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Spark ML – Generalized Linear Regression

Source: R/ml_regression_generalized_linear_regression.R
ml_generalized_linear_regression.Rd

Perform regression using Generalized Linear Model (GLM).

Usage

ml_generalized_linear_regression(
  x,
  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

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".

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.

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 (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.

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.

Details

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.

See also

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()

Examples

if (FALSE) { # \dontrun{
library(sparklyr)

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
} # }