public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
ITransformer model = trainingPipeline.Fit(trainingDataView);
return(model);
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = trainingPipeline.Fit(trainingDataView);
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssue, IssuePrediction>(_trainedModel);
GitHubIssue issue = new GitHubIssue()
{
Title = "WebSockets communication is slow in my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var prediction = _predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
return(trainingPipeline);
}
private static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
// Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.BinaryClassification.CrossValidateNonCalibrated(trainingDataView, trainingPipeline, numberOfFolds: 5, labelColumnName: "Category");
PrintBinaryClassificationFoldsAverageMetrics(crossValidationResults);
}
public CategoricalTransform(TermEstimator term, IEstimator <ITransformer> keyToVector, IDataView input)
{
var chain = term.Append(keyToVector);
_transformer = chain.Fit(input);
}
public static List <float[]> PeekVectorColumnDataInConsole(MLContext mlContext, string columnName, IDataView dataView, IEstimator <ITransformer> pipeline, int numberOfRows = 4)
{
string msg = string.Format("Peek data in DataView: : Show {0} rows with just the '{1}' column", numberOfRows, columnName);
ConsoleWriteHeader(msg);
var transformer = pipeline.Fit(dataView);
var transformedData = transformer.Transform(dataView);
// Extract the 'Features' column.
var someColumnData = transformedData.GetColumn <float[]>(columnName)
.Take(numberOfRows).ToList();
// print to console the peeked rows
someColumnData.ForEach(row => {
String concatColumn = String.Empty;
foreach (float f in row)
{
concatColumn += f.ToString();
}
Console.WriteLine(concatColumn);
});
return(someColumnData);
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = trainingPipeline.Fit(trainingDataView);
_predEngine = _mlContext.Model.CreatePredictionEngine <Supervised, PurposePrediction>(_trainedModel);
return(trainingPipeline);
}
// </SnippetDeclareGlobalVariables>
static void Main(string[] args)
{
// Create MLContext to be shared across the model creation workflow objects
// <SnippetCreateMLContext>
MLContext mlContext = new MLContext();
// </SnippetCreateMLContext>
// Dictionary to encode words as integers.
// <SnippetCreateLookupMap>
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
columns: new[]
{
new TextLoader.Column("Words", DataKind.String, 0),
new TextLoader.Column("Ids", DataKind.Int32, 1),
},
separatorChar: ','
);
// </SnippetCreateLookupMap>
// The model expects the input feature vector to be a fixed length vector.
// This action resizes the integer vector to a fixed length vector. If there
// are less than 600 words in the sentence, the remaining indices will be filled
// with zeros. If there are more than 600 words in the sentence, then the
// array is truncated at 600.
// <SnippetResizeFeatures>
Action <MovieReview, FixedLengthFeatures> ResizeFeaturesAction = (s, f) =>
{
var features = s.VariableLengthFeatures;
Array.Resize(ref features, FeatureLength);
f.Features = features;
};
// </SnippetResizeFeatures>
// Load the TensorFlow model.
// <SnippetLoadTensorFlowModel>
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
// </SnippetLoadTensorFlowModel>
// <SnippetGetModelSchema>
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");
// </SnippetGetModelSchema>
// <SnippetTokenizeIntoWords>
IEstimator <ITransformer> pipeline =
// Split the text into individual words
mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
// </SnippetTokenizeIntoWords>
// <SnippetMapValue>
// Map each word to an integer value. The array of integer makes up the input features.
.Append(mlContext.Transforms.Conversion.MapValue("VariableLengthFeatures", lookupMap,
lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
// </SnippetMapValue>
// <SnippetCustomMapping>
// Resize variable length vector to fixed length vector.
.Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
// </SnippetCustomMapping>
// <SnippetScoreTensorFlowModel>
// Passes the data to TensorFlow for scoring
.Append(tensorFlowModel.ScoreTensorFlowModel("Prediction/Softmax", "Features"))
// </SnippetScoreTensorFlowModel>
// <SnippetCopyColumns>
// Retrieves the 'Prediction' from TensorFlow and and copies to a column
.Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"));
// </SnippetCopyColumns>
// <SnippetCreateModel>
// Create an executable model from the estimator pipeline
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List <MovieReview>());
ITransformer model = pipeline.Fit(dataView);
// </SnippetCreateModel>
// <SnippetCallPredictSentiment>
PredictSentiment(mlContext, model);
// </SnippetCallPredictSentiment>
}
public GreedySolver(IEstimator estimator)
{
this.estimator = estimator;
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline
.Append(_mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label, DefaultColumnNames.Features))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = trainingPipeline.Fit(trainingDataView);
_predEngine = _trainedModel.CreatePredictionEngine <SentimentData, SentimentPrediction>(_mlContext);
SentimentData issue = new SentimentData()
{
Title = "WebSockets communication is slow in my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var prediction = _predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
return(trainingPipeline);
}
private static EvaluationResults Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
// Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.Regression.CrossValidate(trainingDataView, trainingPipeline, 10, "Value");
var results = PrintRegressionFoldsAverageMetrics(crossValidationResults);
return(results);
}
public static void BuildAndTrainModel(string DataSetLocation, string ModelPath, MyTrainerStrategy selectedStrategy)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
// STEP 1: Common data loading configuration
var textLoader = GitHubLabelerTextLoaderFactory.CreateTextLoader(mlContext);
var trainingDataView = textLoader.Read(DataSetLocation);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = GitHubLabelerDataProcessPipelineFactory.CreateDataProcessPipeline(mlContext);
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole <GitHubIssue>(mlContext, trainingDataView, dataProcessPipeline, 2);
//Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
IEstimator <ITransformer> trainer = null;
switch (selectedStrategy)
{
case MyTrainerStrategy.SdcaMultiClassTrainer:
trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label,
DefaultColumnNames.Features);
break;
case MyTrainerStrategy.OVAAveragedPerceptronTrainer:
{
// Create a binary classification trainer.
