public static async Task <PredictionModel <TaxiTrip, TaxiTripFarePrediction> > Train()
{
var pipeline = new LearningPipeline();
var textLoaderPiplelineItem = new TextLoader(_datapath).CreateFrom <TaxiTrip>(useHeader: true, separator: ',');
pipeline.Add(textLoaderPiplelineItem);
pipeline.Add(new ColumnCopier(("FareAmount", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("VendorId",
"RateCode",
"PaymentType"));
pipeline.Add(new ColumnConcatenator("Features",
"VendorId",
"RateCode",
"PassengerCount",
"TripDistance",
"PaymentType"));
pipeline.Add(new FastTreeRegressor());
PredictionModel <TaxiTrip, TaxiTripFarePrediction> model = pipeline.Train <TaxiTrip, TaxiTripFarePrediction>();
await model.WriteAsync(_modelpath);
return(model);
}
internal override async Task <ReturnObj <PredictionModel <T, TK> > > LoadOrGenerateModelAsync <T, TK>(string trainingFileName)
{
PredictionModel <T, TK> model;
if (File.Exists(ModelName))
{
model = await PredictionModel.ReadAsync <T, TK>(ModelName);
return(new ReturnObj <PredictionModel <T, TK> >(model));
}
try
{
var pipeline = new LearningPipeline
{
new TextLoader(trainingFileName).CreateFrom <T>(separator: ','),
new ColumnConcatenator("Features", "Features"),
new FastTreeRegressor()
};
model = pipeline.Train <T, TK>();
await model.WriteAsync(ModelName);
}
catch (Exception ex)
{
return(new ReturnObj <PredictionModel <T, TK> >(ex));
}
return(new ReturnObj <PredictionModel <T, TK> >(model));
}
public void TutorialOne()
{
var pipeline = new LearningPipeline();
var dataPath = @"C:\Test\MLNetTutorials\MLNetTutorials\Data\iris.data.txt";
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(separator: ','));
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
//Learning algorithm
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <IrisData, IrisPrediction>();
var prediction = model.Predict(new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabels}");
}
internal static async Task <PredictionModel <IrisData, ClusterPrediction> > TrainAsync()
{
// LearningPipeline holds all steps of the learning process: data, transforms, learners.
var pipeline = new LearningPipeline
{
// The TextLoader loads a dataset. The schema of the dataset is specified by passing a class containing
// all the column names and their types.
new TextLoader(DataPath).CreateFrom <IrisData>(useHeader: true),
// ColumnConcatenator concatenates all columns into Features column
new ColumnConcatenator("Features",
"SepalLength",
"SepalWidth",
"PetalLength",
"PetalWidth"),
// KMeansPlusPlusClusterer is an algorithm that will be used to build clusters. We set the number of clusters to 3.
new KMeansPlusPlusClusterer()
{
K = 3
}
};
Console.WriteLine("=============== Training model ===============");
var model = pipeline.Train <IrisData, ClusterPrediction>();
Console.WriteLine("=============== End training ===============");
// Saving the model as a .zip file.
