/// <summary>
/// The main program entry point.
/// </summary>
/// <param name="args">The command line parameters.</param>
static void Main(string[] args)
{
// set up a machine learning context
var context = new MLContext();
// load the spam dataset in memory
var data = context.Data.LoadFromTextFile <SpamInput>(
path: dataPath,
hasHeader: true,
separatorChar: '\t');
// use 80% for training and 20% for testing
var partitions = context.Data.TrainTestSplit(
data,
testFraction: 0.2);
// set up a training pipeline
// step 1: transform the 'spam' and 'ham' values to true and false
var pipeline = context.Transforms.CustomMapping <FromLabel, ToLabel>(
mapAction: (input, output) => { output.Label = input.RawLabel == "spam" ? true : false; },
contractName: "MyLambda")
// step 2: featureize the input text
.Append(context.Transforms.Text.FeaturizeText(
outputColumnName: "Features",
inputColumnName: nameof(SpamInput.Message)))
// step 3: use a stochastic dual coordinate ascent learner
.Append(context.BinaryClassification.Trainers.SdcaLogisticRegression());
// test the full data set by performing k-fold cross validation
Console.WriteLine("Performing cross validation...");
var cvResults = context.BinaryClassification.CrossValidate(
data: partitions.TrainSet,
estimator: pipeline,
numberOfFolds: 5);
// report the results
foreach (var r in cvResults)
{
Console.WriteLine($" Fold: {r.Fold}, AUC: {r.Metrics.AreaUnderRocCurve}");
}
Console.WriteLine($" Average AUC: {cvResults.Average(r => r.Metrics.AreaUnderRocCurve)}");
Console.WriteLine();
// train the model on the training set
Console.WriteLine("Training the model...");
var model = pipeline.Fit(partitions.TrainSet);
// evaluate the model on the test set
Console.WriteLine("Evaluating the model...");
var predictions = model.Transform(partitions.TestSet);
var metrics = context.BinaryClassification.Evaluate(
data: predictions,
labelColumnName: "Label",
scoreColumnName: "Score");
// report the results
Console.WriteLine($" Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($" Auc: {metrics.AreaUnderRocCurve:P2}");
Console.WriteLine($" Auprc: {metrics.AreaUnderPrecisionRecallCurve:P2}");
Console.WriteLine($" 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("Predicting spam probabilities for a sample messages...");
var predictionEngine = context.Model.CreatePredictionEngine <SpamInput, SpamPrediction>(model);
// create sample messages
var messages = new SpamInput[] {
new SpamInput()
{
Message = "Hi, wanna grab lunch together today?"
},
new SpamInput()
{
Message = "Win a Nokia, PSP, or €25 every week. Txt YEAHIWANNA now to join"
},
new SpamInput()
{
Message = "Home in 30 mins. Need anything from store?"
},
new SpamInput()
{
Message = "CONGRATS U WON LOTERY CLAIM UR 1 MILIONN DOLARS PRIZE"
},
};
// make the prediction
var myPredictions = from m in messages
select(Message : m.Message, Prediction : predictionEngine.Predict(m));
// show the results
foreach (var p in myPredictions)
{
Console.WriteLine($" [{p.Prediction.Probability:P2}] {p.Message}");
}
}
static void Main(string[] args)
{
var context = new MLContext();
var data = context.Data.LoadFromTextFile <SpamInput>(filePath, hasHeader: false, separatorChar: '\t');
var trainTestPartition = context.Data.TrainTestSplit(data, testFraction: 0.2);
var pipeline = context.Transforms.CustomMapping <FromLabel, ToLabel>((inp, output) => { output.Label = inp.RawLabel.Equals("spam", StringComparison.InvariantCultureIgnoreCase); },
contractName: "SpamClassification")
.Append(context.Transforms.Text.FeaturizeText("Features", nameof(SpamInput.Message)))
.Append(context.BinaryClassification.Trainers.SdcaLogisticRegression());
Console.WriteLine("Performing cross validations...");
var cvResults = context.BinaryClassification.CrossValidate(trainTestPartition.TrainSet, pipeline, numberOfFolds: 5);
foreach (var result in cvResults)
{
Console.WriteLine($"Fold: {result.Fold}, AUC: {result.Metrics.AreaUnderRocCurve}");
}
Console.WriteLine($"Average AUC: {cvResults.Average(r => r.Metrics.AreaUnderRocCurve)}");
Console.WriteLine();
Console.WriteLine("Training the model...");
var model = pipeline.Fit(trainTestPartition.TrainSet);
Console.WriteLine("Evaluating the model...");
var predictions = model.Transform(trainTestPartition.TestSet);
var metrics = context.BinaryClassification.Evaluate(predictions, "Label", "Score");
// report the results
Console.WriteLine($" Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($" Auc: {metrics.AreaUnderRocCurve:P2}");
Console.WriteLine($" Auprc: {metrics.AreaUnderPrecisionRecallCurve: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($" NegativeRecall: {metrics.NegativeRecall:0.##}");
Console.WriteLine();
// set up a prediction engine
Console.WriteLine("Predicting spam probabilities for a sample messages...");
var predictionEngine = context.Model.CreatePredictionEngine <SpamInput, SpamPrediction>(model);
// create sample messages
var messages = new SpamInput[] {
new SpamInput()
{
Message = "Hi, wanna grab lunch together today?"
},
new SpamInput()
{
Message = "Win a Nokia, PSP, or €25 every week. Txt YEAHIWANNA now to join"
},
new SpamInput()
{
Message = "Home in 30 mins. Need anything from store?"
},
new SpamInput()
{
Message = "CONGRATS U WON LOTERY CLAIM UR 1 MILIONN DOLARS PRIZE"
},
};
// make the prediction
var myPredictions = from m in messages
select(Message : m.Message, Prediction : predictionEngine.Predict(m));
// show the results
foreach (var p in myPredictions)
{
Console.WriteLine($" [{p.Prediction.Probability:P2}] {p.Message}");
}
}
