public string Convert(DataTable source, ITransformer transformer)
{
transformer = transformer ?? new NoTransformation();
var objects = DataTableToObjectsConverter.Convert(source);
var transformedObjects = objects.Select(transformer.Transform).ToArray();
return serializer.Serialize(transformedObjects);
}
/// <summary>
/// Translates a code of asset written on TypeScript to JS-code
/// </summary>
/// <param name="asset">Asset</param>
/// <param name="transformer">Transformer</param>
/// <param name="isDebugMode">Flag that web application is in debug mode</param>
/// <returns>Translated asset</returns>
protected override IAsset TranslateAsset(IAsset asset, ITransformer transformer, bool isDebugMode)
{
IAsset processedAsset = InnerTranslateAsset<TypeScriptTranslator>(
Constants.TranslatorName.TypeScriptTranslator, asset, transformer, isDebugMode);
return processedAsset;
}
/// <summary>
/// Translates a code of asset written on EcmaScript2015 to JS-code
/// </summary>
/// <param name="asset">Asset</param>
/// <param name="transformer">Transformer</param>
/// <param name="isDebugMode">Flag that web application is in debug mode</param>
/// <returns>Translated asset</returns>
protected override IAsset TranslateAsset(IAsset asset, ITransformer transformer, bool isDebugMode)
{
IAsset processedAsset = InnerTranslateAsset<BabelJsTranslator>(
AssetTypeCode.EcmaScript2015, asset, transformer, isDebugMode);
return processedAsset;
}
public UsersController(IUserRegisterer userRegisterer, IAuthTokenGenerator authTokenGenerator, IUserRetriever userRetriever, ITransformer transformer)
{
this.userRegisterer = userRegisterer;
this.authTokenGenerator = authTokenGenerator;
this.userRetriever = userRetriever;
this.transformer = transformer;
}
/// <summary>
/// Monitor pentru starea firului de execuţie de rezolvare a cererilor.
/// </summary>
//private ManualResetEvent tcpClientConnected;
/// <param name="iTransformer">Transformatorul de cereri în răspuns</param>
/// <param name="port">Portul pe care se ascultă</param>
public Replyer(ITransformer iTransformer, int port)
{
this.iTransformer = iTransformer;
TcpListener tcpl = new TcpListener(IPAddress.Any, port);
tcpl.Start();
while (true) {
try {
while (!tcpl.Pending()) {
Thread.Sleep(1000);
}
TcpClient client = tcpl.AcceptTcpClient();
ThreadPool.QueueUserWorkItem(receiveTransformAndSendFeedback, client);
}
catch (ThreadAbortException) {
tcpl.Stop();
return;
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="AggregatedTransformer"/> class.
/// </summary>
/// <param name="children">The child transforms.</param>
protected AggregatedTransformer(params ITransformer[] children)
{
if (children == null)
children = new ITransformer[] { };
transforms = children;
}
public At(int positionOffset, ITransformer transformer)
{
this.offset = positionOffset;
this.transformer = transformer;
this.name = string.Format("{0} {1}", transformer.Name,
(offset == 0 ? "" : (offset > 0 ? "+" : "-") + Math.Abs(offset))).Trim();
}
public void Convert(DataTable source, ITransformer transformer, StreamWriter writer)
{
transformer = transformer ?? new NoTransformation();
var objects = DataTableToObjectsConverter.Convert(source);
var transformedObjects = objects.Select(transformer.Transform).ToArray();
serializer.Serialize(transformedObjects, writer);
}
/// <summary>
/// Initializes a new instance of the <see cref="PredicatedTransformer"/> class.
