Spark ML -- Survival Regression

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.

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; currently unused.

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.

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_predictor 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.

Details

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

See also

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

Other ml algorithms: ml_decision_tree_classifier, ml_gbt_classifier, ml_generalized_linear_regression, 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