RemixAutoML (version 0.11.0)

AutoXGBoostMultiClass: AutoXGBoostMultiClass is an automated XGBoost modeling framework with grid-tuning and model evaluation

Description

AutoXGBoostMultiClass is an automated XGBoost modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation metrics, variable importance, and column names used in model fitting.

Usage

AutoXGBoostMultiClass(data, TrainOnFull = FALSE, ValidationData = NULL,
  TestData = NULL, TargetColumnName = NULL, FeatureColNames = NULL,
  IDcols = NULL, eval_metric = "merror", Trees = 50,
  GridTune = FALSE, grid_eval_metric = "merror", TreeMethod = "hist",
  Objective = "multi:softmax", MaxModelsInGrid = 10, NThreads = 8,
  model_path = NULL, metadata_path = NULL, ModelID = "FirstModel",
  Verbose = 0, ReturnModelObjects = TRUE, ReturnFactorLevels = TRUE,
  SaveModelObjects = FALSE, PassInGrid = NULL)

Arguments

data

This is your data set for training and testing your model

TrainOnFull

Set to TRUE to train on full data

ValidationData

This is your holdout data set used in modeling either refine your hyperparameters.

TestData

This is your holdout data set. Catboost using both training and validation data in the training process so you should evaluate out of sample performance with this data set.

TargetColumnName

Either supply the target column name OR the column number where the target is located (but not mixed types). Target should be in factor or character form.

FeatureColNames

Either supply the feature column names OR the column number where the target is located (but not mixed types)

IDcols

A vector of column names or column numbers to keep in your data but not include in the modeling.

eval_metric

This is the metric used to identify best grid tuned model. Choose from "merror", "mlogloss"

Trees

The maximum number of trees you want in your models

GridTune

Set to TRUE to run a grid tuning procedure. Set a number in MaxModelsInGrid to tell the procedure how many models you want to test.

grid_eval_metric

Set to "accuracy" (only option currently)

TreeMethod

Choose from "hist", "gpu_hist"

Objective

Choose from 'multi:softmax' or 'multi:softprob'

MaxModelsInGrid

Number of models to test from grid options (243 total possible options)

NThreads

Set the maximum number of threads you'd like to dedicate to the model run. E.g. 8

model_path

A character string of your path file to where you want your output saved

metadata_path

A character string of your path file to where you want your model evaluation output saved. If left NULL, all output will be saved to model_path.

ModelID

A character string to name your model and output

Verbose

Set to 0 if you want to suppress model evaluation updates in training

ReturnModelObjects

Set to TRUE to output all modeling objects (E.g. plots and evaluation metrics)

SaveModelObjects

Set to TRUE to return all modeling objects to your environment

PassInGrid

Default is NULL. Provide a data.table of grid options from a previous run.

Value

Saves to file and returned in list: VariableImportance.csv, Model, ValidationData.csv, EvaluationMetrics.csv, GridCollect, GridList, and TargetLevels

See Also

Other Automated MultiClass Classification: AutoCatBoostMultiClass, AutoH2oDRFMultiClass, AutoH2oGBMMultiClass

Examples

# NOT RUN {
Correl <- 0.85
N <- 10000
data <- data.table::data.table(Target = runif(N))
data[, x1 := qnorm(Target)]
data[, x2 := runif(N)]
data[, Independent_Variable1 := log(pnorm(Correl * x1 +
                                            sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable2 := (pnorm(Correl * x1 +
                                         sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable3 := exp(pnorm(Correl * x1 +
                                            sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable4 := exp(exp(pnorm(Correl * x1 +
                                                sqrt(1-Correl^2) * qnorm(x2))))]
data[, Independent_Variable5 := sqrt(pnorm(Correl * x1 +
                                             sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable6 := (pnorm(Correl * x1 +
                                         sqrt(1-Correl^2) * qnorm(x2)))^0.10]
data[, Independent_Variable7 := (pnorm(Correl * x1 +
                                         sqrt(1-Correl^2) * qnorm(x2)))^0.25]
data[, Independent_Variable8 := (pnorm(Correl * x1 +
                                         sqrt(1-Correl^2) * qnorm(x2)))^0.75]
data[, Independent_Variable9 := (pnorm(Correl * x1 +
                                         sqrt(1-Correl^2) * qnorm(x2)))^2]
data[, Independent_Variable10 := (pnorm(Correl * x1 +
                                          sqrt(1-Correl^2) * qnorm(x2)))^4]
data[, Target := as.factor(
  ifelse(Independent_Variable2 < 0.20, "A",
         ifelse(Independent_Variable2 < 0.40, "B",
                ifelse(Independent_Variable2 < 0.6,  "C",
                       ifelse(Independent_Variable2 < 0.8,  "D", "E")))))]
data[, Independent_Variable11 := as.factor(
ifelse(Independent_Variable2 < 0.25, "A",
       ifelse(Independent_Variable2 < 0.35, "B",
              ifelse(Independent_Variable2 < 0.65,  "C",
                     ifelse(Independent_Variable2 < 0.75,  "D", "E")))))]
data[, ':=' (x1 = NULL, x2 = NULL)]
TestModel <- AutoXGBoostMultiClass(data,
                                   TrainOnFull = FALSE,
                                   ValidationData = NULL,
                                   TestData = NULL,
                                   TargetColumnName = 1,
                                   FeatureColNames = 2:12,
                                   IDcols = NULL,
                                   eval_metric = "merror",
                                   Trees = 50,
                                   GridTune = TRUE,
                                   grid_eval_metric = "accuracy",
                                   MaxModelsInGrid = 10,
                                   NThreads = 8,
                                   TreeMethod = "hist",
                                   Objective = 'multi:softmax',
                                   model_path = NULL,
                                   metadata_path = NULL,
                                   ModelID = "FirstModel",
                                   ReturnModelObjects = TRUE,
                                   ReturnFactorLevels = TRUE,
                                   SaveModelObjects = FALSE,
                                   PassInGrid = NULL)
# }