/// </summary>
/// <param name="test">The test.</param>
/// <param name="passesTransform">The passes transform.</param>
/// <param name="failsTransform">The fails transform.</param>
public PredicatedTransformer(Predicate<Int32> test, ITransformer passesTransform, ITransformer failsTransform)
{
Helpers.Arguments.NotNull(test, "test");
Helpers.Arguments.NotNull(passesTransform, "passesTransform");
Helpers.Arguments.NotNull(failsTransform, "failsTransform");
_Test = test;
_PassesTransform = passesTransform;
_FailsTrasnform = failsTransform;
}
public PlayedGamesController(
IPlayedGameRetriever playedGameRetriever,
IExcelGenerator excelGenerator,
IPlayedGameSaver playedGameSaver,
IPlayedGameDeleter playedGameDeleter, ITransformer transformer)
{
this.playedGameRetriever = playedGameRetriever;
this.excelGenerator = excelGenerator;
this.playedGameSaver = playedGameSaver;
this.playedGameDeleter = playedGameDeleter;
this.transformer = transformer;
}
public RowParser(int numberOfColumns, AdditionalColumnsProcessing additionalColumnsProcessing,
ITransformer columnExploder)
{
if (columnExploder == null)
{
throw new ArgumentNullException("columnExploder");
}
_numberOfColumns = numberOfColumns;
_additionalColumnsProcessing = additionalColumnsProcessing;
_columnExploder = columnExploder;
}
public HomeController(
IRecentPublicGamesRetriever recentPublicGamesRetriever,
ITopGamingGroupsRetriever topGamingGroupsRetriever,
ITrendingGamesRetriever trendingGamesRetriever,
ITransformer transformer,
IRecentPlayerAchievementsUnlockedRetreiver recentPlayerAchievementsUnlockedRetreiver,
IMapperFactory mapperFactory, IDataContext dataContext) {
_recentPublicGamesRetriever = recentPublicGamesRetriever;
_topGamingGroupsRetriever = topGamingGroupsRetriever;
_trendingGamesRetriever = trendingGamesRetriever;
_transformer = transformer;
_recentPlayerAchievementsUnlockedRetreiver = recentPlayerAchievementsUnlockedRetreiver;
_mapperFactory = mapperFactory;
}
public GameDefinitionController(IGameDefinitionRetriever gameDefinitionRetriever,
ITrendingGamesRetriever trendingGamesRetriever,
IGameDefinitionDetailsViewModelBuilder gameDefinitionTransformation,
IGameDefinitionSaver gameDefinitionCreator,
IBoardGameGeekApiClient boardGameGeekApiClient,
IUserRetriever userRetriever,
IBoardGameGeekGamesImporter boardGameGeekGamesImporter,
ITransformer transformer)
{
_gameDefinitionRetriever = gameDefinitionRetriever;
_trendingGamesRetriever = trendingGamesRetriever;
_gameDefinitionTransformation = gameDefinitionTransformation;
_gameDefinitionSaver = gameDefinitionCreator;
_boardGameGeekApiClient = boardGameGeekApiClient;
_userRetriever = userRetriever;
_boardGameGeekGamesImporter = boardGameGeekGamesImporter;
_transformer = transformer;
}
private static void FileSystemWatcher_Changed(object sender, FileSystemEventArgs e)
{
Console.WriteLine("New model detected!");
trainedModel = null;
LoadModel();
}
public static void IidSpikeDetectorPrediction()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a spike
const int Size = 10;
var data = new List <IidSpikeData>(Size);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidSpikeData(5));
}
// This is a spike
data.Add(new IidSpikeData(10));
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidSpikeData(5));
}
// Convert data to IDataView.
var dataView = ml.Data.ReadFromEnumerable(data);
// Setup IidSpikeDetector arguments
string outputColumnName = nameof(IidSpikePrediction.Prediction);
string inputColumnName = nameof(IidSpikeData.Value);
// The transformed model.
ITransformer model = ml.Transforms.IidChangePointEstimator(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
// Create a time series prediction engine from the model.
var engine = model.CreateTimeSeriesPredictionFunction <IidSpikeData, IidSpikePrediction>(ml);
for (int index = 0; index < 5; index++)
{
// Anomaly spike detection.
var prediction = engine.Predict(new IidSpikeData(5));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2]);
}
// Spike.
var spikePrediction = engine.Predict(new IidSpikeData(10));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 10, spikePrediction.Prediction[0],
spikePrediction.Prediction[1], spikePrediction.Prediction[2]);
// Checkpoint the model.
var modelPath = "temp.zip";
engine.CheckPoint(ml, modelPath);
// Load the model.
using (var file = File.OpenRead(modelPath))
model = TransformerChain.LoadFrom(ml, file);
for (int index = 0; index < 5; index++)
{
// Anomaly spike detection.
var prediction = engine.Predict(new IidSpikeData(5));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2]);
}
// Data Alert Score P-Value
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 10 1 10.00 0.00 <-- alert is on, predicted spike (check-point model)
// 5 0 5.00 0.26 <-- load model from disk.
