AutoH2oGBMRegression: AutoH2oGBMRegression is an automated H2O modeling framework with grid-tuning and model evaluation

Description

AutoH2oGBMRegression 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

AutoH2oGBMRegression(
  data,
  TrainOnFull = FALSE,
  ValidationData,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  WeightsColumn = NULL,
  TransformNumericColumns = NULL,
  Methods = c("YeoJohnson", "BoxCox", "Asinh", "Log", "LogPlus1", "Sqrt", "Asin",
    "Logit"),
  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",
  NumOfParDepPlots = 3,
  ReturnModelObjects = TRUE,
  SaveModelObjects = FALSE,
  SaveInfoToPDF = FALSE,
  IfSaveModel = "mojo",
  H2OShutdown = TRUE,
  H2OStartUp = TRUE,
  GridTune = FALSE,
  GridStrategy = "Cartesian",
  MaxRuntimeSecs = 60 * 60 * 24,
  StoppingRounds = 10,
  MaxModelsInGrid = 2,
  eval_metric = "RMSE",
  Trees = 50,
  LearnRate = 0.1,
  LearnRateAnnealing = 1,
  Alpha = NULL,
  Distribution = "poisson",
  MaxDepth = 20,
  SampleRate = 0.632,
  MTries = -1,
  ColSampleRate = 1,
  ColSampleRatePerTree = 1,
  ColSampleRatePerTreeLevel = 1,
  MinRows = 1,
  NBins = 20,
  NBinsCats = 1024,
  NBinsTopLevel = 1024,
  HistogramType = "AUTO",
  CategoricalEncoding = "AUTO"
)

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

Column name of a weights column

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.

MaxMem

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

NThreads

Set to the mamimum amount of threads you want to use for this function

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. Calibration boxplots will only be created for numerical features (not dummy variables)

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

H2OShutdown

Set to TRUE to shutdown H2O inside the function

H2OStartUp

Defaults to TRUE which means H2O will be started inside the 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

Default "Cartesian"

MaxRuntimeSecs

Default 60*60*24

StoppingRounds

Number of runs

MaxModelsInGrid

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

eval_metric

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

Trees

The maximum number of trees you want in your models

LearnRate

Default 0.10

LearnRateAnnealing

Default 1

Alpha

This is the quantile value you want to use for quantile regression. Must be a decimal between 0 and 1.

Distribution

Choose from gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"

MaxDepth

Default 20

SampleRate

Default 0.632

ColSampleRate

Default 1

ColSampleRatePerTree

Default 1

ColSampleRatePerTreeLevel

Default 1

MinRows

Default 1

NBins

Default 20

NBinsCats

Default 1024

NBinsTopLevel

Default 1024

HistogramType

Default "AUTO"

CategoricalEncoding

Default "AUTO"

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 metadata

Examples

Run this code

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

# Run function
TestModel <- RemixAutoML::AutoH2oGBMRegression(

    # Compute management
    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),
    H2OShutdown = TRUE,
    H2OStartUp = TRUE,
    IfSaveModel = "mojo",

    # Model evaluation
    NumOfParDepPlots = 3,

    # Metadata arguments:
    model_path = normalizePath("./"),
    metadata_path = file.path(normalizePath("./")),
    ModelID = "FirstModel",
    ReturnModelObjects = TRUE,
    SaveModelObjects = FALSE,
    SaveInfoToPDF = FALSE,

    # Data arguments
    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("BoxCox", "Asinh", "Asin", "Log", "LogPlus1", "Sqrt", "Logit","YeoJohnson"),

    # ML grid tuning args
    GridTune = FALSE,
    GridStrategy = "Cartesian",
    MaxRuntimeSecs = 60*60*24,
    StoppingRounds = 10,
    MaxModelsInGrid = 2,

    # Model args
    Trees = 50,
    LearnRate = 0.10,
    LearnRateAnnealing = 1,
    eval_metric = "RMSE",
    Alpha = NULL,
    Distribution = "poisson",
    MaxDepth = 20,
    SampleRate = 0.632,
    ColSampleRate = 1,
    ColSampleRatePerTree = 1,
    ColSampleRatePerTreeLevel  = 1,
    MinRows = 1,
    NBins = 20,
    NBinsCats = 1024,
    NBinsTopLevel = 1024,
    HistogramType = "AUTO",
    CategoricalEncoding = "AUTO")
# }

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Description

Usage

Arguments

Value

See Also

Examples