Theferrn package extracts key components from the data object collected during projection pursuit (PP) guided tour optimisation, produces diagnostic plots, and calculates PP index scores.
Installation
You can install the development version of ferrn fromGitHub with:
# install.packages("remotes")remotes::install_github("huizezhang-sherry/ferrn")Visualise PP optimisation
The data object collected during a PP optimisation can be obtained by assigning thetourr::annimate_xx() function a name. In the following example, the projection pursuit is finding the best projection basis that can detect multi-modality for theboa5 dataset using theholes() index function and the optimisersearch_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_betterThe data structure includes thebasis sampled by the optimiser, their corresponding index values (index_val), aninformation tag explaining the optimisation states, and the optimisationmethod used (search_better). The variablestries andloop describe the number of iterations and samples in the optimisation process, respectively. The variableid 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 55holes_1d_better%>%get_best()%>%pull(basis)%>%.[[1]]#> [,1]#> [1,] 0.005468276#> [2,] 0.990167039#> [3,] -0.054198426#> [4,] 0.088415793#> [5,] 0.093725721holes_1d_better%>%get_best()%>%pull(index_val)#> [1] 0.9136095The 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. Hereholes_1d_geo is the data obtained from the same PP problem asholes_1d_better introduced above, but with asearch_geodesic optimiser. The 5 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 bases is an-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()#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.#> ℹ Please use `linewidth` instead.#> ℹ The deprecated feature was likely used in the ferrn package.#> Please report the issue at#> <https://github.com/huizezhang-sherry/ferrn/issues>.#> This warning is displayed once every 8 hours.#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was#> generated.
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)
