library(sparklyr)
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)
#> f1 impurity_1 num_trees_1 max_depth_1
#> 1 0.9397887 entropy 5 5
#> 2 0.9056526 entropy 10 5
#> 3 0.9397887 entropy 5 10
#> 4 0.9056526 entropy 10 10
#> 5 0.9397887 gini 5 5
#> 6 0.9127092 gini 10 5
#> 7 0.9397887 gini 5 10
#> 8 0.9127092 gini 10 10Spark ML – Tuning
R/ml_tuning.R, R/ml_tuning_cross_validator.R,
ml-tuning
Description
Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.
Usage
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
| Arguments | Description |
|---|---|
| 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 |
Details
ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.
Value
The object returned depends on the class of x.
spark_connection: Whenxis aspark_connection, the function returns an instance of aml_cross_validatororml_traing_validation_splitobject.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the tuning estimator appended to the pipeline.tbl_spark: Whenxis atbl_spark, a tuning estimator is constructed then immediately fit with the inputtbl_spark, returning aml_cross_validation_modelor aml_train_validation_split_modelobject.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.