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⨍ Functions 496◇ Types & classes 87 ↳ Endpoints 1

(self, instances: list[dict[str, Any]])

experiments/bpr/dataset.py:233

(self, instances: list[dict[str, Any]])

experiments/bpr/dataset.py:279

(self, t: torch.Tensor)

revisit_bpr/modules/activation.py:43

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/metric.py:30

( self, output: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None )

revisit_bpr/metrics/metric.py:52

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/map.py:41

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/recall.py:40

( self, output: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None )

revisit_bpr/metrics/auc.py:28

( self, output: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None )

revisit_bpr/metrics/auc.py:80

( self, output: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None )

revisit_bpr/metrics/auc.py:141

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/fbeta.py:48

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/ndcg.py:65

(self, output: torch.Tensor, target: torch.Tensor)

revisit_bpr/metrics/precision.py:40

(self, instances: list[dict[str, Any]])

revisit_bpr/datasets/jsonl.py:72

Method__contains__

experiments/encoder.py:23

Method__getitem__

(self, idx: int)

Method__getitem__

(self, idx: int)

Method__getitem__

(self, idx: int)

experiments/libraries/recbole/dataset.py:30

Method__getitem__

(self, idx: int)

experiments/multae/dataset.py:20

Method__getitem__

(self, idx: int)

experiments/bpr/dataset.py:28

Method__getitem__

(self, idx: int)

experiments/bpr/dataset.py:70

Method__getitem__

(self, idx: int)

experiments/bpr/dataset.py:125

Method__getitem__

(self, idx: int)

experiments/bpr/dataset.py:172

Method__getitem__

(self, idx: int)

revisit_bpr/datasets/jsonl.py:29

( self, path: Path | str, seen_items_path: Path | str, num_users: int,

(self, path: Path | str, seen_items_path: Path | str)

(self, num_items: int, padding_value: float = 0)

(self, exp: Experiment, launcher: Launcher | None = None)

experiments/decorator.py:31

(self, exp: Experiment)

experiments/decorator.py:57

(self, exp: Experiment, dir: Path | None, s3fs: S3FS, clean: bool = False)

experiments/s3_infer.py:40

(self, exp: Experiment, dir: Path | None, s3fs: S3FS, clean: bool = False)

experiments/s3_run.py:45

( self, address: str = "127.0.0.1", backend: str = "nccl", port: str | int = 2

experiments/launcher.py:36

(self, value_type: Callable = str, **kwargs)

experiments/click_options.py:14

(self, attrs: Optional[Union[list[str], dict[str, AttrEncoder]]] = None)

experiments/encoder.py:57

( self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, accelerator:

experiments/trainer.py:20

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/libraries/lightfm/exp.py:37

( self, inner_model: LightFM, num_users: int, num_items: int, epochs:

experiments/libraries/lightfm/model.py:9

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/libraries/cornac/exp.py:27

( self, path: Path | str, seen_items_path: Path | str, mapping: dict[str, dict

experiments/libraries/recbole/dataset.py:13

( self, path: Path | str, seen_items_path: Path | str, mapping: dict[str, dict

experiments/libraries/recbole/dataset.py:42

( self, path: Path | str, mapping: dict[str, dict[str, int]] | None = None, )

experiments/libraries/recbole/dataset.py:75

(self, num_items: int)

experiments/libraries/recbole/dataset.py:106

(self, num_items: int, padding_value: float = 0)

experiments/libraries/recbole/dataset.py:139

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/libraries/recbole/exp.py:27

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/libraries/implicit/exp.py:37

(self, inner_model: RecommenderBase, num_users: int, num_items: int)

experiments/libraries/implicit/model.py:10

( self, bucket: str, dst: str, src: str, exist_ok: bool = False, overwrite: bool = False )

experiments/s3/fs.py:19

( self, bucket: str, dst: str, src: str, exist_ok: bool = False, overwrite: bool = False )

experiments/s3/fs.py:50

(self, bucket: str, s3_client)

experiments/s3/fs.py:105

(self, path: Path | str, num_items: int)

experiments/multae/dataset.py:12

(self, path: Path | str, num_items: int)