// 5 0 5.00 0.26
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
}
/// <summary>
/// Main method used for training the prediction model
/// </summary>
/// <returns>True if successful otherwise false if some error occures</returns>
public bool TrainModel()
{
try
{
ProjectDirectory = Path.GetFullPath(Path.Combine(AppContext.BaseDirectory, "../../../")); //set main directory path
WorkspaceRelativePath = Path.Combine(ProjectDirectory, "workspace"); // set workspace path
AssetsRelativePath = Path.Combine(ProjectDirectory, "assets"); //set assets path
ClearWorkspace();
MLContext context = new MLContext(); // Machine Learning Context
IEnumerable <ImageData> images = ImageLoader.LoadImagesFromDirectory(folder: AssetsRelativePath, useFolderNameAsLabel: true); // Load images from assets dir
IDataView imageData = context.Data.LoadFromEnumerable(images); //fundamental pipeline
IDataView shuffledData = context.Data.ShuffleRows(imageData); // shuffle rows od the pipeline
//Creates a Estimator which converts categorical values into numerical keys from InputModel class
var preprocessingPipeline = context.Transforms.Conversion.MapValueToKey(
inputColumnName: "Label",
outputColumnName: "LabelAsKey")
.Append(context.Transforms.LoadRawImageBytes(
outputColumnName: "Image",
imageFolder: AssetsRelativePath,
inputColumnName: "ImagePath"));
//Pre processsed data used for training/testing/validating the model
IDataView preProcessedData = preprocessingPipeline
.Fit(shuffledData)
.Transform(shuffledData);
//Declare to split into 3 categories train / test / validate
TrainTestData trainSplit = context.Data.TrainTestSplit(data: preProcessedData, testFraction: 0.3);
TrainTestData validationTestSplit = context.Data.TrainTestSplit(trainSplit.TestSet);
IDataView trainSet = trainSplit.TrainSet; // get the training set
IDataView validationSet = validationTestSplit.TrainSet; // get the validation set
IDataView testSet = validationTestSplit.TestSet; // get the test set
//Image trainer options used for training the image classification model
var classifierOptions = new ImageClassificationTrainer.Options()
{
FeatureColumnName = "Image",
LabelColumnName = "LabelAsKey",
ValidationSet = validationSet,
Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
MetricsCallback = (metrics) => Console.WriteLine(metrics),
TestOnTrainSet = false,
Epoch = 100,
BatchSize = 10,
ReuseTrainSetBottleneckCachedValues = true,
ReuseValidationSetBottleneckCachedValues = true,
WorkspacePath = WorkspaceRelativePath
};
//create a training pipeline and append output of prediction as PredictedLabel from OutputModel class
var trainingPipeline = context.MulticlassClassification.Trainers.ImageClassification(classifierOptions)
.Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
//Main trained model used for making image predictions
ITransformer trainedModel = trainingPipeline.Fit(trainSet);
//Prediction engine used for prediction. Created from the trained model
PredictionEngine = context.Model.CreatePredictionEngine <InputModel, OutputModel>(trainedModel);
return(true);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
return(false);
}
}
public ITransformer LoadModel(string modelPath)
{
_trainedModel = _mlContext.Model.Load(modelPath, out var modelInputSchema);
return(_trainedModel);
}
protected abstract void UpdateTransformer(ITransformer transformer, ITransformer parent);
public Analyzer(IReader reader, ITransformer transformer)
{
this.reader = reader;
this.transformer = transformer;
}
public static void SaveModel(MLContext mlContext, DataViewSchema trainingDataViewSchema, ITransformer model)
{
results.Add("Saving the model to a file...");
mlContext.Model.Save(model, trainingDataViewSchema, Config.ModelFile);
}
public static void Train(string filePathTrain, string filePathTest, string fileMLTrainedModel)
{
try
{
// Skip the training of the model if model already exists.
if (System.IO.File.Exists(fileMLTrainedModel))
{
return;
}
/**
* Copied the following code chunk from the official Github of ML.NET
*
* https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/getting-started/MulticlassClassification_MNIST/MNIST/Program.cs
*/
// STEP 1: Common data loading configuration
var trainData = mlContext.Data.LoadFromTextFile(path: filePathTrain,
columns: new[]
{
new TextLoader.Column(nameof(InputData.PixelValues), DataKind.Single, 0, 63),
new TextLoader.Column("Number", DataKind.Single, 64)
},
hasHeader: false,
separatorChar: ','
);
var testData = mlContext.Data.LoadFromTextFile(path: filePathTest,
columns: new[]
{
new TextLoader.Column(nameof(InputData.PixelValues), DataKind.Single, 0, 63),
new TextLoader.Column("Number", DataKind.Single, 64)
},
hasHeader: false,
separatorChar: ','
);
// STEP 2: Common data process configuration with pipeline data transformations
// Use in-memory cache for small/medium datasets to lower training time. Do NOT use it (remove .AppendCacheCheckpoint()) when handling very large datasets.
var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("Label", "Number", keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue).
Append(mlContext.Transforms.Concatenate("Features", nameof(InputData.PixelValues)).AppendCacheCheckpoint(mlContext));
// STEP 3: Set the training algorithm, then create and config the modelBuilder
var trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = dataProcessPipeline.Append(trainer).Append(mlContext.Transforms.Conversion.MapKeyToValue("Number", "Label"));
// STEP 4: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
ITransformer trainedModel = trainingPipeline.Fit(trainData);
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
var predictions = trainedModel.Transform(testData);
var metrics = mlContext.MulticlassClassification.Evaluate(data: predictions, labelColumnName: "Number", scoreColumnName: "Score");
///Common.ConsoleHelper.PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);
mlContext.Model.Save(trainedModel, trainData.Schema, fileMLTrainedModel);
Console.WriteLine("The model is saved to {0}", fileMLTrainedModel);
/*
* End Copy
*/
}
catch (Exception e)
{
Console.WriteLine("ERROR " + e.Message);
}
}
/// <summary> /// Save the model to the stream. /// </summary> /// <param name="model">The trained model to be saved.</param> /// <param name="stream">A writeable, seekable stream to save to.</param> public void Save(ITransformer model, Stream stream) => model.SaveTo(_env, stream);
public InnerTransformHandler(ITransformer transformer)
{
_transformer = transformer;
}
public Current(ITransformer transformer)
{
this.name = "Token " + transformer.Name;
this.transformer = transformer;
}
public GameDefinitionSummaryViewModelBuilder(ITransformer transformer, IWeightTierCalculator weightTierCalculator)
{
this._transformer = transformer;
_weightTierCalculator = weightTierCalculator;
}
public Next(int pos, ITransformer transformer)
: base(pos, transformer)
{
if (pos <= 0)
throw new ArgumentOutOfRangeException();
}
public Next(ITransformer transformer)
: this(1, transformer)
{
}
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// IidChangePointDetector is applied then to identify points where data distribution changed using time series
// prediction engine. The engine is checkpointed and then loaded back from disk into memory and used for prediction.
