RemixAutoML (version 0.5.0)

AutoH2oMLRegression: AutoH2oMLRegression

Description

AutoH2oMLRegression 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

AutoH2oMLRegression(
  data,
  TrainOnFull = FALSE,
  ValidationData = NULL,
  TestData = NULL,
  TargetColumnName = NULL,
  FeatureColNames = NULL,
  ExcludeAlgos = NULL,
  TransformNumericColumns = NULL,
  Methods = c("YeoJohnson", "BoxCox", "Asinh", "Log", "LogPlus1", "Sqrt", "Asin",
    "Logit"),
  eval_metric = "RMSE",
  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
)

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)

ExcludeAlgos

"DRF","GLM","XGBoost","GBM","DeepLearning" and "Stacke-dEnsemble"

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.

eval_metric

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

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

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

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

See Also

Other Automated Supervised Learning - Regression: AutoCatBoostRegression(), AutoH2oDRFRegression(), AutoH2oGAMRegression(), AutoH2oGBMRegression(), AutoH2oGLMRegression(), AutoNLS(), AutoXGBoostRegression()

Examples

# 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::AutoH2oMLRegression(

    # 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:
    #   'eval_metric' is the measure catboost uses when
    #      evaluting on holdout data during its bandit style
    #      process
    #   'NumOfParDepPlots' Number of partial dependence
    #      calibration plots generated.
    #     A value of 3 will return plots for the top 3 variables
    #      based on variable importance
    #     Won't be returned if GrowPolicy is either
    #      "Depthwise" or "Lossguide" is used
    #     Can run the RemixAutoML::ParDepCalPlots() with
    #       the outputted ValidationData
    eval_metric = "RMSE",
    NumOfParDepPlots = 3,

    # Metadata arguments:
    #   'ModelID' is used to create part of the file names
    #     generated when saving to file'
    #   'model_path' is where the minimal model objects
    #     for scoring will be stored
    #      'ModelID' will be the name of the saved model object
    #   'metadata_path' is where model evaluation and model
    #      interpretation files are saved
    #      objects saved to model_path if metadata_path is null
    #      Saved objects include:
    #         'ModelID_ValidationData.csv' is the supplied or
    #            generated TestData with predicted values
    #         'ModelID_VariableImportance.csv' is the variable
    #            importance.
    #            This won't be saved to file if GrowPolicy is either
    #            "Depthwise" or "Lossguide" was used
    #            Results of all model builds including parameter
    #            settings, bandit probs, and grid IDs
    #         'ModelID_EvaluationMetrics.csv' which contains MSE,
    #          MAE, MAPE, R2
    model_path = NULL,
    metadata_path = NULL,
    ModelID = "FirstModel",
    ReturnModelObjects = TRUE,
    SaveModelObjects = FALSE,
    SaveInfoToPDF = FALSE,

    # Data arguments:
    #   'TrainOnFull' is to train a model with 100
    #      percent of your data.
    #     That means no holdout data will be used for evaluation
    #   If ValidationData and TestData are NULL and TrainOnFull
    #      is FALSE then data will be split 70 20 10
    #   'PrimaryDateColumn' is a date column in data that is
    #      meaningful when sorted.
    #     CatBoost categorical treatment is enhanced when supplied
    #   'IDcols' are columns in your data that you don't use for
    #      modeling but get returned with ValidationData
    #   'TransformNumericColumns' is for transforming your target
    #     variable. Just supply the name of it
    TrainOnFull = FALSE,
    ValidationData = NULL,
    TestData = NULL,
    TargetColumnName = "Adrian",
    FeatureColNames = names(data)[!names(data) %in% c("IDcol_1", "IDcol_2","Adrian")],
    TransformNumericColumns = NULL,
    Methods = c("BoxCox", "Asinh", "Asin", "Log", "LogPlus1", "Sqrt", "Logit", "YeoJohnson"),

    # Model args
    ExcludeAlgos = NULL)
# }