The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.