Spark ML – Generalized Linear Regression




Perform regression using Generalized Linear Model (GLM).


  formula = NULL, 
  family = "gaussian", 
  link = NULL, 
  fit_intercept = TRUE, 
  offset_col = NULL, 
  link_power = NULL, 
  link_prediction_col = NULL, 
  reg_param = 0, 
  max_iter = 25, 
  weight_col = NULL, 
  solver = "irls", 
  tol = 1e-06, 
  variance_power = 0, 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  uid = random_string("generalized_linear_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.
family Name of family which is a description of the error distribution to be used in the model. Supported options: “gaussian”, “binomial”, “poisson”, “gamma” and “tweedie”. Default is “gaussian”.
link Name of link function which provides the relationship between the linear predictor and the mean of the distribution function. See for supported link functions.
fit_intercept Boolean; should the model be fit with an intercept term?
offset_col Offset column name. If this is not set, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.
link_power Index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R “statmod” package.
link_prediction_col Link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
reg_param Regularization parameter (aka lambda)
max_iter The maximum number of iterations to use.
weight_col The name of the column to use as weights for the model fit.
solver Solver algorithm for optimization.
tol Param for the convergence tolerance for iterative algorithms.
variance_power Power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.
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. Valid link functions for each family is listed below. The first link function of each family is the default one.

  • gaussian: “identity”, “log”, “inverse”

  • binomial: “logit”, “probit”, “loglog”

  • poisson: “log”, “identity”, “sqrt”

  • gamma: “inverse”, “identity”, “log”

  • tweedie: power link function specified through link_power. The default link power in the tweedie family is 1 - variance_power.


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") 
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE) 
partitions <- mtcars_tbl %>% 
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111) 
mtcars_training <- partitions$training 
mtcars_test <- partitions$test 
# Specify the grid 
family <- c("gaussian", "gamma", "poisson") 
link <- c("identity", "log") 
family_link <- expand.grid(family = family, link = link, stringsAsFactors = FALSE) 
family_link <- data.frame(family_link, rmse = 0) 
# Train the models 
for (i in seq_len(nrow(family_link))) { 
  glm_model <- mtcars_training %>% 
    ml_generalized_linear_regression(mpg ~ ., 
      family = family_link[i, 1], 
      link = family_link[i, 2] 
  pred <- ml_predict(glm_model, mtcars_test) 
  family_link[i, 3] <- ml_regression_evaluator(pred, label_col = "mpg") 
#>     family     link     rmse
#> 1 gaussian identity 2.881163
#> 2    gamma identity 2.954531
#> 3  poisson identity 2.942684
#> 4 gaussian      log 2.613220
#> 5    gamma      log 2.721447
#> 6  poisson      log 2.676343

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_isotonic_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()