await model.WriteAsync(ModelPath);
Console.WriteLine("The model is saved to {0}", ModelPath);
return(model);
}
public static void PredictIris()
{
var pipeline = new LearningPipeline();
string dataPath = "iris-data.txt";
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(separator: ','));
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <IrisData, IrisPrediction>();
var prediction = model.Predict(new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 0.2f,
});
var prediction2 = model.Predict(new IrisData()
{
SepalLength = 5.8f,
SepalWidth = 2.7f,
PetalLength = 5.1f,
PetalWidth = 1.9f
});
Console.WriteLine($"Predicred flower type is: {prediction.PredictedLabels}");
Console.WriteLine($"Predicred 2 flower type is: {prediction2.PredictedLabels}");
}
public static PredictionModel <SentimentData, SentimentPrediction> TrainModelWordEmbeddings(WordEmbeddingsTransformPretrainedModelKind?modelKind)
{
var pipeline = new LearningPipeline
{
new TextLoader(TrainDataPath).CreateFrom <SentimentData>(),
new TextFeaturizer("FeaturesA", "SentimentText")
{
OutputTokens = true
}
};
var we = new WordEmbeddings(("FeaturesA_TransformedText", "FeaturesB"))
{
ModelKind = modelKind
};
pipeline.Add(we);
pipeline.Add(new ColumnConcatenator("Features", "FeaturesA", "FeaturesB"));
pipeline.Add(new FastTreeBinaryClassifier()
{
NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2
});
Console.WriteLine("=============== Training model with Word Embeddings ===============");
var model = pipeline.Train <SentimentData, SentimentPrediction>();
Console.WriteLine("=============== End training ===============");
return(model);
}
static void Main(string[] args)
{
var pipeline = new LearningPipeline();
string dataPath = "data.txt";
pipeline.Add(new TextLoader <IrisData>(dataPath, separator: ","));
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <IrisData, IrisPrediction>();
var prediction = model.Predict(new IrisData()
{
SepalLength = 5.03f,
SepalWidth = 2.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabels}");
}
static PredictionModel <NewsData, NewsPrediction> TrainNews()
{
const string trainingSet = @"news-train.txt";
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(trainingSet).CreateFrom <NewsData>());
pipeline.Add(new TextFeaturizer("Features", "Text")
{
KeepDiacritics = false,
KeepPunctuations = false,
TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
OutputTokens = true,
Language = TextTransformLanguage.English,
StopWordsRemover = new PredefinedStopWordsRemover(),
VectorNormalizer = TextTransformTextNormKind.L2,
CharFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = false
},
WordFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = true
}
});
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
return(pipeline.Train <NewsData, NewsPrediction>());
}
/// <summary>
/// Using training data location that is passed trough constructor this method is building
/// and training machine learning model.
/// </summary>
/// <returns>Trained machine learning model.</returns>
public PredictionModel <WineQualitySample, WineQualityPrediction> BuildAndTrain()
{
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(_trainingDataLocation).CreateFrom <WineQualitySample>(useHeader: true, separator: ';'));
pipeline.Add(new MissingValueSubstitutor("FixedAcidity")
{
ReplacementKind = NAReplaceTransformReplacementKind.Mean
});
pipeline.Add(MakeNormalizer());
pipeline.Add(new ColumnConcatenator("Features",
"FixedAcidity",
"VolatileAcidity",
"CitricAcid",
"ResidualSugar",
"Chlorides",
"FreeSulfurDioxide",
"TotalSulfurDioxide",
"Density",
"Ph",
"Sulphates",
"Alcohol"));
pipeline.Add(_algorythm);
return(pipeline.Train <WineQualitySample, WineQualityPrediction>());
}
public static async Task TrainAsync()
{
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader <GitHubIssue>(DataPath, useHeader: true));
pipeline.Add(new Dictionarizer(("Area", "Label")));
pipeline.Add(new TextFeaturizer("Title", "Title"));
pipeline.Add(new TextFeaturizer("Description", "Description"));
pipeline.Add(new ColumnConcatenator("Features", "Title", "Description"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
Console.WriteLine("=============== Training model ===============");
var model = pipeline.Train <GitHubIssue, GitHubIssuePrediction>();
await model.WriteAsync(ModelPath);
