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_"),
...
)model |
A cross validation or train-validation-split model. |
x |
A |
estimator |
A |
estimator_param_maps |
A named list of stages and hyper-parameter sets to tune. See details. |
evaluator |
A |
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 |
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 |
ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.
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.
## Not run:
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.