ml_aft_survival_regression: Spark ML -- Survival Regression

Description

Fit a parametric survival regression model named accelerated failure time (AFT) model (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.

Usage

ml_aft_survival_regression(
  x,
  formula = NULL,
  censor_col = "censor",
  quantile_probabilities = c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99),
  fit_intercept = TRUE,
  max_iter = 100L,
  tol = 1e-06,
  aggregation_depth = 2,
  quantiles_col = NULL,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("aft_survival_regression_"),
  ...
)
ml_survival_regression(
  x,
  formula = NULL,
  censor_col = "censor",
  quantile_probabilities = c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99),
  fit_intercept = TRUE,
  max_iter = 100L,
  tol = 1e-06,
  aggregation_depth = 2,
  quantiles_col = NULL,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("aft_survival_regression_"),
  response = NULL,
  features = NULL,
  ...
)

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.

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.
censor_col: Censor column name. The value of this column could be 0 or 1. If the value is 1, it means the event has occurred i.e. uncensored; otherwise censored.
quantile_probabilities: Quantile probabilities array. Values of the quantile probabilities array should be in the range (0, 1) and the array should be non-empty.
fit_intercept: Boolean; should the model be fit with an intercept term?
max_iter: The maximum number of iterations to use.
tol: Param for the convergence tolerance for iterative algorithms.
aggregation_depth: (Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).
quantiles_col: Quantiles column name. This column will output quantiles of corresponding quantileProbabilities if it is set.
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.
response: (Deprecated) The name of the response column (as a length-one character vector.)
features: (Deprecated) The name of features (terms) to use for the model fit.

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.

ml_survival_regression() is an alias for ml_aft_survival_regression() for backwards compatibility.

Examples

Run this code

if (FALSE) {

library(survival)
library(sparklyr)

sc <- spark_connect(master = "local")
ovarian_tbl <- sdf_copy_to(sc, ovarian, name = "ovarian_tbl", overwrite = TRUE)

partitions <- ovarian_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

ovarian_training <- partitions$training
ovarian_test <- partitions$test

sur_reg <- ovarian_training %>%
  ml_aft_survival_regression(futime ~ ecog_ps + rx + age + resid_ds, censor_col = "fustat")

pred <- ml_predict(sur_reg, ovarian_test)
pred
}