Console.WriteLine("=============== End training ===============");
Console.WriteLine("The model is saved to {0}", ModelPath);
}
static async Task <PredictionModel <Open311Data, Open311DataPrediction> > TrainOpen311(string dataPath)
{
var pipeline = new LearningPipeline();
var dataSource = CollectionDataSource.Create(OpenFile(dataPath, 3, 0, 1, 2));
pipeline.Add(dataSource);
pipeline.Add(new Dictionarizer(@"Label"));
pipeline.Add(new TextFeaturizer(@"Features", @"Request")
{
KeepDiacritics = false,
KeepPunctuations = false,
TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
OutputTokens = true,
Language = TextTransformLanguage.German,
StopWordsRemover = new PredefinedStopWordsRemover(),
VectorNormalizer = TextTransformTextNormKind.L2,
CharFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = false
},
WordFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = true
}
});
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter {
PredictedLabelColumn = @"PredictedLabel"
});
var model = pipeline.Train <Open311Data, Open311DataPrediction>();
await model.WriteAsync(_modelPath);
return(model);
}
/// <summary>
/// Train and write in model and set <see cref="_model"/>
/// </summary>
/// <returns>Task asynchronous method</returns>
private async Task Train()
{
try
{
Logger.Instance.Info("ML : Training model");
CheckDataFile();
var pipeline = new LearningPipeline {
new TextLoader(Constants.DataPath).CreateFrom <WindowData>(separator: ','),
new Dictionarizer("Label"),
new TextFeaturizer("Program", "Program"),
new TextFeaturizer("WindowTitle", "WindowTitle"),
new ColumnConcatenator("Features", "Program", "WindowTitle", "WindowTop", "WindowLeft", "WindowHeight", "WindowWidth"),
new StochasticDualCoordinateAscentClassifier(),
new PredictedLabelColumnOriginalValueConverter {
PredictedLabelColumn = "PredictedLabel"
}
};
await _semaphore.WaitAsync();
_model = pipeline.Train <WindowData, RegionPrediction>();
_semaphore.Release();
await _model.WriteAsync(Constants.ModelPath);
Logger.Instance.Info("ML : Model trained");
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
TrainAsync(string trainingDataFile, string modelPath)
{
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(trainingDataFile).CreateFrom <InputData>(separator: ','));
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new ColumnConcatenator("Features", "MFCC1", "MFCC2", "MFCC3", "MFCC4",
"MFCC5", "MFCC6", "MFCC7", "MFCC8",
"MFCC9", "MFCC10", "MFCC11",
"MFCC12", "MFCC13", "MFCCDelta1", "MFCCDelta2", "MFCCDelta3", "MFCCDelta4",
"MFCCDelta5", "MFCCDelta6", "MFCCDelta7", "MFCCDelta8",
"MFCCDelta9", "MFCCDelta10", "MFCCDelta11",
"MFCCDelta12", "MFCCDelta13", "MFCCDeltaDelta1", "MFCCDeltaDelta2", "MFCCDeltaDelta3", "MFCCDeltaDelta4",
"MFCCDeltaDelta5", "MFCCDeltaDelta6", "MFCCDeltaDelta7", "MFCCDeltaDelta8",
"MFCCDeltaDelta9", "MFCCDeltaDelta10", "MFCCDeltaDelta11",
"MFCCDeltaDelta12", "MFCCDeltaDelta13"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
PredictionModel <InputData, OutputData> model =
pipeline.Train <InputData, OutputData>();
await model.WriteAsync(modelPath);
Console.WriteLine("Model created");
}
static void Main(string[] args)
{
var pipeline = new LearningPipeline
{
new TextLoader("SalaryData.csv").CreateFrom <SalaryData>(useHeader: true, separator: ','),
new ColumnConcatenator("Features", "YearsExperience"),
new GeneralizedAdditiveModelRegressor()
};
Console.WriteLine("--------------Training----------------");
var model = pipeline.Train <SalaryData, SalaryPrediction>();
// Evaluate
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Evaluating----------------");
var testData = new TextLoader("SalaryData-test.csv").CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Root Mean Squared: {metrics.Rms}");
Console.WriteLine($"R^2: {metrics.RSquared}");
// Predict
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Predicting----------------");
var prediction = model.Predict(new SalaryData {
YearsExperience = PREDICTION_YEARS
});
Console.WriteLine($"After {PREDICTION_YEARS} years you would earn around {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.ReadLine();
}
public static async Task <PredictionModel <SentimentData, SentimentPrediction> > TrainAsync()
{
// LearningPipeline holds all steps of the learning process: data, transforms, learners.
