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CESARDELATORRE Migration to v0.10 of all C# samples (#242)
* BikeSharing sample migrated to 0.10

* Added cultureInfo.InvariantCulture to resolve issue #227

* added Build props file into other samples which are not migrated yet. so that build will not file on remote.

* Did the following changes.
1.Changed the version to 0.10 for global build.props file.
2.Changed the Credit card sample with the following to fix breaking changes.
a. Replace BinaryClassificationContext with BinaryClassificationCatalog
b. Added parameter names to input parameter of ML.Net API calls.
c. Added AppendCacheCheckpoint(mlContext) in the pipeLine.
Resolved some compilation errors according to ML.Net version changes

* Migrated sample Clustering_CustomerSegmentation with the following changes
1.Replaced CreateTextReader with ReadFromTextFile.
2.Changed the order of columns while creating the estimator
3.Added parameter names in API calls.
4.Accessed Fetures and score variables with DefaultColumnNames static class

* Minor changes in Customer Segmentation project
1.Updated Readme File
2.Removed AppendCacheCheckPoint() method as the program is running fine with F5 option in visual studio.

* Minor changes to Credit card Fraud Detection.
1.Changed readme file with version to 0.10 and code changes.
2.Removed AppendCahceCheckPoint(mlContext) as the program runs fine with out delay becuase of F5 option.

* Minor changes to Bike Sharing Demand Sample.
Replaced version with 0.10 in Readme file

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1.Added parameter names in ML.Net API calls
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4.Changed Readme file.
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* Migrated MovieRecommendation Sample to v0.10. with the below changes.
1.Changed code according Ml.Net API calls.
2.Used DefaultColumnNames class to specify common ouput types like Features, score,label etc.
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4. Changed Readme file
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1. Fixed the breaking changes like changing from "MulticlassClassificationContext" to  "MulticlassClassificationCatalog"
2.Changed the parameter orders in the API calls and specified parameter names.
3.Changed ReadMe file.
4.Removed Build.Props file which has version 0.9

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4. Refactored code and specified parameter names in API calls.

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Latest commit ee509d4 Feb 8, 2019

Readme.md

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
v0.10 Dynamic API Up-to-date 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-sensitive 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 training 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.

Build -> Train -> Evaluate -> Consume

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 (FastTree) to train the model with.

The initial code is similar to the following:

    // Create a common ML.NET context.
    // Seed set to any number so you have a deterministic environment for repeateable results
    MLContext mlContext = new MLContext(seed:1);

[...]
    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,
                                                   // First line of the file is a header, not a data row.
                                                   HasHeader = true,
                                                   Separator = ","
                                               };


[...]
    var classification = new BinaryClassificationCatalog(mlContext);

    (trainData, testData) = classification.TrainTestSplit(data, testFraction: 0.2);

[...]

    //Get all the column names for the Features (All except the Label and the StratificationColumn)
    var featureColumnNames = _trainData.Schema.AsQueryable() 
        .Select(column => column.Name) // Get the column names
        .Where(name => name != "Label") // Do not include the Label column
        .Where(name => name != "StratificationColumn") //Do not include the StratificationColumn
        .ToArray();

    var pipeline = _mlContext.Transforms.Concatenate(DefaultColumnNames.Features, featureColumnNames)
                            .Append(_mlContext.Transforms.Normalize(inputColumnName: "Features", outputColumnName: "FeaturesNormalizedByMeanVar", mode: NormalizerMode.MeanVariance))                       
                            .Append(_mlContext.BinaryClassification.Trainers.FastTree(labelColumn: "Label", 
                                                                                      featureColumn: "Features",
                                                                                      numLeaves: 20,
                                                                                      numTrees: 100,
                                                                                      minDatapointsInLeaves: 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 Fit() method while providing the training dataset (trainData.csv) in a DataView object.

    var model = pipeline.Fit(_trainData);

3. Evaluate model

We need this step to conclude how accurate our model is. To do so, the model from the previous step is run against another dataset that was not used in training (testData.csv).

Evaluate() compares the predicted values for the test dataset and produces various metrics, such as accuracy, you can explore.

    var metrics = _context.Evaluate(model.Transform(_testData), "Label");

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;
   using (var file = File.OpenRead(_modelfile))
   {
       model = mlContext.Model.Load(file);
   }

   var predictionEngine = model.CreatePredictionEngine<TransactionObservation, TransactionFraudPrediction>(mlContext);

[...]

    mlContext.CreateEnumerable<TransactionObservation>(dataTest, reuseRowObject: false)
                        .Where(x => x.Label == true)
                        .Take(numberOfTransactions)
                        .Select(testData => testData)
                        .ToList()
                        .ForEach(testData => 
                                    {
                                        Console.WriteLine($"--- Transaction ---");
                                        testData.PrintToConsole();
                                        predictionEngine.Predict(testData).PrintToConsole();
                                        Console.WriteLine($"-------------------");
                                    });
[...]

    mlContext.CreateEnumerable<TransactionObservation>(dataTest, reuseRowObject: false)
                        .Where(x => x.Label == false)
                        .Take(numberOfTransactions)
                        .ToList()
                        .ForEach(testData =>
                                    {
                                        Console.WriteLine($"--- Transaction ---");
                                        testData.PrintToConsole();
                                        predictionEngine.Predict(testData).PrintToConsole();
                                        Console.WriteLine($"-------------------");
                                    });