# Comparing mlr3pipelines to other frameworks

#### Florian Pfisterer

# Comparing mlr3pipelines to other frameworks

Below, we collected some examples, where mlr3pipelines is compared to different other software packages, such as mlr, recipes and sklearn.

Before diving deeper, we give a short introduction to PipeOps.

## An introduction to “PipeOp”s

In this example, we create a ĺinear Pipeline. After scaling all input features, we rotate our data using principal component analysis. After this transformation, we use a simple Decision Tree learner for classification.

As exemplary data, we will use the “`iris`” classification task. This object contains the famous iris dataset and some meta-information, such as the target variable.

      library("mlr3")
      task = mlr_tasks$get("iris")

We quickly split our data into a train and a test set:

      test.idx = sample(seq_len(task$nrow), 30)
      train.idx = setdiff(seq_len(task$nrow), test.idx)
      # Set task to only use train indexes
      task$row_roles$use = train.idx

A Pipeline (or `Graph`) contains multiple pipeline operators (“`PipeOp`”s), where each `PipeOp` transforms the data when it flows through it. For this use case, we require 3 transformations:

- A `PipeOp` that scales the data
- A `PipeOp` that performs PCA
- A `PipeOp` that contains the Decision Tree learner

A list of available `PipeOp`s can be obtained from

      library("mlr3pipelines")
      mlr_pipeops$keys()
    #>  [1] "boxcox"          "branch"          "chunk"
    #>  [4] "classbalancing"  "classifavg"      "colapply"
    #>  [7] "collapsefactors" "copy"            "encode"
    #> [10] "encodelmer"      "featureunion"    "filter"
    #> [13] "fixfactors"      "histbin"         "ica"
    #> [16] "imputehist"      "imputemean"      "imputemedian"
    #> [19] "imputenewlvl"    "imputesample"    "kernelpca"
    #> [22] "learner"         "learner_cv"      "missind"
    #> [25] "modelmatrix"     "mutate"          "nop"
    #> [28] "pca"             "quantilebin"     "regravg"
    #> [31] "removeconstants" "scale"           "scalemaxabs"
    #> [34] "scalerange"      "select"          "smote"
    #> [37] "spatialsign"     "subsample"       "unbranch"
    #> [40] "yeojohnson"

First we define the required `PipeOp`s:

      op1 = PipeOpScale$new()
      op2 = PipeOpPCA$new()
      op3 = PipeOpLearner$new(learner = mlr_learners$get("classif.rpart"))

### A quick glance into a PipeOp

In order to get a better understanding of what the respective PipeOps do, we quickly look at one of them in detail:

The most important slots in a PipeOp are:

- `$train()`: A function used to train the PipeOp.
- `$predict()`: A function used to predict with the PipeOp.

The `$train()` and `$predict()` functions define the core functionality of our PipeOp. In many cases, in order to not leak information from the training set into the test set it is imperative to treat train and test data separately. For this we require a `$train()` function that learns the appropriate transformations from the training set and a `$predict()` function that applies the transformation on future data.

In the case of `PipeOpPCA` this means the following:

- `$train()` learns a rotation matrix from its input and saves this matrix to an additional slot, `$state`. It returns the rotated input data stored in a new `Task`.
- `$predict()` uses the rotation matrix stored in `$state` in order to rotate future, unseen data. It returns those in a new `Task`.

### Constructing the Pipeline

We can now connect the `PipeOp`s constructed earlier to a Pipeline. We can do this using the `%>>%` operator.

      linear_pipeline = op1 %>>% op2 %>>% op3

The result of this operation is a “`Graph`”. A `Graph` connects the input and output of each `PipeOp` to the following `PipeOp`. This allows us to specify linear processing pipelines. In this case, we connect the output of the scaling PipeOp to the input of the PCA PipeOp and the output of the PCA PipeOp to the input of PipeOpLearner.

We can now train the `Graph` using the `iris` Task.

      linear_pipeline$train(task)
    #> $classif.rpart.output
    #> NULL

When we now train the graph, the data flows through the graph as follows:

- The Task flows into the `PipeOpScale`. The `PipeOp` scales each column in the data contained in the Task and returns a new Task that contains the scaled data to its output.
- The scaled Task flows into the `PipeOpPCA`. PCA transforms the data and returns a (possibly smaller) Task, that contains the transformed data.
- This transformed data then flows into the learner, in our case classif.rpart. It is then used to train the learner, and as a result saves a model that can be used to predict new data.

