sparklyr (version 1.7.1)

ml_isotonic_regression: Spark ML -- Isotonic Regression

Description

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.

• 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.

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.

See Also

See http://spark.apache.org/docs/latest/ml-classification-regression.html 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()

Examples

# NOT RUN {
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
# }
# NOT RUN {
# }