public static void IidChangePointDetectorPrediction()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a change
const int Size = 16;
var data = new List <IidChangePointData>(Size);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(5));
}
// This is a change point
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(7));
}
// Convert data to IDataView.
var dataView = ml.Data.ReadFromEnumerable(data);
// Setup IidSpikeDetector arguments
string outputColumnName = nameof(ChangePointPrediction.Prediction);
string inputColumnName = nameof(IidChangePointData.Value);
// Time Series model.
ITransformer model = ml.Transforms.IidChangePointEstimator(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
// Create a time series prediction engine from the model.
var engine = model.CreateTimeSeriesPredictionFunction <IidChangePointData, ChangePointPrediction>(ml);
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(5));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 5, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Change point
var changePointPrediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, changePointPrediction.Prediction[0],
changePointPrediction.Prediction[1], changePointPrediction.Prediction[2], changePointPrediction.Prediction[3]);
// Checkpoint the model.
var modelPath = "temp.zip";
engine.CheckPoint(ml, modelPath);
// Reference to current time series engine because in the next step "engine" will point to the
// checkpointed model being loaded from disk.
var timeseries1 = engine;
// Load the model.
using (var file = File.OpenRead(modelPath))
model = TransformerChain.LoadFrom(ml, file);
// Create a time series prediction engine from the checkpointed model.
engine = model.CreateTimeSeriesPredictionFunction <IidChangePointData, ChangePointPrediction>(ml);
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Prediction from the original time series engine should match the prediction from
// check pointed model.
engine = timeseries1;
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Data Alert Score P-Value Martingale value
// 5 0 5.00 0.50 0.00 <-- Time Series 1.
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 7 1 7.00 0.00 10298.67 <-- alert is on, predicted changepoint (and model is checkpointed).
// 7 0 7.00 0.13 33950.16 <-- Time Series 2 : Model loaded back from disk and prediction is made.
// 7 0 7.00 0.26 60866.34
// 7 0 7.00 0.38 78362.04
// 7 0 7.00 0.50 0.01
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.13 33950.16 <-- Time Series 1 and prediction is made.
// 7 0 7.00 0.26 60866.34
// 7 0 7.00 0.38 78362.04
// 7 0 7.00 0.50 0.01
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
}
public static void UseModelForSinglePrediction(MLContext mlContext, ITransformer model)
{
OsExtensions.WriteOver($"Processing predictions, pass {Config.CurrentIteration}");
var predictionEngine = mlContext.Model.CreatePredictionEngine <BrowseHistory, BrowsePrediction>(model);
foreach (var agent in Agents)
{
var recs = new List <BrowsePrediction>();
for (var i = 1; i < 500000; i++)
{
var testInput = new BrowseHistory {
userId = agent.Id, itemId = i
};
var itemPrediction = predictionEngine.Predict(testInput);
itemPrediction.Iteration = Config.CurrentIteration;
itemPrediction.UserId = testInput.userId;
itemPrediction.ItemId = testInput.itemId;
if (Math.Round(itemPrediction.Score, 1) > 3.5)
{
var site = Sites.FirstOrDefault(o => o.Id == itemPrediction.ItemId);
if (site == null)
{
continue;
}
if (agent.Preference == site.Category)
{
// add matching sites with positive correlation
itemPrediction.Score = 5;
recs.Add(itemPrediction);
//results.Add($"Item {testInput.itemId} is recommended for user {testInput.userId} at {Math.Round(itemPrediction.Score, 1)}");
}
else
{
// add but rate as poor match
itemPrediction.Score = 1;
recs.Add(itemPrediction);
}
}
}
using (StreamWriter w = File.AppendText(Config.OutputFile))
{
//var rnd = new Random();
//var choices = recs.OrderBy(x => rnd.Next()).Take(25);
var choices = recs;
foreach (var rec in choices)
{
TimeSpan t = DateTime.UtcNow - new DateTime(1970, 1, 1);
int secondsSinceEpoch = (int)t.TotalSeconds;
w.WriteLine($"{rec.UserId},{rec.ItemId},{Math.Round(rec.Score, 1)},{secondsSinceEpoch},{rec.Iteration}"); //user_id,item_id,timestamp
}
}
}
}
public XTablePortable(ITable <IData, IData> table, ITransformer <TKey, IData> keyTransformer = null, ITransformer <TRecord, IData> recordTransformer = null)
