# ml_aft_survival_regression

##### 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 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()`

##### 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 {
# }
```

*Documentation reproduced from package sparklyr, version 1.5.2, License: Apache License 2.0 | file LICENSE*