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13

Feature Engineering

Tidy Data Science with the Tidyverse and Tidymodels

W. Jake Thompson

https://tidyds-2021.wjakethompson.com · https://bit.ly/tidyds-2021

Tidy Data Science with the Tidyverse and Tidymodels is licensed under a Creative Commons Attribution 4.0 International License.

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Your Turn 0

  • Open the R Notebook materials/exercises/13-recipes.Rmd
  • Run the setup chunk
01:00

Machine Learning

tidymodels

tidymodels

tidymodels

tidymodels

tidymodels

tidymodels

Pop quiz!

What is multicollinearity?

Pop quiz!

What is multicollinearity?

When multiple predictors are strongly correlated. It can impair linear models.

Principle Components Analysis

Transforms variables into the orthogonal "components" that most concisely capture all of the variation.

Goal

To fit a linear model to the main Principal Components of the ames data.

To build a recipe

1. Start the recipe()

2. Define the variables involved

3. Describe preprocessing step-by-step

recipe()

Creates a recipe for a set of variables.

recipe(Sale_Price ~ ., data = ames)

step_*()

Adds a single transformation to a recipe. Transformations are replayed in order when the recipe is run on data.

rec %>%
  step_novel(all_nominal()) %>%
  step_zv(all_predictors())

step_*()

Complete list at: https://recipes.tidymodels.org/reference/index.html

Selectors

Helper functions fro selecting sets of variables.

rec %>%
  step_novel(all_nominal()) %>%
  step_zv(all_predictors())

selector description

all_predictors()

Each x variable (right side of ~)

all_outcomes()

Each y variable (left side of ~)

all_numeric()

Each numeric variable

all_nominal()

Each categorical variable (e.g. factor, string)

dplyr::select() helpers

starts_with('Lot_'), etc.

Combining selectors

Use commas to separate

rec %>%
  step_novel(all_nominal(), -all_outcomes()) %>%
  step_zv(all_predictors())

Pop quiz!

How does recipes know what is a predictor and what is an outcome?

Pop quiz!

How does recipes know what is a predictor and what is an outcome?

rec <- 
  recipe(Sale_Price ~ .,
         data = ames)

Pop quiz!

How does recipes know what is a predictor and what is an outcome?

rec <- 
  recipe(Sale_Price ~ .,
         data = ames)

The formula ➡️ indicates outcomes vs predictors

Pop quiz!

How does recieps know what is numeric and what is nominal?

Pop quiz!

How does recieps know what is numeric and what is nominal?

rec <- 
  recipe(Sale_Price ~ .,
         data = ames)

Pop quiz!

How does recieps know what is numeric and what is nominal?

rec <- 
  recipe(Sale_Price ~ .,
         data = ames)

The data ➡️ is only used to catalog the names and types of each variable

Pop quiz!

PCA requires variables to be centered and scaled. What does that mean?

Pop quiz!

PCA requires variables to be centered and scaled. What does that mean?

Standardize or z-score

step_center()

Centers numeric variables by subtracting the mean

rec <-
  recipe(Sale_Price ~ .,
         data = ames) %>%
  step_center(all_numeric())

step_scale()

Scales numeric variables by dividing by the standard deviation

rec <-
  recipe(Sale_Price ~ .,
         data = ames) %>%
  step_center(all_numeric()) %>%
  step_scale(all_numeric())

Pop quiz!

Why do you need to "train" a recipe?

Pop quiz!

Why do you need to "train" a recipe?

Imagine "scaling" a new data point. What do you subtract from it? What do you divide it by?

prep() and bake()

"trains" a recipe and then transforms data with the prepped recipe

rec %>%
  prep(training = ames_train) %>%
  bake(new_data = ames_test) # or ames train

prep() and bake()

"trains" a recipe and then transforms data with the prepped recipe

rec %>%
  prep(training = ames_train) %>%
  bake(new_data = ames_test) # or ames train

