simpleml.utils.training.create_persistable module

Module with helper classes to create new persistables

class simpleml.utils.training.create_persistable.DatasetCreator

Bases: simpleml.utils.training.create_persistable.PersistableCreator

classmethod create(registered_name, pipeline=None, **kwargs)

Stateless method to create a new persistable with the desired parameters kwargs are passed directly to persistable

Parameters:

• registered_name – Class name registered in SimpleML
• dataset_pipeline – dataset pipeline object

classmethod determine_filters(name='', version=None, strict=True, **kwargs)

stateless method to determine which filters to apply when looking for existing persistable

Returns: database class, filter dictionary

Parameters:

• registered_name – Class name registered in SimpleML
• strict – whether to assume same class and name = same persistable,

or, load the data and compare the hash

classmethod retrieve_pipeline(**pipeline_kwargs)

class simpleml.utils.training.create_persistable.MetricCreator

Bases: simpleml.utils.training.create_persistable.PersistableCreator

classmethod create(registered_name, model=None, **kwargs)

Stateless method to create a new persistable with the desired parameters kwargs are passed directly to persistable

Parameters:

• registered_name – Class name registered in SimpleML
• model – model class

classmethod determine_filters(name=None, model_id=None, strict=False, **kwargs)

stateless method to determine which filters to apply when looking for existing persistable

Returns: database class, filter dictionary

Parameters:

• registered_name – Class name registered in SimpleML
• strict – whether to fit objects first before assuming they are identical

In theory if all inputs and classes are the same, the outputs should deterministically be the same as well (up to random iter). So, you dont need to fit objects to be sure they are the same

classmethod retrieve_model(**model_kwargs)

class simpleml.utils.training.create_persistable.ModelCreator

Bases: simpleml.utils.training.create_persistable.PersistableCreator

classmethod create(registered_name, pipeline=None, **kwargs)

Stateless method to create a new persistable with the desired parameters kwargs are passed directly to persistable

Parameters:

• registered_name – Class name registered in SimpleML
• pipeline – pipeline object

classmethod determine_filters(name='', version=None, strict=False, **kwargs)

stateless method to determine which filters to apply when looking for existing persistable

Returns: database class, filter dictionary

Parameters:

• registered_name – Class name registered in SimpleML
• strict – whether to fit objects first before assuming they are identical

In theory if all inputs and classes are the same, the outputs should deterministically be the same as well (up to random iter). So, you dont need to fit objects to be sure they are the same

classmethod retrieve_pipeline(**pipeline_kwargs)

class simpleml.utils.training.create_persistable.PersistableCreator

Bases: object

create(**kwargs)

method to create a new persistable with the desired parameters kwargs are passed directly to persistable

determine_filters(strict=False, **kwargs)

method to determine which filters to apply when looking for existing persistable

Parameters:strict – whether to fit objects first before assuming they are identical

In theory if all inputs and classes are the same, the outputs should deterministically be the same as well (up to random iter). So, you dont need to fit objects to be sure they are the same

Default design iterates through 2 (or 3) options when retrieving persistables:

1. By name and version (unique properties that define persistables)

2) By name, registered_name, and computed hash 2.5) Optionally, just use name and registered_name (assumes class

definition is the same and would result in an identical persistable)

Returns: database class, filter dictionary

static retrieve(cls, filters)

Query database using the table model (cls) and filters for a matching persistable

static retrieve_dependency(dependency_cls, **dependency_kwargs)

Base method to query for dependency Raises TrainingError if dependency does not exist

static retrieve_from_registry(registered_name)

stateless method to query registry for class definitions. handles errors

classmethod retrieve_or_create(**kwargs)

Wrapper method to first attempt to retrieve a matching persistable and then create a new one if it isn’t found

class simpleml.utils.training.create_persistable.PipelineCreator

Bases: simpleml.utils.training.create_persistable.PersistableCreator

classmethod create(registered_name, dataset=None, **kwargs)

Stateless method to create a new persistable with the desired parameters kwargs are passed directly to persistable

Parameters:

• registered_name – Class name registered in SimpleML
• dataset – dataset object

classmethod determine_filters(name='', version=None, strict=False, **kwargs)

stateless method to determine which filters to apply when looking for existing persistable

Returns: database class, filter dictionary

Parameters:

• registered_name – Class name registered in SimpleML
• strict – whether to fit objects first before assuming they are identical

In theory if all inputs and classes are the same, the outputs should deterministically be the same as well (up to random iter). So, you dont need to fit objects to be sure they are the same

classmethod retrieve_dataset(**dataset_kwargs)