05_workflows.Rmd

---
title: "05_workflows"
output: html_document
---

layout: false
class: middle, center, inverse

## 4.4 `workflows`:<br><br>Modeling Workflows

---

background-image: url(https://www.tidymodels.org/images/workflows.png)
background-position: 97.5% 2.5%
background-size: 7%
layout: true

---

## 4.4 `workflows`: Modeling Workflows

`workflows` introduces a `workflow` object which bundles the preprocessing recipe and model specification to reduce code clatter. At the same time, it acts as a single entry point to your modeling pipeline.

```{r}
cls_wf <- workflow(preprocessor = mod_recipe, spec = log_cls)
cls_wf
```

.pull-right[.pull-right[.footnote[
*Note: You may even bundle (and later explore) various different combinations of preprocessing recipes and model specifications using the `workflowsets` package.*
]]]

???
- if no preprocessing is required (because the data is already perfect) `add_formula()` could be used (but you can only ever use one of the two)
- in later releases, `workflows` should also be able to encapsulate post-processing steps (e.g., modifying the probability cutoff for binary classification; or calibration of probabilities; or determination of euqivocal zones)

---

## 4.4 `workflows`: Modeling Workflows

When calling `fit()` on a `workflow` object, `tidymodels` performs the following steps for us:
1. It fits the `recipe` object to the training set and produces the in-sample estimates (`prep()`).
2. It applies the fitted recipe to the training set to process the predictors (`bake()`).
3. It trains the specified model on the transformed training set (`fit()`/`fit_xy()`).

```{r, results='hide'}
cls_wf_fit <- cls_wf %>% 
  fit(train_set)

cls_wf_fit
```
```
> Output on next slide

```

???
- workflows abstract away the need for `prep` and `bake`

---

## 4.4 `workflows`: Modeling Workflows

```
> == Workflow [trained] =========================================================================
> Preprocessor: Recipe
> Model: logistic_reg()
> 
> -- Preprocessor -------------------------------------------------------------------------------
> 5 Recipe Steps
> * step_medianimpute()   * step_dummy()
> * step_normalize()      * step_smote()
> * step_other()
> 
> -- Model --------------------------------------------------------------------------------------
> Call:  stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
> 
> Coefficients:
>                (Intercept)                        year                         age  
>                   -2.76543                    -0.44060                     0.05288  
>          peak_name_Cho.Oyu           peak_name_Everest           peak_name_Manaslu  
>                    0.09953                     1.19280                     1.41512  
>            peak_name_other               season_Spring                         ...  
>                    1.31090                     0.02033                         ...  
> 
> Degrees of Freedom: 96469 Total (i.e. Null);  96447 Residual
> Null Deviance:	    127600 
> Residual Deviance: 110100 	AIC: 110200

```

???
output abbreviated

---

## 4.4 `workflows`: Modeling Workflows

Again, after having fitted the workflow, we can proceed to predicting the response in the test data. When calling `predict()` on a `workflow` object, `tidymodels` performs the following steps for us:
1. It applies the fitted recipe to the test set to process the predictors (`bake()`).
2. It applies the trained model to the preprocessed test set predictors to generate predictions (`predict()`).

```{r}
cls_wf_fit %>% 
  predict(new_data = test_set, type = "prob") %>% 
  glimpse
```

.footnote[
*Note: Call `extract_fit_engine()` or `extract_recipe()` to extract the fitted model or the estimated `recipe` object from the workflow.*
]

---

name: example-no-resampling

## Tidymodels: A Complete Example

**Step 1:** Split data into training and test set using `rsample`.
```{r}
set.seed(2021)
climbers_split <- initial_split(climbers_df, prop = 0.8, strata = died)

train_set <- training(climbers_split)
test_set  <- testing(climbers_split)
```

**Step 2:** Define the relevant preprocessing steps using `recipe`.
```{r}
rec <- recipe(formula = died ~ ., data = train_set) %>%
  update_role(member_id, new_role = "id") %>%
  step_impute_median(age) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_other(peak_name, citizenship, expedition_role, threshold = 0.05) %>%
  step_dummy(all_predictors(), -all_numeric(), one_hot = F) %>%
  themis::step_upsample(died, over_ratio = 0.2, seed = 2021, skip = T)
```

**Step 3:** Specify the desired machine learning model using `parsnip`.
```{r}
log_cls <- logistic_reg() %>% 
  set_engine("glm") %>% 
  set_mode("classification")
```

---

## Tidymodels: A Complete Example

**Step 4:** Bring everything together using `workflows`.
```{r}
cls_wf <- workflow() %>%
  add_recipe(rec) %>%
  add_model(log_cls)
```

**Step 5:** Train the workflow (i.e. recipe plus model) and use it for prediction.
```{r}
cls_wf_fit <- cls_wf %>%
  fit(train_set)

cls_wf_fit %>% 
  predict(new_data = test_set, type = "prob") %>% 
  glimpse
```