从内存中的 ASCII 而不是从文件连接反序列化对象

实际问题

如何将对象序列化为 ASCII 并再次从 ASCII 反序列化without必须对文件连接进行写入和读取（即从内存中的 ASCII）？

背景

在无状态的客户端-服务器框架中，我想使某些信息在调用中持久化（序列化 >> 发送到客户端 >> 从客户端返回序列化信息 >> 反序列化）而不将其缓存在服务器端。

请注意，我的 JSON 对象/strong 还包含其他未序列化的信息，因此与序列化信息混合，这就是为什么该方法在这个帖子 https://stackoverflow.com/questions/2258511/r-serialize-objects-to-text-file-and-back-again并不能完全解决问题。

现在，问题是我想反序列化仅基于已读取的 JSON 字符串的对象。可以这么说：来自“内存中 ASCII”而不是来自文件连接。我该怎么做呢？

这是我尝试过的：

require(forecast)

方法一

## SERVER: estimates initial model and writes JSON to socket
model <- auto.arima(AirPassengers, trace = TRUE)

## Model trace:
# ARIMA(2,1,2)(1,1,1)[12]                    : Inf
# ARIMA(0,1,0)(0,1,0)[12]                    : 967.6773
# ARIMA(1,1,0)(1,1,0)[12]                    : 965.4487
# ARIMA(0,1,1)(0,1,1)[12]                    : 957.1797
# ARIMA(0,1,1)(1,1,1)[12]                    : 963.5291
# ARIMA(0,1,1)(0,1,0)[12]                    : 956.7848
# ARIMA(1,1,1)(0,1,0)[12]                    : 959.4575
# ARIMA(0,1,2)(0,1,0)[12]                    : 958.8701
# ARIMA(1,1,2)(0,1,0)[12]                    : 961.3943
# ARIMA(0,1,1)(0,1,0)[12]                    : 956.7848
# ARIMA(0,1,1)(1,1,0)[12]                    : 964.7139
# 
# Best model: ARIMA(0,1,1)(0,1,0)[12]  

fc <- as.data.frame(forecast(model))
deparsed <- deparse(model)

json_out <- list(data = AirPassengers, model = deparsed, fc = fc)
json_out <- jsonlite::toJSON(json_out)

## CLIENT: keeps estimated model, updates data, writes to socket
json_in <- jsonlite::fromJSON(json_out)
json_in$data <- window(AirPassengers, end = 1949 + (1/12 * 14))

## SERVER: reads new JSON and applies model to new data
data <- json_in$data
model_0 <- json_in$model
model_1 <- eval(parse(text = model_0))

## Model trace:
# ARIMA(2,1,2)(1,1,1)[12]                    : Inf
# ARIMA(0,1,0)(0,1,0)[12]                    : 967.6773
# ARIMA(1,1,0)(1,1,0)[12]                    : 965.4487
# ARIMA(0,1,1)(0,1,1)[12]                    : 957.1797
# ARIMA(0,1,1)(1,1,1)[12]                    : 963.5291
# ARIMA(0,1,1)(0,1,0)[12]                    : 956.7848
# ARIMA(1,1,1)(0,1,0)[12]                    : 959.4575
# ARIMA(0,1,2)(0,1,0)[12]                    : 958.8701
# ARIMA(1,1,2)(0,1,0)[12]                    : 961.3943
# ARIMA(0,1,1)(0,1,0)[12]                    : 956.7848
# ARIMA(0,1,1)(1,1,0)[12]                    : 964.7139
# 
# Best model: ARIMA(0,1,1)(0,1,0)[12]  

# Warning message:
#   In auto.arima(x = structure(list(x = structure(c(112, 118, 132,  :
#       Unable to fit final model using maximum likelihood. AIC value approximated

fc <- as.data.frame(forecast(Arima(data, model = model_1)))

## And so on ...

这可行，但请注意eval(parse(text = json_in$model))实际上re-runs打电话给auto.arima()而不是仅仅重新建立/反序列化对象（请注意打印到控制台的跟踪信息，我将其作为注释包含在内）。

这并不完全是我想要的，因为只是想以最快的方式重新建立最终的模型对象。

这就是为什么我转向serialize() next.

方法2

## SERVER: estimates initial model and writes JSON to socket
model <- auto.arima(AirPassengers, trace = TRUE)
fc <- as.data.frame(forecast(model))
serialized <- serialize(model, NULL)
class(serialized)

json_out <- list(data = AirPassengers, model = serialized, fc = fc)
json_out <- jsonlite::toJSON(json_out)

## CLIENT: keeps estimated model, updates data, writes to socket
json_in <- jsonlite::fromJSON(json_out)
json_in$data <- window(AirPassengers, end = 1949 + (1/12 * 14))

## SERVER: reads new JSON and applies model to new data
data <- json_in$data
model_0 <- json_in$model
try(model_1 <- unserialize(model_0))
## --> error:
# Error in unserialize(model_0) : 
#   character vectors are no longer accepted by unserialize()

不幸的是，功能unserialize()需要文件连接而不是“纯 ASCII”。

这就是为什么我需要执行以下解决方法。

方法3

## SERVER: estimates initial model and writes JSON to socket
model <- auto.arima(AirPassengers, trace = TRUE)
fc <- as.data.frame(forecast(model))
con <- file("serialized", "w+")
serialize(model, con)
close(con)

json_out <- list(data = AirPassengers, model = "serialized", fc = fc)
json_out <- jsonlite::toJSON(json_out)

## CLIENT: keeps estimated model, updates data, writes to socket
json_in <- jsonlite::fromJSON(json_out)
json_in$data <- window(AirPassengers, end = 1949 + (1/12 * 14))

## SERVER: reads new JSON and applies model to new data
data <- json_in$data
model_0 <- json_in$model
con <- file(model_0, "r+")
model_1 <- unserialize(con)
close(con)
fc <- as.data.frame(forecast(Arima(data, model = model_1)))

## And so on ...

现在反序列化工作无需实际auto.arima()正在重新评估呼叫。但这违背了我的无状态范例，因为现在实际信息缓存在服务器端，而不是通过 JSON 对象/字符串实际发送。

这符合您的需求吗？

它遵循您的总体策略方法2。唯一的区别是它使用as.character()在将序列化对象传递给之前将其转换为字符向量toJSON()，然后使用as.raw(as.hexmode())将其转换回“另一侧”的原始向量。 （我已经用注释标记了两行已编辑的行## <<- Edited.)

library(forecast)

## SERVER: estimates initial model and writes JSON to socket
model <- auto.arima(AirPassengers, trace = TRUE)
fc <- as.data.frame(forecast(model))
serialized <- as.character(serialize(model, NULL)) ## <<- Edited
class(serialized)


json_out <- list(data = AirPassengers, model = serialized, fc = fc)
json_out <- jsonlite::toJSON(json_out)

## CLIENT: keeps estimated model, updates data, writes to socket
json_in <- jsonlite::fromJSON(json_out)
json_in$data <- window(AirPassengers, end = 1949 + (1/12 * 14))

## SERVER: reads new JSON and applies model to new data
data <- json_in$data
model_0 <- as.raw(as.hexmode(json_in$model))       ## <<- Edited

unserialize(model_0)
## Series: AirPassengers 
## ARIMA(0,1,1)(0,1,0)[12]                    
## 
## Coefficients:
##           ma1
##       -0.3184
## s.e.   0.0877
## 
## sigma^2 estimated as 137.3:  log likelihood=-508.32
## AIC=1020.64   AICc=1020.73   BIC=1026.39