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Turn off parallel processing for NMF, do not load pkgs #685

merged 2 commits into from Apr 15, 2021


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Closes #681

This PR turns off the parallelization that NMF has on by default, which allows us to use NMF and dimRed without fully loading them as before. This solves the problem with fit():

#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

rec <- recipe(HHV ~ ., data = biomass) %>%
  update_role(sample, new_role = "id var") %>%
  update_role(dataset, new_role = "split variable") %>%
  step_nnmf(all_numeric_predictors(), num_comp = 2, seed = 473, num_run = 2) %>%
  prep(training = biomass)

linear_reg() %>% fit(mpg ~ ., data = mtcars)
#> Warning: Engine set to `lm`.
#> parsnip model object
#> Fit time:  3ms 
#> Call:
#> stats::lm(formula = mpg ~ ., data = data)
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat           wt  
#>    12.30337     -0.11144      0.01334     -0.02148      0.78711     -3.71530  
#>        qsec           vs           am         gear         carb  
#>     0.82104      0.31776      2.52023      0.65541     -0.19942

Created on 2021-04-14 by the reprex package (v2.0.0)

In the future, we could consider exposing some kind of parallel option for step_nnmf() for folks who use it outside of our tuning infrastructure and want to run it in parallel.

@juliasilge juliasilge requested a review from topepo April 14, 2021 23:30
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@juliasilge juliasilge merged commit 2c10d53 into master Apr 15, 2021
@juliasilge juliasilge deleted the nnmf-no-require branch April 15, 2021 14:57
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Successfully merging this pull request may close these issues.

Loading the required packages for step_nnmf() makes fit() misbehave
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