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我正在创建自定义学习器，特别是我正在尝试在 mlr 框架中使用h2o机器学习算法。h2o.deeplearning 函数的“隐藏”参数是我要调整的整数向量。我通过以下方式定义了“隐藏”参数：

makeRLearner.classif.h2o_dl = function() {
makeRLearnerClassif(
cl = "classif.h2o_dl",
package = "h2o",
par.set = makeParamSet(
  makeDiscreteLearnerParam(id = "activation",
    values = c("Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "Maxout", "MaxoutWithDropout")),
  makeNumericLearnerParam(id = "epochs", default = 10, lower = 1),
  makeNumericLearnerParam(id = "rate", default = 0.005, lower = 0, upper = 1),
  makeIntegerVectorLearnerParam(id = "hidden", default = c(100,100)),
  makeDiscreteLearnerParam(id = "loss", values = c("Automatic",
            "CrossEntropy", "Quadratic", "Absolute", "Huber"))
  ),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob","missings"),
name = "Deep Learning Neural Network with h2o",
short.name = "h2o_deeplearning_classif",
note = "tbd"
)
}

trainLearner.classif.h2o_dl = function(.learner, .task,.subset,.weights=NULL, ...) {
f = getTaskFormula(.task)
data = getTaskData(.task, .subset)
data_h2o <- as.h2o(data,
                 destination_frame = paste0(
                   "train_",
                   format(Sys.time(), "%m%d%y_%H%M%S")))
h2o::h2o.deeplearning(x = getTaskFeatureNames(.task),
           y = setdiff(names(getTaskData(.task)),
                       getTaskFeatureNames(.task)),
           training_frame = data_h2o, ...)
}

predictLearner.classif.h2o_dl = function(.learner, .model, .newdata, predict.method = "plug-in", ...) {
data <- as.h2o(.newdata,
             destination_frame = paste0("pred_",
                                        format(Sys.time(), "%m%d%y_%H%M%S")))
p = predict(.model$learner.model, newdata = data, method = predict.method, ...)
if (.learner$predict.type == "response") 
return(as.data.frame(p)[,1]) else return(as.matrix(as.numeric(p))[,-1])
}

我尝试使用以下函数通过网格搜索调整参数“隐藏” makeDiscreteParam：

library(mlr)
library(h2o)
h2o.init()

lrn.h2o <- makeLearner("classif.h2o_dl")
n <- getTaskSize(sonar.task)
train.set = seq(1, n, by = 2)
test.set = seq(2, n, by = 2)
mod.h2o = train(lrn.h2o, sonar.task, subset = train.set)
pred.h2o <- predict(mod.h2o,task= sonar.task, subset = train.set)

ctrl = makeTuneControlGrid()
rdesc = makeResampleDesc("CV", iters = 3L)
ps = makeParamSet(
makeDiscreteParam("hidden", values = list(c(10,10),c(100,100))),
makeDiscreteParam("rate", values = c(0.1,0.5))
)

res = tuneParams("classif.h2o_dl", task = sonar.task, resampling = rdesc,par.set = ps,control = ctrl)

这导致了警告消息

Warning messages:
1: In checkValuesForDiscreteParam(id, values) :
 number of items to replace is not a multiple of replacement length
2: In checkValuesForDiscreteParam(id, values) :
 number of items to replace is not a multiple of replacement length

ps看起来像这样：

ps
           Type len Def  Constr Req Tunable Trafo
hidden discrete   -   -  10,100   -    TRUE     -
rate   discrete   -   - 0.1,0.5   -    TRUE     -

这不会导致将隐藏参数调整为向量。我还尝试了其他也不起作用的特殊构造函数（例如makeNumericVectorParam）。有没有人在 mlr 中调整（整数）向量的经验并可以给我一个提示？

4

2 回答 2

2

要调整“隐藏”参数，请使用网格中的这段代码：

makeDiscreteParam(id = "hidden", values = list(a = c(10,10), b = c(100,100)))

看一下这个：

https://github.com/mlr-org/mlr/issues/1305

于 2017-03-15T19:29:59.103 回答
1

警告消息和未能构造正确的 ParamSet 的原因是 ParamHelpers 尝试将名称添加到值列表中，当值是向量时会失败。perevales answer解决了这个问题，这就是它起作用的原因。

但是，当您想要调整整数值向量时，最好使用makeIntegerVectorParam：

ps <- makeParamSet(
  makeIntegerVectorParam("hidden", len = 2, lower = 10, upper = 100),
  makeDiscreteParam("rate", values = c(0.1, 0.5))
)

这不仅会尝试c(10, 10)和c(100, 100)，还会尝试像c(10, 100).

事实上，这也考虑了 10 到 100 之间的所有值（例如c(30, 80)），因此可能需要使用转换稍微减少搜索空间。例子：

ps <- makeParamSet(
  makeIntegerVectorParam("hidden", len = 2, lower = 2, upper = 4,
    trafo = function(x) round(10 ^ (x / 2))),
  makeDiscreteParam("rate", values = c(0.1, 0.5))
)

对于隐藏层，它以任意组合使用值 10 (=10^1)、32 (=10^1.5) 和 100 (=10^2)。

于 2018-10-04T08:59:12.917 回答