from typing import Any, Dict, Optional
import torch
from collie.loss.metadata_utils import ideal_difference_from_metadata
[docs]def bpr_loss(
positive_scores: torch.tensor,
negative_scores: torch.tensor,
num_items: Optional[Any] = None,
positive_items: Optional[torch.tensor] = None,
negative_items: Optional[torch.tensor] = None,
metadata: Optional[Dict[str, torch.tensor]] = dict(),
metadata_weights: Optional[Dict[str, float]] = dict(),
) -> torch.tensor:
"""
Modified Bayesian Personalised Ranking [1]_.
See ``ideal_difference_from_metadata`` docstring for more info on how metadata is used.
Modified from ``torchmf`` and ``Spotlight``:
* https://github.com/EthanRosenthal/torchmf/blob/master/torchmf.py
* https://github.com/maciejkula/spotlight/blob/master/spotlight/losses.py
Parameters
----------
positive_scores: torch.tensor, 1-d
Tensor containing predictions for known positive items of shape ``1 x batch_size``
negative_scores: torch.tensor, 1-d
Tensor containing scores for a single sampled negative item of shape ``1 x batch_size``
num_items: Any
Ignored, included only for compatability with WARP loss
positive_items: torch.tensor, 1-d
Tensor containing ids for known positive items of shape ``1 x batch_size``. This is only
needed if ``metadata`` is provided
negative_items: torch.tensor, 1-d
Tensor containing ids for randomly-sampled negative items of shape ``1 x batch_size``. This
is only needed if ``metadata`` is provided
metadata: dict
Keys should be strings identifying each metadata type that match keys in
``metadata_weights``. Values should be a ``torch.tensor`` of shape (num_items x 1). Each
tensor should contain categorical metadata information about items (e.g. a number
representing the genre of the item)
metadata_weights: dict
Keys should be strings identifying each metadata type that match keys in ``metadata``.
Values should be the amount of weight to place on a match of that type of metadata, with the
sum of all values ``<= 1``.
e.g. If ``metadata_weights = {'genre': .3, 'director': .2}``, then an item is:
* a 100% match if it's the same item,
* a 50% match if it's a different item with the same genre and same director,
* a 30% match if it's a different item with the same genre and different director,
* a 20% match if it's a different item with a different genre and same director,
* a 0% match if it's a different item with a different genre and different director,
which is equivalent to the loss without any partial credit
Returns
-------
loss: torch.tensor
References
----------
.. [1] Hildesheim et al. "BPR: Bayesian Personalized Ranking from Implicit Feedback." BPR |
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 1 June
2009, dl.acm.org/doi/10.5555/1795114.1795167.
"""
preds = positive_scores - negative_scores
if metadata is not None and len(metadata) > 0:
ideal_difference = ideal_difference_from_metadata(
positive_items=positive_items,
negative_items=negative_items,
metadata=metadata,
metadata_weights=metadata_weights,
)
else:
ideal_difference = 1
loss = (ideal_difference - torch.sigmoid(preds))
return (loss.sum() + loss.pow(2).sum()) / len(positive_scores)
[docs]def adaptive_bpr_loss(
positive_scores: torch.tensor,
many_negative_scores: torch.tensor,
num_items: Optional[Any] = None,
positive_items: Optional[torch.tensor] = None,
negative_items: Optional[torch.tensor] = None,
metadata: Optional[Dict[str, torch.tensor]] = dict(),
metadata_weights: Optional[Dict[str, float]] = dict(),
) -> torch.tensor:
"""
Modified adaptive BPR loss function.
Approximates WARP loss by taking the maximum of negative predictions for each user and sending
this to BPR loss.
See ``ideal_difference_from_metadata`` docstring for more info on how metadata is used.
Parameters
----------
positive_scores: torch.tensor, 1-d
Tensor containing scores for known positive items of shape
``num_negative_samples x batch_size``
many_negative_scores: torch.tensor, 2-d
Iterable of tensors containing scores for many (n > 1) sampled negative items of shape
``num_negative_samples x batch_size``. More tensors increase the likelihood of finding
ranking-violating pairs, but risk overfitting
num_items: Any
Ignored, included only for compatability with WARP loss
positive_items: torch.tensor, 1-d
Tensor containing ids for known positive items of shape
``num_negative_samples x batch_size``. This is only needed if ``metadata`` is provided
negative_items: torch.tensor, 2-d
Tensor containing ids for sampled negative items of shape
``num_negative_samples x batch_size``. This is only needed if ``metadata`` is provided
metadata: dict
Keys should be strings identifying each metadata type that match keys in
``metadata_weights``. Values should be a ``torch.tensor`` of shape (num_items x 1). Each
tensor should contain categorical metadata information about items (e.g. a number
representing the genre of the item)
metadata_weights: dict
Keys should be strings identifying each metadata type that match keys in ``metadata``.
Values should be the amount of weight to place on a match of that type of metadata, with the
sum of all values ``<= 1``.
e.g. If ``metadata_weights = {'genre': .3, 'director': .2}``, then an item is:
* a 100% match if it's the same item,
* a 50% match if it's a different item with the same genre and same director,
* a 30% match if it's a different item with the same genre and different director,
* a 20% match if it's a different item with a different genre and same director,
* a 0% match if it's a different item with a different genre and different director,
which is equivalent to the loss without any partial credit
Returns
-------
loss: torch.tensor
"""
highest_negative_scores, highest_negative_inds = torch.max(many_negative_scores, 0)
if negative_items is not None and positive_items is not None:
negative_items = (
negative_items[highest_negative_inds, torch.arange(len(positive_items))].squeeze()
)
return bpr_loss(
positive_scores,
highest_negative_scores.squeeze(),
positive_items=positive_items,
negative_items=negative_items,
metadata=metadata,
metadata_weights=metadata_weights,
)