You don't need to do this! The fit functions do it for you

Artwork by @allison_horst

rec %>% 
  prep(ames_train) %>%
  bake(ames_test) 
#> # A tibble: 732 x 81
#>    MS_SubClass      MS_Zoning     Lot_Frontage Lot_Area Street Alley  Lot_Shape 
#>    <fct>            <fct>                <dbl>    <dbl> <fct>  <fct>  <fct>     
#>  1 One_Story_1946_… Residential_…      2.48     3.00    Pave   No_Al… Slightly_…
#>  2 One_Story_1946_… Residential_…      0.663    0.217   Pave   No_Al… Regular   
#>  3 One_Story_1946_… Residential_…      1.05     0.153   Pave   No_Al… Regular   
#>  4 Two_Story_1946_… Residential_…      0.514   -0.00732 Pave   No_Al… Slightly_…
#>  5 Two_Story_1946_… Residential_…     -0.320    6.00    Pave   No_Al… Moderatel…
#>  6 One_Story_1946_… Residential_…      0.901    0.185   Pave   No_Al… Regular   
#>  7 One_Story_1946_… Residential_…      0.216   -0.221   Pave   No_Al… Regular   
#>  8 Two_Story_PUD_1… Residential_…     -1.09    -1.16    Pave   No_Al… Regular   
#>  9 Two_Story_1946_… Residential_…     -1.72    -0.304   Pave   No_Al… Regular   
#> 10 Two_Story_1946_… Residential_…      0.00782  0.927   Pave   No_Al… Moderatel…
#> # … with 722 more rows, and 74 more variables: Land_Contour <fct>,
#> #   Utilities <fct>, Lot_Config <fct>, Land_Slope <fct>, Neighborhood <fct>,
#> #   Condition_1 <fct>, Condition_2 <fct>, Bldg_Type <fct>, House_Style <fct>,
#> #   Overall_Qual <fct>, Overall_Cond <fct>, Year_Built <dbl>,
#> #   Year_Remod_Add <dbl>, Roof_Style <fct>, Roof_Matl <fct>,
#> #   Exterior_1st <fct>, Exterior_2nd <fct>, Mas_Vnr_Type <fct>,
#> #   Mas_Vnr_Area <dbl>, Exter_Qual <fct>, Exter_Cond <fct>, Foundation <fct>,
#> #   Bsmt_Qual <fct>, Bsmt_Cond <fct>, Bsmt_Exposure <fct>,
#> #   BsmtFin_Type_1 <fct>, BsmtFin_SF_1 <dbl>, BsmtFin_Type_2 <fct>,
#> #   BsmtFin_SF_2 <dbl>, Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, Heating <fct>,
#> #   Heating_QC <fct>, Central_Air <fct>, Electrical <fct>, First_Flr_SF <dbl>,
#> #   Second_Flr_SF <dbl>, Low_Qual_Fin_SF <dbl>, Gr_Liv_Area <dbl>,
#> #   Bsmt_Full_Bath <dbl>, Bsmt_Half_Bath <dbl>, Full_Bath <dbl>,
#> #   Half_Bath <dbl>, Bedroom_AbvGr <dbl>, Kitchen_AbvGr <dbl>,
#> #   Kitchen_Qual <fct>, TotRms_AbvGrd <dbl>, Functional <fct>,
#> #   Fireplaces <dbl>, Fireplace_Qu <fct>, Garage_Type <fct>,
#> #   Garage_Finish <fct>, Garage_Cars <dbl>, Garage_Area <dbl>,
#> #   Garage_Qual <fct>, Garage_Cond <fct>, Paved_Drive <fct>,
#> #   Wood_Deck_SF <dbl>, Open_Porch_SF <dbl>, Enclosed_Porch <dbl>,
#> #   Three_season_porch <dbl>, Screen_Porch <dbl>, Pool_Area <dbl>,
#> #   Pool_QC <fct>, Fence <fct>, Misc_Feature <fct>, Misc_Val <dbl>,
#> #   Mo_Sold <dbl>, Year_Sold <dbl>, Sale_Type <fct>, Sale_Condition <fct>,
#> #   Longitude <dbl>, Latitude <dbl>, Sale_Price <dbl>

Pop quiz!

# A tibble: 6 x 1
  Roof_Style
  <fct>     
1 Hip       
2 Gable     
3 Mansard   
4 Gambrel   
5 Shed      
6 Flat
# A tibble: 2,930 x 6
     Hip Gable Mansard Gambrel  Shed  Flat
   <dbl> <dbl>   <dbl>   <dbl> <dbl> <dbl>
 1     1     0       0       0     0     0
 2     0     1       0       0     0     0
 3     1     0       0       0     0     0
 4     1     0       0       0     0     0
 5     0     1       0       0     0     0
 6     0     1       0       0     0     0
 7     0     1       0       0     0     0
 8     0     1       0       0     0     0
 9     0     1       0       0     0     0
10     0     1       0       0     0     0
# … with 2,920 more rows

