DSSMModel

class rectools.models.dssm.DSSMModel(train_dataset_type: ~typing.Type[~rectools.dataset.torch_datasets.DSSMTrainDatasetBase] = <class 'rectools.dataset.torch_datasets.DSSMTrainDataset'>, user_dataset_type: ~typing.Type[~rectools.dataset.torch_datasets.DSSMUserDatasetBase] = <class 'rectools.dataset.torch_datasets.DSSMUserDataset'>, item_dataset_type: ~typing.Type[~rectools.dataset.torch_datasets.DSSMItemDatasetBase] = <class 'rectools.dataset.torch_datasets.DSSMItemDataset'>, model: ~typing.Optional[~rectools.models.dssm.DSSM] = None, n_factors: int = 128, max_epochs: int = 5, batch_size: int = 128, dataloader_num_workers: int = 0, trainer_sanity_steps: int = 2, trainer_devices: ~typing.Union[str, int] = 1, trainer_accelerator: str = 'auto', callbacks: ~typing.Optional[~typing.Union[~typing.List[~pytorch_lightning.callbacks.callback.Callback], ~pytorch_lightning.callbacks.callback.Callback]] = None, loggers: ~typing.Union[~pytorch_lightning.loggers.logger.Logger, ~typing.Iterable[~pytorch_lightning.loggers.logger.Logger], bool] = True, verbose: int = 0, deterministic: bool = False, recommend_n_threads: int = 0, recommend_use_gpu_ranking: bool = True)[source]

Bases: VectorModel

Wrapper for rectools.models.dssm.DSSM

Parameters

  • train_dataset_type (Type(DSSMTrainDatasetBase), default DSSMTrainDataset) – Type of dataset used for training. A child of torch.utils.data.Dataset that implements from_dataset classmethod. Used to construct torch.utils.data.Dataset from a given rectools.dataset.dataset.Dataset.

  • user_dataset_type (Type(DSSMUserDatasetBase), default DSSMUserDataset) – Type of dataset used for user inference. A child of torch.utils.data.Dataset that implements from_dataset classmethod. Used to construct torch.utils.data.Dataset from a given rectools.dataset.dataset.Dataset.

  • item_dataset_type (Type(DSSMItemDatasetBase), default DSSMItemDataset) – Type of dataset used for item inference. A child of torch.utils.data.Dataset that implements from_dataset classmethod. Used to construct torch.utils.data.Dataset from a given rectools.dataset.dataset.Dataset.

  • model (Optional(DSSM), default None) – Which model to wrap. If model is None, an instance of default DSSM is created during fit.

  • n_factors (int, default 128) – How many hidden units to use in user and item networks. Used only if model is None.

  • max_epochs (int, default 5) – Stop training if this number of epochs is reached. Keep in mind that if any kind of early stopping callback is passed as one of the callbacks along with a validation dataset, then hitting exactly max_epochs is not guaranteed.

  • batch_size (int, default 128) – How many samples per batch to load.

  • dataloader_num_workers (int, default 0) – How many processes to use for data loading. Defaults to 0, which means that all data will be loaded in the main process.

  • trainer_sanity_steps (int, default 2) – Sanity check runs n validation batches before starting the training routine.

  • trainer_devices (str | int, default 1) – “auto” means determine the number of available devices based on the trainer_accelerator type. In case on an integer, it will be mapped to either gpus, tpu_cores, num_processes or ipus, based on the accelerator type.

  • trainer_accelerator (str, default 'auto') – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “auto”). The “auto” option recognizes the machine you are on, and selects the respective.

  • callbacks (Optional(Sequence(Callback)), default None) – Which callbacks to use. For instance, pytorch_lightning.callbacks.TQDMProgressBar, etc.

  • loggers (LightningLoggerBase | iterable(LightningLoggerBase) | bool, default True) – Which loggers to use. For instance, pytorch_lightning.loggers.TensorboardLogger, etc.

  • verbose (int, default 0) – Verbosity level (applies only to recommend loop).

  • deterministic (bool, default False) – If True, sets whether PyTorch operations must use deterministic algorithms. Use pytorch_lightning.seed_everything together with this param to fix the random state.

  • recommend_n_threads (int, default 0) – Number of threads to use for recommendation ranking on CPU. Specifying 0 means to default to the number of cores on the machine. If you want to change this parameter after model is initialized, you can manually assign new value to model recommend_n_threads attribute.

  • recommend_use_gpu_ranking (bool, default True) – Flag to use GPU for recommendation ranking. Please note that GPU and CPU ranking may provide different ordering of items with identical scores in recommendation table. If True, implicit.gpu.HAS_CUDA will also be checked before ranking. If you want to change this parameter after model is initialized, you can manually assign new value to model recommend_use_gpu_ranking attribute.

Inherited-members

Methods

dumps()

Serialize model to bytes.

fit(dataset, *args, **kwargs)

Fit model.

fit_partial(dataset, *args, **kwargs)

Fit model.

from_config(config)

Create model from config.

get_config([mode, simple_types])

Return model config.

get_params([simple_types, sep])

Return model parameters.

get_vectors(dataset)

load(f)

Load model from file.

loads(data)

Load model from bytes.

recommend(users, dataset, k, filter_viewed)

Recommend items for users.

recommend_to_items(target_items, dataset, k)

Recommend items for target items.

save(f)

Save model to file.

Attributes

i2i_dist

recommends_for_cold

recommends_for_warm

u2i_dist