ml

Value Members

1. object Pipeline extends MLReadable[Pipeline] with Serializable

   

   Annotations

   @Since( "1.6.0" )

2. object PipelineModel extends MLReadable[PipelineModel] with Serializable

   

   Annotations

   @Since( "1.6.0" )

3. package attribute

   

   The ML pipeline API uses DataFrames as ML datasets.

   ML attributes

   The ML pipeline API uses DataFrames as ML datasets. Each dataset consists of typed columns, e.g., string, double, vector, etc. However, knowing only the column type may not be sufficient to handle the data properly. For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, which cannot be treated as numeric values in ML algorithms, and, for another instance, we may want to know the names and types of features stored in a vector column. ML attributes are used to provide additional information to describe columns in a dataset.

   ML columns

   A column with ML attributes attached is called an ML column. The data in ML columns are stored as double values, i.e., an ML column is either a scalar column of double values or a vector column. Columns of other types must be encoded into ML columns using transformers. We use Attribute to describe a scalar ML column, and AttributeGroup to describe a vector ML column. ML attributes are stored in the metadata field of the column schema.

4. package classification

   

5. package clustering

   

6. package evaluation

   

7. package feature

   

   The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.

   Feature transformers

   The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting. Most feature transformers are implemented as Transformers, which transform one DataFrame into another, e.g., HashingTF. Some feature transformers are implemented as Estimators, because the transformation requires some aggregated information of the dataset, e.g., document frequencies in IDF. For those feature transformers, calling Estimator!.fit is required to obtain the model first, e.g., IDFModel, in order to apply transformation. The transformation is usually done by appending new columns to the input DataFrame, so all input columns are carried over.

   We try to make each transformer minimal, so it becomes flexible to assemble feature transformation pipelines. Pipeline can be used to chain feature transformers, and VectorAssembler can be used to combine multiple feature transformations, for example:

   import org.apache.spark.ml.feature._
   import org.apache.spark.ml.Pipeline
   
   // a DataFrame with three columns: id (integer), text (string), and rating (double).
   val df = spark.createDataFrame(Seq(
     (0, "Hi I heard about Spark", 3.0),
     (1, "I wish Java could use case classes", 4.0),
     (2, "Logistic regression models are neat", 4.0)
   )).toDF("id", "text", "rating")
   
   // define feature transformers
   val tok = new RegexTokenizer()
     .setInputCol("text")
     .setOutputCol("words")
   val sw = new StopWordsRemover()
     .setInputCol("words")
     .setOutputCol("filtered_words")
   val tf = new HashingTF()
     .setInputCol("filtered_words")
     .setOutputCol("tf")
     .setNumFeatures(10000)
   val idf = new IDF()
     .setInputCol("tf")
     .setOutputCol("tf_idf")
   val assembler = new VectorAssembler()
     .setInputCols(Array("tf_idf", "rating"))
     .setOutputCol("features")
   
   // assemble and fit the feature transformation pipeline
   val pipeline = new Pipeline()
     .setStages(Array(tok, sw, tf, idf, assembler))
   val model = pipeline.fit(df)
   
   // save transformed features with raw data
   model.transform(df)
     .select("id", "text", "rating", "features")
     .write.format("parquet").save("/output/path")

   Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn. The major difference is that most scikit-learn feature transformers operate eagerly on the entire input dataset, while MLlib's feature transformers operate lazily on individual columns, which is more efficient and flexible to handle large and complex datasets.

   See also

   scikit-learn.preprocessing

8. package linalg

   

9. package param

   

10. package recommendation

    

11. package regression

    

12. package source

    

13. package stat

    

14. package tree

    

15. package tuning

    

16. package util