6-branching.Rmd

---
title: "Dynamic branching"
output: html_document
---

# About dynamic branching

For pipelines with hundreds of targets, it is impractical to write out every single target by hand in `_targets.R`. That's why the `targets` package supports dynamic branching, a way to write shorthand for large collections of similar targets.

# Static vs dynamic branching

`targets` also supports static branching, an alternative kind of shorthand. This course does not cover it, but it is easier to understand just by reading about it: <https://books.ropensci.org/targets/static.html>.

# Setup

Start with a fresh data store.

```{r}
library(targets)
tar_destroy()
```

Copy the starting `_targets.R` file into the working directory.

```{r}
tmp <- file.copy("6-branching/initial_targets.R", "_targets.R", overwrite = TRUE)
```

Also load the quiz questions. Try not to peek in advance.

```{r}
source("R/quiz.R")
source("6-branching/answers.R")
```

# Branching with map()

Open `_targets.R` for editing.

```{r}
tar_edit()
```

What is different about this new pipeline?

A. It has a new `activations` target that returns the names of all the activation functions we want to try.
B. All our previous `run_*` targets are combined into a new target called `run`.
C. All of A, B, and D.
D. The `run` target sets `pattern = map(activations)` to declare a separate model run for every element of `activations`.

```{r}
answer5_different("E")
```

Run the pipeline. The `run_*` targets are dynamic branches of `run`, and each is a model fit with a different activation function.

```{r}
tar_make()
```

### Return values

To read the return values of branches, you can use `tar_read()` and `tar_load()` as usual. 

```{r}
tar_read(run)
```

Above, we see a data frame with two rows, one from each branch. By default, `targets` uses `vctrs::vec_c()` to aggregate the results from branches. `vctrs` is smart enough to detect that the branches are data frames, and it automatically binds the rows into a single data frame.

You can select individual branches to read with the `branches` argument.

```{r}
tar_read(run, branches = 2)
```

### Changes


As you develop the workflow, depending on what you change, some branches may run while others are skipped. As an example, add the softmax activation function in `_targets.R`. Sketch:

```{r, eval = FALSE}
tar_target(activations, c("relu", "sigmoid", "softmax"))
```

Now, run the pipeline.

```{r}
tar_make()
```

Which target(s) reran? Why?

A. Just the new run with the softmax activation function. The rest of the targets were already up to date, so `tar_make()` skipped them.
B. Just the new run with the sigmoid activation function. The rest of the targets were already up to date, so `tar_make()` skipped them.
C. The new run with the softmax activation function, the `best_run` target, and possibly `best_model` (only if `best_run` changed). `tar_make()` skipped the other model runs because they were already up to date.
D. All the model runs and everything downstream because the `activations` target changed.

```{r}
answer5_map_runs("E")
```

`map()` can branch over more than one argument. As an example, let's vary the size of the first layer of the neural network. Make the following changes to `_targets.R`:

1. Define a new target called `units` which returns `c(16, 32, 64)`.
2. For the `run` target, change `map(activations)` to `map(activations, units)`.
3. Change the command of `run` to `test_model(act1 = activations, units1 = units, churn_data, churn_recipe)`

Now, run the pipeline again.

```{r}
tar_make()
```

Read the results of the model runs.

```{r}
tar_read(run)
```

Which model runs do you see in the output?

A. 3 model runs: 1 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. Each model run has the same value for `units1`.
B. 3 model runs: 1 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. In the first layer, the relu model has 16 neurons, the sigmoid model has 32 neurons, and the softmax model has 64 runs.
C. 9 model runs: 3 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. Each model run has the same value for `units1`.
D. 9 model runs: one for each combination of activation function and units in the first layer.

```{r}
answer5_map2("E")
```

# Branching with cross()

`cross()` is like `map()`, but it defines a new branch for each combination of grouping variables. In the `run` target in `_targets.R`, change `pattern = map(activations, units)` to `pattern = cross(activations, units)`. Then, run the pipeline again.

```{r}
tar_make()
```

In that call to `tar_make()`, how many `run_*` branches executed?