var pipeline = new LearningPipeline();
// The TextLoader loads a dataset. The schema of the dataset is specified by passing a class containing
// all the column names and their types.
pipeline.Add(new TextLoader(TrainDataPath).CreateFrom <SentimentData>());
// TextFeaturizer is a transform that will be used to featurize an input column to format and clean the data.
pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
// FastTreeBinaryClassifier is an algorithm that will be used to train the model.
// It has three hyperparameters for tuning decision tree performance.
pipeline.Add(new FastTreeBinaryClassifier()
{
NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2
});
Console.WriteLine("=============== Training model ===============");
// The pipeline is trained on the dataset that has been loaded and transformed.
var model = pipeline.Train <SentimentData, SentimentPrediction>();
// Saving the model as a .zip file.
await model.WriteAsync(ModelPath);
Console.WriteLine("=============== End training ===============");
Console.WriteLine("The model is saved to {0}", ModelPath);
return(model);
}
TrainAsync(InputData input)
{
// LearningPipeline allows you to add steps in order to keep everything together
// during the learning process.
var pipeline = new LearningPipeline();
// The TextLoader loads a dataset with comments and corresponding postive or negative sentiment.
// When you create a loader, you specify the schema by passing a class to the loader containing
// all the column names and their types. This is used to create the model, and train it.
//pipeline.Add(new TextLoader(_dataPath).CreateFrom<SentimentData>());
pipeline.Add(new TextLoader(input.TrainingData).CreateFrom <ClassificationData>());
// TextFeaturizer is a transform that is used to featurize an input column.
// This is used to format and clean the data.
pipeline.Add(new TextFeaturizer("Features", "Text"));
// Adds a FastTreeBinaryClassifier, the decision tree learner for this project, and
// three hyperparameters to be used for tuning decision tree performance.
pipeline.Add(new FastTreeBinaryClassifier()
{
NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2
});
// Train the pipeline based on the dataset that has been loaded, transformed.
PredictionModel <ClassificationData, ClassPrediction> model =
pipeline.Train <ClassificationData, ClassPrediction>();
// Saves the model we trained to a zip file.
await model.WriteAsync(_modelpath);
// Returns the model we trained to use for evaluation.
return(model);
}
static void Main(string[] args)
{
// Creating a pipeline
var pipeline = new LearningPipeline();
var fileName = "iris-data.csv";
pipeline.Add(new TextLoader <IrisData>(fileName, separator: ","));
// Assign numeric values to the texts in Label column (4)
pipeline.Add(new Dictionarizer("Label"));
// Put all features into a vector
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
//Adding classifier
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <IrisData, IrisPrediction>();
var prediction = model.Predict(new IrisData
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f
});
System.Console.WriteLine($"Predicted flower type is : {prediction.PredictedLabels}");
}
public static async Task <PredictionModel <IrisData, IrisPrediction> > TrainModel(string dataPath, string modelPath)
{
//Initialize Learning Pipeline
LearningPipeline pipeline = new LearningPipeline();
// Load Data
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(separator: ','));
// Transform Data
// Assign numeric values to text in the "Label" column, because
// only numbers can be processed during model training
pipeline.Add(new Dictionarizer("Label"));
// Vectorize Features
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
// Add Learner
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
// Convert Label back to text
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
// Train Model
var model = pipeline.Train <IrisData, IrisPrediction>();
// Persist Model
await model.WriteAsync(modelPath);
return(model);
}
public void TrainOneVersusAll()
{
string dataPath = GetDataPath("iris.txt");
var pipeline = new LearningPipeline(seed: 1, conc: 1);
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(useHeader: false));
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(OneVersusAll.With(new StochasticDualCoordinateAscentBinaryClassifier()));
