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Functions496 in github.com/Nemexur/revisit-bpr

⨍ Functions 496◇ Types & classes 87↳ Endpoints 1
↓ 43 callersMethodadd_event
( self, engine: str, event_name: Any, handler: Callable, *args: Any, **kwargs: Any )
experiments/trainer.py:44
↓ 33 callersMethodsize
(self, key: str = "user")
experiments/encoder.py:62
↓ 17 callersMethodexists
(self, path: Path | str)
experiments/s3/fs.py:109
↓ 14 callersMethodreset
(self)
revisit_bpr/metrics/map.py:72
↓ 9 callersFunctionflatten_config
(config: dict[str, Any])
experiments/utils.py:9
↓ 8 callersMethodlog
(self)
example.py:131
↓ 8 callersMethodset_accelerator
(self, value: Accelerator)
revisit_bpr/metrics/metric.py:18
↓ 7 callersMethodload
( self, dst: Path | str, src: Path | str, exist_ok: bool = False, overwrite: bool = False )
experiments/s3/fs.py:120
↓ 6 callersFunctionattach_best_exp_saver
(trainer: Trainer, dir: Path)
experiments/options.py:305
↓ 6 callersFunctionattach_checkpointer
( trainer: Trainer, accelerator: Accelerator, early_stopping: EarlyStopping | None = None, che
experiments/options.py:88
↓ 6 callersFunctionattach_debug_handler
(trainer: Trainer, num_iters: int = 100)
experiments/options.py:268
↓ 6 callersFunctionattach_log_epoch_metrics
(trainer: Trainer, accelerator: Accelerator)
experiments/options.py:278
↓ 6 callersFunctionattach_metrics
( trainer: Trainer, accelerator: Accelerator, metrics: dict[str, Metric] | None = None )
experiments/options.py:31
↓ 6 callersFunctionattach_output_saver
(trainer: Trainer, dir: Path, state_attr: str = "result")
experiments/options.py:319
↓ 6 callersFunctionattach_params_watcher
( trainer: Trainer, accelerator: Accelerator, report_freq: int = 1000 )
experiments/options.py:222
↓ 6 callersFunctionattach_progress_bar
( trainer: Trainer, metric_names: dict[str, str | list[str]] | None = None )
experiments/options.py:149
↓ 6 callersFunctionattach_user_metric_saver
( trainer: Trainer, dir: Path, metrics: dict[str, Metric] | None = None, state_attr: str = "us
experiments/options.py:354
↓ 6 callersMethodget_features
(self)
revisit_bpr/models/bpr/model.py:147
↓ 6 callersMethodget_metric
(self, reset: bool = False)
revisit_bpr/metrics/map.py:66
↓ 6 callersFunctionmerge_configs
(*configs: dict[str, Any])
experiments/utils.py:36
↓ 6 callersMethodremove
(self, path: Path | str)
experiments/s3/fs.py:146
↓ 6 callersMethodupload
( self, dst: Path | str, src: Path | str, exist_ok: bool = False, overwrite: bool = False )
experiments/s3/fs.py:133
↓ 5 callersFunctionnumerize
(tp)
experiments/datasets/revisit-ials/generate_data.py:144
↓ 5 callersFunctionprepare_target
(output: torch.Tensor, target: torch.Tensor)
revisit_bpr/metrics/metric.py:110
↓ 4 callersFunction_accelerator_tracker
(tracker_type: Type[T], accelerator: Accelerator)
experiments/options.py:403
↓ 4 callersMethodadd
(self, item: str)
experiments/datasets/time-split/dataset.py:20
↓ 4 callersMethodcompute
(self, output: torch.Tensor, target: torch.Tensor)
revisit_bpr/metrics/map.py:45
↓ 4 callersFunctioncreate_study
( name: str, dir: Path | None = None, storage_url: str | None = None, seed: int = 13 )
experiments/hp.py:14
↓ 4 callersMethodencode
(self, data: Iterable[str], namespace: str)
experiments/datasets/time-split/dataset.py:32
↓ 4 callersMethodfit
(self, data: pd.DataFrame)
experiments/datasets/time-split/dataset.py:40
