```
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 #> # Source: spark<?> [?? x 7]
#> futime fustat age resid_ds rx ecog_ps prediction
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 115 1 74.5 2 1 1 191.
#> 2 353 1 63.2 1 2 2 1369.
#> 3 377 0 58.3 1 2 1 1751.
#> 4 475 1 59.9 2 2 2 818.
#> 5 744 0 50.1 1 2 1 2792.
#> 6 770 0 57.1 2 2 1 928.
```

# Spark ML – Survival Regression

*R/ml_regression_aft_survival_regression.R*

## 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

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`

.

## Examples

## See Also

See https://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()`