextToKeyConverter();
term.Column = new[]
{
new Legacy.Transforms.ValueToKeyMappingTransformerColumn()
{
Source = "Label", Name = "Strat", Sort = Legacy.Transforms.ValueToKeyMappingTransformerSortOrder.Value
}
};
term.Data = filterOutput.OutputData;
var termOutput = experiment.Add(term);
var crossValidate = new Legacy.Models.CrossValidator
{
Data = termOutput.OutputData,
Nodes = subGraph,
Kind = Legacy.Models.MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer,
TransformModel = null,
StratificationColumn = "Strat"
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.PredictorModel = learnerOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
importInput.SetInput(env, experiment);
experiment.Run();
var warnings = experiment.GetOutput(crossValidateOutput.Warnings);
var schema = warnings.Schema;
var b = schema.TryGetColumnIndex("WarningText", out int warningCol);
Assert.True(b);
using (var cursor = warnings.GetRowCursor(col => col == warningCol))
{
var getter = cursor.GetGetter <ReadOnlyMemory <char> >(warningCol);
b = cursor.MoveNext();
Assert.True(b);
var warning = default(ReadOnlyMemory <char>);
getter(ref warning);
Assert.Contains("test instances with class values not seen in the training set.", warning.ToString());
b = cursor.MoveNext();
Assert.True(b);
getter(ref warning);
Assert.Contains("Detected columns of variable length: SortedScores, SortedClasses", warning.ToString());
b = cursor.MoveNext();
Assert.False(b);
}
}
public void TestCrossValidationMacroWithStratification()
{
var dataPath = GetDataPath(@"breast-cancer.txt");
var env = new MLContext(42);
var subGraph = env.CreateExperiment();
var nop = new Legacy.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var learnerInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier
{
TrainingData = nopOutput.OutputData,
NumThreads = 1
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <TransformModel>(nopOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
importInput.Arguments.Column = new Legacy.Data.TextLoaderColumn[]
{
new Legacy.Data.TextLoaderColumn {
Name = "Label", Source = new[] { new Legacy.Data.TextLoaderRange(0) }
},
new Legacy.Data.TextLoaderColumn {
Name = "Strat", Source = new[] { new Legacy.Data.TextLoaderRange(1) }
},
new Legacy.Data.TextLoaderColumn {
Name = "Features", Source = new[] { new Legacy.Data.TextLoaderRange(2, 9) }
}
};
var importOutput = experiment.Add(importInput);
var crossValidate = new Legacy.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
TransformModel = null,
StratificationColumn = "Strat"
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int metricCol);
Assert.True(b);
b = schema.TryGetColumnIndex("Fold Index", out int foldCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol))
{
var getter = cursor.GetGetter <double>(metricCol);
var foldGetter = cursor.GetGetter <ReadOnlyMemory <char> >(foldCol);
ReadOnlyMemory <char> fold = default;
// Get the verage.
b = cursor.MoveNext();
Assert.True(b);
double avg = 0;
getter(ref avg);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold));
// Get the standard deviation.
b = cursor.MoveNext();
Assert.True(b);
double stdev = 0;
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
Assert.Equal(0.00488, stdev, 5);
double sum = 0;
double val = 0;
for (int f = 0; f < 2; f++)
{
b = cursor.MoveNext();
Assert.True(b);
getter(ref val);
foldGetter(ref fold);
sum += val;
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold));
}
Assert.Equal(avg, sum / 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
public void TestCrossValidationMacroWithMultiClass()
{
var dataPath = GetDataPath(@"Train-Tiny-28x28.txt");
var env = new MLContext(42);
var subGraph = env.CreateExperiment();
var nop = new Legacy.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var learnerInput = new Legacy.Trainers.StochasticDualCoordinateAscentClassifier
{
TrainingData = nopOutput.OutputData,
NumThreads = 1
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <TransformModel>(nopOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var crossValidate = new Legacy.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
Kind = Legacy.Models.MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer,
TransformModel = null
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
importInput.SetInput(env, experiment);
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("Accuracy(micro-avg)", out int metricCol);
Assert.True(b);
b = schema.TryGetColumnIndex("Fold Index", out int foldCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol))
{
var getter = cursor.GetGetter <double>(metricCol);
var foldGetter = cursor.GetGetter <ReadOnlyMemory <char> >(foldCol);
ReadOnlyMemory <char> fold = default;
// Get the average.
b = cursor.MoveNext();
Assert.True(b);
double avg = 0;
getter(ref avg);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold));
// Get the standard deviation.
