Spark ML -- Tuning

Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.

ml_sub_models(model)

ml_validation_metrics(model)

ml_cross_validator(x, estimator = NULL, estimator_param_maps = NULL,
  evaluator = NULL, num_folds = 3, collect_sub_models = FALSE,
  parallelism = 1, seed = NULL,
  uid = random_string("cross_validator_"), ...)

ml_train_validation_split(x, estimator = NULL,
  estimator_param_maps = NULL, evaluator = NULL, train_ratio = 0.75,
  collect_sub_models = FALSE, parallelism = 1, seed = NULL,
  uid = random_string("train_validation_split_"), ...)

Arguments

model

A cross validation or train-validation-split model.

x

A spark_connection, ml_pipeline, or a tbl_spark.

estimator

A ml_estimator object.

estimator_param_maps

A named list of stages and hyper-parameter sets to tune. See details.

evaluator

A ml_evaluator object, see ml_evaluator.

num_folds

Number of folds for cross validation. Must be >= 2. Default: 3

collect_sub_models

Whether to collect a list of sub-models trained during tuning. If set to FALSE, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.

parallelism

The number of threads to use when running parallel algorithms. Default is 1 for serial execution.

seed

A random seed. Set this value if you need your results to be reproducible across repeated calls.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; currently unused.

train_ratio

Ratio between train and validation data. Must be between 0 and 1. Default: 0.75

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_cross_validator or ml_traing_validation_split object.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the tuning estimator appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a tuning estimator is constructed then immediately fit with the input tbl_spark, returning a ml_cross_validation_model or a ml_train_validation_split_model object.

For cross validation, ml_sub_models() returns a nested list of models, where the first layer represents fold indices and the second layer represents param maps. For train-validation split, ml_sub_models() returns a list of models, corresponding to the order of the estimator param maps.

ml_validation_metrics() returns a data frame of performance metrics and hyperparameter combinations.

Details

ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.

Examples

if (FALSE) { sc <- spark_connect(master = "local") iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) # Create a pipeline pipeline <- ml_pipeline(sc) %>% ft_r_formula(Species ~ . ) %>% ml_random_forest_classifier() # Specify hyperparameter grid grid <- list( random_forest = list( num_trees = c(5,10), max_depth = c(5,10), impurity = c("entropy", "gini") ) ) # Create the cross validator object cv <- ml_cross_validator( sc, estimator = pipeline, estimator_param_maps = grid, evaluator = ml_multiclass_classification_evaluator(sc), num_folds = 3, parallelism = 4 ) # Train the models cv_model <- ml_fit(cv, iris_tbl) # Print the metrics ml_validation_metrics(cv_model) }