Error in UseMethod("lime") : no applicable method for 'lime' applied to an object of class "H2OFrame" about lime HOT 10 CLOSED

# Load the following packages
library(tidyquant)  # Loads tidyverse and several other pkgs 
library(readxl)     # Super simple excel reader
library(h2o)        # Professional grade ML pkg
library(lime)       # Explain complex black-box ML models
# Read excel data
hr_data_raw <- read_excel(path = "/Users/crawfordw/Downloads/WA_Fn-UseC_-HR-Employee-Attrition.xlsx")

# View first 10 rows
hr_data_raw[1:10,] %>%
    knitr::kable(caption = "First 10 rows")

hr_data <- hr_data_raw %>%
    mutate_if(is.character, as.factor) %>%
    select(Attrition, everything())

glimpse(hr_data)
# Initialize H2O JVM
h2o.init()
h2o.no_progress() # Turn off output of progress bars
# Split data into Train/Validation/Test Sets
hr_data_h2o <- as.h2o(hr_data)

split_h2o <- h2o.splitFrame(hr_data_h2o, c(0.7, 0.15), seed = 1234 )

train_h2o <- h2o.assign(split_h2o[[1]], "train" ) # 70%
valid_h2o <- h2o.assign(split_h2o[[2]], "valid" ) # 15%
test_h2o  <- h2o.assign(split_h2o[[3]], "test" )  # 15%

# Set names for h2o
y <- "Attrition"
x <- setdiff(names(train_h2o), y)

# Run the automated machine learning 
automl_models_h2o <- h2o.automl(
    x = x, 
    y = y,
    training_frame    = train_h2o,
    leaderboard_frame = valid_h2o,
    max_runtime_secs  = 30
    )

# Extract leader model
automl_leader <- automl_models_h2o@leader
# Predict on hold-out set, test_h2o
pred_h2o <- h2o.predict(object = automl_leader, newdata = test_h2o)

# Prep for performance assessment
test_performance <- test_h2o %>%
    tibble::as_tibble() %>%
    select(Attrition) %>%
    add_column(pred = as.vector(pred_h2o$predict)) %>%
    mutate_if(is.character, as.factor)
test_performance
# Confusion table counts
confusion_matrix <- test_performance %>%
    table() 
confusion_matrix

# Performance analysis
tn <- confusion_matrix[1]
tp <- confusion_matrix[4]
fp <- confusion_matrix[3]
fn <- confusion_matrix[2]

accuracy <- (tp + tn) / (tp + tn + fp + fn)
misclassification_rate <- 1 - accuracy
recall <- tp / (tp + fn)
precision <- tp / (tp + fp)
null_error_rate <- tn / (tp + tn + fp + fn)

tibble(
    accuracy,
    misclassification_rate,
    recall,
    precision,
    null_error_rate
) %>% 
    transpose() 


class(automl_leader)

# Setup lime::model_type() function for h2o
model_type.H2OBinomialModel <- function(x, ...) {
    # Function tells lime() what model type we are dealing with
    # 'classification', 'regression', 'survival', 'clustering', 'multilabel', etc
    #
    # x is our h2o model
    
    return("classification")
}

# Setup lime::predict_model() function for h2o
predict_model.H2OBinomialModel <- function(x, newdata, type, ...) {
    # Function performs prediction and returns dataframe with Response
    #
    # x is h2o model
    # newdata is data frame
    # type is only setup for data frame
    
    pred <- h2o.predict(x, as.h2o(newdata))
    
    # return probs
    return(as.data.frame(pred[,-1]))
    
}

# Test our predict_model() function
predict_model(x = automl_leader, newdata = as.data.frame(test_h2o[,-1]), type = 'raw') %>%
    tibble::as_tibble()

# Run lime() on training set
explainer <- lime::lime(
    as.data.frame(train_h2o[,-1]), 
    model          = automl_leader, 
    bin_continuous = FALSE)

explainer <- lime(train_h2o, automl_leader)

# Run lime() on training set
explainer <- lime::lime(
    as.data.frame(train_h2o[,-1]), 
    model          = automl_leader, 
    bin_continuous = FALSE)

# Run explain() on explainer
explanation <- lime::explain(
    as.data.frame(test_h2o[1:10,-1]), 
    explainer    = explainer, 
    n_labels     = 1, 
    n_features   = 4,
    kernel_width = 0.5)

plot_features(explanation)