Add composition to preprocessor blueprints

juliasilge commented 4 years ago

This is one of the first pieces needed for supporting sparse data structures in tidymodels.

library(hardhat)
library(recipes)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#> 
#>     step

train <- iris[1:100,]
test <- iris[101:150,]

rec <- recipe(Species ~ Sepal.Length + Sepal.Width, train) %>%
  step_log(Sepal.Length)

sparse_bp <- default_recipe_blueprint(composition = "dgCMatrix")
sparse_bp
#> Recipe blueprint: 
#>  
#> # Predictors: 0 
#>   # Outcomes: 0 
#>    Intercept: FALSE 
#> Novel Levels: FALSE 
#>  Composition: dgCMatrix

processed <- mold(rec, train, blueprint = sparse_bp)
processed$blueprint
#> Recipe blueprint: 
#>  
#> # Predictors: 2 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#> Novel Levels: FALSE 
#>  Composition: dgCMatrix

forge(train, blueprint = processed$blueprint)
#> $predictors
#> 83 x 2 sparse Matrix of class "dgCMatrix"
#>       Sepal.Length Sepal.Width
#>  [1,]     1.629241         3.5
#>  [2,]     1.589235         3.0
#>  [3,]     1.547563         3.2
#>  [4,]     1.526056         3.1
#>  [5,]     1.609438         3.6
#>  [6,]     1.686399         3.9
#>  [7,]     1.526056         3.4
#>  [8,]     1.609438         3.4
#>  [9,]     1.481605         2.9
#> [10,]     1.589235         3.1
#> [11,]     1.686399         3.7
#> [12,]     1.568616         3.4
#> [13,]     1.568616         3.0
#> [14,]     1.458615         3.0
#> [15,]     1.757858         4.0
#> [16,]     1.740466         4.4
#> [17,]     1.740466         3.8
#> [18,]     1.629241         3.8
#> [19,]     1.686399         3.4
#> [20,]     1.629241         3.7
#> [21,]     1.526056         3.6
#> [22,]     1.629241         3.3
#> [23,]     1.609438         3.0
#> [24,]     1.648659         3.5
#> [25,]     1.648659         3.4
#> [26,]     1.568616         3.1
#> [27,]     1.648659         4.1
#> [28,]     1.704748         4.2
#> [29,]     1.609438         3.2
#> [30,]     1.704748         3.5
#> [31,]     1.589235         3.6
#> [32,]     1.481605         3.0
#> [33,]     1.629241         3.4
#> [34,]     1.609438         3.5
#> [35,]     1.504077         2.3
#> [36,]     1.481605         3.2
#> [37,]     1.526056         3.2
#> [38,]     1.667707         3.7
#> [39,]     1.609438         3.3
#> [40,]     1.945910         3.2
#> [41,]     1.856298         3.2
#> [42,]     1.931521         3.1
#> [43,]     1.704748         2.3
#> [44,]     1.871802         2.8
#> [45,]     1.740466         2.8
#> [46,]     1.840550         3.3
#> [47,]     1.589235         2.4
#> [48,]     1.887070         2.9
#> [49,]     1.648659         2.7
#> [50,]     1.609438         2.0
#> [51,]     1.774952         3.0
#> [52,]     1.791759         2.2
#> [53,]     1.808289         2.9