b = cursor.MoveNext();
Assert.True(b);
double stdev = 0;
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
Assert.Equal(0.015, stdev, 3);
double sum = 0;
double val = 0;
for (int f = 0; f < 2; f++)
{
b = cursor.MoveNext();
Assert.True(b);
getter(ref val);
foldGetter(ref fold);
sum += val;
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold));
}
Assert.Equal(avg, sum / 2);
b = cursor.MoveNext();
Assert.False(b);
}
var confusion = experiment.GetOutput(crossValidateOutput.ConfusionMatrix);
schema = confusion.Schema;
b = schema.TryGetColumnIndex("Count", out int countCol);
Assert.True(b);
b = schema.TryGetColumnIndex("Fold Index", out foldCol);
Assert.True(b);
var type = schema[countCol].Metadata.Schema[MetadataUtils.Kinds.SlotNames].Type;
Assert.True(type is VectorType vecType && vecType.ItemType is TextType && vecType.Size == 10);
var slotNames = default(VBuffer <ReadOnlyMemory <char> >);
schema[countCol].GetSlotNames(ref slotNames);
var slotNameValues = slotNames.GetValues();
for (int i = 0; i < slotNameValues.Length; i++)
{
Assert.True(ReadOnlyMemoryUtils.EqualsStr(i.ToString(), slotNameValues[i]));
}
using (var curs = confusion.GetRowCursor(col => true))
{
var countGetter = curs.GetGetter <VBuffer <double> >(countCol);
var foldGetter = curs.GetGetter <ReadOnlyMemory <char> >(foldCol);
var confCount = default(VBuffer <double>);
var foldIndex = default(ReadOnlyMemory <char>);
int rowCount = 0;
var foldCur = "Fold 0";
while (curs.MoveNext())
{
countGetter(ref confCount);
foldGetter(ref foldIndex);
rowCount++;
Assert.True(ReadOnlyMemoryUtils.EqualsStr(foldCur, foldIndex));
if (rowCount == 10)
{
rowCount = 0;
foldCur = "Fold 1";
}
}
Assert.Equal(0, rowCount);
}
var warnings = experiment.GetOutput(crossValidateOutput.Warnings);
using (var cursor = warnings.GetRowCursor(col => true))
Assert.False(cursor.MoveNext());
}
public void CanSuccessfullyRetrieveSparseData()
{
string dataPath = GetDataPath("SparseData.txt");
var loader = new Legacy.Data.TextLoader(dataPath).CreateFrom <SparseInput>(useHeader: true, allowQuotedStrings: false, supportSparse: true);
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = loader.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
experiment.Compile();
loader.SetInput(environment, experiment);
experiment.Run();
IDataView data = experiment.GetOutput(output.Data);
Assert.NotNull(data);
using (var cursor = data.GetRowCursor((a => true)))
{
var getters = new ValueGetter <float>[] {
cursor.GetGetter <float>(0),
cursor.GetGetter <float>(1),
cursor.GetGetter <float>(2),
cursor.GetGetter <float>(3),
cursor.GetGetter <float>(4)
};
Assert.True(cursor.MoveNext());
float[] targets = new float[] { 1, 2, 3, 4, 5 };
for (int i = 0; i < getters.Length; i++)
{
float value = 0;
getters[i](ref value);
Assert.Equal(targets[i], value);
}
Assert.True(cursor.MoveNext());
targets = new float[] { 0, 0, 0, 4, 5 };
for (int i = 0; i < getters.Length; i++)
{
float value = 0;
getters[i](ref value);
Assert.Equal(targets[i], value);
}
Assert.True(cursor.MoveNext());
targets = new float[] { 0, 2, 0, 0, 0 };
for (int i = 0; i < getters.Length; i++)
{
float value = 0;
getters[i](ref value);
Assert.Equal(targets[i], value);
}
Assert.False(cursor.MoveNext());
}
}
[ConditionalFact(typeof(BaseTestBaseline), nameof(BaseTestBaseline.LessThanNetCore30OrNotNetCore))] // netcore3.0 output differs from Baseline
public void TestCrossValidationMacro()
{
var dataPath = GetDataPath(TestDatasets.generatedRegressionDatasetmacro.trainFilename);
var env = new MLContext(42);
var subGraph = env.CreateExperiment();
var nop = new Legacy.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var generate = new Legacy.Transforms.RandomNumberGenerator();
generate.Column = new[] { new Legacy.Transforms.GenerateNumberTransformColumn()
{
Name = "Weight1"
} };
generate.Data = nopOutput.OutputData;
var generateOutput = subGraph.Add(generate);
var learnerInput = new Legacy.Trainers.PoissonRegressor
{
TrainingData = generateOutput.OutputData,
NumThreads = 1,
WeightColumn = "Weight1"
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <TransformModel>(nopOutput.Model, generateOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath)
{
Arguments = new Legacy.Data.TextLoaderArguments
{
Separator = new[] { ';' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(11) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Features",