In order to predict on new data, we need to save the relevant transformations our data went through while training. As a result, each `PipeOp` saves a state, where information requried to appropriately transform future data is stored. In our case, this is mean and standard deviation of each column for `PipeOpScale`, the PCA rotation matrix for `PipeOpPCA` and the learned model for `PipeOpLearner`.

      # predict on test.idx
      task$row_roles$use = test.idx
      linear_pipeline$predict(task)
    #> $classif.rpart.output
    #> <PredictionClassif> for 30 observations:
    #>     row_id     truth  response
    #>          1    setosa    setosa
    #>          5    setosa    setosa
    #>          8    setosa    setosa
    #> ---
    #>        134 virginica virginica
    #>        144 virginica virginica
    #>        146 virginica virginica

## mlr3pipelines vs. mlr

In order to showcase the benefits of mlr3pipelines over mlr’s `Wrapper` mechanism, we compare the case of imputing missing values before filtering the top 2 features and then applying a learner.

While mlr wrappers are generally less verbose and require a little fewer code, this heavily inhibits flexibility. Wrappers can generally not process data in parallel, i.e.

### mlr

    library("mlr")
    # We first create a learner
    lrn = makeLearner("classif.rpart")
    # Wrap this learner in a FilterWrapper
    lrn.wrp = makeFilterWrapper(lrn, fw.abs = 2L)
    # And wrap the resulting wrapped learner into an ImputeWrapper.
    lrn.wrp = makeImputeWrapper(lrn.wrp)

    # Afterwards, we can train the resulting learner on a task
    train(lrn, iris.task)

### mlr3pipelines

    library("mlr3")
    library("mlr3pipelines")
    library("mlr3filters")

    impute = PipeOpImpute$new()
    filter = PipeOpFilter$new(filter = FilterVariance$new(), param_vals = list(filter.nfeat = 2L))
    rpart = PipeOpLearner$new(mlr_learners$get("classif.rpart"))

    # Assemble the Pipeline
    pipeline = impute %>>% filter %>>% rpart
    # And convert to a 'GraphLearner'
    learner = GraphLearner$new(id = "Pipeline", pipeline)

The fact that mlr’s wrappers have to be applied inside-out, i.e. in the reverse order is often confusing. This is way more straight-forward in `mlr3pipelines`, where we simply chain the different methods using `%>>%`. Additionally, `mlr3pipelines` offers way greater possibilities with respect to the kinds of Pipelines that can be constructed. In `mlr3pipelines`, we allow for the construction of parallel and conditional pipelines. This was previously not possible.

## mlr3pipelines vs. sklearn.pipeline.Pipeline

In order to broaden the horizon, we compare to Python sklearn’s `Pipeline` methods. `sklearn.pipeline.Pipeline` sequentially applies a list of transforms before fitting a final estimator. Intermediate steps of the pipeline are `transforms`, i.e. steps that can learn from the data, but also transform the data while it flows through it. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps

It is thus conceptually very similar to `mlr3pipelines`. Similarly to `mlr3pipelines`, we can tune over a full `Pipeline` using various tuning methods. `Pipeline` mainly supports linear pipelines. This means, that it can execute parallel steps, such as for example Bagging, but it does not support conditional execution, i.e. `PipeOpBranch`.

At the same time, the different `transforms` in the pipeline can be cached, which makes tuning over the configuration space of a `Pipeline` more efficient, as executing some steps multiple times can be avoided.

We compare functionality available in both `mlr3pipelines` and `sklearn.pipeline.Pipeline` to give a comparison.

The following example obtained from the sklearn documentation showcases a Pipeline that first Selects a feature and performs PCA on the original data, concatenates the resulting datasets and applies a Support Vector Machine.

### sklearn

    from sklearn.pipeline import Pipeline, FeatureUnion
    from sklearn.model_selection import GridSearchCV
    from sklearn.svm import SVC
    from sklearn.datasets import load_iris
    from sklearn.decomposition import PCA
    from sklearn.feature_selection import SelectKBest

    iris = load_iris()

    X, y = iris.data, iris.target

    # This dataset is way too high-dimensional. Better do PCA:
    pca = PCA(n_components=2)

    # Maybe some original features where good, too?
    selection = SelectKBest(k=1)

    # Build estimator from PCA and Univariate selection:
    combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])

    # Use combined features to transform dataset:
    X_features = combined_features.fit(X, y).transform(X)

    svm = SVC(kernel="linear")

    # Do grid search over k, n_components and C:
    pipeline = Pipeline([("features", combined_features), ("svm", svm)])

    param_grid = dict(features__pca__n_components=[1, 2, 3],
                      features__univ_select__k=[1, 2],
                      svm__C=[0.1, 1, 10])

    grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5, verbose=10)
    grid_search.fit(X, y)

