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                         Easy Xgboost Implementation

Simple xgboost wrapper

ezXg is a simple R utility, designed as a package, whose goal is to simplify the calibration with the xgboost R library and make it directly usable with new data.

It is inspired from Über's ludwig project, which allows to easily train model with Tensorflow.

It uses 5 functions in order to prepare data, train a model and make predictions. + xg_load_data: load and clean the data. + xg_train: train the model. + xg_gs: grid search for hyperparameter selection. + xg_predict: prediction using the model. + xg_auto_ml: auto ML feature for running a model in 1 line.

Install package

Tha package can be installed from its Github repository with the install_github function from the devtools library.


It relies on the xgboost, data.table and yaml libraries.

Load data

For example purpose, we will use the famous Kaggle titanic dataset, that can be loaded from the library.

titanic <- system.file("extdata", "titanic.csv", package = "ezXg")

The data can easily be loaded with the xg_load_data function:

d <- xg_load_data(titanic,
                  inputs = c("Pclass",
                  output = "Survived",
                  train.size = 0.8)

Train model

The model can be trained with the xg_train function, that is just a wrapper of the xgb.train function.

md <- xg_train(d)

Grid search

The xg_gs function implements a two-step calibration process in order to find the optimal set of hyperparameters for model:

gs <- xg_gs(d)

Make predictions

The prediction function takes the data (as a data.frame or data.table) as an input in order to make predictions on these new values.

new_data <- fread(titanic)
p <- xg_predict(md, new_data)

Auto ML feature

The auto ML feature is simply a wrapper that load the data, implements a grid search and train the model.

conf <- system.file("extdata", "ex_param.yml", package = "ezXg")

md <- xg_auto_ml(titanic, conf)

It is fed by the path to the dataset and a yaml configuration file, whose structure is the one represented below.

  seed: 1 # Seed for the model
  train.size: 0.8 # Training size for validation
  max.levels: 50 # maximum number of levels for factors
  nthread: 2 # Number of thread for the training
  verbose: true # Should information be printed ?
  retrain.full: true # If set to true, the model is trained with full dataset at the end
  inputs: # Input columns (either list or set to "auto")
    - "Pclass"
    - "Sex"
    - "Age"
    - "SibSp"
    - "Parch"
    - "Fare"
    - "Embarked"
  output: "Survived" # Output column
  inputs.class: "auto" # Input class (either list or set to "auto")
  output.class: "auto" # Output class (either "cat", "num" or "auto")
  na.handle: "mean" # Way to handle NA for numeric values
  eta: # Eta parameter, either one value or list if cv > 1
    - 0.05
    - 0.1
    - 0.15
  gamma: # Gamma parameter, either one value or list if cv > 1
    - 0.0
    - 0.1
    - 0.2
    - 0.3
  max_depth: # Max_depth parameter, either one value or list if cv > 1
    - 5
    - 6
    - 8
  colsample_bytree: # Colsample_bytree parameter, either one value or list if cv > 1
    - 0.8
    - 0.9
    - 1.0
  min_child_weight: # Min_child_weight parameter, either one value or list if cv > 1
    - 1
    - 3
  nrounds: 100 # Number of rounds for the training
  objective: "auto" # Objective function
  cv: 5 # Number of folds for cross validation. If set to 1, there will not be any.

The fields are simply the different variables of the functions contained in the library, grouped under three categories: + global for global parameters; + model for model related parameters, such as the input and output values; + param the parameters and hyperparameters for model calibration.

md <- xg_auto_ml(system.file("extdata", "titanic.csv", package = "ezXg"),
                 system.file("extdata", "ex_param.yml", package = "ezXg"))

Most of the fields are optional and have default values.

The only required field is the name of the output value.

The following file structure is thuss correct:

  output: "Survived"

ArnaudBu/ezXg documentation built on Oct. 30, 2019, 4:59 a.m.