AutoH2oMLClassifier: AutoH2oMLClassifier

In AdrianAntico/ModelingTools: AutoQuant

AutoH2oMLClassifier

Description

AutoH2oMLClassifier 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 plot, evaluation metrics, variable importance, partial dependence calibration plots, and column names used in model fitting.

Usage

AutoH2oMLClassifier(
  OutputSelection = c("EvalMetrics", "Score_TrainData"),
  data = NULL,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  ExcludeAlgos = NULL,
  eval_metric = "auc",
  CostMatrixWeights = c(1, 0, 0, 1),
  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),
  MaxModelsInGrid = 2,
  model_path = NULL,
  metadata_path = NULL,
  ModelID = "FirstModel",
  NumOfParDepPlots = 3,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = TRUE,
  IfSaveModel = "mojo",
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  DebugMode = FALSE
)

Arguments

OutputSelection	You can select what type of output you want returned. Choose from c("EvalMetrics", "Score_TrainData")
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). Note that the target column needs to be a 0 | 1 numeric variable.
FeatureColNames	Either supply the feature column names OR the column number where the target is located (but not mixed types)
ExcludeAlgos	"DRF","GLM","XGBoost","GBM","DeepLearning" and "Stacke-dEnsemble"
eval_metric	This is the metric used to identify best grid tuned model. Choose from "AUC" or "logloss"
CostMatrixWeights	A vector with 4 elements c(True Positive Cost, False Negative Cost, False Positive Cost, True Negative Cost). Default c(1,0,0,1),
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
MaxModelsInGrid	Number of models to test from grid options (1080 total possible options)
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
NumOfParDepPlots	Tell the function the number of partial dependence calibration plots you want to create.
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
SaveInfoToPDF	Set to TRUE to print model insights to PDF
IfSaveModel	Set to "mojo" to save a mojo file, otherwise "standard" to save a regular H2O model object
H2OShutdown	Set to TRUE to shutdown H2O after running the function
H2OStartUp	Set to FALSE
DebugMode	Set to TRUE to print out steps taken

Value

Saves to file and returned in list: VariableImportance.csv, Model, ValidationData.csv, EvalutionPlot.png, EvaluationMetrics.csv, ParDepPlots.R a named list of features with partial dependence calibration plots, GridCollect, and GridList

Author(s)

Adrian Antico

See Also

Other Automated Supervised Learning - Binary Classification: AutoCatBoostClassifier(), AutoH2oDRFClassifier(), AutoH2oGAMClassifier(), AutoH2oGBMClassifier(), AutoH2oGLMClassifier(), AutoLightGBMClassifier(), AutoXGBoostClassifier()

Examples

# Create some dummy correlated data with numeric and categorical features
data <- AutoQuant::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000L,
  ID = 2L,
  ZIP = 0L,
  AddDate = FALSE,
  Classification = TRUE,
  MultiClass = FALSE)

TestModel <- AutoQuant::AutoH2oMLClassifier(
  OutputSelection = c("EvalMetrics", "Score_TrainData"),
  data,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = "Adrian",
  FeatureColNames = names(data)[!names(data) %in% c("IDcol_1", "IDcol_2","Adrian")],
  ExcludeAlgos = NULL,
  eval_metric = "auc",
  CostMatrixWeights = c(1,0,0,1),
  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),
  MaxModelsInGrid = 10,
  model_path = normalizePath("./"),
  metadata_path = normalizePath("./"),
  ModelID = "FirstModel",
  NumOfParDepPlots = 3,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = TRUE,
  IfSaveModel = "mojo",
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  DebugMode = FALSE)

AdrianAntico/ModelingTools documentation built on Feb. 1, 2024, 7:33 a.m.