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Introduced ad implemented Ml.Net for prediction purposes
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| <?xml version="1.0" encoding="utf-8" ?> | ||
| <?xml version="1.0" encoding="utf-8"?> | ||
| <configuration> | ||
| <startup> | ||
| <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.8" /> | ||
| </startup> | ||
| <runtime> | ||
| <assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1"> | ||
| <dependentAssembly> | ||
| <assemblyIdentity name="System.Threading.Tasks.Extensions" publicKeyToken="cc7b13ffcd2ddd51" culture="neutral" /> | ||
| <bindingRedirect oldVersion="0.0.0.0-4.2.0.1" newVersion="4.2.0.1" /> | ||
| </dependentAssembly> | ||
| </assemblyBinding> | ||
| </runtime> | ||
| </configuration> |
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| @@ -0,0 +1,91 @@ | ||
| using System; | ||
| using System.IO; | ||
| using System.Linq; | ||
| using Microsoft.ML; | ||
| using Microsoft.ML.Data; | ||
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| // Define a class to hold your data structure | ||
| public class InputData | ||
| { | ||
| [LoadColumn(0)] | ||
| public float Ip1 { get; set; } | ||
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| [LoadColumn(1)] | ||
| public float Ip2 { get; set; } | ||
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| [LoadColumn(2)] | ||
| public float Ip3 { get; set; } | ||
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| [LoadColumn(3)] | ||
| public float Ip4 { get; set; } | ||
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| [LoadColumn(4), ColumnName("Label")] | ||
| public bool Fraud { get; set; } | ||
| } | ||
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| // Define a class to hold your prediction | ||
| public class OutputData | ||
| { | ||
| [ColumnName("PredictedLabel")] | ||
| public bool Prediction { get; set; } | ||
| } | ||
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| public class Predictor | ||
| { | ||
| public static string PredictMovement(InputData inputData) | ||
| { | ||
| // Set paths for data and model | ||
| var dataPath = Path.Combine(Environment.CurrentDirectory, "Data", "input_data.csv"); | ||
| var secondDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "second_input_data.csv"); | ||
| var modelPath = Path.Combine(Environment.CurrentDirectory, "Model", "trained_model.zip"); | ||
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| // Create a ML.NET context | ||
| var mlContext = new MLContext(); | ||
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| // Load data | ||
| var dataView = mlContext.Data.LoadFromTextFile<InputData>( | ||
| path: dataPath, | ||
| hasHeader: true, | ||
| separatorChar: ','); | ||
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| // Split data into training and testing sets | ||
| var trainTestSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.3); | ||
| var trainData = trainTestSplit.TrainSet; | ||
| var testData = trainTestSplit.TestSet; | ||
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| // Define data preparation pipeline | ||
| var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", "Ip1", "Ip2", "Ip3", "Ip4") | ||
| .Append(mlContext.Transforms.NormalizeMinMax("Features")); | ||
|
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| // Create a Logistic Regression model | ||
| var trainer = mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(); | ||
| var trainingPipeline = dataProcessPipeline.Append(trainer); | ||
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| // Train the model | ||
| var model = trainingPipeline.Fit(trainData); | ||
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| // Save the trained model | ||
| mlContext.Model.Save(model, trainData.Schema, modelPath); | ||
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| // Reload the trained model | ||
| var reloadedModel = mlContext.Model.Load(modelPath, out var modelInputSchema); | ||
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| // Load the second input data file (features only) | ||
| var secondDataView = mlContext.Data.LoadFromTextFile<InputData>( | ||
| path: secondDataPath, | ||
| hasHeader: true, | ||
| separatorChar: ','); | ||
| var input = new InputData(); | ||
| // Make predictions on the second input data | ||
| var predictions = mlContext.Model.CreatePredictionEngine<InputData, OutputData>(reloadedModel) | ||
| .Predict(input); | ||
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| // Display predictions | ||
| Console.WriteLine("Predictions for the second input data:"); | ||
| //foreach (var prediction in predictions) | ||
| //{ | ||
| Console.WriteLine($"Prediction: {predictions.Prediction}"); | ||
| //} | ||
| return predictions.Prediction.ToString(); | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,18 @@ | ||
| <?xml version="1.0" encoding="utf-8"?> | ||
| <packages> | ||
| <package id="Microsoft.Bcl.Numerics" version="8.0.0" targetFramework="net48" /> | ||
| <package id="Microsoft.ML" version="3.0.1" targetFramework="net48" /> | ||
| <package id="Microsoft.ML.CpuMath" version="3.0.1" targetFramework="net48" /> | ||
| <package id="Microsoft.ML.DataView" version="3.0.1" targetFramework="net48" /> | ||
| <package id="Newtonsoft.Json" version="13.0.1" targetFramework="net48" /> | ||
| <package id="System.Buffers" version="4.5.1" targetFramework="net48" /> | ||
| <package id="System.CodeDom" version="4.5.0" targetFramework="net48" /> | ||
| <package id="System.Collections.Immutable" version="1.5.0" targetFramework="net48" /> | ||
| <package id="System.Memory" version="4.5.5" targetFramework="net48" /> | ||
| <package id="System.Numerics.Tensors" version="8.0.0" targetFramework="net48" /> | ||
| <package id="System.Numerics.Vectors" version="4.5.0" targetFramework="net48" /> | ||
| <package id="System.Reflection.Emit.Lightweight" version="4.3.0" targetFramework="net48" /> | ||
| <package id="System.Runtime.CompilerServices.Unsafe" version="4.5.3" targetFramework="net48" /> | ||
| <package id="System.Threading.Channels" version="4.7.1" targetFramework="net48" /> | ||
| <package id="System.Threading.Tasks.Extensions" version="4.5.4" targetFramework="net48" /> | ||
| </packages> |