recbole.utils.enum_type

class recbole.utils.enum_type.EvaluatorType(value)[source]

Bases: enum.Enum

Type for evaluation metrics.

  • RANKING: Ranking-based metrics like NDCG, Recall, etc.

  • VALUE: Value-based metrics like AUC, etc.

RANKING = 1
VALUE = 2
class recbole.utils.enum_type.FeatureSource(value)[source]

Bases: enum.Enum

Source of features.

  • INTERACTION: Features from .inter (other than user_id and item_id).

  • USER: Features from .user (other than user_id).

  • ITEM: Features from .item (other than item_id).

  • USER_ID: user_id feature in inter_feat and user_feat.

  • ITEM_ID: item_id feature in inter_feat and item_feat.

  • KG: Features from .kg.

  • NET: Features from .net.

INTERACTION = 'inter'
ITEM = 'item'
ITEM_ID = 'item_id'
KG = 'kg'
NET = 'net'
USER = 'user'
USER_ID = 'user_id'
class recbole.utils.enum_type.FeatureType(value)[source]

Bases: enum.Enum

Type of features.

  • TOKEN: Token features like user_id and item_id.

  • FLOAT: Float features like rating and timestamp.

  • TOKEN_SEQ: Token sequence features like review.

  • FLOAT_SEQ: Float sequence features like pretrained vector.

FLOAT = 'float'
FLOAT_SEQ = 'float_seq'
TOKEN = 'token'
TOKEN_SEQ = 'token_seq'
class recbole.utils.enum_type.InputType(value)[source]

Bases: enum.Enum

Type of Models’ input.

  • POINTWISE: Point-wise input, like uid, iid, label.

  • PAIRWISE: Pair-wise input, like uid, pos_iid, neg_iid.

LISTWISE = 3
PAIRWISE = 2
POINTWISE = 1
class recbole.utils.enum_type.KGDataLoaderState(value)[source]

Bases: enum.Enum

States for Knowledge-based DataLoader.

  • RSKG: Return both knowledge graph information and user-item interaction information.

  • RS: Only return the user-item interaction.

  • KG: Only return the triplets with negative examples in a knowledge graph.

KG = 3
RS = 2
RSKG = 1
class recbole.utils.enum_type.ModelType(value)[source]

Bases: enum.Enum

Type of models.

  • GENERAL: General Recommendation

  • SEQUENTIAL: Sequential Recommendation

  • CONTEXT: Context-aware Recommendation

  • KNOWLEDGE: Knowledge-based Recommendation

CONTEXT = 3
DECISIONTREE = 6
GENERAL = 1
KNOWLEDGE = 4
SEQUENTIAL = 2
TRADITIONAL = 5