var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron(DefaultColumnNames.Label,
DefaultColumnNames.Features,
numIterations: 10);
// Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
// In this strategy, a binary classification algorithm is used to train one classifier for each class, "
// which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
// and choosing the prediction with the highest confidence score.
trainer = new Ova(mlContext, averagedPerceptronBinaryTrainer);
break;
}
default:
break;
}
//Set the trainer/algorithm
var modelBuilder = new Common.ModelBuilder <GitHubIssue, GitHubIssuePrediction>(mlContext, dataProcessPipeline);
modelBuilder.AddTrainer(trainer);
modelBuilder.AddEstimator(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValResults = modelBuilder.CrossValidateAndEvaluateMulticlassClassificationModel(trainingDataView, 6, "Label");
ConsoleHelper.PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
modelBuilder.Train(trainingDataView);
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
GitHubIssue issue = new GitHubIssue()
{
ID = "Any-ID", Title = "WebSockets communication is slow in my machine", Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var modelScorer = new ModelScorer <GitHubIssue, GitHubIssuePrediction>(mlContext, modelBuilder.TrainedModel);
var prediction = modelScorer.PredictSingle(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
//
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
modelBuilder.SaveModelAsFile(ModelPath);
Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
}
public ITransformer TrainFeaturizeText()
{
var textColumns = new List <string>();
for (int i = 0; i < 20; i++) // Only load first 20 columns
{
textColumns.Add($"Column{i}");
}
var featurizers = new List <TextFeaturizingEstimator>();
foreach (var textColumn in textColumns)
{
var featurizer = _mlContext.Transforms.Text.FeaturizeText(textColumn, new TextFeaturizingEstimator.Options()
{
CharFeatureExtractor = null,
WordFeatureExtractor = new WordBagEstimator.Options()
{
NgramLength = 2,
MaximumNgramsCount = new int[] { 200000 }
}
});
featurizers.Add(featurizer);
}
IEstimator <ITransformer> pipeline = featurizers.First();
foreach (var featurizer in featurizers.Skip(1))
{
pipeline = pipeline.Append(featurizer);
}
var model = pipeline.Fit(_dataset);
// BENCHMARK OUTPUT
// * Summary *
//BenchmarkDotNet = v0.11.3, OS = Windows 10.0.18363
//Intel Xeon W - 2133 CPU 3.60GHz, 1 CPU, 12 logical and 6 physical cores
//.NET Core SDK = 3.0.100
//[Host] : .NET Core 2.1.13(CoreCLR 4.6.28008.01, CoreFX 4.6.28008.01), 64bit RyuJIT
//Job - KDKCUJ : .NET Core 2.1.13(CoreCLR 4.6.28008.01, CoreFX 4.6.28008.01), 64bit RyuJIT
//Arguments =/ p:Configuration = Release Toolchain = netcoreapp2.1 IterationCount = 1
//LaunchCount = 3 MaxIterationCount = 20 RunStrategy = ColdStart
//UnrollFactor = 1 WarmupCount = 1
// Method | Mean | Error | StdDev | Extra Metric | Gen 0 / 1k Op | Gen 1 / 1k Op | Gen 2 / 1k Op | Allocated Memory / Op |
//------------------- | --------:| --------:| ---------:| -------------:| -------------:| ------------: | ------------: | --------------------: |
// TrainFeaturizeText | 17.00 s | 6.337 s | 0.3474 s | - | 1949000.0000 | 721000.0000 | 36000.0000 | 315.48 MB |
//// * Legends *
// Mean : Arithmetic mean of all measurements
// Error : Half of 99.9 % confidence interval
// StdDev : Standard deviation of all measurements
// Extra Metric: Value of the provided extra metric
// Gen 0 / 1k Op : GC Generation 0 collects per 1k Operations
// Gen 1 / 1k Op : GC Generation 1 collects per 1k Operations
// Gen 2 / 1k Op : GC Generation 2 collects per 1k Operations
// Allocated Memory/ Op : Allocated memory per single operation(managed only, inclusive, 1KB = 1024B)
// 1 s: 1 Second(1 sec)
//// * Diagnostic Output - MemoryDiagnoser *
//// ***** BenchmarkRunner: End *****
// Run time: 00:01:52(112.92 sec), executed benchmarks: 1
//// * Artifacts cleanup *
// Global total time: 00:01:59(119.89 sec), executed benchmarks: 1
return(model);
}
private void MixMatch(string dataPath)
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();
// Read the data as an IDataView.
