ml_generalized_linear_regression: Spark ML - Generalized Linear Regression

In sparklyr: R Interface to Apache Spark

Spark ML – Generalized Linear Regression

Description

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".
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.

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.

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_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

## Not run: 
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

## End(Not run)

sparklyr documentation built on May 29, 2024, 2:58 a.m.