/// <summary>
/// Read generic model from file.
/// </summary>
/// <typeparam name="TInput">Type for incoming data</typeparam>
/// <typeparam name="TOutput">Type for output data</typeparam>
/// <param name="stream">Stream with model</param>
/// <returns>Model</returns>
public static Task <PredictionModel <TInput, TOutput> > ReadAsync <TInput, TOutput>(Stream stream)
where TInput : class
where TOutput : class, new()
{
if (stream == null)
{
throw new ArgumentNullException(nameof(stream));
}
var environment = new MLContext();
AssemblyRegistration.RegisterAssemblies(environment);
BatchPredictionEngine <TInput, TOutput> predictor =
environment.CreateBatchPredictionEngine <TInput, TOutput>(stream);
return(Task.FromResult(new PredictionModel <TInput, TOutput>(predictor, stream)));
}
private static BatchPredictionEngine <MLSankakuPost, MLSankakuPostLikeagePrediciton> Train(long userId)
{
var context = new DiscordContext();
MLContext mlContext = new MLContext(seed: 0);
var data = GetPosts(userId);
var schemaDef = SchemaDefinition.Create(typeof(MLSankakuPost));
var trainData = mlContext.CreateStreamingDataView <MLSankakuPost>(data, schemaDef);
var pipeline = mlContext.Regression.Trainers.FastTree("Label", "Features", numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 20);
var model = pipeline.Fit(trainData);
return(mlContext.CreateBatchPredictionEngine <MLSankakuPost, MLSankakuPostLikeagePrediciton>(trainData, true,
schemaDef));
//return model.MakePredictionFunction<MLSankakuPost, MLSankakuPost>(mlContext);
}
public void TrainAndPredictSentimentModelWithDirectionInstantiationTestWithWordEmbedding()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
var env = new MLContext(seed: 1, conc: 1);
// Pipeline
var loader = env.Data.ReadFromTextFile(dataPath,
columns: new[]
{
new TextLoader.Column("Label", DataKind.Num, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
},
hasHeader: true
);
var text = TextFeaturizingEstimator.Create(env, new TextFeaturizingEstimator.Arguments()
{
Column = new TextFeaturizingEstimator.Column
{
Name = "WordEmbeddings",
Source = new[] { "SentimentText" }
},
OutputTokens = true,
KeepPunctuations = false,
UsePredefinedStopWordRemover = true,
VectorNormalizer = TextFeaturizingEstimator.TextNormKind.None,
CharFeatureExtractor = null,
WordFeatureExtractor = null,
},
loader);
var trans = WordEmbeddingsExtractingTransformer.Create(env, new WordEmbeddingsExtractingTransformer.Arguments()
{
Column = new WordEmbeddingsExtractingTransformer.Column[1]
{
new WordEmbeddingsExtractingTransformer.Column
{
Name = "Features",
Source = "WordEmbeddings_TransformedText"
}
},
ModelKind = WordEmbeddingsExtractingTransformer.PretrainedModelKind.Sswe,
}, text);
// Train
var trainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features, numLeaves: 5, numTrees: 5, minDatapointsInLeaves: 2);
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
var pred = trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = EvaluateBinary(env, testDataScorer);
// SSWE is a simple word embedding model + we train on a really small dataset, so metrics are not great.
Assert.Equal(.6667, metrics.Accuracy, 4);
Assert.Equal(.71, metrics.Auc, 1);
Assert.Equal(.58, metrics.Auprc, 2);
// Create prediction engine and test predictions
var model = env.CreateBatchPredictionEngine <SentimentData, SentimentPrediction>(testDataScorer);
var sentiments = GetTestData();
var predictions = model.Predict(sentiments, false);
Assert.Equal(2, predictions.Count());
Assert.True(predictions.ElementAt(0).Sentiment);
Assert.True(predictions.ElementAt(1).Sentiment);
// Get feature importance based on feature gain during training
var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
Assert.Equal(1.0, (double)summary[0].Value, 1);
}
public void TrainAndPredictSentimentModelWithDirectionInstantiationTest()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
var env = new MLContext(seed: 1, conc: 1);
// Pipeline
var loader = env.Data.ReadFromTextFile(dataPath,
columns: new[]
{
new TextLoader.Column("Label", DataKind.Num, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
},
hasHeader: true
);
var trans = TextFeaturizingEstimator.Create(env, new TextFeaturizingEstimator.Arguments()
{
Column = new TextFeaturizingEstimator.Column
{
Name = "Features",
Source = new[] { "SentimentText" }
},
OutputTokens = true,
KeepPunctuations = false,
UsePredefinedStopWordRemover = true,
VectorNormalizer = TextFeaturizingEstimator.TextNormKind.L2,
CharFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments()
{
NgramLength = 3, AllLengths = false
},
WordFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments()
{
NgramLength = 2, AllLengths = true
},
},
loader);
// Train
var trainer = new FastTreeBinaryClassificationTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features,
numLeaves: 5, numTrees: 5, minDatapointsInLeaves: 2);
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
var pred = trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = EvaluateBinary(env, testDataScorer);
ValidateBinaryMetrics(metrics);
// Create prediction engine and test predictions
var model = env.CreateBatchPredictionEngine <SentimentData, SentimentPrediction>(testDataScorer);
var sentiments = GetTestData();
var predictions = model.Predict(sentiments, false);
Assert.Equal(2, predictions.Count());
Assert.True(predictions.ElementAt(0).Sentiment);
Assert.True(predictions.ElementAt(1).Sentiment);
// Get feature importance based on feature gain during training
var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
Assert.Equal(1.0, (double)summary[0].Value, 1);
}