我用graphlearner
包开发了一个mlr3
,我想将其发布在Rplumber
服务中。但是,当我收到用于预测的数据(JSON格式的数据)时,graphlearner
难以识别数据,因为fromJSON
的jsonlite
函数无法推断正确的类型(已从中学习图形) 。您对此有解决方案吗?在预测阶段是否有一种机制可以管理mlr3中的JSON数据?
学习步骤
library(mlr3)
imp_missind = po("missind")
imp_fct = po("imputenewlvl", param_vals =list(affect_columns = selector_type("factor")))
imp_num = po("imputehist", param_vals =list(affect_columns = selector_type("numeric")))
learner = lrn('regr.ranger')
graph = po("copy", 2) %>>%
gunion(list(imp_missind, imp_num %>>% imp_fct)) %>>%
po("featureunion") %>>%
po(learner)
t1 = tsk("boston_housing")
g1 = GraphLearner$new(graph)
g1$train(t1)
saveRDS(g1,'my-model')
预测步骤:有效(模拟数据进行预测,删除目标col)
data=t1$data()[1:1,-1]
model = readRDS('my-model')
model$predict_newdata(newdata=data)
预测步骤:它不起作用(将JSON数据模拟为预测)
model = readRDS('my-model')
data = t1$data()[1:1,-1]
json = fromJSON(toJSON(data, na="string"))
model$predict_newdata(newdata=json)
和错误:
Erreur:无法rbind任务:类型与列不匹配:cmedv(数字!=整数)
UPDATE可复制的示例
library(mlr3learners)
library(mlr3)
library(mlr3pipelines)
library(jsonlite)
imp_missind = po("missind")
imp_fct = po("imputenewlvl", param_vals =list(affect_columns = selector_type("factor")))
imp_num = po("imputehist", param_vals =list(affect_columns = selector_type("numeric")))
learner = lrn('regr.ranger')
graph = po("copy", 2) %>>%
gunion(list(imp_missind, imp_num %>>% imp_fct)) %>>%
po("featureunion") %>>%
po(learner)
task = tsk("boston_housing")
graphlearner = GraphLearner$new(graph)
#train model
graphlearner$train(task)
# create data to predict (juste one observation)
data= task$data()
data[1:1, chas := NA]
data = data[1:1,-1]
# look the the types of columns
str(data)
# predictin, this works fine
predict(graphlearner, data)
# simulate the case when json data is received
json_data = toJSON(data, na="string")
print(json_data)
# get R data from json formatted data
data_from_json = fromJSON(json_data)
# look the types of columns, some are different numeric != integer, factor != char
str(data_from_json)
# try to predict, this does not work, get erro : cmedv (numeric != integer)
predict(graphlearner,data_from_json)
我想我们可能会在某个时候解决此问题,但是在有可用的修复程序之前,我建议您通过保存task$feature_types
的模式来修复此问题,以解决此问题。
library(mlr3misc) repair_schema = function(data, feature_types) { imap_dtc(data, function(v, k) { ft_type = feature_types[id == k,][["type"]] if (typeof(v) != ft_type) { fn = switch(ft_type, "character" = as.character, "factor" = as.factor, "numeric" = as.numeric, "integer" = as.integer ) v = fn(v) } return(v) }) } data_from_json2 = repair_schema(data_from_json, task$feature_types) predict(graphlearner,data_from_json2)
此方法还可以为您提供更大的灵活性,因为您可能会遇到各种编码问题,这些问题并非总是可以预期的。