// First, we define the reader: specify the data columns and where to find them in the text file.
var reader = mlContext.Data.CreateTextReader(ctx => (
// The four features of the Iris dataset.
SepalLength: ctx.LoadFloat(0),
SepalWidth: ctx.LoadFloat(1),
PetalLength: ctx.LoadFloat(2),
PetalWidth: ctx.LoadFloat(3),
// Label: kind of iris.
Label: ctx.LoadText(4)
),
// Default separator is tab, but the dataset has comma.
separator: ',');
// Read the data.
var data = reader.Read(dataPath);
// Build the pre-processing pipeline.
var learningPipeline = reader.MakeNewEstimator()
.Append(r => (
// Convert string label to a key.
Label: r.Label.ToKey(),
// Concatenate all the features together into one column 'Features'.
Features: r.SepalLength.ConcatWith(r.SepalWidth, r.PetalLength, r.PetalWidth)));
// Now, at the time of writing, there is no static pipeline for OVA (one-versus-all). So, let's
// append the OVA learner to the dynamic pipeline.
IEstimator <ITransformer> dynamicPipe = learningPipeline.AsDynamic;
// Create a binary classification trainer.
var binaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron("Label", "Features");
// Append the OVA learner to the pipeline.
dynamicPipe = dynamicPipe.Append(new Ova(mlContext, binaryTrainer));
// At this point, we have a choice. We could continue working with the dynamically-typed pipeline, and
// ultimately call dynamicPipe.Fit(data.AsDynamic) to get the model, or we could go back into the static world.
// Here's how we go back to the static pipeline:
var staticFinalPipe = dynamicPipe.AssertStatic(mlContext,
// Declare the shape of the input. As you can see, it's identical to the shape of the reader:
// four float features and a string label.
c => (
SepalLength: c.R4.Scalar,
SepalWidth: c.R4.Scalar,
PetalLength: c.R4.Scalar,
PetalWidth: c.R4.Scalar,
Label: c.Text.Scalar),
// Declare the shape of the output (or a relevant subset of it).
// In our case, we care only about the predicted label column (a key type), and scores (vector of floats).
c => (
Score: c.R4.Vector,
// Predicted label is a key backed by uint, with text values (since original labels are text).
PredictedLabel: c.KeyU4.TextValues.Scalar))
// Convert the predicted label from key back to the original string value.
.Append(r => r.PredictedLabel.ToValue());
// Train the model in a statically typed way.
var model = staticFinalPipe.Fit(data);
// And here is how we could've stayed in the dynamic pipeline and train that way.
dynamicPipe = dynamicPipe.Append(new KeyToValueMappingEstimator(mlContext, "PredictedLabel"));
var dynamicModel = dynamicPipe.Fit(data.AsDynamic);
// Now 'dynamicModel', and 'model.AsDynamic' are equivalent.
}
private static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(trainingDataView, trainingPipeline, numberOfFolds: 5, labelColumnName: "0");
PrintMulticlassClassificationFoldsAverageMetrics(crossValidationResults);
}
public BaseTaskCreator(IEnumerable <IData> data, IEstimator estimator)
{
_data = data;
_estimator = estimator;
PrepareTasks();
}
public OneHotHashEncodingEstimator(IHostEnvironment env, params ColumnInfo[] columns)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(nameof(ValueToKeyMappingEstimator));
_hash = new HashingEstimator(_host, columns.Select(x => x.HashInfo).ToArray());
using (var ch = _host.Start(nameof(OneHotHashEncodingEstimator)))
{
var binaryCols = new List <(string input, string output)>();
var cols = new List <(string input, string output, bool bag)>();
for (int i = 0; i < columns.Length; i++)
{
var column = columns[i];
OneHotEncodingTransformer.OutputKind kind = columns[i].OutputKind;
switch (kind)
{
default:
throw _host.ExceptUserArg(nameof(column.OutputKind));
case OneHotEncodingTransformer.OutputKind.Key:
continue;
case OneHotEncodingTransformer.OutputKind.Bin:
if ((column.HashInfo.InvertHash) != 0)
{
ch.Warning("Invert hashing is being used with binary encoding.");
}
binaryCols.Add((column.HashInfo.Output, column.HashInfo.Output));
break;
case OneHotEncodingTransformer.OutputKind.Ind:
cols.Add((column.HashInfo.Output, column.HashInfo.Output, false));
break;
case OneHotEncodingTransformer.OutputKind.Bag:
cols.Add((column.HashInfo.Output, column.HashInfo.Output, true));
break;
}
}
IEstimator <ITransformer> toBinVector = null;
IEstimator <ITransformer> toVector = null;
if (binaryCols.Count > 0)
{
toBinVector = new KeyToBinaryVectorMappingEstimator(_host, binaryCols.Select(x => new KeyToBinaryVectorMappingTransformer.ColumnInfo(x.input, x.output)).ToArray());
}
if (cols.Count > 0)
{
toVector = new KeyToVectorMappingEstimator(_host, cols.Select(x => new KeyToVectorMappingTransformer.ColumnInfo(x.input, x.output, x.bag)).ToArray());
}
if (toBinVector != null && toVector != null)
{
_toSomething = toVector.Append(toBinVector);
}
else
{
if (toBinVector != null)
{
_toSomething = toBinVector;
}
else
{
_toSomething = toVector;
}
}
}
}
private static ITransformer Train(IDataView trainingDataView, IEstimator <ITransformer> pipeLine)
{
// Train your model based on the data set
return(pipeLine.Fit(trainingDataView));
}
/// <summary>
/// A base template of regression trainer which contains pre-processing likes OHE,PCA with any choosing algorithm.
