The ferrn package extracts key components from the data object collected during projection pursuit (PP) guided tour optimisation, produces diagnostic plots, and calculates PP index scores.
You can install the development version of ferrn from GitHub with:
# install.packages("remotes")
remotes::install_github("huizezhang-sherry/ferrn")
The data object collected during a PP optimisation can be obtained by
assigning the tourr::annimate_xx()
function a name. In the following
example, the projection pursuit is finding the best projection basis
that can detect multi-modality for the boa5
dataset using the
holes()
index function and the optimiser search_better
:
set.seed(123456)
holes_1d_better <- animate_dist(
ferrn::boa5,
tour_path = guided_tour(holes(), d = 1, search_f = search_better),
rescale = FALSE)
holes_1d_better
The data structure includes the basis
sampled by the optimiser, their
corresponding index values (index_val
), an information
tag
explaining the optimisation states, and the optimisation method
used
(search_better
). The variables tries
and loop
describe the number
of iterations and samples in the optimisation process, respectively. The
variable id
serves as the global identifier.
The best projection basis can be extracted via
library(ferrn)
library(dplyr)
holes_1d_better %>% get_best()
#> # A tibble: 1 × 8
#> basis index_val info method alpha tries loop id
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 <dbl [5 × 1]> 0.914 interpolation search_better NA 5 6 55
holes_1d_better %>% get_best() %>% pull(basis) %>% .[[1]]
#> [,1]
#> [1,] 0.005468276
#> [2,] 0.990167039
#> [3,] -0.054198426
#> [4,] 0.088415793
#> [5,] 0.093725721
holes_1d_better %>% get_best() %>% pull(index_val)
#> [1] 0.9136095
The trace plot can be used to view the optimisation progression:
holes_1d_better %>%
explore_trace_interp() +
scale_color_continuous_botanical()
Different optimisers can be compared by plotting their projection bases
on the reduced PCA space. Here holes_1d_geo
is the data obtained from
the same PP problem as holes_1d_better
introduced above, but with a
search_geodesic
optimiser. The 5 $\times$ 1 bases from the two
datasets are first reduced to 2D via PCA, and then plotted to the PCA
space. (PP bases are ortho-normal and the space for $n \times 1$ bases
is an $n$-d sphere, hence a circle when projected into 2D.)
bind_rows(holes_1d_geo, holes_1d_better) %>%
bind_theoretical(matrix(c(0, 1, 0, 0, 0), nrow = 5),
index = tourr::holes(), raw_data = boa5) %>%
explore_space_pca(group = method, details = TRUE) +
scale_color_discrete_botanical()
The same set of bases can be visualised in the original 5-D space via tour animation:
bind_rows(holes_1d_geo, holes_1d_better) %>%
explore_space_tour(flip = TRUE, group = method,
palette = botanical_palettes$fern[c(1, 6)],
max_frames = 20,
point_size = 2, end_size = 5)
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