/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static Scalar <float> LbfgsPoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
float l1Regularization = Options.Defaults.L1Regularization,
float l2Regularization = Options.Defaults.L2Regularization,
float optimizationTolerance = Options.Defaults.OptimizationTolerance,
int historySize = Options.Defaults.HistorySize,
bool enforceNonNegativity = Options.Defaults.EnforceNonNegativity,
Action <PoissonRegressionModelParameters> onFit = null)
{
LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LbfgsPoissonRegressionTrainer(env, labelName, featuresName, weightsName,
l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static Scalar <float> LbfgsPoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights,
LbfgsPoissonRegressionTrainer.Options options,
Action <PoissonRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new LbfgsPoissonRegressionTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
// ===========================================================================================================
public void BuildTrainingPipelineAndModel()
{
if (ErrorHasOccured)
{
return;
}
try
{
EstimatorChain <NormalizingTransformer> dataProcessPipeline = _mlContext.Transforms.Conversion.ConvertType(new[] { new InputOutputColumnPair("CentralAir", "CentralAir") })
.Append(_mlContext.Transforms.Categorical.OneHotEncoding(new[] { new InputOutputColumnPair("MSZoning", "MSZoning"), new InputOutputColumnPair("Street", "Street"), new InputOutputColumnPair("Alley", "Alley"), new InputOutputColumnPair("LotShape", "LotShape"), new InputOutputColumnPair("LandContour", "LandContour"), new InputOutputColumnPair("Utilities", "Utilities"), new InputOutputColumnPair("LotConfig", "LotConfig"), new InputOutputColumnPair("LandSlope", "LandSlope"), new InputOutputColumnPair("Neighborhood", "Neighborhood"), new InputOutputColumnPair("Condition1", "Condition1"), new InputOutputColumnPair("Condition2", "Condition2"), new InputOutputColumnPair("BldgType", "BldgType"), new InputOutputColumnPair("HouseStyle", "HouseStyle"), new InputOutputColumnPair("RoofStyle", "RoofStyle"), new InputOutputColumnPair("RoofMatl", "RoofMatl"), new InputOutputColumnPair("Exterior1st", "Exterior1st"), new InputOutputColumnPair("Exterior2nd", "Exterior2nd"), new InputOutputColumnPair("MasVnrType", "MasVnrType"), new InputOutputColumnPair("ExterQual", "ExterQual"), new InputOutputColumnPair("ExterCond", "ExterCond"), new InputOutputColumnPair("Foundation", "Foundation"), new InputOutputColumnPair("BsmtQual", "BsmtQual"), new InputOutputColumnPair("BsmtCond", "BsmtCond"), new InputOutputColumnPair("BsmtExposure", "BsmtExposure"), new InputOutputColumnPair("BsmtFinType1", "BsmtFinType1"), new InputOutputColumnPair("BsmtFinType2", "BsmtFinType2"), new InputOutputColumnPair("Heating", "Heating"), new InputOutputColumnPair("HeatingQC", "HeatingQC"), new InputOutputColumnPair("Electrical", "Electrical"), new InputOutputColumnPair("KitchenQual", "KitchenQual"), new InputOutputColumnPair("Functional", "Functional"), new InputOutputColumnPair("FireplaceQu", "FireplaceQu"), new InputOutputColumnPair("GarageType", "GarageType"), new InputOutputColumnPair("GarageFinish", "GarageFinish"), new InputOutputColumnPair("GarageQual", "GarageQual"), new InputOutputColumnPair("GarageCond", "GarageCond"), new InputOutputColumnPair("PavedDrive", "PavedDrive"), new InputOutputColumnPair("PoolQC", "PoolQC"), new InputOutputColumnPair("Fence", "Fence"), new InputOutputColumnPair("MiscFeature", "MiscFeature"), new InputOutputColumnPair("SaleType", "SaleType"), new InputOutputColumnPair("SaleCondition", "SaleCondition") }))