#> [54,]     1.722767         2.9
#> [55,]     1.902108         3.1
#> [56,]     1.722767         3.0
#> [57,]     1.757858         2.7
#> [58,]     1.824549         2.2
#> [59,]     1.722767         2.5
#> [60,]     1.774952         3.2
#> [61,]     1.808289         2.8
#> [62,]     1.840550         2.5
#> [63,]     1.856298         2.9
#> [64,]     1.887070         3.0
#> [65,]     1.916923         2.8
#> [66,]     1.902108         3.0
#> [67,]     1.791759         2.9
#> [68,]     1.740466         2.6
#> [69,]     1.704748         2.4
#> [70,]     1.791759         2.7
#> [71,]     1.686399         3.0
#> [72,]     1.791759         3.4
#> [73,]     1.840550         2.3
#> [74,]     1.704748         2.5
#> [75,]     1.704748         2.6
#> [76,]     1.808289         3.0
#> [77,]     1.757858         2.6
#> [78,]     1.609438         2.3
#> [79,]     1.722767         2.7
#> [80,]     1.740466         3.0
#> [81,]     1.740466         2.9
#> [82,]     1.824549         2.9
#> [83,]     1.629241         2.5
#> 
#> $outcomes
#> NULL
#> 
#> $extras
#> $extras$roles
#> NULL
forge(test, blueprint = processed$blueprint)
#> $predictors
#> 44 x 2 sparse Matrix of class "dgCMatrix"
#>       Sepal.Length Sepal.Width
#>  [1,]     1.840550         3.3
#>  [2,]     1.757858         2.7
#>  [3,]     1.960095         3.0
#>  [4,]     1.840550         2.9
#>  [5,]     1.871802         3.0
#>  [6,]     2.028148         3.0
#>  [7,]     1.589235         2.5
#>  [8,]     1.987874         2.9
#>  [9,]     1.902108         2.5
#> [10,]     1.974081         3.6
#> [11,]     1.871802         3.2
#> [12,]     1.856298         2.7
#> [13,]     1.916923         3.0
#> [14,]     1.740466         2.5
#> [15,]     1.757858         2.8
#> [16,]     1.856298         3.2
#> [17,]     2.041220         3.8
#> [18,]     2.041220         2.6
#> [19,]     1.791759         2.2
#> [20,]     1.931521         3.2
#> [21,]     1.722767         2.8
#> [22,]     2.041220         2.8
#> [23,]     1.840550         2.7
#> [24,]     1.902108         3.3
#> [25,]     1.974081         3.2
#> [26,]     1.824549         2.8
#> [27,]     1.808289         3.0
#> [28,]     1.856298         2.8
#> [29,]     1.974081         3.0
#> [30,]     2.001480         2.8
#> [31,]     2.066863         3.8
#> [32,]     1.840550         2.8
#> [33,]     1.808289         2.6
#> [34,]     2.041220         3.0
#> [35,]     1.840550         3.4
#> [36,]     1.856298         3.1
#> [37,]     1.791759         3.0
#> [38,]     1.931521         3.1
#> [39,]     1.902108         3.1
#> [40,]     1.916923         3.2
#> [41,]     1.902108         3.0
#> [42,]     1.840550         2.5
#> [43,]     1.824549         3.4
#> [44,]     1.774952         3.0
#> 
#> $outcomes
#> NULL
#> 
#> $extras
#> $extras$roles
#> NULL

