AutoH2oDRFRegression: AutoH2oDRFRegression
In AdrianAntico/ModelingTools: AutoQuant

AutoH2oDRFRegression

R Documentation

AutoH2oDRFRegression

Description

AutoH2oDRFRegression is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, 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 boxplot, evaluation metrics, variable importance, partial dependence calibration plots, partial dependence calibration box plots, and column names used in model fitting.

Usage

AutoH2oDRFRegression(
  OutputSelection = c("EvalMetrics", "Score_TrainData"),
  data = NULL,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  WeightsColumn = NULL,
  MaxMem = {
     gc()
    
    paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo",
    intern = TRUE))/1e+06)), "G")
 },
  NThreads = max(1L, parallel::detectCores() - 2L),
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  DebugMode = FALSE,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = FALSE,
  IfSaveModel = "mojo",
  model_path = NULL,
  metadata_path = NULL,
  ModelID = "FirstModel",
  TransformNumericColumns = NULL,
  Methods = c("Asinh", "Log", "LogPlus1", "Sqrt", "Asin", "Logit"),
  NumOfParDepPlots = 3,
  eval_metric = "RMSE",
  GridTune = FALSE,
  GridStrategy = "RandomDiscrete",
  MaxRunTimeSecs = 60 * 60 * 24,
  StoppingRounds = 10,
  MaxModelsInGrid = 2,
  Trees = 50,
  MaxDepth = 20,
  SampleRate = 0.632,
  MTries = -1,
  ColSampleRatePerTree = 1,
  ColSampleRatePerTreeLevel = 1,
  MinRows = 1,
  NBins = 20,
  NBinsCats = 1024,
  NBinsTopLevel = 1024,
  HistogramType = "AUTO",
  CategoricalEncoding = "AUTO"
)

Arguments

`OutputSelection`	You can select what type of output you want returned. Choose from "EvalMetrics", "Score_TrainData", "h2o.explain"
`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`	Column name of a weights column
`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
`H2OShutdown`	Set to TRUE to shutdown H2O inside the function
`H2OStartUp`	Defaults to TRUE which means H2O will be started inside the function
`DebugMode`	Set to TRUE to print steps to screen
`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 save insights to PDF
`IfSaveModel`	Set to "mojo" to save a mojo file, otherwise "standard" to save a regular H2O model object
`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
`TransformNumericColumns`	Set to NULL to do nothing; otherwise supply the column names of numeric variables you want transformed
`Methods`	Choose from "YeoJohnson", "BoxCox", "Asinh", "Log", "LogPlus1", "Sqrt", "Asin", or "Logit". If more than one is selected, the one with the best normalization pearson statistic will be used. Identity is automatically selected and compared.
`NumOfParDepPlots`	Tell the function the number of partial dependence calibration plots you want to create. Calibration boxplots will only be created for numerical features (not dummy variables)
`eval_metric`	This is the metric used to identify best grid tuned model. Choose from "MSE", "RMSE", "MAE", "RMSLE"
`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`	Default "Cartesian"
`MaxRunTimeSecs`	Default 86400
`StoppingRounds`	Default 10
`MaxModelsInGrid`	Number of models to test from grid options (1080 total possible options)
`Trees`	The maximum number of trees you want in your models
`MaxDepth`	Default 20
`SampleRate`	Default 0.632
`MTries`	Default -1 means it will default to number of features divided by 3
`ColSampleRatePerTree`	Default 1
`ColSampleRatePerTreeLevel`	Default 1
`MinRows`	Default 1
`NBins`	Default 20
`NBinsCats`	Default 1024
`NBinsTopLevel`	Default 1024
`HistogramType`	Default "AUTO". Select from "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"
`CategoricalEncoding`	Default "AUTO". Other options include "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimite"

Value

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

Author(s)

Adrian Antico

Examples

## Not run: 
# Create some dummy correlated data
data <- AutoQuant::FakeDataGenerator(
  Correlation = 0.85,
  N = 1000,
  ID = 2,
  ZIP = 0,
  AddDate = FALSE,
  Classification = FALSE,
  MultiClass = FALSE)

# Run function
TestModel <- AutoQuant::AutoH2oDRFRegression(

  # Compute management
  MaxMem = {gc();paste0(as.character(floor(as.numeric(system("awk '/MemFree/ {print $2}' /proc/meminfo", intern=TRUE)) / 1000000)),"G")},
  NThreads = max(1L, parallel::detectCores() - 2L),
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  IfSaveModel = "mojo",

  # Model evaluation:
  eval_metric = "RMSE",
  NumOfParDepPlots = 3,

  # Metadata arguments
  OutputSelection = c("EvalMetrics","Score_TrainData"),
  model_path = normalizePath("./"),
  metadata_path = NULL,
  ModelID = "FirstModel",
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = FALSE,
  DebugMode = FALSE,

  # Data Args
  data = data,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = "Adrian",
  FeatureColNames = names(data)[!names(data) %in% c("IDcol_1", "IDcol_2","Adrian")],
  WeightsColumn = NULL,
  TransformNumericColumns = NULL,
  Methods = c("Asinh","Asin","Log","LogPlus1", "Sqrt","Logit"),

  # Grid Tuning Args
  GridStrategy = "RandomDiscrete",
  GridTune = FALSE,
  MaxModelsInGrid = 10,
  MaxRunTimeSecs = 60*60*24,
  StoppingRounds = 10,

  # ML Args
  Trees = 50,
  MaxDepth = 20,
  SampleRate = 0.632,
  MTries = -1,
  ColSampleRatePerTree = 1,
  ColSampleRatePerTreeLevel = 1,
  MinRows = 1,
  NBins = 20,
  NBinsCats = 1024,
  NBinsTopLevel = 1024,
  HistogramType = "AUTO",
  CategoricalEncoding = "AUTO")

## End(Not run)

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