# Multi Class vtreat

#### John Mount

#### 2020-03-10

`vtreat` can now effectively prepare data for multi-class classification or multinomial modeling.

The two functions needed (`mkCrossFrameMExperiment()` and the `S3` method `prepare.multinomial_plan()`) are now part of `vtreat`.

Let’s work a specific example: trying to model multi-class `y` as a function of `x1` and `x2`.

    library("vtreat")

    ## Loading required package: wrapr

    # create example data
    set.seed(326346)
    sym_bonuses <- rnorm(3)
    names(sym_bonuses) <- c("a", "b", "c")
    sym_bonuses3 <- rnorm(3)
    names(sym_bonuses3) <- as.character(seq_len(length(sym_bonuses3)))
    n_row <- 1000
    d <- data.frame(
      x1 = rnorm(n_row),
      x2 = sample(names(sym_bonuses), n_row, replace = TRUE),
      x3 = sample(names(sym_bonuses3), n_row, replace = TRUE),
      y = "NoInfo",
      stringsAsFactors = FALSE)
    d$y[sym_bonuses[d$x2] >
          pmax(d$x1, sym_bonuses3[d$x3], runif(n_row))] <- "Large1"
    d$y[sym_bonuses3[d$x3] >
          pmax(sym_bonuses[d$x2], d$x1, runif(n_row))] <- "Large2"

    knitr::kable(head(d))

x1 | x2 | x3 | y
--- | --- | --- | ---
0.8178292 | a | 2 | NoInfo
0.5867139 | c | 1 | NoInfo
-0.6711920 | a | 3 | Large2
0.1033166 | a | 2 | Large1
-0.3182176 | c | 3 | Large2
-0.5914308 | c | 2 | NoInfo

We define the problem controls and use `mkCrossFrameMExperiment()` to build both a cross-frame and a treatment plan.

    # define problem
    vars <- c("x1", "x2", "x3")
    y_name <- "y"

    # build the multi-class cross frame and treatments
    cfe_m <- mkCrossFrameMExperiment(d, vars, y_name)

The cross-frame is the entity safest for training on (unless you have made separate data split for the treatment design step). It uses cross-validation to reduce nested model bias. Some notes on this issue are available here, and here.

    # look at the data we would train models on
    str(cfe_m$cross_frame)

    ## 'data.frame':    1000 obs. of  16 variables:
    ##  $ x1            : num  0.818 0.587 -0.671 0.103 -0.318 ...
    ##  $ x2_catP       : num  0.333 0.334 0.333 0.333 0.334 0.334 0.333 0.333 0.333 0.333 ...
    ##  $ x3_catP       : num  0.35 0.321 0.329 0.35 0.329 0.35 0.321 0.321 0.321 0.35 ...
    ##  $ x2_lev_x_a    : num  1 0 1 1 0 0 0 0 1 1 ...
    ##  $ x2_lev_x_b    : num  0 0 0 0 0 0 1 1 0 0 ...
    ##  $ x2_lev_x_c    : num  0 1 0 0 1 1 0 0 0 0 ...
    ##  $ x3_lev_x_1    : num  0 1 0 0 0 0 1 1 1 0 ...
    ##  $ x3_lev_x_2    : num  1 0 0 1 0 1 0 0 0 1 ...
    ##  $ x3_lev_x_3    : num  0 0 1 0 1 0 0 0 0 0 ...
    ##  $ Large1_x2_catB: num  1.23 -10.72 1.15 1.16 -10.53 ...
    ##  $ Large1_x3_catB: num  0.7025 0.0903 -10.4833 0.6238 -10.529 ...
    ##  $ Large2_x2_catB: num  0.17979 0.19661 -0.00379 -0.09818 0.00627 ...
    ##  $ Large2_x3_catB: num  -13.12 -13.05 4.49 -4.03 4.71 ...
    ##  $ NoInfo_x2_catB: num  -0.48752 -0.00254 -0.27947 -0.26155 0.15195 ...
    ##  $ NoInfo_x3_catB: num  2.05 2.43 -4.34 1.79 -4.55 ...
    ##  $ y             : chr  "NoInfo" "NoInfo" "Large2" "Large1" ...

`prepare()` can apply the designed treatments to new data. Here we are simulating new data by re-using our design data.

