/// <summary>
/// The main applicaton entry point.
/// </summary>
/// <param name="args">The command line arguments.</param>
public static void Main(string[] args)
{
// set up a machine learning context
var context = new MLContext();
// load training and test data
Console.WriteLine("Loading data...");
var trainData = context.Data.LoadFromTextFile <HeartData>(trainDataPath, hasHeader: true, separatorChar: ',');
var testData = context.Data.LoadFromTextFile <HeartData>(testDataPath, hasHeader: true, separatorChar: ',');
// run an automl experiment on the training data
uint timeout = 60;
Console.WriteLine($"Discovering models for {timeout} seconds...");
var results = context
.Auto()
.CreateBinaryClassificationExperiment(timeout)
.Execute(trainData, "Label");
Console.WriteLine($" Best model is: {results.BestRun.TrainerName}");
// make predictions for the test data set
Console.WriteLine("Evaluating model...");
var predictions = results.BestRun.Model.Transform(testData);
// compare the predictions with the ground truth
var metrics = context.BinaryClassification.Evaluate(
data: predictions,
labelColumnName: "Label",
scoreColumnName: "Score");
// report the results
Console.WriteLine($" Accuracy: {metrics.Accuracy}");
Console.WriteLine($" Auc: {metrics.AreaUnderRocCurve}");
Console.WriteLine($" Auprc: {metrics.AreaUnderPrecisionRecallCurve}");
Console.WriteLine($" F1Score: {metrics.F1Score}");
Console.WriteLine($" LogLoss: {metrics.LogLoss}");
Console.WriteLine($" LogLossReduction: {metrics.LogLossReduction}");
Console.WriteLine($" PositivePrecision: {metrics.PositivePrecision}");
Console.WriteLine($" PositiveRecall: {metrics.PositiveRecall}");
Console.WriteLine($" NegativePrecision: {metrics.NegativePrecision}");
Console.WriteLine($" NegativeRecall: {metrics.NegativeRecall}");
Console.WriteLine();
// set up a prediction engine
Console.WriteLine("Making a prediction for a sample patient...");
var predictionEngine = context.Model.CreatePredictionEngine <HeartData, HeartPrediction>(results.BestRun.Model);
// create a sample patient
var heartData = new HeartData()
{
Age = 36.0f,
Sex = 1.0f,
Cp = 4.0f,
TrestBps = 145.0f,
Chol = 210.0f,
Fbs = 0.0f,
RestEcg = 2.0f,
Thalac = 148.0f,
Exang = 1.0f,
OldPeak = 1.9f,
Slope = 2.0f,
Ca = 1.0f,
Thal = 7.0f,
};
// make the prediction
var prediction = predictionEngine.Predict(heartData);
// report the results
Console.WriteLine($" Age: {heartData.Age} ");
Console.WriteLine($" Sex: {heartData.Sex} ");
Console.WriteLine($" Cp: {heartData.Cp} ");
Console.WriteLine($" TrestBps: {heartData.TrestBps} ");
Console.WriteLine($" Chol: {heartData.Chol} ");
Console.WriteLine($" Fbs: {heartData.Fbs} ");
Console.WriteLine($" RestEcg: {heartData.RestEcg} ");
Console.WriteLine($" Thalac: {heartData.Thalac} ");
Console.WriteLine($" Exang: {heartData.Exang} ");
Console.WriteLine($" OldPeak: {heartData.OldPeak} ");
Console.WriteLine($" Slope: {heartData.Slope} ");
Console.WriteLine($" Ca: {heartData.Ca} ");
Console.WriteLine($" Thal: {heartData.Thal} ");
Console.WriteLine();
Console.WriteLine($"Prediction: {(prediction.Prediction ? "Elevated heart disease risk" : "Normal heart disease risk" )} ");
Console.WriteLine($"Probability: {prediction.Probability:P2} ");
Console.ReadLine();
}
/// <summary>
/// The main applicaton entry point.
