While working on a large client project using `Sparklyr`

and multinomial regression we recently ran into a problem: `Apache Spark`

chooses the order of multinomial regression outcome targets, whereas `R`

users are used to choosing the order of the targets (please see here for some details). So to make things more like `R`

users expect, we need a way to translate one order to another.

Providing good solutions to gaps like this is one of the thing Win-Vector LLC does both in our consulting and training practices.

Let’s take a look at an example. Suppose our two orderings are `o1`

(the ordering `Spark ML`

chooses) and `o2`

(the order the `R`

user chooses).

```
set.seed(326346)
symbols <- letters[1:7]
o1 <- sample(symbols, length(symbols), replace = FALSE)
o1
```

`## [1] "e" "a" "b" "f" "d" "c" "g"`

```
o2 <- sample(symbols, length(symbols), replace = FALSE)
o2
```

`## [1] "d" "g" "f" "e" "b" "c" "a"`

To translate `Spark`

results into `R`

results we need a permutation that takes `o1`

to `o2`

. The idea is: if we had a permeation that takes `o1`

to `o2`

we could use it to re-map predictions that are in `o1`

order to be predictions in `o2`

order.

To solve this we crack open our article on the algebra of permutations.

We are going to use the fact that the `R`

command `base::order(x)`

builds a permutation `p`

such that `x[p]`

is in order.

Given this the solution is: we find permutations `p1`

and `p2`

such that `o1[p1]`

is ordered and `o2[p2]`

is ordered. Then build a permutation `perm`

such that `o1[perm] = (o1[p1])[inverse_permutation(p2)]`

. I.e., to get from `o1`

to `o2`

move `o1`

to sorted order and then move from the sorted order to `o2`

‘s order (by using the reverse of the process that sorts `o2`

). Again, the tools to solve this are in our article on the relation between permutations and indexing.

Below is the complete solution (including combining the two steps into a single permutation):

```
p1 <- order(o1)
p2 <- order(o2)
# invert p2
# see: http://www.win-vector.com/blog/2017/05/on-indexing-operators-and-composition/
p2inv <- seq_len(length(p2))
p2inv[p2] <- seq_len(length(p2))
(o1[p1])[p2inv]
```

`## [1] "d" "g" "f" "e" "b" "c" "a"`

```
# composition rule: (o1[p1])[p2inv] == o1[p1[p2inv]]
# see: http://www.win-vector.com/blog/2017/05/on-indexing-operators-and-composition/
perm <- p1[p2inv]
o1[perm]
```

`## [1] "d" "g" "f" "e" "b" "c" "a"`

The equivilence "`(o1[p1])[p2inv] == o1[p1[p2inv]]`

" is frankly magic (though also quickly follows "by definition"), and studying it is the topic of our original article on permutations.

The above application is a good example of why it is nice to have a little theory worked out, even before you think you need it.

Categories: data science Pragmatic Data Science Pragmatic Machine Learning Programming Statistics Tutorials

### jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.