PrepareData(MLContext mlContext)
{
IDataView data = null;
IDataView trainData = null;
IDataView testData = null;
TextLoader.Column[] columns = new[] {
// A boolean column depicting the 'label'.
new TextLoader.Column("Label", DataKind.BL, 30),
// 29 Features V1..V28 + Amount
new TextLoader.Column("V1", DataKind.R4, 1),
new TextLoader.Column("V2", DataKind.R4, 2),
new TextLoader.Column("V3", DataKind.R4, 3),
new TextLoader.Column("V4", DataKind.R4, 4),
new TextLoader.Column("V5", DataKind.R4, 5),
new TextLoader.Column("V6", DataKind.R4, 6),
new TextLoader.Column("V7", DataKind.R4, 7),
new TextLoader.Column("V8", DataKind.R4, 8),
new TextLoader.Column("V9", DataKind.R4, 9),
new TextLoader.Column("V10", DataKind.R4, 10),
new TextLoader.Column("V11", DataKind.R4, 11),
new TextLoader.Column("V12", DataKind.R4, 12),
new TextLoader.Column("V13", DataKind.R4, 13),
new TextLoader.Column("V14", DataKind.R4, 14),
new TextLoader.Column("V15", DataKind.R4, 15),
new TextLoader.Column("V16", DataKind.R4, 16),
new TextLoader.Column("V17", DataKind.R4, 17),
new TextLoader.Column("V18", DataKind.R4, 18),
new TextLoader.Column("V19", DataKind.R4, 19),
new TextLoader.Column("V20", DataKind.R4, 20),
new TextLoader.Column("V21", DataKind.R4, 21),
new TextLoader.Column("V22", DataKind.R4, 22),
new TextLoader.Column("V23", DataKind.R4, 23),
new TextLoader.Column("V24", DataKind.R4, 24),
new TextLoader.Column("V25", DataKind.R4, 25),
new TextLoader.Column("V26", DataKind.R4, 26),
new TextLoader.Column("V27", DataKind.R4, 27),
new TextLoader.Column("V28", DataKind.R4, 28),
new TextLoader.Column("Amount", DataKind.R4, 29)
};
TextLoader.Arguments txtLoaderArgs = new TextLoader.Arguments
{
Column = columns,
HasHeader = true,
Separators = new char[] { ',' }
};
// Step one: read the data as an IDataView.
// Create the reader: define the data columns
// and where to find them in the text file.
var reader = new TextLoader(mlContext, txtLoaderArgs);
// We know that this is a Binary Classification task,
// so we create a Binary Classification context:
// it will give us the algorithms we need,
// as well as the evaluation procedure.
var classification = new BinaryClassificationCatalog(mlContext);
if (!File.Exists(Path.Combine(_outputPath, "testData.idv")) &&
!File.Exists(Path.Combine(_outputPath, "trainData.idv")))
{
// Split the data 80:20 into train and test sets, train and evaluate.
data = reader.Read(new MultiFileSource(_dataSetFile));
ConsoleHelpers.ConsoleWriteHeader("Show 4 transactions fraud (true) and 4 transactions not fraud (false) - (source)");
ConsoleHelpers.InspectData(mlContext, data, 4);
// Can't do stratification when column type is a boolean, is this an issue?
//(trainData, testData) = classification.TrainTestSplit(data, testFraction: 0.2, stratificationColumn: "Label");
(trainData, testData) = classification.TrainTestSplit(data, testFraction: 0.2);
// save test split
using (var fileStream = File.Create(Path.Combine(_outputPath, "testData.csv")))
{
mlContext.Data.SaveAsText(testData, fileStream, separatorChar: ',', headerRow: true, schema: true);
}
// save train split
using (var fileStream = File.Create(Path.Combine(_outputPath, "trainData.csv")))
{
mlContext.Data.SaveAsText(trainData, fileStream, separatorChar: ',', headerRow: true, schema: true);
}
}
else
{
//Add the "StratificationColumn" that was added by classification.TrainTestSplit()
// And Label is moved to column 0
TextLoader.Column[] columnsPlus = new[] {
// A boolean column depicting the 'label'.