.Append(_mlContext.Transforms.IndicateMissingValues(new[] { new InputOutputColumnPair("LotFrontage_MissingIndicator", "LotFrontage"), new InputOutputColumnPair("MasVnrArea_MissingIndicator", "MasVnrArea"), new InputOutputColumnPair("GarageYrBlt_MissingIndicator", "GarageYrBlt") }))
.Append(_mlContext.Transforms.Conversion.ConvertType(new[] { new InputOutputColumnPair("LotFrontage_MissingIndicator", "LotFrontage_MissingIndicator"), new InputOutputColumnPair("MasVnrArea_MissingIndicator", "MasVnrArea_MissingIndicator"), new InputOutputColumnPair("GarageYrBlt_MissingIndicator", "GarageYrBlt_MissingIndicator") }))
.Append(_mlContext.Transforms.ReplaceMissingValues(new[] { new InputOutputColumnPair("LotFrontage", "LotFrontage"), new InputOutputColumnPair("MasVnrArea", "MasVnrArea"), new InputOutputColumnPair("GarageYrBlt", "GarageYrBlt") }))
.Append(_mlContext.Transforms.Concatenate("Features", new[] { "CentralAir", "MSZoning", "Street", "Alley", "LotShape", "LandContour", "Utilities", "LotConfig", "LandSlope", "Neighborhood", "Condition1", "Condition2", "BldgType", "HouseStyle", "RoofStyle", "RoofMatl", "Exterior1st", "Exterior2nd", "MasVnrType", "ExterQual", "ExterCond", "Foundation", "BsmtQual", "BsmtCond", "BsmtExposure", "BsmtFinType1", "BsmtFinType2", "Heating", "HeatingQC", "Electrical", "KitchenQual", "Functional", "FireplaceQu", "GarageType", "GarageFinish", "GarageQual", "GarageCond", "PavedDrive", "PoolQC", "Fence", "MiscFeature", "SaleType", "SaleCondition", "LotFrontage_MissingIndicator", "MasVnrArea_MissingIndicator", "GarageYrBlt_MissingIndicator", "LotFrontage", "MasVnrArea", "GarageYrBlt", "Id", "MSSubClass", "LotArea", "OverallQual", "OverallCond", "YearBuilt", "YearRemodAdd", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF", "1stFlrSF", "2ndFlrSF", "LowQualFinSF", "GrLivArea", "BsmtFullBath", "BsmtHalfBath", "FullBath", "HalfBath", "BedroomAbvGr", "KitchenAbvGr", "TotRmsAbvGrd", "Fireplaces", "GarageCars", "GarageArea", "WoodDeckSF", "OpenPorchSF", "EnclosedPorch", "3SsnPorch", "ScreenPorch", "PoolArea", "MiscVal", "MoSold", "YrSold" }))
.Append(_mlContext.Transforms.NormalizeMinMax("Features", "Features"))
.AppendCacheCheckpoint(_mlContext);
LbfgsPoissonRegressionTrainer trainer = _mlContext.Regression.Trainers.LbfgsPoissonRegression(new LbfgsPoissonRegressionTrainer.Options()
{
L2Regularization = 0.07404655f, L1Regularization = 0.2087761f, OptimizationTolerance = 0.0001f, HistorySize = 5, MaximumNumberOfIterations = 462473459, InitialWeightsDiameter = 0.5613934f, DenseOptimizer = false, LabelColumnName = "SalePrice", FeatureColumnName = "Features"
});
EstimatorChain <RegressionPredictionTransformer <PoissonRegressionModelParameters> > trainingPipeline = dataProcessPipeline.Append(trainer);
// TODO
// Evaluate quality of Model
// Evaluate(_mlContext, _trainingDataView, trainingPipeline);
// Train Model
_mlModel = trainingPipeline.Fit(_trainingDataView);
// TODO
// Save _mlModel
// SaveModel(_mlContext, mlModel, ModelSettings.ModelFilePath, _trainingDataView.Schema);
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
ErrorHasOccured = true;
FailureInformation = ex.Message;
return;
}
}