/// </summary>
/// <typeparam name="TType">Type of training data.</typeparam>
/// <typeparam name="TTrainer">Type of trainer algorithm.</typeparam>
/// <param name="context">Microsoft.ML context.</param>
/// <param name="trainDataset">Training dataset.</param>
/// <param name="estimator">Algorithm estimator.</param>
/// <returns>Model of training datatype from given estimator.</returns>
private static TransformerChain <TTrainer> RegressionTrainerTemplate <TType, TTrainer>(this MLContext context, IEnumerable <TType> trainDataset, IEstimator <TTrainer> estimator)
where TType : class, new()
where TTrainer : class, ITransformer
{
var type = typeof(TType);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.Append(estimator);
var model = pipeline.Fit(trainDataframe);
return(model);
}
public CategoricalEstimator(IHostEnvironment env, ColumnInfo[] columns,
string file = null, string termsColumn = null,
IComponentFactory <IMultiStreamSource, IDataLoader> loaderFactory = null)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(nameof(TermEstimator));
_term = new TermEstimator(_host, columns, file, termsColumn, loaderFactory);
var binaryCols = new List <(string input, string output)>();
var cols = new List <(string input, string output, bool bag)>();
for (int i = 0; i < columns.Length; i++)
{
var column = columns[i];
CategoricalTransform.OutputKind kind = columns[i].OutputKind;
switch (kind)
{
default:
throw _host.ExceptUserArg(nameof(column.OutputKind));
case CategoricalTransform.OutputKind.Key:
continue;
case CategoricalTransform.OutputKind.Bin:
binaryCols.Add((column.Output, column.Output));
break;
case CategoricalTransform.OutputKind.Ind:
cols.Add((column.Output, column.Output, false));
break;
case CategoricalTransform.OutputKind.Bag:
cols.Add((column.Output, column.Output, true));
break;
}
}
IEstimator <ITransformer> toBinVector = null;
IEstimator <ITransformer> toVector = null;
if (binaryCols.Count > 0)
{
toBinVector = new KeyToBinaryVectorEstimator(_host, binaryCols.Select(x => new KeyToBinaryVectorTransform.ColumnInfo(x.input, x.output)).ToArray());
}
if (cols.Count > 0)
{
toVector = new KeyToVectorEstimator(_host, cols.Select(x => new KeyToVectorTransform.ColumnInfo(x.input, x.output, x.bag)).ToArray());
}
if (toBinVector != null && toVector != null)
{
_toSomething = toVector.Append(toBinVector);
}
else
{
if (toBinVector != null)
{
_toSomething = toBinVector;
}
else
{
_toSomething = toVector;
}
}
}
public EstimatorChain <TNewTrans> Append <TNewTrans>(IEstimator <TNewTrans> estimator, TransformerScope scope = TransformerScope.Everything)
where TNewTrans : class, ITransformer
{
Contracts.CheckValue(estimator, nameof(estimator));
return(new EstimatorChain <TNewTrans>(_host, _estimators.AppendElement(estimator), _scopes.AppendElement(scope), _needCacheAfter.AppendElement(false)));
}
public static void BuildAndTrainModel(string DataSetLocation, string ModelPath, MyTrainerStrategy selectedStrategy)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 1);
// STEP 1: Common data loading configuration
var trainingDataView = mlContext.Data.ReadFromTextFile <GitHubIssue>(DataSetLocation, hasHeader: true, separatorChar: '\t', supportSparse: false);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: DefaultColumnNames.Label, inputColumnName: nameof(GitHubIssue.Area))
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "TitleFeaturized", inputColumnName: nameof(GitHubIssue.Title)))
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "DescriptionFeaturized", inputColumnName: nameof(GitHubIssue.Description)))
.Append(mlContext.Transforms.Concatenate(outputColumnName: DefaultColumnNames.Features, "TitleFeaturized", "DescriptionFeaturized"))
.AppendCacheCheckpoint(mlContext);
// Use in-memory cache for small/medium datasets to lower training time.
// Do NOT use it (remove .AppendCacheCheckpoint()) when handling very large datasets.