new TextLoader.Column("Label", DataKind.BL, 0),
// 30 Features V1..V28 + Amount + StratificationColumn
new TextLoader.Column("V1", DataKind.R4, 1),
new TextLoader.Column("V2", DataKind.R4, 2),
new TextLoader.Column("V3", DataKind.R4, 3),
new TextLoader.Column("V4", DataKind.R4, 4),
new TextLoader.Column("V5", DataKind.R4, 5),
new TextLoader.Column("V6", DataKind.R4, 6),
new TextLoader.Column("V7", DataKind.R4, 7),
new TextLoader.Column("V8", DataKind.R4, 8),
new TextLoader.Column("V9", DataKind.R4, 9),
new TextLoader.Column("V10", DataKind.R4, 10),
new TextLoader.Column("V11", DataKind.R4, 11),
new TextLoader.Column("V12", DataKind.R4, 12),
new TextLoader.Column("V13", DataKind.R4, 13),
new TextLoader.Column("V14", DataKind.R4, 14),
new TextLoader.Column("V15", DataKind.R4, 15),
new TextLoader.Column("V16", DataKind.R4, 16),
new TextLoader.Column("V17", DataKind.R4, 17),
new TextLoader.Column("V18", DataKind.R4, 18),
new TextLoader.Column("V19", DataKind.R4, 19),
new TextLoader.Column("V20", DataKind.R4, 20),
new TextLoader.Column("V21", DataKind.R4, 21),
new TextLoader.Column("V22", DataKind.R4, 22),
new TextLoader.Column("V23", DataKind.R4, 23),
new TextLoader.Column("V24", DataKind.R4, 24),
new TextLoader.Column("V25", DataKind.R4, 25),
new TextLoader.Column("V26", DataKind.R4, 26),
new TextLoader.Column("V27", DataKind.R4, 27),
new TextLoader.Column("V28", DataKind.R4, 28),
new TextLoader.Column("Amount", DataKind.R4, 29),
new TextLoader.Column("StratificationColumn", DataKind.R4, 30)
};
// Load splited data
trainData = mlContext.Data.ReadFromTextFile(Path.Combine(_outputPath, "trainData.csv"),
columnsPlus,
hasHeader: txtLoaderArgs.HasHeader,
separatorChar: txtLoaderArgs.Separators[0]);
testData = mlContext.Data.ReadFromTextFile(Path.Combine(_outputPath, "testData.csv"),
columnsPlus,
hasHeader: txtLoaderArgs.HasHeader,
separatorChar: txtLoaderArgs.Separators[0]);
}
ConsoleHelpers.ConsoleWriteHeader("Show 4 transactions fraud (true) and 4 transactions not fraud (false) - (traindata)");
ConsoleHelpers.InspectData(mlContext, trainData, 4);
ConsoleHelpers.ConsoleWriteHeader("Show 4 transactions fraud (true) and 4 transactions not fraud (false) - (testData)");
ConsoleHelpers.InspectData(mlContext, testData, 4);
return(classification, reader, trainData, testData);
}
/// <summary>
/// Evaluates scored binary classification data.
/// </summary>
/// <typeparam name="T">The shape type for the input data.</typeparam>
/// <param name="catalog">The binary classification catalog.</param>
/// <param name="data">The data to evaluate.</param>
/// <param name="label">The index delegate for the label column.</param>
/// <param name="pred">The index delegate for columns from calibrated prediction of a binary classifier.
/// Under typical scenarios, this will just be the same tuple of results returned from the trainer.</param>
/// <returns>The evaluation results for these calibrated outputs.</returns>
public static CalibratedBinaryClassificationMetrics Evaluate <T>(
this BinaryClassificationCatalog catalog,
DataView <T> data,
Func <T, Scalar <bool> > label,
Func <T, (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel)> pred)