/// </summary>
/// <param name="args">The command line arguments.</param>
public static void Main(string[] args)
{
// set up a machine learning context
var mlContext = new MLContext();
// load training and test data
Console.WriteLine("Loading data...");
var trainingDataView = mlContext.Data.LoadFromTextFile <HeartData>(trainingDataPath, hasHeader: true, separatorChar: ';');
var testDataView = mlContext.Data.LoadFromTextFile <HeartData>(testDataPath, hasHeader: true, separatorChar: ';');
// set up a training pipeline
// step 1: concatenate all feature columns
var pipeline = mlContext.Transforms.Concatenate(
"Features",
"Age",
"Sex",
"Cp",
"TrestBps",
"Chol",
"Fbs",
"RestEcg",
"Thalac",
"Exang",
"OldPeak",
"Slope",
"Ca",
"Thal")
// step 2: set up a fast tree learner
.Append(mlContext.BinaryClassification.Trainers.FastTree(
labelColumnName: DefaultColumnNames.Label,
featureColumnName: DefaultColumnNames.Features));
// train the model
Console.WriteLine("Training model...");
var trainedModel = pipeline.Fit(trainingDataView);
// make predictions for the test data set
Console.WriteLine("Evaluating model...");
var predictions = trainedModel.Transform(testDataView);
// compare the predictions with the ground truth
var metrics = mlContext.BinaryClassification.Evaluate(
data: predictions,
label: DefaultColumnNames.Label,
score: DefaultColumnNames.Score);
// report the results
Console.WriteLine($" Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($" Auc: {metrics.Auc:P2}");
Console.WriteLine($" Auprc: {metrics.Auprc:P2}");
Console.WriteLine($" F1Score: {metrics.F1Score:P2}");
Console.WriteLine($" LogLoss: {metrics.LogLoss:0.##}");
Console.WriteLine($" LogLossReduction: {metrics.LogLossReduction:0.##}");
Console.WriteLine($" PositivePrecision: {metrics.PositivePrecision:0.##}");
Console.WriteLine($" PositiveRecall: {metrics.PositiveRecall:0.##}");
Console.WriteLine($" NegativePrecision: {metrics.NegativePrecision:0.##}");
Console.WriteLine($" NegativeRecall: {metrics.NegativeRecall:0.##}");
Console.WriteLine();
// set up a prediction engine
Console.WriteLine("Making a prediction for a sample patient...");
var predictionEngine = trainedModel.CreatePredictionEngine <HeartData, HeartPrediction>(mlContext);
// create a sample patient
var heartData = new HeartData()
{
Age = 36.0f,
Sex = 1.0f,
Cp = 4.0f,
TrestBps = 145.0f,
Chol = 210.0f,
Fbs = 0.0f,
RestEcg = 2.0f,
Thalac = 148.0f,
Exang = 1.0f,
OldPeak = 1.9f,
Slope = 2.0f,
Ca = 1.0f,
Thal = 7.0f,
};
// make the prediction
var prediction = predictionEngine.Predict(heartData);
// report the results
Console.WriteLine($" Age: {heartData.Age} ");
Console.WriteLine($" Sex: {heartData.Sex} ");
Console.WriteLine($" Cp: {heartData.Cp} ");
Console.WriteLine($" TrestBps: {heartData.TrestBps} ");
Console.WriteLine($" Chol: {heartData.Chol} ");
Console.WriteLine($" Fbs: {heartData.Fbs} ");
Console.WriteLine($" RestEcg: {heartData.RestEcg} ");
Console.WriteLine($" Thalac: {heartData.Thalac} ");
Console.WriteLine($" Exang: {heartData.Exang} ");
Console.WriteLine($" OldPeak: {heartData.OldPeak} ");
Console.WriteLine($" Slope: {heartData.Slope} ");
Console.WriteLine($" Ca: {heartData.Ca} ");
Console.WriteLine($" Thal: {heartData.Thal} ");
Console.WriteLine();
Console.WriteLine($"Prediction: {(prediction.Prediction ? "A disease could be present" : "Not present disease" )} ");
Console.WriteLine($"Probability: {prediction.Probability} ");
Console.ReadLine();
}
/// <summary>
/// The main applicaton entry point.
