public static void Main(string[] args)
{
// Some manually chosen transactions with some modifications.
Console.WriteLine("Loading training data...");
List <TransactionData> trainingData = GetTrainingData();
Console.WriteLine("Training the model...");
var trainingService = new BankTransactionTrainingService();
trainingService.Train(trainingData, "Model.zip");
Console.WriteLine("Prepare transaction labeler...");
var labelService = new BankTransactionLabelService();
labelService.LoadModel("Model.zip");
Console.WriteLine("Predict some transactions based on their description and type...");
Console.WriteLine();
//Starbucks : Coffee
MakePrediction(labelService, "STARBUCKS", "Debit");
//PRET A MANGER : Coffee
MakePrediction(labelService, "MANGER", "Debit");
//This case use training1.json
MakePrediction(labelService, "SIMIT SARAYI", "Debit");
}
private static void MakePrediction(BankTransactionLabelService labelService, string description)
{
string prediction = labelService.PredictCategory(new Transaction
{
Description = description,
});
Console.WriteLine($"{description}\n => {prediction}\n");
}
private static void MakePrediction(BankTransactionLabelService labelService, string information, string creditDebitIndicator)
{
string prediction = labelService.PredictCategory(new TransactionData
{
Information = information,
CreditDebitIndicator = creditDebitIndicator
});
Console.WriteLine($"{information} ({creditDebitIndicator}) => {prediction}");
}
private static void TestPrediction(BankTransactionLabelService labelService, string transactionName, string category)
{
var predict = labelService.Predict(new Transaction(transactionName));
predict.Category.Should().Be(category);
var categories = labelService.GetCategories();
var index = categories.IndexOf(predict.Category);
// Max score should be the predicted category.
predict.Score.Max().Should().Be(predict.Score[index]);
}
public void TestAutoTrain()
{
var mlContext = new MLContext(0);
var trainingService = new BankTransactionTrainingService(mlContext);
string trainingDataFile = Path.Combine(AppContext.BaseDirectory, "Data/training.json");
var trainingData = JsonConvert.DeserializeObject <List <Transaction> >(File.ReadAllText(trainingDataFile));
var mlModel = trainingService.AutoTrain(trainingData, 5);
var labelService = new BankTransactionLabelService(mlContext);
labelService.LoadModel(mlModel);
TestModel(labelService);
}
private static void TestModel(BankTransactionLabelService labelService)
{
// Exact matches from the training data.
labelService.PredictCategory(new Transaction("VISA DEBIT PURCHASE CARD 0012 AMERICAN CONCEPTS PT BRISBANE")).Should().Be("coffee & tea");
labelService.PredictCategory(new Transaction("VISA DEBIT PURCHASE CARD 0012 DOTNETFOUNDATION.ORG 42553885334 10.00 USD INC O/S FEE $0.42")).Should().Be("investment");
// Not exact matches, ML Model needs to be to do "Fuzzy" search on them.
labelService.PredictCategory(new Transaction("coffee")).Should().Be("coffee & tea");
labelService.PredictCategory(new Transaction("DotNetFoundation.org")).Should().Be("investment");
labelService.PredictCategory(new Transaction("Anytime Fitness")).Should().Be("health");
// TODO: Doesn't work for AutoML. Data seems to be too unbalanced and biased toward conferences.
//labelService.PredictCategory(new Transaction("UBER")).Should().Be("transport");
labelService.PredictCategory(new Transaction("PubConf")).Should().Be("conference");
labelService.PredictCategory(new Transaction("DDD")).Should().Be("conference");
labelService.PredictCategory(new Transaction("DDD Perth")).Should().Be("conference");
}
public void TestLoadingMLModel()
{
var mlContext = new MLContext(0);
var trainingService = new BankTransactionTrainingService(mlContext);
string modelFile = Path.Combine(AppContext.BaseDirectory, $"{Guid.NewGuid()}.zip");
string trainingDataFile = Path.Combine(AppContext.BaseDirectory, "Data/training.json");
var trainingData = JsonConvert.DeserializeObject <List <Transaction> >(File.ReadAllText(trainingDataFile));
var model = trainingService.ManualTrain(trainingData);
trainingService.SaveModel(modelFile, model);
var labelService = new BankTransactionLabelService(mlContext);
labelService.LoadModelFromFile(modelFile);
TestModel(labelService);
File.Delete(modelFile);
}
public static void Main(string[] args)
{
bool doTraining = !args.Any(arg => arg.Equals("no-training", StringComparison.OrdinalIgnoreCase));
bool useAutoTrain = args.Any(arg => arg.Equals("auto-ml", StringComparison.OrdinalIgnoreCase));
var mlContext = new MLContext();
// Training is optional as long it's done at least once.
if (doTraining)
{
string trainingDataFile = Path.Combine(AppContext.BaseDirectory, "Data/training.json");
// Some manually chosen transactions with some modifications.
Console.WriteLine("Loading training data...");
IEnumerable <Transaction> trainingData = GetTrainingData(trainingDataFile);
Console.WriteLine("Training the model...");
var trainingService = new BankTransactionTrainingService(mlContext);
var timer = Stopwatch.StartNew();
ITransformer model = useAutoTrain
? trainingService.AutoTrain(trainingData, 15)
: trainingService.ManualTrain(trainingData);
trainingService.SaveModel("Model.zip", model);
timer.Stop();
Console.WriteLine($"Training done in {Math.Round(timer.Elapsed.TotalSeconds, 2)} seconds");
Console.WriteLine();
}
Console.WriteLine("Prepare transaction labeler...");
string modelFile = Path.Combine(AppContext.BaseDirectory, "Model.zip");
var labelService = new BankTransactionLabelService(mlContext);
labelService.LoadModelFromFile(modelFile);
Console.WriteLine("Predict some transactions based on their description and type...");
Console.WriteLine();
// Should be "coffee & tea".
MakePrediction(labelService, "VISA DEBIT PURCHASE CARD 0012 AMERICAN CONCEPTS PT BRISBANE");
// Should be "coffee & tea".
MakePrediction(labelService, "AMERICAN CONCEPTS PT BRISBANE");
// The number in the transaction is always random but it will work despite that. Result: rent
MakePrediction(labelService, "ANZ M-BANKING PAYMENT TRANSFER 513542 TO SPIRE REALITY");
// In fact, searching just for part of the transaction will give us the same result.
MakePrediction(labelService, "SPIRE REALITY");
// Should be "investment".
MakePrediction(labelService, "VISA DEBIT PURCHASE CARD 0012 DOTNETFOUNDATION.ORG 42553885334 10.00 USD INC O/S FEE $0.42");
// Should be "investment".
MakePrediction(labelService, "VISA DEBIT PURCHASE CARD 0012 DOTNETFOUNDATION.ORG 334634543 10.00 USD INC O/S FEE $0.12");
// Will likely fail.
MakePrediction(labelService, "DOTNETFOUNDATION.ORG random text");
}
