pmml.xgb.Booster: Generate PMML for a xgb.Booster object from the package...

In pmml: Generate PMML for Various Models

Generate PMML for a xgb.Booster object from the package xgboost.

Description

Generate PMML for a xgb.Booster object from the package xgboost.

Usage

## S3 method for class 'xgb.Booster'
pmml(
  model,
  model_name = "xboost_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "Extreme Gradient Boosting Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  input_feature_names = NULL,
  output_label_name = NULL,
  output_categories = NULL,
  xgb_dump_file = NULL,
  parent_invalid_value_treatment = "returnInvalid",
  child_invalid_value_treatment = "asIs",
  ...
)

Arguments

model	An object created by the 'xgboost' function.
model_name	A name to be given to the PMML model.
app_name	The name of the application that generated the PMML.
description	A descriptive text for the Header element of the PMML.
copyright	The copyright notice for the model.
model_version	A string specifying the model version.
transforms	Data transformations.
missing_value_replacement	Value to be used as the 'missingValueReplacement' attribute for all MiningFields.
input_feature_names	Input variable names used in training the model.
output_label_name	Name of the predicted field.
output_categories	Possible values of the predicted field, for classification models.
xgb_dump_file	Name of file saved using 'xgb.dump' function.
parent_invalid_value_treatment	Invalid value treatment at the top MiningField level.
child_invalid_value_treatment	Invalid value treatment at the model segment MiningField level.
...	Further arguments passed to or from other methods.

Details

The xgboost function takes as its input either an xgb.DMatrix object or a numeric matrix. The input field information is not stored in the R model object, hence the field information must be passed on as inputs. This enables the PMML to specify field names in its model representation. The R model object does not store information about the fitted tree structure either. However, this information can be extracted from the xgb.model.dt.tree function and the file saved using the xgb.dump function. The xgboost library is therefore needed in the environment and this saved file is needed as an input as well.

The following objectives are currently supported: multi:softprob, multi:softmax, binary:logistic.

The pmml exporter will throw an error if the xgboost model model only has one tree.

The exporter only works with numeric matrices. Sparse matrices must be converted to matrix objects before training an xgboost model for the export to work correctly.

Value

PMML representation of the xgb.Booster object.

Author(s)

Tridivesh Jena

References

xgboost: Extreme Gradient Boosting

See Also

pmml, PMML schema

Examples

## Not run: 
# Example using the xgboost package example model.

library(xgboost)
data(agaricus.train, package = "xgboost")
data(agaricus.test, package = "xgboost")

train <- agaricus.train
test <- agaricus.test

model1 <- xgboost(
  data = train$data, label = train$label,
  max_depth = 2, eta = 1, nthread = 2,
  nrounds = 2, objective = "binary:logistic"
)

# Save the tree information in an external file:
xgb.dump(model1, "model1.dumped.trees")

# Convert to PMML:
model1_pmml <- pmml(model1,
  input_feature_names = colnames(train$data),
  output_label_name = "prediction1",
  output_categories = c("0", "1"),
  xgb_dump_file = "model1.dumped.trees"
)

# Multinomial model using iris data:
model2 <- xgboost(
  data = as.matrix(iris[, 1:4]),
  label = as.numeric(iris[, 5]) - 1,
  max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
  objective = "multi:softprob", num_class = 3
)

# Save the tree information in an external file:
xgb.dump(model2, "model2.dumped.trees")

# Convert to PMML:
model2_pmml <- pmml(model2,
  input_feature_names = colnames(as.matrix(iris[, 1:4])),
  output_label_name = "Species",
  output_categories = c(1, 2, 3), xgb_dump_file = "model2.dumped.trees"
)

## End(Not run)

pmml documentation built on March 18, 2022, 5:49 p.m.