问题描述
我正在使用 R 编程语言 - 我正在尝试遵循此 stackoverflow 帖子中的答案(
在上图中，我确定了一些违反限制中指定的逻辑条件的行.
有谁知道为什么会这样?我是否错误地指定了限制?有人可以告诉我如何解决这个问题吗?
谢谢
更新:
我想我能够解决这个问题——现在逻辑条件"没有了.在最终输出中受到尊重:
#load 库图书馆(dplyr)图书馆(mco)#定义函数funct_set <- 函数 (x) {x1 %选择(a1，b1，c1，猫)b_table = train_data %>%过滤器(猫==b")%>%选择(a1，b1，c1，猫)c_table = train_data %>%过滤器(猫 == c")%>%选择(a1，b1，c1，猫)#计算每个 bin 的分位数(quant")table_a = data.frame(a_table%>% group_by(cat)%>%变异(量化 = ifelse(c1 > x[5],1,0)))table_b = data.frame(b_table%>% group_by(cat)%>%变异(量化 = ifelse(c1 > x[6],1,0)))table_c = data.frame(c_table%>% group_by(cat)%>%变异(量化 = ifelse(c1 > x[7],1,0)))f[1] = 平均值(table_a$quant)f[2] = 平均值(table_b$quant)f[3] = 平均值(table_c$quant)#分组所有表final_table = rbind(table_a, table_b, table_c)# 计算总平均值:这是需要优化的f[4] = 平均值(final_table$quant)返回 (f)；}gn <- 函数(x) {g1 现在，如果我们看一下输出:
#查看输出优化现在遵守所有逻辑条件(即约束")！
注意:如果可能，我仍然有兴趣看到解决此问题的替代方法
谢谢大家！
I am using the R programming language - I am trying to follow the answer from this stackoverflow post (Argument passing in R to functions of several real variables) that shows how to perform "multi objective constrained optimization".
I created some data for this example:
#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)
I then defined a function ("funct_set") with "4 objectives" (f[1], f[2], f[3], f[4]) which are to be minimized for a set of "seven inputs" ([x1], [x2], [x3], x[4], x[5], x[6], x[7]):
#load libraries
library(dplyr)
library(mco)
#define function
funct_set <- function (x) {
x1 <- x[1]; x2 <- x[2]; x3 <- x[3] ; x4 <- x[4]; x5 <- x[5]; x6 <- x[6]; x[7] <- x[7]
f <- numeric(4)
#bin data according to random criteria
train_data <- train_data %>%
mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
ifelse(a1 <= x2 & b1 <= x4, "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 = ifelse(c1 > x[5],1,0 )))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x[6],1,0 )))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x[7],1,0 )))
f[1] = -mean(table_a$quant)
f[2] = -mean(table_b$quant)
f[3] = -mean(table_c$quant)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
f[4] = -mean(final_table$quant)
return (f);
}
Next, I define a series of 4 "restrictions" (i.e. logical conditions/constrains) used in the optimization:
#define restrictions
restrictions <- function (x) {
x1 <- x[1]; x2 <- x[2]; x3 <- x[3]; x4 <- x[4]; x5<- x[5] ; x6 <- x[6]; x7 <- x[7]
restrictions <- logical(4)
restrictions[1] <- (x3 - x1 >= 0)
restrictions[2] <- (x4 - x2 >= 0)
restrictions[3] <- (x7 - x6 >= 0)
restrictions[4] <- (x6 - x5 >= 0)
return (restrictions);
}
Finally, I run the optimization algorithm that attempts to simultaneously minimize all 4 objectives with respect to the restrictions:
#run optimization
optimization <- nsga2(funct_set, idim = 7, odim = 4 , constraints = restrictions, cdim = 4,
generations=150,
popsize=100,
cprob=0.7,
cdist=20,
mprob=0.2,
mdist=20,
lower.bounds=rep(80,80,80,80, 100,200,300),
upper.bounds=rep(120,120,120,120,200,300,400)
)
The above code works fine.
Problem : I noticed that in the output of this code, the optimization algorithm is not respecting the restrictions. For example:
In the above picture, I have identified some rows where the logical conditions specified in the restrictions are violated.
Does anyone know why this is happening? Have I incorrectly specified the restrictions? Can someone please show me how to fix this?
Thanks
UPDATE:
I think I was able to resolve this problem - now the "logical conditions" are respected in the final output:
#load libraries
library(dplyr)
library(mco)
#define function
funct_set <- function (x) {
x1 <- x[1]; x2 <- x[2]; x3 <- x[3] ; x4 <- x[4]; x5 <- x[5]; x6 <- x[6]; x[7] <- x[7]
f <- numeric(4)
#bin data according to random criteria
train_data <- train_data %>%
mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
ifelse(a1 <= x2 & b1 <= x4, "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 = ifelse(c1 > x[5],1,0 )))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x[6],1,0 )))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x[7],1,0 )))
f[1] = mean(table_a$quant)
f[2] = mean(table_b$quant)
f[3] = mean(table_c$quant)
#group all tables
final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
f[4] = mean(final_table$quant)
return (f);
}
gn <- function(x) {
g1 <- x[3] - x[1]
g2<- x[4] - x[2]
g3 <- x[7] - x[6]
g4 <- x[6] - x[5]
return(c(g1,g2,g3,g4))
}
optimization <- nsga2(funct_set, idim = 7, odim = 4 , constraints = gn, cdim = 4,
generations=150,
popsize=100,
cprob=0.7,
cdist=20,
mprob=0.2,
mdist=20,
lower.bounds=rep(80,80,80,80, 100,200,300),
upper.bounds=rep(120,120,120,120,200,300,400)
)
Now, if we take a look at the output:
#view output
optimization
All the logical conditions (i.e. the "constraints") are now respected!
Note: if possible, I would still be interested in seeing alternate ways to solve this problem
Thanks everyone!
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