Sparse pca steps

[topepo]

@dgrtwo

Steps for two types of sparse PCA; step_pca_sparse() can zero out values from the loading matrix anywhere. step_pca_sparse_bayes() does the same but is encouraged to do so in a way that can eliminate all the loadings for a predictor.

All feedback welcome.

An example:

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(embed)
tidymodels_prefer()

data(pd_speech)

rec_sparse <-
  recipe(pd_speech) %>%
  update_role(-class, new_role = "predictor") %>%
  update_role(class, new_role = "outcome") %>%
  step_zv(all_predictors()) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_pca_sparse(all_numeric_predictors(), id = "pca", predictor_prop = 1/2) %>%
  prep()

tidy(rec_sparse, id = "pca") %>%
  select(-id) %>%
  ggplot(aes(x = component, y = terms, fill = value)) +
  geom_raster() +
  scale_fill_gradient2() +
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  ggtitle("irlba")

rec_sparse_bayes <- 
  recipe(pd_speech) %>% 
  update_role(-class, new_role = "predictor") %>% 
  update_role(class, new_role = "outcome") %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_numeric_predictors()) %>% 
  step_pca_sparse_bayes(all_numeric_predictors(), id = "pca") %>% 
  prep()

tidy(rec_sparse_bayes, id = "pca") %>% 
  select(-id) %>% 
  ggplot(aes(x = component, y = terms, fill = value)) +
  geom_raster() + 
  scale_fill_gradient2() + 
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank()
  ) + 
  ggtitle("VBsparsePCA")

^{Created on 2021-06-07 by the reprex package (v2.0.0)}

[juliasilge]

Related to #73 and #82

[dgrtwo]

Excited to see this progress!

It looks like this is solving the problem of sparse PCA with regularization. I think this is important, but I'd suggest distinguishing it from my suggestion in #82 of truncated PCA. Truncated PCA is in my view an easier problem; it doesn't need hyperparameters like predictor_prop since it doesn't do regularization; it's just a (much) faster way to approximate the first num_comp components.

(This is IMO a good recipe step! But I agree with Alex's comment in #82 that this doesn't solve the problem of step_pca's prohibitive slowness)

[hfrick]

looks good! tidy.step_pca_sparse_bayes() is still defined twice (once in R/pca_sparse.R and once in R/pca_sparse_bayes.R), no?

[github-actions]

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