Spark ML - Survival Regression
ml_aft_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,
... )
Arguments
Arguments | Description |
---|---|
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
ml_survival_regression()
is an alias for ml_aft_survival_regression()
for backwards compatibility.
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_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()
Examples
library(survival)
library(sparklyr)
<- spark_connect(master = "local")
sc <- sdf_copy_to(sc, ovarian, name = "ovarian_tbl", overwrite = TRUE)
ovarian_tbl
<- ovarian_tbl %>%
partitions sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
<- partitions$training
ovarian_training <- partitions$test
ovarian_test
<- ovarian_training %>%
sur_reg ml_aft_survival_regression(futime ~ ecog_ps + rx + age + resid_ds, censor_col = "fustat")
<- ml_predict(sur_reg, ovarian_test)
pred
pred