A. All of them, because we changed the branching structure.
B. None, because all targets stayed up to date.
C. The 3 branches from the earlier `map()` call were still up to date, so they were skipped. `cross()` added 6 additional targets to the pipeline, and those 6 ran for the first time.
D. The 9 branches from the earlier `map()` call were still up to date, so they were skipped. `cross()` added 3 additional targets to the pipeline, and those ran for the first time.

```{r}
answer5_cross_reruns("E")
```

View the results.

```{r}
tar_read(run)
```

Which model runs do we have now?

A. 3 model runs, 1 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. Each model run has the same value for `units1`.
B. 3 model runs, 1 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. In the first layer, the relu model has 16 neurons, the sigmoid model has 32 neurons, and the softmax model has 64 runs.
C. 9 model runs, 3 for each of the relu, sigmoid, and softmax activation functions in the first layer of the neural net. Each model run has the same value for `units1`.
D. 9 model runs, one for each combination of activation function and units in the first layer.

```{r}
answer5_cross("E")
```

All targets should be up to date now.

```{r}
tar_make()
```

```{r}
tar_outdated()
```

# List iteration

For this section, let's use just the first part of our crossed pipeline from earlier. Copy it over to `_targets.R`.

```{r}
tmp <- file.copy("6-branching/iteration_targets.R", "_targets.R", overwrite = TRUE)
```

Take a look at the new pipeline.

```{r}
tar_edit()
```

Sometimes, we may not want to aggregate all the branches into a vector or data frame. Set `iteration = "list"` in `tar_target()` for the `run` target.

```{r, eval = FALSE}
# Do not run here.
tar_target(
  run,
  test_model(act1 = activations, units1 = units, churn_data, churn_recipe),
  pattern = cross(activations, units),
  iteration = "list" # list iteration
)
```

Then, run the pipeline.

```{r}
tar_make()
```

Take a look at the results now.

```{r, paged.print = FALSE}
tar_read(run)
```

What do you see?

A. A data frame of fitted model results.
B. A list of one-row data frames of model results.
C. A data frame of lists of model results.
D. A list of trained Keras models.

```{r}
answer5_alt_iteration("E")
```

# Vector iteration

Now, hopefully you see the magic behind the default `iteration = "vector"`. With vector iteration, `tar_read()` and targets downstream of `run` need only mention the symbol `run`, and all the branches will automatically come together in a friendlier data structure.

Switch back to `iteration = "vector"`.

```{r, eval = FALSE}
# Do not run here.
tar_target(
  run,
  test_model(act1 = activations, units1 = units, churn_data, churn_recipe),
  pattern = cross(activations, units),
  iteration = "vector" # vector iteration
)
```

Then, rerun the pipeline.

```{r}
tar_make()
```

When you call `tar_read()`, `vctrs::vec_c()` aggregates the branches into a nice `tibble` behind the scenes.

```{r}
tar_read(run)
```

And yes, I know you just had to fit all 9 models all over again, and that took a lot of runtime. How do we save time if we need to frequently switch iteration methods?

A. Split the `run` target into a model-fitting target that returns a Keras model and summarization step that computes the data frame with accuracy and hyperparameters. That way, the changes are absorbed by computationally cheaper targets.
B. Combine all the `run` branches into a single target that fits all the models together.
C. If your downstream code does not care how the model runs get aggregated, set `cue = tar_cue(iteration = FALSE)` in `tar_target()` so that the change in iteration method does not invalidate all the branches.
D. A or C.

```{r}
answer5_fickle("E")
```

# Group iteration

`targets` lets you dynamically iterate over arbitrary subsets of a data frame. It's like `dplyr::group_by()`, but with branches. The `tar_group()` function is the key. It looks at your `dplyr` groups and creates a special `tar_group` column to explicitly indicate group membership.

```{r, message = FALSE}
library(tidyverse)
tar_read(run) %>%
  group_by(act1) %>%
  tar_group()
```

Then, any pattern that branches over it will iterate over those groups. Open `_targets.R` and try it out yourself:

1. Define a new target called `grouped_run` with the command `run %>% group_by(act1) %>% tar_group()`.
2. Set `iteration = "group"` in `tar_target()` for `grouped_run`. Do not `map()` over `run`. Thanks to vector iteration, `run` will automatically appear to `grouped_run` as a single data frame with our 9 model runs.
3. Define a new branching target (i.e. pattern) called `nrows` which maps over `grouped_run` and counts the number of rows.