{
if (table == null)
{
throw new ArgumentNullException("table");
}
Table = table;
if (keyTransformer == null)
{
keyTransformer = new DataTransformer <TKey>(table.Descriptor.KeyType);
}
if (recordTransformer == null)
{
recordTransformer = new DataTransformer <TRecord>(table.Descriptor.RecordType);
}
KeyTransformer = keyTransformer;
RecordTransformer = recordTransformer;
}
public GameDefinitionDetailsViewModelBuilder(IPlayedGameDetailsViewModelBuilder playedGameDetailsViewModelBuilder, ITransformer transformer)
{
_playedGameDetailsViewModelBuilder = playedGameDetailsViewModelBuilder;
_transformer = transformer;
}
public TransformerThenParser(ITransformer <S, R1> transformer, IParser <R2> parser)
{
this.Transformer = transformer;
this.Parser = parser;
}
public Prev(int pos, ITransformer transformer)
: base(-Math.Abs(pos), transformer)
{
if (pos == 0)
throw new ArgumentOutOfRangeException();
}
public TransformerThenConsumer(ITransformer <S, R> transformer, IConsumer consumer)
{
this.Transformer = transformer;
this.Consumer = consumer;
}
public IEnumerable <float[]> Score(ITransformer model, IDataView data)
{
return(PredictDataUsingModel(data, model));
}
public TransformerThenTransformer(ITransformer <S, I> firstTransformer, ITransformer <I, R> secondTransformer)
{
this.FirstTransformer = firstTransformer;
this.SecondTransformer = secondTransformer;
}
protected override IAsset TranslateAssetCore(IAsset asset, ITransformer transformer, bool isDebugMode)
{
return(InnerTranslateAsset <LessTranslator>("LessTranslator", asset, transformer, isDebugMode));
}
public void Is(ITransformer <S, R> transformer)
{
this.Transformer.Value = transformer;
}
private static void Main(string[] args)
{
//Create the MLContext to share across components for deterministic results
MLContext mlContext = new MLContext(seed: 1); //Seed set to any number so you have a deterministic environment
// STEP 1: Common data loading configuration
IDataView fullData = mlContext.Data.LoadFromTextFile(path: DataPath,
columns: new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column(nameof(IrisData.SepalLength), DataKind.Single, 1),
new TextLoader.Column(nameof(IrisData.SepalWidth), DataKind.Single, 2),
new TextLoader.Column(nameof(IrisData.PetalLength), DataKind.Single, 3),
new TextLoader.Column(nameof(IrisData.PetalWidth), DataKind.Single, 4),
},
hasHeader: true,
separatorChar: '\t');
//Split dataset in two parts: TrainingDataset (80%) and TestDataset (20%)
DataOperationsCatalog.TrainTestData trainTestData = mlContext.Data.TrainTestSplit(fullData, testFraction: 0.2);
trainingDataView = trainTestData.TrainSet;
testingDataView = trainTestData.TestSet;
//STEP 2: Process data transformations in pipeline
var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", nameof(IrisData.SepalLength), nameof(IrisData.SepalWidth), nameof(IrisData.PetalLength), nameof(IrisData.PetalWidth));
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 10);
// STEP 3: Create and train the model
var trainer = mlContext.Clustering.Trainers.KMeans(featureColumnName: "Features", numberOfClusters: 3);
var trainingPipeline = dataProcessPipeline.Append(trainer);
var trainedModel = trainingPipeline.Fit(trainingDataView);
// STEP4: Evaluate accuracy of the model
IDataView predictions = trainedModel.Transform(testingDataView);
var metrics = mlContext.Clustering.Evaluate(predictions, scoreColumnName: "Score", featureColumnName: "Features");
ConsoleHelper.PrintClusteringMetrics(trainer.ToString(), metrics);
// STEP5: Save/persist the model as a .ZIP file
using (var fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(trainedModel, trainingDataView.Schema, fs);
Console.WriteLine("=============== End of training process ===============");
Console.WriteLine("=============== Predict a cluster for a single case (Single Iris data sample) ===============");
// Test with one sample text
var sampleIrisData = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
ITransformer model = mlContext.Model.Load(stream, out var modelInputSchema);
// Create prediction engine related to the loaded trained model
var predEngine = mlContext.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
//Score
var resultprediction = predEngine.Predict(sampleIrisData);
Console.WriteLine($"Cluster assigned for setosa flowers:" + resultprediction.SelectedClusterId);