Source = new [] { new TextLoaderRange(0, 10) },
Type = Legacy.Data.DataKind.Num
}
}
}
};
var importOutput = experiment.Add(importInput);
var crossValidate = new Legacy.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
Kind = Legacy.Models.MacroUtilsTrainerKinds.SignatureRegressorTrainer,
TransformModel = null,
WeightColumn = "Weight1"
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
importInput.SetInput(env, experiment);
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("L1(avg)", out int metricCol);
Assert.True(b);
b = schema.TryGetColumnIndex("Fold Index", out int foldCol);
Assert.True(b);
b = schema.TryGetColumnIndex("IsWeighted", out int isWeightedCol);
using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol || col == isWeightedCol))
{
var getter = cursor.GetGetter <double>(metricCol);
var foldGetter = cursor.GetGetter <ReadOnlyMemory <char> >(foldCol);
ReadOnlyMemory <char> fold = default;
var isWeightedGetter = cursor.GetGetter <bool>(isWeightedCol);
bool isWeighted = default;
double avg = 0;
double weightedAvg = 0;
for (int w = 0; w < 2; w++)
{
// Get the average.
b = cursor.MoveNext();
Assert.True(b);
if (w == 1)
{
getter(ref weightedAvg);
}
else
{
getter(ref avg);
}
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Average", fold));
isWeightedGetter(ref isWeighted);
Assert.True(isWeighted == (w == 1));
// Get the standard deviation.
b = cursor.MoveNext();
Assert.True(b);
double stdev = 0;
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
if (w == 1)
{
Assert.Equal(1.585, stdev, 3);
}
else
{
Assert.Equal(1.39, stdev, 2);
}
isWeightedGetter(ref isWeighted);
Assert.True(isWeighted == (w == 1));
}
double sum = 0;
double weightedSum = 0;
for (int f = 0; f < 2; f++)
{
for (int w = 0; w < 2; w++)
{
b = cursor.MoveNext();
Assert.True(b);
double val = 0;
getter(ref val);
foldGetter(ref fold);
if (w == 1)
{
weightedSum += val;
}
else
{
sum += val;
}
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Fold " + f, fold));
isWeightedGetter(ref isWeighted);
Assert.True(isWeighted == (w == 1));
}
}
Assert.Equal(weightedAvg, weightedSum / 2);
Assert.Equal(avg, sum / 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
public void TestCrossValidationBinaryMacro()
{
var dataPath = GetDataPath("adult.tiny.with-schema.txt");
var env = new MLContext();
var subGraph = env.CreateExperiment();
var catInput = new Legacy.Transforms.CategoricalOneHotVectorizer();
catInput.AddColumn("Categories");
var catOutput = subGraph.Add(catInput);
var concatInput = new Legacy.Transforms.ColumnConcatenator
{
Data = catOutput.OutputData
};
concatInput.AddColumn("Features", "Categories", "NumericFeatures");
var concatOutput = subGraph.Add(concatInput);
var lrInput = new Legacy.Trainers.LogisticRegressionBinaryClassifier
{
TrainingData = concatOutput.OutputData,
NumThreads = 1
};
var lrOutput = subGraph.Add(lrInput);
var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <TransformModel>(catOutput.Model, concatOutput.Model),
PredictorModel = lrOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var crossValidateBinary = new Legacy.Models.BinaryCrossValidator
{
Data = importOutput.Data,
Nodes = subGraph
};
crossValidateBinary.Inputs.Data = catInput.Data;
crossValidateBinary.Outputs.Model = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidateBinary);
experiment.Compile();
importInput.SetInput(env, experiment);
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics[0]);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int aucCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == aucCol))
{
var getter = cursor.GetGetter <double>(aucCol);
b = cursor.MoveNext();
Assert.True(b);
double auc = 0;
getter(ref auc);
Assert.Equal(0.87, auc, 1);
b = cursor.MoveNext();
Assert.False(b);
}
}
private static TransformModel CreateKcHousePricePredictorModel(string dataPath)
{
Experiment experiment = s_environment.CreateExperiment();
var importData = new Legacy.Data.TextLoader(dataPath)
{
Arguments = new TextLoaderArguments
{
Separator = new[] { ',' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Id",
Source = new [] { new TextLoaderRange(0) },
Type = Legacy.Data.DataKind.Text
},
new TextLoaderColumn()
{
Name = "Date",
Source = new [] { new TextLoaderRange(1) },
Type = Legacy.Data.DataKind.Text
},
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(2) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Bedrooms",
