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import logging
from typing import Dict, List, Optional
import lightning as pl
import torch
from lightning.pytorch.trainer.states import RunningStage
from sklearn.metrics import label_ranking_average_precision_score
from relik.common.log import get_logger
from relik.retriever.callbacks.base import DEFAULT_STAGES, NLPTemplateCallback
logger = get_logger(__name__, level=logging.INFO)
class RecallAtKEvaluationCallback(NLPTemplateCallback):
"""
Computes the recall at k for the predictions. Recall at k is computed as the number of
correct predictions in the top k predictions divided by the total number of correct
predictions.
Args:
k (`int`):
The number of predictions to consider.
prefix (`str`, `optional`):
The prefix to add to the metrics.
verbose (`bool`, `optional`, defaults to `False`):
Whether to log the metrics.
prog_bar (`bool`, `optional`, defaults to `True`):
Whether to log the metrics to the progress bar.
"""
def __init__(
self,
k: int = 100,
prefix: Optional[str] = None,
verbose: bool = False,
prog_bar: bool = True,
*args,
**kwargs,
):
super().__init__()
self.k = k
self.prefix = prefix
self.verbose = verbose
self.prog_bar = prog_bar
@torch.no_grad()
def __call__(
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
predictions: Dict,
*args,
**kwargs,
) -> dict:
"""
Computes the recall at k for the predictions.
Args:
trainer (:obj:`lightning.trainer.trainer.Trainer`):
The trainer object.
pl_module (:obj:`lightning.core.lightning.LightningModule`):
The lightning module.
predictions (:obj:`Dict`):
The predictions.
Returns:
:obj:`Dict`: The computed metrics.
"""
if self.verbose:
logger.info(f"Computing recall@{self.k}")
# metrics to return
metrics = {}
stage = trainer.state.stage
if stage not in DEFAULT_STAGES:
raise ValueError(
f"Stage {stage} not supported, only `validate` and `test` are supported."
)
for dataloader_idx, samples in predictions.items():
hits, total = 0, 0
for sample in samples:
# compute the recall at k
# cut the predictions to the first k elements
predictions = sample["predictions"][: self.k]
hits += len(set(predictions) & set(sample["gold"]))
total += len(set(sample["gold"]))
# compute the mean recall at k
recall_at_k = hits / total
metrics[f"recall@{self.k}_{dataloader_idx}"] = recall_at_k
metrics[f"recall@{self.k}"] = sum(metrics.values()) / len(metrics)
if self.prefix is not None:
metrics = {f"{self.prefix}_{k}": v for k, v in metrics.items()}
else:
metrics = {f"{stage.value}_{k}": v for k, v in metrics.items()}
pl_module.log_dict(
metrics, on_step=False, on_epoch=True, prog_bar=self.prog_bar
)
if self.verbose:
logger.info(
f"Recall@{self.k} on {stage.value}: {metrics[f'{stage.value}_recall@{self.k}']}"
)
return metrics
class AvgRankingEvaluationCallback(NLPTemplateCallback):
"""
Computes the average ranking of the gold label in the predictions. Average ranking is
computed as the average of the rank of the gold label in the predictions.
Args:
k (`int`):
The number of predictions to consider.
prefix (`str`, `optional`):
The prefix to add to the metrics.
stages (`List[str]`, `optional`):
The stages to compute the metrics on. Defaults to `["validate", "test"]`.
verbose (`bool`, `optional`, defaults to `False`):
Whether to log the metrics.
"""
def __init__(
self,
k: int,
prefix: Optional[str] = None,
stages: Optional[List[str]] = None,
verbose: bool = True,
*args,
**kwargs,
):
super().__init__()
self.k = k
self.prefix = prefix
self.verbose = verbose
self.stages = (
[RunningStage(stage) for stage in stages] if stages else DEFAULT_STAGES
)
@torch.no_grad()
def __call__(
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
predictions: Dict,
*args,
**kwargs,
) -> dict:
"""
Computes the average ranking of the gold label in the predictions.
Args:
trainer (:obj:`lightning.trainer.trainer.Trainer`):
The trainer object.
pl_module (:obj:`lightning.core.lightning.LightningModule`):
The lightning module.
predictions (:obj:`Dict`):
The predictions.
Returns:
:obj:`Dict`: The computed metrics.
"""
if not predictions:
logger.warning("No predictions to compute the AVG Ranking metrics.")
return {}
if self.verbose:
logger.info(f"Computing AVG Ranking@{self.k}")
# metrics to return
metrics = {}
stage = trainer.state.stage
if stage not in self.stages:
raise ValueError(
f"Stage `{stage}` not supported, only `validate` and `test` are supported."
)
for dataloader_idx, samples in predictions.items():
rankings = []
for sample in samples:
window_candidates = sample["predictions"][: self.k]
window_labels = sample["gold"]
for wl in window_labels:
if wl in window_candidates:
rankings.append(window_candidates.index(wl) + 1)
avg_ranking = sum(rankings) / len(rankings) if len(rankings) > 0 else 0
metrics[f"avg_ranking@{self.k}_{dataloader_idx}"] = avg_ranking
if len(metrics) == 0:
metrics[f"avg_ranking@{self.k}"] = 0
else:
metrics[f"avg_ranking@{self.k}"] = sum(metrics.values()) / len(metrics)
prefix = self.prefix or stage.value
metrics = {
f"{prefix}_{k}": torch.as_tensor(v, dtype=torch.float32)
for k, v in metrics.items()
}
pl_module.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=False)
if self.verbose:
logger.info(
f"AVG Ranking@{self.k} on {prefix}: {metrics[f'{prefix}_avg_ranking@{self.k}']}"
)
return metrics
class LRAPEvaluationCallback(NLPTemplateCallback):
def __init__(
self,
k: int = 100,
prefix: Optional[str] = None,
verbose: bool = False,
prog_bar: bool = True,
*args,
**kwargs,
):
super().__init__()
self.k = k
self.prefix = prefix
self.verbose = verbose
self.prog_bar = prog_bar
@torch.no_grad()
def __call__(
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
predictions: Dict,
*args,
**kwargs,
) -> dict:
if self.verbose:
logger.info(f"Computing recall@{self.k}")
# metrics to return
metrics = {}
stage = trainer.state.stage
if stage not in DEFAULT_STAGES:
raise ValueError(
f"Stage {stage} not supported, only `validate` and `test` are supported."
)
for dataloader_idx, samples in predictions.items():
scores = [sample["scores"][: self.k] for sample in samples]
golds = [sample["gold"] for sample in samples]
# compute the mean recall at k
lrap = label_ranking_average_precision_score(golds, scores)
metrics[f"lrap@{self.k}_{dataloader_idx}"] = lrap
metrics[f"lrap@{self.k}"] = sum(metrics.values()) / len(metrics)
prefix = self.prefix or stage.value
metrics = {
f"{prefix}_{k}": torch.as_tensor(v, dtype=torch.float32)
for k, v in metrics.items()
}
pl_module.log_dict(
metrics, on_step=False, on_epoch=True, prog_bar=self.prog_bar
)
if self.verbose:
logger.info(
f"Recall@{self.k} on {stage.value}: {metrics[f'{stage.value}_recall@{self.k}']}"
)
return metrics