@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("VectorAssembler")
@Description("A transformation stage that leverages the Apache Spark ML VectorAssembler "
+ "to merge multiple numeric (or numeric vector) fields into a single feature vector.")
public class VectorAssembler extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("BucketedLSHBuilder")
@Description("A building stage for an Apache Spark ML Bucketed Random Projection LSH model.")
public class BucketedLSHBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("BucketedLSH")
@Description("A transformation stage that leverages a trained Bucketed Random Projection LSH model "
+ "to project feature vectors onto hash value vectors. Similar feature vectors are mapped onto "
+ "the same hash value vector.")
public class BucketedLSH extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("HashingTF")
@Description("A transformation stage that leverages the Apache Spark ML HashingTF and maps "
+ "a sequence of terms to their term frequencies using the hashing trick. Currently the "
+ "Austin Appleby's MurmurHash 3 algorithm is used.")
public class HashingTF extends FeatureCompute {

      ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("MinHashLSHBuilder")
@Description("A building stage for an Apache Spark ML MinHash LSH model.")
public class MinHashLSHBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("MinHashLSH")
@Description("A transformation stage that leverages a trained MinHash LSH model "
+ "to project feature vectors onto hash value vectors.")
public class MinHashLSH extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("StringIndexerBuilder")
@Description("A building stage for an Apache Spark ML StringIndexer model.")
public class StringIndexerBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("IndexToString")
@Description("A transformation stage that leverages the Apache Spark ML IndexToString transformer. "
+ "This stage requires a trained StringIndexer model.")
public class IndexToString extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("StringToIndex")
@Description("A transformation stage that leverages the Apache Spark ML StringIndexer. "
+ "This stage requires a trained StringIndexer model.")
public class StringToIndex extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("VectorIndexerBuilder")
@Description("A building stage for an Apache Spark ML VectorIndexer model.")
public class VectorIndexerBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("VectorIndexer")
@Description("A transformation stage that leverages the Apache Spark ML VectorIndexer to "
+ "decide which features are categorical and converts the original values into category "
+ "indices. This stage requires a trained VectorIndexer model.")
public class VectorIndexer extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("PCABuilder")
@Description("A building stage for an Apache Spark based Principal Component Analysis "
+ "feature model.")
public class PCABuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("PCA")
@Description("A transformation stage that leverages a trained PCA model to project "
+ "feature vectors onto a lower dimensional vector space.")
public class PCA extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("ScalerBuilder")
@Description("A building stage for an Apache Spark ML feature scaling model. Supported models "
+ "are Min-Max, Max-Abs and Standard Scaler.")
public class ScalerBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("Scaler")
@Description("A transformation stage that leverages a trained Scaler model to project feature "
+ "vectors onto scaled vectors. Supported models are 'Max-Abs', 'Min-Max' and 'Standard'.")
public class Scaler extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("ChiSquaredBuilder")
@Description("A building stage for an Apache Spark ML Chi-Squared Selector model.")
public class ChiSquaredBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("ChiSquaredSelector")
@Description("A transformation stage that leverages a trained Chi-Squared Selector model "
+ "to select categorical features to use for predicting categorical labels.")
public class ChiSquaredSelector extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("Binarizer")
@Description("A transformation stage that leverages the Apache Spark ML Binarizer "
+ "to map continuous features onto binary values.")
public class Binarizer extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("Bucketizer")
@Description("A transformation stage that leverages the Apache Spark ML Feature "
+ "Bucketizer to map continuous features onto feature buckets.")
public class Bucketizer extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("DCT")
@Description("A transformation stage that leverages the Apache Spark ML Discrete "
+ "Cosine Tranform to map a feature vector in the time domain into a feature "
+ "vector in the frequency domain.")
public class DCT extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("NGram")
@Description("A transformation stage that leverages Apache Spark ML N-Gram "
+ "transformer to convert the input array of string into an array of n-grams.")
public class NGram extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("Normalizer")
@Description("A transformation stage to normalize a feature vector to have unit "
+ "norm using the given p-norm.")
public class Normalizer extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("OneHotEncoder")
@Description("A transformation stage to map input labels (indices) to binary vectors. "
+ "This encoding allows algorithms which expect continuous features to use categorical "
+ "features. This transformer expects a numeric input and generates an array of double "
+ "as output.")
public class OneHotEncoder extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("QuantileDiscretizer")
@Description("A transformation stage that leverages the Apache Spark ML Quantile Discretizer "
+ "to map continuous features of a certain input field onto binned categorical feature.")
public class QuantileDiscretizer extends FeatureCompute {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("Tokenizer")
@Description("A transformation stage that leverages the Apache Spark Regex "
+ "ML Tokenizer to split an input text into a sequence of tokens.")
public class Tokenizer extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("CountVecBuilder")
@Description("A building stage for an Apache Spark ML CountVectorizer model.")
public class CountVecBuilder extends FeatureSink {

      ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("CountVec")
@Description("A transformation stage that leverages the Apache Spark ML CountVectorizer. "
+ "This stage requires a trained CountVectorizer model.")
public class CountVec extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("TFIDFBuilder")
@Description("A building stage for an Apache Spark ML TF-IDF feature model.")
public class TFIDFBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("TFIDF")
@Description("A transformation stage that leverages an Apache Spark ML TF-IDF model "
+ "to map a sequence of words into its feature vector. Term frequency-inverse "
+ "document frequency (TF-IDF) is a feature vectorization method widely used in "
+ "text mining to reflect the importance of a term to a document in the corpus.")
public class TFIDF extends FeatureCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("W2VecBuilder")
@Description("A building stage for an Apache Spark ML Word2Vec feature model.")
public class W2VecBuilder extends FeatureSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("W2Vec")
@Description("A transformation stage that turns a sentence into a vector to represent "
+ "the whole sentence. The transform is performed by averaging all word vectors it "
+ "contains, based on a trained Word2Vec model.")
public class W2Vec extends FeatureCompute {

      ...

}