用FeatureImp$new和H2O提取变量重要性时出错。

Question

我正试图使用 iml 包裹 R起初，我以为这个错误是由于我的实施，但发现不是这样的，当我重现同样的例子，它已经工作正常。此处.这里是代码，相当简单、直接、可重复。

library(rsample)   # data splitting
library(ggplot2)   # allows extension of visualizations
library(dplyr)     # basic data transformation
library(h2o)       # machine learning modeling
library(iml)       # ML interprtation

# initialize h2o session
h2o.no_progress()
h2o.init()

# classification data
df <- rsample::attrition %>% 
  mutate_if(is.ordered, factor, ordered = FALSE) %>%
  mutate(Attrition = recode(Attrition, "Yes" = "1", "No" = "0") %>% factor(levels = c("1", "0")))

# convert to h2o object
df.h2o <- as.h2o(df)

# create train, validation, and test splits
set.seed(123)
splits <- h2o.splitFrame(df.h2o, ratios = c(.7, .15), destination_frames = 
    c("train","valid","test"))
names(splits) <- c("train","valid","test")

# variable names for resonse & features
y <- "Attrition"
x <- setdiff(names(df), y) 

# elastic net model 
glm <- h2o.glm(
  x = x, 
  y = y, 
  training_frame = splits$train,
  validation_frame = splits$valid,
  family = "binomial",
  seed = 123
  )

# 1. create a data frame with just the features
features <- as.data.frame(splits$valid) %>% select(-Attrition)

# 2. Create a vector with the actual responses
response <- as.numeric(as.vector(splits$valid$Attrition))

# 3. Create custom predict function that returns the predicted values as a
#    vector (probability of purchasing in our example)
pred <- function(model, newdata)  {
  results <- as.data.frame(h2o.predict(model, as.h2o(newdata)))
  return(results[[3L]])
}

# create predictor object to pass to explainer functions
predictor.glm <- Predictor$new(
  model = glm, 
  data = features, 
  y = response, 
  predict.fun = pred,
  class = "classification"
  )

imp.glm <- FeatureImp$new(predictor.glm, loss = "mse")

这是我得到的错误。

Error in `[.data.frame`(prediction, , self$class, drop = FALSE): undefined columns 
selected
Traceback:

1. FeatureImp$new(predictor.glm, loss = "mse")

2. .subset2(public_bind_env, "initialize")(...)

3. private$run.prediction(private$sampler$X)

4. self$predictor$predict(data.frame(dataDesign))

5. prediction[, self$class, drop = FALSE]

6. `[.data.frame`(prediction, , self$class, drop = FALSE)

7. stop("undefined columns selected")

我该如何解决这个问题？

Answer 1

在H2O中，你可以通过使用 varimp() 方法。您可以使用 predictor.varimp().