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
IEstimator <ITransformer> trainer = null;
switch (selectedStrategy)
{
case MyTrainerStrategy.SdcaMultiClassTrainer:
trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label,
DefaultColumnNames.Features);
break;
case MyTrainerStrategy.OVAAveragedPerceptronTrainer:
{
// Create a binary classification trainer.
var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron(DefaultColumnNames.Label,
DefaultColumnNames.Features,
numIterations: 10);
// Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
// In this strategy, a binary classification algorithm is used to train one classifier for each class, "
// which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
// and choosing the prediction with the highest confidence score.
trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(averagedPerceptronBinaryTrainer);
break;
}
default:
break;
}
//Set the trainer/algorithm and map label to value (original readable state)
var trainingPipeline = dataProcessPipeline.Append(trainer)
.Append(mlContext.Transforms.Conversion.MapKeyToValue(DefaultColumnNames.PredictedLabel));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
//Measure cross-validation time
var watchCrossValTime = System.Diagnostics.Stopwatch.StartNew();
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(data: trainingDataView, estimator: trainingPipeline, numFolds: 6, labelColumn: DefaultColumnNames.Label);
//Stop measuring time
watchCrossValTime.Stop();
long elapsedMs = watchCrossValTime.ElapsedMilliseconds;
Console.WriteLine($"Time Cross-Validating: {elapsedMs} miliSecs");
ConsoleHelper.PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValidationResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
//Measure training time
var watch = System.Diagnostics.Stopwatch.StartNew();
var trainedModel = trainingPipeline.Fit(trainingDataView);
//Stop measuring time
watch.Stop();
long elapsedCrossValMs = watch.ElapsedMilliseconds;
Console.WriteLine($"Time Training the model: {elapsedCrossValMs} miliSecs");
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
GitHubIssue issue = new GitHubIssue()
{
ID = "Any-ID", Title = "WebSockets communication is slow in my machine", Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
// Create prediction engine related to the loaded trained model
var predEngine = trainedModel.CreatePredictionEngine <GitHubIssue, GitHubIssuePrediction>(mlContext);
//Score
var prediction = predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
//
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
using (var fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(trainedModel, fs);
Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
}
public SuggestedTransform(PipelineNode pipelineNode, IEstimator <ITransformer> estimator)
{
PipelineNode = pipelineNode;
Estimator = estimator;
}
public static void PeekDataViewInConsole(MLContext mlContext, IDataView dataView, IEstimator <ITransformer> pipeline, int numberOfRows = 4)
{
string msg = string.Format("Peek data in DataView: Showing {0} rows with the columns", numberOfRows.ToString());
ConsoleWriteHeader(msg);
//https://github.com/dotnet/machinelearning/blob/master/docs/code/MlNetCookBook.md#how-do-i-look-at-the-intermediate-data
var transformer = pipeline.Fit(dataView);
var transformedData = transformer.Transform(dataView);
// 'transformedData' is a 'promise' of data, lazy-loading. call Preview
//and iterate through the returned collection from preview.
var preViewTransformedData = transformedData.Preview(maxRows: numberOfRows);
foreach (var row in preViewTransformedData.RowView)
{
var ColumnCollection = row.Values;
string lineToPrint = "Row--> ";
foreach (KeyValuePair <string, object> column in ColumnCollection)
{
lineToPrint += $"| {column.Key}:{column.Value}";
}
Console.WriteLine(lineToPrint + "\n");
}
}
private static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(trainingDataView, trainingPipeline, numberOfFolds: 5, labelColumnName: "Area");
PrintMulticlassClassificationFoldsAverageMetrics(crossValidationResults);
}
private static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
// Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
}
private static IEstimator <ITransformer> BuildTrainingPipeline(MLContext mlContext, IEstimator <ITransformer> dataProcessingPipeline)
{
return(dataProcessingPipeline.Append(mlContext.BinaryClassification.Trainers.FastTree(new FastTreeBinaryTrainer.Options()
{
NumberOfLeaves = 10, NumberOfTrees = 50, LabelColumnName = "isFraud", FeatureColumnName = "Features"
})));
}
public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator <ITransformer> trainingPipeline)
{
Console.WriteLine("=============== Training model ===============");
ITransformer model = trainingPipeline.Fit(trainingDataView);
Console.WriteLine("=============== End of training process ===============");
return(model);
}
/// <summary>
/// Produces the estimator. Note that this is made out of <see cref="ReconcileCore(IHostEnvironment, string[])"/>'s
/// return value, plus whatever usages of <see cref="CopyColumnsEstimator"/> are necessary to avoid collisions with
/// the output names fed to the constructor. This class provides the implementation, and subclasses should instead
/// override <see cref="ReconcileCore(IHostEnvironment, string[])"/>.