^{Created on 2020-09-28 by the reprex package (v0.3.0.9001)}

juliasilge commented 4 years ago

Related to tidymodels/tidymodels#42

juliasilge commented 4 years ago

Closes #100 eventually

juliasilge commented 4 years ago

I worked more on this today, and now during forge() the recipe is only baked one time on the predictors but everything else wires up correctly. 🎉

library(hardhat)
library(recipes)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#> 
#>     step

train <- iris[1:100,]
test <- iris[101:150,]

rec <- recipe(Species ~ Sepal.Length + Sepal.Width, train) %>%
  step_normalize(Sepal.Length)

sparse_bp <- default_recipe_blueprint(composition = "dgCMatrix")
sparse_bp
#> Recipe blueprint: 
#>  
#> # Predictors: 0 
#>   # Outcomes: 0 
#>    Intercept: FALSE 
#> Novel Levels: FALSE 
#>  Composition: dgCMatrix

processed <- mold(rec, train, blueprint = sparse_bp)
processed$blueprint
#> Recipe blueprint: 
#>  
#> # Predictors: 2 
#>   # Outcomes: 1 
#>    Intercept: FALSE 
#> Novel Levels: FALSE 
#>  Composition: dgCMatrix

forge(train, blueprint = processed$blueprint)
#> $predictors
#> 100 x 2 sparse Matrix of class "dgCMatrix"
#>        Sepal.Length Sepal.Width
#>   [1,]  -0.57815327         3.5
#>   [2,]  -0.88982620         3.0
#>   [3,]  -1.20149912         3.2
#>   [4,]  -1.35733558         3.1
#>   [5,]  -0.73398974         3.6
#>   [6,]  -0.11064389         3.9
#>   [7,]  -1.35733558         3.4
#>   [8,]  -0.73398974         3.4
#>   [9,]  -1.66900851         2.9
#>  [10,]  -0.88982620         3.1
#>  [11,]  -0.11064389         3.7
#>  [12,]  -1.04566266         3.4
#>  [13,]  -1.04566266         3.0
#>  [14,]  -1.82484497         3.0
#>  [15,]   0.51270196         4.0
#>  [16,]   0.35686550         4.4
#>  [17,]  -0.11064389         3.9
#>  [18,]  -0.57815327         3.5
#>  [19,]   0.35686550         3.8
#>  [20,]  -0.57815327         3.8
#>  [21,]  -0.11064389         3.4
#>  [22,]  -0.57815327         3.7
#>  [23,]  -1.35733558         3.6
#>  [24,]  -0.57815327         3.3
#>  [25,]  -1.04566266         3.4
#>  [26,]  -0.73398974         3.0
#>  [27,]  -0.73398974         3.4
#>  [28,]  -0.42231681         3.5
#>  [29,]  -0.42231681         3.4
#>  [30,]  -1.20149912         3.2
#>  [31,]  -1.04566266         3.1
#>  [32,]  -0.11064389         3.4
#>  [33,]  -0.42231681         4.1
#>  [34,]   0.04519257         4.2
#>  [35,]  -0.88982620         3.1
#>  [36,]  -0.73398974         3.2
#>  [37,]   0.04519257         3.5
#>  [38,]  -0.88982620         3.6
#>  [39,]  -1.66900851         3.0
#>  [40,]  -0.57815327         3.4
#>  [41,]  -0.73398974         3.5
#>  [42,]  -1.51317205         2.3
#>  [43,]  -1.66900851         3.2
#>  [44,]  -0.73398974         3.5
#>  [45,]  -0.57815327         3.8
#>  [46,]  -1.04566266         3.0
#>  [47,]  -0.57815327         3.8
#>  [48,]  -1.35733558         3.2
#>  [49,]  -0.26648035         3.7
#>  [50,]  -0.73398974         3.3
#>  [51,]   2.38273950         3.2
#>  [52,]   1.44772073         3.2
#>  [53,]   2.22690304         3.1
#>  [54,]   0.04519257         2.3
#>  [55,]   1.60355719         2.8
#>  [56,]   0.35686550         2.8
#>  [57,]   1.29188427         3.3
#>  [58,]  -0.88982620         2.4
#>  [59,]   1.75939366         2.9
#>  [60,]  -0.42231681         2.7
#>  [61,]  -0.73398974         2.0
#>  [62,]   0.66853842         3.0
#>  [63,]   0.82437488         2.2
#>  [64,]   0.98021135         2.9
#>  [65,]   0.20102904         2.9