Dummy Variables

lm(Sale_Price ~ Roof_Style, data = ames)
#> # A tibble: 6 x 5
#>   term              estimate std.error statistic  p.value
#>   <chr>                <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)        184799.    17167.    10.8   1.57e-26
#> 2 Roof_StyleGable    -14487.    17240.    -0.840 4.01e- 1
#> 3 Roof_StyleGambrel  -46514.    23719.    -1.96  5.00e- 2
#> 4 Roof_StyleHip       41891.    17475.     2.40  1.66e- 2
#> 5 Roof_StyleMansard  -18573.    28818.    -0.644 5.19e- 1
#> 6 Roof_StyleShed       8401.    38386.     0.219 8.27e- 1

step_dummy()

Converts nominal data into dummy variables which are numeric and suitable for linear algebra.

rec %>%
  step_dummy(all_nominal())

You don't need this for decision trees or ensembles of trees

Consider

Let's think about the modeling.

What if there were no homes with shed roofs in the training data?

Consider

Let's think about the modeling.

What if there were no homes with shed roofs in the training data?

Will the model have a coefficient for shed roof?

Consider

Let's think about the modeling.

What if there were no homes with shed roofs in the training data?

Will the model have a coefficient for shed roof?

No

Consider

Let's think about the modeling.

What if there were no homes with shed roofs in the training data?

Will the model have a coefficient for shed roof?

No

What will happen if the test data has a home with a shed roof?

Consider

Let's think about the modeling.

What if there were no homes with shed roofs in the training data?

Will the model have a coefficient for shed roof?

No

What will happen if the test data has a home with a shed roof?

Error!

step_novel()

Adds a catch-all level to a factor for any new values, which lets R intelligently predict new levels in the test set.

rec %>%
  step_novel(all_nominal()) %>%
  step_dummy(all_nominal())

Use before step_dummy() so new level is dummified

Consider

What would happen if you try to scale a variable that doesn't vary?

Consider

What would happen if you try to scale a variable that doesn't vary?

Error! You'd be dividing by zero!

step_zv()

Intelligently handles zero variance variables (variables that contain only a single value)

rec %>%
  step_novel(all_nominal()) %>%
  step_dummy(all_nominal()) %>%
  step_zv(all_predictors())

Pop quiz!

What step function would do PCA?

Pop quiz!

What step function would do PCA?

step_pca()

Replaces variables with components

rec %>%
  step_pca(all_numeric(),
           num_comp = 5)

Your Turn 1

Write a recipe for the Sale_Price ~ . variables that:

  1. Adds a novel level to all factors
  2. Converts all factors to dummy variables
  3. Catches any zero variance variables
  4. Centers all of the predictors
  5. Scales all of the predictors
  6. Computes the first 5 principal components

Save the result as pca_rec

05:00 
pca_rec <- 
  recipe(Sale_Price ~ ., data = ames) %>%
  step_novel(all_nominal()) %>%
  step_dummy(all_nominal()) %>%
  step_zv(all_predictors()) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) %>%
  step_pca(all_predictors(), num_comp = 5)
pca_rec
#> Data Recipe
#> 
#> Inputs:
#> 
#>       role #variables
#>    outcome          1
#>  predictor         80
#> 
#> Operations:
#> 
#> Novel factor level assignment for all_nominal()
#> Dummy variables from all_nominal()
#> Zero variance filter on all_predictors()
#> Centering for all_predictors()
#> Scaling for all_predictors()
#> No PCA components were extracted.

Roles

You can also give variables a "role" within a recipe and then select by roles.

has_role(match = "privacy")
add_role(rec, Fence, new_role = "privacy")
update_role(rec, Fence, new_role = "privacy", old_role = "yard")
remove_role(rec, Fence, old_role = "yard")

Pop quiz!

If we use add_model() to add a model to a workflow, what would we use to add a recipe?

Pop quiz!

If we use add_model() to add a model to a workflow, what would we use to add a recipe?

Let's see!

Your Turn 2

Make a workflow that combines pca_rec and with lm_spec.

02:00 
pca_wf <-
  workflow() %>%
  add_recipe(pca_rec) %>%
  add_model(lm_spec)
pca_wf
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 6 Recipe Steps
#> 
#> • step_novel()
#> • step_dummy()
#> • step_zv()
#> • step_center()
#> • step_scale()
#> • step_pca()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm

add_recipe()

Adds a recipe to a workflow.

pca_wf <-
  workflow() %>%
  add_recipe(pca_rec) %>%
  add_model(lm_spec)

Pop quiz!

