void Extensibility()
{
using (var env = new LocalEnvironment())
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
Action <IrisData, IrisData> action = (i, j) =>
{
j.Label = i.Label;
j.PetalLength = i.SepalLength > 3 ? i.PetalLength : i.SepalLength;
j.PetalWidth = i.PetalWidth;
j.SepalLength = i.SepalLength;
j.SepalWidth = i.SepalWidth;
};
var lambda = LambdaTransform.CreateMap(env, loader, action);
var term = TermTransform.Create(env, lambda, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Transform(term);
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(scorer);
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
public void SdcaWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var data = TextLoader.CreateReader(Env, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var binaryTrainer = new SdcaBinaryTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
TestEstimatorCore(binaryTrainer, data.AsDynamic);
var regressionTrainer = new SdcaRegressionTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
TestEstimatorCore(regressionTrainer, data.AsDynamic);
var mcTrainer = new SdcaMultiClassTrainer(Env, "Label", "Features", advancedSettings: (s) => s.ConvergenceTolerance = 1e-2f);
TestEstimatorCore(mcTrainer, data.AsDynamic);
Done();
}
public static EstimatorChain <KeyToValueMappingTransformer> BuildAndTrainModel(IDataView trainingDataView, EstimatorChain <ITransformer> pipeline)
{
var trainer = new SdcaMultiClassTrainer(_mlContext, DefaultColumnNames.Label, DefaultColumnNames.Features);
var trainingPipeline = pipeline.Append(trainer)
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
Console.WriteLine($"=============== Training the model ===============");
_trainedModel = trainingPipeline.Fit(trainingDataView);
Console.WriteLine($"=============== Finished Training the model Ending time: {DateTime.Now.ToString()} ===============");
Console.WriteLine($"=============== Single Prediction just-trained-model ===============");
_predEngine = _trainedModel.CreatePredictionEngine <СonformChecker, CheckerPrediction>(_mlContext);
СonformChecker conf = new СonformChecker()
{
Name = "Электронный аукцион"
};
var prediction = _predEngine.Predict(conf);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Con} ===============");
SaveModelAsFile(_mlContext, _trainedModel);
return(trainingPipeline);
}
void DecomposableTrainAndPredict()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = TermTransform.Create(env, loader, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Cut out term transform from pipeline.
var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term);
var keyToValue = new KeyToValueTransform(env, "PredictedLabel").Transform(newScorer);
var model = env.CreatePredictionEngine <IrisDataNoLabel, IrisPrediction>(keyToValue);
var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisDataNoLabel>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == "Iris-setosa");
}
}
}
public void TrainSentiment()
{
var env = new MLContext(seed: 1);
// Pipeline
var arguments = new TextLoader.Arguments()
{
Column = new TextLoader.Column[]
{
new TextLoader.Column()
{
Name = "Label",
Source = new[] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.Num
},
new TextLoader.Column()
{
Name = "SentimentText",
Source = new[] { new TextLoader.Range()
{
Min = 1, Max = 1
} },
Type = DataKind.Text
}
},
HasHeader = true,
AllowQuoting = false,
AllowSparse = false
};
var loader = env.Data.ReadFromTextFile(_sentimentDataPath, arguments);
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 SdcaMultiClassTrainer(env, "Label", "Features", maxIterations: 20);
var predicted = trainer.Fit(trans);
_consumer.Consume(predicted);
}
public void TrainSentiment()
{
using (var env = new ConsoleEnvironment(seed: 1))
{
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
AllowQuoting = false,
AllowSparse = false,
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.Num
},
new TextLoader.Column()
{
Name = "SentimentText",
Source = new [] { new TextLoader.Range()
{
Min = 1, Max = 1
} },
Type = DataKind.Text
}
}
}, new MultiFileSource(_sentimentDataPath));
var text = TextFeaturizingEstimator.Create(env,
new TextFeaturizingEstimator.Arguments()
{
Column = new TextFeaturizingEstimator.Column
{
Name = "WordEmbeddings",
Source = new[] { "SentimentText" }
},
OutputTokens = true,
KeepPunctuations = false,
StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(),
VectorNormalizer = TextFeaturizingEstimator.TextNormKind.None,
CharFeatureExtractor = null,
WordFeatureExtractor = null,
}, loader);
var trans = WordEmbeddingsTransform.Create(env,
new WordEmbeddingsTransform.Arguments()
{
Column = new WordEmbeddingsTransform.Column[1]
{
new WordEmbeddingsTransform.Column
{
Name = "Features",
Source = "WordEmbeddings_TransformedText"
}
},
ModelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe,
}, text);
// Train
var trainer = new SdcaMultiClassTrainer(env, "Features", "Label", maxIterations: 20);
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
var predicted = trainer.Train(trainRoles);
_consumer.Consume(predicted);
}
}
public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
{
string dataPath = GetDataPath("iris.txt");
string testDataPath = dataPath;
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env,
new TextLoader.Arguments()
{
HasHeader = false,
Column = new[] {
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "SepalLength",
Source = new [] { new TextLoader.Range()
{
Min = 1, Max = 1
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "SepalWidth",
Source = new [] { new TextLoader.Range()
{
Min = 2, Max = 2
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "PetalLength",
Source = new [] { new TextLoader.Range()
{
Min = 3, Max = 3
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "PetalWidth",
Source = new [] { new TextLoader.Range()
{
Min = 4, Max = 4
} },
Type = DataKind.R4
}
}
}, new MultiFileSource(dataPath));
IDataTransform trans = new ConcatTransform(env, loader, "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
// Normalizer is not automatically added though the trainer has 'NormalizeFeatures' On/Auto
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "Features");
// Train
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments());
// Explicity adding CacheDataView since caching is not working though trainer has 'Caching' On/Auto
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = TrainUtils.CreateExamples(cached, label: "Label", feature: "Features");
trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
var pred = trainer.CreatePredictor();
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = Evaluate(env, testDataScorer);
CompareMatrics(metrics);
// Create prediction engine and test predictions
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(testDataScorer);
ComparePredictions(model);
// Get feature importance i.e. weight vector
var summary = ((MulticlassLogisticRegressionPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
Assert.Equal(7.757867, Convert.ToDouble(summary[0].Value), 5);
}
}
private static IPredictor TrainSentimentCore()
{
var dataPath = s_sentimentDataPath;
using (var env = new TlcEnvironment(seed: 1))
{
// Pipeline
var loader = new TextLoader(env,
new TextLoader.Arguments()
{
AllowQuoting = false,
AllowSparse = false,
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.Num
},
new TextLoader.Column()
{
Name = "SentimentText",
Source = new [] { new TextLoader.Range()
{
Min = 1, Max = 1
} },
Type = DataKind.Text
}
}
}, new MultiFileSource(dataPath));
var text = TextTransform.Create(env,
new TextTransform.Arguments()
{
Column = new TextTransform.Column
{
Name = "WordEmbeddings",
Source = new[] { "SentimentText" }
},
KeepDiacritics = false,
KeepPunctuations = false,
TextCase = Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode.Lower,
OutputTokens = true,
StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(),
VectorNormalizer = TextTransform.TextNormKind.None,
CharFeatureExtractor = null,
WordFeatureExtractor = null,
}, loader);
var trans = new WordEmbeddingsTransform(env,
new WordEmbeddingsTransform.Arguments()
{
Column = new WordEmbeddingsTransform.Column[1]
{
new WordEmbeddingsTransform.Column
{
Name = "Features",
Source = "WordEmbeddings_TransformedText"
}
},
ModelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe,
}, text);
// Train
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments()
{
MaxIterations = 20
});
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}