Check the graph.

```{r}
tar_visnetwork()
```

It should look like this:

```{r}
readRDS("6-branching/group_graph.rds")
```

Also check the manifest.

```{r, paged.print = FALSE}
tar_manifest()
```

It should look like this:

```{r, paged.print = FALSE}
readRDS("6-branching/group_manifest.rds")
```

Then, run the pipeline.

```{r}
tar_make()
```

How many branches of `grouped_run` were created? How many branches of `nrows`?

A. None for `grouped_run`, none for `nrows`.
B. None for `grouped_run`, 3 for `nrows`.
C. 3 for `grouped_run`, none for `nrows`.
D. 3 for `grouped_run`, 3 for `nrows`.

```{r}
answer5_grouped("E")
```

Read `nrows`.

```{r}
tar_read(nrows)
```

What is in `nrows`? Why?

A. The number 9, because `grouped_df` had 9 rows
B. A vector of length 9 with elements all equal to 1, because we mapped over `grouped_df`, which has 9 rows.
C. A vector of length 3 with each element equal to 3. We mapped over the groups in `grouped_df` and counted the rows in each group.
D. A list of 3 data frames, each with 3 rows, because we mapped over the groups in `grouped_df`.

```{r}
answer5_nrows("E")
```

# Branching over files and patterns.

In `targets`, it is possible to branch over multiple data files. To see this, let's switch to a new pipeline.

```{r}
tmp <- file.copy("6-branching/files_targets.R", "_targets.R", overwrite = TRUE)
```

Take a look at the new `_targets.R`.

```{r}
tar_edit()
```

This pipeline uses the `tar_files()` helper from the `tarchetypes` package. Here, we use `tar_files()` to ensure we can correctly branch over the files in the `data/` folder. The mechanism requires two different targets to work correctly. `churn_file_files` does the actual work and returns the output files we want to track, and `churn_file` separates the output files into iterable branches.

```{r}
tar_manifest()
```

We can now branch over `churn_file`, which actually tracks multiple files (one per branch). Add two targets to the pipeline:

1. A target named `churn_data` that maps over `churn_file` and has the command `split_data(churn_file)`.
2. A target named `churn_recipe` that maps over `churn_data` and has the command ` prepare_recipe(churn_data)`. 

Check the abridged graph.

```{r}
tar_glimpse()
```

It should look like this:

```{r}
readRDS("6-branching/files_graph.rds")
```

Then run `tar_make()`.

```{r}
tar_make()
```

How many branches were created for each of `churn_data` and `churn_recipe`? Why?

A. 3 for both. `churn_data` maps over the 3 branches of `churn_file`, so each `churn_data` branch corresponds to one of the CSV files in `data/`. `churn_recipe` produces one branch for each branch of `churn_data` (i.e. each dataset).
B. 3 for both. `churn_data` knows to create 3 branches because it checks the files in `data/`. 
C. 1 for both. We can branch over vectors and data frames, but not patterns (branching targets).
D. 1 for both. We do not use any branching after `churn_file`.

```{r}
answer5_files_branch("E")
```

If we try to read in the datasets, we get an error.

```{r}
library(rsample)
tar_read(churn_data)
```

What is the cause of the error?

A. The saved data in `_targets/` is corrupted.
B. We did not map over `churn_file` correctly.
C. The return value from each individual branch is incorrect.
D. `tar_read()` assumed vector iteration because we did not set the `iteration` argument of `tar_target()`. That means `vctrs::vec_c()` tried to combine a bunch of `rsplit` objects into a vector-like object, which is not possible. We need list iteration for both `churn_data` and `churn_recipe`.

```{r}
answer5_error("E")
```

Fix the error in `_targets.R` and rerun the pipeline.

```{r}
tar_make()
```

Try again to read the data.

```{r}
library(rsample)
tar_read(churn_data)
```

What data structure do you see?

A. A list of data frames.
B. A vector of data frames.
C. A list of `rsplit` objects with training and testing datasets.
D. A vector of `rsplit` objects with training and testing datasets.

```{r}
answer5_churn_data("E")
```