public static PredictionEngine <TIn, TOut> NaiveBayes <TIn, TOut>(IEnumerable <TIn> trainDataset, string outputColumnName = "PredictedLabel", Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Conversion.MapValueToKey(labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.NaiveBayes(
labelColumnName: labelColumnName,
featureColumnName: "Features"
))
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
/// <summary>
/// Create engine of Stochastic Dual Coordination Ascent (optimization as such Stochastic Gradient Descent) algorithm using training dataset and hyperparameters
/// </summary>
/// <typeparam name="TIn"></typeparam>
/// <typeparam name="TOut"></typeparam>
/// <param name="trainDataset">Enumerable of TIn type.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="outputColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column.</param>
/// <param name="l2Regularization">The L2 regularization hyperparameter.</param>
/// <param name="l1Regularization">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maximumNumberOfIterations">The maximum number of passes to perform over the data.</param>
/// <returns></returns>
public static PredictionEngine <TIn, TOut> SdcaMaximumEntropy <TIn, TOut>(IEnumerable <TIn> trainDataset, string outputColumnName = "PredictedLabel", string exampleWeightColumnName = null, ISupportSdcaClassificationLoss loss = null, float?l2Regularization = null, float?l1Regularization = null, int?maximumNumberOfIterations = null, Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Conversion.MapValueToKey(labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.SdcaMaximumEntropy(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
l2Regularization: l2Regularization,
l1Regularization: l1Regularization,
maximumNumberOfIterations: maximumNumberOfIterations
))
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
public static PredictionEngine <TIn, TOut> LinearSVM <TIn, TOut>(
IEnumerable <TIn> trainDataset,
string exampleWeightColumnName = null,
int numberOfIterations = 1,
Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray())
.Append(preprocessor.OneHotEncodingEstimator)
.AppendCacheCheckpoint(context)
.Append(context.BinaryClassification.Trainers.LinearSvm(labelColumnName, featureColumnName: "Features", exampleWeightColumnName, numberOfIterations));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
/// <summary>
/// A base template of regression trainer which contains pre-processing likes OHE,PCA with any choosing algorithm.
/// </summary>
/// <typeparam name="TType">Type of training data.</typeparam>
/// <typeparam name="TTrainer">Type of trainer algorithm.</typeparam>
/// <param name="context">Microsoft.ML context.</param>
/// <param name="trainDataset">Training dataset.</param>
/// <param name="estimator">Algorithm estimator.</param>
/// <returns>Model of training datatype from given estimator.</returns>
private static TransformerChain <TTrainer> RegressionTrainerTemplate <TType, TTrainer>(this MLContext context, IEnumerable <TType> trainDataset, IEstimator <TTrainer> estimator)
where TType : class, new()
where TTrainer : class, ITransformer
{
var type = typeof(TType);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.Append(estimator);
var model = pipeline.Fit(trainDataframe);
return(model);
}
public static PredictionEngine <TIn, TOut> FastTree <TIn, TOut>(
IEnumerable <TIn> trainDataset,
string exampleWeightColumnName = null,
int numberOfLeaves = 20,
int numberOfTrees = 100,
int minimumExampleCountPerLeaft = 10,
double learningRate = 0.2,
Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline =
preprocessor.OneHotEncodingEstimator
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.AppendCacheCheckpoint(context)
.Append(context.BinaryClassification.Trainers.FastTree(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
numberOfLeaves: numberOfLeaves,
numberOfTrees: numberOfTrees,
minimumExampleCountPerLeaf: minimumExampleCountPerLeaft,
learningRate: learningRate
));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
public static PredictionEngine <TIn, TOut> AveragedPerceptron <TIn, TOut>(
IEnumerable <TIn> trainDataset,
IClassificationLoss lossFunction = null,
float learningRate = 1f,
bool decreaseLearningRate = false,
float l2Regularization = 0f,
int numberOfIterations = 1,
Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray())
.Append(preprocessor.OneHotEncodingEstimator)
.AppendCacheCheckpoint(context)
.Append(context.BinaryClassification.Trainers.AveragedPerceptron(
labelColumnName,
featureColumnName: "Features",
lossFunction,
learningRate,
decreaseLearningRate,
l2Regularization,
numberOfIterations
));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
public static PredictionEngine <TIn, TOut> KMeans <TIn, TOut>(IEnumerable <TIn> trainDataset, int numberOfClusters = 5, string exampleWeightColumnName = null, Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessing = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Concatenate("Features", preprocessing.CombinedFeatures.ToArray())
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.Append(preprocessing.OneHotEncodingEstimator)
.Append(context.Clustering.Trainers.KMeans(featureColumnName: "Features", numberOfClusters: numberOfClusters));
var model = pipeline.Fit(trainDataframe);
var engine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(engine);
}
/// <summary>
/// Create engine of Limited-Memory Broyden–Fletcher–Goldfarb–Shanno algorithm using training dataset and hyperparameters
/// </summary>
/// <typeparam name="TIn"></typeparam>
/// <typeparam name="TOut"></typeparam>
/// <param name="trainDataset">Enumerable of TIn type.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="outputColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <param name="historySize">Memory size. Low=faster, less accurate.</param>
/// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
/// <returns></returns>
public static PredictionEngine <TIn, TOut> LbfgsMaximumEntropy <TIn, TOut>(IEnumerable <TIn> trainDataset, string outputColumnName = "PredictedLabel", string exampleWeightColumnName = null, float l1Regularization = 1, float l2Regularization = 1, double optimizationTolerance = 1e-07, int historySize = 20, bool enforceNonNegativity = false, Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Conversion.MapValueToKey(labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.LbfgsMaximumEntropy(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
l1Regularization: l1Regularization,
l2Regularization: l2Regularization,
optimizationTolerance: (float)optimizationTolerance,
historySize: historySize,
enforceNonNegativity: enforceNonNegativity
)
)
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}