#>  [66,]   1.91523012         3.1
#>  [67,]   0.20102904         3.0
#>  [68,]   0.51270196         2.7
#>  [69,]   1.13604781         2.2
#>  [70,]   0.20102904         2.5
#>  [71,]   0.66853842         3.2
#>  [72,]   0.98021135         2.8
#>  [73,]   1.29188427         2.5
#>  [74,]   0.98021135         2.8
#>  [75,]   1.44772073         2.9
#>  [76,]   1.75939366         3.0
#>  [77,]   2.07106658         2.8
#>  [78,]   1.91523012         3.0
#>  [79,]   0.82437488         2.9
#>  [80,]   0.35686550         2.6
#>  [81,]   0.04519257         2.4
#>  [82,]   0.04519257         2.4
#>  [83,]   0.51270196         2.7
#>  [84,]   0.82437488         2.7
#>  [85,]  -0.11064389         3.0
#>  [86,]   0.82437488         3.4
#>  [87,]   1.91523012         3.1
#>  [88,]   1.29188427         2.3
#>  [89,]   0.20102904         3.0
#>  [90,]   0.04519257         2.5
#>  [91,]   0.04519257         2.6
#>  [92,]   0.98021135         3.0
#>  [93,]   0.51270196         2.6
#>  [94,]  -0.73398974         2.3
#>  [95,]   0.20102904         2.7
#>  [96,]   0.35686550         3.0
#>  [97,]   0.35686550         2.9
#>  [98,]   1.13604781         2.9
#>  [99,]  -0.57815327         2.5
#> [100,]   0.35686550         2.8
#> 
#> $outcomes
#> NULL
#> 
#> $extras
#> $extras$roles
#> NULL
forge(test, blueprint = processed$blueprint, outcomes = TRUE)
#> $predictors
#> 50 x 2 sparse Matrix of class "dgCMatrix"
#>       Sepal.Length Sepal.Width
#>  [1,]    1.2918843         3.3
#>  [2,]    0.5127020         2.7
#>  [3,]    2.5385760         3.0
#>  [4,]    1.2918843         2.9
#>  [5,]    1.6035572         3.0
#>  [6,]    3.3177583         3.0
#>  [7,]   -0.8898262         2.5
#>  [8,]    2.8502489         2.9
#>  [9,]    1.9152301         2.5
#> [10,]    2.6944124         3.6
#> [11,]    1.6035572         3.2
#> [12,]    1.4477207         2.7
#> [13,]    2.0710666         3.0
#> [14,]    0.3568655         2.5
#> [15,]    0.5127020         2.8
#> [16,]    1.4477207         3.2
#> [17,]    1.6035572         3.0
#> [18,]    3.4735947         3.8
#> [19,]    3.4735947         2.6
#> [20,]    0.8243749         2.2
#> [21,]    2.2269030         3.2
#> [22,]    0.2010290         2.8
#> [23,]    3.4735947         2.8
#> [24,]    1.2918843         2.7
#> [25,]    1.9152301         3.3
#> [26,]    2.6944124         3.2
#> [27,]    1.1360478         2.8
#> [28,]    0.9802113         3.0
#> [29,]    1.4477207         2.8
#> [30,]    2.6944124         3.0
#> [31,]    3.0060854         2.8
#> [32,]    3.7852677         3.8
#> [33,]    1.4477207         2.8
#> [34,]    1.2918843         2.8
#> [35,]    0.9802113         2.6
#> [36,]    3.4735947         3.0
#> [37,]    1.2918843         3.4
#> [38,]    1.4477207         3.1
#> [39,]    0.8243749         3.0
#> [40,]    2.2269030         3.1
#> [41,]    1.9152301         3.1
#> [42,]    2.2269030         3.1
#> [43,]    0.5127020         2.7
#> [44,]    2.0710666         3.2
#> [45,]    1.9152301         3.3
#> [46,]    1.9152301         3.0
#> [47,]    1.2918843         2.5
#> [48,]    1.6035572         3.0
#> [49,]    1.1360478         3.4
#> [50,]    0.6685384         3.0
#> 
#> $outcomes
#> # A tibble: 50 x 1
#>    Species  
#>    <fct>    
#>  1 virginica
#>  2 virginica
#>  3 virginica
#>  4 virginica
#>  5 virginica
#>  6 virginica
#>  7 virginica
#>  8 virginica
#>  9 virginica
#> 10 virginica
#> # … with 40 more rows
#> 
#> $extras
#> $extras$roles
#> NULL

