knitr::opts_chunk$set( cache = FALSE, collapse = TRUE, comment = "#>" ) set.seed(8008135) compiler::enableJIT(0) library("mlr3") library("mlr3pipelines")
This vignette is an in-depth introduction to
mlr3pipelines, the dataflow programming toolkit for machine learning in
mlr3. It will go through basic concepts and then give a few examples that both show the simplicity as well as the power and versaitility of using
What's the Point
Machine learning toolkits often try to abstract away the processes happening inside machine learning algorithms. This makes it easy for the user to switch out one algorithm for another without having to worry about what is happening inside it, what kind of data it is able to operate on etc. The benefit of using
mlr3, for example, is that one can create a
Resampling etc. and use them for typical machine learning operations. It is trivial to exchange individual components and therefore use, for example, a different
Learner in the same experiment for comparison.
task = TaskClassif$new("iris", as_data_backend(iris), "Species") lrn = mlr_learners$get("classif.rpart") rsmp = mlr_resamplings$get("holdout") resample(task, lrn, rsmp)
However, this modularity breaks down as soon as the learning algorithm encompasses more than just model fitting, like data preprocessing, ensembles or other meta models.
mlr3pipelines takes modularity one step further than
mlr3: it makes it possible to build individual steps within a "
Learner" out of building blocks called
PipeOp: Pipeline Operators
The most basic unit of functionality within
mlr3pipelines is the
PipeOp, short for "pipeline operator", which represents a transformative operation on input (for example a training dataset) leading to output. It can therefore be seen as a generalised notion of a function, with a certain twist:
PipeOps behave differently during a "training phase" and a "prediction phase". The training phase will typically generate a certain model of the data that is saved as internal state. The prediction phase will then operate on the input data depending on the trained model.
An example of this behaviour is the principal component analysis operation ("
PipeOpPCA"): During training, it will transform incoming data by rotating it in a way that leads to uncorrelated features ordered by their contribution to total variance. It will also save the rotation matrix to be used during for new data. This makes it possible to perform "prediction" with single rows of new data, where a row's scores on each of the principal components (the components of the training data!) is computed.
po = mlr_pipeops$get("pca") po$train(list(task))[]$data()
single_line_task = task$clone()$filter(1) po$predict(list(single_line_task))[]$data()
This shows the most important primitives incorporated in a
$train(), taking a list of input arguments, turning them into a list of outputs, meanwhile saving a state in
$predict(), taking a list of input arguments, turning them into a list of outputs, making use of the saved
$state, the "model" trained with
$train()and utilised during
Schematically we can represent the
PipeOp like so:
It is important to take a moment and notice the importance of a
$state variable and the
$predict() dichotomy in a
PipeOp. There are many preprocessing methods, for example scaling of parameters or imputation, that could in theory just be applied to training data and prediction / validation data separately, or they could be applied to a task before resampling is performed. This would, however, be fallacious:
- The preprocessing of each instance of prediction data should not depend on the remaining prediction dataset. A prediction on a single instance of new data should give the same result as prediction performed on a whole dataset.
- If preprocessing is performed on a task before resampling is done, information about the test set can leak into the training set. Resampling should evaluate the generalisation performance of the entire machine learning method, therefore the behaviour of this entire method must only depend only on the content of the training split during resampling.
Where to Get
PipeOp is an instance of an "
R6" class, many of which are provided by the
mlr3pipelines package itself. They can be constructed explicitly ("
PipeOpPCA$new()") or retrieved from the
mlr_pipeops$get("pca"). The entire list of available
PipeOps, and some meta-information, can be retrieved using
as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num")]
mlr_pipeops$get("pca", param_vals = list(rank. = 3))
Just like functions,
PipeOps can take multiple inputs. These multiple inputs are always given as elements in the input list. For example, there is a
PipeOpFeatureUnion that combines multiple tasks with different features and "
cbind()s" them together, creating one combined task. When two halves of the
iris task are given, for example, it recreates the original task:
iris_first_half = task$clone()$select(c("Petal.Length", "Petal.Width")) iris_second_half = task$clone()$select(c("Sepal.Length", "Sepal.Width")) pofu = mlr_pipeops$get("featureunion", innum = 2) pofu$train(list(iris_first_half, iris_second_half))[]$data()
PipeOpFeatureUnion effectively takes two input arguments here, we can say it has two input channels. An input channel also carries information about the type of input that is acceptable. The input channels of the
pofu object constructed above, for example, each accept a
Task during training and prediction. This information can be queried from the
PipeOps may have channels that take different types during different phases. The
PipeOp, for example, takes a
NULL and a
Task during training, and a
Prediction and a
Task during prediction:
# TODO this is an important case to handle here, do not delete unless there is a better example. # mlr_pipeops$get("backuplearner")$input
Unlike the typical notion of a function,
PipeOps can also have multiple output channels.
