Build better training data

---

# Build a better training set with `recipes`

---

## `recipes`

---

## Preprocessing options

- Encode categorical predictors
- Center and scale variables
- Handle class imbalance
- Impute missing data
- Perform dimensionality reduction 
- *A lot more!*

---

---

## To build a recipe

1. Start the `recipe()`
1. Define the .display[variables] involved
1. Describe **prep**rocessing .display[step-by-step]

---

## `recipe()`

Creates a recipe for a set of variables

```r
recipe(test ~ ., data = bechdel)
```

---

## `recipe()`

Creates a recipe for a set of variables

```r
rec <- recipe(test ~ ., data = bechdel)
```

---

## `step_*()`

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

```r
rec <- recipe(test ~ ., data = bechdel) %>%
  step_other(genre, threshold = .05)
```

---

---

## Before recipe

```
## # A tibble: 14 × 2
##    genre         n
##    <chr>     <int>
##  1 Action      334
##  2 Comedy      286
##  3 Drama       236
##  4 Adventure    86
##  5 Animation    82
##  6 Crime        77
##  7 Horror       67
##  8 Biography    58
##  9 Mystery      10
## 10 Fantasy       6
## # … with 4 more rows
```

]

## After recipe

```
## # A tibble: 8 × 2
##   genre         n
##   <fct>     <int>
## 1 Action      334
## 2 Comedy      286
## 3 Drama       236
## 4 Adventure    86
## 5 other        85
## 6 Animation    82
## 7 Crime        77
## 8 Horror       67
```

]

---

## `step_*()`

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

---

## K Nearest Neighbors (KNN)

To predict the outcome of a new data point:

- Find the K most similar old data points
- Take the average/mode/etc. outcome

---

## To specify a model with `parsnip`

1. Pick a .display[model]
1. Set the .display[engine]
1. Set the .display[mode] (if needed)

---

## To specify a KNN model with `parsnip`

```r
knn_mod <- nearest_neighbor() %>%              
  set_engine("kknn") %>%             
  set_mode("classification")        
```

---

## Fact

KNN requires all numeric predictors, and all need to be **centered** and **scaled**.

What does that mean?

---

## 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?

---

---

---

---

## Guess

```
# A tibble: 5 × 1
  rated
  <chr>
1 R    
2 PG-13
3 PG   
4 G    
5 NC-17
```

]

```
# A tibble: 1,394 × 5
       R `PG-13`    PG     G `NC-17`
   <dbl>   <dbl> <dbl> <dbl>   <dbl>
 1     1       0     0     0       0
 2     1       0     0     0       0
 3     0       1     0     0       0
 4     0       1     0     0       0
 5     1       0     0     0       0
 6     1       0     0     0       0
 7     0       1     0     0       0
 8     0       1     0     0       0
 9     1       0     0     0       0
10     1       0     0     0       0
# … with 1,384 more rows
```

]

---

## Dummy Variables

```r
glm(test ~ rated, family = "binomial", data = bechdel)
```

```
## # A tibble: 5 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   -0.470     0.403    -1.17    0.244
## 2 ratedNC-17    -1.14      1.17     -0.976   0.329
## 3 ratedPG        0.225     0.427     0.527   0.598
## 4 ratedPG-13     0.354     0.412     0.859   0.391
## 5 ratedR         0.198     0.411     0.482   0.630
```

---

## `step_dummy()`

Converts nominal data into numeric dummy variables, needed as predictors for models like KNN.

```r
rec <- recipe(test ~ ., data = bechdel) %>%
  step_other(genre, threshold = .05) %>% 
* step_dummy(all_nominal_predictors())
```

---

## Quiz

How does `recipes` know which variables are **numeric** and which are **nominal**?

```r
rec <- recipe(
  test ~ ., 
* data = bechdel
  )
```

---

## Quiz

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

```r
rec <- recipe(
* test ~ .,
  data = bechdel
  )
```

---

## Selectors

Helper functions for selecting sets of variables

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

---

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  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">selector</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">description</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_predictors()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each x variable  (right side of ~)</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_outcomes()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each y variable  (left side of ~)</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_numeric()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each numeric variable</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_nominal()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each categorical variable (e.g. factor, string)</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_nominal_predictors()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each categorical variable (e.g. factor, string) that is defined as a predictor</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>all_numeric_predictors()</code></p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p>Each numeric variable that is defined as a predictor</p>
</div></td></tr>
    <tr><td class="gt_row gt_left"><div class='gt_from_md'><p><code>dplyr::select()</code> helpers</p>
</div></td>
<td class="gt_row gt_left"><div class='gt_from_md'><p><code>starts_with('NY_')</code>, etc.</p>
</div></td></tr>
  </tbody>
  
  
</table>
</div>

---

## Let's think about the modeling.

What if there were no films with `rated` NC-17 in the training data?