var model = pipeline.Train <IrisData, IrisPrediction>();
var testData = new TextLoader(dataPath).CreateFrom <IrisData>(useHeader: false);
var evaluator = new ClassificationEvaluator();
ClassificationMetrics metrics = evaluator.Evaluate(model, testData);
CheckMetrics(metrics);
var trainTest = new TrainTestEvaluator()
{
Kind = MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer
}.TrainTestEvaluate <IrisData, IrisPrediction>(pipeline, testData);
CheckMetrics(trainTest.ClassificationMetrics);
}
static void Main()
{
var pipeline = new LearningPipeline
{
new TextLoader(FileName).CreateFrom <AgeRange>(separator: ',', useHeader: true),
new Dictionarizer("Label"),
new TextFeaturizer("Gender", "Gender"),
new ColumnConcatenator("Features", "Age", "Gender"),
new StochasticDualCoordinateAscentClassifier(),
new PredictedLabelColumnOriginalValueConverter {
PredictedLabelColumn = "PredictedLabel"
}
};
var model = pipeline.Train <AgeRange, AgeRangePrediction>();
var converter = new OnnxConverter
{
Onnx = OnnxPath,
Json = OnnxAsJsonPath,
Domain = "com.elbruno"
};
converter.Convert(model);
// Strip the version.
var fileText = File.ReadAllText(OnnxAsJsonPath);
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(OnnxAsJsonPath, fileText);
}
static void Main(string[] args)
{
var agesRangesCsv = "AgeRangeData.csv";
var pipeline = new LearningPipeline
{
new TextLoader <AgeRangeData>(agesRangesCsv, separator: ","),
new Dictionarizer("Label"),
new ColumnConcatenator("Features", "AgeStart", "AgeEnd"),
new StochasticDualCoordinateAscentClassifier(),
new PredictedLabelColumnOriginalValueConverter {
PredictedLabelColumn = "PredictedLabel"
}
};
var model = pipeline.Train <AgeRangeData, AgeRangePrediction>();
var prediction = model.Predict(new AgeRangeData()
{
AgeStart = 1,
AgeEnd = 2
});
Console.WriteLine($"Predicted age range is: {prediction.PredictedLabels}");
prediction = model.Predict(new AgeRangeData()
{
AgeStart = 7,
AgeEnd = 7
});
Console.WriteLine($"Predicted age range is: {prediction.PredictedLabels}");
Console.ReadLine();
}
public PredictStock()
{
// Creating a pipeline and loading the data
var pipeline = new LearningPipeline();
// Pipelining the training file
string dataPath = System.AppDomain.CurrentDomain.BaseDirectory + @"\Profit-Train.txt";
pipeline.Add(new TextLoader(dataPath).CreateFrom <StockData>(separator: ','));
// Labeling the data
pipeline.Add(new Dictionarizer("Label"));
// Putting features into a vector
pipeline.Add(new ColumnConcatenator("Features", "CurrentPrice", "DayHigh", "DayLow"));
// Adding learning algorithm
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
// Converting the Label back into original text
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
// Train the model
this.model = pipeline.Train <StockData, StockPrediction>();
}
//train the model
public static async Task <PredictionModel <SentimentData, SentimentPrediction> > Train()
{
//Instance used to load,process,featurize the data
var pipeline = new LearningPipeline();
//to load train data
pipeline.Add(new TextLoader(_dataPath).CreateFrom <SentimentData>(useHeader: true));
pipeline.Add(new Dictionarizer("Label"));
// TextFeaturizer to convert the SentimentText column into a numeric vector called Features used by the ML algorithm
pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
//choose learning algorithm
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
//pipeline.Add(new LogisticRegressionClassifier());
//pipeline.Add(new NaiveBayesClassifier());
//pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
//train the model
PredictionModel <SentimentData, SentimentPrediction> model = pipeline.Train <SentimentData, SentimentPrediction>();
//save model
await model.WriteAsync(_modelpath);
return(model);
}
public void Train()
{
if (pipeline == null)
{
Init();
}
pipeline.Add(new TextLoader(dataPath).CreateFrom <SubjectData>(separator: ','));
// STEP 3: Transform your data
// Assign numeric values to text in the "Label" column, because only
// numbers can be processed during model training
pipeline.Add(new Dictionarizer("Label"));
// Puts all features into a vector
pipeline.Add(new TextFeaturizer("Features", "SubjectName"));
// STEP 4: Add learner
// Add a learning algorithm to the pipeline.
