sparkxgb is a sparklyr extension that provides an interface to XGBoost on Spark.
You can install the development version of sparkxgb with:
sparkxgb supports the familiar formula interface for specifying models:
library(sparkxgb)
library(sparklyr)
library(dplyr)
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris)
xgb_model <- xgboost_classifier(
iris_tbl,
Species ~ .,
num_class = 3,
num_round = 50,
max_depth = 4
)
xgb_model %>%
ml_predict(iris_tbl) %>%
select(Species, predicted_label, starts_with("probability_")) %>%
glimpse()
#> Rows: ??
#> Columns: 5
#> Database: spark_connection
#> $ Species <chr> "setosa", "setosa", "setosa", "setosa", "setosa…
#> $ predicted_label <chr> "setosa", "setosa", "setosa", "setosa", "setosa…
#> $ probability_setosa <dbl> 0.9971547, 0.9948581, 0.9968392, 0.9968392, 0.9…
#> $ probability_versicolor <dbl> 0.002097376, 0.003301427, 0.002284616, 0.002284…
#> $ probability_virginica <dbl> 0.0007479066, 0.0018403779, 0.0008762418, 0.000…It also provides a Pipelines API, which means you can use a xgboost_classifier or xgboost_regressor in a pipeline as any Estimator, and do things like hyperparameter tuning:
pipeline <- ml_pipeline(sc) %>%
ft_r_formula(Species ~ .) %>%
xgboost_classifier(num_class = 3)
param_grid <- list(
xgboost = list(
max_depth = c(1, 5),
num_round = c(10, 50)
)
)
cv <- ml_cross_validator(
sc,
estimator = pipeline,
evaluator = ml_multiclass_classification_evaluator(
sc,
label_col = "label",
raw_prediction_col = "rawPrediction"
),
estimator_param_maps = param_grid
)
cv_model <- cv %>%
ml_fit(iris_tbl)
summary(cv_model)
#> Summary for CrossValidatorModel
#> <cross_validator__13c346ec_bc09_4b8a_952d_92f9711299d7>
#>
#> Tuned Pipeline
#> with metric f1
#> over 4 hyperparameter sets
#> via 3-fold cross validation
#>
#> Estimator: Pipeline
#> <pipeline__bf0a05c1_6f0e_4875_ac1a_c77fbd6635f3>
#> Evaluator: MulticlassClassificationEvaluator
#> <multiclass_classification_evaluator__387ea4db_61da_45cb_813e_8c6f63811fff>
#>
#> Results Summary:
#> f1 max_depth_1 num_round_1
#> 1 0.9134404 1 10
#> 2 0.8993533 5 10
#> 3 0.9064859 1 50
#> 4 0.9064859 5 50
spark_disconnect(sc)