BinaryClassification_CreditCardFraudDetection

Fraud detection in credit cards based on binary classification and PCA

ML.NET version	API type	Status	App Type	Data type	Scenario	ML Task	Algorithms
v0.7	Dynamic API	Needs update to 0.7 and README.md	Two console apps	.csv file	Fraud Detection	Two-class classification	FastTree Binary Classification

In this introductory sample, you'll see how to use ML.NET to predict a credit card fraud. In the world of machine learning, this type of prediction is known as binary classification.

API version: Dynamic and Estimators-based API

It is important to note that this sample uses the dynamic API with Estimators.

Problem

This problem is centered around predicting if credit card transaction (with its related info/variables) is a fraud or no.

The input information of the transactions contain only numerical input variables which are the result of PCA transformations. Unfortunately, due to confidentiality issues, the original features and additional background information are not available, but the way you build the model doesn't change.

Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'.

The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

Using those datasets you build a model that when predicting it will analyze a transaction's input variables and predict a fraud value of false or true.

DataSet

The trainig and testing data is based on a public dataset available at Kaggle originally from Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles), collected and analysed during a research collaboration.

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.

By: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML

ML Task - Binary Classification

Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule. Contexts requiring a decision as to whether or not an item has some qualitative property, some specified characteristic

Solution

To solve this problem, first you need to build a machine learning model. Then you train the model on existing training data, evaluate how good its accuracy is, and lastly you consume the model (deploying the built model in a different app) to predict a fraud for a sample credit card transaction.

1. Build model

Building a model includes:

• Define the data's schema maped to the datasets to read with a DataReader

• Split data for training and tests

• Create an Estimator and transform the data with a ConcatEstimator() and Normalize by Mean Variance.

• Choosing a trainer/learning algorithm (SDCA or FastTree) to train the model with.

The initial code is similar to the following:

    //1. Create ML.NET context/environment
    var env = new LocalEnvironment();

[...]
    //2. Create DataReader with data schema mapped to file's columns
    var reader = new TextLoader(env, 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 ),
        },
        HasHeader = true,
        Separator = ","
    }); 

    var classification = new BinaryClassificationContext(env);

    //3. Split data for training and test
    (trainData, testData) = classification.TrainTestSplit(data, testFraction: 0.2);

[...]

    //4. Create an estimator to use afterwards for creating/traing the model.
    var logMeanVarNormalizer =   new Normalizer(_env,Normalizer.NormalizerMode.MeanVariance ,("Features", "FeaturesNormalizedByMeanVar"));

    var estimator = new ConcatEstimator(_env, "Features", new[] { "Amount", "V1", "V2", "V3", "V4", "V5", "V6",
                                                                        "V7", "V8", "V9", "V10", "V11", "V12",
                                                                        "V13", "V14", "V15", "V16", "V17", "V18",
                                                                        "V19", "V20", "V21", "V22", "V23", "V24",
                                                                        "V25", "V26", "V27", "V28" })                      
                    .Append(new Normalizer(_env, Normalizer.NormalizerMode.MeanVariance, ("Features", "FeaturesNormalizedByMeanVar")))
                    .Append(new FastTreeBinaryClassificationTrainer(_env, "Label", "Features",
                                                    
                                                numLeaves: 20,
                                                numTrees: 100,
                                                minDocumentsInLeafs : 10,
                                                learningRate: 0.2));

2. Train model

Training the model is a process of running the chosen algorithm on a training data (with known fraud values) to tune the parameters of the model. It is implemented in the Fit() method from the Estimator object.

To perform training you need to call the CrossValidate() method while providing the training dataset in a DataView object.

    // Now run the n-fold cross-validation experiment, using the same pipeline.
    var cvResults = _context.CrossValidate(_trainData, estimator, labelColumn: "Label", numFolds: cvNumFolds);

3. Evaluate model

We need this step to conclude how accurate our model is. To do so, inspect results from Cross Validation and save best model.

    int count = 1;
    var cvModels = cvResults.ToList();
    cvModels.ForEach(result =>
    {
        ConsoleHelpers.ConsoleWriteHeader($"Train Metrics Cross Validate [{count}/{cvNumFolds}]:");
        result.metrics.ToConsole();
        ConsoleHelpers.ConsoleWriteHeader($"Show 4 [model {count}]");
        ConsoleHelpers.InspectScoredData(_env, result.scoredTestData);
        count++;
    });
    // save model with best accuracy
    var bestmodel = cvModels.OrderByDescending(result => result.metrics.Accuracy).Select(result => result.model).FirstOrDefault();           
    bestmodel.SaveModel(_env, Path.Combine(_outputPath, "cv-fastTree.zip"));

4. Consume model

After the model is trained, you can use the Predict() API to predict if a transaction is a fraud, using a IDataSet.

[...]

   ITransformer model = env.ReadModel(_modelfile);
   var predictionFunc = model.MakePredictionFunction<TransactionVectorModel, TransactionEstimatorModel>(env);

[...]

    dataTest.AsEnumerable<TransactionVectorModel>(env, reuseRowObject: false)
            .Where(x => x.Label == true)
            .Take(4)
            .Select(testData => testData)
            .ToList()
            .ForEach(testData => {
                predictionFunc.Predict(testData).PrintToConsole();
            });
[...]

    dataTest.AsEnumerable<TransactionVectorModel>(env, reuseRowObject: false)
                    .Where(x => x.Label == false)
                    .Take(4)
                    .ToList()
                    .ForEach(testData => {
                        predictionFunc.Predict(testData).PrintToConsole();
                    });