// This is a classification scenario (What type of iris is this?)
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
// Convert the Label back into original text (after converting to number in step 3)
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
// STEP 5: Train your model based on the data set
model = pipeline.Train <SubjectData, SubjectPrediction>();
}
public void TransformOnlyPipeline()
{
const string _dataPath = @"..\..\Data\breast-cancer.txt";
var pipeline = new LearningPipeline();
pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom <InputData>(useHeader: false));
pipeline.Add(new CategoricalHashOneHotVectorizer("F1")
{
HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag
});
var model = pipeline.Train <InputData, TransformedData>();
var predictionModel = model.Predict(new InputData()
{
F1 = "5"
});
Assert.NotNull(predictionModel);
Assert.NotNull(predictionModel.TransformedF1);
Assert.Equal(1024, predictionModel.TransformedF1.Length);
for (int index = 0; index < 1024; index++)
{
if (index == 265)
{
Assert.Equal(1, predictionModel.TransformedF1[index]);
}
else
{
Assert.Equal(0, predictionModel.TransformedF1[index]);
}
}
}
/// <summary>
/// Source:
/// https://stackoverflow.com/questions/50497593/how-to-predict-integer-values-using-ml-net
/// https://github.com/Rowandish/MachineLearningTest
/// </summary>
internal static void DigitsDataPrediction()
{
Console.WriteLine();
Console.WriteLine();
Console.WriteLine("2> Training and predicting Digits data:");
var dataPath = @"Models\PredictDigits\Data\segments.txt";
var pipeline = new LearningPipeline
{
new TextLoader(dataPath).CreateFrom <Digit>(separator: ','),
new ColumnConcatenator("Features", nameof(Digit.Features)),
new StochasticDualCoordinateAscentClassifier()
};
var model = pipeline.Train <Digit, DigitPrediction>();
var prediction = model.Predict(new Digit
{
Up = 1,
Middle = 1,
Bottom = 0,
UpLeft = 1,
BottomLeft = 1,
TopRight = 1,
BottomRight = 1
});
Console.WriteLine($"Predicted digit is: {prediction.ExpectedDigit - 1}");
}
public override void Train(List <DataSet> data, List <float> labels = null)
{
if (TrainedModel != null)
{
throw new InvalidOperationException("May only train/load a model once");
}
#if ML_LEGACY
var pipeline = new LearningPipeline();
// add data
pipeline.Add(CollectionDataSource.Create(data));
// choose what to predict
pipeline.Add(new ColumnCopier(("Score", "Label")));
// add columns as features
// do not include the features which should be predicted
pipeline.Add(new ColumnConcatenator("Features", DataSet.ColumnNames()));
// add a regression prediction
pipeline.Add(new FastTreeRegressor());
// train the model
TrainedModel = pipeline.Train <DataSet, DataSetPrediction>();
#else
// add data
var textLoader = GetTextLoader(Context);
// spill to disk !?!?! since there is no way to load from a collection
var pathToData = "";
try
{
// write data to disk
pathToData = WriteToDisk(data);
// read in data
IDataView dataView = textLoader.Load(pathToData);
InputSchema = dataView.Schema;
// configurations
var dataPipeline = Context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: nameof(DataSet.Score))
.Append(Context.Transforms.Concatenate("Features", DataSet.ColumnNames()));
// set the training algorithm
var trainer = Context.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = dataPipeline.Append(trainer);
TrainedModel = trainingPipeline.Fit(dataView);
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