}
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public OptionalTransformer(ITransformer <R, R> transformer)
{
this.Transformer = transformer;
}
//public double getProbabilityThreshold()
//{
// return lrModel.getThreshold();
//}
public void setLogisticRegressionModel(ITransformer lrm)
{
lrModel = lrm;
}
public void CreateTransformer()
{
XmlSerializerTransformer xmlTransformer = new XmlSerializerTransformer();
// just a fake out to test the transformer
RuntimeConfigurationView configurationView = new RuntimeConfigurationView(Context);
xmlTransformer.CurrentSectionName = "ApplConfig1";
xmlTransformer.Initialize(configurationView);
transformer = xmlTransformer;
}
private static void LoadModel()
{
mlContext = new MLContext(1);
trainedModel = mlContext.Model.Load(modelFileName, out var modelInputSchema);
predEngine = mlContext.Model.CreatePredictionEngine <SentimentIssue, SentimentPrediction>(trainedModel);
}
public EmployeeRepository(ITransformer<EmployeeModel, Employee> transformer)
{
this.transformer = transformer;
}
public static void EvaluateModel(MLContext mlContext, IDataView testDataView, ITransformer model)
{
using (StreamWriter w = File.AppendText(Config.StatsFile))
{
results.Add("Evaluating model...");
var prediction = model.Transform(testDataView);
var metrics = mlContext.Regression.Evaluate(prediction, labelColumnName: "Label", scoreColumnName: "Score");
results.Add("Root Mean Squared Error : " + metrics.RootMeanSquaredError.ToString());
results.Add("RSquared: " + metrics.RSquared.ToString());
w.WriteLine($"{Config.CurrentIteration},{metrics.RootMeanSquaredError},{metrics.RSquared},{metrics.LossFunction},{metrics.MeanAbsoluteError},{metrics.MeanSquaredError}");
}
}
public HtmlAwareMultiTransformer(ITransformer delegateTransformer)
{
this.delegateTransformer = delegateTransformer;
}
public static BrowseRecommendationsResults Run(Configuration config = null)
{
if (config == null)
{
Config = new Configuration();
}
else
{
Config = config;
}
var stopwatch = System.Diagnostics.Stopwatch.StartNew();
results.Add($"Building test {Config.TestNumber} directories...");
if (!Directory.Exists(Configuration.BaseDirectory))
{
Directory.CreateDirectory(Configuration.BaseDirectory);
}
if (!Directory.Exists($"{Configuration.BaseDirectory}/{Config.TestNumber}"))
{
Directory.CreateDirectory($"{Configuration.BaseDirectory}/{Config.TestNumber}");
}
if (!Directory.Exists($"{Configuration.BaseDirectory}/dependencies"))
{
Directory.CreateDirectory($"{Configuration.BaseDirectory}/dependencies");
}
if (!File.Exists(Config.AgentsFile))
{
results.Add("Generating agents file...");
Generators.GenerateAgentsFile(Config);
}
if (!File.Exists(Config.SitesFile))
{
results.Add("Generating sites file...");
Generators.GenerateSitesFile(Config);
}
if (!File.Exists(Config.InputFilePref) || !File.Exists(Config.InputFileRand))
{
results.Add("Generating browse history files...");
Generators.GenerateNewBrowseFiles(Config);
}
var typesToProcess = new[] { "pref", "rand" };
foreach (var typeToProcess in typesToProcess)
{
Config.CurrentType = typeToProcess;
results.Add($"Initializing {Config.CurrentType}...");
results.Add("Extracting test file...");
if (!File.Exists(Config.TestFile))
{
//build test file from input
var lines = File.ReadAllLines(Config.InputFile);
var numberForTest = (lines.Length * Config.PercentOfDataIsTest);
var linesToRemove = new List <int>();
using (StreamWriter w = File.AppendText(Config.TestFile))
{
w.WriteLine("user_id,item_id,rating,timestamp,iteration".ToLower());
int recordsCopied = 0;
while (recordsCopied < numberForTest)
{
var r = new Random();
var randomLineNumber = r.Next(1, lines.Length - 1);
while (linesToRemove.Contains(randomLineNumber))
{
randomLineNumber = r.Next(1, lines.Length - 1);
}
var line = lines[randomLineNumber];
w.WriteLine(line);
linesToRemove.Add(randomLineNumber);
recordsCopied++;
}
}
//remove test data from input file
if (File.Exists(Config.InputFile + ".backup"))
{
File.Delete(Config.InputFile + ".backup");
}
File.Move(Config.InputFile, Config.InputFile + ".backup");
using (StreamWriter w = File.AppendText(Config.InputFile))
{
w.WriteLine("user_id,item_id,rating,timestamp,iteration".ToLower());
int i = -1;
foreach (var line in lines)
{
i++;
if (i == 0 || linesToRemove.Contains(i))
{
continue;
}
w.WriteLine(line);
}
}
}
MLContext mlContext = new MLContext();