^{Created on 2020-09-29 by the reprex package (v0.3.0.9001)}

A few things to note:

The outcome is still a tibble, not a matrix or sparse matrix. We'll need those factors for classification models and such.
For situations where there are "extras", e.g. columns that are not predictors or outcomes, they are baked with the predictors. This means that they have to be numeric and the baking will error if they are categorical/nominal. Baking them with the outcomes does not work very well in any way I have tried, so I think this may just be a restriction for using a non-tibble composition.

I set this:

#' @param composition Either "tibble", "matrix", or "dgCMatrix" for the format
#' of the processed predictors.

I don't think there's much point to handling an explicit data.frame case, although it is a composition option in recipes.

topepo commented 4 years ago

This might be more of a workflows issue but .fit_pre() still generates a tibble

# See https://github.com/tidymodels/hardhat/pull/150
# remotes::install_github("tidymodels/hardhat@recipe-blueprint-composition")

# See https://github.com/tidymodels/parsnip/pull/373
# remotes::install_github("tidymodels/parsnip@sparsity")

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────────────────────────── tidymodels 0.1.1 ──
#> ✓ broom     0.7.0          ✓ recipes   0.1.13    
#> ✓ dials     0.0.9          ✓ rsample   0.0.8     
#> ✓ dplyr     1.0.2          ✓ tibble    3.0.3     
#> ✓ ggplot2   3.3.2          ✓ tidyr     1.1.2     
#> ✓ infer     0.5.2          ✓ tune      0.1.1     
#> ✓ modeldata 0.0.2          ✓ workflows 0.2.0     
#> ✓ parsnip   0.1.3.9000     ✓ yardstick 0.0.7     
#> ✓ purrr     0.3.4
#> ── Conflicts ───────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter()  masks stats::filter()
#> x dplyr::lag()     masks stats::lag()
#> x recipes::step()  masks stats::step()
library(hardhat)

data(ames)

ames <- 
  ames %>% 
  mutate(Sale_Price = log10(Sale_Price)) %>% 
  select(Sale_Price, Longitude, Latitude, Neighborhood)

rec <- 
  recipe(Sale_Price ~ ., data = ames) %>% 
  step_dummy(Neighborhood) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_predictors())

sparse_bp <- default_recipe_blueprint(composition = "dgCMatrix")


processed <- mold(rec, ames, blueprint = sparse_bp)
class(forge(ames, blueprint = processed$blueprint)$predictors)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"


lm_spec <- linear_reg(mixture = .5) %>% set_engine("glmnet")

lm_wflow <- 
  workflow() %>% 
  add_recipe(rec, blueprint = sparse_bp) %>% 
  add_model(lm_spec)