Will the model have a coefficient for `rated` NC-17?

What will happen if the test data includes a film with `rated` NC-17?

---

## `step_novel()`

Adds a catch-all level to a factor for any new values not encountered in model training, which lets R intelligently predict new levels in the test set.

```r
rec <- recipe(test ~ ., data = bechdel) %>%
  step_other(genre, threshold = .05) %>% 
* step_novel(all_nominal_predictors()) %>%
  step_dummy(all_nominal_predictors())
```

---

## Guess

What would happen if you try to normalize 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)

```r
rec <- recipe(test ~ ., data = bechdel) %>%
  step_other(genre, threshold = .05) %>% 
  step_novel(all_nominal_predictors()) %>%
  step_dummy(all_nominal_predictors()) %>% 
* step_zv(all_predictors())
```

---

## `step_normalize()`

Centers then scales numeric variable (mean = 0, sd = 1)

---

## ⏱ Your Turn 1

Unscramble! You have all the steps from our `knn_rec`- your challenge is to *unscramble* them into the right order!

Save the result as `knn_rec`

---

```r
knn_rec <- recipe(formula = test ~ ., data = bechdel) %>% 
  step_other(genre, threshold = .05) %>% 
  step_novel(all_nominal_predictors()) %>% 
  step_dummy(all_nominal_predictors()) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_numeric_predictors()) 
knn_rec
## Recipe
## 
## Inputs:
## 
##       role #variables
##    outcome          1
##  predictor          8
## 
## Operations:
## 
## Collapsing factor levels for genre
## Novel factor level assignment for all_nominal_predictors()
## Dummy variables from all_nominal_predictors()
## Zero variance filter on all_predictors()
## Centering and scaling for all_numeric_predictors()
```

---

## `usemodels`

<https://tidymodels.github.io/usemodels/>

---

```r
library(usemodels)
use_kknn(test ~ ., data = bechdel, verbose = TRUE, tune = FALSE)
## kknn_recipe <- 
##   recipe(formula = test ~ ., data = bechdel) %>% 
##   ## For modeling, it is preferred to encode qualitative data as factors 
##   ## (instead of character). 
##   step_string2factor(one_of("rated", "genre")) %>% 
##   step_novel(all_nominal_predictors()) %>% 
##   ## This model requires the predictors to be numeric. The most common 
##   ## method to convert qualitative predictors to numeric is to create 
##   ## binary indicator variables (aka dummy variables) from these 
##   ## predictors. 
##   step_dummy(all_nominal_predictors()) %>% 
##   ## Since distance calculations are used, the predictor variables should 
##   ## be on the same scale. Before centering and scaling the numeric 
##   ## predictors, any predictors with a single unique value are filtered 
##   ## out. 
##   step_zv(all_predictors()) %>% 
##   step_normalize(all_numeric_predictors()) 
## 
## kknn_spec <- 
##   nearest_neighbor() %>% 
##   set_mode("classification") %>% 
##   set_engine("kknn") 
## 
## kknn_workflow <- 
##   workflow() %>% 
##   add_recipe(kknn_recipe) %>% 
##   add_model(kknn_spec)
```

---

```r
use_glmnet(test ~ ., data = bechdel, verbose = TRUE, tune = FALSE)
## glmnet_recipe <- 
##   recipe(formula = test ~ ., data = bechdel) %>% 
##   ## For modeling, it is preferred to encode qualitative data as factors 
##   ## (instead of character). 
##   step_string2factor(one_of("rated", "genre")) %>% 
##   step_novel(all_nominal_predictors()) %>% 
##   ## This model requires the predictors to be numeric. The most common 
##   ## method to convert qualitative predictors to numeric is to create 
##   ## binary indicator variables (aka dummy variables) from these 
##   ## predictors. 
##   step_dummy(all_nominal_predictors()) %>% 
##   ## Regularization methods sum up functions of the model slope 
##   ## coefficients. Because of this, the predictor variables should be on 
##   ## the same scale. Before centering and scaling the numeric predictors, 
##   ## any predictors with a single unique value are filtered out. 
##   step_zv(all_predictors()) %>% 
##   step_normalize(all_numeric_predictors()) 
## 
## glmnet_spec <- 
##   logistic_reg() %>% 
##   set_mode("classification") %>% 
##   set_engine("glmnet") 
## 
## glmnet_workflow <- 
##   workflow() %>% 
##   add_recipe(glmnet_recipe) %>% 
##   add_model(glmnet_spec)
```

---
class: inverse, middle

## Now we've built a recipe.

## But, how do we *use* a recipe?

---

## Axiom

Feature engineering and modeling are two halves of a single predictive workflow.

