Spark ML – Gradient Boosted Trees

R/ml_classification_gbt_classifier.R,

ml_gbt_classifier

Description

Perform binary classification and regression using gradient boosted trees. Multiclass classification is not supported yet.

Usage

 
ml_gbt_classifier( 
  x, 
  formula = NULL, 
  max_iter = 20, 
  max_depth = 5, 
  step_size = 0.1, 
  subsampling_rate = 1, 
  feature_subset_strategy = "auto", 
  min_instances_per_node = 1L, 
  max_bins = 32, 
  min_info_gain = 0, 
  loss_type = "logistic", 
  seed = NULL, 
  thresholds = NULL, 
  checkpoint_interval = 10, 
  cache_node_ids = FALSE, 
  max_memory_in_mb = 256, 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  probability_col = "probability", 
  raw_prediction_col = "rawPrediction", 
  uid = random_string("gbt_classifier_"), 
  ... 
) 
 
ml_gradient_boosted_trees( 
  x, 
  formula = NULL, 
  type = c("auto", "regression", "classification"), 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  probability_col = "probability", 
  raw_prediction_col = "rawPrediction", 
  checkpoint_interval = 10, 
  loss_type = c("auto", "logistic", "squared", "absolute"), 
  max_bins = 32, 
  max_depth = 5, 
  max_iter = 20L, 
  min_info_gain = 0, 
  min_instances_per_node = 1, 
  step_size = 0.1, 
  subsampling_rate = 1, 
  feature_subset_strategy = "auto", 
  seed = NULL, 
  thresholds = NULL, 
  cache_node_ids = FALSE, 
  max_memory_in_mb = 256, 
  uid = random_string("gradient_boosted_trees_"), 
  response = NULL, 
  features = NULL, 
  ... 
) 
 
ml_gbt_regressor( 
  x, 
  formula = NULL, 
  max_iter = 20, 
  max_depth = 5, 
  step_size = 0.1, 
  subsampling_rate = 1, 
  feature_subset_strategy = "auto", 
  min_instances_per_node = 1, 
  max_bins = 32, 
  min_info_gain = 0, 
  loss_type = "squared", 
  seed = NULL, 
  checkpoint_interval = 10, 
  cache_node_ids = FALSE, 
  max_memory_in_mb = 256, 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  uid = random_string("gbt_regressor_"), 
  ... 
)

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.
max_iter	Maxmimum number of iterations.
max_depth	Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.
step_size	Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)
subsampling_rate	Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)
feature_subset_strategy	The number of features to consider for splits at each tree node. See details for options.
min_instances_per_node	Minimum number of instances each child must have after split.
max_bins	The maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.
min_info_gain	Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
loss_type	Loss function which GBT tries to minimize. Supported: `"squared"` (L2) and `"absolute"` (L1) (default = squared) for regression and `"logistic"` (default) for classification. For `ml_gradient_boosted_trees`, setting `"auto"` will default to the appropriate loss type based on model type.
seed	Seed for random numbers.
thresholds	Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value `p/t` is predicted, where `p` is the original probability of that class and `t` is the class’s threshold.
checkpoint_interval	Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
cache_node_ids	If `FALSE`, the algorithm will pass trees to executors to match instances with nodes. If `TRUE`, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Defaults to `FALSE`.
max_memory_in_mb	Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. Defaults to 256.
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.
probability_col	Column name for predicted class conditional probabilities.
raw_prediction_col	Raw prediction (a.k.a. confidence) column name.
uid	A character string used to uniquely identify the ML estimator.
…	Optional arguments; see Details.
type	The type of model to fit. `"regression"` treats the response as a continuous variable, while `"classification"` treats the response as a categorical variable. When `"auto"` is used, the model type is inferred based on the response variable type – if it is a numeric type, then regression is used; classification otherwise.
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. The supported options for feature_subset_strategy are

"auto": Choose automatically for task: If num_trees == 1, set to "all". If num_trees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
"all": use all features
"onethird": use 1/3 of the features
"sqrt": use use sqrt(number of features)
"log2": use log2(number of features)
"n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

ml_gradient_boosted_trees is a wrapper around ml_gbt_regressor.tbl_spark and ml_gbt_classifier.tbl_spark and calls the appropriate method based on model type.

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

library(sparklyr)
 
sc <- spark_connect(master = "local") 
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) 
 
partitions <- iris_tbl %>% 
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111) 
 
iris_training <- partitions$training 
iris_test <- partitions$test 
 
gbt_model <- iris_training %>% 
  ml_gradient_boosted_trees(Sepal_Length ~ Petal_Length + Petal_Width) 
 
pred <- ml_predict(gbt_model, iris_test) 
 
ml_regression_evaluator(pred, label_col = "Sepal_Length") 
#> [1] 0.4036941