lm_wflow_1 <- .fit_pre(lm_wflow, data = ames)
lm_wflow_1$pre$mold$predictors
#> # A tibble: 2,930 x 29
#>    Longitude Latitude Neighborhood_Co… Neighborhood_Ol… Neighborhood_Ed…
#>        <dbl>    <dbl>            <dbl>            <dbl>            <dbl>
#>  1     0.901    1.06            -0.317           -0.298           -0.266
#>  2     0.900    1.01            -0.317           -0.298           -0.266
#>  3     0.915    0.987           -0.317           -0.298           -0.266
#>  4     0.995    0.911           -0.317           -0.298           -0.266
#>  5     0.154    1.43            -0.317           -0.298           -0.266
#>  6     0.155    1.43            -0.317           -0.298           -0.266
#>  7     0.354    1.55            -0.317           -0.298           -0.266
#>  8     0.353    1.43            -0.317           -0.298           -0.266
#>  9     0.391    1.45            -0.317           -0.298           -0.266
#> 10     0.149    1.34            -0.317           -0.298           -0.266
#> # … with 2,920 more rows, and 24 more variables: Neighborhood_Somerset <dbl>,
#> #   Neighborhood_Northridge_Heights <dbl>, Neighborhood_Gilbert <dbl>,
#> #   Neighborhood_Sawyer <dbl>, Neighborhood_Northwest_Ames <dbl>,
#> #   Neighborhood_Sawyer_West <dbl>, Neighborhood_Mitchell <dbl>,
#> #   Neighborhood_Brookside <dbl>, Neighborhood_Crawford <dbl>,
#> #   Neighborhood_Iowa_DOT_and_Rail_Road <dbl>, Neighborhood_Timberland <dbl>,
#> #   Neighborhood_Northridge <dbl>, Neighborhood_Stone_Brook <dbl>,
#> #   Neighborhood_South_and_West_of_Iowa_State_University <dbl>,
#> #   Neighborhood_Clear_Creek <dbl>, Neighborhood_Meadow_Village <dbl>,
#> #   Neighborhood_Briardale <dbl>, Neighborhood_Bloomington_Heights <dbl>,
#> #   Neighborhood_Veenker <dbl>, Neighborhood_Northpark_Villa <dbl>,
#> #   Neighborhood_Blueste <dbl>, Neighborhood_Greens <dbl>,
#> #   Neighborhood_Green_Hills <dbl>, Neighborhood_Landmark <dbl>