(IDataView trainingDataView, IDataView testDataView) = LoadData(mlContext);
Agents = new List <Agent>();
using (var fileStream = File.OpenRead(Config.AgentsFile))
{
using (var streamReader = new StreamReader(fileStream, Encoding.UTF8, true, 128))
{
var i = -1;
String line;
while ((line = streamReader.ReadLine()) != null)
{
i++;
if (i == 0)
{
continue;
}
var o = line.Split(Convert.ToChar(","));
Agents.Add(new Agent(Convert.ToInt32(o[0]), o[1], Convert.ToInt32(Convert.ToDouble(o[2]))));
}
}
}
Sites = new List <Site>();
using (var fileStream = File.OpenRead(Config.SitesFile))
{
using (var streamReader = new StreamReader(fileStream, Encoding.UTF8, true, 128))
{
String line;
while ((line = streamReader.ReadLine()) != null)
{
var o = line.Split(Convert.ToChar(","));
try
{
Sites.Add(new Site(Convert.ToInt32(o[0]), o[1]));
}
catch { } //lazy, don't @ me
}
}
}
results.Add($"Initializing model and associated requirements...");
if (!File.Exists(Config.ModelFile))
{
ITransformer model = BuildAndTrainModel(mlContext, trainingDataView);
EvaluateModel(mlContext, testDataView, model);
UseModelForSinglePrediction(mlContext, model);
SaveModel(mlContext, trainingDataView.Schema, model);
}
/*
* results.Add("=============== Running Experiment ===============");
* var experimentSettings = new RecommendationExperimentSettings();
* experimentSettings.MaxExperimentTimeInSeconds = 3600;
* experimentSettings.OptimizingMetric = RegressionMetric.MeanSquaredError;
* var experiment = mlContext.Auto().CreateRecommendationExperiment(experimentSettings);
* ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto()
* .CreateRecommendationExperiment(new RecommendationExperimentSettings() { MaxExperimentTimeInSeconds = 3600 })
* .Execute(trainingDataView, testDataView,
* new ColumnInformation()
* {
* LabelColumnName = "Label",
* UserIdColumnName = "userId",
* ItemIdColumnName = "itemId"
* });
* // STEP 3: Print metric from best model
* RunDetail<RegressionMetrics> bestRun = experimentResult.BestRun;
* results.Add($"Total models produced: {experimentResult.RunDetails.Count()}");
* results.Add($"Best model's trainer: {bestRun.TrainerName}");
* results.Add($"Metrics of best model from validation data --");
* PrintMetrics(bestRun.ValidationMetrics);
* Environment.Exit(1);
*/
//now that we have a model, we'll loop through that model x times - same model, growing dataset over iteration
for (var i = 1; i < Config.Iterations; i++)
{
Config.CurrentIteration = i;
//Define DataViewSchema for data preparation pipeline and trained model
DataViewSchema modelSchema;
// Load trained model
var trainedModel = mlContext.Model.Load(Config.ModelFile, out modelSchema);
// Load data preparation pipeline and trained model
UseModelForSinglePrediction(mlContext, trainedModel);
}
results.Add("Generating final reports...");
Generators.GenerateReportFile(Config);
results.Add($"{Config.CurrentType} completed in {stopwatch.ElapsedMilliseconds} ms");
}
stopwatch.Stop();
results.Add($"Test completed in {stopwatch.ElapsedMilliseconds} ms");
//load result file
var recommendations = RecommendationsService.Load(config.ResultFileOut);
var browseRecommendationsResults = new BrowseRecommendationsResults {
JobOutput = results, Recommendations = recommendations
};
return(browseRecommendationsResults);
}
public static float GetPrediction(ITransformer trainedModel, Employee employee)
{
var predictionEngine = MlContext.Model.CreatePredictionEngine <EmployeeDto, SalaryPrediction>(trainedModel);
return(GetPrediction(predictionEngine, employee));
}
public CSharpOperation(string outKey, string outType, string script, Dictionary<string, Script> scripts, IParameters parameters)
: base(string.Empty, outKey) {
var csc = new CSharpCodeProvider();
var ca = Assembly.GetExecutingAssembly();
var cp = new CompilerParameters { GenerateInMemory = true };
var testRow = new Row();
cp.ReferencedAssemblies.Add("System.dll");
cp.ReferencedAssemblies.Add("System.Core.dll");
cp.ReferencedAssemblies.Add("mscorlib.dll");
cp.ReferencedAssemblies.Add(ca.Location);
var scriptBuilder = new StringBuilder(string.Empty);
foreach (var s in scripts) {
scriptBuilder.AppendLine($"// {s.Value.Name} script");
scriptBuilder.AppendLine(s.Value.Content);
}
var castBuilder = new StringBuilder(string.Empty);
if (!parameters.Any()) {
castBuilder.AppendLine(string.Format("{1} {0} = ({1}) row[\"{0}\"];", OutKey, Common.ToSystemType(outType)));
