from typing import Iterable
from catalyst.metrics._topk_metric import TopKMetric
from catalyst.metrics.functional._ndcg import ndcg
[docs]class NDCGMetric(TopKMetric):
"""
Calculates the Normalized discounted cumulative gain (NDCG)
score given model outputs and targets
The precision metric summarizes the fraction of relevant items
Computes mean value of NDCG and it's approximate std value
Args:
topk_args: list of `topk` for ndcg@topk computing
compute_on_call: if True, computes and returns metric value during metric call
prefix: metric prefix
suffix: metric suffix
Examples:
.. code-block:: python
import torch
from catalyst import metrics
outputs = torch.Tensor([
[0.5, 0.2, 0.1],
[0.5, 0.2, 0.1],
])
targets = torch.tensor([
[1.0, 0.0, 1.0],
[1.0, 0.0, 1.0],
])
metric = metrics.NDCGMetric(topk_args=[1, 2])
metric.reset()
metric.update(outputs, targets)
metric.compute()
# (
# (1.0, 0.6131471991539001), # mean for @01, @02
# (0.0, 0.0) # std for @01, @02
# )
metric.compute_key_value()
# {
# 'ndcg01': 1.0,
# 'ndcg02': 0.6131471991539001,
# 'ndcg01/std': 0.0,
# 'ndcg02/std': 0.0
# }
metric.reset()
metric(outputs, targets)
# (
# (1.0, 0.6131471991539001), # mean for @01, @02
# (0.0, 0.0) # std for @01, @02
# )
# ((0.5, 0.75), (0.0, 0.0)) # mean, std for @01, @03
.. code-block:: python
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(
input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
num_epochs=3,
verbose=True,
callbacks=[
dl.BatchTransformCallback(
transform=torch.sigmoid,
scope="on_batch_end",
input_key="logits",
output_key="scores"
),
dl.CriterionCallback(
input_key="logits", target_key="targets", metric_key="loss"
),
dl.AUCCallback(input_key="scores", target_key="targets"),
dl.HitrateCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.HitrateCallback(
input_key="scores", target_key="targets", topk_args=(1, 3, 5)
),
dl.MAPCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.NDCGCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.OptimizerCallback(metric_key="loss"),
dl.SchedulerCallback(),
dl.CheckpointCallback(
logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
),
]
)
.. note::
Metric names depending on input parameters:
- ``topk_args = (1,) or None`` ---> ``"ndcg01"``
- ``topk_args = (1, 3)`` ---> ``"ndcg01"``, ``"ndcg03"``
- ``topk_args = (1, 3, 5)`` ---> ``"ndcg01"``, ``"ndcg03"``, ``"ndcg05"``
You can find them in ``runner.batch_metrics``, ``runner.loader_metrics`` or
``runner.epoch_metrics``.
.. note::
Please follow the `minimal examples`_ sections for more use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
"""
def __init__(
self,
topk_args: Iterable[int] = None,
compute_on_call: bool = True,
prefix: str = None,
suffix: str = None,
):
"""Init NDCGMetric"""
super().__init__(
metric_name="ndcg",
metric_function=ndcg,
topk_args=topk_args,
compute_on_call=compute_on_call,
prefix=prefix,
suffix=suffix,
)
__all__ = ["NDCGMetric"]