---

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---
class: middle, inverse

# Workflows

---

## `workflow()`

Creates a workflow to which you can add a model (and more)

```r
workflow()
```

---

## `add_formula()`

Adds a formula to a workflow `*`

```r
workflow() %>% add_formula(test ~ metascore)
```

---

## `add_model()`

Adds a parsnip model spec to a workflow

```r
workflow() %>% add_model(knn_mod)
```

---

## Guess

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

Fill in the blanks to make a workflow that combines `knn_rec` and with `knn_mod`.

---

```r
knn_wf <- workflow() %>% 
  add_recipe(knn_rec) %>% 
  add_model(knn_mod)
knn_wf
## ══ Workflow ════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: nearest_neighbor()
## 
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 5 Recipe Steps
## 
## • step_other()
## • step_novel()
## • step_dummy()
## • step_zv()
## • step_normalize()
## 
## ── Model ───────────────────────────────────────────────────────────────────────
## K-Nearest Neighbor Model Specification (classification)
## 
## Computational engine: kknn
```

---

## `add_recipe()`

Adds a recipe to a workflow.

```r
knn_wf <- workflow() %>%
* add_recipe(knn_rec) %>%
  add_model(knn_mod)
```

---

## Guess

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

Nope!

```r
rec <- recipe(
* test ~ .,
  data = bechdel
)
```

---

## `fit()`

Fit a workflow that bundles a recipe`*` and a model.

```r
_wf %>% 
  fit(data = bechdel_train) %>% 
  predict(bechdel_test)...
```

---

## Preprocess k-fold resamples?

```r
set.seed(100)
bechdel_folds <- vfold_cv(bechdel_train, v = 10, strata = test)
```

---

## `fit_resamples()`

Fit a workflow that bundles a recipe`*` and a model with resampling.

```r
_wf %>% 
  fit_resamples(resamples = bechdel_folds)
```

---

## ⏱ Your Turn 3

Run the first chunk. Then try our KNN workflow on `bechdel_folds`. What is the ROC AUC?

---

```r
set.seed(100)
bechdel_folds <- vfold_cv(bechdel_train, v = 10, strata = test)

knn_wf %>% 
  fit_resamples(resamples = bechdel_folds) %>% 
  collect_metrics()
## # A tibble: 2 × 6
##   .metric  .estimator  mean     n std_err .config           
##   <chr>    <chr>      <dbl> <int>   <dbl> <chr>             
## 1 accuracy binary     0.591    10  0.0165 Preprocessor1_Mod…
## 2 roc_auc  binary     0.604    10  0.0176 Preprocessor1_Mod…
```

---

## Feature Engineering

]

]

---

## `update_recipe()`

Replace the recipe in a workflow.

```r
_wf %>%
* update_recipe(glmnet_rec)
```

---

## `update_model()`

Replace the model in a workflow.

```r
_wf %>%
* update_model(tree_mod)
```

---

## ⏱ Your Turn 4

Turns out, the same `knn_rec` recipe can also be used to fit a penalized logistic regression model. Let's try it out!

```r
plr_mod <- logistic_reg(penalty = .01, mixture = 1) %>% 
  set_engine("glmnet") %>% 
  set_mode("classification")

plr_mod %>% 
  translate()
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = 0.01
##   mixture = 1
## 
## Computational engine: glmnet 
## 
## Model fit template:
## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     alpha = 1, family = "binomial")
```

---

```r
glmnet_wf <- knn_wf %>% 
  update_model(plr_mod)

glmnet_wf %>% 
  fit_resamples(resamples = bechdel_folds) %>% 
  collect_metrics() 
## # A tibble: 2 × 6
##   .metric  .estimator  mean     n std_err .config           
##   <chr>    <chr>      <dbl> <int>   <dbl> <chr>             
## 1 accuracy binary     0.615    10  0.0139 Preprocessor1_Mod…
## 2 roc_auc  binary     0.647    10  0.0193 Preprocessor1_Mod…
```