frex.models package

Subpackages

• frex.models.constraints package
  • Submodules
  • frex.models.constraints.attribute_constraint module
  • frex.models.constraints.constraint_solution module
  • frex.models.constraints.constraint_solution_section module
  • frex.models.constraints.constraint_solution_section_set module
  • frex.models.constraints.constraint_type module
  • frex.models.constraints.item_constraint module
  • frex.models.constraints.section_assignment_constraint module
  • frex.models.constraints.section_constraint_hierarchy module
  • frex.models.constraints.section_set_constraint module
  • Module contents
• frex.models.explanation_types package
  • Submodules
  • frex.models.explanation_types.case_based_explanation module
  • frex.models.explanation_types.contextual_explanation module
  • frex.models.explanation_types.contrastive_explanation module
  • frex.models.explanation_types.counterfactual_explanation module
  • frex.models.explanation_types.scientific_explanation module
  • frex.models.explanation_types.simulation_based_explanation module
  • frex.models.explanation_types.statistical_explanation module
  • Module contents

Submodules

frex.models.candidate module

class frex.models.candidate.Candidate(context: Any, domain_object: frex.models.domain_object.DomainObject, applied_explanations: List[frex.models.explanation.Explanation], applied_scores: List[float])

Bases: object

A Candidate should store some context for the current application, a domain-specific object (that is, the candidate to return as part of the recommendation), and lists of applied explanations and scores. We expect applied explanations and scores to always have the same length, as pipeline stages should apply both when they are passed through.

applied_explanations: List[frex.models.explanation.Explanation]

applied_scores: List[float]

context: Any

domain_object: frex.models.domain_object.DomainObject

classmethod from_dict(kvs: Optional[Union[dict, list, str, int, float, bool]], *, infer_missing=False) → dataclasses_json.api.A

classmethod from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → dataclasses_json.api.A

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → dataclasses_json.mm.SchemaF[dataclasses_json.mm.A]

to_dict(encode_json=False) → Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Optional[Union[int, str]] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) → str

property total_score: float

Get the sum of scores in applied_scores for this candidate. :return: The sum of self.applied_scores

frex.models.domain_object module

class frex.models.domain_object.DomainObject(uri: rdflib.term.URIRef)

Bases: object

DomainObject is the base class for objects related to a recommendation application that have some URI.

classmethod from_dict(kvs: Optional[Union[dict, list, str, int, float, bool]], *, infer_missing=False) → dataclasses_json.api.A

classmethod from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → dataclasses_json.api.A

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → dataclasses_json.mm.SchemaF[dataclasses_json.mm.A]

to_dict(encode_json=False) → Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Optional[Union[int, str]] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) → str

uri: rdflib.term.URIRef

frex.models.explanation module

class frex.models.explanation.Explanation(explanation_string: str)

Bases: object

An Explanation in its most simple form consists of a string.

More complex explanations can be defined by subclassing this Explanation class.

explanation_string: str

classmethod from_dict(kvs: Optional[Union[dict, list, str, int, float, bool]], *, infer_missing=False) → dataclasses_json.api.A

classmethod from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → dataclasses_json.api.A

classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → dataclasses_json.mm.SchemaF[dataclasses_json.mm.A]

to_dict(encode_json=False) → Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Optional[Union[int, str]] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) → str

Module contents