/// </summary>
public sealed override IEstimator <ITransformer> Reconcile(IHostEnvironment env,
PipelineColumn[] toOutput,
IReadOnlyDictionary <PipelineColumn, string> inputNames,
IReadOnlyDictionary <PipelineColumn, string> outputNames,
IReadOnlyCollection <string> usedNames)
{
Contracts.AssertValue(env);
env.AssertValue(toOutput);
env.AssertValue(inputNames);
env.AssertValue(outputNames);
env.AssertValue(usedNames);
// The reconciler should have been called with all the input columns having names.
env.Assert(inputNames.Keys.All(_inputs.Contains) && _inputs.All(inputNames.Keys.Contains));
// The output name map should contain only outputs as their keys. Yet, it is possible not all
// outputs will be required in which case these will both be subsets of those outputs indicated
// at construction.
env.Assert(outputNames.Keys.All(Outputs.Contains));
env.Assert(toOutput.All(Outputs.Contains));
env.Assert(Outputs.Count() == _outputNames.Length);
IEstimator <ITransformer> result = null;
// In the case where we have names used that conflict with the fixed output names, we must have some
// renaming logic.
var collisions = new HashSet <string>(_outputNames);
collisions.IntersectWith(usedNames);
var old2New = new Dictionary <string, string>();
if (collisions.Count > 0)
{
// First get the old names to some temporary names.
int tempNum = 0;
foreach (var c in collisions)
{
old2New[c] = $"#TrainTemp{tempNum++}";
}
// In the case where the input names have anything that is used, we must reconstitute the input mapping.
if (inputNames.Values.Any(old2New.ContainsKey))
{
var newInputNames = new Dictionary <PipelineColumn, string>();
foreach (var p in inputNames)
{
newInputNames[p.Key] = old2New.ContainsKey(p.Value) ? old2New[p.Value] : p.Value;
}
inputNames = newInputNames;
}
result = new CopyColumnsEstimator(env, old2New.Select(p => (p.Key, p.Value)).ToArray());
}
// Map the inputs to the names.
string[] mappedInputNames = _inputs.Select(c => inputNames[c]).ToArray();
// Finally produce the trainer.
var trainerEst = ReconcileCore(env, mappedInputNames);
if (result == null)
{
result = trainerEst;
}
else
{
result = result.Append(trainerEst);
}
// OK. Now handle the final renamings from the fixed names, to the desired names, in the case
// where the output was desired, and a renaming is even necessary.
var toRename = new List <(string source, string name)>();
foreach ((PipelineColumn outCol, string fixedName) in Outputs.Zip(_outputNames, (c, n) => (c, n)))
{
if (outputNames.TryGetValue(outCol, out string desiredName))
{
toRename.Add((fixedName, desiredName));
}
else
{
env.Assert(!toOutput.Contains(outCol));
}
}
// Finally if applicable handle the renaming back from the temp names to the original names.
foreach (var p in old2New)
{
toRename.Add((p.Value, p.Key));
}
if (toRename.Count > 0)
{
result = result.Append(new CopyColumnsEstimator(env, toRename.ToArray()));
}
return(result);
}
GeneralFunctionAnalyzer <TIn, TDelegateInput, TOutShape>(
IHostEnvironment env,
IChannel ch,
TDelegateInput input,
ReaderReconciler <TIn> baseReconciler,
Func <TDelegateInput, TOutShape> mapper,
out IEstimator <ITransformer> estimator,
Func <PipelineColumn, string> inputNameFunction)
{
Contracts.CheckValue(mapper, nameof(mapper));
var method = mapper.Method;
var output = mapper(input);
KeyValuePair <string, PipelineColumn>[] outPairs = StaticPipeInternalUtils.GetNamesValues(output, method.ReturnParameter);
// Map where the key depends on the set of things in the value. The value contains the yet unresolved dependencies.
var keyDependsOn = new Dictionary <PipelineColumn, HashSet <PipelineColumn> >();
// Map where the set of things in the value depend on the key.
var dependsOnKey = new Dictionary <PipelineColumn, HashSet <PipelineColumn> >();
// The set of columns detected with zero dependencies.
var zeroDependencies = new List <PipelineColumn>();
// First we build up the two structures above, using a queue and visiting from the outputs up.
var toVisit = new Queue <PipelineColumn>(outPairs.Select(p => p.Value));
while (toVisit.Count > 0)
{
var col = toVisit.Dequeue();
ch.CheckParam(col != null, nameof(mapper), "The delegate seems to have null columns returned somewhere in the pipe.");
if (keyDependsOn.ContainsKey(col))
{
continue; // Already visited.
}
var dependsOn = new HashSet <PipelineColumn>();
foreach (var dep in col.Dependencies ?? Enumerable.Empty <PipelineColumn>())
{
dependsOn.Add(dep);
if (!dependsOnKey.TryGetValue(dep, out var dependsOnDep))
{
dependsOnKey[dep] = dependsOnDep = new HashSet <PipelineColumn>();
toVisit.Enqueue(dep);
}
dependsOnDep.Add(col);
}
keyDependsOn[col] = dependsOn;
if (dependsOn.Count == 0)
{
zeroDependencies.Add(col);
}
}
// Get the base input columns.
var baseInputs = keyDependsOn.Select(p => p.Key).Where(col => col.ReconcilerObj == baseReconciler).ToArray();
// The columns that utilize the base reconciler should have no dependencies. This could only happen if
// the caller of this function has introduced a situation whereby they are claiming they can reconcile
// to a data-reader object but still have input data dependencies, which does not make sense and
// indicates that there is a bug in that component code. Unfortunately we can only detect that condition,
// not determine exactly how it arose, but we can still do so to indicate to the user that there is a
// problem somewhere in the stack.
