Spark ML – Isotonic Regression

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

Usage

ml_isotonic_regression(
  x,
  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

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.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

See also

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()

Examples

if (FALSE) { # \dontrun{
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)

pred
} # }