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AutoH2oGAMClassifier 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.
AutoH2oGAMClassifier(
data,
TrainOnFull = FALSE,
ValidationData = NULL,
TestData = NULL,
TargetColumnName = NULL,
FeatureColNames = NULL,
WeightsColumn = NULL,
GamColNames = NULL,
Distribution = "binomial",
Link = "logit",
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),
model_path = NULL,
metadata_path = NULL,
ModelID = "FirstModel",
NumOfParDepPlots = 3,
ReturnModelObjects = TRUE,
SaveModelObjects = FALSE,
SaveInfoToPDF = FALSE,
IfSaveModel = "mojo",
H2OShutdown = FALSE,
H2OStartUp = TRUE,
DebugMode = FALSE,
GridTune = FALSE,
GridStrategy = "Cartesian",
StoppingRounds = 10,
MaxRunTimeSecs = 3600 * 24 * 7,
MaxModelsInGrid = 2,
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
)This is your data set for training and testing your model
Set to TRUE to train on full data
This is your holdout data set used in modeling either refine your hyperparameters.
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.
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.
Either supply the feature column names OR the column number where the target is located (but not mixed types)
Weighted classification
GAM column names. Up to 9 features
"binomial", "quasibinomial"
identity, logit, log, inverse, tweedie
This is the metric used to identify best grid tuned model. Choose from "AUC" or "logloss"
A vector with 4 elements c(True Positive Cost, False Negative Cost, False Positive Cost, True Negative Cost). Default c(1,0,0,1),
Set the maximum amount of memory you'd like to dedicate to the model run. E.g. "32G"
Set the number of threads you want to dedicate to the model building
A character string of your path file to where you want your output saved
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.
A character string to name your model and output
Tell the function the number of partial dependence calibration plots you want to create.
Set to TRUE to output all modeling objects (E.g. plots and evaluation metrics)
Set to TRUE to return all modeling objects to your environment
Set to TRUE to save modeling information to PDF. If model_path or metadata_path aren't defined then output will be saved to the working directory
Set to "mojo" to save a mojo file, otherwise "standard" to save a regular H2O model object
Set to TRUE to shutdown H2O after running the function
Set to TRUE to start up H2O inside function
Set to TRUE to get a print out of steps taken internally
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.
"RandomDiscrete" or "Cartesian"
Iterations in grid tuning
Max run time in seconds
Number of models to test from grid options (1080 total possible options)
Numeric values for gam
Logical
Default "AUTO". Options include "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"
Gridable. 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.
Gridable. Default NULL. Regularization strength.
Default FALSE.
Default -1
Default TRUE. Standardize numerical columns
Default FALSE. Removes some of the linearly dependent columns
Default TRUE
Default FALSE
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
Other Automated Supervised Learning - Binary Classification:
AutoCatBoostClassifier(),
AutoH2oDRFClassifier(),
AutoH2oGBMClassifier(),
AutoH2oGLMClassifier(),
AutoH2oMLClassifier(),
AutoXGBoostClassifier()
# NOT RUN {
# Create some dummy correlated data
data <- RemixAutoML::FakeDataGenerator(
Correlation = 0.85,
N = 1000,
ID = 2,
ZIP = 0,
AddDate = FALSE,
Classification = TRUE,
MultiClass = FALSE)
# 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::AutoH2oGAMClassifier(
# 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 args
CostMatrixWeights = c(1,0,0,1),
eval_metric = "auc",
NumOfParDepPlots = 3,
# Metadata arguments:
model_path = NULL,
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,
GamColNames = GamCols,
# ML args
num_knots = NULL,
keep_gam_cols = TRUE,
GridTune = FALSE,
GridStrategy = "Cartesian",
StoppingRounds = 10,
MaxRunTimeSecs = 3600 * 24 * 7,
MaxModelsInGrid = 10,
Distribution = "binomial",
Link = "logit",
Solver = "AUTO",
Alpha = 0.5,
Lambda = NULL,
LambdaSearch = FALSE,
NLambdas = -1,
Standardize = TRUE,
RemoveCollinearColumns = FALSE,
InterceptInclude = TRUE,
NonNegativeCoefficients = FALSE)
# }
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