^{Created on 2020-09-30 by the reprex package (v0.3.0)}

Session info

``` r devtools::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.2 (2020-06-22) #> os macOS Catalina 10.15.5 #> system x86_64, darwin17.0 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/New_York #> date 2020-09-30 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) #> backports 1.1.10 2020-09-15 [1] CRAN (R 4.0.2) #> broom * 0.7.0 2020-07-09 [1] CRAN (R 4.0.0) #> callr 3.4.4 2020-09-07 [1] CRAN (R 4.0.2) #> class 7.3-17 2020-04-26 [1] CRAN (R 4.0.2) #> cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.0) #> codetools 0.2-16 2018-12-24 [1] CRAN (R 4.0.2) #> colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0) #> crayon 1.3.4.9000 2020-08-18 [1] Github (r-lib/crayon@6b3f0c6) #> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0) #> devtools 2.3.1 2020-07-21 [1] CRAN (R 4.0.2) #> dials * 0.0.9 2020-09-16 [1] CRAN (R 4.0.2) #> DiceDesign 1.8-1 2019-07-31 [1] CRAN (R 4.0.0) #> digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.0) #> dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.0) #> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0) #> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) #> foreach 1.5.0 2020-03-30 [1] CRAN (R 4.0.2) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) #> furrr 0.1.0 2018-05-16 [1] CRAN (R 4.0.0) #> future 1.19.1 2020-09-22 [1] CRAN (R 4.0.2) #> generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.0) #> ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.0) #> globals 0.13.0 2020-09-17 [1] CRAN (R 4.0.2) #> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2) #> gower 0.2.2 2020-06-23 [1] CRAN (R 4.0.0) #> GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.0.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0) #> hardhat * 0.1.4.9000 2020-09-30 [1] Github (tidymodels/hardhat@b763b1f) #> highr 0.8 2019-03-20 [1] CRAN (R 4.0.0) #> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.0) #> infer * 0.5.2 2020-06-14 [1] CRAN (R 4.0.0) #> ipred 0.9-9 2019-04-28 [1] CRAN (R 4.0.2) #> iterators 1.0.12 2019-07-26 [1] CRAN (R 4.0.0) #> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.2) #> lattice 0.20-41 2020-04-02 [1] CRAN (R 4.0.2) #> lava 1.6.8 2020-09-26 [1] CRAN (R 4.0.2) #> lhs 1.1.0 2020-09-29 [1] CRAN (R 4.0.2) #> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.0.0) #> lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2) #> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.0) #> MASS 7.3-51.6 2020-04-26 [1] CRAN (R 4.0.2) #> Matrix 1.2-18 2019-11-27 [1] CRAN (R 4.0.2) #> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0) #> modeldata * 0.0.2 2020-06-22 [1] CRAN (R 4.0.2) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0) #> nnet 7.3-14 2020-04-26 [1] CRAN (R 4.0.2) #> parsnip * 0.1.3.9000 2020-09-30 [1] Github (tidymodels/parsnip@659b5ad) #> pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.0) #> pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0) #> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.0) #> plyr 1.8.6 2020-03-03 [1] CRAN (R 4.0.2) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0) #> pROC 1.16.2 2020-03-19 [1] CRAN (R 4.0.2) #> processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2) #> prodlim 2019.11.13 2019-11-17 [1] CRAN (R 4.0.0) #> ps 1.3.4 2020-08-11 [1] CRAN (R 4.0.2) #> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.0) #> R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.0) #> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.0) #> recipes * 0.1.13 2020-06-23 [1] CRAN (R 4.0.2) #> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2) #> rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.0) #> rmarkdown 2.3 2020-06-18 [1] CRAN (R 4.0.2) #> rpart 4.1-15 2019-04-12 [1] CRAN (R 4.0.2) #> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.0) #> rsample * 0.0.8 2020-09-23 [1] CRAN (R 4.0.2) #> rstudioapi 0.11 2020-02-07 [1] CRAN (R 4.0.0) #> scales * 1.1.1 2020-05-11 [1] CRAN (R 4.0.2) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2) #> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.0) #> survival 3.1-12 2020-04-10 [1] CRAN (R 4.0.2) #> testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.2) #> tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.0) #> tidymodels * 0.1.1 2020-07-14 [1] CRAN (R 4.0.0) #> tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2) #> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.0) #> timeDate 3043.102 2018-02-21 [1] CRAN (R 4.0.0) #> tune * 0.1.1 2020-07-08 [1] CRAN (R 4.0.2) #> usethis 1.9.0.9000 2020-09-30 [1] Github (r-lib/usethis@b993e83) #> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.0) #> vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2) #> withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.2) #> workflows * 0.2.0 2020-09-15 [1] CRAN (R 4.0.2) #> xfun 0.17 2020-09-09 [1] CRAN (R 4.0.2) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) #> yardstick * 0.0.7 2020-07-13 [1] CRAN (R 4.0.2) #> #> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library ```

juliasilge commented 4 years ago

OK, I think this is getting there! 👍 I now have mold() working as well, and also wired up to the important various bits:

library(tidymodels)
library(hardhat)

data(ames, package = "modeldata")
x <- ames %>%
