pimlico.datatypes.sklearn module

Datatypes for storing and loading Scikit-learn models.

class SklearnModelWriter(base_dir, **kwargs)

Bases: pimlico.datatypes.base.PimlicoDatatypeWriter

write_model(model)

class SklearnModel(base_dir, pipeline, **kwargs)

Bases: pimlico.datatypes.base.PimlicoDatatype

Datatype for storing Scikit-learn models.

Very simple storage mechanism: we just pickle the model to a file. Instead of the standard Python pickle package, we use Joblib, which stores large data objects (especially Numpy arrays) more efficiently.

get_software_dependencies()

Check that all software required to read this datatype is installed and locatable. This is separate to metadata config checks, so that you don’t need to satisfy the dependencies for all modules in order to be able to run one of them. You might, for example, want to run different modules on different machines. This is called when a module is about to be executed and each of the dependencies is checked.

Returns a list of instances of subclasses of :class:~pimlico.core.dependencies.base.SoftwareDependency, representing the libraries that this module depends on.

Take care when providing dependency classes that you don’t put any import statements at the top of the Python module that will make loading the dependency type itself dependent on runtime dependencies. You’ll want to run import checks by putting import statements within this method.

You should call the super method for checking superclass dependencies.

load_model()