Tuning parameters for media transforms

dials parameter objects for the quantities step_adstock() and step_saturation() expose to tuning. Having these is the entire reason to be a recipe step rather than a function you call beforehand: they let carryover decay, saturation shape and a model's own penalty be tuned jointly, in one tune::tune_bayes() call over proper rolling-origin resampling, rather than fixing the transform parameters by eye and tuning only the model.

Usage

carryover_decay(range = c(0, 0.95), trans = NULL)

carryover_max_lag(range = c(1L, 26L), trans = NULL)

saturation_half_max(range = c(0.05, 1), trans = NULL)

saturation_shape(range = c(0.5, 3), trans = NULL)

Arguments

range

A two-element vector giving the range of the parameter.

trans

A transformation from scales, or NULL for none.

Value

A dials parameter object.

Details

carryover_decay() spans [0, 0.95] rather than [0, 1). The upper bound is a practical one: a decay of 0.99 implies a half-life of 69 periods, which no ordinary media dataset can identify, and leaving it in the range mostly wastes tuning iterations on parameter values that trade off against the intercept.

carryover_max_lag() spans 1 to 26 periods. It is finite by necessity: Inf is a legitimate value for step_adstock() but not a tunable one, since a search cannot propose an infinite integer. So tuning max_lag over a finite grid and leaving it fixed at Inf are genuinely different searches – the first over truncated kernels, the second over the recursive one. Most of the time, tuning decay with max_lag = Inf is the better use of the budget: the truncation point is usually a modelling decision rather than something the data speaks to.

saturation_half_max() is a fraction of each column's reference level, so its range is unitless and comparable across channels. See step_saturation().

saturation_shape() spans [0.5, 3]. Values above 1 give an S-shaped response with a threshold below which media barely registers; below 1 the curve bends harder than a plain hyperbola. Note that step_saturation() narrows this range to (0.1, 1] when type = "power", where shape is the exponent itself and a value above 1 would not be saturating at all.

See also

step_adstock(), step_saturation()

Examples

carryover_decay()
#> Carryover Decay (quantitative)
#> Range: [0, 0.95]
dials::value_seq(carryover_decay(), 5)
#> [1] 0.0000 0.2375 0.4750 0.7125 0.9500

saturation_shape()
#> Saturation Shape (quantitative)
#> Range: [0.5, 3]
dials::grid_regular(carryover_decay(), saturation_shape(), levels = 3)
#> # A tibble: 9 × 2
#>   decay shape
#>   <dbl> <dbl>
#> 1 0      0.5 
#> 2 0.475  0.5 
#> 3 0.95   0.5 
#> 4 0      1.75
#> 5 0.475  1.75
#> 6 0.95   1.75
#> 7 0      3   
#> 8 0.475  3   
#> 9 0.95   3