Question

我有以下问题。
第一步，假设我有一个包含3个事实（a，b，c）的数据框。

library(dplyr)
library(tidyr)
library(CVXR)
library(RcppRoll)
library(purrr)

set.seed(1234)

df = tibble(
  a = c(4.88,5.03,5.11,4.77,5.04,5.05,4.94,4.95,4.94,4.91)
  ,b = c(652,600,622,706,796,689,649,609,616,942)
  ,c = c(101,95,96,105,93,86,106,90,100,91)
)

然后，我对b列和c列进行一些转换（例如，它是滚动总和，但在这里我想做更复杂的事情），并计算目标列（y）。

df = df %>% 
  mutate(b_roll_sum = roll_sum(b, n=3, fill=NA, align="right", na.rm = TRUE),
         c_roll_sum = roll_sum(c, n=3, fill=NA, align="right", na.rm = TRUE)) %>% 
  mutate(y = (-1)*a+0.0002*b_roll_sum+0.0007*c_roll_sum+1)

# A tibble: 10 x 6
       a     b     c b_roll_sum c_roll_sum     y
   <dbl> <dbl> <dbl>      <dbl>      <dbl> <dbl>
 1  4.88   652   101         NA         NA NA   
 2  5.03   600    95         NA         NA NA   
 3  5.11   622    96       1874        292 -3.53
 4  4.77   706   105       1928        296 -3.18
 5  5.04   796    93       2124        294 -3.41
 6  5.05   689    86       2191        284 -3.41
 7  4.94   649   106       2134        285 -3.31
 8  4.95   609    90       1947        282 -3.36
 9  4.94   616   100       1874        296 -3.36
10  4.91   942    91       2167        281 -3.28

现在的目标是在b和c列之间重新定位数字：

像开始时一样保持b和c中的分布（如果给定的总和大于0）
保持b和c列的总和不变（7844）
b和c都应> = 0

最大化y 。

我试图为此使用CVXR包，在这里我将目标定义为数据帧和对象Variable（）的自定义函数。该代码似乎可以运行，但是结果是错误的，因为解决方案应该是将所有内容“重定位”到列c。输出却是另一种方式。

# calculate distribution in rows to keep them like before
dist_by_rows <- df %>% map2_dfr(.x = df %>% select(b, c)
                                ,.y = df %>% select(b, c) %>% summarise_all(sum)
                                ,.f = ~(.x/.y))
names(dist_by_rows) <- paste0(names(dist_by_rows), "_rows_dist")
df <- bind_cols(df, dist_by_rows)


# A tibble: 10 x 8
       a     b     c b_roll_sum c_roll_sum     y b_rows_dist c_rows_dist
   <dbl> <dbl> <dbl>      <dbl>      <dbl> <dbl>       <dbl>       <dbl>
 1  4.88   652   101         NA         NA NA          0.116       0.132
 2  5.03   600    95         NA         NA NA          0.107       0.124
 3  5.11   622    96       1874        292 -3.53       0.110       0.125
 4  4.77   706   105       1928        296 -3.18       0.125       0.137
 5  5.04   796    93       2124        294 -3.41       0.141       0.121
 6  5.05   689    86       2191        284 -3.41       0.122       0.112
 7  4.94   649   106       2134        285 -3.31       0.115       0.138
 8  4.95   609    90       1947        282 -3.36       0.108       0.117
 9  4.94   616   100       1874        296 -3.36       0.109       0.130
10  4.91   942    91       2167        281 -3.28       0.167       0.119


# define function to optimize
funk <- function(df, vars_to_opt) {

df_new <- df %>% 
  mutate(
    new_b = value(vars_to_opt)[1],
    new_c = value(vars_to_opt)[2],
    b = new_b*b_rows_dist,
    c = new_c*c_rows_dist) %>% 
  mutate(b_roll_sum = roll_sum(b, n=3, fill=NA, align="right", na.rm = TRUE),
         c_roll_sum = roll_sum(c, n=3, fill=NA, align="right", na.rm = TRUE)) %>% 
  mutate(y = (-1)*a+0.0002*b_roll_sum+0.0007*c_roll_sum+1)

df_new %>%
  select(y) %>%
  sum(., na.rm = T)

}

# test of function on "current status"
test <- Variable(2)
value(test) <- matrix(c(6881, 963), nrow = 2) #currently sum of b and c is 6881 and 963, respectively

> funk(df, vars_to_opt = test)
[1] -26.8452

> df %>% select(y) %>% sum(na.rm = T)
[1] -26.8452


# CVXR with constraints
mix_hat <- Variable(2)
objective <- Maximize(funk(df, vars_to_opt = mix_hat))

A <- matrix(rep(1, 2), nrow = 1) 
B <- diag(1, nrow = 2)

constraint1 <- A %*% mix_hat == 7844 #sum of b and c keep like it was 7844
constraint2 <- B %*% mix_hat >= 0 #b & c non negative


problem <- Problem(objective, constraints = list(constraint1, constraint2))
result <- solve(problem, b = "GLPK")

> result$getValue(mix_hat)
     [,1]
[1,] 7844
[2,]    0
> result$value
[1] -31.71

使用CVXR包可以优化在数据帧上工作的功能吗？

0 个答案: