explain_h2o: Create explainer from your h2o model

In DALEXtra: Extension for 'DALEX' Package

Create explainer from your h2o model

Description

DALEX is designed to work with various black-box models like tree ensembles, linear models, neural networks etc. Unfortunately R packages that create such models are very inconsistent. Different tools use different interfaces to train, validate and use models. One of those tools, we would like to make more accessible is H2O.

Usage

explain_h2o(
  model,
  data = NULL,
  y = NULL,
  weights = NULL,
  predict_function = NULL,
  predict_function_target_column = NULL,
  residual_function = NULL,
  ...,
  label = NULL,
  verbose = TRUE,
  precalculate = TRUE,
  colorize = !isTRUE(getOption("knitr.in.progress")),
  model_info = NULL,
  type = NULL
)

Arguments

model	object - a model to be explained
data	data.frame or matrix - data which will be used to calculate the explanations. If not provided, then it will be extracted from the model. Data should be passed without a target column (this shall be provided as the y argument). NOTE: If the target variable is present in the data, some of the functionalities may not work properly.
y	numeric vector with outputs/scores. If provided, then it shall have the same size as data
weights	numeric vector with sampling weights. By default it's NULL. If provided, then it shall have the same length as data
predict_function	function that takes two arguments: model and new data and returns a numeric vector with predictions. By default it is yhat.
predict_function_target_column	Character or numeric containing either column name or column number in the model prediction object of the class that should be considered as positive (i.e. the class that is associated with probability 1). If NULL, the second column of the output will be taken for binary classification. For a multiclass classification setting, that parameter cause switch to binary classification mode with one vs others probabilities.
residual_function	function that takes four arguments: model, data, target vector y and predict function (optionally). It should return a numeric vector with model residuals for given data. If not provided, response residuals (y-\hat{y}) are calculated. By default it is residual_function_default.
...	other parameters
label	character - the name of the model. By default it's extracted from the 'class' attribute of the model
verbose	logical. If TRUE (default) then diagnostic messages will be printed
precalculate	logical. If TRUE (default) then predicted_values and residual are calculated when explainer is created. This will happen also if verbose is TRUE. Set both verbose and precalculate to FALSE to omit calculations.
colorize	logical. If TRUE (default) then WARNINGS, ERRORS and NOTES are colorized. Will work only in the R console. Now by default it is FALSE while knitting and TRUE otherwise.
model_info	a named list (package, version, type) containing information about model. If NULL, DALEX will seek for information on it's own.
type	type of a model, either classification or regression. If not specified then type will be extracted from model_info.

Value

explainer object (explain) ready to work with DALEX

Examples

# load packages and data
library(h2o)
library(DALEXtra)

# data <- DALEX::titanic_imputed

# init h2o
 cluster <- try(h2o::h2o.init())
if (!inherits(cluster, "try-error")) {
# stop h2o progress printing
 h2o.no_progress()

# split the data
# h2o_split <- h2o.splitFrame(as.h2o(data))
# train <- h2o_split[[1]]
# test <- as.data.frame(h2o_split[[2]])
# h2o automl takes target as factor
# train$survived <- as.factor(train$survived)

# fit a model
# automl <- h2o.automl(y = "survived",
#                   training_frame = train,
#                    max_runtime_secs = 30)


# create an explainer for the model
# explainer <- explain_h2o(automl,
#                        data = test,
#                         y = test$survived,
#                          label = "h2o")


titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
titanic_train <- read.csv(system.file("extdata", "titanic_train.csv", package = "DALEXtra"))
titanic_h2o <- h2o::as.h2o(titanic_train)
titanic_h2o["survived"] <- h2o::as.factor(titanic_h2o["survived"])
titanic_test_h2o <- h2o::as.h2o(titanic_test)
model <- h2o::h2o.gbm(
training_frame = titanic_h2o,
y = "survived",
distribution = "bernoulli",
ntrees = 500,
max_depth = 4,
min_rows =  12,
learn_rate = 0.001
)
explain_h2o(model, titanic_test[,1:17], titanic_test[,18])

try(h2o.shutdown(prompt = FALSE))
 }

DALEXtra documentation built on May 31, 2023, 5:30 p.m.