public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_predictionEngine = _trainedModel.CreatePredictionEngine <IrisData, IrisPrediction>(_env);
_consumer.Consume(_predictionEngine.Predict(_example));
var reader = new TextLoader(_env,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
},
hasHeader: true
);
IDataView testData = reader.Read(_dataPath);
IDataView scoredTestData = _trainedModel.Transform(testData);
var evaluator = new MultiClassClassifierEvaluator(_env, new MultiClassClassifierEvaluator.Arguments());
_metrics = evaluator.Evaluate(scoredTestData, DefaultColumnNames.Label, DefaultColumnNames.Score, DefaultColumnNames.PredictedLabel);
_batches = new IrisData[_batchSizes.Length][];
for (int i = 0; i < _batches.Length; i++)
{
var batch = new IrisData[_batchSizes[i]];
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = _example;
}
_batches[i] = batch;
}
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
ClassGenerator classGenerator = new ClassGenerator("GeneratedIris", "CustomClass");
classGenerator.AddField("SepalLength", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("SepalWidth", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("PetalLength", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("PetalWidth", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("Label", typeof(string), System.CodeDom.MemberAttributes.Public);
classGenerator.Compile();
List <object> generatedDataSet = new List <object>();
dataset.ToList().ForEach((d) =>
{
generatedDataSet.Add(GetDynamicClass(d, classGenerator.GetInstance()));
});
var instance = classGenerator.GetInstance().GetType();
DataViewGenerator listGenerator = new DataViewGenerator("ListIris", "CustomGenerator", instance, classGenerator.NamespaceName);
var type = listGenerator.GeneratorType;
var methodInfo = type.GetMethod("GetDataView");
var dataView = methodInfo.Invoke(null, new object[] { generatedDataSet.ToList() });
IDataView trainingDataView = (IDataView)dataView;
trainingDataView.Schema.ToList().Add(new DataViewSchema.Column());
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Label", featureColumnName: "Features"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
TransformerChain <Microsoft.ML.Transforms.KeyToValueMappingTransformer> model = pipeline.Fit(trainingDataView);
var prediction = model.CreatePredictionEngine <IrisData, IrisPrediction>(mlContext).Predict(
new IrisData()
{
SepalLength = 5.9f,
SepalWidth = 3.0f,
PetalLength = 5.1f,
PetalWidth = 1.8f,
});
Console.WriteLine(prediction.PredictedLabels);
Console.ReadLine();
}
private void LoadModel()
{
// Here the model is being loaded from a file. We could also embed the model in an
// assembly as a resource. This would then allow us to update the model via a NuGet
// package.
using (FileStream fileStream = new FileStream("test-model.zip", FileMode.Open, FileAccess.Read, FileShare.Read))
{
// The MLContext is the starting point for all ML things using ML.Net.
MLContext context = new MLContext();
// Build the model from the data contained within the zip file.
TransformerChain <ITransformer> model = TransformerChain.LoadFrom(context, fileStream);
// Create the predictor we'll use whenever the user clicks a button.
_predictor = model.CreatePredictionEngine <SentimentData, SentimentPrediction>(context);
}
}
public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_predictionEngine = _trainedModel.CreatePredictionEngine <IrisData, IrisPrediction>(mlContext);
_consumer.Consume(_predictionEngine.Predict(_example));
// Create text loader.
var options = new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("SepalLength", DataKind.Single, 1),
new TextLoader.Column("SepalWidth", DataKind.Single, 2),
new TextLoader.Column("PetalLength", DataKind.Single, 3),
new TextLoader.Column("PetalWidth", DataKind.Single, 4),
},
HasHeader = true,
};
var loader = new TextLoader(mlContext, options: options);
IDataView testData = loader.Load(_dataPath);
IDataView scoredTestData = _trainedModel.Transform(testData);
var evaluator = new MulticlassClassificationEvaluator(mlContext, new MulticlassClassificationEvaluator.Arguments());
_metrics = evaluator.Evaluate(scoredTestData, DefaultColumnNames.Label, DefaultColumnNames.Score, DefaultColumnNames.PredictedLabel);
_batches = new IrisData[_batchSizes.Length][];
for (int i = 0; i < _batches.Length; i++)
{
var batch = new IrisData[_batchSizes[i]];
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = _example;
}
_batches[i] = batch;
}
}
static void Main(string[] args)
{
// Download the dataset if it doesn't exist.
if (!File.Exists(TrainDataPath))
{
using (var client = new WebClient())
{
//The code below will download a dataset from a third-party, UCI (link), and may be governed by separate third-party terms.
//By proceeding, you agree to those separate terms.
client.DownloadFile("https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip", "spam.zip");
}
ZipFile.ExtractToDirectory("spam.zip", DataDirectoryPath);
}
// Set up the MLContext, which is a catalog of components in ML.NET.
MLContext mlContext = new MLContext();
// Specify the schema for spam data and read it into DataView.
var data = mlContext.Data.ReadFromTextFile <SpamInput>(path: TrainDataPath, hasHeader: true, separatorChar: '\t');
// Create the estimator which converts the text label to boolean, featurizes the text, and adds a linear trainer.
var dataProcessPipeLine = mlContext.Transforms.CustomMapping <MyInput, MyOutput>(mapAction: MyLambda.MyAction, contractName: "MyLambda")
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: DefaultColumnNames.Features, inputColumnName: nameof(SpamInput.Message)));
//Create the training pipeline
Console.WriteLine("=============== Training the model ===============");
var trainingPipeLine = dataProcessPipeLine.Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent());
// Evaluate the model using cross-validation.
// Cross-validation splits our dataset into 'folds', trains a model on some folds and
// evaluates it on the remaining fold. We are using 5 folds so we get back 5 sets of scores.
// Let's compute the average AUC, which should be between 0.5 and 1 (higher is better).
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.BinaryClassification.CrossValidate(data: data, estimator: trainingPipeLine, numFolds: 5);
var aucs = crossValidationResults.Select(r => r.metrics.Auc);
Console.WriteLine("The AUC is {0}", aucs.Average());
// Now let's train a model on the full dataset to help us get better results
var model = trainingPipeLine.Fit(data);
// The dataset we have is skewed, as there are many more non-spam messages than spam messages.
// While our model is relatively good at detecting the difference, this skewness leads it to always
// say the message is not spam. We deal with this by lowering the threshold of the predictor. In reality,
// it is useful to look at the precision-recall curve to identify the best possible threshold.
var inPipe = new TransformerChain <ITransformer>(model.Take(model.Count() - 1).ToArray());
var lastTransformer = new BinaryPredictionTransformer <IPredictorProducing <float> >(mlContext, model.LastTransformer.Model, inPipe.GetOutputSchema(data.Schema), model.LastTransformer.FeatureColumn, threshold: 0.15f, thresholdColumn: DefaultColumnNames.Probability);
ITransformer[] parts = model.ToArray();
parts[parts.Length - 1] = lastTransformer;
ITransformer newModel = new TransformerChain <ITransformer>(parts);
// Create a PredictionFunction from our model
var predictor = newModel.CreatePredictionEngine <SpamInput, SpamPrediction>(mlContext);
Console.WriteLine("=============== Predictions for below data===============");
// Test a few examples
ClassifyMessage(predictor, "That's a great idea. It should work.");
ClassifyMessage(predictor, "free medicine winner! congratulations");
ClassifyMessage(predictor, "Yes we should meet over the weekend!");
ClassifyMessage(predictor, "you win pills and free entry vouchers");
Console.WriteLine("=============== End of process, hit any key to finish =============== ");
Console.ReadLine();
}