↓ 4 callersFunctionget_count
(tp, id)
experiments/datasets/revisit-ials/generate_data.py:31
↓ 4 callersMethodrun
(self)
experiments/decorator.py:69
↓ 3 callersMethod__init__
( self, user_emb: torch.nn.Embedding, item_emb: torch.nn.Embedding, item_bias:
revisit_bpr/models/bpr/model.py:97
↓ 3 callersMethodadd
(self, item: Any)
experiments/encoder.py:26
↓ 3 callersFunctionattach_checkpoint_loader
( trainer: Trainer, accelerator: Accelerator, datasets: dict[str, DataLoader] )
experiments/options.py:116
↓ 3 callersFunctionattach_preemptible
(trainer: Trainer, accelerator: Accelerator)
experiments/options.py:188
↓ 3 callersMethodclean
(self)
experiments/decorator.py:81
↓ 3 callersMethodrun
(self)
experiments/s3_run.py:55
↓ 3 callersFunctions3_save_handler
( s3fs: S3FS, dir: Path | None, clean: bool = False )
experiments/s3_run.py:77
↓ 3 callersMethodstep
(self)
revisit_bpr/models/ae/kl_scheduler.py:56
↓ 3 callersFunctionvalidate_metric_inputs
(output: torch.Tensor, target: torch.Tensor)
revisit_bpr/metrics/metric.py:100
↓ 2 callersFunction_accelerator_save
(accelerator: Accelerator)
experiments/options.py:391
↓ 2 callersMethod_evaluate
(self, loader: DataLoader)
experiments/libraries/recbole/exp.py:104
↓ 2 callersMethod_log_metrics
(self)
experiments/libraries/recbole/exp.py:118
↓ 2 callersFunction_sampling_weights
(item_weights: torch.Tensor, seen_items: torch.Tensor)
revisit_bpr/modules/neg_samplers.py:135
↓ 2 callersMethod_sampling_weights
(self, seen_items: torch.Tensor)
experiments/bpr/exp.py:282
↓ 2 callersMethod_upload
(self, s3_client, src: str, initial_src: str = "")
experiments/s3/fs.py:85
↓ 2 callersMethodclean
(self)
experiments/decorator.py:42
↓ 2 callersMethodclean
(self)
experiments/s3_run.py:70
↓ 2 callersFunctionfilter_ratings
( data: pd.DataFrame, user_idx: str, item_idx: str, min_user_count: int = 3, min_item_coun
experiments/datasets/time-split/dataset.py:83
↓ 2 callersMethodfit
(self, data: Iterable[dict[str, Any]])
experiments/encoder.py:65
↓ 2 callersFunctiongenerate_data
Generates and writes train, validation and test data. The raw_data is first split into train, validation and test by user. For the validation
experiments/datasets/revisit-ials/generate_data.py:107
↓ 2 callersFunctionget_count
(data: pd.DataFrame, column: str)
experiments/datasets/time-split/dataset.py:58
↓ 2 callersFunctionget_valid
(dataset: pl.DataFrame, group_col: str, count_col: str, min_count: int)
experiments/bpr/cmd/cutoff_samples.py:8
↓ 2 callersFunctionprepare_dataset
(path: str)
experiments/libraries/cornac/exp.py:171
↓ 2 callersMethodrun
(self)
experiments/decorator.py:39
↓ 2 callersFunctionsample_params
(trial: optuna.Trial, config: dict[str, Any])
experiments/utils.py:45
↓ 2 callersMethodsave
(self, path: Path)
experiments/encoder.py:94
↓ 2 callersFunctionsplit_train_test_proportion
Splits a DataFrame into train and test sets. Args: data: a DataFrame of (userId, itemId, rating). test_prop: the proportion of test r
experiments/datasets/revisit-ials/generate_data.py:65
↓ 2 callersMethodupdate_stats
(self)
revisit_bpr/modules/neg_samplers.py:127
↓ 2 callersMethodweight
(self)
revisit_bpr/models/ae/kl_scheduler.py:53
↓ 1 callersMethod_adaptive_sampling
(self, batch: dict[str, torch.Tensor], num: int = 1)
experiments/bpr/exp.py:295
↓ 1 callersMethod_add_events
(self)
experiments/trainer.py:97
↓ 1 callersMethod_calc_metrics