testRow[OutKey] = new DefaultFactory(Logger).Convert(null, outType);
} else {
var map = Common.GetLiteral();
foreach (var pair in parameters) {
if (pair.Value.HasValue()) {
castBuilder.AppendLine($"{Common.ToSystemType(pair.Value.SimpleType)} {pair.Value.Name} = {map[pair.Value.SimpleType](pair.Value.Value)};");
} else {
castBuilder.AppendLine(string.Format("{1} {0} = ({1}) row[\"{0}\"];", pair.Value.Name, Common.ToSystemType(pair.Value.SimpleType)));
}
testRow[pair.Value.Name] = new DefaultFactory(Logger).Convert(null, pair.Value.SimpleType);
}
}
var code = $@"using System;
using System.Text;
using System.Collections.Generic;
using System.Linq;
using Transformalize.Operations.Transform;
using Transformalize.Libs.Rhino.Etl;
{scriptBuilder}
public class Transformer : ITransformer
{{
public object Transform(Row row)
{{
{castBuilder}
//User's script
{script}
}}
}}";
Logger.EntityDebug(EntityName, "Compiling this code:");
Logger.EntityDebug(EntityName, code);
var res = csc.CompileAssemblyFromSource(
cp,
code
);
if (res.Errors.Count == 0) {
var type = res.CompiledAssembly.GetType("Transformer");
_transformer = (ITransformer)Activator.CreateInstance(type);
try {
var test = _transformer.Transform(testRow);
Logger.EntityDebug(EntityName, "CSharp transform compiled and passed test. {0}", test);
} catch (Exception e) {
Logger.EntityDebug(EntityName, "CSharp transform compiled but failed test. {0}", e.Message);
Logger.EntityDebug(EntityName, e.StackTrace);
}
} else {
foreach (var error in res.Errors) {
Logger.EntityError(EntityName, error.ToString());
}
throw new TransformalizeException(Logger, EntityName, "Failed to compile code. {0}", code);
}
Name = $"CSharpOperation ({outKey})";
}
public static IEnumerable <KeyValuePair <Employee, float> > GetPrediction(ITransformer trainedModel, IEnumerable <Employee> employees)
{
var predictionEngine = MlContext.Model.CreatePredictionEngine <EmployeeDto, SalaryPrediction>(trainedModel);
return(employees.Select(e => new KeyValuePair <Employee, float>(e, GetPrediction(predictionEngine, e))));
}
public PlayerStatsController(IPlayerRetriever playerRetriever, ITransformer transformer)
{
this.playerRetriever = playerRetriever;
this.transformer = transformer;
}
internal XFindIndex(DynamicDelegate f, ITransformer xf) : base(f, xf)
{
idx = -1;
}
public ParserThenTransformer(IParser <R1> parser, ITransformer <R1, R2> transformer)
{
this.Parser = parser;
this.Transformer = transformer;
}
public Prev(ITransformer transformer)
: this(1, transformer)
{
}
/// <summary>
/// Register new <see cref="ITransformer"/>.
/// </summary>
/// <param name="transformer"><see cref="ITransformer"/> instance.</param>
public void RegisterTransformer(ITransformer transformer)
{
this.transformers.Add(transformer.Name, transformer);
}
public OneOrMoreTransformer(ITransformer <R, R> transformer)
{
this.Transformer = transformer;
}
public ModificationHistoryProjectPlugin(IFarmService farmService, IPhysicalApplicationPathProvider pathProvider)
{
this.farmService = farmService;
transformer = new XslTransformer();
this.pathProvider = pathProvider;
}
private static Func <Schema, IRowToRowMapper> TransformerChecker(IExceptionContext ectx, ITransformer transformer)
{
ectx.CheckValue(transformer, nameof(transformer));
ectx.CheckParam(transformer.IsRowToRowMapper, nameof(transformer), "Must be a row to row mapper");
return(transformer.GetRowToRowMapper);
}
public override bool Transform(ITransformer transformer)
{
return transformer.Transform(this);
}
/// <summary>
/// Method transforms TSource type collection to TResult type collection.
/// </summary>
/// <typeparam name="TSource">
/// Type of collection.
/// </typeparam>
/// <typeparam name="TResult">
/// Type of transformed collection.
/// </typeparam>
/// <param name="collection">
/// Collection to transform.
/// </param>
/// <param name="transformer">
/// Format to transform.
/// </param>
/// <returns>
/// Returns an IEnumerator for the transformed collection.
/// </returns>
/// <exception cref="ArgumentNullException">
/// Collection must not be null. Collection elements must not be null.
/// </exception>
public static IEnumerable <TResult> Transform <TSource, TResult>(this IEnumerable <TSource> collection, ITransformer <TSource, TResult> transformer)
{
return(collection.Transform(transformer.Transform));
}
