Skip to content

Latest commit

 

History

History
99 lines (83 loc) · 3.13 KB

linear_pipelines.md

File metadata and controls

99 lines (83 loc) · 3.13 KB
Raw

Linear Pipelines

In MLJ a pipeline is a composite model in which models are chained together in a linear (non-branching) chain. Pipelines can include learned or static target transformations, if one of the models is supervised.

To illustrate basic construction of a pipeline, consider the following toy data:

using MLJ
MLJ.color_off()

using MLJ
X = (age    = [23, 45, 34, 25, 67],
	 gender = categorical(['m', 'm', 'f', 'm', 'f']));
height = [67.0, 81.5, 55.6, 90.0, 61.1]
nothing # hide

The code below defines a new model type, and an instance of that ype called pipe, for performing the following operations:

  • standardize the target variable :height to have mean zero and standard deviation one
  • coerce the :age field to have Continuous scitype
  • one-hot encode the categorical feature :gender
  • train a K-nearest neighbor model on the transformed inputs and transformed target
  • restore the predictions of the KNN model to the original :height scale (i.e., invert the standardization)
const KNNRegressor = @load KNNRegressor pkg=NearestNeighborModels

KNNRegressor = @load KNNRegressor
pipe = @pipeline(X -> coerce(X, :age=>Continuous),
				 OneHotEncoder,
				 KNNRegressor(K=3),
				 target = Standardizer())

Pipeline326(
	one_hot_encoder = OneHotEncoder(
			features = Symbol[],
			drop_last = false,
			ordered_factor = true,
			ignore = false),
	knn_regressor = KNNRegressor(
			K = 3,
			algorithm = :kdtree,
			metric = Distances.Euclidean(0.0),
			leafsize = 10,
			reorder = true,
			weights = :uniform),
	target = Standardizer(
			features = Symbol[],
			ignore = false,
			ordered_factor = false,
			count = false)) @287

Notice that field names for the composite are automatically generated based on the component model type names. The automatically generated name of the new model composite model type, Pipeline406, can be replaced with a user-defined one by specifying, say, name=MyPipe. If you are planning on serializing (saving) a pipeline-machine, you will need to specify a name..

The new model can be used just like any other non-composite model:

pipe.knn_regressor.K = 2
pipe.one_hot_encoder.drop_last = true
evaluate(pipe, X, height, resampling=Holdout(), measure=l2, verbosity=2)

[ Info: Training Machine{Pipeline406} @959.
[ Info: Training Machine{UnivariateStandardizer} @422.
[ Info: Training Machine{OneHotEncoder} @745.
[ Info: Spawning 1 sub-features to one-hot encode feature :gender.
[ Info: Training Machine{KNNRegressor} @005.
┌───────────┬───────────────┬────────────┐
│ _.measure │ _.measurement │ _.per_fold │
├───────────┼───────────────┼────────────┤
│ l2        │ 55.5          │ [55.5]     │
└───────────┴───────────────┴────────────┘
_.per_observation = [[[55.502499999999934]]]

For important details on including target transformations, see below.

@pipeline