/// </summary>
/// <param name="args">The command line arguments.</param>
public static void Main(string[] args)
{
// set up a machine learning context
var context = new MLContext();
// load training and test data
Console.WriteLine("Loading data...");
var data = context.Data.LoadFromTextFile <HeartData>(dataPath, hasHeader: false, separatorChar: ',');
// split the data into a training and test partition
var partitions = context.Data.TrainTestSplit(data, testFraction: 0.2);
// set up a training pipeline
// step 1: convert the label value to a boolean
var pipeline = context.Transforms.CustomMapping <FromLabel, ToLabel>(
(input, output) => { output.Label = input.RawLabel > 0; },
"LabelMapping"
)
// step 2: concatenate all feature columns
.Append(context.Transforms.Concatenate(
"Features",
"Age",
"Sex",
"Cp",
"TrestBps",
"Chol",
"Fbs",
"RestEcg",
"Thalac",
"Exang",
"OldPeak",
"Slope",
"Ca",
"Thal"))
// step 3: set up a fast tree learner
.Append(context.BinaryClassification.Trainers.FastTree(
labelColumnName: "Label",
featureColumnName: "Features"));
// train the model
Console.WriteLine("Training model...");
var model = pipeline.Fit(partitions.TrainSet);
// make predictions for the test data set
Console.WriteLine("Evaluating model...");
var predictions = model.Transform(partitions.TestSet);
// compare the predictions with the ground truth
var metrics = context.BinaryClassification.Evaluate(
data: predictions,
labelColumnName: "Label",
scoreColumnName: "Score");
// report the results
Console.WriteLine($" Accuracy: {metrics.Accuracy}");
Console.WriteLine($" Auc: {metrics.AreaUnderRocCurve}");
Console.WriteLine($" Auprc: {metrics.AreaUnderPrecisionRecallCurve}");
Console.WriteLine($" F1Score: {metrics.F1Score}");
Console.WriteLine($" LogLoss: {metrics.LogLoss}");
Console.WriteLine($" LogLossReduction: {metrics.LogLossReduction}");
Console.WriteLine($" PositivePrecision: {metrics.PositivePrecision}");
Console.WriteLine($" PositiveRecall: {metrics.PositiveRecall}");
Console.WriteLine($" NegativePrecision: {metrics.NegativePrecision}");
Console.WriteLine($" NegativeRecall: {metrics.NegativeRecall}");
Console.WriteLine();
// set up a prediction engine
Console.WriteLine("Making a prediction for a sample patient...");
var predictionEngine = context.Model.CreatePredictionEngine <HeartData, HeartPrediction>(model);
// create a sample patient
var heartData = new HeartData()
{
Age = 36.0f,
Sex = 1.0f,
Cp = 4.0f,
TrestBps = 145.0f,
Chol = 210.0f,
Fbs = 0.0f,
RestEcg = 2.0f,
Thalac = 148.0f,
Exang = 1.0f,
OldPeak = 1.9f,
Slope = 2.0f,
Ca = 1.0f,
Thal = 7.0f,
};
// make the prediction
var prediction = predictionEngine.Predict(heartData);
// report the results
Console.WriteLine($" Age: {heartData.Age} ");
Console.WriteLine($" Sex: {heartData.Sex} ");
Console.WriteLine($" Cp: {heartData.Cp} ");
Console.WriteLine($" TrestBps: {heartData.TrestBps} ");
Console.WriteLine($" Chol: {heartData.Chol} ");
Console.WriteLine($" Fbs: {heartData.Fbs} ");
Console.WriteLine($" RestEcg: {heartData.RestEcg} ");
Console.WriteLine($" Thalac: {heartData.Thalac} ");
Console.WriteLine($" Exang: {heartData.Exang} ");
Console.WriteLine($" OldPeak: {heartData.OldPeak} ");
Console.WriteLine($" Slope: {heartData.Slope} ");
Console.WriteLine($" Ca: {heartData.Ca} ");
Console.WriteLine($" Thal: {heartData.Thal} ");
Console.WriteLine();
Console.WriteLine($"Prediction: {(prediction.Prediction ? "Elevated heart disease risk" : "Normal heart disease risk" )} ");
Console.WriteLine($"Probability: {prediction.Probability:P2} ");
Console.ReadLine();
}