### mlr3pipelines

    library("mlr3verse")
    iris = mlr_tasks$get("iris")

    # Build the steps
    copy = PipeOpCopy$new(2)
    pca = PipeOpPCA$new()
    selection = PipeOpFilter$new(filter = FilterVariance$new())
    union = PipeOpFeatureUnion$new(2)
    svm = PipeOpLearner$new(mlr_learners$get("classif.svm", param_vals = list(kernel = "linear")))

    # Assemble the Pipeline
    pipeline = copy %>>% gunion(list(pca, selection)) %>>% union %>>% svm
    learner = GraphLearner$new(id = "Pipeline", pipeline)

    # For tuning, we define the resampling and the Parameter Space
    resampling = mlr3::mlr_resamplings$get("cv", param_vals = list(folds = 5L))

    param_set = paradox::ParamSet$new(params = list(
      paradox::ParamDbl$new("classif.svm.cost", lower = 0.1, upper = 1),
      paradox::ParamInt$new("pca.rank.",  lower = 1, upper = 3),
      paradox::ParamInt$new("variance.filter.nfeat",  lower = 1, upper = 2)
    ))

    pe = PerformanceEvaluator$new(iris, learner, resampling, param_set)
    terminator = TerminatorEvaluations$new(60)
    tuner = TunerGridSearch$new(pe, terminator, resolution = 10)$tune()

    # Set the learner to the optimal values and train
    learner$param_set$values = tuner$tune_result()$values

In summary, we can achieve similar results with a comparable number of lines, while at the same time offering greater flexibility with respect to which kinds of pipelines we want to optimize over. At the same time, experiments using `mlr3` can now be arbitrarily parallelized using `futures`.

## mlr3pipelines vs recipes

recipes is a new package, that covers some of the same applications steps as mlr3pipelines. Both packages feature the possiblility to connect different pre- and post-processing methods using a pipe-operator. As `recipes` package tightly integrates with the `tidymodels` ecosystem, much of the functionality integrated there can be used in `recipes`. We compare `recipes` to `mlr3pipelines` using an example from the `recipes` vignette.

The aim of the analysis is to predict whether customers pay back their loans given some information on the customers. In order to do this, we build a model that does the following: 1. It first imputes missing values using k-nearest neighbours 2. All factor variables are converted to numerics using dummy encoding 3. The data is first centered then scaled.

In order to validate the algorithm, data is first split into a train and test set using `initial_split`, `training`, `testing`. The recipe trained on the train data (see steps above) is then applied to the test data.

### recipes

    library("tidymodels")
    library("rsample")
    data("credit_data")

    set.seed(55)
    train_test_split = initial_split(credit_data)
    credit_train = training(train_test_split)
    credit_test = testing(train_test_split)

    rec = recipe(Status ~ ., data = credit_train) %>%
      step_knnimpute(all_predictors()) %>%
      step_dummy(all_predictors(), -all_numeric()) %>%
      step_center(all_numeric()) %>%
      step_scale(all_numeric())

    trained_rec = prep(rec, training = credit_train)

    # Apply to train and test set
    train_data <- bake(trained_rec, new_data = credit_train)
    test_data  <- bake(trained_rec, new_data = credit_test)

Afterwards, the transformed data can be used during train and predict

    # Train
    rf = rand_forest(mtry = 12, trees = 200) %>%
      set_engine("ranger", importance = 'impurity') %>%
      fit(Status ~ ., data = train_data)

    # Predict
    prds = predict(rf, test_data)

### mlr3pipelines

The same analysis can be performed in mlr3pipelines

    library("data.table")
    library("mlr3")
    library("mlr3learners")
    library("mlr3pipelines")
    data("credit_data", package = "recipes")
    set.seed(55)

    # Create the task
    tsk = TaskClassif$new(id = "credit_task", target = "Status",
      backend = as_data_backend(data.table(credit_data)))

    # Build up the Pipeline:
    g = PipeOpImpute$new() %>>%
      # PipeOpEncode$new() %>>%
      # PipeOpScale$new() %>>%
      PipeOpLearner$new(mlr_learners$get("classif.ranger"))

    # We can visualize what happens to the data using the `plot` function:
    g$plot()

    # And we can use `mlr3's` full functionality be wrapping the Graph into a GraphLearner.
    glrn = GraphLearner$new(g)
    resample(tsk, glrn, mlr_resamplings$get("holdout"))