public void RunMultiplePredictions(int numberOfTransactions, int?seed = 1)
{
var env = new LocalEnvironment(seed);
var binTestData = new BinaryLoader(env, new BinaryLoader.Arguments(), new MultiFileSource(_dasetFile));
var testRoles = new RoleMappedData(binTestData, roles: TransactionObservation.Roles());
var dataTest = testRoles.Data;
//Inspect/Peek data from datasource
ConsoleHelpers.ConsoleWriterSection($"Inspect {numberOfTransactions} transactions observed as fraud and {numberOfTransactions} not observed as fraud, from the test datasource:");
ConsoleHelpers.InspectData(env, dataTest, numberOfTransactions);
ConsoleHelpers.ConsoleWriteHeader($"Predictions from saved model:");
ITransformer model = env.ReadModel(_modelfile);
var predictionFunc = model.MakePredictionFunction <TransactionObservation, TransactionFraudPrediction>(env);
ConsoleHelpers.ConsoleWriterSection($"Test {numberOfTransactions} transactions, from the test datasource, that should be predicted as fraud (true):");
dataTest.AsEnumerable <TransactionObservation>(env, reuseRowObject: false)
.Where(x => x.Label == true)
.Take(numberOfTransactions)
.Select(testData => testData)
.ToList()
.ForEach(testData =>
{
Console.WriteLine($"--- Transaction ---");
testData.PrintToConsole();
predictionFunc.Predict(testData).PrintToConsole();
Console.WriteLine($"-------------------");
});
ConsoleHelpers.ConsoleWriterSection($"Test {numberOfTransactions} transactions, from the test datasource, that should NOT be predicted as fraud (false):");
dataTest.AsEnumerable <TransactionObservation>(env, reuseRowObject: false)
.Where(x => x.Label == false)
.Take(numberOfTransactions)
.ToList()
.ForEach(testData =>
{
Console.WriteLine($"--- Transaction ---");
testData.PrintToConsole();
predictionFunc.Predict(testData).PrintToConsole();
Console.WriteLine($"-------------------");
});
}
PrepareData(MLContext mlContext)
{
IDataView data = null;
IDataView trainData = null;
IDataView testData = null;
// 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, new TextLoader.Arguments
{
Column = 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),
},
// First line of the file is a header, not a data row.
HasHeader = true,
Separator = ","
});
// 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 BinaryClassificationContext(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
IHostEnvironment env = (IHostEnvironment)mlContext;
using (var ch = env.Start("SaveData"))
using (var file = env.CreateOutputFile(Path.Combine(_outputPath, "testData.idv")))
{
var saver = new BinarySaver(mlContext, new BinarySaver.Arguments());
DataSaverUtils.SaveDataView(ch, saver, testData, file);
}
// save train split
using (var ch = ((IHostEnvironment)env).Start("SaveData"))
using (var file = env.CreateOutputFile(Path.Combine(_outputPath, "trainData.idv")))
{
var saver = new BinarySaver(mlContext, new BinarySaver.Arguments());
DataSaverUtils.SaveDataView(ch, saver, trainData, file);
}
}
else
{
// Load splited data
var binTrainData = new BinaryLoader(mlContext, new BinaryLoader.Arguments(), new MultiFileSource(Path.Combine(_outputPath, "trainData.idv")));
var trainRoles = new RoleMappedData(binTrainData, roles: TransactionObservation.Roles());
trainData = trainRoles.Data;
var binTestData = new BinaryLoader(mlContext, new BinaryLoader.Arguments(), new MultiFileSource(Path.Combine(_outputPath, "testData.idv")));
var testRoles = new RoleMappedData(binTestData, roles: TransactionObservation.Roles());
testData = testRoles.Data;
}
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);
}