R: Spark ML - Survival Regression

ml_aft_survival_regression {sparklyr}

R Documentation

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 = list(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 = 2L, 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 = list(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 = 2L, quantiles_col = NULL, features_col = "features",
  label_col = "label", prediction_col = "prediction",
  uid = random_string("aft_survival_regression_"), response = NULL,
  features = NULL, ...)

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.

Value

The object returned depends on the class of x.