我想在R中使用h2o(深度学习)创建的模型上实现LIME。为了使用模型中的数据,我创建了h2oFrames并将其转换回数据帧,然后在LIME中使用它(lime函数) ,因为LIME的解释函数无法识别h2oFrame)。在这里,我可以运行函数
下一步是对测试数据使用explain函数来生成解释。这里R抛出了使用数据帧和h2oFrame的错误。
这是使用数据帧时产生的错误:
Error in chk.H2OFrame(x) : must be an H2OFrame
这是使用h2oframe时产生的错误:
Error in UseMethod("permute_cases") : no applicable method for 'permute_cases' applied to an object of class "H2OFrame"
if(!require(pacman)) install.packages("pacman")
pacman::p_load(h2o, lime, data.table, e1071)
data(iris)
h2o.init( nthreads = -1 )
h2o.no_progress()
# Split up the data set
iris <- as.h2o(iris)
split <- h2o.splitFrame( iris, c(0.6, 0.2), seed = 1234 )
iris_train <- h2o.assign( split[[1]], "train" ) # 60%
iris_valid <- h2o.assign( split[[2]], "valid" ) # 20%
iris_test <- h2o.assign( split[[3]], "test" ) # 20%
output <- 'Species'
input <- setdiff(names(iris),output)
model_dl_1 <- h2o.deeplearning(
model_id = "dl_1",
training_frame = iris_train,
validation_frame = iris_valid,
x = input,
y = output,
hidden = c(32, 32, 32),
epochs = 10, # hopefully converges earlier...
score_validation_samples = 10000,
stopping_rounds = 5,
stopping_tolerance = 0.01
)
pred1 <- h2o.predict(model_dl_1, iris_test)
list(dimension = dim(pred1), pred1$predict)
#convert to df from h2ofdataframe
train_org<-as.data.frame(iris_train)
#converting train h2oframe to dataframe
sapply(train_org,class) #checking the class of train_org
test_df <- as.data.frame(iris_test)
#converting test data h2oFrame to dataframe
test_sample <- test_df[1:1,]
#works
#lime is used to get explain on the train data
explain <- lime(train_org, model_dl_1, bin_continuous = FALSE, n_bins =
5, n_permutations = 1000)
# Explain new observation
explanation <- explain(test_sample, n_labels = 1, n_features = 1)
h2o.shutdown(prompt=F)
任何人都可以帮我找到解决方案或使用LIME的解释功能与相应的dataFrame
的方法答案 0 :(得分:7)
引擎盖下的lime
软件包使用两个函数predict_model()
和model_type()
,您需要为当前不支持的任何模型设置这些函数。
对于您的具体示例,这是您需要做的事情。
第1步:为类model_type
的模型设置通用H2OMultinomialModel
函数。你在这里所做的只是告诉你lime
你希望它执行的模型类型,例如“分类”或“回归”。
model_type.H2OMultinomialModel <- 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")
}
第2步:为类predict_model
的模型设置通用H2OMultinomialModel
函数。这里的关键是理解为了使石灰工作,它需要分类概率而不是预测(这需要我花一点时间来弄清楚,它必须处理lime:::output_type(explaination)
变量。)
predict_model.H2OMultinomialModel <- 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 classification probabilities only
return(as.data.frame(pred[,-1]))
}
正确设置这些功能后,您可以运行lime
脚本。
# Lime is used to get explain on the train data
explainer <- lime(train_org, model_dl_1, bin_continuous = FALSE, n_bins = 5, n_permutations = 1000)
# Explain new observation
explanation <- explain(test_sample, explainer, n_labels = 1, n_features = 1)