    # pretend original data is new data to be treated
    # NA out top row to show processing
    for(vi in vars) {
      d[[vi]][[1]] <- NA
    }
    str(prepare(cfe_m$treat_m, d))

    ## 'data.frame':    1000 obs. of  16 variables:
    ##  $ x1            : num  0.0205 0.5867 -0.6712 0.1033 -0.3182 ...
    ##  $ x2_catP       : num  0.0005 0.334 0.333 0.333 0.334 0.334 0.333 0.333 0.333 0.333 ...
    ##  $ x3_catP       : num  0.0005 0.321 0.329 0.35 0.329 0.35 0.321 0.321 0.321 0.35 ...
    ##  $ x2_lev_x_a    : num  0 0 1 1 0 0 0 0 1 1 ...
    ##  $ x2_lev_x_b    : num  0 0 0 0 0 0 1 1 0 0 ...
    ##  $ x2_lev_x_c    : num  0 1 0 0 1 1 0 0 0 0 ...
    ##  $ x3_lev_x_1    : num  0 1 0 0 0 0 1 1 1 0 ...
    ##  $ x3_lev_x_2    : num  0 0 0 1 0 1 0 0 0 1 ...
    ##  $ x3_lev_x_3    : num  0 0 1 0 1 0 0 0 0 0 ...
    ##  $ Large1_x2_catB: num  0 -10.58 1.18 1.18 -10.58 ...
    ##  $ Large1_x3_catB: num  0 0.284 -10.584 0.529 -10.584 ...
    ##  $ Large2_x2_catB: num  0 0.1 0.0242 0.0242 0.1 ...
    ##  $ Large2_x3_catB: num  0 -13.08 4.72 -4.43 4.72 ...
    ##  $ NoInfo_x2_catB: num  0 0.0685 -0.3392 -0.3392 0.0685 ...
    ##  $ NoInfo_x3_catB: num  0 2.39 -4.55 2.05 -4.55 ...
    ##  $ y             : chr  "NoInfo" "NoInfo" "Large2" "Large1" ...

Obvious issues include: computing variable importance, and blow up and co-dependency of produced columns. These we leave for the next modeling step to deal with (this is our philosophy with most issues that involve joint distributions of variables).

We also have per-outcome variable importance.

    knitr::kable(
      cfe_m$score_frame[,
                        c("varName", "rsq", "sig", "outcome_level"),
                        drop = FALSE])

varName | rsq | sig | outcome_level
--- | --- | --- | ---
x1 | 0.0427675 | 0.0002015 | Large1
x2_catP | 0.0979334 | 0.0000000 | Large1
x2_lev_x_a | 0.2681130 | 0.0000000 | Large1
x2_lev_x_b | 0.0975700 | 0.0000000 | Large1
x2_lev_x_c | 0.0979334 | 0.0000000 | Large1
x3_catP | 0.0125618 | 0.0439536 | Large1
x3_lev_x_1 | 0.0053772 | 0.1874933 | Large1
x3_lev_x_2 | 0.0266092 | 0.0033678 | Large1
x3_lev_x_3 | 0.0961219 | 0.0000000 | Large1
x1 | 0.0003984 | 0.4784542 | Large2
x2_catP | 0.0008969 | 0.2875322 | Large2
x2_lev_x_a | 0.0000512 | 0.7994128 | Large2
x2_lev_x_b | 0.0013961 | 0.1845435 | Large2
x2_lev_x_c | 0.0008969 | 0.2875322 | Large2
x3_catP | 0.0574052 | 0.0000000 | Large2
x3_lev_x_1 | 0.2546121 | 0.0000000 | Large2
x3_lev_x_2 | 0.2659830 | 0.0000000 | Large2
x3_lev_x_3 | 0.9308590 | 0.0000000 | Large2
x1 | 0.0035420 | 0.0312177 | NoInfo
x2_catP | 0.0004091 | 0.4641054 | NoInfo
x2_lev_x_a | 0.0108027 | 0.0001684 | NoInfo
x2_lev_x_b | 0.0072297 | 0.0020855 | NoInfo
x2_lev_x_c | 0.0004091 | 0.4641054 | NoInfo
x3_catP | 0.0416046 | 0.0000000 | NoInfo
x3_lev_x_1 | 0.1848006 | 0.0000000 | NoInfo
x3_lev_x_2 | 0.1796720 | 0.0000000 | NoInfo
x3_lev_x_3 | 0.7228777 | 0.0000000 | NoInfo
Large1_x2_catB | 0.2679354 | 0.0000000 | Large1
Large1_x3_catB | 0.0835409 | 0.0000002 | Large1
Large2_x2_catB | 0.0002176 | 0.6004146 | Large2
Large2_x3_catB | 0.9064823 | 0.0000000 | Large2
NoInfo_x2_catB | 0.0080585 | 0.0011565 | NoInfo
NoInfo_x3_catB | 0.7143906 | 0.0000000 | NoInfo

One can relate these per-target and per-treatment performances back to original columns by aggregating.

    tapply(cfe_m$score_frame$rsq,
           cfe_m$score_frame$origName,
           max)

    ##         x1         x2         x3
    ## 0.04276746 0.26811298 0.93085900

    tapply(cfe_m$score_frame$sig,
           cfe_m$score_frame$origName,
           min)

    ##            x1            x2            x3
    ##  2.015164e-04  1.315559e-20 2.777723e-257