experiments/multae/dataset.py:32

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/multae/exp.py:40

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/ease/exp.py:37

(self, user_idx: str = "user", item_idx: str = "item")

experiments/datasets/time-split/dataset.py:27

(self, path: Path | str, seen_items_path: Path | str)

experiments/bpr/dataset.py:17

(self, path: Path | str, seen_items_path: Path | str)

experiments/bpr/dataset.py:37

(self, path: Path | str, seen_items_path: Path | str, seed: int = 13)

experiments/bpr/dataset.py:58

(self, path: Path | str, seen_items_path: Path | str, seed: int = 13)

experiments/bpr/dataset.py:80

( self, path: Path | str, seen_items_path: Path | str, num_users: int,

experiments/bpr/dataset.py:105

( self, path: Path | str, seen_items_path: Path | str, num_users: int,

experiments/bpr/dataset.py:143

(self, num_items: int)

experiments/bpr/dataset.py:194

(self, num_items: int, padding_value: float = 0)

experiments/bpr/dataset.py:229

(self, num_items: int, padding_value: float = 0)

experiments/bpr/dataset.py:275

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/bpr/exp.py:45

( self, exp_config: dict[str, Any] | Callable[[], dict[str, Any]], dir: Path | None =

experiments/popularity/exp.py:37

( self, linears: list[torch.nn.Linear], activations: Act | list[Act] = F.relu,

revisit_bpr/modules/mlp.py:25

(self, num_items: int, neg_gen: torch.Generator)

revisit_bpr/modules/neg_samplers.py:27

( self, model: BPR, num_items: int, sampling_prob: float, neg_gen: tor

revisit_bpr/modules/neg_samplers.py:59

(self, name: str, *args, **kwargs)

revisit_bpr/modules/activation.py:31

(self, topk: int, normalized: bool = True)

revisit_bpr/metrics/map.py:22

(self, topk: int)

revisit_bpr/metrics/recall.py:19

revisit_bpr/metrics/auc.py:12

revisit_bpr/metrics/auc.py:64

revisit_bpr/metrics/auc.py:125

(self, topk: int, beta: float = 1.0)

revisit_bpr/metrics/fbeta.py:23

(self, topk: int, gain_function: str = "exp")

revisit_bpr/metrics/ndcg.py:42

(self, topk: int)

revisit_bpr/metrics/precision.py:19

(self, path: Path | str)

revisit_bpr/datasets/jsonl.py:22

(self, path: Path | str)

revisit_bpr/datasets/jsonl.py:43

(self, pad: list[str] | None = None, padding_value: float = 0)

revisit_bpr/datasets/jsonl.py:68

(self, size_average: bool = False)

revisit_bpr/models/ae/loss.py:5

(self, weight: float = 1.0)

revisit_bpr/models/ae/kl_scheduler.py:24

( self, annealing_steps: int, zero_weight_steps: int = 0, max_weight: float =

revisit_bpr/models/ae/kl_scheduler.py:41

( self, encoder: MLP, decoder: MLP, normalize: bool = True, dropout_prob: float = 0.0 )

revisit_bpr/models/ae/multdae.py:9

( self, encoder: MLP, decoder: MLP, latent_dim: int, normalize: bool =

revisit_bpr/models/ae/multvae.py:18

( self, num_items: int, lambda_weight: float = 100.0, threshold: float = 0.0,

revisit_bpr/models/ease/model.py:6

(self, size_average: bool = True)

revisit_bpr/models/bpr/loss.py:15

( self, logits_model: BaseLogitModel, reg_alphas: dict[str, float] | None = None,

revisit_bpr/models/bpr/model.py:28

( self, num_items: int, hidden_dim: int, padding_idx: int = 0, bias: bool = False )

revisit_bpr/models/bpr/model.py:157

(self, num_items: int, padding_idx: int = 0, bias: bool = False)

revisit_bpr/models/bpr/model.py:202

(self, num_items: int)

revisit_bpr/models/popularity/model.py:14

experiments/libraries/recbole/dataset.py:55

experiments/libraries/recbole/dataset.py:83

experiments/multae/dataset.py:36

experiments/bpr/dataset.py:44

experiments/bpr/dataset.py:88

revisit_bpr/datasets/jsonl.py:46

experiments/encoder.py:20

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