(self, model: Recommender, train_set: Dataset, test_set: Dataset)
experiments/libraries/cornac/exp.py:102
↓ 1 callersMethod_decode
(self, latent: torch.Tensor)
revisit_bpr/models/ae/multdae.py:28
↓ 1 callersMethod_decode
(self, latent: torch.Tensor)
revisit_bpr/models/ae/multvae.py:56
↓ 1 callersMethod_encode
(self, source: torch.Tensor)
revisit_bpr/models/ae/multdae.py:21
↓ 1 callersMethod_encode
(self, source: torch.Tensor)
revisit_bpr/models/ae/multvae.py:47
↓ 1 callersFunction_filter_ratings
( data: pd.DataFrame, user_idx: str, item_idx: str, min_user_count: int = 3, min_item_coun
experiments/datasets/time-split/dataset.py:63
↓ 1 callersMethod_get_accelerator
(self)
experiments/libraries/lightfm/exp.py:107
↓ 1 callersMethod_get_accelerator
(self)
experiments/libraries/implicit/exp.py:107
↓ 1 callersMethod_get_accelerator
(self)
experiments/multae/exp.py:122
↓ 1 callersMethod_get_accelerator
(self)
experiments/ease/exp.py:105
↓ 1 callersMethod_get_accelerator
(self)
experiments/bpr/exp.py:160
↓ 1 callersMethod_get_accelerator
(self)
experiments/popularity/exp.py:110
↓ 1 callersMethod_get_trainer
(self, model: torch.nn.Module)
experiments/libraries/lightfm/exp.py:127
↓ 1 callersMethod_get_trainer
(self, model: torch.nn.Module)
experiments/libraries/implicit/exp.py:127
↓ 1 callersMethod_get_trainer
( self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, datasets: di
experiments/multae/exp.py:143
↓ 1 callersMethod_get_trainer
(self, model: torch.nn.Module)
experiments/ease/exp.py:125
↓ 1 callersMethod_get_trainer
( self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, datasets: di
experiments/bpr/exp.py:181
↓ 1 callersMethod_get_trainer
( self, model: torch.nn.Module, datasets: dict[str, DataLoader], )
experiments/popularity/exp.py:129
↓ 1 callersMethod_is_s3_dir
(self, dst: Path | str)
experiments/s3/fs.py:159
↓ 1 callersMethod_kl_loss
(self, out: EncoderOut)
revisit_bpr/models/ae/multvae.py:61
↓ 1 callersMethod_load
(self, s3_client, src: str, initial_src: str = "")
experiments/s3/fs.py:65
↓ 1 callersMethod_load_checkpoint_if_needed
(self)
experiments/multae/exp.py:186
↓ 1 callersMethod_load_checkpoint_if_needed
(self)
experiments/bpr/exp.py:249
↓ 1 callersMethod_load_checkpoint_if_needed
(self)
experiments/popularity/exp.py:161
↓ 1 callersMethod_log_metrics
(self)
experiments/libraries/cornac/exp.py:118
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
example.py:116
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/libraries/recbole/dataset.py:130
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/libraries/recbole/dataset.py:163
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/multae/dataset.py:65
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/bpr/dataset.py:220
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/bpr/dataset.py:266
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
experiments/bpr/dataset.py:299
↓ 1 callersMethod_make_batch
(instances: list[dict[str, Any]])
revisit_bpr/datasets/jsonl.py:90
↓ 1 callersMethod_process
(self, batch)
experiments/libraries/recbole/dataset.py:113
↓ 1 callersMethod_process
(self, batch)
experiments/bpr/dataset.py:201
↓ 1 callersMethod_sample
(self, mu: torch.Tensor, log_var: torch.Tensor)
revisit_bpr/models/ae/multvae.py:41
↓ 1 callersMethod_save_objects
(self)
experiments/decorator.py:97
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