  select(Longitude, Latitude, Year_Built)
y <- log10(ames$Sale_Price)

## works for xy
bp1 <- default_xy_blueprint(composition = "dgCMatrix")
x1 <- mold(x, y, blueprint = bp1)
class(x1$predictors)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"
colnames(x1$predictors)
#> [1] "Longitude"  "Latitude"   "Year_Built"

## works for formula
bp2 <- default_formula_blueprint(composition = "matrix")
x2 <- mold(log10(Sale_Price) ~ Longitude + Latitude + Neighborhood, ames, blueprint = bp2)
class(x2$predictors)
#> [1] "matrix" "array"
colnames(x2$predictors)
#>  [1] "Longitude"                                          
#>  [2] "Latitude"                                           
#>  [3] "NeighborhoodNorth_Ames"                             
#>  [4] "NeighborhoodCollege_Creek"                          
#>  [5] "NeighborhoodOld_Town"                               
#>  [6] "NeighborhoodEdwards"                                
#>  [7] "NeighborhoodSomerset"                               
#>  [8] "NeighborhoodNorthridge_Heights"                     
#>  [9] "NeighborhoodGilbert"                                
#> [10] "NeighborhoodSawyer"                                 
#> [11] "NeighborhoodNorthwest_Ames"                         
#> [12] "NeighborhoodSawyer_West"                            
#> [13] "NeighborhoodMitchell"                               
#> [14] "NeighborhoodBrookside"                              
#> [15] "NeighborhoodCrawford"                               
#> [16] "NeighborhoodIowa_DOT_and_Rail_Road"                 
#> [17] "NeighborhoodTimberland"                             
#> [18] "NeighborhoodNorthridge"                             
#> [19] "NeighborhoodStone_Brook"                            
#> [20] "NeighborhoodSouth_and_West_of_Iowa_State_University"
#> [21] "NeighborhoodClear_Creek"                            
#> [22] "NeighborhoodMeadow_Village"                         
#> [23] "NeighborhoodBriardale"                              
#> [24] "NeighborhoodBloomington_Heights"                    
#> [25] "NeighborhoodVeenker"                                
#> [26] "NeighborhoodNorthpark_Villa"                        
#> [27] "NeighborhoodBlueste"                                
#> [28] "NeighborhoodGreens"                                 
#> [29] "NeighborhoodGreen_Hills"                            
#> [30] "NeighborhoodLandmark"                               
#> [31] "NeighborhoodHayden_Lake"

## can forge
xx1 <- forge(ames, blueprint = x1$blueprint)
class(xx1$predictors)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"

## workflow can get to the "recomposed" data
rec <- 
  recipe(Sale_Price ~  Longitude + Latitude + Neighborhood, data = ames) %>% 
  step_dummy(Neighborhood) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_predictors())

lasso_spec <- linear_reg(mixture = 1) %>% set_engine("glmnet")
bp3 <- default_recipe_blueprint(composition = "dgCMatrix")

lasso_wflow <- 
  workflow() %>% 
  add_recipe(rec, blueprint = bp3) %>% 
  add_model(lasso_spec)

wflow_1 <- .fit_pre(lasso_wflow, data = ames)
class(wflow_1$pre$mold$predictors)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"
colnames(wflow_1$pre$mold$predictors)
#>  [1] "Longitude"                                           
#>  [2] "Latitude"                                            
#>  [3] "Neighborhood_College_Creek"                          
#>  [4] "Neighborhood_Old_Town"                               
#>  [5] "Neighborhood_Edwards"                                
#>  [6] "Neighborhood_Somerset"                               
#>  [7] "Neighborhood_Northridge_Heights"                     
#>  [8] "Neighborhood_Gilbert"                                
#>  [9] "Neighborhood_Sawyer"                                 
#> [10] "Neighborhood_Northwest_Ames"                         
#> [11] "Neighborhood_Sawyer_West"                            
#> [12] "Neighborhood_Mitchell"                               
#> [13] "Neighborhood_Brookside"                              
#> [14] "Neighborhood_Crawford"                               
#> [15] "Neighborhood_Iowa_DOT_and_Rail_Road"                 
#> [16] "Neighborhood_Timberland"                             
#> [17] "Neighborhood_Northridge"                             
#> [18] "Neighborhood_Stone_Brook"                            
#> [19] "Neighborhood_South_and_West_of_Iowa_State_University"
#> [20] "Neighborhood_Clear_Creek"                            
#> [21] "Neighborhood_Meadow_Village"                         
#> [22] "Neighborhood_Briardale"                              
#> [23] "Neighborhood_Bloomington_Heights"                    
#> [24] "Neighborhood_Veenker"                                
#> [25] "Neighborhood_Northpark_Villa"                        
#> [26] "Neighborhood_Blueste"                                
#> [27] "Neighborhood_Greens"                                 
#> [28] "Neighborhood_Green_Hills"                            
#> [29] "Neighborhood_Landmark"

^{Created on 2020-09-30 by the reprex package (v0.3.0.9001)}

I added significant changes to the tests for the preprocessors FYI.

github-actions[bot] commented 3 years ago

This pull request has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.

tidymodels / hardhat

Add composition to preprocessor blueprints #150