ch.CheckParam(baseInputs.All(col => keyDependsOn[col].Count == 0),
nameof(input), "Bug detected where column producing object was yielding columns with dependencies.");
// This holds the mappings of columns to names and back. Note that while the same column could be used on
// the *output*, e.g., you could hypothetically have `(a: r.Foo, b: r.Foo)`, we treat that as the last thing
// that is done.
var nameMap = new BidirectionalDictionary <string, PipelineColumn>();
// Check to see if we have any set of initial names. This is important in the case where we are mapping
// in an input data view.
foreach (var col in baseInputs)
{
string inputName = inputNameFunction(col);
if (inputName != null)
{
ch.Assert(!nameMap.ContainsKey(col));
ch.Assert(!nameMap.ContainsKey(inputName));
nameMap[col] = inputName;
ch.Trace($"Using input with name {inputName}.");
}
}
estimator = null;
var toCopy = new List <(string src, string dst)>();
int tempNum = 0;
// For all outputs, get potential name collisions with used inputs. Resolve by assigning the input a temporary name.
foreach (var p in outPairs)
{
// If the name for the output is already used by one of the inputs, and this output column does not
// happen to have the same name, then we need to rename that input to keep it available.
if (nameMap.TryGetValue(p.Key, out var inputCol) && p.Value != inputCol)
{
ch.Assert(baseInputs.Contains(inputCol));
string tempName = $"#Temp_{tempNum++}";
ch.Trace($"Input/output name collision: Renaming '{p.Key}' to '{tempName}'.");
toCopy.Add((p.Key, tempName));
nameMap[tempName] = nameMap[p.Key];
ch.Assert(!nameMap.ContainsKey(p.Key));
}
// If we already have a name for this output column, maybe it is used elsewhere. (This can happen when
// the only thing done with an input is we rename it, or output it twice, or something like this.) In
// this case it is most appropriate to delay renaming till after all other processing has been done in
// that case. But otherwise we may as well just take the name.
if (!nameMap.ContainsKey(p.Value))
{
nameMap[p.Key] = p.Value;
}
}
// If any renamings were necessary, create the CopyColumns estimator.
if (toCopy.Count > 0)
{
estimator = new CopyColumnsEstimator(env, toCopy.ToArray());
}
// First clear the inputs from zero-dependencies yet to be resolved.
foreach (var col in baseInputs)
{
ch.Assert(zeroDependencies.Contains(col));
ch.Assert(col.ReconcilerObj == baseReconciler);
zeroDependencies.Remove(col); // Make more efficient...
if (!dependsOnKey.TryGetValue(col, out var depends))
{
continue;
}
// If any of these base inputs do not have names because, for example, they do not directly appear
// in the outputs and otherwise do not have names, assign them a name.
if (!nameMap.ContainsKey(col))
{
nameMap[col] = $"Temp_{tempNum++}";
}
foreach (var depender in depends)
{
var dependencies = keyDependsOn[depender];
ch.Assert(dependencies.Contains(col));
dependencies.Remove(col);
if (dependencies.Count == 0)
{
zeroDependencies.Add(depender);
}
}
dependsOnKey.Remove(col);
}
// Call the reconciler to get the base reader estimator.
var readerEstimator = baseReconciler.Reconcile(env, baseInputs, nameMap.AsOther(baseInputs));
ch.AssertValueOrNull(readerEstimator);
// Next we iteratively find those columns with zero dependencies, "create" them, and if anything depends on
// these add them to the collection of zero dependencies, etc. etc.
while (zeroDependencies.Count > 0)
{
// All columns with the same reconciler can be transformed together.
// Note that the following policy of just taking the first group is not optimal. So for example, we
// could have three columns, (a, b, c). If we had the output (a.X(), b.X() c.Y().X()), then maybe we'd
// reconcile a.X() and b.X() together, then reconcile c.Y(), then reconcile c.Y().X() alone. Whereas, we
// could have reconciled c.Y() first, then reconciled a.X(), b.X(), and c.Y().X() together.
var group = zeroDependencies.GroupBy(p => p.ReconcilerObj).First();
// Beyond that first group that *might* be a data reader reconciler, all subsequent operations will
// be on where the data is already loaded and so accept data as an input, that is, they should produce
// an estimator. If this is not the case something seriously wonky is going on, most probably that the
// user tried to use a column from another source. If this is detected we can produce a sensible error
// message to tell them not to do this.
if (!(group.Key is EstimatorReconciler rec))
{
throw ch.Except("Columns from multiple sources were detected. " +
"Did the caller use a " + nameof(PipelineColumn) + " from another delegate?");
}
PipelineColumn[] cols = group.ToArray();
// All dependencies should, by this time, have names.
ch.Assert(cols.SelectMany(c => c.Dependencies).All(dep => nameMap.ContainsKey(dep)));
foreach (var newCol in cols)
{
if (!nameMap.ContainsKey(newCol))
{
nameMap[newCol] = $"#Temp_{tempNum++}";
}
}
var localInputNames = nameMap.AsOther(cols.SelectMany(c => c.Dependencies ?? Enumerable.Empty <PipelineColumn>()));
var localOutputNames = nameMap.AsOther(cols);
var usedNames = new HashSet <string>(nameMap.Keys1.Except(localOutputNames.Values));
var localEstimator = rec.Reconcile(env, cols, localInputNames, localOutputNames, usedNames);
readerEstimator = readerEstimator?.Append(localEstimator);
estimator = estimator?.Append(localEstimator) ?? localEstimator;
foreach (var newCol in cols)
{
zeroDependencies.Remove(newCol); // Make more efficient!!
