Version: 0.95

snorkelflow.lfs.LF

class snorkelflow.lfs.LF(name, label=None, templates=None, multipolar_template=None, graph=None, is_trusted=False)

Bases: object

Class to represent an immutable LF.

Examples

Create an LF.

>>> from snorkelflow.lfs import LF
>>> lf_a = LF(name="lf_a", label=0, templates=...)

Due to the immutability, a new LF should be created if you want to change its attributes.

>>> lf_config = lf_a.to_dict()
>>> lf_config["name"] = "lf_b"
>>> lf_b = LF(**lf_config)

__init__(name, label=None, templates=None, multipolar_template=None, graph=None, is_trusted=False)

Initialize an LF object.

Parameters:

name (str) – Name of the LF
label (Optional[Any], default: None) – Label associated with the LF. This shouldn’t be provided for a multi-polar LF.
templates (Optional[List[Dict[str, Any]]], default: None) – Templates for the LF. Each template is assigned with a 0-th based index that can be used in graph.
multipolar_template (Optional[Dict[str, Any]], default: None) – Template for the multi-polar LF
graph (Union[str, int, List[Any], None], default: None) – Provide this argument to construct a compound LF, one that’s made of multiple templates. Optional if there is only one template. It is a list that starts with a logical operator, followed by template indexes or sub-lists. The logical operator can be "$AND", "$OR", or "$NOT". The list can be nested but the depth should be 3 or less. For example, a compound LF with ["$AND", 0, ["$NOT", 1]] votes if the 0th template returns True and the 1st template returns False, and its depth is 2.
is_trusted (Optional[bool], default: False) – If True, designate the LF as trusted

Methods

`__init__`(name[, label, templates, ...])	Initialize an LF object.
`to_dict`()	Get a dictionary representation of the LF.

Attributes

to_dict()

Get a dictionary representation of the LF.