Do you need to add a formula if you have a recipe?

Pop quiz!

Do you need to add a formula if you have a recipe?

Nope!

rec <-
  recipe(Sale_Price ~ .,
         data = ames)

Your Turn 3

Try our PCA workflow on ames_folds. What is the estimated RMSE?

04:00 
pca_wf %>%
  fit_resamples(resamples = ames_folds) %>%
  collect_metrics()
#> # A tibble: 2 x 6
#>   .metric .estimator      mean     n   std_err .config             
#>   <chr>   <chr>          <dbl> <int>     <dbl> <chr>               
#> 1 rmse    standard   38937.       10 1349.     Preprocessor1_Model1
#> 2 rsq     standard       0.761    10    0.0158 Preprocessor1_Model1

update_recipe()

Replace the recipe in a workflow.

pca_wf %>%
  update_recipe(bc_rec)

Your Turn 4

Modify the code to build a new PCA recipe that uses a BoxCox transformation instead of centering and scaling the data.

Then update pca_wf to use the new recipe.

Hint: Guess. Use tab completion. Or visit https://recipes.tidymodels.org/reference/index.html.

03:00 
bc_rec <-
  recipe(Sale_Price ~ ., data = ames) %>%
  step_novel(all_nominal()) %>%
  step_dummy(all_nominal()) %>%
  step_zv(all_predictors()) %>%
  step_BoxCox(all_predictors()) %>%
  step_pca(all_predictors(), num_comp = 5)
bc_wf <- 
  pca_wf %>%
  update_recipe(bc_rec)
bc_wf %>%
  fit_resamples(resamples = ames_folds) %>%
  collect_metrics()
#> # A tibble: 2 x 6
#>   .metric .estimator      mean     n   std_err .config             
#>   <chr>   <chr>          <dbl> <int>     <dbl> <chr>               
#> 1 rmse    standard   44203.       10 2286.     Preprocessor1_Model1
#> 2 rsq     standard       0.699    10    0.0287 Preprocessor1_Model1

Feature Engineering

Before

Feature Engineering

Before

After

StackOverflow Data

library(modeldata)
data(stackoverflow)
glimpse(stackoverflow)
#> Rows: 5,594
#> Columns: 21
#> $ Country                              <fct> United Kingdom, United States, Un…
#> $ Salary                               <dbl> 100000.000, 130000.000, 175000.00…
#> $ YearsCodedJob                        <int> 20, 20, 16, 4, 1, 1, 13, 4, 7, 17…
#> $ OpenSource                           <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
#> $ Hobby                                <dbl> 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, …
#> $ CompanySizeNumber                    <dbl> 5000, 1000, 10000, 1000, 5000, 20…
#> $ Remote                               <fct> Remote, Remote, Not remote, Not r…
#> $ CareerSatisfaction                   <int> 8, 9, 7, 9, 5, 8, 7, 7, 8, 9, 10,…
#> $ Data_scientist                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Database_administrator               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Desktop_applications_developer       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, …
#> $ Developer_with_stats_math_background <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
#> $ DevOps                               <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Embedded_developer                   <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Graphic_designer                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Graphics_programming                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Machine_learning_specialist          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Mobile_developer                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
#> $ Quality_assurance_engineer           <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Systems_administrator                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ Web_developer                        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, …

Bigger version of what we used earlier.

Pop quiz!

Name that package!

set.seed(100) # Important!
so_split <- initial_split(stackoverflow, strata = Remote)
so_train <- training(so_split)
so_test  <- testing(so_split)

Pop quiz!

Name that package!

set.seed(100) # Important!
so_split <- initial_split(stackoverflow, strata = Remote)
so_train <- training(so_split)
so_test  <- testing(so_split)

Pop quiz!

Name that package!

tree_spec <- 
  decision_tree() %>%         
  set_engine("rpart") %>%      
  set_mode("classification")

Pop quiz!

Name that package!

tree_spec <- 
  decision_tree() %>%         
  set_engine("rpart") %>%      
  set_mode("classification")

Pop quiz!

Name that package!

so_rec <- recipe(Remote ~ ., data = so_train) %>% 
  step_dummy(all_nominal(), -all_outcomes()) %>% 
  step_lincomb(all_predictors())

Pop quiz!

