public CategoricalTransform(ValueToKeyMappingEstimator term, IEstimator <ITransformer> toVector, IDataView input)
{
if (toVector != null)
{
_transformer = term.Append(toVector).Fit(input);
}
else
{
_transformer = new TransformerChain <ITransformer>(term.Fit(input));
}
}
private void buildAndTrainModel(IDataView data)
{
_answerKeyEstimator = _mlContext.Transforms.Conversion.MapValueToKey(
inputColumnName: nameof(MLEntry.Text),
outputColumnName: "Label");
var trainingPipeline =
_answerKeyEstimator
.Append(_mlContext.Transforms.Text.FeaturizeText(inputColumnName: nameof(MLEntry.Text),
outputColumnName: "TextFeaturized"))
.Append(_mlContext.Transforms.Text.FeaturizeText(inputColumnName: nameof(MLEntry.Intent),
outputColumnName: "IntentFeaturized"))
.Append(_mlContext.Transforms.Text.FeaturizeText(inputColumnName: nameof(MLEntry.PreviousIntents),
outputColumnName: "PreviousIntentFeaturized"))
.Append(_mlContext.Transforms.Concatenate("Featurized", "TextFeaturized", "IntentFeaturized", "PreviousIntentFeaturized"))
.AppendCacheCheckpoint(_mlContext)
.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "QuestionFeaturized"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
_trainedModel = trainingPipeline.Fit(data);
_predictionEngine = _mlContext.Model.CreatePredictionEngine <MLEntry, IntentPrediction>(_trainedModel);
}
public SchemaShape GetOutputSchema(SchemaShape inputSchema) => _term.Append(_toSomething).GetOutputSchema(inputSchema);
