我使用 R 编程语言。
我正在尝试按照 R(https://cran.r-project.org/web/packages/optimization/optimization.pdf)中的“优化”包中的说明进行操作,并在特定功能上使用此包中的功能。
对于这个例子,我首先生成一些随机数据:
#load libraries
library(dplyr)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
从这里,我定义了我想要优化的函数 (“适应度”)。这个函数需要 7 个输入,并计算一个“总”均值 (单个标量值)。这个函数所需的输入是:
“random_1”(介于 80 和 120 之间)
“random_2”(介于“random_1”和 120 之间)
“random_3”(介于 85 和 120 之间)
“random_4”(介于 random_2 和 120 之间)
“split_1”(介于 0 和 1 之间)
“split_2”(介于 0 和 1 之间)
“split_3”(介于 0 和 1 之间)
要优化的函数定义如下:
library(optimization)
fitness <- function(x) {
#bin data according to random criteria
train_data <- train_data %>%
mutate(cat = ifelse(a1 <= x[1] & b1 <= x[3], "a",
ifelse(a1 <= x[2] & b1 <= x[4], "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[5])))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[6])))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[7])))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
}
从这里,我试图运行以下优化功能:
Output <- optim_nm(fitness, k = 7, trace = TRUE)
plot(output)
plot(Output, 'contour')
但这些返回以下错误:
Error: Problem with `mutate()` column `quant`.
i `quant = quantile(c1, prob = x[6])`.
x 'probs' outside [0,1]
Run `rlang::last_error()` to see where the error occurred.
Error in plot(Output) : object 'Output' not found
我认为错误是“split_1”,“split_2”和“split_3”变量被分配 0 和 1 之外的值:由于函数使用这些变量用于计算百分位数(例如quant = quantile(c1, prob = x[5]
),这自然会导致错误?
我试图使用这个包中的另一个优化算法,其中明确定义了这 7 个输入的范围,但这也产生了相同的错误:
ro_sa <- optim_sa(fun = fitness,
start = c(runif(7, min = -1, max = 1)),
lower = c(80,80,80,80,0,0,0),
upper = c(120,120,120,120,1,1,1),
trace = TRUE,
control = list(t0 = 100,
nlimit = 550,
t_min = 0.1,
dyn_rf = FALSE,
rf = 1,
r = 0.7
)
)
Error: Problem with `mutate()` column `quant`.
i `quant = quantile(c1, prob = x[6])`.
x 'probs' outside [0,1]
如果您提供初始起点,这也不起作用:
optim_nm(fitness, start = c(80,80,80,80,0.5,0.6,0.7))
Error: Problem with `mutate()` column `quant`.
i `quant = quantile(c1, prob = x[5])`.
x 'probs' outside [0,1]
i The error occurred in group 1: cat = a.
问题:有人可以告诉我如何解决这个问题,以便我可以运行优化功能?
#desired functions to run:
Output <- optim_nm(fitness, k = 7, trace = TRUE)
plot(output)
plot(Output, 'contour')
ro_sa <- optim_sa(fun = fitness,
start = c(runif(7, min = -1, max = 1)),
lower = c(80,80,80,80,0,0,0),
upper = c(120,120,120,120,1,1,1),
trace = TRUE,
control = list(t0 = 100,
nlimit = 550,
t_min = 0.1,
dyn_rf = FALSE,
rf = 1,
r = 0.7
)
)
optim_nm(fitness, start = c(80,80,80,80,0.5,0.6,0.7))
谢谢
生成的x
值是随机的,它们可以是正的或负的。quantile
的probs
参数需要具有 0 到 1 之间的值。一种方法是采用x[5:7]
的绝对值,然后使用prop.table
将它们转换为比率。
x[5:7] <- prop.table(abs(x[5:7]))
完整功能-
library(optimization)
fitness <- function(x) {
#bin data according to random criteria
train_data <- train_data %>%
mutate(cat = ifelse(a1 <= x[1] & b1 <= x[3], "a",
ifelse(a1 <= x[2] & b1 <= x[4], "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
x[5:7] <- prop.table(abs(x[5:7]))
#calculate quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[5])))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[6])))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = x[7])))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
mean = mean(final_table$diff)
}
您可以应用和绘制此函数-
Output <- optim_nm(fitness, k = 7, trace = TRUE)
plot(Output)
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