RemixAutoML (version 0.4.2)

AutoH2oGAMMultiClass: AutoH2oGAMMultiClass is an automated H2O modeling framework with grid-tuning and model evaluation

Description

AutoH2oGAMMultiClass is an automated H2O 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, confusion matrix, and variable importance.

Usage

AutoH2oGAMMultiClass(
  data,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  WeightsColumn = NULL,
  GamColNames = NULL,
  eval_metric = "logloss",
  MaxMem = {     gc()    
    paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo",
    intern = TRUE))/1e+06)), "G") },
  NThreads = max(1, parallel::detectCores() - 2),
  model_path = NULL,
  metadata_path = NULL,
  ModelID = "FirstModel",
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  IfSaveModel = "mojo",
  H2OShutdown = FALSE,
  H2OStartUp = TRUE,
  GridTune = FALSE,
  GridStrategy = "Cartesian",
  StoppingRounds = 10,
  MaxRunTimeSecs = 3600 * 24 * 7,
  MaxModelsInGrid = 2,
  Distribution = "multinomial",
  Link = "Family_Default",
  num_knots = NULL,
  keep_gam_cols = TRUE,
  Solver = "AUTO",
  Alpha = 0.5,
  Lambda = NULL,
  LambdaSearch = FALSE,
  NLambdas = -1,
  Standardize = TRUE,
  RemoveCollinearColumns = FALSE,
  InterceptInclude = TRUE,
  NonNegativeCoefficients = FALSE
)

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).

FeatureColNames

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

WeightsColumn

Weighted classification

GamColNames

GAM column names. Up to 9 features

eval_metric

This is the metric used to identify best grid tuned model. Choose from "logloss", "r2", "RMSE", "MSE"

MaxMem

Set the maximum amount of memory you'd like to dedicate to the model run. E.g. "32G"

NThreads

Set the number of threads you want to dedicate to the model building

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

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

IfSaveModel

Set to "mojo" to save a mojo file, otherwise "standard" to save a regular H2O model object

H2OShutdown

Set to TRUE to have H2O shutdown after running this function

H2OStartUp

Set to TRUE to start up H2O inside function

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.

GridStrategy

"RandomDiscrete" or "Cartesian"

StoppingRounds

Iterations in grid tuning

MaxRunTimeSecs

Max run time in seconds

MaxModelsInGrid

Number of models to test from grid options (1080 total possible options)

num_knots

Numeric values for gam

keep_gam_cols

Logical

Solver

Default "AUTO". Options include "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"

Alpha

Default 0.5 Otherwise supply a value between 0 and 1. 1 is equivalent to Lasso regression. 0 is equivalent to Ridge regression. Inbetween for a blend of the two.

Lambda

Default NULL. Regularization strength.

LambdaSearch

Default FALSE.

NLambdas

Default -1

Standardize

Default TRUE. Standardize numerical columns

RemoveCollinearColumns

Default FALSE. Removes some of the linearly dependent columns

InterceptInclude

Default TRUE

NonNegativeCoefficients

Default FALSE

Value

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

See Also

Other Automated Supervised Learning - Multiclass Classification: AutoCatBoostMultiClass(), AutoH2oDRFMultiClass(), AutoH2oGBMMultiClass(), AutoH2oGLMMultiClass(), AutoH2oMLMultiClass(), AutoXGBoostMultiClass()

Examples

# NOT RUN {
# Create some dummy correlated data with numeric and categorical features
data <- RemixAutoML::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000L,
  ID = 2L,
  ZIP = 0L,
  AddDate = FALSE,
  Classification = FALSE,
  MultiClass = TRUE)

# Define GAM Columns to use - up to 9 are allowed
GamCols <- names(which(unlist(lapply(data, is.numeric))))
GamCols <- GamCols[!GamCols %in% c("Adrian","IDcol_1","IDcol_2")]
GamCols <- GamCols[1L:(min(9L,length(GamCols)))]

# Run function
TestModel <- RemixAutoML::AutoH2oGAMMultiClass(
   data,
   TrainOnFull = FALSE,
   ValidationData = NULL,
   TestData = NULL,
   TargetColumnName = "Adrian",
   FeatureColNames = names(data)[!names(data) %in% c("IDcol_1", "IDcol_2","Adrian")],
   WeightsColumn = NULL,
   GamColNames = GamCols,
   eval_metric = "logloss",
   MaxMem = {gc();paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo", intern=TRUE)) / 1000000)),"G")},
   NThreads = max(1, parallel::detectCores()-2),
   model_path = normalizePath("./"),
   metadata_path = NULL,
   ModelID = "FirstModel",
   ReturnModelObjects = TRUE,
   SaveModelObjects = FALSE,
   IfSaveModel = "mojo",
   H2OShutdown = FALSE,
   H2OStartUp = TRUE,

   # ML args
   num_knots = NULL,
   keep_gam_cols = TRUE,
   GridTune = FALSE,
   GridStrategy = "Cartesian",
   StoppingRounds = 10,
   MaxRunTimeSecs = 3600 * 24 * 7,
   MaxModelsInGrid = 10,
   Distribution = "multinomial",
   Link = "Family_Default",
   Solver = "AUTO",
   Alpha = 0.5,
   Lambda = NULL,
   LambdaSearch = FALSE,
   NLambdas = -1,
   Standardize = TRUE,
   RemoveCollinearColumns = FALSE,
   InterceptInclude = TRUE,
   NonNegativeCoefficients = FALSE)
# }