$predict() always return a list, so certain
PipeOps may return lists with more than one element. Similar to input channels, the information about the number and type of outputs given by a
PipeOp is available in the
$output slot. The
chunk PipeOp, for example, chunks a given
Task into subsets and consequently returns multiple
Task objects, both during training and prediction. The number of output channels must be given during construction through the
mlr_pipeops$get("chunk", outnum = 3)$output
Note that the number of output channels during training and prediction is the same. A schema of a
PipeOp with two output channels:
PipeOps have only one input channel (so they take a list with a single element), but there are a few with more than one; in many cases, the number of input or output channels is determined during construction, e.g. through the
outnum arguments. The
output.num columns of the
mlr_pipeops-table above show the default number of channels, and
NA if the number depends on a construction argument.
The default printer of a
PipeOp gives information about channel names and types:
Graph: Networks of
What is the advantage of this tedious way of declaring input and output channels and handling in/output through lists? Because each
PipeOp has a known number of input and output channels that always produce or accept data of a known type, it is possible to network them together in
Graph is a collection of
PipeOps with "edges" that mandate that data should be flowing along them. Edges always pass between
PipeOp channels, so it is not only possible to explicitly prescribe which position of an input or output list an edge refers to, it makes it possible to make different components of a
PipeOp's output flow to multiple different other
PipeOps, as well as to have a
PipeOp gather its input from multiple other
A schema of a simple graph of
Graph is empty when first created, and
PipeOps can be added using the
$add_pipeop() method. The
$add_edge() method is used to create connections between them. While the printer of a
Graph gives some information about its layout, the most intuitive way of visualizing it is using the
gr = Graph$new() gr$add_pipeop(mlr_pipeops$get("scale")) gr$add_pipeop(mlr_pipeops$get("subsample", param_vals = list(frac = 0.1))) gr$add_edge("scale", "subsample")
gr$plot(html = TRUE)
Graph itself has a
$train() and a
$predict() method that accept some data and propagate this data through the network of
PipeOps. The return value corresponds to the output of the
PipeOp output channels that are not connected to other
The collection of
PipeOps inside a
Graph can be accessed through the
$pipeops slot. The set of edges in the Graph can be inspected through the
$edges slot. It is possible to modify individual
PipeOps and edges in a Graph through these slots, but this is not recommended because no error checking is performed and it may put the
Graph in an unsupported state.
The example above showed a linear preprocessing pipeline, but it is in fact possible to build true "graphs" of operations, as long as no loops are introduced^[It is tempting to denote this as a "directed acyclic graph", but this would not be entirely correct because edges run between channels of
PipeOps with multiple output channels can feed their data to multiple different subsequent
PipeOps with multiple input channels can take results from different
PipeOps. When a
PipeOp has more than one input / output channel, then the
$add_edge() method needs an additional argument that indicates which channel to connect to. This argument can be given in the form of an integer, or as the name of the channel.
The following constructs a
Graph that copies the input and gives one copy each to a "scale" and a "pca"
PipeOp. The resulting columns of each operation are put next to each other by "featureunion".
