Spark ML – Isotonic Regression




Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.


  formula = NULL, 
  feature_index = 0, 
  isotonic = TRUE, 
  weight_col = NULL, 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  uid = random_string("isotonic_regression_"), 


Arguments Description
x A spark_connection, ml_pipeline, or a tbl_spark.
formula Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
feature_index Index of the feature if features_col is a vector column (default: 0), no effect otherwise.
isotonic Whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true
weight_col The name of the column to use as weights for the model fit.
features_col Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.
label_col Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.
prediction_col Prediction column name.
uid A character string used to uniquely identify the ML estimator.
Optional arguments; see Details.


When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.


The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.

  • tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.


sc <- spark_connect(master = "local") 
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) 
partitions <- iris_tbl %>% 
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111) 
iris_training <- partitions$training 
iris_test <- partitions$test 
iso_res <- iris_tbl %>% 
  ml_isotonic_regression(Petal_Length ~ Petal_Width) 
pred <- ml_predict(iso_res, iris_test) 
#> # Source: spark<?> [?? x 6]
#>    Sepal_Length Sepal_Width Petal_…¹ Petal…² Species predi…³
#>           <dbl>       <dbl>    <dbl>   <dbl> <chr>     <dbl>
#>  1          4.4         2.9      1.4     0.2 setosa     1.4 
#>  2          4.6         3.1      1.5     0.2 setosa     1.4 
#>  3          4.6         3.4      1.4     0.3 setosa     1.52
#>  4          4.8         3        1.4     0.3 setosa     1.52
#>  5          4.9         2.4      3.3     1   versic…    3.3 
#>  6          4.9         3        1.4     0.2 setosa     1.4 
#>  7          5           3.4      1.5     0.2 setosa     1.4 
#>  8          5           3.5      1.6     0.6 setosa     1.73
#>  9          5.2         3.5      1.5     0.2 setosa     1.4 
#> 10          5.2         4.1      1.5     0.1 setosa     1.31
#> # … with more rows, and abbreviated variable names
#> #   ¹​Petal_Length, ²​Petal_Width, ³​prediction

See Also

See for more information on the set of supervised learning algorithms. Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_linear_regression(), ml_linear_svc(), ml_logistic_regression(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()