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# Source code for catalyst.metrics._ndcg

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: 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=[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 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)

dataset = TensorDataset(X, y)

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
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,
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=(1, 3, 5)
),
dl.MAPCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
dl.NDCGCallback(input_key="scores", target_key="targets", topk=(1, 3)),
dl.OptimizerCallback(metric_key="loss"),
dl.SchedulerCallback(),
dl.CheckpointCallback(
),
]
)

.. note::
Metric names depending on input parameters:

- topk = (1,) or None ---> "ndcg01"
- topk = (1, 3) ---> "ndcg01", "ndcg03"
- topk = (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  # noqa: E501, W505
"""

def __init__(
self,
topk: 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=topk,
compute_on_call=compute_on_call,
prefix=prefix,
suffix=suffix,
)

__all__ = ["NDCGMetric"]