gr = Graph$new()$ add_pipeop(mlr_pipeops$get("copy", outnum = 2))$ add_pipeop(mlr_pipeops$get("scale"))$ add_pipeop(mlr_pipeops$get("pca"))$ add_pipeop(mlr_pipeops$get("featureunion", innum = 2)) gr$ add_edge("copy", "scale", src_channel = 1)$ # designating channel by index add_edge("copy", "pca", src_channel = "output2")$ # designating channel by name add_edge("scale", "featureunion", dst_channel = 1)$ add_edge("pca", "featureunion", dst_channel = 2) gr$plot(html = TRUE)
Although it is possible to create intricate
Graphs with edges going all over the place (as long as no loops are introduced), there is usually a clear direction of flow between "layers" in the
Graph. It is therefore convenient to build up a
Graph from layers, which can be done using the
%>>% ("double-arrow") operator. It takes either a
PipeOp or a
Graph on each of its sides and connects all of the outputs of its left-hand side to one of the inputs each of its right-hand side--the number of inputs therefore must match the number of outputs. Together with the
gunion() operation, which takes
Graphs and arranges them next to each other akin to a (disjoint) graph union, the above network can more easily be constructed as follows:
gr = mlr_pipeops$get("copy", outnum = 2) %>>% gunion(list(mlr_pipeops$get("scale"), mlr_pipeops$get("pca"))) %>>% mlr_pipeops$get("featureunion", innum = 2) gr$plot(html = TRUE)
PipeOp IDs and ID Name Clashes
PipeOps within a graph are addressed by their
$id-slot. It is therefore necessary for all
PipeOps within a
Graph to have a unique
$id can be set during or after construction, but it should not directly be changed after a
PipeOp was inserted in a
Graph. At that point, the
$set_names()-method can be used to change
po1 = mlr_pipeops$get("scale") po2 = mlr_pipeops$get("scale") po1 %>>% po2 # name clash
po2$id = "scale2" gr = po1 %>>% po2 gr
# Alternative ways of getting new ids: mlr_pipeops$get("scale", id = "scale2") PipeOpScale$new(id = "scale2")
# sometimes names of PipeOps within a Graph need to be changed gr2 = mlr_pipeops$get("scale") %>>% mlr_pipeops$get("pca") gr %>>% gr2
gr2$set_names("scale", "scale3") gr %>>% gr2
Learners in Graphs, Graphs in Learners
The true power of
mlr3pipelines derives from the fact that it can be integrated seamlessly with
mlr3. Two components are mainly responsible for this:
PipeOpthat encapsulates a
Learnerand creates a
PredictionDataobject in its
Learnerthat can be used in place of any other
Learner, but which does prediction using a
Graphgiven to it
Note that these are dual to each other: One takes a
Learner and produces a
PipeOp (and by extension a
Graph); the other takes a
Graph and produces a
PipeOpLearner is constructed using a
Learner and will use it to create
PredictionData in the
$predict() phase. The output during
NULL. It can be used after a preprocessing pipeline, and it is even possible to perform operations on the
PredictionData, for example by averaging multiple predictions or by using the "
PipeOpBackupLearner" operator to impute predictions that a given model failed to create.
The following is a very simple
Graph that performs training and prediction on data after performing principal component analysis.
gr = mlr_pipeops$get("pca") %>>% mlr_pipeops$get("learner", mlr_learners$get("classif.rpart"))
$predict() functions, it can not be used directly in places where
Learners can be used like resampling or benchmarks. For this, it needs to be wrapped in a
GraphLearner object, which is a thin wrapper that enables this functionality. The resulting
Learner is extremely versatile, because every part of it can be modified, replaced, parameterized and optimized over. Resampling the graph above can be done the same way that resampling of the
Learner was performed in the introductory example.
lrngrph = GraphLearner$new(gr) resample(task, lrngrph, rsmp)
mlr3pipelines relies on the
paradox package to provide parameters that can modify each
paradox parameters provide information about the parameters that can be changed, as well as their types and ranges. They provide a unified interface for benchmarks and parameter optimization ("tuning"). For a deep dive into
paradox, see the mlr3book.
ParamSet, representing the space of possible parameter configurations of a
PipeOp, can be inspected by accessing the
$param_set slot of a
PipeOp or a
op_pca = mlr_pipeops$get("pca") op_pca$param_set
To set or retrieve a parameter, the
$param_set$values slot can be accessed. Alternatively, the
param_vals value can be given during construction.
op_pca$param_set$values$center = FALSE op_pca$param_set$values
op_pca = mlr_pipeops$get("pca", param_vals = list(center = TRUE)) op_pca$param_set$values
PipeOp can bring its own individual parameters which are collected together in the
PipeOp's parameter names are prefixed with its
$id to prevent parameter name clashes.
gr = op_pca %>>% mlr_pipeops$get("scale") gr$param_set
GraphLearner preserve parameters of the objects they encapsulate.
op_rpart = mlr_pipeops$get("learner", mlr_learners$get("classif.rpart")) op_rpart$param_set
glrn = GraphLearner$new(gr %>>% op_rpart) glrn$param_set