private static ITransformModel CreateKcHousePricePredictorModel(string dataPath)
{
var dataSchema = "col=Id:TX:0 col=Date:TX:1 col=Label:R4:2 col=Bedrooms:R4:3 col=Bathrooms:R4:4 col=SqftLiving:R4:5 col=SqftLot:R4:6 col=Floors:R4:7 col=Waterfront:R4:8 col=View:R4:9 col=Condition:R4:10 col=Grade:R4:11 col=SqftAbove:R4:12 col=SqftBasement:R4:13 col=YearBuilt:R4:14 col=YearRenovated:R4:15 col=Zipcode:R4:16 col=Lat:R4:17 col=Long:R4:18 col=SqftLiving15:R4:19 col=SqftLot15:R4:20 header+ sep=,";
Experiment experiment = s_environment.CreateExperiment();
var importData = new Data.TextLoader();
importData.CustomSchema = dataSchema;
Data.TextLoader.Output imported = experiment.Add(importData);
var numericalConcatenate = new Transforms.ColumnConcatenator();
numericalConcatenate.Data = imported.Data;
numericalConcatenate.AddColumn("NumericalFeatures", "SqftLiving", "SqftLot", "SqftAbove", "SqftBasement", "Lat", "Long", "SqftLiving15", "SqftLot15");
Transforms.ColumnConcatenator.Output numericalConcatenated = experiment.Add(numericalConcatenate);
var categoryConcatenate = new Transforms.ColumnConcatenator();
categoryConcatenate.Data = numericalConcatenated.OutputData;
categoryConcatenate.AddColumn("CategoryFeatures", "Bedrooms", "Bathrooms", "Floors", "Waterfront", "View", "Condition", "Grade", "YearBuilt", "YearRenovated", "Zipcode");
Transforms.ColumnConcatenator.Output categoryConcatenated = experiment.Add(categoryConcatenate);
var categorize = new Transforms.CategoricalOneHotVectorizer();
categorize.AddColumn("CategoryFeatures");
categorize.Data = categoryConcatenated.OutputData;
Transforms.CategoricalOneHotVectorizer.Output categorized = experiment.Add(categorize);
var featuresConcatenate = new Transforms.ColumnConcatenator();
featuresConcatenate.Data = categorized.OutputData;
featuresConcatenate.AddColumn("Features", "NumericalFeatures", "CategoryFeatures");
Transforms.ColumnConcatenator.Output featuresConcatenated = experiment.Add(featuresConcatenate);
var learner = new Trainers.StochasticDualCoordinateAscentRegressor();
learner.TrainingData = featuresConcatenated.OutputData;
learner.NumThreads = 1;
Trainers.StochasticDualCoordinateAscentRegressor.Output learnerOutput = experiment.Add(learner);
var combineModels = new Transforms.ManyHeterogeneousModelCombiner();
combineModels.TransformModels = new ArrayVar <ITransformModel>(numericalConcatenated.Model, categoryConcatenated.Model, categorized.Model, featuresConcatenated.Model);
combineModels.PredictorModel = learnerOutput.PredictorModel;
Transforms.ManyHeterogeneousModelCombiner.Output combinedModels = experiment.Add(combineModels);
var scorer = new 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));
}
private static ITransformModel CreateKcHousePricePredictorModel(string dataPath)
{
Experiment experiment = s_environment.CreateExperiment();
var importData = new Data.TextLoader(dataPath)
{
Arguments = new TextLoaderArguments
{
Separator = new[] { ',' },
HasHeader = true,
Column = new[]
{
new TextLoaderColumn()
{
Name = "Id",
Source = new [] { new TextLoaderRange(0) },
Type = Data.DataKind.Text
},
new TextLoaderColumn()
{
Name = "Date",
Source = new [] { new TextLoaderRange(1) },
Type = Data.DataKind.Text
},
new TextLoaderColumn()
{
Name = "Label",
Source = new [] { new TextLoaderRange(2) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Bedrooms",
Source = new [] { new TextLoaderRange(3) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Bathrooms",
Source = new [] { new TextLoaderRange(4) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLiving",
Source = new [] { new TextLoaderRange(5) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLot",
Source = new [] { new TextLoaderRange(6) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Floors",
Source = new [] { new TextLoaderRange(7) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Waterfront",
Source = new [] { new TextLoaderRange(8) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "View",
Source = new [] { new TextLoaderRange(9) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Condition",
Source = new [] { new TextLoaderRange(10) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Grade",
Source = new [] { new TextLoaderRange(11) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftAbove",
Source = new [] { new TextLoaderRange(12) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftBasement",
Source = new [] { new TextLoaderRange(13) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "YearBuilt",
Source = new [] { new TextLoaderRange(14) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "YearRenovated",
Source = new [] { new TextLoaderRange(15) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Zipcode",
Source = new [] { new TextLoaderRange(16) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Lat",
Source = new [] { new TextLoaderRange(17) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "Long",
Source = new [] { new TextLoaderRange(18) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLiving15",
Source = new [] { new TextLoaderRange(19) },
Type = Data.DataKind.Num
},
new TextLoaderColumn()
{
Name = "SqftLot15",
Source = new [] { new TextLoaderRange(20) },
Type = Data.DataKind.Num
},
}
}
//new Data.CustomTextLoader();
// importData.CustomSchema = dataSchema;
//
};
Data.TextLoader.Output imported = experiment.Add(importData);
var numericalConcatenate = new Transforms.ColumnConcatenator();
numericalConcatenate.Data = imported.Data;
numericalConcatenate.AddColumn("NumericalFeatures", "SqftLiving", "SqftLot", "SqftAbove", "SqftBasement", "Lat", "Long", "SqftLiving15", "SqftLot15");
Transforms.ColumnConcatenator.Output numericalConcatenated = experiment.Add(numericalConcatenate);
var categoryConcatenate = new Transforms.ColumnConcatenator();
categoryConcatenate.Data = numericalConcatenated.OutputData;
categoryConcatenate.AddColumn("CategoryFeatures", "Bedrooms", "Bathrooms", "Floors", "Waterfront", "View", "Condition", "Grade", "YearBuilt", "YearRenovated", "Zipcode");
Transforms.ColumnConcatenator.Output categoryConcatenated = experiment.Add(categoryConcatenate);
var categorize = new Transforms.CategoricalOneHotVectorizer();
categorize.AddColumn("CategoryFeatures");
categorize.Data = categoryConcatenated.OutputData;
Transforms.CategoricalOneHotVectorizer.Output categorized = experiment.Add(categorize);
var featuresConcatenate = new Transforms.ColumnConcatenator();
featuresConcatenate.Data = categorized.OutputData;
featuresConcatenate.AddColumn("Features", "NumericalFeatures", "CategoryFeatures");
Transforms.ColumnConcatenator.Output featuresConcatenated = experiment.Add(featuresConcatenate);
var learner = new Trainers.StochasticDualCoordinateAscentRegressor();
learner.TrainingData = featuresConcatenated.OutputData;
learner.NumThreads = 1;
Trainers.StochasticDualCoordinateAscentRegressor.Output learnerOutput = experiment.Add(learner);
var combineModels = new Transforms.ManyHeterogeneousModelCombiner();
combineModels.TransformModels = new ArrayVar <ITransformModel>(numericalConcatenated.Model, categoryConcatenated.Model, categorized.Model, featuresConcatenated.Model);
combineModels.PredictorModel = learnerOutput.PredictorModel;
Transforms.ManyHeterogeneousModelCombiner.Output combinedModels = experiment.Add(combineModels);
var scorer = new 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));
}