#endif
}
public static void GetMyPrediction()
{
Console.WriteLine("Begin ML.NET demo run");
Console.WriteLine("Income from age, sex, politics");
var pipeline = new LearningPipeline();
string dataPath = AppDomain.CurrentDomain.BaseDirectory + "/PeopleData.txt";
pipeline.Add(new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' '));
pipeline.Add(new ColumnCopier(("Income", "Label")));
//pipeline.Add(new CategoricalOneHotVectorizer("Politic"));
pipeline.Add(new ColumnConcatenator("Features", "pre10",
"pre9", "pre8", "pre7", "pre6", "pre5", "pre4", "pre3"
, "pre2", "pre1"));
var sdcar = new StochasticDualCoordinateAscentRegressor();
sdcar.MaxIterations = 1000;
sdcar.NormalizeFeatures = NormalizeOption.Auto;
pipeline.Add(sdcar);
// pipeline.N
Console.WriteLine("\nStarting training \n");
var model = pipeline.Train <myLottery, myPrediction>();
Console.WriteLine("\nTraining complete \n");
string modelPath = AppDomain.CurrentDomain.BaseDirectory + "/IncomeModel.zip";
Task.Run(async() =>
{
await model.WriteAsync(modelPath);
}).GetAwaiter().GetResult();
var testData = new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' ');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
double rms = metrics.Rms;
Console.WriteLine("Root mean squared error = " +
rms.ToString("F4"));
Console.WriteLine("Income age 40 conservative male: ");
myLottery newPatient = new myLottery()
{
pre10 = 6824298f,
pre9 = 2589916f,
pre8 = 2602089f,
pre7 = 2915497f,
pre6 = 8507838f,
pre5 = 7679324f,
pre4 = 607461f,
pre3 = 5806877,
pre2 = 6776442f,
pre1 = 9975203
};
myPrediction prediction = model.Predict(newPatient);
float predIncome = prediction.Income;
Console.WriteLine("Predicted income = $" +
predIncome.ToString("F2"));
Console.WriteLine("\nEnd ML.NET demo");
Console.ReadLine();
}
private static async Task RebuildModelAsync(Config config)
{
Console.WriteLine("RebuildModel:");
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(config.DatabasePath).CreateFrom <TrainingDatabaseEntry>(useHeader: true, separator: ','));
var e = new TrainingDatabaseEntry();
pipeline.Add(new CategoricalOneHotVectorizer(nameof(e.globalTolerance)));
pipeline.Add(new ColumnConcatenator("Features", nameof(e.toleranceValue), nameof(e.numNotTolerance), nameof(e.numTolerance), nameof(e.percentTolerance), nameof(e.globalTolerance), nameof(e.dayOfWeek)));
pipeline.Add(new FastTreeBinaryClassifier()
{
NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2
});
var model = pipeline.Train <TrainingDatabaseEntry, TimelinessPrediction>();
Console.WriteLine($" Saving model to '{config.ModelPath}'...");
await model.WriteAsync(config.ModelPath);
Console.WriteLine(" Model rebuilt.");
}
static void Main(string[] args)
{
//Create a Pipeline and Load the Data
var pipeline = new LearningPipeline();
string dataPath = "flowers.txt";
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(separator: ','));
//Transform data from string to numeric
pipeline.Add(new Dictionarizer("Label"));
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
//adding learning/training algorithm
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
//Train the model
var model = pipeline.Train <IrisData, IrisPrediction>();
//Using the model to make predictions
var prediction = model.Predict(new IrisData()
{
SepalLength = 0.3f,
SepalWidth = 0.6f,
PetalLength = 1.2f,
PetalWidth = 1.1f
});
Console.WriteLine($"Pridicted flower class is : {prediction.PredictedLabels}");
Console.Read();
}