Source = new [] { new TextLoaderRange(3) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Bathrooms",
Source = new [] { new TextLoaderRange(4) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLiving",
Source = new [] { new TextLoaderRange(5) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLot",
Source = new [] { new TextLoaderRange(6) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Floors",
Source = new [] { new TextLoaderRange(7) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Waterfront",
Source = new [] { new TextLoaderRange(8) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "View",
Source = new [] { new TextLoaderRange(9) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Condition",
Source = new [] { new TextLoaderRange(10) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Grade",
Source = new [] { new TextLoaderRange(11) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftAbove",
Source = new [] { new TextLoaderRange(12) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftBasement",
Source = new [] { new TextLoaderRange(13) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "YearBuilt",
Source = new [] { new TextLoaderRange(14) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "YearRenovated",
Source = new [] { new TextLoaderRange(15) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Zipcode",
Source = new [] { new TextLoaderRange(16) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Lat",
Source = new [] { new TextLoaderRange(17) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Long",
Source = new [] { new TextLoaderRange(18) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLiving15",
Source = new [] { new TextLoaderRange(19) },
Type = Legacy.Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLot15",
Source = new [] { new TextLoaderRange(20) },
Type = Legacy.Data.DataKind.Num
},
}
}
//new Data.CustomTextLoader();
// importData.CustomSchema = dataSchema;
//
};
Legacy.Data.TextLoader.Output imported = experiment.Add(importData);
var numericalConcatenate = new Legacy.Transforms.ColumnConcatenator();
numericalConcatenate.Data = imported.Data;
numericalConcatenate.AddColumn("NumericalFeatures", "SqftLiving", "SqftLot", "SqftAbove", "SqftBasement", "Lat", "Long", "SqftLiving15", "SqftLot15");
Legacy.Transforms.ColumnConcatenator.Output numericalConcatenated = experiment.Add(numericalConcatenate);
var categoryConcatenate = new Legacy.Transforms.ColumnConcatenator();
categoryConcatenate.Data = numericalConcatenated.OutputData;
categoryConcatenate.AddColumn("CategoryFeatures", "Bedrooms", "Bathrooms", "Floors", "Waterfront", "View", "Condition", "Grade", "YearBuilt", "YearRenovated", "Zipcode");
Legacy.Transforms.ColumnConcatenator.Output categoryConcatenated = experiment.Add(categoryConcatenate);
var categorize = new Legacy.Transforms.CategoricalOneHotVectorizer();
categorize.AddColumn("CategoryFeatures");
categorize.Data = categoryConcatenated.OutputData;
Legacy.Transforms.CategoricalOneHotVectorizer.Output categorized = experiment.Add(categorize);
var featuresConcatenate = new Legacy.Transforms.ColumnConcatenator();
featuresConcatenate.Data = categorized.OutputData;
featuresConcatenate.AddColumn("Features", "NumericalFeatures", "CategoryFeatures");
Legacy.Transforms.ColumnConcatenator.Output featuresConcatenated = experiment.Add(featuresConcatenate);
var learner = new Legacy.Trainers.StochasticDualCoordinateAscentRegressor();
learner.TrainingData = featuresConcatenated.OutputData;
learner.NumThreads = 1;
Legacy.Trainers.StochasticDualCoordinateAscentRegressor.Output learnerOutput = experiment.Add(learner);
var combineModels = new Legacy.Transforms.ManyHeterogeneousModelCombiner();
combineModels.TransformModels = new ArrayVar <TransformModel>(numericalConcatenated.Model, categoryConcatenated.Model, categorized.Model, featuresConcatenated.Model);
combineModels.PredictorModel = learnerOutput.PredictorModel;
Legacy.Transforms.ManyHeterogeneousModelCombiner.Output combinedModels = experiment.Add(combineModels);
var scorer = new Legacy.Transforms.Scorer
{
PredictorModel = combinedModels.PredictorModel
};
var scorerOutput = experiment.Add(scorer);
experiment.Compile();
experiment.SetInput(importData.InputFile, new SimpleFileHandle(s_environment, dataPath, false, false));
experiment.Run();
return(experiment.GetOutput(scorerOutput.ScoringTransform));
}