// Finally, we find all columns that depend on this one. If this happened to be the last pending
// dependency, then we add it to the list.
if (dependsOnKey.TryGetValue(newCol, out var depends))
{
foreach (var depender in depends)
{
var dependencies = keyDependsOn[depender];
Contracts.Assert(dependencies.Contains(newCol));
dependencies.Remove(newCol);
if (dependencies.Count == 0)
{
zeroDependencies.Add(depender);
}
}
dependsOnKey.Remove(newCol);
}
}
}
if (keyDependsOn.Any(p => p.Value.Count > 0))
{
// This might happen if the user does something incredibly strange, like, say, take some prior
// lambda, assign a column to a local variable, then re-use it downstream in a different lambdas.
// The user would have to go to some extraorindary effort to do that, but nonetheless we want to
// fail with a semi-sensible error message.
throw ch.Except("There were some leftover columns with unresolved dependencies. " +
"Did the caller use a " + nameof(PipelineColumn) + " from another delegate?");
}
// Now do the final renaming, if any is necessary.
toCopy.Clear();
foreach (var p in outPairs)
{
// TODO: Right now we just write stuff out. Once the copy-columns estimator is in place
// we ought to do this for real.
Contracts.Assert(nameMap.ContainsKey(p.Value));
string currentName = nameMap[p.Value];
if (currentName != p.Key)
{
ch.Trace($"Will copy '{currentName}' to '{p.Key}'");
toCopy.Add((currentName, p.Key));
}
}
// If any final renamings were necessary, insert the appropriate CopyColumns transform.
if (toCopy.Count > 0)
{
var copyEstimator = new CopyColumnsEstimator(env, toCopy.ToArray());
if (estimator == null)
{
estimator = copyEstimator;
}
else
{
estimator = estimator.Append(copyEstimator);
}
}
ch.Trace($"Exiting {nameof(ReaderEstimatorAnalyzerHelper)}");
return(readerEstimator);
}
private static void TrainModel(string dataFile, string modelFile)
{
// Create MLContext to be shared across the model creation workflow objects
var mlContext = new MLContext(seed: 0);
// STEP 1: Loading the data
Console.WriteLine($"Step 1: Loading the data ({dataFile})");
var textLoader = mlContext.Data.TextReader(
new TextLoader.Arguments
{
Separator = ",",
HasHeader = true,
AllowQuoting = true,
AllowSparse = true,
Column = new[]
{
new TextLoader.Column("Id", DataKind.Text, 0),
new TextLoader.Column("Category", DataKind.Text, 1),
new TextLoader.Column("Content", DataKind.Text, 2),
}
});
var trainingDataView = textLoader.Read(dataFile);
// STEP 2: Common data process configuration with pipeline data transformations
Console.WriteLine("Step 2: Map raw input data columns to ML.NET data");
var dataProcessPipeline = mlContext.Transforms.Categorical.MapValueToKey("Category", DefaultColumnNames.Label)
.Append(mlContext.Transforms.Text.FeaturizeText("Content", DefaultColumnNames.Features));
// (OPTIONAL) Peek data (few records) in training DataView after applying the ProcessPipeline's transformations into "Features"
// DataViewToConsole<JokeModel>(mlContext, trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
Console.WriteLine("Step 3: Create and configure the selected training algorithm (trainer)");
IEstimator <ITransformer> trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent();
// Alternative training
//// var averagedPerceptionBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron(
//// DefaultColumnNames.Label,
//// DefaultColumnNames.Features,
//// numIterations: 10);
//// trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(averagedPerceptronBinaryTrainer);
// Set the trainer/algorithm and map label to value (original readable state)
var trainingPipeline = dataProcessPipeline.Append(trainer).Append(
mlContext.Transforms.Conversion.MapKeyToValue(DefaultColumnNames.PredictedLabel));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("Step 4: Cross-Validate with single dataset (alternatively we can divide it 80-20)");
var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(
trainingDataView,
trainingPipeline,
numFolds: 10,
labelColumn: "Label");
PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValidationResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("Step 5: Train the model fitting to the DataSet");
var trainedModel = trainingPipeline.Fit(trainingDataView);
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine($"Step 6: Save the model to a file ({modelFile})");
using (var fs = new FileStream(modelFile, FileMode.Create, FileAccess.Write, FileShare.Write))
{
mlContext.Model.Save(trainedModel, fs);
}
}
