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 = 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,
...
)x |
A |
formula |
Used when |
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 |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
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. |
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.
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()
## 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 ## End(Not run)
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