Name that package!

so_rec <- recipe(Remote ~ ., data = so_train) %>% 
  step_dummy(all_nominal(), -all_outcomes()) %>% 
  step_lincomb(all_predictors())

Pop quiz!

Name that package!

so_wf <- workflow() %>% 
  add_model(tree_spec) %>% 
  add_recipe(so_rec)

Pop quiz!

Name that package!

so_wf <- workflow() %>% 
  add_model(tree_spec) %>% 
  add_recipe(so_rec)

fit()

set.seed(1980)
so_fit <- so_wf %>%
  fit(data = so_train)
so_preds <- bind_cols(
  predict(so_fit, new_data = so_test, type = "class"),
  predict(so_fit, new_data = so_test, type = "prob")
) %>%
  mutate(truth = so_test$Remote)
so_metric_set <- metric_set(accuracy, roc_auc)
so_metric_set(so_preds, truth = truth, .pred_Remote, estimate = .pred_class)
#> # A tibble: 2 x 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.898
#> 2 roc_auc  binary         0.5
so_metric_set <- metric_set(accuracy, roc_auc, sens, spec)
so_metric_set(so_preds, truth = truth, .pred_Remote, estimate = .pred_class)
#> # A tibble: 4 x 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.898
#> 2 sens     binary         0    
#> 3 spec     binary         1    
#> 4 roc_auc  binary         0.5

Pop quiz!

Can you guess what the confusion matrix looks like?

Pop quiz!

Can you guess what the confusion matrix looks like?

uh oh

conf_mat(so_preds, truth = truth, estimate = .pred_class)
#>             Truth
#> Prediction   Remote Not remote
#>   Remote          0          0
#>   Not remote    143       1254

Pop quiz!

Can you guess what the confusion matrix looks like?

uh oh

so_train %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       432
#> 2 Not remote  3765
so_test %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       143
#> 2 Not remote  1254

How can we get better at identifying the less frequent class?

How can we get better at identifying the less frequent class?

Sub-class sampling

Downsampling

Downsampling

Upsampling

Upsampling

Your Turn 5

Add a recipe step to downsample the remote variable majority class in the training set prior to model training. Edit your workflow, then re-fit the model and examine the metrics. Is the ROC AUC better than chance (.5)?

05:00 
so_down <- recipe(Remote ~ ., data = so_train) %>%
  step_dummy(all_nominal(), -all_outcomes()) %>%
  step_lincomb(all_predictors()) %>%
  step_downsample(all_outcomes())
so_downwf <- so_wf %>%
  update_recipe(so_down)
set.seed(1980)
so_downfit <- so_downwf %>%
  fit(data = so_train)
so_downpreds <- bind_cols(
  predict(so_downfit, new_data = so_test, type = "class"),
  predict(so_downfit, new_data = so_test, type = "prob")
) %>%
  mutate(truth = so_test$Remote)
so_metric_set(so_downpreds, truth = truth, .pred_Remote, estimate = .pred_class)
#> # A tibble: 4 x 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.658
#> 2 sens     binary         0.552
#> 3 spec     binary         0.670
#> 4 roc_auc  binary         0.630

Ahh!

juice()

Get the preprocessed training data back from a prepped recipe. Returns a tibble.

so_down %>%
  prep(training = so_train) %>%
  juice()

Downsampling

so_train %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       432
#> 2 Not remote  3765

Downsampling

so_train %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       432
#> 2 Not remote  3765
so_down %>%
  prep(training = so_train) %>%
  juice() %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       432
#> 2 Not remote   432

step_downsample()

Down-sampling is performed on the training set only. Default is skip = TRUE.

so_test %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       143
#> 2 Not remote  1254

step_downsample()

Down-sampling is performed on the training set only. Default is skip = TRUE.

so_test %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       143
#> 2 Not remote  1254
so_down %>%
  prep(training = so_train) %>%
  bake(new_data = so_test) %>%
  count(Remote)
#> # A tibble: 2 x 2
#>   Remote         n
#>   <fct>      <int>
#> 1 Remote       143
#> 2 Not remote  1254

Feature Engineering

Tidy Data Science with the Tidyverse and Tidymodels

W. Jake Thompson

https://tidyds-2021.wjakethompson.com · https://bit.ly/tidyds-2021

Tidy Data Science with the Tidyverse and Tidymodels is licensed under a Creative Commons Attribution 4.0 International License.

Your Turn 0

  • Open the R Notebook materials/exercises/13-recipes.Rmd
  • Run the setup chunk
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