# ml_aft_survival_regression

0th

Percentile

##### 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.

##### 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, ...)
##### 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.

• 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.

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

##### Aliases
• ml_aft_survival_regression
• ml_survival_regression
##### Examples
# NOT RUN {
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
# }
# NOT RUN {
# }